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1. Estimating multi-scale irrigation amounts using multi-resolution soil moisture data: A data-driven approach using PrISM

Author

Giovanni Paolini, Maria Jose Escorihuela, Olivier Merlin, Pierre Laluet, Joaquim Bellvert, and Thierry Pellarin

Subjects
Irrigation estimates, Irrigation Water Use, Soil Moisture, Remote Sensing, PrISM, Agriculture (General), S1-972, Agricultural industries, HD9000-9495
Abstract

Irrigated agriculture is the primary driver of freshwater use and is continuously expanding. Precise knowledge of irrigation amounts is critical for optimizing water management, especially in semi-arid regions where water is a limited resource. This study proposed to adapt the PrISM (Precipitation inferred from Soil Moisture) methodology to detect and estimate irrigation events from soil moisture remotely sensed data. PrISM was originally conceived to correct precipitation products, assimilating Soil Moisture (SM) observations into an antecedent precipitation index (API) formula, using a particle filter scheme. This novel application of PrISM uses initial precipitation and SM observations to detect instances of water excess in the soil (not caused by precipitation) and estimates the amount of irrigation, along with its uncertainty. This newly proposed approach does not require extensive calibration and is adaptable to different spatial and temporal scales. The objective of this study was to analyze the performance of PrISM for irrigation amount estimation and compare it with current state-of-the-art approaches. To develop and test this methodology, a synthetic study was conducted using SM observations with various noise levels to simulate uncertainties and different spatial and temporal resolutions. The results indicated that a high temporal resolution (less than 3 days) is crucial to avoid underestimating irrigation amounts due to missing events. However, including a constraint on the frequency of irrigation events, deduced from the system of irrigation used at the field level, could overcome the limitation of low temporal resolution and significantly reduce underestimation of irrigation amounts. Subsequently, the developed methodology was applied to actual satellite SM products at different spatial scales (1 km and 100 m) over the same area. Validation was performed using in situ data at the district level of Algerri-Balaguer in Catalunya, Spain, where in situ irrigation amounts were available for various years. The validation resulted in a total Pearson’s correlation coefficient (r) of 0.80 and a total root mean square error (rmse) of 7.19 mm∕week for the years from 2017 to 2021. Additional validation was conducted at the field level in the Segarra-Garrigues irrigation district using in situ data from a field where SM profiles and irrigation amounts were continuously monitored. This validation yielded a total bi-weekly r of 0.81 and a total rmse of −9.34 mm∕14-days for the years from 2017 to 2021. Overall, the results suggested that PrISM can effectively estimate irrigation from SM remote sensing data, and the methodology has the potential to be applied on a large scale without requiring extensive calibration or site-specific knowledge.

Published
2023
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2. PrISM at Operational Scale: Monitoring Irrigation District Water Use during Droughts

Author

Giovanni Paolini, Maria Jose Escorihuela, Joaquim Bellvert, Olivier Merlin, and Thierry Pellarin

Subjects
irrigation estimates, data assimilation, soil moisture, remote sensing, PrISM, Science
Abstract

Efficient water management strategies are of utmost importance in drought-prone regions, given the fundamental role irrigation plays in avoiding yield losses and food shortages. Traditional methodologies for estimating irrigation amounts face limitations in terms of overall precision and operational scalability. This study proposes to estimate irrigation amounts from soil moisture (SM) data by adapting the PrISM (Precipitation Inferred from Soil Moisture) methodology. The PrISM assimilates SM into a simple Antecedent Precipitation Index (API) model using a particle filter approach, which allows the creation and estimation of irrigation events. The methodology is applied in a semi-arid region in the Ebro basin, located in the north-east of Spain (Catalonia), from 2016 to 2023. Multi-year drought, which started in 2020, particularly affected the region starting from the spring of 2023, which led to significant reductions in irrigation district water allocations in some of the areas of the region. This study demonstrates that the PrISM approach can correctly identify areas where water restrictions were adopted in 2023, and monitor the water usage with good performances and reliable results. When compared with in situ data for 8 consecutive years, PrISM showed a significant person’s correlation between 0.58 and 0.76 and a cumulative weekly root mean squared error (rmse) between 7 and 11 mm. Additionally, PrISM was applied to three irrigation districts with different levels of modernization, due to the different predominant irrigation systems: flood, sprinkler, and drip. This analysis underlined the strengths and limitations of PrISM depending on the irrigation techniques monitored. PrISM has good performances in areas irrigated by sprinkler and flood systems, while difficulties are present over drip irrigated areas, where the very localized and limited irrigation amounts could not be detected from SM observations.

Published
2024
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3. Datasets of high-resolution water level and discharge from the Saigon-Dong Nai estuary system impacted by a developing megacity, Ho Chi Minh City - Vietnam

Author

Francisco Rodrigues do Amaral, Tin Nguyen Trung, Thierry Pellarin, and Nicolas Gratiot

Subjects
Tidal river, Low-cost installation, ADCP, Tidal propagation in rivers, Computer applications to medicine. Medical informatics, R858-859.7, Science (General), Q1-390
Abstract

We present a new hydrological dataset collected during a field campaign in the Saigon-Dong Nai estuary system, Vietnam. These data include water level and water temperature measurements at five locations along the Saigon river and 2 locations along the Dong Nai river as well as discharge measurements from four 24-hour Acoustic Doppler Current Profiler (ADCP) campaigns at 2 locations in the Saigon river and 1 location in the Dong Nai river. Additionally, water level was barometrically compensated using air pressure measurements. Data were sampled between October 21st, 2022 and December 16th, 2022 and are provided in three processing stages namely, direct measurements as provided by the sensors (raw), barometricaly compensated measurements (pre-processed) and corrected measurements (post-processed). Even though of short duration (about 2 months), this dataset provides water level measurements at unprecedented spatial and temporal resolution in a region where data is scarce and not freely available. The synchronous logging of multiple water level sensors along river provides an opportunity to study profiles of water surface slope and upstream tidal propagation. Furthermore, the concurrent discharge measurements can be used to calibrate hydrological and/or hydraulic models of this estuary system. Additionally, the spatial resolution of this dataset is similar to the prospective measurements that the novel Surface Water and Ocean Topography (SWOT) satellite will provide. Thus, it enables the study of synthetic SWOT measurements to evaluate the future potential of the SWOT satellite over this estuary system.

Published
2023
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4. Validation of Soil Moisture Data Products From the NASA SMAP Mission

Author

Andreas Colliander, Rolf H. Reichle, Wade T. Crow, Michael H. Cosh, Fan Chen, Steven Chan, Narendra Narayan Das, Rajat Bindlish, Julian Chaubell, Seungbum Kim, Qing Liu, Peggy E. O'Neill, R. Scott Dunbar, Land B. Dang, John S. Kimball, Thomas J. Jackson, Hala Khalid Al-Jassar, Jun Asanuma, Bimal K. Bhattacharya, Aaron A. Berg, David D. Bosch, Laura Bourgeau-Chavez, Todd Caldwell, Jean-Christophe Calvet, Chandra Holifield Collins, Karsten H. Jensen, Stan Livingston, Ernesto Lopez-Baeza, Jose Martinez-Fernandez, Heather McNairn, Mahta Moghaddam, Carsten Montzka, Claudia Notarnicola, Thierry Pellarin, Isabella Greimeister-Pfeil, Jouni Pulliainen, Judith Gpe. Ramos, Mark Seyfried, Patrick J. Starks, Zhongbo Su, R. van der Velde, Yijian Zeng, Marc Thibeault, Mariette Vreugdenhil, Jeffrey P. Walker, Mehrez Zribi, Dara Entekhabi, and Simon H. Yueh

Subjects
Core validation sites (CVS), soil moisture (SM), Soil Moisture Active Passive (SMAP), validation, Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809
Abstract

The National Aeronautics and Space Administration Soil Moisture Active Passive (SMAP) mission has been validating its soil moisture (SM) products since the start of data production on March 31, 2015. Prior to launch, the mission defined a set of criteria for core validation sites (CVS) that enable the testing of the key mission SM accuracy requirement (unbiased root-mean-square error 3/m3). The validation approach also includes other (“sparse network”) in situ SM measurements, satellite SM products, model-based SM products, and field experiments. Over the past six years, the SMAP SM products have been analyzed with respect to these reference data, and the analysis approaches themselves have been scrutinized in an effort to best understand the products’ performance. Validation of the most recent SMAP Level 2 and 3 SM retrieval products (R17000) shows that the L-band (1.4 GHz) radiometer-based SM record continues to meet mission requirements. The products are generally consistent with SM retrievals from the European Space Agency Soil Moisture Ocean Salinity mission, although there are differences in some regions. The high-resolution (3-km) SM retrieval product, generated by combining Copernicus Sentinel-1 data with SMAP observations, performs within expectations. Currently, however, there is limited availability of 3-km CVS data to support extensive validation at this spatial scale. The most recent (version 5) SMAP Level 4 SM data assimilation product providing surface and root-zone SM with complete spatio–temporal coverage at 9-km resolution also meets performance requirements. The SMAP SM validation program will continue throughout the mission life; future plans include expanding it to forested and high-latitude regions.

Published
2022
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5. From SMOS Soil Moisture to 3-hour Precipitation Estimates at 0.1° Resolution in Africa

Author

Thierry Pellarin, Alexandre Zoppis, Carlos Román-Cascón, Yann H. Kerr, Nemesio Rodriguez-Fernandez, Geremy Panthou, Nathalie Philippon, and Jean-Martial Cohard

Subjects
satellite precipitation product, PrISM, soil moisture, Africa, real-time, IMERG, Science
Abstract

Several recent studies have shown that knowledge of the spatiotemporal dynamics of soil moisture intrinsically contains information on precipitation. In this study, we show how SMOS measurements can be used to generate a near-real-time precipitation product with a spatial resolution of 0.1° and a temporal resolution of 3 h. The principle consists of assimilating the SMOS data into a model that simulates the evolution of soil moisture, which is forced by a satellite precipitation product. The assimilation of SMOS soil moisture leads to an adjustment of the satellite precipitation rates. Using data from more than 200 rain gauges set up in Africa between 2010 and 2021, we show that the PrISM algorithm (for Precipitation Inferred from Soil Moisture) almost systematically improves the initial precipitation product. One of the original features of this study is that we used the IMERG-Early satellite precipitation product, which has a finer spatial resolution (0.1°) than SMOS (~0.25°). Despite this, the methodology reduces both the RMSE and bias of IMERG-Early. The RMSE is reduced from 8.0 to 6.3 mm/day, and the absolute bias is reduced from 0.81 to 0.63 mm/day on average over the 200 rain gauges. PrISM performs even slightly better on average than IMERG-Final in terms of RMSE (6.8 mm/day for IMERG-Final) but better scores are obtained by IMERG-Final in terms of absolute bias (0.35 mm/day), which utilizes a network of field measurements to correct the biases of the IMERG-Early product with a 2.5-month delay. Therefore, the use of SMOS soil moisture measurements for Africa can be an advantageous alternative to the use of gauge measurements for debiasing rainfall satellite products in real time.

Published
2022
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6. Can We Use Satellite-Based Soil-Moisture Products at High Resolution to Investigate Land-Use Differences and Land–Atmosphere Interactions? A Case Study in the Savanna

Author

Carlos Román-Cascón, Marie Lothon, Fabienne Lohou, Nitu Ojha, Olivier Merlin, David Aragonés, María P. González-Dugo, Ana Andreu, Thierry Pellarin, Aurore Brut, Ramón C. Soriguer, Ricardo Díaz-Delgado, Oscar Hartogensis, and Carlos Yagüe

Subjects
soil moisture, satellite data, land use, heterogeneity, savanna, DISPATCH, Science
Abstract

The use of soil moisture (SM) measurements from satellites has grown in recent years, fostering the development of new products at high resolution. This opens the possibility of using them for certain applications that were normally carried out using in situ data. We investigated this hypothesis through two main analyses using two high-resolution satellite-based soil moisture (SBSM) products that combined microwave with thermal and optical data: (1) The Disaggregation based on Physical And Theoretical scale Change (DISPATCH) and, (2) The Soil Moisture Ocean Salinity-Barcelona Expert Center (SMOS-BEC Level 4). We used these products to analyse the SM differences among pixels with contrasting vegetation. This was done through the comparison of the SM measurements from satellites and the measurements simulated with a simple antecedent precipitation index (API) model, which did not account for the surface characteristics. Subsequently, the deviation of the SM from satellite with respect to the API model (bias) was analysed and compared for contrasting land use categories. We hypothesised that the differences in the biases of the varied categories could provide information regarding the water retention capacity associated with each type of vegetation. From the satellite measurements, we determined how the SM depended on the tree cover, i.e., the denser the tree cover, the higher the SM. However, in winter periods with light rain events, the tree canopy could dampen the moistening of the soil through interception and conducted higher SM in the open areas. This evolution of the SM differences that depended on the characteristics of each season was observed both from satellite and from in situ measurements taken beneath a tree and in grass on the savanna landscape. The agreement between both types of measurements highlighted the potential of the SBSM products to investigate the SM of each type of vegetation. We found that the results were clearer for DISPATCH, whose data was not smoothed spatially as it was in SMOS-BEC. We also tested whether the relationships between SM and evapotranspiration could be investigated using satellite data. The answer to this question was also positive but required removing the unrealistic high-frequency SM oscillations from the satellite data using a low pass filter. This improved the performance scores of the products and the agreement with the results from the in situ data. These results demonstrated the possibility of using SM data from satellites to substitute ground measurements for the study of land–atmosphere interactions, which encourages efforts to improve the quality and resolution of these measurements.

Published
2020
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7. The Precipitation Inferred from Soil Moisture (PrISM) Near Real-Time Rainfall Product: Evaluation and Comparison

Author

Thierry Pellarin, Carlos Román-Cascón, Christian Baron, Rajat Bindlish, Luca Brocca, Pierre Camberlin, Diego Fernández-Prieto, Yann H. Kerr, Christian Massari, Geremy Panthou, Benoit Perrimond, Nathalie Philippon, and Guillaume Quantin

Subjects
precipitation, soil moisture, africa, satellite rainfall products, comparison, Science
Abstract

Near real-time precipitation is essential to many applications. In Africa, the lack of dense rain-gauge networks and ground weather radars makes the use of satellite precipitation products unavoidable. Despite major progresses in estimating precipitation rate from remote sensing measurements over the past decades, satellite precipitation products still suffer from quantitative uncertainties and biases compared to ground data. Consequently, almost all precipitation products are provided in two modes: a real-time mode (also called early-run or raw product) and a corrected mode (also called final-run, adjusted or post-processed product) in which ground precipitation measurements are integrated in algorithms to correct for bias, generally at a monthly timescale. This paper describes a new methodology to provide a near-real-time precipitation product based on satellite precipitation and soil moisture measurements. Recent studies have shown that soil moisture intrinsically contains information on past precipitation and can be used to correct precipitation uncertainties. The PrISM (Precipitation inferred from Soil Moisture) methodology is presented and its performance is assessed for five in situ rainfall measurement networks located in Africa in semi-arid to wet areas: Niger, Benin, Burkina Faso, Central Africa, and East Africa. Results show that the use of SMOS (Soil Moisture and Ocean Salinity) satellite soil moisture measurements in the PrISM algorithm most often improves the real-time satellite precipitation products, and provides results comparable to existing adjusted products, such as TRMM (Tropical Rainfall Measuring Mission), GPCC (Global Precipitation Climatology Centre) and IMERG (Integrated Multi-satellitE Retrievals for GPM), which are available a few weeks or months after their detection.

Published
2020
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8. On The Need of a New High-Resolution L-Band Mission to Study Land/Water/Ice Interfaces.

Author

Nemesio Rodriguez-Fernandez, Jacqueline Boutin, Lars Kaleschke, Gabrielle De Lannoy, Giovanni Macelloni, Kimmo Rautiainen, Maria José Escorihuela, Peter Weston, Patricia de Rosnay, Jean-Christophe Calvet, Frédéric Frappart, Alexandre Roy, Thierry Pellarin, Andreas Colliander, Alexandre Supply, Eric Anterrieu, Philippe Richaume, Arnaud Mialon, Cécile Cheymol, Thierry Amiot, Louise Yu, Manuel Martín-Neira, Asma Kallel, Benjamin Carayon, Josep Closa, Alberto Zurita, and Yann Kerr

Published
2023
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9. The SMOS-HR Mission: Science Case and Project Status.

Author

Nemesio J. Rodríguez-Fernández, Eric Anterrieu, Jacqueline Boutin, Alexandre Supply, Gilles Reverdin, G. Alory, Elisabeth Rémy, Ghislain Picard, Thierry Pellarin, Philippe Richaume, Arnaud Mialon, Ali Khazaal, Ahmad Al Bitar, Raquel Rodriguez Suquet, Louise Yu, Patrice Gonzalez, Cécile Cheymol, Thierry Amiot, Philippe Maisongrande, Nicolas Jeannin, Thibaut Decoopman, Abdelaziz Kallel, Jean-Michel Morel, Miguel Colom, Max Dunitz, Clovis Thouvenin-Masson, L. Olivier, and Yann H. Kerr

Published
2022
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10. A Follow-Up for the Soil Moisture and Ocean Salinity Mission.

Author

Nemesio J. Rodríguez-Fernández, Eric Anterrieu, François Cabot, Jacqueline Boutin, Ghislain Picard, Thierry Pellarin, Olivier Merlin, Jérôme Vialard, Frédéric Vivier, Josiane Costeraste, Baptiste Palacin, Raquel Rodriguez-Suquet, Louise Yu, Thierry Amiot, Ali Khazaal, Thibaut Decoopman, Nicolas Jeannin, Laurent Costes, Romain Caujolle, Maria José Escorihuela, Ahmad Al Bitar, Philippe Richaume, Arnaud Mialon, Christophe Suere, and Yann H. Kerr

Published
2021
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11. A New L-Band Passive Radiometer For Earth Observation: SMOS-High Resolution (SMOS-HR).

Author

Nemesio J. Rodríguez-Fernández, Eric Anterrieu, François Cabot, Jacqueline Boutin, Ghislain Picard, Thierry Pellarin, Olivier Merlin, Jérôme Vialard, Frédéric Vivier, Josiane Costeraste, Baptiste Palacin, Raquel Rodriguez-Suquet, Thierry Amiot, Ali Khaazal, Bernard Rougé, Jean-Michel Morel, Miguel Colom, Thibaut Decoopman, Nicolas Jeannin, Romain Caujolle, Maria José Escorihuela, Ahmad Al Bitar, Philippe Richaume, Arnaud Mialon, Christophe Suere, and Yann H. Kerr

Published
2020
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12. SMOS-HR: A High Resolution L-Band Passive Radiometer for Earth Science and Applications.

Author

Nemesio J. Rodríguez-Fernández, Arnaud Mialon, Olivier Merlin, Christophe Suere, François Cabot, Ali Khazaal, Josiane Costeraste, Baptiste Palacin, Raquel Rodriguez-Suquet, Thierry Tournier, Thibaut Decoopman, Eric Anterrieu, Miguel Colom, Jean-Michel Morel, Yann H. Kerr, Bernard Rougé, Jacqueline Boutin, Ghislain Picard, Thierry Pellarin, Maria José Escorihuela, Ahmad Al Bitar, and Philippe Richaume

Published
2019
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15. Soil Moisture Product Validation Good Practices Protocol

Author

Carsten Montzka, Michael Cosh, Jaime Nickeson, Fernando Camacho, Bagher Bayat, Ahmad Al Bitar, Aaron Berg, Rajat Bindlish, Heye Reemt Bogena, John D Bolten, Francois Cabot, Todd Caldwell, Steven Chan, Andreas Colliander, Wade Crow, Narendra Das, Gabrielle De Lannoy, Wouter Dorigo, Steven R Evett, Alexander Gruber, Sebastian Hahn, Thomas Jagdhuber, Scott Jones, Yann Kerr, Seungbum Kim, Christian Koyama, Mehmet Kurum, Ernesto Lopez-Baeza, Francesco Mattia, Kaighin A McColl, Susanne Mecklenburg, Binayak Mohanty, Peggy E O'Neill, Dani Or, Thierry Pellarin, George P Petropoulos, Maria Piles, Rolf H Reichle, Nemesio Rodriguez-Fernandez, Christoph Rüdiger, Tracy Scanlon, Robert C Schwartz, Daniel Spengler, Prashant Srivastav, Swati Suman, Robin van der Schalie, Wolfgang Wagner, Urs Wegmuller, and Jean-Pierre Wigneron

Subjects
Earth Resources And Remote Sensing
Published
2021
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16. Inversion of irrigation from satellite soil moisture data with a model based on PrISM (Precipitation Inferred from Soil Moisture)

Author

Giovanni Paolini, Thierry Pellarin, Maria jose Escorihuela, Olivier Merlin, Joaquim Bellvert, Victor Altes, Josep Maria Villar, and Xavier Petit

Abstract

Accurate irrigation water management is crucial for maximizing crop yield and minimizing water waste. Remote sensing technology offers a promising solution for efficiently estimating irrigation water use at the field scale.In this study, we adapted the PrISM (Precipitation inferred from Soil Moisture) methodology to detect and estimate irrigation events from soil moisture remotely sensed data. PrISM is a well-known approach to correct precipitation estimates using soil moisture data. Its main application is to provide a near real-time corrected precipitation product. PrISM employs an antecedent precipitation index (API) formula coupled with a particle filter assimilation scheme for soil moisture.In this study, we adapted the PrISM methodology to estimate irrigation amounts from soil moisture. The methodology uses initial precipitation estimates and soil moisture profile to detect whenever water excess is present in the soil (not caused by precipitation) and estimates its amount, together with its uncertainty. The methodology does not need extensive calibration and it is adaptable to different spatial and temporal scales. A synthetic study was performed to investigate the effect of a degraded soil moisture signal in terms of temporal resolution (lowering the temporal sampling of the soil moisture time-series), spatial resolution (lowering the percentage of irrigated area in a pixel), and random noise (increasing RMSE values). Results from this study suggested that high spatial resolution is critical in order to avoid underestimation of irrigation amounts. Ideally, a field-level soil moisture (with more than 75% of the pixel irrigated) and a product with low RMSE (0.02 m3/m3) is required for precise estimations (in order to keep the error of annual cumulative irrigation below 20%). Temporal resolution has a lower impact, especially when an assumption on the frequency of irrigation events (deduced from the system of irrigation used at the field-level) is included in the algorithm.Consequently, the developed algorithm was applied to actual satellite soil moisture products at different spatial scales over the same area. Validation was performed using in situ data at the district level of Algerri-Balaguer from the study area in Catalunya, Spain, where ground-based irrigation amounts were available for various years. Additional validation was performed at the field-level at the Segarra-Garrigues irrigation district using in-situ data from a few fields where soil moisture profiles and irrigation amounts were continuously monitored. Our results suggest that PrISM can be used effectively to estimate irrigation from soil moisture remote sensing data and that this methodology could be potentially applied on a large scale, with the only limitation being the quality and spatial resolution of the satellite soil moisture product.

Published
2023
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17. Reducing the carbon footprint of a public research laboratory in Geosciences. Assessing a reduction strategy built with laboratory members after a 3-year experimentation

Author

Thierry Pellarin, Nicolas Champollion, Nicolas Gratiot, Claudia Teran-Escobar, Isabelle Ruin, Geremy Panthou, Benoit Hingray, Gilles Delaygue, Eliot Jager, Alexis Lamothe, Guillaume Piton, Guillaume Evin, Juliette Blanchet, Nathalie Philippon, Armelle Philip, Patricia Martinerie, and Ghislain Picard

Abstract

The Institute of Environmental Geosciences (IGE) is a public research laboratory in Earth and Environmental Sciences with a staff of about 300 people, which conducts research on climate, the anthropisation of our planet and environmental risks, combining glaciology, hydrology, oceanography, mechanics, atmospheric sciences and human sciences. An important part of its activity consists of field experiments in remote sites (Antarctica, Asia, South America, Africa), numerical simulations using significant computer resources (several million CPU hours/year), using expensive and sometimes energy intensive scientific equipment (e.g. 170 m² of cold rooms).In 2019, the laboratory collectively decided to adopt a strategy to reduce its Carbon Footprint (CFP) by 7% per year in order to achieve a 50% reduction by 2030 and thus to comply with the objectives of the Paris Agreement. The first CFP budget (2018 and 2019, using the GES1point5 tool) showed a predominance of emissions from professional travels (~640 tCO2e out of 1850 tCO2e, i.e. 2.6 tCO2e/person). In this context, the strategy consisted in defining CO2 budgets for each of the 8 research teams of the IGE on the basis of the 2018/2019 emissions, imposing a 10% reduction per year from 2020. Given the pandemic in 2020 and 2021, the reduction targets for professional travel were easily achieved (-81% and -64%) and the reduction in 2022 was -39% compared to 2018/2019 instead of the targeted -27%.For all emission items (commuting, professional travel, heating, electricity, digital computing, purchasing, refrigerants), the reduction was -45% in 2020, -30% in 2021 and -15% in 2022. To consider the evolution of the number of people in the laboratory (and in the teams), the mean individual CFP has been defined as the ratio between the CO2 emissions and the number of people in the laboratory. The IGE's mean individual CFP was 7.22 tCO2e/person in 2018/2019 and 5.45 tCO2e/person in 2022 (for a target of 6.0 tCO2/person). It should be 3.61 tCO2e/person in 2030.The strategy (the long-term reduction trajectory and the team-based reduction objectives) is well received by the IGE laboratory staff, even if some staff are still reluctant to any form of reduction. To ease its implementation and check whether it is being kept, a bimonthly monitoring of the teams’ emissions and the mean personal CFP was set up. The IGE also proposes participation in awareness-raising tools (La Fresque du Climat, Ma Terre en 180'). Significant changes in travel habits have followed. For instance, out of the 30 members of the IGE who come to the EGU in Vienna each year, 90% came by plane and 10% by train (a 20-hour long journey) in 2018/2019, and this ratio was 25% by plane and 75% by train in 2022.To achieve our objective, further actions need to be identified to reduce the "purchase" and "digital computing" emission posts. What will help is that the insulation of the buildings was initiated in 2022, and the cold rooms which emitted a very strong greenhouse gas (refrigerant gas R508b) were changed in 2022 for a model operating with CO2.

Published
2023
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18. Hydraulic response of a tropical urban estuary to a typhoon event: case study of Ho Chi Minh City and typhoon Usagi

Author

Francisco Rodrigues do Amaral, Nicolas Gratiot, and Thierry Pellarin

Abstract

We investigate the most severe rainfall event ever experienced in Ho Chi Minh City (HCMC), Vietnam. It occurred on November 25th, 2018 when typhoon (TY) Usagi directly hit HCMC. During this event, there was more than 300 mm in rainfall over 24 h which led to flooding and considerable material damages. We propose an in-depth study of the hydrological response of this urban estuary region at a short time scale by focusing on the days before and after typhoon Usagi. We use a set of data analysis and signal processing tools to characterize and quantify both coastal and continental effects on the hydrosystem. We found that TY Usagi made landfall without forming a significant storm surge. The extreme rainfall does not translate in immediate river discharge but presents a 10 hour time lag between peak precipitation and peak residual discharge. Nevertheless, increased water levels can be seen at both urban and upstream river stations with a similar time lag. At the upstream station, residual discharge represents 1.5 % of available rain water and evidence of upstream wide spread flooding was found. At the urban station, we assess the potential surface run off during the event to be 8.9 % of the upstream residual discharge. Hence, the Saigon river would be capable of evacuating the urban flood water after the event. However, a time lag in peak water level and peak rainfall was found and attributed to the combination of high tide and impervious streets which prevented the evacuation of rain water and resulted in street flooding of up to 0.8 m. Overall, it was found that despite not having a significant storm surge, the coastal tidal forcing is predominant in the hydrodynamics of the river even during severe heavy rainfall with tidal fluctuations of water level and respective discharge much larger than the residuals.

Published
2023

20. Assessment of the SMAP Level-4 Surface and Root-Zone Soil Moisture Product Using In Situ Measurements

Author

Rolf H. Reichle, Gabrielle J. M. De Lannoy, Qing Liu, Joseph V. Ardizzone, Andreas Colliander, Austin Conaty, Wade Crow, Thomas J. Jackson, Lucas A. Jones, John S. Kimball, Randal D. Koster, Sarith P. Mahanama, Edmond B. Smith, Aaron Berg, Simone Bircher, David Bosch, Todd G. Caldwell, Michael Cosh, Ángel González-Zamora, Chandra D. Holifield Collins, Karsten H. Jensen, Stan Livingston, Ernesto Lopez-Baeza, José Martínez-Fernández, Heather McNairn, Mahta Moghaddam, Anna Pacheco, Thierry Pellarin, John Prueger, Tracy Rowlandson, Mark Seyfried, Patrick Starks, Zhongbo Su, Marc Thibeault, Rogier van der Velde, Jeffrey Walker, Xiaoling Wu, and Yijian Zeng

Published
2017
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21. Saigon's river response to Typhoon Usagi

Author

Francisco Rodrigues do Amaral, Nicolas Gratiot, and Thierry Pellarin

Subjects
Harmonic Tidal Analysis, Hydrodynamics, Saigon River, Storm Surge, Typhoon Usagi
Abstract

A good understanding of the hydrological cycle, and discharge in particular, in tidal rivers enables reliable forecasting and decision making by researchers and policy makers. We develop the work of Camenen et al. 2021 where the hydrological dynamics of the tidal Saigon river are studied. The study proposes a low-cost technique to estimate tidallyinfluenced river discharge based on high-frequency in-situ measurements. This technique estimates discharge based on a stage-fall-discharge rating curve (Camenen et al., 2017) which requires the estimation of the river’s slope. The application to the Saigon river as in Camenen et al. 2021 shows that its hydrological dynamics are ruled by a complex combination of factors.

Published
2022
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24. From SMOS Soil Moisture to 3-hour Precipitation Estimates at 0.1 degrees Resolution in Africa

Author

Thierry Pellarin, Alexandre Zoppis, Carlos Román-Cascón, Yann H. Kerr, Nemesio Rodriguez-Fernandez, Geremy Panthou, Nathalie Philippon, Jean-Martial Cohard, Institut des Géosciences de l’Environnement (IGE), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Centre d'études spatiales de la biosphère (CESBIO), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), and Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
PrISM, satellite precipitation product, soil moisture, Africa, real-time, IMERG, SMOS, [SDE.MCG]Environmental Sciences/Global Changes, Science, General Earth and Planetary Sciences, Geofísica
Abstract

International audience; Several recent studies have shown that knowledge of the spatiotemporal dynamics of soil moisture intrinsically contains information on precipitation. In this study, we show how SMOS measurements can be used to generate a near-real-time precipitation product with a spatial resolution of 0.1° and a temporal resolution of 3 h. The principle consists of assimilating the SMOS data into a model that simulates the evolution of soil moisture, which is forced by a satellite precipitation product. The assimilation of SMOS soil moisture leads to an adjustment of the satellite precipitation rates. Using data from more than 200 rain gauges set up in Africa between 2010 and 2021, we show that the PrISM algorithm (for Precipitation Inferred from Soil Moisture) almost systematically improves the initial precipitation product. One of the original features of this study is that we used the IMERG-Early satellite precipitation product, which has a finer spatial resolution (0.1°) than SMOS (~0.25°). Despite this, the methodology reduces both the RMSE and bias of IMERG-Early. The RMSE is reduced from 8.0 to 6.3 mm/day, and the absolute bias is reduced from 0.81 to 0.63 mm/day on average over the 200 rain gauges. PrISM performs even slightly better on average than IMERG-Final in terms of RMSE (6.8 mm/day for IMERG-Final) but better scores are obtained by IMERG-Final in terms of absolute bias (0.35 mm/day), which utilizes a network of field measurements to correct the biases of the IMERG-Early product with a 2.5-month delay. Therefore, the use of SMOS soil moisture measurements for Africa can be an advantageous alternative to the use of gauge measurements for debiasing rainfall satellite products in real time.

Published
2022
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26. Soil moisture and vegetation optical depth retrievals over heterogeneous scenes using LEWIS L-band radiometer

Author

Yann Kerr, S. Gascoin, M. Barrée, Arnaud Mialon, Romain Biron, Marie Parrens, Thierry Pellarin, F. Lemaître, Centre d'études spatiales de la biosphère (CESBIO), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut des Géosciences de l’Environnement (IGE), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Dynamiques et écologie des paysages agriforestiers (DYNAFOR), École nationale supérieure agronomique de Toulouse (ENSAT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), ONERA / DOTA, Université de Toulouse [Toulouse], ONERA-PRES Université de Toulouse, The authors acknowledge support by CNES (Centre National d’Etudes Spatiales) TOSCA program, the 'Centre Aval de Traitement des Données SMOS' (CATDS), operated for the CNES (France) by IFREMER (Brest, France)., Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), DOTA, ONERA [Salon], ONERA, and The authors acknowledge support by CNES (Centre National d’Etudes Spatiales) TOSCA program, the 'Centre Aval de Traitement des Donn ́ees SMOS' (CATDS), operated for the CNES (France) by IFREMER (Brest, France).

Subjects
Brightness, L band, 010504 meteorology & atmospheric sciences, 0211 other engineering and technologies, 02 engineering and technology, Management, Monitoring, Policy and Law, 01 natural sciences, Footprint, Cal/Val, Radiative transfer, 14. Life underwater, Computers in Earth Sciences, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, ComputingMilieux_MISCELLANEOUS, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Earth-Surface Processes, Remote sensing, Global and Planetary Change, Radiometer, VOD, Vegetation, 15. Life on land, Surface soil moisture, L-band, [SDU]Sciences of the Universe [physics], Environmental science, Radiometry, Satellite, SMOS
Abstract

International audience; The Soil Moisture and Ocean Salinity (SMOS) satellite has been providing global data for more than 11 years. The algorithm to retrieve surface soil moisture (SM) and vegetation optical depth (VOD) from Brightness Temperatures (TB) has constantly evolved as our understanding of the instrument and of the responses to various ecosystems increased. The evolution of the radiative transfer modelling used in the inversion process is in part based upon ground measurements done on experimental sites. We present the particular case of an L-band radiometer placed on top of a 800- meter-high cliff. This design allows us to study large and various scenes when compared to classical experiments where a radiometer is placed 10–20 m above a surface. With our set up, the footprints cover areas larger than 300 m in diameter, covering thus several land use types and the footprint can be directed towards different dominant land classes (forest, crops, urban etc…). This paper presents the whole set up consisting of in situ measurements (soil moisture and surface temperatures) and L-band radiometry acquired by the LEWIS instrument at high incidence angles. We perform SM/VOD retrievals from brightness temperatures using various configurations which are compared to the in situ observations. We test in particular a 2-VOD retrieval approach that consists in deriving one SM for the entire scene but two VOD, one for agricultural/low vegetation and a VOD for the forest parts, which improves the performance of the SM/VOD retrievals.

Published
2021
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27. Soil moisture retrieval by SMOS: a global feasibility study.

Author

Thierry Pellarin, Jean-Christophe Calvet, Jean-Pierre Wigneron, Lester P. Simmonds, Ernesto López-Baeza, Michael Berger 0002, Adriano Camps, André Chanzy, Paolo Ferrazzoli, Martti T. Hallikainen, Yann H. Kerr, Christian Mätzler, Wolfram Mauser, Adriaan A. Van de Griend, Bart Van den Hurk, Peter J. van Oevelen, Pedro Viterbo, and Philippe Waldteufel

Published
2002
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29. A Follow-Up for the Soil Moisture and Ocean Salinity Mission

Author

J. Vialard, M. J. Escorihuela, N. Jeannin, Yann Kerr, J. Costeraste, T. Amiot, R. Rodriguez-Suquet, Ahmad Al Bitar, Philippe Richaume, Baptiste Palacin, Francois Cabot, L. Costes, Arnaud Mialon, R. Caujolle, Ali Khazaal, Jacqueline Boutin, Eric Anterrieu, Thibaut Decoopman, Thierry Pellarin, Christophe Suere, L. Yu, Ghislain Picard, F. Vivier, Nemesio Rodriguez-Fernandez, and Olivier Merlin

Subjects
Data set, Salinity, Moisture, Radiometry, Environmental science, Satellite, Angular resolution, Water content, Downscaling, Remote sensing
Abstract

The Soil Moisture and Ocean Salinity (SMOS) satellite is performing systematic L-band observations since 2009, allowing a large number of science and operational applications. Several recent studies have shown the need of the continuity of L-band observations, in particular with an increased angular resolution. In this contribution, two instrumental concepts are presented to reach native resolutions of 5–10 km. In addition, using airborne data, it is also shown that the accuracy of downscaling coarser resolution L-band data to 5–10 km using a high resolution auxiliary data set, is significantly lower than that of native high resolution observations.

Published
2021
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30. Assessment and inter-comparison of recently developed/reprocessed microwave satellite soil moisture products using ISMN ground-based measurements

Author

Amen Al-Yaari, Yann Kerr, Andreas Colliander, Jean-Pierre Wigneron, Roberto Fernandez-Moran, Thierry Pellarin, P. Richaume, Sebastian Hahn, Arnaud Mialon, Lei Fan, Wouter Dorigo, G. De Lannoy, Interactions Sol Plante Atmosphère (UMR ISPA), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro), INRA Bioclimatologie, Institut National de la Recherche Agronomique (INRA), Institut des Géosciences de l’Environnement (IGE), Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut de Recherche pour le Développement (IRD)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Université Catholique de Louvain = Catholic University of Louvain (UCL), Institut de Recherche pour le Développement (IRD)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Interactions Sol Plante Atmosphère (ISPA), Laboratoire d'étude des transferts en hydrologie et environnement (LTHE), Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Université Fédérale Toulouse Midi-Pyrénées-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), and Université Catholique de Louvain

Subjects
Technology, Passive microwave remote sensing, 010504 meteorology & atmospheric sciences, 0208 environmental biotechnology, Active microwave remote sensing, Review, 02 engineering and technology, 01 natural sciences, 7. Clean energy, law.invention, Remote Sensing, law, Radar, Evaluation, ComputingMilieux_MISCELLANEOUS, evaluation, Geology, passive microwave remote sensing, DATA SETS, Life Sciences & Biomedicine, active microwave remote sensing, SMOS, LAND SURFACES, review, Soil Science, Climate change, Environmental Sciences & Ecology, Land cover, VALIDATION, RETRIEVALS, International soil moisture network, Computers in Earth Sciences, Imaging Science & Photographic Technology, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, 0105 earth and related environmental sciences, Remote sensing, Science & Technology, Radiometer, AMSR-E, SMAP, Scatterometer, international soil moisture network, 020801 environmental engineering, CLIMATE, ASCAT, 13. Climate action, Soil water, Environmental science, Spatial variability, Satellite, Soil moisture, soil moisture, Environmental Sciences, L-BAND
Abstract

Soil moisture (SM) is a key state variable in understanding the climate system through its control on the land surface energy, water budget partitioning, and the carbon cycle. Monitoring SM at regional scale has become possible thanks to microwave remote sensing. In the past two decades, several satellites were launched carrying on board either radiometer (passive) or radar (active) or both sensors in different frequency bands with various spatial and temporal resolutions. Soil moisture algorithms are in rapid development and their improvements/revisions are ongoing. The latest SM retrieval products and versions of products that have been recently released are not yet, to our knowledge, comprehensively evaluated and inter-compared over different ecoregions and climate conditions. The aim of this paper is to comprehensively evaluate the most recent microwave-based SM retrieval products available from NASA's (National Aeronautics and Space Administration) SMAP (Soil Moisture Active Passive) satellite, ESA's led mission (European Space Agency) SMOS (Soil Moisture and Ocean Salinity) satellite, ASCAT (Advanced Scatterometer) sensor on board the meteorological operational (Metop) platforms Metop-A and Metop-B, and the ESA Climate Change Initiative (CCI) blended long-term SM time series. More specifically, in this study we compared SMAPL3 V4, SMOSL3 V300, SMOSL2 V650, ASCAT H111, and CCI V04.2 and the new SMOS-IC (V105) SM product. This evaluation was achieved using four statistical scores: Pearson correlation (considering both original observations and anomalies), RMSE, unbiased RMSE, and Bias between remotely-sensed SM retrievals and ground-based measurements from >1000 stations from 17 monitoring networks, spread over the globe, disseminated through the International Soil Moisture Network (ISMN). The analysis reveals that the performance of the remotely-sensed SM retrievals generally varies depending on ecoregions, land cover types, climate conditions, and between the monitoring networks. It also reveals that temporal sampling of the data, the frequency of data in time and the spatial coverage, affect the performance metrics. Overall, the performance of SMAP and SMOS-IC products compared slightly better with respect to the ISMN in situ observations than the other remotely-sensed products. ispartof: REMOTE SENSING OF ENVIRONMENT vol:224 pages:289-303 status: published

Published
2019
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31. River flow prediction in data scarce regions: soil moisture integrated satellite rainfall products outperform rain gauge observations in West Africa

Author

Paolo Filippucci, Stefania Camici, Luca Ciabatta, Thierry Pellarin, Yann Kerr, Luca Brocca, Christian Massari, Diego Fernández-Prieto, Research Institute for Geo-hydrological Protection [Perugia] (IRPI), Consiglio Nazionale delle Ricerche (CNR), Institut des Géosciences de l’Environnement (IGE), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS)-Université Fédérale Toulouse Midi-Pyrénées-Centre National d'Études Spatiales [Toulouse] (CNES)-Météo France-Centre National de la Recherche Scientifique (CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Météo France, ESA ERSIN FRASCATI ITA, Partenaires IRSTEA, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), and Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
Rainfall, SM2RAIN, 010504 meteorology & atmospheric sciences, Hydrological modelling, 0208 environmental biotechnology, lcsh:Medicine, 02 engineering and technology, 01 natural sciences, Article, Remote Sensing, Streamflow, Precipitation, lcsh:Science, 0105 earth and related environmental sciences, 2. Zero hunger, Multidisciplinary, Rain gauge, Discharge, lcsh:R, Natural hazards, flood, 6. Clean water, 020801 environmental engineering, Water resources, 13. Climate action, Climatology, [SDE]Environmental Sciences, Environmental science, lcsh:Q, Satellite, Soil moisture, Hydrology, Global Precipitation Measurement, GPM
Abstract

Satellite precipitation products have been largely improved in the recent years particularly with the launch of the global precipitation measurement (GPM) core satellite. Moreover, the development of techniques for exploiting the information provided by satellite soil moisture to complement/enhance precipitation products have improved the accuracy of accumulated rainfall estimates over land. Such satellite enhanced precipitation products, available with a short latency (

Published
2020
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32. Can we use satellite-based soil-moisture products at high resolution to investigate land-use differences and land-atmosphere interactions? a case study in the savanna

Author

Ana Andreu, Oscar Hartogensis, María P. González-Dugo, Ricardo Díaz-Delgado, Nitu Ojha, Carlos Yagüe, Aurore Brut, Fabienne Lohou, Thierry Pellarin, David Aragonés, Ramón C. Soriguer, C. Román-Cascón, Olivier Merlin, Marie Lothon, Laboratoire d'aérologie (LAERO), Centre National de la Recherche Scientifique (CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées, Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Laboratoire d'aérologie (LA), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), and Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS)

Subjects
010504 meteorology & atmospheric sciences, Science, soil moisture, satellite data, land use, heterogeneity, savanna, DISPATCH, SMOS-BEC, 0207 environmental engineering, 02 engineering and technology, Atmospheric sciences, 01 natural sciences, Atmosphere, Meteorology, Evapotranspiration, Savanna, 14. Life underwater, [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology, 020701 environmental engineering, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, Water content, Meteorologie, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, Tree canopy, WIMEK, Física atmosférica, Vegetation, 15. Life on land, 13. Climate action, Satellite data, Land use, General Earth and Planetary Sciences, Environmental science, Satellite, Soil moisture, Interception, Heterogeneity, Scale (map)
Abstract

The use of soil moisture (SM) measurements from satellites has grown in recent years, fostering the development of new products at high resolution. This opens the possibility of using them for certain applications that were normally carried out using in situ data. We investigated this hypothesis through two main analyses using two high-resolution satellite-based soil moisture (SBSM) products that combined microwave with thermal and optical data: (1) The Disaggregation based on Physical And Theoretical scale Change (DISPATCH) and, (2) The Soil Moisture Ocean Salinity-Barcelona Expert Center (SMOS-BEC Level 4). We used these products to analyse the SM differences among pixels with contrasting vegetation. This was done through the comparison of the SM measurements from satellites and the measurements simulated with a simple antecedent precipitation index (API) model, which did not account for the surface characteristics. Subsequently, the deviation of the SM from satellite with respect to the API model (bias) was analysed and compared for contrasting land use categories. We hypothesised that the differences in the biases of the varied categories could provide information regarding the water retention capacity associated with each type of vegetation. From the satellite measurements, we determined how the SM depended on the tree cover, i.e., the denser the tree cover, the higher the SM. However, in winter periods with light rain events, the tree canopy could dampen the moistening of the soil through interception and conducted higher SM in the open areas. This evolution of the SM differences that depended on the characteristics of each season was observed both from satellite and from in situ measurements taken beneath a tree and in grass on the savanna landscape. The agreement between both types of measurements highlighted the potential of the SBSM products to investigate the SM of each type of vegetation. We found that the results were clearer for DISPATCH, whose data was not smoothed spatially as it was in SMOS-BEC. We also tested whether the relationships between SM and evapotranspiration could be investigated using satellite data. The answer to this question was also positive but required removing the unrealistic high-frequency SM oscillations from the satellite data using a low pass filter. This improved the performance scores of the products and the agreement with the results from the in situ data. These results demonstrated the possibility of using SM data from satellites to substitute ground measurements for the study of land–atmosphere interactions, which encourages efforts to improve the quality and resolution of these measurements.&nbsp

Published
2020
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33. A daily 25 km short-latency rainfall product for data-scarce regions based on the integration of the Global Precipitation Measurement mission rainfall and multiple-satellite soil moisture products

Author

Luca Ciabatta, Diego Fernandez Prieto, Thierry Pellarin, Yann Kerr, Viviana Maggioni, Christian Massari, Luca Brocca, Paolo Filippucci, Gab Abramowitz, Research Institute for Geo-hydrological Protection [Perugia] (IRPI), Consiglio Nazionale delle Ricerche (CNR), Institut des Géosciences de l’Environnement (IGE), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), ESA Centre for Earth Observation (ESRIN), European Space Agency (ESA), National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Agence Spatiale Européenne = European Space Agency (ESA)

Subjects
SM2RAIN, 010504 meteorology & atmospheric sciences, Meteorology, 0208 environmental biotechnology, rainfall, 02 engineering and technology, 01 natural sciences, lcsh:Technology, lcsh:TD1-1066, remote sensing, Short latency, Product (category theory), lcsh:Environmental technology. Sanitary engineering, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, Water content, lcsh:Environmental sciences, 0105 earth and related environmental sciences, General Environmental Science, 2. Zero hunger, lcsh:GE1-350, lcsh:T, lcsh:Geography. Anthropology. Recreation, Scatterometer, 6. Clean water, 020801 environmental engineering, Salinity, lcsh:G, [SDE]Environmental Sciences, General Earth and Planetary Sciences, Environmental science, Satellite, soil moisture, Global Precipitation Measurement
Abstract

Rain gauges are unevenly spaced around the world with extremely low gauge density over developing countries. For instance, in some regions in Africa the gauge density is often less than one station per 10 000 km2. The availability of rainfall data provided by gauges is also not always guaranteed in near real time or with a timeliness suited for agricultural and water resource management applications, as gauges are also subject to malfunctions and regulations imposed by national authorities. A potential alternative is satellite-based rainfall estimates, yet comparisons with in situ data suggest they are often not optimal. In this study, we developed a short-latency (i.e. 2–3 d) rainfall product derived from the combination of the Integrated Multi-Satellite Retrievals for GPM (Global Precipitation Measurement) Early Run (IMERG-ER) with multiple-satellite soil-moisture-based rainfall products derived from ASCAT (Advanced Scatterometer), SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active and Passive) L3 (Level 3) satellite soil moisture (SM) retrievals. We tested the performance of this product over four regions characterized by high-quality ground-based rainfall datasets (India, the conterminous United States, Australia and Europe) and over data-scarce regions in Africa and South America by using triple-collocation (TC) analysis. We found that the integration of satellite SM observations with in situ rainfall observations is very beneficial with improvements of IMERG-ER up to 20 % and 40 % in terms of correlation and error, respectively, and a generalized enhancement in terms of categorical scores with the integrated product often outperforming reanalysis and ground-based long-latency datasets. We also found a relevant overestimation of the rainfall variability of GPM-based products (up to twice the reference value), which was significantly reduced after the integration with satellite soil-moisture-based rainfall estimates. Given the importance of a reliable and readily available rainfall product for water resource management and agricultural applications over data-scarce regions, the developed product can provide a valuable and unique source of rainfall information for these regions.

Published
2020
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34. Groundwater dynamics retrievals in Africa using SMOS soil moisture measurements

Author

Thierry Pellarin, Laurent Oxarango, Jean-Martial Cohard, Alban Depeyre, Basile Hector, Yann Kerr, and Jean-Pierre Vandervaere

Abstract

ESA’s SMOS mission is celebrating 10 years of measurements in 2020 and is still producing soil moisture data of interest for many applications. One of the successes of this mission is its unexpected applications of soil moisture, such as thin ice sheets over the ocean, above ground biomass and carbon stocks, crop yields or rainfall estimation. We believe that knowledge of soil moisture time series contains information that are closely related to the functioning of the hydrosphere (infiltration, evaporation, groundwater recharge) and the biosphere (vegetation development, crop yield, carbon storage). These two compartments are traditionally studied using models forced by precipitation rates and atmospheric variables. However, beyond the difficulty of measuring the precipitation rate accurately from space, a non-negligible portion of rain does not infiltrate the soil either because it is intercepted by vegetation or because of the surface runoff.In this study, we assume that SMOS retrieved soil moisture dynamics (0-5 cm) can inform us on much deeper soil horizons. Given that the water that reaches the root zone (0-200cm) and groundwater necessarily transits at some point through the surface, we can hypothesize that surface soil moisture dynamics intrinsically contains information on water dynamics in deeper layers.To test this idea, we used Richards' 1D model and forced the first layer of the model with 5-cm in-situ soil moisture measurements from the AMMA-CATCH observatory sites in West-Africa. A variation of soil moisture at the surface generates moisture variations in the deeper layers according to the hydrodynamic parameters of the model: soil conductivity at saturation (Ks), shape parameters of the retention curve (α and m), soil porosity (θsat). For highly permeable soils, water rapidly infiltrates the soil column and creates a groundwater table with its seasonal dynamics. For more impermeable soils, water remains close to the surface and there is no groundwater recharge. This approach satisfyingly compares with in-situ measurements concerning both root zone soil moisture profiles and water table dynamics.In a second step, the proposed methodology was applied to measurements derived from the SMOS satellite over the whole of Africa. To substitute in situ measurements, the GRACE satellite gravity data is used to compare with simulated soil water variations. This comparison allows to reject a lot of hydrodynamic parameters, and to select the best combination of the 4 parameters. Finally, the method makes it possible to produce maps of water table depths and their temporal dynamics at the scale of the African continent from information on surface soil moisture from SMOS (0-5cm) and soil water content from GRACE satellite.

Published
2020
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35. Large scale high resolution modelling of the West African rivers and aquifers

Author

Reed M. Maxwell, Basile Hector, Jean-Martial Cohard, Alban Depeyre, and Thierry Pellarin

Subjects
West african, geography, geography.geographical_feature_category, Scale (ratio), Earth science, High resolution, Aquifer, Geology
Abstract

West Africa has been classified as one of the most vulnerable regions in the world for water resources to face global changes, both climatic and demographic. The population is expected to double by 2050 leading to increased pressure on the use of water resources. In this context, it is necessary to understand the dynamics of major African hydrosystems as large rivers (Niger river, Senegal river...) and transboundary aquifers in order to predict the fate of water resources for the next decades. The ParFlow-CLM physical-based model was chosen for its ability to simulate surface water and groundwater dynamics in a coupled manner. This type of modelling makes it possible to represent the main hydrological processes observed over the whole West Africa region. It operates at a relatively fine spatial resolution (1 km²). The main challenge is to determine the hydrodynamic parameters of the soil for the entire region and on a 100 m thickness (i.e. 3.5 million pixels times 11 layers).As a first step, the model was implemented on two catchments monitored by the AMMA-CATCH observatory. These two watersheds are representative of the major and contrasted processes found in WA : being respectively representative of Sudanian and Sahelian climates. In order to assess the relevance of the regional databases (SoilGrids and GLHYMPS), simulations were carried out with original and adjusted (based on observations) soil parameters and results were evaluated with local measurements. It appears that the deep weathered lithology is not considered in databases for most of hard-rock areas in intertropical areas with no tectonic uplift. Aquifer thicknesses, permeabilities and porosities have to be significantly enhanced for the model to represent the correct flow paths. Furthermore, in the Sahel where most of the annual precipitation falls during a dozen events only, a crust layer (consistent with observations) has been added to represent the large runoff coefficients which lead to the early season floods.In a second step, the model was implemented at the West Africa scale using the adjusted soil parameters. These parameters were obtained using a simple linear law that have been applied uniformly over the entire domain and a mask over a part of the Sahel representative of the crusting zones. Results will be compared with both remotely sensed and in situ data : GRACE provides water stock variations at a very large scale, MERRA and ERA reanalysis provide evapotranspiration data. Altimeters and in situ measurements provide river flow data. In the near future the launch of the SWOT satellite will bring new observations to complete the current one. The evaluation of the different compartments of the hydrological cycle should reveal spatial discrepancies in the model's ability to represent processes, highlighting the points on which further work should focus.

Published
2020
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36. Soil moisture retrieval using GNSS-R data

Author

Mireille Huc, Nicolas Baghdadi, Thierry Pellarin, Mehrez Zribi, Nazzareno Pierdicca, Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Institut des Géosciences de l’Environnement (IGE), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome], Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), and Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome] (UNIROMA)

Subjects
Moisture, GNSS-R, Inversion (meteorology), Shuttle Radar Topography Mission, 15. Life on land, [SDE.ES]Environmental Sciences/Environmental and Society, 6. Clean water, GNSS applications, CYGNSS, [SDE]Environmental Sciences, Environmental science, soil moisture, Digital elevation model, change detection, Water content, Image resolution, Change detection, Remote sensing
Abstract

The aim of this study is to propose an inversion algorithm for CYGNSS constellation data, which can be used for the monitoring of soil moisture. A change detection approach based on a 21-month CYGNSS data time series is proposed, and is processed to retrieve daily average reflectivity over a grid with a spatial resolution equal to $0.5 ^{\circ} \mathrm{x}0.5 ^{\circ}$. Auxiliary data, including PROBA-V optical measurements and SRTM Digital Terrain Model products are used to account for the influence of vegetation and relief. The proposed estimates are validated at three different African sites (Merguellil, Tunisia; Basso Dallo, Niger; and Oueme, Benin), using field moisture measurements acquired at 5 cm soil depth.

Published
2020

37. Estimating 500-m Resolution Soil Moisture Using Sentinel-1 and Optical Data Synergy

Author

Jean-Christophe Calvet, Mehrez Zribi, Thierry Pellarin, Myriam Foucras, Nicolas Baghdadi, Clément Albergel, Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut des Géosciences de l’Environnement (IGE), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), and Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )

Subjects
ascat, lcsh:Hydraulic engineering, 010504 meteorology & atmospheric sciences, Geography, Planning and Development, 0211 other engineering and technologies, Soil science, 02 engineering and technology, Aquatic Science, 01 natural sciences, Biochemistry, lcsh:Water supply for domestic and industrial purposes, lcsh:TC1-978, Image resolution, Water content, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Water Science and Technology, modis, lcsh:TD201-500, sentinel-2, sentinel-1, Vegetation, 15. Life on land, Scatterometer, [SDE.ES]Environmental Sciences/Environmental and Society, Spectroradiometer, change detection algorithm, Temporal resolution, Soil water, Environmental science, soil moisture, Change detection
Abstract

The aim of this study is to estimate surface soil moisture at a spatial resolution of 500 m and a temporal resolution of at least 6 days, by combining remote sensing data from Sentinel-1 and optical data from Sentinel-2 and MODIS (Moderate-Resolution Imaging Spectroradiometer). The proposed methodology is based on the change detection technique, applied to a series of measurements over a three-year period (2015 to 2018). The algorithm described here as &ldquo, Soil Moisture Estimations from the Synergy of Sentinel-1 and optical sensors (SMES)&rdquo, proposes different options, allowing information from vegetation densities and seasonal conditions to be taken into account. The output from this algorithm is a moisture index ranging between 0 and 1, with 0 corresponding to the driest soils and 1 to the wettest soils. This methodology has been tested at different test sites (South of France, Central Tunisia, Western Benin and Southwestern Niger), characterized by a wide range of different climatic conditions. The resulting surface soil moisture estimations are compared with in situ measurements and already existing satellite-derived soil moisture ASCAT (Advanced SCATterometer) products. They are found to be well correlated, for the African regions in particular (RMSE below 6 vol.%). This outcome indicates that the proposed algorithm can be used with confidence to estimate the surface soil moisture of a wide range of climatically different sites.

Published
2020
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38. Correcting satellite-based precipitation products through SMOS soil moisture data assimilation in two land-surface models of different complexity: API and SURFEX

Author

Yann Kerr, Wade T. Crow, François Gibon, Emmanuel Cosme, Diego Fernández-Prieto, Luca Brocca, Carlos Román-Cascón, Thierry Pellarin, and Christian Massari

Subjects
Rainfall, 010504 meteorology & atmospheric sciences, Meteorology, 0208 environmental biotechnology, Soil Science, Context (language use), 02 engineering and technology, 01 natural sciences, Data assimilation, Particle filter, Precipitation, Computers in Earth Sciences, Water content, SURFEX, 0105 earth and related environmental sciences, Remote sensing, Land use, Geology, 020801 environmental engineering, API, PERSIANN, Environmental science, Satellite, Soil moisture, Scale (map), SMOS
Abstract

Global rainfall information is useful for many applications. However, real-time versions of satellite-based rainfall products are known to contain errors. Recent studies have demonstrated how the information about rainfall intrinsically contained in soil moisture data can be utilised for improving rainfall estimates. That is, soil moisture dynamics are impacted for several days by the accumulated amount of rainfall following within a particular event. In this context, soil moisture data from the Soil Moisture Ocean Salinity (SMOS) satellite is used in this study to correct rainfall accumulation estimates provided by satellite-based real-time precipitation products such as CMORPH, TRMM-3B42RT or PERSIANN. An algorithm based on the SMOS measurements data assimilation is tested in two land-surface models of different complexity: a simple hydrological model (Antecedent Precipitation Index (API)) and a more sophisticated state-of-the-art land-surface model (SURFEX ( Surface Externalisee )). We show how the assimilation technique, based on a particle filter method, generally leads to a significant improvement in rainfall estimates, with slightly better results for the simpler (and less computationally demanding) API model. This methodology has been evaluated for six years at ten sites around the world with different land use and climatological features. The results also show the limitations of the methodology in regions highly affected by mountainous terrain, forest or intense radio-frequency interference (RFI), which can notably affect the quality of the retrievals. The satisfactory results shown here invite the future operational application of the methodology in near-real time on a global scale.

Published
2017
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39. A daily/25 km short-latency rainfall product for data scarce regions based on the integration of the GPM IMERG Early Run with multiple satellite soil moisture products

Author

Thierry Pellarin, Luca Ciabatta, Viviana Maggioni, Paolo Filippucci, Yann Kerr, Gab Abramowitz, Luca Brocca, Diego Fernandez Prieto, and Christian Massari

Subjects
Meteorology, Agriculture, business.industry, Environmental science, Triple collocation, Satellite, Short latency, Product (category theory), business, Water content, Categorical variable, Long latency
Abstract

Rain gauges are unevenly spaced around the world with extremely low gauge density over developing countries. For instance, in some regions in Africa the gauge density is often less than one station per 10 000 km2. The availability of rainfall data provided by gauges is also not always guaranteed in near real time or with a timeliness suited for agricultural and water resource management applications as gauges are also subject to malfunctions and regulations imposed by national authorities. A potential alternative are satellite-based rainfall estimates, yet comparisons with in-situ data suggest they're often not optimal. In this study, we developed a short-latency (i.e., 2–3 days) rainfall product derived from the combination of the Integrated Multi-Satellite Retrievals for GPM early run (IMERG-ER) with multiple satellite soil moisture-based rainfall products derived from ASCAT, SMOS and SMAP L3 satellite soil moisture (SM) retrievals. We tested the performance of this product over four regions characterized by high quality ground-based rainfall datasets (India, Conterminous United States, Australia and Europe) and over data scarce regions in Africa and South America by using Triple Collocation analysis (TC). We found the integration of satellite SM observations with in-situ rainfall observations is very beneficial with improvements of IMERG-ER up to 20 % and 40 % in terms of correlation and error, respectively, and a generalized enhancement in terms of categorical scores with the integrated product often outperforming reanalysis and ground-based long latency datasets. Given the importance of a reliable and readily available rainfall product for water resource management and agricultural applications over data scarce regions, the developed product can provide a valuable and unique source of rainfall information for these regions.

Published
2019
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41. SMOS-HR: A High Resolution L-Band Passive Radiometer for Earth Science and Applications

Author

Francois Cabot, Ali Khazaal, R. Rodriguez-Suquet, B. Rouge, Christophe Suere, Thibaut Decoopman, Olivier Merlin, Philippe Richaume, Thierry Pellarin, J. Costeraste, Arnaud Mialon, Maria Jose Escorihuela, Miguel Colom, Jean-Michel Morel, Nemesio Rodriguez-Fernandez, Yann Kerr, Baptiste Palacin, Ahmad Al Bitar, Jaqueline Boutin, Eric Anterrieu, Thierry Tournier, and Ghislain Picard

Subjects
L band, Radiometer, 010504 meteorology & atmospheric sciences, 0211 other engineering and technologies, 02 engineering and technology, Vegetation, 01 natural sciences, Salinity, Soil water, Cryosphere, Environmental science, Satellite, Image resolution, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing
Abstract

The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite has provided, for the first time, systematic passive L-band (1.4 GHz) measurements from space. This new data set, with a spatial resolution of ~40 km, has allowed a number of outstanding results over land (soil moisture, vegetation properties, frozen soils, …), ocean (salinity, meso-scale phenomena, river plumes, high winds, …) and cryosphere. SMOS, together with the NASA missions SMAP and Aquarius, have demonstrated the interest of the continuity of L-band observations. However, higher spatial resolution (1–10 km) is needed for applications related to water resources management and food security, for instance. Over the ocean as well as in coastal areas, higher resolution will bring the possibility to study in detail meso-scale processes and salinity (and density) variations closer to the coast. Over ice, higher spatial resolution will allow to monitor melting events in the coastal regions of Antarctica, for instance. In order to ensure the continuity of Earth observations in the L-band, while improving the resolution of the current generation of radiometers, new mission concepts are needed. We present the SMOS-HR (High-Resolution) project, which is currently in Phase 0 at CNES (Centre National d’Etudes Spatiales).

Published
2019
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42. Evaluation of remotely sensed rainfall products over Central Africa

Author

Sylvain Bigot, Jean-Claude Maki Mateso, Vincent Moron, Fils Makanzu Imwangana, Olivier Dewitte, Gaston Samba, Nadège Martiny, Geoffrey Barraud, Nathalie Philippon, Elise Monsieurs, Pierre Camberlin, Muhindo Sahani, Thierry Pellarin, Biogéosciences [UMR 6282] [Dijon] (BGS), Centre National de la Recherche Scientifique (CNRS)-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Institut des Géosciences de l’Environnement (IGE), Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut de Recherche pour le Développement (IRD)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Royal Museum for Central Africa [Tervuren] (RMCA), Unité de Recherche et de Formation en Gestion des Risques Naturels, Université Libre de Kinshasa (ULK), Department of Geophysics, Centre de Recherche en Sciences Naturelles [Lwiro, Congo] (CRSN), Faculty of Bioscience Engineering [Ghent], Universiteit Gent = Ghent University [Belgium] (UGENT), Earth and Life Institute [Louvain-La-Neuve] (ELI), Université Catholique de Louvain = Catholic University of Louvain (UCL), Department of Geography, Université de Liège, Fonds National de la Recherche Scientifique [Bruxelles] (FNRS), Centre européen de recherche et d'enseignement des géosciences de l'environnement (CEREGE), Aix Marseille Université (AMU)-Institut national des sciences de l'Univers (INSU - CNRS)-Collège de France (CdF (institution))-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Recherche Agronomique (INRA), Université catholique du Graben, Centre de Recherches et d'Etudes sur l'Environnement (CREE), Université Marien Ngouabi, Financial support from BELSPO for the RESIST (SR/00/305) research project (http://resist. africamuseum.be/), a F.R.S.-FNRS PhD scholarship, theDynAfFor project supported by the French Fund for the Global Environment (FFEM, grant no. CZZ1636.01D), the WaTFor project supported by the French Centre National d'Etudes Spatiales (CNES)., Institut de Recherche pour le Développement (IRD)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Ghent University [Belgium] (UGENT), Université Catholique de Louvain (UCL), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Collège de France (CdF)-Institut national des sciences de l'Univers (INSU - CNRS)-Aix Marseille Université (AMU)-Institut National de la Recherche Agronomique (INRA), Biogéosciences [UMR 6282] (BGS), Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Universiteit Gent = Ghent University (UGENT), and Institut de Recherche pour le Développement (IRD)-Institut National de la Recherche Agronomique (INRA)-Aix Marseille Université (AMU)-Collège de France (CdF (institution))-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Atmospheric Science, 010504 meteorology & atmospheric sciences, Central africa, DRC, Precipitation, Remote sensing, 01 natural sciences, 010305 fluids & plasmas, Central African Republic, Congo Basin, Ranking, 13. Climate action, [SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology, Climatology, 0103 physical sciences, PERSIANN, Dry season, Spatial ecology, Environmental science, Satellite, Cameroon, Gabon, Southern Hemisphere, 0105 earth and related environmental sciences
Abstract

24 pages; International audience; An intercomparison of seven gridded rainfall products incorporating satellite data (ARC, CHIRPS, CMORPH, PERSIANN, TAPEER, TARCAT, TMPA) is carried out over Central Africa, by evaluating them against three observed datasets: (a) the WaTFor database, consisting of 293 (monthly records) and 154 (daily records) rain‐gauge stations collected from global datasets, national meteorological services and monitoring projects, (b) the WorldClim v2 gridded database, and (c) a set of stations expanded from the FAOCLIM network, these two latter sets describing climate normals. All products fairly well reproduce the mean rainfall regimes and the spatial patterns of mean annual rainfall, although with some discrepancies in the east–west gradient. A systematic positive bias is found in the CMORPH product. Despite its lower spatial resolution, TAPEER shows reasonable skills. When considering daily rainfall amounts, TMPA shows best skills, followed by CMORPH, but over the central part of the Democratic Republic of the Congo, TARCAT is amongst the best products. Skills ranking is however different at the interannual time‐scale, with CHIRPS and TMPA performing best, though PERSIANN has comparable skills when only fully independent stations are used as reference. A preliminary study of Southern Hemisphere dry season variability, from the example of Kinshasa, shows that it is a difficult variable to capture with satellite‐based rainfall products. Users should still be careful when using any product in the most data‐sparse regions, especially for trend assessment.

Published
2019
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43. Three years of L-band brightness temperature measurements in a mountainous area: Topography, vegetation and snowmelt issues

Author

I. Redor, Romain Biron, Samuel Morin, Mike Schwank, I. Völksch, Yann Kerr, Thierry Pellarin, François Gibon, Arnaud Mialon, C. Coulaud, Bernard Mercier, and Matthieu Lafaysse

Subjects
Radiometer, 010504 meteorology & atmospheric sciences, Moisture, 0211 other engineering and technologies, Soil Science, Geology, 02 engineering and technology, Vegetation, Snow, 01 natural sciences, Normalized Difference Vegetation Index, Atmospheric radiative transfer codes, Brightness temperature, Snowmelt, Environmental science, Computers in Earth Sciences, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing
Abstract

L-band passive measurements (1.4 GHz) over continental areas are known to be related to surface soil moisture. Two satellite missions were recently launched to measure land surface emissions at this frequency band (SMOS-Soil Moisture and Ocean Salinity in 2009 and SMAP-Soil Moisture Active/Passive in 2015). In order to improve soil moisture retrievals from satellite data, ground-based radiometer systems operating at the same frequency were deployed over specified areas to investigate the L-band emission of various land covers under various climatological conditions. In this study, three years of L-band passive measurements from a radiometer installed on top of a steep mountain in the French Alps were analyzed and compared to L-band passive simulations. The innovative radiometer location led to large footprints due to the distance between the radiometer and the area under study. This experiment also produced microwave measurements affected by various potential difficulties typically encountered in SMOS/SMAP satellite missions: topography, heterogeneous footprints, dry/wet snow events, dew and vegetation litter. Based on in situ and modeling data, this paper investigates the potential of a radiative transfer model (L-band Microwave Emission of the Biosphere, L-MEB) to simulate L-band measurements and analyzes the differences with ELBARA observations. First, it was found that the topography generated a mixing of the horizontal and vertical polarizations. In addition, a large positive bias was found on ELBARA measurements (31 K and 12 K in horizontal and vertical polarizations respectively). Investigations showed that the sky reflection measured by the radiometer was partially substituted by land reflection coming from the surrounding topography. Second, the low-vegetation emission was investigated and highlighted the inability of the MODIS NDVI product to correctly represent the vegetation dynamics. Finally, dry snow conditions were found to have non-negligible impact at L-band and a particular signature was found during snow melting periods, with potential applications at the SMOS/SMAP spatial scales (~ 40 km).

Published
2016
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44. Overview of SMOS performance in terms of global soil moisture monitoring after six years in operation

Author

Philippe Waldteufel, Thierry Pellarin, Delphine Leroux, Susanne Mecklenburg, Thomas J. Jackson, Marie Parrens, Christoph Rudiger, Philippe Richaume, S. Bircher, Ali Mahmoodi, Arnaud Mialon, Nemesio Rodriguez-Fernandez, Yann Kerr, Ahmad Al Bitar, Beatriz Molero, Steven Delwart, Amen Al-Yaari, Rajat Bindlish, Jean-Pierre Wigneron, Centre d'études spatiales de la biosphère (CESBIO), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Interactions Sol Plante Atmosphère (UMR ISPA), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro), Laboratoire d'étude des transferts en hydrologie et environnement (LTHE), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Grenoble (OSUG ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), European Space Research and Technology Centre (ESTEC), Agence Spatiale Européenne = European Space Agency (ESA), USDA Agricultural Research Service [Maricopa, AZ] (USDA), United States Department of Agriculture (USDA), Department of Civil Engineering [Clayton], Monash University [Clayton], ESTER - LATMOS, Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), ESA Centre for Earth Observation (ESRIN), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Université Fédérale Toulouse Midi-Pyrénées-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Interactions Sol Plante Atmosphère (ISPA), Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), European Space Agency (ESA), United States Department of Agriculture - USDA (USA), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), and Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Observatoire des Sciences de l'Univers de Grenoble (OSUG )

Subjects
010504 meteorology & atmospheric sciences, Meteorology, 0211 other engineering and technologies, Soil Science, 02 engineering and technology, 01 natural sciences, Latitude, Validation, Satellite imagery, [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology, Computers in Earth Sciences, Water content, Product inter-comparison, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, [PHYS.PHYS.PHYS-AO-PH]Physics [physics]/Physics [physics]/Atmospheric and Oceanic Physics [physics.ao-ph], [SDE.IE]Environmental Sciences/Environmental Engineering, Simulation modeling, Geology, Soil classification, 15. Life on land, Retrieval accuracy metrics, Tropical rain forest, 13. Climate action, Soil water, Environmental science, Satellite, Soil moisture, Neural networks, SMOS
Abstract

International audience; The Soil Moisture and Ocean Salinity satellite (SMOS) was launched in November 2009 and started delivering data in January 2010. The commissioning phase ended in May 2010. Subsequently, the satellite has been in operation for over six years while the retrieval algorithms from Level 1 (L1) to Level 2 (L2) underwent significant evolutions as knowledge improved. Moreover, other approaches for retrieval at L2 over land were investigated while Level 3 (L3) and Level 4 (L4) were initiated. In this paper, these improvements were assessed by inter-comparisons of the current L2 (V620) against the previous version (V551) and new products (using neural networks referred to as SMOS-NN) and L3 (referred to as SMOS-L3). In addition, a global evaluation of different SMOS soil moisture (SM) products (SMOS-L2, SMOS-L3, and SMOS-NN) was performed comparing products with those of model simulations and other satellites. Finally, all products were evaluated against in situ measurements of soil moisture (SM). To achieve such a goal a set of metrics to evaluate different satellite products are suggested.The study demonstrated that the V620 shows a significant improvement (including those at L1 improving L2) with respect to the earlier version V551. Results also show that neural network based approaches can often yield excellent results over areas where other products are poor. Finally, global comparison indicates that SMOS behaves very well when compared to other sensors/approaches and gives consistent results over all surfaces from very dry (African Sahel, Arizona), to wet (tropical rain forests). RFI (Radio Frequency Interference) is still an issue even though detection has been greatly improved through the significant reduction of RFI sources in several areas of the world. When compared to other satellite products, the analysis shows that SMOS achieves its expected goals and is globally consistent over different eco climate regions from low to high latitudes and throughout the seasons.

Published
2016
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45. Millet yield estimates in the Sahel using satellite derived soil moisture time series

Author

Carlos Román-Cascón, Yann Kerr, Christian Baron, Seydou B. Traoré, Thierry Pellarin, Agali Alhassane, François Gibon, Danny Lo Seen, Institut des Géosciences de l’Environnement (IGE), Institut de Recherche pour le Développement (IRD)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Centre Régional AGRHYMET (CRA), Université de Reims Champagne-Ardenne (URCA), Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Gestion de l'Eau, Acteurs, Usages (UMR G-EAU), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut de Recherche pour le Développement (IRD), CNES (TOSCA), French Ministry of Higher Education and Research, Laboratoire d'étude des transferts en hydrologie et environnement (LTHE), Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Université Fédérale Toulouse Midi-Pyrénées-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), and Institut de Recherche pour le Développement (IRD)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-AgroParisTech-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)

Subjects
Atmospheric Science, 010504 meteorology & atmospheric sciences, millet, 0208 environmental biotechnology, 02 engineering and technology, Imagerie par satellite, 01 natural sciences, Data assimilation, F01 - Culture des plantes, Yield (wine), Sahel, Niger, Water content, ComputingMilieux_MISCELLANEOUS, 2. Zero hunger, Global and Planetary Change, Forestry, Vegetation, Rendement des cultures, C30 - Documentation et information, Banque de données, SMOS, Télédétection, Crop, Donnée climatique, Crop yield, Eau du sol, P10 - Ressources en eau et leur gestion, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, 0105 earth and related environmental sciences, business.industry, 15. Life on land, 020801 environmental engineering, FAO, Agronomy, 13. Climate action, Agriculture, Environmental science, Satellite, Soil moisture, U30 - Méthodes de recherche, business, Agronomy and Crop Science
Abstract

International audience; In the Sahel, crop growth and yield are strongly linked to climate fluctuations. The low and erratic rainfall the Sahel region has experienced for several years led to poor harvests, associated with dramatic food crises and famines. Consequently, numerous studies were conducted to develop innovative techniques to estimate crop yield based on satellite measurements. Unlike most approaches which use rainfall, temperature or vegetation indices to derive crop yield estimates, the present study investigates the potential of satellite-derived soil moisture products. This study focuses on millet, a major food crop in Africa. A first step was devoted to analyzing the relation between soil moisture and millet yield at the local scale using ground-based soil moisture and millet yield measurements obtained at ten site locations in Niger. Then, the statistical relationship obtained at the local scale was assessed at the regional scale (Niger, Mali, Senegal and Burkina Faso) using satellite-based soil moisture mapping (based on a simple land-surface model and a satellite precipitation product) and compared to millet yield estimates from the Food and Agriculture Organization (FAO) database. It was shown that millet yield variations are closely linked to soil moisture variations during two key periods of the plant growth: the "grain filling" and the "reproductive" periods. Soil moisture variations during these two periods led to explain 81% (R-2 = 0.81) of the FAO millet yield variations from 1998 to 2014 in the Sahel.

Published
2018
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46. SMOS-IC: Current status and overview of soil moisture and VOD applications

Author

Amen Al-Yaari, Roberto Fernandez-Moran, Feng Tian, Arnaud Mialon, Mohsen Ebrahimi, Thierry Pellarin, Jan J. Quets, Lei Fan, Rasmus Fensholt, G. De Lannoy, Yann Kerr, Jean-Pierre Wigneron, Nemesio Rodriguez-Fernandez, Martin Brandt, Interactions Sol Plante Atmosphère (UMR ISPA), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro), Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Université Catholique de Louvain = Catholic University of Louvain (UCL), University of Tehran, Institut des Géosciences de l’Environnement (IGE), Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut de Recherche pour le Développement (IRD)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), and University of Copenhagen = Københavns Universitet (KU)

Subjects
Vegetation optical depth, 010504 meteorology & atmospheric sciences, [SDV]Life Sciences [q-bio], 0211 other engineering and technologies, Biosphere, Inversion (meteorology), 02 engineering and technology, 01 natural sciences, Microwave emission, [SDE]Environmental Sciences, Environmental science, Water content, Retrieval algorithm, Carbon stock, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing
Abstract

© 2018 IEEE. In 2017, the new SMOS-IC retrieval product of soil moisture (SM) and L-band Vegetation Optical depth (L-VOD) was developed. This product relies on a two-parameter inversion of the L-MEB model (L-band Microwave Emission of the Biosphere) which requires little ancillary information and was found to be accurate, making it very well-suited for application in agriculture, hydrology, climate and vegetation monitoring. In this communication we present recent improvements in the SMOS-IC retrieval algorithm and recent applications using the soil moisture or VOD retrievals from the SMOS-IC data set. SMOS-IC SM is available at the French CATDS center. ispartof: pages:1451-1454 ispartof: IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM vol:2018-July pages:1451-1454 ispartof: 38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS) location:Valencia Spain date:22 Jul - 27 Jul 2018 status: Published online

Published
2018
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47. Validation of satellite microwave retrieved soil moisture with global ground-based measurements

Author

Yann Kerr, Wouter Dorigo, Amen Al-Yaari, Jean-Pierre Wigneron, Thierry Pellarin, Lei Fan, Arnaud Mialon, Andreas Colliander, Interactions Sol Plante Atmosphère (UMR ISPA), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro), Centre d'études spatiales de la biosphère (CESBIO), Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Vienna University of Technology, Jet Propulsion Laboratory (JPL), NASA-California Institute of Technology (CALTECH), and Université Grenoble Alpes (COMUE) (UGA)

Subjects
010504 meteorology & atmospheric sciences, [SDV]Life Sciences [q-bio], 0208 environmental biotechnology, 02 engineering and technology, 01 natural sciences, 020801 environmental engineering, 13. Climate action, Remote sensing (archaeology), [SDE]Environmental Sciences, Environmental science, Microwave remote sensing, Satellite, Water content, Microwave, 0105 earth and related environmental sciences, Remote sensing
Abstract

International audience; Soil moisture retrieval from microwave remote sensing brightness temperatures is in continuous development. In this study, the most recent and latest microwave remote sensing soil moisture products were evaluated against ground-based measurements. We compared, for the first time, the latest versions of SMOS (L2V650 and SMOS-IC V105), SMAP (L3V4), and CCI (V03.2) soil moisture products with respect to ground-based measurements obtained from ISMN (International Soil Moisture Network). Time series were plotted over some sites and it was found that all these products capture well the temporal dynamics over all the sites used in this study. However, CCI was wetter than the in situ measurements over Niger and both SMOS products (IC and L2) and SMAP were drier than the in situ observations over Biebrza site in Poland.

Published
2018
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48. EnOI Optimization for SMOS Soil Moisture Over West Africa

Author

Yann Kerr, Thierry Pellarin, and Ju Hyoung Lee

Subjects
Atmospheric Science, Stationary process, Meteorology, Initialization, Numerical weather prediction, Physics::Geophysics, Data modeling, Data assimilation, Environmental science, Satellite, Ensemble Kalman filter, Computers in Earth Sciences, Physics::Atmospheric and Oceanic Physics, Remote sensing, Interpolation
Abstract

In land surface or numerical weather prediction (NWP) models, a soil moisture initialization scheme is important not to drift the prognostic variables to errors. We propose a novel approach for a stationary data assimilation scheme of ensemble optimal interpolation (EnOI) effective for soil moisture and ocean salinity (SMOS) soil moisture initialization. For the optimization of EnOI, the satellite retrieval error specification was conducted rather than ensemble evolution. As combining two ensembles generated from a satellite retrieval and a land surface model, this approach is termed as “two-step EnOI” in this study: (first step) the SMOS soil moisture retrieval ensembles (i.e., errors in brightness temperature, landscape, and geophysical parameters) were merged with SMOS L3 data; (second step) the data assimilation result from the first step was further used for the observations of the EnOI. This two-step EnOI was compared with a sequential ensemble Kalman filter (EnKF) evolving model state ensembles over time but assuming global constant a priori random errors for the SMOS observations. The point-scale comparison results showed that two-step EnOI was better matched with the field measurements than the SMOS L3 data and a sequential ensemble KF scheme. On meso-scale, a spatial average of two-step EnOI reached that of a sequential ensemble KF with the significantly reduced ensemble size. These results suggest that the performance of two-step EnOI is comparable to a sequential ensemble KF but computationally more effective. From this, it is illustrated that appropriate error specification of satellite retrieval is more important than a sequential evolution of model state ensembles, and brightness temperature ensemble mean can reduce the SMOS retrieval biases without sequential evolution.

Published
2015
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49. Changes in Vegetation and Rainfall over West Africa during the Last Three Decades (1981-2010)

Author

Arona Diedhiou, Issiaka Savane, Ahmed A. Balogun, Bamory Kamagate, Adama Bamba, Nadine Dessay, Abdourahamane Konaré, Bastien Dieppois, Thierry Pellarin, Coventry University, Laboratoire de physique de l'atmosphère et de mécaniques des fluides (LAPA-MF), Université de Cocody, Laboratoire d'étude des transferts en hydrologie et environnement (LTHE), Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), UMR 228 Espace-Dev, Espace pour le développement, Université de Guyane (UG)-Université des Antilles (UA)-Institut de Recherche pour le Développement (IRD)-Université de Perpignan Via Domitia (UPVD)-Avignon Université (AU)-Université de La Réunion (UR)-Université de Montpellier (UM), Hydrosciences Montpellier (HSM), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Université Montpellier 2 - Sciences et Techniques (UM2)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD)-Université de Perpignan Via Domitia (UPVD)-Avignon Université (AU)-Université de La Réunion (UR)-Université de Montpellier (UM)-Université de Guyane (UG)-Université des Antilles (UA), Observatoire des Sciences de l'Univers de Grenoble (OSUG), Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), and Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[SPI.OTHER]Engineering Sciences [physics]/Other, Decadal Variability, 010504 meteorology & atmospheric sciences, NDVI, [SDE.MCG]Environmental Sciences/Global Changes, 0211 other engineering and technologies, [SCCO.COMP]Cognitive science/Computer science, Distribution (economics), 02 engineering and technology, 01 natural sciences, Normalized Difference Vegetation Index, West africa, Atmosphere, Precipitation, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, business.industry, Significant t-Test, Cru, [SHS.GEO]Humanities and Social Sciences/Geography, General Medicine, Vegetation, 15. Life on land, [SDE.ES]Environmental Sciences/Environmental and Society, Geography, [SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology, 13. Climate action, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Rainfall Recovery, Period (geology), [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Physical geography, business, Re-Greening
Abstract

International audience; The decadal variability of rainfall and vegetation over West Africa have been studied over the last three decades, 1981-1990, 1991-2000 and 2001-2010 denoted as 1980s, 1990s and 2000s, respectively. Climate Research Unit (CRU) monthly precipitation and Normalized Difference Vegetation Index (NDVI) from the National Oceanic and Atmosphere Administration (NOAA), all covering the period 1981-2010 have been used. This study aimed to assess the changes in the land surface condition and the spatio-temporal distribution of rainfall over West Africa region. The relationship between rainfall and vegetation indices over this region was determined using Pearson’s correlation. Also, the decadal comparison between rainfall and NDVI over the region was based on the significant t-test and the Pearson’s correlation. Results showed that significant return to wet conditions is observed between decade 1980s and decade 1990s over West Africa, and also during decade 2000s with the exception of central Benin and the western Nigeria. Meanwhile, a regreening of the central Sahel and Sudano-Sahel regions is noted. From 1990s to 2000s, this regreening belt is located in the South and the coastal areas: the Guinea Coast, Sudano-Guinea and western Sahel regions. A northward displacement of this re-greening belt is also detected. Thus, a linear relationship occurs between rainfall and NDVI in the Sudanian savannah region, but it is not the case in the rest of West Africa. This may suggest that the re-growth of vegetation in the Sudanian savannah region may be linked to rainfall supplies. Therefore, re-greening over Sahel region in 1990s is related to rainfall recovery. However, this re-greening was not sustained in the decade 2000s due to a slight decrease in rainfall.

Published
2015
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