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1. Early-life dispersal traits of coastal fishes: an extensive database combining observations and growth models

Author: M. Di Stefano, D. Nerini, I. Alvarez, G. Ardizzone, P. Astruch, G. Basterretxea, A. Blanfuné, D. Bonhomme, A. Calò, I. Catalan, C. Cattano, A. Cheminée, R. Crec'hriou, A. Cuadros, A. Di Franco, C. Diaz-Gil, T. Estaque, R. Faillettaz, F. C. Félix-Hackradt, J. A. Garcia-Charton, P. Guidetti, L. Guilloux, J.-G. Harmelin, M. Harmelin-Vivien, M. Hidalgo, H. Hinz, J.-O. Irisson, G. La Mesa, L. Le Diréach, P. Lenfant, E. Macpherson, S. Matić-Skoko, M. Mercader, M. Milazzo, T. Monfort, J. Moranta, M. Muntoni, M. Murenu, L. Nunez, M. P. Olivar, J. Pastor, Á. Pérez-Ruzafa, S. Planes, N. Raventos, J. Richaume, E. Rouanet, E. Roussel, S. Ruitton, A. Sabatés, T. Thibaut, D. Ventura, L. Vigliola, D. Vrdoljak, and V. Rossi
Subjects: Environmental sciences, GE1-350, Geology, QE1-996.5
Abstract: Early-life stages play a key role in the dynamics of bipartite life cycle marine fish populations. Difficult to monitor, observations of these stages are often scattered in space and time. While Mediterranean coastlines have often been surveyed, no effort has been made to assemble historical observations. Here we build an exhaustive compilation of dispersal traits for coastal fish species, considering in situ observations and growth models (Di Stefano et al., 2023; https://doi.org/10.17882/91148). Our database contains over 110 000 entries collected from 1993 to 2021 in various subregions. All observations are harmonized to provide information on dates and geolocations of both spawning and settlement, along with pelagic larval durations. When applicable, missing data and associated confidence intervals are reconstructed from dynamic energy budget theory. Statistical analyses allow traits’ variability to be revisited and sampling biases to be revealed across taxa, space and time, hence providing recommendations for future studies and sampling. Comparison of observed and modelled entries provides suggestions to improve the feed of observations into models. Overall, this extensive database is a crucial step to investigate how marine fish populations respond to global changes across environmental gradients.
Published: 2024
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2. Evaluating soil moisture retrieval in Arctic and sub-Arctic environments using passive microwave satellite data

Author: Juliette Ortet, Arnaud Mialon, Yann Kerr, Alain Royer, Aaron Berg, Julia Boike, Elyn Humphreys, François Gibon, Philippe Richaume, Simone Bircher-Adrot, Azza Gorrab, and Alexandre Roy
Subjects: Arctic and sub-Arctic, CCI, L-band, SMAP, SMOS, soil moisture, Mathematical geography. Cartography, GA1-1776
Abstract: Soil Moisture (SM) is a key parameter in northern Arctic and sub-Arctic (A-SA) environments that are highly vulnerable to climate change. We evaluated six SM satellite passive microwave datasets using thirteen ground-based SM stations across Northwestern America. The best agreement was obtained with SMAP (Soil Moisture Active Passive) products with the lowest RMSD (Root Mean Square Difference) (0.07 m[Formula: see text] m[Formula: see text]) and the highest R (0.55). ESA CCI (European Space Agency Climate Change Initiative) also performed well in terms of correlation with a similar R (0.55) but showed a strong variation among sites. Weak results were obtained over sites with high water body fractions. This study also details and evaluates a dedicated retrieval of SM from SMOS (Soil Moisture and Ocean Salinity) brightness temperatures based on the [Formula: see text] model. Two soil dielectric models (Mironov and Bircher) and a dedicated soil roughness and single scattering albedo parameterization were tested. Water body correction in the retrieval shows limited improvement. The metrics of our retrievals (RMSD = 0.08 m[Formula: see text] m[Formula: see text] and R = 0.41) are better than SMOS but outperformed by SMAP. Passive microwave satellite remote sensing is suitable for SM retrieval in the A-SA region, but a dedicated approach should be considered.
Published: 2024
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3. Estimating the uncertainties of satellite derived soil moisture at global scale

Author: François Gibon, Arnaud Mialon, Philippe Richaume, Nemesio Rodríguez-Fernández, Daniel Aberer, Alexander Boresch, Raffaele Crapolicchio, Wouter Dorigo, Alexander Gruber, Irene Himmelbauer, Wolfgang Preimesberger, Roberto Sabia, Pietro Stradiotti, Monika Tercjak, and Yann H. Kerr
Subjects: Soil moisture, Uncertainty, Passive microwave remote sensing, SMOS, ISMN, Physical geography, GB3-5030, Science
Abstract: This study attempts to derive the uncertainty of the soil moisture estimation from passive microwave satellite mission at global scale. To do so, the approach is based on the sensitivity of the Soil Moisture and Ocean Salinity (SMOS) soil moisture retrieval quality to the land surface characteristics within its footprint (presence of forest, topography, open water bodies, sand, clay, bulk density and soil organic carbon content). First, we performed a global assessment of SMOS using in situ measurements from the International Soil Moisture Network (ISMN) as reference, with more than 1900 ISMN stations and 10 years of SMOS data. This assessment shows that the ubRMSD scores vary greatly between locations (with a mean of 0.074 m3m−3 and an interquartile range of 0.030 m3m−3). Second, the scores are analyzed for different surface conditions within the satellite footprint. The best agreement between the ground measurement and SMOS time series are obtained for low forest cover, low topographic complexity, and marginal presence of open water bodies within the SMOS footprint. Soil parameters also have an impact, with better scores for sandier soils with a high bulk-density and low soil organic carbon content. Finally, we propose to extrapolate the obtained relationships, using a multiple linear regression, in order to derive a global map of SMOS uncertainties based on surface conditions. This map of predicted uncertainties show a diverse range of ubRMSD values across the globe (with a mean of 0.076 m3m−3 and an interquartile range of 0.031 m3m−3) depending on the surface characteristics. At the ISMN site location, the predicted ubRMSD shows similar results than the comparison between SMOS and the in situ measurements. The map of predicted SMOS ubRMSD represents an upper bound estimate of the SMOS uncertainty, as it includes the uncertainties of the in situ sensor measurements and the scale mismatch. Further investigations will focus on the different components of this uncertainty budget to obtain a better assessment of the absolute uncertainties of SMOS soil moisture retrievals across the globe.
Published: 2024
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4. Assessment of SMOS Root Zone Soil Moisture: A Comparative Study Using SMAP, ERA5, and GLDAS

Author: Nitu Ojha, Ali Mahmoodi, Arnaud Mialon, Philippe Richaume, Sylvain Ferrant, and Yann H. Kerr
Subjects: Surface soil moisture, root zone soil moisture, SMOS, SMAP, GLDAS, ERA5, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract: Root zone soil moisture (RZSM) refers to the amount of water present in the soil layer where plants can freely absorb water, and its information is crucial for various applications such as hydrology and agriculture. SMOS and SMAP remote sensing satellites provide soil moisture (SM) data on a global scale but limit their sensing capability to a depth of approximately 5 cm. However, for a comprehensive understanding of soil water content in the root zone, a deeper insight into RZSM is essential, which extends from 5 cm to 100 cm. Hence, to bridge the gap between the surface SM and RZSM, SMOS surface SM information (5 cm) was integrated into the root zone (100 cm) using a simple subsurface physical model. SMOS RZSM data are available on a global scale with 25 km sampling on EASE grid 2, which provides a daily temporal scale from 2010 to the present. The main aims of this study were to i) check the efficacy of a simple model-based approach and ii) investigate the importance of remote sensing SM observations for the retrieval of RZSM. Here, we investigate the benefit of the simple model-based approach by comparing SMOS RZSM (simple model) and SMAP, ERA5, and GLDAS RZSM (complex model or data-assimilation) with in-situ SM. We then investigated the role of remote sensing SM observation in the retrieval of RZSM by comparing SMOS RZSM and SMAP RZSM products over rice-irrigated areas for dry seasons (minimal rainfall) in Telangana, South India. First, SMOS RZSM was evaluated with in-situ SM data for four distinct networks: SCAN, HOBE, SMOSMANIA, and Amma catch from 2011 to 2017. The results between SMOS RZSM and in-situ SM show an average correlation coefficient between 0.54 and 0.8 with an average unbiased root mean square difference (ubRMSD) within the threshold of 0.04 m3/m3. The average correlation coefficient between the RZSM and in-situ SM for the SMOS and SMAP RZSM shows better performance in the range (0.55 to 0.93) than the ERA5 and GLDAS RZSM in the range (0.20 to 0.93). Finally, the outcomes of SMOS and SMAP RZSM over irrigated areas show that only SMOS RZSM captures changes in SM dynamics due to irrigation, particularly during the dry season.
Published: 2024
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5. Unveiling microbiome changes in Mediterranean octocorals during the 2022 marine heatwaves: quantifying key bacterial symbionts and potential pathogens

Author: Camille Prioux, Romie Tignat-Perrier, Ophélie Gervais, Tristan Estaque, Quentin Schull, Stéphanie Reynaud, Eric Béraud, Bastien Mérigot, Anaïs Beauvieux, Maria-Isabelle Marcus, Justine Richaume, Olivier Bianchimani, Adrien Cheminée, Denis Allemand, and Christine Ferrier-Pagès
Subjects: Corallium rubrum, Paramuricea clavata, Marine heatwaves, Climate change, Bacterial communities, Holobiont, Microbial ecology, QR100-130
Abstract: Abstract Background Climate change has accelerated the occurrence and severity of heatwaves in the Mediterranean Sea and poses a significant threat to the octocoral species that form the foundation of marine animal forests (MAFs). As coral health intricately relies on the symbiotic relationships established between corals and microbial communities, our goal was to gain a deeper understanding of the role of bacteria in the observed tissue loss of key octocoral species following the unprecedented heatwaves in 2022. Results Using amplicon sequencing and taxon-specific qPCR analyses, we unexpectedly found that the absolute abundance of the major bacterial symbionts, Spirochaetaceae (C. rubrum) and Endozoicomonas (P. clavata), remained, in most cases, unchanged between colonies with 0% and 90% tissue loss. These results suggest that the impairment of coral health was not due to the loss of the main bacterial symbionts. However, we observed a significant increase in the total abundance of bacterial opportunists, including putative pathogens such as Vibrio, which was not evident when only their relative abundance was considered. In addition, there was no clear relation between bacterial symbiont loss and the intensity of thermal stress, suggesting that factors other than temperature may have influenced the differential response of octocoral microbiomes at different sampling sites. Conclusions Our results indicate that tissue loss in octocorals is not directly caused by the decline of the main bacterial symbionts but by the proliferation of opportunistic and pathogenic bacteria. Our findings thus underscore the significance of considering both relative and absolute quantification approaches when evaluating the impact of stressors on coral microbiome as the relative quantification does not accurately depict the actual changes in the microbiome. Consequently, this research enhances our comprehension of the intricate interplay between host organisms, their microbiomes, and environmental stressors, while offering valuable insights into the ecological implications of heatwaves on marine animal forests. Video Abstract
Published: 2023
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6. Unveiling microbiome changes in Mediterranean octocorals during the 2022 marine heatwaves: quantifying key bacterial symbionts and potential pathogens

Author: Prioux, Camille, Tignat-Perrier, Romie, Gervais, Ophélie, Estaque, Tristan, Schull, Quentin, Reynaud, Stéphanie, Béraud, Eric, Mérigot, Bastien, Beauvieux, Anaïs, Marcus, Maria-Isabelle, Richaume, Justine, Bianchimani, Olivier, Cheminée, Adrien, Allemand, Denis, and Ferrier-Pagès, Christine
Published: 2023
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7. Above-Ground Biomass Estimation Based on Multi-Angular L-Band Measurements of Brightness Temperatures

Author: Julio Cesar Salazar-Neira, Arnaud Mialon, Philippe Richaume, Stephane Mermoz, Yann H. Kerr, Alexandre Bouvet, Thuy Le Toan, Simon Boitard, and Nemesio J. Rodriguez-Fernandez
Subjects: Above-ground biomass (AGB), forest biomass, $L$ -band, machine learning, neural networks, passive microwaves (PMWs), Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809
Abstract: There is growing interest in using passive microwave observations and vegetation optical depth (VOD) to study the above-ground biomass (AGB) and carbon stocks evolution. L-band observations, in particular, have been shown to be very sensitive to AGB. Here, thanks to the multiangle capabilities of the soil moisture and ocean salinity mission, a new approach to estimate AGB directly from multiangular L-band brightness temperatures (TBs) is proposed, thus surpassing the use of intermediate variables such as VOD. The European Space Agency (ESA) Climate Change Initiative (CCI) Biomass maps for the years 2010, 2017, and 2018 are used as the AGB reference. AGB estimates from artificial neural networks (ANN) using a purely data-driven approach explained up to 88% of AGB variability globally; even so, a decrease in retrieval performance was observed when models are applied to data from years different than the year used for their training. A new training methodology based on multiyear training sets is presented, leading to results showing more stability for temporal analyses. The best set of predictors and an optimal learning dataset configuration are proposed based on an assessment of the accuracy of the estimates. The ANN methodology using TBs is a promising alternative with respect to the common method of using a parametric function to estimate AGB from VOD. ANNs AGB estimates showed a higher correlation with CCI AGB maps ($R$2 $\sim$0.87 instead of $\sim$0.84) and presented a stronger agreement with their spatial structure and less differences in residual maps.
Published: 2023
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8. Unfolding H-convex Manhattan Towers

Author: Richaume, Lydie, Andres, Eric, Largeteau-Skapin, Gaëlle, and Zrour, Rita
Published: 2022
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9. SMOS L-VOD Retrieved by Level 2 Algorithm and its Correlation With GEDI LIDAR Products

Author: Cristina Vittucci, Leila Guerriero, Paolo Ferrazzoli, Philippe Richaume, and Yann H. Kerr
Subjects: LIDAR, microwave radiometry, vegetation height, vegetation optical depth, Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809
Abstract: In this article, L-band vegetation optical depth (L-VOD) retrieved by Soil Moisture and Ocean Salinity (SMOS) in four large continents is compared against vegetation parameters (RH100 and PAI) retrieved by Global Ecosystem Dynamics Investigation (GEDI) LIDAR instrument, recently launched by NASA. In order to manage the different spatial resolutions, GEDI parameters were averaged within SMOS pixels and a threshold to the minimum number of GEDI samples per SMOS pixel was applied. Spatial correlations between monthly averages were investigated from May 2019 to April 2020. For continents mostly covered by tropical vegetation (Africa and South America), the Pearson correlation coefficients between L-VOD and RH100 are higher than 0.8 in all months of the year. Conversely, seasonal effects are observed in North America and Asia, producing a lower correlation in colder months. RMS differences between L-VODs retrieved by SMOS and the ones obtained using a linear regression over RH100 are lower than 0.2 for all cases, and close to 0.1 for most cases. Using PAI in place of RH100 slightly lower spatial correlations are generally achieved. Overall, the obtained results confirm the good potential of L-VOD to monitor vegetation height in different environments.
Published: 2021
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10. 3D Photogrammetry Modeling Highlights Efficient Reserve Effect Apparition After 5 Years and Stillness After 40 for Red Coral (Corallium rubrum) Conservation in French MPAs

Author: Justine Richaume, Adrien Cheminée, Pierre Drap, Patrick Bonhomme, Frederic Cadene, Bruno Ferrari, Virginie Hartmann, Noëmie Michez, and Olivier Bianchimani
Subjects: MPAs, photogrammetry, Corallium rubrum, reserve effect, BACI design, Science, General. Including nature conservation, geographical distribution, QH1-199.5
Abstract: Imaging the marine environment is more and more useful to understand relationships between species, as well as natural processes. Developing photogrammetry allowed the use of 3D measuring to study populations dynamics of sessile organisms at various scales: from colony to population. This study focuses on red coral (Corallium rubrum), as known as precious coral. Metrics measured at a colony scale (e.g., maximum height, diameter and number of branches) allowed population understanding and a comparison between an old (Cerbère-Banyuls reserve) vs. a new (Calanques National Park) MPA. Our results suggested a 5-year time step allows the appearance of a significant difference between populations inside vs. outside the Calanques National Park no-take zones. Red coral colonies were taller and had more branches inside no-take zones. A significant difference was still observable for the populations inside the Cerbère-Banyuls reserve after 40 years of protection, reflecting the sustainability and effectiveness of precautionary measures set by the reserve. The impacts at the local level (mechanical destruction) and those presumed to occur via global change (climatic variations) underline the need to develop strategies both to follow the evolutions of red coral populations but also to understand their resilience. Photogrammetry induced modeling is a time and cost effective as well as non-invasive method which could be used to understand population dynamics at a seascape scale on coralligenous reefs.
Published: 2021
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11. An evaluation of SMOS L-band vegetation optical depth (L-VOD) data sets: high sensitivity of L-VOD to above-ground biomass in Africa

Author: N. J. Rodríguez-Fernández, A. Mialon, S. Mermoz, A. Bouvet, P. Richaume, A. Al Bitar, A. Al-Yaari, M. Brandt, T. Kaminski, T. Le Toan, Y. H. Kerr, and J.-P. Wigneron
Subjects: Ecology, QH540-549.5, Life, QH501-531, Geology, QE1-996.5
Abstract: The vegetation optical depth (VOD) measured at microwave frequencies is related to the vegetation water content and provides information complementary to visible/infrared vegetation indices. This study is devoted to the characterization of a new VOD data set obtained from SMOS (Soil Moisture and Ocean Salinity) satellite observations at L-band (1.4 GHz). Three different SMOS L-band VOD (L-VOD) data sets (SMOS level 2, level 3 and SMOS-IC) were compared with data sets on tree height, visible/infrared indexes (NDVI, EVI), mean annual precipitation and above-ground biomass (AGB) for the African continent. For all relationships, SMOS-IC showed the lowest dispersion and highest correlation. Overall, we found a strong (R > 0.85) correlation with no clear sign of saturation between L-VOD and four AGB data sets. The relationships between L-VOD and the AGB data sets were linear per land cover class but with a changing slope depending on the class type, which makes it a global non-linear relationship. In contrast, the relationship linking L-VOD to tree height (R = 0.87) was close to linear. For vegetation classes other than evergreen broadleaf forest, the annual mean of L-VOD spans a range from 0 to 0.7 and it is linearly correlated with the average annual precipitation. SMOS L-VOD showed higher sensitivity to AGB compared to NDVI and K/X/C-VOD (VOD measured at 19, 10.7 and 6.9 GHz). The results showed that, although the spatial resolution of L-VOD is coarse ( ∼ 40 km), the high temporal frequency and sensitivity to AGB makes SMOS L-VOD a very promising indicator for large-scale monitoring of the vegetation status, in particular biomass.
Published: 2018
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12. Validation Practices for Satellite Soil Moisture retrievals: What are (the) errors?

Author: A. Gruber, G. De Lannoy, C. Albergel, A. Al-Yaari, L. Brocca, J.-C. Calvet, Andreas Colliander, M. Cosh, W. Crow, W. Dorigo, Clara S Draper, M. Hirschi, Y. Kerr, A. Konings, W. Lahoz, K. McColl, C. Montzka, J. Munoz Sabater, J. Peng, Rolf Helmut Reichle, P. Richaume, C. Rudiger, T. Scanlon, R. van der Schalie, J.-P. Wigneron, and W. Wagner
Subjects: Geosciences (General)
Abstract: This paper presents a community effort to develop good practice guidelines for the validation of global coarse-scale satellite soil moisture products. We provide theoretical background, a review of state-of-the-art methodologies for estimating errors in soil moisture data sets, practical recommendations on data pre-processing and presentation of statistical results, and a recommended validation protocol that is supplemented with an example validation exercise focused on microwave-based surface soil moisture products. We conclude by identifying research gaps that should be addressed in the near future.
Published: 2020
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13. SMOS near-real-time soil moisture product: processor overview and first validation results

Author: N. J. Rodríguez-Fernández, J. Muñoz Sabater, P. Richaume, P. de Rosnay, Y. H. Kerr, C. Albergel, M. Drusch, and S. Mecklenburg
Subjects: Technology, Environmental technology. Sanitary engineering, TD1-1066, Geography. Anthropology. Recreation, Environmental sciences, GE1-350
Abstract: Measurements of the surface soil moisture (SM) content are important for a wide range of applications. Among them, operational hydrology and numerical weather prediction, for instance, need SM information in near-real-time (NRT), typically not later than 3 h after sensing. The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite is the first mission specifically designed to measure SM from space. The ESA Level 2 SM retrieval algorithm is based on a detailed geophysical modelling and cannot provide SM in NRT. This paper presents the new ESA SMOS NRT SM product. It uses a neural network (NN) to provide SM in NRT. The NN inputs are SMOS brightness temperatures for horizontal and vertical polarizations and incidence angles from 30 to 45°. In addition, the NN uses surface soil temperature from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS). The NN was trained on SMOS Level 2 (L2) SM. The swath of the NRT SM retrieval is somewhat narrower (∼ 915 km) than that of the L2 SM dataset (∼ 1150 km), which implies a slightly lower revisit time. The new SMOS NRT SM product was compared to the SMOS Level 2 SM product. The NRT SM data show a standard deviation of the difference with respect to the L2 data of 3 m−3 in most of the Earth and a Pearson correlation coefficient higher than 0.7 in large regions of the globe. The NRT SM dataset does not show a global bias with respect to the L2 dataset but can show local biases of up to 0.05 m3 m−3 in absolute value. The two SMOS SM products were evaluated against in situ measurements of SM from more than 120 sites of the SCAN (Soil Climate Analysis Network) and the USCRN (US Climate Reference Network) networks in North America. The NRT dataset obtains similar but slightly better results than the L2 data. In summary, the NN SMOS NRT SM product exhibits performances similar to those of the Level 2 SM product but it has the advantage of being available in less than 3.5 h after sensing, complying with NRT requirements. The new product is processed at ECMWF and it is distributed by ESA and via the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) multicast service (EUMETCast).
Published: 2017
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14. The global SMOS Level 3 daily soil moisture and brightness temperature maps

Author: A. Al Bitar, A. Mialon, Y. H. Kerr, F. Cabot, P. Richaume, E. Jacquette, A. Quesney, A. Mahmoodi, S. Tarot, M. Parrens, A. Al-Yaari, T. Pellarin, N. Rodriguez-Fernandez, and J.-P. Wigneron
Subjects: Environmental sciences, GE1-350, Geology, QE1-996.5
Abstract: The objective of this paper is to present the multi-orbit (MO) surface soil moisture (SM) and angle-binned brightness temperature (TB) products for the SMOS (Soil Moisture and Ocean Salinity) mission based on a new multi-orbit algorithm. The Level 3 algorithm at CATDS (Centre Aval de Traitement des Données SMOS) makes use of MO retrieval to enhance the robustness and quality of SM retrievals. The motivation of the approach is to make use of the longer temporal autocorrelation length of the vegetation optical depth (VOD) compared to the corresponding SM autocorrelation in order to enhance the retrievals when an acquisition occurs at the border of the swath. The retrieval algorithm is implemented in a unique operational processor delivering multiple parameters (e.g. SM and VOD) using multi-angular dual-polarisation TB from MO. A subsidiary angle-binned TB product is provided. In this study the Level 3 TB V310 product is showcased and compared to SMAP (Soil Moisture Active Passive) TB. The Level 3 SM V300 product is compared to the single-orbit (SO) retrievals from the Level 2 SM processor from ESA with aligned configuration. The advantages and drawbacks of the Level 3 SM product (L3SM) are discussed. The comparison is done on a global scale between the two datasets and on the local scale with respect to in situ data from AMMA-CATCH and USDA ARS Watershed networks. The results obtained from the global analysis show that the MO implementation enhances the number of retrievals: up to 9 % over certain areas. The comparison with the in situ data shows that the increase in the number of retrievals does not come with a decrease in quality, but rather at the expense of an increased time lag in product availability from 6 h to 3.5 days, which can be a limiting factor for applications like flood forecast but reasonable for drought monitoring and climate change studies. The SMOS L3 soil moisture and L3 brightness temperature products are delivered using an open licence and free of charge using a web application (https://www.catds.fr/sipad/). The RE04 products, versions 300 and 310, used in this paper are also available at ftp://ext-catds-cpdc:catds2010@ftp.ifremer.fr/Land_products/GRIDDED/L3SM/RE04/.
Published: 2017
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15. Recovery of SMOS Salinity Variability in RFI-Contaminated Regions

Author: Bonjean, Fabrice, Boutin, Jacqueline, Vergely, Jean-Luc, Richaume, Philippe, and Sabia, Roberto
Abstract: The Soil Moisture and Ocean Salinity (SMOS) satellite mission, operational since 2010, relies on an L-band microwave interferometric radiometer to generate brightness temperature (BT) images along the swath, with global coverage every three days. These images are then used to derive sea surface salinity (SSS) with an effective resolution of less than 50 km. However, signal acquisition in some ocean regions is intermittently and significantly disrupted by radio frequency interferences (RFIs) from various terrestrial military or civilian sources worldwide. We develop a new methodology based on principal component and regression analyses to extract the RFI signatures in time and space, thereby enabling the construction of a corrected SSS estimate along the swath. This method successfully filters out many disruptive features characterized by long and wide branches occurring around the RFI sources, hence recovering SSS variability as demonstrated in comparison to in situ reference data. This correction methodology is an alternative to separate filtering procedures that were applied on BT at Level 1. Independent information indicating the probability of RFI occurrence on land areas or nearby is used to verify the timing of oceanic RFI contamination inferred by the correction process. The methodology performs particularly well in areas where the probability is close to 1 for a significant and contiguous portion of the entire period. Already applied with significant improvement in three selected regions, this exemplary study is a starting point for expanding and systematizing the methodology to treat as many RFI-polluted regions as possible and to recover SMOS SSS variability.
Published: 2024
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16. Performance of SMOS Soil Moisture Products over Core Validation Sites

Author: A. Colliander, Y. Kerr, J-P. Wigneron, A. Al-Yaari, N. Rodriguez-Fernandez, X. Li, J. Chaubell, P. Richaume, A. Mialon, J. Asanuma, A. Berg, D.D. Bosch, T. Caldwell, M.H. Cosh, C. Holifield Collins, J. Martínez-Fernández, H. McNairn, M.S. Seyfried, P.J. Starks, Z. Su, M. Thibeault, J.P. Walker, Jet Propulsion Laboratory (JPL), NASA-California Institute of Technology (CALTECH), 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), Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université Grenoble Alpes (UGA), Université de Tsukuba = University of Tsukuba, University of Guelph, USDA-ARS : Agricultural Research Service, University of Texas-Pan American (UTPA), University of Texas-Pan, Universidad de Salamanca, Agriculture and Agri-Food (AAFC), University of Twente, Comisión Nacional de Actividades Espaciales - Argentinian Space Agency (CONAE), and Monash university
Subjects: [SDE.IE]Environmental Sciences/Environmental Engineering, Soil Moisture, Validation, SMAP, Electrical and Electronic Engineering, [SDV.SA.SDS]Life Sciences [q-bio]/Agricultural sciences/Soil study, Geotechnical Engineering and Engineering Geology, SMOS
Abstract: International audience; The European Space Agency (ESA) launched the SMOS (Soil Moisture Ocean Salinity) mission in 2009; currently, multiple global soil moisture (SM) products are based on the measurements of its L-band (1.4 GHz) radiometer. We compared four SMOS products with each other: Level 2, Level 3, IC (INRA-CESBIO), and Near Real Time products. The comparisons focused on core validation sites (CVS), whose spatial representativeness errors allow the estimation of the SM product performance for bias-insensitive metrics (unbiased root mean square error (ubRMSE) and correlation (R), and anomaly R) with negligible uncertainty and for bias-sensitive metrics (mean difference (MD) and root mean square difference or RMSD) with acceptable uncertainty. When the products were compared with CVS independently, the results showed that the ubRMSE, R, and anomaly R of the IC product were better than those of the other products, while the MD was larger. However, the differences between the performances were smaller when the products were assessed using only the data points when each product had a valid retrieval. This indicates that the algorithms have similar performance and that data screening and quality flagging of the retrievals markedly affects the performance. The NASA Soil Moisture Active Passive (SMAP) mission produces a similar SM product as SMOS using an L-band radiometer. The closeness of the ubRMSE, R, and anomaly R performance of the IC product and the SMAP product (0.039 m 3 /m 3 vs. 0.041 m 3 /m 3 , 0.80 vs. 0.81, and 0.75 vs. 0.75) demonstrate that the SMOS and SMAP radiometers can achieve similar SM sensitivity.
Published: 2023
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17. Digital Surface of Revolution with Hand-Drawn Generatrix

Author: Andres, Eric, Richaume, Lydie, and Largeteau-Skapin, Gaelle
Published: 2017
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18. SMOS Third Mission Reprocessing after 10 Years in Orbit

Author: Roger Oliva, Manuel Martín-Neira, Ignasi Corbella, Josep Closa, Albert Zurita, François Cabot, Ali Khazaal, Philippe Richaume, Juha Kainulainen, Jose Barbosa, Gonçalo Lopes, Joseph Tenerelli, Raul Díez-García, Veronica González-Gambau, and Raffaele Crapolicchio
Subjects: SMOS, calibration, radiometry, reprocessing, Science
Abstract: After more than 10 years in orbit, the SMOS team has started a new reprocessing campaign for the SMOS measurements, which includes the changes in calibration and image reconstruction that have been made to the Level 1 Operational Processor (L1OP) during the past few years. The current L1 processor, version v620, was used for the second mission reprocessing in 2014. The new version, v724, is the one run in the third mission reprocessing and will become the new operational processor. The present paper explains the major changes applied and analyses the quality of the data with different metrics. The results have been obtained with numerous individual tests that have confirmed the benefits of the evolutions and an end-to-end processing campaign involving three years of data used to assess the improvements of the SMOS measurements quantitatively.
Published: 2020
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19. Negative Effects of Copper Oxide Nanoparticles on Carbon and Nitrogen Cycle Microbial Activities in Contrasting Agricultural Soils and in Presence of Plants

Author: Marie Simonin, Amélie A. M. Cantarel, Armelle Crouzet, Jonathan Gervaix, Jean M. F. Martins, and Agnès Richaume
Subjects: metal-oxide nanomaterials, agro-ecosystem, microbial ecotoxicology, wheat, nitrification, denitrification, Microbiology, QR1-502
Abstract: Metal-oxide nanoparticles (NPs) such as copper oxide (CuO) NPs offer promising perspectives for the development of novel agro-chemical formulations of pesticides and fertilizers. However, their potential impact on agro-ecosystem functioning still remains to be investigated. Here, we assessed the impact of CuO-NPs (0.1, 1, and 100 mg/kg dry soil) on soil microbial activities involved in the carbon and nitrogen cycles in five contrasting agricultural soils in a microcosm experiment over 90 days. Additionally, in a pot experiment, we evaluated the influence of plant presence on the toxicity of CuO-NPs on soil microbial activities. CuO-NPs caused significant reductions of the three microbial activities measured (denitrification, nitrification, and soil respiration) at 100 mg/kg dry soil, but the low concentrations (0.1 and 1 mg/kg) had limited effects. We observed that denitrification was the most sensitive microbial activity to CuO-NPs in most soil types, while soil respiration and nitrification were mainly impacted in coarse soils with low organic matter content. Additionally, large decreases in heterotrophic microbial activities were observed in soils planted with wheat, even at 1 mg/kg for soil substrate-induced respiration, indicating that plant presence did not mitigate or compensate CuO-NP toxicity for microorganisms. These two experiments show that CuO-NPs can have detrimental effects on microbial activities in soils with contrasting physicochemical properties and previously exposed to various agricultural practices. Moreover, we observed that the negative effects of CuO-NPs increased over time, indicating that short-term studies (hours, days) may underestimate the risks posed by these contaminants in soils.
Published: 2018
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20. The SMOS-HR Mission: Science Case and Project Status

Author: N.J. Rodriguez-Fernandez, E. Anterrieu, J. Boutin, A. Supply, G. Reverdin, G. Alory, E. Remy, G. Picard, T. Pellarin, P. Richaume, A. Mialon, A. Khazaal, A. Al Bitar, R. Rodriguez-Suquet, L. Yu, P. Gonzalez, C. Cheymol, T. Amiot, P. Maisongrande, N. Jeannin, T. Decoopman, A. Kallel, J.-M. Morel, M. Colom, M. Dunitz, C. Thouvenin-Masson, L. Olivier, and Y. H. Kerr
Published: 2022
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21. Above Ground Biomass Estimation from Passive Microwaves Brightness Temperatures Using Neural Networks

Author: J. C. Salazar-Neira, N. J. Rodriguez-Fernandez, A. Mialon, S. Mermoz, A. Bouvet, Y. Kerr, T. Le Toan, and P. Richaume
Published: 2022
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22. SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact

Author: Nemesio Rodríguez-Fernández, Patricia de Rosnay, Clement Albergel, Philippe Richaume, Filipe Aires, Catherine Prigent, and Yann Kerr
Subjects: soil moisture, L-band, passive radiometry, data assimilation, numerical weather prediction, Science
Abstract: The assimilation of Soil Moisture and Ocean Salinity (SMOS) data into the ECMWF (European Centre for Medium Range Weather Forecasts) H-TESSEL (Hydrology revised-Tiled ECMWF Scheme for Surface Exchanges over Land) model is presented. SMOS soil moisture (SM) estimates have been produced specifically by training a neural network with SMOS brightness temperatures as input and H-TESSEL model SM simulations as reference. This can help the assimilation of SMOS information in several ways: (1) the neural network soil moisture (NNSM) data have a similar climatology to the model, (2) no global bias is present with respect to the model even if local biases can remain. Experiments performing joint data assimilation (DA) of NNSM, 2 m air temperature and relative humidity or NNSM-only DA are discussed. The resulting SM was evaluated against a large number of in situ measurements of SM obtaining similar results to those of the model with no assimilation, even if significant differences were found from site to site. In addition, atmospheric forecasts initialized with H-TESSEL runs (without DA) or with the analysed SM were compared to measure of the impact of the satellite information. Although NNSM DA has an overall neutral impact in the forecast in the Tropics, a significant positive impact was found in other areas and periods, especially in regions with limited in situ information. The joint NNSM, T2m and RH2m DA improves the forecast for all the seasons in the Southern Hemisphere. The impact is mostly due to T2m and RH2m but SMOS NN DA alone also improves the forecast in July- September. In the Northern Hemisphere, the joint NNSM, T2m and RH2m DA improves the forecast in April−September, while NNSM alone has a significant positive effect in July−September. Furthermore, forecasting skill maps show that SMOS NNSM improves the forecast in North America and in Northern Asia for up to 72 h lead time.
Published: 2019
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23. Evaluating Soil Moisture Retrievals from ESA's SMOS and NASA's SMAP Brightness Temperature Datasets

Author: A Al-Yaari, J-P Wigernon, Y Kerr, N Rodriguez-Fernandez, P E O'Neill, T J Jackson, G J M De Lannoy, A Al Bitar, A Mialon, P Richaume, J P Walker, A Mahmoodi, and S Yueh
Subjects: Earth Resources And Remote Sensing
Abstract: Two satellites are currently monitoring surface soil moisture (SM) using L-band observations: SMOS (Soil Moisture and Ocean Salinity), a joint ESA (European Space Agency), CNES (Centre national d'tudes spatiales), and CDTI (the Spanish government agency with responsibility for space) satellite launched on November 2, 2009 and SMAP (Soil Moisture Active Passive), a National Aeronautics and Space Administration (NASA) satellite successfully launched in January 2015. In this study, we used a multilinear regression approach to retrieve SM from SMAP data to create a global dataset of SM, which is consistent with SM data retrieved from SMOS. This was achieved by calibrating coefficients of the regression model using the CATDS (Centre Aval de Traitement des Donnes) SMOS Level 3 SM and the horizontally and vertically polarized brightness temperatures (TB) at 40 deg incidence angle, over the 2013 - 2014 period. Next, this model was applied to SMAP L3 TB data from Apr 2015 to Jul 2016. The retrieved SM from SMAP (referred to here as SMAP_Reg) was compared to: (i) the operational SMAP L3 SM (SMAP_SCA), retrieved using the baseline Single Channel retrieval Algorithm (SCA); and (ii) the operational SMOSL3 SM, derived from the multiangular inversion of the L-MEB model (L-MEB algorithm) (SMOSL3). This inter-comparison was made against in situ soil moisture measurements from more than 400 sites spread over the globe, which are used here as a reference soil moisture dataset. The in situ observations were obtained from the International Soil Moisture Network (ISMN; https:ismn.geo.tuwien.ac.at) in North of America (PBO_H2O, SCAN, SNOTEL, iRON, and USCRN), in Australia (Oznet), Africa (DAHRA), and in Europe (REMEDHUS, SMOSMANIA, FMI, and RSMN). The agreement was analyzed in terms of four classical statistical criteria: Root Mean Squared Error (RMSE),Bias, Unbiased RMSE (UnbRMSE), and correlation coefficient (R). Results of the comparison of these various products with in situ observations show that the performance of both SMAP products i.e. SMAP_SCA and SMAP_Reg is 48 similar and marginally better to that of the SMOSL3 product particularly over the PBO_H2O, SCAN, and USCRN sites. However, SMOSL3 SM was closer to the in situ observations over the DAHRA and Oznet sites. We found that the correlation between all three datasets and in situ measurements is best (R 0.80) over the Oznet sites and worst (R 0.58) over the SNOTEL sites for SMAP_SCA and over the DAHRA and SMOSMANIA sites (R 0.51 and R 0.45 for SMAP_Reg and SMOSL3, respectively). The Bias values showed that all products are generally dry, except over RSMN, DAHRA, and Oznet (and FMI for SMAP_SCA). Finally, our analysis provided interesting insights that can be useful to improve the consistency between SMAP and SMOS datasets.
Published: 2017
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24. 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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25. The Soil Moisture and Ocean Salinity (SMOS) Mission: first results and achievements

Author: Yann Kerr, Philippe Waldteufel, Jean-Pierre Wigneron, Jacqueline Boutin, Nicolas Reul, Ahmad Al Bitar, Delphine Leroux, Arnaud Mialon, Philippe Richaume, and Susanne Mecklenburg
Subjects: Instruments and machines, QA71-90, Applied optics. Photonics, TA1501-1820, Cellular telephone services industry. Wireless telephone industry, HE9713-9715
Abstract: La mission SMOS (Soil moisture and Ocean Salinity) a été lancée avec succès le 2 novembre 2009. Cette mission menée par l'ESA (Agence Spatiale Europénne) est dédiée à la mesure de l'humidité superficielle des sols sur les continents (avec une précision recherchée de 0,04m3/m3) et la salinité des océans (objectif 0.1psu). Ces deux quantités géophysiques sont très importantes car elle contrôle le budget énergétique à l'interface sol-atmosphère. Leur connaissance à l'échelle globale est utile pour les recherches sur le climat et la météorologie, en particulier pour les modèles de prévision numérique. Elles ont aussi un très grand potentiel un très grand nombre d'application, comme par exemple pour le suivi des ouragans ou la gestion des ressources en eau. Les six premiers mois ont été dédiés à la recette en vol qui a permis de vérifier le satellite le segment sol et l'étalonnage. Cette phase s'est achevée avec succès en mai 2010 et SMOS fonctionne de façon opérationnelle depuis, fournissant de données à la communauté internationale. Les performances de l'instrument sont globalement conformes aux spécifications. Cependant, les interférences radio sont présentes au-dessus de l'Europe, du Moyen-Orient et de l'Asie. Ces émissions parasites dans la bande protégée perturbent la mesure de façon significative. La génération des produits de niveau 2 et 3 est une activité en cours avec des améliorations régulières de sorties.
Published: 2014
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26. Long Term Global Surface Soil Moisture Fields Using an SMOS-Trained Neural Network Applied to AMSR-E Data

Author: Nemesio J. Rodríguez-Fernández, Yann H. Kerr, Robin van der Schalie, Amen Al-Yaari, Jean-Pierre Wigneron, Richard de Jeu, Philippe Richaume, Emanuel Dutra, Arnaud Mialon, and Matthias Drusch
Subjects: soil moisture, passive radiometry, neural networks, SMOS (Soil Moisture and Ocean Salinity) mission, Science
Abstract: A method to retrieve soil moisture (SM) from Advanced Scanning Microwave Radiometer—Earth Observing System Sensor (AMSR-E) observations using Soil Moisture and Ocean Salinity (SMOS) Level 3 SM as a reference is discussed. The goal is to obtain longer time series of SM with no significant bias and with a similar dynamical range to that of the SMOS SM dataset. This method consists of training a neural network (NN) to obtain a global non-linear relationship linking AMSR-E brightness temperatures ( T b ) to the SMOS L3 SM dataset on the concurrent mission period of 1.5 years. Then, the NN model is used to derive soil moisture from past AMSR-E observations. It is shown that in spite of the different frequencies and sensing depths of AMSR-E and SMOS, it is possible to find such a global relationship. The sensitivity of AMSR-E T b ’s to soil temperature ( T s o i l ) was also evaluated using European Centre for Medium-Range Weather Forecast Interim/Land re-analysis (ERA-Land) and Modern-Era Retrospective analysis for Research and Applications-Land (MERRA-Land) model data. The best combination of AMSR-E T b ’s to retrieve T s o i l is H polarization at 23 and 36 GHz plus V polarization at 36 GHz. Regarding SM, several combinations of input data show a similar performance in retrieving SM. One NN that uses C and X bands and T s o i l information was chosen to obtain SM in the 2003–2011 period. The new dataset shows a low bias (
Published: 2016
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27. Global-Scale Comparison of Passive (SMOS) and Active (ASCAT) Satellite Based Microwave Soil Moisture Retrievals with Soil Moisture Simulations (MERRA-Land)

Author: A Al-Yaari, J P Wigneron, A Ducharne, Y H Kerr, W Wagner, G De Lannoy, R Reichle, A Al Bitar, W Dorigo, P Richaume, and A Mialon
Subjects: Earth Resources And Remote Sensing
Abstract: Global surface soil moisture (SSM) datasets are being produced based on active and passive microwave satellite observations and simulations from land surface models (LSM). This study investigates the consistency of two global satellite-based SSM datasets based on microwave remote sensing observations from the passive Soil Moisture and Ocean Salinity (SMOS;SMOSL3 version 2.5) and the active Advanced Scatterometer (ASCAT; version TUWien- WARP 5.5) with respect to LSM SSM from the MERRA-Land data product. The relationship between the global-scale SSM products was studied during the 2010-2012 period using (1) a time series statistics (considering both original SSM data and anomalies), (2) a space-time analysis using Hovmoller diagrams, and (3) a triple collocation error model. The SMOSL3 and ASCAT retrievals are consistent with the temporal dynamics of modeled SSM (correlation R (is) greater than 0.70 for original SSM) in the transition zones between wet and dry climates, including the Sahel, the Indian subcontinent, the Great Plains of North America, eastern Australia, and southeastern Brazil. Over relatively dense vegetation covers, a better consistency with MERRA-Land was obtained with ASCAT than with SMOSL3. However, it was found that ASCAT retrievals exhibit negative correlation versus MERRA-Land in some arid regions (e.g., the Sahara and the Arabian Peninsula). In terms of anomalies, SMOSL3 better captures the short term SSM variability of the reference dataset (MERRA-Land) than ASCAT over regions with limited radio frequency interference (RFI) effects (e.g., North America, South America, and Australia). The seasonal and latitudinal variations of SSM are relatively similar for the three products, although the MERRALand SSM values are generally higher and their seasonal amplitude is much lower than for SMOSL3 and ASCAT. Both SMOSL3 and ASCAT have relatively comparable triple collocation errors with similar spatial error patterns: (i) lowest errors in arid regions (e.g., Sahara and Arabian Peninsula), due to the very low natural variability of soil moisture in these areas, and Central America, and (ii) highest errors over most of the vegetated regions (e.g., northern Australia, India, central Asia, and South America). However, the ASCAT SSM product is prone to larger random errors in some regions (e.g., north-western Africa, Iran, and southern South Africa). Vegetation density was found to be a key factor to interpret the consistency with MERRA-Land between the two remotely sensed products (SMOSL3 and ASCAT) which provides complementary information on SSM. This study shows that both SMOS and ASCAT have thus a potential for data fusion into long-term data records.
Published: 2014
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28. Multi-Timescale Analysis of the Spatial Representativeness of In Situ Soil Moisture Data within Satellite Footprints

Author: Mh Cosh, Rajat Bindlish, P. Richaume, Dj Leroux, Beatriz Molero, Olivier Merlin, and Yh Kerr
Subjects: Atmospheric Science, 010504 meteorology & atmospheric sciences, Anomaly (natural sciences), 0208 environmental biotechnology, Wavelet transform, 02 engineering and technology, 01 natural sciences, Stability (probability), 020801 environmental engineering, Footprint, Geophysics, Wavelet, Space and Planetary Science, Earth and Planetary Sciences (miscellaneous), Environmental science, Satellite, Scale (map), 0105 earth and related environmental sciences, Downscaling, Remote sensing
Abstract: We conduct a novel comprehensive investigation that seeks to prove the connection between spatial and time scales in surface soil moisture (SM) within the satellite footprint (~50 km). Modeled and measured point series at Yanco and Little Washita in situ networks are first decomposed into anomalies at time scales ranging from 0.5 to 128 days, using wavelet transforms. Then, their degree of spatial representativeness is evaluated on a per time-scale basis by comparison to large-spatial scale datasets (the in situ spatial average, SMOS, AMSR2 and ECMWF). Four methods are used for this: temporal stability analysis (TStab), triple collocation (TC), the percentage of correlated areas (CArea) and a new proposed approach that uses wavelet-based correlations (WCor). We found that the mean of the spatial representativeness values tends to increase with the time scale but so does their dispersion. Locations exhibit poor spatial representativeness at scales below 4 days, while either very good or poor representativeness at seasonal scales. Regarding the methods, TStab cannot be applied to the anomaly series due to their multiple zero-crossings and TC is suitable for week and month scales but not for other scales where datasets cross-correlations are found low. In contrast, WCor and CArea give consistent results at all time-scales. WCor is less sensitive to the spatial sampling density, so it is a robust method that can be applied to sparse networks (1 station per footprint). These results are promising to improve the validation and downscaling of satellite SM series and the optimization of SM networks.
Published: 2018
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29. Heterogeneous Cell Density and Genetic Structure of Bacterial Pools Associated with Various Soil Microenvironments as Determined by Enumeration and DNA Fingerprinting Approach (RISA)

Author: Ranjard, L., Poly, F., Combrisson, J., Richaume, A., Gourbière, F., Thioulouse, J., and Nazaret, S.
Published: 2000
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30. SMOS near-real-time soil moisture product: processor overview and first validation results

Author: Patricia de Rosnay, Yann Kerr, Matthias Drusch, Clément Albergel, J. M. Sabater, P. Richaume, Susanne Mecklenburg, Nemesio Rodriguez-Fernandez, Groupe d'étude de l'atmosphère météorologique (CNRM-GAME), Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS), 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), European Space Research and Technology Centre (ESTEC), European Space Agency (ESA), Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-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)-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), 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: 010504 meteorology & atmospheric sciences, Meteorology, 0211 other engineering and technologies, 02 engineering and technology, 01 natural sciences, lcsh:Technology, Standard deviation, lcsh:TD1-1066, Soil temperature, Range (statistics), lcsh:Environmental technology. Sanitary engineering, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, Water content, Retrieval algorithm, lcsh:Environmental sciences, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, General Environmental Science, Remote sensing, lcsh:GE1-350, lcsh:T, lcsh:Geography. Anthropology. Recreation, Numerical weather prediction, lcsh:G, [SDU]Sciences of the Universe [physics], 13. Climate action, Product (mathematics), General Earth and Planetary Sciences, Environmental science, Satellite
Abstract: Measurements of the surface soil moisture (SM) content are important for a wide range of applications. Among them, operational hydrology and numerical weather prediction, for instance, need SM information in near-real-time (NRT), typically not later than 3 h after sensing. The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite is the first mission specifically designed to measure SM from space. The ESA Level 2 SM retrieval algorithm is based on a detailed geophysical modelling and cannot provide SM in NRT. This paper presents the new ESA SMOS NRT SM product. It uses a neural network (NN) to provide SM in NRT. The NN inputs are SMOS brightness temperatures for horizontal and vertical polarizations and incidence angles from 30 to 45°. In addition, the NN uses surface soil temperature from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS). The NN was trained on SMOS Level 2 (L2) SM. The swath of the NRT SM retrieval is somewhat narrower (∼ 915 km) than that of the L2 SM dataset (∼ 1150 km), which implies a slightly lower revisit time. The new SMOS NRT SM product was compared to the SMOS Level 2 SM product. The NRT SM data show a standard deviation of the difference with respect to the L2 data of 3 m−3 in most of the Earth and a Pearson correlation coefficient higher than 0.7 in large regions of the globe. The NRT SM dataset does not show a global bias with respect to the L2 dataset but can show local biases of up to 0.05 m3 m−3 in absolute value. The two SMOS SM products were evaluated against in situ measurements of SM from more than 120 sites of the SCAN (Soil Climate Analysis Network) and the USCRN (US Climate Reference Network) networks in North America. The NRT dataset obtains similar but slightly better results than the L2 data. In summary, the NN SMOS NRT SM product exhibits performances similar to those of the Level 2 SM product but it has the advantage of being available in less than 3.5 h after sensing, complying with NRT requirements. The new product is processed at ECMWF and it is distributed by ESA and via the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) multicast service (EUMETCast).
Published: 2017
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31. Lessons Learnt from SMOS RFI Activities After 10 Years in Orbit: RFI Detection and Reporting to Claim Protection and Increase Awareness of the Interference Problem in the 1400–1427 MHZ Passive Band

Author: Elena Daganzo, Antonio de la Fuente, Roger Oliva, Susanne Mecklenburg, Yann Kerr, Ekhi Uranga, Alvaro Llorente, Manuel Martin-Neira, and P. Richaume
Subjects: Interference problem, L band radiometry, Orbit (dynamics), Environmental science, Data loss, Electromagnetic interference, Remote sensing
Abstract: The European Space Agency’s Soil Moisture and Ocean Salinity (SMOS) mission is perturbed by Radio Frequency Interference (RFI) that jeopardizes part of its scientific retrieval in certain areas of the world. Areas affected by RFI experience data loss or underestimation of soil moisture and ocean salinity retrieval values. Close to the 10th anniversary of SMOS launch, this paper provides an overview of SMOS RFI activities during these years, the evolution of the RFI scenario worldwide and the lessons learned. The main challenges faced in the RFI detection and geo-location will be introduced, with some interesting examples of RFI cases detected. Another topic addressed in this paper is the impact of the RFI contamination from a scientific perspective, with some estimations of the data percentage that has to be discarded due to interference.
Published: 2019
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32. Lessons learned from SMOS RFI processing, perspectives for future interferometry missions

Author: Yann Kerr, Ali Khazaal, Francois Cabot, P. Richaume, and Eric Anterrieu
Subjects: Interferometry, 010504 meteorology & atmospheric sciences, Brightness temperature, 0208 environmental biotechnology, Environmental science, Satellite, 02 engineering and technology, 01 natural sciences, 020801 environmental engineering, 0105 earth and related environmental sciences, Remote sensing
Abstract: Since 2009, the SMOS mission has been acquiring brightness temperature measurements to derive soil moisture and ocean salinity. With a revisit time of three days everywhere on the globe, it has become one of the first mission to provide global assessment of these two parameters in near real time. The only instrument carried by SMOS satellite is a two dimensional radiometric interferometer, operating between 1400 and 1420 MHz. Despite this being a protected band, it has been obvious since day one that man-made emissions, either close and powerful or directly in-band, were contaminating the measurements. This was foreseen to some extent and some filtering algorithms had been designed prior to the launch. Although quite efficient, the very high diversity of RFI source characteristics made it very difficult to identify reliably all contaminations. Thus, since then, it has been a constant effort to try to identify better all these sources and assess their impact on SMOS measurements.
Published: 2019
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33. SMOS RFI Experience in the 1400-1427 MHz Passive Band: Case of Extended Interference Caused By Broadcasting Satellite Home-TV Receivers

Author: Ekhi Uranga, P. Richaume, Roger Oliva, Y. H. Kerr, Alvaro Llorente, and E. Daganzo
Subjects: Microwave Imaging Radiometer with Aperture Synthesis, Intermediate frequency, business.industry, Environmental science, Satellite, Broadcasting, Interference (wave propagation), business, Electromagnetic interference, Poor quality, ITU Radio Regulations, Remote sensing
Abstract: The ESA’s Soil Moisture and Ocean Salinity (SMOS) mission has been in orbit over nine years, with its L-Band Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) functioning well. Between October 2011 and March 2012, a sudden increase in the distribution of RF interference (RFI) was observed over most urban areas in Japan, and this RFI scenario has remained unchanged during the last years. The interference has also polluted the observations of the SMAP (Soil Moisture Active/Passive) and Aquarius missions operated by NASA. This case of harmful interference was reported by ESA to the Japanese Regulatory Authorities in accordance to the procedure contained in the ITU Radio-Regulations. Investigations have shown that the main RFI source is originated by the aggregate radiations from the intermediate frequency (IF) circuits of broadcasting-satellite receiving installations. The main RFI causes appear to be due to poor quality cable, poor connections and/or issues with installation practices resulting in insufficient RF screening. This paper presents the SMOS experience in addressing this unique RFI problem encountered, provides an overview of the investigations conducted and the on-going actions led by the Japanese authorities in order to mitigate the impact of the interference.
Published: 2019
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34. Is vegetation optical depth needed to estimate biomass from passive microwave radiometers? A statistical study using neural networks

Author: Yann Kerr, Ahmad Al Bitar, P. Richaume, Emma Bousquet, Nemesio Rodriguez-Fernandez, S.S. Saatchi, and Arnaud Mialon
Subjects: Brightness, Biomass (ecology), L band, Radiometer, 010504 meteorology & atmospheric sciences, Artificial neural network, 0211 other engineering and technologies, 02 engineering and technology, Vegetation, 01 natural sciences, Environmental science, Water content, Microwave, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing
Abstract: Neural networks were used to estimate the ability of different sets of predictors to capture the variability of above ground biomass (AGB). SMOS brightness temperatures (TBs) for only two incidence angles capture 85% of the AGB variance. Adding soil moisture or L-band vegetation optical depth (L-VOD) increase the ability to capture the AGB variance to 90 % and 92 %, respectively. With respect to using only TBs, L-VOD improves the AGB estimation in regions of low vegetation.
Published: 2019
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35. Preliminary System Studies on a High-Resolution SMOS Follow-On: SMOS-HR

Author: N. Jeannin, Baptiste Palacin, Jean-Michel Morel, T. Tournier, Y. H. Kerr, Eric Anterrieu, R. Caujolle, Miguel Colom, R. Rodriguez-Suquet, P. Richaume, B. Rouge, Thibaut Decoopman, J. Costerate, Francois Cabot, F. Payot, Ali Khazaal, Nemesio Rodriguez-Fernandez, and L. Costes
Subjects: Aperture synthesis, Resolution (electron density), 0211 other engineering and technologies, Environmental science, Satellite, 02 engineering and technology, Microwave radiometry, Image resolution, 021101 geological & geomatics engineering, Remote sensing
Abstract: The Soil Moisture and Ocean Salinity (SMOS) satellite has provided, for the very first time, systematic passive L-band (1420−1427 MHz) measurements from space with a spatial resolution of ~50 Km. This contribution presents preliminary results of studies conducted for a High Resolution (HR) follow-on mission. The SMOS-HR project is currently undergoing a Phase 0 study by the French space agency. The goal is to ensure continuity of L-band measurements while increasing the spatial resolution to ~10 Km without degrading the radiometric sensitivity and keeping the revisit time of 3 days unchanged.
Published: 2019
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36. Improving the Spatial Bias Correction Algorithm in SMOS Image Reconstruction Processor: Validation of Soil Moisture Retrievals With In Situ Data

Author: P. Richaume, Yann Kerr, Eric Anterrieu, Francois Cabot, Arnaud Mialon, Ali Khazaal, Université Fédérale Toulouse Midi-Pyrénées, and ESA
Subjects: Pixel, inverse problems, [SDV]Life Sciences [q-bio], 0211 other engineering and technologies, Field of view, Gibbs oscillations, 02 engineering and technology, Iterative reconstruction, Residual, Physics::Geophysics, Sea surface temperature, remote sensing, 13. Climate action, Aliasing, synthetic aperture imaging, Brightness temperature, spatial biases, General Earth and Planetary Sciences, Environmental science, Electrical and Electronic Engineering, Water cycle, Physics::Atmospheric and Oceanic Physics, 021101 geological & geomatics engineering, Remote sensing
Abstract: International audience; SMOS is a space mission led by the European Space Agency and designed to provide global maps of Soil Moisture and Ocean salinity, two important geophysical parameters for understanding the water cycle variations and climate change. The SMOS payload is a 2-D interferometer operating at L-band that consists of 69 elementary antennas located along a Y-shaped structure. Important spatial biases persist in the retrieved brightness temperature (BT) images mainly due to the phenomenon of aliasing inside the field of view of SMOS but also due to the Gibbs oscillations near land/ocean transitions. To minimize these biases, a differential image reconstruction algorithm is used in the operational processor that reduces the contrast of the image to be retrieved. To do that, the contribution of a constant artificial temperature map is removed from the measurements prior to reconstruction and then added back after the reconstruction. In this paper, we show that strong residual biases are still present in the retrieved images. To reduce them, we propose to improve the bias correction algorithm by using a more realistic artificial temperature scene based on separating the land and ocean regions and assigning a constant temperature over land and a Fresnel BT model over the ocean. The artificial scene is also improved by means of representing each pixel by its water fraction percentage to smooth the land/ocean transitions. The improved algorithm is validated over the ocean by comparing the retrieved temperatures to a forward geophysical model but also over land by comparing the retrieved soil moisture to in situ measurements.
Published: 2019
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37. Comparison of SMOS and AMSR-E vegetation optical depth to four MODIS-based vegetation indices

Author: R.A.M. de Jeu, Matthias Drusch, Heather Lawrence, P. Richaume, Y. H. Kerr, Jennifer Grant, Ahmad Al Bitar, Arnaud Mialon, Jean-Pierre Wigneron, M. van Marle, Hydrology and Geo-environmental sciences, Earth and Climate, European Space Agency (ESA), 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), Transmissivity, European Centre for Medium-Range Weather Forecasts (ECMWF), 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), European Space Research and Technology Centre (ESTEC), Department of Earth and Climate, Vrije universiteit = Free university of Amsterdam [Amsterdam] (VU), VU University Amsterdam, Interactions Sol Plante Atmosphère (ISPA), 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), and Université Fédérale Toulouse Midi-Pyrénées-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Vegetation optical depth, 010504 meteorology & atmospheric sciences, 0211 other engineering and technologies, Soil Science, 02 engineering and technology, Normalized difference water index, donnée satellite, 01 natural sciences, Normalized Difference Vegetation Index, Optical vegetation indices, SDG 14 - Life Below Water, 14. Life underwater, Computers in Earth Sciences, Time series, Leaf area index, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, Northern Hemisphere, Geology, AMSR-E, Enhanced vegetation index, 15. Life on land, Vegetation dynamics, microonde passive, MODIS, 13. Climate action, Environmental science, indice de végétation, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Passive microwaves, SMOS
Abstract: The main objectives of this study were to provide a proxy “validation” of the Soil Moisture and Ocean Salinity (SMOS) mission's vegetation optical depth product ( τ SMOS ) on a global scale, to give a first indication of the potential of τ SMOS to capture large-scale vegetation dynamics, and to contribute towards investigations into the possible use of optical vegetation indices (VI's) for the estimation of τ . The analyses were performed by comparing the spatial and temporal behaviour of τ SMOS relative to four MODIS-based VI's, with that of the vegetation optical depth from a similar sensor, AMSR-E ( τ AMSR-E ). 16-day and annual average values of the passive microwave optical depth ( τ ) for the year 2010 were obtained from SMOS (1.4 GHz) and AMSR-E (6.9 GHz) observations. The VI's chosen for this study were the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI) and Normalized Difference Water Index (NDWI). The highest global-scale, annual correlation was found between τ SMOS and τ AMSR-E from ascending orbits (Spearman's R = 0.80). On global, annual scales, τ SMOS showed higher correlations with τ AMSR-E than with the VI's, while τ AMSR-E was more highly correlated with VI's than with τ SMOS . Timeseries of both τ and the VI's were made per landcover class, for the northern hemisphere, tropics and southern hemisphere. Although the large-scale spatial and spatio-temporal behaviour of τ SMOS and τ AMSR-E is generally similar, the results highlight some notable differences in observing vegetation with optical vs . passive microwave sensors, and certain crucial differences between the two passive microwave sensors themselves. Overall, the results found in this study give a good first confidence in the SMOS L3 τ product and its potential use in vegetation studies. These results provide an essential general reference for future (global-scale) vegetation monitoring with passive microwaves, for the future inclusion of τ SMOS in long-term, multi-sensor datasets, and for passive microwave algorithm development.
Published: 2016
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38. Constraining terrestrial carbon fluxes through assimilation of SMOS products

Author: N. Rodrigucz-Femander, Marko Scholze, Mousong Wu, Matthias Drusch, M. Vossbeck, Cristina Vittucci, Y. H. Kerr, Wolfgang Knorr, Paolo Ferrazzoli, P. Richaume, Arnaud Mialon, Thomas Kaminski, Jean-Pierre Wigneron, The Inversion Lab, Lund University [Lund], Università degli Studi di Roma Tor Vergata [Roma], 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), 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), and ESA/ESTEC
Subjects: Biosphere model, Vegetation optical depth, 010504 meteorology & atmospheric sciences, [SDV]Life Sciences [q-bio], Biosphere, Assimilation (biology), 010103 numerical & computational mathematics, 15. Life on land, Atmospheric sciences, 01 natural sciences, Carbon cycle, Data assimilation, Carbon assimilation, 13. Climate action, [SDE]Environmental Sciences, Environmental science, 0101 mathematics, 0105 earth and related environmental sciences, Carbon flux
Abstract: International audience; The ongoing ESA funded `SMOS + Vegetation' project combines a retrieval component that aims at further improving the SMOS VOD product with an assimilation component that aims at demonstrating the added value of this product in constraining simulated land surface fluxes of carbon dioxide. This contribution focuses on the project's modelling and assimilation component. We describe the construction of dedicated observation operators that link the state of the terrestrial biosphere model to simulated VOD and surface layer soil moisture. We present our carbon assimilation system around a terrestrial biosphere model and demonstrate its operation through simultaneous assimilation of the SMOS VOD product over seven sites covering a range of plant functional types.
Published: 2018
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39. Multi-Timescale Analysis of the Spatial Representativeness of In Situ Soil Moisture Data within Satellite Footprints

Author: B, Molero, D J, Leroux, P, Richaume, Y H, Kerr, O, Merlin, M H, Cosh, R, Bindlish, 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 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: spatial representativeness, satellite validation, spatial scales, timescales, wavelet decomposition, soil moisture, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, Article
Abstract: International audience; We conduct a novel comprehensive investigation that seeks to prove the connection between spatial scales and timescales in surface soil moisture (SM) within the satellite footprint (similar to 50 km). Modeled and measured point series at Yanco and Little Washita in situ networks are first decomposed into anomalies at timescales ranging from 0.5 to 128 days, using wavelet transforms. Then, their degree of spatial representativeness is evaluated on a per-timescale basis by comparison to large spatial scale data sets (the in situ spatial average, SMOS, AMSR2, and ECMWF). Four methods are used for this: temporal stability analysis (TStab), triple collocation (TC), percentage of correlated areas (CArea), and a new proposed approach that uses wavelet-based correlations (WCor). We found that the mean of the spatial representativeness values tends to increase with the timescale but so does their dispersion. Locations exhibit poor spatial representativeness at scales below 4 days, while either very good or poor representativeness at seasonal scales. Regarding the methods, TStab cannot be applied to the anomaly series due to their multiple zero-crossings, and TC is suitable for week and month scales but not for other scales where data set cross-correlations are found low. In contrast, WCor and CArea give consistent results at all timescales. WCor is less sensitive to the spatial sampling density, so it is a robust method that can be applied to sparse networks (one station per footprint). These results are promising to improve the validation and downscaling of satellite SM series and the optimization of SM networks.
Published: 2018
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40. Soil moisture retrieval using SMOS brightness temperatures and a neural network trained on in situ measurements

Author: Yann Kerr, Ahmad Al Bitar, Veronica de Souza, P. Richaume, and Nemesio Rodriguez-Fernandez
Subjects: In situ, Brightness, 010504 meteorology & atmospheric sciences, Artificial neural network, 0211 other engineering and technologies, 02 engineering and technology, 01 natural sciences, SNOTEL, Environmental science, Training phase, Scale (map), Water content, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing
Abstract: An algorithm using in situ measurements for training a neural network (NN) to retrieve soil moisture (SM) from SMOS observations is discussed. The in situ data are measurements of the SM content in the 0–5 cm depth layer from the SCAN, SNOTEL and USCRN networks. It is shown that this approach can be used to retrieve SM at continental scale in North America. The NN retrieval (NN inSitu ) is evaluated against in situ data not used during the training phase and against maps of the SMOS level 3 SM product and ECMWF SM models. NN inSitu SM values are closer to ECMWF values for wet areas. A method to use NNs as a tool to classify in situ sites representative of the remote sensing observations scale is briefly discussed.
Published: 2017
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41. Modelling the passive microwave signature from land surfaces: A review of recent results and application to the L-band SMOS & SMAP soil moisture retrieval algorithms

Author: Narendra N. Das, P. de Rosnay, Yann Kerr, Peggy O'Neill, Ahmad Al Bitar, G. De Lannoy, Roberto Fernandez-Moran, Heather Lawrence, Thomas J. Jackson, Valery Mironov, Jeffrey P. Walker, Marie Parrens, Joaquín Muñoz-Sabater, Amen Al-Yaari, P. Richaume, Simone Bircher, Mike Schwank, Jean-Pierre Wigneron, Steven Delwart, Arnaud Mialon, Jennifer Grant, Paolo Ferrazzoli, Mehmet Kurum, Alain Royer, 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), United States Department of Agriculture, NASA Goddard Space Flight Center (GSFC), Université Catholique de Louvain = Catholic University of Louvain (UCL), European Centre for Medium-Range Weather Forecasts (ECMWF), Monash University [Clayton], Università degli Studi di Roma Tor Vergata [Roma], Kirenski Institute, 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 la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD [France-Ouest]), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées, Netherlands Space Office, Mississippi State University [Mississippi], Gamma Remote Sensing and WSL-Birmensdorf, Jet Propulsion Laboratory (JPL), NASA-California Institute of Technology (CALTECH), Centre d'Applications et de Recherches en TELédétection [Sherbrooke] (CARTEL), Département de géomatique appliquée [Sherbrooke] (UdeS), Université de Sherbrooke (UdeS)-Université de Sherbrooke (UdeS), European Space Research Institute (ESRIN), European Space Agency (ESA), Interactions Sol Plante Atmosphère (ISPA), Université Catholique de Louvain, 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), California Institute of Technology (CALTECH)-NASA, Centre d'Applications et de Recherches en TELédétection (CARTEL), Université de Sherbrooke [Sherbrooke], and ESA ESRIN
Subjects: L band, 010504 meteorology & atmospheric sciences, Meteorology, multi-objective particle swarm optimization (mopso), [SDV]Life Sciences [q-bio], 0211 other engineering and technologies, Soil Science, 02 engineering and technology, 01 natural sciences, Radiative transfer, humidité du sol, Computers in Earth Sciences, Water content, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, parametrization, Geology, Inversion (meteorology), Settore ING-INF/02 - Campi Elettromagnetici, Interferometry, paramétrisation, 13. Climate action, Brightness temperature, Soil water, Environmental science, Microwave, algorithme
Abstract: Two passive microwave missions are currently operating at L-band to monitor surface soil moisture (SM) over continental surfaces. The SMOS sensor, based on an innovative interferometric technology enabling multi-angular signatures of surfaces to be measured, was launched in November 2009. The SMAP sensor, based on a large mesh reflector 6 m in diameter providing a conically scanning antenna beam with a surface incidence angle of 40°, was launched in January of 2015. Over the last decade, an intense scientific activity has focused on the development of the SM retrieval algorithms for the two missions. This activity has relied on many field (mainly tower-based) and airborne experimental campaigns, and since 2010–2011, on the SMOS and Aquarius space-borne L-band observations. It has relied too on the use of numerical, physical and semi-empirical models to simulate the microwave brightness temperature of natural scenes for a variety of scenarios in terms of system configurations (polarization, incidence angle) and soil, vegetation and climate conditions. Key components of the inversion models have been evaluated and new parameterizations of the effects of the surface temperature, soil roughness, soil permittivity, and vegetation extinction and scattering have been developed. Among others, global maps of select radiative transfer parameters have been estimated very recently. Based on this intense activity, improvements of the SMOS and SMAP SM inversion algorithms have been proposed. Some of them have already been implemented, whereas others are currently being investigated. In this paper, we present a review of the significant progress which has been made over the last decade in this field of research with a focus on L-band, and a discussion on possible applications to the SMOS and SMAP soil moisture retrieval approaches.
Published: 2017
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42. Comparison of SMOS and SMAP soil moisture retrieval approaches using tower-based radiometer data over a vineyard field

Author: Ernesto Lopez-Baeza, Yann Kerr, Peggy O'Neill, Ahmad Al Bitar, P. Richaume, Heather Lawrence, Roberto Fernandez Moran, Gabrielle De Lannoy, Jean-Pierre Wigneron, Richard de Jeu, Arnaud Mialon, Simone Bircher, Mike Schwank, Thomas J. Jackson, Maciej Miernecki, University of Hamburg, 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), Universitat de València (UV), 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), Vrije universiteit = Free university of Amsterdam [Amsterdam] (VU), Atmospheric and Environmental Research, Inc. (AER), Hydrology and Remote Sensing Laboratory, US Department of Agriculture [Beltsville] (USDA), Gamma Remote Sensing, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, European Centre for Medium-Range Weather Forecasts (ECMWF), VU University Amsterdam, Earth and Climate, and Amsterdam Global Change Institute
Subjects: 010504 meteorology & atmospheric sciences, Mean squared error, Meteorology, [SDE.MCG]Environmental Sciences/Global Changes, 0211 other engineering and technologies, Soil Science, 02 engineering and technology, Astrophysics::Cosmology and Extragalactic Astrophysics, 01 natural sciences, Physics::Geophysics, 14. Life underwater, Computers in Earth Sciences, Time series, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, Atmospheric sounding, Valencia Anchor Station, Radiometer, Geology, Inversion (meteorology), SMAP, 15. Life on land, Brightness temperature, Soil water, Environmental science, Radiometry, Soil moisture retrieval, ELBARA, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, SMOS
Abstract: International audience; The objective of this study was to compare several approaches to soil moisture (SM) retrieval using l-band microwave radiometry. The comparison was based on a brightness temperature (TB) data set acquired since 2010 by the L-band radiometer ELBARA-II over a vineyard field at the Valencia Anchor Station (VAS) site. ELBARA-II, provided by the European Space Agency (ESA) within the scientific program of the SMOS (Soil Moisture and Ocean Salinity) mission, measures multiangular TB data at horizontal and vertical polarization for a range of incidence angles (30°–60°). Based on a three year data set (2010–2012), several SM retrieval approaches developed for spaceborne missions including AMSR-E (Advanced Microwave Scanning Radiometer for EOS), SMAP (Soil Moisture Active Passive) and SMOS were compared. The approaches include: the Single Channel Algorithm (SCA) for horizontal (SCA-H) and vertical (SCA-V) polarizations, the Dual Channel Algorithm (DCA), the Land Parameter Retrieval Model (LPRM) and two simplified approaches based on statistical regressions (referred to as ‘Mattar’ and ‘Saleh’). Time series of vegetation indices required for three of the algorithms (SCA-H, SCA-V and ‘Mattar’) were obtained from MODIS observations. The SM retrievals were evaluated against reference SM values estimated from a multiangular 2-Parameter inversion approach. As no in situ SM data was used, the evaluation made here is relative to the use of this specific reference data set. The results obtained with the current base line algorithms developed for SMAP (SCA-H and -V) are in very good agreement with the ‘reference’ SM data set derived from the multi-angular observations (R2 ≈ 0.90, RMSE varying between 0.035 and 0.056 m3/m3 for several retrieval configurations). This result showed that, provided the relationship between vegetation optical depth and a remotely-sensed vegetation index can be calibrated, the SCA algorithms can provide results very close to those obtained from multi-angular observations in this study area. The approaches based on statistical regressions provided similar results and the best accuracy was obtained with the ‘Saleh’ methods based on either bi-angular or bipolarization observations (R2 ≈ 0.93, RMSE ≈ 0.035 m3/m3). The LPRM and DCA algorithms were found to be slightly less successful in retrieving the ‘reference’ SM time series (R2 ≈ 0.75, RMSE ≈ 0.055 m3/m3). However, the two above approaches have the great advantage of not requiring any model calibrations previous to the SM retrievals.
Published: 2014
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43. SMOS forest optical depth intercomparisons over pan-tropical biomes

Author: P. Richaume, G. Vaglio Laurin, Y. H. Kerr, Leila Guerriero, Cristina Vittucci, and Paolo Ferrazzoli
Subjects: Biomass (ecology), Vegetation optical depth, 010504 meteorology & atmospheric sciences, Biome, 0211 other engineering and technologies, AMSR2, Forests Biomass, Microwaves, SMOS, Vegetation Optical Depth, Settore ING-INF/02 - Campi Elettromagnetici, Model parameters, 02 engineering and technology, Vegetation, Atmospheric sciences, 01 natural sciences, Soil water, Environmental science, Water content, Optical depth, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing
Abstract: The main objective of SMOS over land is to retrieve soil moisture. Anyway, for soils covered by vegetation two model parameters, soil moisture and vegetation optical depth, are provided as outputs of the Level 2 algorithm.
Published: 2016
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44. SMOS Radio Frequency Interference Scenario: Status and Actions Taken to Improve the RFI Environment in the 1400–1427-MHz Passive Band

Author: Susanne Mecklenburg, C. Gruhier, E. Daganzo, P. Richaume, Sonia Nieto, Roger Oliva, Yann Kerr, Agence Spatiale Européenne (ESA), European Space Agency (ESA), European Space Research and Technology Centre (ESTEC), 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), European Space Research Institute (ESRIN), Agence Spatiale Européenne = European Space Agency (ESA), 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: satellite remote sensing, 010504 meteorology & atmospheric sciences, Meteorology, Data loss, 01 natural sciences, Electromagnetic interference, [SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems, L band radiometry, radio frequency interferences (RFIs), L-band radiometry, Electrical and Electronic Engineering, 0105 earth and related environmental sciences, Remote sensing, [SDE.IE]Environmental Sciences/Environmental Engineering, Soil Moisture and Ocean Salinity (SMOS) mission, 04 agricultural and veterinary sciences, Sea surface temperature, 13. Climate action, Satellite remote sensing, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, General Earth and Planetary Sciences, Environmental science, Radiometry, Radio frequency, Microwave radiometry
Abstract: International audience; The European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission is perturbed by radio frequency interferences (RFIs) that jeopardize part of its scientific retrieval in certain areas of the world, particularly over continental areas in Europe, Southern Asia, and the Middle East. Areas affected by RFImight experience data loss or underestimation of soilmoisture and ocean salinity retrieval values. To alleviate this situation, the SMOS team has put strategies in place that, one year after launch, have already improved the RFI situation in Europe where half of the sources have been successfully localized and switched off.
Published: 2012
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45. Disaggregation of SMOS Soil Moisture in Southeastern Australia

Author: Yann Kerr, Ahmad Al Bitar, Jeffrey P. Walker, Olivier Merlin, P. Richaume, Christoph Rudiger, 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), Groupe d'étude de l'atmosphère météorologique (CNRM-GAME), Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS), Department of Civil and Environmental Engineering, The University of Melbourne, University of Melbourne, 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), Centre national de recherches météorologiques (CNRM), 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), 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 -Centre National de la Recherche Scientifique (CNRS)
Subjects: 010504 meteorology & atmospheric sciences, Correlation coefficient, 0211 other engineering and technologies, Evaporation, Soil science, 02 engineering and technology, 01 natural sciences, Australian Airborne Calibration/validation Experiments for SMOS (AACES), [SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems, field campaign, Electrical and Electronic Engineering, Water content, calibration/validation, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Transpiration, Moderate Resolution Imaging Spectroradiometer (MODIS), Atmospheric correction, Vegetation, 15. Life on land, Soil Moisture and Ocean Salinity (SMOS), 13. Climate action, disaggregation, Disaggregation based on Physical And Theoretical scale Change (DisPATCh), General Earth and Planetary Sciences, Radiometry, Environmental science, Moderate-resolution imaging spectroradiometer
Abstract: Disaggregation based on Physical And Theoretical scale Change (DisPATCh) is an algorithm dedicated to the disaggregation of soil moisture observations using high-resolution soil temperature data. DisPATCh converts soil temperature fields into soil moisture fields given a semi-empirical soil evaporative efficiency model and a first-order Taylor series expansion around the field-mean soil moisture. In this study, the disaggregation approach is applied to Soil Moisture and Ocean Salinity (SMOS) satellite data over the 500 km by 100 km Australian Airborne Calibration/validation Experiments for SMOS (AACES) area. The 40-km resolution SMOS surface soil moisture pixels are disaggregated at 1-km resolution using the soil skin temperature derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data, and subsequently compared with the AACES intensive ground measurements aggregated at 1-km resolution. The objective is to test DisPATCh under various surface and atmospheric conditions. It is found that the accuracy of disaggregation products varies greatly according to season: while the correlation coefficient between disaggregated and in situ soil moisture is about 0.7 during the summer AACES, it is approximately zero during the winter AACES, consistent with a weaker coupling between evaporation and surface soil moisture in temperate than in semi-arid climate. Moreover, during the summer AACES, the correlation coefficient between disaggregated and in situ soil moisture is increased from 0.70 to 0.85, by separating the 1-km pixels where MODIS temperature is mainly controlled by soil evaporation, from those where MODIS temperature is controlled by both soil evaporation and vegetation transpiration. It is also found that the 5-km resolution atmospheric correction of the official MODIS temperature data has a significant impact on DisPATCh output. An alternative atmospheric correction at 40-km resolution increases the correlation coefficient between disaggregated and in situ soil moisture from 0.72 to 0.82 during the summer AACES. Results indicate that DisPATCh has a strong potential in low-vegetated semi-arid areas where it can be used as a tool to evaluate SMOS data (by reducing the mismatch in spatial extent between SMOS observations and localized in situ measurements), and as a further step, to derive a 1-km resolution soil moisture product adapted for large-scale hydrological studies.
Published: 2012
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46. Evaluation of SMOS Soil Moisture Products Over Continental U.S. Using the SCAN/SNOTEL Network

Author: Delphine Leroux, P. Richaume, Eric F. Wood, Olivier Merlin, A. K. Sahoo, Yann Kerr, Ahmad Al Bitar, 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), Princeton University, 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: L band, microwave, 010504 meteorology & atmospheric sciences, 0207 environmental engineering, 02 engineering and technology, Antenna footprint, 01 natural sciences, Soil Climate Analysis Network (SCAN), [SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems, validation, Electrical and Electronic Engineering, 020701 environmental engineering, Water content, 0105 earth and related environmental sciences, Remote sensing, Moisture, Snowpack, L-band, Soil Moisture and Ocean Salinity (SMOS), SNOTEL, General Earth and Planetary Sciences, Radiometry, Environmental science, Satellite, soil moisture, SNOwpack TELemetry (SNOTEL)
Abstract: The Soil Moisture and Ocean Salinity (SMOS) satellite has opened the era of soil moisture products from passive L-band observations. In this paper, validation of SMOS products over continental U.S. is done by using the Soil Climate Analysis Network (SCAN)/SNOwpack TELemetry (SNOTEL) soil moisture monitoring stations. The SMOS operational products and the SMOS reprocessing products are both used and compared over year 2010. First, a direct node-to-site comparison is performed by taking advantage of the oversampling of the SMOS product grid. The comparison is performed over several adjacent nodes to site, and several representative couples of site-node are identified. The impact of forest fraction is shown through the analysis of different cases across the U.S. Also, the impact of water fraction is shown through two examples in Florida and in Utah close to Great Salt Lake. A radiometric aggregation approach based on the antenna footprint and spatial description is used. A global comparison of the SCAN/SNOTEL versus SMOS is made. Statistics show an underestimation of the soil moisture from SMOS compared to the SCAN/SNOTEL local measurements. The results suggest that SMOS meets the mission requirement of 0.04 m3/m3 over specific nominal cases, but differences are observed over many sites and need to be addressed.
Published: 2012
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47. Estimating the uncertainties of satellite derived soil moisture at global scale

Author: Gibon, François, Mialon, Arnaud, Richaume, Philippe, Rodríguez-Fernández, Nemesio, Aberer, Daniel, Boresch, Alexander, Crapolicchio, Raffaele, Dorigo, Wouter, Gruber, Alexander, Himmelbauer, Irene, Preimesberger, Wolfgang, Sabia, Roberto, Stradiotti, Pietro, Tercjak, Monika, and Kerr, Yann H.
Abstract: This study attempts to derive the uncertainty of the soil moisture estimation from passive microwave satellite mission at global scale. To do so, the approach is based on the sensitivity of the Soil Moisture and Ocean Salinity (SMOS) soil moisture retrieval quality to the land surface characteristics within its footprint (presence of forest, topography, open water bodies, sand, clay, bulk density and soil organic carbon content). First, we performed a global assessment of SMOS using in situmeasurements from the International Soil Moisture Network (ISMN) as reference, with more than 1900 ISMN stations and 10 years of SMOS data. This assessment shows that the ubRMSD scores vary greatly between locations (with a mean of 0.074 m3m−3and an interquartile range of 0.030 m3m−3). Second, the scores are analyzed for different surface conditions within the satellite footprint. The best agreement between the ground measurement and SMOS time series are obtained for low forest cover, low topographic complexity, and marginal presence of open water bodies within the SMOS footprint. Soil parameters also have an impact, with better scores for sandier soils with a high bulk-density and low soil organic carbon content. Finally, we propose to extrapolate the obtained relationships, using a multiple linear regression, in order to derive a global map of SMOS uncertainties based on surface conditions. This map of predicted uncertainties show a diverse range of ubRMSD values across the globe (with a mean of 0.076 m3m−3and an interquartile range of 0.031 m3m−3) depending on the surface characteristics. At the ISMN site location, the predicted ubRMSD shows similar results than the comparison between SMOS and the in situmeasurements. The map of predicted SMOS ubRMSD represents an upper bound estimate of the SMOS uncertainty, as it includes the uncertainties of the in situsensor measurements and the scale mismatch. Further investigations will focus on the different components of this uncertainty budget to obtain a better assessment of the absolute uncertainties of SMOS soil moisture retrievals across the globe.
Published: 2024
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48. Status of RFI in the 1400–1427 MHz passive band: The SMOS perspective

Author: J. Barbosa, Yan Soldo, G. Lopes, Roger Oliva, E. Daganzo, E. Anterrieu, A. Gutierrez, P. Richaume, Yann Kerr, and Francois Cabot
Subjects: Computer science, Electromagnetic interference, Remote sensing
Abstract: The current paper describes the status of Radio Frequency interference (RFI) in the SMOS observations and the recent efforts performed by the SMOS team to mitigate the negative effects of the interfering sources. This efforts comprise new methodologies being introduced at the new level 1 processor in order to flag the observations affected by RFI and a continuous monitoring and reporting of the RFI sources worldwide, to request countries to protect the spectrum from illegal transmissions.
Published: 2014
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49. Merging two passive microwave remote sensing (SMOS and AMSR_E) datasets to produce a long term record of Soil Moisture

Author: P. de Rosnay, R.A.M. de Jeu, Amen Al-Yaari, Joaquín Muñoz-Sabater, J. P. Wigneron, Ajit Govind, P. Richaume, Clément Albergel, Arnaud Mialon, Yann Kerr, Ahmad Al Bitar, Agnès Ducharne, 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), Milieux Environnementaux, Transferts et Interactions dans les hydrosystèmes et les Sols (METIS), Centre National de la Recherche Scientifique (CNRS)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Pierre et Marie Curie - Paris 6 (UPMC), 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), European Centre for Medium-Range Weather Forecasts (ECMWF), Vrije universiteit = Free university of Amsterdam [Amsterdam] (VU), Institut national des sciences de l'Univers (INSU - CNRS), IEEE Geoscience and Remote Sensing Society (GRSS). USA., Université Pierre et Marie Curie - Paris 6 (UPMC)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), 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), Vrije Universiteit Amsterdam [Amsterdam] (VU), Université Pierre et Marie Curie - Paris 6 (UPMC)-École pratique des hautes études (EPHE), and VU University Amsterdam
Subjects: Brightness, Radiometer, 010504 meteorology & atmospheric sciences, Meteorology, Mean squared error, Calibration (statistics), [SDV]Life Sciences [q-bio], 0211 other engineering and technologies, 1.4 GHZ, Regression analysis, regression analyses, AMSR-E, 02 engineering and technology, 01 natural sciences, Term (time), 13. Climate action, [SDE]Environmental Sciences, Microwave remote sensing, Environmental science, soil moisture, Water content, SMOS, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing
Abstract: International audience; This study investigated the use of physically based statistical regressions to retrieve a global and long term (e.g. 2003–2014) surface soil moisture (SSM) record based on a combination of passive microwave remote sensing observations from the Advanced Microwave Scanning Radiometer (AMSR-E; 2003-Sept. 2011) and the Soil Moisture and Ocean Salinity (SMOS; 2010–2014) sensors. Statistical regression methods based on bi-polarization (horizontal and vertical) brightness temperatures (Tb) observations obtained from AMSR-E. The coefficients of these regression equations were calibrated using SMOS level 3 SSM maps (SMOSL3) as a reference. This calibration process was carried out over the June 2010-Sept. 2011 period, over which both SMOS and AMSR-E observations coincide. Based on these calibrated coefficients global SSM maps could be computed from the AMSR-E Tb observations over the whole 2003–2011 period. In this study, the SSM maps were successfully evaluated against the SMOSL3 SSM products over the period of calibration (Jun. 2010-Sept. 2011). Correlations (R) and Root Mean Square Error (RMSE) were computed between the AMSR-E retrievals and the reference (SMOSL3) SSM products. The R (mostly > 0.75) and RMSE (mostly < 0.04 m3/m3) maps showed a good agreement between the retrieved and SMOSL3 SSM products particularly over Australia, central USA, central Asia, and the Sahel. In conclusion, the statistical regression method is capable of retrieving a coherent "SMOS-AMSR-E" SSM time series for the period 2003–2014.
Published: 2014
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50. Global-scale evaluation of two satellite-based passive microwave soil moisture datasets (SMOS and AMSR-E) with respect to Land Data Assimilation System estimates

Author: Agnès Ducharne, Amen Al-Yaari, Jean-Pierre Wigneron, Yann Kerr, Ahmad Al Bitar, Joaquín Muñoz-Sabater, Ajit Govind, P. Richaume, R.A.M. de Jeu, Arnaud Mialon, Clément Albergel, P. de Rosnay, Écologie fonctionnelle et physique de l'environnement (EPHYSE - UR1263), Institut National de la Recherche Agronomique (INRA), Milieux Environnementaux, Transferts et Interactions dans les hydrosystèmes et les Sols (METIS), Centre National de la Recherche Scientifique (CNRS)-École pratique des hautes études (EPHE)-Université Pierre et Marie Curie - Paris 6 (UPMC), Centre d'études spatiales de la biosphère (CESBIO), 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), European Centre for Medium-Range Weather Forecasts (ECMWF), Department of Earth Sciences [Amsterdam], VU University Amsterdam, Groupe d'étude de l'atmosphère météorologique (CNRM-GAME), Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS), Université Pierre et Marie Curie - Paris 6 (UPMC)-École pratique des hautes études (EPHE)-Centre National de la Recherche Scientifique (CNRS), Écologie fonctionnelle et physique de l'environnement (EPHYSE), Université Pierre et Marie Curie - Paris 6 (UPMC)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), 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), Vrije universiteit = Free university of Amsterdam [Amsterdam] (VU), TOSCA (Terre Ocean Surfaces Continentales et Atmosphere) CNES programme, Islamic Development Bank (IDB), Earth and Climate, Amsterdam Global Change Institute, Université Pierre et Marie Curie - Paris 6 (UPMC)-École Pratique des Hautes Études (EPHE), 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), Vrije Universiteit Amsterdam [Amsterdam] (VU), Centre national de recherches météorologiques (CNRM), 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), 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 -Centre National de la Recherche Scientifique (CNRS)
Subjects: 010504 meteorology & atmospheric sciences, Correlation coefficient, [SDE.MCG]Environmental Sciences/Global Changes, Biome, 0211 other engineering and technologies, Soil Science, 02 engineering and technology, Passive, 01 natural sciences, Data assimilation, 14. Life underwater, SDG 14 - Life Below Water, Computers in Earth Sciences, Leaf area index, [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology, Microwaves, C-band, Water content, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, Radiometer, Geology, Vegetation, AMSR-E, 15. Life on land, LAI, L-band, Environmental science, Satellite, Soil moisture, SMOS
Abstract: Global Level-3 surface soil moisture (SM) maps derived from the passive microwave SMOS (Soil Moisture and Ocean Salinity) observations at L-band have recently been released. In this study, a comparative analysis of this Level 3 product (referred to as SMOSL3) along with another Surface SM (SSM) product derived from the observations of the Advanced Microwave Scanning Radiometer (AMSR-E) at C-band is presented (this latter product is referred to as AMSRM). SM-DAS-2, a SSM product produced by the European Centre for Medium Range Weather Forecasts (ECMWF) Land Data Assimilation System (LDAS) was used to monitor both SMOSL3 and AMSRM qualities. The present study was carried out from 03/2010 to 09/2011, a period during which both SMOS and AMSR-E products were available at global scale. Three statistical metrics were used for the evaluation; the correlation coefficient (R), the Root Mean Squared Difference (RMSD), and the bias. Results were analysed using maps of biomes and Leaf Area Index (LAI). It is shown that both SMOSL3 and AMSRM captured well the spatio-temporal variability of SM-DAS-2 for most of the biomes. In terms of correlation values, the SMOSL3 product was found to better capture the SSM temporal dynamics in highly vegetated biomes ("tropical humid", "temperate humid", etc.) while best results for AMSRM were obtained over arid and semi-arid biomes ("desert temperate", "desert tropical", etc.). Finally, we showed that the accuracy of the remotely sensed SSM products is strongly related to LAI. Both the SMOSL3 and AMSRM (marginally better) SSM products correlated well with the SM-DAS-2 product over regions with sparse vegetation for values of LAI ≤. 1 (these regions represent almost 50% of the pixels considered in this global study). In regions where LAI. >1, SMOSL3 showed better correlations with SM-DAS-2 than AMSRM: SMOSL3 had a consistent performance up to LAI. =. 6, whereas the AMSRM performance deteriorated with increasing values of LAI. This study reveals that SMOS and AMSR-E complement one another in monitoring SSM over a wide range in conditions of vegetation density and that there are valuable satellite observed SSM data records over more than 10. years, which can be used to study land-atmosphere processes. © 2014 Elsevier Inc.
Published: 2014
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