Search

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

Observation and modeling of the coupled energy and water balance is the key to understand hydrospheric and cryospheric processes at high elevation. The paper summarizes the progress to address this aspect in relation with different earth system elements, from glaciers to wetlands. The energy budget of two glaciers, i.e. Xiao Dongkemadi and Parlung No.4, was studied by means of extended field measurements and a distributed model of the coupled energy and mass balance was developed and evaluated. The need for accurate characterization of surface albedo was further documented for the entire Qinghai Tibet Plateau by numerical experiments with Weather Research and Forecast (WRF) on the sensitivity of the atmospheric boundary layer to the parameterization of land surface processes. A new approach to the calibration of a coupled distributed watershed model of the energy and water balance was demonstrated by a case study on the Heihe River Basin in northwestern China. The assimilation of land surface temperature did lead to the retrieval of critical soil and vegetation properties as the soil permeability and the canopy resistance to the exchange of vapour and carbon dioxide. The retrievals of actual Evapo-Transpiration (ET) were generated by the ETMonitor system and evaluated against eddy covariance measurements at sites spread throughout Asia. As regards glacier response to climate variability, the combined findings based on satellite data and model experiments showed that the spatial variability of surface albedo and temperature is significant and controls both glacier mass balance and flow. Experiments with both atmospheric and hydrosphere-cryosphere models documented the need and advantages of using accurate retrievals of land surface albedo to capture lan-atmosphere interactions at high elevation.

This research aims at introducing a new methodology to create maps of irrigation types at very high resolution, with yearly updates. While different studies were already performed on simply mapping irrigated areas, there is still no research on classifying irrigation types based on remotely sensed data. This information has a critical scientific value since detailed information on irrigation greatly improves the understanding of human activities on the water cycle. In particular, precise knowledge of different irrigation types is needed in order to correctly model the anthropogenic impact in various land surface models (Ozdogan et al., 2010; Evans and Zaitchik, 2008). Additionally, these maps are also useful for administrative purposes, to estimate the percentage of different irrigation types, monitor changes in irrigation practices and consequently encourage more sustainable use of the freshwater resources. In this research, we produce maps of irrigation types combining state-of-the-art supervised AI classification algorithms for time series classification together with a selection of hydrological variables. In order to train and test the AI models, a field campaign to collect ground truth data was performed in November 2020 around the intensely cultivated region of Catalunya, Spain. From this campaign, important information about crop types and irrigation types (sprinkler, flood, drip/subsurface and non-irrigated) were retrieved for a large number of fields, ensuring to collect a representative sample of the different cultivation and irrigation types employed in the area. Three different models were tested using as inputs a large variety of hydrological variables both alone and combined in multivariate models. Two machine learning models, Time-Series Forest and Rocket, and one Deep Neural Network model, ResNET, were selected for this classification task. The classification was performed using time-series from three different years in order to train the models with a more general and robust dataset, independent from specific meteorological conditions of a single year. The main finding of the research was that Soil Moisture (SM) and Actual Evapotranspiration (ETa) at very high spatial resolution (20 m) consistently showed the highest accuracy, when combined together, with respect to the other variables considered, regardless of the AI model used. Additionally, ResNET showed consistently better performance than the other two AI models over all the metrics used for the comparison (accuracy, precision, recall and kappa). The final classification accuracy retrieved from ResNET using SM and ETa as inputs was 86.59 +/- 2.79, obtained from 10 different runs of the model trained each time with different ground truth data subsamples. As a result of these findings, yearly maps of irrigation types can be created for large areas at field level, delivering detailed information on the status and evolution of irrigation practices. REFERENCE:Ozdogan, M.; Rodell, M.; Beaudoing, H.K.; Toll, D.L. Simulating the effects of irrigation over the United States in a land surface model based on satellite-derived agricultural data. J. Hydrometeorol 2010, 11, 171–184.Evans, J.P.; Zaitchik, B.F. Modeling the large-scale water balance impact of different irrigation systems. Water Res. 2008, 44, W08448.

Swath mode processing of CryoSat-2 Synthetic Aperture Radar Interferometric (SARIn) mode has been used to monitor elevation of areas with complex topography such as over ice sheet and ice cap margins. Swath processing relies on an accurate measure of the angle of arrival of the measured echo and, therefore, requires custom strategies in order to resolve the ambiguous phase measurement. In mountainous regions of complex terrain, it may be necessary to apply different phase ambiguities across a waveform record when returns come from different scatters distributed perpendicularly to the CryoSat-2 ground tracks. In this letter, we present modifications to the conventional swath processing method whereby a multisurface retracker is first applied to the record in order to identify potential different scattering surfaces. Phase ambiguity is then independently resolved for each of these subsurfaces. The improvements with this new method over the Karakoram glaciers are a 10% increase in the number of measurements with improvements of almost 50% for individual glaciers and a reduction in the median absolute deviation of the elevations from 20.18 to 14.69 m.

International audience; Remote sensing data, crop modelling, and statistical methods are combined in an original method to overcome current limitations of crop yield estimation. It is then tested for timely estimation of maize grain yields and their year-on-year variability in Burkina Faso. Outputs from the SARRA-O crop model were used as a proxy for observed data for calibration. The final remote sensing-based yield model was constructed on the interaction between aboveground biomass at flowering (AGB-F) and crop water stress (Cstr) over the reproductive and maturation phases. Various vegetation and drought-related indices were derived from different spectral domains and tested. Model performance was evaluated by cross-validation against (a) simulated yields and (b) independent yields from ground surveys aggregated at village level. The results showed that the RF (Random Forest) model outperformed the MLR (Multiple Linear Regression) model for year-on-year yield estimation at the end of the season when compared to simulated yields. Surface soil moisture (SSM) information, as a proxy for soil water available for plant growth, together with information on the temperature of the canopy cover, helped to improve the RF maize yield model, impacting more particularly the estimation of crop water stress. Lastly, two months before harvest the RF model predicted 46% of the observed end-of-season maize grain yield variability. The combined remote sensing, crop model and machine learning method is thus an effective approach for estimating and forecasting inter-annual maize crop yields in environments where field data are scarce, such as in most parts of the African continent. However, more research is needed to better retrieve the spatial variability of yields in order to strengthen current agricultural monitoring systems, and to address societal challenges, such as declining food security.

Root-zone soil moisture (RZSM) plays a key role for most water and energy budgets, as it is particularly relevant in controlling plant transpiration and hydraulic redistribution. RZSM data is needed for a variety of different applications, such as forecasting crop yields, improving flood predictions and monitoring agricultural drought, among others. Remote sensing provides surface soil moisture (SSM) retrievals, whose key advantage is the large spatial coverage on a systematic basis. This study tests a simple method to retrieve RZSM estimates from high-resolution SSM derived from SMAP (Soil Moisture Active Passive). A recursive exponential filter using a time constant τ is calibrated per land cover type, which uses as an intermediate step a long-term ISBA-DIF (Interaction Soil Biosphere Atmosphere—Diffusion scheme) dataset over an area located in Catalonia, NE of Spain. The τ values thus obtained are then used as an input to the same recursive exponential filter, to derive 1 km resolution RZSM estimates from 1 km SMAP SSM, which are obtained from the original data by downscaling to a 1 km resolution, through the DISPATCH (DISaggregation based on a Physical and Theoretical scale CHange) methodology. The results are then validated with scaled in situ observations at different depths, over two different areas, one representative of rainfed crops, and the other of irrigated crops. In general, the estimates agree well with the observations over the rainfed crops, especially at a 10 cm and 25 cm depth. Nash–Sutcliffe (NS) scores ranging between 0.33 and 0.58, and between 0.37 and 0.56 have been found, respectively. Correlation coefficients for these depths are high, between 0.76 and 0.91 (10 cm), and between 0.71 and 0.90 (25 cm). For the irrigated sites, results are poorer (partly due to the extremely high heterogeneity present), with NS scores ranging between −2.57 and 0.16, and correlations ranging between −0.56 and 0.48 at 25 cm. Given the strong correlations and NS scores found in the surface, the sensitivity of the filter to different τ values was investigated. For the rainfed site, it was found, as expected, with increasing τ, increasing NS and correlations with the deeper layers, suggesting a better coupling. Nevertheless, a strong correlation with the surface (5 cm) or shallower depths (10 cm) observed over certain sites indicates a certain lack of skill of the filter to represent processes which occur at lower levels in the SM column. All in all, a calibration accounting for the vegetation was shown to be an adequate methodology in applying the recursive exponential filter to derive the RZSM estimates over large areas. Nevertheless, the relative shallow surface at which the estimates correlate in some cases seem to indicate that an effect of evapotranspiration in the profile is not well captured by the filter.

Droughts in the Iberian Peninsula are a natural hazard of great relevance due to their recurrence, severity and impact on multiple environmental and socioeconomic aspects. The Ebro Basin, located in the NE of the Iberian Peninsula, is particularly vulnerable to drought with consequences on agriculture, urban water supply and hydropower. This study, performed within the Project HUMID (CGL2017-85687-R), aims at evaluating the influence of the climatic, land cover and soil characteristics on the interactions between rainfall, evapotranspiration and soil moisture anomalies which define the spatio-temporal drought patterns in the basin.The onset, propagation and mitigation of droughts in the Iberian Peninsula is driven by anomalies of rainfall, evapotranspiration and soil moisture, which are related by feedback processes. To test the relative importance of such anomalies, we evaluate the contribution of climatic, land-cover and geologic heterogeneity on the definition of the spatio-temporal patterns of drought. We use the Köppen-Geiger climatic classification to assess how the contrasting climatic types within the basin determine differences on drought behavior. Land-cover types that govern the partition between evaporation and transpiration are also of great interest to discern the influence of vegetation and crop types on the anomalies of evapotranspiration across the distinct regions of the basin (e.g. forested mountains vs. crop-dominated areas). The third physical characteristic whose effect on drought we investigate is the impact of soil properties on soil moisture anomalies.The maps and time series used for the spatio-temporal analysis are based on drought indices calculated with high-resolution datasets from remote sensing (MOD16A2ET and SMOS1km) and the land-surface model SURFEX-ISBA. The Standardized Precipitation Index (SPI), the EvapoTranspiration Deficit Index (ETDI) and the Soil Moisture Deficit Index (SMDI) are the three indices chosen to characterize the anomalies of the corresponding rainfall (atmospheric), evapotranspiration (atmosphere-land interface) and soil moisture (land) anomalies (components of the water balance). The comparison of the correlations of the indices (with different time lags) between contrasting regions offers insights about the impact of climate, land-cover and soil properties in the dominance, the timing of the response and memory aspects of the interactions. The high spatial and temporal resolution of remote sensing and land-surface model data allows adopting time and spatial scales suitable to investigate the influence of these physical factors with detail beyond comparison with ground-based datasets.The spatial and temporal analysis prove useful to investigate the physical factors of influence on the anomalies between rainfall, evapotranspiration and soil moisture. This approach facilitates the physical interpretation of the anomalies of drought indices aiming to improve the characterization of drought in heterogeneous semi-arid areas like the Ebro River Basin.

Agriculture is by far the largest consumer of water, with about 70% of the diverted water being used in irrigation. Agriculture is also considered as a key source of diffuse pollution with inefficient practices resulting in high water and nutrient (particularly N and P) surpluses that are transferred to water bodies through diffuse processes (runoff and leaching), promoting eutrophication with associated biodiversity loss. WATER4EVER aims to demonstrate the connectivity between agricultural practices carried out at the plot scale and the resulting environmental effects that are usually first perceived further downstream. For that, the project combines the most performant monitoring strategies at the plot scale to provide detailed information of water flow and nutrient transport, integrating then this information at the catchment scale to close the gap between diffuse loads and water quality degradation. The specific objectives are: (i) to develop an automatic irrigation and fertilization Decision Support System (DSS) based on online data and forecast models; (ii) to develop new strategies for continuous monitoring of crop development based on optical sensors installed on fixed and mobile ground, drone and satellite platforms; (iii) to improve process-based models for the dynamics of water and its implication on nutrient budgets, crop development, and soil erosion; (iv) to connect plot and catchment scale models to quantify the effect of local agriculture practices on downstream water availability and quality; and (v) to disseminate and transfer knowledge and technology by means of public information adapted to different stakeholders. WATER4EVER was implemented in multiple case studies in Portugal, Spain, Italy, and Turkey. Earlier work was on the collection of field datasets for the calibration/validation of some of the innovative tools and approaches developed in the project. These included: (i) a modular smart camera for crop monitoring, which was integrated with other modular and open-source IoT based technologies to upgrade conventional machinery with variable rate technologies to reach higher levels of precision for crop monitoring; (ii) irrigation scheduling tools for improving irrigation water use efficiency in water scarce regions; (iii) the IrrigaSys DSS for irrigation water management at the plot scale based on online, open source tools; (iv) 1 km soil moisture maps for monitoring surface soil moisture at the catchment scale; (v) 100 m soil moisture maps for plot scale monitoring of soil moisture conditions; and (vi) vegetation products (LAI and NDVI) (30m resolution) for crop growth monitoring. Project results further included: (i) validated protocols for sustainable irrigation water management; (ii) guidelines on the best practices for integrating earth observation data in model-based irrigation and fertilization optimization strategies; and (iii) guidelines on the best practices related to soil and water management for each case study. WATER4EVER results can contribute directly to the implementation of the Nitrates Directive and the Water Framework Directive. The Project is particularly dedicated to raising awareness on the causes of diffuse pollution and respective impacts on the availability and quality of downstream water bodies; on reducing costs of production factors by improving irrigation water and fertilizers efficiency; and on demonstrating the capabilities of emerging technologies (smart cameras, satellite data, process-based models) for improving irrigation water and fertilization management. WATER4EVER is led by Instituto Superior Técnico (Portugal) and includes Deimos Engenharia SA (Portugal), Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência (Portugal), isardSAT (Spain), Universidad Politécnica de Cartagena (Spain), Institute for Agricultural and Earthmoving Machines of the National Research Council of Italy (Italy), and the Abant Izzet Baysal University (Turkey) in the consortium. More information on WATER4EVER is available at http://water4ever.eu/.

Although irrigation practices affect food production and water resource management, with ever more impacting effects under climate change and population increasing scenarios, detailed knowledge of irrigation is still lacking. In fact, explicit information on the spatial occurrence of irrigation and on the amounts of water used for this purpose is often not available, thus making irrigation the missing variable to comprehensively understand the hydrological cycle dynamics over agricultural areas. Nevertheless, remote sensing techniques can be used to delimit the irrigation extent. In this study, the capability of five remotely sensed soil moisture products to detect the irrigation signal over an area intensely equipped for irrigation in the North East of Spain is investigated; moreover, a method to map the actually irrigated areas based on the K-means clustering algorithm is proposed. The remote sensing soil moisture data sets used in this study are SMOS (Soil Moisture and Ocean Salinity) at 1 km, SMAP (Soil Moisture Active Passive) at 1 km and 9 km, Sentinel-1 at 1 km, and ASCAT (Advanced SCATterometer) at 12.5 km. The 1 km resolution versions of SMOS and SMAP are obtained by downscaling coarser SMOS and SMAP data through the DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) algorithm. The analyses are supported by an additional data set of soil moisture at 1 km resolution simulated by the SURFEX-ISBA (SURFace EXternalisee – Interaction Sol Biosphere Atmosphere) land surface model. Among all the considered data sets, the L-band passive microwave downscaled products show the best performances in detecting the irrigation signal over the pilot area, especially SMAP at 1 km. The proposed maps of irrigated areas derived by exploiting soil moisture from SMAP at 1 km data set agree well (up to 78%) with the ground truth derived irrigated areas. Furthermore, the method is able to well distinguish the actually irrigated areas from rainfed agricultural areas, thus representing a useful tool to obtain reliable spatial information about the areas where irrigation actually occurs.

International audience; The Sentinel-3 Mission Performance Centre (S3MPC) is tasked by the European Space Agency (ESA) to monitor the health of the Copernicus Sentinel-3 satellites and ensure a high data quality to the users. This paper deals exclusively with the effort devoted to the altimeter and microwave radiometer, both components of the Surface Topography Mission (STM). The altimeters on Sentinel-3A and -3B are the first to operate in delay-Doppler or SAR mode over all Earth surfaces, which enables better spatial resolution of the signal in the along-track direction and improved noise reduction through multi-looking, whilst the radiometer is a two-channel nadir-viewing system. There are regular routine assessments of the instruments through investigation of telemetered housekeeping data, calibrations over selected sites and comparisons of geophysical retrievals with models, in situ data and other satellite systems. These are performed both to monitor the daily production, assessing the uncertainties and errors on the estimates, and also to characterize the long-term performance for climate science applications. This is critical because an undetected drift in performance could be misconstrued as a climate variation. As the data are used by the Copernicus Services (e.g., CMEMS, Global Land Monitoring Services) and by the research community over open ocean, coastal waters, sea ice, land ice, rivers and lakes, the validation activities encompass all these domains, with regular reports openly available. The S3MPC is also in charge of preparing improvements to the processing, and of the development and tuning of algorithms to improve their accuracy. This paper is thus the first refereed publication to bring together the analysis of SAR altimetry across all these different domains to highlight the benefits and existing challenges.

The resolution of current satellite surface soil moisture (SM) estimates is very low, of tens of kilometers, which proves to be insufficient for various agricultural and hydrological applications. Amongst the existing downscaling approaches of remotely sensed SM, DISPATCH (DISaggregation based on a Physical And Theoretical scale CHange) improves the resolution of SMOS (Soil Moisture and Ocean Salinity) soil moisture data using soil evaporative efficiency (SEE) estimates at high resolution (HR) and a SEE(SM) model implemented at low resolution (LR). Defined as the ratio of actual to potential soil evaporation, SEE can be derived from the remotely sensed land surface temperature (LST) and normalized difference vegetation index (NDVI). The current version of DISPATCH uses a linear SEE(SM) model. This study aims at improving the SEE(SM) model and testing different calibration strategies, to ultimately have more robust and better downscaled SM products. A nonlinear SEE(SM) model is introduced and its influence on the derived HR SM products is studied over a range of conditions. Each model, linear and nonlinear, is calibrated from remote sensing data on a daily and a multi-date basis. The approaches were tested over two mixed dry and irrigated areas in Catalonia, Spain, and over one dry area in Morocco. When using the linear model, better statistical results were generally obtained using a daily calibration (current version of DISPATCH), most notably over one Spanish site. However, the best results were systematically obtained for an annually calibrated nonlinear model, in terms of all metrics considered: correlation coefficient, slope of the linear regression, bias, unbiased root mean square error. In particular, when using the annually calibrated nonlinear SEE (SM) model, the temporal slope of the linear regression between disaggregated and in situ soil moisture increased to 1.16 and 0.75 for one Spanish site and for the Moroccan site (as opposed to 0.44 and 0.58, respectively, when using the linear model with a daily calibration). The temporal correlation coefficient increased to 0.47 and 0.54 over the Spanish sites (as opposed to 0.18 and 0.27, respectively, when using the linear model with a daily calibration). Those contrasted results indicate compensation effects between the model type and the calibration strategy. Taking into account studies that report the strong nonlinear behavior of the SEE with respect to SM, the introduction of the nonlinear SEE(SM) model in DISPATCH, combined with a multi-date calibration, is proven to perform significantly better under various conditions, leading to more robust disaggregated SM products. The SEE modeling based on the nonlinear SM model, with a multi-date calibration, could be integrated into the CATDS&mdash, Centre Aval de Traitement des Donn&eacute, es SMOS as a future product, as well as into existing evapotranspiration models, which are based on a combination of thermal and microwave data.

High-resolution soil moisture product is important for agriculture-related managements including irrigation. We have investigated the Change Detection (CD) method using Sentinel-1 data for 100 m resolution soil moisture retrieval and got a Root Mean Square Error (RMSE) about 0.6 m3/m3. However, the result of this approach is not accurate enough for high-density crops like corn. Another approach needs to be studied to get better accuracy over all types of crops. The artificial neural network (NN) technique, which involves nonlinear parameterized mapping from an input vector to an output vector, is an appropriate tool for retrieving geophysical parameters from remote sensing data. Many studies have explored the NN approach for processing remotely sensed data, including retrieving soil moisture, however, only a few studies [Notarnicola et al., 2010; Paloscia et al., 2013, etc.] had investigated NN for soil moisture estimation over vegetation-covered areas, especially in a large scale.The objective of this study is to develop an approach based on neural networks to estimate soil moisture at high resolution over vegetation-covered areas from Sentinel-1 C-band SAR data. The quality of the output results depends directly on the quality of the input data used to train the NN and the reference data for the training, therefore, we performed our study over Catalonia, where we have many auxiliary data. The study is performed using both VV and VH polarization over the whole Catalonia. Apart from Sentinel-1 SAR data, auxiliary data including Sentinel-2 NDVI, SMAP soil moisture, CCI (ESA Climate Change Initiative) land cover, SIGPAC (Sistema de Información Geográfica de Parcelas Agrícolas) land cover, irrigation index and crop type information from SIGPAC, and DEM (Digital elevation model) are also used for approach development. DISPATCH (Disaggregation based on Physical and Theoretical scale Change) soil moisture product at 1 km resolution is considered as the target in the Neural Network training, adding great value to our study. To prepare the Neural Network training, all data sets are co-registered at 1 km resolution within the same size and resampled for the same dates within one year (2017). Two indexes describing the normalized backscatter difference and soil moisture are introduced as equation (1) and (2):Index1 = (σ0i - σ0min) / (σ0max - σ0min) (1) Index2 = SMmin + (SMmax - SMmin) * Index1 (2) Different parameters were tested to train the Neural Network approach, the preliminary results show a correlation value compared with DISPATCH product about 0.71 over croplands, 0.73 over irrigated fields, and 0.65 over forests, considering Index1, Index2 and SMAP soil moisture. Works are still on-going to try to improve the results by better analyzing the SAR data performance over different fields and conditions. The final goal of the study is to produce 100 m resolution soil moisture product. After 1 km resolution study, we will apply the approach at 100m resolution, and the in-situ soil moisture will be used for validation.This work is inscribed within the Water4Ever project, which is funded by the European Commission under the framework of the ERA-NET COFUND WATERWORKS 2015 Programme.

The Mediterranean climate of the Iberian Peninsula defines high spatial and temporal variability of drought at multiple scales. These droughts impact human activities such as water management, agriculture or forestry, and may alter valuable natural ecosystems as well. An accurate understanding and monitoring of drought processes are crucial in this area. The HUMID project (CGL2017-85687-R) is studying how remote sensing data and models (Quintana-Seguí et al., 2019; Barella-Ortiz and Quintana-Seguí, 2019) can improve our current knowledge on Iberian droughts, in general, and in the Ebro basin, more specifically.The traditional ground-based monitoring of drought lacks the spatial resolution needed to identify the microclimatic mechanisms of drought at sub-basin scale, particularly when considering relevant variables for drought such as soil moisture and evapotranspiration. In situ data of these two variables is very scarce.The increasing availability of remote sensing products such as MODIS16 A2 ET and the high-resolution SMOS 1km facilitates the use of distributed observations for the analysis of drought patterns across scales. The data is used to generate standardized drought indexes: the soil moisture deficit index (SMDI) based on SMOS 1km data (2010-2019) and the evapotranspiration deficit index (ETDI) based on MODIS16 A2 ET 500m. The study aims to identify the spatio-temporal mechanisms of drought generation, propagation and mitigation within the Ebro River basin and sub-basins, located in NE Spain where dynamic Atlantic, Mediterranean and Continental climatic influences dynamically mix, causing a large heterogeneity in climates.Droughts in the 10-year period 2010-2019 of study exhibit spatio-temporal patterns at synoptic and mesoscale scales. Mesoscale spatio-temporal patterns prevail for the SMDI while the ETDI ones show primarily synoptic characteristics. The study compares the patterns of drought propagation identified with remote sensing data with the patterns estimated using the land surface model SURFEX-ISBA at 5km. The comparison provides further insights about the capabilities and limitations of both tools, while emphasizes the value of combining approaches to improve our understanding about the complexity of drought processes across scales.Additionally, the periods of quick change of drought indexes comprise valuable information about the response of evapotranspiration to water deficits as well as on the resilience of soil to evaporative stress. The lag analysis ranges from weeks to seasons. Results show lags between the ETDI and SMDI ranging from days to weeks depending on the precedent drought status and the season/month of drought’s generation or mitigation. The comparison of the lags observed on remote sensing data and land surface model data aims at evaluating the adequacy of the data sources and the indexes to represent the nonlinear interaction between soil moisture and evapotranspiration. This aspect is particularly relevant for developing drought monitoring aiming at managing the impact of drought in semi-arid environments and improving the adaptation to drought alterations under climate change.

Drought is a major climatic risk resulting from complex interactions between the atmosphere, the continental surface and water resources management. Droughts have large socioeconomic impacts and recent studies show that drought is increasing in frequency and severity due to the changing climate.Drought is a complex phenomenon and there is not a common understanding about drought definition. In fact, there is a range of definitions for drought. In increasing order of severity, we can talk about: meteorological drought is associated to a lack of precipitation, agricultural drought, hydrological drought and socio-economic drought is when some supply of some goods and services such as energy, food and drinking water are reduced or threatened by changes in meteorological and hydrological conditions. A number of different indices have been developed to quantify drought, each with its own strengths and weaknesses. The most commonly used are based on precipitation such as the precipitation standardized precipitation index (SPI; McKee et al., 1993, 1995), on precipitation and temperature like the Palmer drought severity index (PDSI; Palmer 1965), others rely on vegetation status like the crop moisture index (CMI; Palmer, 1968) or the vegetation condition index (VCI; Liu and Kogan, 1996). Drought indices can also be derived from climate prediction models outputs. Drought indices base on remote sensing based have traditionally been limited to vegetation indices, notably due to the difficulty in accurately quantifying precipitation from remote sensing data. The main drawback in assessing drought through vegetation indices is that the drought is monitored when effects are already causing vegetation damage. In order to address drought in their early stages, we need to monitor it from the moment the lack of precipitation occurs.Thanks to recent technological advances, L-band (21 cm, 1.4 GHz) radiometers are providing soil moisture fields among other key variables such as sea surface salinity or thin sea ice thickness. Three missions have been launched: the ESA’s SMOS was the first in 2009 followed by Aquarius in 2011 and SMAP in 2015.A wealth of applications and science topics have emerged from those missions, many being of operational value (Kerr et al. 2016, Muñoz-Sabater et al. 2016, Mecklenburg et al. 2016). Those applications have been shown to be key to monitor the water and carbon cycles. Over land, soil moisture measurements have enabled to get access to root zone soil moisture, yield forecasts, fire and flood risks, drought monitoring, improvement of rainfall estimates, etc.The advent of soil moisture dedicated missions (SMOS, SMAP) paves the way for drought monitoring based on soil moisture data. Initial assessment of a drought index based on SMOS soil moisture data has shown to be able to precede drought indices based on vegetation by 1 month (Albitar et al. 2013).In this presentation we will be analysing different drought episodes in the Ebro basin using both soil moisture and vegetation based indices to compare their different performances and test the hypothesis that soil moisture based indices are earlier indicators of drought than vegetation ones.

International audience; Soil moisture (SM) data can be reliably obtained at high spatial resolution through the disaggregation of passive microwave-derived SM. Optical/thermal data are classically used as fine scale information in such a disaggregation procedure. The point is that optical data are unavailable under cloudy conditions and their relationship with SM is strongly affected by vegetation cover. Alternatively, Sentinel-1 provides radar data at an unprecedented spatio- temporal resolution, despite the complex relationship between backscatter and SM that often requires ancillary data for calibration. Hence the idea of combining disaggregation- and Sentinel-1-based SM retrieval approaches on clear sky days and to run the so-calibrated radar-based algorithm irrespective of weather conditions. This study undertakes preliminary analyses before both approaches can be efficiently merged. It consists of 1) extending the applicability of DISPATCH (disaggregation) algorithm to full-vegetated areas, in order to optimize the spatial match between disaggregation- and radar-based approaches and 2) providing a first comparative assessment of DISPATCH and the recently released Copernicus 1 km resolution SM data sets over a contrasted area including dryland and irrigated crops. Results indicate significant differences between both data sets, thus highlighting the potential of synergies between them.

Despite irrigation being one of the main sources of anthropogenic water consumption, detailed information about water amounts destined for this purpose are often lacking worldwide. In this study, a methodology which can be used to estimate irrigation amounts over a pilot area in Spain by exploiting remotely sensed soil moisture is proposed. Two high-resolution DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) downscaled soil moisture products have been used: SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) at 1 km. The irrigation estimates have been obtained through the SM2RAIN algorithm, in which the evapotranspiration term has been improved to adequately reproduce the crop evapotranspiration over irrigated areas according to the FAO (Food and Agriculture Organization) model. The experiment exploiting the SMAP data at 1 km represents the main work analyzed in this study and covered the period from January 2016 to September 2017. The experiment with the SMOS data at 1 km, for which a longer time series is available, allowed the irrigation estimates to be extended back to 2011. For both of the experiments carried out, the proposed method performed well in reproducing the magnitudes of the irrigation amounts that actually occurred in four of the five pilot irrigation districts. The SMAP experiment, for which a more detailed analysis was performed, also provided satisfactory results in representing the spatial distribution and the timing of the irrigation events. In addition, the investigation into which term of the SM2RAIN algorithm plays the leading role in determining the amount of water entering into the soil highlights the importance of correct representation of the evapotranspiration process.

Irrigation practices introduce imbalances in the natural hydrological cycle at different spatial scales and put pressure on water resources, especially under climate changing and population increasing scenarios. Despite the implications of irrigation on food production and on the rational management of the available freshwater, detailed information about the areas where irrigation actually occurs is still lacking. For this reason, the comprehensive knowledge of the dynamics of the hydrological cycle over agricultural areas is often tricky.The first aim of this study is to evaluate the capability of five remote sensing soil moisture data sets to detect the irrigation signal over an intensely irrigated area located within the Ebro river basin, in the North of Spain, during the biennium 2016-2017. As a second objective, a methodology to map the irrigated areas through the K-means clustering algorithm is proposed. The remotely sensed soil moisture products used in this study are: SMOS (Soil Moisture and Ocean Salinity) at 1 km, SMAP (Soil Moisture Active Passive) at 1 km and 9 km, Sentinel-1 at 1 km and ASCAT (Advanced SCATterometer) at 12.5 km. The 1 km versions of SMOS and SMAP are DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) downscaled versions of the corresponding coarser resolution products. An additional data set of soil moisture simulated by the SURFEX-ISBA (Surface Externalisée - Interaction Sol Biosphère Atmosphère) land surface model is used as a support for the performed analyses.The capability of soil moisture products to detect irrigation has been investigated by exploiting indices representing the spatial and temporal dynamics of soil moisture. The L-band passive microwave downscaled products, especially SMAP at 1 km, result the best performing ones in detecting the irrigation signal over the pilot area; on the basis of these data sets, the K-means algorithm has been employed to classify three kinds of surfaces within the study area: the dryland, the forest or natural areas, and the actually irrigated areas. The resulting maps have been validated by exploiting maps of crops in Catalonia as ground truth data set. The percentage of irrigated areas well classified by the proposed method reaches the value of 78%; this result is obtained for the period May - September 2017. In addition, the method performs well in distinguishing the irrigated areas from rainfed agricultural areas, which are dry during summer, thus representing a useful tool to obtain explicit spatial information about where irrigation practices actually occur over agricultural areas equipped for this purpose.

Mapping the time-variable calving front location (CFL) of Antarctic ice shelves is important for estimating the freshwater budget, as an indicator of changing ocean and structural conditions or as a precursor of dynamic instability. Here, we present a novel approach for deriving regular and consistent CFLs based on CryoSat-2 swath altimetry. The CFL detection is based on the premise that the shelf edge is usually characterized by a steep ice cliff, which is clearly resolved in the surface elevation data. Our method applies edge detection and vectorization of the sharp ice edge in gridded elevation data to generate vector shapefiles of the calving front. To show the feasibility of our approach, we derived a unique data set of ice-front positions for the Filchner-Ronne Ice Shelf (FRIS) between 2011 and 2018 at a 200 m spatial resolution and biannual temporal frequency. The observed CFLs compare well with independently derived ice front positions from Sentinel-1 Synthetic Aperture Radar imagery and are used to calculate area change, advance rates, and iceberg calving rates. We measure an area increase of 810 ± 40 km2 a−1 for FRIS and calving rates of 9 ± 1 Gt a−1 and 7 ± 1 Gt a−1 for the Filchner and Ronne Ice Shelves, respectively, which is an order of magnitude smaller than their steady-state calving flux. Our findings demonstrate that the “elevation-edge” method is complementary to standard CFL detection techniques. Although at a reduced spatial resolution and less suitable for smaller glaciers in steep terrain, it enables to provide CFLs at regular intervals and to fill existing gaps in time and space. Moreover, the method simultaneously provides ice thickness, required for mass budget calculation, and has a degree of automation which removes the need for heavy manual intervention. In the future, altimetry data has the potential to deliver a systematic and continuous record of change in ice shelf calving front positions around Antarctica. This will greatly benefit the investigation of environmental forcing on ice flow and terminus dynamics by providing a valuable climate data record and improving our knowledge of the constraints for calving models and ice shelf freshwater budget.

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

This paper presents the comparison of three global soil moisture products (ASCAT, AMSR and SMOS) versus a land surface model over a region representative of several Mediterranean landscapes located in the Northeast of the Iberian Peninsula. Our approach has been for agricultural and water management applications at the regional and local scale. Despite being a rather small area, we were able to observe different signal behaviours corresponding to major land cover classes in Mediterranean areas i.e.: dryland and irrigated crops, forests and natural vegetation (grass-shrubs). The area also allowed assessing the impact of topography. The first result of the study is that the results are very dependent on the normalizations used to make the data comparable, thus their impact must be carefully analysed. In this study, we applied two different normalisation methods (called ZV35 and ZV) and different moving average windows (1, 10 and 30 days) in order to enhance seasonal effects. Using no smoothing window, ASCAT is the soil moisture product that correlates best with the LSM over all cover classes, whatever the method. Using smoothing window, AMSR-E tends to outperform other soil moisture products with the ZV method. The ZV35 method is not able to identify a small heavily irrigated area. The reason for these different results is that ZV35, tends to eliminate the monthly scale soil moisture memory and therefore becomes more sensitive to precipitation and less sensitive to the monthly evolution of superficial soil moisture. The comparison shows in general good agreement for all soil moisture products with the LSM on the temporal series simulated over flat, non irrigated areas which are not close to the sea. SMOS has difficulties in areas close to the sea and in areas with steep relief and the current version of the L2 Operational Algorithm (V5.51) depicts few values in forested areas. ASCAT, in its turn, shows some limitations over agricultural and natural vegetation where it shows an increase of soil moisture from June to October probably due to increase of penetration depth in dry soil moisture conditions. AMSR-E LPRM shows a clear vegetation cycle over all the land cover classes. From all the remote sensing products, SMOS is the only one able to see irrigation and the only that does not show clear vegetation or roughness effects. In this study, we were able to assess the impact of higher resolution soil moisture products to map irrigated areas.

[Departement_IRSTEA]Territoires [TR1_IRSTEA]SYNERGIE [Axe_IRSTEA]TETIS-ATTOS [TR2_IRSTEA]SYNERGIE [ADD1_IRSTEA]Équiper l'agriculture [ADD2_IRSTEA]Dynamiques spatiales d'anthropisation; International audience; The recently launched Sentinel-1 satellite with a Synthetic Aperture Radar (SAR) sensor onboard offers a powerful tool for irrigation monitoring under various weather conditions, with high spatial and temporal resolution. This research discusses the potential of different metrics calculated from the Sentinel-1 time series for mapping irrigated fields. A methodology for irrigation mapping using SAR data is proposed. The study is performed using VV (vertical-vertical) and VH (vertical-horizontal) polarizations over an agricultural site in Urgell, Catalunya (Spain). With field segmentation information from SIGPAC (the Geographic Information System for Agricultural Parcels), the backscatter intensities are averaged within each field. From the Sentinel-1 time series for each field, the statistics and metrics, including the mean value, the variance of the signal, the correlation length, and the fractal dimension, are analyzed. With the Support Vector Machine (SVM), the classification of irrigated crops, irrigated trees, and non-irrigated fields is performed with the metrics vector. The results derived from the SVM are validated with ground truthing from SIGPAC over the whole study area, with a good overall accuracy of 81.08%. Random Forest (RF) machine classification is also tested in this study, which gives an accuracy of around 82.2% when setting the tree depth at three. The methodology is based only on SAR data, which makes it applicable to all areas, even with frequent cloud cover, but this method may be less robust when irrigation is less dominated to soil moisture change.

International audience; Radar data have been used to retrieve and monitor the surface soil moisture (SM) changes in various conditions. However, the calibration of radar models whether empirically or physically-based, is still subject to large uncertainties especially at high-spatial resolution. To help calibrate radar-based retrieval approaches to supervising SM at high resolution, this paper presents an innovative synergistic method combining Sentinel-1 (S1) microwave and Landsat-7/8 (L7/8) thermal data. First, the S1 backscatter coefficient was normalized by its maximum and minimum values obtained during 2015–2016 agriculture season. Second, the normalized S1 backscatter coefficient was calibrated from reference points provided by a thermal-derived SM proxy named soil evaporative efficiency (SEE, defined as the ratio of actual to potential soil evaporation). SEE was estimated as the radiometric soil temperature normalized by its minimum and maximum values reached in a water-saturated and dry soil, respectively. We estimated both soil temperature endmembers by using a soil energy balance model forced by available meteorological forcing. The proposed approach was evaluated against in situ SM measurements collected over three bare soil fields in a semi-arid region in Morocco and we compared it against a classical approach based on radar data only. The two polarizations VV (vertical transmit and receive) and VH (vertical transmit and horizontal receive) of the S1 data available over the area are tested to analyse the sensitivity of radar signal to SM at high incidence angles (39°–43°). We found that the VV polarization was better correlated to SM than the VH polarization with a determination coefficient of 0.47 and 0.28, respectively. By combining S1 (VV) and L7/8 data, we reduced the root mean square difference between satellite and in situ SM to 0.03 m3 m−3, which is far smaller than 0.16 m3 m−3 when using S1 (VV) only.

International audience; The main objective of this study is to analyze the potential use of Sentinel-l (S1) radar data for the estimation of soil moisture in agricultural areas. Simultaneously to several S1 acquisitions made between 2015 and 2017 over different sites, ground measurements of soil roughness, soil water content, LAI and crop height were recorded. The sensitivity of S1 signal to variations in soil moisture is discussed. A modelling based on Water Cloud Model was proposed to simulate radar signal backscattered over covered vegetation surfaces. Three inversion approaches were proposed to retrieve surface soil moisture at field scale.

International audience; The FAO-56 dual crop coefficient (FAO-2Kc) model has been extensively used at the field scale to estimate the crop water requirements by means of the simulated evapotranspiration (ET) and its two components evaporation (E) and transpiration (T). Given that the main limitation of FAO-2Kc for operational irrigation management over large areas is the unavailability (over most irrigated areas) of irrigation data, this study investigates the feasibility 1) to constrain the FAO-2Kc ET from LST and VI data, 2) to retrieve irrigation amounts and dates from LST and VI data and 3) to estimate the root-zone soil moisture (RZSM) at the daily scale. In practice, the vegetation and soil temperatures retrieved from LST/VI data are used to estimate the FAO-2Kc vegetation stress coefficient (Ks) and soil evaporation reduction coefficient (Kr), respectively. The modeling and remote sensing combined approach is tested over a wheat crop field in central Morocco, and results are evaluated in terms of ET, irrigation and RZSM estimates. ET is estimated with a RMSE of 0.68 mm day-1 compared to 0.84 mm day-1 for the standard (without using LST data) FAO-2Kc based on tabulated values for the parameters. The total irrigation depth (67 mm) is correctly estimated and is very close to the actual effective irrigation (69.8 mm) applied by the farmer. Daily RZSM is estimated with an R2 value of 0.68 (0.42) and a RMSE value of 0.034 (0.061) m3 m-3 by forcing FAO-2Kc using the retrieved irrigation (from LST-derived estimates and precipitation only). Since spaceborne LST data are currently not available at both high-spatial and high-temporal resolution, a sensitivity analysis is finally undertaken to assess the potential and applicability of the proposed methodology to temporally sparse thermal data.

International audience; This paper presents the the first attempt to include soil moisture information from remote sensing in the tools available to desert locust managers. The soil moisture requirements were first assessed with the users. The main objectives of this paper are: i) to describe and validate the algorithms used to produce a soil moisture dataset at 1 km resolution relevant to desert locust management based on DisPATCh methodology applied to SMOS and ii) the development of an innovative approach to derive high (100 m) resolution soil moisture products from Sentinel-1 in synergy with SMOS data. For the purpose of soil moisture validation, 4 soil moisture stations where installed in desert areas (one in each user country). The soil moisture 1km product was thoroughly validated and its accuracy is amongst the best available soil moisture products. Current comparison with in-situ soil moisture stations shows good values of correlation (R > 0.7) and low RMSE (below 0.04 m(3) m(-3)). The low number of acquisitions on wet dates has limited the development of the soil moisture 100m product over the Users Areas. The Soil Moisture product at 1km will be integrated into the national and global Desert Locust early warning systems in national locust centres and at DLIS-FAO, respectively.

The 40 km resolution SMOS (Soil Moisture and Ocean Salinity) soil moisture, previously disaggregated at a 1 km resolution using the DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) method based on MODIS optical/thermal data, is further disaggregated to 100 m resolution using Sentinel-1 backscattering coefficient (σ°). For this purpose, three distinct radar-based disaggregation methods are tested by linking the spatio-temporal variability of σ° and soil moisture data at the 1 km and 100 m resolution. The three methods are: (1) the weight method, which estimates soil moisture at 100 m resolution at a certain time as a function of σ° ratio (100 m to 1 km resolution) and the 1 km DISPATCH products of the same time; (2) the regression method which estimates soil moisture as a function of σ° where the regression parameters (e.g., intercept and slope) vary in space and time; and (3) the Cumulative Distribution Function (CDF) method, which estimates 100 m resolution soil moisture from the cumulative probability of 100 m resolution backscatter and the maximum to minimum 1 km resolution (DISPATCH) soil moisture difference. In each case, disaggregation results are evaluated against in situ measurements collected between 1 January 2016 and 11 October 2016 over a bare soil site in central Morocco. The determination coefficient (R2) between 1 km resolution DISPATCH and localized in situ soil moisture is 0.31. The regression and CDF methods have marginal effect on improving the DISPATCH accuracy at the station scale with a R2 between remotely sensed and in situ soil moisture of 0.29 and 0.34, respectively. By contrast, the weight method significantly improves the correlation between remotely sensed and in situ soil moisture with a R2 of 0.52. Likewise, the soil moisture estimates show low root mean square difference with in situ measurements (RMSD = 0.032 m3 m−3). This work is a contribution to the REC project funded by the European Commission Horizon 2020 Programme for Research and Innovation (H2020) in the context of the Marie Skłodowska-Curie Research and Innovation Staff Exchange (RISE) action under grant agreement no: 645642. In addition, this work has been partially funded by a public grant of Ministerio de Economía y Competitividad (DI-14-06587) and AGAUR-Generalitat de Catalunya (DI-2015-058).

International audience; This paper presents the methodology for irrigation mapping using the Sentinel-1 SAR data. The study is performed using VV polarization over an agricultural site in Urgell, Catalunya (Spain). From the time series for each field, the indices including the mean value and variance of the signal, the correlation length, the fractal dimension which are derived from the backscatter time series are analyzed. The classification of irrigated and nonirrigated fields is done with the indices vector formed by the parameters analyzed. The result is compared with the supervised classification from Sentinel-2 multi-band data. The accuracy is 77%. The methodology uses only SAR data, which makes it usable for all areas even with cloud cover most times of the year.

The soil evaporation is under atmospheric conditions non-limited in energy-strongly linked to the near-surface soil moisture sensed by microwave radiometers. This has been the rationale for developing the DisPATCh (Disaggregation based on Physical And Theoretical scale Change) method, which relies on thermal-derived evaporation to improve the spatial resolution of SMOS (Soil Moisture and Ocean Salinity) like data. In practice, the disaggregation scheme estimates the 0–5 cm soil moisture at 1 km resolution by combining 40 km SMOS soil moisture, 1 km resolution MODIS (MODerate resolution Imaging Spectroradiometer) data, and a multi-scale soil evaporation model. This paper provides an overview of 1) the current status and main assumptions of DisPATCh, 2) the DisPATCh-based processor implemented in the Centre Aval de Traitement des Donnees SMOS (CATDS), and 3) related ongoing research including advanced modeling of soil evaporation and the prospect of coupling thermal- and radar-based soil moisture downscaling approaches.

This paper explores the effect of rain intercepted by vegetation and mulch on the L-band emission of natural grass. The study is based on radiometric, meteorological, and biophysical measurements obtained during the SMOSREX Experiment (Toulouse, France). Several approaches were followed to evaluate interception effects. Firstly, the analysis of microwave brightness temperature (TB) measurements at L-band indicated that interception increases vegetation emission at both polarisations. Secondly, the use of microwave indices to detect the presence/absence of interception was examined. In particular, a modified polarisation ratio at 50° was found to be well related to the interception status of the standing vegetation. Finally, the vegetation optical depth (?), which parameterises the extinction across the vegetation layer, was retrieved from the TB observations. It was found that ? increases with the increase in the water content stored within the vegetation and mulch after rainfall. The study highlights the strong impact of intercepted water in otherwise weakly attenuating covers such as grasses. Interception might therefore be an issue to consider in order to improve soil moisture retrieval algorithms from L-band observations.

International audience; The Soil Moisture and Ocean Salinity (SMOS) mission is the first satellite dedicated to providing global surface soil moisture (SM). SMOS operates at L-band and at this frequency, the signal depends on soil moisture but is also significantly affected by surface soil roughness. Using the Combined soil Roughness & Vegetation Effects (CRVE) method detailed in this paper, the effect of vegetation and soil roughness can be combined using a single parameter, referred to as TR here. SM and TR were retrieved by inverting the SMOS observations using the forward emission model (L-MEB). Assuming a linear relationship between TR and LAI obtained by the MODIS data, an Australian map of soil roughness was computed. This map could lead to improved soil moisture retrievals for present and future microwave remote sensing missions such as SMOS and the Soil Moisture Active Passive (SMAP) scheduled for launch in November 2014.

International audience; A disaggregation algorithm is applied to 40 km resolution SMOS (Soil Moisture and Ocean Salinity) surface soil moisture using 1 km resolution MODIS (MODerature resolution Imaging Spectroradiometer), 90 m resolution ASTER (Advanced Spaceborne Thermal Emission and Reflection radiometer), and 60 m resolution Landsat-7 data. DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) distributes high-resolution soil moisture around the low-resolution observed mean value using the instantaneous spatial link between optical-derived soil evaporative efficiency (ratio of actual to potential evaporation) and near-surface soil moisture. The objective is three-fold: (i) evaluating DISPATCH at a range of spatial resolutions using readily available multi-sensor thermal data, (ii) deriving a robust calibration procedure solely based on remotely sensed data, and (iii) testing the linear or nonlinear behavior of soil evaporative efficiency. Disaggregated soil moisture is compared with the 0-5 cm in situ measurements collected each month from April to October 2011 in a 20 km square spanning an irrigated and dry land area in Catalunya, Spain. The target downscaling resolution is set to 3 km using MODIS data and to 100 m using ASTER and Landsat data. When comparing 40 km SMOS, 3 km disaggregated and 100 m disaggregated data with the in situ measurements aggregated at corresponding resolution, results indicate that DISPATCH improves the spatio-temporal correlation with in situ measurements at both 3 km and 100 m resolutions. A yearly calibration of DISPATCH is more efficient than a daily calibration. Assuming a linear soil evaporative efficiency model is adequate at kilometric resolution. At 100 mresolution, the very high spatial variability in the irrigated area makes the linear approximation poorer. By accounting for non-linearity effects, the slope of the linear regression between disaggregated and in situ measurements is increased from 0.2 to 0.5. Such a multi-sensor remote sensing approach has potential for operational multi-resolution monitoring of surface soil moisture and is likely to help parameterize soil evaporation at integrated spatial scales.

Characterization of snow pack evolution is a key parameter for regions where water supply is mainly due to snow melt and runoff. Main goal of the project "AGORA" is to study the impact of assimilating earth observed data in a water prediction numerical model. The site chosen for the study cover the Pyrenees area in Catalonia. As first step, an observation of the water balance terms, such as snow cover, snow water equivalent, changes in soil water content, have been done through extensive in situ campaigns in the area of study. Collocated earth observed data from both passive, e.g. MERIS, MODIS, and active, e.g. ENVISAT-ASAR, ENVISATRA, sensors have been collected during the surveys. These measurements will be used to develop and validate algorithms for the characterization of the snow pack appropriately tuned for the area of interest. The final phase of the project will evaluate the impact of assimilating remote sensing data into a hydrological model specifically developed to cope with the significant weather changes in time and space characteristic of the area of study. Preliminary results of the activity scheduled during the first year of the project will be highlighted. The importance of developing application based on both remote sensed and in situ data will be discussed.