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The present study aims to evaluate the performance of a hydrological model to simulate spring runoff processes, analyze the effect of snowmelt on seasonal flow, and identify the snowmelt contribution rate based on the snow water equivalent (SWE) in the Moroccan High Atlas watersheds.The main objective of this study is to evaluate the daily SWE in a poorly instrumented mountainous watershed using an improved hydrological model. The model algorithm improvement is considered an essential approach for better understanding the initial basin conditions that influence these hydrogeological behaviors. For this purpose, a seasonal analysis was performed to select flood events that reproduce this phenomenon.To this end, the calibration has been done by forcing the model with rainfall, runoff, temperature, and snow water equivalent (SWE), with an amelioration of the model algorithm. Interestingly enough, this improvement achieved 13% based on the Nash-Sutcliffe efficiency coefficients. Hence, the spring event flows were influenced by the snowmelt process, these results will have direct implications for flood event replication modeling and flood forecasting in these regions.The study demonstrates that this region is sensitive to the seasonal effect of snowmelt. Therefore, it is essential to take into account the contribution of snow in hydrological studies developed at the level of the Moroccan High Atlas mountainous watersheds. This approach is a great challenge that will improve the reproduction of seasonal flood events and allow a better forecast of flood events to reduce the uncertainties and risks of flooding in mountainous basin areas facing the same climate conditions.Keywords: Precipitation, SWE, Hydrological modeling, Calibration, Mediterranean climate, flood events, Zat basin.

Accurate estimation of the partitioning of actual evapotranspiration (ETa) into plant transpiration (Tr) and soil evaporation (E) is difficult but important for assessing biomass production and the allocation of increasingly scarce water resources. This work aims to evaluate the performance of the AquaCrop model to estimate actual crop ETa and its components (Tr and E) over drip irrigated wheat fields in the semi-arid region of Morocco. Field experiments were carried out during 2016-2017 season on an irrigated winter wheat field in semi-arid region of Morocco. Wheat ETa and its partitioning components (Tr and E) were measured by using the eddy covariance (EC) system and the sap flow system (SF). The obtained results showed that the AquaCrop model adequately simulated canopy cover (CC), ETa and wheat biomass. The coefficient of determination (R2) between observed and measured CC, ETa and biomass were 0.98, 0.72 and 0.98 respectively. With regard to the ETa partitioning, the results indicate that the estimate of Tr using the AquaCrop model is well consistent with those of the in-situ measurements with SF. The Root mean square error (RMSE) between the observed and simulated Tr was about 0.60 mm.day-1. This work demonstrates that the AquaCrop model has reliable accuracy in simulating wheat growth, production and ETa partitioning. As a result, this model provides a technical means of application to formulate optimal irrigation schedules.

The main goal of this work was to evaluate the potential of the Shuttleworth–Wallace (SW) model for mapping actual crop evapotranspiration (ET) over complex surface located in the foothill of the Atlas Mountain (Morocco). This model needs many input variables to compute soil $$({r}_{\mathrm{s}}^{\mathrm{s}})$$ and vegetation ( $${r}_{\mathrm{s}}^{\mathrm{v}})$$ resistances, which are often difficult to estimate at large scale particularly soil moisture. In this study, a new approach to spatialize $${r}_{\mathrm{s}}^{\mathrm{s}}$$ and $${r}_{\mathrm{s}}^{\mathrm{v}}$$ based on two thermal-based proxy variables was proposed. Land Surface Temperature ( $$\mathrm{LST}$$ ) and Normalized Difference Vegetation Index (NDVI) derived from Landsat data were combined with the endmember temperatures for soil ( $${T\mathrm{s}}_{\mathrm{min}}$$ and $${T\mathrm{s}}_{\mathrm{max}}$$ ) and vegetation ( $${T\mathrm{v}}_{\mathrm{min}}$$ and $${T\mathrm{v}}_{\mathrm{max}}$$ ), which were simulated by a surface energy balance model, to compute the soil ( $$T\mathrm{s}$$ ) and the vegetation ( $$T\mathrm{v}$$ ) temperatures. Based on these temperatures, two thermal proxies ( $${\mathrm{SI}}_{\mathrm{ss}}$$ for soil and $${\mathrm{SI}}_{\mathrm{sv}}$$ for vegetation) were calculated and related to $${r}_{\mathrm{s}}^{\mathrm{s}}$$ and $${r}_{\mathrm{s}}^{\mathrm{v}}$$ , with an empirical exponential relationship [with a correlation coefficient (R) of about 0.6 and 0.5 for soil and vegetation, respectively]. The proposed approach was initially evaluated at a local scale, by comparing the results to observations by an eddy covariance system installed over an area planted with olive trees intercropped with wheat. In a second step, the new approach was applied over a large area which contains a mixed vegetation (tall and short) crossed by a river to derive $${r}_{\mathrm{s}}^{\mathrm{s}}$$ and $${r}_{\mathrm{s}}^{\mathrm{v}}$$ , and thereafter to estimate ET. A Large aperture scintillometer (LAS) installed over a line transect of 1.4 km and spanning the total area was used to validate the obtained ET. The comparison confirmed the ability of the proposed approach to provide satisfactory ET maps with an RMSE, bias and R2 equal to 0.08 mm/h, 0.06 mm/h and 0.80, respectively.

International audience; Accurate estimation of evapotranspiration (ET) is of crucial importance in water science and hydrological process understanding especially in semi-arid/arid areas since ET represents more than 85% of the total water budget. FAO-56 is one of the widely used formulations to estimate the actual crop evapotranspiration (ET c act) due to its operational nature and since it represents a reasonable compromise between simplicity and accuracy. In this vein, the objective of this paper was to examine the possibility of improving ET c act estimates through remote sensing data assimilation. For this purpose, remotely sensed soil moisture (SM) and Land surface temperature (LST) data were simultaneously assimilated into FAO-dualK c. Surface SM observations were assimilated into the soil evaporation (E s) component through the soil evaporation coefficient, and LST data were assimilated into the actual crop transpiration (T c act) component through the crop stress coefficient. The LST data were used to estimate the water stress coefficient (K s) as a proxy of LST (LST proxy). The FAO-Ks was corrected by assimilating LST proxy derived from Landsat data based on the variances of predicted errors on K s estimates from FAO-56 model and thermal-derived K s. The proposed approach was tested over a semi-arid area in Morocco using first, in situ data collected during 2002-2003 and 2015-2016 wheat growth seasons over two different fields and then, remotely sensed data derived from disaggregated Soil Moisture Active Passive (SMAP) SM and Landsat-LST sensors were used. Assimilating SM data leads to an improvement of the ET c act model prediction: the root mean square error (RMSE) decreased from 0.98 to 0.65 mm/day compared to the classical FAO-dualK c using in situ SM. Moreover, assimilating both in situ SM and LST data provided more accurate results with a RMSE error of 0.55 mm/day. By using SMAP-based SM and Landsat-LST, results also improved in comparison with standard FAO and reached a RMSE of 0.73 mm/day against eddy-covariance ET c act measurements.

Accurate seasonal forecasting of cereal yields is an important decision support tool for countries, such as Morocco, that are not self-sufficient in order to predict, as early as possible, importation needs. This study aims to develop an early forecasting model of cereal yields (soft wheat, barley and durum wheat) at the scale of the agricultural province considering the 15 most productive over 2000–2017 (i.e., 15 × 18 = 270 yields values). To this objective, we built on previous works that showed a tight linkage between cereal yields and various datasets including weather data (rainfall and air temperature), regional climate indices (North Atlantic Oscillation in particular), and drought indices derived from satellite observations in different wavelengths. The combination of the latter three data sets is assessed to predict cereal yields using linear (Multiple Linear Regression, MLR) and non-linear (Support Vector Machine, SVM; Random Forest, RF, and eXtreme Gradient Boost, XGBoost) machine learning algorithms. The calibration of the algorithmic parameters of the different approaches are carried out using a 5-fold cross validation technique and a leave-one-out method is implemented for model validation. The statistical metrics of the models are first analyzed as a function of the input datasets that are used, and as a function of the lead times, from 4 months to 2 months before harvest. The results show that combining data from multiple sources outperformed models based on one dataset only. In addition, the satellite drought indices are a major source of information for cereal prediction when the forecasting is carried out close to harvest (2 months before), while weather data and, to a lesser extent, climate indices, are key variables for earlier predictions. The best models can accurately predict yield in January (4 months before harvest) with an R2 = 0.88 and RMSE around 0.22 t. ha−1. The XGBoost method exhibited the best metrics. Finally, training a specific model separately for each group of provinces, instead of one global model, improved the prediction performance by reducing the RMSE by 10% to 35% depending on the provinces. In conclusion, the results of this study pointed out that combining remote sensing drought indices with climate and weather variables using a machine learning technique is a promising approach for cereal yield forecasting.

Food and Agriculture Organization (FAO) dual crop coefficient (FAO-2Kc) is one of the widely used formulation to estimate soil evaporation (E) due to its operationality and simplicity. The FAO-2Kc method could explicitly distinguish the contribution of E and plant transpiration, separately. However, systematic and random uncertainty in E observations still exist. In this vein, this paper attempts to improve FAO-2Kc evapotranspiration estimates through assimilating SMAP-based disaggregated soil moisture (SM) into FAO-2Kc E component via the soil evaporation coefficient. Sequential data assimilation methods (Kalman filter) was used for this purpose, where E is strongly linked to SM especially under arid atmospheric conditions where energy is not the limited factor. The proposed approach is applied over a semi-arid site in central Morocco. Results revealed that the assimilation approach provides better results in term of evapotranspiration by decreasing the root mean square error from 0.98 mm/day to 0.75 mm/day compared to the standard FAO-2Kc.

A precise estimate of surface evapotranspiration (ET) is fundamental in water science for determining the crop water needs and for optimizing water management practices and irrigation regimes. FAO-56 dual crop coefficient (FAO-2Kc) based on a water balance model allows the partitioning between bare soil evaporation (E) and plant transpiration (Tr). However, its performance for estimating the water use efficiency is limited by uncertainties in the modeled evaporation/transpiration partitioning [1], [2] due to its simplicity. This paper aims to improve the accuracy of the ET components, through assimilating remotely sensed data. Remotely sensed soil moisture (SM) and land surface temperature (LST) are simultaneously assimilated into FAO-2Kc. SM was used to improve the E component while LST to update the plant Tr element. SM and LST data were derived from SMAP and Landsat 7/8 remotely sensed observations during the 2015–2016 wheat season, respectively. The standard FAO-2Kc yields an error of 0.98 mm/day with a bias of 0.47 mm/day. Assimilating combined SM and LST into FAO-2Kc leads to an improvement of the ET prediction with an error of 0.73 mm/day compared to eddy correlation measurements.

Cereals are the main crop in Morocco. Its production exhibits a high inter-annual due to uncertain rainfall and recurrent drought periods. Considering the importance of this resource to the country's economy, it is thus important for decision makers to have reliable forecasts of the annual cereal production in order to pre-empt importation needs. In this study, we assessed the joint use of satellite-based drought indices, weather (precipitation and temperature) and climate data (pseudo-oscillation indices including NAO and the leading modes of sea surface temperature -SST- in the mid-latitude and in the tropical area) to predict cereal yields at the level of the agricultural province using machine learning algorithms (Support Vector Machine -SVM-, Random forest -FR- and eXtreme Gradient Boost -XGBoost-) in addition to Multiple Linear Regression (MLR). Also, we evaluate the models for different lead times along the growing season from January (about 5 months before harvest) to March (2 months before harvest). The results show the combination of data from the different sources outperformed the use of a single dataset; the highest accuracy being obtained when the three data sources were all considered in the model development. In addition, the results show that the models can accurately predict yields in January (5 months before harvesting) with an R² = 0.90 and RMSE about 3.4 Qt.ha-1. When comparing the model’s performance, XGBoost represents the best one for predicting yields. Also, considering specific models for each province separately improves the statistical metrics by approximately 10-50% depending on the province with regards to one global model applied to all the provinces. The results of this study pointed out that machine learning is a promising tool for cereal yield forecasting. Also, the proposed methodology can be extended to different crops and different regions for crop yield forecasting.

Crop evapotranspiration (ET) is a fundamental component of the hydrological cycle, especially in arid/semi-arid regions. The FAO-56 offers an operational method for deriving ET from the reduction (dual crop coefficient Kc) of the atmospheric evaporative demand (ET0). The dual coefficient approach (FAO-2Kc) is intended to improve the daily estimation of ET by separating the contribution of bare soil evaporation (E) and crop transpiration components. The FAO-2Kc has been a well-known reference for the operational monitoring of crop water needs. However, its performance for estimating the water use efficiency is limited by uncertainties in the modeled evaporation/transpiration partitioning. This paper aims at improving the soil module of the FAO-2Kc by modifying the E reduction coefficient (Kr) according to soil texture information and state-of-the-art formulations, hence, to amend the mismatch between FAO-2Kc and field-measured data beyond standard conditions. In practice this work evaluates the performance of two evaporation models, using the classical Kr (Kr,FAO) and a new texture-based Kr (Kr,text) over 33 bare soil sites under different evaporative demand and soil conditions. An offline validation is investigated by forcing both models with observed soil moisture ( θ s ) data as input. The Kr,text methodology provides more accurate E estimations compared to the Kr,FAO method and systematically reduces biases. Using Kr,text allows reaching the lowest root means square error (RMSE) of 0.16 mm/day compared to the Kr,FAO where the lowest RMSE reached is 0.88 mm/day. As a step further in the assessment of the proposed methodology, ET was estimated in three wheat fields across the entire agricultural season. Both approaches were thus inter-compared in terms of ET estimates forced by SM estimated as a residual of the water balance model (online validation). Compared to ET measurements, the new formulation provided more accurate results. The RMSE was 0.66 mm/day (0.71 mm/day) and the R2 was 0.83 (0.78) for the texture-based (classical) Kr.

The main goal of this work was to evaluate the potential of the Shuttleworth-Wallace (SW) model for mapping actual crop evapotranspiration (ET) over complex terrain located within the foothill of the Atlas Mountain (Morocco). This model needs many input variables to compute soil (rss) and vegetation (rsv) resistances, which are often difficult to estimate at large scale particularly soil moisture. In this study, a new approach to spatialize rss and rsv based on two thermal-based proxy variables is proposed. Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) derived from LANDSAT data were combined with the endmember temperatures for soil (Tsmin and Tsmax) and vegetation (Tvmin and Tvmax), which are simulated by a surface energy balance model, to compute the temperature of the two components, namely the soil (Ts) and the vegetation (Tv). Based on these temperatures, two thermal proxies (SIss for soil and SIsv for vegetation) were calculated and related to rss and rsv, with an empirical exponential relationship (with a correlation coefficient (R) of about 0,6 and 0,5 for soil and vegetation, respectively). The proposed approach was firstly evaluated at a local scale, by comparing the results to observations by an eddy covariance system installed over an area planted with olive trees intercropped with wheat. In a second step, the new approach was applied over a large area which contains a mixed vegetation (tall and short vegetation) crossed by a river to derive rss and rsv, and thereafter to estimate ET. A Large aperture scintillometer (LAS) installed over a transect of 1.4 km and spanning the total area is used to validate the obtained ET. The comparison confirms the ability of the proposed approach to provide satisfactory ET maps with an RMSE and R2 equal to 52.51 W/m2 and 0.80, respectively.

International audience; Soil moisture (SM) data are required at high spatio-temporal resolution—typically the crop field scale every 3–6 days—for agricultural and hydrological purposes. To provide such high-resolution SM data, many remote sensing methods have been developed from passive microwave, active microwave and thermal data. Despite the pros and cons of each technique in terms of spatio-temporal resolution and their sensitivity to perturbing factors such as vegetation cover, soil roughness and meteorological conditions, there is currently no synergistic approach that takes advantage of all relevant (passive, active microwave and thermal) remote sensing data. In this context, the objective of the paper is to develop a new algorithm that combines SMAP L-band passive microwave, MODIS/Landsat optical/thermal and Sentinel-1 C-band radar data to provide SM data at the field scale at the observation frequency of Sentinel-1. In practice, it is a three-step procedure in which: (1) the 36 km resolution SMAP SM data are disaggregated at 100 m resolution using MODIS/Landsat optical/thermal data on clear sky days, (2) the 100 m resolution disaggregated SM data set is used to calibrate a radar-based SM retrieval model and (3) the so-calibrated radar model is run at field scale on each Sentinel-1 overpass. The calibration approach also uses a vegetation descriptor as ancillary data that is derived either from optical (Sentinel-2) or radar (Sentinel-1) data. Two radar models (an empirical linear regression model and a non-linear semi-empirical formulation derived from the water cloud model) are tested using three vegetation descriptors (NDVI, polarization ratio (PR) and radar coherence (CO)) separately. Both models are applied over three experimental irrigated and rainfed wheat crop sites in central Morocco. The field-scale temporal correlation between predicted and in situ SM is in the range of 0.66–0.81 depending on the retrieval configuration. Based on this data set, the linear radar model using PR as a vegetation descriptor offers a relatively good compromise between precision and robustness all throughout the agricultural season with only three parameters to set. The proposed synergistical approach combining multi-resolution/multi-sensor SM-relevant data offers the advantage of not requiring in situ measurements for calibration.

Monitoring agricultural crops is of paramount importance for preserving water resources and increasing water efficiency over semi-arid areas. This can be achieved by modelling the water resources all along the growing season through the coupled water–surface energy balance. Surface albedo is a key land surface variable to constrain the surface radiation budget and hence the coupled water–surface energy balance. In order to capture the hydric status changes over the growing season, optical remote sensing becomes impractical due to cloud cover in some periods, especially over irrigated winter crops in semi-arid regions. To fill the gap, this paper aims to generate cloudless surface albedo product from Sentinel-1 data that offers a source of high spatio-temporal resolution images. This can help to better capture the vegetation development along the growth season through the surface radiation budget. Random Forest (RF) algorithm was implemented using Sentinel-1 backscatters as input. The approach was tested over an irrigated semi-arid zone in Morocco, which is known by its heterogeneity in term of soil conditions and crop types. The obtained results are evaluated against Landsat-derived albedo with quasi-concurrent Landsat/Sentinel-1 overpasses (up to one day offset), while a further validation was investigated using in situ field scale albedo data. The best model-hyperparameters selection was dependent on two validation approaches (K-fold cross-validation ‘k = 10’, and holdout). The more robust and accurate model parameters are those that represent the best statistical metrics (root mean square error ‘RMSE’, bias and correlation coefficient ‘R’). Coefficient values ranging from 0.70 to 0.79 and a RMSE value between 0.0002 and 0.00048 were obtained comparing Landsat and predicted albedo by RF method. The relative error ratio equals 4.5, which is acceptable to predict surface albedo.

Global soil moisture (SM) products are currently available from passive microwave sensors at typically 40 km spatial resolution. Although recent efforts have been made to produce 1 km resolution data from the disaggregation of coarse scale observations, the targeted resolution of available SM data is still far from the requirements of fine-scale hydrological and agricultural studies. To fill the gap, a new disaggregation scheme of Soil Moisture Active and Passive (SMAP) data is proposed at 100 m resolution by using the disaggregation based on physical and theoretical scale change (DISPATCH) algorithm. The main objectives of this paper is (i) to implement DISPATCH algorithm at 100 m resolution using SMAP SM and Landsat land surface temperature and vegetation index data and (ii) to investigate the usefulness of an intermediate spatial resolution (ISR) between the SMAP 36 km resolution and the targeted 100 m resolution. The sequential disaggregation approach from 36 km to ISR (ranging from 1 km to 30 km) and from ISR to 100 m resolution is evaluated over 22 irrigated field crops in central Morocco using in-situ SM measurements collected from January to May 2016. The lowest root mean square difference (RMSD) between the 100 m resolution disaggregated and in-situ SM is obtained when the ISR is around 10 km. Therefore, the two-step disaggregation is more efficient than the direct disaggregation from SMAP to 100 m resolution. Moreover, we propose a moving average window algorithm to increase the accuracy in the 100 m resolution SM as well as to reduce the low-resolution boxy artifacts on disaggregated images. The correlation coefficient between 100 m resolution disaggregated and in situ SM ranges between 0.5–0.9 for four out of the six extensive sampling dates. This methodology relies solely on remote sensing data and can be easily implemented to monitor SM at a high spatial resolution over irrigated regions.

International audience; The use of land surface temperature (LST) for monitoring the consumption and water status of crops requires data at fine spatial and temporal resolutions. Unfortunately, the current spaceborne thermal sensors provide data at either high temporal (e.g. MODIS: Moderate Resolution Imaging Spectro-radiometer) or high spatial (e.g. Landsat) resolution separately. Disaggregating low spatial resolution (LR) LST data using ancillary data available at high spatio-temporal resolution could compensate for the lack of high spatial resolution (HR) LST observations. Existing LST downscaling approaches generally rely on the fractional green vegetation cover (fgv) derived from HR reflectances but they do not take into account the soil water availability to explain the spatial variability in LST at HR. In this context, a new method is developed to disaggregate kilometric MODIS LST at 100 m resolution by including the Sentinel-1 (S-1) backscatter, which is indirectly linked to surface soil moisture, in additionto the Landsat-7 and Landsat-8 (L-7 & L-8) reflectances. The approach is tested over two different sites-an 8 km by 8 km irrigated crop area named "R3" and a 12 km by 12 km rainfed area named "Sidi Rahal" in central Morocco (Marrakech)-on the seven dates when S-1, and L-7 or L-8 acquisitions coincide with a one-day precision during the 2015-2016 growing season. The downscaling methods are applied to the 1 km resolution MODIS-Terra LST data, and their performance is assessed by comparing the 100 m disaggregated LST to Landsat LST in three cases: no disaggregation, disaggregation using Landsat fgv only, disaggregation using both Landsat fgv and S-1 backscatter. When including fgv only in the disaggregation procedure, the mean root mean square error in LST decreases from 4.20 to 3.60 °C and the mean correlation coefficient (R) increases from 0.45 to 0.69 compared to the non-disaggregated case within R3. The new methodology including the S-1 backscatter as input to the disaggregation is found to be systematically more accurate on the available dates with a disaggregation mean error decreasing to 3.35 °C and a mean R increasing to 0.75.

A precise estimate of the evapotranspiration (ET) partitioning is fundamental for determining the crop water needs and optimizing irrigation management. The plant transpiration (T) is generally considered to be the most desirable component, while reducing the soil evaporation (E) could be one of the most important water-saving actions in semi-arid agricultural regions. Given the lack of reference method to estimate the E/T partitioning of wheat crop, this study inter-compares four different methods based on eddy covariance, sap flow and lysimetry measurements and FAO modeling. The objectives are: i) to quantify the systematic and random uncertainty in E and T observations, ii) to evaluate the partitioning ratio (T/ET) at the daily/field scale and iii) to assess the performance of the FAO model over two drip irrigated wheat fields. Results indicate that despite the small surface sensed by mini-lysimeters, the partitioning ratio is evaluated more precisely (19% relative error) with lysimetry than with the other systems (any combination of eddy covariance, lysimetry and sap flow measurements). Moreover, stem-scale T measurements from sap flow sensors are subject to representativeness issues at the field scale, and to systematic errors during water-stress and senescence periods. The lysimeter-derived partitioning ratio increases from about 0.50 to 0.85 during the growth stage and rapidly drops towards 0 during senescence. Its dynamics is found to be significantly correlated (R>0.7) with the 5-cm soil moisture. By comparing FAO simulations with observations, it is found that the FAO method overestimates T and underestimates E, while keeping satisfying ET estimates for drip irrigated wheat. This study suggests that different independent measurement techniques should be implemented to both quantify and reduce uncertainties in the T/ET ratio, and that accurate observations are still needed to improve the modeling of E/T components.

International audience; The main goal of this research was to evaluate the potential of the HYDRUS-1D numerical model for estimating the soil moisture (θ) at different depths, actual crop evapotranspiration (ETa) and its components (crop transpiration, Ta and soil evaporation, Ea) as well as the deep percolation (DP) of irrigated winter wheat under different water managements in the semi-arid region of Tensift-basin (central Morocco). The HYDRUS-1D simulations were performed at daily time step during the two growing seasons: 2002/2003 and 2015/2016. The model was firstly calibrated based on one field "denoted F1" data during the 2002/2003 cropping season by using the Levenberg-Marquardt method implemented in HYDRUS-1D model for optimizing various parameters of Van Genuchten equation that provide the minimum difference between measured and simulated soil moisture at four layers of soil (0-5, 5-10, 10-20, 20-30, 30-50 cm). Afterwards, the model validation was done based on the data from four fields of wheat: two fields "denoted F2 and F3" during the 2002/2003 and two other fields "denoted F4 and F5" during the 2015/2016 cropping season. All fields were irrigated with flooding system except the field F5 where drip irrigation was undertaken. In-situ measurements of θ was carried out using Time Domain Reflectometry (TDR) and gravimetric method ETa was measured by the Eddy Covariance system Ta and Ea were monitored using a lysimeter in F5 field. The results showed that the HYDRUS-1D model simulates the θ, ETa, Ta and Ea reasonably well. Additionally, the evaluation of the irrigation system on DP losses was investigated by comparing the simulation results over flood (F4) and drip (F5) irrigated fields. It was found that about 56% and 20% of seasonal supplied water were lost by DP in F4 and F5 sites, respectively. Such unexpected high amount of DP taking place in F5 field is due to the improper use of the drip irrigation system..

A meta-analysis data-driven approach is developed to represent the soil evaporative efficiency (SEE) defined as the ratio of actual to potential soil evaporation. The new model is tested across a bare soil database composed of more than 30 sites around the world, a clay fraction range of 0.02-0.56, a sand fraction range of 0.05-0.92, and about 30,000 acquisition times. SEE is modeled using a soil resistance ($r_{ss}$) formulation based on surface soil moisture ($\theta$) and two resistance parameters $r_{ss,ref}$ and $\theta_{efolding}$. The data-driven approach aims to express both parameters as a function of observable data including meteorological forcing, cut-off soil moisture value $\theta_{1/2}$ at which SEE=0.5, and first derivative of SEE at $\theta_{1/2}$, named $\Delta\theta_{1/2}^{-1}$. An analytical relationship between $(r_{ss,ref};\theta_{efolding})$ and $(\theta_{1/2};\Delta\theta_{1/2}^{-1})$ is first built by running a soil energy balance model for two extreme conditions with $r_{ss} = 0$ and $r_{ss}\sim\infty$ using meteorological forcing solely, and by approaching the middle point from the two (wet and dry) reference points. Two different methods are then investigated to estimate the pair $(\theta_{1/2} ; \Delta\theta_{1/2}^{-1})$ either from the time series of SEE and $\theta$ observations for a given site, or using the soil texture information for all sites. The first method is based on an algorithm specifically designed to accomodate for strongly nonlinear $\text{SEE}(\theta)$ relationships and potentially large random deviations of observed SEE from the mean observed $\text{SEE}(\theta)$. The second method parameterizes $\theta_{1/2}$ as a multi-linear regression of clay and sand percentages, and sets $\Delta\theta_{1/2}^{-1}$ to a constant mean value for all sites. The new model significantly outperformed the evaporation modules of ISBA (Interaction Sol-Biosph\`{e}re-Atmosph\`{e}re), H-TESSEL (Hydrology-Tiled ECMWF Scheme for Surface Exchange over Land), and CLM (Community Land Model). It has potential for integration in various land-surface schemes, and real calibration capabilities using combined thermal and microwave remote sensing data.

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.

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).

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.

International audience; Modeling soil evaporation has been a notorious challenge due to the complexity of the phenomenon and the lack of data to constrain it. In this context, a parsimonious model is developed to estimate soil evaporative efficiency (SEE) defined as the ratio of actual to potential soil evaporation. It uses a soil resistance driven by surface (0-5 cm) soil moisture, meteorological forcing and time (hour) of day, and has the capability to be calibrated using the radiometric surface temperature derived from remotely sensed thermal data. The new approach is tested over a rainfed semi-arid site, which had been under bare soil conditions during a 9-month period in 2016. Three calibration strategies are adopted based on SEE time series derived from (1) eddy-covariance measurements , (2) thermal measurements, and (3) eddy-covariance measurements used only over separate drying periods between significant rainfall events. The correlation coefficients (and slopes of the linear regression) between simulated and observed (eddy-covariance-derived) SEE are 0.85, 0.86 and 0.87 (and 0.91, 0.87 and 0.91) for calibration strategies 1, 2 and 3, respectively. Moreover, the correlation coefficient (and slope of the linear regression) between simulated and observed SEE is improved from 0.80 to 0.85 (from 0.86 to 0.91) when including hour of day in the soil resistance. The reason is that, under non-energy-limited conditions, the receding evaporation front during daytime makes SEE decrease at the hourly time scale. The soil resistance formulation can be integrated into state-of-the-art dual-source surface models and has calibration capabilities across a range of spatial scales from spaceborne microwave and thermal data.

International audience; Evapotranspiration (ET) plays an essential role for detecting plant water status, estimating crop water needs and optimising irrigation management. Accurate estimates of ET at field scale are therefore critical. The present paper investigates a remote sensing and modelling coupled approach for monitoring actual ET of irrigated wheat crops in the semi-arid region of Tensift Al Haouz (Morocco). The ET modelling is based on a modified Penman-Monteith equation obtained by introducing a simple empirical relationship between surface resistance (r(c)) and a stress index (SI). SI is estimated from Landsat-derived land surface temperature (LST) combined with the LST endmembers (in wet and dry conditions) simulated by a surface energy balance model driven by meteorological forcing and Landsat-derived fractional vegetation cover. The proposed model is first calibrated using eddy covariance measurements of ET during one growing season (2015-2016) over an experimental flood irrigated wheat field located within the irrigated perimeter named R3. It is then validated during the same growing season over another drip-irrigated wheat field located in the same perimeter. Next, the proposed ET model is implemented over a 10 x 10 km(2) area in R3 using a time series of Landsat-7/8 reflectance and LST data. The comparison between modelled and measured ET fluxes indicates that the model works well. The Root Mean Square Error (RMSE) values over drip and flood sites were 13 and 12 W m(-2), respectively. The proposed approach has a great potential for detecting crop water stress and estimating crop water requirements over large areas along the agricultural season