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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 minilysimeters, 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.

Monitoring irrigation is essential for an efficient management of water resources in arid and semi-arid regions. We propose to estimate the timing and the amount of irrigation throughout the agricultural season using optical and thermal Landsat-7/8 data. The approach is implemented in four steps: i) partitioning the Landsat land surface temperature (LST) to derive the crop water stress coefficient (Ks), ii) estimating the daily root zone soil moisture (RZSM) from the integration of Landsat-derived Ks into a crop water balance model, iii) retrieving irrigation at the Landsat pixel scale and iv) aggregating pixel-scale irrigation estimates at the crop field scale. The new irrigation retrieval method is tested over three agricultural areas during four seasons and is evaluated over five winter wheat fields under different irrigation techniques (drip, flood and no-irrigation). The model is very accurate for the seasonal accumulated amounts (R ~ 0.95 and RMSE ~ 44 mm). However, lower agreements with observed irrigations are obtained at the daily scale. To assess the performance of the irrigation retrieval method over a range of time periods, the daily predicted and observed irrigations are cumulated from 1 to 90 days. Generally, acceptable errors (R = 0.52 and RMSE = 27 mm) are obtained for irrigations cumulated over 15 days and the performance gradually improves by increasing the accumulation period, depicting a strong link to the frequency of Landsat overpasses (16 days or 8 days by combining Landsat-7 and-8). Despite the uncertainties in retrieved irrigations at daily to weekly scales, the daily RZSM and evapotranspiration simulated from the retrieved daily irrigations are estimated accurately and are very close to those estimated from actual irrigations. This research demonstrates the utility of high spatial resolution optical and thermal data for estimating irrigation and consequently for better closing the water budget over agricultural areas. We also show that significant improvements can be expected at daily to weekly time scales by reducing the revisit time of high-spatial resolution thermal data, as included in the TRISHNA future mission requirements.

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.

Monitoring evapotranspirationin arid and semi-arid environments plays a key role in water irrigation scheduling for water use efficiency. This work presents an operational method for evapotranspiration retrievals based ondisaggregated Land Surface Temperature (LST). The LSTs retrieved from Landsat-8 and MODIS data weremerged in order to provide an 8-day composite LSTproduct at 100 x 100 m resolution.The method wastested in the arid region of Copiapó, Chile using data from years 2013-2014 and validated using data from years 2015-2016.In-situ measurements from agrometeorological stations were used as input to the disaggregated method such as air temperature and potential evapotranspiration (ET0)estimated at the location. The disaggregation method was developed bytaking into account1) the spatial relationship between

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) eddycovariance 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

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 R 2 value of 0.68 (0.42) and a RMSE value of 0.034 (0.061) m 3 m-3 by forcing FAO-2Kc using the retrieved irrigation (from LSTderived 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.