The appraisal of crop water requirements (CWR) is crucial for the management of water resources, especially in arid and semi-arid regions where irrigation represents the largest consumer of water, such as the Doukkala area, western Morocco. Simple and (semi) empirical approaches have been applied to estimate CWR: the first one is called K_c-NDVI method, based on the correlation between the Normalized Difference Vegetation Index (NDVI) and the crop coefficient (K_c); the second one is the analytical approach based on the direct application of the Penman-Monteith equation with reflectance-based estimates of canopy biophysical variables, such as surface albedo (r), leaf area index (LAI) and crop height (h_c). A time series of high spatial resolution RapidEye (REIS), SPOT4 (HRVIR1) and Landsat 8 (OLI) images acquired during the 2012/2013 agricultural season has been used to assess the spatial and temporal variability of crop evapotranspiration ET_c and biophysical variables. The validation using the dual crop coefficient approach (K_cb) showed that the satellite-based estimates of daily ET_c were in good agreement with ground-based ET_c, i.e., R² = 0.75 and RMSE = 0.79 versus R² = 0.73 and RMSE = 0.89 for the K_c-NDVI, respective of the analytical approach. The assessment of irrigation performance in terms of adequacy between water requirements and allocations showed that CWR were much larger than allocated surface water for the entire area, with this difference being small at the beginning of the growing season. Even smaller differences were observed between surface water allocations and Irrigation Water Requirements (IWR) throughout the irrigation season. Finally, surface water allocations were rather close to Net Irrigation Water Requirements (NIWR).

Currently, Digital Elevation Models (DEMs) are the main source for representing Earth’s surface and have been an integral part of any geospatial analysis, in particular, geomorphometric research. Among existing techniques, photogrammetry is considered as a common method for obtaining high-resolution elevation data, especially over large or/and inaccessible areas. However, this DEMs are disposed to a severe amount of uncertainty. The aim of this research is to provide an optimal filtering strategy that can remove possible errors and improve the quality of raw photogrammetric DEMs for geomorphometric research over regions with relatively low relief. Results reveal that a combination of three digital filters, namely Gaussian filter, Median filter, and Slope Based filter, is able to reduce different sources of errors and improve the elevation accuracy of DEM by more than 5%, resulting in improved quality of the derived geomorphometric parameters.

Currently, Digital Elevation Models (DEMs) are the main source for representing Earth’s surface and have been an integral part of any geospatial analysis, in particular, geomorphometric research. Among existing techniques, photogrammetry is considered as a common method for obtaining high-resolution elevation data, especially over large or/and inaccessible areas. However, this DEMs are disposed to a severe amount of uncertainty. The aim of this research is to provide an optimal filtering strategy that can remove possible errors and improve the quality of raw photogrammetric DEMs for geomorphometric research over regions with relatively low relief. Results reveal that a combination of three digital filters, namely Gaussian filter, Median filter, and Slope Based filter, is able to reduce different sources of errors and improve the elevation accuracy of DEM by more than 5%, resulting in improved quality of the derived geomorphometric parameters.