Delineation of aquatic plants and estimation of its surface extent are crucial to the efficient control of its proliferation, and this information can be derived accurately with fine resolution remote sensing products. However, small swath and low observation frequency associated with them may be prohibitive for application to large water bodies with rapid proliferation and dynamic floating aquatic plants. The information can be derived from products with large swath and high observation frequency, but with coarse resolution; and the quality of so derived information must be eventually assessed using finer resolution data. In this study, we evaluate two methods: Normalized Difference Vegetation Index (NDVI) slicing and maximum likelihood in terms of delineation; and two methods: Gutman and Ignatov’s NDVI-based fractional cover retrieval and linear spectral unmixing in terms of area estimation of aquatic plants from 300 m Medium Resolution Imaging Spectrometer (MERIS) data, using as reference results obtained with 30 m Landsat-7 ETM+. Our results show for delineation, that maximum likelihood with an average classification accuracy of 80% is better than NDVI slicing at 75%, both methods showing larger errors over sparse vegetation. In area estimation, we found that Gutman and Ignatov’s method and spectral unmixing produce almost the same root mean square (RMS) error of about 0.10, but the former shows larger errors of about 0.15 over sparse vegetation while the latter remains invariant. Where an endmember spectral library is available, we recommend the spectral unmixing approach to estimate extent of vegetation with coarse resolution data, as its performance is relatively invariant to the fragmentation of aquatic vegetation cover.

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).

This study on a large irrigation scheme in Morocco addressed a two-fold question: (a) does the local water management authority adapt water releases to atmospheric water demand ET₀-P? and (b) does crop actual evapotranspiration respond to inter- and intra-annual variability in water releases? We have evaluated the inter-annual variability of ET₀-P during the period 1992–2017 and compared its anomalies (i.e., deviations from average) with anomalies in annual water release. Overall, it appeared that anomalies in water release were consistent with anomalies in ET₀-P. The actual evapotranspiration (ET_a) was estimated using a time series of multi-spectral satellite image data by applying the Surface Energy Balance (SEBAL) algorithm in a dry, wet, and reference year. We have determined the quartiles of the distribution of the ET₀-P time series to identify these three years. The dry year was 2015–2016, the wet year was 2014–2015, and the reference (median of ET₀-P) was 2013–2014. Finally, we compared seasonal and annual anomalies in ET₀-P, ET_a and release, W_d of irrigation water. In the period 1992–2017, the relative anomalies in ET₀-P and W_d were similar only in 2000–2001 and 2015–2016. The analysis of anomalies in water inflow and stocks confirmed the response in increased W_d following wet years with higher inflow and replenishment of the reservoir. The response of crop water use to the irrigation water supply was evaluated by comparing anomalies in the ratio of actual to maximum ET, i.e., ET_a/ET_c with anomalies in W_d. As regards the Sidi Bennour, Faregh, and Gharbia districts, the relative anomalies in ET_a/ET_c and W_d were consistent, i.e., they had the same sign and comparable magnitude. Overall, the study shows that water delivery W_d responds to inter-annual variability in the pre-irrigation season water inflows into the reservoirs, rather than in ET₀-P. On the other hand, actual crop water use, i.e., ET_a/ET_c, does respond to inter- and intra-annual variability in W_d. This evidence suggests that there is scope for adaptive water management based on a flexible adaptation of water release to inter- and intra-annual variability in water demand.

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.