The use of remote sensing data provides valuable information to ensure sustainable land cover management. In this paper, the potential of phenological metrics data, derived from Sentinel-2A (S2) and Landsat 8 (L8) NDVI time series, was evaluated using Random Forest (RF) classification to identify and map various crop classes over two irrigated perimeters in Morocco. The smoothed NDVI time series obtained by the TIMESAT software was used to extract profiles and phenological metrics, which constitute potential explanatory variables for cropland classification. The method of classification applied involves the use of a supervised Random Forest (RF) classifier. The results demonstrated the capability of moderate-to-high spatial resolution (10-30 m) satellite imagery to capture the phenological stages of different cropping systems over the study area. Furthermore, the classification based on S2 data presents a higher overall accuracy of 93% and a kappa coefficient of 0.91 than those produced by L8 data, which are 90% and 0.88, respectively. In other words, phenological metrics obtained from S2 time series data showed high potential for agricultural crop-types classification in semi-arid regions and thus can constitute a valuable tool for decision makers to use in managing and monitoring a complex landscape such as an irrigated perimeter.
The enhancement of water efficiency requires controlling the high demand for irrigated agriculture which depends on improving the capabilities to accurately simulate the water cycle and its components. Among these, evapotranspiration is widely studied to estimate reference evapotranspiration (ET
0
) but the performance and accuracy of the estimates vary. Moreover, these estimates require some hardly available or misrepresentative meteorological data which lead, mainly in arid and semi-arid areas, to errors and inaccuracies. Here, ET
0
of five empirical temperature-based estimates are compared to the standard FAO Penman–Monteith estimate (ET
0-PM
) under the representative and wide-ranging settings of 22 weather stations of Morocco. We found a significant positive correlation between ET
0-PM
and solar radiation, average and maximum air temperatures. We have determined that the Dorji estimate shows relatively better precision and stability while it requires advanced calibration to accommodate arid and semi-arid conditions. After hundreds of calibration repetitions, we concluded a new estimate (ET
0-Hadria
) which demonstrates an overall improvement in the quality and precision of ET
0
assessment, mainly in flat areas. This estimate improved the precision and enhanced the precision in almost 68% of the stations. This simple calibrated estimate is an accurate, improved, and transferable tool achieved through a precise methodical process of selection and configuration.
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