New Investigation and Challenge for Spatiotemporal Drought Monitoring Using Bottom-Up Precipitation Dataset (SM2RAIN-ASCAT) and NDVI in Moroccan Arid and Semi-Arid Rangelands

Abstract Remotely sensed soil moisture products showed sensitivity to vegetation cover density and typology at regional dryland level. In these regions, drought monitoring is significantly performed using index rainfall data. Recently, observations have increasingly become available. This has hampered scientific progress as regards characterization of land surface processes not just in meteorology. The purpose this study was investigate the relationship between a newly developed precipitation dataset, SM2RAIN (Advanced SCATterometer (SM2RAIN-ASCAT), NDVI (eMODIS-TERRA) events over diverse rangeland regions Morocco. Results indicated that highest polynomial correlation coefficient lowest root mean square error (RMSE) SM2RAIN-ASCAT were found 10-year period from 2007 2017 all rangelands ( R = 0.81; RMSE 0.05). strong for degraded rangeland, where there positive coefficients 0.99). High correlations sparse moderate shrub 0.82 0.61, respectively). anomalies maps very good similarity Normalized Difference Vegetation Index (NDVI) results revealed product could accurately predict arid semi-arid rangelands.