1. bookVolume 41 (2022): Issue 1 (March 2022)
Journal Details
License
Format
Journal
eISSN
1337-947X
First Published
24 Aug 2013
Publication timeframe
4 times per year
Languages
English
access type Open Access

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

Published Online: 22 Apr 2022
Volume & Issue: Volume 41 (2022) - Issue 1 (March 2022)
Page range: 90 - 100
Received: 14 Aug 2021
Accepted: 20 Dec 2021
Journal Details
License
Format
Journal
eISSN
1337-947X
First Published
24 Aug 2013
Publication timeframe
4 times per year
Languages
English
Abstract

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

Keywords

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