Detection of a Real Time Remote Sensing Indices and Soil Moisture for Drought Monitoring and Assessment in Jordan

Abstract

Drought monitoring represents a challenge for water and agricultural sector as this natural hazard accelerates water deficiency and leads to adverse environmental and socioeconomic impacts. The use of remote sensing data and geospatial techniques to monitor and map drought severity expanded in the last decades with progressive developments in data sources and processing. This study investigates the correlations among drought indices derived with soil moisture stress (K) obtained from ground data collected from fields cultivated with barley. The study, carried out in Yarmouk basin in the north of Jordan, includes NDVI, PDI, MPDI and PVI derived from Landsat 8-OLI and Sentinel 2-MSI. Results showed different behavior among the indices and throughout the 2016/2017 growing season, with maximum correlation between PDI and MPDI followed by NDVI with PVI. Correlations among the remote sensing indices and K for different soil depths during March-April were significant for most indices with a maximum (R 2) of 0.82 for K 30-50 and MPDI, followed by K 30-50 with NDVI. Drought severity maps for the month of March showed different trends for the different indices, with similarities between MPDI and PDI. The map of drought severity combined from the remote sensing indices and K showed that PDI and soil moisture could significantly explain 56% of variations in spatial patterns of drought, while the combination of MPDI, PDI and NDVI could significantly explain up to 59% of variations in drought severity map. Therefore, the study recommends the adoption of these remotely sensed indices for monitoring and mapping of agricultural droughts.

Soil Moisture Analysis by Means of Multispectral Images According to Land Use and Spatial Resolution on Andosols in the Colombian Andes

“…Farhan and Al Bakri [36] report that the NDVI mainly reflects seasonal vegetation conditions, showing higher correlations with seasonal soil moisture stress, whereas the PDI does not show this relationship.…”

Delineation of Suitable Cropland Areas Using a Gis Based Multi-Criteria Evaluation Approach in the Central Agricultural Region of Mongolia

“…The DEM dataset was derived from a topographic map with a resolution of 30 m. The data of raster parameters such as the slope and aspect resulted from the DEM dataset at dimensions of 30x30m. The slope percentage dataset was processed using ArcMap software and divided into different categories according to (Mazahreh et al, 2018) for representing levels of slope suitability for rainfed agricultural and irrigated crops (Farhan and Al-Bakri.,2019). Approximately 70% of the land in the Central agriculture region has below 30% slope, 0-30% slope has good conditions for cropland in this region.…”