Wadi Kufranja catchment (126.3 km 2 ), northern Jordan, was selected to estimate annual soil loss using the Revised Universal Soil Loss Equation (RUSLE), remote sensing (RS), and geographic information system (GIS). RUSLE factors (R, K, LS, C and P) were computed and presented by raster layers in a GIS environment, then multiplied together to predict soil erosion rates, and to generate soil erosion risk categories and soil erosion severity maps. The estimated potential average annual soil loss is 10 ton·ha . Apart from the gentle slopes of the alluvial fan (Krayma town and surroundings), the lower and the middle reaches of the watershed suffer from severe to extreme erosion risk. High terrain, slope steepness, removal of vegetation, and poor conservation practices are the most prominent causes of soil erosion. This investigation demonstrates that remote sensing (RS) and GIS technologies are effective tools in modeling erosion, thus enabling extraction of significant information for implementing soil conservation plans in the north Jordan highlands.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.