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 (R2) of 0.82 for K30-50 and MPDI, followed by K30-50 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.
Highlights
Drought is a natural disaster that has many interrelated environmental and socioeconomic impacts related to the lack or shortage of water
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 data used in the study included satellite images of Landsat 8-Operational Land Imager (OLI), Sentinel 2-Multispectral Instrument (MSI) and climatic data from Jordan Meteorological Department (JMD) and Ministry of Water and Irrigation (MWI), in addition to ground data collected during several field visits
Summary
Drought is a natural disaster that has many interrelated environmental and socioeconomic impacts related to the lack or shortage of water. The alternatives for the in-situ soil moisture measurements are the land surface models that integrate climatic and remote sensing data [2] These coupled and uncoupled models for soil moisture estimation are limited by the level of accuracy which is highly limited by the input data for initial conditions and the coarse spatial resolution [3]. The techniques of remote sensing data are based on the assimilation of digital numbers (DNs), representing surface spectral reflectance at certain wavelength, to derive indices that are related to drought and reflecting soil moisture conditions. Most of these indices are derived from the red and near-infrared bands and assumed to reflect vegetation fractions and conditions
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