Remote sensing-based agricultural drought mapping in Northern Jordan using Landsat and MODIS data

Abstract

Monitoring agricultural drought in a semi-arid environment is critical, especially during the growing season, as it negatively impacts vegetation health and crop yield. This study aimed to monitor the spatiotemporal variation of agricultural drought in Northern Jordan using Landsat-8 and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. The Spatio-Temporal Image Fusion Model (STI-FM) was used to produce synthetic Landsat images with a high spatiotemporal resolution (30 m / 8 days) by utilizing a pair of successive MODIS images at two points in time (time-1 and time-2) and one Landsat-8 image at time-1. Agricultural drought was mapped and monitored using spectral indices namely, Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Vegetation Health Index (VHI). Additionally, the Standard Precipitation Index (SPI) -based meteorological rainfall data was used to validate the accuracy of the drought maps. The results revealed significant spatiotemporal variations in drought conditions, with April 2020 showing the least dry conditions, while 2019 was identified as the driest year. Validation through SPI indicated high accuracy, with kappa values ranging from 0.70 to 0.85 and overall accuracy ranging from 80 % to 92 %. Furthermore, the STI-FM data fusion algorithm effectively generated high-resolution Landsat-8 images, demonstrating a strong correlation between original and synthetic images for the red and NIR spectral bands (0.93 and 0.84, respectively). These findings highlight the effectiveness of integrating STI-FM with spectral indices and SPI for accurate and high-resolution agricultural drought monitoring, which can support improved water resource management and agricultural planning in semi-arid regions.