Monitoring agricultural drought in Peshawar Valley, Pakistan using long -term satellite and meteorological data

Abstract

Monitoring agricultural drought across a large area is challenging, especially in regions with limited data availability, like the Peshawar Valley, which holds great agricultural significance in Pakistan. Although remote sensing provides biophysical variables such as precipitation (P), land surface temperature (LST), normalized difference vegetation index (NDVI), and relative soil moisture (RSM) to assess drought conditions at various spatiotemporal scales, these variables have limited capacity to capture the complex nature of agricultural drought and associated crop responses. Here, we developed a composite drought index named "Temperature Vegetation ET Dryness Index" (TVEDI) by modifying the Temperature Vegetation Precipitation Dryness Index (TVPDI) and integrating NDVI, LST, and remotely sensed evapotranspiration (ET) using 3D space and Euclidean distance. Several statistical techniques were employed to examine TVPDI and TVEDI trends and relationships with other commonly used drought indices such as the standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), and standardized soil moisture index (SSI), as well as crop yield, to better understand how these indices captured the spatial and temporal distribution of agricultural drought in the Peshawar valley between 1986 and 2018. Results indicated that while the temporal patterns of the 3-month SPI, SPEI, and SSI generally align with those of TVEDI and TVPDI, TVEDI was more strongly correlated with these indices (e.g., correlation coefficient, r = 0.78-0.84 from TVEDI and r = 0.73-0.79 from TVPDI). Moreover, the crop yield, a measure of crop response to agricultural drought, demonstrated a significant positive correlation with TVEDI (r = 0.60-0.80), much higher than its correlation with TVPDI (r = 0.30-0.48). These outcomes indicate that the inclusion of ET in TVEDI effectively captured changes in soil moisture, crop water status, and their impact on crop yield. Overall, TVEDI exhibited enhanced capability to identify drought impacts compared to TVPDI, showing its potential for characterizing agricultural drought in regions with limited data availability.

Keywords: Agricultural Drought; And TVPDI; Auto-correlation; Evapotranspiration; TVEDI.