Integrating UAV thermal imagery and in-situ data for high-resolution crop water stress–soil moisture dynamics over India’s agricultural hotspot

Abstract

Airborne remote sensing has facilitated high-resolution canopy and soil water assessment, especially in agriculture-intensive regions. This study presents a field-scale assessment of crop water stress index (CWSI) and soil moisture (SM) using unmanned aerial vehicle (UAV)-mounted thermal imagery combined with in-situ hydrometeorological data over a key agricultural site in India’s Ganga basin. The UAV-based LST (LST-Aerial) is modelled using the spectral emissivity from ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) spectral library and atmospheric radiances from a radiative transfer model. The LST-Aerial is subsequently utilized to estimate field-scale CWSI (CWSI-Aerial) and SM (SM-Aerial) across two principal crop seasons (paddy and wheat) using an empirical and multiple linear regression model, respectively. While the radiometer data reveals a significant correlation (R2 = 0.58 to 0.76, p < 0.001) between canopy–air temperature difference and vapour pressure deficit, a temperature difference of ~ 5°C was noticed between non-transpiring baselines of paddy and wheat for the airborne window. The radiometer-derived CWSI (CWSI-Rad) showed a relatively higher value (0.57) during the wheat season compared to paddy (0.19), reflecting the influence of monsoon-fed cropping in north India. Partial least squares regression reveals solar radiation and relative humidity as major meteorological drivers of CWSI-Rad during the paddy and wheat growing period, respectively. While CWSI-Aerial demonstrated superior accuracy (R2 = 0.85, p < 0.05) to CWSI-Rad, it exhibited a high negative correlation (R = −0.75 to −0.97) to concurrent SM and SM-Aerial. Additionally, the predicted SM-Aerial agrees well with ground-based SM with errors ranging from 0.01–0.14 m3.m−3, showcasing the robust UAV-based SM prediction. Findings of this study offer valuable insights into smart and precision agriculture, enabling well-informed regulation of crop water resources in tropical water-limited regions.