Estimation of regional-scale near real time reference evapotranspiration using remote sensing and weather data to improve agriculture advisory

Accurate and timely information on evapotranspiration (ET₀) is essential for multiple agricultural applications, including irrigation scheduling, studying crop-specific water loss at different growth stages, predicting crop yields, and forecasting drought conditions. This study aims to examine the spatiotemporal patterns of ET₀ and facilitate the monitoring of crop water demand, optimize irrigation water usage, and enhance agricultural advisory services. This paper estimates regional-level daily ET₀ gridded data with a spatial resolution of 12.5 km by integrating observed weather data, IMD GFS-T1534 reanalysis data, and INSAT-3D satellite-based insolation product using the standard FAO56 Penman–Monteith empirical equation. The estimated monthly mean of ET₀ across India ranged from 10 to 400 mm. ET₀ exhibited an increasing trend from January to May, reaching its maximum values in May. In June, ET₀ significantly decreased as the monsoon arrived, coinciding with the movement of rainfall patterns. The month of December exhibited the lowest ET₀ values. The estimated daily gridded ET₀ was compared with station-based ET₀, resulting in daily correlation coefficient R² and daily maximum absolute percentage errors ranging from 0.34 to 0.90 and 10% to 27% respectively. However, these errors decreased to a large extent when considering multiday accumulated values. A comparison was conducted between the GLDAS model ET₀ and the station-estimated values, revealing an overestimation of ET₀ by the GLDAS model. Additionally, significant variations were observed among the meteorological subdivisions. This highlights the necessity for proper calibration of the GLDAS model ET₀ or its effective agricultural application.