Remote Sensing Data Fusion to Evaluate Patterns of Regional Evapotranspiration: A Case Study for Dynamics of Film-Mulched Drip-Irrigated Cotton in China’s Manas River Basin over 20 Years

Abstract

The accurate quantification of evapotranspiration (ET) is critical to the sustainable management of irrigated agriculture. In this study, we proposed a remote sensing data fusion method for predicting ET, coupling a surface energy balance system model with an enhanced spatial and temporal adaptive reflectance fusion model utilizing remote sensing inversion with satellite data from Landsat and MODIS. The method was tested for a case study with cotton fields under film-mulched drip irrigation (FMDI) in the Manas River Basin. Areas under FMDI were identified, and ET patterns were evaluated for a 21-year period spanning from 2000 to 2020. A field experiment, a regional survey, and data retrieved from the literature provided results demonstrating that the method allowed reliable estimation of ET distribution with simultaneously, relatively high spatial and temporal resolutions at both field and regional scales. ET was found to decline from upstream to downstream in the basin, with the difference gradually diminishing over time. Supported by the promotion of FMDI technology, the area under cotton production in the basin increased by an average of 4.9% annually. Limited irrigation quotas to farmers and, therefore, water application per area led to a decreasing ratio of relative water supply for potential ET and, thus, to a reduction in average actual ET of 7.5 mm year−1. The average ET in the basin declined to about 415.7 mm in 2020, 287.2 mm lower than its water demand. The dynamics of fused ET provide a reliable scientific basis for agricultural water resources planning and management and for the sustainable utilization of water and soil resources in the basin. The method, with simultaneously high temporal and spatial resolutions, should have both local and global practical potential.