Revealing irrigation uniformity with remote sensing: A comparative analysis of satellite-derived uniformity coefficient

Abstract

This study investigates the use of satellite-derived Christiansen Uniformity Coefficient (SDCUC) values for evaluating irrigation uniformity. In the context of global water scarcity and the imperative for sustainable water management, we explore the potential of remote sensing methods to evaluate irrigation uniformity across large agricultural areas. The findings reveal a consistent tendency for SDCUC to overestimate irrigation uniformity, with an average overestimation rate of 7.83 %. However, accuracy improved with the appropriate method, vegetation index, or spectral band selection. Employing the entire satellite image for SDCUC (SDCUCTOT) assessment improved accuracy. For Sentinel-1 (S1), using the dual-band cross-polarization horizontal transmit/vertical receive band (VH), the bias confidence interval was −0.39–0.69 %, while for Sentinel-2 (S2), using the normalized difference red edge 3 index (NDRE3), it was −1.47–0.66 %, and for Landsat 8 (L8) and Landsat 9 (L9) using the shortwave infrared water stress index (SIWSI) it ranged from 0.36 % to 2.28 %. Improved results were also observed when the normalized difference vegetation index (NDVI) ranged between 0.4 and 0.8 or the evapotranspiration and potential evapotranspiration ratio (ET/PET) ranged between 0.30 and 0.55. In these conditions, SDCUCTOT for the S2, L8, and L9 using the simple ratio index (SR) ranged from 1.00 % to 2.33 %, 0.00–1.83 %, 0.23–2.00 %, respectively, and for S2, the normalized difference water index (NDWI) and NDRE3 ranged from −1.39–0.71 %, and −1.43–2.31 % respectively. These findings underscore the potential of remote sensing techniques to revolutionize water resource management and promote sustainable agriculture, emphasizing the synergistic role of ground-based measurements and the need for continued methodological refinements to improve accuracy. Further advancements and research are warranted to refine the methodology and enhance the accuracy and reliability of remote sensing-based irrigation uniformity assessment, ultimately contributing to more sustainable agricultural irrigation practices.