Phenology-Based Remote Sensing Assessment of Crop Water Productivity

Abstract

The assessment of crop water productivity (CWP) is of practical significance for improving regional agricultural water use efficiency and water conservation levels. The remote sensing method is a common method for estimating large scale CWP, and the assessment errors in CWP by remote sensing originate mainly from remote sensing inversion errors in crop yield and evapotranspiration (ET). The phenological period is the important factor in crop ET and yield estimation. The crop coefficient (Kc) and harvest index (HI), which are closely related to different phenological periods, are considered during the processes of crop ET and yield estimation. The crop phenological period is detected from enhanced vegetation index (EVI) curves using Moderate Resolution Imaging Spectroradiometer (MODIS) data and Sentinel-2 data. The crop ET is estimated using the surface–energy balance algorithm for land (SEBAL) model and Penman‒Monteith (P-M) equation, and the crop yield is estimated using the dry matter mass–harvest index method. The CWP is calculated as the ratio of the crop yield to ET during the growing season. The results show that the daily ET and crop yield estimated from remote sensing images are consistent with the measured values. It is found from the variation in daily ET that the peaks appear at the heading period of wheat and maize, which are in good agreement with the rainfall and growth characteristics of the crop. The relationship between crop yield and ET shows a negative parabolic correlation, and that between CWP and crop yield shows a linear correlation. The average CWPs of wheat and maize are 1.60 kg/m3 and 1.39 kg/m3, respectively. The results indicate that the phenology-based remote sensing inversion method has a good effect on the assessment of CWP in Lixin County.