A Climate Smart Framework for Forecasting Field-level Potential Evapotranspiration and Irrigation Requirement with Numerical Weather Predictions and Satellite Remote Sensing

A key challenge for climate-smart water management is timely and reliably forecasting potential evapotranspiration (ETc) and irrigation water requirement (IWR) at field level with high spatial and temporal resolution. In this study, we develop a framework for forecasting ETc and IWR using multi-model numerical weather predictions and harmonized Landsat Sentinel-2 remote sensing product. Multiple numerical weather predictions from The International Grand Global Ensemble (TIGGE) are used as input into the Food and Agriculture Organization (FAO) Penman-Monteith equation to produce reference evapotranspiration (ETo) forecasts. The non-homogeneous Gaussian regression method is used to post-process the ETo forecasts. ETo forecasts are evaluated against meteorological observations and compare with the forecasts from the National Weather Service Digital Forest Database over the contiguous United States. Crop parameters (leaf area index and surface albedo) and crop coefficients are derived from visible and near-infrared images from Harmonized Landsat Sentinel-2 product. The satellite derivations are evaluated against ground crop measurements from agricultural fields in Alabama and California. Potential crop evapotranspiration (ETc) forecasts are estimated using two approaches: 1) crop coefficient-based approach, and 2) crop parameter-based approach with the post-processed ETo forecasts. The ETc and irrigation water requirement (IWR) calculated using the FAO-56 method with observed weather data and field collected crop data are used as observational reference. ETc and IWR forecasts are evaluated against observational references using different metrics. In general, statistical post-processing using non-homogeneous Gaussian regression greatly improved ETo forecasting performance. The crop parameter-based approach showed better performance compared to the crop coefficient approach, contingent upon the choice of TIGGE predictions. The study demonstrates the capability of Harmonized Landsat Sentinel-2 and TIGGE for forecasting ETc and IWR and has implications for informing site-specific climate smart water management.