Estimation of root-zone soil moisture using crop water stress index (CWSI) in agricultural fields - Venkata Radha Akuraju; Dongryeol Ryu; Biju George (GIScience & Remote Sensing, 2021)
Due to the limited availability of Root-Zone Soil Moisture (RZSM) information at the regional scale, this paper explores the use of thermal infrared remote sensing to estimate RZSM in agricultural fields. This study presents the Crop Water Stress Index (CWSI) derived from thermal infrared data used as an indicator to estimate root zone soil moisture. Theoretical limits were calculated using canopy and air temperature difference, which is related to vapor pressure deficit. An empirical model was developed using continuous remotely sensed optical, thermal infrared data with limited meteorological data collected from a wheat site, the Dookie experimental farm, Victoria, Australia during 2012 and 2013. Linear and exponential models predicting RZSM using CWSI were constructed and compared in two different cropping seasons. Cross-validation results demonstrate that the linear model predicted RZSM with an error of 3.9% in 2012 and 5.3% in 2013 cropping seasons. The proposed method is applied to another root-zone soil moisture dataset collected during 2002–04 cropping seasons from a corn field site in the Optimizing Production Inputs for Economic and Environmental Enhancement (OPE3) site in the USA. Validation results showed that the model produces reasonable RZSM estimates, except for the high rainfall distribution during cropping seasons even though the crop types of the calibration and validation sites were different. The efficacy of canopy temperature in RZSM estimations was demonstrated using Dookie and OPE3 RZSM dataset. The potential limitation is that sparse vegetation in the initial growth stages produces negative values in CWSI due to the dominant soil surface. Overall, the results support the potential role of the theoretical crop water stress index in root-zone soil moisture estimations.