Estimation of root-zone soil moisture using thermal infrared data

Abstract

© 2017 Dr. Venkata Radha AkurajuThis thesis focuses on Root-Zone Soil Moisture (RZSM) estimation using Thermal Infrared (TIR) observations. RZSM plays an important role in hydrological modelling and agricultural applications. Conventional point-based measurements such as gravimetric and TDR measurements may not be useful for agricultural applications to understand the spatial and temporal behaviour of soil moisture. Microwave remote sensing is a useful tool to retrieve soil moisture information in large scales, but their retrievals are limited to surface soil moisture and sparse vegetation conditions. Thermal infrared remote sensing is an alternate approach to predict soil moisture until root-zone, even under dense vegetation conditions and in high spatial and temporal resolutions. Since optical and thermal observations linked to soil water status of deeper layers, developing a model to estimate RZSM is particularly important for hydrological modelling. Being able to predict root-zone soil moisture using TIR observations, understanding the interactions between surface fluxes and soil moisture is necessary. This research builds on understanding the links between ET derived from TIR data and surface to root-zone soil moisture in dryland wheat field, Dookie experimental site, Victoria, Australia. In the first step of this research, a hydro-meteorological dataset has been collected for created for three cropping seasons. By monitoring two cropping seasons, it is shown that there exists a strong relationship between ET and soil moisture in water-limited conditions. The relationship between ET and RZSM is highly conditional based on net radiation, crop growth stage and rainfall distribution. More appropriate linkages between ET and available water fraction was found by incorporating root depth and density simulated from Agricultural Production Systems sIMulator (APSIM) model. A new model, CWSI (Crop Water Stress Index) based on the theoretical limits obtained from canopy temperature and air temperature is developed by considering the impacts of root depth variation, growth stage. The sensitivity of CWSI and RZSM from two cropping seasons is explored and compared with another cropping season. Cross-validation results demonstrate that the linear model can predict RZSM with an average error of 3.9% and 5.3% in different cropping seasons. The proposed method is also applied to another root-zone soil moisture dataset collected during 2002-04 cropping seasons in a cornfield site in the Optimizing Production Inputs for Economic and Environmental Enhancement (OPE3) site in the U.S. Validation results showed that the model produces reasonable RZSM estimates except for the high rainfall distribution during cropping seasons. Overall, this research demonstrates the links between surface fluxes/TIR observations and root-zone soil moisture. The implications of the close links contribute towards reliable root-zone soil moisture estimations in large scales using thermal infrared observations