A Review of Root Zone Soil Moisture Estimation Methods Based on Remote Sensing

Abstract

Root zone soil moisture (RZSM) controls vegetation transpiration and hydraulic distribution processes and plays a key role in energy and water exchange between land surface and atmosphere; hence, accurate estimation of RZSM is crucial for agricultural irrigation management practices. Traditional methods to measure soil moisture at stations are laborious and spatially uneven, making it difficult to obtain soil moisture data on a large scale. Remote sensing techniques can provide soil moisture in a large-scale range, but they can only provide surface soil moisture (SSM) with a depth of approximately 5–10 cm. In order to obtain a large range of soil moisture for deeper soil layers, especially the crop root zone with a depth of about 100–200 cm, numerous methods based on remote sensing inversion have been proposed. This paper analyzes and summarizes the research progress of remote sensing-based RZSM estimation methods in the past few decades and classifies these methods into four categories: empirical methods, semi-empirical methods, physics-based methods, and machine learning methods. Then, the advantages and disadvantages of various methods are outlined. Additionally an outlook on the future development of RZSM estimation methods is made and discussed.

Coupled DSSAT and HYDRUS-1D Simulation of the Farmland–Crop Water Cycling Process in the Dengkouyangshui Irrigation District

“…In the coupled model, the root mean square error (RMSE) for the soil moisture content ranged from 0.02 to 0.04, while the RMSE for the leaf area index varied from 0.16 to 0.20. These results are comparable to those obtained by Li Lei [33], who integrated remote sensing data on soil moisture in the maize root zone and developed a coupled model combining the crop growth model (WOFOST) and the hydrological model (HYDRUS-1D), achieving soil moisture RMSE values between 0.073 and 0.101 and leaf area index RMSE values between 0.45 and 1.05. The precision of the RMSE in our model surpassed that of Li Lei's study, possibly due to the latter's method of dividing the 0-100 cm soil profile into just two layers, which might have reduced the accuracy.…”

UAV-Based Remote Sensing to Evaluate Daily Water Demand Characteristics of Maize: A Case Study from Yuci Lifang Organic Dry Farming Experimental Base in Jinzhong City, China

“…Recent studies highlight the significant potential of remote sensing for monitoring soil moisture [34][35][36] and assessing crop growth [37]. However, some of these methods face challenges in adaptability across different geographical and climatic conditions, but the limitations are gradually diminishing with advances in small satellite technology and UAV image processing methods [38,39]. Furthermore, current models struggle to accurately reflect crop water demands at specific growth stages.…”