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.

Soil moisture is an important component of the hydrologic cycle and ecosystem functioning, and it has a significant impact on agricultural production, climate change and natural disasters. Despite the availability of machine-learning techniques for estimating soil moisture from high-resolution remote-sensing imagery, including synthetic aperture radar (SAR) data and optical remote sensing, comprehensive comparative studies of these techniques remain limited. This paper addresses this gap by systematically comparing the performance of four tree-based ensemble-learning models (random forest (RF), extreme gradient boosting (XGBoost), light gradient-boosting machine (LightGBM), and category boosting (CatBoost)) and three deep-learning models (deep neural network (DNN), convolutional neural network (CNN), and gated recurrent unit (GRU)) in terms of soil moisture estimation. Additionally, we introduce and evaluate the effectiveness of four different stacking methods for model fusion, an approach that is relatively novel in this context. Moreover, Sentinel-1 C-band dual-polarization SAR and Sentinel-2 multispectral data, as well as NASADEM and geographical code and temporal code features, are used as input variables to retrieve the soil moisture in the ShanDian River Basin in China. Our findings reveal that the tree-based ensemble-learning models outperform the deep-learning models, with LightGBM being the best individual model, while the stacking approach can further enhance the accuracy and robustness of soil moisture estimation. Moreover, the stacking all boosting classes ensemble-learning model (SABM), which integrates only boosting-type models, demonstrates superior accuracy and robustness in soil moisture estimation. The SHAP value analysis reveals that ensemble learning can utilize more complex features than deep learning. This study provides an effective method for retrieving soil moisture using machine-learning and high-resolution remote-sensing data, demonstrating the application value of SAR data and high-resolution optical remote-sensing data in soil moisture monitoring.

Droughts frequently occur in Yunnan province, the southwest of China, which leads to crop loss, ecosystem degradation, and difficulties in drinking water for people. In order to assess and compare the extremeness for different drought events, this study quantified it by utilizing the joint return period of drought multi-characteristics. Three characteristics at the regional scale: drought duration, severity, and affected areas were obtained by a simple regional drought process methodology, and their relationship was considered based on three types of Archimedean Copulas. Standard Precipitation Evapotranspiration Index at a six-month time scale was selected as the optimal drought index based on actual drought impact data. Results showed that drought events in Yunnan province were mostly short drought duration, low severity, and high drought-affected areas. By comparing the historical reported droughts’ loss, the return periods of drought events calculated by the combination of duration and severity and drought-affected area are much more suitable to reflect the real drought situations than those calculated by one- or two-dimensional drought characteristics, especially for extreme drought events. On average, the drought in Yunnan province was almost shown a return period of ~10 yr. The frequency of droughts in Yunnan province has gradually increased due to climate change, and droughts with ~100 yr or even larger return periods occurred in 2009–2010 and 2011–2013.