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.

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.

Piping is a major factor contributing to river embankment breaches, particularly during flood season in small and medium rivers. To reduce the costs of earth rock embankment inspections, avoid the need for human inspectors and enable the quick and widespread detection of piping hazards, a UAV image-acquisition function was introduced in this study. Through the collection and analysis of thermal infrared and visible (TIR & V) images from several piping field simulation experiments, temperature increases, and diffusion centered on the piping point were discovered, so an automatic algorithm for piping identification was developed to capture this phenomenon. To verify the identification capabilities, the automatic identification algorithm was applied to detect potential piping hazards during the 2022 flooding of the Dingjialiu River, Liaoning, China. The algorithm successfully identified all five piping hazard locations, demonstrating its potential for detecting embankment piping.

The analysis of temporal and spatial variability in risk has garnered significant research attention, particularly regarding flash flood disasters in the context of warming and wetting conditions on the Qinghai–Tibetan Plateau. Focusing on the Lhasa River basin, this study develops a framework that integrates geographic information systems and a combined subjective–objective weighting approach to comprehensively assess flash flood risk despite limited observations. This paper investigates the distribution patterns of hazard, vulnerability, and the integrated risk of pluvial flash floods; demonstrates the reliability of the assessment results; and provides mitigation recommendations for disaster risk management at the county level. The results showed a trend in increasing flash flood risk in recent decades compared to the 1990s. Moreover, very-high- and high-risk areas were concentrated in downstream regions with frequent precipitation extremes and anthropogenic activity. From 1991 to 2020, the high to very high-risk areas gradually expanded from central Lhasa to neighbouring counties. This study contributes valuable insights into flash flood risk assessment cand mapping, which are crucial in terms of the protection of life and property in the plateau basin.

Computing power network (CPN) is designed to utilize multi-dimensional resources to complete computing tasks. However, in practical applications, the CPN architecture has difficulty in coordinating cross-domain heterogeneous resources, making it impossible to achieve the real-time and high scalability requirements of computationally intensive and time-sensitive tasks such as levee piping hazard inspection via remote sensing in emergency scenarios. Based on this, we propose a communication and computation integrated network architecture, referred to as (Com)²INet, that integrates “sensing”, “transmission”, and “computation” phases. In the sensing phase, thermal infrared imagery is utilized to retrieve land surface temperature fields through radiative transfer mechanisms, providing a reliable foundation for visual segmentation of piping hazards. In the transmission phase, we adopt the designed multi-path transmission mechanism to promote the efficient data flow across heterogeneous networks. In the computation phase, the proposed SACM algorithm, which is functionally decomposed and implemented as service chains within the proposed network architecture, dynamically processes the retrieved temperature fields to achieve precise hazard identification. This integrated framework ensures seamless interaction between sensing, communication, and computation, addressing the challenges of real-time hazard detection in emergency scenarios.

China is one of the countries most severely affected by natural disasters, which are characterized by diverse types, wide distribution, and high frequency. As the cornerstone and first line of defense of the emergency management system, grassroots emergency management plays a decisive role in determining disaster evolution and loss outcomes. However, current grassroots emergency management still faces challenges such as insufficient functional integration, difficulties in institutional operation, and inadequate collaborative governance. Drawing on nationwide statistical data, policy document analysis, and an in-depth county-level case study of District W, we conduct a process-based analysis of grassroots emergency management system reform. The case analysis systematically examines the reform’s background, implementation practices, and post-reform constraints, with particular attention to functional integration, institutional materialization, and coordination mechanisms. On this basis, a problem-oriented reform framework is proposed from the perspective of collaborative governance, based on empirical diagnosis and theoretical synthesis. The proposed framework focuses on integrating grassroots emergency management functions, advancing the substantive operation of grassroots emergency management institutions, and establishing a multi-stakeholder collaborative governance mechanism. The study suggests that collaborative governance provides a practical and effective framework for addressing structural constraints in grassroots emergency management and for enhancing its overall effectiveness and sustainability.