The agriculture sector is highly vulnerable to natural disasters and climate change, leading to severe impacts on food security, economic stability, and rural livelihoods. The use of geospatial information and technology has been recognized as a valuable tool to help farmers reduce the adverse impacts of natural disasters on agriculture. Remote sensing and GIS are gaining traction as ways to improve agricultural disaster response due to recent advancements in spatial resolution, accessibility, and affordability. This paper presents a comprehensive overview of the FAIR agricultural disaster services. It holistically introduces the current status, case studies, technologies, and challenges, and it provides a big picture of exploring geospatial applications for agricultural disaster “from farm to space”. The review begins with an overview of the governments and organizations worldwide. We present the major international and national initiatives relevant to the agricultural disaster context. The second part of this review illustrates recent research on remote sensing-based agricultural disaster monitoring, with a special focus on drought and flood events. Traditional, integrative, and machine learning-based methods are highlighted in this section. We then examine the role of spatial data infrastructure and research on agricultural disaster services and systems. The generic lifecycle of agricultural disasters is briefly introduced. Eventually, we discuss the grand challenges and emerging opportunities that range from analysis-ready data to decision-ready services, providing guidance on the foreseeable future.

Natural disaster response and assessment are key elements of natural hazard monitoring and risk management. Currently, the existing systems are not able to meet the specific needs of many regional stakeholders worldwide; traditional approaches with field surveys are labor-intensive, time-consuming, and expensive, especially for severe disasters that affect a large geographic area. Recent studies have demonstrated that Earth observation (EO) data and technologies provide powerful support for the natural disaster emergency response. However, challenges still exist in support of the entire disaster lifecycle—preparedness, response, and recovery—which build the gaps between the disaster Spatial Data Infrastructure (SDI) already-in-place requirements and the EO capabilities. In order to tackle some of the above challenges, this paper demonstrates how to facilitate typhoon-triggered flood disaster-ready information delivery using an SDI services approach, and proposes a web-based remote sensing disaster decision support system to facilitate natural disaster response and impact assessment, which implements on-demand disaster resource acquisition, on-the-fly analysis, automatic thematic mapping, and decision report release. The system has been implemented with open specifications to facilitate interoperability. The typhoons and floods in Hainan Province, China, are used as typical scenarios to verify the system’s applicability and effectiveness. The system improves the automation level of the natural disaster emergency response service, and provides technical support for regional remote-sensing-based disaster mitigation in China.

The assessment of ecological space quality (ESQ) and its spatio-temporal change monitoring are crucial for regional ecological management and sustainable development. However, there are few studies on how to construct a comprehensive ESQ assessment system to reveal the spatial and temporal change patterns of ESQ over a long time series. Therefore, this study constructs an ESQ evaluation model with comprehensive ecological characteristics to quantitatively assess the spatio-temporal dynamics of ESQ from 2001 to 2020 based on policy objectives and public demands, using the urban agglomeration in the middle reaches of the Yangtze River as an example. The results show that, in the past 20 years, the mean value of ESQ in urban agglomeration has decreased (−0.179·year⁻¹), and the overall ESQ is dominated by a good level. The ESQ has shifted from improvement (2001–2010) to deterioration (2010–2020) and shows the spatial distribution characteristics of “high in the periphery and center, low in the interior”. From the trend of change, the degraded area of ESQ is greater than the improved area, and the degraded area of ESQ will increase in the future compared with the trend of 2001–2020. The distribution of ESQ has a significant spatial agglomeration and scale effect. The hot spots of ESQ at the town scale are mainly concentrated in the central part of urban agglomeration and mountainous areas in the periphery. The cold spots are mainly concentrated in the surrounding areas of central cities (Wuhan, Changsha, and Nanchang). The proposed assessment framework can be used to quantify spatial and temporal changes in ESQ and identify potential ecological space management issues, providing basic information for implementing ecological space protection, restoration, and developing adaptive ESQ management measures. The research results are of significant importance for ecosystem restoration and long-term development in the Yangtze River Basin.

Hydrological models play a critical role in advancing environmental modeling. They are particularly significant in contexts requiring short-term decision-making, where real-time simulation capabilities support timely and informed actions. The advancement of Internet of Things (IoT) technology has provided new opportunities for enhancing real-time hydrological modeling. However, most widely used hydrological models were originally designed as desktop applications with process-oriented execution workflows, which hinder fine-grained state access and standardized integration with IoT systems, thereby limiting their suitability for real-time, observation-driven modeling scenarios. This paper proposes a method for describing hydrological model components and data using a standard IoT conceptual model. By establishing a generic object-oriented framework, we integrate hydrological models with IoT systems, systematically representing model elements and data while mapping them to the Open Geospatial Consortium (OGC) SensorThings API conceptual model. This approach enables real-time, observation-driven hydrological modeling and facilitates fine-grained state acquisition. Finally, we developed a prototype system based on the Storm Water Management Model (SWMM) and validated the feasibility of our methodology through case studies.