The application of machine learning (ML) and remote sensing (RS) in soil and water conservation has become a powerful tool. As analytical tools continue to advance, the variety of ML algorithms and RS sources has expanded, providing opportunities for more sophisticated analyses. At the same time, researchers are required to select appropriate technologies based on the research objectives, topic, and scope of the study area. In this paper, we present a comprehensive review of the application of ML algorithms and RS that has been implemented to advance research in soil and water conservation. The key contribution of this review paper is that it provides an overview of current research areas within soil and water conservation and their effectiveness in improving prediction accuracy and resource management in categorized subfields, including soil properties, hydrology and water resources, and wildfire management. We also highlight challenges and future directions based on limitations of ML and RS applications in soil and water conservation. This review aims to serve as a reference for researchers and decision-makers by offering insights into the effectiveness of ML and RS applications in the fields of soil and water conservation.

The Soil and Water Assessment Tool (SWAT) model has been used worldwide for many hydrologic and Non-Point Source (NPS) Pollution analyses on a watershed scale. However, it has many limitations in simulating the Vegetative Filter Strip (VFS) because it considers only ‘filter strip width’ when the model estimates sediment trapping efficiency and does not consider the routing of sediment with overland flow which is expected to maximize the sediment trapping efficiency from upper agricultural subwatersheds to lower spatially-explicit filter strips. Therefore, the SWAT overland flow option between landuse-subwatersheds with sediment routing capability was enhanced by modifying the SWAT watershed configuration and SWAT engine based on the numerical model VFSMOD applied to South-Korean conditions. The enhanced SWAT can simulate the VFS sediment trapping efficiency for South-Korean conditions in a manner similar to the desktop VFSMOD-w system. Due to this enhancement, SWAT is applicable to simulate the effects of overland flow from upper subwatersheds to reflect increased runoff volume at the lower subwatershed, which occurs in the field if no diversion channel is installed. In this study, the enhanced SWAT model was applied to small watersheds located at Jaun-ri in South-Korea to simulate a diversion channel and spatially-explicit VFS. Sediment can be reduced by 31%, 65%, and 68%, with a diversion channel, the VFS, and the VFS with diversion channel, respectively. The enhanced SWAT should be used in estimating site-specific effects on sediment reduction with diversion channels and VFS, instead of the currently available SWAT, which does not simulate sediment routing in overland flow and does not consider other sensitive factors affecting sediment reduction with VFS.

This study evaluates the applicability of the InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) model for assessing wildfire-induced changes in water-related ecosystem services at a localized scale. Wildfires significantly alter hydrological processes by reducing vegetation cover, which in turn affects water-yield dynamics. To quantify these changes, we applied the InVEST annual water-yield model to a 4.95 km² wildfire-affected area and validated its outputs against the physically based SWAT model. The study utilized Sentinel-2 imagery to create pre- and post-wildfire land cover maps, which served as key inputs for the InVEST model. The results showed a 7.05% increase in water yield after the wildfire. Validation using SWAT confirmed that InVEST could capture localized hydrological changes with accuracy. While InVEST simplifies hydrological processes by relying primarily on land cover data, it remains a valuable tool for rapid and low-resource assessments in wildfire-prone regions. This study highlights the potential of InVEST for rapid post-fire evaluations, offering a practical decision-support model for post-fire land and water resource management in the context of climate change.

The application of machine learning (ML) and remote sensing (RS) in soil and water conserva-tion has become a powerful tool. As analytical tools continue to advance, the variety of ML al-gorithms and RS sources has expanded, providing opportunities for more sophisticated analyses. At the same time, researchers are required to select appropriate technologies based on research objectives, topic, and scope of the study area. In this paper, we present a comprehensive review on the application of ML algorithms and RS that has been implemented to advance research in soil and water conservation. The key contribution of this review paper is that it provides an overview of current research areas within soil and water conservation and their effectiveness in improving prediction accuracy and resource management in categorized subfields, including biomass-vegetation, soil properties, hydrology and water resources, and wildfire management. We also highlight challenges and future directions based on limitations of ML and RS applica-tions in soil and water conservation. This review aims to serve as a reference for researchers and decision-makers by offering insights into the effectiveness of ML and RS applications in the field of soil and water conservation.

This study is to develop the Web GIS-based surface soil erosion prediction system that informs soil information such as daily potential soil erosion, soil quality, and best management practices (BMPs). The system involves three functions that are: 1) to predict daily potential soil erosion in the study areas (e.g., Jaun-Cheon, Bukhan-Gang, Namhan-Gang, and Gyoungan-Cheon); 2) to provide the current levels of soil qualities at field scale; 3) to recommend BMPs which can improve soil qualities. This study developed a module based on MUSLE and assessed the availability of the module comparing with the measured data at sample fields (3%, 9% slope). After verification of the module, the Web GIS-based system was developed using a user-friendly interface. The users can obtain the visualized soil erosion information through the interface and compare the amount of soil erosion using the single field or multi-fields analysis tool developed in this study. Moreover, the users can find the current level of soil qualities at fields they selected and gain various applicable BMPs information. The system enables to inform non-experts to soil information without using a complex model and equation. Therefore, the system can play a significant role in recognizing the importance of soil resources and enacting laws relative to soil conservation.