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.

Agriculture is vulnerable to drought indicating that the increasing climate crisis requires the necessity of sustainable crop production. In this study, we developed the Irrigation Schedule and Management (ISM) model based on a simulation–optimization (Soil Water Atmosphere Plant-SWAP model with Genetic Algorithm-GA) framework. The ISM model finds an optimal combination of Irrigation Water Amount (IWA) and Irrigation Interval (II) by adjusting Water Stress (WS) responding to environmental conditions (weather, soils, crops and bottom boundary conditions) throughout growing periods. By conditioning the crop (WS) and water management (IWA and II) variables, ISM improves the sustainability of optimal crop productions under different climatic-land surface conditions. The Regional Agromet Center (RAC) site in Faisalabad (at Punjab, Pakistan) was selected to test the proposed ISM model for the field validation/synthetic numerical experiments with various crops (Wheat, Corn and Potato) and soils. We demonstrated that the ISM model that reflects the relationship between crop and water management variables improved the sustainability of crop productions and Water Productivity (WP) compared to those of the conventional irrigation method at the RAC site under various environment conditions. Additionally, the ISM-based long-term crop productions showed the variations along the yearly precipitation changes indicating that optimal combinations of the crop and water management variables are considerably influenced by environmental conditions. Although uncertainties exist, our proposed ISM model can contribute to the establishment of efficient irrigation schedule/management plans under agricultural drought.

The upsurges in population, internal migration, and various development works have caused significant land use and land cover (LULC) changes in the Bagmati Basin of Nepal. The effects of climate change such as increased precipitation and temperature are affecting the provision of ecosystem services (ES). In this regard, this study particularly treated water yield (WY), soil loss, nitrogen export, and carbon fluctuation in the basin. Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) tools were used to carry out a comparative analysis of ES based on LULC data for 2000 and 2010 and corresponding climate data. To analyze the future period (2010–2099), we have used climate data from the multi-model ensemble (MME) of statistically downscaled and bias-corrected 12 best global climate models (GCMs). A raw GCM analysis (based on historical observational data) from 29 GCMs was done first. The results shows with a subsequent degradation of ES providers like forests and an increment in agricultural and urban areas, ES are on a verge of degradation. Furthermore, a projection of future climate patterns depicts increased precipitation and temperature. Thus, urgent measures are required for the sustainable provision of ES. Outcomes of the study are expected to help in the incorporation of ES in development policies promoting low-impact development along with maintaining ecological and economic goals. The study closes by presenting a recommendation for model application and future study needs.

Distinct seasonal characteristics of monsoon climate significantly affect river streamflow in South Korea. The roles of direct runoff and baseflow on streamflow have become more important to ecosystems and human communities in various watersheds of South Korea. Understanding river characteristics, including direct runoff and baseflow, is the first step of river management and can make a significant contribution to maintaining a sustainable and effective river environment. In this regard, this study involves twin objectives: (1) developing the web-based BFlow system to gain advantages in the time and effort required relative to the DOS (Disk Operation System)-based BFlow program; and (2) assessing the contributions of baseflow and direct runoff to streamflow for river management at the national level. For this, we investigated all streamflow gauge stations in South Korea and, then, used the BFlow program to separate baseflow from the available streamflow data. The results showed that baseflow index for 254 streamflow gauge stations ranged from 0.28 to 0.89. Gauge stations with a baseflow index greater than 0.5 accounted for 64% of total stations. The web-based system developed in this study is a more MS (Microsoft) user-friendly version of BFlow. Furthermore, this study illustrated that high baseflow indexes were generally found at gauge stations with a low coefficient of variation of streamflow. The web-based BFlow system will provide understanding to strategically control rivers and improve the efficiency and safety of river management at the national level.

Agricultural drought is triggered by a depletion of moisture content in the soil, which hinders photosynthesis and thus increases carbon dioxide (CO₂) concentrations in the atmosphere. The aim of this study is to analyze the relationship between soil moisture (SM) and vegetation activity toward quantifying CO₂ concentration in the atmosphere. To this end, the MODerate resolution imaging spectroradiometer (MODIS), an optical multispectral sensor, was used to evaluate two regions in South Korea for validation. Vegetation activity was analyzed through MOD13A1 vegetation indices products, and MODIS gross primary productivity (GPP) product was used to calculate the CO₂ flux based on its relationship with respiration. In the case of SM, it was calculated through the method of applying apparent thermal inertia (ATI) in combination with land surface temperature and albedo. To validate the SM and CO₂ flux, flux tower data was used which are the observed measurement values for the extreme drought period of 2014 and 2015 in South Korea. These two variables were analyzed for temporal variation on flux tower data as daily time scale, and the relationship with vegetation index (VI) was synthesized and analyzed on a monthly scale. The highest correlation between SM and VI (correlation coefficient (r) = 0.82) was observed at a time lag of one month, and that between VI and CO₂ (r = 0.81) at half month. This regional study suggests a potential capability of MODIS-based SM, VI, and CO₂ flux, which can be applied to an assessment of the global view of the agricultural drought by using available satellite remote sensing products.

Several inversion modeling-based approaches have been developed/used to extract soil hydraulic properties (α, n, θ_res, θ_sat, K_sat) from remotely sensed (RS) soil moisture footprints. Hydrological models with shallow ground water (SGW) table depths in soils simulate daily root zone soil moisture dynamics based on the extracted soil parameters. The presence of SGW table depths in soils significantly influences model performances; however, SGW table depths are usually unknown in the field, thus, unknown SGW table depths might cause uncertainties in the model outputs. In order to overcome these drawbacks, we developed a dynamic ground water (DGW) data assimilation approach that can consider SGW table depths across time for quantifying effective soil hydraulic properties in the unsaturated zone. In order to verify the DGW data assimilation scheme, numerical experiments comprising synthetic and field validation experiments were conducted. For the numerical studies, the Little Washita (LW) watershed in Oklahoma and Olney (OLN)/Bondville (BOND) sites in Illinois were selected as different hydroclimatic regions. For the synthetic conditions, we tested the DGW scheme using various soil textures and vegetation covers with fixed and dynamically changing SGW table depths across time in homogeneous and heterogeneous (layered) soil columns. The DGW-based soil parameters matched the observations under various synthetic conditions better than those that only consider fixed ground water (FGW) table depths in time. For the field validations, our proposed data assimilation scheme performed well in predicting the soil hydraulic properties and SGW table depths at the point, airborne sensing, and satellite scales, even though uncertainties exist. These findings support the robustness of our proposed DGW approach in application to regional fields. Thus, the DGW scheme could improve the availability and applicability of pixel-scale soil moisture footprints based on satellite platforms.