Sustainable Land Management Research Articles

Pinelands: Impacts of Different Long-Term Land Uses on Soil Physical Properties in Red Ferrosols

Century-long land-use practices have a profound impact on soil physical and chemical properties, with direct implications for soil health and agricultural sustainability. This study aimed to assess the effects of four contrasting land uses—remnant vegetation, pasture, cultivated areas, and loafing areas—on the physical and chemical properties of Red Ferrosols in the Toowoomba region, Queensland, Australia. Soil samples were collected from upper and lower slope positions for each land use. Physical properties, including bulk density, porosity, water retention, and permeability, as well as chemical properties such as organic carbon, nitrogen, phosphorus, and potassium, were analysed. The results showed that remnant vegetation preserved the most favourable soil conditions, with lower bulk density, higher porosity, and greater water retention. Cultivated areas exhibited significant soil degradation, marked by compaction, reduced infiltration, and depleted organic matter. Loafing areas displayed localised nutrient enrichment but higher compaction due to livestock trampling. Pastures maintained intermediate conditions, retaining some beneficial soil characteristics. These findings emphasise the critical need for sustainable land management strategies to protect soil structure and function, supporting the long-term productivity and resilience of Red Ferrosols.

Emerging trends in AI-based soil health assessment: A review

The application of artificial intelligence (AI) in soil health assessment presents significant advancements over conventional methods by enabling more efficient and precise measurements. This review examines and supports how AI monitors soil health and its significance for sustainable land management. AI technologies, including machine learning, remote sensing and big data analytics, enable researchers and practitioners to analyse diverse data sources, model soil-plant relations and predict soil health trends with greater accuracy. AI-integrated soil health monitoring enables tracking of key soil parameters, facilitating efficient nutrient management, soil erosion control and overall ecosystem sustainability. AI-driven precision agriculture helps stakeholders predict the long-term impacts of farming practices, optimize resource use, enhance crop yields and reduce environmental impacts. This review also demonstrates how updated highlights recent research, case studies and best practices that demonstrate how AI-based soil health monitoring contributes to agricultural sustainability, conservation and food security.

How size and resource traits control species' biomass in monoculture and mixture and drive biodiversity–ecosystem functioning relationships

Humans are driving unprecedented environmental change, causing the loss of species from local ecosystems. This local species loss is likely to result in declines in ecosystem functioning but understanding why these so‐called biodiversity‐ecosystem functioning relationships vary is crucial for conservation and sustainable land management. Previous studies have shown that variation among biodiversity–ecosystem functioning (BEF) relationships can be explained by a ‘function‐dominance correlation', i.e. the correlation of species' biomass in monoculture (‘functioning') versus mixtures (‘dominance'). One potential reason for the importance of the function–dominance correlation is its relationship to underlying plant traits. Here, we explore which traits control species' biomass in monoculture and mixture and thereby drive the function–dominance correlation, and hence BEF relationships. To do this, we perform a modeling experiment with six trait‐based models of plant community dynamics and classify model traits as either ‘size' or ‘resource' traits. This approach allows us to better generalize across systems that differ in terms of their key traits and/or how a given trait affects individual performance and ecosystem functioning. We found that size traits, but not resource traits, predicted species' monoculture biomass in five out of the six models. However, in mixture, resource traits became more important and – in addition to size traits – explained substantial variation in species' biomass in four models. In models where size traits were consistently important predictors of biomass variance in monoculture and mixture, the function–dominance correlation was high, and BEF relationships were strongly positive. Our analysis shows how generalizable categories of functional traits allow predicting BEF relationships across systems, and thereby the potential effects of losing species on ecosystem functioning.

Soil erosion vulnerability analysis of Damodar River Basin, India using Revised Universal Soil Loss Equation (RUSLE) in Google Earth Engine (GEE)

Soil erosion is an important environmental issue worldwide. Therefore, data on spatio-temporal patterns of soil erosion and successive soil loss would be of immense significance for the sustainable management of land and water resources. Despite being coal mining-intensive area, Damodar River Basin of India, suffers with the lack of adequate measurements of soil erosion over the entire basin, which hampers the holistic planning and conservation initiative. Therefore, present study employs the Revised Universal Soil Loss Equation (RUSLE) integrated with Geographic Information System (GIS), massive databases and processing capabilities of Google Earth Engine (GEE) and attempts to estimate the soil loss in the entire basin. To estimate soil loss for the years 2017 and 2024 for the study area, RUSLE considers several factors, including the steepness factor (S), crop/cover management component (C), rainfall erosivity factor (R), soil erodibility factor (K), slope length (L), and conservation support practice factor (P). The mean soil loss in the Damodar River Basin is decreased from 12.86 t ha⁻1 yr⁻1 in 2017 to 12.06 t ha⁻1 yr⁻1 in 2024. The present study identifies areas affected by prominent soil loss (> 20 t ha⁻1 yr⁻1) covering 36.47% of the total area in 2017 mostly concentrated on northwestern and central region of the entire basin with extensive mining activities, which slightly declined to 35.07% in 2024. Among all the factors, R factor is the primary reason for such decline which is attributed to the decrease in rainfall in the study area. The findings underscore the urgent need for focused soil conservation measures in the Damodar River Basin, considering its ecological relevance and socioeconomic worth. Due to the limitations in obtaining comprehensive field observations, across such an immense river basin, might affect the accuracy of predictions using the RUSLE model. However, present study estimates soil loss and identify vulnerable zone over two time periods within the expansive and mining-scarred river basin to enhance the reliability of estimations and thus it may contribute to the development of viable strategies for the long-term management of the Damodar River Basin's natural resources.

Potential of solar-induced chlorophyll fluorescence for monitoring long-term dynamics of soil salinity in Central Asia the Xinjiang Region China

IntroductionSoil salinization in Central Asia and Xinjiang, China, poses serious threats to agriculture and ecosystems. Solar-induced chlorophyll fluorescence (SIF), which reflects plant photosynthetic status and stress, shows promise for monitoring salinity but remains underutilized in this region.MethodsThis study integrated SIF-derived indices (SIFI) with soil salinity data to build a region-specific prediction model. Using a random forest algorithm, soil salinity was classified into five levels based on satellite data and ground references from 2000–2020. Model performance, seasonal sensitivity, and spatial variation were analyzed across Central Asian countries and Xinjiang.ResultsSIF effectively detected salinization dynamics, with highest sensitivity in Kazakhstan and Xinjiang. April was identified as the most responsive month, with SIFI1 being the key indicator. The model achieved over 80% accuracy in typical regions and around 70% in atypical regions. Kazakhstan had the largest salt-affected area, followed by Turkmenistan and Xinjiang. Tajikistan showed high variability, while Xinjiang remained relatively stable. Most areas exhibited increasing salinity and expansion of saline lands.DiscussionThese findings demonstrate the potential of SIF-based monitoring for large-scale salinity assessment. The integration of plant physiological signals with machine learning provides a valuable tool for early warning and sustainable land management in arid regions.

Integrated Modeling of Water Table Depth Using Controlled-Source Electromagnetic Sounding (ADMT-200S) and SRTM-Derived Terrain Data: A Case Study of Ile-Ife, Southwestern Nigeria

This study investigated the integrated modeling of water table depth in the Ile-Ife region of Southwestern Nigeria, addressing the critical need for precise groundwater mapping in a rapidly urbanizing environment characterized by water scarcity and reliance on costly private boreholes. The research employed a synergistic approach combining Controlled- Source Electromagnetic Sounding (ADMT-200S) for subsurface resistivity, Shuttle Radar Topographic Mission (SRTM) data for terrain analysis, and interpolated borehole records. Methodologies included Ordinary Kriging interpolation of 20 borehole depths to generate a continuous water table surface, followed by GIS-based overlay analysis to integrate ADMT- 200S resistivity maps (identifying fracture zones and low-resistivity regions, typically <30 ohm-m, indicative of water presence) with SRTM-derived elevation data. Key findings revealed significant spatial variability in water table depths across the study area. Higher elevations generally corresponded to deeper water tables, as demonstrated by the comparison of modeled depths with SRTM topography. Observed depths ranged from 80m to 100m in high overburden areas, for instance, Ede road (20m overburden, 100m depth) and Olugbodo (15m overburden, 80m depth). The integrated model successfully classified high groundwater potential areas where low resistivity overlapped with shallower interpolated depths. This research provides crucial, localized insights into the complex interplay of surface terrain and subsurface hydrogeology, enhancing the predictive accuracy for borehole siting. The findings underscore the potential for such integrated approaches to inform sustainable land and water management practices in Ile-Ife and similar regions to ensure long-term water security.

Wildfire Severity Reduction Through Prescribed Burning in the Southeastern United States

With wildfires becoming more frequent and severe in fire-prone regions affected by warmer and drier climate conditions, reducing hazardous fuels is increasingly recognized as a preventative strategy for promoting sustainability and safeguarding valued resources. Prescribed fire is one of the most cost-effective methods for reducing hazardous fuels and hence wildfire severity, yet empirical research on its effectiveness at minimizing damage to highly valued resources and assets (HVRAs) remains limited. The overarching objective of this study was to evaluate wildfire severity under differing weather conditions across various HVRAs characterized by diverse land uses, vegetation types, and treatment histories. The findings from this study reveal that wildfire severity was generally lower in areas treated with prescribed fire, although the significance of this effect varied among HVRAs and diminished as post-treatment duration increased. The wildland–urban interface experienced the greatest initial reduction in wildfire severity following prescribed fire, but burn severity increased more rapidly over time relative to other HVRAs. Elevated drought conditions had a significant effect, increasing wildfire severity across all HVRAs. The implications of this study underscore the role of prescribed fire in promoting sustainable land management by reducing wildfire severity and safeguarding both natural and built environments, particularly in the expanding wildland–urban interface.

Impacts of introduced sustainable land management (ISLM) practices on crop yield and net farm income of smallholder farmers, Eastern and Southern Ethiopia

Land degradation is a major serious threat to the world’s competence to achieve agricultural productivity and environmental sustainability. It is most severe in developing countries, mainly in Sub-Saharan Africa. To reverse the situation, targeted programs promoting introduced sustainable land management (ISLM) practices have been implemented in eastern and southern Ethiopia. However, there are limited impact assessments on the contributions of adopting ISLM practices on the welfare of farmers. This study investigates the impacts of adopting ISLM practices on crop yield and net farm income in eastern and southern Ethiopia. The data were gathered from 384 randomly selected household heads (190 adopters and 194 non-adopters) using multistage sampling procedures. Descriptive statistics and econometric models were applied to analyze the data. The key ISLM practices adopted in the study area were improved soil bunds, bench terraces, chemical fertilizers, and forage production. The results of the binary logit model revealed that age of household head, education level, livestock size, social group membership, frequency of extension contacts, access to ISLM information, access to credit, land slope, farm-house distance, farmers’ perception of land degradation, and attitude toward ISLM effectiveness significantly affected the probability of adoption of ISLM practices. The propensity score matching (PSM) model showed that ISLM practices adoption has a positive impact on crop yield and net farm income of farmers. On average, it has increased crop yield per hectare and net farm income per year of ISLM practices adopted farmers by 16.8% (418.42 kg) and 19.1% (7604.99 birr), respectively, compared to non-adopter farmers. This study concluded that farmers’ welfare was enhanced by adopted ISLM practices through SLM interventions. Therefore, it is recommended that to increase the adoption of ISLM practices and enhance farmers’ welfare, policymakers and project planners should design SLM interventions based on the identified significant covariates.

Predicting soil chemical characteristics in the arid region of central Iran using remote sensing and machine learning models

Digital Soil Mapping (DSM) techniques have advanced significantly in recent decades, helping to close critical gaps in soil data and knowledge. This study was conducted in the arid Gavkhouni sub-basin of Isfahan Province, central Iran, where environmental stresses such as salinity and water scarcity challenge sustainable land management. We employed 34 environmental covariates derived from Landsat 8 imagery and a digital elevation model, combined with 96 surface soil samples (0 to 20 cm depth), to assess the performance of six machine-learning models: Random Forest (RF), Classification and Regression Tree (CART), Support Vector Regression (SVR), Generalized Additive Model (GAM), Generalized Linear Model (GLM), and an ensemble approach. Unlike many previous studies that have focused on a single soil attribute with a limited set of predictors, our work adopts an integrated approach to map four salinity-related soil properties: Ca, CaCO3, CaSO4, and SO4. Predictor selection involved multicollinearity testing using the Variance Inflation Factor (VIF) and the Boruta algorithm. Model performance was assessed using tenfold cross-validation. The ensemble model performed best, achieving R2 values of 0.89 for Ca, 0.84 for CaCO3, 0.79 for SO4, and 0.73 for CaSO4. Elevation and the Temperature-Vegetation Dryness Index (TVDI) were the most influential predictors for Ca, while the Tasseled Cap Brightness (TCB) and Tasseled Cap Wetness (TCW) indices were most important for CaCO3. For CaSO4, Band 5 (B5) and TCB were the most effective, whereas SO4 predictions were driven by TCB along with Bands 5 and 7. These findings highlight the potential of remote sensing-based DSM to enhance soil monitoring in data-scarce, arid environments. The growing availability of free satellite data, such as Landsat, offers valuable opportunities to improve soil assessment and promote sustainable land management in resource-limited regions like Iran.

Evaluating restoration success: long-term impact of sustainable land management practices in Ethiopia using synthetic control with matrix completion method

Abstract Efforts to combat land degradation globally have led to the widespread promotion of sustainable land management practices (SLMPs) aimed at reducing surface runoff and erosion. Despite their extensive implementation, long-term evaluations of these practices are limited, especially in data-scarce regions. In our study, we assess the long-term impact of large-scale SLMPs in Ethiopia using remotely sensed data from the past 24 years on 122 watersheds. Using a synthetic control method that does not require an explicit control group, we find statistically significant positive effects of SLMPs in both wet and dry seasons. These benefits persist at least eight years beyond the intervention period. Our findings highlight the need for multi-season impact assessments. Focusing only on the wet season may overlook key outcomes in dryland regions, underestimating the effectiveness of large-scale, multi-year projects.
We further find that effects were most positive in drought-prone agricultural highlands, and that some administrative zones appear more effective than others at implementation. Efficient and affordable monitoring of sustainable agricultural water and land management and watershed conservation is crucial for understanding which interventions are effective and can provide opportunities for alternative financing mechanisms.

The impact of biochar application on sponge function, water erosion, and vegetation cover in a Mediterranean vineyard soil.

The impact of biochar application on sponge function, water erosion, and vegetation cover in a Mediterranean vineyard soil.

Integrating Multi-Temporal Remote Sensing and Advanced Drought Modeling to Assess Desertification Dynamics in Semi-Arid Andhra Pradesh, India: A Framework for Sustainable Land Management

Integrating Multi-Temporal Remote Sensing and Advanced Drought Modeling to Assess Desertification Dynamics in Semi-Arid Andhra Pradesh, India: A Framework for Sustainable Land Management

Urban Heat Island phenomenon and the role of urban green spaces in regulating thermal comfort in Bogor City, Indonesia

This study examined the intensification of the Urban Heat Island (UHI) phenomenon in Bogor City, Indonesia, over a ten-year period from 2013 to 2023. Rapid urbanization has led to extensive changes in land cover, primarily the conversion of vegetated areas into built-up zones. This research integrated remote sensing analysis using Landsat 8 OLI/TIRS imagery with field-based measurements of the Temperature Humidity Index (THI) to assess spatial patterns of Land Surface Temperature (LST), vegetation cover (NDVI), and built-up area expansion (NDBI). The results indicated a notable increase in UHI intensity, as reflected in the expansion of high LST zones (29-32 °C) and a reduction in cooler zones (23-26 °C). Built-up areas increased most significantly in Tanah Sareal (11.98%) and West Bogor (8.49%), while vegetation cover declined sharply, especially in North and Central Bogor. Regression analysis showed a strong negative correlation between NDVI and LST (R² = 0.59) and a positive correlation between NDBI and LST (R² = 0.60), confirming the thermal buffering role of vegetation and the heat-amplifying effect of built surfaces. THI measurements indicate widespread thermal discomfort (THI >27 °C) in densely populated urban areas. However, Central Bogor maintains lower LST and THI values, indicating better thermal comfort. These findings highlight the crucial role of urban green infrastructure in mitigating urban heat island (UHI) effects, underscoring the importance of adopting nature-based solutions, such as expanding green spaces and implementing sustainable land management practices, to enhance urban climate resilience.

Mapping previously undetected trees reveals overlooked changes in pan-tropical tree cover

Detecting tree cover is crucial for sustainable land management and climate mitigation. Here we develop an automatic detection algorithm using high-resolution satellite data (<5 m) to map pan-tropical tree cover (2015–2022), enabling identification and change analysis for previously undetected tree cover (PUTC). Our findings reveal that neglecting PUTC represents 17.31 ± 1.78% of the total pan-tropical tree cover. Tree cover net decreased by 61.05 ± 2.36 Mha in both forested areas (63.93%) and non-forested areas (36.07%) between 2015 and 2022. Intense changes in tree cover are primarily observed in regions with PUTC, where the World Cover dataset with a resolution of 10 m often fails to accurately detect tree cover. We also conduct a sensitivity analysis to quantify the contributions f climate factors and anthropogenic impacts (including human activities and land use cover change) to tree cover dynamics. Our findings indicate that 43.98% of tree cover gain is linked to increased precipitation, while 56.03% of tree cover loss is associated with anthropogenic impacts. These findings highlight the need to include undetected tree cover in strategies combating degradation, climate change, and promoting sustainability. Fine-scale mapping can improve biogeochemical cycles modeling and vegetation-climate interactions, improving global change understanding.

The role of sustainable land management practices on enhancing ecosystem services in the highlands of Ethiopia

The role of sustainable land management practices on enhancing ecosystem services in the highlands of Ethiopia

Morphometric Characteristics and Flooding Risk of Zhrzy Valley in the Rania Plain, Northern Iraq

Employing GIS alongside multi-criteria analysis for morphometric and flood risk mapping is an efficient method to pinpoint areas susceptible to flooding. This method helps water resource planners and decision-makers pinpoint specific regions for detailed flood risk assessments using hydrological and hydraulic models, minimizes resource demands while ensuring precision. The Wadi Zhrzy basin is located in the Rania Plain area in the Sulaymaniyah Governorate, northeastern Iraq. All river levels converge into the main valley course, with the basin containing 796 streams across five levels, totaling 952.87 km in length. The number of stream levels increases with the basin area, and variations in valley levels are associated with differences in valley sizes. The rise in secondary valleys correlates with basin size, with variations influenced by the hardness of rock formations and tectonic activity, affecting fluvial erosion and waterway efficiency. The bifurcation ratio, which reflects water discharge control in rivers, is a key drainage network characteristic. A higher bifurcation ratio, such as 4.234 in this case, indicates increased flood risk and discharge, highlighting geological and climatic consistency within the basin. The river frequency is 1.4318 valleys per square kilometer, and the cutting rate is 6.9056, attributed to the extensive basin area and numerous waterways. The drainage density, influenced by climatic factors like rainfall and temperature, is 1.71399, indicating erosion and carving by waterways. The basin's drainage pattern transitions from dendritic to parallel dendritic. The results indicated that the eastern part of the study area, as well as some western regions, are at high risk of flooding. This outcome aligns with precipitation patterns, slope degree, proximity to streams, and the nature of land cover characteristics. The study highlights GIS and multi-criteria analysis as cost-efficient tools for identifying flood-prone areas. In the Wadi Zhrzy basin, significant flood risks are noted, especially in the eastern and some western regions, due to local topography, precipitation, and land cover. Recommendations include targeted hydrological and hydraulic evaluations, sustainable land management, and improved drainage infrastructure to mitigate severe flooding.

Temporal Assessment of Land Use Land Cover (LULC), Land Surface Temperature, and Urban Heat Island Changes in Benin City, Edo State, Nigeria: A Case Study of 2017 and 2023

Land use and land cover (LULC) define how land is shaped by human activities and natural processes. As cities grow, forests shrink, farmlands expand, and concrete landscapes replace green spaces. These changes disrupt environmental balance, influencing land surface temperature (LST) and intensifying the urban heat island (UHI) effect, where cities trap more heat than surrounding rural areas. Between 2017 and 2023, Benin City experienced rapid urban transformation. Tree cover dropped from 82.06% to 70.16%, an 11.9% decline, primarily due to urban expansion and land conversion. Built-up areas grew from 9.49% to 15.29%, while cropland and rangeland expanded by 2.15% and 4.19%, respectively. These shifts fueled rising temperatures, with hightemperature zones (&gt;35°C) increasing by 1.52% and moderate-temperature areas (30-35°C) shrinking by 6.11%. The UHI effect worsened as cooler zones (&lt;- 0.45) decreased by 26.48%, while urban heat accumulation intensified, with moderate and high UHI areas expanding by 14.56% and 11.92%. Unchecked urban growth threatens environmental stability. Reversing these trends requires afforestation programs to restore lost vegetation, stricter urban planning to control expansion, and heat mitigation strategies such as reflective roofing and urban greenery. Sustainable land management and continuous monitoring through remote sensing technologies will help build a more resilient and livable Benin City.

Концепция за устойчиво земеползване в земеделските земи в условията на съвременните екологични изисквания

The present paper examines the concept of sustainable land use in agricultural areas within the context of contemporary environmental requirements. It analyzes the legal framework and environmental standards at global, European, and national levels that influence the planning and management of agricultural resources. Special attention is given to the principles of organic farming, the Natura 2000 network, and the requirements for good agricultural and environmental condition. Based on the ecosystem approach, the paper proposes a structured land-use planning framework that integrates measures for soil, water, and biodiversity conservation. The proposed guidelines aim to support the sustainable management of agricultural land by ensuring a balance between the environmental, economic, and social aspects of land use.

Soil spectral libraries and their role in soil analysis

Soil Spectral Libraries (SSLs) play a crucial role for advancing soil testing by integrating soil spectroscopic tools. This article aimed to examines the development, utilizations and implications of the SSLs in soil science studies. The SSLs offer rapid and cost-effective quantitative estimation of different soil properties such as soil pH, salinity, nutrients, texture, organic carbon and others which is essential for environmental monitoring and precision agriculture. Furthermore, this article highlights the potentialities of the visible/near-infrared (Vis-NIR) soil spectroscopy for creating robust prediction models and demonstrating the necessity for large, variable reference datasets of the soil samples and their corresponding spectral reflectance data to enhance the applied prediction models’ accuracy. Additionally, Open Soil Spectral Library (OSSL) aims to provide an access to the soil data and engaging the external communities in the soil data collection. There are many advantages of using the SSLs, but there are some challenges especially in predicting certain soil properties accurately; and the factors related to the used prediction models and soil types. There is a necessity for creating the SSLs in India due to its importance in achieving better soil monitoring, planning and management. The prospects for SSLs are promising whereas the applicability of the machine learning models for better estimation of soil properties can be enhanced globally through collaborative efforts and increased accessibility for stakeholders in developing regions. A key limitation of this study is that the accuracy of SSLs in predicting certain soil properties can be affected by the variability in soil types and the choice of prediction models, which may limit the generalizability of the results across diverse Indian soils. Thus, this review article comprehensive overview underscores the transformative potential of SSLs in soil analysis and their critical role in sustainable land management practices.

Evaluating theimpactof land use and land cover change on soil moisture variability usingGIS and remote sensing technology in southwestern Ethiopia.

Soil moisture dynamics are critical for agriculture, water resources, and climate resilience in Ethiopia, influencing crop yields, water availability, and ecosystem health. Assessing soil moisture variability in relation to land use and cover(LULC) changes is essential for effective ecosystem conservation and climate change adaptation, ensuring sustainable development and resilience to environmental challenges. This study examines the impact of LULC changes on soil moisture variability in Southwestern Ethiopia over 30years (1994-2024). Utilizing Landsat Thematic Mapper (1994), Enhanced Thematic Mapper Plus (2004), and Operational Land Imager/Thermal Infrared Sensor (2024) data, LULC changes were analyzed using supervised classification approach with the Maximum Likelihood Algorithm. The classification process was conducted using ERDAS Imagine 2015 software. In addition, vegetation health and soil moisture dynamics were assessed through the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Moisture Index (NDMI). Results reveal a significant increase in cultivated land from 3595.2 km2 (42.0%) to 6183.3 km2 (72.2%), with a corresponding decrease in forest cover from 3119.2 km2 (36.4%) to 2030.4 km2 (23.7%).This significant shift indicates intensive agricultural expansion at the expense of forest cover, highlighting increased land conversion pressures. NDVI values dropped from 0.71 in 1994 to 0.52 in 2024, this decline in NDVI values signifies a substantial reduction in vegetation cover and density over the 30-year period. The NDMI results indicated a decrease in peak moisture levels and average soil moisture, emphasizing a growing trend of intensified dryness. This soil moisture decline is attributed to factors such as reduced precipitation, increased evaporation, and changes in LULC. The conversion of forest land to cultivation led to a significant decrease in NDMI values, reflecting a reduction in soil moisture due to the loss of forest cover and increased evapotranspiration from agricultural activities. A strong correlation (R2 = 0.98) between NDVI and NDMI highlights that higher vegetation cover is associated with higher soil moisture. Therefore, the study highlights the profound impact of land use and land cover changes on soil moisture, underscoring the urgent need for sustainable land management practices. These practices are critical to combat environmental degradation, improve soil moisture retention, and bolster ecosystem resilience, ensuring a sustainable and climate-resilient future.