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Evaluating Modified Soil Erodibility Factors with the Aid of Pedotransfer Functions and Dynamic Remote-Sensing Data for Soil Health Management

Author

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  • Pooja Preetha

    (Department of Mechanical & Civil Engineering and Construction Management, College of Engineering, Technology, and Physical Sciences, Alabama A&M University, Huntsville, AL 35811, USA)

  • Naveen Joseph

    (Department of Geospatial Science, Artis College of Science and Technology, Radford University, Radford, VA 24142, USA)

Abstract

Soil erosion is a critical factor impacting soil health and agricultural productivity, with soil erodibility often quantified using the K-factor in erosion models such as the universal soil loss equation (USLE). Traditional K-factor estimation lacks spatiotemporal precision, particularly under varying soil moisture and land cover conditions. This study introduces modified K-factor pedotransfer functions (Kmlr) integrating dynamic remotely sensed data on land use land cover to enhance K-factor accuracy for diverse soil health management applications. The Kmlr functions from multiple approaches, including dynamic crop and cover management factor (Cdynamic), high resolution satellite data, and downscaled remotely sensed data, were evaluated across spatial and temporal scales within the Fish River watershed in Alabama, a coastal watershed with significant soil–water interactions. The results highlighted that the Kmlr model provided more accurate sediment yield (SY) predictions, particularly in agricultural areas, where traditional models overestimated erosion by upto 59.23 ton/ha. SY analysis across the 36 hydrological response units (HRUs) in the watershed showed that the Kmlr model captured more accurate soil loss estimates, especially in regions with varying land use. The modified K-factor model (Kmlr-c) using Cdynamic and high-resolution soil surface moisture data outperformed the traditional USLE K-factors in predicting SY, with a strong correlation to observed SY data (R² = 0.980 versus R² = 0.911). The total sediment yield predicted by Kmlr-c (525.11 ton/ha) was notably lower than that of USLE-based estimates (828.62 ton/ha), highlighting the overestimation in conventional models. The identification of erosive hotspots revealed that 6003 ha of land was at high erosion risk (K-factor > 0.25), with an average soil loss of 24.2 ton/ha. The categorization of erosive hotspots highlighted critical areas at high risk for erosion, underscoring the need for targeted soil conservation practices. This research underscores the improvement of remotely sensed data-based models and perfects them for the application of soil erodibility assessments thus promoting the development of such models.

Suggested Citation

  • Pooja Preetha & Naveen Joseph, 2025. "Evaluating Modified Soil Erodibility Factors with the Aid of Pedotransfer Functions and Dynamic Remote-Sensing Data for Soil Health Management," Land, MDPI, vol. 14(3), pages 1-22, March.
    • Handle: RePEc:gam:jlands:v:14:y:2025:i:3:p:657-:d:1616387
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    References listed on IDEAS

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    1. Tesfa Gebrie Andualem & Guna A. Hewa & Baden R. Myers & Stefan Peters & John Boland, 2023. "Erosion and Sediment Transport Modeling: A Systematic Review," Land, MDPI, vol. 12(7), pages 1-20, July.
    2. Mukhtar Iderawumi Abdulraheem & Wei Zhang & Shixin Li & Ata Jahangir Moshayedi & Aitazaz A. Farooque & Jiandong Hu, 2023. "Advancement of Remote Sensing for Soil Measurements and Applications: A Comprehensive Review," Sustainability, MDPI, vol. 15(21), pages 1-32, October.
    3. Bor-Shiun Lin & Chun-Kai Chen & Kent Thomas & Chen-Kun Hsu & Hsing-Chuan Ho, 2019. "Improvement of the K-Factor of USLE and Soil Erosion Estimation in Shihmen Reservoir Watershed," Sustainability, MDPI, vol. 11(2), pages 1-16, January.
    4. Pooja Preetha & Mahbub Hasan, 2023. "Scrutinizing the Hydrological Responses of Chennai, India Using Coupled SWAT-FEM Model under Land Use Land Cover and Climate Change Scenarios," Land, MDPI, vol. 12(5), pages 1-21, April.
    5. Jiaqi Wang & Jiuchun Yang & Zhi Li & Liwei Ke & Qingyao Li & Jianwei Fan & Xue Wang, 2024. "Research on Soil Erosion Based on Remote Sensing Technology: A Review," Agriculture, MDPI, vol. 15(1), pages 1-31, December.
    6. Rhavel Salviano Dias Paulista & Frederico Terra de Almeida & Adilson Pacheco de Souza & Aaron Kinyu Hoshide & Daniel Carneiro de Abreu & Jaime Wendeley da Silva Araujo & Charles Campoe Martim, 2023. "Estimating Suspended Sediment Concentration Using Remote Sensing for the Teles Pires River, Brazil," Sustainability, MDPI, vol. 15(9), pages 1-22, April.
    7. Sarita Gajbhiye Meshram & Vijay P. Singh & Ozgur Kisi & Vahid Karimi & Chandrashekhar Meshram, 2020. "Application of Artificial Neural Networks, Support Vector Machine and Multiple Model-ANN to Sediment Yield Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(15), pages 4561-4575, December.
    8. Pooja P. Preetha & Naveen Joseph & Balaji Narasimhan, 2021. "Quantifying Surface Water and Ground Water Interactions using a Coupled SWAT_FEM Model: Implications of Management Practices on Hydrological Processes in Irrigated River Basins," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(9), pages 2781-2797, July.
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