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Soil salinity prediction using hybrid machine learning and remote sensing in Ben Tre province on Vietnam’s Mekong River Delta

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Abstract

Soil salinization is considered one of the disasters that have significant effects on agricultural activities in many parts of the world, particularly in the context of climate change and sea level rise. This problem has become increasingly essential and severe in the Mekong River Delta of Vietnam. Therefore, soil salinity monitoring and assessment are critical to building appropriate strategies to develop agricultural activities. This study aims to develop a low-cost method based on machine learning and remote sensing to map soil salinity in Ben Tre province, which is located in Vietnam’s Mekong River Delta. This objective was achieved by using six machine learning algorithms, including Xgboost (XGR), sparrow search algorithm (SSA), bird swarm algorithm (BSA), moth search algorithm (MSA), Harris hawk optimization (HHO), grasshopper optimization algorithm (GOA), particle swarm optimization algorithm (PSO), and 43 factors extracted from remote sensing images. Various indices were used, namely, root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2) to estimate the efficiency of the prediction models. The results show that six optimization algorithms successfully improved XGR model performance with an R2 value of more than 0.98. Among the proposed models, the XGR-HHO model was better than the other models with a value of R2 of 0.99 and a value of RMSE of 0.051, by XGR-GOA (R2 = 0.931, RMSE = 0.055), XGR-MSA (R2 = 0.928, RMSE = 0.06), XGR-BSA (R2 = 0.926, RMSE = 0.062), XGR-SSA (R2 = 0.917, 0.07), XGR-PSO (R2 = 0.916, RMSE = 0.08), XGR (R2 = 0.867, RMSE = 0.1), CatBoost (R2 = 0.78, RMSE = 0.12), and RF (R2 = 0.75, RMSE = 0.19), respectively. These proposed models have surpassed the reference models (CatBoost and random forest). The results indicated that the soils in the eastern areas of Ben Tre province are more saline than in the western areas. The results of this study highlighted the effectiveness of using hybrid machine learning and remote sensing in soil salinity monitoring. The finding of this study provides essential tools to support farmers and policymakers in selecting appropriate crop types in the context of climate change to ensure food security.

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Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request

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Authors and Affiliations

  1. Faculty of Geography, VNU University of Science, Vietnam National University, Hanoi, Vietnam

    Huu Duy Nguyen, Quoc-Huy Nguyen & Quang-Thanh Bui

  2. Thuyloi University, 175 Tay Son, Dong Da, Hanoi, Vietnam

    Chien Pham Van

  3. Faculty of Hydrology, Meteorology and Oceanography, VNU University of Science, Vietnam National University, 334 Nguyen Trai, Thanh Xuan District, Hanoi, Vietnam

    Tien Giang Nguyen & Dinh Kha Dang

  4. Center for Environmental Fluid Dynamics, VNU University of Science, Vietnam National University, 334 Nguyen Trai, Thanh Xuan District, Hanoi, Vietnam

    Thi Thuy Nga Pham

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Huu Duy Nguyen, Tien Giang Nguyen, and Quang Thanh Bui: conceptualization and methodology. Huu Duy Nguyen, Tien Giang Nguyen, Quang Thanh Bui, Dinh Kha Dang, Thi Thuy Nga Pham, Van Chien Nguyen, and Quoc Huy Nguyen: methodology, material preparation, validation, analysis, writing the original draft, reviewing and editing, and writing the review and editing. Huu Duy Nguyen and Quoc Huy Nguyen: data collection. All authors read and approved the final manuscript.

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Nguyen, H.D., Van, C.P., Nguyen, T.G. et al. Soil salinity prediction using hybrid machine learning and remote sensing in Ben Tre province on Vietnam’s Mekong River Delta. Environ Sci Pollut Res 30, 74340–74357 (2023). https://doi.org/10.1007/s11356-023-27516-x

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