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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

An Integrated Remote Sensing and Machine Learning Approach to Assess the Impact of Soil Salinity on Rice Yield in Northeastern Thailand

by
Jurawan Nontapon
1,
Neti Srihanu
2,
Niwat Bhumiphan
3,
Nopanom Kaewhanam
1,
Anongrit Kangrang
1,
Umesh Bhurtyal
4,
Niraj KC
5,
Siwa Kaewplang
1,* and
Alfredo Huete
6
1
Faculty of Engineering, Mahasarakham University, Kantharawichai District, Maha Sarakham 44150, Thailand
2
Faculty of Engineering, Northeastern University, Khon Kaen 40000, Thailand
3
Faculty of Technology and Engineering, Udon Thani Rajabhat University, Sam Phrao Subdistrict, Mueang District, Udon Thani 41000, Thailand
4
Department of Geomatics Engineering, Pashchimanchal Campus, Tribhuvan University, Kaski 33700, Nepal
5
Institute of Engineering and IT, Lumbini Technological University (LTU), Nepalgunj 21900, Nepal
6
School of Life Sciences, University of Technology Sydney, Sydney, NSW 2007, Australia
*
Author to whom correspondence should be addressed.
Geomatics 2025, 5(4), 80; https://doi.org/10.3390/geomatics5040080 (registering DOI)
Submission received: 6 September 2025 / Revised: 23 November 2025 / Accepted: 9 December 2025 / Published: 13 December 2025
Versions Notes

Abstract

The Northeast region of Thailand covers approximately 16.89 million hectares, with about 6.17 million hectares of seasonal rice cultivation and 2.85 million hectares affected by soil salinity—a major constraint to agricultural productivity in this region. This study develops an integrated data fusion framework combining multi-temporal Landsat-8 and Sentinel-2 imagery to train machine learning (ML) models for the prediction of rice yield and soil salinity, allowing for an analysis of their relationship. The field data comprised 380 rice yield and 625 soil electrical conductivity (EC) samples collected in 2023. Three ML models—Random Forest (RF), Classification and Regression Trees (CART), and Support Vector Regression (SVR)—were applied for variable reduction and optimal predictor selection. RF achieved the highest accuracy for yield prediction (R2 = 0.86, RMSE = 0.19 t ha−1) and salinity estimation (R2 = 0.93, RMSE = 0.87 dS/m) when using fused Landsat–Sentinel data. Spatial analysis of 5000 matched points showed a strong negative relationship between seedling stage EC and yield (R2 = 0.71), with yields declining sharply above 5 dS/m and remaining below 1.5 t ha−1 beyond 15 dS/m. These results demonstrate the potential of multi-sensor fusion and ensemble ML approaches for precise soil salinity monitoring and sustainable rice production.
Keywords: remote sensing; multi-sensor integration; precision agriculture; saline soils; Northeast Thailand remote sensing; multi-sensor integration; precision agriculture; saline soils; Northeast Thailand

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MDPI and ACS Style

Nontapon, J.; Srihanu, N.; Bhumiphan, N.; Kaewhanam, N.; Kangrang, A.; Bhurtyal, U.; KC, N.; Kaewplang, S.; Huete, A. An Integrated Remote Sensing and Machine Learning Approach to Assess the Impact of Soil Salinity on Rice Yield in Northeastern Thailand. Geomatics 2025, 5, 80. https://doi.org/10.3390/geomatics5040080

AMA Style

Nontapon J, Srihanu N, Bhumiphan N, Kaewhanam N, Kangrang A, Bhurtyal U, KC N, Kaewplang S, Huete A. An Integrated Remote Sensing and Machine Learning Approach to Assess the Impact of Soil Salinity on Rice Yield in Northeastern Thailand. Geomatics. 2025; 5(4):80. https://doi.org/10.3390/geomatics5040080

Chicago/Turabian Style

Nontapon, Jurawan, Neti Srihanu, Niwat Bhumiphan, Nopanom Kaewhanam, Anongrit Kangrang, Umesh Bhurtyal, Niraj KC, Siwa Kaewplang, and Alfredo Huete. 2025. "An Integrated Remote Sensing and Machine Learning Approach to Assess the Impact of Soil Salinity on Rice Yield in Northeastern Thailand" Geomatics 5, no. 4: 80. https://doi.org/10.3390/geomatics5040080

APA Style

Nontapon, J., Srihanu, N., Bhumiphan, N., Kaewhanam, N., Kangrang, A., Bhurtyal, U., KC, N., Kaewplang, S., & Huete, A. (2025). An Integrated Remote Sensing and Machine Learning Approach to Assess the Impact of Soil Salinity on Rice Yield in Northeastern Thailand. Geomatics, 5(4), 80. https://doi.org/10.3390/geomatics5040080

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