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August 7, 2026European Journal of Remote SensingOpen Access

Prediction of soil organic carbon and nitrogen: a WMTR-CatBoost model based on multi-feature fusion

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Authors

JZJing ZhangNHNanzhou HouHYHongwei Yang

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Overview

Randomized trial demonstrates improved predictions of soil nutrients in agriculture, indicating potential for better management.

Key Points

  • The aim is to enhance soil nutrient prediction by integrating diverse features and improving model accuracy.
  • Developed a weighted multi-target regression CatBoost model using multi-feature fusion techniques.
  • Combined spectral, land classification, and remote sensing features from the LUCAS 2015 dataset.
  • Evaluated model performance through coefficients of determination (R2) for soil organic carbon and nitrogen.
  • Achieved R2 values of 0.945 for soil organic carbon and 0.923 for nitrogen.
  • Showed relative improvements of 2.5% for SOC and 4.6% for N compared to baseline models.
  • Gains of 1%–3.3% for SOC and 1%–8.1% for N against single-target regression models.

Cite This Study

Zhang et al. (2026) studied this question.

synapsesocial.com/papers/6a75abca847ab6d26c020708https://doi.org/10.1080/22797254.2026.2711851
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