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Modeling the Spatial Distribution of Soil Nitrogen Content at Smallholder Maize Farms Using Machine Learning Regression and Sentinel-2 Data

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  • Zinhle Mashaba-Munghemezulu

    (Department of Geography, Geoinformatics and Meteorology, University of Pretoria, Pretoria 0028, South Africa
    Geoinformation Science Division, Agricultural Research Council, Natural Resources and Engineering, Pretoria 0001, South Africa)

  • George Johannes Chirima

    (Department of Geography, Geoinformatics and Meteorology, University of Pretoria, Pretoria 0028, South Africa
    Geoinformation Science Division, Agricultural Research Council, Natural Resources and Engineering, Pretoria 0001, South Africa)

  • Cilence Munghemezulu

    (Geoinformation Science Division, Agricultural Research Council, Natural Resources and Engineering, Pretoria 0001, South Africa)

Abstract

Nitrogen is one of the key nutrients that indicate soil quality and an important component for plant development. Accurate knowledge and management of soil nitrogen is crucial for food security in rural communities, especially for smallholder maize farms. However, less research has been done on generating digital soil nitrogen maps for these farmers. This study examines the utility of Sentinel-2 satellite data and environmental variables to map soil nitrogen at smallholder maize farms. Three machine learning algorithms—random forest (RF), gradient boosting (GB), and extreme gradient boosting (XG) were investigated for this purpose. The findings indicate that the RF (R 2 = 0.90, RMSE = 0.0076%) model performs slightly better than the GB (R 2 = 0.88, RMSE = 0.0083%) and XG (R 2 = 0.89, RMSE = 0.0077%) models. Furthermore, the variable importance measure showed that the Sentinel-2 bands, particularly the red and red-edge bands, have a superior performance in comparison to the environmental variables and soil indices. The digital maps generated in this study show the high capability of Sentinel-2 satellite data to generate accurate nitrogen content maps with the application of machine learning. The developed framework can be implemented to map the spatial pattern of soil nitrogen. This will also contribute to soil fertility interventions and nitrogen fertilization management to improve food security in rural communities. This application contributes to Sustainable Development Goal number 2.

Suggested Citation

  • Zinhle Mashaba-Munghemezulu & George Johannes Chirima & Cilence Munghemezulu, 2021. "Modeling the Spatial Distribution of Soil Nitrogen Content at Smallholder Maize Farms Using Machine Learning Regression and Sentinel-2 Data," Sustainability, MDPI, vol. 13(21), pages 1-21, October.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:21:p:11591-:d:660676
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    References listed on IDEAS

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    1. P. M. Lerman, 1980. "Fitting Segmented Regression Models by Grid Search," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(1), pages 77-84, March.
    2. Gerald Forkuor & Ozias K L Hounkpatin & Gerhard Welp & Michael Thiel, 2017. "High Resolution Mapping of Soil Properties Using Remote Sensing Variables in South-Western Burkina Faso: A Comparison of Machine Learning and Multiple Linear Regression Models," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-21, January.
    3. Magali J. López-Calderón & Juan Estrada-Ávalos & Víctor M. Rodríguez-Moreno & Jorge E. Mauricio-Ruvalcaba & Aldo R. Martínez-Sifuentes & Gerardo Delgado-Ramírez & Enrique Miguel-Valle, 2020. "Estimation of Total Nitrogen Content in Forage Maize ( Zea mays L.) Using Spectral Indices: Analysis by Random Forest," Agriculture, MDPI, vol. 10(10), pages 1-15, October.
    4. Jones, Peter G. & Thornton, Philip K., 2015. "Representative soil profiles for the Harmonized World Soil Database at different spatial resolutions for agricultural modelling applications," Agricultural Systems, Elsevier, vol. 139(C), pages 93-99.
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