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Mapping Cropland Soil Nutrients Contents Based on Multi-Spectral Remote Sensing and Machine Learning

Author

Listed:
  • Wenjie Zhang

    (College of Geomatics Science and Technology, Nanjing Tech University, Nanjing 211816, China)

  • Liang Zhu

    (State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China)

  • Qifeng Zhuang

    (College of Geomatics Science and Technology, Nanjing Tech University, Nanjing 211816, China)

  • Dong Chen

    (College of Geomatics Science and Technology, Nanjing Tech University, Nanjing 211816, China)

  • Tao Sun

    (College of Geomatics Science and Technology, Nanjing Tech University, Nanjing 211816, China)

Abstract

Nitrogen (N) and phosphorus (P) are primary indicators of soil nutrients in agriculture. Accurate management of these nutrients is essential for ensuring food security. High-resolution, multi-spectral remote sensing images can provide crucial information for mapping soil nutrients at the field scale. This study compares the capabilities of ZH-1 and Sentinel-2 satellite data, along with different spectral indices, in mapping soil nutrients (total N and Olsen-P) using two machine learning algorithms, random forest (RF) and XGBoost (XGB). Two agricultural fields in Suihua City were selected as the study areas for this investigation. The results showed that Sentinel-2 data performed best in computing the total N content in soil using the RF model ( R 2 = 0.74, RMSE = 0.10 g/kg). However, for the soil Olsen-P content, the XGBoost model performed better with ZH-1 data ( R 2 = 0.75, RMSE = 9.79 mg/kg) than the RF model. This study demonstrates that both ZH-1 and Sentinel-2 satellite data perform well in terms of accurately mapping soil total N and Olsen-P contents using machine learning. Due to its higher spectral and spatial resolution, ZH-1 remote sensing data provides more detailed information on soil nutrient content during Olsen-P inversion and exhibits comparable accuracy.

Suggested Citation

  • Wenjie Zhang & Liang Zhu & Qifeng Zhuang & Dong Chen & Tao Sun, 2023. "Mapping Cropland Soil Nutrients Contents Based on Multi-Spectral Remote Sensing and Machine Learning," Agriculture, MDPI, vol. 13(8), pages 1-19, August.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:8:p:1592-:d:1215189
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    References listed on IDEAS

    as
    1. Lin Zhang & Zhe Liu & Diyou Liu & Quan Xiong & Ning Yang & Tianwei Ren & Chao Zhang & Xiaodong Zhang & Shaoming Li, 2019. "Crop Mapping Based on Historical Samples and New Training Samples Generation in Heilongjiang Province, China," Sustainability, MDPI, vol. 11(18), pages 1-17, September.
    2. Kingsley JOHN & Isong Abraham Isong & Ndiye Michael Kebonye & Esther Okon Ayito & Prince Chapman Agyeman & Sunday Marcus Afu, 2020. "Using Machine Learning Algorithms to Estimate Soil Organic Carbon Variability with Environmental Variables and Soil Nutrient Indicators in an Alluvial Soil," Land, MDPI, vol. 9(12), pages 1-20, December.
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    Cited by:

    1. Xiantao He & Jinting Zhu & Pinxuan Li & Dongxing Zhang & Li Yang & Tao Cui & Kailiang Zhang & Xiaolong Lin, 2024. "Research on a Multi-Lens Multispectral Camera for Identifying Haploid Maize Seeds," Agriculture, MDPI, vol. 14(6), pages 1-12, May.

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