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Assessment of Organic Matter Content of Winter Wheat Inter-Row Topsoil Based on Airborne Hyperspectral Imaging

Author

Listed:
  • Jiachen He

    (National Chengdu Agricultural Science and Technology Center, Chengdu 610213, China
    Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu 610213, China)

  • Wei Ma

    (Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu 610213, China)

  • Jing He

    (Faculty of Geography and Planning, Chengdu University of Technology, Chengdu 610059, China)

Abstract

Soil organic matter (SOM) is an essential factor affecting the growth and development of crops, so the establishment of an efficient and rapid method for detecting SOM content is of great significance for crop cultivation and management. The spatial distribution map of SOM content in the study area was obtained by using the optimal model, and a distribution map of aboveground wheat biomass under different fertilization conditions was drawn. The results of this study showed that the fertilization treatments significantly increased the SOM content, and its spatial distribution showed obvious heterogeneity. By plotting the spatial distribution of SOM content and wheat growth under different fertilization conditions, it was found that the wheat biomass of fertilized fields was significantly higher than that of non-fertilized fields. Further analysis showed that there was a significant positive correlation between SOM content and wheat biomass, and a quantitative model between the two was established. This study provides scientific evidence and technical support for soil nutrient management and crop productivity enhancement in precision agriculture, as well as a reference for the application of hyperspectral imagery in agroecosystem monitoring.

Suggested Citation

  • Jiachen He & Wei Ma & Jing He, 2025. "Assessment of Organic Matter Content of Winter Wheat Inter-Row Topsoil Based on Airborne Hyperspectral Imaging," Sustainability, MDPI, vol. 17(11), pages 1-24, June.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:11:p:5160-:d:1671677
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    References listed on IDEAS

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    2. Du, Ruiqi & Xiang, Youzhen & Zhang, Fucang & Chen, Junying & Shi, Hongzhao & Liu, Hao & Yang, Xiaofei & Yang, Ning & Yang, Xizhen & Wang, Tianyang & Wu, Yuxiao, 2024. "Combing transfer learning with the OPtical TRApezoid Model (OPTRAM) to diagnosis small-scale field soil moisture from hyperspectral data," Agricultural Water Management, Elsevier, vol. 298(C).
    3. Ziwei Mei & Zhentao Shi, 2022. "On LASSO for High Dimensional Predictive Regression," Papers 2212.07052, arXiv.org, revised Jan 2024.
    4. Mei, Ziwei & Shi, Zhentao, 2024. "On LASSO for high dimensional predictive regression," Journal of Econometrics, Elsevier, vol. 242(2).
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