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Research on Inversion Model of Cultivated Soil Moisture Content Based on Hyperspectral Imaging Analysis

Author

Listed:
  • Tinghui Wu

    (College of Engineering, Nanjing Agricultural University, Nanjing 210031, China)

  • Jian Yu

    (College of Engineering, Nanjing Agricultural University, Nanjing 210031, China)

  • Jingxia Lu

    (College of Engineering, Nanjing Agricultural University, Nanjing 210031, China)

  • Xiuguo Zou

    (College of Engineering, Nanjing Agricultural University, Nanjing 210031, China
    Jiangsu Province Engineering Laboratory of Modern Facility Agriculture Technology and Equipment, Nanjing 210031, China)

  • Wentian Zhang

    (Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney NSW 2007, Australia)

Abstract

Based on hyperspectral imaging technology, rapid and efficient prediction of soil moisture content (SMC) can provide an essential basis for the formulation of precise agricultural programs (e.g., forestry irrigation and environmental management). To build an efficient inversion model of SMC, this paper collected 117 cultivated soil samples from the Chair Hill area and tested them using the GaiaSorter hyperspectral sorter. The collected soil reflectance dataset was preprocessed by wavelet transform, before the combination of competitive adaptive reweighted sampling algorithm and successive projections algorithm (CARS-SPA) was used to select the bands optimally. Seven wavelengths of 695, 711, 736, 747, 767, 778, and 796 nm were selected and used as the factors of the SMC inversion model. The popular linear regression algorithm was employed to construct this model. The result indicated that the inversion model established by the multiple linear regression algorithm (the predicted R 2 was 0.83 and the RMSE was 0.0078) was feasible and highly accurate, indicating it could play an important role in predicting SMC of cultivated soils over a large area for agricultural irrigation and remote monitoring of crop yields.

Suggested Citation

  • Tinghui Wu & Jian Yu & Jingxia Lu & Xiuguo Zou & Wentian Zhang, 2020. "Research on Inversion Model of Cultivated Soil Moisture Content Based on Hyperspectral Imaging Analysis," Agriculture, MDPI, vol. 10(7), pages 1-14, July.
    • Handle: RePEc:gam:jagris:v:10:y:2020:i:7:p:292-:d:383819
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    References listed on IDEAS

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    1. Benyoh Emmanuel Kigha Nsafon & Sang-Chul Lee & Jeung-Soo Huh, 2020. "Responses of Yield and Protein Composition of Wheat to Climate Change," Agriculture, MDPI, vol. 10(3), pages 1-13, March.
    2. Guopeng Jiang & Miles Grafton & Diane Pearson & Mike Bretherton & Allister Holmes, 2019. "Integration of Precision Farming Data and Spatial Statistical Modelling to Interpret Field-Scale Maize Productivity," Agriculture, MDPI, vol. 9(11), pages 1-22, November.
    3. Luca Stevanato & Gabriele Baroni & Yafit Cohen & Cristiano Lino Fontana & Simone Gatto & Marcello Lunardon & Francesco Marinello & Sandra Moretto & Luca Morselli, 2019. "A Novel Cosmic-Ray Neutron Sensor for Soil Moisture Estimation over Large Areas," Agriculture, MDPI, vol. 9(9), pages 1-14, September.
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    Citations

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    Cited by:

    1. Xueqin Jiang & Shanjun Luo & Qin Ye & Xican Li & Weihua Jiao, 2022. "Hyperspectral Estimates of Soil Moisture Content Incorporating Harmonic Indicators and Machine Learning," Agriculture, MDPI, vol. 12(8), pages 1-17, August.
    2. Haiming Yu & Yuhui Hu & Lianxing Qi & Kai Zhang & Jiwen Jiang & Haiyuan Li & Xinyue Zhang & Zihan Zhang, 2023. "Hyperspectral Detection of Moisture Content in Rice Straw Nutrient Bowl Trays Based on PSO-SVR," Sustainability, MDPI, vol. 15(11), pages 1-20, May.
    3. Kuifeng Luan & Hui Li & Jie Wang & Chunmei Gao & Yujia Pan & Weidong Zhu & Hang Xu & Zhenge Qiu & Cheng Qiu, 2022. "Quantitative Inversion Method of Surface Suspended Sand Concentration in Yangtze Estuary Based on Selected Hyperspectral Remote Sensing Bands," Sustainability, MDPI, vol. 14(20), pages 1-22, October.
    4. Wei Zhang & Zhijun Li & Yang Pu & Yunteng Zhang & Zijun Tang & Junyu Fu & Wenjie Xu & Youzhen Xiang & Fucang Zhang, 2023. "Estimation of the Leaf Area Index of Winter Rapeseed Based on Hyperspectral and Machine Learning," Sustainability, MDPI, vol. 15(17), pages 1-13, August.

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