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Estimation of Cultivated Land Quality Based on Soil Hyperspectral Data

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  • Chenjie Lin

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
    Guangdong Province Engineering Research Center for Land Information Technology, South China Agricultural University, Guangzhou 510642, China)

  • Yueming Hu

    (Guangdong Province Engineering Research Center for Land Information Technology, South China Agricultural University, Guangzhou 510642, China
    College of Tropical Crops, Hainan University, Haikou 570228, China
    Guangzhou South China Academy of Science and Technology of Natural Resources, South China Agricultural University, Guangzhou 510642, China)

  • Zhenhua Liu

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
    Guangdong Province Engineering Research Center for Land Information Technology, South China Agricultural University, Guangzhou 510642, China)

  • Yiping Peng

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
    Guangdong Province Engineering Research Center for Land Information Technology, South China Agricultural University, Guangzhou 510642, China)

  • Lu Wang

    (Guangdong Province Engineering Research Center for Land Information Technology, South China Agricultural University, Guangzhou 510642, China
    College of Tropical Crops, Hainan University, Haikou 570228, China
    Guangzhou South China Academy of Science and Technology of Natural Resources, South China Agricultural University, Guangzhou 510642, China)

  • Dailiang Peng

    (Aerospace Information Research Institute, China Academy of Sciences, Beijing 100094, China)

Abstract

Efficient monitoring of cultivated land quality (CLQ) plays a significant role in cultivated land protection. Soil spectral data can reflect the state of cultivated land. However, most studies have used crop spectral information to estimate CLQ, and there is little research on using soil spectral data for this purpose. In this study, soil hyperspectral data were utilized for the first time to evaluate CLQ. We obtained the optimal spectral variables from dry soil spectral data using a gradient boosting decision tree (GBDT) algorithm combined with the variance inflation factor (VIF). Two estimation algorithms (partial least-squares regression (PLSR) and back-propagation neural network (BPNN)) with 10-fold cross-validation were employed to develop the relationship model between the optimal spectral variables and CLQ. The optimal algorithms were determined by the degree of fit (determination coefficient, R 2 ). In order to estimate CLQ at the regional scale, HuanJing-1A Hyperspectral Imager (HJ-1A HSI) data were transformed into dry soil spectral data using the linkage model of original soil spectral reflectance to dry soil spectral reflectance. This study was conducted in the Guangdong Province, China and the Conghua district within the same province. The results showed the following: (1) the optimal spectral variables selected from the dry soil spectral variables were 478 nm, 502 nm, 614 nm, 872 nm, 966 nm, 1007 nm, and 1796 nm. (2) The BPNN was the optimal model, with an R 2 (C) of 0.71 and a normalized root mean square error (NRMSE) of 12.20%. (3) The results showed the R 2 of the regional-scale CLQ estimation based on the proposed method was 0.05 higher, and the NRMSE was 0.92% lower than that of the CLQ map obtained using the traditional method. Additionally, the NRMSE of the regional-scale CLQ estimation base on dry soil spectral variables from HJ-1A HSI data was 2.00% lower than that of the model base on the original HJ-1A HSI data.

Suggested Citation

  • Chenjie Lin & Yueming Hu & Zhenhua Liu & Yiping Peng & Lu Wang & Dailiang Peng, 2022. "Estimation of Cultivated Land Quality Based on Soil Hyperspectral Data," Agriculture, MDPI, vol. 12(1), pages 1-13, January.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:1:p:93-:d:722213
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    References listed on IDEAS

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    1. Hualin Xie & Jinlang Zou & Hailing Jiang & Ning Zhang & Yongrok Choi, 2014. "Spatiotemporal Pattern and Driving Forces of Arable Land-Use Intensity in China: Toward Sustainable Land Management Using Emergy Analysis," Sustainability, MDPI, vol. 6(6), pages 1-17, May.
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    Cited by:

    1. Chengqiang Li & Junxiao Wang & Liang Ge & Yujie Zhou & Shenglu Zhou, 2022. "Optimization of Sample Construction Based on NDVI for Cultivated Land Quality Prediction," IJERPH, MDPI, vol. 19(13), pages 1-17, June.

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