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Crop Growth Stage GPP-Driven Spectral Model for Evaluation of Cultivated Land Quality Using GA-BPNN

Author

Listed:
  • Mingbang Zhu

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China)

  • Shanshan Liu

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China)

  • Ziqing Xia

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China)

  • Guangxing Wang

    (Department of Geography and Environmental Resources, Southern Illinois University Carbondale (SIUC), Carbondale, IL 62901, USA)

  • Yueming Hu

    (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
    Key Laboratory of Construction Land Transformation, Ministry of Land and Resources, South China Agricultural University, Guangzhou 510642, China
    Guangdong Provincial Key Laboratory of Land Use and Consolidation, South China Agricultural University, Guangzhou 510642, China)

  • Zhenhua Liu

    (College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China)

Abstract

Rapid and accurate evaluation of cultivated land quality (CLQ) using remotely sensed images plays an important role for national food security and social stability. Current approaches for evaluating CLQ do not consider spectral response relationships between CLQ and spectral indicators based on crop growth stages. This study aimed to propose an accurate spectral model to evaluate CLQ based on late rice phenology. In order to increase the accuracy of evaluation, the Empirical Bayes Kriging (EBK) interpolation was first performed to scale down gross primary production (GPP) products from a 500 m spatial resolution to 30 m. As an indicator, the ability of MODIS-GPPs from critical growth stages (tillering, jointing, heading, and maturity stages) was then investigated by combining Pearson correlation analysis and variance inflation factor (VIF) to select the phases of CLQ evaluation. Finally, a linear Partial Least Squares Regression (PLSR) and two nonlinear models, including Support Vector Regression (SVR) and Genetic Algorithm-Based Back Propagation Neural Network (GA-BPNN), were driven to develop an accurate spectral model of evaluating CLQ based on MODIS-GPPs. The models were tested and compared in the Conghua and Zengcheng districts of Guangzhou City, Guangdong, China. The results showed that based on field measured GPP data, the validation accuracy of 30 m spatial resolution MODIS GPP products with a root mean square error (RMSE) of 7.43 and normalized RMSE (NRMSE) of 1.59% was higher than that of the 500 m MODIS GPP products, indicating that the downscaled 30 m MODIS GPP products by EBK were more appropriate than the 500 m products. Compared with PLSR (R 2 = 0.38 and RMSE = 87.97) and SVR (R 2 = 0.64 and RMSE = 64.38), the GA-BPNN model (R 2 = 0.69 and RMSE = 60.12) was more accurate to evaluate CLQ, implying a non-linear relationship of CLQ with the GPP spectral indicator. This is the first study to improve the accuracy of estimating CLQ using the rice growth stage GPP-driven spectral model by GA-BPNN and can thus advance the literature in this field.

Suggested Citation

  • Mingbang Zhu & Shanshan Liu & Ziqing Xia & Guangxing Wang & Yueming Hu & Zhenhua Liu, 2020. "Crop Growth Stage GPP-Driven Spectral Model for Evaluation of Cultivated Land Quality Using GA-BPNN," Agriculture, MDPI, vol. 10(8), pages 1-16, August.
    • Handle: RePEc:gam:jagris:v:10:y:2020:i:8:p:318-:d:393258
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    References listed on IDEAS

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    Cited by:

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    2. 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.
    3. Li Wang & Yong Zhou & Qing Li & Tao Xu & Zhengxiang Wu & Jingyi Liu, 2021. "Application of Three Deep Machine-Learning Algorithms in a Construction Assessment Model of Farmland Quality at the County Scale: Case Study of Xiangzhou, Hubei Province, China," Agriculture, MDPI, vol. 11(1), pages 1-23, January.
    4. Quanfeng Li & Wenhao Guo & Xiaobing Sun & Aizheng Yang & Shijin Qu & Wenfeng Chi, 2021. "The Differentiation in Cultivated Land Quality between Modern Agricultural Areas and Traditional Agricultural Areas: Evidence from Northeast China," Land, MDPI, vol. 10(8), pages 1-15, August.

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