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Estimating Corn Growth Parameters by Integrating Optical and Synthetic Aperture Radar Features into the Water Cloud Model

Author

Listed:
  • Yanyan Wang

    (Aerospace Information Research Institute, Henan Academy of Sciences, Zhengzhou 450046, China
    School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China)

  • Zhaocong Wu

    (Aerospace Information Research Institute, Henan Academy of Sciences, Zhengzhou 450046, China)

  • Shanjun Luo

    (Aerospace Information Research Institute, Henan Academy of Sciences, Zhengzhou 450046, China
    School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China)

  • Xinyan Liu

    (Aerospace Information Research Institute, Henan Academy of Sciences, Zhengzhou 450046, China
    School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China)

  • Shuaibing Liu

    (School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China)

  • Xinxin Huang

    (School of Economics & Management, Northwest University, Xi’an 710127, China)

Abstract

Crop growth parameters are the basis for evaluation of crop growth status and crop yield. The aim of this study was to develop a more accurate estimation model for corn growth parameters combined with multispectral vegetation indexes (VI opt ) and the differential radar information (DRI) derived from SAR data. Targeting the estimation of corn plant height (H) and the BBCH (Biologische Bundesanstalt, Bundessortenamt and CHemical industry) phenological parameters, this study compared the estimation accuracies of various multispectral vegetation indexes (VI opt ) and the corresponding VI DRI (vegetation index corrected by DRI) indexes in inverting the corn growth parameters. (1) When comparing the estimation accuracies of four multispectral vegetation indexes (NDVI, NDVIre1, NDVIre2, and S2REP), NDVI showed the lowest estimation accuracy, with a normalized root mean square error (nRMSE) of 20.84% for the plant height, while S2REP showed the highest estimation accuracy (nRMSE = 16.05%). In addition, NDVIre2 (nRMSE = 16.18%) and S2REP (16.05%) exhibited a higher accuracy than NDVIre1 (nRMSE = 19.27%). Similarly, for BBCH, the nRMSEs of the four indexes were 24.17%, 22.49%, 17.04% and 16.60%, respectively. This confirmed that the multispectral vegetation indexes based on the red-edge bands were more sensitive to the growth parameters, especially for the Sentinel-2 red-edge 2 band. (2) The constructed VI DRI indexes were more beneficial than the VI opt indexes in enhancing the estimation accuracy of corn growth parameters. Specifically, the nRMSEs of the four VI DRI indexes (NDVI DRI , NDVIre1 DRI , NDVIre2 DRI , and S2REP DRI ) decreased to 19.64%, 18.11%, 15.00%, and 14.64% for plant height, and to 23.24%, 21.58%, 15.79%, and 15.91% for BBCH, indicating that even in cases of high vegetation coverage, the introduction of SAR DRI features can further improve the estimation accuracy of growth parameters. Our findings also demonstrated that the NDVIre2 DRI and S2REP DRI indexes constructed using red-edge 2 band information of Sentinel-2 and SAR DRI features had more advantages in improving the estimation accuracy of corn growth parameters.

Suggested Citation

  • Yanyan Wang & Zhaocong Wu & Shanjun Luo & Xinyan Liu & Shuaibing Liu & Xinxin Huang, 2024. "Estimating Corn Growth Parameters by Integrating Optical and Synthetic Aperture Radar Features into the Water Cloud Model," Agriculture, MDPI, vol. 14(5), pages 1-20, April.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:5:p:695-:d:1385169
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