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Development of soil water content retrieving method for irrigation agriculture areas using the red-edge band of Gaofen-6 satellite

Author

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  • Wang, Rong
  • Zhao, Hongli
  • Zhang, Chi
  • Hao, Zhen
  • Chen, Aiqi
  • Xu, Ran
  • He, Junyan

Abstract

Soil water content (SWC) is an essential index that reflects water conditions in the soil surface during drought, and changes in SWC are critical in guiding agricultural irrigation management. Remote sensing is a crucial means of monitoring SWC, and the red and near-infrared bands of optical remote sensing data are often used to monitor SWC through remote sensing retrieval. With the development of remote sensing technology, red-edge band has also been used to retrieve SWC. It exhibits higher sensitivity to changes in vegetation and SWC, relatively more precise differentiation between vegetation and soil, and more accurate SWC detection. However, there has been relatively little research regarding the use of the red-edge band for retrieving SWC. In this study, we developed a novel method, based on the principle of spectral space and the calculation of modified perpendicular drought index, to calculate the soil water index using the red-edge band of the Gaofen-6 (GF-6) satellite by comparing the retrieval accuracy of GF-6 using different combinations of red, near-infrared, and red-edge bands. Based on the measured SWC in the Shijin Irrigation District of Hebei Province, an SWC retrieval model was established using linear regression. Moreover, the new method was used to retrieve SWC from GF-6 and Sentinel-2 with the same central wavelength, and the reliability of the new method was compared and analyzed. The results revealed that the combination of red band and red-edge band 2 with a central wavelength of 0.74 μm substantial. improved the retrieval accuracy of SWC. Our study provides a new method for retrieving SWC using optical remote sensing, which can effectively improve retrieval accuracy and provide data support for agricultural irrigation monitoring and water management.

Suggested Citation

  • Wang, Rong & Zhao, Hongli & Zhang, Chi & Hao, Zhen & Chen, Aiqi & Xu, Ran & He, Junyan, 2024. "Development of soil water content retrieving method for irrigation agriculture areas using the red-edge band of Gaofen-6 satellite," Agricultural Water Management, Elsevier, vol. 303(C).
    • Handle: RePEc:eee:agiwat:v:303:y:2024:i:c:s0378377424003809
    DOI: 10.1016/j.agwat.2024.109045
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    References listed on IDEAS

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    1. Xu, Zhenheng & Sun, Hao & Zhang, Tian & Xu, Huanyu & Wu, Dan & Gao, JinHua, 2023. "Evaluating established deep learning methods in constructing integrated remote sensing drought index: A case study in China," Agricultural Water Management, Elsevier, vol. 286(C).
    2. Sun, Haoyang & Wang, Sufen & Hao, Xinmei, 2017. "An Improved Analytic Hierarchy Process Method for the evaluation of agricultural water management in irrigation districts of north China," Agricultural Water Management, Elsevier, vol. 179(C), pages 324-337.
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    4. Yang, Gaiqiang & Guo, Ping & Huo, Lijuan & Ren, Chongfeng, 2015. "Optimization of the irrigation water resources for Shijin irrigation district in north China," Agricultural Water Management, Elsevier, vol. 158(C), pages 82-98.
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