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Soil moisture retrieval over croplands using novel dual-polarization SAR vegetation index

Author

Listed:
  • Zhang, Rui
  • Bao, Xin
  • Hong, Ruikai
  • He, Xu
  • Yin, Gaofei
  • Chen, Jie
  • Ouyang, Xiaoying
  • Wang, Yongxun
  • Liu, Guoxiang

Abstract

Synthetic Aperture Radar (SAR) data, known for its high spatial resolution and all-weather observation capabilities, holds immense promise in soil moisture monitoring. The Water Cloud Model (WCM) is widely applied in soil moisture inversion using SAR data. However, the optical vegetation indices employed in traditional WCM cannot synchronize with SAR data, and the polarimetric scattering information contained in current SAR vegetation indices is incomplete, consequently compromising the accuracy of SAR-based soil moisture retrieval. Therefore, this study proposes a method for soil moisture retrieval over cropland using a novel dual-polarization SAR vegetation index. The method initially combines SAR data covariance elements with backscatter information to establish a polarization scattering contrast parameter (mcp). Then, based on mcp and incorporating the degree of polarization, a polarization scattering correlation contrast parameter (Rcp) is defined. Rcp integrates the distinctive features of scattering differences and polarization states. Building on Rcp, a novel dual-polarization SAR vegetation index (DRVIs) is introduced. Ultimately, DRVIs are utilized in the WCM to achieve surface soil moisture retrieval in cropland. This research conducts experiments in four crop cover areas of the Carman test site in Canada, namely soybean, wheat, canola, and corn. Under VV and VH polarizations, the overall correlation coefficients between measured in-situ data and SSM estimates reach 0.89 and 0.84, respectively. Compared to SSM estimates based on NDVI and LAI products, SSM estimates based on DRVIs exhibit a notable improvement in accuracy, with enhancements of approximately 7.2 % and 12.7 %, respectively. This novel DRVIs is poised to expand the utilization of SAR data in monitoring vegetation growth and soil moisture retrieval.

Suggested Citation

  • Zhang, Rui & Bao, Xin & Hong, Ruikai & He, Xu & Yin, Gaofei & Chen, Jie & Ouyang, Xiaoying & Wang, Yongxun & Liu, Guoxiang, 2024. "Soil moisture retrieval over croplands using novel dual-polarization SAR vegetation index," Agricultural Water Management, Elsevier, vol. 306(C).
    • Handle: RePEc:eee:agiwat:v:306:y:2024:i:c:s0378377424004955
    DOI: 10.1016/j.agwat.2024.109159
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    References listed on IDEAS

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    1. Nanda, Arunav & Das, Narendra & Singh, Gurjeet & Bindlish, Rajat & Andreadis, Konstantinos M. & Jayasinghe, Susantha, 2024. "Harnessing SMAP satellite soil moisture product to optimize soil properties to improve water resource management for agriculture," Agricultural Water Management, Elsevier, vol. 300(C).
    2. Wu, Zongjun & Cui, Ningbo & Zhang, Wenjiang & Gong, Daozhi & Liu, Chunwei & Liu, Quanshan & Zheng, Shunsheng & Wang, Zhihui & Zhao, Lu & Yang, Yenan, 2024. "Inversion of large-scale citrus soil moisture using multi-temporal Sentinel-1 and Landsat-8 data," Agricultural Water Management, Elsevier, vol. 294(C).
    3. Gao, Yu-Xin & Leng, Pei & Li, Jing & Shang, Guo-Fei & Zhang, Xia & Li, Zhao-Liang, 2024. "Identification of irrigation events using Bayesian statistics-based change detection and soil moisture measurements," Agricultural Water Management, Elsevier, vol. 302(C).
    4. Wang, Weizhen & Ma, Chunfeng & Wang, Xufeng & Feng, Jiaojiao & Dong, Leilei & Kang, Jian & Jin, Rui & Li, Xingze, 2024. "A soil moisture experiment for validating high-resolution satellite products and monitoring irrigation at agricultural field scale," Agricultural Water Management, Elsevier, vol. 304(C).
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