Feature parameters collaborative inversion based on optical and SAR data

Bei Zhang; Zhengqiu He; Huanyao Dai; Jianlu Wang

doi:10.1117/12.3010781

15 November 2023 Feature parameters collaborative inversion based on optical and SAR data

Bei Zhang, Zhengqiu He, Huanyao Dai, Jianlu Wang

Proceedings Volume 12815, International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023); 128150P (2023) https://doi.org/10.1117/12.3010781
Event: International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023), 2023, Kaifeng, China

Abstract

Soil moisture, as the main characteristic parameter of the surface, plays an important role in climate change, energy exchange, and crop yield estimation. In order to remove the influence of vegetation coverage and roughness on soil moisture retrieval, a cooperative inversion method based on Water Cloud model and Oh model is proposed to retrieve soil moisture in winter wheat covered farmland areas using Sentinel-1/2 multi-source remote sensing data. The influence of winter wheat coverage on radar backscattering coefficient is removed by using the improved Water Cloud model with vegetation coverage and vegetation index fusion extracted from Sentinel-2 data. The attenuation factor in the Water Cloud model is solved by using the optimization theory. The soil moisture in the study area is retrieved based on the backscattering coefficient of VV and VH polarization Sentinel-1 data through the Look Up Table established by Oh model. The experimental results show that, based on the proposed method, compared with VH polarization, the soil moisture inversion from VV polarization data has a better effect, with a determination coefficient of 0.6577, a Root Mean Square Error of 0.0391 cm3 /cm3 , and a Mean Absolute Error of 0.0303 cm3 /cm3 , demonstrating the application potential of the proposed method in soil moisture retrieval.

Citation Download Citation

Bei Zhang, Zhengqiu He, Huanyao Dai, and Jianlu Wang "Feature parameters collaborative inversion based on optical and SAR data", Proc. SPIE 12815, International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023), 128150P (15 November 2023); https://doi.org/10.1117/12.3010781

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KEYWORDS

Soil moisture

Vegetation

Backscatter

Data modeling

Polarization

Radar

Synthetic aperture radar

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