基于Sentinel多源遥感数据的河北省景县农田土壤水分协同反演

doi:10.19741/j.issn.1673-4831.2019.0944

摘要/Abstract

摘要： 植被覆盖层对微波遥感反演地表土壤水分产生重要影响。以河北省景县为研究区，基于Sentinel-1 SAR遥感数据和Sentinel-2光学遥感数据，采用改进水云模型和Oh模型的组合方法，对植被覆盖地表土壤水分进行定量反演研究。结果表明：在Sentinel-1 VV极化条件下，改进水云模型和Oh模型的组合方法具有较高的反演精度，决定系数（R²）为0.653 0，均方根误差（RMSE）为0.040 1 cm³·cm^-3，平均绝对误差（MAE）为0.032 7 cm³·cm^-3，这3项反演精度评价指标均优于VH极化。该方法在获取高空间分辨率和高精度的植被覆盖区农田土壤水分信息方面具有较高的应用价值。

关键词: 改进水云模型, Oh模型, 土壤水分, 植被含水量, Sentinel-1/2

Abstract: Vegetation has significant effect on the retrieval of surface soil moisture by microwave remote sensing. A related study was carried out in Jingxian County of Hebei Province. The quantitative inversion research on the surface soil moisture in vegetation covered agricultural areas was based on the combination method of the modified water cloud model and the Oh model using Sentinel-1 SAR and Sentinel-2 optical remote sensing data. The results show that the combination method had good inversion accuracy in the VV polarization mode, with R² of 0.653 0, RMSE of 0.040 1 cm³·cm^-3, MAE of 0.032 7 cm³·cm^-3. Compared with the VH polarization mode, the VV polarization mode had better ability for the detection of the change of surface soil water content. The proposed method can obtain the data of the surface soil moisture with high spatial resolution and high accuracy in the vegetation covered areas.

Key words: modified water cloud model, Oh model, soil moisture, vegetation water content, Sentinel-1/2

中图分类号:

P237
S127

李伯祥, 陈晓勇. 基于Sentinel多源遥感数据的河北省景县农田土壤水分协同反演[J]. 生态与农村环境学报, 2020, 36(6): 752-761.

LI Bo-xiang, CHEN Xiao-yong. Synergic Use of Sentinel-1 and Sentinel-2 Images for Soil Moisture Retrieval in Vegetation Covered Agricultural Areas of Jingxian County of Heibei Province[J]. Journal of Ecology and Rural Environment, 2020, 36(6): 752-761.

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