基于神经网络算法的Sentinel-1和Sentinel-2遥感数据联合反演土壤湿度研究

Joint retrieval of soil moisture from Sentinel-1 and Sentinel-2 remote sensing data based on neural network algorithm

摘要:

以西班牙萨拉曼卡地区为研究区域，联合Sentinel-1后向散射系数和入射角信息、Sentinel-2光学数据提取的植被指数以及地面实测数据，构建了BP神经网络土壤湿度反演模型，并将该模型应用于试验区土壤湿度反演。结果表明：1）基于Sentinel-1卫星VV和VH极化雷达后向散射系数、雷达入射角和Sentinel-2植被指数数据构建的BP神经网络土壤湿度反演模型，能够实现对该地区土壤湿度高精度反演；2）在光学与微波数据联合反演植被覆盖区土壤湿度中，Sentinel-2的NDVI、NDWI1和NDWI2指数都可以用于削弱植被对土壤湿度反演的影响，但基于SWRI1波段的NDWI1能够获得更高精度的土壤湿度反演结果（RMSE为0.049 cm³/cm³，ubRMSE为0.048 cm³/cm³，Bias为0.008 cm³/cm³，r为0.681）；3）相比于Sentinel-1 VH极化模式，Sentinel-1 VV极化模式在土壤湿度中表现出更大优势，说明Sentinel-1 VV极化模式更适用于土壤湿度反演。

关键词:土壤水分;Sentinel-1;Sentinel-2;BP神经网络

Abstract:

Soil moisture is an important parameter of ecological environment and an important part of water cycle.The retrieval of surface soil moisture based on multi-source remote sensing data is a hotspot and trend in recent years.As a new generation of Sentinel satellites, the Sentinel-1 SAR data combined with the Sentinel-2 optical data have broad application prospects.Taking Salamanca, Spain as the research area, a BP neural network soil moisture retrieval model is constructed by combining the Sentinel-1 backscatter coefficient and incidence angle information, the vegetation index extracted from the Sentinel-2 optical data, and the ground observation data, and the model is applied to retrieve the soil moisture in the area.Finally, the model retrieval results are tested and evaluated.Results show that:(1) Based on the Sentinel-1 satellite VV and VH polarization radar backscatter coefficients and radar incidence angles and the Sentinel-2 vegetation index data, the BP neural network soil moisture retrieval model can realize high-precision retrieval of soil moisture in Salamanca area;(2) In the joint retrieval of soil moisture of optical and microwave data in vegetation coveragearea, the NDVI, NDWI1 and NDWI2 indices from the Sentinel-2 can be used to weaken the influence of vegetation on soil moisture retrieval, but the NDWI1 based on SWRI1 band can obtain more accurate soil moisture retrieval results (RMSE=0.049 cm³/cm³, ubRMSE=0.048 cm³/cm³, Bias=0.008 cm³/cm³, r=0.681);(3) Comparing with the Sentinel-1 VH polarization model, the Sentinel-1 VV polarization model shows greater advantages in soil moisture, indicating that the Sentinel-1 VV polarization model is more suitable for soil moisture retrieval.

Key words:soil moisture;Sentinel-1;Sentinel-2;BP neural network