Cooperative Inversion of Winter Wheat Covered Surface Soil Moisture Based on Sentinel-1/2 Remote Sensing Data
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摘要: 冬小麦是我国重要粮食作物之一，对冬小麦覆盖地表土壤水分进行监测有助于解决因土壤供水导致的冬小麦歉收和农业用水浪费等问题。为了降低冬小麦覆盖地表土壤水分微波遥感反演过程中冬小麦对雷达后向散射系数的影响，该文基于Sentinel-1携带的合成孔径雷达(SAR)数据和Sentinel-2携带的多光谱成像仪(MSI)数据，结合水云模型，开展冬小麦覆盖地表土壤水分协同反演研究。首先，基于MSI数据，该文定义了一种新的植被指数，即融合植被指数(FVI)，用于冬小麦含水量反演；然后，该文发展了一种基于主被动遥感数据的冬小麦覆盖地表土壤水分反演半经验模型，校正冬小麦在土壤水分反演过程中对雷达后向散射系数的影响；最后，以河南省某地冬小麦农田为研究区域，开展归一化水体指数(NDWI)和FVI两种指数与VV, VH, VV/VH 3种极化组合而成的6种反演方式下的土壤水分反演对比实验。结果表明：以FVI为植被指数，能够更好地去除冬小麦在土壤水分反演过程中对雷达后向散射系数的影响；6种反演方式中，FVI与VV/VH组合下的反演效果最优，其决定系数为0.7642，均方根误差为0.0209 cm3/cm3，平均绝对误差为0.0174 cm3/cm3，展示了该文所提土壤水分反演模型的研究价值和应用潜力。
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关键词:
- 雷达土壤水分反演 /
- 水云模型 /
- 融合植被指数 /
- Sentinel-1/2
Abstract: Winter wheat is one of the most important food crops in China. Monitoring the soil moisture over winter wheat covered surface can help to solve the problem of poor harvest of winter wheat and waste of agricultural water due to soil water supply. In order to reduce the influence of winter wheat on radar backscattering coefficient in the process of microwave remote sensing retrieval of soil moisture covered by winter wheat, based on the Synthetic Aperture Radar (SAR) data carried by Sentinel-1 and the MultiSpectral Imager (MSI) data carried by Sentinel-2, combined with the water cloud model, the collaborative inversion of soil moisture over winter wheat mulching surface is carried out. Firstly, based on the MSI data from Sentinel-2, a new vegetation index called Fusion Vegetation Index (FVI) is defined for inversion of winter wheat moisture. Secondly, a semi-empirical soil moisture inversion model based on active and passive remote sensing data is developed to correct the influence of winter wheat on radar backscatter coefficient. Finally, by taking a winter wheat field in Henan Province as the study area, the comparative experiments of soil moisture inversion are carried out under six combinations, which are composed of two vegetation indexes, Normalized Difference Water Index (NDWI) and FVI respectively, and three types of polarization data, VV, VH and VV/VH respectively. Through the experimental results, FVI shows a better performance than NDWI in reducing the influence of winter wheat on radar backscatter coefficient. Meanwhile, among the six inversion combinations, the one of FVI and VV/VH achieves the optimal inversion precision, with a determination coefficient of 0.7642, a Root Mean Square Error of 0.0209 cm3/cm3, and a Mean Absolute Error of 0.0174 cm3/cm3, demonstrating the application potential of the soil inversion model developed in this paper. -
表 1 基于水云模型和本文所发展模型的土壤水分反演精度对比结果
反演模型 反演组合方式 R2 RMSE 水云模型 VV-NDWI 0.6915 0.0245 VV-FVI 0.7212 0.0243 本文所发展模型 VV/VH-NDWI 0.7266 0.0240 VV/VH-FVI 0.7642 0.0209 
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表 2 本文所发展模型的6种组合反演方式下土壤水分反演精度对比结果
反演组合方式 R2 RMSE MAE VH-NDWI 0.4727 0.0326 0.0263 VV-NDWI 0.6733 0.0253 0.0202 VV/VH-NDWI 0.7266 0.0240 0.0202 VH-FVI 0.5151 0.0289 0.0246 VV-FVI 0.6791 0.0249 0.0219 VV/VH-FVI 0.7642 0.0209 0.0174
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