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首页> 外文会议>IEEE International Geoscience and Remote Sensing Symposium >Parametrization of a Dielectric Mixture Model to Retrieve Soil Moisture at Field Scale Using Sentinel-1 Data and in Situ Soil Moisture Measurements
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Parametrization of a Dielectric Mixture Model to Retrieve Soil Moisture at Field Scale Using Sentinel-1 Data and in Situ Soil Moisture Measurements

机译：使用Sentinel-1数据和原位土壤水分测量方法在田间尺度上获取介电混合物模型的参数化

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摘要

In the framework of the H2020 APOLLO project a significant focus has been put on the use of high resolution satellite based products to serve small farmers in their agricultural practices. This paper presents first results achieved by using Sentinel-1 Synthetic Aperture Radar (SAR) data as input to a semi-empirical soil moisture (SM) retrieval model based on an algorithm originally developed for ERS SAR data and successively modified to handle ENVISAT ASAR acquisitions. In this work the model has been adapted to Sentinel-1 images and calibrated by using ground measurements taken in two test sites characterized by bare soil and cotton cultivation, aiming at testing its capability to represent the SM behavior at different stages of the vegetation cycle. The model performance has been assessed through the correlation R and root mean squared error (RMSE) between in situ and satellite retrieved SM data. Very good results have been achieved for bare soil (R>0.8, RMSE<;0.04 m3m-3); however, the model performed worse in the cotton fields (R<;0.6, RMSE>0.08 m3m-3).
机译：在H2020 APOLLO项目的框架内，重点已放在使用高分辨率卫星产品上，以服务于小农的农业实践。本文介绍了通过使用Sentinel-1合成孔径雷达（SAR）数据作为半经验土壤水分（SM）检索模型的输入而获得的第一个结果，该模型基于最初为ERS SAR数据开发的算法并相继修改以处理ENVISAT ASAR采集。在这项工作中，该模型已适应Sentinel-1图像，并通过在两个以裸露土壤和棉花种植为特征的测试地点进行的地面测量进行了校准，旨在测试其代表植被周期不同阶段的SM行为的能力。已通过就地和卫星检索到的SM数据之间的相关性R和均方根误差（RMSE）评估了模型的性能。裸土取得了非常好的结果（R> 0.8，RMSE <; 0.04 m 3 米 -3 ）;然而，该模型在棉田中表现较差（R <; 0.6，RMSE> 0.08 m 3 米 -3 ）。

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