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Investigation of SMAP Fusion Algorithms With Airborne Active and Passive L-Band Microwave Remote Sensing

机译:机载有源和无源L波段微波遥感SMAP融合算法研究

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The objective of the NASA Soil Moisture Active Passive (SMAP) mission is to provide global measurements of soil moisture and freeze/thaw states. SMAP integrates L-band radar and radiometer instruments as a single observation system combining the respective strengths of active and passive remote sensing for enhanced soil moisture mapping. Airborne instruments are a key part of the SMAP validation program. Here, we present an airborne campaign in the Rur catchment, Germany, in which the passive L-band system Polarimetric L-band Multi-beam Radiometer and the active L-band system F-SAR of DLR were flown simultaneously on six dates in 2013. The flights covered the full heterogeneity of the area under investigation, i.e., the main land cover types and all experimental monitoring sites. Here, we used the obtained data sets as a test bed for the analysis of three active–passive fusion techniques: 1) estimation of soil moisture by passive sensor data and subsequent disaggregation by active sensor backscatter data; 2) disaggregation of passive microwave brightness temperature by active microwave backscatter and subsequent inversion to soil moisture; and 3) fusion of two single-source soil moisture products from radar and radiometer. Results indicate that the regression parameters are dependent on the radar vegetation index. The best performance was obtained by the fusion of radiometer brightness temperatures and radar backscatter, which was able to reach the same accuracy as single-source coarse-scale radiometer soil moisture retrieval but on a higher spatial resolution.
机译:NASA土壤水分主动被动(SMAP)任务的目的是提供土壤水分和冻结/融化状态的全局度量。 SMAP将L波段雷达和辐射计仪器集成为一个单独的观测系统,结合了主动和被动遥感各自的优势来增强土壤湿度测绘。机载仪器是SMAP验证计划的关键部分。在这里,我们介绍了一次在德国Rur流域进行的空降战役,其中,无源L波段系统极化L波段多波束辐射计和DLR的有源L波段系统F-SAR在6个日期同时飞行飞行覆盖了被调查区域的全部异质性,即主要的土地覆盖类型和所有实验监测点。在这里,我们将获得的数据集用作测试三种主动-被动融合技术的试验台:1)通过被动传感器数据估算土壤水分,然后通过主动传感器反向散射数据进行分解; 2)通过主动微波反向散射分解被动微波亮度温度,然后转化为土壤水分; 3)融合雷达和辐射计的两种单一来源的土壤水分产品。结果表明,回归参数取决于雷达植被指数。辐射计亮度温度和雷达反向散射的融合获得了最佳性能,它能够达到与单源粗尺度辐射计土壤水分反演相同的精度,但具有更高的空间分辨率。

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