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首页> 外文期刊>Water resources research >Synergies for Soil Moisture Retrieval Across Scales From Airborne Polarimetric SAR, Cosmic Ray Neutron Roving, and an In Situ Sensor Network
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Synergies for Soil Moisture Retrieval Across Scales From Airborne Polarimetric SAR, Cosmic Ray Neutron Roving, and an In Situ Sensor Network

机译：机载极化SAR，宇宙射线中子漫游和原位传感器网络在各个尺度上实现土壤水分反演的协同作用

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摘要

The consistent determination of soil moisture across scales is a persistent challenge in hydrology. Several measurement methods exist at distinct scales, each of which is challenging in terms of data processing, removal of vegetation and surface effects, and calibration. While in situ measurements are trusted at the point scale, distributed sensor networks extend the areal representation to the field scale. At this scale, also cosmic ray neutron sensing (CRNS) has become an established method to derive volume-averaged, root zone soil moisture over several tens of hectometers, but the signal is often biased due to biomass water. With airborne synthetic aperture radar (SAR)remote sensing, it is possible to cover regional scales, but the method is limited to the topmost soil layer and sensitive to vegetation parameters. In this study, the performance and synergistic potential of these complementary methods is investigated for the determination of soil moisture within a 55-km(2) Alpine foothill river catchment in Southern Germany. The individual approaches are evaluated and brought into synergy for a 9-ha grassland and several other locations within the catchment. The results indicate that the sensor network data provide valuable information to calibrate the mobile CRNS rover, and to optimize the vegetation removal within the polarimetric SAR retrieval algorithm. The root-mean-square errors for polarimetric synthetic aperture radar soil permittivity are 9.32 with the standard agriculture approach, 4.29 with the semi-stand-alone approach, and 0.31 with the sensor network optimized approach. Furthermore, the CRNS soil moisture product was improved by considering the remotely sensed cross-polarized backscatter product as a biomass water proxy.
机译：在各个尺度上一致确定土壤湿度是水文学的一个持续挑战。有几种不同规模的测量方法，每种方法在数据处理，去除植被和表面效应以及校准方面都具有挑战性。虽然可以在点尺度上进行现场测量，但分布式传感器网络将区域表示扩展到了场尺度。在这样的规模下，宇宙射线中子传感（CRNS）也已成为一种建立方法，可以在数十公顷的土地上获得体积平均的根区土壤水分，但是由于生物质水的存在，信号经常会产生偏差。借助机载合成孔径雷达（SAR）遥感，可以覆盖区域范围，但是该方法仅限于最顶层的土壤层并且对植被参数敏感。在这项研究中，对这些互补方法的性能和协同潜力进行了研究，以确定德国南部一个55 km（2）高山山麓河流域的土壤湿度。对于9公顷的草地和集水区中的其他几个位置，将对各个方法进行评估并使其协同作用。结果表明，传感器网络数据可提供宝贵的信息，以校准移动CRNS流动站，并在极化SAR检索算法内优化植被去除。极化合成孔径雷达土壤介电常数的均方根误差在标准农业法下为9.32，在半独立法下为4.29，在传感器网络优化法下为0.31。此外，通过将遥感的交叉极化反向散射产物视为生物量水的替代物，可改善CRNS的土壤水分产物。

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