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首页> 外文期刊>Geoscience and Remote Sensing, IEEE Transactions on >Surface Soil Moisture Retrieval Using the L-Band Synthetic Aperture Radar Onboard the Soil Moisture Active–Passive Satellite and Evaluation at Core Validation Sites
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Surface Soil Moisture Retrieval Using the L-Band Synthetic Aperture Radar Onboard the Soil Moisture Active–Passive Satellite and Evaluation at Core Validation Sites

机译:利用土壤主动和被动卫星上的L波段合成孔径雷达反演地面土壤水分,并在核心验证点进行评估

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摘要

This paper evaluates the retrieval of soil moisture in the top 5-cm layer at 3-km spatial resolution using L-band dual-copolarized Soil Moisture Active-Passive (SMAP) synthetic aperture radar (SAR) data that mapped the globe every three days from mid-April to early July, 2015. Surface soil moisture retrievals using radar observations have been challenging in the past due to complicating factors of surface roughness and vegetation scattering. Here, physically based forward models of radar scattering for individual vegetation types are inverted using a time-series approach to retrieve soil moisture while correcting for the effects of static roughness and dynamic vegetation. Compared with the past studies in homogeneous field scales, this paper performs a stringent test with the satellite data in the presence of terrain slope, subpixel heterogeneity, and vegetation growth. The retrieval process also addresses any deficiencies in the forward model by removing any time-averaged bias between model and observations and by adjusting the strength of vegetation contributions. The retrievals are assessed at 14 core validation sites representing a wide range of global soil and vegetation conditions over grass, pasture, shrub, woody savanna, corn, wheat, and soybean fields. The predictions of the forward models used agree with SMAP measurements to within 0.5 dB unbiased-root-mean-square error (ubRMSE) and −0.05 dB (bias) for both copolarizations. Soil moisture retrievals have an accuracy of 0.052 m3/m3 ubRMSE, −0.015 m3/m3 bias, and a correlation of 0.50, compared to in situ measurements, thus meeting the accuracy target of 0.06 m3/m3 ubRMSE. The successful retrieval demonstrates the feasibility of a physically based time series retrieval with L-band SAR data for characterizing soil moisture over diverse conditions of soil moisture, surface roughness, and vegetation.
机译:本文使用L波段双共极化土壤水分主动-被动(SMAP)合成孔径雷达(SAR)数据(每三天绘制一次地球图)评估3公里空间分辨率下5 cm顶层的土壤水分的反演从2015年4月中旬到7月上旬。由于复杂的表面粗糙度和植被散布因素,使用雷达观测方法检索地表土壤水分在过去一直很困难。在这里,使用时间序列方法对基于雷达的物理散射模型(针对单个植被类型)进行反演,以获取土壤水分,同时校正静态粗糙度和动态植被的影响。与以往在均匀场尺度上的研究相比,本文在存在地形坡度,亚像素异质性和植被生长的情况下,对卫星数据进行了严格测试。检索过程还通过消除模型与观测值之间的任何时间平均偏差并通过调整植被贡献强度来解决前向模型中的任何缺陷。在14个核心验证站点对检索结果进行了评估,这些站点代表草,牧场,灌木,木质大草原,玉米,小麦和大豆田上广泛的全球土壤和植被状况。对于两个共极化,所使用的前向模型的预测与SMAP测量值一致,误差在0.5 dB以内,无偏差均方根误差(ubRMSE)和-0.05 dB(偏置)。与原位测量相比,土壤水分反演的精度为0.052 m3 / m3 ubRMSE,偏差为-0.015 m3 / m3,相关系数为0.50,因此达到了0.06 m3 / m3 ubRMSE的精度目标。成功的检索证明了使用基于L波段SAR数据的物理时间序列检索在各种土壤水分,表面粗糙度和植被条件下表征土壤水分的可行性。

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