Kim Seung-Bum, van Zyl Jakob J, Johnson Joel T, Moghaddam Matha, Tsang Leung, Colliander Andreas, Dunbar Roy Scott, Jackson Thomas J, Jaruwatanadilok Sermsak, West Richard, Berg Aaron, Caldwell Todd, Cosh Michael H, Goodrich David C, Livingston Stanley, López-Baeza Ernesto, Rowlandson Tracy, Thibeault Marc, Walker Jeffrey P, Entekhabi Dara, Njoku Eni G, O'Neill Peggy E, Yueh Simon H
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109 USA.
The Ohio State University, Columbus, OH 43212 USA.
IEEE Trans Geosci Remote Sens. 2017 Jan 19;Volume 55(Iss 4):1897-1914. doi: 10.1109/TGRS.2016.2631126.
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 m/m ubRMSE, -0.015 m/m bias, and a correlation of 0.50, compared to measurements, thus meeting the accuracy target of 0.06 m/m 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.
本文利用L波段双共极化土壤湿度主动-被动（SMAP）合成孔径雷达（SAR）数据评估了2015年4月中旬至7月初每隔三天对全球进行一次测绘的3公里空间分辨率下5厘米表层土壤湿度的反演情况。过去，由于地表粗糙度和植被散射等复杂因素，利用雷达观测进行表层土壤湿度反演一直具有挑战性。在此，采用时间序列方法对针对不同植被类型的基于物理的雷达散射前向模型进行反演，以反演土壤湿度，同时校正静态粗糙度和动态植被的影响。与过去在均匀田间尺度上的研究相比，本文在存在地形坡度、亚像素异质性和植被生长的情况下，利用卫星数据进行了严格测试。反演过程还通过消除模型与观测之间的任何时间平均偏差以及调整植被贡献强度来解决前向模型中的任何缺陷。在14个核心验证点对反演结果进行了评估，这些验证点代表了草地、牧场、灌木、木本稀树草原、玉米、小麦和大豆田等广泛的全球土壤和植被条件。所使用的前向模型的预测结果与SMAP测量值在两种共极化情况下的无偏均方根误差（ubRMSE）在0.5 dB以内，偏差在-0.05 dB以内。与测量值相比，土壤湿度反演的精度为ubRMSE 0.052 m/m、偏差-0.015 m/m，相关性为0.50，从而达到了ubRMSE 0.06 m/m的精度目标。成功的反演证明了利用L波段SAR数据基于物理的时间序列反演在不同土壤湿度、地表粗糙度和植被条件下表征土壤湿度的可行性。
