Fang Bin, Lakshmi Venkat, Jackson Thomas J, Bindlish Rajat, Colliander Andreas
Earth and Ocean Sciences, University of South Carolina, Columbia SC 29223.
Hydrology and Remote Sensing Laboratory, Beltsville Agricultural, Research Center, United States Department of Agriculture, Beltsville MD 20705.
J Hydrol (Amst). 2019 Jul;574:1085-1098. doi: 10.1016/j.jhydrol.2019.04.082. Epub 2019 Apr 30.
The SMAPVEX12 (Soil Moisture Active Passive (SMAP) Validation Experiment 2012) experiment was conducted during June-July 2012 in Manitoba, Canada with the goal of collecting remote sensing data and ground measurements for the development and testing of soil moisture retrieval algorithms under varying vegetation and soil conditions for the SMAP satellite. The aircraft based soil moisture data provided by the passive/active microwave sensor PALS (Passive and Active L-band System) has a nominal spatial resolution of 1600 m. However, this resolution is not compatible with agricultural, meteorological and hydrological studies that require high spatial resolutions and this issue can be solved by soil moisture disaggregation. The soil moisture disaggregation algorithm integrates radiometer soil moisture retrievals and high-resolution radar observations and it can provide soil moisture estimates at a finer scale than the radiometer data alone. In this study, a change detection algorithm was used for disaggregation of coarse resolution passive microwave soil moisture retrievals with radar backscatter coefficients obtained from the higher spatial resolution UAVSAR (Unmanned Air Vehicle Synthetic Aperture Radar) at crop field scale. The accuracy of the disaggregated change in soil moisture was evaluated using ground based soil moisture measurements collected during SMAPVEX12 campaign. The results showed that soil moisture spatial variabilities were better characterized by the disaggregated change in soil moisture estimates at 5 m / 800 m resolution as well as good agreement with measurements. It also showed that VWC (Vegetation Water Content) did not have a big impact on disaggregation algorithm performance, with R of the disaggregated results ranging 0.628-0.794. The 5 m and 800m resolution disaggregated soil moisture did no show significant difference in statistical performance variables.
SMAPVEX12（2012年土壤湿度主动被动（SMAP）验证实验）实验于2012年6月至7月在加拿大马尼托巴省进行，目的是收集遥感数据和地面测量数据，以开发和测试在不同植被和土壤条件下用于SMAP卫星的土壤湿度反演算法。由被动/主动微波传感器PALS（被动和主动L波段系统）提供的基于飞机的土壤湿度数据的标称空间分辨率为1600米。然而，这种分辨率与需要高空间分辨率的农业、气象和水文研究不兼容，这个问题可以通过土壤湿度分解来解决。土壤湿度分解算法整合了辐射计土壤湿度反演结果和高分辨率雷达观测数据，并且它能够比单独的辐射计数据提供更精细尺度的土壤湿度估计。在本研究中，一种变化检测算法被用于在作物田尺度上，利用从更高空间分辨率的无人机合成孔径雷达（UAVSAR）获得的雷达后向散射系数，对粗分辨率被动微波土壤湿度反演结果进行分解。利用在SMAPVEX12活动期间收集的地面土壤湿度测量数据，评估了分解后的土壤湿度变化的准确性。结果表明，在5米/800米分辨率下，分解后的土壤湿度估计变化能更好地表征土壤湿度空间变异性，并且与测量结果吻合良好。研究还表明，植被含水量（VWC）对分解算法性能影响不大，分解结果的R值范围为0.628 - 0.794。5米和800米分辨率下分解后的土壤湿度在统计性能变量上没有显示出显著差异。
