Akbar Ruzbeh, Cosh Michael H, O'Neill Peggy E, Entekhabi Dara, Moghaddam Mahta
Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA 90089 USA.
U.S. Department of Agriculture, Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705 USA.
IEEE Trans Geosci Remote Sens. 2017 Jul;55(7):4098-4110. doi: 10.1109/TGRS.2017.2688403. Epub 2017 Apr 26.
A robust physics-based combined radar-radiometer, or Active-Passive, surface soil moisture and roughness estimation methodology is presented. Soil moisture and roughness retrieval is performed via optimization, i.e., minimization, of a joint objective function which constrains similar resolution radar and radiometer observations simultaneously. A data-driven and noise-dependent regularization term has also been developed to automatically regularize and balance corresponding radar and radiometer contributions to achieve optimal soil moisture retrievals. It is shown that in order to compensate for measurement and observation noise, as well as forward model inaccuracies, in combined radar-radiometer estimation surface roughness can be considered a free parameter. Extensive Monte-Carlo numerical simulations and assessment using field data have been performed to both evaluate the algorithm's performance and to demonstrate soil moisture estimation. Unbiased root mean squared errors (RMSE) range from 0.18 to 0.03 cm3/cm3 for two different land cover types of corn and soybean. In summary, in the context of soil moisture retrieval, the importance of consistent forward emission and scattering development is discussed and presented.
本文提出了一种基于物理的稳健的雷达-辐射计组合，即有源-无源地表土壤湿度和粗糙度估计方法。土壤湿度和粗糙度反演通过联合目标函数的优化（即最小化）来实现，该目标函数同时约束了具有相似分辨率的雷达和辐射计观测数据。还开发了一个数据驱动且依赖噪声的正则化项，以自动对雷达和辐射计的相应贡献进行正则化和平衡，从而实现最优的土壤湿度反演。结果表明，为了补偿雷达-辐射计组合估计中的测量和观测噪声以及前向模型的不准确性，地表粗糙度可被视为一个自由参数。已进行了广泛的蒙特卡洛数值模拟，并使用现场数据进行了评估，以评估该算法的性能并展示土壤湿度估计结果。对于玉米和大豆这两种不同土地覆盖类型，无偏均方根误差（RMSE）范围为0.18至0.03 cm³/cm³。总之，在土壤湿度反演的背景下，讨论并介绍了一致的前向发射和散射发展的重要性。
