This study investigates soil moisture retrievals using airborne passive microwave data at two different resolutions collected during the Soil Moisture Active Passive Validation Experiments in 2012 and 2016 (SMAPVEX12 and SMAPVEX16-MB). Based on the fine-resolution passive data (500 m), we integrate the surface roughness parameters which are traditionally used in the radar backscatter models into the passive emission models. To parameterize the effective roughness H_r in L-band Microwave Emission of the Biosphere (L-MEB) model, we analyze two different functions which include only surface Root Mean Square height (s), as well as both s and the autocorrelation length l (z_s = s²/l). For each of the two roughness functions, the b vegetation parameters are optimized for canola, soybean and wheat. The transferability of the b parameters between 2012 and 2016 is also evaluated by comparing the L-MEB model simulated and the measured brightness temperature. Then, the calibrated L-MEB model is applied to the subpixels of the coarse-resolution passive data (1500 m) given the vegetation heterogeneity, to map the soil moisture over the SMAPVEX entire experimental site. The results indicated the H_r model with the z_s parameter outperformed that with the s parameter. This suggests that the inclusion of the roughness autocorrelation length in the L-MEB model improved the accuracy of modeling the brightness temperature. The vegetation attenuation on the brightness temperature at V-polarization was stronger than that at H-polarization, due to the dominant vertical structure of the crop canopy. Since the airborne passive observations exhibited remarkable consistency between the 2012 and 2016 measurements, the b parameters obtained in 2012 can be transferred to the 2016. Based on the obtained H_r and b parameters, the soil moisture maps were retrieved using the calibrated L-MEB model applied to the sub-pixel of the coarse-resolution passive data, implying a Root Mean Square Errors (RMSEs) of 0.049–0.058 m³/m³ and correlation coefficients of 0.82–0.87. This paper suggests that the physical roughness z_s in the radar domain can be coupled into the L-MEB model to refine the soil moisture retrievals from passive brightness temperature.

本研究使用在2012年和2016年的土壤水分主动无源验证实验（SMAPVEX12和SMAPVEX16-MB）中收集的两种不同分辨率的机载被动微波数据调查土壤水分的反演。基于高分辨率的无源数据（500 m），我们将雷达反散射模型中传统使用的表面粗糙度参数整合到无源发射模型中。为了在生物圈的L波段微波辐射（L-MEB）模型中参数化有效粗糙度H _r，我们分析了两个不同的函数，这些函数仅包括表面均方根高度（s），s和自相关长度l（z _s  = s² / l）。对于这两个粗糙度函数中的每一个，均针对低芥酸菜籽，大豆和小麦优化了b植被参数。通过比较模拟的L-MEB模型和测得的亮度温度，还可以评估2012年至2016年之间b参数的可传递性。然后，将校准的L-MEB模型应用于具有植被异质性的粗分辨率无源数据（1500 m）的子像素，以绘制SMAPVEX整个实验地点的土壤湿度图。结果表明，具有z _s参数的H _r模型优于具有_s参数的H _r模型。参数。这表明在L-MEB模型中包括粗糙度自相关长度可以提高对亮度温度进行建模的准确性。由于作物冠层的垂直结构占主导地位，V极化时植物对亮度温度的衰减要强于H极化时。由于机载被动观测在2012年和2016年的测量之间显示出显着的一致性，因此2012年获得的b参数可以转移到2016年。基于获得的H _r和b参数，使用适用于粗分辨率无源数据的子像素的校准L-MEB模型检索土壤湿度图，这意味着均方根误差（RMSE）为0.049–0.058 m ³ / m ³和相关系数为0.82-0.87。本文建议将雷达域中的物理粗糙度z _s耦合到L-MEB模型中，以从被动亮度温度中细化土壤水分。