An algorithm is developed for retrieving soil moisture at plateau scale by combined usage of Aquarius active and passive L-band observations. In this algorithm, Look-Up-Tables (LUTs) are established for bare soil and vegetated areas by using the physical based Tor Vergata discrete electromagnetic model (hereafter, TV-DEM). In the case of vegetated area, the LUT is built based on simulations with varying soil moisture and Leaf Area Index (LAI). Only soil moisture is variable for the bare soil case, and values calibrated in previous works are adopted for the other TV-DEM parameters. Soil moisture is then retrieved by minimizing a squared difference object function based on the emissivity and backscatter coefficient observed by Aquarius and the corresponding TV-DEM simulations included in the LUT. The soil moisture retrievals are assessed at Aquarius footprint scale using in-situ measurements collected at three regional scale networks spread across the Tibetan Plateau. The unbiased root mean squared differences (ubRMSDs) from the three networks vary from 0.016 to 0.050 m³ m⁻³ and coefficients of determination (R²) are from 0.274 to 0.499 (-). This ubRMSD and R² is in the same order of the passive-only official Aquarius product, Metop-A Advanced SCATterometer (ASCAT) L2 soil moisture product (hereafter ASCAT) as well as reanalysis data generated by European Centre for Medium-Range Weather Forecasts (hereafter, ERA-Interim). At Plateau-scale, all four soil moisture products capture the seasonal trend, whereby the dynamic range during the monsoon season captured by the ERA-Interim product is relatively small. Further, the northwest-southeast dry-wet gradient due to precipitation and evapotranspiration is well captured by the soil moisture spatial distributions produced by TV-DEM Aquarius, official Aquarius and ASCAT products, but less pronounced in the ERA-Interim product. This study demonstrates that the TV-DEM based algorithm can be used to retrieve soil moisture over a Plateau scale with robust results in terms of error statistics (e.g. bias, R² and ubRMSD) and can generate realistic spatial patterns for soil moisture at Plateau-scale.

通过结合使用水瓶座主动和被动L波段观测，开发了一种在高原规模上检索土壤水分的算法。在该算法中，通过使用基于物理的Tor Vergata离散电磁模型（以下称为TV-DEM），为裸土和植被区建立了查找表（LUT）。对于有植被的地区，LUT是基于模拟的土壤水分和叶面积指数（LAI）的变化而构建的。对于裸土情况，仅土壤水分是可变的，而其他TV-DEM参数采用先前工作中校准的值。然后，根据水瓶座观测到的发射率和反向散射系数以及LUT中包含的相应TV-DEM模拟，通过最小化平方差目标函数来检索土壤水分。利用在青藏高原上分布的三个区域规模网络收集的原位测量值，以水瓶座足迹规模评估土壤水分的取回量。三个网络的无偏均方根差（ubRMSD）在0.016至0.050 m之间变化³ m ^-3，并且确定系数（R ²）为0.274至0.499（-）。这个ubRMSD和R ²与仅限被动使用的官方Aquarius产品，Metop-A高级SCATterometer（ASCAT）L2土壤水分产品（以下称ASCAT）以及欧洲中型天气预报中心（以下称ERA-临时）。在高原规模上，所有四种土壤水分产品都反映了季节趋势，因此ERA-Interim产品在季风季节期间捕获的动态范围相对较小。此外，TV-DEM水瓶座，官方水瓶座和ASCAT产品产生的土壤水分空间分布可以很好地捕捉到由于降水和蒸散引起的西北-东南干湿梯度，但在ERA-Interim产品中不那么明显。²和ubRMSD），并且可以在高原尺度上生成逼真的土壤水分空间格局。