Due to the success of the SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active Passive) missions, new satellite missions are on the horizon. The current and future missions can benefit from investigations that seek to improve retrieval algorithms that quantitatively map global soil moisture and vegetation optical depth (tau) from Earth's microwave emissions. In this study, we explore multi-angular and multi-frequency approaches for the retrieval of soil moisture and vegetation tau, considering the payload configurations of current and future satellite missions (such as the Copernicus Imaging Microwave Radiometer, the Water Cycle Observation Mission, and the Terrestrial Water Resources Satellite) using a new set of ground observations. Two ground-based microwave radiometry datasets collected in Inner Mongolia during the Soil Moisture Experiment in the Luan River from July to August 2017 (cropland) and August to September 2018 (grassland) are used for this study. The corn field, which covers an entire growth period, indicated that the degree of information increases linearly as the number of channels (in terms of the incidence angle and frequency) increases, and that the multi-frequency observations contain slightly more independent information than do the multi-angular observations under the same number of channels. The polarization difference in brightness temperature is sensitive to both soil moisture and vegetation water content, especially at L-band due to its penetrating ability. Soil moisture explains most of the variance in frequency differences of brightness temperature at adjacent frequencies (L- & C-bands, C- & X-bands), while the variance in incidence-angle differences of brightness temperature is mostly associated with the vegetation water content. A multi-channel collaborative algorithm (MCCA) is developed based on the two-component version of the omega-tau model, which utilizes information from collaborative channels expressed as an analytical form of brightness temperature at the core channel to rule out the parameters to be retrieved. Results of soil moisture retrieval show that the multi-angular approach used by the MCCA generally has a better performance, unbiased root mean square difference (ubRMSD) varying from 0.028 cm³/cm³ to 0.037 cm³/cm³, than the multi-frequency approach (ubRMSD from 0.028 cm³/cm³ to 0.089 cm³/cm³) for the corn field. This is attributed to the dependence of vegetation tau on the frequency being more significant than that on the incidence angle. Except for in the C- & X-band combination, the multi-frequency approach used by the MCCA performs better (ubRMSD from 0.018 cm³/cm³ to 0.023 cm³/cm³) than the multi-angular version (ubRMSD from 0.026 cm³/cm³ to 0.034 cm³/cm³) for the grass field due to reduced vegetation effects for this type of cover. It is affirmed that increasing the number of observation channels could make the soil moisture retrieval more robust, but might also limit the retrieval performance, as the probability that the model estimations will not match the observations is increased. This study provides new insights into the design of potential satellite missions to improve soil moisture retrieval. A satellite with simultaneous multi-angular and multi-frequency observation capabilities is highly recommended.

由于SMOS（土壤水分和海洋盐度）和SMAP（土壤水分主动无源）任务的成功，即将出现新的卫星任务。当前和将来的任务可以从旨在改进检索算法的调查中受益，这些算法可以定量地绘制地球微波辐射中的全球土壤水分和植被光学深度（tau）。在这项研究中，我们考虑了当前和将来的卫星任务（例如，哥白尼成像微波辐射计，水循环观测任务和卫星观测）的有效载荷配置，探索了多角度和多频率的方法来检索土壤水分和植被tau。陆地水资源卫星）使用一组新的地面观测数据。这项研究使用了2017年7月至2017年8月（农田）和2018年8月至2018年9月（草原））河土壤水分实验期间在内蒙古收集的两个地面微波辐射数据集。覆盖整个生长期的玉米田表明，信息的程度随通道数量的增加而线性增加（就入射角和频率而言），并且多频观测所包含的独立信息比在相同通道数下的多角度观测。亮度温度的极化差异对土壤水分和植被含水量均敏感，特别是在L波段，由于其穿透能力，对它们都敏感。土壤水分解释了相邻频率（L和C波段，C和X波段）的亮度温度频率差异的大部分变化，而亮度温度的入射角差异的变化主要与植被水有关。内容。基于omega-tau模型的两部分版本开发了一种多通道协作算法（MCCA），该算法利用来自协作通道的信息表示为核心通道亮度温度的解析形式，从而排除了需要检索。土壤水分反演的结果表明，MCCA使用的多角度方法通常具有更好的性能，无偏方根均方差（ubRMSD）介于0.028 cm 亮度温度的入射角差的变化主要与植被含水量有关。基于omega-tau模型的两部分版本开发了一种多通道协作算法（MCCA），该算法利用来自协作通道的信息表示为核心通道亮度温度的解析形式，从而排除了需要检索。土壤水分反演的结果表明，MCCA使用的多角度方法通常具有更好的性能，无偏方根均方差（ubRMSD）介于0.028 cm 亮度温度的入射角差的变化主要与植被含水量有关。基于omega-tau模型的两部分版本开发了一种多通道协作算法（MCCA），该算法利用来自协作通道的信息表示为核心通道亮度温度的解析形式，从而排除了需要检索。土壤水分反演的结果表明，MCCA使用的多角度方法通常具有更好的性能，无偏方根均方差（ubRMSD）介于0.028 cm 它利用来自协作渠道的信息表示为核心渠道亮度温度的分析形式，以排除要检索的参数。土壤水分反演的结果表明，MCCA使用的多角度方法通常具有更好的性能，无偏方根均方差（ubRMSD）介于0.028 cm 它利用来自协作渠道的信息表示为核心渠道亮度温度的分析形式，以排除要检索的参数。土壤水分反演的结果表明，MCCA使用的多角度方法通常具有更好的性能，无偏方根均方差（ubRMSD）介于0.028 cm³ / cm ³到0.037 cm ³ / cm ³，比玉米田的多频方法（ubRMSD从0.028 cm ³ / cm ³到0.089 cm ³ / cm ³）高。这归因于植被tau对频率的依赖性比对入射角的依赖性更重要。除了在C波段和X波段组合中，MCCA所使用的多频方法的性能（ubRMSD从0.018 cm ³ / cm ³到0.023 cm ³ / cm ³）比多角度版本的性能更好（ubRMSD从0.026开始） cm ³ / cm ³至0.034 cm ³/ cm ³），因为这种类型的覆盖物减少了植被的影响。可以肯定的是，增加观测通道的数量可以使土壤水分的反演更加稳健，但也可能会限制反演的效果，因为模型估计与观测值不匹配的可能性会增加。这项研究为设计潜在的卫星任务以改善土壤水分获取提供了新的见识。强烈建议同时具有多角度和多频率观测功能的卫星。