Soil moisture (SM) is one of the key land surface parameters, but the coarse spatial resolution of the passive microwave SM products constrains the precise monitoring of surface changes. The existing SM downscaling methods typically either utilize spatio-temporal information or leverage auxiliary parameters, without fully mining the complementary information between them. In this paper, a generalized spatio-temporal-spectral integrated fusion-based downscaling method is proposed to fully utilize the complementary features between multi-source auxiliary parameters and multi-temporal SM data. Specifically, we define the spectral characteristic of geographic objects as an assemblage of diverse attribute characteristics at specific spatio-temporal locations and scales. Based on this, the SM-related auxiliary parameter data can be treated as the generalized spectral characteristics of SM, and a generalized spatio-temporal-spectral integrated fusion framework is proposed to integrate the spatio-temporal features of the SM products and the generalized spectral features from the auxiliary parameters to generate fine spatial resolution SM data with high quality. In addition, considering the high heterogeneity of multi-source data, the proposed framework is based on a spatio-temporal constrained cycle generative adversarial network (STC-CycleGAN). The proposed STC-CycleGAN network comprises a forward integrated fusion stage and a backward spatio-temporal constraint stage, between which spatio-temporal cycle-consistent constraints are formed. Numerous experiments were conducted on Soil Moisture Active Passive (SMAP) SM products. The qualitative, quantitative, and in-situ site verification results demonstrate the capability of the proposed method to mine the complementary information of multi-source data and achieve high-accuracy downscaling of global daily SM data from 36 km to 9 km.

中文翻译：

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广义时空光谱一体化融合用于土壤水分降尺度


土壤水分 （SM） 是关键的地表参数之一，但无源微波 SM 产品的粗略空间分辨率限制了对地表变化的精确监测。现有的 SM 降尺度方法通常要么利用时空信息，要么利用辅助参数，而不充分挖掘它们之间的互补信息。该文提出了一种基于广义时空谱一体化融合的降尺度方法，以充分利用多源辅助参数与多时相 SM 数据之间的互补特征。具体来说，我们将地理对象的光谱特征定义为特定时空位置和尺度上不同属性特征的集合。基于此，可以将SM相关的辅助参数数据视为SM的广义谱特征，并提出了广义的时空谱一体化融合框架，将SM产品的时空特征与辅助参数的广义谱特征进行融合，生成高质量的精细空间分辨率SM数据。此外，考虑到多源数据的高度异构性，所提出的框架基于时空约束循环生成对抗网络 （STC-CycleGAN）。所提出的 STC-CycleGAN 网络包括前向集成融合阶段和后向时空约束阶段，两者之间形成时空周期一致约束。对土壤水分主动被动 （SMAP） SM 产品进行了大量实验。 定性、定量和原位站点验证结果表明，所提方法能够挖掘多源数据的互补信息，实现全球每日 SM 数据从 36 km 到 9 km 的高精度缩小。