Surface soil moisture (SSM) is of great importance in understanding global climate change and studies related to environmental and earth science. However, neither of current SSM products or algorithms can generate SSM with High spatial resolution, High spatio-temporal continuity (cloud-free and daily), and High accuracy simultaneously (i.e., 3H SSM data). Without 3H SSM data, fine-scale environmental and hydrological modeling cannot be easily achieved. To address this issue, we proposed a novel and integrated SSM downscaling framework inspired by deep learning-based point-surface fusion, which was designed to produce 1 km spatially seamless and temporally continuous SSM with high accuracy by fusing remotely sensed, model-based, and ground data. First, SSM auxiliary variables (e.g., land surface temperature, surface reflectance) were gap filled to ensure the spatial continuity. Meanwhile, the extended triple collocation method was adopted to select reliable in-situ stations to address the scale mismatch issue in SSM downscaling. Then, the deep belief model was utilized to downscale the original 9 km SMAP SSM and 0.1º. ERA5-Land SSM to 1 km. The downscaling framework was validated over three ISMN soil moisture networks covering diverse ground conditions in Southwestern US. Three validation strategies were adopted, including in-situ validation, time-series validation, and spatial distribution validation. Results showed that the average Pearson correlation coefficient (PCC), unbiased root mean squared error (ubRMSE), and mean absolute error (MAE) achieved 0.89, 0.034 $m^3{m}^{-3}$, and 0.032 $m^3{m}^{-3}$, respectively. The use of point-surface fusion greatly improved the downscaling accuracy, of which the PCC, ubRMSE, and MAE were improved by 3.73, 20.93, and 39.62% compared to surface-surface fusion method, respectively. Comparative analyses have also been carefully conducted to confirm the effectiveness of the framework, in terms of other downscaling algorithms, scale variations, and fusion methods. The proposed method is promising for fine-scale studies and applications in agricultural, hydrological, and environmental domains.

表层土壤水分（SSM）对于了解全球气候变化以及与环境和地球科学相关的研究具有重要意义。然而，目前的SSM产品或算法都不能同时生成具有高空间分辨率、高时空连续性（无云和日常）和高精度（即3H SSM数据）的SSM。如果没有 3H SSM 数据，就无法轻松实现精细的环境和水文建模。为了解决这个问题，我们提出了一种新颖的集成 SSM 缩减框架，其灵感来自基于深度学习的点面融合，旨在通过融合遥感、基于模型、和地面数据。首先，SSM 辅助变量（例如，地表温度，表面反射率）被间隙填充以确保空间连续性。同时，采用扩展的三重搭配方法来选择可靠的现场站，以解决 SSM 降尺度中的尺度不匹配问题。然后，利用深度信念模型将原始 9 km SMAP SSM 和 0.1º 缩小。ERA5-Land SSM 到 1 公里。该降尺度框架在覆盖美国西南部不同地面条件的三个 ISMN 土壤水分网络上得到验证。采用了三种验证策略，包括原位验证、时间序列验证和空间分布验证。结果表明，平均皮尔逊相关系数（PCC）、无偏均方根误差（ubRMSE）和平均绝对误差（MAE）分别达到0.89、0.034${米}^3{米}^{-3}$, 和 0.032${米}^3{米}^{-3}$， 分别。点面融合的使用大大提高了降尺度精度，其中PCC、ubRMSE和MAE与面面融合方法相比分别提高了3.73%、20.93%和39.62%。还仔细进行了比较分析，以确认框架在其他降尺度算法、尺度变化和融合方法方面的有效性。该方法有望用于农业、水文和环境领域的精细研究和应用。