Soil moisture (SM) downscaling has been extensively investigated in recent years to improve coarse resolution of SM products. However, available methods for downscaling are generally based on pixel-to-pixel strategy, which ignores the information among pixels. Hence, a new downscaling method based on a convolutional neural network (CNN) is proposed to solve the problem. Furthermore, a weight layer is designed for the input, and residual SM is treated as the output of the CNN to improve the accuracy. This method is applied to downscale Soil Moisture Active Passive (SMAP) SM products (i.e., 36-km $\mathbf {L3{\_}SM{\_}P}$ and 9-km $\mathbf {L3{\_}SM{\_}P{\_}E}$ ) from January 1, 2018 to December 30, 2018. Compared with 9-km $\mathbf {L3{\_}SM{\_}P{\_}E}$ , the 9-km downscaling result is satisfactory with obtained correlation coefficient ( $R$ ), root mean square error (RMSE), and unbiased RMSE (ubRMSE) values of 95.81%, 2.77%, and 2.67%, respectively. Moreover, SMAP SM products (36 and 9 km) and downscaling SM (3 and 1 km) are validated by the in situ data, which are collected by the 109 stations of the Oklahoma Mesonet SM monitoring network. Mean $R$ , RMSE, and ubRMSE values are 67.92%, 7.94%, and 4.87% for 36-km $\mathbf {L3{\_}SM{\_}P}$ ; 67.78%, 8.35%, and 4.95% for 9-km $\mathbf {L3{\_}SM{\_}P{\_}E}$ ; 67.28%, 8.34%, and 4.97% for 3-km downscaling SM; 65.90%, 8.40%, and 5.18% for 1-km downscaling SM, respectively. The 3-km downscaling SM generated by this method can improve the coarse resolution of 9-km $\mathbf {L3{\_}SM{\_}P{\_}E}$ while preserving its accuracy. However, error will remarkably increase in the 1-km downscaling SM. Therefore, the proposed method provides a new strategy for SM downscaling and obtains satisfactory results in practice. Additional studies can be conducted in the future.

中文翻译：

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利用卷积神经网络缩小SMAP土壤水分产品的比例

近年来，已经对土壤水分（SM）降尺度进行了广泛的研究，以提高SM产品的粗分辨率。然而，用于缩小的可用方法通常基于像素到像素策略，该策略忽略了像素之间的信息。因此，提出了一种新的基于卷积神经网络的降尺度方法。此外，为输入设计了权重层，并将残余SM视为CNN的输出，以提高准确性。此方法适用于规模较小的土壤水分主动被动（SMAP）SM产品（即36公里$ \ mathbf {L3 {\ _} SM {\ _} P} $ 和9公里 $ \ mathbf {L3 {\ _} SM {\ _} P {\ _} E} $ ）从2018年1月1日至2018年12月30日。与9公里相比 $ \ mathbf {L3 {\ _} SM {\ _} P {\ _} E} $ ，在9 km的降尺度结果中，获得的相关系数是令人满意的（ $ R $ ），均方根误差（RMSE）和无偏RMSE（ubRMSE）值分别为95.81％，2.77％和2.67％。此外，SMAP SM产品（36公里和9公里）和降级SM产品（3公里和1公里）已通过原位数据，由俄克拉荷马州Mesonet SM监控网络的109个站收集。吝啬的$ R $ ，RMSE和ubRMSE值在36公里内分别为67.92％，7.94％和4.87％ $ \ mathbf {L3 {\ _} SM {\ _} P} $ ; 9公里为67.78％，8.35％和4.95％$ \ mathbf {L3 {\ _} SM {\ _} P {\ _} E} $ ; 3公里缩小SM分别为67.28％，8.34％和4.97％；1公里缩小比例SM分别为65.90％，8.40％和5.18％。通过这种方法生成的3 km缩小比例SM可以提高9 km的粗分辨率$ \ mathbf {L3 {\ _} SM {\ _} P {\ _} E} $同时保持其准确性。但是，在1 km缩小比例的SM中，误差会显着增加。因此，该方法为SM的降尺度提供了一种新的策略，并在实践中取得了令人满意的结果。将来可以进行其他研究。