Abstract

Estimating terrestrial water storage (TWS) with high spatial resolution is crucial for hydrological and water resource management. Comparing to traditional in-situ data measurement, observation from space borne sensor such as Gravity Recovery and Climate Experiment (GRACE) satellites is quite effective to obtain a large-scale TWS data. However, the coarse resolution of the GRACE data restricts its application at a local scale. This paper presents three novel convolutional neural network (CNN) based approaches including the Super-Resolution CNN (SRCNN), the Very Deep Super-Resolution (VDSR), and the Residual Channel Attention Networks (RCAN) to spatial downscaling of the monthly GRACE TWS products using the outputs of the Ecological Assimilation of Land and Climate Observations (EALCO) model over Canada. We also compare the performance of CNN-based methods with the empirical linear regression-based downscaling method. All comparison results were evaluated by root mean square error (RMSE) between the reconstructed GRACE TWS and the original one. RMSEs over the matched pixels are 22.3, 14.4, 18.4 and 71.6 mm of SRCNN, VDSR, RCAN and linear regression-based method respectively. Obviously, VDSR shows the best accuracy among all methods. The result shows all CNN-based super resolution methods preform much better than traditional method in spatial downscaling.

摘要

以高空间分辨率估算陆地储水量 (TWS) 对水文和水资源管理至关重要。与传统的原位数据测量相比，重力恢复和气候实验（GRACE）卫星等星载传感器的观测对于获取大规模TWS数据非常有效。然而，GRACE 数据的粗分辨率限制了其在局部尺度上的应用。本文介绍了三种基于卷积神经网络 (CNN) 的新型方法，包括超分辨率 CNN (SRCNN)、超深超分辨率 (VDSR) 和残差通道注意网络 (RCAN)，以对每月 GRACE TWS 进行空间缩小使用加拿大土地和气候观测生态同化 (EALCO) 模型输出的产品。我们还将基于 CNN 的方法与基于经验线性回归的降尺度方法的性能进行了比较。所有比较结果均通过重建的 GRACE TWS 与原始 TWS 之间的均方根误差 (RMSE) 进行评估。SRCNN、VDSR、RCAN 和基于线性回归的方法的匹配像素的 RMSE 分别为 22.3、14.4、18.4 和 71.6 毫米。显然，VDSR 在所有方法中表现出最好的准确度。结果表明，所有基于 CNN 的超分辨率方法在空间缩小方面都比传统方法要好得多。分别基于 RCAN 和基于线性回归的方法。显然，VDSR 在所有方法中表现出最好的准确度。结果表明，所有基于 CNN 的超分辨率方法在空间缩小方面都比传统方法要好得多。分别基于 RCAN 和基于线性回归的方法。显然，VDSR 在所有方法中表现出最好的准确度。结果表明，所有基于 CNN 的超分辨率方法在空间缩小方面都比传统方法要好得多。