Abstract
Accurate high spatial resolution snow depth mapping in arid and semi-arid regions is of great importance for snow disaster assessment and hydrological modeling. However, due to the complex topography and low spatial-resolution microwave remote-sensing data, the existing snow depth datasets have large errors and uncertainty, and actual spatiotemporal heterogeneity of snow depth cannot be effectively detected. This paper proposed a deep learning approach based on downscaling snow depth retrieval by fusion of satellite remote-sensing data with multiple spatial scales and diverse characteristics. The (Fengyun-3 Microwave Radiation Imager) FY-3 MWRI data were downscaled to 500 m resolution to match Moderate-resolution Imaging Spectroradiometer (MODIS) snow cover, meteorological and geographic data. A deep neural network was constructed to capture detailed spectral and radiation signals and trained to retrieve the higher spatial resolution snow depth from the aforementioned input data and ground observation. Verified by in situ measurements, downscaled snow depth has the lowest root mean square error (RMSE) and mean absolute error (MAE) (8.16 cm, 4.73 cm respectively) among Environmental and Ecological Science Data Center for West China Snow Depth (WESTDC_SD, 9.38 cm and 5.36 cm), the Microwave Radiation Imager (MWRI) Ascend Snow Depth (MWRI_A_SD, 9.45 cm and 5.49 cm) and MWRI Descend Snow Depth (MWRI_D_SD, 10.55 cm and 6.13 cm) in the study area. Meanwhile, downscaled snow depth could provide more detailed information in spatial distribution, which has been used to analyze the decrease of retrieval accuracy by various topography factors.
Highlights
Snow is an important indicator for global climate change, which has significant positive and noteworthy negative feedbacks on climate system, water resources and ecological environment [1,2,3,4]
Accurate high spatial resolution snow depth mapping is of great importance for regional snow disaster assessment and hydrological simulation, especially for Northern Xinjiang (NX) with a complex topography and abundant snow
Microwave data with coarse spatial resolution are not enough to detect the spatiotemporal heterogeneity of snow depth
Summary
Snow is an important indicator for global climate change, which has significant positive and noteworthy negative feedbacks on climate system, water resources and ecological environment [1,2,3,4]. Due to the high surface albedo and large-scale cooling effect, snow cover (SC) participates in energy exchange between the atmosphere and Earth’s land, regulating regional and global climate. Snowmelt can provide water resources for ecosystem and participate in regional water cycles, which is beneficial for Remote Sens. Snow water equivalent (SWE) is an important driving factor for meteorological, climate and hydrological models, which can enhance the understanding of regional water resources environment and improve the accuracy of a weather forecast. As the most sensitive and active response to climate change, snow has become an important field of research. Snow cover mapping using optical remote sensing has developed many mature datasets, such as Landsat [6], Systeme Probatoire d’Observation de la Terre (SPOT) [7], Advanced Very High Resolution Radiometer (AVHRR) [8] and Moderate-resolution Imaging Spectroradiometer (MODIS) snow cover datasets [9,10,11,12,13]
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