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Snow depth (SD) is an indispensable parameter for many studies. Launched in 2018, the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) is designed to obtain global glacial elevations, but it can also acquire canopy and terrain elevations. Whether the depth of seasonal snow can be estimated by directly comparing the difference in elevations in snow-cover and snow-free cases, many people may reasonably ask. In this letter, we conduct such an investigation in Altay, Northwest China, using ICESat-2 ATL08 elevation products. Our investigation suggests: 1) in mountainous areas, the answer maybe is no because the estimation is obviously affected by rugged topography; 2) but in flat regions, SDs have been effectively estimated. (The R² is up to 0.88 between estimates and ground measurements.); and 3) as expected, land-cover types also affect the accuracy of the results, and the best estimation happens over the type of bare land. Therefore, estimating the depth of seasonal snow from the ICESat-2 product may be feasible, but we must check the results carefully.
Xiaojing Hu; Xiaohua Hao; Jian Wang; Guanghui Huang; Hongyi Li; Qian Yang. Can the Depth of Seasonal Snow be Estimated From ICESat-2 Products: A Case Investigation in Altay, Northwest China. IEEE Geoscience and Remote Sensing Letters 2021, PP, 1 -5.
AMA StyleXiaojing Hu, Xiaohua Hao, Jian Wang, Guanghui Huang, Hongyi Li, Qian Yang. Can the Depth of Seasonal Snow be Estimated From ICESat-2 Products: A Case Investigation in Altay, Northwest China. IEEE Geoscience and Remote Sensing Letters. 2021; PP (99):1-5.
Chicago/Turabian StyleXiaojing Hu; Xiaohua Hao; Jian Wang; Guanghui Huang; Hongyi Li; Qian Yang. 2021. "Can the Depth of Seasonal Snow be Estimated From ICESat-2 Products: A Case Investigation in Altay, Northwest China." IEEE Geoscience and Remote Sensing Letters PP, no. 99: 1-5.
Endmember extraction is a primary and indispensable component of the spectral mixing analysis model applicated to quantitatively retrieve fractional snow cover (FSC) from satellite observation. In this study, a new endmember extraction algorithm, the spatial–spectral–environmental (SSE) endmember extraction algorithm, is developed, in which spatial, spectral and environmental information are integrated together to automatically extract different types of endmembers from moderate resolution imaging spectroradiometer (MODIS) images. Then, combining the linear spectral mixture analysis model (LSMA), the SSE endmember extraction algorithm is practically applied to retrieve FSC from standard MODIS surface reflectance products in China. The new algorithm of MODIS FSC retrieval is named as SSEmod. The accuracy of SSEmod is quantitatively validated with 16 higher spatial-resolution FSC maps derived from Landsat 8 binary snow cover maps. Averaged over all regions, the average root-mean-square-error (RMSE) and mean absolute error (MAE) are 0.136 and 0.092, respectively. Simultaneously, we also compared the SSEmod with MODImLAB, MODSCAG and MOD10A1. In all regions, the average RMSE of SSEmod is improved by 2.3%, 2.6% and 5.3% compared to MODImLAB for 0.157, MODSCAG for 0.157 and MOD10A1 for 0.189. Therefore, our SSE endmember extraction algorithm is reliable for the MODIS FSC retrieval and may be also promising to apply other similar satellites in view of its accuracy and efficiency.
Hongyu Zhao; Xiaohua Hao; Jian Wang; Hongyi Li; Guanghui Huang; Donghang Shao; Bo Su; Huajin Lei; Xiaojing Hu. The Spatial–Spectral–Environmental Extraction Endmember Algorithm and Application in the MODIS Fractional Snow Cover Retrieval. Remote Sensing 2020, 12, 3693 .
AMA StyleHongyu Zhao, Xiaohua Hao, Jian Wang, Hongyi Li, Guanghui Huang, Donghang Shao, Bo Su, Huajin Lei, Xiaojing Hu. The Spatial–Spectral–Environmental Extraction Endmember Algorithm and Application in the MODIS Fractional Snow Cover Retrieval. Remote Sensing. 2020; 12 (22):3693.
Chicago/Turabian StyleHongyu Zhao; Xiaohua Hao; Jian Wang; Hongyi Li; Guanghui Huang; Donghang Shao; Bo Su; Huajin Lei; Xiaojing Hu. 2020. "The Spatial–Spectral–Environmental Extraction Endmember Algorithm and Application in the MODIS Fractional Snow Cover Retrieval." Remote Sensing 12, no. 22: 3693.
Seasonal snow cover is closely related to regional climate and hydrological processes. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow cover products from 2001 to 2018 were applied to analyze the snow cover variation in northern Xinjiang, China. As cloud obscuration causes significant spatiotemporal discontinuities in the binary snow cover extent (SCE), we propose a conditional probability interpolation method based on a space-time cube (STCPI) to remove clouds completely after combining Terra and Aqua data. First, the conditional probability that the central pixel and every neighboring pixel in a space-time cube of 5 × 5 × 5 with the same snow condition is counted. Then the snow probability of the cloud pixels reclassified as snow is calculated based on the space-time cube. Finally, the snow condition of the cloud pixels can be recovered by snow probability. The validation experiments with the cloud assumption indicate that STCPI can remove clouds completely and achieve an overall accuracy of 97.44% under different cloud fractions. The generated daily cloud-free MODIS SCE products have a high agreement with the Landsat–8 OLI image, for which the overall accuracy is 90.34%. The snow cover variation in northern Xinjiang, China, from 2001 to 2018 was investigated based on the snow cover area (SCA) and snow cover days (SCD). The results show that the interannual change of SCA gradually decreases as the elevation increases, and the SCD and elevation have a positive correlation. Furthermore, the interannual SCD variation shows that the area of increase is higher than that of decrease during the 18 years.
Siyong Chen; Xiaoyan Wang; Hui Guo; Peiyao Xie; Jian Wang; Xiaohua Hao. A Conditional Probability Interpolation Method Based on a Space-Time Cube for MODIS Snow Cover Products Gap Filling. Remote Sensing 2020, 12, 3577 .
AMA StyleSiyong Chen, Xiaoyan Wang, Hui Guo, Peiyao Xie, Jian Wang, Xiaohua Hao. A Conditional Probability Interpolation Method Based on a Space-Time Cube for MODIS Snow Cover Products Gap Filling. Remote Sensing. 2020; 12 (21):3577.
Chicago/Turabian StyleSiyong Chen; Xiaoyan Wang; Hui Guo; Peiyao Xie; Jian Wang; Xiaohua Hao. 2020. "A Conditional Probability Interpolation Method Based on a Space-Time Cube for MODIS Snow Cover Products Gap Filling." Remote Sensing 12, no. 21: 3577.
Snow surface spectral reflectance is very important in the Earth’s climate system. Traditional land surface models with parameterized schemes can simulate broadband snow surface albedo but cannot accurately simulate snow surface spectral reflectance with continuous and fine spectral wavebands, which constitute the major observations of current satellite sensors; consequently, there is an obvious gap between land surface model simulations and remote sensing observations. Here, we suggest a new integrated scheme that couples a radiative transfer model with a land surface model to simulate high spectral resolution snow surface reflectance information specifically targeting multisource satellite remote sensing observations. Our results indicate that the new integrated model can accurately simulate snow surface reflectance information over a large spatial scale and continuous time series. The integrated model extends the range of snow spectral reflectance simulation to the whole shortwave band and can predict snow spectral reflectance changes in the solar spectrum region based on meteorological element data. The kappa coefficients (K) of both the narrowband snow albedo targeting Moderate Resolution Imaging Spectroradiometer (MODIS) data simulated by the new integrated model and the retrieved snow albedo based on MODIS reflectance data are 0.5, and both exhibit good spatial consistency. Our proposed narrowband snow albedo simulation scheme targeting satellite remote sensing observations is consistent with remote sensing satellite observations in time series and can predict narrowband snow albedo even during periods of missing remote sensing observations. This new integrated model is a significant improvement over traditional land surface models for the direct spectral observations of satellite remote sensing. The proposed model could contribute to the effective combination of snow surface reflectance information from multisource remote sensing observations with land surface models.
Donghang Shao; Wenbo Xu; Hongyi Li; Jian Wang; Xiaohua Hao. Modeling Snow Surface Spectral Reflectance in a Land Surface Model Targeting Satellite Remote Sensing Observations. Remote Sensing 2020, 12, 3101 .
AMA StyleDonghang Shao, Wenbo Xu, Hongyi Li, Jian Wang, Xiaohua Hao. Modeling Snow Surface Spectral Reflectance in a Land Surface Model Targeting Satellite Remote Sensing Observations. Remote Sensing. 2020; 12 (18):3101.
Chicago/Turabian StyleDonghang Shao; Wenbo Xu; Hongyi Li; Jian Wang; Xiaohua Hao. 2020. "Modeling Snow Surface Spectral Reflectance in a Land Surface Model Targeting Satellite Remote Sensing Observations." Remote Sensing 12, no. 18: 3101.
Atmospheric water vapor plays an important role in the water cycle, especially in arid Central Asia, where precipitation is invaluable to water resources. Understanding and quantifying the relationship between water vapor source regions and precipitation is a key problem in water resource research in typical arid Central Asia, Northern Xinjiang. However, the relationship between precipitation and water vapor sources is still unclear of snow season. This paper aimed at studying the role of water vapor source supply in the Northern Xinjiang precipitation trend, which was investigated using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. The results showed that the total water vapor contributed from Western Eurasia and the North Polar area presented upward trends similar to the precipitation change trend, which indicated that the water vapor contribution from the two previous water vapor source regions supplied abundant water vapor and maintained the upward precipitation trend from 1980 to 2017 in Northern Xinjiang. From the climatology of water vapor transport, the region was controlled by midlatitude westerlies and major water vapor input from the western boundary, and the net water vapor flux of this region also showed an annual increasing trend. Western Eurasia had the largest moisture percentage contribution to Northern Xinjiang (48.11%) over the past 38 years. Northern Xinjiang precipitation was correlated with water vapor from Western Eurasia, the North Polar area, and Siberia, and the correlation coefficients were 0.66, 0.45, and 0.57, respectively. These results could aid in better understanding the water cycle process and climate change in this typical arid region of Central Asia.
Weiguo Wang; Hongyi Li; Jian Wang; Xiaohua Hao. Water Vapor from Western Eurasia Promotes Precipitation during the Snow Season in Northern Xinjiang, a Typical Arid Region in Central Asia. Water 2020, 12, 141 .
AMA StyleWeiguo Wang, Hongyi Li, Jian Wang, Xiaohua Hao. Water Vapor from Western Eurasia Promotes Precipitation during the Snow Season in Northern Xinjiang, a Typical Arid Region in Central Asia. Water. 2020; 12 (1):141.
Chicago/Turabian StyleWeiguo Wang; Hongyi Li; Jian Wang; Xiaohua Hao. 2020. "Water Vapor from Western Eurasia Promotes Precipitation during the Snow Season in Northern Xinjiang, a Typical Arid Region in Central Asia." Water 12, no. 1: 141.
The Normalized Difference Snow Index (NDSI) is an effective index for snow-cover mapping at large scales, but in forested regions the identification accuracy for snow using the NDSI is low because of forest cover effects. In this study, typical evergreen coniferous forest zones on Qilian Mountain in the Upper Heihe River Basin (UHRB) were chosen as example regions. By analyzing the spectral signature of snow-covered and snow-free evergreen coniferous forests with Landsat Operational Land Imager (OLI) data, a novel spectral band ratio using near-infrared (NIR) and shortwave infrared (SWIR) bands, defined as (ρnir − ρswir)/(ρnir + ρswir), is proposed. Our research shows that this band ratio, named the normalized difference forest snow index (NDFSI), can be used to effectively distinguish snow-covered evergreen coniferous forests from snow-free evergreen coniferous forests in UHRB.
Xiao-Yan Wang; Jian Wang; Zhi-Yong Jiang; Hong-Yi Li; Xiao-Hua Hao. An Effective Method for Snow-Cover Mapping of Dense Coniferous Forests in the Upper Heihe River Basin Using Landsat Operational Land Imager Data. Remote Sensing 2015, 7, 17246 -17257.
AMA StyleXiao-Yan Wang, Jian Wang, Zhi-Yong Jiang, Hong-Yi Li, Xiao-Hua Hao. An Effective Method for Snow-Cover Mapping of Dense Coniferous Forests in the Upper Heihe River Basin Using Landsat Operational Land Imager Data. Remote Sensing. 2015; 7 (12):17246-17257.
Chicago/Turabian StyleXiao-Yan Wang; Jian Wang; Zhi-Yong Jiang; Hong-Yi Li; Xiao-Hua Hao. 2015. "An Effective Method for Snow-Cover Mapping of Dense Coniferous Forests in the Upper Heihe River Basin Using Landsat Operational Land Imager Data." Remote Sensing 7, no. 12: 17246-17257.
High-resolution snow distributions are essential for studying cold regions. However, the temporal and spatial resolutions of current remote sensing snow maps remain limited. Remotely sensed snow cover fraction (SCF) data only provide quantitative descriptions of snow area proportions and do not provide information on subgrid-scale snow locations. We present a downscaling method based on simulated inhomogeneous snow ablation capacities that are driven by air temperature and solar radiation data. This method employs a single parameter to adjust potential snow ablation capacities. Using this method, SCF data with a resolution of 500 m are downscaled to a resolution of 30 m. Then, 18 remotely sensed TM, CHRIS and EO-1 snow maps are used to verify the downscaled results. The mean overall accuracy is 0.69, the average root-mean-square error (RMSE) of snow-covered slopes between the downscaled snow map and the real snow map is 3.9°, and the average RMSE of the sine of the snow covered aspects between the downscaled snow map and the real snow map is 0.34, which is equivalent to 19.9°. This method can be applied to high-resolution snow mapping in similar mountainous regions.
Hong Yi Li; Yong Qi He; Xiao Hua Hao; Tao Che; Jian Wang; Xiao Dong Huang. Downscaling Snow Cover Fraction Data in Mountainous Regions Based on Simulated Inhomogeneous Snow Ablation. Remote Sensing 2015, 7, 8995 -9019.
AMA StyleHong Yi Li, Yong Qi He, Xiao Hua Hao, Tao Che, Jian Wang, Xiao Dong Huang. Downscaling Snow Cover Fraction Data in Mountainous Regions Based on Simulated Inhomogeneous Snow Ablation. Remote Sensing. 2015; 7 (7):8995-9019.
Chicago/Turabian StyleHong Yi Li; Yong Qi He; Xiao Hua Hao; Tao Che; Jian Wang; Xiao Dong Huang. 2015. "Downscaling Snow Cover Fraction Data in Mountainous Regions Based on Simulated Inhomogeneous Snow Ablation." Remote Sensing 7, no. 7: 8995-9019.