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Hongyi Li
Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China

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Journal article
Published: 01 July 2021 in Journal of Hydrology
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Remote sensing can potentially be used to monitor river ice, which is of significance for water resource utilization and hydrological research, especially on the Tibetan Plateau, over which field observations are severely limited. However, methods to monitor the river ice distribution over a large scale while overcoming snow interference have not been reported yet. To monitor river ice on the Tibetan Plateau on a large scale, we present a relative difference river ice (RDRI) identification method based on the differential spectral characteristics of river ice. The difference between the red and near-infrared (NIR) band reflectance of river ice is divided by the sum of the NIR reflectance and infrared band reflectance. The resulting values are notably different for river ice and other neighboring similar landscapes, such as snow cover. This method can overcome the interference caused by snow cover and other surrounding landscapes and enable the monitoring of river ice in different forms at different elevations on the Tibetan Plateau. The RDRI method is applied to Landsat and Sentinel-2 images. The validation results show that the mean overall accuracy and Kappa coefficient were 99.76% and 0.94, respectively. The comparison of the RDRI and normalized difference snow index (NDSI) methods indicates that the accuracies of both methods for river ice monitoring are similar in the absence of any snow disturbance. The RDRI can enable more accurate remote sensing of river ice on a large scale, which can support river ice distribution research over the Tibetan Plateau.

ACS Style

Haojie Li; Hongyi Li; Jian Wang; Xiaohua Hao. Identifying river ice on the Tibetan Plateau based on the relative difference in spectral bands. Journal of Hydrology 2021, 601, 126613 .

AMA Style

Haojie Li, Hongyi Li, Jian Wang, Xiaohua Hao. Identifying river ice on the Tibetan Plateau based on the relative difference in spectral bands. Journal of Hydrology. 2021; 601 ():126613.

Chicago/Turabian Style

Haojie Li; Hongyi Li; Jian Wang; Xiaohua Hao. 2021. "Identifying river ice on the Tibetan Plateau based on the relative difference in spectral bands." Journal of Hydrology 601, no. : 126613.

Journal article
Published: 25 May 2021 in IEEE Geoscience and Remote Sensing Letters
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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.

ACS Style

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 Style

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 (99):1-5.

Chicago/Turabian Style

Xiaojing 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.

Journal article
Published: 11 November 2020 in Remote Sensing
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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.

ACS Style

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 Style

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 (22):3693.

Chicago/Turabian Style

Hongyu 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.

Journal article
Published: 22 September 2020 in Remote Sensing
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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.

ACS Style

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 Style

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 (18):3101.

Chicago/Turabian Style

Donghang 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.

Journal article
Published: 07 July 2020 in Journal of Geophysical Research: Solid Earth
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The Hispar Glacier is a useful site for studying surge mechanisms. Prior to this study only two‐dimensional (2D) flow velocities having low temporal resolution were available for this glacier, providing inadequate information about its surge evolution. In this study, 139 Sentinel‐1A images were used to obtain 3D flow velocity time series for the Hispar Glacier during the recent surge (2014‐2016). The 3D flow velocities were sampled at an interval of 11 days, which is much greater than in previous studies. Besides, the SRTM DEM and two TanDEM‐X images were used to determine glacier thickness changes prior to and following the recent surge. Combining the results and geomorphologic features, we deduced that the recent surge was because of saturated basal water pressure in the Yutmaru tributary. The mass from the Yutmaru tributary squeezed into the trunk and rapidly flowed downslope along the northern margin, generating strong normal pressure to the trunk mass. Pushed by the Yutmaru tributary, the trunk began to surge in September 2014. The flow velocity reached a first peak in May 2015, and then decreased to October 2015, as part of the basal meltwater ran off. However, basal meltwater accumulated again during the following four months, and correspondingly the trunk accelerated again after October 2015. Finally, as kinetic energy was released and resisting force increased, the trunk became almost stagnant in August 2016. The surge mass was blocked downstream in the trunk by the mass transferred from the Kunyang tributary, and consequently the glacier did not advance.

ACS Style

Lei Guo; Jia Li; Zhi‐Wei Li; Li‐Xin Wu; Xin Li; Jun Hu; Hui‐Lin Li; Hong‐Yi Li; Ze‐Lang Miao; Zhong‐Qin Li. The Surge of the Hispar Glacier, Central Karakoram: SAR 3‐D Flow Velocity Time Series and Thickness Changes. Journal of Geophysical Research: Solid Earth 2020, 125, 1 .

AMA Style

Lei Guo, Jia Li, Zhi‐Wei Li, Li‐Xin Wu, Xin Li, Jun Hu, Hui‐Lin Li, Hong‐Yi Li, Ze‐Lang Miao, Zhong‐Qin Li. The Surge of the Hispar Glacier, Central Karakoram: SAR 3‐D Flow Velocity Time Series and Thickness Changes. Journal of Geophysical Research: Solid Earth. 2020; 125 (7):1.

Chicago/Turabian Style

Lei Guo; Jia Li; Zhi‐Wei Li; Li‐Xin Wu; Xin Li; Jun Hu; Hui‐Lin Li; Hong‐Yi Li; Ze‐Lang Miao; Zhong‐Qin Li. 2020. "The Surge of the Hispar Glacier, Central Karakoram: SAR 3‐D Flow Velocity Time Series and Thickness Changes." Journal of Geophysical Research: Solid Earth 125, no. 7: 1.

Journal article
Published: 13 June 2020 in Remote Sensing of Environment
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River ice monitoring is important for hydrological research and water resource management of the Tibetan Plateau but limited by the serious shortage of field observations, and remote sensing can be used as an effective supplementary means for monitoring river ice. However, remote sensing high-altitude river ice is scarce and a basin-scale understanding of river ice is lacking on the Tibetan Plateau. To ascertain the spatial and temporal distribution characteristics of high-altitude river ice at the basin scale, we selected the Babao River basin as the study area, which is a typical river basin located in the northeastern Tibetan Plateau. Utilizing 447 available Landsat images during the river ice period from 1999 to 2018 and the classical normalized difference snow index (NDSI) algorithm, we monitored the river ice in a long time series at the Babao River basin. The average Khat of accuracy validation reached 0.973. The average area of river ice in the river ice period of this basin showed a weak decreasing trend and was negatively correlated with air temperature. We also found that gentle slopes and high elevations are beneficial for the development of river ice. The melting of river ice supplements river discharge in spring. This study is the first to reveal the distribution characteristics and changing trend of river ice at the basin scale on the Tibetan Plateau, and the results provide a reference for river ice research in this region.

ACS Style

Haojie Li; Hongyi Li; Jian Wang; Xiaohua Hao. Monitoring high-altitude river ice distribution at the basin scale in the northeastern Tibetan Plateau from a Landsat time-series spanning 1999–2018. Remote Sensing of Environment 2020, 247, 111915 .

AMA Style

Haojie Li, Hongyi Li, Jian Wang, Xiaohua Hao. Monitoring high-altitude river ice distribution at the basin scale in the northeastern Tibetan Plateau from a Landsat time-series spanning 1999–2018. Remote Sensing of Environment. 2020; 247 ():111915.

Chicago/Turabian Style

Haojie Li; Hongyi Li; Jian Wang; Xiaohua Hao. 2020. "Monitoring high-altitude river ice distribution at the basin scale in the northeastern Tibetan Plateau from a Landsat time-series spanning 1999–2018." Remote Sensing of Environment 247, no. : 111915.

Journal article
Published: 02 January 2020 in Water
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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.

ACS Style

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 Style

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 (1):141.

Chicago/Turabian Style

Weiguo 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.

Journal article
Published: 01 November 2019 in Water Resources Research
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River discharge gauging is scarce in high mountainous regions, especially on the Tibetan Plateau, where rivers are widely distributed. Although remote sensing is an important mean of monitoring river discharge, previous methods are most suitable for large rivers, and the ability to monitor small rivers (with widths less than 100 m) is limited. To resolve this issue, a multiple pixel ratio (MPR) method is presented for monitoring the discharge of small rivers based on the relationship between river discharge and the near‐infrared reflectivity. Utilizing 281 Landsat images (1990‐2015), we monitored river discharges in two sub‐basins in the upstream region of the Heihe River located on the northeastern Tibetan Plateau. Our results indicate that the performance of the MPR method is more stable than the previous calibration/measurement (C/M) method. The monitoring accuracy was correlated with the length and location of the selected inundated river channel (SIRC). Using SIRC lengths between 300 and 600 m can provide better monitoring accuracy. The Nash‐Sutcliffe efficiency (NSE) of monitoring results (2013‐2014) of Qilian station was 0.82; the Zhamashike station (2015) was 0.45; the two stations multi‐years (1990‐2013) monitoring results was 0.32, 0.41, respectively; the ungauged basin was 0.45. Our results suggest that the MPR method can expand the ability of remote sensing to monitor discharge in small rivers with widths greater than 30 m on the Tibetan Plateau. In addition, the new method also has the potential to monitor the discharge of ungauged small rivers (with widths greater than 30 m).

ACS Style

Haojie Li; Hongyi Li; Jian Wang; Xiaohua Hao. Extending the Ability of Near‐Infrared Images to Monitor Small River Discharge on the Northeastern Tibetan Plateau. Water Resources Research 2019, 55, 8404 -8421.

AMA Style

Haojie Li, Hongyi Li, Jian Wang, Xiaohua Hao. Extending the Ability of Near‐Infrared Images to Monitor Small River Discharge on the Northeastern Tibetan Plateau. Water Resources Research. 2019; 55 (11):8404-8421.

Chicago/Turabian Style

Haojie Li; Hongyi Li; Jian Wang; Xiaohua Hao. 2019. "Extending the Ability of Near‐Infrared Images to Monitor Small River Discharge on the Northeastern Tibetan Plateau." Water Resources Research 55, no. 11: 8404-8421.

Journal article
Published: 17 August 2019 in Journal of Geophysical Research: Atmospheres
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ACS Style

Hongyi Li; Xin Li; Dawen Yang; Jian Wang; Bing Gao; Xiaoduo Pan; Yanlin Zhang; Xiaohua Hao. Tracing Snowmelt Paths in an Integrated Hydrological Model for Understanding Seasonal Snowmelt Contribution at Basin Scale. Journal of Geophysical Research: Atmospheres 2019, 124, 8874 -8895.

AMA Style

Hongyi Li, Xin Li, Dawen Yang, Jian Wang, Bing Gao, Xiaoduo Pan, Yanlin Zhang, Xiaohua Hao. Tracing Snowmelt Paths in an Integrated Hydrological Model for Understanding Seasonal Snowmelt Contribution at Basin Scale. Journal of Geophysical Research: Atmospheres. 2019; 124 (16):8874-8895.

Chicago/Turabian Style

Hongyi Li; Xin Li; Dawen Yang; Jian Wang; Bing Gao; Xiaoduo Pan; Yanlin Zhang; Xiaohua Hao. 2019. "Tracing Snowmelt Paths in an Integrated Hydrological Model for Understanding Seasonal Snowmelt Contribution at Basin Scale." Journal of Geophysical Research: Atmospheres 124, no. 16: 8874-8895.

Journal article
Published: 13 December 2018 in Vadose Zone Journal
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Research on land surface processes at the catchment scale has drawn much attention over the past few decades, and a number of watershed observatories have been established worldwide. The Heihe River Basin (HRB), which contains the second largest inland river in China, is an ideal natural field experimental area for investigation of land surface processes involving diverse landscapes and the coexistence of cold and arid regions. The Heihe Integrated Observatory Network was established in 2007. For long-term observations, a hydrometeorological observatory, ecohydrological wireless sensor network, and satellite remote sensing are now in operation. In 2012, a multiscale observation experiment on evapotranspiration over heterogeneous land surfaces was conducted in the midstream region of the HRB, which included a flux observation matrix, wireless sensor network, airborne remote sensing, and synchronized ground measurements. Under an open data policy, the datasets have been publicly released following careful data processing and quality control. The outcomes highlight the integrated research on land surface processes in the HRB and include observed trends, scaling methods, high spatiotemporal resolution remote sensing products, and model–data integration in the HRB, all of which are helpful to other endorheic basins in the “Silk Road Economic Belt.” Henceforth, the goal of the Heihe Integrated Observatory Network is to develop an intelligent monitoring system that incorporates ground-based observatory networks, unmanned aerial vehicles, and multi-source satellites through the Internet of Things technology. Furthermore, biogeochemical processes observation will be improved, and the study of integrating ground observations, remote sensing, and large-scale models will be promoted further. Copyright © 2018. . Copyright © by the Soil Science Society of America, Inc.

ACS Style

Shaomin Liu; Xin Li; Ziwei Xu; Tao Che; Qing Xiao; Mingguo Ma; Qinhuo Liu; Rui Jin; Jianwen Guo; Liangxu Wang; Weizhen Wang; Yuan Qi; Hongyi Li; Tongren Xu; Youhua Ran; Xiaoli Hu; ShengJin Shi; Zhongli Zhu; Junlei Tan; Yang Zhang; Zhiguo Ren. The Heihe Integrated Observatory Network: A Basin-Scale Land Surface Processes Observatory in China. Vadose Zone Journal 2018, 17, 180072 .

AMA Style

Shaomin Liu, Xin Li, Ziwei Xu, Tao Che, Qing Xiao, Mingguo Ma, Qinhuo Liu, Rui Jin, Jianwen Guo, Liangxu Wang, Weizhen Wang, Yuan Qi, Hongyi Li, Tongren Xu, Youhua Ran, Xiaoli Hu, ShengJin Shi, Zhongli Zhu, Junlei Tan, Yang Zhang, Zhiguo Ren. The Heihe Integrated Observatory Network: A Basin-Scale Land Surface Processes Observatory in China. Vadose Zone Journal. 2018; 17 (1):180072.

Chicago/Turabian Style

Shaomin Liu; Xin Li; Ziwei Xu; Tao Che; Qing Xiao; Mingguo Ma; Qinhuo Liu; Rui Jin; Jianwen Guo; Liangxu Wang; Weizhen Wang; Yuan Qi; Hongyi Li; Tongren Xu; Youhua Ran; Xiaoli Hu; ShengJin Shi; Zhongli Zhu; Junlei Tan; Yang Zhang; Zhiguo Ren. 2018. "The Heihe Integrated Observatory Network: A Basin-Scale Land Surface Processes Observatory in China." Vadose Zone Journal 17, no. 1: 180072.

Journal article
Published: 12 July 2018 in IEEE Transactions on Geoscience and Remote Sensing
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Snow albedo plays an important role in the global climate system. There are notable missing data and error uncertainties in the current remote sensing snow albedo products that are attributed to the limits of remote-sensing technology. Due to the uncertainties of meteorological factors and the differences in various forward model simulation methods, snow albedo forward simulations also have considerable uncertainties. This paper suggests a long-time-series reconstruction of snow albedo utilizing a forward radiation-transferring model and a remote-sensing retrieval model together with multisource remotely sensed data and meteorological data. The key to this paper is to estimate snow information for areas lacking data utilizing a forward model for snow albedo with clear physical mechanisms. The estimated snow information can be used as reliable data for snow albedo reconstructions. The results indicate that the long time series of snow albedo data obtained by coupling the snow albedo retrieval model and forward simulation model is highly accurate. The mean absolute error, root mean square error, Pearson's correlation coefficient (R), and Nash-Sutcliffe efficiency coefficient of the observed and reconstructed snow albedos are 0.11, 0.14, 0.79, and 0.69, respectively. The reconstructed snow albedo data are underestimated by only 11% relative to the in situ snow surface albedo measurements. In the alpine mountain regions, the proposed method has a simulation accuracy that is 6% greater than that of the MOD10A1 SAD. This paper provides an effective reconstruction solution that improves the accuracy of estimations of snow albedo and fills gaps in the data.

ACS Style

Donghang Shao; Wenbo Xu; Hongyi Li; Jian Wang; Xiaohua Hao. Reconstruction of Remotely Sensed Snow Albedo for Quality Improvements Based on a Combination of Forward and Retrieval Models. IEEE Transactions on Geoscience and Remote Sensing 2018, 56, 6969 -6985.

AMA Style

Donghang Shao, Wenbo Xu, Hongyi Li, Jian Wang, Xiaohua Hao. Reconstruction of Remotely Sensed Snow Albedo for Quality Improvements Based on a Combination of Forward and Retrieval Models. IEEE Transactions on Geoscience and Remote Sensing. 2018; 56 (12):6969-6985.

Chicago/Turabian Style

Donghang Shao; Wenbo Xu; Hongyi Li; Jian Wang; Xiaohua Hao. 2018. "Reconstruction of Remotely Sensed Snow Albedo for Quality Improvements Based on a Combination of Forward and Retrieval Models." IEEE Transactions on Geoscience and Remote Sensing 56, no. 12: 6969-6985.

Article
Published: 24 January 2018 in Journal of Geophysical Research: Atmospheres
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Endorheic basins around the world are suffering from water and ecosystem crisis. To pursue sustainable development, quantifying the hydrological cycle is fundamentally important. However, knowledge gaps exist in how climate change and human activities influence the hydrological cycle in endorheic basins. We used an integrated eco-hydrological model, in combination with systematic observations, to analyze the hydrological cycle in the Heihe River Basin, a typical endorheic basin in arid region of China. The water budget was closed for different landscapes, river channel sections, and irrigation districts of the basin from 2001 to 2012. The results showed that climate warming, which has led to greater precipitation, snowmelt, glacier melt, and runoff, is a favorable factor in alleviating water scarcity. Human activities, including ecological water diversion, cropland expansion, and groundwater overexploitation, have both positive and negative effects. The natural oasis ecosystem has been restored considerably, but the overuse of water in midstream and the use of environmental flow for agriculture in downstream have exacerbated the water stress, resulting in unfavorable changes in surface-ground water interactions and raising concerns regarding how to fairly allocate water resources. Our results suggest that the water resource management in the region should be adjusted to adapt to a changing hydrological cycle and cropland area must be reduced and the abstraction of groundwater must be controlled. To foster long-term benefits, water conflicts should be handled from a broad socioeconomic perspective. The findings can provide useful information on endorheic basins to policy makers and stakeholders around the world.

ACS Style

Xin Li; Guodong Cheng; Yingchun Ge; Hongyi Li; Feng Han; Ge Yingchun; Wei Tian; Yong Tian; Xiaoduo Pan; Yanyun Nian; Yanlin Zhang; Youhua Ran; Yi Zheng; Bing Gao; Dawen Yang; Chunmiao Zheng; Xusheng Wang; Shaomin Liu; Ximing Cai. Hydrological Cycle in the Heihe River Basin and Its Implication for Water Resource Management in Endorheic Basins. Journal of Geophysical Research: Atmospheres 2018, 123, 890 -914.

AMA Style

Xin Li, Guodong Cheng, Yingchun Ge, Hongyi Li, Feng Han, Ge Yingchun, Wei Tian, Yong Tian, Xiaoduo Pan, Yanyun Nian, Yanlin Zhang, Youhua Ran, Yi Zheng, Bing Gao, Dawen Yang, Chunmiao Zheng, Xusheng Wang, Shaomin Liu, Ximing Cai. Hydrological Cycle in the Heihe River Basin and Its Implication for Water Resource Management in Endorheic Basins. Journal of Geophysical Research: Atmospheres. 2018; 123 (2):890-914.

Chicago/Turabian Style

Xin Li; Guodong Cheng; Yingchun Ge; Hongyi Li; Feng Han; Ge Yingchun; Wei Tian; Yong Tian; Xiaoduo Pan; Yanyun Nian; Yanlin Zhang; Youhua Ran; Yi Zheng; Bing Gao; Dawen Yang; Chunmiao Zheng; Xusheng Wang; Shaomin Liu; Ximing Cai. 2018. "Hydrological Cycle in the Heihe River Basin and Its Implication for Water Resource Management in Endorheic Basins." Journal of Geophysical Research: Atmospheres 123, no. 2: 890-914.

Journal article
Published: 13 October 2017 in Remote Sensing
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The change in snow cover under climate change is poorly understood in Tianshan Mountains. Here, we investigate the spatiotemporal characteristics and trends of snow-covered area (SCA) and snow-covered days (SCD) in the Tianshan Mountains by using the cloud-removed MODIS fractional snow cover datasets from 2001–2015. The possible linkage between the snow cover and temperature and precipitation changes over the Tianshan Mountains is also investigated. The results are as follows: (1) The distribution of snow cover over the Tianshan Mountains exhibits a large spatiotemporal heterogeneity. The areas with SCD greater than 120 days are distributed in the principal mountains with elevations of above 3000 m. (2) In total, 26.39% (5.09% with a significant decline) and 34.26% (2.81% with a significant increase) of the study area show declining and increasing trend in SCD, respectively. The SCD mainly decreases in Central and Eastern Tianshan (decreased by 11.88% and 8.03%, respectively), while it increases in Northern and Western Tianshan (increased by 9.36% and 7.47%). (3) The snow cover variations are linked to the temperature and precipitation changes. Temperature tends to be the major factor effecting the snow cover changes in the Tianshan Mountains during 2001–2015.

ACS Style

Zhiguang Tang; Xiaoru Wang; Jian Wang; Xin Wang; Hongyi Li; Zongli Jiang. Spatiotemporal Variation of Snow Cover in Tianshan Mountains, Central Asia, Based on Cloud-Free MODIS Fractional Snow Cover Product, 2001–2015. Remote Sensing 2017, 9, 1045 .

AMA Style

Zhiguang Tang, Xiaoru Wang, Jian Wang, Xin Wang, Hongyi Li, Zongli Jiang. Spatiotemporal Variation of Snow Cover in Tianshan Mountains, Central Asia, Based on Cloud-Free MODIS Fractional Snow Cover Product, 2001–2015. Remote Sensing. 2017; 9 (10):1045.

Chicago/Turabian Style

Zhiguang Tang; Xiaoru Wang; Jian Wang; Xin Wang; Hongyi Li; Zongli Jiang. 2017. "Spatiotemporal Variation of Snow Cover in Tianshan Mountains, Central Asia, Based on Cloud-Free MODIS Fractional Snow Cover Product, 2001–2015." Remote Sensing 9, no. 10: 1045.

Journal article
Published: 30 June 2017 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Snow distribution has a profound impact on natural processes such as the hydrological cycle, the climate system, and ecological evolution. Many studies suggest that elevation, temperature, and precipitation are the three major factors controlling snow distribution. Our study explores the influence of wind on the snow distribution and finds that wind is another important factor controlling the snow distribution in the northeastern Tibet Plateau. We select the Qilian Mountains in the northeastern Tibetan Plateau as the study area, and the data include the moderate-resolution imaging spectroradiometer snow area product and the atmosphere dataset generated by the Weather Research and Forecasting model. The results indicate that there is a threshold elevation for the correlation between the fractional snow cover (FSC) area and the wind speed in the study area. At elevations above 3900 m, the FSC and wind speed exhibit a significant negative correlation, and at elevations below 3900 m, they exhibit a significant positive correlation. Our analyses indicate that the probability for the occurrence of snowdrifts is higher in regions above 3900 m and that the wind transports snow from regions above 3900 m to lower elevations.

ACS Style

Donghang Shao; Hongyi Li; Jian Wang; Xiaoduo Pan; Xiaohua Hao. Distinguishing the Role of Wind in Snow Distribution by Utilizing Remote Sensing and Modeling Data: Case Study in the Northeastern Tibetan Plateau. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2017, 10, 4445 -4456.

AMA Style

Donghang Shao, Hongyi Li, Jian Wang, Xiaoduo Pan, Xiaohua Hao. Distinguishing the Role of Wind in Snow Distribution by Utilizing Remote Sensing and Modeling Data: Case Study in the Northeastern Tibetan Plateau. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2017; 10 (10):4445-4456.

Chicago/Turabian Style

Donghang Shao; Hongyi Li; Jian Wang; Xiaoduo Pan; Xiaohua Hao. 2017. "Distinguishing the Role of Wind in Snow Distribution by Utilizing Remote Sensing and Modeling Data: Case Study in the Northeastern Tibetan Plateau." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10, no. 10: 4445-4456.

Journal article
Published: 01 December 2016 in Agricultural and Forest Meteorology
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Guanghui Huang; Xin Li; Mingguo Ma; Hongyi Li; Chunlin Huang. High resolution surface radiation products for studies of regional energy, hydrologic and ecological processes over Heihe river basin, northwest China. Agricultural and Forest Meteorology 2016, 230-231, 67 -78.

AMA Style

Guanghui Huang, Xin Li, Mingguo Ma, Hongyi Li, Chunlin Huang. High resolution surface radiation products for studies of regional energy, hydrologic and ecological processes over Heihe river basin, northwest China. Agricultural and Forest Meteorology. 2016; 230-231 ():67-78.

Chicago/Turabian Style

Guanghui Huang; Xin Li; Mingguo Ma; Hongyi Li; Chunlin Huang. 2016. "High resolution surface radiation products for studies of regional energy, hydrologic and ecological processes over Heihe river basin, northwest China." Agricultural and Forest Meteorology 230-231, no. : 67-78.

Journal article
Published: 18 December 2015 in Remote Sensing
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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.

ACS Style

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 Style

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 (12):17246-17257.

Chicago/Turabian Style

Xiao-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.

Journal article
Published: 20 July 2015 in Remote Sensing
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To obtain long term accurate high resolution precipitation for the Heihe River Basin (HRB), Weather Research and Forecasting (WRF) model simulations were performed using two different initial boundary conditions, with nine microphysical processes for different analysis parameterization schemes. High spatial-temporal precipitation was simulated from 2000 to 2013 and a suitable set of initial, boundary, and micro parameters for the HRB was evaluated from the Heihe Watershed Allied Telemetry Experimental Research project and Chinese Meteorological Administration data at hourly, daily, monthly, and annual time scales using various statistical indicators. It was found that annual precipitation has gradually increased over the HRB since 2000. Precipitation mostly occurs in summer and is higher in monsoon-influenced areas. High elevations experience winter snowfall. Precipitation is higher in the eastern upstream area than in the western upstream, area; however, the converse occurs in winter. Precipitation gradually increases with elevation from 1000 m to 4000 m, and the maximum precipitation occurs at the height of 3500–4000 m, then the precipitation slowly decreases with elevation from 4000 m to the top over the Qilian Mountains. Precipitation is scare and has a high temporal variation in the downstream area. Results are systematically validated using the in situ observations in this region and it was found that precipitation simulated by the WRF model using suitable physical configuration agrees well with the observation over the HRB at hourly, daily, monthly and yearly scales, as well as at spatial pattern. We also conclude that the dynamic downscaling using the WRF model is capable of producing high-resolution and reliable precipitation over complex mountainous areas and extremely arid environments. The downscaled data can meet the requirement of river basin scale hydrological modeling and water balance analysis.

ACS Style

Xiaoduo Pan; Xin Li; Guodong Cheng; Hongyi Li; Xiaobo He. Development and Evaluation of a River-Basin-Scale High Spatio-Temporal Precipitation Data Set Using the WRF Model: A Case Study of the Heihe River Basin. Remote Sensing 2015, 7, 9230 -9252.

AMA Style

Xiaoduo Pan, Xin Li, Guodong Cheng, Hongyi Li, Xiaobo He. Development and Evaluation of a River-Basin-Scale High Spatio-Temporal Precipitation Data Set Using the WRF Model: A Case Study of the Heihe River Basin. Remote Sensing. 2015; 7 (7):9230-9252.

Chicago/Turabian Style

Xiaoduo Pan; Xin Li; Guodong Cheng; Hongyi Li; Xiaobo He. 2015. "Development and Evaluation of a River-Basin-Scale High Spatio-Temporal Precipitation Data Set Using the WRF Model: A Case Study of the Heihe River Basin." Remote Sensing 7, no. 7: 9230-9252.

Journal article
Published: 16 July 2015 in Remote Sensing
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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.

ACS Style

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 Style

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 (7):8995-9019.

Chicago/Turabian Style

Hong 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.

Journal article
Published: 01 January 2014 in Journal of Applied Remote Sensing
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Snowline altitude (SLA) is the most sensitive indicator for monitoring climatic behavior among all the cryosphere elements. In this study, the snowline and SLA over the Tibetan plateau (TP) during 2001 to 2013 are extracted using the cloud-removed MODIS daily fractional snow cover (FSC) products combined with digital elevation model (DEM), and the spatiotemporal changes of SLA and their response to the changing temperature are examined. The proposed MODIS-based SLA-extracting methodology includes cloud removal from MODIS FSC data, the determination of the snowline and SLA, and the establishment of the snowline altitude field (SLAF). Results show that the SLA in the interior of the TP is obviously higher than the peripheral mountainous area due to the complex terrain. There is no obvious trend of SLA change during the examined period although a strong seasonal and interannual variability of SLA is discovered. The interannual fluctuation of SLA in the snowmelt period can be explained by the high-positive correlations between the SLA and temperature. The MODIS-based SLA-extracting method described has a good application potential in SLA monitoring for other regions.

ACS Style

Zhiguang Tang; Jian Wang; Hongyi Li; Ji Liang; Chaokui Li; Xin Wang. Extraction and assessment of snowline altitude over the Tibetan plateau using MODIS fractional snow cover data (2001 to 2013). Journal of Applied Remote Sensing 2014, 8, 084689 -084689.

AMA Style

Zhiguang Tang, Jian Wang, Hongyi Li, Ji Liang, Chaokui Li, Xin Wang. Extraction and assessment of snowline altitude over the Tibetan plateau using MODIS fractional snow cover data (2001 to 2013). Journal of Applied Remote Sensing. 2014; 8 (1):084689-084689.

Chicago/Turabian Style

Zhiguang Tang; Jian Wang; Hongyi Li; Ji Liang; Chaokui Li; Xin Wang. 2014. "Extraction and assessment of snowline altitude over the Tibetan plateau using MODIS fractional snow cover data (2001 to 2013)." Journal of Applied Remote Sensing 8, no. 1: 084689-084689.

Journal article
Published: 01 January 2014 in Journal of Applied Remote Sensing
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Hongyi Li; Zhiguang Tang; Jian Wang; Tao Che; Xiaoduo Pan; Chunlin Huang; Xufeng Wang; Xiaohua Hao; Shaobo Sun. Synthesis method for simulating snow distribution utilizing remotely sensed data for the Tibetan Plateau. Journal of Applied Remote Sensing 2014, 8, 84696 .

AMA Style

Hongyi Li, Zhiguang Tang, Jian Wang, Tao Che, Xiaoduo Pan, Chunlin Huang, Xufeng Wang, Xiaohua Hao, Shaobo Sun. Synthesis method for simulating snow distribution utilizing remotely sensed data for the Tibetan Plateau. Journal of Applied Remote Sensing. 2014; 8 (1):84696.

Chicago/Turabian Style

Hongyi Li; Zhiguang Tang; Jian Wang; Tao Che; Xiaoduo Pan; Chunlin Huang; Xufeng Wang; Xiaohua Hao; Shaobo Sun. 2014. "Synthesis method for simulating snow distribution utilizing remotely sensed data for the Tibetan Plateau." Journal of Applied Remote Sensing 8, no. 1: 84696.