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Long-term surface soil moisture (SM) data are increasingly needed in water budget and energy balance analysis of watersheds. The performance of nine remotely sensed SM products from AMSR2, SMOS and SMAP missions, are evaluated based on observations collected from distributed observation networks in the Heihe River Basin (HRB) of China during 2013 to 2017. Results show that the SMAP Level 3 Dual Channel Algorithm (DCA) SM retrievals reflect the seasonal SM variations well with high temporal correlations of ~0.7 and high accuracy within 0.04 m3/m3 in terms of unbiased Root Mean Squared Error (ubRMSE) over the grassland in the HRB. The SMOS level 3 SM retrievals present increased underestimation and ubRMSE of ~0.10 m3/m3 as the vegetation increases. The newly published SMOS-IC product in version 2 outperforms the SMOS level 3 product with improved temporal correlation coefficient above 0.4 and lower ubRMSE of ~0.05 m3/m3. AMSR2 Land Parameter Retrieval Algorithm (LPRM) SM products show extremely large overestimation over the vegetated regions in HRB, especially the C-band products. Drastically high underestimation biases are observed in the Japan Aerospace Exploration Agency (JAXA) AMSR2 SM product. Parameter uncertainty analyses indicate that the different parameterization schemes of vegetation optical depth (VOD) inputs could be one of the main reasons resulting in the systematic overestimation/underestimation in the AMSR2/SMOS/SMAP SM retrievals. The findings aim to provide insights into studies on algorithms refinements and data fusions of SM products in HRB.
Zengyan Wang; Tao Che; Tianjie Zhao; Liyun Dai; Xiaojun Li; Jean Pierre Wigneron. Evaluation of SMAP, SMOS and AMSR2 soil moisture products based on distributed ground observation network in cold and arid regions of China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021, PP, 1 -1.
AMA StyleZengyan Wang, Tao Che, Tianjie Zhao, Liyun Dai, Xiaojun Li, Jean Pierre Wigneron. Evaluation of SMAP, SMOS and AMSR2 soil moisture products based on distributed ground observation network in cold and arid regions of China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021; PP (99):1-1.
Chicago/Turabian StyleZengyan Wang; Tao Che; Tianjie Zhao; Liyun Dai; Xiaojun Li; Jean Pierre Wigneron. 2021. "Evaluation of SMAP, SMOS and AMSR2 soil moisture products based on distributed ground observation network in cold and arid regions of China." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing PP, no. 99: 1-1.
Ground surface elevation changes, soil moisture, and snow depth are all essential variables for studying the dynamics of the active layer and permafrost. GPS interferometric reflectometry (GPS-IR) has been used to measure surface elevation changes and snow depth in permafrost areas. However, its applicability to estimating soil moisture in permafrost regions has not been assessed. Moreover, these variables were usually measured separately at different sites. Integrating their estimates at one site facilitates the comprehensive utilization of GPS-IR in permafrost studies. In this study, we run simulations to elucidate that the commonly used GPS-IR algorithm for estimating soil moisture content cannot be directly used in permafrost areas, because it does not consider the bias introduced by the seasonal surface elevation changes due to active layer thawing. We propose a solution to improve this default method by introducing modeled surface elevation changes. We validate this modified method using the GPS data and in situ observations at a permafrost site in the northeastern Qinghai–Tibet Plateau (QTP). The root-mean-square error and correlation coefficient between the GPS-IR estimates of soil moisture content and the in situ ones improve from 1.85 % to 1.51 % and 0.71 to 0.82, respectively. We also propose a framework to integrate the GPS-IR estimates of these three variables at one site and illustrate it using the same site in the QTP as an example. This study highlights the improvement to the default algorithm, which makes the GPS-IR valid in estimating soil moisture content in permafrost areas. The three-in-one framework is able to fully utilize the GPS-IR in permafrost areas and can be extended to other sites such as those in the Arctic. This study is also the first to use GPS-IR to estimate environmental variables in the QTP, which fills a spatial gap and provides complementary measurements to ground temperature and active layer thickness.
Jiahua Zhang; Lin Liu; Lei Su; Tao Che. Three in one: GPS-IR measurements of ground surface elevation changes, soil moisture, and snow depth at a permafrost site in the northeastern Qinghai–Tibet Plateau. The Cryosphere 2021, 15, 3021 -3033.
AMA StyleJiahua Zhang, Lin Liu, Lei Su, Tao Che. Three in one: GPS-IR measurements of ground surface elevation changes, soil moisture, and snow depth at a permafrost site in the northeastern Qinghai–Tibet Plateau. The Cryosphere. 2021; 15 (6):3021-3033.
Chicago/Turabian StyleJiahua Zhang; Lin Liu; Lei Su; Tao Che. 2021. "Three in one: GPS-IR measurements of ground surface elevation changes, soil moisture, and snow depth at a permafrost site in the northeastern Qinghai–Tibet Plateau." The Cryosphere 15, no. 6: 3021-3033.
Using the Google Earth Engine (GEE) platform, a long-term AVHRR snow cover extent (SCE) product from 1981 until 2019 over China has been generated by the snow research team in the Northwest Institute of Eco-Environment and Resources (NIEER), Chinese Academy of Sciences. The new NIEER product has the spatial resolution of 5-km and the daily temporal resolution, and is a completely gap-free product, which is produced through a series of processes such as the quality control, cloud detection, snow discrimination and gap-filling. A comprehensive validation with reference to ground snow-depth measurements during snow seasons in China revealed the overall accuracy is 87.4 %, the producer’s accuracy was 81.0 % the user’s accuracy was 81.3 %, and the Cohen’s kappa value was 0.717. Another validation with reference to higher-resolution snow maps derived from Landsat-5 Thematic Mapper (TM) images demonstrates an overall accuracy of 89.4 %, a producer’s accuracy of 90.2 %, a user’s accuracy of 96.1 %, and a Cohen’s kappa value of 0.713. These accuracies were significantly higher than those of currently existing AVHRR products. For example, compared with the well-known JASMES AVHRR product, the overall accuracy increased approximately 15 percent, the omission error dropped from nearly 40 % to 19.7 %, the commission error dropped from 31.9 % to 21.3 %, and the CK value increased by more than 114 %. The new AVHRR product is now already available at https://dx.doi.org/10.11888/Snow.tpdc.271381 (Hao et al. 2021).
Xiaohua Hao; Guanghui Huang; Tao Che; Wenzheng Ji; Xingliang Sun; Qin Zhao; Hongyu Zhao; Jian Wang; Hongyi Li; Qian Yang. The NIEER AVHRR snow cover extent product over China – A long-term daily snow record for regional climate research. 2021, 2021, 1 -42.
AMA StyleXiaohua Hao, Guanghui Huang, Tao Che, Wenzheng Ji, Xingliang Sun, Qin Zhao, Hongyu Zhao, Jian Wang, Hongyi Li, Qian Yang. The NIEER AVHRR snow cover extent product over China – A long-term daily snow record for regional climate research. . 2021; 2021 ():1-42.
Chicago/Turabian StyleXiaohua Hao; Guanghui Huang; Tao Che; Wenzheng Ji; Xingliang Sun; Qin Zhao; Hongyu Zhao; Jian Wang; Hongyi Li; Qian Yang. 2021. "The NIEER AVHRR snow cover extent product over China – A long-term daily snow record for regional climate research." 2021, no. : 1-42.
The accurate and timely estimation of regional crop biomass at different growth stages is of great importance in guiding crop management decision making. The recent availability of long time series of remote sensing data offers opportunities for crop monitoring. In this paper, four machine learning models, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), and extreme gradient boosting (XGBoost) were adopted to estimate the seasonal corn biomass based on field observation data and moderate resolution imaging spectroradiometer (MODIS) reflectance data from 2012 to 2019 in the middle reaches of the Heihe River basin, China. Nine variables were selected with the forward feature selection approach from among twenty-seven variables potentially influencing corn biomass: soil-adjusted total vegetation index (SATVI), green ratio vegetation index (GRVI), Nadir_B7 (2105–2155 nm), Nadir_B6 (1628–1652 nm), land surface water index (LSWI), normalized difference vegetation index (NDVI), Nadir_B4 (545–565 nm), and Nadir_B3 (459–479 nm). The results indicated that the corn biomass was suitably estimated (the coefficient of determination (R2) was between 0.72 and 0.78) with the four machine learning models. The XGBoost model performed better than the other three models (R2 = 0.78, root mean squared error (RMSE) = 2.86 t/ha and mean absolute error (MAE) = 1.86 t/ha). Moreover, the RF model was an effective method (R2 = 0.77, RMSE = 2.91 t/ha and MAE = 1.91 t/ha), with a performance comparable to that of the XGBoost model. This study provides a reference for estimating crop biomass from MOD43A4 datasets. In addition, the research demonstrates the potential of machine learning techniques to achieve a relatively accurate estimation of daily corn biomass at a large scale.
Liying Geng; Tao Che; Mingguo Ma; Junlei Tan; Haibo Wang. Corn Biomass Estimation by Integrating Remote Sensing and Long-Term Observation Data Based on Machine Learning Techniques. Remote Sensing 2021, 13, 2352 .
AMA StyleLiying Geng, Tao Che, Mingguo Ma, Junlei Tan, Haibo Wang. Corn Biomass Estimation by Integrating Remote Sensing and Long-Term Observation Data Based on Machine Learning Techniques. Remote Sensing. 2021; 13 (12):2352.
Chicago/Turabian StyleLiying Geng; Tao Che; Mingguo Ma; Junlei Tan; Haibo Wang. 2021. "Corn Biomass Estimation by Integrating Remote Sensing and Long-Term Observation Data Based on Machine Learning Techniques." Remote Sensing 13, no. 12: 2352.
The MODIS land surface temperature (LST) product is one of the most widely used data sources to study the climate and energy-water cycle at a global scale. However, the large number of invalid values caused by cloud cover limits the wide application of the MODIS LST. In this study, a two-step improved similar pixels (TISP) method was proposed for cloudy sky LST reconstruction. The TISP method was validated using a temperature-based method over various land cover types. The ground measurements were collected at fifteen stations from 2013 to 2018 during the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) field campaign in China. The estimated theoretical clear-sky temperature indicates that clouds cool the land surface during the daytime and warm it at nighttime. For bare land, the surface temperature shows a clear seasonal trend and very similar across stations, with a cooling amplitude of 4.14 K in the daytime and a warming amplitude of 3.99 K at nighttime, as a yearly average. The validation result showed that the reconstructed LST is highly consistent with in situ measurements and comparable with MODIS LST validation accuracy, with a mean bias of 0.15 K at night (−0.43 K in the day), mean RMSE of 2.91 K at night (4.41 K in the day), and mean R 2 of 0.93 at night (0.90 in the day). The developed method maximizes the potential of obtaining quality MODIS LST retrievals, ancillary data, and in situ observations, and the results show high accuracy for most land cover types.
Junlei Tan; Tao Che; Jian Wang; Ji Liang; Yang Zhang; Zhiguo Ren. Reconstruction of the Daily MODIS Land Surface Temperature Product Using the Two-Step Improved Similar Pixels Method. Remote Sensing 2021, 13, 1671 .
AMA StyleJunlei Tan, Tao Che, Jian Wang, Ji Liang, Yang Zhang, Zhiguo Ren. Reconstruction of the Daily MODIS Land Surface Temperature Product Using the Two-Step Improved Similar Pixels Method. Remote Sensing. 2021; 13 (9):1671.
Chicago/Turabian StyleJunlei Tan; Tao Che; Jian Wang; Ji Liang; Yang Zhang; Zhiguo Ren. 2021. "Reconstruction of the Daily MODIS Land Surface Temperature Product Using the Two-Step Improved Similar Pixels Method." Remote Sensing 13, no. 9: 1671.
Ice phenology data of 22 large lakes of the Northern Hemisphere for 40 years (1979–2018) have been retrieved from passive microwave remote sensing brightness temperature (Tb). The results were compared with site-observation data and visual interpretation from Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectivity products images (MOD09GA). The mean absolute errors of four lake ice phenology parameters, including freeze-up start date (FUS), freeze-up end date (FUE), break-up start date (BUS), and break-up end date (BUE) against MODIS-derived ice phenology were 2.50, 2.33, 1.98, and 3.27 days, respectively. The long-term variation in lake ice phenology indicates that FUS and FUE are delayed; BUS and BUE are earlier; ice duration (ID) and complete ice duration (CID) have a general decreasing trend. The average change rates of FUS, FUE, BUS, BUE, ID, and CID of lakes in this study from 1979 to 2018 were 0.23, 0.23, −0.17, −0.33, −0.67, and −0.48 days/year, respectively. Air temperature and latitude are two dominant driving factors of lake ice phenology. Lake ice phenology for the period 2021–2100 was predicted by the relationship between ice phenology and air temperature for each lake. Compared with lake ice phenology changes from 1990 to 2010, FUS is projected to be delayed by 3.1 days and 11.8 days under Representative Concentration Pathways (RCPs) 2.6 and 8.5 scenarios, respectively; BUS is projected to be earlier by 3.3 days and 10.7 days, respectively; and ice duration from 2080 to 2100 will decrease by 6.5 days and 21.9 days, respectively.
Lei Su; Tao Che; Liyun Dai. Variation in Ice Phenology of Large Lakes over the Northern Hemisphere Based on Passive Microwave Remote Sensing Data. Remote Sensing 2021, 13, 1389 .
AMA StyleLei Su, Tao Che, Liyun Dai. Variation in Ice Phenology of Large Lakes over the Northern Hemisphere Based on Passive Microwave Remote Sensing Data. Remote Sensing. 2021; 13 (7):1389.
Chicago/Turabian StyleLei Su; Tao Che; Liyun Dai. 2021. "Variation in Ice Phenology of Large Lakes over the Northern Hemisphere Based on Passive Microwave Remote Sensing Data." Remote Sensing 13, no. 7: 1389.
In this study, a machine learning algorithm was introduced to fuse gridded snow depth datasets. The input variables of the machine learning method included geolocation (latitude and longitude), topographic data (elevation), gridded snow depth datasets and in situ observations. A total of 29,565 in situ observations were used to train and optimize the machine learning algorithm. A total of five gridded snow depth datasets—Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) snow depth, Global Snow Monitoring for Climate Research (GlobSnow) snow depth, Long time series of daily snow depth over the Northern Hemisphere (NHSD) snow depth, ERA-Interim snow depth and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) snow depth—were used as input variables. The first three snow depth datasets are retrieved from passive microwave brightness temperature or assimilation with in situ observations, while the last two are snow depth datasets obtained from meteorological reanalysis data with a land surface model and data assimilation system. Then, three machine learning methods, i.e., Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Random Forest Regression (RFR), were used to produce a fused snow depth dataset from 2002 to 2004. The RFR model performed best and was thus used to produce a new snow depth product from the fusion of the five snow depth datasets and auxiliary data over the Northern Hemisphere from 2002 to 2011. The fused snow-depth product was verified at five well-known snow observation sites. The R2 of Sodankylä, Old Aspen, and Reynolds Mountains East were 0.88, 0.69, and 0.63, respectively. At the Swamp Angel Study Plot and Weissfluhjoch observation sites, which have an average snow depth exceeding 200 cm, the fused snow depth did not perform well. The spatial patterns of the average snow depth were analyzed seasonally, and the average snow depths of autumn, winter, and spring were 5.7, 25.8, and 21.5 cm, respectively. In the future, random forest regression will be used to produce a long time series of a fused snow depth dataset over the Northern Hemisphere or other specific regions.
Yanxing Hu; Tao Che; Liyun Dai; Lin Xiao. Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere. Remote Sensing 2021, 13, 1250 .
AMA StyleYanxing Hu, Tao Che, Liyun Dai, Lin Xiao. Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere. Remote Sensing. 2021; 13 (7):1250.
Chicago/Turabian StyleYanxing Hu; Tao Che; Liyun Dai; Lin Xiao. 2021. "Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere." Remote Sensing 13, no. 7: 1250.
Volume scattering (VS) estimation plays a critical role in microwave emission modeling of the snowpack. However, it is challenging to obtain VS accurately for different frequencies by using the microwave emission model of layered snowpacks (MEMLS), which is one of the representative microwave emission models. This article develops a new VS method to consider frequency and exponential correlation length based on a snowfield campaign from November 2015 to April 2016 in Altay, China. Compared with the commonly used empirical and improved Born approximation (IBA) algorithms, the proposed VS algorithm exhibits better performances at both 18 and 36 GHz with a wide range of snow grain sizes. The bias of brightness temperatures at vertical polarization from the proposed algorithm against the observed brightness temperatures are 1.1 K and -0.4 K at 18 and 36 GHz, respectively; the root mean square errors (RMSEs) are 1.8 K and 2.6 K, respectively. The RMSEs decreased by 16.2 K at 18 GHz and 6.5 K at 36 GHz compared with those from the empirical methods and by 2.1 K and 22.2 K compared with those from the IBA. This work demonstrates that the VS difference between 18 and 36 GHz is larger and the dependence of VS on grain size is weaker than those represented by existing methods.
L. Dai; T. Che; Lin Xiao; M. Akynbekkyzy; K. Zhao; L. Leppanen. Improving the Snow Volume Scattering Algorithm in a Microwave Forward Model by Using Ground-Based Remote Sensing Snow Observations. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -17.
AMA StyleL. Dai, T. Che, Lin Xiao, M. Akynbekkyzy, K. Zhao, L. Leppanen. Improving the Snow Volume Scattering Algorithm in a Microwave Forward Model by Using Ground-Based Remote Sensing Snow Observations. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-17.
Chicago/Turabian StyleL. Dai; T. Che; Lin Xiao; M. Akynbekkyzy; K. Zhao; L. Leppanen. 2021. "Improving the Snow Volume Scattering Algorithm in a Microwave Forward Model by Using Ground-Based Remote Sensing Snow Observations." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-17.
Ski tourism is extremely sensitive to climate change and is also heavily affected by socioeconomic conditions. Although some ski areas are still profitable under current climate and socioeconomic conditions, they will become difficult to operate in the face of rising winter temperatures, which will result in further economic losses, resource waste and environmental damage. This study projects variability in the suitability of ski area development across China in the coming decades. Natural suitability under three representative concentration pathway emission scenarios (RCP2.6, RCP4.5 and RCP8.5), socioeconomic suitability under four shared socioeconomic pathways (SSP1, SSP2, SSP3, and SSP5) and integrated suitability under four climatic-socioeconomic scenarios (RCP2.6-SSP1, RCP4.5-SSP2, RCP8.5-SSP3, and RCP8.5-SSP5) are reported. Furthermore, the suitability of 731 existing ski areas in China is assessed. The results show a substantial decline in integrated suitability for most regions of China except for some very cold areas, where higher air temperatures will make visitors feel more comfortable and the relatively poor socioeconomic conditions will improve in the 2030s, 2050s and 2090s. The average higher integrated suitability area (integrated suitability values greater than 0.5) under four climatic-socioeconomic scenarios decreases from the current 29.9% to 14.4%, 5.0% and 4.5% by the 2030s, 2050s and 2090s, respectively. Under RCP2.6-SSP1, the higher integrated suitability area is projected to decrease from the current 28.0% to 5.2% by the 2050s and then increase to 5.3% by the 2090s. Under RCP4.5-SSP2, RCP8.5-SSP3, and RCP8.5-SSP5, the higher integrated suitability area is projected to continuously decrease from 30.3%, 30.6% and 30.6% in the 2010s to 4.1%, 4.4% and 4.4% in the 2090s, respectively. By the 2090s, 41, 138 and 277 existing ski areas are projected to be closed under RCP2.6-SSP1, RCP4.5-SSP2, and RCP8.5-SSP3/RCP8.5-SSP5, respectively. It is clear that emission pathways and climate change adaptation and mitigation strategies will greatly shape the development of China’s regional ski tourism.
Jie Deng; Tao Che; Tong Jiang; Li-Yun Dai. Suitability projection for Chinese ski areas under future natural and socioeconomic scenarios. Advances in Climate Change Research 2021, 12, 224 -239.
AMA StyleJie Deng, Tao Che, Tong Jiang, Li-Yun Dai. Suitability projection for Chinese ski areas under future natural and socioeconomic scenarios. Advances in Climate Change Research. 2021; 12 (2):224-239.
Chicago/Turabian StyleJie Deng; Tao Che; Tong Jiang; Li-Yun Dai. 2021. "Suitability projection for Chinese ski areas under future natural and socioeconomic scenarios." Advances in Climate Change Research 12, no. 2: 224-239.
Accurate and continuous monitoring of leaf area index (LAI), a widelyused vegetation structural parameter, is crucial to characterize crop growth conditions and forecast crop yield. Meanwhile, advancements in collecting field LAI measurements have provided strong support for validating remotesensingderived LAI. This paper evaluates the performance of LAI retrieval from multisource, remotely sensed data through comparisons with continuous field LAI measurements. Firstly, field LAI was measured continuously over periods of time in 2018 and 2019 using LAINet, a continuous LAI measurement system deployed using wireless sensor network (WSN) technology, over an agricultural region located at the Heihe watershed at northwestern China. Then, cloudfree images from optical satellite sensors, including Landsat 7 the Enhanced Thematic Mapper Plus (ETM+), Landsat 8 the Operational Land Imager (OLI), and Sentinel2A/B Multispectral Instrument (MSI), were collected to derive LAI through inversion of the PROSAIL radiation transfer model using a lookuptable (LUT) approach. Finally, field LAI data were used to validate the multi-temporal LAI retrieved from remotesensing data acquired by different satellite sensors. The results indicate that good accuracy was obtained using different inversion strategies for each sensor, while Green Chlorophyll Index (CIgreen) and a combination of three red-edge bands perform better for Landsat 7/8 and Sentinel2 LAI inversion, respectively. Furthermore, the estimated LAI has good consistency with in situ measurements at vegetative stage (coefficient of determination R2 = 0.74, and root mean square error RMSE = 0.53 m2 m−2). At the reproductive stage, a significant underestimation was found (R2 = 0.41, and 0.89 m2 m−2 in terms of RMSE). This study suggests that timeseries LAI can be retrieved from multisource satellite data through model inversion, and the LAINet instrument could be used as a lowcost tool to provide continuous field LAI measurements to support LAI retrieval.
Lihong Yu; Jiali Shang; Zhiqiang Cheng; Zebin Gao; Zixin Wang; Luo Tian; Dantong Wang; Tao Che; Rui Jin; Jiangui Liu; Taifeng Dong; Yonghua Qu. Assessment of Cornfield LAI Retrieved from Multi-Source Satellite Data Using Continuous Field LAI Measurements Based on a Wireless Sensor Network. Remote Sensing 2020, 12, 3304 .
AMA StyleLihong Yu, Jiali Shang, Zhiqiang Cheng, Zebin Gao, Zixin Wang, Luo Tian, Dantong Wang, Tao Che, Rui Jin, Jiangui Liu, Taifeng Dong, Yonghua Qu. Assessment of Cornfield LAI Retrieved from Multi-Source Satellite Data Using Continuous Field LAI Measurements Based on a Wireless Sensor Network. Remote Sensing. 2020; 12 (20):3304.
Chicago/Turabian StyleLihong Yu; Jiali Shang; Zhiqiang Cheng; Zebin Gao; Zixin Wang; Luo Tian; Dantong Wang; Tao Che; Rui Jin; Jiangui Liu; Taifeng Dong; Yonghua Qu. 2020. "Assessment of Cornfield LAI Retrieved from Multi-Source Satellite Data Using Continuous Field LAI Measurements Based on a Wireless Sensor Network." Remote Sensing 12, no. 20: 3304.
Snow cover is a key parameter of the climate system and its significant seasonal and annual variability have significant impacts on the surface energy balance and global water circulation. However, current snow depth datasets show large inconsistencies and uncertainties, which limit their applications in climate change projections and hydrological processes simulations. In this study, a comprehensive assessment of five hemispheric snow depth datasets was carried out against ground observations from 43,391 stations. The five snow depth datasets included three remote sensing datasets, i.e., Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), Advanced Microwave Scanning Radiometer-2 (AMSR2), Global Snow Monitoring for Climate Research (GlobSnow), and two reanalysis datasets, i.e., ERA-Interim and the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). Assessment results imply that the spatial distribution of GlobSnow and ERA-Interim exhibit overall better agreements with ground observations than other datasets. GlobSnow and ERA-Interim exhibit less uncertainty during the snow accumulation and ablation periods, respectively. In plain and forested regions, GlobSnow, ERA-Interim and MERRA-2 show better performances, while in mountain and forested mountain areas, GlobSnow exhibits the best performance. AMSR-E and AMSR2 agree better with ground observations in shallow snow condition (0–10 cm), while MERRA-2 shows more satisfying performance when snow depth exceeds 50 cm. These systematic and integrated understanding of the five representative snow depth datasets provides information on data selection and data refinement, as well as data fusion, which is our next work of interest.
Lin Xiao; Tao Che; Liyun Dai. Evaluation of Remote Sensing and Reanalysis Snow Depth Datasets over the Northern Hemisphere during 1980–2016. Remote Sensing 2020, 12, 3253 .
AMA StyleLin Xiao, Tao Che, Liyun Dai. Evaluation of Remote Sensing and Reanalysis Snow Depth Datasets over the Northern Hemisphere during 1980–2016. Remote Sensing. 2020; 12 (19):3253.
Chicago/Turabian StyleLin Xiao; Tao Che; Liyun Dai. 2020. "Evaluation of Remote Sensing and Reanalysis Snow Depth Datasets over the Northern Hemisphere during 1980–2016." Remote Sensing 12, no. 19: 3253.
Ground surface elevation changes, soil moisture, and snow depth are all essential variables for studying the dynamics of the active layer and permafrost. GPS interferometric reflectometry (GPS-IR) has been used to measure surface elevation changes and snow depth in permafrost areas. However, its applicability to estimating soil moisture in permafrost regions has not been assessed. Moreover, these variables were usually measured separately at different sites. Integrating their estimates at one site facilitates the comprehensive utilization of GPS-IR in permafrost studies. In this study, we run simulations to elucidate that the commonly-used GPS-IR method for estimating soil moisture content cannot be directly used in permafrost areas, because it does not consider the bias introduced by the seasonal surface elevation changes due to thawing of the active layer. We propose a solution to improve this default method by introducing modeled surface elevation changes. We validate this modified method using the GPS data and in situ observations at a permafrost site in the northeastern Qinghai-Tibet Plateau (QTP). The root-mean-square error and correlation coefficient between the GPS-IR estimates of soil moisture content and the in situ ones improve from 1.85 % to 1.51 % and 0.71 to 0.82, respectively. We also implement a framework to integrate the GPS-IR estimates of these three variables at one site and illustrate it using the same site in the QTP as an example. This study highlights the improvement to the default method, which makes the GPS-IR valid in estimating soil moisture content in permafrost areas. The three-in-one framework is able to fully utilize the GPS-IR in permafrost areas and can be extended to other sites such as those in the Arctic. This study is also the first to use GPS-IR to estimate environmental variables in the QTP, which fills a spatial gap and provides complementary measurements to those of ground temperature and active layer thickness.
Jiahua Zhang; Lin Liu; Lei Su; Tao Che. Three-in-one: GPS-IR measurements of ground surface elevation changes, soil moisture, and snow depth at a permafrost site in the northeastern Qinghai-Tibet Plateau. 2020, 2020, 1 -27.
AMA StyleJiahua Zhang, Lin Liu, Lei Su, Tao Che. Three-in-one: GPS-IR measurements of ground surface elevation changes, soil moisture, and snow depth at a permafrost site in the northeastern Qinghai-Tibet Plateau. . 2020; 2020 ():1-27.
Chicago/Turabian StyleJiahua Zhang; Lin Liu; Lei Su; Tao Che. 2020. "Three-in-one: GPS-IR measurements of ground surface elevation changes, soil moisture, and snow depth at a permafrost site in the northeastern Qinghai-Tibet Plateau." 2020, no. : 1-27.
Jiahua Zhang; Lin Liu; Lei Su; Tao Che. Supplementary material to "Three-in-one: GPS-IR measurements of ground surface elevation changes, soil moisture, and snow depth at a permafrost site in the northeastern Qinghai-Tibet Plateau". 2020, 1 .
AMA StyleJiahua Zhang, Lin Liu, Lei Su, Tao Che. Supplementary material to "Three-in-one: GPS-IR measurements of ground surface elevation changes, soil moisture, and snow depth at a permafrost site in the northeastern Qinghai-Tibet Plateau". . 2020; ():1.
Chicago/Turabian StyleJiahua Zhang; Lin Liu; Lei Su; Tao Che. 2020. "Supplementary material to "Three-in-one: GPS-IR measurements of ground surface elevation changes, soil moisture, and snow depth at a permafrost site in the northeastern Qinghai-Tibet Plateau"." , no. : 1.
The hydrological regimes in permafrost-dominated catchments have unique characteristics. However, studies based on plot-scale experiments of runoff generation processes and the factors influencing these processes are limited on the Tibetan Plateau, which is experiencing rapid warming and permafrost degradation. Runoff generation processes were studied on a standard runoff plot (5×20 m) in a permafrost-dominated catchment on the Tibetan Plateau with an alpine meadow cover to analyze these processes during the spring–summer transition period (from mid-May to early June) and in the summer months of June and July. The discharge, including surface and subsurface runoff, the soil hydrothermal and moisture conditions, and the meteorological conditions were monitored from May 2018 to May 2019. Partial Least-Squares Path Modeling was used to identify the influencing factors in the two time periods. The lateral subsurface flow at 30−60 cm depth accounted for the majority of the discharge from the runoff plot (> 99%) and showed a sharp increase when the soil water contents of the thawed soil layers exceeded a threshold value that varied from 0.41 to 0.52. During the spring–summer transition period, the lateral subsurface flow at 30−60 cm depth was promoted by both event precipitation and the antecedent precipitation with an average runoff ratio of 1.7, indicating that the antecedent moisture condition maintained by the underlying permafrost drives runoff generation via meltwater from snow and frozen soil. In the summer months, the lateral subsurface flow at 30−60 cm depth was directly promoted by event precipitation, especially the moderate precipitation that occurred in this period, with a reduced runoff ratio of 0.5 as a result of deepening of the thawed soil layer from 30 to > 60 cm, enhanced evapotranspiration and an increased soil water storage capacity caused by an increase of air temperature and the soil temperature. Prediction models obtained via Multiple Linear Regressions of the identified influencing factors were able to accurately estimate the discharge. This study shows the important role of lateral subsurface flow in runoff generation processes in this permafrost area and the most important influencing factors in different seasons.
Xiong Xiao; Fan Zhang; Tao Che; Xiaonan Shi; Chen Zeng; Guanxing Wang. Changes in plot-scale runoff generation processes from the spring–summer transition period to the summer months in a permafrost-dominated catchment. Journal of Hydrology 2020, 587, 124966 .
AMA StyleXiong Xiao, Fan Zhang, Tao Che, Xiaonan Shi, Chen Zeng, Guanxing Wang. Changes in plot-scale runoff generation processes from the spring–summer transition period to the summer months in a permafrost-dominated catchment. Journal of Hydrology. 2020; 587 ():124966.
Chicago/Turabian StyleXiong Xiao; Fan Zhang; Tao Che; Xiaonan Shi; Chen Zeng; Guanxing Wang. 2020. "Changes in plot-scale runoff generation processes from the spring–summer transition period to the summer months in a permafrost-dominated catchment." Journal of Hydrology 587, no. : 124966.
Accurate spatiotemporal information of snow cover not only is important for investigating the mechanisms of climate change but also greatly contributes to hydrological modelling in mountainous regions. The Suomi-National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) (referred to as VNP) daily snow cover product is recently released and expected to take place of Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products in near future. As an important addition to the widely used MODIS products, there is also an urgent need for a reliable accuracy evaluation and comparison of VNP for future large-scale daily snow cover mapping. This study for the first time evaluates the accuracy of VNP daily snow cover data in China using daily snow depth observations from 330 stations. The accuracy of VNP data is generally good with the averaged CK (Cohen's Kappa) and FS (F-Score) as high as 0.72 and 0.75, respectively, but considerably decreases to 0.50 and 0.52 for the Tibetan Plateau. VNP shows slightly better accuracy than MODIS TERRA for stations outside the Tibetan Plateau owing to its higher spatial resolution, but its accuracy is lower than TERRA for those within the Tibetan Plateau possibly due to its longer time interval between ground observation and satellite overpass time. By contrast, VNP shows much better accuracy than MODIS AQUA in China including both outside and within the Tibetan Plateau. This study provides important implications for optimal use of VNP and MODIS daily snow cover products in China, which may further contribute to more accurate snow variation information for climate analysis and cryospheric hydrological modelling.
Hongbo Zhang; Fan Zhang; Tao Che; Shijin Wang. Comparative evaluation of VIIRS daily snow cover product with MODIS for snow detection in China based on ground observations. Science of The Total Environment 2020, 724, 138156 .
AMA StyleHongbo Zhang, Fan Zhang, Tao Che, Shijin Wang. Comparative evaluation of VIIRS daily snow cover product with MODIS for snow detection in China based on ground observations. Science of The Total Environment. 2020; 724 ():138156.
Chicago/Turabian StyleHongbo Zhang; Fan Zhang; Tao Che; Shijin Wang. 2020. "Comparative evaluation of VIIRS daily snow cover product with MODIS for snow detection in China based on ground observations." Science of The Total Environment 724, no. : 138156.
National Tibetan Plateau Data Center (TPDC) is one of the first 20 national data centers authorized by the Ministry of Science and Technology of China in 2019 . It is the only data center in China with the most complete scientific data for the Tibetan Plateau and surrounding regions. There are more than 1700 datasets covering many disciplines such as geography, atmospheric science, cryospheric science, hydrology, ecology, geology, geophysics, natural resource science, social economy, and other fields. All data are sorted and integrated in a strict way accordance with the data standards specified by TPDC and the relevant data acquisition specifications. The mission of the data center is to establish a big data center for Third-Pole Earth System Sciences to integrate ThirdPole data resources, particularly those obtained through the implementation of the Third-Pole "Super Monitoring" plan; to develop cutting edge observation technology for extreme environments; and to build a comprehensive and intelligent Internet of Things (IoT) observation system for the Pan-Third Pole region. These developments will facilitate the modeling of environmental changes in the Pan-Third Pole with improved accuracy and performance, as well as support decision-making for sustainable development of the Pan-Third Pole region.
TPDC complies with the “findable, accessible, interoperable and reusable (FAIR)” data sharing principles, in which, the scientific data and metadata can be 'findable' by anyone for exploring and using, can be 'accessible' for being examined, can be 'interoperable' for being analyzed and integrated with comparable data through the use of common vocabulary and formats, can be 'reusable' for public as a result of robust metadata, provenance information and clear usage license. Under the guidance of FAIR data sharing principle, Pan-Third big data system provides online sharing manner for data users, supplemented by offline sharing manner, with bilingual data sharing in Chinese and English.
TPDC has joined WMO (World Meteorological Organization) to promote the project of Integrated Global Cryosphere Information System (IGCryoIS), aiming to collect and share multi-source data in global regions where data is difficult to obtain. Recently TPDC and NSIDC (National Snow and Ice Data Center) officially signed a memorandum of collaboration on data sharing and research to start comprehensive cooperation. TPDC is strengthening cooperation with the international data organizations (e.g. CODATA, WDS) and providing data support for the international science programs of the Tibetan Plateau (e.g. TPE, ANSO). TPDC is applying to become a recommended data repository for the international mainstream journals so as to encourage data authors to share their well-documented, useful and preserved data by giving them credit and recognition.
In a word, TPDC stores, integrates, analyses, excavates and publishes scientific data such as resources, environment, ecology and atmosphere in Pan-third polar region, gathers Pan-third polar core data resources, forms Pan-third polar key scientific data products, and gradually develops online large data analysis, model application and other functions. Furthermore, a cloud service platform will be built for the extensive integration of data, methods, models and services in Pan-Third Pole Science and to promote the application of large data methods in Pan-Third Pole Science Research.
Xin Li; Xiaoduo Pan; Xuejun Guo; Xiaolei Niu; Xiaojuan Yang; Min Feng; Tao Che; Rui Jin; Youhua Ran; Jianwen Guo. National Tibetan Plateau Data Center. 2020, 1 .
AMA StyleXin Li, Xiaoduo Pan, Xuejun Guo, Xiaolei Niu, Xiaojuan Yang, Min Feng, Tao Che, Rui Jin, Youhua Ran, Jianwen Guo. National Tibetan Plateau Data Center. . 2020; ():1.
Chicago/Turabian StyleXin Li; Xiaoduo Pan; Xuejun Guo; Xiaolei Niu; Xiaojuan Yang; Min Feng; Tao Che; Rui Jin; Youhua Ran; Jianwen Guo. 2020. "National Tibetan Plateau Data Center." , no. : 1.
Validation of the snow process model is an important preliminary work for the snow parameter estimation. The snow grain growth is a continuous and accumulative process, which cannot be evaluated without comparing with the observations in snow season scale. In order to understand the snow properties in the Asian Water Tower region (including Xinjiang province and the Tibetan Plateau) and enhance the use of modeling tools, an extended snow experiment at the foot of the Altay Mountain was designed to validate and improve the coupled physical Snow Thermal Model (SNTHERM) and the Microwave Emission Model of Layered Snowpacks (MEMLS). By matching simultaneously the observed snow depth, geometric grain size, and observed brightness temperature (TB), with an RMSE of 1.91 cm, 0.47 mm, and 4.43 K (at 36.5 GHz, vertical polarization), respectively, we finalized the important model coefficients, which are the grain growth coefficient and the grain size to exponential correlation length conversion coefficients. When extended to 102 meteorological stations in the 2008–2009 winter, the SNTHERM predicted the daily snow depth with an accuracy of 2–4 cm RMSE, and the coupled SNTHERM-MEMLS model predicted the satellite-observed TB with an accuracy of 13.34 K RMSE at 36.5 GHz, vertical polarization, with the fractional snow cover considered.
Tao Chen; Jinmei Pan; Shunli Chang; Chuan Xiong; Jiancheng Shi; Mingyu Liu; Lifu Wang; Hongrui Liu. Validation of the SNTHERM Model Applied for Snow Depth, Grain Size, and Brightness Temperature Simulation at Meteorological Stations in China. Remote Sensing 2020, 12, 507 .
AMA StyleTao Chen, Jinmei Pan, Shunli Chang, Chuan Xiong, Jiancheng Shi, Mingyu Liu, Lifu Wang, Hongrui Liu. Validation of the SNTHERM Model Applied for Snow Depth, Grain Size, and Brightness Temperature Simulation at Meteorological Stations in China. Remote Sensing. 2020; 12 (3):507.
Chicago/Turabian StyleTao Chen; Jinmei Pan; Shunli Chang; Chuan Xiong; Jiancheng Shi; Mingyu Liu; Lifu Wang; Hongrui Liu. 2020. "Validation of the SNTHERM Model Applied for Snow Depth, Grain Size, and Brightness Temperature Simulation at Meteorological Stations in China." Remote Sensing 12, no. 3: 507.
Global warming has leaded to permafrost degradation, with potential impacts on the runoff generation processes of permafrost influenced alpine meadow hillslope. Stable isotopes have the potential to trace the complex runoff generation processes. In this study, precipitation, hillslope surface and subsurface runoff, stream water, and mobile soil water (MSW) at different hillslope positions and depths were collected during the summer rainfall period to analyze the major flow pathway based on stable isotopic signatures. The results indicated that: (1) Compared to precipitation, the δ2H values of MSW showed little temporal variation but strong heterogeneity with enriched isotopic ratios at lower hillslope positions and in deeper soil layers. (2) The δ2H values of middle‐slope surface runoff and shallow subsurface flow were similar to those of precipitation and MSW of the same soil layer, respectively. (3) Middle‐slope shallow subsurface flow was the major flow pathway of the permafrost influenced alpine meadow hillslope, which turned into surface runoff at the riparian zone before contributing to the streamflow. (4) The slight variation of δ2H values in stream water were shown to be related to mixing processes of new water (precipitation, 2%) and old water (middle‐slope shallow subsurface flow, 98%) in the highly transmissive shallow thawed soil layers. It was inferred that supra‐permafrost water levels would be lowered to a less conductive, deeper soil layer under further warming and thawing permafrost, which would result in a declined streamflow and delayed runoff peak. This study explained the “rapid mobilization of old water” paradox in permafrost influenced alpine meadow hillslope and improved our understanding of permafrost hillslope hydrology in alpine regions.
Xiong Xiao; Fan Zhang; Xiaoyan Li; Chen Zeng; Xiaonan Shi; Huawu Wu; Muhammad Dodo Jagirani; Tao Che. Using stable isotopes to identify major flow pathways in a permafrost influenced alpine meadow hillslope during summer rainfall period. Hydrological Processes 2019, 34, 1104 -1116.
AMA StyleXiong Xiao, Fan Zhang, Xiaoyan Li, Chen Zeng, Xiaonan Shi, Huawu Wu, Muhammad Dodo Jagirani, Tao Che. Using stable isotopes to identify major flow pathways in a permafrost influenced alpine meadow hillslope during summer rainfall period. Hydrological Processes. 2019; 34 (5):1104-1116.
Chicago/Turabian StyleXiong Xiao; Fan Zhang; Xiaoyan Li; Chen Zeng; Xiaonan Shi; Huawu Wu; Muhammad Dodo Jagirani; Tao Che. 2019. "Using stable isotopes to identify major flow pathways in a permafrost influenced alpine meadow hillslope during summer rainfall period." Hydrological Processes 34, no. 5: 1104-1116.
The alpine region is important in riverine and watershed ecosystems as a contributor of freshwater, providing and stimulating specific habitats for biodiversity. In parallel, recent climate change, human activities and other perturbations may disturb hydrological processes and eco-functions, creating the need for next-generation observational and modeling approaches to advance a predictive understanding of such processes in the alpine region. However, several formidable challenges, including the cold and harsh climate, high altitude and complex topography, inhibit complete and consistent data collection where and when it is needed, which hinders the development of remote-sensing technologies and alpine hydrological models. The current study presents a suite of datasets consisting of long-term hydrometeorological, snow cover and frozen-ground data for investigating watershed science and functions from an integrated, distributed and multiscale observation network in the upper reaches of the Heihe River Basin (HRB) in China. Meteorological and hydrological data were monitored from an observation network connecting a group of automatic meteorological stations (AMSs). In addition, to capture snow accumulation and ablation processes, snow cover properties were collected from a snow observation superstation using state-of-the-art techniques and instruments. High-resolution soil physics datasets were also obtained to capture the freeze–thaw processes from a frozen-ground observation superstation. The updated datasets were released to scientists with multidisciplinary backgrounds (i.e., cryospheric science, hydrology and meteorology), and they are expected to serve as a testing platform to provide accurate forcing data and validate and evaluate remote-sensing products and hydrological models for a broader community. The datasets are available from the Cold and Arid Regions Science Data Center at Lanzhou (https://doi.org/10.3972/hiwater.001.2019.db, Li, 2019).
Tao Che; Xin Li; Shaomin Liu; Hongyi Li; Ziwei Xu; Junlei Tan; Yang Zhang; Zhiguo Ren; Lin Xiao; Jie Deng; Rui Jin; Mingguo Ma; Jian Wang; Xiaofan Yang. Integrated hydrometeorological, snow and frozen-ground observations in the alpine region of the Heihe River Basin, China. Earth System Science Data 2019, 11, 1483 -1499.
AMA StyleTao Che, Xin Li, Shaomin Liu, Hongyi Li, Ziwei Xu, Junlei Tan, Yang Zhang, Zhiguo Ren, Lin Xiao, Jie Deng, Rui Jin, Mingguo Ma, Jian Wang, Xiaofan Yang. Integrated hydrometeorological, snow and frozen-ground observations in the alpine region of the Heihe River Basin, China. Earth System Science Data. 2019; 11 (3):1483-1499.
Chicago/Turabian StyleTao Che; Xin Li; Shaomin Liu; Hongyi Li; Ziwei Xu; Junlei Tan; Yang Zhang; Zhiguo Ren; Lin Xiao; Jie Deng; Rui Jin; Mingguo Ma; Jian Wang; Xiaofan Yang. 2019. "Integrated hydrometeorological, snow and frozen-ground observations in the alpine region of the Heihe River Basin, China." Earth System Science Data 11, no. 3: 1483-1499.
The successful bid for the 2022 Winter Olympics (Beijing 2022, officially known as the XXIV Olympic Winter Games) has greatly stimulated Chinese enthusiasm towards winter sports participation. Consequently, the Chinese ski industry is rapidly booming due to enormous market demand and government support. However, investing in ski areas in unreasonable locations will cause problems from an economic perspective (in terms of operation and management) as well as geographical concerns (such as environmental degradation). Therefore, evaluating the suitability of a ski area based on scientific metrics has become a prerequisite for the sustainable development of the ski industry. In this study, we evaluate the locational suitability of ski areas in China by integrating their natural and socioeconomic conditions using a linearly weighted method based on geographic information system (GIS) spatial analysis combined with remote sensing, online, and field survey data. The key indexes for evaluating natural suitability include snow cover, air temperature, topographic conditions, water resources, and vegetation, whereas socioeconomic suitability is evaluated based on economic conditions, accessibility of transportation, distance to a tourist attraction, and distance to a city. As such, metrics ranging from 0 to 1 considering both natural and socioeconomic conditions are used to define a suitability threshold for each candidate region for ski area development. A ski area is considered to be a dismal prospect when the locational integrated index is less than 0.5. The results show that 84 % of existing ski areas are located in areas with an integrated index greater than 0.5. Finally, corresponding development strategies for decision-makers are proposed based on the multicriteria metrics, which will be extended to incorporate potential influences from future climate change and socioeconomic development. However, the snowmaking model with local data should to be used to further analyze the suitability for a specific ski area.
Jie Deng; Tao Che; Cunde Xiao; Shijin Wang; Liyun Dai; Akynbekkyzy Meerzhan. Suitability analysis of ski areas in China: an integrated study based on natural and socioeconomic conditions. The Cryosphere 2019, 13, 2149 -2167.
AMA StyleJie Deng, Tao Che, Cunde Xiao, Shijin Wang, Liyun Dai, Akynbekkyzy Meerzhan. Suitability analysis of ski areas in China: an integrated study based on natural and socioeconomic conditions. The Cryosphere. 2019; 13 (8):2149-2167.
Chicago/Turabian StyleJie Deng; Tao Che; Cunde Xiao; Shijin Wang; Liyun Dai; Akynbekkyzy Meerzhan. 2019. "Suitability analysis of ski areas in China: an integrated study based on natural and socioeconomic conditions." The Cryosphere 13, no. 8: 2149-2167.