Articles | Volume 15, issue 2
https://doi.org/10.5194/tc-15-835-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-15-835-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Feb 2021
Research article |
| 18 Feb 2021
| 18 Feb 2021
Estimating fractional snow cover from passive microwave brightness temperature data using MODIS snow cover product over North America
Xiongxin Xiao
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Shunlin Liang
Department of Geographical Sciences, University of Maryland, College
Park, MD 20742, USA
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Daiqiang Wu
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Congyuan Pei
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Jianya Gong
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Related authors
Xinyan Liu, Tao He, Qingxin Wang, Xiongxin Xiao, Yichuan Ma, Yanyan Wang, Shanjun Luo, Lei Du, and Zhaocong Wu
Earth Syst. Sci. Data, 17, 2405–2435, https://doi.org/10.5194/essd-17-2405-2025, https://doi.org/10.5194/essd-17-2405-2025, 2025
Short summary
Short summary
This study addresses the challenge of how clouds affect the Earth's energy balance, which is vital for understanding climate change. We developed a new method to create long-term cloud radiative kernels to improve the accuracy of measurements of sunlight reaching the surface, which significantly reduces errors. Findings suggest that prior estimates of cloud cooling effects may have been overstated, emphasizing the need for better strategies to manage climate change impacts in the Arctic.
Xinyan Liu, Tao He, Shunlin Liang, Ruibo Li, Xiongxin Xiao, Rui Ma, and Yichuan Ma
Earth Syst. Sci. Data, 15, 3641–3671, https://doi.org/10.5194/essd-15-3641-2023, https://doi.org/10.5194/essd-15-3641-2023, 2023
Short summary
Short summary
We proposed a data fusion strategy that combines the complementary features of multiple-satellite cloud fraction (CF) datasets and generated a continuous monthly 1° daytime cloud fraction product covering the entire Arctic during the sunlit months in 2000–2020. This study has positive significance for reducing the uncertainties for the assessment of surface radiation fluxes and improving the accuracy of research related to climate change and energy budgets, both regionally and globally.
Xinyan Liu, Tao He, Qingxin Wang, Xiongxin Xiao, Yichuan Ma, Yanyan Wang, Shanjun Luo, Lei Du, and Zhaocong Wu
Earth Syst. Sci. Data, 17, 2405–2435, https://doi.org/10.5194/essd-17-2405-2025, https://doi.org/10.5194/essd-17-2405-2025, 2025
Short summary
Short summary
This study addresses the challenge of how clouds affect the Earth's energy balance, which is vital for understanding climate change. We developed a new method to create long-term cloud radiative kernels to improve the accuracy of measurements of sunlight reaching the surface, which significantly reduces errors. Findings suggest that prior estimates of cloud cooling effects may have been overstated, emphasizing the need for better strategies to manage climate change impacts in the Arctic.
Hui Liang, Shunlin Liang, Bo Jiang, Tao He, Feng Tian, Jianglei Xu, Wenyuan Li, Fengjiao Zhang, and Husheng Fang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-136, https://doi.org/10.5194/essd-2025-136, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
This paper describes 1 km daily mean land surface sensible heat flux (H) and land surface – air temperature difference (Tsa) datasets on the global scale during 2000–2020. The datasets were developed using a data-driven approach and rigorously validated against in situ observations and existing H and Tsa datasets, demonstrating both high accuracy and exceptional spatial resolution.
Yufang Zhang, Shunlin Liang, Han Ma, Tao He, Feng Tian, Guodong Zhang, and Jianglei Xu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-553, https://doi.org/10.5194/essd-2024-553, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
Soil moisture (SM) plays a vital role in climate, agriculture, and hydrology, yet reliable long-term seamless global datasets remain scarce. To fill this gap, we developed a four-decade seamless global daily 5 km SM product using multi-source datasets and deep learning techniques. This product has long-term coverage, spatial and temporal integrity, and high accuracy, making it a valuable tool for applications like SM trend analysis, drought monitoring, and assessing vegetation responses.
Xinlian Liang, Yinrui Wang, Jun Pan, Mi Wang, Jiansi Yang, and Jianya Gong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-3-2024, 637–641, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-637-2024, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-637-2024, 2024
Mi Zhang, Bingnan Yang, Jianya Gong, and Xiangyun Hu
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-3-2024, 445–452, https://doi.org/10.5194/isprs-annals-X-3-2024-445-2024, https://doi.org/10.5194/isprs-annals-X-3-2024-445-2024, 2024
Peng Yue, Kaixuan Wang, Hanwen Xu, Jianya Gong, and Longgang Xiang
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-4-2024, 441–446, https://doi.org/10.5194/isprs-annals-X-4-2024-441-2024, https://doi.org/10.5194/isprs-annals-X-4-2024-441-2024, 2024
Xianyuan Zhang, Longgang Xiang, Peng Yue, Jianya Gong, and Huayi Wu
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-4-2024, 453–459, https://doi.org/10.5194/isprs-annals-X-4-2024-453-2024, https://doi.org/10.5194/isprs-annals-X-4-2024-453-2024, 2024
Bing Li, Shunlin Liang, Han Ma, Guanpeng Dong, Xiaobang Liu, Tao He, and Yufang Zhang
Earth Syst. Sci. Data, 16, 3795–3819, https://doi.org/10.5194/essd-16-3795-2024, https://doi.org/10.5194/essd-16-3795-2024, 2024
Short summary
Short summary
This study describes 1 km all-weather instantaneous and daily mean land surface temperature (LST) datasets on the global scale during 2000–2020. It is the first attempt to synergistically estimate all-weather instantaneous and daily mean LST data on a long global-scale time series. The generated datasets were evaluated by the observations from in situ stations and other LST datasets, and the evaluation indicated that the dataset is sufficiently reliable.
Jiageng Zhong, Ming Li, Armin Gruen, Jianya Gong, Deren Li, Mingjie Li, and Jiangying Qin
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-2-2024, 247–254, https://doi.org/10.5194/isprs-annals-X-2-2024-247-2024, https://doi.org/10.5194/isprs-annals-X-2-2024-247-2024, 2024
Xinyan Liu, Tao He, Shunlin Liang, Ruibo Li, Xiongxin Xiao, Rui Ma, and Yichuan Ma
Earth Syst. Sci. Data, 15, 3641–3671, https://doi.org/10.5194/essd-15-3641-2023, https://doi.org/10.5194/essd-15-3641-2023, 2023
Short summary
Short summary
We proposed a data fusion strategy that combines the complementary features of multiple-satellite cloud fraction (CF) datasets and generated a continuous monthly 1° daytime cloud fraction product covering the entire Arctic during the sunlit months in 2000–2020. This study has positive significance for reducing the uncertainties for the assessment of surface radiation fluxes and improving the accuracy of research related to climate change and energy budgets, both regionally and globally.
Yufang Zhang, Shunlin Liang, Han Ma, Tao He, Qian Wang, Bing Li, Jianglei Xu, Guodong Zhang, Xiaobang Liu, and Changhao Xiong
Earth Syst. Sci. Data, 15, 2055–2079, https://doi.org/10.5194/essd-15-2055-2023, https://doi.org/10.5194/essd-15-2055-2023, 2023
Short summary
Short summary
Soil moisture observations are important for a range of earth system applications. This study generated a long-term (2000–2020) global seamless soil moisture product with both high spatial and temporal resolutions (1 km, daily) using an XGBoost model and multisource datasets. Evaluation of this product against dense in situ soil moisture datasets and microwave soil moisture products showed that this product has reliable accuracy and more complete spatial coverage.
Aolin Jia, Shunlin Liang, Dongdong Wang, Lei Ma, Zhihao Wang, and Shuo Xu
Earth Syst. Sci. Data, 15, 869–895, https://doi.org/10.5194/essd-15-869-2023, https://doi.org/10.5194/essd-15-869-2023, 2023
Short summary
Short summary
Satellites are now producing multiple global land surface temperature (LST) products; however, they suffer from data gaps caused by cloud cover, seriously restricting the applications, and few products provide gap-free global hourly LST. We produced global hourly, 5 km, all-sky LST data from 2011 to 2021 using geostationary and polar-orbiting satellite data. Based on the assessment, it has high accuracy and can be used to estimate evapotranspiration, drought, etc.
Fa Li, Qing Zhu, William J. Riley, Lei Zhao, Li Xu, Kunxiaojia Yuan, Min Chen, Huayi Wu, Zhipeng Gui, Jianya Gong, and James T. Randerson
Geosci. Model Dev., 16, 869–884, https://doi.org/10.5194/gmd-16-869-2023, https://doi.org/10.5194/gmd-16-869-2023, 2023
Short summary
Short summary
We developed an interpretable machine learning model to predict sub-seasonal and near-future wildfire-burned area over African and South American regions. We found strong time-lagged controls (up to 6–8 months) of local climate wetness on burned areas. A skillful use of such time-lagged controls in machine learning models results in highly accurate predictions of wildfire-burned areas; this will also help develop relevant early-warning and management systems for tropical wildfires.
Han Ma, Shunlin Liang, Changhao Xiong, Qian Wang, Aolin Jia, and Bing Li
Earth Syst. Sci. Data, 14, 5333–5347, https://doi.org/10.5194/essd-14-5333-2022, https://doi.org/10.5194/essd-14-5333-2022, 2022
Short summary
Short summary
The fraction of absorbed photosynthetically active radiation (FAPAR) is one of the essential climate variables. This study generated a global land surface FAPAR product with a 250 m resolution based on a deep learning model that takes advantage of the existing FAPAR products and MODIS time series of observation information. Direct validation and intercomparison revealed that our product better meets user requirements and has a greater spatiotemporal continuity than other existing products.
Rui Ma, Jingfeng Xiao, Shunlin Liang, Han Ma, Tao He, Da Guo, Xiaobang Liu, and Haibo Lu
Geosci. Model Dev., 15, 6637–6657, https://doi.org/10.5194/gmd-15-6637-2022, https://doi.org/10.5194/gmd-15-6637-2022, 2022
Short summary
Short summary
Parameter optimization can improve the accuracy of modeled carbon fluxes. Few studies conducted pixel-level parameterization because it requires a high computational cost. Our paper used high-quality spatial products to optimize parameters at the pixel level, and also used the machine learning method to improve the speed of optimization. The results showed that there was significant spatial variability of parameters and we also improved the spatial pattern of carbon fluxes.
Y. Xu, X. Hu, J. Gong, X. Huang, and J. Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 223–228, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-223-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-223-2022, 2022
Jianglei Xu, Shunlin Liang, and Bo Jiang
Earth Syst. Sci. Data, 14, 2315–2341, https://doi.org/10.5194/essd-14-2315-2022, https://doi.org/10.5194/essd-14-2315-2022, 2022
Short summary
Short summary
Land surface all-wave net radiation (Rn) is a key parameter in many land processes. Current products have drawbacks of coarse resolutions, large uncertainty, and short time spans. A deep learning method was used to obtain global surface Rn. A long-term Rn product was generated from 1981 to 2019 using AVHRR data. The product has the highest accuracy and a reasonable spatiotemporal variation compared to three other products. Our product will play an important role in long-term climate change.
Qing Zhu, Fa Li, William J. Riley, Li Xu, Lei Zhao, Kunxiaojia Yuan, Huayi Wu, Jianya Gong, and James Randerson
Geosci. Model Dev., 15, 1899–1911, https://doi.org/10.5194/gmd-15-1899-2022, https://doi.org/10.5194/gmd-15-1899-2022, 2022
Short summary
Short summary
Wildfire is a devastating Earth system process that burns about 500 million hectares of land each year. It wipes out vegetation including trees, shrubs, and grasses and causes large losses of economic assets. However, modeling the spatial distribution and temporal changes of wildfire activities at a global scale is challenging. This study built a machine-learning-based wildfire surrogate model within an existing Earth system model and achieved high accuracy.
Xueyuan Gao, Shunlin Liang, Dongdong Wang, Yan Li, Bin He, and Aolin Jia
Earth Syst. Dynam., 13, 219–230, https://doi.org/10.5194/esd-13-219-2022, https://doi.org/10.5194/esd-13-219-2022, 2022
Short summary
Short summary
Numerical experiments with a coupled Earth system model show that large-scale nighttime artificial lighting in tropical forests will significantly increase carbon sink, local temperature, and precipitation, and it requires less energy than direct air carbon capture for capturing 1 t of carbon, suggesting that it could be a powerful climate mitigation option. Side effects include CO2 outgassing after the termination of the nighttime lighting and impacts on local wildlife.
Xiaona Chen, Shunlin Liang, Lian He, Yaping Yang, and Cong Yin
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-279, https://doi.org/10.5194/essd-2021-279, 2021
Preprint withdrawn
Short summary
Short summary
The present study developed a 39 year consistent 8-day 0.05 degree gap-free SCE dataset over the NH for the period 1981–2019 as part of the Global LAnd Surface Satellite dataset (GLASS) product suite based on the NOAA AVHRR-SR CDR and several contributory datasets. Compared with published SCE datasets, GLASS SCE has several advantages in snow cover studies, including long time series, finer spatial resolution (especially for years before 2000), and complete spatial coverage.
Diyang Cui, Shunlin Liang, Dongdong Wang, and Zheng Liu
Earth Syst. Sci. Data, 13, 5087–5114, https://doi.org/10.5194/essd-13-5087-2021, https://doi.org/10.5194/essd-13-5087-2021, 2021
Short summary
Short summary
Large portions of the Earth's surface are expected to experience changes in climatic conditions. The rearrangement of climate distributions can lead to serious impacts on ecological and social systems. Major climate zones are distributed in a predictable pattern and are largely defined following the Köppen climate classification. This creates an urgent need to compile a series of Köppen climate classification maps with finer spatial and temporal resolutions and improved accuracy.
Yan Chen, Shunlin Liang, Han Ma, Bing Li, Tao He, and Qian Wang
Earth Syst. Sci. Data, 13, 4241–4261, https://doi.org/10.5194/essd-13-4241-2021, https://doi.org/10.5194/essd-13-4241-2021, 2021
Short summary
Short summary
This study used remotely sensed and assimilated data to estimate all-sky land surface air temperature (Ta) using a machine learning method, and developed an all-sky 1 km daily mean land Ta product for 2003–2019 over mainland China. Validation results demonstrated that this dataset has achieved satisfactory accuracy and high spatial resolution simultaneously, which fills the current dataset gap in this field and plays an important role in studies of climate change and the hydrological cycle.
Yongkang Xue, Tandong Yao, Aaron A. Boone, Ismaila Diallo, Ye Liu, Xubin Zeng, William K. M. Lau, Shiori Sugimoto, Qi Tang, Xiaoduo Pan, Peter J. van Oevelen, Daniel Klocke, Myung-Seo Koo, Tomonori Sato, Zhaohui Lin, Yuhei Takaya, Constantin Ardilouze, Stefano Materia, Subodh K. Saha, Retish Senan, Tetsu Nakamura, Hailan Wang, Jing Yang, Hongliang Zhang, Mei Zhao, Xin-Zhong Liang, J. David Neelin, Frederic Vitart, Xin Li, Ping Zhao, Chunxiang Shi, Weidong Guo, Jianping Tang, Miao Yu, Yun Qian, Samuel S. P. Shen, Yang Zhang, Kun Yang, Ruby Leung, Yuan Qiu, Daniele Peano, Xin Qi, Yanling Zhan, Michael A. Brunke, Sin Chan Chou, Michael Ek, Tianyi Fan, Hong Guan, Hai Lin, Shunlin Liang, Helin Wei, Shaocheng Xie, Haoran Xu, Weiping Li, Xueli Shi, Paulo Nobre, Yan Pan, Yi Qin, Jeff Dozier, Craig R. Ferguson, Gianpaolo Balsamo, Qing Bao, Jinming Feng, Jinkyu Hong, Songyou Hong, Huilin Huang, Duoying Ji, Zhenming Ji, Shichang Kang, Yanluan Lin, Weiguang Liu, Ryan Muncaster, Patricia de Rosnay, Hiroshi G. Takahashi, Guiling Wang, Shuyu Wang, Weicai Wang, Xu Zhou, and Yuejian Zhu
Geosci. Model Dev., 14, 4465–4494, https://doi.org/10.5194/gmd-14-4465-2021, https://doi.org/10.5194/gmd-14-4465-2021, 2021
Short summary
Short summary
The subseasonal prediction of extreme hydroclimate events such as droughts/floods has remained stubbornly low for years. This paper presents a new international initiative which, for the first time, introduces spring land surface temperature anomalies over high mountains to improve precipitation prediction through remote effects of land–atmosphere interactions. More than 40 institutions worldwide are participating in this effort. The experimental protocol and preliminary results are presented.
Diyang Cui, Shunlin Liang, Dongdong Wang, and Zheng Liu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-53, https://doi.org/10.5194/essd-2021-53, 2021
Preprint withdrawn
Short summary
Short summary
The Köppen-Geiger climate classification has been widely applied in climate change and ecology studies to characterize climatic conditions. We present a new 1-km global dataset of Köppen-Geiger climate classification and bioclimatic variables for historical and future climates. The new climate maps offer higher classification accuracy, correspond well with distributions of vegetation and topographic features, and demonstrate the ability to identify recent and future changes in climate zones.
Jin Ma, Ji Zhou, Frank-Michael Göttsche, Shunlin Liang, Shaofei Wang, and Mingsong Li
Earth Syst. Sci. Data, 12, 3247–3268, https://doi.org/10.5194/essd-12-3247-2020, https://doi.org/10.5194/essd-12-3247-2020, 2020
Short summary
Short summary
Land surface temperature is an important parameter in the research of climate change and many land surface processes. This article describes the development and testing of an algorithm for generating a consistent global long-term land surface temperature product from 20 years of NOAA AVHRR radiance data. The preliminary validation results indicate good accuracy of this new long-term product, which has been designed to simplify applications and support the scientific research community.
Yi Zheng, Ruoque Shen, Yawen Wang, Xiangqian Li, Shuguang Liu, Shunlin Liang, Jing M. Chen, Weimin Ju, Li Zhang, and Wenping Yuan
Earth Syst. Sci. Data, 12, 2725–2746, https://doi.org/10.5194/essd-12-2725-2020, https://doi.org/10.5194/essd-12-2725-2020, 2020
Short summary
Short summary
Accurately reproducing the interannual variations in vegetation gross primary production (GPP) is a major challenge. A global GPP dataset was generated by integrating the regulations of several major environmental variables with long-term changes. The dataset can effectively reproduce the spatial, seasonal, and particularly interannual variations in global GPP. Our study will contribute to accurate carbon flux estimates at long timescales.
Cited articles
Amante, C. and Eakins, B. W.: ETOPO1 1 Arc-Minute Global Relief Model:
Procedures, Data Sources and Analysis, National Geophysical Data Center,
NOAA, https://doi.org/10.7289/V5C8276M, 2009.
Arsenault, K. R., Houser, P. R., and De Lannoy, G. J. M.: Evaluation of the
MODIS snow cover fraction product, Hydrol. Process., 28, 980–998,
https://doi.org/10.1002/hyp.9636, 2014.
Barnett, T. P., Adam, J. C., and Lettenmaier, D. P.: Potential impacts of a
warming climate on water availability in snow-dominated regions, Nature,
438, 303–309, https://doi.org/10.1038/nature04141, 2005.
Belgiu, M. and Drăgu, L.: Random forest in remote sensing: A review of
applications and future directions, ISPRS J. Photogramm., 114, 24–31, https://doi.org/10.1016/j.isprsjprs.2016.01.011, 2016.
Berman, E. E., Bolton, D. K., Coops, N. C., Mityok, Z. K., Stenhouse, G. B.,
and Moore, R. D.: Daily estimates of Landsat fractional snow cover driven by
MODIS and dynamic time-warping, Remote Sens. Environ., 216, 635–646,
https://doi.org/10.1016/j.rse.2018.07.029, 2018.
Bormann, K. J., Brown, R. D., Derksen, C., and Painter, T. H.: Estimating
snow-cover trends from space, Nat. Clim. Change, 8, 924–928,
https://doi.org/10.1038/s41558-018-0318-3, 2018.
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32,
https://doi.org/10.1023/a:1010933404324, 2001.
Brodzik, M., Long, D., and Hardman, M.: Best Practices in Crafting the
Calibrated, Enhanced-Resolution Passive-Microwave EASE-Grid 2.0 Brightness
Temperature Earth System Data Record, Remote Sens.-Basel, 10, 1793, https://doi.org/10.3390/rs10111793, 2018.
Brown, R., Derksen, C., and Wang, L.: A multi-data set analysis of
variability and change in Arctic spring snow cover extent, 1967–2008,
J. Geophys. Res., 115, 751–763, https://doi.org/10.1029/2010jd013975, 2010.
Brown, R. D. and Derksen, C.: Is Eurasian October snow cover extent
increasing?, Environ. Res. Lett., 8, 024006, https://doi.org/10.1088/1748-9326/8/2/024006, 2013.
Chang, A., Foster, J., and Hall, D.: Nimbus-7 SMMR derived global snow cover
parameters, Ann. Glaciol, 9, 39–44, 1987.
Che, T., Xin, L., Jin, R., Armstrong, R., and Zhang, T.: Snow depth derived
from passive microwave remote-sensing data in China, Ann. Glaciol.,
49, 145–154, 2008.
Che, T., Dai, L., Zheng, X., Li, X., and Zhao, K.: Estimation of snow depth
from passive microwave brightness temperature data in forest regions of
northeast China, Remote Sens. Environ., 183, 334–349, 2016.
Chen, X., Long, D., Liang, S., He, L., Zeng, C., Hao, X., and Hong, Y.:
Developing a composite daily snow cover extent record over the Tibetan
Plateau from 1981 to 2016 using multisource data, Remote Sens. Environ., 215, 284–299, https://doi.org/10.1016/j.rse.2018.06.021, 2018.
Cheng, Z., Guo, Z., Tan, Z., Yang, J., and Wang, Q.: Waste heat recovery
from high-temperature solid granular materials: Energy challenges and
opportunities, Renew. Sust. Energ. Rev., 116, 024006, https://doi.org/10.1016/j.rser.2019.109428, 2019.
Cohen, J., Lemmetyinen, J., Pulliainen, J., Heinila, K., Montomoli, F.,
Seppanen, J., and Hallikainen, M. T.: The Effect of Boreal Forest Canopy in
Satellite Snow Mapping – A Multisensor Analysis, IEEE T. Geosci. Remote, 53, 6593–6607, https://doi.org/10.1109/tgrs.2015.2444422,
2015.
Colditz, R.: An Evaluation of Different Training Sample Allocation Schemes
for Discrete and Continuous Land Cover Classification Using Decision
Tree-Based Algorithms, Remote Sens.-Basel, 7, 9655–9681, https://doi.org/10.3390/rs70809655,
2015.
Coll, J., and Li, X.: Comprehensive accuracy assessment of MODIS daily snow
cover products and gap filling methods, ISPRS J. Photogramm., 144, 435–452, https://doi.org/10.1016/j.isprsjprs.2018.08.004, 2018.
Czyzowska-Wisniewski, E. H., van Leeuwen, W. J. D., Hirschboeck, K. K.,
Marsh, S. E., and Wisniewski, W. T.: Fractional snow cover estimation in
complex alpine-forested environments using an artificial neural network,
Remote Sens. Environ., 156, 403–417, https://doi.org/10.1016/j.rse.2014.09.026,
2015.
Dai, L., Che, T., Wang, J., and Zhang, P.: Snow depth and snow water
equivalent estimation from AMSR-E data based on a priori snow
characteristics in Xinjiang, China, Remote Sens. Environ., 127,
14–29, 2012.
Dai, L., Che, T., Ding, Y., and Hao, X.: Evaluation of snow cover and snow depth on the Qinghai–Tibetan Plateau derived from passive microwave remote sensing, The Cryosphere, 11, 1933–1948, https://doi.org/10.5194/tc-11-1933-2017, 2017.
De Lannoy, G. J. M., Reichle, R. H., Arsenault, K. R., Houser, P. R., Kumar, S., Verhoest, N. E. C., and Pauwels, V. R. N.: Multiscale assimilation of
Advanced Microwave Scanning Radiometer-EOS snow water equivalent and
Moderate Resolution Imaging Spectroradiometer snow cover fraction
observations in northern Colorado, Water Resour. Res., 48, W01522, https://doi.org/10.1029/2011wr010588, 2012.
Derksen, C., LeDrew, E., Walker, A., and Goodison, B.: Influence of sensor
overpass time on passive microwave-derived snow cover parameters, Remote
Sens. Environ., 71, 297–308, 2000.
Dietz, A. J., Kuenzer, C., Gessner, U., and Dech, S.: Remote sensing of snow
– a review of available methods, Int. J. Remote Sens.,
33, 4094–4134, https://doi.org/10.1080/01431161.2011.640964, 2011.
Dobreva, I. D. and Klein, A. G.: Fractional snow cover mapping through
artificial neural network analysis of MODIS surface reflectance, Remote
Sens. Environ., 115, 3355–3366, https://doi.org/10.1016/j.rse.2011.07.018, 2011.
Dong, C. and Menzel, L.: Producing cloud-free MODIS snow cover products
with conditional probability interpolation and meteorological data, Remote
Sens. Environ., 186, 439–451, https://doi.org/10.1016/j.rse.2016.09.019, 2016.
Dong, J., Ek, M., Hall, D., Peters-Lidard, C., Cosgrove, B., Miller, J.,
Riggs, G., and Xia, Y.: Using Air Temperature to Quantitatively Predict the
MODIS Fractional Snow Cover Retrieval Errors over the Continental United
States, J. Hydrometeorol., 15, 551–562, https://doi.org/10.1175/jhm-d-13-060.1,
2014.
Du, P., Samat, A., Waske, B., Liu, S., and Li, Z.: Random Forest and
Rotation Forest for fully polarized SAR image classification using
polarimetric and spatial features, ISPRS J. Photogramm., 105, 38–53, https://doi.org/10.1016/j.isprsjprs.2015.03.002, 2015.
Flanner, M. G., Shell, K. M., Barlage, M., Perovich, D. K., and Tschudi, M. A.: Radiative forcing and albedo feedback from the Northern Hemisphere
cryosphere between 1979 and 2008, Nat. Geosci., 4, 151–155,
https://doi.org/10.1038/ngeo1062, 2011.
Foody, G. M.: Explaining the unsuitability of the kappa coefficient in the
assessment and comparison of the accuracy of thematic maps obtained by image
classification, Remote Sens. Environ., 239, 111630, https://doi.org/10.1016/j.rse.2019.111630, 2020.
Foster, J. L., Hall, D. K., Eylander, J. B., Riggs, G. A., Nghiem, S. V.,
Tedesco, M., Kim, E., Montesano, P. M., Kelly, R. E. J., Casey, K. A., and
Choudhury, B.: A blended global snow product using visible, passive
microwave and scatterometer satellite data, Int. J. Remote
Sens., 32, 1371–1395, https://doi.org/10.1080/01431160903548013, 2011.
Frank, E., Hall, M., Trigg, L., Holmes, G., and Witten, I. H.: Data mining
in bioinformatics using Weka, Bioinformatics, 20, 2479–2481, 2004.
Friedman, J. H.: Multivariate Adaptive Regression Splines, Ann. Stat., 19, 1–67, 1991.
Gafurov, A. and Bárdossy, A.: Cloud removal methodology from MODIS snow cover product, Hydrol. Earth Syst. Sci., 13, 1361–1373, https://doi.org/10.5194/hess-13-1361-2009, 2009.
Gao, J., Yao, T., Masson-Delmotte, V., Steen-Larsen, H. C., and Wang, W.:
Collapsing glaciers threaten Asia's water supplies, Nature, 19–21, https://doi.org/10.1038/d41586-018-07838-4, 2019.
Gascoin, S., Hagolle, O., Huc, M., Jarlan, L., Dejoux, J.-F., Szczypta, C., Marti, R., and Sánchez, R.: A snow cover climatology for the Pyrenees from MODIS snow products, Hydrol. Earth Syst. Sci., 19, 2337–2351, https://doi.org/10.5194/hess-19-2337-2015, 2015.
Gascoin, S., Grizonnet, M., Bouchet, M., Salgues, G., and Hagolle, O.: Theia Snow collection: high-resolution operational snow cover maps from Sentinel-2 and Landsat-8 data, Earth Syst. Sci. Data, 11, 493–514, https://doi.org/10.5194/essd-11-493-2019, 2019.
Grippa, M., Mognard, N., Le Toan, T., and Josberger, E.: Siberia snow depth
climatology derived from SSM/I data using a combined dynamic and static
algorithm, Remote Sens. Environ., 93, 30–41, 2004.
Grody, N. C. and Basist, A. N.: Global identification of snowcover using
SSM/I measurements, IEEE T. Geosci. Remote, 34,
237–249, 1996.
Hall, D. K. and Riggs, G. A.: Accuracy assessment of the MODIS snow
products, Hydrol. Process., 21, 1534–1547, https://doi.org/10.1002/hyp.6715, 2007.
Hall, D. K. and Riggs, G. A.: MODIS/Terra Snow Cover Daily L3 Global 500 m
SIN Grid, Version 6, NASA National Snow and Ice Data Center Distributed
Active Archive Center, Boulder, Colorado USA, https://doi.org/https://doi.org/10.5067/MODIS/MOD10A1.006, 2016a.
Hall, D. K. and Riggs, G. A.: MODIS/Aqua Snow Cover Daily L3 Global 500 m
SIN Grid, Version 6., NASA National Snow and Ice Data Center Distributed
Active Archive Center, Boulder, Colorado USA, https://doi.org/10.5067/MODIS/MYD10A1.006, 2016b.
Hall, D. K., Riggs, G. A., and Salomonson, V. V.: Development of methods for
mapping global snow cover using moderate resolution imaging
spectroradiometer data, Remote Sens. Environ., 54, 127–140, 1995.
Hall, D. K., Foster, J. L., Verbyla, D. L., Klein, A. G., and Benson, C. S.:
Assessment of Snow-Cover Mapping Accuracy in a Variety of Vegetation-Cover
Densities in Central Alaska, Remote Sens. Environ., 66, 129–137,
https://doi.org/10.1016/S0034-4257(98)00051-0, 1998.
Hall, D. K., Riggs, G. A., and Salomonson, V. V.: Algorithm Theoretical
Basis Document (ATBD) for the MODIS Snow and Sea Ice-Mapping Algorithms, NASA Goddard Space Flight Center, Greenbelt, MD, USA,
2001.
Han, M., Yang, K., Qin, J., Jin, R., Ma, Y., Wen, J., Chen, Y., Zhao, L.,
Lazhu, and Tang, W.: An Algorithm Based on the Standard Deviation of Passive
Microwave Brightness Temperatures for Monitoring Soil Surface Freeze/Thaw
State on the Tibetan Plateau, IEEE T. Geosci. Remote, 53, 2775–2783, https://doi.org/10.1109/tgrs.2014.2364823, 2015.
Hao, S., Jiang, L., Shi, J., Wang, G., and Liu, X.: Assessment of
MODIS-Based Fractional Snow Cover Products Over the Tibetan Plateau, IEEE
J. Sel. Top. Appl.,
12, 533–548, https://doi.org/10.1109/jstars.2018.2879666, 2019.
Hao, X., Luo, S., Che, T., Wang, J., Li, H., Dai, L., Huang, X., and Feng, Q.: Accuracy assessment of four cloud-free snow cover products over the
Qinghai-Tibetan Plateau, Int. J. Digit. Earth, 12,
375–393, https://doi.org/10.1080/17538947.2017.1421721, 2018.
Haykin, S. O.: Neural Networks and Learning Machines, 3rd Edn., Prentice Hall, Pearson Education, New Jersey,
2009.
He, T., Liang, S., and Song, D.-X.: Analysis of global land surface albedo
climatology and spatial-temporal variation during 1981–2010 from multiple
satellite products, J. Geophys. Res.-Atmos., 119,
10281–210298, https://doi.org/10.1002/2014jd021667, 2014.
Hecht-Nielsen, R.: Theory of the backpropagation neural network, in: Neural
networks for perception, Elsevier, IJCNN, International Joint Conference, 65–93, 1992.
Henderson, G. R., Peings, Y., Furtado, J. C., and Kushner, P. J.:
Snow–atmosphere coupling in the Northern Hemisphere, Nat. Clim. Change,
8, 954–963, https://doi.org/10.1038/s41558-018-0295-6, 2018.
Hori, M., Sugiura, K., Kobayashi, K., Aoki, T., Tanikawa, T., Kuchiki, K.,
Niwano, M., and Enomoto, H.: A 38-year (1978–2015) Northern Hemisphere
daily snow cover extent product derived using consistent objective criteria
from satellite-borne optical sensors, Remote Sens. Environ., 191,
402–418, https://doi.org/10.1016/j.rse.2017.01.023, 2017.
Huang, X., Deng, J., Ma, X., Wang, Y., Feng, Q., Hao, X., and Liang, T.: Spatiotemporal dynamics of snow cover based on multi-source remote sensing data in China, The Cryosphere, 10, 2453–2463, https://doi.org/10.5194/tc-10-2453-2016, 2016.
Huang, Y., Liu, H., Yu, B., Wu, J., Kang, E. L., Xu, M., Wang, S., Klein, A., and Chen, Y.: Improving MODIS snow products with a HMRF-based
spatio-temporal modeling technique in the Upper Rio Grande Basin, Remote
Sens. Environ., 204, 568–582, https://doi.org/10.1016/j.rse.2017.10.001, 2018.
Jin, H., Stehman, S. V., and Mountrakis, G.: Assessing the impact of
training sample selection on accuracy of an urban classification: a case
study in Denver, Colorado, Int. J. Remote Sens., 35,
2067–2081, 2014.
Josberger, E. G. and Mognard, N. M.: A passive microwave snow depth
algorithm with a proxy for snow metamorphism, Hydrol. Process., 16,
1557–1568, 2002.
Kattenborn, T., Lopatin, J., Förster, M., Braun, A. C., and Fassnacht, F. E.: UAV data as alternative to field sampling to map woody invasive
species based on combined Sentinel-1 and Sentinel-2 data, Remote Sens. Environ., 227, 61–73, https://doi.org/10.1016/j.rse.2019.03.025, 2019.
Kelly, R.: The AMSR-E snow depth algorithm: Description and initial results,
Journal of The Remote Sensing Society of Japan, 29, 307–317,
2009.
Kim, R. S., Durand, M., and Liu, D.: Spectral analysis of airborne passive
microwave measurements of alpine snowpack: Colorado, USA, Remote Sens.
Environ., 205, 469–484, https://doi.org/10.1016/j.rse.2017.07.025, 2018.
Kim, R. S., Durand, M., Li, D., Baldo, E., Margulis, S. A., Dumont, M., and
Morin, S.: Estimating alpine snow depth by combining multifrequency passive
radiance observations with ensemble snowpack modeling, Remote Sens.
Environ., 226, 1–15, https://doi.org/10.1016/j.rse.2019.03.016, 2019.
Kostadinov, T. S. and Lookingbill, T. R.: Snow cover variability in a
forest ecotone of the Oregon Cascades via MODIS Terra products, Remote
Sens. Environ., 164, 155–169, https://doi.org/10.1016/j.rse.2015.04.002, 2015.
Kuter, S., Weber, G.-W., Akyürek, Z., and Özmen, A.: Inversion of
top of atmospheric reflectance values by conic multivariate adaptive
regression splines, Inverse Probl. Sci. En., 23,
651–669, https://doi.org/10.1080/17415977.2014.933828, 2015.
Kuter, S., Akyurek, Z., and Weber, G.-W.: Retrieval of fractional snow
covered area from MODIS data by multivariate adaptive regression splines,
Remote Sens. Environ., 205, 236–252, https://doi.org/10.1016/j.rse.2017.11.021,
2018.
Lemmetyinen, J., Derksen, C., Rott, H., Macelloni, G., King, J., Schneebeli, M., Wiesmann, A., Leppänen, L., Kontu, A., and Pulliainen, J.: Retrieval
of Effective Correlation Length and Snow Water Equivalent from Radar and
Passive Microwave Measurements, Remote Sens.-Basel, 10, 170, https://doi.org/10.3390/rs10020170,
2018.
Li, X., Liu, Y., Zhu, X., Zheng, Z., and Chen, A.: Snow Cover Identification
with SSM/I Data in China, J. Appl. Meteorol. Sci., 18,
12–20, 2007.
Liang, H., Huang, X., Sun, Y., Wang, Y., and Liang, T.: Fractional
Snow-Cover Mapping Based on MODIS and UAV Data over the Tibetan Plateau,
Remote Sens.-Basel, 9, 1332, https://doi.org/10.3390/rs9121332, 2017.
Liu, X., Jiang, L., Wu, S., Hao, S., Wang, G., and Yang, J.: Assessment of
Methods for Passive Microwave Snow Cover Mapping Using FY-3C/MWRI Data in
China, Remote Sens.-Basel, 10, 524, https://doi.org/10.3390/rs10040524, 2018.
Long, D. G. and Brodzik, M. J.: Optimum Image Formation for Spaceborne
Microwave Radiometer Products, IEEE T. Geosci. Remote, 54, 2763–2779, https://doi.org/10.1109/TGRS.2015.2505677, 2016.
Luojus, K., Cohen, J., Ikonen, J., Pulliainen, J., Takala, M., Veijola, K.,
Lemmetyinen, J., Nagler, T., Derksen, C., and Brown, R.: Assessment of
Seasonal snow Cover Mass in Northern Hemisphere During the Satellite-ERA,
IGARSS 2018 – 2018 IEEE International Geoscience and Remote Sensing
Symposium, 2018, Valencia, 6255–6258, https://doi.org/10.1109/IGARSS.2018.8517494, 2018.
Lyons, M. B., Keith, D. A., Phinn, S. R., Mason, T. J., and Elith, J.: A
comparison of resampling methods for remote sensing classification and
accuracy assessment, Remote Sens. Environ., 208, 145–153,
https://doi.org/10.1016/j.rse.2018.02.026, 2018.
Marchane, A., Jarlan, L., Hanich, L., Boudhar, A., Gascoin, S., Tavernier, A., Filali, N., Le Page, M., Hagolle, O., and Berjamy, B.: Assessment of daily MODIS snow cover products to monitor snow cover dynamics over the Moroccan Atlas mountain range, Remote Sens. Environ., 160, 72–86, 2015.
Masson, T., Dumont, M., Mura, M., Sirguey, P., Gascoin, S., Dedieu, J.-P.,
and Chanussot, J.: An Assessment of Existing Methodologies to Retrieve Snow
Cover Fraction from MODIS Data, Remote Sens.-Basel, 10, 619, https://doi.org/10.3390/rs10040619,
2018.
Menne, M. J., Durre, I., Korzeniewski, B., McNeal, S., Thomas, K., Yin, X.,
Anthony, S., Ray, R., Vose, R. S., E.Gleason, B., and Houston, T. G.: Global
Historical Climatology Network – Daily (GHCN-Daily), Version 3, NOAA
National Climatic Data Center, https://doi.org/10.7289/V5D21VHZ, 2012a.
Menne, M. J., Durre, I., Vose, R. S., Gleason, B. E., and Houston, T. G.: An
Overview of the Global Historical Climatology Network-Daily Database,
J. Atmos. Ocean. Tech., 29, 897–910,
https://doi.org/10.1175/jtech-d-11-00103.1, 2012b.
Metsämäki, S. J., Anttila, S. T., Markus, H. J., and
Vepsäläinen, J. M.: A feasible method for fractional snow cover
mapping in boreal zone based on a reflectance model, Remote Sens.
Environ., 95, 77–95, https://doi.org/10.1016/j.rse.2004.11.013, 2005.
Millard, K. and Richardson, M.: On the Importance of Training Data Sample
Selection in Random Forest Image Classification: A Case Study in Peatland
Ecosystem Mapping, Remote Sens.-Basel, 7, 8489–8515, https://doi.org/10.3390/rs70708489, 2015.
Moosavi, V., Malekinezhad, H., and Shirmohammadi, B.: Fractional snow cover
mapping from MODIS data using wavelet-artificial intelligence hybrid models,
J. Hydrol., 511, 160–170, https://doi.org/10.1016/j.jhydrol.2014.01.015, 2014.
Mutanga, O., Adam, E., and Cho, M. A.: High density biomass estimation for
wetland vegetation using WorldView-2 imagery and random forest regression
algorithm, Int. J. Appl. Earth Obs., 18, 399–406, https://doi.org/10.1016/j.jag.2012.03.012, 2012.
Neale, C. M. U., McFarland, M. J., and Chang, K.: Land-surface-type
classification using microwave brightness temperatures from the Special
Sensor Microwave/Imager, IEEE T. Geosci. Remote,
28, 829–838, https://doi.org/10.1109/36.58970, 1990.
Nguyen, L. H., Joshi, D. R., Clay, D. E., and Henebry, G. M.: Characterizing
land cover/land use from multiple years of Landsat and MODIS time series: A
novel approach using land surface phenology modeling and random forest
classifier, Remote Sens. Environ., 238, 111017, https://doi.org/10.1016/j.rse.2018.12.016,
2018.
Pan, J., Jiang, L., and Zhang, L.: Wet snow detection in the south of China
by passive microwave remote sensing, 2012 IEEE Int. Geosci. Remote, 2012, 4863–4866, 2012.
Pan, M., Sahoo, A. K., and Wood, E. F.: Improving soil moisture retrievals
from a physically-based radiative transfer model, Remote Sens. Environ., 140, 130–140, https://doi.org/10.1016/j.rse.2013.08.020, 2014.
Parajka, J. and Blöschl, G.: Validation of MODIS snow cover images over Austria, Hydrol. Earth Syst. Sci., 10, 679–689, https://doi.org/10.5194/hess-10-679-2006, 2006.
Parajka, J. and Blöschl, G.: Spatio-temporal combination of MODIS
images – potential for snow cover mapping, Water Resour. Res., 44, 72–84, https://doi.org/10.1029/2007wr006204, 2008.
Parajka, J., Holko, L., Kostka, Z., and Blöschl, G.: MODIS snow cover mapping
accuracy in a small mountain catchment – comparison between open and forest
sites, Hydrol. Earth Syst. Sci., 16, 2365–2377,
https://doi.org/10.5194/hess-16-2365-2012, 2012.
Peng, G., Meier, W. N., Scott, D. J., and Savoie, M. H.: A long-term and reproducible passive microwave sea ice concentration data record for climate studies and monitoring, Earth Syst. Sci. Data, 5, 311–318, https://doi.org/10.5194/essd-5-311-2013, 2013.
Qu, Y., Zhu, Z., Chai, L., Liu, S., Montzka, C., Liu, J., Yang, X., Lu, Z.,
Jin, R., Li, X., Guo, Z., and Zheng, J.: Rebuilding a Microwave Soil
Moisture Product Using Random Forest Adopting AMSR-E/AMSR2 Brightness
Temperature and SMAP over the Qinghai–Tibet Plateau, China, Remote Sens.-Basel,
11, 683, https://doi.org/10.3390/rs11060683, 2019.
Quirós, E., Felicísimo, Á., and Cuartero, A.: Testing
Multivariate Adaptive Regression Splines (MARS) as a Method of Land Cover
Classification of TERRA-ASTER Satellite Images, Sensors, 9, 9011–9028,
https://doi.org/10.3390/s91109011, 2009.
Revuelto, J., López-Moreno, J. I., Azorin-Molina, C., and Vicente-Serrano, S. M.: Topographic control of snowpack distribution in a small catchment in the central Spanish Pyrenees: intra- and inter-annual persistence, The Cryosphere, 8, 1989–2006, https://doi.org/10.5194/tc-8-1989-2014, 2014.
Riggs, G. A. and Hall, D. K.: MODIS Snow Products Collection 6 User Guide, available at: https://modis-snow-ice.gsfc.nasa.gov/uploads/C6_MODIS Snow_User_Guide.pdf (last access: 10 February 2021),
2016.
Riggs, G. A., Hall, D. K., and Román, M. O.: Overview of NASA's MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) snow-cover Earth System Data Records, Earth Syst. Sci. Data, 9, 765–777, https://doi.org/10.5194/essd-9-765-2017, 2017.
Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J., Chica-Olmo, M., and
Rigol-Sanchez, J. P.: An assessment of the effectiveness of a random forest
classifier for land-cover classification, ISPRS J. Photogramm., 67, 93–104, https://doi.org/10.1016/j.isprsjprs.2011.11.002, 2012.
Romanov, P. and Tarpley, D.: Enhanced algorithm for estimating snow depth
from geostationary satellites, Remote Sens. Environ., 108, 97–110,
2007.
Rosenthal, W. and Dozier, J.: Automated Mapping of Montane Snow Cover at
Subpixel Resolution from the Landsat Thematic Mapper, Water Resour.
Res., 32, 115–130, https://doi.org/10.1029/95WR02718, 1996.
Saberi, N.: Snow Properties Retrieval Using Passive Microwave Observations,
Doctor of Philosophy, Geography Environmental Management, University of
Waterloo, Waterloo, Ontario, Canada, 156 pp., 2019.
Salomonson, V. V. and Appel, I.: Estimating fractional snow cover from
MODIS using the normalized difference snow index, Remote Sens. Environ., 89, 351–360, https://doi.org/10.1016/j.rse.2003.10.016, 2004.
Salomonson, V. V. and Appel, I.: Development of the Aqua MODIS NDSI
fractional snow cover algorithm and validation results, IEEE T. Geosci. Remote, 44, 1747–1756, https://doi.org/10.1109/TGRS.2006.876029,
2006.
Savoie, M. H., Armstrong, R. L., Brodzik, M. J., and Wang, J. R.:
Atmospheric corrections for improved satellite passive microwave snow cover
retrievals over the Tibet Plateau, Remote Sens. Environ., 113,
2661–2669, 2009.
Singh, P. R. and Gan, T. Y.: Retrieval of snow water equivalent using
passive microwave brightness temperature data, Remote Sens. Environ., 74, 275–286, 2000.
Smith, T. and Bookhagen, B.: Assessing uncertainty and sensor biases in
passive microwave data across High Mountain Asia, Remote Sens. Environ., 181, 174–185, 2016.
Sturm, M., Taras, B., Liston, G. E., Derksen, C., Jonas, T., and Lea, J.:
Estimating snow water equivalent using snow depth data and climate classes,
J. Hydrometeorol., 11, 1380–1394, 2010.
Sturm, M.: White water: Fifty years of snow research in WRR and the outlook
for the future, Water Resour. Res., 51, 4948–4965,
https://doi.org/10.1002/2015wr017242, 2015.
Sulla-Menashe, D. and Friedl, M. A.: Use Guide to collection 6 MODIS land
cover (MCD12Q1 and MCD12C1) Product, USGS, Reston, VA, USA, 2018.
Takala, M., Luojus, K., Pulliainen, J., Derksen, C., Lemmetyinen, J.,
Kärnä, J.-P., Koskinen, J., and Bojkov, B.: Estimating northern
hemisphere snow water equivalent for climate research through assimilation
of space-borne radiometer data and ground-based measurements, Remote Sens.
Environ., 115, 3517–3529, 2011.
Tedesco, M., Pulliainen, J., Takala, M., Hallikainen, M., and Pampaloni, P.:
Artificial neural network-based techniques for the retrieval of SWE and snow
depth from SSM/I data, Remote Sens. Environ., 90, 76–85, 2004.
Tedesco, M. and Jeyaratnam, J.: A New Operational Snow Retrieval Algorithm
Applied to Historical AMSR-E Brightness Temperatures, Remote Sens.-Basel, 8, 1037, https://doi.org/10.3390/rs8121037, 2016.
Tran, H., Nguyen, P., Ombadi, M., Hsu, K. L., Sorooshian, S., and Qing, X.:
A cloud-free MODIS snow cover dataset for the contiguous United States from
2000 to 2017, Sci. Data, 6, 180300, https://doi.org/10.1038/sdata.2018.300, 2019.
Tsai, Y., Dietz, A., Oppelt, N., and Kuenzer, C.: Wet and Dry Snow Detection Using
Sentinel-1 SAR Data for Mountainous Areas with a Machine Learning Technique,
Remote Sens.-Basel, 11, 895, https://doi.org/10.3390/rs11080895, 2019.
Wang, G., Jiang, L., Wu, S., Shi, J., Hao, S., and Liu, X.: Fractional Snow
Cover Mapping from FY-2 VISSR Imagery of China, Remote Sens.-Basel, 9, 983, https://doi.org/10.3390/rs9100983, 2017.
Wang, X., Xie, H., and Liang, T.: Evaluation of MODIS snow cover and cloud
mask and its application in Northern Xinjiang, China, Remote Sens. Environ., 112, 1497–1513, https://doi.org/10.1016/j.rse.2007.05.016, 2008.
Wang, Y., Huang, X., Wang, J., Zhou, M., and Liang, T.: AMSR2 snow depth
downscaling algorithm based on a multifactor approach over the Tibetan
Plateau, China, Remote Sens. Environ., 231, 111268, https://doi.org/10.1016/j.rse.2019.111268, 2019.
Wei, J., Huang, W., Li, Z., Xue, W., Peng, Y., Sun, L., and Cribb, M.:
Estimating 1-km-resolution PM2.5 concentrations across China using the
space-time random forest approach, Remote Sens. Environ., 231, 111221, https://doi.org/10.1016/j.rse.2019.111221, 2019.
Wiesmann, A. and Mätzler, C.: Microwave Emission Model of Layered
Snowpacks, Remote Sens. Environ., 70, 307–316, 1999.
Witten, I., Frank, E., Hall, M., and Pal, C.: Data Mining: Practical Machine Learning Tools and Techniques, 4th. Edn., Morgan Kaufmann, https://doi.org/10.1016/c2009-0-19715-5, 2016.
Wolfe, R. E., Nishihama, M., Fleig, A. J., Kuyper, J. A., Roy, D. P.,
Storey, J. C., and Patt, F. S.: Achieving sub-pixel geolocation accuracy in
support of MODIS land science, Remote Sens. Environ., 83, 31–49,
https://doi.org/10.1016/S0034-4257(02)00085-8, 2002.
Xiao, X., Zhang, T., Zhong, X., Shao, W., and Li, X.: Support vector
regression snow-depth retrieval algorithm using passive microwave remote
sensing data, Remote Sens. Environ., 210, 48–64, 2018.
Xiao, X., Zhang, T., Zhong, X., and Li, X.: Spatiotemporal variation of snow
depth in the Northern Hemisphere from 1992 to 2016, Remote Sens.-Basel, 12,
2728, https://doi.org/10.3390/rs12172728, 2020.
Xin, Q., Woodcock, C. E., Liu, J., Tan, B., Melloh, R. A., and Davis, R. E.:
View angle effects on MODIS snow mapping in forests, Remote Sens.
Environ., 118, 50–59, https://doi.org/10.1016/j.rse.2011.10.029, 2012.
Xu, X., Liu, X., Li, X., Xin, Q., Chen, Y., Shi, Q., and Ai, B.: Global snow
cover estimation with Microwave Brightness Temperature measurements and
one-class in situ observations, Remote Sens. Environ., 182, 227–251,
2016.
Yu, J., Zhang, G., Yao, T., Xie, H., Zhang, H., Ke, C., and Yao, R.:
Developing Daily Cloud-Free Snow Composite Products From MODIS Terra–Aqua
and IMS for the Tibetan Plateau, IEEE T. Geosci. Remote, 54, 2171–2180, https://doi.org/10.1109/tgrs.2015.2496950, 2016.
Zhang, H., Zhang, F., Zhang, G., Che, T., Yan, W., Ye, M., and Ma, N.:
Ground-based evaluation of MODIS snow cover product V6 across China:
Implications for the selection of NDSI threshold, Sci. Total
Environ., 651, 2712–2726, https://doi.org/10.1016/j.scitotenv.2018.10.128, 2019.
Zhang, T.: Influence of the seasonal snow cover on the ground thermal
regime: An overview, Rev. Geophys., 43, 589–590, 2005.
Zhang, W., and Goh, A. T. C.: Multivariate adaptive regression splines and
neural network models for prediction of pile drivability, Geosci.
Front., 7, 45–52, https://doi.org/10.1016/j.gsf.2014.10.003, 2016.
Zhao, W., Wu, H., Yin, G., and Duan, S.-B.: Normalization of the temporal
effect on the MODIS land surface temperature product using random forest
regression, ISPRS J. Photogramm., 152,
109–118, https://doi.org/10.1016/j.isprsjprs.2019.04.008, 2019.
Zhong, L., Hu, L., and Zhou, H.: Deep learning based multi-temporal crop
classification, Remote Sens. Environ., 221, 430–443,
https://doi.org/10.1016/j.rse.2018.11.032, 2019.
Zona, D., Gioli, B., Commane, R., Lindaas, J., Wofsy, S. C., Miller, C. E.,
Dinardo, S. J., Dengel, S., Sweeney, C., Karion, A., Chang, R. Y. W.,
Henderson, J. M., Murphy, P. C., Goodrich, J. P., Moreaux, V., Liljedahl, A., Watts, J. D., Kimball, J. S., Lipson, D. A., and Oechel, W. C.: Cold
season emissions dominate the Arctic tundra methane budget,
P. Natl. Acad. Sci. USA, 113, 40–45, https://doi.org/10.1073/pnas.1516017113, 2016.
Short summary
Daily time series and full space-covered sub-pixel snow cover area data are urgently needed for climate and reanalysis studies. Due to the fact that observations from optical satellite sensors are affected by clouds, this study attempts to capture dynamic characteristics of snow cover at a fine spatiotemporal resolution (daily; 6.25 km) accurately by using passive microwave data. We demonstrate the potential to use the passive microwave and the MODIS data to map the fractional snow cover area.
Daily time series and full space-covered sub-pixel snow cover area data are urgently needed for...
