Articles | Volume 17, issue 11
https://doi.org/10.5194/essd-17-6273-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-6273-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
20 Nov 2025
Data description paper |
| 20 Nov 2025
| 20 Nov 2025
A high-resolution (0.05°) global seamless continuity record (2002–2023) of near-surface soil freeze-thaw states via passive microwave and optical satellite data
Defeng Feng
State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
University of Chinese Academy of Sciences, Beijing 100049, China
Tianjie Zhao
CORRESPONDING AUTHOR
State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
University of Chinese Academy of Sciences, Beijing 100049, China
Jingyao Zheng
State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Yu Bai
State Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Youhua Ran
University of Chinese Academy of Sciences, Beijing 100049, China
State Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
Xiaokang Kou
School of Civil Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
Key Laboratory of Roads and Railway Engineering Safety Control, Shijiazhuang Tiedao University, Ministry of Education, Shijiazhuang 050043, China
Lingmei Jiang
State Key Laboratory of Remote Sensing and Digital Earth, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Ziqian Zhang
State Key Laboratory of Remote Sensing and Digital Earth, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Pei Yu
School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, China
Jinbiao Zhu
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Aeronautical Remote Sensing Center, Chinese Academy of Sciences, Beijing 100094, China
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China
Jie Pan
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Aeronautical Remote Sensing Center, Chinese Academy of Sciences, Beijing 100094, China
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China
Jiancheng Shi
National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China
Yuei-An Liou
Hydrology Remote Sensing Laboratory, Center for Space and Remote Sensing Research, National Central University, Taoyuan 320317, China
Related authors
No articles found.
Jingtian Zhou, Yang Lei, Jinmei Pan, Cunren Liang, Zhang Yunjun, Weiliang Li, Chuan Xiong, Jiancheng Shi, and Wei Ma
The Cryosphere, 19, 5361–5388, https://doi.org/10.5194/tc-19-5361-2025, https://doi.org/10.5194/tc-19-5361-2025, 2025
Short summary
Short summary
Understanding how much water is stored in snow is important for tracking climate change and managing water supply. This study used satellite radar data from 2019 to 2021 to measure snow water changes in a mountain region of China. The results matched ground data well, especially in cold, dry conditions without heavy snowfall. A new phase calibration method helped improve accuracy, offering a useful reference for global snow monitoring using widely available satellite data.
Yanghai Yu, Yang Lei, Paul Siqueira, Xiaotong Liu, Denuo Gu, Anmin Fu, Yong Pang, Wenli Huang, and Jiancheng Shi
Earth Syst. Sci. Data, 17, 4397–4429, https://doi.org/10.5194/essd-17-4397-2025, https://doi.org/10.5194/essd-17-4397-2025, 2025
Short summary
Short summary
This study proposes a global-to-local approach for estimating forest height by fusing repeat-pass synthetic aperture radar interferometry and Global Ecosystem Dynamics Investigation (GEDI) data. Using Advanced Land Observing Satellite (ALOS-1) data and a twofold strategy to address temporal gaps, the method produced 30 m gridded forest height mosaics for the northeastern United States and China, demonstrating promising accuracies and offering potential for fusing data from future missions.
Qixiang Sun, Dabin Ji, Husi Letu, Yongqian Wang, Peng Zhang, Hong Liang, Chong Shi, Shuai Yin, and Jiancheng Shi
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-365, https://doi.org/10.5194/essd-2025-365, 2025
Preprint under review for ESSD
Short summary
Short summary
The Tibetan Plateau plays a vital role in Asia’s water cycle, but tracking water vapor in this mountainous region is difficult, especially under cloudy conditions. We developed a new satellite-based method to generate hourly water vapor data at 0.02-degree resolution from 2016 to 2022, now available at https://data.tpdc.ac.cn/en/data/4bb3c256-3cdb-4373-9924-f7ac16ddc717, which improves accuracy and reveals fine-scale moisture transport critical for understanding rainfall and extreme weather.
Jiajie Ying, Lingmei Jiang, Jinmei Pan, Chuan Xiong, and Jianwei Yang
EGUsphere, https://doi.org/10.5194/egusphere-2025-276, https://doi.org/10.5194/egusphere-2025-276, 2025
Short summary
Short summary
The Sentinel-1-based C-snow product has been widely used as reference data across various scales, but its reliability remains unknown. This study systematically evaluates its performance at 1, 10, and 25 km scales using ground-based measurements and airborne LiDAR data. The results show that performance is influenced by factors such as forest fraction, DEM, permanent ice, and wet snow. We also identify scale patterns differences compared to station and airborne datasets and explore the reasons.
Duc-Vinh Hoang and Yuei-An Liou
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-215, https://doi.org/10.5194/nhess-2024-215, 2024
Manuscript not accepted for further review
Short summary
Short summary
This paper presents a pioneering study using GIS, remote sensing, and machine learning to predict flash flood susceptibility in mountainous areas of Vietnam. It optimizes machine learning algorithms, including Support Vector Machine, Random Forest, and Extreme Gradient Boosting, with Particle Swarm Optimization and Genetic Algorithm. The hybrid models, particularly PSO-XGB, GA-XGB, and GA-RF, significantly improve flood prediction and provide valuable insights for similar regions globally.
Fangbo Pan, Lingmei Jiang, Gongxue Wang, Jinmei Pan, Jinyu Huang, Cheng Zhang, Huizhen Cui, Jianwei Yang, Zhaojun Zheng, Shengli Wu, and Jiancheng Shi
Earth Syst. Sci. Data, 16, 2501–2523, https://doi.org/10.5194/essd-16-2501-2024, https://doi.org/10.5194/essd-16-2501-2024, 2024
Short summary
Short summary
It is important to strengthen the continuous monitoring of snow cover as a key indicator of imbalance in the Asian Water Tower (AWT) region. We generate long-term daily gap-free fractional snow cover products over the AWT at 0.005° resolution from 2000 to 2022 based on the multiple-endmember spectral mixture analysis algorithm and the gap-filling algorithm. They can provide highly accurate, quantitative fractional snow cover information for subsequent studies on hydrology and climate.
Jinmei Pan, Michael Durand, Juha Lemmetyinen, Desheng Liu, and Jiancheng Shi
The Cryosphere, 18, 1561–1578, https://doi.org/10.5194/tc-18-1561-2024, https://doi.org/10.5194/tc-18-1561-2024, 2024
Short summary
Short summary
We developed an algorithm to estimate snow mass using X- and dual Ku-band radar, and tested it in a ground-based experiment. The algorithm, the Bayesian-based Algorithm for SWE Estimation (BASE) using active microwaves, achieved an RMSE of 30 mm for snow water equivalent. These results demonstrate the potential of radar, a highly promising sensor, to map snow mass at high spatial resolution.
Peilin Song, Yongqiang Zhang, Jianping Guo, Jiancheng Shi, Tianjie Zhao, and Bing Tong
Earth Syst. Sci. Data, 14, 2613–2637, https://doi.org/10.5194/essd-14-2613-2022, https://doi.org/10.5194/essd-14-2613-2022, 2022
Short summary
Short summary
Soil moisture information is crucial for understanding the earth surface, but currently available satellite-based soil moisture datasets are imperfect either in their spatiotemporal resolutions or in ensuring image completeness from cloudy weather. In this study, therefore, we developed one soil moisture data product over China that has tackled most of the above problems. This data product has the potential to promote the investigation of earth hydrology and be extended to the global scale.
Shu Fang, Kebiao Mao, Xueqi Xia, Ping Wang, Jiancheng Shi, Sayed M. Bateni, Tongren Xu, Mengmeng Cao, Essam Heggy, and Zhihao Qin
Earth Syst. Sci. Data, 14, 1413–1432, https://doi.org/10.5194/essd-14-1413-2022, https://doi.org/10.5194/essd-14-1413-2022, 2022
Short summary
Short summary
Air temperature is an important parameter reflecting climate change, and the current method of obtaining daily temperature is affected by many factors. In this study, we constructed a temperature model based on weather conditions and established a correction equation. The dataset of daily air temperature (Tmax, Tmin, and Tavg) in China from 1979 to 2018 was obtained with a spatial resolution of 0.1°. Accuracy verification shows that the dataset has reliable accuracy and high spatial resolution.
Youhua Ran, Xin Li, Guodong Cheng, Jingxin Che, Juha Aalto, Olli Karjalainen, Jan Hjort, Miska Luoto, Huijun Jin, Jaroslav Obu, Masahiro Hori, Qihao Yu, and Xiaoli Chang
Earth Syst. Sci. Data, 14, 865–884, https://doi.org/10.5194/essd-14-865-2022, https://doi.org/10.5194/essd-14-865-2022, 2022
Short summary
Short summary
Datasets including ground temperature, active layer thickness, the probability of permafrost occurrence, and the zonation of hydrothermal condition with a 1 km resolution were released by integrating unprecedentedly large amounts of field data and multisource remote sensing data using multi-statistical\machine-learning models. It updates the understanding of the current thermal state and distribution for permafrost in the Northern Hemisphere.
Guoqing Zhang, Youhua Ran, Wei Wan, Wei Luo, Wenfeng Chen, Fenglin Xu, and Xin Li
Earth Syst. Sci. Data, 13, 3951–3966, https://doi.org/10.5194/essd-13-3951-2021, https://doi.org/10.5194/essd-13-3951-2021, 2021
Short summary
Short summary
Lakes can be effective indicators of climate change, especially over the Qinghai–Tibet Plateau. Here, we provide the most comprehensive lake mapping covering the past 100 years. The new features of this data set are (1) its temporal length, providing the longest period of lake observations from maps, (2) the data set provides a state-of-the-art lake inventory for the Landsat era (from the 1970s to 2020), and (3) it provides the densest lake observations for lakes with areas larger than 1 km2.
Xiangjin Meng, Kebiao Mao, Fei Meng, Jiancheng Shi, Jiangyuan Zeng, Xinyi Shen, Yaokui Cui, Lingmei Jiang, and Zhonghua Guo
Earth Syst. Sci. Data, 13, 3239–3261, https://doi.org/10.5194/essd-13-3239-2021, https://doi.org/10.5194/essd-13-3239-2021, 2021
Short summary
Short summary
In order to improve the accuracy of China's regional agricultural drought monitoring and climate change research, we produced a long-term series of soil moisture products by constructing a time and depth correction model for three soil moisture products with the help of ground observation data. The spatial resolution is improved by building a spatial weight decomposition model, and validation indicates that the new product can meet application needs.
Cited articles
Beer, C., Porada, P., Ekici, A., and Brakebusch, M.: Effects of short-term variability of meteorological variables on soil temperature in permafrost regions, The Cryosphere, 12, 741–757, https://doi.org/10.5194/tc-12-741-2018, 2018.
Bierkens, M. F. P., Finke, P. A., and de Willigen, P.: Upscaling and downscaling methods for environmental research, Kluwer Academic Publishers, Dordrecht, the Netherlands, 190 pp., ISBN 9780792363392, 2000.
Black, T. A., Chen, W. J., Barr, A. G., Arain, M. A., Chen, Z., Nesic, Z., Hogg, E. H., Neumann, H. H., and Yang, P. C.: Increased carbon sequestration by a boreal deciduous forest in years with a warm spring, Geophysical Research Letters, 27, 1271–1274, https://doi.org/10.1029/1999GL011234, 2000.
Bojinski, S., Verstraete, M., Peterson, T. C., Richter, C., Simmons, A., and Zemp, M.: The concept of essential climate variables in support of climate research, applications, and policy, Bulletin of the American Meteorological Society, 95, 1431–1443, https://doi.org/10.1175/BAMS-D-13-00047.1, 2014.
Canny, J.: A computational approach to edge detection, IEEE Trans. Pattern Anal. Mach. Intell., PAMI-8, 679–698, https://doi.org/10.1109/TPAMI.1986.4767851, 1986.
Cary, J. W., Papendick, R. I., and Campbell, G. S.: Water and Salt Movement in Unsaturated Frozen Soil: Principles and Field Observations, Soil Science Soc. of Amer. J., 43, 3–8, https://doi.org/10.2136/sssaj1979.03615995004300010001x, 1979.
Chai, L., Zhang, L., Zhang, Y., Hao, Z., Jiang, L., and Zhao, S.: Comparison of the classification accuracy of three soil freeze–thaw discrimination algorithms in China using SSMIS and AMSR-E passive microwave imagery, International Journal of Remote Sensing, 35, 7631–7649, https://doi.org/10.1080/01431161.2014.975376, 2014.
Chander, G., Hewison, T. J., Fox, N., Wu, X., Xiong, X., and Blackwell, W. J.: Overview of Intercalibration of Satellite Instruments, IEEE Trans. Geosci. Remote Sensing, 51, 1056–1080, https://doi.org/10.1109/TGRS.2012.2228654, 2013.
Chang, X., Jin, H., Zhang, Y., He, R., Luo, D., Wang, Y., Lü, L., and Zhang, Q.: Thermal impacts of boreal forest vegetation on active layer and permafrost soils in northern da xing'anling (hinggan) mountains, northeast China, Arctic, Antarctic, and Alpine Research, 47, 267–279, https://doi.org/10.1657/AAAR00C-14-016, 2015.
Chen, Y., Nan, Z., Zhao, S., and Xu, Y.: A Bayesian Approach for Interpolating Clear-Sky MODIS Land Surface Temperatures on Areas With Extensive Missing Data, IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing, 14, 515–528, https://doi.org/10.1109/JSTARS.2020.3038188, 2021.
Cho, E., Su, C.-H., Ryu, D., Kim, H., and Choi, M.: Does AMSR2 produce better soil moisture retrievals than AMSR-E over Australia?, Remote Sensing of Environment, 188, 95–105, https://doi.org/10.1016/j.rse.2016.10.050, 2017.
Dorigo, W., Himmelbauer, I., Aberer, D., Schremmer, L., Petrakovic, I., Zappa, L., Preimesberger, W., Xaver, A., Annor, F., Ardö, J., Baldocchi, D., Bitelli, M., Blöschl, G., Bogena, H., Brocca, L., Calvet, J.-C., Camarero, J. J., Capello, G., Choi, M., Cosh, M. C., van de Giesen, N., Hajdu, I., Ikonen, J., Jensen, K. H., Kanniah, K. D., de Kat, I., Kirchengast, G., Kumar Rai, P., Kyrouac, J., Larson, K., Liu, S., Loew, A., Moghaddam, M., Martínez Fernández, J., Mattar Bader, C., Morbidelli, R., Musial, J. P., Osenga, E., Palecki, M. A., Pellarin, T., Petropoulos, G. P., Pfeil, I., Powers, J., Robock, A., Rüdiger, C., Rummel, U., Strobel, M., Su, Z., Sullivan, R., Tagesson, T., Varlagin, A., Vreugdenhil, M., Walker, J., Wen, J., Wenger, F., Wigneron, J. P., Woods, M., Yang, K., Zeng, Y., Zhang, X., Zreda, M., Dietrich, S., Gruber, A., van Oevelen, P., Wagner, W., Scipal, K., Drusch, M., and Sabia, R.: The International Soil Moisture Network: serving Earth system science for over a decade, Hydrol. Earth Syst. Sci., 25, 5749–5804, https://doi.org/10.5194/hess-25-5749-2021, 2021.
Dorigo, W. A., Xaver, A., Vreugdenhil, M., Gruber, A., Hegyiová, A., Sanchis-Dufau, A. D., Zamojski, D., Cordes, C., Wagner, W., and Drusch, M.: Global Automated Quality Control of In Situ Soil Moisture Data from the International Soil Moisture Network, Vadose Zone Journal, 12, 1–21, https://doi.org/10.2136/vzj2012.0097, 2013.
Du, J., Kimball, J. S., Azarderakhsh, M., Dunbar, R. S., Moghaddam, M., and McDonald, K. C.: Classification of Alaska Spring Thaw Characteristics Using Satellite L-Band Radar Remote Sensing, IEEE Trans. Geosci. Remote Sensing, 53, 542–556, https://doi.org/10.1109/TGRS.2014.2325409, 2015.
England, A. W.: Radiobrightness Of Diurnally Heated, Freezing Soil, IEEE Trans. Geosci. Remote Sensing, 28, 464–476, https://doi.org/10.1109/TGRS.1990.572923, 1990.
Fan, L., Fu, C., and Chen, D.: Review on creating future climate change scenarios by statistical downscaling techniques, Advances in Earth Science, 20, 320–329, 2005.
Friedl, M. A. and Sulla-Menashe, D.: MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 0.05Deg CMG V061, NASA EOSDIS Land Processes Distributed Active Archive Center [data set], https://doi.org/10.5067/MODIS/MCD12C1.061, 2022.
Frolking, S., McDonald, K. C., Kimball, J. S., Way, J. B., Zimmermann, R., and Running, S. W.: Using the space-borne NASA scatterometer (NSCAT) to determine the frozen and thawed seasons, J. Geophys. Res., 104, 27895–27907, https://doi.org/10.1029/1998JD200093, 1999.
Giorgi, F. and Mearns, L. O.: Approaches to the simulation of regional climate change: A review, Reviews of Geophysics, 29, 191–216, https://doi.org/10.1029/90RG02636, 1991.
Gouttevin, I., Menegoz, M., Dominé, F., Krinner, G., Koven, C., Ciais, P., Tarnocai, C., and Boike, J.: How the insulating properties of snow affect soil carbon distribution in the continental pan-Arctic area, J. Geophys. Res., 117, 2011JG001916, https://doi.org/10.1029/2011JG001916, 2012.
Gray, D. M., Landine, P. G., and Granger, R. J.: Simulating infiltration into frozen Prairie soils in streamflow models, Can. J. Earth Sci., 22, 464–472, https://doi.org/10.1139/e85-045, 1985.
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 Trans. Geosci. Remote Sensing, 53, 2775–2783, https://doi.org/10.1109/TGRS.2014.2364823, 2015.
Hanssen-Bauer, I., Achberger, C., Benestad, R., Chen, D., and Førland, E.: Statistical downscaling of climate scenarios over Scandinavia, Clim. Res., 29, 255–268, https://doi.org/10.3354/cr029255, 2005.
Hertig, E. and Jacobeit, J.: Assessments of Mediterranean precipitation changes for the 21st century using statistical downscaling techniques, Intl. Journal of Climatology, 28, 1025–1045, https://doi.org/10.1002/joc.1597, 2008.
Hu, T., Zhao, T., Shi, J., Wu, S., Liu, D., Qin, H., and Zhao, K.: High-Resolution Mapping of Freeze/Thaw Status in China via Fusion of MODIS and AMSR2 Data, Remote Sensing, 9, 1339, https://doi.org/10.3390/rs9121339, 2017.
Hu, T., Zhao, T., Zhao, K., and Shi, J.: A continuous global record of near-surface soil freeze/thaw status from AMSR-E and AMSR2 data, International Journal of Remote Sensing, 40, 6993–7016, https://doi.org/10.1080/01431161.2019.1597307, 2019.
Jiang, H., Yi, Y., Zhang, W., Yang, K., and Chen, D.: Sensitivity of soil freeze/thaw dynamics to environmental conditions at different spatial scales in the central tibetan plateau, Science of The Total Environment, 734, 139261, https://doi.org/10.1016/j.scitotenv.2020.139261, 2020.
Jin, R., Li, X., and Che, T.: A decision tree algorithm for surface soil freeze/thaw classification over China using SSM/I brightness temperature, Remote Sensing of Environment, 113, 2651–2660, https://doi.org/10.1016/j.rse.2009.08.003, 2009.
Judge, J., Galantowicz, J. F., England, A. W., and Dahl, P.: Freeze/thaw classification for prairie soils using SSM/I radiobrightnesses, in: IGARSS '96. 1996 International Geoscience and Remote Sensing Symposium, IGARSS '96. 1996 International Geoscience and Remote Sensing Symposium, Lincoln, NE, USA, 2270–2272, https://doi.org/10.1109/IGARSS.1996.516958, 1996.
Kendall, M. G.: Rank correlation methods, Charles Griffin, London, ISBN 0195208374, 1948.
Kim, Y., Kimball, J. S., McDonald, K. C., and Glassy, J.: Developing a Global Data Record of Daily Landscape Freeze/Thaw Status Using Satellite Passive Microwave Remote Sensing, IEEE Trans. Geosci. Remote Sensing, 49, 949–960, https://doi.org/10.1109/TGRS.2010.2070515, 2011.
Kim, Y., Kimball, J. S., Zhang, K., and McDonald, K. C.: Satellite detection of increasing northern hemisphere non-frozen seasons from 1979 to 2008: Implications for regional vegetation growth, Remote Sensing of Environment, 121, 472–487, https://doi.org/10.1016/j.rse.2012.02.014, 2012.
Kimball, J. S., McDonald, K. C., Running, S. W., and Frolking, S. E.: Satellite radar remote sensing of seasonal growing seasons for boreal and subalpine evergreen forests, Remote Sensing of Environment, 90, 243–258, https://doi.org/10.1016/j.rse.2004.01.002, 2004.
Knowles, K., Savoie, M., Armstrong, R. and Brodzik, M. J.: AMSR-E/aqua daily global quarter-degree gridded brightness temperatures, version 1, https://doi.org/10.5067/RRR4WWORG070, 2006.
Kou, X., Jiang, L., Yan, S., Zhao, T., Lu, H., and Cui, H.: Detection of land surface freeze-thaw status on the Tibetan Plateau using passive microwave and thermal infrared remote sensing data, Remote Sensing of Environment, 199, 291–301, https://doi.org/10.1016/j.rse.2017.06.035, 2017.
Kou, X., Jiang, L., Yan, S., Wang, J., and Gao, L.: Research on the Improvement of Passive Microwave Freezing and Thawing Discriminant Algorithms for Complicated Surface Conditions, in: IGARSS 2018–2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018–2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, 7161–7164, https://doi.org/10.1109/IGARSS.2018.8518731, 2018.
Langer, M., Westermann, S., Heikenfeld, M., Dorn, W., and Boike, J.: Satellite-based modeling of permafrost temperatures in a tundra lowland landscape, Remote Sensing of Environment, 135, 12–24, https://doi.org/10.1016/j.rse.2013.03.011, 2013.
Li, X., Zhou, Y., Asrar, G. R., and Zhu, Z.: Creating a seamless 1 km resolution daily land surface temperature dataset for urban and surrounding areas in the conterminous united states, Remote Sensing of Environment, 206, 84–97, https://doi.org/10.1016/j.rse.2017.12.010, 2018.
Liang, X., Liu, Q., Wang, J., Chen, S., and Gong, P.: Global 500 m seamless dataset (2000–2022) of land surface reflectance generated from MODIS products, Earth Syst. Sci. Data, 16, 177–200, https://doi.org/10.5194/essd-16-177-2024, 2024.
Liu, Q., Wang, L., Qu, Y., Liu, N., Liu, S., Tang, H., and Liang, S.: Preliminary evaluation of the long-term GLASS albedo product, International Journal of Digital Earth, 6, 69–95, https://doi.org/10.1080/17538947.2013.804601, 2013.
Mann, H. B.: Nonparametric Tests Against Trend, Econometrica, 13, 245–259, https://doi.org/10.2307/1907187, 1945.
McDonald, K. C. and Kimball, J. S.: Estimation of Surface Freeze–Thaw States Using Microwave Sensors, in: Encyclopedia of Hydrological Sciences, John Wiley & Sons, Ltd., https://doi.org/10.1002/0470848944.hsa059a, 2006.
McDonald, K. C., Kimball, J. S., Zhao, M., Njoku, E., Zimmermann, R., and Running, S. W.: Spaceborne microwave remote sensing of seasonal freeze-thaw processes in the terrestrial high latitudes: Relationships with land-atmosphere CO2 exchange, Fourth International Asia-Pacific Environmental Remote Sensing Symposium 2004: Remote Sensing of the Atmosphere, Ocean, Environment, and Space, Honolulu, HI, 167–178, https://doi.org/10.1117/12.578906, 2004a.
McDonald, K. C., Kimball, J. S., Njoku, E., Zimmermann, R., and Zhao, M.: Variability in springtime thaw in the terrestrial high latitudes: Monitoring a major control on the biospheric assimilation of atmospheric CO2 with spaceborne microwave remote sensing, Earth Interact., 8, 1–23, https://doi.org/10.1175/1087-3562(2004)8<1:VISTIT>2.0.CO;2, 2004b.
Okuyama, A. and Imaoka, K.: Intercalibration of Advanced Microwave Scanning Radiometer-2 (AMSR2) Brightness Temperature, IEEE Trans. Geosci. Remote Sensing, 53, 4568–4577, https://doi.org/10.1109/TGRS.2015.2402204, 2015.
Panneer Selvam, B., Laudon, H., Guillemette, F., and Berggren, M.: Influence of soil frost on the character and degradability of dissolved organic carbon in boreal forest soils, JGR Biogeosciences, 121, 829–840, https://doi.org/10.1002/2015JG003228, 2016.
Peng, X., Frauenfeld, O. W., Cao, B., Wang, K., Wang, H., Su, H., Huang, Z., Yue, D., and Zhang, T.: Response of changes in seasonal soil freeze/thaw state to climate change from 1950 to 2010 across china, JGR Earth Surface, 121, 1984–2000, https://doi.org/10.1002/2016JF003876, 2016.
Poutou, E., Krinner, G., Genthon, C., and De Noblet-Ducoudré, N.: Role of soil freezing in future boreal climate change, Climate Dynamics, 23, 621–639, https://doi.org/10.1007/s00382-004-0459-0, 2004.
Qin, J., Yang, K., Lu, N., Chen, Y., Zhao, L., and Han, M.: Spatial upscaling of in-situ soil moisture measurements based on MODIS-derived apparent thermal inertia, Remote Sensing of Environment, 138, 1–9, https://doi.org/10.1016/j.rse.2013.07.003, 2013.
Rautiainen, K., Parkkinen, T., Lemmetyinen, J., Schwank, M., Wiesmann, A., Ikonen, J., Derksen, C., Davydov, S., Davydova, A., Boike, J., Langer, M., Drusch, M., and Pulliainen, J.: SMOS prototype algorithm for detecting autumn soil freezing, Remote Sensing of Environment, 180, 346–360, https://doi.org/10.1016/j.rse.2016.01.012, 2016.
Roy, A., Royer, A., Derksen, C., Brucker, L., Langlois, A., Mialon, A., and Kerr, Y. H.: Evaluation of Spaceborne L-Band Radiometer Measurements for Terrestrial Freeze/Thaw Retrievals in Canada, IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing, 8, 4442–4459, https://doi.org/10.1109/JSTARS.2015.2476358, 2015.
Running, S. W.: A blueprint for improved global change monitoring of the terrestrial biosphere, The Earth Observer, 10, 8–13, 1998.
Schaefer, K., Zhang, T., Bruhwiler, L., and Barrett, A. P.: Amount and timing of permafrost carbon release in response to climate warming, Tellus B, 63, 165–180, https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1600-0889.2011.00527.x (last access 03 November 2025), 2011.
Sohrabinia, M., Rack, W., and Zawar-Reza, P.: Soil moisture derived using two apparent thermal inertia functions over Canterbury, New Zealand, J. Appl. Remote Sens, 8, 083624, https://doi.org/10.1117/1.JRS.8.083624, 2014.
Song, C. and Jia, L.: A Method for Downscaling FengYun-3B Soil Moisture Based on Apparent Thermal Inertia, Remote Sensing, 8, https://doi.org/10.3390/rs8090703, 2016.
Su, Z., Wen, J., Dente, L., van der Velde, R., Wang, L., Ma, Y., Yang, K., and Hu, Z.: The Tibetan Plateau observatory of plateau scale soil moisture and soil temperature (Tibet-Obs) for quantifying uncertainties in coarse resolution satellite and model products, Hydrol. Earth Syst. Sci., 15, 2303–2316, https://doi.org/10.5194/hess-15-2303-2011, 2011.
Vaittinada Ayar, P., Vrac, M., Bastin, S., Carreau, J., Déqué, M., and Gallardo, C.: Intercomparison of statistical and dynamical downscaling models under the EURO- and MED-CORDEX initiative framework: present climate evaluations, Clim. Dyn., 46, 1301–1329, https://doi.org/10.1007/s00382-015-2647-5, 2016.
Van Doninck, J., Peters, J., De Baets, B., De Clercq, E. M., Ducheyne, E., and Verhoest, N. E. C.: The potential of multitemporal Aqua and Terra MODIS apparent thermal inertia as a soil moisture indicator, International Journal of Applied Earth Observation and Geoinformation, 13, 934–941, https://doi.org/10.1016/j.jag.2011.07.003, 2011.
Veroustraete, F., Li, Q., Verstraeten, W. W., Chen, X., Bao, A., Dong, Q., Liu, T., and Willems, P.: Soil moisture content retrieval based on apparent thermal inertia for Xinjiang province in China, International Journal of Remote Sensing, 33, 3870–3885, https://doi.org/10.1080/01431161.2011.636080, 2012.
Verstraeten, W. W., Veroustraete, F., Van Der Sande, C. J., Grootaers, I., and Feyen, J.: Soil moisture retrieval using thermal inertia, determined with visible and thermal spaceborne data, validated for European forests, Remote Sensing of Environment, 101, 299–314, https://doi.org/10.1016/j.rse.2005.12.016, 2006.
Wang, C., Yang, N., Zhao, T., Xue, H., Peng, Z., Zheng, J., Pan, J., Yao, P., Gao, X., Yan, H., Song, P., Liou, Y.-A., and Shi, J.: All-season liquid soil moisture retrieval from SMAP, IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing, 17, 8258–8270, https://doi.org/10.1109/JSTARS.2024.3382315, 2024.
Wang, P., Zhao, T., Shi, J., Hu, T., Roy, A., Qiu, Y., and Lu, H.: Parameterization of the freeze/thaw discriminant function algorithm using dense in-situ observation network data, International Journal of Digital Earth, 12, 980–994, https://doi.org/10.1080/17538947.2018.1452300, 2019a.
Wang, P., Zhao, T., Shi, J., Hu, T., Roy, A., Qiu, Y., and Lu, H.: Parameterization of the freeze/thaw discriminant function algorithm using dense in-situ observation network data, International Journal of Digital Earth, 12, 980–994, https://doi.org/10.1080/17538947.2018.1452300, 2019b.
Way, J., Zimmermann, R., Rignot, E., McDonald, K., and Oren, R.: Winter and spring thaw as observed with imaging radar at BOREAS, J. Geophys. Res., 102, 29673–29684, https://doi.org/10.1029/96JD03878, 1997.
Wei, Z., Jin, H., Zhang, J., Yu, S., Han, X., Ji, Y., He, R., and Chang, X.: Prediction of permafrost changes in Northeastern China under a changing climate, Sci. China Earth Sci., 54, 924–935, https://doi.org/10.1007/s11430-010-4109-6, 2011.
Wu, S., Zhao, T., Pan, J., Xue, H., Zhao, L., and Shi, J.: Improvement in modeling soil dielectric properties during freeze-thaw transitions, IEEE Geosci. Remote Sensing Lett., 19, 1–5, https://doi.org/10.1109/LGRS.2022.3154291, 2022.
Xu, L., Myneni, R. B., Chapin Iii, F. S., Callaghan, T. V., Pinzon, J. E., Tucker, C. J., Zhu, Z., Bi, J., Ciais, P., Tømmervik, H., Euskirchen, E. S., Forbes, B. C., Piao, S. L., Anderson, B. T., Ganguly, S., Nemani, R. R., Goetz, S. J., Beck, P. S. A., Bunn, A. G., Cao, C., and Stroeve, J. C.: Temperature and vegetation seasonality diminishment over northern lands, Nature Clim. Change, 3, 581–586, https://doi.org/10.1038/nclimate1836, 2013.
Xu, Z., Han, Y., and Yang, Z.: Dynamical downscaling of regional climate: A review of methods and limitations, Sci. China Earth Sci., 62, 365–375, https://doi.org/10.1007/s11430-018-9261-5, 2019.
Yang, J., Yang, S., Li, M., and Tan, C.: Vulnerability of frozen ground to climate change in China, Journal of Glaciology and Geocryology, 35, 1436–1445, 2013.
Yao, R., Wang, L., Huang, X., Cao, Q., Wei, J., He, P., Wang, S., and Wang, L.: Global seamless and high-resolution temperature dataset (GSHTD), 2001–2020, Remote Sensing of Environment, 286, 113422, https://doi.org/10.1016/j.rse.2022.113422, 2023.
Yu, P., Zhao, T., Shi, J., Ran, Y., Jia, L., Ji, D., and Xue, H.: Global spatiotemporally continuous MODIS land surface temperature dataset, Sci. Data, 9, 143, https://doi.org/10.1038/s41597-022-01214-8, 2022.
Zhang, L., Zhao, T., Jiang, L., and Zhao, S.: Estimate of Phase Transition Water Content in Freeze–Thaw Process Using Microwave Radiometer, IEEE Trans. Geosci. Remote Sensing, 48, 4248–4255, https://doi.org/10.1109/TGRS.2010.2051158, 2010.
Zhang, P., Zheng, D., Wen, J., Zeng, Y., Wang, X., Wang, Z., Ma, Y., and Su, Z.: A 10-year surface soil moisture dataset produced based on in situ measurements collected from the Tibet-Obs (2009–2019), National Tibetan Plateau/Third Pole Environment Data Center [data set], https://doi.org/10.4121/12763700.v7, 2021a.
Zhang, P., Zheng, D., van der Velde, R., Wen, J., Zeng, Y., Wang, X., Wang, Z., Chen, J., and Su, Z.: Status of the Tibetan Plateau observatory (Tibet-Obs) and a 10-year (2009–2019) surface soil moisture dataset, Earth Syst. Sci. Data, 13, 3075–3102, https://doi.org/10.5194/essd-13-3075-2021, 2021b.
Zhang, T., Zhou, Y., Zhu, Z., Li, X., and Asrar, G. R.: A global seamless 1 km resolution daily land surface temperature dataset (2003–2020), Earth Syst. Sci. Data, 14, 651–664, https://doi.org/10.5194/essd-14-651-2022, 2022.
Zhang, X., Sun, S. F., and Xue, Y.: Development and Testing of a Frozen Soil Parameterization for Cold Region Studies, Journal of Hydrometeorology, 8, 690–701, https://doi.org/10.1175/JHM605.1, 2007.
Zhang, X., Zhou, J., Liang, S., Chai, L., Wang, D., and Liu, J.: Estimation of 1-km all-weather remotely sensed land surface temperature based on reconstructed spatial-seamless satellite passive microwave brightness temperature and thermal infrared data, ISPRS Journal of Photogrammetry and Remote Sensing, 167, 321–344, https://doi.org/10.1016/j.isprsjprs.2020.07.014, 2020.
Zhao, L.: A new map of permafrost distribution on the Tibetan Plateau (2017), National Tibetan Plateau/Third Pole Environment Data Center [data set], https://doi.org/10.11888/Geocry.tpdc.270468, 2017.
Zhao, M., Ramage, J., Semmens, K., and Obleitner, F.: Recent ice cap snowmelt in Russian High Arctic and anti-correlation with late summer sea ice extent, Environ. Res. Lett., 9, 045009, https://doi.org/10.1088/1748-9326/9/4/045009, 2014.
Zhao, T. and Yu, P.: Global daily 0.05° spatiotemporal continuous land surface temperature dataset (2002–2022), National Tibetan Plateau/Third Pole Environment Data Center [data set], https://doi.org/10.11888/Meteoro.tpdc.271663, 2021.
Zhao, T., Zhang, L., Jiang, L., Zhao, S., Chai, L., and Jin, R.: A new soil freeze/thaw discriminant algorithm using AMSR-E passive microwave imagery, Hydrol. Process., 25, 1704–1716, https://doi.org/10.1002/hyp.7930, 2011.
Zhao, T., Shi, J., Hu, T., Zhao, L., Zou, D., Wang, T., Ji, D., Li, R., and Wang, P.: Estimation of high-resolution near-surface freeze/thaw state by the integration of microwave and thermal infrared remote sensing data on the Tibetan Plateau, Earth and Space Science, 4, 472–484, https://doi.org/10.1002/2017EA000277, 2017.
Zhao, T., Wang, S., Ouyang, C., Chen, M., Liu, C., Zhang, J., Yu, L., Wang, F., Xie, Y., Li, J., Wang, F., Grunwald, S., Wong, B. M., Zhang, F., Qian, Z., Xu, Y., Yu, C., Han, W., Sun, T., Shao, Z., Qian, T., Chen, Z., Zeng, J., Zhang, H., Letu, H., Zhang, B., Wang, L., Luo, L., Shi, C., Su, H., Zhang, H., Yin, S., Huang, N., Zhao, W., Li, N., Zheng, C., Zhou, Y., Huang, C., Feng, D., Xu, Q., Wu, Y., Hong, D., Wang, Z., Lin, Y., Zhang, T., Kumar, P., Plaza, A., Chanussot, J., Zhang, J., Shi, J., and Wang, L.: Artificial intelligence for geoscience: Progress, challenges, and perspectives, The Innovation, 5, 100691, https://doi.org/10.1016/j.xinn.2024.100691, 2024a.
Zhao, T., Feng, D., Yu, P., and Zhang, Z.: Global 0.05° near-surface freeze/thaw state dataset v2.0 (2002–2023), National Tibetan Plateau/Third Pole Environment Data Center [data set], https://doi.org/10.11888/Cryos.tpdc.301551, 2024b.
Zhou, Z., Zhao, S., and Jiang, L.: Downscaling methods of passive microwave remote sensing of soil moisture, Journal of Beijing Normal University (Natural Science), 52, 479–485, 2016.
Zou, D., Zhao, L., Sheng, Y., Chen, J., Hu, G., Wu, T., Wu, J., Xie, C., Wu, X., Pang, Q., Wang, W., Du, E., Li, W., Liu, G., Li, J., Qin, Y., Qiao, Y., Wang, Z., Shi, J., and Cheng, G.: A new map of permafrost distribution on the Tibetan Plateau, The Cryosphere, 11, 2527–2542, https://doi.org/10.5194/tc-11-2527-2017, 2017.
Zuerndorfer, B. and England, A. W.: Radiobrightness decision criteria for freeze/thaw boundaries, IEEE Trans. Geosci. Remote Sensing, 30, 89–102, https://doi.org/10.1109/36.124219, 1992.
Zuerndorfer, B. W., England, A. W., Dobson, M. C., and Ulaby, F. T.: Mapping freeze/thaw boundaries with SMMR data, Agricultural and Forest Meteorology, 52, 199–225, https://doi.org/10.1016/0168-1923(90)90106-G, 1990.
Short summary
This study introduces a downscaling approach integrating passive microwave and optical satellite data to generate a long-term (2002–2023), high-resolution (0.05°) global near-surface freeze-thaw (FT) state dataset with daily seamless continuity. The dataset attains 83.78 % overall accuracy, consistent with the microwave-based products but with finer spatial detail. This detailed FT record provides valuable information to enhance the understanding of global hydrological and ecological impacts.
This study introduces a downscaling approach integrating passive microwave and optical satellite...
Altmetrics
Final-revised paper
Preprint