Articles | Volume 20, issue 1
https://doi.org/10.5194/tc-20-29-2026
© Author(s) 2026. 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-20-29-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The influence of snow cover on gross primary productivity of cultivated land in Northeast China
Lue Li
State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, 130102, China
Qian Yang
CORRESPONDING AUTHOR
State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, 130102, China
Meng Cui
Cultivated Land Quality and Farmland Engineering Supervision and Protection Center, Ministry of Agriculture and Rural Affairs, Beijing, 100125, China
Huanjun Liu
State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, 130102, China
Xiaohua Hao
Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, China
Yiyang Peng
Cultivated Land Quality and Farmland Engineering Supervision and Protection Center, Ministry of Agriculture and Rural Affairs, Beijing, 100125, China
Junyi Chang
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Remote Sensing and Geographic Information Research Centre, Changchun, 130102, China
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Jinliang Hou, Mingkai Zhang, Xiaohua Hao, Jifu Guo, Peng Dou, Ying Zhang, and Chunlin Huang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-662, https://doi.org/10.5194/essd-2025-662, 2025
Preprint under review for ESSD
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ChinaAI-FSC provides the first large-scale, AI-ready snow dataset for mainland China, spanning 2000–2022. By integrating MODIS, Landsat, and Sentinel-2 observations with advanced quality control, it supports AI model training, benchmarking, and large-scale snow mapping. The dataset enhances snow monitoring accuracy and fosters reproducible research on climate and hydrological processes.
Qian Yang, Xiaoguang Shi, Weibang Li, Kaishan Song, Zhijun Li, Xiaohua Hao, Fei Xie, Nan Lin, Zhidan Wen, Chong Fang, and Ge Liu
The Cryosphere, 17, 959–975, https://doi.org/10.5194/tc-17-959-2023, https://doi.org/10.5194/tc-17-959-2023, 2023
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A large-scale linear structure has repeatedly appeared on satellite images of Chagan Lake in winter, which was further verified as being ice ridges in the field investigation. We extracted the length and the angle of the ice ridges from multi-source remote sensing images. The average length was 21 141.57 ± 68.36 m. The average azimuth angle was 335.48° 141.57 ± 0.23°. The evolution of surface morphology is closely associated with air temperature, wind, and shoreline geometry.
Xiaohua Hao, Guanghui Huang, Zhaojun Zheng, Xingliang Sun, Wenzheng Ji, Hongyu Zhao, Jian Wang, Hongyi Li, and Xiaoyan Wang
Hydrol. Earth Syst. Sci., 26, 1937–1952, https://doi.org/10.5194/hess-26-1937-2022, https://doi.org/10.5194/hess-26-1937-2022, 2022
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We develop and validate a new 20-year MODIS snow-cover-extent product over China, which is dedicated to addressing known problems of the standard snow products. As expected, the new product significantly outperforms the state-of-the-art MODIS C6.1 products; improvements are particularly clear in forests and for the daily cloud-free product. Our product has provided more reliable snow knowledge over China and can be accessible freely https://dx.doi.org/10.11888/Snow.tpdc.271387.
Donghang Shao, Hongyi Li, Jian Wang, Xiaohua Hao, Tao Che, and Wenzheng Ji
Earth Syst. Sci. Data, 14, 795–809, https://doi.org/10.5194/essd-14-795-2022, https://doi.org/10.5194/essd-14-795-2022, 2022
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The temporal series and spatial distribution discontinuity of the existing snow water equivalent (SWE) products in the pan-Arctic region severely restricts the use of SWE data in cryosphere change and climate change studies. Using a ridge regression machine learning algorithm, this study developed a set of spatiotemporally seamless and high-precision SWE products. This product could contribute to the study of cryosphere change and climate change at large spatial scales.
Xiaohua Hao, Guanghui Huang, Tao Che, Wenzheng Ji, Xingliang Sun, Qin Zhao, Hongyu Zhao, Jian Wang, Hongyi Li, and Qian Yang
Earth Syst. Sci. Data, 13, 4711–4726, https://doi.org/10.5194/essd-13-4711-2021, https://doi.org/10.5194/essd-13-4711-2021, 2021
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Long-term snow cover data are not only of importance for climate research. Currently China still lacks a high-quality snow cover extent (SCE) product for climate research. This study develops a multi-level decision tree algorithm for cloud and snow discrimination and gap-filled technique based on AVHRR surface reflectance data. We generate a daily 5 km SCE product across China from 1981 to 2019. It has high accuracy and will serve as baseline data for climate and other applications.
Cited articles
Abebe, S. A., Qin, T., Zhang, X., and Yan, D.: Wavelet transform-based trend analysis of streamflow and precipitation in Upper Blue Nile River basin, Journal of Hydrology, Regional Studies, 44, https://doi.org/10.1016/j.ejrh.2022.101251, 2022.
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.
Beer, C., Reichstein, M., Tomelleri, E., Ciais, P., Jung, M., Carvalhais, N., Rödenbeck, C., Arain, M. A., Baldocchi, D., Bonan, G. B., Bondeau, A., Cescatti, A., Lasslop, G., Lindroth, A., Lomas, M., Luyssaert, S., Margolis, H., Oleson, K. W., Roupsard, O., Veenendaal, E., Viovy, N., Williams, C., Woodward, F. I., and Papale, D.: Terrestrial gross carbon dioxide uptake: global distribution and covariati on with climate, Science, 329, 834–838, https://doi.org/10.1126/science.1184984, 2010.
Blankinship, J. C. and Hart, S. C.: Consequences of manipulated snow cover on soil gaseous emission and N retention in the growing season: a meta-analysis, Ecosphere, 3, 1–20, 2012.
Bodner, G., Nakhforoosh, A., Kaul, H.-P. 0: Management of crop water under drought: a review, Agronomy for Sustainable Development, 35, 401–442, 2015.
Brooks, P. D., Grogan, P., Templer, P. H., Groffman, P., Öquist, M. G., and Schimel, J.: Carbon and nitrogen cycling in snow-covered environments, Geography Compass, 5, 682–699, 2011.
Chen, S., Huang, Y., and Wang, G.: Response of vegetation carbon uptake to snow-induced phenological and physiological changes across temperate China, Science of the Total Environment, 692, 188–200, 2019.
Endsley, K. A., Zhao, M., Kimball, J. S., and Devadiga, S.: Continuity of Global MODIS Terrestrial Primary Productivity Estimates in the VIIRS Era Using Model-Data Fusion, Journal of Geophysical Research, Biogeosciences, 128, https://doi.org/10.1029/2023JG007457, 2023.
Gonzalez, R.: Applied Multivariate Statistics for the Social Sciences, The American Statistician, 57, 68–69, https://doi.org/10.1198/tas.2003.s213, 2003
Hair, J. F., Risher, J. J., Sarstedt, M., and Ringle, C. M.: When to use and how to report the results of PLS-SEM, European Business Review, 31, 2–24, 2019.
Jiang, L., Yang, J., Zhang, C., Wu, S., Li, Z., Dai, L., Li, X., and Qiu, Y.: Daily snow water equivalent product with SMMR, SSM/I and SSMIS from 1980 to 2020 over China, Big Earth Data, 6, 420–434, 2022.
Kashyap, R. and Kuttippurath, J.: Warming-induced soil moisture stress threatens food security in India, Environmental Science and Pollution Research, 31, 59202–59218, 2024.
Kendall, M. G.: Rank Correlation Methods, 1st edn., Charles Griffin & Company, London, 160 pp., 1948.
Knowles, J. F., Lestak, L. R., and Molotch, N. P.: On the use of a snow aridity index to predict remotely sensed forest productivity in the presence of bark beetle disturbance, Water Resources Research, 53, 4891–4906, 2017.
Li, D., Ouyang, W., Wang, L., Chen, J., Zhang, H., Sharkhuu, A., Tseren-Ochir, S.-E., and Yang, Y.: Revisiting snowmelt dynamics and its impact on soil moisture and vegetation in mid-high latitude watershed over four decades, Agric. Forest Meteorol., 362, https://doi.org/10.1016/j.agrformet.2024.110353, 2025.
Li, W., Perera, S., Linstead, E., Thomas, R., El-Askary, H., Piechota, T., and Struppa, D.: Investigating decadal changes of multiple hydrological products and land-cover changes in the Mediterranean Region for 2009–2018, Earth Systems and Environment, 5, 285–302, 2021.
Li, Y., Liu, D., Li, T., Fu, Q., Liu, D., Hou, R., Meng, F., Li, M., and Li, Q.: Responses of spring soil moisture of different land use types to snow cover in Northeast China under climate change background, Journal of Hydrology, 608, https://doi.org/10.1016/j.jhydrol.2022.127610, 2022.
Liu, H., Xiao, P., Zhang, X., Chen, S., Wang, Y.,Wang, W.: Winter snow cover influences growing-season vegetation productivity non-uniformly in the Northern Hemisphere, Communications Earth and Environment, 4, 487, https://doi.org/10.1038/s43247-023-01167-9, 2023.
Luo, Z., Feng, W., Luo, Y., Baldock, J., and Wang, E.: Soil organic carbon dynamics jointly controlled by climate, carbon inputs, soil properties and soil carbon fractions, Global Change Biology, 23, 4430–4439, 2017.
Mann, H. B.: Nonparametric tests against trend, Econometrica: Journal of the econometric society, 13, 245–259, 1945.
McNally, A.: FLDAS noah land surface model L4 global monthly 0.1×0.1° (MERRA-2 and CHIRPS), Atmos. Compos, Water Energy Cycles Clim, Var, 2018.
Meredith, M., Sommerkorn, M., Cassotta, S., Derksen, C., Ekaykin, A., Hollowed, A., Kofinas, G., Mackintosh, A., Melbourne-Thomas, J., Muelbert, M. M. C., Ottersen, G., Pritchard, H., and Schuur, E. A. G.: Polar Regions, in: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate, edited by: Pörtner, H.-O., Roberts, D. C., Masson-Delmotte, V., Zhai, P., Tignor, M., Poloczanska, E., Mintenbeck, K., Alegría, A., Nicolai, M., Okem, A., Petzold, J., Rama, B., and Weyer, N. M., Cambridge University Press, Cambridge, UK, and New York, NY, USA, 203–320, https://doi.org/10.1017/9781009157964.005, 2019.
Mihalevich, B. A., Neilson, B. T., and Buahin, C. A.: Evaluation of the ERA5-land reanalysis data set for process-based river temperature modeling over data sparse and topographically complex regions, Water Resources Research, 58, https://doi.org/10.1029/2021WR031294, 2022.
Mudryk, L., Santolaria-Otín, M., Krinner, G., Ménégoz, M., Derksen, C., Brutel-Vuilmet, C., Brady, M., and Essery, R.: Historical Northern Hemisphere snow cover trends and projected changes in the CMIP6 multi-model ensemble, The Cryosphere, 14, 2495–2514, https://doi.org/10.5194/tc-14-2495-2020, 2020.
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021.
Pan, M., Zhao, F., Ma, J., Zhang, L., Qu, J., Xu, L., and Li, Y.: Effect of snow cover on spring soil moisture content in key agricultural areas of Northeast China, Sustainability, 14, https://doi.org/10.3390/su14031527, 2022.
Peng, S.: 1 km monthly mean temperature dataset for china (1901–2022), National Tibetan Plateau Data Center: Beijing, China, 2019.
Peng, S.: 1 km monthly precipitation dataset for China (1901–2022), National Tibetan Plateau Data Center: Beijing, China, 2020.
Peng, S., Piao, S., Ciais, P., Fang, J., and Wang, X.: Change in winter snow depth and its impacts on vegetation in China, Global Change Biology, 16, 3004–3013, 2010.
Pulliainen, J., Aurela, M., Laurila, T., Aalto, T., Takala, M., Salminen, M., Kulmala, M., Barr, A., Heimann, M., Lindroth, A., Laaksonen, A., Derksen, C., Mäkelä, A., Markkanen, T., Lemmetyinen, J., Susiluoto, J., Dengel, S., Mammarella, I., Tuovinen, J., and Vesala, T.: Early snowmelt significantly enhances boreal springtime carbon uptake, Proceedings of the National Academy of Sciences, 114, 11081–11086, 2017.
Pulliainen, J., Luojus, K., Derksen, C., Mudryk, L., Lemmetyinen, J., Salminen, M., Ikonen, J., Takala, M., Cohen, J., Smolander, T., and Norberg, J.: Patterns and trends of Northern Hemisphere snow mass from 1980 to 2018, Nature, 581, 294–298, 2020.
Running, S., Mu, Q., and Zhao, M.: MODIS/Terra Gross Primary Productivity 8-Day L4 Global 500 m SIN Grid V061, no title, 2021.
Sen, P. K.: Estimates of the regression coefficient based on Kendall's tau, Journal of the American statistical association, 63, 1379–1389, 1968.
Sjöström, M., Zhao, M. Archibald, S., Arneth, A., Cappelaere, B., Falk, U., Grandcourt, A. D., Hanan, N., Kergoat, L., Kutsch, W., Merbold, L., Mougin, E., Nickless, A., Nouvellon, Y., Scholes, R. J., Veenendaal, E. M., and Ardö, J.: Evaluation of MODIS gross primary productivity for Africa using eddy covariance data, Remote Sensing of Environment, 131, 275–286, 2013.
Wagle, P., Xiao, X., and Suyker, A. E.: Estimation and analysis of gross primary production of soybean under various management practices and drought conditions, ISPRS Journal of Photogrammetry and Remote Sensing, 99, 70–83, 2015.
Wang, L., Zhu, H., Lin, A., Zou, L., Qin, W., and Du, Q.: Evaluation of the latest MODIS GPP products across multiple biomes using global eddy covariance flux data, Remote Sensing, 9, https://doi.org/10.3390/rs9050418, 2017.
Wang, L., Faye, B., Li, Q., and Li, Y.: A spatio-temporal analysis of the ecological compensation for cultivated land in northeast China, Land, 12, https://doi.org/10.3390/land12122179, 2023a.
Wang, W., Yin, S., Yu, J., He, Z., and Xie, Y.: Long-term trends of precipitation and erosivity over Northeast China during 1961–2020, International Soil and Water Conservation Research, 11, 743–754, 2023b.
Wang, X., Feng, L., Qi, W., Cai, X., Zheng, Y., Gibson, L., Tang, J., Song, X., Liu, J., Zheng, C., and Bryan, B. A.: Continuous Loss of Global Lake Ice Across Two Centuries Revealed by Satellite Observations and Numerical Modeling, Geophysical Research Letters, 49, 2022.
Wang, Y., Xiao, P., Zhang, X., Liu, H., Wu, Y., and Sun, L.: Unraveling the effects of snow cover change on vegetation productivity: Insights from underlying surface types, Ecosphere, 15, https://doi.org/10.1002/ecs2.4855, 2024.
Wei, X., Yang, J., Luo, P., Lin, L., Lin, K., and Guan, J: Assessment of the variation and influencing factors of vegetation NPP and carbon sink capacity under different natural conditions, Ecological Indicators, 138, https://doi.org/10.1016/j.ecolind.2022.108834, 2022.
Wetzels, M., Odekerken-Schröder, G., and Van Oppen, C.: Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration, MIS Quarterly, 33, 177–195, https://doi.org/10.2307/20650284, 2009.
Xu, X., Liu, J., Zhang, S., Li, R., Yan, C., and Wu, S.: China many periods of land use land cover remote sensing monitoring data set (CNLUCC), Chinese Academy of Sciences, Resources and Environment Science Data Center Data Registration and Publication System [data set], https://doi.org/10.12078/2018070201 (in Chinese), 2018.
Xu, Y., Xu, Y., Wang, Y., Wu, L., Li, G., and Song, S.: Spatial and temporal trends of reference crop evapotranspiration and its influential variables in Yangtze River Delta, eastern China, Theoretical and Applied Climatology, 130, 945–958, 2017.
Xu, Y., Pei, J., Li, S., Zou, H. T., Wang, J. K., and Zhang, J. B.: Main Characteristics and Utilization Countermeasures for Black Soils in Different Regions of Northeast China, Chinese Journal of Soil Science, 54, 495–504, 2023.
Xue, L., Kappas, M., Wyss, D., and Putzenlechner, B.: Assessing the drought variability in northeast china over multiple temporal and spatial scales, Atmosphere, 13, https://doi.org/10.3390/atmos13091506, 2022.
Zhao, Q., Hao, X., Wang, J., Sun, X., and Li, H.: A dataset of snow cover phenology in China based on MODIS during 2000–2020, National Cryosphere Desert Date Center, [data set] (in Chinese), https://doi.org/10.12072/ncdc.I-SNOW.db0011.2021, 2022.
Zhao, Y., Chen, Y., Wu, C., Li, G., Ma, M., Fan, L., Zheng, H., Song, L., and Tang, X.: Exploring the contribution of environmental factors to evapotranspiration dynamics in the Three-River-Source region, China, Journal of Hydrology, 626, https://doi.org/10.1016/j.jhydrol.2023.130222, 2023.
Zhu, H., Lin, A., Wang, L., Xia, Y., and Zou, L.: Evaluation of MODIS gross primary production across multiple biomes in China using eddy covariance flux data, Remote Sensing, 8, https://doi.org/10.3390/rs8050395, 2016.
Short summary
This study demonstrates that snow cover generally suppresses crop growth (GPP) in Northeast China's croplands through thermal effects. Snow cover generally exerts a negative effect on GPP through the thermal influence. Regionally, snow indirectly inhibits GPP via water-mediated effects in the Liaohe and Songnen Plains, but promotes it in the Sanjiang Plain, Xing'an and Changbai Mountains, and Western Sandy Area.
This study demonstrates that snow cover generally suppresses crop growth (GPP) in Northeast...