Chakraborty, T., Venter, Z. S., Demuzere, M., Zhan, W., Gao, J., Zhao, L., and Qian, Y.: Large disagreements in estimates of urban land across scales and their implications, Nat. Commun., 15,
https://doi.org/10.1038/s41467-024-52241-5, 2024.
a
Chander, G., Markham, B. L., and Helder, D. L.: Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM
+, and EO-1 ALI sensors, Remote Sens. Environ., 113, 893–903,
https://doi.org/10.1016/j.rse.2009.01.007, 2009.
a
Chen, B., Wu, S., Song, Y., Webster, C., Xu, B., and Gong, P.: Contrasting inequality in human exposure to greenspace between cities of Global North and Global South, Nat. Commun., 13,
https://doi.org/10.1038/s41467-022-32258-4, 2022.
a
Chen, J., Dun, C., and Kyrillidis, A.: Fast FixMatch: Faster Semi-Supervised Learning with Curriculum Batch Size, in: 2024 IEEE International Symposium on Information Theory (ISIT), IEEE, 1836–1841,
https://doi.org/10.1109/isit57864.2024.10619518, 2024.
a
Cheng, M., He, W., Li, Z., Yang, G., and Zhang, H.: Harmony in diversity: Content cleansing change detection framework for very-high-resolution remote-sensing images, ISPRS J. Photogramm., 218, 1–19,
https://doi.org/10.1016/j.isprsjprs.2024.09.002, 2024.
a
Cheng, M., Li, Z., Li, L., He, W., Zhang, L., and Zhang, H.: LP-QLC10: 25-year quarterly land change mapping in China’s Loess Plateau reveals long-term and substantial soil erosion mitigation, Science Data Bank [data set],
https://doi.org/10.57760/sciencedb.33656, 2026.
a,
b,
c
Di, B., Zeng, H., Zhang, M., Ustin, S. L., Tang, Y., Wang, Z., Chen, N., and Zhang, B.: Quantifying the spatial distribution of soil mass wasting processes after the 2008 earthquake in Wenchuan, China, Remote Sens. Environ., 114, 761–771,
https://doi.org/10.1016/j.rse.2009.11.011, 2010.
a
Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., Hoersch, B., Isola, C., Laberinti, P., Martimort, P., Meygret, A., Spoto, F., Sy, O., Marchese, F., and Bargellini, P.: Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services, Remote Sens. Environ., 120, 25–36,
https://doi.org/10.1016/j.rse.2011.11.026, 2012.
a
Feng, X., Fu, B., Lu, N., Zeng, Y., and Wu, B.: How ecological restoration alters ecosystem services: an analysis of carbon sequestration in China’s Loess Plateau, Sci. Rep., 3,
https://doi.org/10.1038/srep02846, 2013.
a
Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A., and Huang, X.: MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets, Remote Sens. Environ., 114, 168–182,
https://doi.org/10.1016/j.rse.2009.08.016, 2010.
a
Fritz, S., See, L., Perger, C., McCallum, I., Schill, C., Schepaschenko, D., Duerauer, M., Karner, M., Dresel, C., Laso-Bayas, J.-C., Lesiv, M., Moorthy, I., Salk, C. F., Danylo, O., Sturn, T., Albrecht, F., You, L., Kraxner, F., and Obersteiner, M.: A global dataset of crowdsourced land cover and land use reference data, Scientific Data, 4,
https://doi.org/10.1038/sdata.2017.75, 2017.
a,
b
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., and Michaelsen, J.: The climate hazards infrared precipitation with stations – a new environmental record for monitoring extremes, Scientific Data, 2,
https://doi.org/10.1038/sdata.2015.66, 2015.
a
Gao, H., Li, Z., Jia, L., Li, P., Xu, G., Ren, Z., Pang, G., and Zhao, B.: Capacity of soil loss control in the Loess Plateau based on soil erosion control degree, J. Geogr. Sci., 26, 457–472,
https://doi.org/10.1007/s11442-016-1279-y, 2016.
a
Ge, J., Pitman, A. J., Guo, W., Zan, B., and Fu, C.: Impact of revegetation of the Loess Plateau of China on the regional growing season water balance, Hydrol. Earth Syst. Sci., 24, 515–533,
https://doi.org/10.5194/hess-24-515-2020, 2020.
a
Gong, P., Liu, H., Zhang, M., Li, C., Wang, J., Huang, H., Clinton, N., Ji, L., Li, W., Bai, Y., Chen, B., Xu, B., Zhu, Z., Yuan, C., Ping Suen, H., Guo, J., Xu, N., Li, W., Zhao, Y., Yang, J., Yu, C., Wang, X., Fu, H., Yu, L., Dronova, I., Hui, F., Cheng, X., Shi, X., Xiao, F., Liu, Q., and Song, L.: Stable classification with limited sample: transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017, Sci. Bull., 64, 370–373,
https://doi.org/10.1016/j.scib.2019.03.002, 2019.
a
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R.: Google Earth Engine: Planetary-scale geospatial analysis for everyone, Remote Sens. Environ., 202, 18–27,
https://doi.org/10.1016/j.rse.2017.06.031, 2017.
a
GPRC: Outline of Comprehensive Management Plan for the Loess Plateau Region (2010–2030), Government of the People’s Republic of China, Beijing,
https://www.ndrc.gov.cn/fzggw/jgsj/njs/sjdt/201101/W020240430572756442125.pdf (last access: 9 May 2026), 2010.
a,
b
He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R.: Momentum Contrast for Unsupervised Visual Representation Learning, in: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 9726–9735,
https://doi.org/10.1109/cvpr42600.2020.00975, 2020.
a
Hengl, T. and Wheeler, I.: Soil organic carbon content in
x 5 g/kg at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution, Zenodo [data set],
https://doi.org/10.5281/zenodo.2525553, 2018.
a
Hermosilla, T., Wulder, M. A., White, J. C., and Coops, N. C.: Land cover classification in an era of big and open data: Optimizing localized implementation and training data selection to improve mapping outcomes, Remote Sens. Environ., 268, 112780,
https://doi.org/10.1016/j.rse.2021.112780, 2022.
a
Hua, W., Liang, D., Li, J., Liu, X., Zou, Z., Ye, X., and Bai, X.: SOOD: Towards Semi-Supervised Oriented Object Detection, in: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 15558–15567,
https://doi.org/10.1109/cvpr52729.2023.01493, 2023.
a
Huang, Z., Du, H., Mao, F., Li, X., Zhou, G., Sun, J., Xu, Y., Xuan, J., Lu, Y., Huang, L., and Song, M.: Assessing the impact of land use and cover change on above-ground carbon storage in subtropical forests: a case study of Zhejiang Province, China, Geo-Spatial Information Science, 28, 2781–2807,
https://doi.org/10.1080/10095020.2024.2440615, 2025.
a
IPCC: Climate Change and Land: An IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse Gas Fluxes in Terrestrial Ecosystems, Intergovernmental Panel on Climate Change, Geneva, Switzerland,
https://www.ipcc.ch/srccl/ (last access: 9 May 2026), 2019. a
Jiang, C., Zhang, H., Wang, X., Feng, Y., and Labzovskii, L.: Challenging the land degradation in China’s Loess Plateau: Benefits, limitations, sustainability, and adaptive strategies of soil and water conservation, Ecol. Eng., 127, 135–150,
https://doi.org/10.1016/j.ecoleng.2018.11.018, 2019.
a
Kang, L., Han, X., Zhang, Z., and Sun, O. J.: Grassland ecosystems in China: review of current knowledge and research advancement, Philos. T. R. Soc. B, 362, 997–1008,
https://doi.org/10.1098/rstb.2007.2029, 2007.
a
Karra, K., Kontgis, C., Statman-Weil, Z., Mazzariello, J. C., Mathis, M., and Brumby, S. P.: Global land use/land cover with Sentinel 2 and deep learning, in: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, IEEE, 4704–4707,
https://doi.org/10.1109/igarss47720.2021.9553499, 2021.
a
Kodl, G., Streeter, R., Cutler, N., and Bolch, T.: Arctic tundra shrubification can obscure increasing levels of soil erosion in NDVI assessments of land cover derived from satellite imagery, Remote Sens. Environ., 301, 113935,
https://doi.org/10.1016/j.rse.2023.113935, 2024.
a
Kraamwinkel, C., Beaulieu, A., Dias, T., and Howison, R.: Planetary limits to soil degradation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4970,
https://doi.org/10.5194/egusphere-egu22-4970, 2022.
a
Li, Z., He, W., Cheng, M., Hu, J., Yang, G., and Zhang, H.: SinoLC-1: the first 1 m resolution national-scale land-cover map of China created with a deep learning framework and open-access data, Earth Syst. Sci. Data, 15, 4749–4780,
https://doi.org/10.5194/essd-15-4749-2023, 2023.
a,
b,
c
Li, Z., He, W., Li, J., Lu, F., and Zhang, H.: Learning without Exact Guidance: Updating Large-Scale High-Resolution Land Cover Maps from Low-Resolution Historical Labels, in: 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 27717–27727,
https://doi.org/10.1109/cvpr52733.2024.02618, 2024.
a,
b
Li, Z., Li, L., Hu, T., Cheng, M., He, W., Qiu, T., Zhang, L., and Zhang, H.: Satellite mapping of every building’s function in urban China reveals deep built environment disparities, Nat. Commun., 17,
https://doi.org/10.1038/s41467-026-69589-5, 2026.
a
Lin, M., Yang, G., and Zhang, H.: Transition Is a Process: Pair-to-Video Change Detection Networks for Very High Resolution Remote Sensing Images, IEEE T. Image Process., 32, 57–71,
https://doi.org/10.1109/tip.2022.3226418, 2023.
a
Liu, J., Li, S., Ouyang, Z., Tam, C., and Chen, X.: Ecological and socioeconomic effects of China’s policies for ecosystem services, P. Natl. Acad. Sci. USA, 105, 9477–9482,
https://doi.org/10.1073/pnas.0706436105, 2008.
a
Liu, L., Zhang, H., Li, F., and Chen, X.: Research progress and prospect of soil and water conservation measures in the Loess Plateau, in: Fifth International Conference on Traffic Engineering and Transportation System (ICTETS 2021), edited by: Xing, Y., SPIE, p. 51,
https://doi.org/10.1117/12.2619650, 2021.
a
Liu, Q., Wang, Y., Zhang, J., and Chen, Y.: Filling Gullies to Create Farmland on the Loess Plateau, Environ. Sci. Technol., 47, 7589–7590,
https://doi.org/10.1021/es402460r, 2013.
a
Liu, Y., Liu, R., Chen, J., Wei, X., Qi, L., and Zhao, L.: A global annual fractional tree cover dataset during 2000–2021 generated from realigned MODIS seasonal data, Scientific Data, 11,
https://doi.org/10.1038/s41597-024-03671-9, 2024.
a
Liu, Z., Shao, M., and Wang, Y.: Effect of environmental factors on regional soil organic carbon stocks across the Loess Plateau region, China, Agr. Ecosyst. Environ., 142, 184–194,
https://doi.org/10.1016/j.agee.2011.05.002, 2011.
a
MEE China: Technical Specification for Investigation and Assessment of National Ecological Status – Ecosystem Services Assessment, Tech. Rep. HJ 1173–2021, Ministry of Ecology and Environment of the People's Republic of China, Beijing,
https://www.mee.gov.cn/ywgz/fgbz/bz/bzwb/stzl/202106/W020210910457959297347.pdf (last access: 9 May 2026), 2021a (in Chinese).
a,
b,
c,
d
MEE China: Technical specification for investigation and assessment of national ecological status – Ecosystem problems assessment, Tech. Rep. HJ 1174–2021, Ministry of Ecology and Environment of the People's Republic of China, Beijing,
https://www.mee.gov.cn/ywgz/fgbz/bz/bzwb/stzl/202106/W020210910459257234201.pdf (last access: 9 May 2026) 2021b (in Chinese). a
Miyato, T., Maeda, S.-I., Koyama, M., and Ishii, S.: Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning, IEEE T. Pattern Anal., 41, 1979–1993,
https://doi.org/10.1109/tpami.2018.2858821, 2019.
a
Moltchanova, E., Lesiv, M., See, L., Mugford, J., and Fritz, S.: Optimizing Crowdsourced Land Use and Land Cover Data Collection: A Two-Stage Approach, Land, 11, 958,
https://doi.org/10.3390/land11070958, 2022.
a
Nut, N., Mihara, M., Jeong, J., Ngo, B., Sigua, G., Prasad, P. V., and Reyes, M. R.: Land Use and Land Cover Changes and Its Impact on Soil Erosion in Stung Sangkae Catchment of Cambodia, Sustainability, 13, 9276,
https://doi.org/10.3390/su13169276, 2021.
a
Peili, S., Ning, W., and Rawat, G. S.: The Distribution Patterns of Timberline and Its Response to Climate Change in the Himalayas, Journal of Resources and Ecology, 11, 342,
https://doi.org/10.5814/j.issn.1674-764x.2020.04.002, 2020.
a
Pettorelli, N., Laurance, W. F., O’Brien, T. G., Wegmann, M., Nagendra, H., and Turner, W.: Satellite remote sensing for applied ecologists: opportunities and challenges, J. Appl. Ecol., 51, 839–848,
https://doi.org/10.1111/1365-2664.12261, 2014.
a
Rafiei-Sardooi, E., Mirchooli, F., Azareh, A., and Clague, J. J.: Impact of soil erosion on agricultural sustainability based on crop water productivity in semi-arid Iran, Sci. Rep., 15,
https://doi.org/10.1038/s41598-025-24353-5, 2025.
a
Renard, K. G., Foster, G. R., Weesies, G. A., and Porter, J. P.: RUSLE: Revised universal soil loss equation, J. Soil Water Conserv., 46, 30–33,
https://doi.org/10.1080/00224561.1991.12456571, 1991.
a
Roy, D., Wulder, M., Loveland, T., C.E., W., Allen, R., Anderson, M., Helder, D., Irons, J., Johnson, D., Kennedy, R., Scambos, T., Schaaf, C., Schott, J., Sheng, Y., Vermote, E., Belward, A., Bindschadler, R., Cohen, W., Gao, F., Hipple, J., Hostert, P., Huntington, J., Justice, C., Kilic, A., Kovalskyy, V., Lee, Z., Lymburner, L., Masek, J., McCorkel, J., Shuai, Y., Trezza, R., Vogelmann, J., Wynne, R., and Zhu, Z.: Landsat-8: Science and product vision for terrestrial global change research, Remote Sens. Environ., 145, 154–172,
https://doi.org/10.1016/j.rse.2014.02.001, 2014.
a
Sala, O. E., Stuart Chapin, F., III, Armesto, J. J., Berlow, E., Bloomfield, J., Dirzo, R., Huber-Sanwald, E., Huenneke, L. F., Jackson, R. B., Kinzig, A., Leemans, R., Lodge, D. M., Mooney, H. A., Oesterheld, M., Poff, N. L., Sykes, M. T., Walker, B. H., Walker, M., and Wall, D. H.: Global Biodiversity Scenarios for the Year 2100, Science, 287, 1770–1774,
https://doi.org/10.1126/science.287.5459.1770, 2000.
a
Sharma, H. and Ehlers, T. A.: Effects of seasonal variations in vegetation and precipitation on catchment erosion rates along a climate and ecological gradient: insights from numerical modeling, Earth Surf. Dynam., 11, 1161–1181,
https://doi.org/10.5194/esurf-11-1161-2023, 2023.
a
Singh, D., Singh, N., Singh, H., Kumawat, A., Jeet, P., Yadav, D., Gupta, A. K., and Kumar, G.: Biological and mechanical measures for runoff and soil erosion control in India and beyond, Discover Applied Sciences, 7,
https://doi.org/10.1007/s42452-025-07287-5, 2025.
a
Tang, Q., Jiang, W., Li, Z., Wang, J., and Lan, G.: Potential analysis of the comprehensive land consolidation for entire karst region based on improved random forest method: a case study in the Lijiang River Basin, Geo-Spatial Information Science, 1–18,
https://doi.org/10.1080/10095020.2025.2583814, 2025.
a
UNCCD: Global Land Outlook, Second Edition, United Nations Convention to Combat Desertification, Bonn, Germany,
https://www.unccd.int/resources/global-land-outlook (last access: 9 May 2026), 2022. a
U.S. Geological Survey: Landsat Collection 2 Level-2 Science Product Guide, Tech. rep., Department of the Interior, U.S. Geological Survey,
https://www.usgs.gov/landsat-missions/landsat-collection-2-level-2-science-products (last access: 31 August 2025), 2020. a
Wang, S., Fu, B., Piao, S., Lü, Y., Ciais, P., Feng, X., and Wang, Y.: Reduced sediment transport in the Yellow River due to anthropogenic changes, Nat. Geosci., 9, 38–41,
https://doi.org/10.1038/ngeo2602, 2015.
a
Wang, Y., Yu, P., Feger, K., Wei, X., Sun, G., Bonell, M., Xiong, W., Zhang, S., and Xu, L.: Annual runoff and evapotranspiration of forestlands and non‐forestlands in selected basins of the Loess Plateau of China, Ecohydrology, 4, 277–287,
https://doi.org/10.1002/eco.215, 2011.
a
Wang, Z., Shi, X., Dou, S., Cheng, M., and Miao, L.: The 30 m land cover dataset for capturing land cover changes induced by ecological restoration from 1990 to 2022 on the Chinese Loess Plateau, Scientific Data, 12,
https://doi.org/10.1038/s41597-025-04575-y, 2025.
a
Wuyun, D., Sun, L., Chen, Z., Li, Y., Han, M., Shi, Z., Ren, T., and Zhao, H.: A 10-meter resolution dataset of abandoned and reclaimed cropland from 2016 to 2023 in Inner Mongolia, China, Scientific Data, 12,
https://doi.org/10.1038/s41597-025-04614-8, 2025.
a
Xie, B., Jia, X., Qin, Z., Shen, J., and Chang, Q.: Vegetation dynamics and climate change on the Loess Plateau, China: 1982–2011, Reg. Environ. Change, 16, 1583–1594,
https://doi.org/10.1007/s10113-015-0881-3, 2015.
a
Yan, J., Wang, S., Feng, J., He, H., Wang, L., Sun, Z., and Zheng, C.: New 30-m resolution dataset reveals declining soil erosion with regional increases across Chinese mainland (1990–2022), Remote Sens. Environ., 323, 114681,
https://doi.org/10.1016/j.rse.2025.114681, 2025.
a,
b
Yang, L., Qi, L., Feng, L., Zhang, W., and Shi, Y.: Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation, in: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 7236–7246,
https://doi.org/10.1109/cvpr52729.2023.00699, 2023.
a
Yang, L., Shi, L., Li, J., and Kong, H.: Spatio-temporal pattern change of LULC and its response to climate in the Loess Plateau, China, Sci. Rep., 14,
https://doi.org/10.1038/s41598-024-73945-0, 2024.
a
Yang, S., Guan, Y., Zhao, C., Zhang, C., Bai, J., and Chen, K.: Determining the influence of catchment area on intensity of gully erosion using high-resolution aerial imagery: A 40-year case study from the Loess Plateau, northern China, Geoderma, 347, 90–102,
https://doi.org/10.1016/j.geoderma.2019.03.042, 2019.
a
Yu, Z., Di, L., Yang, R., Tang, J., Lin, L., Zhang, C., Rahman, M. S., Zhao, H., Gaigalas, J., Yu, E. G., and Sun, Z.: Selection of Landsat 8 OLI Band Combinations for Land Use and Land Cover Classification, in: 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics), IEEE, 5 pp.,
https://doi.org/10.1109/agro-geoinformatics.2019.8820595, 2019.
a
Yuan, Y., Fu, R., Huang, L., Lin, W., Zhang, C., Chen, X., and Wang, J.: HRFormer: High-Resolution Transformer for Dense Prediction, Neural Information Processing Systems (NeurIPS),
https://arxiv.org/abs/2110.09408 (last access: 9 May 2026), 2021.
a,
b,
c
Zanaga, D., Van De Kerchove, R., Daems, D., De Keersmaecker, W., Brockmann, C., Kirches, G., Wevers, J., Cartus, O., Santoro, M., Fritz, S., Lesiv, M., Herold, M., Tsendbazar, N.-E., Xu, P., Ramoino, F., and Arino, O.: ESA WorldCover 10 m 2021 v200, Zenodo [data set],
https://doi.org/10.5281/zenodo.7254221, 2022.
a,
b,
c,
d,
e
Zeng, Z., Estes, L., Ziegler, A. D., Chen, A., Searchinger, T., Hua, F., Guan, K., Jintrawet, A., and Wood, E. F.: Highland cropland expansion and forest loss in Southeast Asia in the twenty-first century, Nat. Geosci., 11, 556–562,
https://doi.org/10.1038/s41561-018-0166-9, 2018.
a
Zhang, K., Shu, A., Xu, X., Yang, Q., and Yu, B.: Soil erodibility and its estimation for agricultural soils in China, J. Arid Environ., 72, 1002–1011,
https://doi.org/10.1016/j.jaridenv.2007.11.018, 2008.
a,
b
Zhang, X., Liu, L., Chen, X., Gao, Y., Xie, S., and Mi, J.: GLC_FCS30: global land-cover product with fine classification system at 30 m using time-series Landsat imagery, Earth Syst. Sci. Data, 13, 2753–2776,
https://doi.org/10.5194/essd-13-2753-2021, 2021.
a
Zhang, X., Zhao, T., Xu, H., Liu, W., Wang, J., Chen, X., and Liu, L.: GLC_FCS30D: the first global 30 m land-cover dynamics monitoring product with a fine classification system for the period from 1985 to 2022 generated using dense-time-series Landsat imagery and the continuous change-detection method, Earth Syst. Sci. Data, 16, 1353–1381,
https://doi.org/10.5194/essd-16-1353-2024, 2024.
a
Zhao, J., Wang, X., and Zhou, Y.: A 30-meter resolution LS-factor dataset for the China Region, China Scientific Data, 10, 1–12,
https://doi.org/10.11922/11-6035.csd.2024.0026.zh, 2025.
a,
b
Zhu, X. X., Tuia, D., Mou, L., Xia, G.-S., Zhang, L., Xu, F., and Fraundorfer, F.: Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources, IEEE Geoscience and Remote Sensing Magazine, 5, 8–36,
https://doi.org/10.1109/mgrs.2017.2762307, 2017.
a
Zhu, Y., Li, W., Wang, D., Wu, Z., and Shang, P.: Spatial Pattern of Soil Erosion in Relation to Land Use Change in a Rolling Hilly Region of Northeast China, Land, 11, 1253,
https://doi.org/10.3390/land11081253, 2022.
a
Zweifel, L., Meusburger, K., and Alewell, C.: Spatio-temporal pattern of soil degradation in a Swiss Alpine grassland catchment, Remote Sens. Environ., 235, 111441,
https://doi.org/10.1016/j.rse.2019.111441, 2019.
a
