88 citations as recorded by crossref.

1. Unbalanced forest displacement across the coastal urban groups of eastern China in recent decades Y. Ji et al. https://doi.org/10.1016/j.scitotenv.2019.135900
2. Effects of land use change on ecosystem services in the China–Mongolia–Russia economic corridor J. Li et al. https://doi.org/10.1016/j.jclepro.2022.132175
3. Improvement of Spatio-Temporal Inconsistency of Time Series Land Cover Products L. Zhu et al. https://doi.org/10.3390/su16188127
4. Heterogeneous elevation-dependence of grassland greenness and greening on the Tibetan plateau J. Xie et al. https://doi.org/10.1016/j.agrformet.2025.110764
5. China’s Land Cover Fraction Change during 2001–2015 Based on Remote Sensed Data Fusion between MCD12 and CCI-LC H. Wang et al. https://doi.org/10.3390/rs13030341
6. Uncertainty Analysis of Multisource Land Cover Products in China L. Wang & J. Jin https://doi.org/10.3390/su13168857
7. A database of global reference sites to support validation of satellite surface albedo datasets (SAVS 1.0) A. Loew et al. https://doi.org/10.5194/essd-8-425-2016
8. Analyzing Space–Time Coherence in Precipitation Seasonality across Different European Climates M. Lanfredi et al. https://doi.org/10.3390/rs12010171
9. Reliability and consistency assessment of land cover products at macro and local scales in typical cities W. Shi et al. https://doi.org/10.1080/17538947.2023.2181992
10. Hidden Markov Models for Annual Land Cover Mapping—Increasing Temporal Consistency and Completeness P. Bogaert et al. https://doi.org/10.1109/TGRS.2021.3123738
11. Satellite-Detected Contrasting Responses of Canopy Structure and Leaf Physiology to Drought H. Gu et al. https://doi.org/10.1109/JSTARS.2023.3247422
12. Mapping forest cover and change as continuous variables is essential to advance consistency across forest monitoring products V. Gutiérrez-Vélez et al. https://doi.org/10.1080/15481603.2024.2427305
13. Enhanced carbon storage in the Sichuan-Yunnan Ecological Barrier Zone: the impact of land use changes driven by ecological engineering M. Yan et al. https://doi.org/10.1016/j.ecoleng.2025.107588
14. Global-scale patterns and determinants of cropping frequency in irrigation dam command areas P. Rufin et al. https://doi.org/10.1016/j.gloenvcha.2018.02.011
15. Land Cover Classification using Google Earth Engine and Random Forest Classifier—The Role of Image Composition T. Phan et al. https://doi.org/10.3390/rs12152411
16. Mapping irrigated, rainfed and paddy croplands from time-series Sentinel-2 images by integrating pixel-based classification and image segmentation on Google Earth Engine H. Xing et al. https://doi.org/10.1080/10106049.2022.2076923
17. Assessing the impact of land cover map resolution and geolocation accuracy on evapotranspiration simulations by a land surface model N. Ghilain & F. Gellens-Meulenberghs https://doi.org/10.1080/2150704X.2014.927081
18. Development and assessment of a 250m spatial resolution MODIS annual land cover time series (2000–2011) for the forest region of Canada derived from change-based updating D. Pouliot et al. https://doi.org/10.1016/j.rse.2013.10.004
19. Automated annual cropland mapping using knowledge-based temporal features F. Waldner et al. https://doi.org/10.1016/j.isprsjprs.2015.09.013
20. Land Cover Mapping for Namdapha National Park (Arunachal Pradesh), India Using Harmonized Land Cover Legends M. Lodhi et al. https://doi.org/10.1007/s12524-013-0326-8
21. An in situ‐based analysis of the relationship between land surface “skin” and screen‐level air temperatures E. Good https://doi.org/10.1002/2016JD025318
22. Comparison and Assessment of Regional and Global Land Cover Datasets for Use in CLASS over Canada L. Wang et al. https://doi.org/10.3390/rs11192286
23. Major trends in the land surface phenology (LSP) of Africa, controlling for land-cover change T. Adole et al. https://doi.org/10.1080/01431161.2018.1479797
24. Mapping of ESA's Climate Change Initiative land cover data to plant functional types for use in the CLASSIC land model L. Wang et al. https://doi.org/10.5194/bg-20-2265-2023
25. ESM-SnowMIP: assessing snow models and quantifying snow-related climate feedbacks G. Krinner et al. https://doi.org/10.5194/gmd-11-5027-2018
26. Dealing with the uncertainty of technical changes in the CORINE Land Cover dataset: The Portuguese approach D. García-Álvarez et al. https://doi.org/10.1016/j.jag.2023.103389
27. Exploring the multitemporal surface urban heat island effect and its driving relation in the Beijing-Tianjin-Hebei urban agglomeration X. Fu et al. https://doi.org/10.1016/j.apgeog.2022.102714
28. Land cover change detection by integrating object-based data blending model of Landsat and MODIS M. Lu et al. https://doi.org/10.1016/j.rse.2016.07.028
29. A global 1‐km consensus land‐cover product for biodiversity and ecosystem modelling M. Tuanmu & W. Jetz https://doi.org/10.1111/geb.12182
30. Cropland Mapping over Sahelian and Sudanian Agrosystems: A Knowledge-Based Approach Using PROBA-V Time Series at 100-m M. Lambert et al. https://doi.org/10.3390/rs8030232
31. Large net forest loss in Cambodia’s Tonle Sap Lake protected areas during 1992–2019 A. Chen et al. https://doi.org/10.1007/s13280-022-01704-4
32. A Synergy Cropland of China by Fusing Multiple Existing Maps and Statistics M. Lu et al. https://doi.org/10.3390/s17071613
33. Land Cover Mapping of a Tropical Region by Integrating Multi-Year Data into an Annual Time Series J. Anaya et al. https://doi.org/10.3390/rs71215833
34. Large Uncertainty on Forest Area Change in the Early 21st Century among Widely Used Global Land Cover Datasets H. Chen et al. https://doi.org/10.3390/rs12213502
35. Does ASCAT observe the spring reactivation in temperate deciduous broadleaf forests? I. Pfeil et al. https://doi.org/10.1016/j.rse.2020.112042
36. Detecting Change Areas in Mexico Between 2005 and 2010 Using 250 m MODIS Images R. Colditz et al. https://doi.org/10.1109/JSTARS.2013.2280711
37. Benchmarking CMIP5 models with a subset of ESA CCI Phase 2 data using the ESMValTool A. Lauer et al. https://doi.org/10.1016/j.rse.2017.01.007
38. Automatic training sample collection utilizing multi-source land cover products and time-series Sentinel-2 images Y. Wang et al. https://doi.org/10.1080/15481603.2024.2352957
39. Seasonal Land Cover Dynamics in Beijing Derived from Landsat 8 Data Using a Spatio-Temporal Contextual Approach J. Wang et al. https://doi.org/10.3390/rs70100865
40. Assessment of TerraClass and MapBiomas data on legend and map agreement for the Brazilian Amazon biome A. NEVES et al. https://doi.org/10.1590/1809-4392201900981
41. Uncertainty in land use obscures global soil organic carbon stock estimates C. Gang et al. https://doi.org/10.1016/j.agrformet.2023.109585
42. A Synthesis of Land Use/Land Cover Studies: Definitions, Classification Systems, Meta-Studies, Challenges and Knowledge Gaps on a Global Landscape R. Nedd et al. https://doi.org/10.3390/land10090994
43. Cropland expansion outweighs the monetary effect of declining natural vegetation on ecosystem services in sub-Saharan Africa A. Fenta et al. https://doi.org/10.1016/j.ecoser.2020.101154
44. Major forest changes and land cover transitions based on plant functional types derived from the ESA CCI Land Cover product W. Li et al. https://doi.org/10.1016/j.jag.2015.12.006
45. Annual maps of global artificial impervious area (GAIA) between 1985 and 2018 P. Gong et al. https://doi.org/10.1016/j.rse.2019.111510
46. Revisiting PM2.5 pollution along urban-rural gradient and its coupling with urbanization process, a new perspective from urban pollution island analysis L. Yao et al. https://doi.org/10.1016/j.uclim.2022.101270
47. Monitoring the Variation of Vegetation Water Content with Machine Learning Methods: Point–Surface Fusion of MODIS Products and GNSS-IR Observations Q. Yuan et al. https://doi.org/10.3390/rs11121440
48. A review of regional and Global scale Land Use/Land Cover (LULC) mapping products generated from satellite remote sensing Y. Wang et al. https://doi.org/10.1016/j.isprsjprs.2023.11.014
49. Landsat-Based Land Cover Change in the Beijing-Tianjin-Tangshan Urban Agglomeration in 1990, 2000 and 2010 A. Yang & G. Sun https://doi.org/10.3390/ijgi6030059
50. Improving estimation of forest aboveground biomass using Landsat 8 imagery by incorporating forest crown density as a dummy variable C. Li et al. https://doi.org/10.1139/cjfr-2019-0216
51. More realistic land‐use and vegetation parameters in a regional climate model reduce model biases over China J. Gou et al. https://doi.org/10.1002/joc.6110
52. A review of statistical methods used for developing large-scale and long-term PM2.5 models from satellite data Z. Ma et al. https://doi.org/10.1016/j.rse.2021.112827
53. Recent divergence in the contributions of tropical and boreal forests to the terrestrial carbon sink T. Tagesson et al. https://doi.org/10.1038/s41559-019-1090-0
54. Land cover changes in the Brazilian Cerrado and Caatinga biomes from 1990 to 2010 based on a systematic remote sensing sampling approach R. Beuchle et al. https://doi.org/10.1016/j.apgeog.2015.01.017
55. Habitat quality dynamics in China's first group of national parks in recent four decades: Evidence from land use and land cover changes X. Chen et al. https://doi.org/10.1016/j.jenvman.2022.116505
56. Plant functional type classification for earth system models: results from the European Space Agency's Land Cover Climate Change Initiative B. Poulter et al. https://doi.org/10.5194/gmd-8-2315-2015
57. High agricultural water consumption led to the continued shrinkage of the Aral Sea during 1992–2015 Y. Su et al. https://doi.org/10.1016/j.scitotenv.2021.145993
58. Development of high resolution land surface parameters for the Community Land Model Y. Ke et al. https://doi.org/10.5194/gmd-5-1341-2012
59. Progress in space-borne studies of permafrost for climate science: Towards a multi-ECV approach A. Trofaier et al. https://doi.org/10.1016/j.rse.2017.05.021
60. High-resolution forest canopy cover estimation in ecodiverse landscape using machine learning and Google Earth Engine: Validity and reliability assessment H. Zurqani https://doi.org/10.1016/j.rsase.2023.101095
61. Does quality control matter? Surface urban heat island intensity variations estimated by satellite-derived land surface temperature products J. Lai et al. https://doi.org/10.1016/j.isprsjprs.2018.03.012
62. Global spatially-distributed sectoral GDP map for disaster risk analysis T. Shoji et al. https://doi.org/10.5194/essd-17-6669-2025
63. An automated approach for developing a regional-scale 1-m forest canopy cover dataset using machine learning and Google Earth Engine cloud computing platform H. Zurqani https://doi.org/10.1016/j.simpa.2023.100607
64. Use of MODIS Sensor Images Combined with Reanalysis Products to Retrieve Net Radiation in Amazonia G. De Oliveira et al. https://doi.org/10.3390/s16070956
65. Assessment of long time-series greening signatures across the urban–rural gradient in Chinese cities L. Yao https://doi.org/10.1016/j.ecolind.2024.111826
66. Gross and net land cover changes in the main plant functional types derived from the annual ESA CCI land cover maps (1992–2015) W. Li et al. https://doi.org/10.5194/essd-10-219-2018
67. Climate effects of the GlobeLand30 land cover dataset on the Beijing Climate Center climate model simulations X. Shi et al. https://doi.org/10.1007/s11430-016-5320-x
68. Remote sensing approach in evaluating anthropogenic impacts on the spatiotemporal changes in net primary productivity of the Niger river basin, from 2000 to 2020 O. Chukwuka et al. https://doi.org/10.1016/j.heliyon.2023.e21246
69. A multi-resolution global land cover dataset through multisource data aggregation L. Yu et al. https://doi.org/10.1007/s11430-014-4919-z
70. Interannual asymmetric transitions of gross primary productivity in the grasslands of Northern China Y. Liu et al. https://doi.org/10.1016/j.ecolind.2024.112631
71. Human alterations of the global floodplains 1992–2019 A. Rajib et al. https://doi.org/10.1038/s41597-023-02382-x
72. Global land cover mapping using Earth observation satellite data: Recent progresses and challenges Y. Ban et al. https://doi.org/10.1016/j.isprsjprs.2015.01.001
73. Estimation of the Land Surface Albedo Changes in the Broader Mediterranean Area, Based on 12 Years of Satellite Observations N. Benas & N. Chrysoulakis https://doi.org/10.3390/rs71215816
74. Use of Automated Change Detection and VGI Sources for Identifying and Validating Urban Land Use Change A. Olteanu-Raimond et al. https://doi.org/10.3390/rs12071186
75. Are CORINE land cover classes reliable proxies of plant species assemblages? A test in Mediterranean forest landscapes V. Amici et al. https://doi.org/10.1080/11263504.2017.1407372
76. Revealing the proximate drivers behind global tree cover loss using multisourced remote sensing products during 2000–2020 W. Liu et al. https://doi.org/10.1016/j.foreco.2025.122501
77. Sensitivity of Convection-Permitting Regional Climate Simulations to Changes in Land Cover Input Data: Role of Land Surface Characteristics for Temperature and Climate Extremes M. Tölle & E. Churiulin https://doi.org/10.3389/feart.2021.722244
78. Addressing the need for improved land cover map products for policy support Z. Szantoi et al. https://doi.org/10.1016/j.envsci.2020.04.005
79. Analysing the inconsistencies of CORINE status layers (CLC) and layers of changes (CHA) (1990-2018) for a Spanish case study D. García Álvarez & M. Camacho Olmedo https://doi.org/10.1080/19475683.2023.2166583
80. Reviews and syntheses: Systematic Earth observations for use in terrestrial carbon cycle data assimilation systems M. Scholze et al. https://doi.org/10.5194/bg-14-3401-2017
81. Land surface modeling over the Dry Chaco: the impact of model structures, and soil, vegetation and land cover parameters M. Maertens et al. https://doi.org/10.5194/hess-25-4099-2021
82. Drought decreased annual cumulative carbon uptake in Southwest China mainly through its influence on phenology rather than physiology J. Xie et al. https://doi.org/10.1016/j.ecolind.2023.111359
83. Trends of urban surface temperature and heat island characteristics in the Mediterranean N. Benas et al. https://doi.org/10.1007/s00704-016-1905-8
84. The ESA Climate Change Initiative (CCI): A European contribution to the generation of the Global Climate Observing System S. Plummer et al. https://doi.org/10.1016/j.rse.2017.07.014
85. Land cover in Norway based on an area frame survey of vegetation types A. Bryn et al. https://doi.org/10.1080/00291951.2018.1468356
86. Supervised methods of image segmentation accuracy assessment in land cover mapping H. Costa et al. https://doi.org/10.1016/j.rse.2017.11.024
87. Land Cover Changes in Ghana over the Past 24 Years P. Ampim et al. https://doi.org/10.3390/su13094951
88. Evaluation of MODIS, Climate Change Initiative, and CORINE Land Cover Products Based on a Ground Truth Dataset in a Mediterranean Landscape M. Bachantourian et al. https://doi.org/10.3390/land11091453