138 citations as recorded by crossref.

1. Characterizing Forest Cover and Landscape Pattern Using Multi-Source Remote Sensing Data with Ensemble Learning Y. Wang et al. 10.3390/rs14215470
2. Spatiotemporal variation of enhanced vegetation index in the Amazon Basin and its response to climate change R. Zhong et al. 10.1016/j.pce.2021.103024
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4. Decreasing control of precipitation on grassland spring phenology in temperate China Y. Fu et al. 10.1111/geb.13234
5. Blue-Sky Albedo Reduction and Associated Influencing Factors of Stable Land Cover Types in the Middle-High Latitudes of the Northern Hemisphere during 1982–2015 S. Yuan et al. 10.3390/rs14040895
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9. Vegetation Dynamics and Its Trends Associated with Extreme Climate Events in the Yellow River Basin, China Y. Cao et al. 10.3390/rs15194683
10. Are there suitable global datasets for monitoring of land use and land cover in the tropics? Evidences from mainland Southeast Asia J. Zhai et al. 10.1016/j.gloplacha.2023.104233
11. A global historical twice-daily (daytime and nighttime) land surface temperature dataset produced by Advanced Very High Resolution Radiometer observations from 1981 to 2021 J. Li et al. 10.5194/essd-15-2189-2023
12. Spatiotemporal variations and driving factors for potential wind erosion on the Mongolian Plateau X. Wei et al. 10.1016/j.scitotenv.2022.160829
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14. Did Climate Change Influence the Emergence, Transmission, and Expression of the COVID-19 Pandemic? S. Gupta et al. 10.3389/fmed.2021.769208
15. Impact of Urbanization on Seismic Risk: A Study Based on Remote Sensing Data L. An & J. Zhang 10.3390/su14106132
16. Reconstructing Long‐Term Forest Cover in China by Fusing National Forest Inventory and 20 Land Use and Land Cover Data Sets X. Xia et al. 10.1029/2022JG007101
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19. Flash Droughts Identification Based on an Improved Framework and Their Contrasting Impacts on Vegetation Over the Loess Plateau, China X. Zheng et al. 10.1029/2021WR031464
20. Effects of Cropland Expansion on Temperature Extremes in Western India from 1982 to 2015 J. Liu et al. 10.3390/land10050489
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24. Evolution Simulation and Risk Analysis of Land Use Functions and Structures in Ecologically Fragile Watersheds Y. Wang et al. 10.3390/rs14215521
25. Spatio-temporal land use/land cover dynamics and its driving forces in the Mekong Basin using Landsat imageries from 1988 to 2017 H. Li & L. Hong 10.1080/10106049.2022.2089736
26. Land Cover and Land Use Mapping of the East Asian Summer Monsoon Region from 1982 to 2015 Y. He et al. 10.3390/land11030391
27. Soil Moisture Mapping Based on Multi-Source Fusion of Optical, Near-Infrared, Thermal Infrared, and Digital Elevation Model Data via the Bayesian Maximum Entropy Framework L. Han et al. 10.3390/rs12233916
28. Spatiotemporal Analysis of Land Cover and the Effects on Ecosystem Service Values in Rupandehi, Nepal from 2005 to 2020 A. KC et al. 10.3390/ijgi10100635
29. Exploring the Relationships between Land Surface Temperature and Its Influencing Factors Using Multisource Spatial Big Data: A Case Study in Beijing, China X. Wang et al. 10.3390/rs15071783
30. Stable Prototype-Guided Single-Temporal Supervised Learning for Change Detection and Extraction of Building S. Hou et al. 10.1109/TGRS.2023.3297850
31. Continuous Long Time Series Monitoring of Urban Construction Land in Supporting the SDG 11.3.1—A Case Study of Nanning, Guangxi, China Z. Ling et al. 10.3390/land12020452
32. The land–energy–water nexus of global bioenergy potentials from abandoned cropland J. Næss et al. 10.1038/s41893-020-00680-5
33. Gradient Boosting Machine and Object-Based CNN for Land Cover Classification Q. Bui et al. 10.3390/rs13142709
34. A spatiotemporal ensemble machine learning framework for generating land use/land cover time-series maps for Europe (2000–2019) based on LUCAS, CORINE and GLAD Landsat M. Witjes et al. 10.7717/peerj.13573
35. Decomposition of contribution to runoff changes and spatial differences of major tributaries in the middle reaches of the Yellow River based on the Budyko framework Y. Dai et al. 10.2166/nh.2023.061
36. Modeling the impact of urbanization and climate changes on terrestrial vegetation productivity in China by a neighborhood substitution analysis Z. Qin & Z. Sha 10.1016/j.ecolmodel.2023.110405
37. A Simple and Automatic Method for Detecting Large-Scale Land Cover Changes Without Training Data G. Lei et al. 10.1109/JSTARS.2023.3299413
38. On the potential of documenting decadal-scale avifaunal change from before-and-after comparisons of museum and observational data across North America F. Machado-Stredel et al. 10.1016/j.avrs.2022.100005
39. Recent Developments in Some Long-Term Drought Drivers A. de Jager et al. 10.3390/cli10030031
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41. A Method of Evaluating Safe Operating Space: Focus on Geographic Regions, Income Levels and Developing Pathway Y. Zhang et al. 10.1007/s00267-022-01730-8
42. Mapping N deposition impacts on soil microbial biomass across global terrestrial ecosystems C. Chen et al. 10.1016/j.geoderma.2023.116429
43. Precipitation changes and its interaction with terrestrial water storage determine water yield variability in the world's water towers T. Ning et al. 10.1016/j.scitotenv.2023.163285
44. The Effects of Financial Development and Pandemics Prevalence on Forests: Evidence From Asia-Pacific Region J. Wang et al. 10.3389/fenvs.2022.850724
45. Exploring spatio-temporal patterns of sediment load and driving factors in Lancang-Mekong River basin before operation of mega-dams (1968–2002) F. Tian et al. 10.1016/j.jhydrol.2022.128922
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47. Spatio-temporal analysis and simulation of land cover changes and their impacts on land surface temperature in urban agglomeration of Bisha Watershed, Saudi Arabia J. Mallick et al. 10.1080/10106049.2021.1980616
48. Struggle for Climate Rescue: The Euphoria of Plans versus Cold Reality V. Klimenko et al. 10.1134/S0040601523030011
49. AGTML: A novel approach to land cover classification by integrating automatic generation of training samples and machine learning algorithms on Google Earth Engine Y. Cui et al. 10.1016/j.ecolind.2023.110904
50. GCI30: a global dataset of 30 m cropping intensity using multisource remote sensing imagery M. Zhang et al. 10.5194/essd-13-4799-2021
51. An improved global land cover mapping in 2015 with 30 m resolution (GLC-2015) based on a multisource product-fusion approach B. Li et al. 10.5194/essd-15-2347-2023
52. Global impact of the COVID-19 lockdown on surface concentration and health risk of atmospheric benzene C. Ling et al. 10.5194/acp-23-3311-2023
53. Mapping Land Use/Cover Dynamics of the Yellow River Basin from 1986 to 2018 Supported by Google Earth Engine Q. Ji et al. 10.3390/rs13071299
54. Self-Supervised Remote Sensing Feature Learning: Learning Paradigms, Challenges, and Future Works C. Tao et al. 10.1109/TGRS.2023.3276853
55. Production of global daily seamless data cubes and quantification of global land cover change from 1985 to 2020 - iMap World 1.0 H. Liu et al. 10.1016/j.rse.2021.112364
56. Intra-annual land cover mapping and dynamics analysis with dense satellite image time series: a spatiotemporal cube based spatiotemporal contextual method W. Xi et al. 10.1080/15481603.2021.1973216
57. Time-series land cover mapping and urban expansion analysis using OpenStreetMap data and remote sensing big data: A case study of Guangdong-Hong Kong-Macao Greater Bay Area, China Q. Ding et al. 10.1016/j.jag.2022.103001
58. Identifying the driving forces of global ecosystem services balance, 2000–2020 Y. Peng et al. 10.1016/j.jclepro.2023.139019
59. Energy potentials, negative emissions, and spatially explicit environmental impacts of perennial grasses on abandoned cropland in Europe C. Iordan et al. 10.1016/j.eiar.2022.106942
60. Comparing spatial patterns of 11 common cancers in Mainland China L. Zhang et al. 10.1186/s12889-022-13926-y
61. Changes in different land cover areas and NDVI values in northern latitudes from 1982 to 2015 S. Xue et al. 10.1016/j.accre.2021.04.003
62. Assessing and Characterizing Carbon Storage in Wetlands of the Guangdong–Hong Kong–Macau Greater Bay Area, China, During 1995–2020 Y. Deng et al. 10.1109/JSTARS.2022.3192267
63. Tropospheric Ozone Perturbations Induced by Urban Land Expansion in China from 1980 to 2017 X. Zhang et al. 10.1021/acs.est.1c06664
64. Urban Renewal Mapping: A Case Study in Beijing from 2000 to 2020 H. Ni et al. 10.34133/remotesensing.0072
65. Spatial distribution and risk assessments of mercury in topsoils of Central Asia Z. Yang et al. 10.1016/j.gsf.2023.101585
66. A Modified Self-adaptive Method for Mapping Annual 30-m Land Use/Land Cover Using Google Earth Engine: A Case Study of Yangtze River Delta L. Qu et al. 10.1007/s11769-021-1226-4
67. 全球沙漠变化的气候影响 瑶. 陈 et al. 10.1360/SSTe-2022-0128
68. Global desert variation under climatic impact during 1982–2020 Y. Chen et al. 10.1007/s11430-022-1052-1
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70. Implementation of an Improved Water Change Tracking (IWCT) Algorithm: Monitoring the Water Changes in Tianjin over 1984–2019 Using Landsat Time-Series Data X. Han et al. 10.3390/rs13030493
71. Evaluation and analysis of upscaling of different land use/land cover products (FORM-GLC30, GLC_FCS30, CCI_LC, MCD12Q1 and CNLUCC): a case study in China S. He et al. 10.1080/10106049.2022.2127926
72. Regional proximity effects of landscape pattern evolution: Evidence from 325 county-level areas in the middle reaches of the Yangtze River, China H. Wang et al. 10.1016/j.scitotenv.2023.166134
73. Coupling coordinated development of the food-energy-water nexus and its relationship with landscape ecological security in China from a regional perspective J. Zhang & C. Zang 10.1007/s40974-022-00254-5
74. Uncertainty in land use obscures global soil organic carbon stock estimates C. Gang et al. 10.1016/j.agrformet.2023.109585
75. Evaluation of the influence of ENSO on tropical vegetation in long time series using a new indicator Y. Yan et al. 10.1016/j.ecolind.2021.107872
76. An Interannual Transfer Learning Approach for Crop Classification in the Hetao Irrigation District, China Y. Hu et al. 10.3390/rs14051208
77. Fine-scale forest biological hazard in China show significant spatial and temporal heterogeneity Y. Liu et al. 10.1016/j.ecolind.2022.109676
78. Identification and evaluation of soil moisture flash drought by a nonstationary framework considering climate and land cover changes X. Zha et al. 10.1016/j.scitotenv.2022.158953
79. Global land use changes are four times greater than previously estimated K. Winkler et al. 10.1038/s41467-021-22702-2
80. New insights of global vegetation structural properties through an analysis of canopy clumping index, fractional vegetation cover, and leaf area index H. Fang et al. 10.1016/j.srs.2021.100027
81. A Multifactor Weighting Method for Improved Clear View Compositing Using All Available Landsat 8 and Sentinel 2 Images in Google Earth Engine S. Meng et al. 10.34133/remotesensing.0086
82. Spatial resolution enhancement method for Landsat imagery using a Generative Adversarial Network V. Pham & Q. Bui 10.1080/2150704X.2021.1918789
83. Detection and attribution of urbanization impact on summer extreme heat based on nonstationary models in the Yangtze River Delta, China P. Xu et al. 10.1016/j.uclim.2022.101376
84. Optimal parameters of random forest for land cover classification with suitable data type and dataset on Google Earth Engine J. Sun & S. Ongsomwang 10.3389/feart.2023.1188093
85. Time series analysis for global land cover change monitoring: A comparison across sensors L. Xu et al. 10.1016/j.rse.2022.112905
86. Trends in Lakeshore Zone Development: A Comparison of Polish and Hungarian Lakes over 30-Year Period G. Furgała-Selezniow et al. 10.3390/ijerph19042141
87. Estimating forest aboveground biomass using temporal features extracted from multiple satellite data products and ensemble machine learning algorithm Y. Zhang & J. Liu 10.1080/10106049.2022.2153930
88. Rapid Land-Cover and Land-Use Change in the Indo-Malaysian Region over the Last Thirty-Four Years Based on AVHRR NDVI Data Y. He et al. 10.1080/24694452.2022.2077168
89. Reforestation in Southern China Enhances the Convective Afternoon Rainfall During the Post-flood Season X. Li et al. 10.3389/fenvs.2022.942974
90. Crop production response to soil moisture and groundwater depletion in the Nile Basin based on multi-source data Z. Nigatu et al. 10.1016/j.scitotenv.2022.154007
91. Comparison of time-integrated NDVI and annual maximum NDVI for assessing grassland dynamics J. Yan et al. 10.1016/j.ecolind.2022.108611
92. Impact of COVID-19 restrictions on the concentration and source apportionment of atmospheric ammonia (NH3) across India L. Cui 10.1016/j.scitotenv.2023.163443
93. Mapping the presence and distribution of tree species in Canada's forested ecosystems T. Hermosilla et al. 10.1016/j.rse.2022.113276
94. Mapping the annual dynamics of land cover in Beijing from 2001 to 2020 using Landsat dense time series stack S. Xie et al. 10.1016/j.isprsjprs.2022.01.014
95. Land use/cover and land degradation across the Eurasian steppe: Dynamics, patterns and driving factors Z. Du et al. 10.1016/j.scitotenv.2023.168593
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98. Comparison and Assessment of Different Land Cover Datasets on the Cropland in Northeast China P. Cui et al. 10.3390/rs15215134
99. Long-Term Changes of Land Use and Land Cover in the Yangtze River Basin from 1990–2020 Landsat Data J. Yao & S. Jin 10.14358/PERS.22-00050R2
100. Mapping a European Spruce Bark Beetle Outbreak Using Sentinel-2 Remote Sensing Data M. Dalponte et al. 10.3390/rs14133135
101. Assessing spatiotemporal variations and predicting changes in ecosystem service values in the Guangdong–Hong Kong–Macao Greater Bay Area J. Zhang et al. 10.1080/15481603.2021.2022427
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116. Land use and cover changes on the Loess Plateau: A comparison of six global or national land use and cover datasets W. Sun et al. 10.1016/j.landusepol.2022.106165
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137. A global 5 km monthly potential evapotranspiration dataset (1982–2015) estimated by the Shuttleworth–Wallace model S. Sun et al. 10.5194/essd-15-4849-2023
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