116 citations as recorded by crossref.

1. Quarterly cropland mapping in sample shortage regions using Sentinel-2 images based on the knowledge-driven OCSVM-RF R. Wang et al. https://doi.org/10.1016/j.atech.2025.101572
2. Spatially divergent cropland disturbance and driving mechanisms in the transboundary Heilongjiang River basin, 1990–2021 J. Jiang et al. https://doi.org/10.1016/j.ecolind.2026.115058
3. Crop spatial concentration enhances water productivity while threatening regional agricultural water sustainability H. Huang et al. https://doi.org/10.1016/j.jhydrol.2026.135693
4. Spatio-Temporal Pattern of Cropland Abandonment in Hilly and Mountainous Areas: A Case Study of Jiangxi, China W. Jiaming & X. Hualin https://doi.org/10.5814/j.issn.1674-764x.2024.05.018
5. Large-Scale High-Resolution Essential Urban Land Cover Category Mapping Using a Semantic-Augmented and Noise-Tolerant Approach Z. Li et al. https://doi.org/10.1109/TGRS.2025.3601626
6. A framework to identify winter fallow arable lands through machine learning methods D. Li et al. https://doi.org/10.1016/j.asr.2026.02.086
7. Reserve asset or empty promise? Revealing the role of new croplands for improving food production potential in China H. Chen et al. https://doi.org/10.1016/j.resconrec.2025.108675
8. Fusing very high-resolution aerial imagery with Sentinel-1 and Sentinel-2 time series improves multi-year orchard type classification L. Fine et al. https://doi.org/10.1016/j.jag.2026.105495
9. DFGNet: A CropLand Change Detection Network Combining Deformable Convolution and Grouped Residual Self-Attention X. Feng & X. Liu https://doi.org/10.3390/app152413133
10. A Landsat-Based Framework for Long-Term Mapping of Topsoil Sand Content in Croplands H. Wang et al. https://doi.org/10.3390/rs18091303
11. Spatial-temporal assessment and optimization of ecological cropland utilization: A case study of China's Huang-Huai-Hai region L. Yang et al. https://doi.org/10.1016/j.eiar.2026.108383
12. The Accuracy, Spatial Consistency, and Impact Factors of Global Cropland Products in Karst Landscapes: A Case Study of the Yunnan–Guizhou Plateau Y. Xia et al. https://doi.org/10.3390/land15020343
13. <bold>2001~2023</bold>年中国植被持续变绿 善. 尉 et al. https://doi.org/10.1360/N072024-0239
14. Incorporating the Number of Patches into an Integrated Land Use Optimization Framework: Toward Sustainable Land Use Configurations in Urbanizing Basins Y. Liu et al. https://doi.org/10.3390/su17219810
15. Annual Cropping Intensity Dynamics in China from 2001 to 2023 J. Ren et al. https://doi.org/10.3390/rs16244801
16. Cropland abandonment under the socio-economic changes on the Loess Plateau from 2000 to 2020 C. Ge et al. https://doi.org/10.1016/j.indic.2025.101013
17. Investigating the ability of state-of-the-art remote sensing datasets in capturing the spatiotemporal patterns of cropland area: Implications for agricultural monitoring in the largest river basin of China’s arid region Z. Liu et al. https://doi.org/10.1016/j.atech.2025.101580
18. A phenology-weighted cross-correlation method for long-term monitoring of non-grain cultivation expansion in the Guanzhong Plain, China R. Jia et al. https://doi.org/10.1080/10106049.2025.2610042
19. Increasing Cropland Area and Its Associated Human-Induced Water Consumption Put Ebinur Lake at Risk of Drying Up H. Zeng et al. https://doi.org/10.34133/remotesensing.0496
20. Indirect Urbanization Impacts Shape Vegetation Growth and Carbon Sink Dynamics: Synergies and Trade-Offs across China B. Wu et al. https://doi.org/10.1021/acs.est.5c11267
21. TWDTW-Based Maize Mapping Using Optimal Time Series Features of Sentinel-1 and Sentinel-2 Images H. Yan et al. https://doi.org/10.3390/rs17173113
22. Impact of Construction Land Transition on Staple Crop Diversity in China X. Yao et al. https://doi.org/10.1002/ldr.70503
23. Advancing sustainable agricultural: A novel framework for mapping annual 30-m resolution national-level irrigated croplands E. Du et al. https://doi.org/10.1016/j.jag.2025.104589
24. Opposite effect on soil organic carbon between grain and non-grain crops: Evidence from Main Grain Land, China S. Liu et al. https://doi.org/10.1016/j.agee.2024.109364
25. Spatiotemporal Differentiation of Fertilizer and Pesticide Use and Its Driving Factors in the Yangtze River Delta of China: An Analysis at the County Scale K. Wu & C. Chen https://doi.org/10.3390/land14061180
26. Revealing remote sensing blind spots in human-driven land transitions: a systematic evaluation of state-of-the-art land use and land cover datasets on the Tibetan Plateau J. Ye et al. https://doi.org/10.1080/20964471.2025.2589642
27. Declining Ecological Water Consumption of Marsh Wetlands and the Driving Forces in Semi-Arid Plateau Region: A Case Study in the Bashang Plateau, China C. Li et al. https://doi.org/10.3390/land15030450
28. OasisMap30: a 30 m annual land cover dataset of China's oases from 1987 to 2024 P. Chen et al. https://doi.org/10.5194/essd-18-3289-2026
29. Decoupling temporal groundwater depletion drivers using partial least squares structural equation modelling A. Yawe et al. https://doi.org/10.1016/j.pce.2026.104271
30. Underestimated shrubland distribution in the Eurasian drylands L. Liu et al. https://doi.org/10.1016/j.jag.2026.105144
31. Multi-source assessment of permafrost deformation along the Bei'an–Hei'he highway in Northeast China A. Yan et al. https://doi.org/10.1016/j.rse.2025.115143
32. Mapping Land-Cover Dynamics in Arid Regions Using Spectral Mixture Analysis and Representative Training Samples Y. Cui et al. https://doi.org/10.1109/TGRS.2024.3472080
33. Monitoring the 2024 Abrupt Flood Event in East Dongting Lake via Deep Learning and Multisource Remote Sensing Data Y. Xiao et al. https://doi.org/10.1109/JSTARS.2026.3653452
34. Optimizing coordination and trade‐offs between food security and biodiversity conservation goals W. Chen et al. https://doi.org/10.1002/ecog.07939
35. Spatiotemporal Heterogeneity and Zonal Adaptation Strategies for Agricultural Risks of Compound Dry and Hot Events in China’s Middle Yangtze River Basin Y. Wang et al. https://doi.org/10.3390/rs17162892
36. Mapping 10-m Plastic Greenhouses of China Based on Multisource Remote Sensing Products X. Zhang et al. https://doi.org/10.1109/JSTARS.2025.3625292
37. An Increasing Misalignment Between Crop Distribution and Environmental Resources Under Climate Change in China Z. Su et al. https://doi.org/10.1002/advs.202516290
38. Accounting for consumption-based cultivated land use in China: A structural driver-pathway framework S. Guo et al. https://doi.org/10.1016/j.ecolmodel.2025.111199
39. A large-scale image–text dataset benchmark for farmland segmentation C. Tao et al. https://doi.org/10.5194/essd-17-4835-2025
40. A method of measuring soil moisture by piezoelectric sensors based on manifold learning and wavelet analysis X. Li et al. https://doi.org/10.1016/j.jdd.2025.100031
41. Cross-sensor cropland mapping: evaluating Chinese satellites and Sentinel-2 in the Nile Delta Z. Lou et al. https://doi.org/10.1080/17538947.2025.2562054
42. Unpacking spatially explicit cropland non-agriculturalization in China based on satellite remote sensing data: A 30-year analysis of spatial patterns and conversion types D. Jiang et al. https://doi.org/10.1016/j.landusepol.2026.108137
43. Impact of cropland use changes based on non-agriculturalization, non-grainization and abandonment on grain potential production in Northeast China L. Pu https://doi.org/10.1038/s41598-025-09205-6
44. Construction of nitrogen critical dilution curve and differences in nitrogen nutrition characteristics in different organs of maize varieties with contrasting nitrogen efficiencies S. Bao et al. https://doi.org/10.3389/fpls.2025.1577962
45. Mapping complex cropping patterns in China (2018–2021) at 10 m resolution: a data-driven framework based on multi-product integration and Google satellite embedding X. Li & L. Yu https://doi.org/10.5194/essd-18-5601-2026
46. Agricultural Drought Dynamics With Satellite Soil Moisture and Impacts on Crop Loss in China During 2003–2022 Y. Gu et al. https://doi.org/10.1109/JSTARS.2025.3572312
47. Long-Term Spatiotemporal Information Extraction of Cultivated Land in the Nomadic Area: A Case Study of the Selenge River Basin Y. Sun et al. https://doi.org/10.3390/rs17121970
48. A 10-m resolution dataset of plastic-mulched farmland distributions on the Chinese Loess Plateau (2019–2021) C. Zhao et al. https://doi.org/10.1038/s41597-025-06304-x
49. Integrating phenological time-series and machine learning for remote sensing crop classification: a case study in the Hetao Plain, China Y. Xing et al. https://doi.org/10.3389/fagro.2026.1797361
50. Diminishing returns and rising ecological costs of cropland reclamation in China’s four northern provinces J. Yin et al. https://doi.org/10.1038/s44458-026-00089-9
51. Dynamics of Cropland Non-Agriculturalization in Shaanxi Province of China and Its Attribution Using a Machine Learning Approach H. Yan et al. https://doi.org/10.3390/land14010190
52. Drivers of agricultural ecosystem resilience under compound atmospheric-soil drought in Northeast China L. Wang et al. https://doi.org/10.1016/j.ecolind.2025.114525
53. A Global Review of Monitoring Cropland Abandonment Using Remote Sensing: Temporal–Spatial Patterns, Causes, Ecological Effects, and Future Prospects T. Liu et al. https://doi.org/10.34133/remotesensing.0584
54. Land surface energy balance modeling over rotational croplands: The added value of remotely sensed latent heat-leaf area coupling W. Lv et al. https://doi.org/10.1016/j.rse.2026.115415
55. The 30 m land cover dataset for capturing land cover changes induced by ecological restoration from 1990 to 2022 on the Chinese Loess Plateau Z. Wang et al. https://doi.org/10.1038/s41597-025-04575-y
56. The dynamic evaluation of cropland resource reserves in Mid-inner Zhejiang under urban network development S. Zhang et al. https://doi.org/10.1080/22797254.2026.2662672
57. Spatiotemporal patterns and driving factors of ecological restoration regions: Insights from multi-scale analysis Y. Zhang et al. https://doi.org/10.1016/j.eiar.2025.108020
58. A 30 m resolution dataset of soil and water conservation terraces across China for 2000, 2010, and 2020 E. Zhang et al. https://doi.org/10.5194/essd-17-6315-2025
59. Spatio–temporal variations and driving forces of wind erosion and sand stabilization in the middle Heihe River Basin Y. Liu et al. https://doi.org/10.1080/15324982.2026.2628718
60. A 10-meter resolution dataset of abandoned and reclaimed cropland from 2016 to 2023 in Inner Mongolia, China D. Wuyun et al. https://doi.org/10.1038/s41597-025-04614-8
61. Groundwater Level Response Processes in Arid Northwest China Based on Remote Sensing and Causal Inference: From Influential Variables to Transmission Pathways L. Zeng & S. Chen https://doi.org/10.3390/rs18091378
62. 1 km annual forest cover and plant functional type dataset for China from 1981 to 2023 B. Liu et al. https://doi.org/10.5194/essd-18-1103-2026
63. Increasing proportion of mildly aged population in rural mitigates farmland abandonment in the farming-pastoral ecotone of northern China Y. Jin et al. https://doi.org/10.1371/journal.pone.0328483
64. Spatiotemporal dynamics and food security implications of cropland abandonment in China Z. Han et al. https://doi.org/10.1016/j.jia.2025.09.001
65. Spatial distribution dataset of 30-m resolution cropland in agricultural regions, Qinghai-Tibet Plateau X. Xia et al. https://doi.org/10.1038/s41597-025-05841-9
66. Assessing the Consistency of Five Remote Sensing-Based Land Cover Products for Monitoring Cropland Changes in China F. Deng et al. https://doi.org/10.3390/rs16234498
67. An improved optical trapezoid model to identify wetlands and irrigation water ponds in the arid irrigation district using Google Earth Engine X. Chen et al. https://doi.org/10.1080/15481603.2024.2422930
68. Design and multi-objective optimization of an active attitude adjustment mechanism for side-slope agricultural vehicles Y. Fan et al. https://doi.org/10.1177/09544070261439199
69. Knowledge-informed cascaded sampling for corn mapping across heterogeneous growing environments G. Xiao et al. https://doi.org/10.1016/j.jag.2026.105259
70. The impacts of climate variability and agricultural expansion on ecosystem functions in Xinjiang drylands D. Zhai et al. https://doi.org/10.3389/frsen.2026.1815646
71. Spatiotemporal Variations of Cropland Quality and Morphology Under the Requisition–Compensation Balance Policy Z. Lin et al. https://doi.org/10.3390/land14061235
72. CropLayer: a 2 m resolution cropland map of China for 2020 from Mapbox and Google satellite imagery H. Jiang et al. https://doi.org/10.5194/essd-17-6703-2025
73. 30 m-resolution annual crop type maps in Northeast China from 2001 to 2022 Y. Di et al. https://doi.org/10.1038/s41597-025-06516-1
74. A Full-Life-Cycle Modeling Framework for Cropland Abandonment Detection Based on Dense Time Series of Landsat-Derived Vegetation and Soil Fractions Q. Sun et al. https://doi.org/10.3390/rs17132193
75. A 30 m annual cropland dataset of China from 1986 to 2021 Y. Tu et al. https://doi.org/10.5194/essd-16-2297-2024
76. Mapping cropland use changes in the Guanzhong Plain of China based on a two-level classification framework W. Zhang et al. https://doi.org/10.1080/01431161.2025.2496528
77. Research on the Influencing Factors of the Cropland Abandonment Behavior of Different Typical Types of Farming Households: Based on a Survey in Mountainous Areas Y. Feng et al. https://doi.org/10.3390/land14102057
78. ALCC: Temporally Consistent Annual Land Cover Maps over China from 1985 to 2022 Based on an Ensemble Change Detection Method B. Li et al. https://doi.org/10.34133/remotesensing.1029
79. A data-driven approach to estimate leaf area index for Landsat images over China H. Li et al. https://doi.org/10.1080/22797254.2026.2654470
80. Releasing the compound potential of abandoned cropland through recultivation, afforestation, and photovoltaic solutions T. Liu et al. https://doi.org/10.1016/j.resconrec.2025.108760
81. Mapping irrigated cropland at 30 m spatial resolution in northern China over the past three decades L. Li et al. https://doi.org/10.1080/15481603.2025.2563394
82. Optimising the geometry of protected areas to mitigate exposure risk from anthropogenic pressure in urbanising regions J. Song et al. https://doi.org/10.1016/j.eiar.2026.108469
83. Structural Transformation or Crisis? The Dynamics of Cultivated Land Abandonment and Reuse in China’s Rural Development, 1992–2022 B. Guo et al. https://doi.org/10.3390/land15071244
84. Monitoring of Farmland Abandonment Based on Google Earth Engine and Interpretable Machine Learning Y. Jiang et al. https://doi.org/10.3390/agriculture15192090
85. Consistency and divergence in drivers of soil erosion risk: Insights from heterogeneous watersheds Z. Qi et al. https://doi.org/10.1016/j.eiar.2025.108068
86. CANSD v1.0: the first map of agricultural nitrogen surplus across China from 2000 to 2022 Y. Li et al. https://doi.org/10.1038/s41597-026-07165-8
87. Unmasking human-subsidized resilience through hydrological drivers of water use efficiency during compound droughts across the North China Plain K. Rahman et al. https://doi.org/10.1016/j.ejrh.2026.103394
88. A Comprehensive Review of Agricultural Parcel and Boundary Delineation From Remote Sensing Images: Recent progress and future perspectives J. Zheng et al. https://doi.org/10.1109/MGRS.2026.3658493
89. Evaluation of cropland utilization efficiency and analysis of risk mechanisms under abandonment patterns in the Guanzhong region T. Zhou et al. https://doi.org/10.1016/j.jenvman.2025.128348
90. Multi-Dimensional Assessment, Regional Differences, and Influencing Factors of Agricultural Water Pollution from the Perspective of Grey Water Footprint in Zhejiang Province, China H. Zhu et al. https://doi.org/10.3390/agriculture14112031
91. Mapping local energy zones in China to support urban-scale energy-carbon research R. Wang et al. https://doi.org/10.1016/j.buildenv.2026.114653
92. Orchard plantation mapping using remote sensing phenological feature fusion and interpretable ML algorithms in Hunan Province, China Y. Liu et al. https://doi.org/10.1016/j.ecoinf.2026.103628
93. Massive hidden cropland transitions undermine China's agricultural sustainability N. Fang et al. https://doi.org/10.1016/j.landusepol.2026.108199
94. Optimising county-level farmland allocation among crops in Northeast China under climate change and adaptive practices Y. Sun et al. https://doi.org/10.1016/j.agsy.2026.104885
95. Spatiotemporal trends and driving forces of pig production in China and the United States Y. Zhao et al. https://doi.org/10.1016/j.ecz.2025.100036
96. Sustainable Agricultural Development in China: An Empirical Analysis of Temporal and Spatial Evolution, Regional Differences, and Convergence Mechanisms Z. Zhang et al. https://doi.org/10.3390/land15040567
97. Slope grade difference of cropland spatial pattern in China P. Zhu et al. https://doi.org/10.1007/s11629-025-9890-y
98. Multi-Decadal Remote Sensing of Crop Planting Structure and Surface Water Dynamics in the Ningxia Plain: Drivers and Scale-Dependent Responses C. Jiang & X. Song https://doi.org/10.3390/w18080978
99. HSRRSFPD and MEGRDNet: empowering high-resolution farmland parcel mapping in complex terrains Y. Yan et al. https://doi.org/10.1016/j.jag.2026.105346
100. Monitoring of Cropland Abandonment Integrating Machine Learning and Google Earth Engine—Taking Hengyang City as an Example Y. Jiang & Z. Guo https://doi.org/10.3390/land14101984
101. Discrepancies in cropland mapping and their implications for biodiversity conservation in China C. Ou & Y. Liu https://doi.org/10.1016/j.srs.2025.100332
102. Geographical Zoning-Based Classification of Agricultural Land Use in Hilly and Mountainous Areas Using High-Resolution Remote Sensing Images J. Zhang et al. https://doi.org/10.3390/rs18081259
103. From urban to nature: A continuous landscape gradient mapping framework in Southwest China J. Fang et al. https://doi.org/10.1016/j.jenvman.2025.128037
104. Advancing the earliest identifiable timing of cotton fields by the unique mechanized plastic-mulching practices in Xinjiang R. Zeng et al. https://doi.org/10.1016/j.rse.2026.115416
105. Desertification Mitigation in Northern China Was Promoted by Climate Drivers after 2000 H. Li et al. https://doi.org/10.3390/rs16193706
106. Digital financial inclusion and rural labor allocation: The roles of credit and land markets H. Ye et al. https://doi.org/10.1016/j.eap.2026.02.003
107. Accelerating increases in soil heatwaves and cropland exposure in China L. Lin et al. https://doi.org/10.1088/1748-9326/ae2ca5
108. Pixel-wise parameter assignment in LandTrendr algorithm: Enhancing cropland abandonment monitoring using satellite-based NDVI time-series D. Wuyun et al. https://doi.org/10.1016/j.compag.2024.109541
109. A 1 km datasets for urban–suburban–rural–natural continuous gradient landscapes in Southwest China J. Fang et al. https://doi.org/10.1038/s41597-025-06266-0
110. Two decades of persistent greening in China despite 2023 climate extremes S. Wei et al. https://doi.org/10.1007/s11430-024-1530-7
111. The dataset of main grain land changes in China over 1985–2020 S. Liu et al. https://doi.org/10.1038/s41597-024-04292-y
112. Irrigated agriculture on the southern and northern slopes of the Tianshan Mountains faces different risks of water scarcity Z. Yang et al. https://doi.org/10.1016/j.accre.2025.10.009
113. Impact of the Auditing of Natural Resource Assets on Farmland Protection T. Yu et al. https://doi.org/10.3390/land15030396
114. Annual cropland change detection in arid areas with long time series remote sensing images: A case study of the economic belt of the north Tianshan Mountains, Xinjiang Province, China Z. Wang et al. https://doi.org/10.1016/j.jaridenv.2025.105522
115. Hcropland30: A hybrid 30-m global cropland map generated by leveraging global land cover products and Landsat data via deep learning Q. Hu et al. https://doi.org/10.1016/j.rse.2026.115552
116. Integrating horizontal-vertical heterogeneity and environmental factors to unravel heavy metal distributions and phytoaccumulation in a soil-maize system W. Liu et al. https://doi.org/10.1016/j.jhazmat.2026.142382