85 citations as recorded by crossref.

1. Intensive mining and vegetation greening jointly drive the decline of terrestrial water storage on the loess plateau W. Zheng et al. https://doi.org/10.1080/15481603.2026.2635779
2. Implementation of XGBoost Models for Predicting CO2 Emission and Specific Tractor Fuel Consumption N. Balać et al. https://doi.org/10.3390/agriculture15111209
3. A novel application with explainable machine learning (SHAP and LIME) to predict soil N, P, and K nutrient content in cabbage cultivation T. Abekoon et al. https://doi.org/10.1016/j.atech.2025.100879
4. Spatiotemporal changes of vegetation in the northern foothills of Qinling Mountains based on kNDVI considering climate time-lag effects and human activities L. Chen et al. https://doi.org/10.1016/j.envres.2025.120959
5. Eco-zoning management based on thresholds of NPP driving factors Y. Jiang et al. https://doi.org/10.3389/ffgc.2025.1712019
6. AI models uncover factors influencing scorpionism in Northern Brazil T. Moura et al. https://doi.org/10.1016/j.toxicon.2025.108342
7. Trends, turning points, and driving forces of desertification in global arid land based on the segmental trend method and SHAP model X. Meng et al. https://doi.org/10.1080/15481603.2024.2367806
8. Assessing urban resilience in Northwest China using a resistance-recovery-adaptability framework and explainable machine learning approach Y. Ji et al. https://doi.org/10.1016/j.jenvman.2026.130648
9. eXplainable expert systems for potato tuber yield prediction in the Maritime provinces of Canada M. Jamei et al. https://doi.org/10.1016/j.compag.2025.110831
10. Explanations of Machine Learning Models in Repeated Nested Cross-Validation: An Application in Age Prediction Using Brain Complexity Features R. Scheda & S. Diciotti https://doi.org/10.3390/app12136681
11. An agricultural drought early warning threshold model with considering copula combined with diminishing marginal benefit theory: A case study in the Yellow River basin T. Qian et al. https://doi.org/10.1016/j.agwat.2025.109582
12. Spatiotemporal Dynamics and Predictors of Cropland Change in Hunan Province, China: An XGBoost-SHAP Approach A. Zhou et al. https://doi.org/10.3390/land15071190
13. Prediction and optimization of regional land-use patterns considering nonpoint-source pollution control under conditions of uncertainty Q. Rong et al. https://doi.org/10.1016/j.jenvman.2022.114432
14. Soil physicochemical properties explain land use/cover histories in the last sixty years in China H. Chen et al. https://doi.org/10.1016/j.geoderma.2024.116908
15. Vapor pressure deficit and temperature variability drive future changes to carbon sink stability in China’s terrestrial ecosystems Z. Zhou et al. https://doi.org/10.3389/ffgc.2024.1518578
16. Impacts of Extreme Climate on NDVI in China—Response Patterns and Threshold Effects M. Li et al. https://doi.org/10.3390/rs18132180
17. Spatial Heterogeneity and Nonlinear Differentiation of Ethnic Village Development in Sichuan: A Complex-System-Informed Analysis D. Fan et al. https://doi.org/10.1007/s40980-026-00170-w
18. Changes and Driving Factors of Ecological Environment Quality in the Agro-Pastoral Ecotone of Northern China from 2000 to 2020 S. Yang et al. https://doi.org/10.3390/land14122309
19. Towards a News Recommendation System to increase Reader Engagement through Newsletter Content Personalization E. Fernandes et al. https://doi.org/10.1016/j.procs.2024.06.165
20. Insights into the vulnerability of vegetation to tephra fallouts from interpretable machine learning and big Earth observation data S. Biass et al. https://doi.org/10.5194/nhess-22-2829-2022
21. The application of game theory-based machine learning modelling to assess climate variability effects on the sensitivity of lagoon ecosystem parameters M. Kruk et al. https://doi.org/10.1016/j.ecoinf.2021.101462
22. Optimization of Sensor Combinations for Simplified Estimation of Reference Crop Evapotranspiration Using Machine Learning and SHAP Interpretation Q. Zhang et al. https://doi.org/10.3390/agriculture16010093
23. Assessing drought risk of grassland ecosystem in Hulunbuir, China J. Han et al. https://doi.org/10.1016/j.ecolind.2025.113522
24. Insights from land sparing and land sharing frameworks for land productivity degradation governance in the Yangtze River Delta urban agglomeration, China J. Qian et al. https://doi.org/10.1002/ldr.5219
25. Research and application of XGBoost in imbalanced data P. Zhang et al. https://doi.org/10.1177/15501329221106935
26. Analysis of Key Influencing Factors of Water Quality in Tai Lake Basin Based on XGBoost-SHAP W. Li et al. https://doi.org/10.3390/w17111619
27. Policy mixes for a just, effective, and public budget-conscious household energy transition in Switzerland A. Torné & E. Trutnevyte https://doi.org/10.1016/j.enpol.2025.114872
28. PIXAL: a physics-inspired explainable machine learning architecture for Greenland ice albedo modeling R. Antwerpen et al. https://doi.org/10.5194/tc-20-3131-2026
29. Surgery duration: Optimized prediction and causality analysis O. Babayoff et al. https://doi.org/10.1371/journal.pone.0273831
30. Predicting sepsis in-hospital mortality with machine learning: a multi-center study using clinical and inflammatory biomarkers G. Zhang et al. https://doi.org/10.1186/s40001-024-01756-0
31. Interpretable machine learning to forecast hypoxia in a lagoon D. Politikos et al. https://doi.org/10.1016/j.ecoinf.2021.101480
32. Climate-driven vegetation vulnerability in a monsoon-dominated dryland: a dual-index (kNDVI–VHI) assessment for Pakistan K. Mehmood et al. https://doi.org/10.3389/fenvs.2026.1745938
33. Peripheral blood mononuclear cell derived biomarker detection using eXplainable Artificial Intelligence (XAI) provides better diagnosis of breast cancer S. Kumar & A. Das https://doi.org/10.1016/j.compbiolchem.2023.107867
34. Improving Hospital Outpatient Clinics Appointment Schedules by Prediction Models O. Babayoff et al. https://doi.org/10.1007/s10916-022-01902-3
35. Modelling of fatty acids signatures predicts macroalgal carbon in marine sediments . Erlania et al. https://doi.org/10.1016/j.ecolind.2024.111715
36. Exploring the Spatiotemporal Alterations in China’s GPP Based on the DTEC Model J. Peng et al. https://doi.org/10.3390/rs16081361
37. GRADIENT-BOOSTED CAUSAL INFERENCE FRAMEWORK FOR POLICY RECOMMENDATION IN SMART GOVERNANCE SYSTEMS P. Khairnar https://doi.org/10.29121/shodhkosh.v4.i2.2023.5543
38. Global map of a comprehensive drought/flood index and analysis of controlling environmental factors J. Pang & H. Zhang https://doi.org/10.1007/s11069-022-05673-5
39. A data‐driven approach to improve online consumer subscriptions by combining data visualization and machine learning methods E. Fernandes et al. https://doi.org/10.1111/ijcs.13030
40. Adaptive multi-scale feature refinement for wheat phenology recognition using cross-scale attention mechanisms H. Sun et al. https://doi.org/10.3389/fpls.2026.1730706
41. Partial Order Ranking of the Key Drivers of Grassland Conversion in the Urban–Grassland Interface: A Case Study of the Hohhot–Baotou–Ordos Region X. Li et al. https://doi.org/10.3390/app15115906
42. Controls of groundwater-dependent vegetation coverage in the yellow river basin, china: Insights from interpretable machine learning T. Bai et al. https://doi.org/10.1016/j.jhydrol.2024.130747
43. Local climate zone framework: seasonal dynamics of surface urban heat island and its influencing factors in three Chinese urban agglomerations H. Deng et al. https://doi.org/10.1080/15481603.2025.2490317
44. Evaluating the seasonal impact of urbanisation on surface air temperature in Beijing over three decades: 1990s, 2010s, and 2030s L. Vitanova et al. https://doi.org/10.1088/2515-7620/adc908
45. Effects of climate change and human activities on vegetation coverage change in northern China considering extreme climate and time-lag and -accumulation effects M. Ma et al. https://doi.org/10.1016/j.scitotenv.2022.160527
46. Using explainable machine learning methods to evaluate vulnerability and restoration potential of ecosystem state transitions J. Delaney & D. Larson https://doi.org/10.1111/cobi.14203
47. Enhanced Solar Power Prediction Models With Integrating Meteorological Data Toward Sustainable Energy Forecasting M. Atiea et al. https://doi.org/10.1155/er/8022398
48. Precision under pressure: leveraging interpretable AI and machine learning to predict multifactorial abiotic stress in climate-smart crops L. Abhishek et al. https://doi.org/10.1007/s13562-026-01064-0
49. Explaining the contrast responses of evergreen needle-leaved forest and grassland carbon fluxes during the growing season droughts over the North Temperate Zone X. Li et al. https://doi.org/10.1016/j.ecolind.2025.114016
50. WBGT prediction with high spatial resolution using actual measurement data and data acquired using infrared sensors mounted on UAVs H. Niwa & R. Manabe https://doi.org/10.1016/j.scs.2024.105470
51. Shrub dendrochronology reveals the last 150 years of spatiotemporal patterns of desertification in the Tengger Desert Y. Ren et al. https://doi.org/10.1016/j.catena.2026.109844
52. MODIS-Based Spatiotemporal Inversion and Driving-Factor Analysis of Cloud-Free Vegetation Cover in Xinjiang from 2000 to 2024 H. Yang et al. https://doi.org/10.3390/s25082394
53. The Modèle Atmosphérique Régional – Intelligence Artificielle (MAR-IA): surface meltwater over Greenland M. Tedesco et al. https://doi.org/10.5194/tc-20-4235-2026
54. Topographic and economic drivers of agricultural–ecological coupling in Gansu Province: spatial differentiation and threshold effects B. Xie et al. https://doi.org/10.1016/j.ecolind.2026.115463
55. Assessing multi-year-drought vulnerability in dense Mediterranean-climate forests using water-balance-based indicators G. Cui et al. https://doi.org/10.1016/j.jhydrol.2022.127431
56. Using an Explainable Machine Learning Approach to Characterize Earth System Model Errors: Application of SHAP Analysis to Modeling Lightning Flash Occurrence S. Silva et al. https://doi.org/10.1029/2021MS002881
57. Integrating Remote Sensing, Machine Learning, and Degree-Day Models for Predicting Grasshopper Habitat Suitability in Temperate Grasslands R. Ahmed et al. https://doi.org/10.3390/rs17243955
58. A machine learning framework for predicting agricultural terrace suitability using positive and unlabelled datasets: an application for the Troodos Mountains, Cyprus A. Meena et al. https://doi.org/10.1016/j.jag.2026.105454
59. Filling-well: An effective technique to handle incomplete well-log data for lithology classification using machine learning algorithms S. Garini et al. https://doi.org/10.1016/j.mex.2024.103127
60. Integrating land use dynamics and human health risk: A multi-scale assessment of future urban heat exposure using PLUS and explainable AI Y. Zhu et al. https://doi.org/10.1016/j.jenvman.2026.128586
61. Modeling and Evaluation of the Permeate Flux in Forward Osmosis Process with Machine Learning F. Shi et al. https://doi.org/10.1021/acs.iecr.2c03064
62. Influential factors for the emission inspection results of urban in-use vehicles: From an ensemble learning perspective Q. Zhimei et al. https://doi.org/10.1080/10962247.2022.2035851
63. Biological response of Rhopalosiphum padi and Sipha flava (Hemiptera: Aphididae) changes over generations A. Auad et al. https://doi.org/10.1515/flaent-2024-0088
64. Multi-scale response patterns of texture features derived from high-resolution remote sensing imagery in relation to stand spatial structure in coniferous–broadleaf mixed forests F. Yang et al. https://doi.org/10.1016/j.biombioe.2026.109976
65. Predicting Pediatric Asthma Readmissions Through Machine Learning: Performance With a National Administrative Database J. Gabbay et al. https://doi.org/10.1002/ppul.71624
66. Attribution of vegetation dynamics using an interpretable machine learning model in the Haihe River Basin, China Q. Wang et al. https://doi.org/10.1016/j.ecofro.2025.05.022
67. Improving interpretation of sea-level projections through a machine-learning-based local explanation approach J. Rohmer et al. https://doi.org/10.5194/tc-16-4637-2022
68. Polarization of dryland vegetation response to spring flood S. Zhang et al. https://doi.org/10.1016/j.jenvman.2025.126175
69. A Physically Driven Interpretable Machine Learning Framework for Early Forecasting of Summer Hypoxia in the Semi-Enclosed Bohai Sea Using Remote Sensing Data Y. Jin et al. https://doi.org/10.3390/rs18071097
70. The counteracting effects of large-scale vegetation restoration and increased precipitation on drought in the Huang-Huai-Hai-Yangtze River basin M. Ma et al. https://doi.org/10.1016/j.jhydrol.2023.129733
71. A machine learning approach to map the potential agroecological complexity in an indigenous community of Colombia C. Ojeda Riaños et al. https://doi.org/10.1016/j.jenvman.2024.122655
72. Modeling the Subpixel Land-Use Dynamics and Its Influence on Urban Heat Islands: Impacts of Factors and Scale, and Population Exposure Risk X. Liang et al. https://doi.org/10.1016/j.scs.2024.105417
73. Optimizing Sentinel-2 temporal composites for soil organic carbon mapping and cropland management insights X. Ji et al. https://doi.org/10.1016/j.eti.2026.104971
74. Characteristics and Drivers of Vegetation Change in Xinjiang, 2000–2020 G. Li et al. https://doi.org/10.3390/f15020231
75. Clustering and Interpretation of time-series trajectories of chronic pain using evidential c-means A. Soubeiga et al. https://doi.org/10.1016/j.eswa.2024.125369
76. Quantifying soil wind erosion attribution in Inner Mongolia’s desert grassland H. Jiang et al. https://doi.org/10.1038/s41598-025-98339-8
77. Trade‐Offs Between Carbon and Water Fluxes Along a Land Use Intensity Gradient in Southeast Asian Forests and Plantations B. Hanggara et al. https://doi.org/10.1111/gcb.70753
78. An artificial intelligence-based assessment of soil erosion probability indices and contributing factors in the Abha-Khamis watershed, Saudi Arabia S. Alqadhi et al. https://doi.org/10.3389/fevo.2023.1189184
79. Dynamics and Drivers of Ecosystem Service Values in the Qionglai–Daxiangling Region of China’s Giant Panda National Park (1990–2020) Y. Chen et al. https://doi.org/10.3390/systems13090807
80. Comparison of vegetation succession patterns in the monsoon and arid regions of northern China over the past two centuries: Implications for ecosystem restoration projects J. Pang et al. https://doi.org/10.1016/j.palaeo.2025.113295
81. Climate change enhances greening while human activities accelerate degradation in northern China's grasslands F. Cao et al. https://doi.org/10.1016/j.scitotenv.2025.178570
82. Monitoring of Farmland Abandonment Based on Google Earth Engine and Interpretable Machine Learning Y. Jiang et al. https://doi.org/10.3390/agriculture15192090
83. Hybrid Intelligent Model for Estimating the Cost of Huizhou Replica Traditional Vernacular Dwellings J. Huang et al. https://doi.org/10.3390/buildings14092623
84. How does the ambient environment respond to the industrial heat island effects? An innovative and comprehensive methodological paradigm for quantifying the varied cooling effects of different landscapes J. Gao et al. https://doi.org/10.1080/15481603.2022.2127463
85. Climate-induced tree-mortality pulses are obscured by broad-scale and long-term greening Y. Yan et al. https://doi.org/10.1038/s41559-024-02372-1