77 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. 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
9. 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
10. 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
11. 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
12. 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
13. 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
14. 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
15. 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
16. 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
17. 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
18. 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
19. Assessing drought risk of grassland ecosystem in Hulunbuir, China J. Han et al. https://doi.org/10.1016/j.ecolind.2025.113522
20. 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
21. Research and application of XGBoost in imbalanced data P. Zhang et al. https://doi.org/10.1177/15501329221106935
22. 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
23. 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
24. 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
25. Surgery duration: Optimized prediction and causality analysis O. Babayoff et al. https://doi.org/10.1371/journal.pone.0273831
26. 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
27. Interpretable machine learning to forecast hypoxia in a lagoon D. Politikos et al. https://doi.org/10.1016/j.ecoinf.2021.101480
28. 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
29. 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
30. Improving Hospital Outpatient Clinics Appointment Schedules by Prediction Models O. Babayoff et al. https://doi.org/10.1007/s10916-022-01902-3
31. Modelling of fatty acids signatures predicts macroalgal carbon in marine sediments . Erlania et al. https://doi.org/10.1016/j.ecolind.2024.111715
32. Exploring the Spatiotemporal Alterations in China’s GPP Based on the DTEC Model J. Peng et al. https://doi.org/10.3390/rs16081361
33. GRADIENT-BOOSTED CAUSAL INFERENCE FRAMEWORK FOR POLICY RECOMMENDATION IN SMART GOVERNANCE SYSTEMS P. Khairnar https://doi.org/10.29121/shodhkosh.v4.i2.2023.5543
34. 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
35. 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
36. 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
37. 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
38. 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
39. 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
40. 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
41. 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
42. 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
43. Enhanced Solar Power Prediction Models With Integrating Meteorological Data Toward Sustainable Energy Forecasting M. Atiea et al. https://doi.org/10.1155/er/8022398
44. 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
45. 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
46. 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
47. 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
48. 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
49. 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
50. 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
51. 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
52. 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
53. 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
54. 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
55. 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
56. 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
57. Predicting Pediatric Asthma Readmissions Through Machine Learning: Performance With a National Administrative Database J. Gabbay et al. https://doi.org/10.1002/ppul.71624
58. 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
59. 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
60. Polarization of dryland vegetation response to spring flood S. Zhang et al. https://doi.org/10.1016/j.jenvman.2025.126175
61. 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
62. 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
63. 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
64. 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
65. 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
66. Characteristics and Drivers of Vegetation Change in Xinjiang, 2000–2020 G. Li et al. https://doi.org/10.3390/f15020231
67. 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
68. Quantifying soil wind erosion attribution in Inner Mongolia’s desert grassland H. Jiang et al. https://doi.org/10.1038/s41598-025-98339-8
69. 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
70. 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
71. 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
72. 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
73. 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
74. Monitoring of Farmland Abandonment Based on Google Earth Engine and Interpretable Machine Learning Y. Jiang et al. https://doi.org/10.3390/agriculture15192090
75. Hybrid Intelligent Model for Estimating the Cost of Huizhou Replica Traditional Vernacular Dwellings J. Huang et al. https://doi.org/10.3390/buildings14092623
76. 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
77. 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