64 citations as recorded by crossref.

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4. Spatial prediction of permafrost occurrence in Sikkim Himalayas using logistic regression, random forests, support vector machines and neural networks P. Baral & M. Haq 10.1016/j.geomorph.2020.107331
5. UAV data and deep learning: efficient tools to map ant mounds and their ecological impact J. Monsimet et al. 10.1002/rse2.400
6. Spatial Prediction of Soil Organic Carbon Stock in the Moroccan High Atlas Using Machine Learning M. Meliho et al. 10.3390/rs15102494
7. Modeling the Spatial Distribution of Soil Organic Carbon and Carbon Stocks in the Casanare Flooded Savannas of the Colombian Llanos J. Martín-López et al. 10.1007/s13157-023-01705-3
8. The Spatial Analysis of Vegetation Cover and Permafrost Degradation for a Subarctic Palsa Mire Based on UAS Photogrammetry and GPR Data in the Kola Peninsula N. Krutskikh et al. 10.3390/rs15071896
9. Deep learning approaches in remote sensing of soil organic carbon: a review of utility, challenges, and prospects O. Odebiri et al. 10.1007/s10661-021-09561-6
10. Iron mineral dissolution releases iron and associated organic carbon during permafrost thaw M. Patzner et al. 10.1038/s41467-020-20102-6
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12. Determining Subarctic Peatland Vegetation Using an Unmanned Aerial System (UAS) M. Palace et al. 10.3390/rs10091498
13. Estimating Ecosystem Respiration in the Grasslands of Northern China Using Machine Learning: Model Evaluation and Comparison X. Zhu et al. 10.3390/su12052099
14. Small-Scale Variability of Soil Quality in Permafrost Peatland of the Great Hing’an Mountains, Northeast China X. Wang et al. 10.3390/w14172597
15. Comparing machine learning algorithms for predicting and digitally mapping surface soil available phosphorous: a case study from southwestern Iran S. Hojati et al. 10.1007/s11119-023-10099-5
16. Spatial models with covariates improve estimates of peat depth in blanket peatlands D. Young et al. 10.1371/journal.pone.0202691
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18. Mitigation of greenhouse gas emissions and improved yield by plastic mulching in rice production H. Gao et al. 10.1016/j.scitotenv.2023.162984
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21. Mapping the yields of lignocellulosic bioenergy crops from observations at the global scale W. Li et al. 10.5194/essd-12-789-2020
22. Soil organic and phytomass carbon stocks in mountain periglacial settings of Vindelfjällen (Sweden) C. Fröjd et al. 10.1080/15230430.2024.2366333
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24. Towards a Monitoring Approach for Understanding Permafrost Degradation and Linked Subsidence in Arctic Peatlands B. de la Barreda-Bautista et al. 10.3390/rs14030444
25. Improved soil carbon stock spatial prediction in a Mediterranean soil erosion site through robust machine learning techniques H. Mosaid et al. 10.1007/s10661-024-12294-x
26. Digital Mapping of Soil Organic Carbon Using UAV Images and Soil Properties in a Thermo-Erosion Gully on the Tibetan Plateau M. Ding et al. 10.3390/rs15061628
27. Developing a digital mapping of soil organic carbon on a national scale using Sentinel-2 and hybrid models at varying spatial resolutions X. Ji et al. 10.1016/j.ecolind.2024.112654
28. Mapping soil organic carbon in northern France using adaptive zoning regression kriging based on LUCAS dataset X. Wang et al. 10.1080/10106049.2024.2379842
29. High-resolution broad-scale mapping of soil parent material using object-based image analysis (OBIA) of LiDAR elevation data A. Prince et al. 10.1016/j.catena.2019.104422
30. Relationships between above‐ground plant traits and carbon cycling in tundra plant communities K. Happonen et al. 10.1111/1365-2745.13832
31. Rhizosphere allocation by canopy‐forming species dominates soil CO2 efflux in a subarctic landscape T. Parker et al. 10.1111/nph.16573
32. Optical and radar Earth observation data for upscaling methane emissions linked to permafrost degradation in sub-Arctic peatlands in northern Sweden S. Sjögersten et al. 10.5194/bg-20-4221-2023
33. Permafrost Causes Unique Fine‐Scale Spatial Variability Across Tundra Soils M. Siewert et al. 10.1029/2020GB006659
34. Spatial modeling of soil organic carbon using remotely sensed indices and environmental field inventory variables A. Katebikord et al. 10.1007/s10661-022-09842-8
35. Leveraging the application of Earth observation data for mapping cropland soils in Brazil J. Safanelli et al. 10.1016/j.geoderma.2021.115042
36. Insights of Machine Learning Approach for Soil Fertility Assessment and Management Strategy S. Muthulakshmi et al. 10.1080/00103624.2024.2416920
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38. Inversion of soil organic carbon content based on the two-point machine learning method C. Wang et al. 10.1016/j.scitotenv.2024.173608
39. Soil organic carbon mapping in cultivated land using model ensemble methods L. Wang et al. 10.1080/03650340.2021.1925651
40. Environmental spaces for palsas and peat plateaus are disappearing at a circumpolar scale O. Leppiniemi et al. 10.5194/tc-17-3157-2023
41. VIS-NIR spectroscopy and environmental factors coupled with PLSR models to predict soil organic carbon and nitrogen J. Zhu et al. 10.1016/j.iswcr.2024.02.001
42. Mapping soil organic carbon content using multi-source remote sensing variables in the Heihe River Basin in China T. Zhou et al. 10.1016/j.ecolind.2020.106288
43. Improving Soil Thickness Estimations Based on Multiple Environmental Variables with Stacking Ensemble Methods X. Li et al. 10.3390/rs12213609
44. Macro-and/or microplastics as an emerging threat effect crop growth and soil health H. Gao et al. 10.1016/j.resconrec.2022.106549
45. Winters are changing: snow effects on Arctic and alpine tundra ecosystems C. Rixen et al. 10.1139/as-2020-0058
46. InSAR-measured permafrost degradation of palsa peatlands in northern Sweden S. Valman et al. 10.5194/tc-18-1773-2024
47. UAV reveals substantial but heterogeneous effects of herbivores on Arctic vegetation M. Siewert & J. Olofsson 10.1038/s41598-021-98497-5
48. Using Machine Learning Algorithms to Estimate Soil Organic Carbon Variability with Environmental Variables and Soil Nutrient Indicators in an Alluvial Soil K. JOHN et al. 10.3390/land9120487
49. Ensemble Machine Learning Approach Improves Predicted Spatial Variation of Surface Soil Organic Carbon Stocks in Data-Limited Northern Circumpolar Region U. Mishra et al. 10.3389/fdata.2020.528441
50. A High-resolution Soil Organic Carbon Map for Great Britain S. Mahmood et al. 10.1080/27658511.2024.2415166
51. Precipitation isotope time series predictions from machine learning applied in Europe D. Nelson et al. 10.1073/pnas.2024107118
52. Significant Improvement in Soil Organic Carbon Estimation Using Data-Driven Machine Learning Based on Habitat Patches W. Yu et al. 10.3390/rs16040688
53. Multi-predictor mapping of soil organic carbon in the alpine tundra: a case study for the central Ecuadorian páramo J. Ayala Izurieta et al. 10.1186/s13021-021-00195-2
54. National soil organic carbon map of agricultural lands in Nepal S. Lamichhane et al. 10.1016/j.geodrs.2022.e00568
55. Predicting Soil Respiration from Plant Productivity (NDVI) in a Sub-Arctic Tundra Ecosystem O. Azevedo et al. 10.3390/rs13132571
56. Improving Spatial Disaggregation of Crop Yield by Incorporating Machine Learning with Multisource Data: A Case Study of Chinese Maize Yield S. Chen et al. 10.3390/rs14102340
57. Prediction of Soil Organic Carbon Content Using Sentinel-1/2 and Machine Learning Algorithms in Swamp Wetlands in Northeast China H. Zhang et al. 10.1109/JSTARS.2023.3281732
58. Use of machine learning in Moroccan soil fertility prediction as an alternative to laborious analyses Y. Al Masmoudi et al. 10.1007/s40808-021-01329-8
59. Digital mapping of topsoil organic carbon content in an alluvial plain area of the Terai region of Nepal S. Lamichhane et al. 10.1016/j.catena.2021.105299
60. Scale-dependency of Arctic ecosystem properties revealed by UAV M. Siewert & J. Olofsson 10.1088/1748-9326/aba20b
61. Permafrost carbon emissions in a changing Arctic K. Miner et al. 10.1038/s43017-021-00230-3
62. High-resolution digital mapping of soil organic carbon and soil total nitrogen using DEM derivatives, Sentinel-1 and Sentinel-2 data based on machine learning algorithms T. Zhou et al. 10.1016/j.scitotenv.2020.138244
63. Lability classification of soil organic matter in the northern permafrost region P. Kuhry et al. 10.5194/bg-17-361-2020
64. Short and Long‐Term Controls on Active Layer and Permafrost Carbon Turnover Across the Arctic S. Faucherre et al. 10.1002/2017JG004069