45 citations as recorded by crossref.

1. Characteristics, impacts, and future research directions of Mongolian peatlands X. Wu et al. 10.1139/er-2024-0070
2. Socio-Ecological Approach to a Forest-Swamp-Savannah Mosaic Landscape Using Remote Sensing and Local Knowledge: a Case Study in the Bas-Ogooué Ramsar Site, Gabon C. Demichelis et al. 10.1007/s00267-023-01827-8
3. Disentangling the effect of temperature and moisture on boreal peatland microbial activity and function P. Ferguson & Z. Lindo 10.1016/j.pedobi.2025.151063
4. SatViT: Pretraining Transformers for Earth Observation A. Fuller et al. 10.1109/LGRS.2022.3201489
5. Predicting the potential geographical distribution of peatlands in Northeast China based on the ensemble model H. Wu et al. 10.1016/j.gloplacha.2025.104866
6. Detecting and Mapping Peatlands in the Tibetan Plateau Region Using the Random Forest Algorithm and Sentinel Imagery Z. Pan et al. 10.3390/rs17020292
7. Control of local topography and surface patterning on the formation and stability of a slope permafrost peatland at 4800-m elevation on the central Qinghai-Tibetan Plateau Y. Li et al. 10.1016/j.ecolind.2023.111475
8. Application of machine learning techniques for wetland type mapping in the Numto Nature Park (Western Siberia) M. Moskovchenko 10.1007/s12145-024-01654-3
9. Analysis of peat soil testing errors based on its characteristics and appropriate recommendation of peat soil testing A. Khoerani et al. 10.1051/e3sconf/202342904018
10. Montane peatland response to drought: Evidence from multispectral and thermal UAS monitoring J. Langhammer et al. 10.1016/j.ecolind.2024.112587
11. The role of peatland degradation, protection and restoration for climate change mitigation in the SSP scenarios J. Doelman et al. 10.1088/2752-5295/acd5f4
12. Using the Canadian Model for Peatlands (CaMP) to examine greenhouse gas emissions and carbon sink strength in Canada's boreal and temperate peatlands K. Bona et al. 10.1016/j.ecolmodel.2024.110633
13. An assessment of recent peat forest disturbances and their drivers in the Cuvette Centrale, Africa K. Nesha et al. 10.1088/1748-9326/ad6679
14. Mapping the distribution and condition of mountain peatlands in Colombia for sustainable ecosystem management P. Skillings-Neira et al. 10.1016/j.jenvman.2025.124915
15. Mapping and monitoring peatland conditions from global to field scale B. Minasny et al. 10.1007/s10533-023-01084-1
16. Warming-induced vapor pressure deficit suppression of vegetation growth diminished in northern peatlands N. Chen et al. 10.1038/s41467-023-42932-w
17. Research Progress in the Field of Peatlands in 1990–2022: A Systematic Analysis Based on Bibliometrics J. Shi et al. 10.3390/land13040549
18. High-resolution mapping of peatland CO2 fluxes using drone multispectral images R. Walcker et al. 10.1016/j.ecoinf.2025.103060
19. Peatland microalgae are unsung heroes of climate change mitigation 10.1038/s41558-025-02272-7
20. Towards a roadmap for space-based observations of the land sector for the UNFCCC global stocktake O. Ochiai et al. 10.1016/j.isci.2023.106489
21. On the detailed mapping of peat (raised bogs) using airborne radiometric data D. Beamish & J. White 10.1016/j.jenvrad.2024.107462
22. Genes and genome‐resolved metagenomics reveal the microbial functional make up of Amazon peatlands under geochemical gradients M. Pavia et al. 10.1111/1462-2920.16469
23. Predicting Soil Organic Carbon Stock in Plateau Swamp Wetlands Using Multisource Remote Sensing and Spectral Measurements: A Case Study of the Dianchi Basin F. Cai et al. 10.1109/TGRS.2025.3541122
24. A meta-analysis of peatland microbial diversity and function responses to climate change M. Le Geay et al. 10.1016/j.soilbio.2023.109287
25. Mapping high-altitude peatlands to inform a landscape conservation strategy in the Andes of northern Peru G. Curatola Fernández et al. 10.1017/S0376892923000267
26. A new data-driven map predicts substantial undocumented peatland areas in Amazonia A. Hastie et al. 10.1088/1748-9326/ad677b
27. Hidden becomes clear: Optical remote sensing of vegetation reveals water table dynamics in northern peatlands I. Burdun et al. 10.1016/j.rse.2023.113736
28. Unveiling the Past: Deep-Learning-Based Estimation of Historical Peatland Distribution S. Cha et al. 10.3390/land13030328
29. Initial assessment of the peatlands of the upper-Ucayali Valley, Central Peruvian Amazon: Basic analysis of geographic products & predictors B. Crnobrna et al. 10.1016/j.gecco.2024.e03056
30. Widespread carbon-dense peatlands in the Colombian lowlands R. Winton et al. 10.1088/1748-9326/adbc03
31. Microbial photosynthesis mitigates carbon loss from northern peatlands under warming S. Hamard et al. 10.1038/s41558-025-02271-8
32. Topographic and climatic controls of peatland distribution on the Tibetan Plateau J. Sun et al. 10.1038/s41598-023-39699-x
33. Peatlands and their carbon dynamics in northern high latitudes from 1990 to 2300: a process-based biogeochemistry model analysis B. Zhao & Q. Zhuang 10.5194/bg-20-251-2023
34. Geospatial Data and Deep Learning Expose ESG Risks to Critical Raw Materials Supply: The Case of Lithium C. Lawley et al. 10.3389/esss.2024.10109
35. Drainage density and land cover interact to affect fire occurrence in Indonesian peatlands R. Salmayenti et al. 10.1088/1748-9326/adc755
36. Global increase in biomass carbon stock dominated by growth of northern young forests over past decade H. Yang et al. 10.1038/s41561-023-01274-4
37. Adapting machine learning for environmental spatial data - A review M. Jemeļjanova et al. 10.1016/j.ecoinf.2024.102634
38. Digital mapping of peat thickness and extent in Finland using remote sensing and machine learning J. Pohjankukka et al. 10.1016/j.geoderma.2025.117216
39. Digital mapping of peat thickness and carbon stock of global peatlands M. Widyastuti et al. 10.1016/j.catena.2025.109243
40. Mismatch Between Global Importance of Peatlands and the Extent of Their Protection K. Austin et al. 10.1111/conl.13080
41. The spatial distribution and paleoecology of Caribbean peatlands E. Rabel & J. Loisel 10.1038/s43247-024-01903-9
42. A map of global peatland extent created using machine learning (Peat-ML) J. Melton et al. 10.5194/gmd-15-4709-2022
43. A deep learning approach for high‐resolution mapping of Scottish peatland degradation F. Macfarlane et al. 10.1111/ejss.13538
44. Modeling Carbon Accumulation and Permafrost Dynamics of Northern Peatlands Since the Holocene B. Zhao et al. 10.1029/2022JG007009
45. A probabilistic map of Costa Rican peatlands based on vegetation, ecosystem, and soil inventories E. Rabel et al. 10.1038/s41598-025-03126-0