119 citations as recorded by crossref.

1. Maps with 1 km resolution reveal increases in above- and belowground forest biomass carbon pools in China over the past 20 years Y. Chen et al. 10.5194/essd-15-897-2023
2. A global map of root biomass across the world's forests Y. Huang et al. 10.5194/essd-13-4263-2021
3. Remote Sensing Monitoring on the Territory of the Volga Carbon Polygon (Republic of Tatarstan, Russia) B. Usmanov et al. 10.1051/e3sconf/202346302021
4. Fire facilitates ground layer plant diversity in a Miombo ecosystem J. Wieczorkowski et al. 10.1093/aob/mcae035
5. Dealing with clustered samples for assessing map accuracy by cross-validation S. de Bruin et al. 10.1016/j.ecoinf.2022.101665
6. Forest Area and Structural Variable Estimation in Boreal Forest Using Suomi NPP VIIRS Data and a Sample from VHR Imagery T. Häme et al. 10.3390/rs15123029
7. Spatiotemporal Assessment of Forest Fire Vulnerability in China Using Automated Machine Learning H. Ren et al. 10.3390/rs14235965
8. Constraining a land cover map with satellite-based aboveground biomass estimates over Africa G. Marie et al. 10.5194/gmd-15-2599-2022
9. Satellite observed aboveground carbon dynamics in Africa during 2003–2021 M. Wang et al. 10.1016/j.rse.2023.113927
10. ReUse: REgressive Unet for Carbon Storage and Above-Ground Biomass Estimation A. Pascarella et al. 10.3390/jimaging9030061
11. A Comparative Assessment of Machine-Learning Techniques for Forest Degradation Caused by Selective Logging in an Amazon Region Using Multitemporal X-Band SAR Images T. Kuck et al. 10.3390/rs13173341
12. A systematic evaluation of multi-resolution ICESat-2 ATL08 terrain and canopy heights in boreal forests T. Feng et al. 10.1016/j.rse.2023.113570
13. Monitoring post-fire recovery of various vegetation biomes using multi-wavelength satellite remote sensing E. Bousquet et al. 10.5194/bg-19-3317-2022
14. How small firms build resilience to ward off crises: a paradox perspective S. Chaudhary et al. 10.1080/08985626.2023.2265327
15. Large Area Aboveground Biomass and Carbon Stock Mapping in Woodlands in Mozambique with L-Band Radar: Improving Accuracy by Accounting for Soil Moisture Effects Using the Water Cloud Model Y. Gou et al. 10.3390/rs14020404
16. A systematic review of remote sensing and machine learning approaches for accurate carbon storage estimation in natural forests C. Matiza et al. 10.2989/20702620.2023.2251946
17. SWAT-3PG: Improving forest growth simulation with a process-based forest model in SWAT R. Karki et al. 10.1016/j.envsoft.2023.105705
18. Mercury Uptake, Accumulation, and Translocation in Roots of Subtropical Forest: Implications of Global Mercury Budget W. Yuan et al. 10.1021/acs.est.2c04217
19. Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring U. Ahmad et al. 10.3390/agronomy12030555
20. Characterizing the Accelerated Global Carbon Emissions from Forest Loss during 1985–2020 Using Fine-Resolution Remote Sensing Datasets W. Liu et al. 10.3390/rs16060978
21. Mapping Above-Ground Carbon Stocks at the Landscape Scale to Support a Carbon Compensation Mechanism: The Chocó Andino Case Study F. Cuesta et al. 10.3390/f14091903
22. Non-Destructive Allometric Modeling for Tree Volume Estimation in Tropical Dry Deciduous Forests of India Using Terrestrial Laser Scanner S. Rodda et al. 10.1007/s12524-022-01664-y
23. Comparison of Three Active Microwave Models of Forest Growing Stock Volume Based on the Idea of the Water Cloud Model T. Zhang et al. 10.3390/rs15112848
24. From Ecosystem Observation to Environmental Decision-Making: Model-Data Fusion as an Operational Tool T. Smallman et al. 10.3389/ffgc.2021.818661
25. Improving Estimates and Change Detection of Forest Above-Ground Biomass Using Statistical Methods A. Turton et al. 10.3390/rs14194911
26. Estimating Forest Above-Ground Biomass in Central Amazonia Using Polarimetric Attributes of ALOS/PALSAR Images I. Narvaes et al. 10.3390/f14050941
27. Climate Change Will Increase Biomass Proportion of Global Forest Carbon Stocks Under an SSP5–8.5 Climate Trajectory Z. Mekonnen & W. Riley 10.1029/2023GL104612
28. Uncertainty of spatial averages and totals of natural resource maps A. Wadoux & G. Heuvelink 10.1111/2041-210X.14106
29. The Role of Forest Stands Characteristics on Formation of Exterior Migratory Outbreak Spots by the Siberian Silk Moth Dendrolimus sibiricus (Tschetv.) during Population Collapse D. Demidko et al. 10.3390/f14061078
30. Priority science can accelerate agroforestry as a natural climate solution D. Terasaki Hart et al. 10.1038/s41558-023-01810-5
31. Revealing the spatial variation in biomass uptake rates of Brazil’s secondary forests N. Chen et al. 10.1016/j.isprsjprs.2023.12.013
32. Effect of Socioeconomic Variables in Predicting Global Fire Ignition Occurrence T. Mukunga et al. 10.3390/fire6050197
33. Afforestation triggers water mining and a single pulse of water for carbon trade-off in deep soil H. Li et al. 10.1016/j.agee.2023.108655
34. Carbon stock in litter of middle taiga forest ecosystems of Central Siberia O. Sergeeva et al. 10.1088/1755-1315/875/1/012085
35. The overlooked contribution of trees outside forests to tree cover and woody biomass across Europe S. Liu et al. 10.1126/sciadv.adh4097
36. Estimation of National Forest Aboveground Biomass from Multi-Source Remotely Sensed Dataset with Machine Learning Algorithms in China Z. Tang et al. 10.3390/rs14215487
37. Forest Landscape Model Initialization with Remotely Sensed-Based Open-Source Databases in the Absence of Inventory Data I. Bychkov & A. Popova 10.3390/f14101995
38. Changes in land use and management led to a decline in Eastern Europe’s terrestrial carbon sink K. Winkler et al. 10.1038/s43247-023-00893-4
39. Temporal Changes in Mediterranean Pine Forest Biomass Using Synergy Models of ALOS PALSAR-Sentinel 1-Landsat 8 Sensors E. Velasco Pereira et al. 10.3390/rs15133430
40. Accuracy evaluation and effect factor analysis of GEDI aboveground biomass product for temperate forests in the conterminous United States D. Jia et al. 10.1080/15481603.2023.2292374
41. Forest structure and individual tree inventories of northeastern Siberia along climatic gradients T. Miesner et al. 10.5194/essd-14-5695-2022
42. A Multi-Scale Forest Above-Ground Biomass Mapping Approach: Employing a Step-by-Step Spatial Downscaling Method with Bias-Corrected Ensemble Machine Learning J. Liu & Y. Zhang 10.3390/rs16071228
43. Role of space station instruments for improving tropical carbon flux estimates using atmospheric data P. Palmer et al. 10.1038/s41526-022-00231-6
44. Comparison of Multiple Machine Learning Models for Estimating the Forest Growing Stock in Large-Scale Forests Using Multi-Source Data H. Huang et al. 10.3390/f13091471
45. Near Real-Time Change Detection System Using Sentinel-2 and Machine Learning: A Test for Mexican and Colombian Forests A. Pacheco-Pascagaza et al. 10.3390/rs14030707
46. Low-Cost Non-Contact Forest Inventory: A Case Study of Kieni Forest in Kenya C. Kiplimo et al. 10.3390/challe15010016
47. Mobile phone adoption, deforestation, and agricultural land use in Uganda S. Jung & M. Rogers 10.1016/j.worlddev.2024.106618
48. GriddingMachine, a database and software for Earth system modeling at global and regional scales Y. Wang et al. 10.1038/s41597-022-01346-x
49. Towards an Integrated Approach to Wildfire Risk Assessment: When, Where, What and How May the Landscapes Burn E. Chuvieco et al. 10.3390/fire6050215
50. Wall-to-Wall Mapping of Forest Biomass and Wood Volume Increment in Italy F. Giannetti et al. 10.3390/f13121989
51. Reconstructing Long‐Term Forest Age of China by Combining Forest Inventories, Satellite‐Based Forest Age and Forest Cover Data Sets J. Xia et al. 10.1029/2023JG007492
52. Does Decentralized and Voluntary Commitment Reduce Deforestation? The Effects of Programa Municípios Verdes M. Moz-Christofoletti et al. 10.1007/s10640-022-00659-0
53. Modelling forest biomass dynamics in relation to climate change in Romania using complex data and machine learning algorithms R. Prăvălie et al. 10.1007/s00477-022-02359-z
54. A data-driven estimate of litterfall and forest carbon turnover and the drivers of their inter-annual variabilities in forest ecosystems across China X. Zhao et al. 10.1016/j.scitotenv.2022.153341
55. Past decade above-ground biomass change comparisons from four multi-temporal global maps A. Araza et al. 10.1016/j.jag.2023.103274
56. Edge Effects in Amazon Forests: Integrating Remote Sensing and Modelling to Assess Changes in Biomass and Productivity L. Bauer et al. 10.3390/rs16030501
57. Recent advances and challenges in monitoring and modeling of disturbances in tropical moist forests J. He et al. 10.3389/frsen.2024.1332728
58. Satellite-based global maps are rarely used in forest reference levels submitted to the UNFCCC J. Melo et al. 10.1088/1748-9326/acba31
59. Sub-continental-scale carbon stocks of individual trees in African drylands C. Tucker et al. 10.1038/s41586-022-05653-6
60. Joint optimization of land carbon uptake and albedo can help achieve moderate instantaneous and long-term cooling effects A. Graf et al. 10.1038/s43247-023-00958-4
61. Lessons learned in developing reference data sets with the contribution of citizens: the Geo-Wiki experience L. See et al. 10.1088/1748-9326/ac6ad7
62. A 29-year time series of annual 300 m resolution plant-functional-type maps for climate models K. Harper et al. 10.5194/essd-15-1465-2023
63. Aboveground forest biomass varies across continents, ecological zones and successional stages: refined IPCC default values for tropical and subtropical forests D. Rozendaal et al. 10.1088/1748-9326/ac45b3
64. Spatial predictions and uncertainties of forest carbon fluxes for carbon accounting A. Araza et al. 10.1038/s41598-023-38935-8
65. Helical graphs to visualize the NDVI temporal variation of forest vegetation in an open source space E. Thouverai et al. 10.1016/j.ecoinf.2022.101956
66. A Survey of Computer Vision Techniques for Forest Characterization and Carbon Monitoring Tasks S. Illarionova et al. 10.3390/rs14225861
67. Prediction of Strawberry Dry Biomass from UAV Multispectral Imagery Using Multiple Machine Learning Methods C. Zheng et al. 10.3390/rs14184511
68. Disentangling contributions of allometry, species composition and structure to high aboveground biomass density of high-elevation forests S. Khanal et al. 10.1016/j.foreco.2023.121679
69. Analysis of CYGNSS coherent reflectivity over land for the characterization of pan-tropical inundation dynamics P. Zeiger et al. 10.1016/j.rse.2022.113278
70. Estimating Forest Aboveground Biomass Combining Pléiades Satellite Imagery and Field Inventory Data in the Peak–Cluster Karst Region of Southwestern China Y. Guo et al. 10.3390/f14091760
71. Spaceborne satellite remote sensing of tropical montane forests: a review of applications and future trends R. Altarez et al. 10.1080/10106049.2022.2060330
72. Recent expansion of oil palm plantations into carbon-rich forests Y. Xu et al. 10.1038/s41893-022-00872-1
73. The three major axes of terrestrial ecosystem function M. Migliavacca et al. 10.1038/s41586-021-03939-9
74. The hidden warming effects of the degradation of tropical moist forests 10.1038/s41561-023-01139-w
75. Deforestation-induced climate change reduces carbon storage in remaining tropical forests Y. Li et al. 10.1038/s41467-022-29601-0
76. SMOS L-VOD Retrieved by Level 2 Algorithm and its Correlation With GEDI LIDAR Products C. Vittucci et al. 10.1109/JSTARS.2021.3128022
77. On the NASA GEDI and ESA CCI biomass maps: aligning for uptake in the UNFCCC global stocktake N. Hunka et al. 10.1088/1748-9326/ad0b60
78. Global-scale biomass estimation based on machine learning and deep learning methods S. Talebiesfandarani & A. Shamsoddini 10.1016/j.rsase.2022.100868
79. Global review and state-of-the-art of biomass and carbon stock in the Amazon E. Araujo et al. 10.1016/j.jenvman.2023.117251
80. Satellite-Observed Increase in Aboveground Carbon over Southwest China during 2013-2021 L. Fan et al. 10.34133/remotesensing.0113
81. Retrieval of forest canopy height in a mountainous region with ICESat-2 ATLAS S. Pang et al. 10.1016/j.fecs.2022.100046
82. Estimating Aboveground Carbon Dynamic of China Using Optical and Microwave Remote-Sensing Datasets from 2013 to 2019 Z. Chang et al. 10.34133/remotesensing.0005
83. Validation of ESA-CCI Forest Biomass Products over India: Methodological and Data Challenges and Results Y. Bhat et al. 10.1007/s12524-023-01741-w
84. Evaluating the Performance of the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC) Tailored to the Pan‐Canadian Domain S. Curasi et al. 10.1029/2022MS003480
85. Remote sensing analysis on primary productivity and forest cover dynamics: A Western Ghats India case study M. Baldo et al. 10.1016/j.ecoinf.2022.101922
86. Change Detection of Selective Logging in the Brazilian Amazon Using X-Band SAR Data and Pre-Trained Convolutional Neural Networks T. Kuck et al. 10.3390/rs13234944
87. Influence of Vegetation Height, Plant Area Index, and Forest Intactness on SMOS L-VOD, for Different Seasons and Latitude Ranges C. Vittucci et al. 10.1109/TGRS.2023.3299529
88. Local and Regional Effects of Land-Use Intensity on Aboveground Biomass and Tree Diversity in Tropical Montane Cloud Forests A. Uscanga et al. 10.1007/s10021-023-00861-1
89. Estimating Aboveground Forest Biomass Using Radar Methods I. Babiy et al. 10.1134/S1995425522050031
90. Monitoring vegetation dynamics with open earth observation tools: the case of fire-modulated savanna to forest transitions in Central Africa L. Sagang et al. 10.1016/j.isprsjprs.2022.04.008
91. A global fuel characteristic model and dataset for wildfire prediction J. McNorton & F. Di Giuseppe 10.5194/bg-21-279-2024
92. The effectiveness of global protected areas for climate change mitigation L. Duncanson et al. 10.1038/s41467-023-38073-9
93. Canada’s Green Gold: Unveiling Challenges, Opportunities, and Pathways for Sustainable Forestry Offsets C. Pan et al. 10.3390/f14112206
94. Perspective: Monitoring global forests using only structural metrics – problems and solutions from a savanna viewpoint P. Scogings 10.1016/j.foreco.2023.121381
95. Sentinel-1 Imagery Used for Estimation of Soil Organic Carbon by Dual-Polarization SAR Vegetation Indices E. Santos et al. 10.3390/rs15235464
96. Weekly mapping of surface water extent in the intertropical wetlands using spaceborne GNSS reflectometry P. Zeiger et al. 10.1016/j.jhydrol.2023.130305
97. Mapping global forest age from forest inventories, biomass and climate data S. Besnard et al. 10.5194/essd-13-4881-2021
98. Mapping high-resolution forest aboveground biomass of China using multisource remote sensing data Q. Yang et al. 10.1080/15481603.2023.2203303
99. Global estimation of above-ground biomass from spaceborne C-band scatterometer observations aided by LiDAR metrics of vegetation structure M. Santoro et al. 10.1016/j.rse.2022.113114
100. Carbon stock in living biomass of Russian forests: new quantification based on data from the first cycle of the State Forest Inventory A. Filipchuk et al. 10.2478/forj-2023-0021
101. Application of MaxEnt Model in Biomass Estimation: An Example of Spruce Forest in the Tianshan Mountains of the Central-Western Part of Xinjiang, China X. Ding et al. 10.3390/f14050953
102. Are Terrestrial Biosphere Models Fit for Simulating the Global Land Carbon Sink? C. Seiler et al. 10.1029/2021MS002946
103. A new circa 2007 biomass map for China differs significantly from existing maps W. Dong et al. 10.1038/s41597-024-03092-8
104. Siberian carbon sink reduced by forest disturbances L. Fan et al. 10.1038/s41561-022-01087-x
105. Comparable biophysical and biogeochemical feedbacks on warming from tropical moist forest degradation L. Zhu et al. 10.1038/s41561-023-01137-y
106. Evaluation of optical and microwave-derived vegetation indices for monitoring aboveground biomass over China Z. Chang et al. 10.1080/10095020.2024.2311858
107. A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps A. Araza et al. 10.1016/j.rse.2022.112917
108. Uncovering the Hidden Carbon Treasures of the Philippines’ Towering Mountains: A Synergistic Exploration Using Satellite Imagery and Machine Learning R. Altarez et al. 10.1007/s41064-023-00264-w
109. Framework for Accounting Reference Levels for REDD+ in Tropical Forests: Case Study from Xishuangbanna, China G. Liu et al. 10.3390/rs13030416
110. Carbon Stocks, Species Diversity and Their Spatial Relationships in the Yucatán Peninsula, Mexico J. Hernández-Stefanoni et al. 10.3390/rs13163179
111. Global and Regional Trends and Drivers of Fire Under Climate Change M. Jones et al. 10.1029/2020RG000726
112. The Timber Footprint of the German Bioeconomy—State of the Art and Past Development V. Egenolf et al. 10.3390/su13073878
113. Uncovering the Hidden Carbon Treasures of the Philippines’ Towering Mountains: A Synergistic Exploration Using Satellite Imagery and Machine Learning R. Altarez et al. 10.1007/s41064-023-00264-w
114. Apparent ecosystem carbon turnover time: uncertainties and robust features N. Fan et al. 10.5194/essd-12-2517-2020
115. Rapid remote monitoring reveals spatial and temporal hotspots of carbon loss in Africa’s rainforests O. Csillik et al. 10.1038/s43247-022-00383-z
116. Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach P. Bispo et al. 10.3390/rs12172685
117. Fire enhances forest degradation within forest edge zones in Africa Z. Zhao et al. 10.1038/s41561-021-00763-8
118. Spatial Optimization and Tradeoffs of Alternative Forest Management Scenarios in Macedonia, Greece P. Palaiologou et al. 10.3390/f12060697
119. Modeling Forest Aboveground Carbon Density in the Brazilian Amazon with Integration of MODIS and Airborne LiDAR Data X. Jiang et al. 10.3390/rs12203330