Nation-wide Sentinel-2 mosaics were used with National Forest Inventory (NFI) data for modelling and subsequent mapping of spruce, pine and deciduous forest in Norway in 16 m $\times$ 16 m resolution. The accuracies of the best model ranged between 74% for spruce and 87% for deciduous forest. An overall accuracy of 90% was found on stand level using independent data from more than 42.000 stands. Errors mostly resulting from a forest mask reduced the model accuracies by approximately 10%. Nonetheless, efficiencies of national forest area estimates increased by 20% to 50% for model-assisted (MA) estimates and up to 90% for poststratification (PS). Greater minimum number of observations, however, constrained the use of PS. For estimates of sub-populations (municipalities), efficiencies improved by up to a factor of 8. However, even for municipalities with a decent number of NFI plots, direct NFI estimates were sometimes more precise than MA estimates. PS estimates were always more precise than direct and MA estimates but were applicable in fewer municipalities. The tree species prediction map is freely available as part of the Norwegian forest resource map and is used, among others, to improve maps of other variables of interest such as timber volume and biomass.
Country-wide, high-resolution monitoring of forest browning with Sentinel-2
ETH Zurich（苏黎世联邦理工学院）；ETH AI Center（ETH人工智能中心）；Swiss Data Science Center（瑞士数据科学中心）；EPFL（洛桑联邦理工学院）；Swiss Federal Institute for Forest, Snow and Landscape Research WSL（瑞士联邦森林、雪与景观研究所）；University of Bern（伯尔尼大学）；Oeschger Centre for Climate Change Research（厄施格气候变化研究中心）
Estimating optical vegetation indices and biophysical variables for temperate forests with Sentinel-1 SAR data using machine learning techniques: A case study for Czechia
Charles University（查理大学）；European Space Agency (ESA/ESRIN)（欧洲空间局（欧洲空间局地球观测中心））；AI4EARTH
Within-fire estimates of biomass loss across national forests of the US West Coast: inventory-informed inference with uncertainty
Michigan State University（密歇根州立大学）；South Dakota School of Mines & Technology（南达科他矿业理工学院）；USDA Forest Service, Pacific Northwest Research Station（美国农业部林务局，太平洋西北研究站）；Northern Research Station, United States Department of Agriculture Forest Service（美国农业部林务局北方研究站）；USDA Forest Service, Rocky Mountain Research Station（美国农业部林务局，落基山研究站）
New allometric models for the USA create a step-change in forest carbon estimation, modeling, and mapping
State University of New York College of Environmental Science and Forestry（纽约州立大学环境科学与林业学院）；USDA Forest Service（美国农业部林业局）；Northern Research Station（北方研究站）
Soil Property and Class Maps of the Conterminous US at 100 meter Spatial Resolution based on a Compilation of National Soil Point Observations and Machine Learning
