This file contains the identifiers of the 1,250 Sentinel-2 images used in the analysis presented in [Mapping forest types of semi-arid regions of DR Congo, the need to account for leaf phenology]

Content

This dataset contains 30k images that are 1024 by 1024 rgb. They are 1m satellite images from two patches of Pacific Northwest mountain ranges

Acknowledgements

Data collected by USDA and I got it distributed through google earth engine.

Rapid and accurate estimation of forest biomass are essential to drive sustainable management of forests. Field-based measurements of forest above-ground biomass (AGB) can be costly and difficult to conduct. Multi-source remote sensing data offers the potential to improve the accuracy of modelled AGB predictions. Here, four machine learning methods: Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Classification and Regression Trees (CART), and Minimum Distance (MD) were used to construct forest AGB models of Taiyue Mountain forest, Shanxi Province, China using single and multi-sourced remote sensing data and the Google Earth Engine platform. Results showed that the machine learning method that most accurately predicted AGB were GBDT and spectral index for coniferous (R2 = 0.99; RMSE = 65.52 Mg/ha), broadleaved (R2 = 0.97; RMSE = 29.14 Mg/ha), and mixed-species (R2 = 0.97; RMSE = 81.12 Mg/ha) forest types. Models constructed using bivariate variable combinations that included the spectral index improved the AGB estimation accuracy of mixed-species (R2 = 0.99; RMSE = 59.52 Mg/ha) forest types and reduced slightly the accuracy of coniferous (R2 = 0.99; RMSE = 101.46 Mg/ha) and broadleaved (R2 = 0.97; RMSE = 37.59 Mg/ha) forest AGB estimation. Overall, parameterizing machine learning algorithms with multi-source remote sensing variables can improve the prediction accuracy of mixed-species forests.

The Cropland Data Layer (CDL) is a crop-specific land cover data layer created annually for the continental United States using moderate resolution satellite imagery and extensive agricultural ground truth. The CDL is created by the USDA, National Agricultural Statistics Service (NASS), Research and Development Division, Geospatial Information Branch, Spatial Analysis Research Section. For detailed FAQ please visit CropScape and Cropland Data Layers - FAQs. To explore details about the classification accuracies and utility of the data, see state-level omission and commission errors by crop type and year. The asset date is aligned with the calendar year of harvest. For most crops the planted and harvest year are the same. Some exceptions: winter wheat is unique, as it is planted in the prior year. A hay crop like alfalfa could have been planted years prior. For winter wheat the data also have a class called "Double Crop Winter Wheat/Soybeans". Some mid-latitude areas of the US have conditions such that a second crop (usually soybeans) can be planted immediately after the harvest of winter wheat and itself still be harvested within the same year. So for mapping winter wheat areas use both classes (use both values 24 and 26). While the CDL date is aligned with year of harvest, the map itself is more representative of what was planted. In other words, a small percentage of fields on a given year will not be harvested. Some non-agricultural categories are duplicate due to two very different epochs in methodology. The non-ag codes 63-65 and 81-88 are holdovers from the older methodology and will only appear in CDLs from 2007 and earlier. The non-ag codes from 111-195 are from the current methodology which uses the USGS NLCD as non-ag training and will only appear in CDLs 2007 and newer. 2007 was a transition year so there may be both sets of categories in the 2007 national product but will not appear within the same state. Note: The 2024 CDL only has the data band. The cultivated and confidence bands are yet to be released by the provider.

The global map of forest types provides a spatially explicit representation of primary forest, naturally regenerating forest and planted forest (including plantation forest) for the year 2020 at 10m spatial resolution. The base layer for mapping these forest types is the extent of forest cover of version 1 of the …

Sub-dataset: soil type probability - suborder: Boralfs Description Global annual maps of soil properties for 2000—2022 produced within the scope of the Land & Carbon Lab, integrating Digital surface/terrain model, vegetation/tillage indices, climatic/bioclimatic variables, and based on tree-based spatiotemporal Machine Learning. While the primary focus is on improving monitoring in global soil properties, the dataset provides wall-to-wall coverage across all terrestrial ecosystems and is organized into 300+ global mosaics in COG (Cloud Optimized GeoTIFF) format. Data are presented at 5-year intervals, across 3 standard depth intervals (0–30 cm, 30–60 cm, 60–100 cm), and cover 79 USDA soil taxonomy suborders. Original layers use the WGS84 Coordinate System (EPSG:4326) at a pixel resolution of 0.00025 degrees, and 0.00075 degrees with uncertainty (STAC and GEE). Layers archived on Zenodo are at 0.00075 degrees with uncertainty but include only the initial and final periods (2000–2005 & 2020–2022), including: Soil Organic Carbon Content (g/kg) As a key indicator of soil fertility, structure, and microbial activity, it represents the concentration of organic carbon in the fine earth fraction of the soil. Standard method of measurement is dry combustion using elemental analyzers (e.g., ISO 10694). Soil Organic Carbon Density (kg/m³) Represents the mass of organic carbon per unit volume of soil. It is derived as: SOC content × bulk density × (1 − coarse fragment volume fraction). This value is critical for estimating total carbon stocks and monitoring soil carbon changes over time. Soil pH Indicates the acidity or alkalinity of soil, affecting nutrient availability and microbial processes. Reported as pH measured in water solution (pH in H₂O). Bulk Density (g/cm³) Refers to the mass of dry fine earth (<2 mm) per unit volume, excluding coarse fragments. It reflects soil compaction and porosity, influencing water retention and root penetration. Commonly determined using the core method or calculated from pedotransfer functions. Soil Texture Fraction Defines the relative proportions of mineral particles by size. Texture influences water movement, nutrient holding capacity, and plant growth. Clay content (%): Proportion of particles <0.002 mm in diameter. Sand content (%): Proportion of particles between 0.05–2.0 mm (some definitions use 0.063 mm as lower threshold). Silt content (%): Particles sized between 0.002–0.05 mm or up to 0.063 mm depending on classification system. Textural fractions follow USDA or FAO particle size classifications. Soil Type Probability Probabilistic classification of soils based on USDA Soil Taxonomy at the subgroup level. Each pixel is assigned a probability distribution across potential soil types, based on legacy point data and environmental covariates. 30m layers can be accessed through STAC and Google Earth Engine GEE) through: OpenLandMap STAC https://stac.openlandmap.org Google Earth Engine https://code.earthengine.google.com/?asset=projects/global-pasture-watch/assets/gsm-30m All modeling framework is publicly available at OpenLandMap GitHub - soildb Data Detail Time period: 2000-2022, in 5-year intervals (last period covers 2020–2022) for soil properties ; 2000-2022 static for soil type Type of data: Spatiotemporal soil data base, with depth ranges and weighted percentage data for soil assessments and static soil type classification. How the data was collected or derived: The data was derived using machine learning models. Statistical methods used: Tree-based spatiotemporal machine learning Depth reference: b30cm..60cm = below ground at 30-60cm interval Limitations or exclusions in the data: no Antarctica; masking out permanent ice and deserts Coordinate reference system: EPSG:4326 Bounding box (Xmin, Ymin, Xmax, Ymax): (-180, -56, 180, 76) Spatial resolution: 0.00075 degree (~120m) Image size: 360,000P, 132,000L File format: Cloud Optimized Geotiff (COG) format Dataset Contents This dataset includes: soil type probability - suborder: Boralfs Related Identifiers SOC density: below ground 0cm-30cm 2000-2005 , below ground 0cm-30cm 2020-2022 , below ground 30cm-60cm 2000-2005 , below ground 30cm-60cm 2020-2022 , below ground 60cm-100cm 2000-2005 , below ground 60cm-100cm 2020-2022 , SOC content: below ground 0cm-30cm 2000-2005 - part 1 , below ground 0cm-30cm 2000-2005 - part 2 , below ground 0cm-30cm 2020-2022 - part 1 , below ground 0cm-30cm 2020-2022 - part 2 , below ground 30cm-60cm 2000-2005 , below ground 30cm-60cm 2020-2022 , below ground 60cm-100cm 2000-2005 , below ground 60cm-100cm 2020-2022 , Bulk density: below ground 0cm-30cm 2000-2005 , below ground 0cm-30cm 2020-2022 , below ground 30cm-60cm 2000-2005 , below ground 30cm-60cm 2020-2022 , below ground 60cm-100cm 2000-2005 , below ground 60cm-100cm 2020-2022 , Soil ph of water: below ground 0cm-30cm 2000-2005 , below ground 0cm-30cm 2020-2022 , below ground 30cm-60cm 2000-2005 , below ground 30cm-60cm 2020-2022 , below ground 60cm-100cm 2000-2005 , below ground 60cm-100cm 2020-2022 , Soil textures fraction: clay below ground 0cm-30cm 2000-2005 , clay below ground 0cm-30cm 2020-2022 , clay below ground 30cm-60cm 2000-2005 , clay below ground 30cm-60cm 2020-2022 , clay below ground 60cm-100cm 2000-2005 , clay below ground 60cm-100cm 2020-2022 , sand below ground 0cm-30cm 2000-2005 , sand below ground 0cm-30cm 2020-2022 , sand below ground 30cm-60cm 2000-2005 , sand below ground 30cm-60cm 2020-2022 , sand below ground 60cm-100cm 2000-2005 , sand below ground 60cm-100cm 2020-2022 , silt below ground 0cm-30cm 2000-2005 , silt below ground 0cm-30cm 2020-2022 , silt below ground 30cm-60cm 2000-2005 , silt below ground 30cm-60cm 2020-2022 , silt below ground 60cm-100cm 2000-2005 , silt below ground 60cm-100cm 2020-2022 , Soil type (Suborder): Uderts , Calcids , Xerands , Orthents , Cryands , Ustalfs , Cryalfs , Aquepts , Udalfs , Cryolls , Durids , Usterts , Boralfs , Orthids , Udands , Torrerts , Histels , Rendolls , Aqualfs , Udepts , Xeralfs , Gelepts , Xerults , Fibrists , Ustepts , Xererts , Ustults , Aquands , Perox , Xerolls , Tropepts , Turbels , Udults , Aquents , Aquerts , Ustox , Aquods , Aquolls , Xerepts , Udox , Cryods , Ustolls , Aquults , Psamments , Arents , Fluvents , Humults , Vitrands , Udolls , Borolls , Orthels , Hemists , Wassents , Albolls , Salids , Cryepts , Saprists , Folists , Gypsids , Ochrepts , Cambids , Argids , Orthods , Data Details Time period: 2000-2022 Type of data: soil type probability - suborder: Boralfs How the data was collected or derived: Machine learning models. Statistical Methods used: Random Forest. Limitations or exclusions in the data: The dataset does not include Antarctica. Coordinate reference system: EPSG:4326 Bounding box (Xmin, Ymin, Xmax, Ymax): (-180, -56, 180, 76) Spatial resolution: 120m Image size: 360,000P x 132,000L File format: Cloud Optimized Geotiff (COG) format. Layer information: File Name Unit Scale Data Type No Data Description oc_iso.10694.1995.mg.cm3 kg/m³ 10 UInt16 32767 Organic carbon density derived by multiply fine earth bulk density and organic carbon content oc_iso.10694.1995.wpml g/kg 10 UInt16 32767 Organic carbon content based on dry combustion weight percent ph.h2o_iso.10390.2021.index - 10 Byte 255 The pH, 1:1 soil-water suspension is the pH of a sample measured in distilled water at a 1:1 soil:solution ratio bd.core_iso.11272.2017.g.cm3 g/cm³ 100 UInt16 32767 Bulk density, <2mm fraction, dry is the weight per unit volume of the <2 mm fraction, with volume measured in laboratory sand.tot_iso.11277.2020.wpct % 1 Byte 255 Total laboratory-estimated sand 0.063 to 2.0 mm particle diameter silt.tot_iso.11277.2020.wpct % 1 Byte 255 Total laboratory-estimated silt 0.002 to 0.063 mm particle size clay.tot_iso.11277.2020.wpct % 1 Byte 255 Total clay is the soil separate with <0.002 mm particle diameter soil.types_ensemble % 1 Byte 255 Probability of soil type occurrence Support If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue here Naming convention To ensure consistency and ease of use across and within the projects, we follow the standard Ai4SoilHealth and Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describe important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. For example, for oc_iso.10694.1995.wpml_m_30m_b30cm..60cm_20000101_20051231_g_epsg.4326_v20250204.tif, the fields are: generic variable

Annual (1986-2020) land-use/land cover maps at 30-meter resolution of the Tucson metropolitan area, Arizona and the greater Santa Cruz Watershed including Nogales, Sonora, Mexico. Maps were created using a combination of Landsat imagery, derived transformation and indices, texture analysis and other ancillary data fed to a Random Forest classifier in Google Earth Engine. The maps contain 13 classes based on the National Land Cover Classification scheme and modified to reflect local land cover types. Data are presented as a stacked, multi-band raster with one "band" for each year (Band 1 = 1986, Band 2 = 1987 and so on). Note that the year 2012 was left out of our time series because of lack of quality Landsat data. A color file (.clr) is included that can be imported to match the color of the National Land Cover Classification scheme. This data release also contains two JavaScript files with the Google Earth Engine code developed for pre-processing Landsat imagery and for image classification, and a zip folder "Accuracy Data" with five excel files: 1) Accuracy Statistics describing overall accuracy for each LULC year, 2) Confusion Matrices for each LULC year, 3) Land Cover Evolution - changes in pixel count for each class per year, 4) LULC Change Matrix - to and from class changes over the period, and 5) Variable Importance - results of the Random Forest Classification.

Dataset Summary

This dataset is developed for pixel-level classification of urban informal settlements using satellite imagery. The input data consists of Sentinel-2 imagery (2015–2016), and the ground truth is derived from a government-conducted survey available as a KML vector file, rasterized to align with the imagery.Formats include NumPy arrays and HDF5 files for easy ML integration. Intended for land‑use/land‑cover classification tasks.

🛰️ Data Source

Satellite Imagery: Sentinel‑2 L2A (Surface Reflectance) images from 2015–16, accessed via Google Earth Engine (GEE)

Ground Truth: Official government KML vector file, manually rasterized to match imagery resolution and alignment

📦 Data Format

Ground Truth Source: Government survey KML converted to raster via QGIS

Satellite Data: Sentinel‑2 L2A (Surface Reflectance) images from 2015–16

CRS & Extent: EPSG:4326

Bounding Box: Longitude: 72.7827462580 to 72.9718317340

Latitude: 18.9086328640 to 19.2638524900

Spatial Accuracy: ~±2 m (WGS84)

Raster Size: 2105 × 3954 pixels (Float64 GeoTIFF)

Formats: NumPy (.npy) and HDF5 (.h5) for image bands and per-pixel labels

Pixel size: ~10m (based on Sentinel-2 native resolution)

Label Values:

        1 → Informal/Slum

        0 → Formal/Non-slum

Data Type: float64 (image), uint8 (labels)

📜 Coordinate System Details

CRS Name: EPSG:4326 - WGS 84

Datum: World Geodetic System 1984 (EPSG:6326)

Units: Geographic (degrees)

Accuracy: ≤ 2 meters (approximate)

Type: Geographic 2D

Celestial Body: Earth

Reference: Dynamic (not plate-fixed)

Additional Details

1.Processing Pipeline KML to Raster: Ground truth polygons from KML rasterized using GDAL to match Sentinel-2 extent and resolution. Image Preprocessing: Cloud masking and band selection (R, G, B, NIR) through Google Earth Engine. Export Format: .tif downloaded, converted to .npy and .h5 using rasterio, numpy, and h5py. Alignment: Verified pixel-wise correspondence between image and label arrays.

2.Authorship & Provenance Creators: M Rupesh Kumar Yadav, Mtech, Dept of Centre of Studies in Resources Engineering, IIT Bombay. You can contact through mail rupesh32003@gmail.com, 24m0319@iitb.ac.in, or checkout github for further resources/assistance. orcid id, github, LinkedIn

3.Content & Structure Bands per sample: RGB (3 bands) + NIR (1 band) Ground truth: Per-pixel labels aligned with imagery Data splits: (e.g.) train/val/test percentages or file lists File naming conventions: Explain if files correspond to tiles, dates, etc. Example sample: Show dimensions, dtype, label values, and their mapping to classes.

4.Collection & Processing Satellite imagery: Retrieved via Google Earth Engine over 2015–16; filtered by cloud cover threshold Ground truth conversion: KML survey data rasterized using same spatial resolution and CRS Alignment: Resampled and aligned bands using GEE reprojection Preprocessing steps: Cloud masking, atmospheric correction (L2A), normalization, dtype cast to Float64 Label handling: Ensured spatial overlap and clipping; labeled invalid/missing areas as class 0 or mask

5.Usage & Intended Applications Tasks: Semantic segmentation or pixel-level land-cover mapping Ideal for: Land use change detection, agricultural mapping, validation of remote sensing models Not suitable for: Tasks needing multispectral beyond NIR, very high-res (<10 m) labeling, temporal sequence modeling

6.Limitations & Bias Temporal span: Only covers 2015–2016; may not reflect current conditions Spatial scope bias: Limited geographic area (Mumbai region) Labeling bias: Dependent on government survey accuracy and rasterization fidelity Cloud coverage: Some tiles may still contain residual cloud pixels

A European Local Climate Zone map at a 100 m spatial resolution, derived from multiple earth observation datasets and expert LCZ class labels. There are 10 urban LCZ types, each associated with a set of relevant variables such that the map represent a valuable database of urban properties.

Leaf Area Index (LAI) is a fundamental vegetation structural variable that drives energy and mass exchanges between the plant and the atmosphere. Moderate-resolution (300m – 7km) global LAI data products have been widely applied to track global vegetation changes, drive Earth system models, monitor crop growth and productivity, etc. Yet, cutting-edge applications in climate adaptation, hydrology, and sustainable agriculture require LAI information at higher spatial resolution (< 100m) to model and understand heterogeneous landscapes. This dataset was built to assist a machine-learning-based approach for mapping LAI from 30m-resolution Landsat images across the contiguous US (CONUS). The data was derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) Version 6 LAI/FPAR, Landsat Collection 1 surface reflectance, and NLCD Land Cover datasets over 2006 – 2018 using Google Earth Engine. Each record/sample/row includes a MODIS LAI value, corresponding Landsat surface reflectance in green, red, NIR, SWIR1 bands, a land cover (biome) type, geographic location, and other auxiliary information. Each sample represents a MODIS LAI pixel (500m) within which a single biome type dominates 90% of the area. The spatial homogeneity of the samples was further controlled by a screening process based on the coefficient of variation of the Landsat surface reflectance. In total, there are approximately 1.6 million samples, stratified by biome, Landsat sensor, and saturation status from the MODIS LAI algorithm. This dataset can be used to train machine learning models and generate LAI maps for Landsat 5, 7, 8 surface reflectance images within CONUS. Detailed information on the sample generation and quality control can be found in the related journal article. Resources in this dataset:Resource Title: README. File Name: LAI_train_samples_CONUS_README.txtResource Description: Description and metadata of the main datasetResource Software Recommended: Notepad,url: https://www.microsoft.com/en-us/p/windows-notepad/9msmlrh6lzf3?activetab=pivot:overviewtab Resource Title: LAI_training_samples_CONUS. File Name: LAI_train_samples_CONUS_v0.1.1.csvResource Description: This CSV file consists of the training samples for estimating Leaf Area Index based on Landsat surface reflectance images (Collection 1 Tire 1). Each sample has a MODIS LAI value and corresponding surface reflectance derived from Landsat pixels within the MODIS pixel. Contact: Yanghui Kang (kangyanghui@gmail.com)
Column description

UID: Unique identifier. Format: LATITUDE_LONGITUDE_SENSOR_PATHROW_DATE
Landsat_ID: Landsat image ID Date: Landsat image date in "YYYYMMDD" Latitude: Latitude (WGS84) of the MODIS LAI pixel center Longitude: Longitude (WGS84) of the MODIS LAI pixel center MODIS_LAI: MODIS LAI value in "m2/m2" MODIS_LAI_std: MODIS LAI standard deviation in "m2/m2" MODIS_LAI_sat: 0 - MODIS Main (RT) method used no saturation; 1 - MODIS Main (RT) method with saturation NLCD_class: Majority class code from the National Land Cover Dataset (NLCD) NLCD_frequency: Percentage of the area cover by the majority class from NLCD Biome: Biome type code mapped from NLCD (see below for more information) Blue: Landsat surface reflectance in the blue band Green: Landsat surface reflectance in the green band Red: Landsat surface reflectance in the red band Nir: Landsat surface reflectance in the near infrared band Swir1: Landsat surface reflectance in the shortwave infrared 1 band Swir2: Landsat surface reflectance in the shortwave infrared 2 band Sun_zenith: Solar zenith angle from the Landsat image metadata. This is a scene-level value. Sun_azimuth: Solar azimuth angle from the Landsat image metadata. This is a scene-level value. NDVI: Normalized Difference Vegetation Index computed from Landsat surface reflectance EVI: Enhanced Vegetation Index computed from Landsat surface reflectance NDWI: Normalized Difference Water Index computed from Landsat surface reflectance GCI: Green Chlorophyll Index = Nir/Green - 1

Biome code

1 - Deciduous Forest
2 - Evergreen Forest
3 - Mixed Forest
4 - Shrubland
5 - Grassland/Pasture
6 - Cropland
7 - Woody Wetland
8 - Herbaceous Wetland

Reference Dataset: All data was accessed through Google Earth Engine Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment. MODIS Version 6 Leaf Area Index/FPAR 4-day L5 Global 500m Myneni, R., Y. Knyazikhin, T. Park. MOD15A2H MODIS/Terra Leaf Area Index/FPAR 8-Day L4 Global 500m SIN Grid V006. 2015, distributed by NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/MODIS/MOD15A2H.006 Landsat 5/7/8 Collection 1 Surface Reflectance Landsat Level-2 Surface Reflectance Science Product courtesy of the U.S. Geological Survey. Masek, J.G., Vermote, E.F., Saleous N.E., Wolfe, R., Hall, F.G., Huemmrich, K.F., Gao, F., Kutler, J., and Lim, T-K. (2006). A Landsat surface reflectance dataset for North America, 1990–2000. IEEE Geoscience and Remote Sensing Letters 3(1):68-72. http://dx.doi.org/10.1109/LGRS.2005.857030. Vermote, E., Justice, C., Claverie, M., & Franch, B. (2016). Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment. http://dx.doi.org/10.1016/j.rse.2016.04.008. National Land Cover Dataset (NLCD) Yang, Limin, Jin, Suming, Danielson, Patrick, Homer, Collin G., Gass, L., Bender, S.M., Case, Adam, Costello, C., Dewitz, Jon A., Fry, Joyce A., Funk, M., Granneman, Brian J., Liknes, G.C., Rigge, Matthew B., Xian, George, A new generation of the United States National Land Cover Database—Requirements, research priorities, design, and implementation strategies: ISPRS Journal of Photogrammetry and Remote Sensing, v. 146, p. 108–123, at https://doi.org/10.1016/j.isprsjprs.2018.09.006 Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel

To assess the distribution, frequency, and global extent of riverine hypoxia, we compiled 118 million paired dissolved oxygen (DO) and water temperature measurements from 125,158 unique locations in rivers in 93 countries and territories across the globe. The dataset also includes site characteristics derived from StreamCat, the National Hydrography and HydroAtlas datasets and proximal land cover derived from MODIS-based IGBP land cover types compiled using Google Earth Engine (GEE).

Forest cover is rapidly changing at the global scale as a result of land-use change (principally deforestation in many tropical regions and afforestation in many temperate regions) and climate change. However, a detailed map of global forest gain is still lacking at fine spatial and temporal resolutions. In this study, we developed a new automatic framework to map annual forest gain across the globe, based on Landsat time series, the LandTrendr algorithm and the Google Earth Engine (GEE) platform. First, samples of stable forest collected based on the Global Forest Change product (GFC) were used to determine annual Normalized Burn Ratio (NBR) thresholds for forest gain detection. Secondly, with the NBR time-series from 1982 to 2020 and LandTrendr algorithm, we produced dataset of global forest gain year from 1984 to 2020 based on a set of decision rules. Our results reveal that large areas of forest gain occurred in China, Russia, Brazil and North America, and the vast majority of the global forest gain has occurred since 2000. The new dataset was consistent in both spatial extent and years of forest gain with data from field inventories and alternative remote sensing products. Our dataset is valuable for policy-relevant research on the net impact of forest cover change on the global carbon cycle and provides an efficient and transferable approach for monitoring other types of land cover dynamics.