CTrees Global Aboveground Biomass 100m (2000–2025)¶

Accurate, high-resolution estimation of forest aboveground biomass (AGB) is critical for quantifying terrestrial carbon stocks and informing climate mitigation policy, yet existing global products remain constrained by coarse spatial resolution and limited temporal coverage. This dataset from CTrees presents a wall-to-wall global AGB product at 100 m native spatial resolution spanning 2000–2025, trained with a DenseNet deep learning architecture using independent AGB reference labels from ALS-derived AGB, field-plot and forest-inventory samples from the research community, selected regional and national inventories, and mangrove AGB training samples from global coastal regions. The predictor stack integrates Landsat surface reflectance time series, ALOS PALSAR-½ L-band backscatter, GEDI and ICESat-2 canopy-height metrics (used only as ancillary structural layers), and topographic data. On a held-out validation set of approximately 1.01 million pixels compiled across eco-regions, the product achieves R² = 0.741, RMSE = 59.5 Mg ha⁻¹, and bias = −4.82 Mg ha⁻¹. Further details about the dataset and underlying methodology can be found in the CTrees AGB Carbon Data Brief.

Band Description¶

Each annual image in the collection contains two bands. Raw pixel values are stored as signed int16 with a scale factor of 0.1; divide raw values by 10 to obtain values in Mg ha⁻¹.

Image Properties¶

Each image carries the following metadata properties:

Citation¶

Yang, Y., Saatchi, S., Kwon, N., Lin, W.T., Liu, Z., Li, S., DalAgnol, R., Sagang, L.B., 2026.
From Snapshot Maps to Continuous Monitoring of Global Forest Carbon at 100 m Resolution
(2000–2025). EarthArXiv (preprint). https://doi.org/10.31223/X5KJ4Q

Dataset Citation¶

CTrees. (2026). CTrees Global Aboveground Biomass 100m (2000–2025) [Data set].
https://doi.org/10.82924/7vmb-zv66

Earth Engine Snippet¶

var collection = ee.ImageCollection("projects/sat-io/open-datasets/CTREES-GLOBAL-AGB-100M");
print(collection);
print(collection.size());

// --- Rescale function: apply scale factor and mask pixels <= 0 ---
function rescaleAGB(image) {
  var scaled = image.multiply(image.getNumber('agb_scale_factor'));
  return scaled.updateMask(scaled.gt(0)).copyProperties(image, image.propertyNames());
}

// --- Temporal window ---
var startDate = '2000-01-01';
var endDate   = '2025-12-31';

var filtered = collection
  .filterDate(startDate, endDate)
  .map(rescaleAGB);

var agb = filtered.sort('system:time_start', false);

// --- Visualization parameters ---
var visParams = {
  bands: ['agb'],
  min: 0,
  max: 400,
  palette: [
    '#f7fcf5', '#d9f0d3', '#b2e0a8', '#84ca7e', '#5db96a',
    '#38a152', '#1f7f3b', '#0d5c26', '#00441b', '#023640',
    '#042864', '#08306b'
  ]
};

Map.setCenter(-62, -4, 4);
Map.addLayer(agb, visParams, 'AGB (Mg/ha)');

Sample Code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:agriculture-vegetation-forestry/CTREES-GLOBAL-AGB100M

License & Usage¶

This dataset is licensed under a Creative Commons Attribution 4.0 International (CC-BY 4.0) license. You are free to copy, redistribute, and build upon the material for any purpose, including commercially, provided you give appropriate credit, provide a link to the license, and indicate if changes were made.

Provided by: Yang et al 2026

Curated in GEE by: Samapriya Roy

Keywords: Aboveground Biomass, AGB, Forest Carbon, Global, 100m, Deep Learning, DenseNet, Landsat, ALOS PALSAR, GEDI, ICESat-2, Temporal Change, REDD+, Carbon Monitoring, CTrees

Last updated: 2026-06-02

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