Derivation and evaluation of Landsat-derived annual aboveground biomass maps for Arctic and Boreal North America, 1984-2022. Liang, W., van Lier, O., Friedl, M. A., Randerson, J. T., Rogers, B. M., Burrell, A., Hu, K., Tompalski, P., Macander, M. J., Yang, D., Morton, D. C., Bullock, E., Lu, J., Zhang, Y., Zhu, X., & Wang, J. A. Remote Sensing of Environment, 341:115446, August, 2026.
Derivation and evaluation of Landsat-derived annual aboveground biomass maps for Arctic and Boreal North America, 1984-2022 [link]Paper  doi  abstract   bibtex   
Arctic and boreal regions (ABRs) are experiencing rapid warming and increasingly severe wildfires, threatening their roles as global carbon sinks. High quality time series maps of aboveground biomass (AGB) are key for characterizing and attributing spatiotemporal dynamics of carbon stocks in these regions. However, existing maps at regional to global scales often lack the spatial resolution or temporal coverage needed to capture the heterogeneous and dynamic nature of Arctic-boreal AGB change. To address these limitations, we developed annual (1984-2022) 30-m resolution AGB density maps for Alaska and Canada (11.2 x 106 km2). The maps were produced by using extensive training datasets, including 45,002 unique ground plots and 100,000 km2 of airborne lidar data, and time-series spectral features derived from the Continuous Change Detection and Classification (CCDC) algorithm fitted on Landsat Collection 2 Surface Reflectance. Using the eXtreme Gradient Boosting model, we generated annual wall-to-wall maps of AGB along with associated uncertainties. Our maps suggest a ∼ 41 Pg stock of AGB at 2022, representing a 12% increase from 1984. Our maps achieve high accuracy and low bias on holdout testing data (R2 = 0.72, Bias= -5.03%, RMSE% = 62.7%), representing an average of 16.0 percentage points increase in R2 values and 22.7 percentage points decrease in relative bias compared to six existing AGB products. We show using repeat ground measurements that these maps effectively capture AGB loss and recovery due to fire and harvest, gradual AGB changes, and both live and dead tree AGB components in boreal regions. By integrating extensive calibration data with multi-decadal satellite observations and advanced machine learning techniques, these map products provide a robust tool for advancing the understanding of carbon dynamics under global change in Arctic-boreal North America.
@article{liang_derivation_2026,
	title = {Derivation and evaluation of {Landsat}-derived annual aboveground biomass maps for {Arctic} and {Boreal} {North} {America}, 1984-2022},
	volume = {341},
	issn = {0034-4257},
	url = {https://www.sciencedirect.com/science/article/pii/S0034425726002166},
	doi = {10.1016/j.rse.2026.115446},
	abstract = {Arctic and boreal regions (ABRs) are experiencing rapid warming and increasingly severe wildfires, threatening their roles as global carbon sinks. High quality time series maps of aboveground biomass (AGB) are key for characterizing and attributing spatiotemporal dynamics of carbon stocks in these regions. However, existing maps at regional to global scales often lack the spatial resolution or temporal coverage needed to capture the heterogeneous and dynamic nature of Arctic-boreal AGB change. To address these limitations, we developed annual (1984-2022) 30-m resolution AGB density maps for Alaska and Canada (11.2 x 106 km2). The maps were produced by using extensive training datasets, including 45,002 unique ground plots and 100,000 km2 of airborne lidar data, and time-series spectral features derived from the Continuous Change Detection and Classification (CCDC) algorithm fitted on Landsat Collection 2 Surface Reflectance. Using the eXtreme Gradient Boosting model, we generated annual wall-to-wall maps of AGB along with associated uncertainties. Our maps suggest a ∼ 41 Pg stock of AGB at 2022, representing a 12\% increase from 1984. Our maps achieve high accuracy and low bias on holdout testing data (R2 = 0.72, Bias= -5.03\%, RMSE\% = 62.7\%), representing an average of 16.0 percentage points increase in R2 values and 22.7 percentage points decrease in relative bias compared to six existing AGB products. We show using repeat ground measurements that these maps effectively capture AGB loss and recovery due to fire and harvest, gradual AGB changes, and both live and dead tree AGB components in boreal regions. By integrating extensive calibration data with multi-decadal satellite observations and advanced machine learning techniques, these map products provide a robust tool for advancing the understanding of carbon dynamics under global change in Arctic-boreal North America.},
	urldate = {2026-05-27},
	journal = {Remote Sensing of Environment},
	author = {Liang, Wanwan and van Lier, Olivier and Friedl, Mark A. and Randerson, James T. and Rogers, Brendan M. and Burrell, Arden and Hu, Kai-Ting and Tompalski, Piotr and Macander, Matthew J. and Yang, Daryl and Morton, Douglas C. and Bullock, Eric and Lu, Jiaming and Zhang, Yingtong and Zhu, Xiaoran and Wang, Jonathan A.},
	month = aug,
	year = {2026},
	keywords = {NALCMS, Terrestrial Ecoregions (CEC 1997)},
	pages = {115446},
}
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