Aguilar-Amuchastegui, NaikoaBelenky, Lucas GregoryBurt, AndrewBecerra Leal, CatalinaGalindo, GustavoBotero, Maria Fernanda JaramilloKiesslich, NormanMatteson, RyanMichel Fuentes, Jose MariaPeneva-Reed, EllieVilca, Beisit Luz PumaSanchez, NatalieSoares, MuriOdorico, HerciloOsterbur, NicholasPerez Lara, MartinTurriago, JuanEspejo, Andres B.2025-02-112025-02-112025-02-11https://hdl.handle.net/10986/42789This study explored the use of a state-of-the-art collection of high-quality in situ datasets, following best practices13 to inform biomass estimates derived through remote sensing. The intention was to circumvent (1) the challenge of extrapolating data coverage from plot-level to satellite-level (for example, for the entire ERP area) and (2) the limitations of using allometric equations to estimate biomass from national forest inventories. The ASA also explored a novel method of creating data synergies within a 50,000-hectare (ha) region of interest. It was expected that these processes would improve the accuracy and bias of estimates so they could be extrapolated to the larger ERP area with the support of colleagues from Sylvera.en-USCC BY-NC 3.0 IGOBIOMASS ESTIMATESEXTRAPOLATING DATA COVERAGENATIONAL FOREST INVENTORIESLIFE ON LANDCLIMATE ACTIONReportWorld Bank10.1596/42789https://doi.org/10.1596/42789