Background:Information on above-ground biomass(AGB) is important for managing forest resource use at local levels,land management planning at regional levels,and carbon emissions reporting at national and international levels.In many tropical developing countries,this information may be unreliable or at a scale too coarse for use at local levels.There is a vital need to provide estimates of AGB with quantifiable uncertainty that can facilitate land use management and policy development improvements.Model-based methods provide an efficient framework to estimate AGB.Methods:Using National Forest Inventory(NFI) data for a~1,000,000 ha study area in the miombo ecoregion,Zambia,we estimated AGB using predicted canopy cover,environmental data,disturbance data,and Landsat 8 OLI satellite imagery.We assessed different combinations of these datasets using three models,a semiparametric generalized additive model(GAM) and two nonlinear models(sigmoidal and exponential),employing a genetic algorithm for variable selection that minimized root mean square prediction error(RMSPE),calculated through cross-validation.We compared model fit statistics to a null model as a baseline estimation method.Using bootstrap resampling methods,we calculated 95% confidence intervals for each model and compared results to a simple estimate of mean AGB from the NFI ground plot data.Results:Canopy cover,soil moisture,and vegetation indices were consistently selected as predictor variables.The sigmoidal model and the GAM performed similarly;for both models the RMSPE was -36.8 tonnes per hectare(i.e.,57% of the mean).However,the sigmoidal model was approximately 30% more efficient than the GAM,assessed using bootstrapped variance estimates relative to a null model.After selecting the sigmoidal model,we estimated total AGB for the study area at 64,526,209 tonnes(+/- 477,730),with a confidence interval 20 times more precise than a simple designbased estimate.Conclusions:Our findings demonstrate that NFI data may be combined with freely available satellite imagery and soils data to estimate total AGB with quantifiable uncertainty,while also providing spatially explicit AGB maps useful for management,planning,and reporting purposes. Background: Information on above-ground biomass (AGB) is important for managing forest resource use at local levels, land management planning at regional levels, and carbon emissions reporting at national and international levels. Many of developing countries, this information may be unreliable or at a scale too coarse for use at local levels. Here is a vital need to provide estimates of AGB with quantifiable uncertainty that can facilitate land use management and policy development improvements. Model-based methods provide an efficient framework to estimate AGB.Methods: Using National Forest Inventory (NFI) data for a ~ 1,000,000 ha study area in the miombo ecoregion, Zambia, we estimated AGB using predicted canopy cover, environmental data, disturbance data, and Landsat 8 OLI satellite imagery. We assess different combinations of these datasets using three models, a semiparametric generalized additive model (GAM) and two nonlinear models (sigmoidal and exponential), employing a genetic algorit hm for variable selection that minimized root mean square prediction error (RMSPE), calculated through cross-validation. We compared model fit statistics to a null model as a baseline estimation method. Using bootstrap resampling methods, we calculated 95% confidence intervals for each model and compared results to a simple estimate of mean AGB from the NFI ground plot data. Results: Canopy cover, soil moisture, and vegetation indices were consistently selected as predictor variables. the sigmoidal model and the GAM performed similarly; for both models the RMSPE was After the sigmoidal model was approximately 30% more efficient than the GAM, assessed using bootstrapped variance estimates relative to a null model. After selecting the sigmoidal model, we estimated total (ie, 57% of the mean) AGB for the study area at 64,526,209 tonnes (+/- 477,730) with a confidence interval 20 times more precise than a simple designbased estimate. Conclusions: Our findings demonstrate that NFIdata may be combined with freely available satellite imagery and soils data to estimate total AGB with quantifiable uncertainty, while also providing spatially explicit AGB maps useful for management, planning, and reporting purposes.