Accuracy assessment on the number of flux terms needed to estimate in situ fAPAR Abstract   The fraction of Absorbed Photosynthetically Active Radiation (fAPAR) is a crucial variable for assessing global carbon balances and currently, there is an urgent need for reference data to validate satellitederived fAPAR products. However, it is wellknown that fAPAR ground measurements are associated with considerable uncertainties. Generally, fAPAR measurements can be carried out with two, three and fourflux approaches, depending on the number of flux terms measured. Currently, not much is known about the number of flux terms needed to satisfactorily reduce systematic errors. This study investigates the accuracy of different fAPAR estimates based on permanent, 10min PAR measurements using Wireless Sensor Networks (WSNs) at three forest sites, located in Central Europe (mixedconiferous forest), North America (borealdeciduous forest) and Central America (tropical dry forest). All fAPAR estimates reflect the seasonal course of fAPAR. The highest average biases of different fAPAR estimates account to 0.02 at the temperate, 0.08 at the boreal and 0.05 at the tropical site, respectively, thereby generally fulfilling the uncertainty threshold of a maximum of 10 % or 0.05 fAPAR units set by the Global Climate Observing System (GCOS, 2016). During high wind speed conditions at the boreal site, the bias of the twoflux fAPAR estimate exceeded the 0.05uncertainty threshold. Threeflux fAPAR estimates were not found to be advantageous, especially at the tropical site. Our findings are beneficial for the development of sampling protocols that are needed to validate global satellitederived fAPAR products.

Uncertainty Analysis of Remote Sensing Pretreatment for Biomass Estimation on Landsat OLI and Landsat ETM The accurate quantification of biomass helps to understand forest productivity and carbon cycling dynamics. Research on uncertainty during pretreatment is still lacking despite it being one of the major sources of uncertainty and an essential step in biomass estimation. In this study, we investigated pretreatment uncertainty and conducted a comparative study on the uncertainty of three optical imagery preprocessing stages (radiometric calibration, atmospheric and terrain correction) in biomass estimation. A combination of statistical models (random forest) and multisource data (Landsat enhanced thematic mapper plus (ETM), Landsat operational land imager (OLI), national forest inventory (NFI)) was used to estimate forest biomass. Particularly, mean absolute error (MAE) and relative error (RE) were used to assess and quantify the uncertainty of each pretreatment, while the coefficient of determination (R2) was employed to evaluate the accuracy of the model. The results obtained show that random forest (RF) and 10fold cross validation algorithms provided reliable accuracy for biomass estimation to better understand the uncertainty in pretreatments. In this study, there was a considerable uncertainty in biomass estimation using original OLI and ETM images from. Uncertainty was lower after data processing, emphasizing the importance of pretreatments for improving accuracy in biomass estimation. Further, the effects of three pretreatments on uncertainty of biomass estimation were objectively quantified. In this study (results of test sample), a 33.70% uncertainty was found in biomass estimation using original images from the OLI, and a 34.28% uncertainty in ETM. Radiometric calibration slightly increased the uncertainty of biomass estimation (OLI increased by 1.38%, ETM increased by 2.08%). Moreover, atmospheric correction (5.56% for OLI, 4.41% for ETM) and terrain correction (1.00% for OLI, 1.67% for ETM) significantly reduced uncertainty for OLI and ETM, respectively. This is an important development in the field of improving the accuracy of biomass estimation by remote sensing. Notably, the three pretreatments presented the same trend in uncertainty during biomass estimation using OLI and ETM. This may exhibit the same effects in other optical images. This article aims to quantify uncertainty in pretreatment and to analyze the resultant effects to provide a theoretical basis for improving the accuracy of biomass estimation.