Remote estimation of the fraction of absorbed photosynthetically active radiation for a maize canopy in Northeast China

Aims accurate remote estimation of the fraction of absorbed photosynthetically active radiation(fAPAR)is essential for the light use efficiency(LUE)models.Currently,one challenge for the LUE models is lack of knowledge about the relationship between fAPAR and the normalized difference vegetation index(NDVI).Few studies have tested this relationship against field measurements and evaluated the accuracy of the remote estimation method.this study aimed to reveal the empirical relationship between NDVI and fAPAR and to improve algorithms for remote estimation of fAPAR.Methods to investigate the method of remote estimation of fAPAR seasonal dynamics,the CASA(Carnegie-ames-stanford approach)model and spectral vegetation indices(VIs)were used for in situ measure-ments of spectral reflectance and fAPAR during the growing season of a maize canopy in Northeast China.Important Findingsthe results showed that the fAPAR increased rapidly with the day of year during the vegetative stage,it remained relatively stable at the stage of reproduction,and finally decreased slowly during the senescence stage.In addition,fAPAR green[fAPAR_(green)=fAPAR_(green) -fAPAR_(green) LAI_(max))]showed clearer seasonal trends than fAPAR.the NDVI,red-edge NDVI,wide dynamic range vegetation index,red-edge position(REP)and REP with sentinel-2 bands derived from hyperspectral remote sensing data were all significantly positively related to fAPAR green during the entire growing season.In a comparison of the predictive performance of VIs for the whole growing season,REP was the most appropriate spectral index,and can be recommended for monitoring seasonal dynamics of fAPAR in a maize canopy.

Improving GPP estimates by partitioning green APAR from total APAR in two deciduous forest sites

Non-photosynthetic components within a forest ecosystem account for a large proportion of the canopy but are not involved in photosynthesis.Therefore,the accuracy of gross primary production(GPP)estimates is expected to improve by removing these components.However,their infl uence in GPP estimations has not been quantitatively evaluated for deciduous forests.Several vegetation indices have been used recently to estimate the fraction of photosynthetically active radiation absorbed by photosynthetic components(FAPAR_(green))for partitioning APAR green(photosynthetically active radiation absorbed by photosynthetic components).In this study,the enhanced vegetation index(EVI)estimated FAPAR_(green)and to separate the photosynthetically active radiation absorbed by photosynthetic components(APAR green)from total APAR observations(APAR_(total))at two deciduous forest sites.The eddy covariance-light use effi ciency(EC-LUE)algorithm was employed to evaluate the infl uence of non-photosynthetic components and to test the performance of APAR green in GPP estimation.The results show that the infl uence of non-photosynthetic components have a seasonal pattern at deciduous forest sites,large diff erences are observed with normalized root mean square error(RMSE*)values of APAR green-based GPP and APAR_(total)-based GPP between tower-based GPP during the early and end stages,while slight diff erences occurred during peak growth seasons.In addition,daily GPP estimation was significantly improved using the APAR green-based method,giving a higher coeffi cient of determination and lower normalized root mean square error against the GPP estimated by the APAR_(total)-based method.The results demonstrate the signifi cance of partitioning APAR green from APAR_(total)for accurate GPP estimation in deciduous forests.

Tropical forest canopies and their relationships with climate and disturbance： results from a global dataset of consistent field-based measurements

Background： Canopy structure, defined by leaf area index （LAI）, fractional vegetation cover （FCover） and fraction of absorbed photosynthetically active radiation （fAPAR）, regulates a wide range of forest functions and ecosystem services. Spatially consistent field-measurements of canopy structure are however lacking, particularly for the tropics. Methods： Here, we introduce the Global LAI database： a global dataset of field-based canopy structure measurements spanning tropical forests in four continents （Africa, Asia, Australia and the Americas）. We use these measurements to test for climate dependencies within and across continents, and to test for the potential of anthropogenic disturbance and forest protection to modulate those dependences. Results： Using data collected from 887 tropical forest plots, we show that maximum water deficit, defined across the most arid months of the year, is an important predictor of canopy structure, with all three canopy attributes declining significantly with increasing water deficit. Canopy attributes also increase with minimum temperature, and with the protection of forests according to both active （within protected areas） and passive measures （through topography）. Once protection and continent effects are accounted for, other anthropogenic measures （e.g. human population） do not improve the model. Conclusions： We conclude that canopy structure in the tropics is primarily a consequence of forest adaptation to the maximum water deficits historically experienced within a given region. Climate change, and in particular changes in drought regimes may thus affect forest structure and function, but forest protection may offer some resilience against this effect.