Please use this identifier to cite or link to this item:
|Title:||Improving the performance of remote sensing models for capturing intra- and inter-annual variations in daily GPP: An analysis using global FLUXNET tower data|
|Authors:||VERMA M.; FRIEDL MARK A.; LAW BEVERLY; BONAL DAMIEN; KIELY GERALD; BLACK A.; WOHLFAHRT G.; MOORS E.J.; MONTAGNANI LEONARDO; MARCOLLA B.; TOSCANO P; VARLAGIN ANDREJ; ROUPSARD OLIVIER; CESCATTI ALESSANDRO; ARAIN ALTAF M.; D'ODORICO PETRA|
|Citation:||AGRICULTURAL AND FOREST METEOROLOGY vol. 214-215 p. 416-429|
|Publisher:||ELSEVIER SCIENCE BV|
|Type:||Articles in periodicals and books|
|Abstract:||Accurate and reliable estimates of gross primary productivity (GPP) are required for monitoring the globalcarbon cycle at different spatial and temporal scales. Because GPP displays high spatial and temporal vari-ation, remote sensing plays a major role in producing gridded estimates of GPP across spatiotemporalscales. In this context, understanding the strengths and weaknesses of remote sensing-based models ofGPP and improving their performance is a key contemporary scientific activity. We used measurementsfrom 157 research sites (∼470 site-years) in the FLUXNET “La Thuile” data and compared the skills of11 different remote sensing models in capturing intra- and inter-annual variations in daily GPP in sevendifferent biomes. Results show that the models were able to capture significant intra-annual variationin GPP (Index of Agreement = 0.4–0.80) in all biomes. However, the models’ ability to track inter-annualvariation in daily GPP was significantly weaker (IoA < 0.45). We examined whether the inclusion of differ-ent mechanisms that are missing in the models could improve their predictive power. The mechanismsincluded the effect of sub-daily variation in environmental variables on daily GPP, factoring-in differentialrates of GPP conversion efficiency for direct and diffuse incident radiation, lagged effects of environmen-tal variables, better representation of soil-moisture dynamics, and allowing spatial variation in modelparameters. Our analyses suggest that the next generation remote sensing models need better represen-tation of soil-moisture, but other mechanisms that have been found to influence GPP in site-level studiesmay not have significant bearing on model performance at continental and global scales.|
|JRC Directorate:||Space, Security and Migration|
Files in This Item:
There are no files associated with this item.
Items in repository are protected by copyright, with all rights reserved, unless otherwise indicated.