Improving the performance of remote sensing models for capturing intra- and inter-annual variations in daily GPP: An analysis using global FLUXNET tower data

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.

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; 

2018-01-16

ELSEVIER SCIENCE BV

JRC99997

0168-1923,   

http://www.sciencedirect.com/science/article/pii/S0168192315007108?via%3Dihub,    https://publications.jrc.ec.europa.eu/repository/handle/JRC99997,   

10.1016/j.agrformet.2015.09.005,