Estimation of FAPAR over croplands using MISR data and the Earth Observation Land Data Assimilation System (EO-LDAS)

The fraction of photosynthetically active radiation (FAPAR) is an important parameter in climate and carbon cycle studies. In this paper, we use the Earth Observation Land Data Assimilation System (EO-LDAS) framework to retrieve FAPAR from observations of directional surface reflectance measurements from the Multi-angle Imaging SpectroRadiometer (MISR) instrument. The procedure works by interpreting the reflectance data via the semi-discrete radiative transfer (RT) model, supported by a prior parameter distribution and a dynamic regularisation model, and resulting in an inference of land surface parameters, such as effective leaf area index (LAI), leaf chlorophyll concentration and fraction of senescent leaves, with full uncertainty quantification. The method is demonstrated over three agricultural FLUXNET sites, and the EO-LDAS results are compared with 8 years of in situ measurements of FAPAR and LAI, resulting in a total of 24 site years. We additionally compare three other widely used EO FAPAR products, namely the MEdium Resolution Imaging Spectrometer (MERIS) Full Resolution, theMISR High Resolution (HR) Joint Research Centre Two-stream Inversion Package (JRC-TIP) and MODIS MCD15 FAPAR products. The EO-LDAS MISR FAPAR retrievals show a high correlation with the ground measurements (r2>0.8), as well as the lowest average RMSE (0.14), in line with the MODIS product. As the EO-LDAS solution is effectively interpolated, if only measurements that are coincident with MISR observations are considered, the correlation increases (r2>0.85), the RMSE is lower by 4-5%, and the bias is 2 and 7%. The EO-LDAS MISR LAI estimates show a strong correlation with ground based LAI (average r2=0.76), but an underestimate of LAI for optically thick canopies due to saturation (average RMSE=2.23). These results suggest that the EO-LDAS approach is successful in retrieving both FAPAR and other land surface parameters. A large part of this success is based on the use of a dynamic regularisation model that counteracts the poor temporal sampling from the MISR instrument.

CHERNETSKIY Maxim;  GOMEZ-DANS J.;  GOBRON Nadine;  MORGAN Olivier;  LEWIS P.;  TRUCKENBRODT Sina;  SCHMULLIUS Christine; 

2017-10-12

MDPI AG

JRC107158

2072-4292,   

http://www.mdpi.com/2072-4292/9/7/656,    https://publications.jrc.ec.europa.eu/repository/handle/JRC107158,   

10.3390/rs9070656,