Estimation of FAPAR over Croplands Using MISR Data and the Earth Observation Land Data Assimilation System (EO-LDAS)

Nadine Gobron,Christiane Schmullius,Philip Lewis,Jose Gómez-Dans,Sina Truckenbrodt,Olivier Morgan,Maxim Chernetskiy

doi:10.3390/rs9070656

Abstract

The Fraction of Absorbed 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 eight 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, the MISR 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 ( r 2 > 0.8), as well as the lowest average R M S E (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 ( r 2 > 0.85); the R M S E 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 r 2 = 0.76), but an underestimate of LAI for optically-thick canopies due to saturation (average R M S E = 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.

Highlights

The Fraction of Absorbed Photosynthetically-Active Radiation (FAPAR) is recognised as an essential climate variable, and it plays an important role in biosphere and climate modelling [1].FAPAR is defined as incident solar radiation in the range 400–700 nm that is absorbed by the photosynthetic tissue of canopy [2] and, is an important control on the photosynthetic activity of vegetation
The temporal trajectories of the leaf pigments are characterised by large uncertainties, but the posterior mean shows a clear annual cycle, with chlorophyll leading senescence, as expected. This is a significant observation, as the temporal dynamics are not fixed by the prior term
We have compared results from a number of different approaches and sensors: the Earth Observation Land Data Assimilation System (EO-LDAS) and Joint Research Centre Two-stream Inversion Package (JRC-TIP) approaches both use MISR data. These two methods rely on the inversion of an radiative transfer (RT) model, auxiliated by prior parameter distributions, and in the case of EO-LDAS, a dynamic regularisation model that allows the inference even at times where no satellite observations are present

Summary

Introduction

The Fraction of Absorbed Photosynthetically-Active Radiation (FAPAR) is recognised as an essential climate variable, and it plays an important role in biosphere and climate modelling [1]. FAPAR is defined as incident solar radiation in the range 400–700 nm that is absorbed by the photosynthetic tissue of canopy [2] and, is an important control on the photosynthetic activity of vegetation. We consider FAPAR to be a land surface parameter (e.g., only related to the land), the amount of direct and diffuse radiation affects its value [9,10]. In order to infer the state of the land surface, the inversion of physically-based models that describe the interaction of incoming radiation with the soil-leaf-canopy medium, typically based on radiative transfer (RT) theory, are generally used [11,12].

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Discussion

Conclusion