Earth Observation Data Group

Abstract:

The recent advent of very high spectral resolution measurements by the Fourier Transform Spectrometer (FTS) on board the Greenhouse gases Observing SATellite (GOSAT) platform has made possible the retrieval of sun-induced terrestrial chlorophyll fluorescence (F s) on a global scale. The basis for this retrieval is the modeling of the in-filling of solar Fraunhofer lines by fluorescence. This contribution to the field of space-based carbon cycle science presents an alternative method for the retrieval of F s from the Fraunhofer lines resolved by GOSAT-FTS measurements. The method is based on a linear forward model derived by a singular vector decomposition technique, which enables a fast and robust inversion of top-of-atmosphere radiance spectra. Retrievals are performed in two spectral micro-windows (~2-3nm width) containing several strong Fraunhofer lines. The statistical nature of this approach allows to avoid potential retrieval errors associated with the modeling of the instrument line shape or with a given extraterrestrial solar irradiance data set. The method has been tested on 22 consecutive months of global GOSAT-FTS measurements. The fundamental basis of this F s retrieval approach and the results from the analysis of the global F s data set produced with it are described in this work. Among other findings, the data analysis has shown (i) a very good comparison of F s intensity levels and spatial patterns with the state-of-the-art physically-based F s retrieval approach described in Frankenberg et al. (2011a), (ii) the overall good agreement between F s annual and seasonal patterns and other space-based vegetation parameters, (iii) the need for a biome-dependent scaling from F s to gross primary production, and (iv) the apparent existence of strong directional effects in the F s emission from forest canopies. These results reinforce the confidence in the feasibility of F s retrievals with GOSAT-FTS and open several points for future research in this emerging field. © 2012 Elsevier Inc.

Abstract:

The aerosol component of the Oxford-Rutherford Appleton Laboratory (RAL) Aerosol and Clouds (ORAC) retrieval scheme for the Advanced Along-Track Scanning Radiometer (AATSR) uses data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) to constrain the brightness of the surface. However, the spectral response functions of the channels used (centred near 550 nm, 660 nm, 870 nm, and 1.6 μm) do not exactly match between the two sensors. It is shown that failure to account for differences between the instruments' spectral response functions leads to errors of typically 0.001-0.01 in spectral surface albedo, and distinct biases, dependent on wavelength and surface type. A technique based on singular value decomposition (SVD) is used to reduce these random errors by an average of 35% at 670. nm and over 60% at the other wavelengths used. The technique reduces the biases so that they are negligible. In principle, the method can be extended to any combination of sensors. The SVD-based scheme is applied to AATSR data from the month of July 2008 and found to increase the number of successful aerosol retrievals, the speed of retrieval convergence, and improve the level of consistency between the measurements and the retrieved state. Additionally, retrieved aerosol optical depth at 550. nm shows an improvement in correspondence when compared to Aerosol Robotic Network (AERONET) data. © 2011 Elsevier Inc.

Abstract:

The aerosol component of the Oxford-Rutherford Appleton Laboratory (RAL) Aerosol and Clouds (ORAC) retrieval scheme for the Advanced Along-Track Scanning Radiometer (AATSR) uses data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) to constrain the brightness of the surface. However, the spectral response functions of the channels used (centred near 550nm, 660nm, 870nm, and 1.6μm) do not exactly match between the two sensors. It is shown that failure to account for differences between the instruments' spectral response functions leads to errors of typically 0.001–0.01 in spectral surface albedo, and distinct biases, dependent on wavelength and surface type. A technique based on singular value decomposition (SVD) is used to reduce these random errors by an average of 35% at 670nm and over 60% at the other wavelengths used. The technique reduces the biases so that they are negligible. In principle, the method can be extended to any combination of sensors. The SVD-based scheme is applied to AATSR data from the month of July 2008 and found to increase the number of successful aerosol retrievals, the speed of retrieval convergence, and improve the level of consistency between the measurements and the retrieved state. Additionally, retrieved aerosol optical depth at 550nm shows an improvement in correspondence when compared to Aerosol Robotic Network (AERONET) data.