. Globally mapped terrestrial chlorophyll fluorescence retrievals are of high interest because they can provide information on the functional status of vegetation including light-use efficiency and global primary productivity that can be used for global carbon cycle modeling and agricultural applications. Previous satellite retrievals of fluorescence have relied solely upon the filling-in of solar Fraunhofer lines that are not significantly affected by atmospheric absorption. Although these measurements provide near-global coverage on a monthly basis, they suffer from relatively low precision and sparse spatial sampling. Here, we describe a new methodology to retrieve global far-red fluorescence information; we use hyperspectral data with a simplified radiative transfer model to disentangle the spectral signatures of three basic components: atmospheric absorption, surface reflectance, and fluorescence radiance. An empirically based principal component analysis approach is employed, primarily using cloudy data over ocean, to model and solve for the atmospheric absorption. Through detailed simulations, we demonstrate the feasibility of the approach and show that moderate-spectral-resolution measurements with a relatively high signal-to-noise ratio can be used to retrieve far-red fluorescence information with good precision and accuracy. The method is then applied to data from the Global Ozone Monitoring Instrument 2 (GOME-2). The GOME-2 fluorescence retrievals display similar spatial structure as compared with those from a simpler technique applied to the Greenhouse gases Observing SATellite (GOSAT). GOME-2 enables global mapping of far-red fluorescence with higher precision over smaller spatial and temporal scales than is possible with GOSAT. Near-global coverage is provided within a few days. We are able to show clearly for the first time physically plausible variations in fluorescence over the course of a single month at a spatial resolution of 0.5° × 0.5°. We also show some significant differences between fluorescence and coincident normalized difference vegetation indices (NDVI) retrievals.

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Global monitoring of terrestrial chlorophyll fluorescence from moderate-spectral-resolution near-infrared satellite measurements: methodology, simulations, and application to GOME-2

Prospects for Chlorophyll Fluorescence Remote Sensing from the Orbiting Carbon Observatory-2

. The Orbiting Carbon Observatory-2 (OCO-2) carries and points a three-channel imaging grating spectrometer designed to collect high-resolution, co-boresighted spectra of reflected sunlight within the molecular oxygen (O2) A-band at 0.765 microns and the carbon dioxide (CO2) bands at 1.61 and 2.06 microns. These measurements are calibrated and then combined into soundings that are analyzed to retrieve spatially resolved estimates of the column-averaged CO2 dry-air mole fraction, XCO2. Variations of XCO2 in space and time are then analyzed in the context of the atmospheric transport to quantify surface sources and sinks of CO2. This is a particularly challenging remote-sensing observation because all but the largest emission sources and natural absorbers produce only small ( To meet its demanding measurement requirements, each OCO-2 spectrometer channel collects 24 spectra s−1 across a narrow (  17 000), dynamic range (∼ 104), and sensitivity (continuum signal-to-noise ratio > 400). The OCO-2 instrument performance was extensively characterized and calibrated prior to launch. In general, the instrument has performed as expected during its first 18 months in orbit. However, ongoing calibration and science analysis activities have revealed a number of subtle radiometric and spectroscopic challenges that affect the yield and quality of the OCO-2 data products. These issues include increased numbers of bad pixels, transient artifacts introduced by cosmic rays, radiance discontinuities for spatially non-uniform scenes, a misunderstanding of the instrument polarization orientation, and time-dependent changes in the throughput of the oxygen A-band channel. Here, we describe the OCO-2 instrument, its data products, and its on-orbit performance. We then summarize calibration challenges encountered during its first 18 months in orbit and the methods used to mitigate their impact on the calibrated radiance spectra distributed to the science community.

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The on-orbit performance of the Orbiting Carbon Observatory-2 (OCO-2) instrument and its radiometrically calibrated products

. We describe a method of evaluating systematic errors in measurements of total column dry-air mole fractions of CO2 (XCO2) from space, and we illustrate the method by applying it to the v2.8 Atmospheric CO2 Observations from Space retrievals of the Greenhouse Gases Observing Satellite (ACOS-GOSAT) measurements over land. The approach exploits the lack of large gradients in XCO2 south of 25° S to identify large-scale offsets and other biases in the ACOS-GOSAT data with several retrieval parameters and errors in instrument calibration. We demonstrate the effectiveness of the method by comparing the ACOS-GOSAT data in the Northern Hemisphere with ground truth provided by the Total Carbon Column Observing Network (TCCON). We use the observed correlation between free-tropospheric potential temperature and XCO2 in the Northern Hemisphere to define a dynamically informed coincidence criterion between the ground-based TCCON measurements and the ACOS-GOSAT measurements. We illustrate that this approach provides larger sample sizes, hence giving a more robust comparison than one that simply uses time, latitude and longitude criteria. Our results show that the agreement with the TCCON data improves after accounting for the systematic errors, but that extrapolation to conditions found outside the region south of 25° S may be problematic (e.g., high airmasses, large surface pressure biases, M-gain, measurements made over ocean). A preliminary evaluation of the improved v2.9 ACOS-GOSAT data is also discussed.

/pdf/a-method-for-evaluating-bias-in-global-measurements-of-co-2-1fx35tcthj.pdf

A method for evaluating bias in global measurements of CO 2 total columns from space

. The column-averaged dry-air mole fractions of carbon dioxide and methane (XCO2 and XCH4) have been retrieved from Greenhouse gases Observing SATellite (GOSAT) Short-Wavelength InfraRed (SWIR) observations and released as a SWIR L2 product from the National Institute for Environmental Studies (NIES). XCO2 and XCH4 retrieved using the version 01.xx retrieval algorithm showed large negative biases and standard deviations (−8.85 and 4.75 ppm for XCO2 and −20.4 and 18.9 ppb for XCH4, respectively) compared with data of the Total Carbon Column Observing Network (TCCON). Multiple reasons for these error characteristics (e.g., solar irradiance database, handling of aerosol scattering) are identified and corrected in a revised version of the retrieval algorithm (version 02.xx). The improved retrieval algorithm shows much smaller biases and standard deviations (−1.48 and 2.09 ppm for XCO2 and −5.9 and 12.6 ppb for XCH4, respectively) than the version 01.xx. Also, the number of post-screened measurements is increased, especially at northern mid- and high-latitudinal areas.

/pdf/improvement-of-the-retrieval-algorithm-for-gosat-swir-xco2-15c15whckz.pdf

Improvement of the retrieval algorithm for GOSAT SWIR XCO2 and XCH4 and their validation using TCCON data

. This work describes the NASA Atmospheric CO2 Observations from Space (ACOS) XCO2 retrieval algorithm, and its performance on highly realistic, simulated observations. These tests, restricted to observations over land, are used to evaluate retrieval errors in the face of realistic clouds and aerosols, polarized non-Lambertian surfaces, imperfect meteorology, and uncorrelated instrument noise. We find that post-retrieval filters are essential to eliminate the poorest retrievals, which arise primarily due to imperfect cloud screening. The remaining retrievals have RMS errors of approximately 1 ppm. Modeled instrument noise, based on the Greenhouse Gases Observing SATellite (GOSAT) in-flight performance, accounts for less than half the total error in these retrievals. A small fraction of unfiltered clouds, particularly thin cirrus, lead to a small positive bias of ~0.3 ppm. Overall, systematic errors due to imperfect characterization of clouds and aerosols dominate the error budget, while errors due to other simplifying assumptions, in particular those related to the prior meteorological fields, appear small.

/pdf/the-acos-co-2-retrieval-algorithm-part-1-description-and-j1zxxq2qqb.pdf

D. Eldering

Papers

The ACOS CO 2 retrieval algorithm – Part 1: Description and validation against synthetic observations