Retrieval and global assessment of terrestrial chlorophyll fluorescence from GOSAT space measurements

Guanter, Luis; Frankenberg, Christian; Dudhia, A.; Lewis, P.; Gómez-Dans, José; Kuze, Akihiko; Shimada, Hiroshi; Grainger, R. G.

doi:10.1016/j.rse.2012.02.006

Cited by 486 publications

(532 citation statements)

References 28 publications

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“…The GOME-2 spatial footprint (i.e., support) of the observations is 40 km × 80 km (Joiner et al, 2013), and the volume of available data is approximately 2 × 10 5 SIF observations per week. Multiple recent studies have demonstrated the potential use of satellite observations of SIF for understanding the photosynthetic CO 2 uptake at large scales (Joiner et al, 2011(Joiner et al, , 2012(Joiner et al, , 2013Frankenberg et al, 2011Frankenberg et al, , 2012Frankenberg et al, , 2014Guanter et al, 2012;Lee et al, 2013). Satellite SIF measurements can be used with land surface models to understand gross primary production (GPP) response to environmental stress (e.g., Lee et al, 2013) and to improve the representation of GPP.…”

Section: Global Land Solar-induced Fluorescence Fieldsmentioning

confidence: 99%

Spatio-temporal approach to moving window block kriging of satellite data v1.0

et al. 2017

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Abstract. Numerous existing satellites observe physical or environmental properties of the Earth system. Many of these satellites provide global-scale observations, but these observations are often sparse and noisy. By contrast, contiguous, global maps are often most useful to the scientific community (i.e., Level 3 products). We develop a spatio-temporal moving window block kriging method to create contiguous maps from sparse and/or noisy satellite observations. This approach exhibits several advantages over existing methods: (1) it allows for flexibility in setting the spatial resolution of the Level 3 map, (2) it is applicable to observations with variable density, (3) it produces a rigorous uncertainty estimate, (4) it exploits both spatial and temporal correlations in the data, and (5) it facilitates estimation in real time. Moreover, this approach only requires the assumption that the observable quantity exhibits spatial and temporal correlations that are inferable from the data. We test this method by creating Level 3 products from satellite observations of CO 2 (XCO 2 ) from the Greenhouse Gases Observing Satellite (GOSAT), CH 4 (XCH 4 ) from the Infrared Atmospheric Sounding Interferometer (IASI) and solar-induced chlorophyll fluorescence (SIF) from the Global Ozone Monitoring Experiment-2 (GOME-2). We evaluate and analyze the difference in performance of spatio-temporal vs. recently developed spatial kriging methods.

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Section: Global Land Solar-induced Fluorescence Fieldsmentioning

confidence: 99%

Spatio-temporal approach to moving window block kriging of satellite data v1.0

et al. 2017

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“…Recently, satellite observations of SIF have become available, opening new possibilities for the global monitoring of photosynthesis (Frankenberg et al, 2011Guanter et al, 2012;Joiner et al, 2013;Schimel et al, 2015;Xu et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Water, Energy, and Carbon with Artificial Neural Networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence

et al. 2017

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Abstract.A new global estimate of surface turbulent fluxes, latent heat flux (LE) and sensible heat flux (H ), and gross primary production (GPP) is developed using a machine learning approach informed by novel remotely sensed solarinduced fluorescence (SIF) and other radiative and meteorological variables. This is the first study to jointly retrieve LE, H , and GPP using SIF observations. The approach uses an artificial neural network (ANN) with a target dataset generated from three independent data sources, weighted based on a triple collocation (TC) algorithm. The new retrieval, named Water, Energy, and Carbon with Artificial Neural Networks (WECANN), provides estimates of LE, H , and GPP from 2007 to 2015 at 1 • × 1 • spatial resolution and at monthly time resolution. The quality of ANN training is assessed using the target data, and the WECANN retrievals are evaluated using eddy covariance tower estimates from the FLUXNET network across various climates and conditions. When compared to eddy covariance estimates, WECANN typically outperforms other products, particularly for sensible and latent heat fluxes. Analyzing WECANN retrievals across three extreme drought and heat wave events demonstrates the capability of the retrievals to capture the extent of these events.Uncertainty estimates of the retrievals are analyzed and the interannual variability in average global and regional fluxes shows the impact of distinct climatic events -such as the 2015 El Niño -on surface turbulent fluxes and GPP.

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“…These include the Greenhouse gases Observing SATellite (GOSAT) [32][33][34][35], the Global Ozone Monitoring Experiment 2 (GOME-2) [21,36], the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) [33,37], and the Orbiting Carbon Observatory 2 (OCO-2) [38]. Future instruments capable of measuring SiF are the TROPOspheric Monitoring Instrument (TROPOMI) [39] and the dedicated FLuorescence EXplorer mission (FLEX) [40].…”

Section: Introductionmentioning

confidence: 99%