Validation of the operational MERIS FAPAR

Gobron, Nadine; Pinty, B.; Aussedat, Ophélie; Lee, Thomas; Mélin, Frédéric; Robustelli, Monica; Taberner, Malcolm

doi:10.1109/igarss.2007.4423325

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…Vegetation indexes are subjected to intrinsic limitations (e.g., saturation of the signal) and contaminations from different sources (e.g., illumination and observation geometry, 3D structure of the vegetated medium, and background reflectance) [ 5]. More modern approaches make use of canopy radiative transfer models to derive key vegetation variables (e.g., LAI, leaf area index; FAPAR, Fraction of Absorbed Photosynthetically Active Radiation) from canopy reflectances [6][7][8]. The advantage of these methods is that they provide access to inherent vegetation properties largely decontaminated of external factors [ 9].…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Based Yield Estimation in a Stochastic Framework — Case Study of Durum Wheat in Tunisia

Meroni

Marinho

Sghaier³

et al. 2013

Remote Sensing

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Multitemporal optical remote sensing constitutes a useful, cost efficient method for crop status monitoring over large areas. Modelers interested in yield monitoring can rely on past and recent observations of crop reflectance to estimate aboveground biomass and infer the likely yield. Therefore, in a framework constrained by information availability, remote sensing data to yield conversion parameters are to be estimated. Statistical models are suitable for this purpose, given their ability to deal with statistical errors. This paper explores the performance in yield estimation of various remote sensing indicators based on varying degrees of bio-physical insight, in interaction with statistical methods (linear regressions) that rely on different hypotheses. Performances in estimating the temporal and spatial variability of yield, and implications of data scarcity in both dimensions are investigated. Jackknifed results (leave one year out) are presented for the case of wheat yield regional estimation in Tunisia using the SPOT-VEGETATION instrument. Best performances, up to 0.8 of R 2 , are achieved using the most physiologically sound remote sensing indicator, in conjunction with statistical specifications allowing for parsimonious spatial adjustment of the parameters. OPEN ACCESSRemote Sens. 2013, 5 540

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Section: Introductionmentioning

confidence: 99%

Remote Sensing Based Yield Estimation in a Stochastic Framework — Case Study of Durum Wheat in Tunisia

Meroni

Marinho

Sghaier³

et al. 2013

Remote Sensing

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“…QA4ECV TIP LAI/FAPAR Product top level Traceability Chain.4.5 AVHRR FAPAR Joint Research Centre (JRC) methodology was used to compute daily Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) from daily spectral measurements acquired by Advanced Very High Resolution Radiometer (AVHRR) onboard a series of National Oceanic and Atmospheric Administration (NOAA) platforms, namely 07, 09, 11, 14 and 16. The methodology itself is based on previous JRC-FAPAR algorithms such as the ones developed for the Medium Resolution Instrument Sensor (MERIS) and the Ocean Land Colour Instrument (OLCI)[32, 33], except surface reflectances in Band 1 and Band 2 instead of top of atmosphere ones were used as inputs data[18].…”

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confidence: 99%

Quality Assurance Framework Development Based on Six New ECV Data Products to Enhance User Confidence for Climate Applications

Nightingale¹,

Boersma²,

Müller³

et al. 2018

Preprint

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Data from Earth Observation (EO) satellites are increasingly used to monitor the environment, understand variability and change, inform evaluations of climate model forecasts and manage natural resources. Policy makers are progressively relying on the information derived from these datasets to make decisions on mitigating and adapting to climate change. These decisions should be evidence based, which requires confidence in derived products as well as the reference measurements used to calibrate, validate or inform product development. In support of the European Union’s Earth Observation Programmes Copernicus Climate Change Service, the Quality Assurance for Essential Climate Variables (QA4ECV) project fulfilled a gap in the delivery of climate quality satellite derived datasets by prototyping a robust, generic system for the implementation and evaluation of Quality Assurance (QA) measures for satellite-derived ECV climate data record products. The project demonstrated the QA system on six new long-term, climate quality ECV data records for surface Albedo, Leaf Area Index, FAPAR, NO2, HCHO and CO. Provision of standardized QA information provides data users with evidence-based confidence in the products and enables judgement on the fitness-for-purpose of various ECV data products their specific applications.

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Validation of the operational MERIS FAPAR

Cited by 2 publications

References 14 publications

Remote Sensing Based Yield Estimation in a Stochastic Framework — Case Study of Durum Wheat in Tunisia

Remote Sensing Based Yield Estimation in a Stochastic Framework — Case Study of Durum Wheat in Tunisia

Quality Assurance Framework Development Based on Six New ECV Data Products to Enhance User Confidence for Climate Applications

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