Intercomparison of fraction of absorbed photosynthetically active radiation products derived from satellite data over Europe

D’Odorico, Petra; Gonsamo, Alemu; Pinty, B.; Gobron, Nadine; Coops, Nicholas C.; Mendez, Elias; Schaepman, Michael E.

doi:10.1016/j.rse.2013.12.005

Cited by 75 publications

(77 citation statements)

References 58 publications

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“…In addition, they noted that MODIS fPAR (C5) produced larger seasonal variation in fPAR for needleleaf evergreen forests than what was estimated based on flux tower measurements. On the other hand, for a southern boreal forest site in Finland, satellite-based JRC-TIP (Joint Research Centre Two-stream Inversion Package) and ESA/JRC MGVI (European Space Agency/JRC Global Vegetation Index) estimates of summer fPAR produced very low values (~0.4) compared to ground measurements [5]. When no good quality fPAR products are available NDVI based products can be used to approximate fPAR (e.g., [7,8]).…”

Section: Introductionmentioning

confidence: 93%

“…Recent studies have concentrated on the intercomparison and evaluation of different fPAR products over large areas, e.g., over Northern Eurasia [4], Europe [5], Australia [6], Scandinavia [7], Alaska [8,9], Iberia Peninsula [10], temperate forests in USA [11], tropical forests of Amazon [12] or over the entire globe [13,14]. There are large differences among fPAR products for forested biomes [5,6,10]. D'Odorico et al [5] found, for example, that for Scandinavian and Russian boreal forests, MODIS fPAR (MCD product, C5: Collection five) overestimated fPAR by almost 0.5 units.…”

Section: Introductionmentioning

confidence: 99%

“…There are large differences among fPAR products for forested biomes [5,6,10]. D'Odorico et al [5] found, for example, that for Scandinavian and Russian boreal forests, MODIS fPAR (MCD product, C5: Collection five) overestimated fPAR by almost 0.5 units. In addition, they discovered that the same product displayed an anomalous peak in summertime fPAR values, and noted considerable disagreements between other fPAR products for July.…”

Section: Introductionmentioning

confidence: 99%

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Validation of MODIS and GEOV1 fPAR Products in a Boreal Forest Site in Finland

et al. 2015

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Remote sensing of the fraction of absorbed Photosynthetically Active Radiation (fPAR) has become a timely option to monitor forest productivity. However, only a few studies have had ground reference fPAR datasets containing both forest canopy and understory fPAR from boreal forests for the validation of satellite products. The aim of this paper was to assess the performance of two currently available satellite-based fPAR products: MODIS fPAR (MOD15A2, C5) and GEOV1 fPAR (g2_BIOPAR_FAPAR), as well as an NDVI-fPAR relationship applied to the MODIS surface reflectance product and a Landsat 8 image, in a boreal forest site in Finland. Our study area covered 16 km 2 and field data were collected from 307 forest plots. For all plots, we obtained both forest canopy fPAR and understory fPAR. The ground reference total fPAR agreed better with GEOV1 fPAR than with MODIS fPAR, which showed much more temporal variation during the peak-season than GEOV1 fPAR. At the chosen intercomparison date in peak growing season, MODIS NDVI based fPAR estimates were similar to GEOV1 fPAR, and produced on average 0.01 fPAR units smaller fPAR estimates than ground reference total fPAR. MODIS fPAR and Landsat 8 NDVI based fPAR estimates were similar to forest canopy fPAR.

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Section: Introductionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Validation of MODIS and GEOV1 fPAR Products in a Boreal Forest Site in Finland

et al. 2015

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“…TC is a method to estimate the RMSE (and, if desired, correlation coefficients) of three spatially and temporally collocated measurements by assuming a linear error model between the measurements (McColl et al, 2014;Stoffelen, 1998). This methodology has been widely used in error estimation of land and ocean parameters, such as wind speed, sea surface temperature, soil moisture, evaporation, precipitation, f APAR, and in the rescaling of measurement systems to reference system for data assimilation purposes (Alemohammad et al, 2015;D'Odorico et al, 2014;Gruber et al, 2016;Hain et al, 2011;Lei et al, 2015;Miralles et al, 2010Miralles et al, , 2011bParinussa et al, 2011), as well as in validating categorical variables such as the soil freeze-thaw state (McColl et al, 2016). The relationship between each measurement and the true value is assumed to follow a linear model:…”

Section: Target Dataset: a Bayesian Prior Using Triple Collocationmentioning

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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“…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]. The main benefits of using physically-based RT models is their ability to cope with different sensor properties (angular and spectral sampling characteristics, etc.)…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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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 RMSE (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 RMSE 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 RMSE = 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.

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Intercomparison of fraction of absorbed photosynthetically active radiation products derived from satellite data over Europe

Cited by 75 publications

References 58 publications

Validation of MODIS and GEOV1 fPAR Products in a Boreal Forest Site in Finland

Validation of MODIS and GEOV1 fPAR Products in a Boreal Forest Site in Finland

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

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

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