An automatic procedure to identify key vegetation phenology events using the JRC-FAPAR products

Verstraete, M. M.; Gobron, Nadine; Aussedat, Ophélie; Robustelli, Monica; Pinty, B.; Widlowski, Jean-Luc; Taberner, Malcolm

doi:10.1016/j.asr.2007.05.066

Cited by 47 publications

(38 citation statements)

References 22 publications

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“…In June (b p , BBCH 12-50, LAI > 1) and July (c p BBCH 50-90, LAI > 3) the cereal vegetation indices are higher due to increased photosynthetic activity with maximum LAI values usually observed in June [41]. The PAR ND Index (Model IV) is similar to JRC-FAPAR algorithm (Fraction of Absorbed Photosynthetically Active Radiation [68]). Green vegetation with completely closed canopies (LAI > 1) strongly absorbs solar radiation in the red spectral region (ρ RED ) and, respectively, emits and scatters in near-infrared (ρ NIR ).…”

Section: Implications From the Optical Infrared Reflectance Data For mentioning

confidence: 56%

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Cereal Yield Modeling in Finland Using Optical and Radar Remote Sensing

et al. 2010

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Optical VGI yield estimates were validated with CropWatN crop model yield estimates using SPOT and NOAA data (mean R 2 0.71, RMSE 436 kg/ha) and with composite SAR/ASAR and NDVI models (mean R 2 0.61, RMSE 402 kg/ha) using both reflectance and backscattering data. CropWatN and Composite SAR/ASAR & NDVI model mean yields were 4,754/4,170 kg/ha for wheat, 4,192/3,848 kg/ha for barley and 4,992/2,935 kg/ha for oats.

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Section: Implications From the Optical Infrared Reflectance Data For mentioning

confidence: 56%

“…Kadett) denoting absorbed infrared covariance between partly covered top soil and early emergence of spring cereals with partly closed canopy structures (LAI < 1) [41,[55][56][57][58][59][67][68][69]. Correspondingly in May, the near-infrared (ρ NIR /Rf 4 ) obtained higher emitted reflectance.…”

Section: Cereal Intracultivar Variancementioning

confidence: 99%

Cereal Yield Modeling in Finland Using Optical and Radar Remote Sensing

et al. 2010

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“…FAPAR is defined as incident solar radiation in the range 400-700 nm that is absorbed by the photosynthetic tissue of canopy [2] and, thus, is an important control on the photosynthetic activity of vegetation. FAPAR has been widely used for monitoring drought, biodiversity, land degradation, phenology, CO 2 emission studies and Dynamic Global Vegetation Models (DGVM) [3][4][5][6][7][8].…”

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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“…These include Normalized Difference Water Index (NDWI) (Delbart et al, 2005), FPAR (Verstraete et al, 2008), and LAI (Obrist et al, 2003).…”

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confidence: 99%

“…These products include: (1) the MODIS Land Cover Dynamics Product (MCD12Q2) derived from MODIS NBAR (nadir bidirectional reflectance distribution function adjusted reflectance) EVI (enhanced vegetation index) (500m-1000m), which is the only global product that is produced on an operational basis from 2001 to present Ganguly et al, 2010); (2) the MODIS-based product generated at NASA-GSFC (Goddard Space Flight Center) in support of the North American Carbon Program, which was produced using MODIS data at a spatial resolution of 250m-500m (Morisette et al, 2009;Tan et al, 2011); (3) the MODIS phenology product being generated for the contiguous United States (CONUS) by the US Forest Service (Hargrove et al, 2009); (4) the USGS long-term 1-km AVHRR phenology product for CONUS (1989-present;Reed et al, 1994); (5) the NOAA 4-km GVIx phenology over North America from 1982(Zhang et al, 2007; (6) the global 4.6 km product for 2005 from the Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) (Dash et al, 2010); and (6) the global product based on FPAR (Fraction of Photosynthetically Active Radiation) developed by the European Space Agency (Verstraete et al, 2008).…”

mentioning

confidence: 99%