Intercomparison and sensitivity analysis of Leaf Area Index retrievals from LAI-2000, AccuPAR, and digital hemispherical photography over croplands

Garrigues, Sébastien; Shabanov, Nikolay V.; Swanson, Kerry; Morisette, Jeffrey T.; Baret, Frédéric; Myneni, Ranga B.

doi:10.1016/j.agrformet.2008.02.014

Cited by 172 publications

(103 citation statements)

References 42 publications

(85 reference statements)

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“…Historic field measurements [35] also published low LAI measurements demonstrating that the relative standard deviation of LAI field measurements in the SEVI area can be up to 60%. This is confirmed by [74], who showed large discrepancies between different ground-based LAI measurements for heterogeneous and short crop canopies due to the substantial differences in the spatial sampling of transmittance (up to RMSE of 0.16). For shrub and grass land, at least the same error for the LAI measurements is expected, as well as for remote sensing-derived LAI products.…”

Section: Resultssupporting

confidence: 66%

Global Gap-Free MERIS LAI Time Series (2002–2012)

et al. 2016

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This article describes the principles used to generate global gap-free Leaf Area Index (LAI) time series from 2002-2012, based on MERIS (MEdium Resolution Imaging Spectrometer) full-resolution Level1B data. It is produced as a series of 10-day composites in geographic projection at 300-m spatial resolution. The processing chain comprises geometric correction, radiometric correction, pixel identification, LAI calculation with the BEAM (Basic ERS & Envisat (A)ATSR and MERIS Toolbox) MERIS vegetation processor, re-projection to a global grid and temporal aggregation selecting the measurement closest to the mean value. After the LAI pre-processing, we applied time series analysis to fill data gaps and to filter outliers using the technique of harmonic analysis (HA) in combination with mean annual and multiannual phenological data. Data gaps are caused by clouds, sensor limitations due to the solar zenith angle (<10˝), topography and intermittent data reception. We applied our technique for the whole period of observation (July 2002-March 2012. Validation, carried out with VALERI (Validation of Land European Remote Sensing Instruments) and BigFoot data, revealed a high degree (R 2 : 0.88) of agreement on a global scale.

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Section: Resultssupporting

confidence: 66%

Global Gap-Free MERIS LAI Time Series (2002–2012)

et al. 2016

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“…For instance, LAI estimates can be derived using the Li-Cor LAI-2000 sensor (Li-Cor, Inc. USA); digital hemispherical photography (DHP) [20]; quantum sensors, such as TRAC (3 rd Wave Engineering, ON, Canada) and Decagon AccuPAR (Decagon Devices, Inc.) or ceptometers (Delta-T devices Ltd, UK). However, hemispherical sensors (such as LAI-2000 and DHP) have been shown to be more robust than other sensors [31,32] in terms of their limited sensitivity to environmental conditions, operational procedures and assumptions, for example in relation to canopy architecture. The LAI-2000 instrument uses a fish-eye lens to measure the canopy light interception in multiple directions at five angles.…”

Section: Validation Of Eo-based Medium Spatial Resolution CCC Productsmentioning

confidence: 99%

“…The digital hemispherical photography technique is based on upward-looking fish-eye photographs taken from beneath the plant canopy that are processed using dedicated software to estimate LAI [34,35]. A detailed review of field techniques for the estimation of LAI can be found in [20,[30][31][32]. Non-destructive estimates of leaf chlorophyll concentration can be obtained from portable leaf chlorophyll meters such as a Minolta SPAD-502 (Minolta Camera Co. Ltd., Japan) or an Opti-Sciences CCM-200 meter (Opti-Sciences, Inc., USA).…”

Section: Validation Of Eo-based Medium Spatial Resolution CCC Productsmentioning

confidence: 99%

Methodologies and Uncertainties in the Use of the Terrestrial Chlorophyll Index for the Sentinel-3 Mission

et al. 2012

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A methodology is described for the validation of Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) data over heterogeneous land surfaces in an agricultural region in Southern Italy. The approach involves the use inverse canopy reflectance modeling techniques to derive maps of canopy chlorophyll content (CCC) and leaf area index (LAI) at fine spatial resolution. Indirect field measurements are used for validation of the fine spatial resolution data. Subsequently, these maps are aggregated based on a regular grid at 1 km spatial resolution to validate MERIS Level 2 MTCI (300 m). RapidEye satellite sensor data with a pixel size of 6.5 m are used for this purpose. Based on a set of independent ground measurements, fine spatial resolution maps achieved an R 2 = 0.78 and RMSE = 0.39 for CCC and R 2 = 0.76 and RMSE = 0.64 for LAI. The relationship between MERIS L2 MTCI and CCC [g·machieved a coefficient of determination of 0.74 and it resulted to be extremely statistically significant (p-value < 0.001). Additionally, a relative validation of two other satellite products at medium resolution spatial scale, namely MERIS leaf area index (LAI) and

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“…A variety of approaches for estimating PAI using direct and indirect methods were presented by Bréda [49], Jonckheere et al [50] and Weiss et al [51]. Vegetation gap fraction (G) is defined as the fraction of sky seen from below the canopy [49][50][51][52]. Gap fraction is related to PAI ( 1 .…”

Section: Pai and Fvc Retrievalmentioning

confidence: 99%

Use of Radarsat-2 and Landsat TM Images for Spatial Parameterization of Manning’s Roughness Coefficient in Hydraulic Modeling

et al. 2015

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Vegetation resistance influences water flow in floodplains. Characterization of vegetation for hydraulic modeling includes the description of the spatial variability of vegetation type, height and density. In this research, we explored the use of dual polarized Radarsat-2 wide swath mode backscatter coefficients (σ°) and Landsat 5 TM to derive spatial hydraulic roughness. The spatial roughness parameterization included four steps: (i) land use classification from Landsat 5 TM; (ii) establishing a relationship between σ° statistics and vegetation parameters; (iii) relative surface roughness (Ks) determination from Synthetic Aperture Radar (SAR) backscatter temporal variability; (iv) derivation of the spatial distribution of the spatial hydraulic roughness both from Manning's roughness coefficient look up table (LUT) and relative surface roughness. Hydraulic simulations were performed using the FLO-2D hydrodynamic model to evaluate model performance under three different hydraulic modeling simulations results with different Manning's coefficient parameterizations, which includes SWL1, SWL2 and SWL3. SWL1 is simulated water OPEN ACCESSRemote Sens. 2015, 7 837 levels with optimum floodplain roughness (np) with channel roughness nc = 0.03 m −1/3 /s; SWL2 is simulated water levels with calibrated values for both floodplain roughness np = 0.65 m −1/3 /s and channel roughness nc = 0.021 m −1/3 /s; and SWL3 is simulated water levels with calibrated channel roughness nc and spatial Manning's coefficients as derived with aid of relative surface roughness. The model performance was evaluated using Nash-Sutcliffe model efficiency coefficient (E) and coefficient of determination (R 2 ), based on water levels measured at a gauging station in the wetland. The overall performance of scenario SWL1 was characterized with E = 0.75 and R 2 = 0.95, which was improved in SWL2 to E = 0.95 and R 2 = 0.99. When spatially distributed Manning values derived from SAR relative surface values were parameterized in the model, the model also performed well and yielding E = 0.97 and R 2 = 0.98. Improved model performance using spatial roughness shows that spatial roughness parameterization can support flood modeling and provide better flood wave simulation over the inundated riparian areas equally as calibrated models.

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Intercomparison and sensitivity analysis of Leaf Area Index retrievals from LAI-2000, AccuPAR, and digital hemispherical photography over croplands

Cited by 172 publications

References 42 publications

Global Gap-Free MERIS LAI Time Series (2002–2012)

Global Gap-Free MERIS LAI Time Series (2002–2012)

Methodologies and Uncertainties in the Use of the Terrestrial Chlorophyll Index for the Sentinel-3 Mission

Use of Radarsat-2 and Landsat TM Images for Spatial Parameterization of Manning’s Roughness Coefficient in Hydraulic Modeling

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