Neural network estimation of LAI, fAPAR, fCover and LAI×Cab, from top of canopy MERIS reflectance data : Principles and validation

By Cédric Bacour, Frédéric Baret, D. Beal, Marie Weiss, K. Pavageau. 2006

420 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Plant Science

View Paper: https://www.semanticscholar.org/paper/2c1cfd739adc25c9818ae6615bfa7742ddbddf65

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Citations in the corpus

GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part1: Principles of development and production (2013, neutral): “The blue band was not considered here since it brings only little extra information on the surface as compared to the red, NIR and SWIR bands, while being very sensitive to errors in the atmospheric correction (Bacour et al. 2006).”
A comparison of methods for smoothing and gap filling time series of remote sensing observations - application to MODIS LAI products (2012, methodology): “Satellite observations in the reflective solar domain have been used intensively for more than a decade to monitor LAI dynamics over the globe using medium resolution sensors such as MODIS (Moderate Resolution Imaging Spectroradiometer) (Myneni et al., 2002), VEGETATION (Deng et al., 2006; Baret et ”
Estimating winter wheat (Triticum aestivum) LAI and leaf chlorophyll content from canopy reflectance data by integrating agronomic prior knowledge with the PROSAIL model (2015, methodology): “Bacour et al. (2006) proposed a neural network algorithm (NNA) for the estimation of canopy biophysical variables from a Medium Resolution Imaging Spectrometer (MERIS) image.”
Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance (2014, background): “These empirical methods are computationally efficient in operating with a large amount of data, but they do not exploit the full spectral-directional information conveyed by the radiometric signal [7].”
Estimating crop chlorophyll content with hyperspectral vegetation indices and the hybrid inversion method (2016, methodology): “Additionally, chlorophyll content inversion involves the leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC, calculated as LCC × LAI), two indicators that are measured at different scales (Daughtry et al. 2000; Bacour et al. 2006; Darvishzadeh et al. 2008).”
Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and -3 (2012, methodology): “By feeding simulated training data into a generic NN, Bacour et al. (2006) achieved an accuracy of 0.09.”
Theoretical uncertainty analysis of global MODIS, CYCLOPES, and GLOBCARBON LAI products using a triple collocation method (2012, background): “Underestimation of CYCLOPES for higher LAI is related to the saturation effect in the radiative transfer simulation process and the neural network inversion algorithm (Bacour et al., 2006; Weiss et al., 2007).”
Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass (2008, methodology): “As noted by Bacour et al. (2006) , inversion techniques based on pre-computed reflectance database are often preferred to more computationally heavy iterative methods for operational applications.”
Experimental Sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods - A comparison (2015, methodology): “This is suggested to be due to saturation effects, a well-known phenomenon with PROSAIL simulations (Bacour et al., 2006; Weiss et al., 2007; Garrigues et al., 2008).”