Inversion of a radiative transfer model with hyperspectral observations for LAI mapping in poplar plantations

By Michele Meroni, Roberto Colombo, C. Panigada. 2004

219 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/d24848eee73d72652629f805da5eb0b2af86a476

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Citations in the corpus

Linking plant strategies and plant traits derived by radiative transfer modelling (2017, background): “RTMs are often applied in agriculture (Meroni et al. 2004; Duan et al. 2014), while canopies exhibiting greater architectural and physiological 717”
Experimental Sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods - A comparison (2015, background): “Suggestions for improvements typically refer to the addition of prior information at the level of individual parcels (e.g. Atzberger, 2004; Combal et al., 2003; Knyazikhin et al., 1998; Meroni et al., 2004; Richter et al., 2009; Verrelst et al., 2012c).”
Review of Machine Learning Approaches for Biomass and Soil Moisture Retrievals from Remote Sensing Data (2015, methodology): “This limits the possibility to extend their use to different areas and different remote sensing systems, since they remain valid only under the conditions in which reference samples have been collected [22,26].”
Calibration of a water and energy balance model: Recursive parameter estimation versus particle swarm optimization (2009, background): “, 2009] and Leaf Area Indices [Fang et al., 2003; Meroni et al., 2004; Casa and Jones, 2005], a large number of model parameters are still very difficult to measure in situ and even impossible to observe in a spatially distributed or catchment-averaged context.”
Remote sensing techniques for mangrove mapping (2006, background): “For example, image processing of remotely sensed imagery for accurate plant biophysical studies (Bruce and Li, 2001; Zarco-Tejada et al., 2001; Imanishi et al., 2004; le Maire et al., 2004; Meroni et al., 2004) and vegetation discrimination and classification (Tsai and Philpot, 2002; Hochberg and At”
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): “Meroni, Colombo, and Panigada (2004) coupled the SAILH canopy reflectance model with the PROSPECT leaf model to retrieve LAI using a quasi-Newton iterative method.”
A Bayesian object based approach for estimating vegetation biophysical and biochemical variables from APEX at sensor radiance data (2013, background): “Most studies avoid these problems by summarizing Co and Ca matrices into a single weight vector containing the ratios of information contained in the observations to the information contained in the a priori data for each variable (Combal et al., 2002; Li et al., 2001; Meroni et al., 2004).”
Applicability of the PROSPECT model for estimating protein and cellulose + lignin in fresh leaves (2015, unknown): “…confirms the statistical models (Asner, Martin, Knapp, Tupayachi, Anderson, Carranza, et al., 2011; Martin, Plourde, Ollinger, Smith & McNeil, 2008; Mutanga & Skidmore, 2004; Skidmore, Ferwerda, Mutanga, VanWieren, Peel, Grant, et al., 2010) that have consistently demonstrated leaf biochemical…”
Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties - A review (2015, background): “sured and estimated LAI perceptibly improves when only a few but well-chosen wavelengths are selected for model inversion (Meroni et al., 2004; Fang and Liang, 2005; Schlerf et al., 2005; Darvishzadeh et al., 2011).”
Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study (2018, background): “Note that the issue of optimal spectral sampling has been treated by many studies but goes beyond the scope of the present study [5,6,42,90,111,157,158].”