Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties - A review

By Jochem Verrelst, Gustau Camps-Valls, Jordi Muñoz-Marí, Juan Pablo Rivera, Frank Veroustraete, Jan G. P. W. Clevers, José F. Moreno. 2015

709 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/582f3b7e2917a907300c60472b0eef96a14683b8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Gustau Camps-Valls
2. Achim Walter
3. Clement Atzberger
4. Helge Aasen
5. Frank Liebisch
6. Lukas Roth

In Ecology

1. Stephane Jacquemoud
2. Jean-Baptiste F'eret
3. Sylvain Jay
4. F. Maupas
5. Nathalie Gorretta
6. Ryad Bendoula
7. Z. Malenovsky
8. Katja Berger

In Media Technology

1. Gustau Camps-Valls
2. Clement Atzberger
3. Helge Aasen
4. Anatoly A. Gitelson
5. Stephane Jacquemoud
6. Jon Atli Benediktsson
7. Lorenzo Bruzzone
8. Antonio J. Plaza

Citations in the corpus

• PROSPECT-PRO for estimating content of nitrogen-containing leaf proteins and other carbon-based constituents (2020, methodology): “The most unsuitable spectral wavelengths can be identified and removed by hybrid methods featuring band selection, feature extraction or band weighting procedures (Fassnacht et al., 2014; Feilhauer et al., 2015; Verrelst et al., 2015).”
• Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms (2016, background): “Moreover, a comprehensive review of ML algorithms in biophysical parameter estimation can be found in Verrelst et al. (2015).”
• PROSPECT-D: towards modeling leaf optical properties through a complete lifecycle (2017, positive): “It includes univariate statistical models derived from spectral indices (e.g., Gitelson et al., 2006), multivariate statistical models such as partial least squared regressions (e.g., Asner and Martin, 2009) and machine learning algorithms (e.g., Verrelst et al., 2015).”
• A physically-based model for retrieving foliar biochemistry and leaf orientation using close-range imaging spectroscopy (2016, neutral): “…the numerical implementation of the iterative optimization or by considering regularization strategies as reviewed by Baret & Buis (2008) and Verrelst et al. (2015),e.g.,byusingpriorknowledgetoconstrainsomemodelvariables, or by generating a look-up table and averaging the N best solutions.”
• Extracting leaf area index using viewing geometry effects—A new perspective on high-resolution unmanned aerial system photography (2018, methodology): “Hybrid methods62 combine regression and radiative transfer models (Verrelst et al., 2015).”
• Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study (2018, methodology): “The combination of the generic physical approach using a CRM with flexible, fast and effective non-parametric non-linear regression methods is considered a “hybrid approach” [77].”
• Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angular optical remote sensing: Comparison of vegetation indices and PROSAIL inversion for field phenotyping (2017, methodology): “Numerous methods have been developed to extract canopy properties from (mono- or multi-angular) remotely sensed spectral data (Baret and Buis 2008; Dorigo et al. 2007; Verrelst et al. 2015).”