Evaluation of envisat MERIS terrestrial chlorophyll index-based models for the estimation of terrestrial gross primary productivity
Authors: Boyd, D.S., Almond, S., Dash, J., Curran, P.J., Hill, R.A. and Foody, G.M.
Journal: IEEE Geoscience and Remote Sensing Letters
Volume: 9
Issue: 3
Pages: 457-461
ISSN: 1545-598X
DOI: 10.1109/LGRS.2011.2170810
Abstract:This letter evaluates three Envisat Medium Resolution Imaging Spectrometer Terrestrial Chlorophyll Index (MTCI)-based models for the estimation of terrestrial gross primary productivity (GPP) across a range of vegetation types. Correlations between flux tower measures of GPP and models for years between 2003 and 2007 were established for 30 sites across USA, Canada, and Brazil. Correlations were seen to range from very strong to weak, depending on seasonal variation in photosynthetic capacity (which is influenced by chlorophyll content) exhibited by the vegetation at each site. At least one of the three models obtained a statistically significant relationship with GPP at every site. Results indicate that chlorophyll content (as measured by the MTCI) is a most relevant community property for estimating primary productivity and chlorophyll-related vegetation indexes provide favorable approximations of the GPP of terrestrial vegetation. The inclusion of radiation information (photosynthetically active radiation (PAR) and fraction of photosynthetically active radiation (fPAR)) into the models extended the applicability of the models and the accuracy of the GPP estimate. Although further investigation is required to fully understand the applicability of these models and their parameters, these results point to the possibility of a total remote sensing approach to GPP estimation. © 2012 IEEE.
Source: Scopus
Evaluation of Envisat MERIS Terrestrial Chlorophyll Index-Based Models for the Estimation of Terrestrial Gross Primary Productivity
Authors: Boyd, D.S., Almond, S., Dash, J., Curran, P.J., Hill, R.A. and Foody, G.M.
Journal: IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume: 9
Issue: 3
Pages: 457-461
ISSN: 1545-598X
DOI: 10.1109/LGRS.2011.2170810
Source: Web of Science (Lite)
Evaluation of Envisat MERIS Terrestrial Chlorophyll Index-based models for the estimation of terrestrial Gross Primary Productivity.
Authors: Boyd, D.S., Almond, S.F., Dash, J., Curran, P., Hill, R.A. and Foody, G.M.
Journal: IEEE Geoscience and Remote Sensing Letters
Volume: 9
Pages: 457-461
ISSN: 1545-598X
DOI: 10.1109/LGRS.2011.2170810
Abstract:This letter evaluates three Envisat Medium Resolution Imaging Spectrometer Terrestrial Chlorophyll Index (MTCI)-based models for the estimation of terrestrial gross primary productivity (GPP) across a range of vegetation types. Correlations between flux tower measures of GPP and models for years between 2003 and 2007 were established for 30 sites across USA, Canada, and Brazil. Correlations were seen to range from very strong to weak, depending on seasonal variation in photosynthetic capacity (which is influenced by chlorophyll content) exhibited by the vegetation at each site. At least one of the three models obtained a statistically significant relationship with GPP at every site. Results indicate that chlorophyll content (as measured by the MTCI) is a most relevant community property for estimating primary productivity and chlorophyll-related vegetation indexes provide favorable approximations of the GPP of terrestrial vegetation. The inclusion of radiation information (photosynthetically active radiation (PAR) and fraction of photosynthetically active radiation (fPAR)) into the models extended the applicability of the models and the accuracy of the GPP estimate. Although further investigation is required to fully understand the applicability of these models and their parameters, these results point to the possibility of a total remote sensing approach to GPP estimation.
Source: Manual
Preferred by: Ross Hill
Evaluation of Envisat MERIS Terrestrial Chlorophyll Index-Based Models for the Estimation of Terrestrial Gross Primary Productivity.
Authors: Boyd, D.S., Almond, S., Dash, J., Curran, P.J., Hill, R.A. and Foody, G.M.
Journal: IEEE Geosci. Remote. Sens. Lett.
Volume: 9
Pages: 457-461
DOI: 10.1109/LGRS.2011.2170810
Source: DBLP