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Validating the MERIS Terrestrial Chlorophyll Index (MTCI) with ground chlorophyll content data at MERIS spatial resolution

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Validating the MERIS Terrestrial Chlorophyll Index (MTCI) with ground chlorophyll content data at MERIS spatial resolution. / Dash, J.; Curran, P.; Tallis, Matthew; Llewellyn, G.; Taylor, G.; Snoeij, P.

In: International Journal of Remote Sensing, Vol. 31, No. 20, 2010, p. 5513-5532.

Research output: Contribution to journalArticlepeer-review

Harvard

Dash, J, Curran, P, Tallis, M, Llewellyn, G, Taylor, G & Snoeij, P 2010, 'Validating the MERIS Terrestrial Chlorophyll Index (MTCI) with ground chlorophyll content data at MERIS spatial resolution', International Journal of Remote Sensing, vol. 31, no. 20, pp. 5513-5532. https://doi.org/10.1080/01431160903376340

APA

Dash, J., Curran, P., Tallis, M., Llewellyn, G., Taylor, G., & Snoeij, P. (2010). Validating the MERIS Terrestrial Chlorophyll Index (MTCI) with ground chlorophyll content data at MERIS spatial resolution. International Journal of Remote Sensing, 31(20), 5513-5532. https://doi.org/10.1080/01431160903376340

Vancouver

Dash J, Curran P, Tallis M, Llewellyn G, Taylor G, Snoeij P. Validating the MERIS Terrestrial Chlorophyll Index (MTCI) with ground chlorophyll content data at MERIS spatial resolution. International Journal of Remote Sensing. 2010;31(20):5513-5532. https://doi.org/10.1080/01431160903376340

Author

Dash, J. ; Curran, P. ; Tallis, Matthew ; Llewellyn, G. ; Taylor, G. ; Snoeij, P. / Validating the MERIS Terrestrial Chlorophyll Index (MTCI) with ground chlorophyll content data at MERIS spatial resolution. In: International Journal of Remote Sensing. 2010 ; Vol. 31, No. 20. pp. 5513-5532.

Bibtex

@article{c0210ee2eaf64d3a98f051b0b381c387,
title = "Validating the MERIS Terrestrial Chlorophyll Index (MTCI) with ground chlorophyll content data at MERIS spatial resolution",
abstract = "The Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI), a standard level 2 European Space Agency (ESA) product, provides information on the chlorophyll content of vegetation (amount of chlorophyll per unit area of ground). This is a combination of information on Leaf Area Index (LAI, area of leaves per unit area of ground) and the chlorophyll concentration of those leaves. The MTCI correlates strongly with chlorophyll content when using model, laboratory and field spectrometry data. However, MTCI calculated with MERIS data has only been correlated with surrogate chlorophyll content data. This is because of the logistical difficulties of determining the chlorophyll content of the area covered by a MERIS pixel (9 × 104 m2). This paper reports the first attempt to determine the relationship between MTCI and chlorophyll content using actual MERIS data and actual chlorophyll content data. During the summer of 2006 LAI and chlorophyll concentration data were collected for eight large (> 25 ha) fields around Dorchester in southern England. The fields contained six crops (beans, linseed, wheat, grass, oats and maize) at different stages of maturity and with different canopy structures, LAIs and chlorophyll concentrations. A stratified sampling method was used in which each field contained sampling units in proportion to the spatial variability of the crop. Within each unit 25 random points were sampled. This approach captured the variability of the field and reduced the potential bias introduced by the planting pattern or later agricultural treatments (e.g. pesticides or herbicides). At each random point LAI was estimated using an LAI-2000 plant canopy analyser and chlorophyll concentration was estimated using a Minolta-SPAD chlorophyll meter. In addition, for each field a calibration set of 30 contiguous SPAD measurements and associated leaf samples were collected. The relationship between MTCI and chlorophyll content was positive. The coefficient of determination (R 2) was 0.62, root mean square error (RMSE) was 244 g per MERIS pixel and accuracy of estimation (in relation to the mean) was 65%. However, one field included a high proportion of seed heads, which artificially increased the measured LAI and thus chlorophyll content. Removal of this field from the dataset resulted in a stronger relationship between MTCI and chlorophyll content with an R 2 of 0.8, an RMSE of 192 g per MERIS pixel and accuracy of estimation (in relation to the mean) of 71%.",
author = "J. Dash and P. Curran and Matthew Tallis and G. Llewellyn and G. Taylor and P. Snoeij",
year = "2010",
doi = "10.1080/01431160903376340",
language = "English",
volume = "31",
pages = "5513--5532",
journal = "International Journal of Remote Sensing",
issn = "0143-1161",
publisher = "Taylor and Francis Ltd.",
number = "20",

}

RIS

TY - JOUR

T1 - Validating the MERIS Terrestrial Chlorophyll Index (MTCI) with ground chlorophyll content data at MERIS spatial resolution

AU - Dash, J.

AU - Curran, P.

AU - Tallis, Matthew

AU - Llewellyn, G.

AU - Taylor, G.

AU - Snoeij, P.

PY - 2010

Y1 - 2010

N2 - The Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI), a standard level 2 European Space Agency (ESA) product, provides information on the chlorophyll content of vegetation (amount of chlorophyll per unit area of ground). This is a combination of information on Leaf Area Index (LAI, area of leaves per unit area of ground) and the chlorophyll concentration of those leaves. The MTCI correlates strongly with chlorophyll content when using model, laboratory and field spectrometry data. However, MTCI calculated with MERIS data has only been correlated with surrogate chlorophyll content data. This is because of the logistical difficulties of determining the chlorophyll content of the area covered by a MERIS pixel (9 × 104 m2). This paper reports the first attempt to determine the relationship between MTCI and chlorophyll content using actual MERIS data and actual chlorophyll content data. During the summer of 2006 LAI and chlorophyll concentration data were collected for eight large (> 25 ha) fields around Dorchester in southern England. The fields contained six crops (beans, linseed, wheat, grass, oats and maize) at different stages of maturity and with different canopy structures, LAIs and chlorophyll concentrations. A stratified sampling method was used in which each field contained sampling units in proportion to the spatial variability of the crop. Within each unit 25 random points were sampled. This approach captured the variability of the field and reduced the potential bias introduced by the planting pattern or later agricultural treatments (e.g. pesticides or herbicides). At each random point LAI was estimated using an LAI-2000 plant canopy analyser and chlorophyll concentration was estimated using a Minolta-SPAD chlorophyll meter. In addition, for each field a calibration set of 30 contiguous SPAD measurements and associated leaf samples were collected. The relationship between MTCI and chlorophyll content was positive. The coefficient of determination (R 2) was 0.62, root mean square error (RMSE) was 244 g per MERIS pixel and accuracy of estimation (in relation to the mean) was 65%. However, one field included a high proportion of seed heads, which artificially increased the measured LAI and thus chlorophyll content. Removal of this field from the dataset resulted in a stronger relationship between MTCI and chlorophyll content with an R 2 of 0.8, an RMSE of 192 g per MERIS pixel and accuracy of estimation (in relation to the mean) of 71%.

AB - The Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI), a standard level 2 European Space Agency (ESA) product, provides information on the chlorophyll content of vegetation (amount of chlorophyll per unit area of ground). This is a combination of information on Leaf Area Index (LAI, area of leaves per unit area of ground) and the chlorophyll concentration of those leaves. The MTCI correlates strongly with chlorophyll content when using model, laboratory and field spectrometry data. However, MTCI calculated with MERIS data has only been correlated with surrogate chlorophyll content data. This is because of the logistical difficulties of determining the chlorophyll content of the area covered by a MERIS pixel (9 × 104 m2). This paper reports the first attempt to determine the relationship between MTCI and chlorophyll content using actual MERIS data and actual chlorophyll content data. During the summer of 2006 LAI and chlorophyll concentration data were collected for eight large (> 25 ha) fields around Dorchester in southern England. The fields contained six crops (beans, linseed, wheat, grass, oats and maize) at different stages of maturity and with different canopy structures, LAIs and chlorophyll concentrations. A stratified sampling method was used in which each field contained sampling units in proportion to the spatial variability of the crop. Within each unit 25 random points were sampled. This approach captured the variability of the field and reduced the potential bias introduced by the planting pattern or later agricultural treatments (e.g. pesticides or herbicides). At each random point LAI was estimated using an LAI-2000 plant canopy analyser and chlorophyll concentration was estimated using a Minolta-SPAD chlorophyll meter. In addition, for each field a calibration set of 30 contiguous SPAD measurements and associated leaf samples were collected. The relationship between MTCI and chlorophyll content was positive. The coefficient of determination (R 2) was 0.62, root mean square error (RMSE) was 244 g per MERIS pixel and accuracy of estimation (in relation to the mean) was 65%. However, one field included a high proportion of seed heads, which artificially increased the measured LAI and thus chlorophyll content. Removal of this field from the dataset resulted in a stronger relationship between MTCI and chlorophyll content with an R 2 of 0.8, an RMSE of 192 g per MERIS pixel and accuracy of estimation (in relation to the mean) of 71%.

U2 - 10.1080/01431160903376340

DO - 10.1080/01431160903376340

M3 - Article

VL - 31

SP - 5513

EP - 5532

JO - International Journal of Remote Sensing

JF - International Journal of Remote Sensing

SN - 0143-1161

IS - 20

ER -

ID: 195094