Next Article in Journal
Validation of NOAA-Interactive Multisensor Snow and Ice Mapping System (IMS) by Comparison with Ground-Based Measurements over Continental United States
Previous Article in Journal
A Real-Time Method to Detect and Track Moving Objects (DATMO) from Unmanned Aerial Vehicles (UAVs) Using a Single Camera
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2012, 4(5), 1112-1133; doi:10.3390/rs4051112

Methodologies and Uncertainties in the Use of the Terrestrial Chlorophyll Index for the Sentinel-3 Mission

1
Institute of Surveying, Remote Sensing and Land Information (IVFL), University of Natural Resources and Life Sciences (BOKU), Peter Jordan Str. 82, A-1190 Vienna, Austria
2
School of Geography, University of Southampton, Highfield, Southampton SO17 1BJ, UK
3
City University London, Northampton Square, London EC1V 0HB, UK
4
European Space Research and Technology Centre (ESTEC), European Space Agency (ESA), Keplerlaan 1, P.O. Box 299, 2200 AG Noordwijk, The Netherlands
5
EUMETSAT, Eumetsat Allee 1, D-64295 Darmstadt, Germany
*
Author to whom correspondence should be addressed.
Received: 15 March 2012 / Revised: 12 April 2012 / Accepted: 13 April 2012 / Published: 25 April 2012
View Full-Text   |   Download PDF [1157 KB, uploaded 19 June 2014]   |  

Abstract

A methodology is described for the validation of Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) data over heterogeneous land surfaces in an agricultural region in Southern Italy. The approach involves the use inverse canopy reflectance modeling techniques to derive maps of canopy chlorophyll content (CCC) and leaf area index (LAI) at fine spatial resolution. Indirect field measurements are used for validation of the fine spatial resolution data. Subsequently, these maps are aggregated based on a regular grid at 1 km spatial resolution to validate MERIS Level 2 MTCI (300 m). RapidEye satellite sensor data with a pixel size of 6.5 m are used for this purpose. Based on a set of independent ground measurements, fine spatial resolution maps achieved an R2 = 0.78 and RMSE = 0.39 for CCC and R2 = 0.76 and RMSE = 0.64 for LAI. The relationship between MERIS L2 MTCI and CCC [g∙m−2] achieved a coefficient of determination of 0.74 and it resulted to be extremely statistically significant (p-value < 0.001). Additionally, a relative validation of two other satellite products at medium resolution spatial scale, namely MERIS leaf area index (LAI) and Moderate Resolution Imaging Spectrometer (MODIS) LAI was performed by comparison with the fine spatial resolution LAI map. Results indicated a better accuracy in LAI estimation of MERIS (RMSE = 0.33) compared to MODIS (RMSE = 0.81) data. View Full-Text
Keywords: Envisat-MERIS; MERIS Terrestrial Chlorophyll Index (MTCI); canopy chlorophyll content (CCC); leaf area index (LAI); Sentinel-3; OLCI Terrestrial Chlorophyll Index (OTCI) Envisat-MERIS; MERIS Terrestrial Chlorophyll Index (MTCI); canopy chlorophyll content (CCC); leaf area index (LAI); Sentinel-3; OLCI Terrestrial Chlorophyll Index (OTCI)
Figures

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Vuolo, F.; Dash, J.; Curran, P.J.; Lajas, D.; Kwiatkowska, E. Methodologies and Uncertainties in the Use of the Terrestrial Chlorophyll Index for the Sentinel-3 Mission. Remote Sens. 2012, 4, 1112-1133.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top