Next Article in Journal
Derivation of Relationships between Spectral Vegetation Indices from Multiple Sensors Based on Vegetation Isolines
Previous Article in Journal
Two Linear Unmixing Algorithms to Recognize Targets Using Supervised Classification and Orthogonal Rotation in Airborne Hyperspectral Images
Remote Sens. 2012, 4(3), 561-582; doi:10.3390/rs4030561
Article

Optimal Exploitation of the Sentinel-2 Spectral Capabilities for Crop Leaf Area Index Mapping

1,* , 1
,
2
,
1
 and
3
Received: 20 January 2012 / Revised: 15 February 2012 / Accepted: 21 February 2012 / Published: 28 February 2012
View Full-Text   |   Download PDF [744 KB, uploaded 19 June 2014]   |   Browse Figures

Abstract

The continuously increasing demand of accurate quantitative high quality information on land surface properties will be faced by a new generation of environmental Earth observation (EO) missions. One current example, associated with a high potential to contribute to those demands, is the multi-spectral ESA Sentinel-2 (S2) system. The present study focuses on the evaluation of spectral information content needed for crop leaf area index (LAI) mapping in view of the future sensors. Data from a field campaign were used to determine the optimal spectral sampling from available S2 bands applying inversion of a radiative transfer model (PROSAIL) with look-up table (LUT) and artificial neural network (ANN) approaches. Overall LAI estimation performance of the proposed LUT approach (LUTN50) was comparable in terms of retrieval performances with a tested and approved ANN method. Employing seven- and eight-band combinations, the LUTN50 approach obtained LAI RMSE of 0.53 and normalized LAI RMSE of 0.12, which was comparable to the results of the ANN. However, the LUTN50 method showed a higher robustness and insensitivity to different band settings. Most frequently selected wavebands were located in near infrared and red edge spectral regions. In conclusion, our results emphasize the potential benefits of the Sentinel-2 mission for agricultural applications.
Keywords: Sentinel-2; leaf area index; model inversion; look-up table; artificial neural networks; optimal spectral sampling; PROSAIL model Sentinel-2; leaf area index; model inversion; look-up table; artificial neural networks; optimal spectral sampling; PROSAIL model
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Share & Cite This Article

Further Mendeley | CiteULike
Export to BibTeX |
EndNote
MDPI and ACS Style

Richter, K.; Hank, T.B.; Vuolo, F.; Mauser, W.; D’Urso, G. Optimal Exploitation of the Sentinel-2 Spectral Capabilities for Crop Leaf Area Index Mapping. Remote Sens. 2012, 4, 561-582.

View more citation formats

Article Metrics

Comments

Citing Articles

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