Next Article in Journal
Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network
Previous Article in Journal
Monitoring Forest Dynamics in the Andean Amazon: The Applicability of Breakpoint Detection Methods Using Landsat Time-Series and Genetic Algorithms
Article Menu
Issue 1 (January) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(1), 72; doi:10.3390/rs9010072

Assessment of Mono- and Split-Window Approaches for Time Series Processing of LST from AVHRR—A TIMELINE Round Robin

German Remote Sensing Data Center (DFD), Earth Observation Center (EOC), German Aerospace Center (DLR), Oberpfaffenhofen, D-82234 Wessling, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Zhaoliang Li and Prasad S. Thenkabail
Received: 21 October 2016 / Revised: 21 December 2016 / Accepted: 10 January 2017 / Published: 13 January 2017
View Full-Text   |   Download PDF [5761 KB, uploaded 13 January 2017]   |  

Abstract

Processing of land surface temperature from long time series of AVHRR (Advanced Very High Resolution Radiometer) requires stable algorithms, which are well characterized in terms of accuracy, precision and sensitivity. This assessment presents a comparison of four mono-window (Price 1983, Qin et al., 2001, Jiménez-Muñoz and Sobrino 2003, linear approach) and six split-window algorithms (Price 1984, Becker and Li 1990, Ulivieri et al., 1994, Wan and Dozier 1996, Yu 2008, Jiménez-Muñoz and Sobrino 2008) to estimate LST from top of atmosphere brightness temperatures, emissivity and columnar water vapour. Where possible, new coefficients were estimated matching the spectral response curves of the different AVHRR sensors of the past and present. The consideration of unique spectral response curves is necessary to avoid artificial anomalies and wrong trends when processing time series data. Using simulated data on the base of a large atmospheric profile database covering many different states of the atmosphere, biomes and geographical regions, it was assessed (a) to what accuracy and precision LST can be estimated using before mentioned algorithms and (b) how sensitive the algorithms are to errors in their input variables. It was found, that the split-window algorithms performed almost equally well, differences were found mainly in their sensitivity to input bands, resulting in the Becker and Li 1990 and Price 1984 split-window algorithm to perform best. Amongst the mono-window algorithms, larger deviations occurred in terms of accuracy, precision and sensitivity. The Qin et al., 2001 algorithm was found to be the best performing mono-window algorithm. A short comparison of the application of the Becker and Li 1990 coefficients to AVHRR with the MODIS LST product confirmed the approach to be physically sound. View Full-Text
Keywords: LST; AVHRR; time series; accuracy; precision; sensitivity LST; AVHRR; time series; accuracy; precision; sensitivity
Figures

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. (CC BY 4.0).

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

Frey, C.M.; Kuenzer, C.; Dech, S. Assessment of Mono- and Split-Window Approaches for Time Series Processing of LST from AVHRR—A TIMELINE Round Robin. Remote Sens. 2017, 9, 72.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

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