Performance Assessment of Tailored Split-Window Coefficients for the Retrieval of Lake Surface Water Temperature from AVHRR Satellite Data

Lieberherr, Gian; Riffler, Michael; Wunderle, Stefan

doi:10.3390/rs9121334

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Performance Assessment of Tailored Split-Window Coefficients for the Retrieval of Lake Surface Water Temperature from AVHRR Satellite Data

Gian Lieberherr

^1,2,*

Michael Riffler

³ and

Stefan Wunderle

^1,2

Institute of Geography, University of Bern, Hallerstrasse 12, CH-3012 Bern, Switzerland

Oeschger Centre for Climate Change Research, University of Bern, Falkenplatz 16, CH-3012 Bern, Switzerland

GeoVille Information Systems and Data Processing GmbH, 6020 Innsbruck, Austria

Author to whom correspondence should be addressed.

Remote Sens. 2017, 9(12), 1334; https://doi.org/10.3390/rs9121334

Received: 18 October 2017 / Revised: 2 December 2017 / Accepted: 14 December 2017 / Published: 20 December 2017

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Abstract

Although lake surface water temperature (LSWT) is defined as an essential climate variable (ECV) within the global climate observing system (GCOS), current satellite-based retrieval techniques do not fulfill the GCOS accuracy requirements. The split-window (SW) retrieval method is well-established, and the split-window coefficients (SWC) are the key elements of its accuracy. Performances of SW depends on the degree of SWC customization with respect to its application, where accuracy increases when SWC is tailored for specific situations. In the literature, different SWC customization approaches have been investigated, however, no direct comparisons have been conducted among them. This paper presents the results of a sensitivity analysis to address this gap. We show that the performance of SWC is most sensitive to customizations for specific time-windows (Sensitivity Index SI of 0.85) or spatial extents (SI 0.27). Surprisingly, the study highlights that the use of separated SWC for daytime and night-time situations has limited impact (SI 0.10). The final validation with AVHRR satellite data showed that the subtle differences among different SWC customizations were not traceable to the final uncertainty of the LSWT product. Nevertheless, this study provides a basis to critically evaluate current assumptions regarding SWC generation by directly comparing the performance of multiple customization approaches for the first time. View Full-Text

Keywords: LSWT; AVHRR; dual channel; split-window coefficients; thermal infrared; radiative transfer

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MDPI and ACS Style

Lieberherr, G.; Riffler, M.; Wunderle, S. Performance Assessment of Tailored Split-Window Coefficients for the Retrieval of Lake Surface Water Temperature from AVHRR Satellite Data. Remote Sens. 2017, 9, 1334. https://doi.org/10.3390/rs9121334

AMA Style

Lieberherr G, Riffler M, Wunderle S. Performance Assessment of Tailored Split-Window Coefficients for the Retrieval of Lake Surface Water Temperature from AVHRR Satellite Data. Remote Sensing. 2017; 9(12):1334. https://doi.org/10.3390/rs9121334

Chicago/Turabian Style

Lieberherr, Gian, Michael Riffler, and Stefan Wunderle. 2017. "Performance Assessment of Tailored Split-Window Coefficients for the Retrieval of Lake Surface Water Temperature from AVHRR Satellite Data" Remote Sensing 9, no. 12: 1334. https://doi.org/10.3390/rs9121334

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Performance Assessment of Tailored Split-Window Coefficients for the Retrieval of Lake Surface Water Temperature from AVHRR Satellite Data

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