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

Gian Lieberherr,Michael Riffler,Stefan Wunderle

doi:10.3390/rs9121334

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.

Highlights

The intrinsic error associated with the GRE site is the highest, followed by the error associated with the EEU, ALP sites, and lowest in the SSC and NSC sites (Figure 4)
Tailoring the split window coefficients to certain atmospheric conditions or geographical locations generally increased the accuracy associated with the split window retrieval method
During validation with in-situ measurements, the subtle performance differences of the tested split window coefficients (SWC) could not be linked to the accuracy associated with the final lake surface water temperature (LSWT) product

Summary

Introduction

Studies have revealed global warming trends for LSWT within different climate zones (e.g., [4,5]), and there is evidence that indications of climate change are sometimes even stronger within lake than in air temperature records (e.g., [6,7]). To further explore these trends on a continental or even a global scale, there is a need to generate accurate and homogeneous LSWT time series from different climatic regions. The World Meteorological Organisation (WMO) recommends that time series prepared for climate studies should ideally consist of data records that exceed 30 years

Methods

Results

Conclusion