Remote sensing of sun-induced chlorophyll-a fluorescence in inland and coastal waters: Current state and future prospects

REMOTE SENSING OF ENVIRONMENT

卷:262

Review

Gupana, Remika S.^1,2  Odermatt, Daniel^1,2  Cesana, Ilaria³  Giardino, Claudia⁴  Nedbal, Ladislav⁵  Damm, Alexander^1,2 

[1] Eawag, Swiss Fed Inst Aquat Sci & Technol, Surface Waters Res & Management, Uberlandstr 133, CH-8600 Dubendorf, Switzerland

[2] Univ Zurich, Dept Geog, Winterthurerstr 190, CH-8057 Zurich, Switzerland

[3] Univ Milano Bicocca, DISAT, Remote Sensing Environm Dynam Lab, Piazza Sci 1, I-20126 Milan, Italy

[4] Natl Res Council CNR IREA, Inst Electromagent Sensing Environm, Via Bassini 15, I-20133 Milan, Italy

[5] Forschungszentrum Julich, Inst Bio & Geosci Plant Sci IBG 2, Wilhelm Johnen Str, D-52428 Julich, Germany

关键词: Phytoplankton fluorescence;    Optically complex waters;    Case-2 waters;    Hyperspectral data;    Phytoplankton remote sensing;    Water quality;    Review;   

DOI  :  10.1016/j.rse.2021.112482

来源: Elsevier

【 摘 要 】

Sun-induced fluorescence (SIF) retrieved from satellite measurements has been widely used as proxy for chlorophyll-a concentration and as indicator of phytoplankton physiological status in oceans. The practical use of this naturally occurring light signal in environmental research is, however, under-exploited, particularly in research focusing on optically complex waters such as inland and coastal waters. In this study, we investigated methodological and knowledge gaps in remote sensing of chlorophyll-a SIF in optically complex waters by reviewing the theory behind SIF occurrence, the availability of existing and upcoming instrumentation, the availability of SIF retrieval schemes, and the applications for aquatic research. Starting with an overview of factors that influence SIF leaving the water body, we further investigated available and upcoming observational capacity by in situ, airborne and satellite sensors. We discuss requirements for spatial, spectral, temporal, and radiometric resolution of observing systems in the context of SIF dynamics. We assessed viable retrieval techniques able to disentangle SIF from non-SIF contribution to the upwelling radiance, ranging from the established multispectral Fluorescence Line Height algorithm (FLH) approach to hyperspectral approaches including model inversion, spectral fitting methods and machine learning regression procedures. Finally, we provide an overview of applications, which could potentially benefit from improved SIF emission estimates such as biomass estimation, algal bloom investigation and primary productivity modelling.

【 授权许可】

Free