Publications
Following publications have been announced by our department Optical Oceanography. For further information please contact Dr. Martin Hieronymi, co-author of the publications:
Ansper-Toomsalu, A., Uusõue, M., Kangro, K., Hieronymi, M., & Alikas, K. (2024): Suitability of different in-water algorithms for eutrophic and absorbing waters applied to Sentinel-2 MSI and Sentinel-3 OLCI data. Front. Remote Sens., 5:1423332, doi:10.3389/frsen.2024.1423332
Abstract:
Optically complex waters present significant challenges for remote sensing due to high concentrations of optically active substances (OASs) and their inherent optical properties (IOPs), as well as the adjacency effect. OASs and IOPs can be derived from atmospheric correction processors’ in-water algorithms applied to data from Sentinel-2 MultiSpectral Instrument (S2 MSI) and Sentinel-3 Ocean and Land Color Instrument (S3 OLCI). This study compared S3 OLCI Level-2 in-water products for Case-2 waters with alternative in-water algorithms derived from ACOLITE, POLYMER, C2RCC, and A4O. Fifty in-water algorithms were evaluated using an extensive match-up dataset from lakes and coastal areas, focusing particularly on small lakes with high colored dissolved organic matter absorption at 442 nm (up to 48 m-1). The Chl a band ratio introduced by Gons et al. (2022) applied to data processed by ACOLITE performed best for S3 OLCI Chl a retrieval (dispersion = 23%, bias = 10%). Gons et al. (2022) band ratio also showed consistent agreement between S3 OLCI and S2 MSI resampled data (intercept of 6.27 and slope of 0.83, close to the 1:1 line); however, lower Chl a values (<20 mg/m3) were overestimated by S2 MSI. When estimating errors associated with proximity to land, S2 MSI Chl a in-water algorithms had higher errors close to the shore (on average 315%) compared to S3 OLCI (on average 150%). Chl a retrieved with POLYMER had the lowest errors close to the shore for both S2 MSI and S3 OLCI data (on average 70%). Total suspended matter (TSM) retrieval with C2RCC performed well for S2 MSI (dispersion 24% and bias −12%). Total absorption was most accurately derived from C2RCC applied to S3 OLCI L1 data (dispersion < 43% and bias < −39%), and it was better estimated than its individual components: phytoplankton, mineral particles, and colored dissolved organic matter absorption. However, none of the colored dissolved organic matter absorption in-water algorithms performed well (dispersion > 59% and bias < −29%).
Ogashawara, I., Wollrab, S., Berger, S.A., Kiel, C., Jechow, A., Guislain, A.L.N., Gege, P., Ruhtz, T., Hieronymi, M., Schneider, T., Lischeid, G., Singer, G.A., Hölker, F., Grossart, H.-P., & Nejstgaard, J.C. (2024): Unleashing the power of remote sensing data in aquatic research: Guidelines for optimal utilization. Limnol. Oceanogr. Lett., doi:10.1002/lol2.10427
Scientific Significance Statement:
The growing utilization of remote sensing data in lake studies provides crucial spatial insights into biogeochemistry and biology. However, clarity regarding the development and intended use of remote sensing products is often lacking. This letter aims to elucidate the tradeoffs for the utilization of remote sensing data in limnological studies with an example of based on the estimation of chlorophyll a due to its importance as a water quality indicator. The analysis initiates with a meticulous product selection, requiring an evaluation of its capacity to address the optical complexity of freshwater systems. Assessing atmospheric correction and product limitations ensures alignment with the study’s objectives. Subsequently, rigorous validation of remote sensing products is essential, accompanied by a cautious interpretation of the data. This letter advocates for the use of remote sensing data, offering key strategies for their optimal utilization in lake studies.
