A case study of airborne and satellite remote sensing of a spring bloom event in the Gulf of Finland

Koponen, Sampsa; Attila, Jenni; Pulliainen, Jouni; Kallio, Kari; Pyhälahti, Timo; Lindfors, Antti; Rasmus, Kai; Hallikainen, M.

doi:10.1016/j.csr.2006.10.006

Cited by 86 publications

(50 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Different remote sensing products (chlorophyll a, CDOM, suspended matter, diffuse attenuation coefficient, etc.) have been developed or adopted for the Baltic Sea [3,4,6,8,11,[17][18][19][20][21][22][23][24][25][26][27][28]. Many of the recent studies rely on MERIS data and the results can probably be implemented on Sentinel-3 OLCI data as well.…”

Section: Introductionmentioning

confidence: 99%

Mapping Water Quality Parameters with Sentinel-3 Ocean and Land Colour Instrument imagery in the Baltic Sea

et al. 2017

View full text Add to dashboard Cite

Abstract:The launch of Ocean and Land Colour Instrument (OLCI) on board Sentinel-3A in 2016 is the beginning of a new era in long time, continuous, high frequency water quality monitoring of coastal waters. Therefore, there is a strong need to validate the OLCI products to be sure that the technical capabilities provided will be used in the best possible way in water quality monitoring and research. The Baltic Sea is an optically complex waterbody where many ocean colour products, performing well in other waterbodies, fail. We tested the performance of standard Case-2 Regional/Coast Colour (C2RCC) processing chain in retrieving water reflectance, inherent optical properties (IOPs), and water quality parameters such as chlorophyll a, total suspended matter (TSM) and coloured dissolved organic matter (CDOM) in the Baltic Sea. The reflectance spectra produced by the C2RCC are realistic in both shape and magnitude. However, the IOPs, and consequently the water quality parameters estimated by the C2RCC, did not have correlation with in situ data. On the other hand, some tested empirical remote sensing algorithms performed well in retrieving chlorophyll a, TSM, CDOM and Secchi depth from the reflectance produced by the C2RCC. This suggests that the atmospheric correction part of the processor performs relatively well while IOP retrieval part of the neural network needs extensive training with actual IOP data before it can produce reasonable estimates for the Baltic Sea.

show abstract

Section: Introductionmentioning

confidence: 99%

Mapping Water Quality Parameters with Sentinel-3 Ocean and Land Colour Instrument imagery in the Baltic Sea

et al. 2017

View full text Add to dashboard Cite

show abstract

“…The spectrum taken near the tributary of Lake Vänern (Sweden) (Fig. 1e) is typical of those taken in CDOM-rich waters where CDOM absorption lowers reflectance at short wavelengths (Kutser et al, 2005;Koponen et al, 2007), Finally, Lake Neusiedl (Austria, Hungary) ( Fig. 1f), characterised by very turbid waters (SPM=100 gm -3 ), hence producing an high backscattering of downwelling light, hence generating high water reflectance (Bukata et al, 1995).…”

Section: Optical Remote Sensingmentioning

confidence: 87%

Optical remote sensing of lakes: an overview on Lake Maggiore

Giardino

Bresciani²,

Stroppiana³

et al. 2013

J Limnol

View full text Add to dashboard Cite

show abstract

“…This is in agreement with [19,43], as high reflectance in the green (0.56 µm) and red/NIR regions was found for sediment-dominated waters. The region near 0.71 µm was found to correlate positively with the turbidity values [6,16,17,44,45] whereas not being overlapping with the absorptions caused by other optically active substances (e.g., phytoplankton and dissolved organic matter).…”

Section: Linking the Chemical And Optical Properties Of The Sampled Wmentioning

confidence: 89%

Applying Spectral Unmixing to Determine Surface Water Parameters in a Mining Environment

Kopačková

Hladíková

2014

Remote Sensing

View full text Add to dashboard Cite

Compared to natural waters, mine waters represent an extreme water type that is frequently heavily polluted. Although they have been traditionally monitored by in situ measurements of point samples taken at regular intervals, the emergence of a new generation of multispectral and hyperspectral (HS) sensors means that image spectroscopy has the potential to become a modern method for monitoring polluted surface waters. This paper describes an approach employing linear Spectral Unmixing (LSU) for analysis of hyperspectral image data to map the relative abundances of mine water components (dissolved Fe-Fediss, dissolved organic carbon-DOC, undissolved particles). The ground truth data (8 monitored ponds) were used to validate the results of spectral mapping. The same approach applied to HS data was tested using the image data resampled to WorldView2 (WV2) spectral resolution. A key aspect of the image data processing was to define the proper pure image end members for the fundamental water types. The highest correlations detected between the studied water parameters and the fractional images using the HyMap and the resampled WV2 data, respectively, were: dissolved Fe (R 2 = 0.74 and R 2 vw2 = 0.6), undissolved particles (R 2 = 0.57 and R 2 vw2 = 0.49) and DOC (R 2 = 0.42 and R 2 vw2 < 0.40). These fractional images were further classified to create semi-quantitative maps. In conclusion, the classification still benefited from the higher spectral resolution of the HyMap data; however the WV2 reflectance data can be suitable for mapping specific inherent optical properties (SIOPs), which significantly differ from one another from an optical point of view OPEN ACCESSRemote Sens. 2014, 6 11205 (e.g., mineral suspension, dissolved Fe and phytoplankton), but it seems difficult to differentiate among diverse suspension particles, especially when the waters have more complex properties (e.g., mineral particles, DOC together with tripton or other particles, etc.).

show abstract

A case study of airborne and satellite remote sensing of a spring bloom event in the Gulf of Finland

Cited by 86 publications

References 19 publications

Mapping Water Quality Parameters with Sentinel-3 Ocean and Land Colour Instrument imagery in the Baltic Sea

Mapping Water Quality Parameters with Sentinel-3 Ocean and Land Colour Instrument imagery in the Baltic Sea

Optical remote sensing of lakes: an overview on Lake Maggiore

Applying Spectral Unmixing to Determine Surface Water Parameters in a Mining Environment

Contact Info

Product

Resources

About