https://digital.csic.es/bitstream/10261/254160/1/cyanoHABs.pdf

Monitoring cyanoHABs and water quality in Laguna Lake (Philippines) with Sentinel-2 satellites during the 2020 Pacific typhoon season

Mapping algal bloom dynamics in small reservoirs using Sentinel-2 imagery in Google Earth Engine

Satellite temporal resolutions are vital for the timely detection of phytoplankton blooms in inland waters using satellite observations due to rapid phytoplankton migration and reproduction. However, the effects of satellite temporal resolutions on the remote derivation of trends in phytoplankton have not yet been assessed. To address this issue, we first used the daily Moderate–resolution Imaging Spectroradiometer (MODIS)–derived phytoplankton blooms in a typical bloom–frequent lake (Lake Taihu, China) between 2001 and 2020 to simulate a series of phytoplankton bloom data sets with temporal resolutions of 2 days to 30 days. We then revealed the differences in the trends of the phytoplankton blooms and their responses to the climatic factors derived from these data sets; we subsequently quantified the relationships between the differences and the temporal resolutions at daily, monthly, and yearly scales. We found that there were significant inconsistencies in the trends in the phytoplankton bloom areas derived from the simulated series data sets with different temporal resolutions; the relations of the MODIS–derived phytoplankton bloom areas to those derived from the simulated series data sets became weaker as the temporal resolutions of the simulated series data sets decreased; the responses of the phytoplankton blooms to the climatic factors quantified by the simulated series data sets were also varied with the temporal resolutions at daily, monthly, and yearly scales. These results from simulated data were also validated by satellite data of Lake Taihu, Lake Chaohu, and Lake Dianchi, three eutrophic lakes in China. In brief, our results cautioned against the use of satellite data with low temporal resolutions to reveal long–term phytoplankton–bloom dynamics. 

Effects of satellite temporal resolutions on the remote derivation of trends in phytoplankton blooms in inland waters

Climate change has increased the temperature and altered the mixing regime of high-value lakes in the subalpine region of Northern Italy. Remote sensing of chlorophyll-a can help provide a time series to allow an assessment of the ecological implications of this. Non-parametric multiplicative regression (NPMR) was used to visualize and understand the changes that have occurred between 2003–2018 in Lakes Garda, Como, Iseo, and Maggiore. In all four deep subalpine lakes, there has been a disruption from a traditional pattern of a significant spring chlorophyll-a peak followed by a clear water phase and summer/autumn peaks. This was replaced after 2010–2012, with lower spring peaks and a tendency for annual maxima to occur in summer. There was a tendency for this switch to be interspersed by a two-year period of low chlorophyll-a. Variables that were significant in NPMR included time, air temperature, total phosphorus, winter temperature, and winter values for the North Atlantic Oscillation. The change from spring to summer chlorophyll-a maxima, relatively sudden in an ecological context, could be interpreted as a regime shift. The cause was probably cascading effects from increased winter temperatures, reduced winter mixing, and altered nutrient dynamics. Future trends will depend on climate change and inter-decadal climate drivers.

https://www.mdpi.com/2073-4441/13/6/866/pdf

Detecting Climate Driven Changes in Chlorophyll-a in Deep Subalpine Lakes Using Long Term Satellite Data

Space-time monitoring of water quality in an eutrophic reservoir using SENTINEL-2 data - A case study of San Roque, Argentina

Hot moments and hotspots of cyanobacteria hyperblooms in the Curonian Lagoon (SE Baltic Sea) revealed via remote sensing-based retrospective analysis.

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Hot moments and hotspots of cyanobacteria hyperblooms in the Curonian Lagoon (SE Baltic Sea) revealed via remote sensing-based retrospective analysis.