# Advances in the Monitoring of Algal Blooms by Remote Sensing: A Bibliometric Analysis
## 4 Discussion
The Medium Resolution Imaging Spectrometer (MERIS) on board the European Space Agency (ESA) ENVISAT platform already provided better capability for monitoring inland water systems from 2002 until 2012 than previous sensors (e.g., MERIS spatial resolution was 300 m and SeaWiFS was 1100 m, and MERIS had 15 spectral bands and SeaWiFS 8 bands) [47]. More recent sensors, such as the OLCI (Ocean and Land Color Instrument) on board the Sentinel-3, have a number of improvements when compared to MERIS, including an increase from 15 to 21 spectral bands, improved signal-to-noise ratio (SNR), global spatial resolution of 300 m at full resolution, and improved coverage [47]. To be able to distinguish between climate variability and changes of anthropogenic origin for a specific variable is necessary to define the baseline. The baseline can be defined as the mean value of the studied variable over which fluctuations can occur within an interval that contains most observed cases [50; 51]. Based on this concept we can calculate simple anomalies, simple standardised anomalies, or climatic anomalies [51]. Values above the baseline are described as positive anomalies (increase) and values below as negative anomalies (decrease). In the climatic anomalies, the average value of the variable is replaced by the 12 monthly averages that constitute the climatology of the variable. Both simple anomalies and climatological anomalies are defined locally. Its definition is valid only for the place where the data series used to define it is available. As the time series is longer, the definition of the baseline will be more precise, and the extreme values will be highlighted in a better way. The anomalies methodology has been widely applied for the study of oceanographic processes [52; 53; 54; 55]. To apply this methodology, it is essential to establish continuous monitoring programs. However, if monitoring programs measuring a simple variable continuously such as temperature are scarce [51], long-term monitoring programs of phytoplankton are even scarcer. According to our database results, most of the existing algal blooms studies globally are based on sporadic sampling and short-term research programs. Sporadic sampling is usually done in the context of monitoring programs that only take samples when there is a noticeable change in the sea colour or when dead fish appear, and samples are taken to characterize the event. This has been noticed by [PERSON] et al. [56] in Mexico, but our results show that it happens the same around the world. Only a few programs are based on an ongoing monitoring strategy that could allow the building of a good quality baseline if maintained on time (e.g., HELCOM in the Baltic sea WWW.HELCOM.FI/ENVIRONMENT/ALGALBLOOMS.HTML [57]; Coastal Intensive Site Network (CISNet) program around the U.S. marine and Great Lakes coasts [58]; Water Framework Directive monitoring [[https://ec.europa.eu/environment/water/water-framework/index_en.html](https://ec.europa.eu/environment/water/water-framework/index_en.html)]([https://ec.europa.eu/environment/water/water-framework/index_en.html](https://ec.europa.eu/environment/water/water-framework/index_en.html))). The scarcity of in situ monitoring programs is worst in non-developed countries, probably due to economic constraints and lack of individual training. In our results (Figure 3), we observe that South America and Africa have nearly no studied areas. Even if remote sensing images are mostly free, in situ remote sensing is expensive and poses a limitation for non-developed countries. It is important to say that remote sensing and in situ sampling are complementary techniques and deriving results only from remote sensing can lead to wrong conclusions. Finally, to be able to detect algal blooms with remote sensing, some considerations about the bloom's characteristics must be considered: patchy distribution, duration, and depth, because there can be limitations.