Sentinel-2 and Landsat-8 Observations for Harmful Algae Blooms in a Small Eutrophic Lake

Abstract

Widespread harmful cyanobacterial bloom is one of the most pressing concerns in lakes and reservoirs, resulting in a lot of negative ecological consequences and threatening public health. Ocean color instruments with low spatial resolution have been used to monitor cyanobacterial bloom in large lakes; however, they cannot be applied to small water bodies well. Here, the Multi-Spectral Instrument (MSI) onboard Sentinel-2A and -2B and the Operational Landsat Imager (OLI) onboard Landsat-8 were employed to assemble the virtual constellation and to track spatial and seasonal variations in floating algae blooms from 2016 to 2020 in a small eutrophic plateau lake: Lake Xingyun in China. The floating algae index (FAI) was calculated using Rayleigh-corrected reflectance in the red, near-infrared, and short-wave infrared bands. The MSI-derived FAI had a similar pattern to the OLI-derived FAI, with a mean absolute percentage error of 19.98% and unbiased percentage difference of 17.05%. Then, an FAI threshold, 0.0693, was determined using bimodal histograms of FAI images for floating algae extraction. The floating algae had a higher occurrence in the northern region than the southern region in this lake, whilst the occurrence of floating algae in summer and autumn was higher than that in spring and winter. Such a spatial and seasonal pattern was related to the variability in air temperature, wind speed and direction, and nutrients. The climatological annual mean occurrence of floating algae from 2016 to 2020 in Lake Xingyun exhibited a significant decrease, which was related to decreases in nutrients, resulting from efficient ecological restoration by the local government. This research highlighted the application of OLI-MSI virtual constellation on monitoring floating algae in a small lake, providing a practical and theoretical reference to monitor aquatic environments in small water bodies.

Harmful Cyanobacterial Blooms: Going beyond the “Green” to Monitor and Predict HCBs

“…There are also limitations in PC estimation for blooms of cyanobacteria in inland waters, namely under oligotrophic conditions or during blooms with other phytoplankton (where other pigments' signatures overlap in the signature curves) [162][163][164]. More recently, the launch of Sentinel-2 MSI (Multiple Spectral Instrument) and Lansat-8 OLI (Operational Land Imager) has provided the acquisition of data that could be comparable and with higher spatial resolution (10-60 m and 30 m, respectively), which is more adequate to monitor medium to small lakes and reservoirs [165][166][167][168]. However, the design of their imagery sensors was not originally thought for aquatic appli-cations and the lack of an orange band (~620 nm) has been challenging and has required the development of complex algorithms for PC estimation [169,170].…”

Estimation Method of Chlorophyll Concentration Distribution Based on UAV Aerial Images Considering Turbid Water Distribution in a Reservoir

“…To supplement this, there are examples of combining various types of satellite images and discussing the short-term decline in HABs. Liu et al [37] calculated the floating algae index (FAI) from Sentinel-2 MSI and Landsat-8 OLI images to monitor the spatial and seasonal variability in floating algae in a small lake from 2016 to 2020. The consistency of the FAI between MSI and OLI was confirmed, and the FAI threshold to distinguish floating algae was found using a bimodal histogram; the results suggest that the spatial distributions based on the different satellites can also be used in common.…”

“…In the study, the brightness values of each spectrum were corrected based on the amount of insolation. The need to correct insolation conditions is an issue not only for aerial images taken by UAVs but also when comparing images taken on different dates in satellite image analysis [37,47,48].…”

Remote Sensing Identification and Spatiotemporal Change Analysis of Cladophora with Different Morphologies

“…At present, this type of data has been used to extract the area, degree, and duration of algal outbreaks through spectral indices and classification methodologies [4,[15][16][17][18][19][20][21][22][23]. Additionally, visual interpretation has been widely employed to obtain increasingly precise information about algal blooms within a region [6,[24][25][26][27].…”