High-Frequency Observations of Cyanobacterial Blooms in Lake Taihu (China) from FY-4B/AGRI

Abstract

China’s FY-4B satellite, launched on 3 June 2021, is a new-generation geostationary meteorological satellite. The Advanced Geosynchronous Radiation Imager (AGRI) onboard FY-4B has 15 spectral channels, including 2 visible (470 and 650 nm), 1 near infrared (825 nm), and 3 shortwave infrared (1379, 1610, and 2225 nm) bands, which can be used to observe the Earth system with the highest spatial resolution of 500 m and 15 min temporal resolution. In this study, FY-4B/AGRI observations were applied for the first time to monitor cyanobacterial blooms in Lake Taihu, China. The AGRI reflectance at visible and near-infrared bands was first corrected to surface reflectance using the 6S radiative transfer model. Due to the similar spectral reflectance characteristics to those of land-based vegetation, the normalized difference vegetation index (NDVI) and some other remote sensing vegetation indices are usually used for the retrieval of cyanobacterial blooms. The fractional vegetation cover (FVC) of algae, defined as the fraction of green vegetation in the nadir view, was adopted to depict the status and trend of cyanobacterial blooms. NDVI and FVC, the two remote sensing indices developed for the retrieval of land vegetation, were used for the detection of cyanobacteria blooms in Lake Taihu. Finally, the FVC derived from AGRI measurements was compared with that obtained from the Advanced Himawari Imager (AHI) onboard the Himawari-8 satellite to validate the effectiveness of our method. It was found that atmospheric correction can substantially improve the determination of the normalized difference vegetation index (NDVI) values of cyanobacterial blooms in the lake. As a proof of the robustness of the algorithm, the NDVIs are both derived from both AGRI and AHI and their magnitudes are similar. In addition, the distribution of cyanobacterial blooms derived from AGRI FVC is highly consistent with that derived from FY-3D/MERSI and EOS/MODIS. While a lower spatial resolution of FY-4B/AGRI might restrict its capability in capturing some spatial details of cyanobacterial blooms, the high-frequency measurements can provide information for the timely and effective management of aquatic ecosystems and help researchers better quantify and understand the dynamics of cyanobacterial blooms. In particular, AGRI can provide greater details on the diurnal variation in the distribution of cyanobacterial blooms owing to the high temporal resolution.

Variation of Local Wind Fields under the Background of Climate Change and Its Impact on Algal Blooms in Lake Taihu, China

“…According to the algal spectral characteristics, pixels in satellite images containing algae can be identified using several vegetation indexes such as the EVI (Enhanced Vegetation Index), NDVI (Normalized Difference Vegetation Index), and FAI (Floating Algae Index), which include red light and near-infrared bands [45][46][47]. Here, we adopted the most commonly used NDVI for the inversion of algal blooms, and it can be calculated as follows [43,48]:…”

“…The 𝑁𝐷𝑉𝐼 ranges from −1 to 1, with a smaller value indicating a lower density of algal blooms, while a larger value indicates a higher density of algal blooms. The spectra values were determined as the average spectra of the three types of observation targets [48].…”

“…To determine an appropriate NDVI threshold for detecting algal blooms, a method integrating visual analysis with statistics, a commonly used method for threshold selection, was used in this paper [48]. Firstly, boundaries of algal blooms and water were manually determined in RGB (Red-Green-Blue) imagery containing algal blooms.…”

“…Following the definition of fractional vegetation cover [24,52], we defined the fractional algae cover index (FAC) as the proportion of algal blooms at the lowest point, which can reflect the area size for photosynthetic and coverage density of algal blooms. Assuming that a pixel on the lake surface is always composed of algae and water proportions, the NDVI of a mixed pixel can be expressed as the following equation [48]: Field measurement reflectance spectra of dense algae, algae-water, and water in Lake Taihu. The spectra values were determined as the average spectra of the three types of observation targets [48].…”

“…Assuming that a pixel on the lake surface is always composed of algae and water proportions, the NDVI of a mixed pixel can be expressed as the following equation [48]: Field measurement reflectance spectra of dense algae, algae-water, and water in Lake Taihu. The spectra values were determined as the average spectra of the three types of observation targets [48].…”