Retrievals of Chlorophyll-a from GOCI and GOCI-II Data in Optically Complex Lakes

Abstract

The chlorophyll-a (Chla) concentration is a key parameter to evaluate the eutrophication conditions of water, which is very important for monitoring algal blooms. Although Geostationary Ocean Color Imager (GOCI) has been widely used in Chla inversion, the consistency of the Rayleigh-corrected reflectance (Rrc) of GOCI and GOCI-II sensors still needs to be further evaluated, and a model suitable for lakes with complex optical properties needs to be constructed. The results show that (1) the derived Chla values of the GOCI and GOCI-II synchronous data were relatively consistent and continuous in three lakes in China. (2) The accuracy of the random forest (RF) model (R2 = 0.84, root mean square error (RMSE) =11.77 μg/L) was higher than that of the empirical model (R2 = 0.79, RMSE = 12.63 μg/L) based on the alternative floating algae index (AFAI). (3) The interannual variation trend fluctuated, with high Chla levels in Lake Chaohu in 2015 and 2019, while those in Lake Hongze were high in 2013, 2015, and 2022, and those in Lake Taihu reached their peak in 2017 and 2019. There were three types of diurnal variation patterns, namely, near-continuous increase (Class 1), near-continuous decrease (Class 2), and first an increase and then a decrease (Class 3), among which Lake Chaohu and Lake Taihu occupied the highest proportion in Class 3. The results analyzed the temporal and spatial variations of Chla in three lakes for 12 years and provided support for the use of GOCI and GOCI-II data and monitoring of Chla in optical complex inland waters.

A Systematic Review of the Application of GOCI to Water Quality Monitoring of Inland and Coastal Waters

“…Simulated hyperspectral 𝑅 𝑟𝑠 (𝜆) facilitate the construction of more accurate Chla estimation models for optically complex turbid inland waters. [142] compared the performance of different Chla algorithms for GOCI and GOCI-II in Lake Taihu, Lake Chaohu, and Lake Hongze with 𝐿 𝑟𝑐 (𝜆) as input. The results indicated that the RF algorithm outperformed the commonly used spectral index models.…”

A Systematic Review of the Application of the Geostationary Ocean Color Imager to the Water Quality Monitoring of Inland and Coastal Waters

“…Simulated hyperspectral R rs (λ) facilitate the construction of more accurate Chla estimation models for optically complex turbid inland waters. Guo [141] compared the performance of different Chla algorithms for the GOCI and the GOCI-II in Lake Taihu, Lake Chaohu, and Lake Hongze, with L rc (λ) as input. The results indicated that the RF algorithm outperformed the commonly used spectral index models.…”

Analysis of the impact of the COVID-19 pandemic lockdown on the spatiotemporal variations in water quality in three wetland areas in Oran, western Algeria

“…Along with certain agricultural practices, domestic and industrial wastewater discharge are identi ed as the main sources of this eutrophication (Converse 1995 Currently, due to the vastness, remoteness, and di culty of accessing certain wetland areas, groundbased monitoring methods have become expensive, costly, and often unfeasible. Due to the ease of acquisition and rapid processing of images (Guo et al 2023), remote sensing techniques based on satellite imagery have become indispensable for monitoring water quality parameters, including chlorophyll-a and water transparency (Mishra and Mishra 2012); (Alikas and Kratzer 2017). Several multispectral satellites have been launched, including Landsat series data, MODIS data, and MERIS data.…”