The worldwide expansion of phytoplankton blooms has severely threatened water quality, food webs, habitat stability and human health. Due to the rapidity of phytoplankton migration and reproduction, high-frequency information on phytoplankton bloom dynamics is crucial for their forecasting, treatment, and management. While several approaches involving satellites, in situ observations and automated underwater monitoring stations have been widely used in the past several decades, they cannot fully provide high-frequency and continuous observations of phytoplankton blooms at low cost and with high accuracy. Thus, we propose a novel ground-based remote sensing system (GRSS) that can monitor real-time chlorophyll a concentrations (Chla) in inland waters with a high frequency. The GRSS mainly consists of three platforms: the spectral measurement platform, the data-processing platform, and the remote access control, display and storage platform. The GRSS is capable of obtaining a remote sensing irradiance ratio (R(λ)) of 400–1000 nm at a high frequency of 20 s. Eight different Chla retrieval algorithms were calibrated and validated using a dataset of 481 pairs of GRSS R(λ) and in situ Chla measurements collected from four inland waters. The results showed that random forest regression achieved the best performance in deriving Chla (R²= 0.95, root mean square error = 13.40 μg/L, and mean relative error = 25.7%). The GRSS successfully captured two typical phytoplankton bloom events in August 2021 with rapid changes in Chla from 20 μg/L to 325 μg/L at the minute level, highlighting the critical role that this GRSS can play in the high-frequency monitoring of phytoplankton blooms. Although the algorithm embedded into the GRSS may be limited by the size of the training dataset, the high-frequency, continuous and real-time data acquisition capabilities of the GRSS can effectively compensate for the limitations of traditional observations. The initial application demonstrated that the GRSS can capture rapid changes of phytoplankton blooms in a short time and thus will play a critical role in phytoplankton bloom management. From a broader perspective, this approach can be extended to other carriers, such as aircraft, ships and unmanned aerial vehicles, to achieve the networked monitoring of phytoplankton blooms.

Weijia Wang, Kun Shi, Yibo Zhang, Na Li, Xiao Sun, Dong Zhang, Yunlin Zhang, Boqiang Qin, Guangwei Zhu, A ground-based remote sensing system for high-frequency and real-time monitoring of phytoplankton blooms, Journal of Hazardous Materials, 439, 2022, 129623, doi.org/10.1016/j.jhazmat.2022.129623.