Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; Nanjing Zhongke Deep Insight Technology Research Institute Co., Ltd, Nanjing 211899, China.
J Hazard Mater. 2022 Oct 5;439:129623. doi: 10.1016/j.jhazmat.2022.129623. Epub 2022 Jul 16.
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 (R = 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.
全球范围内浮游植物水华的扩张严重威胁着水质、食物网、栖息地稳定性和人类健康。由于浮游植物的迁移和繁殖速度很快，因此对浮游植物水华动态进行高频信息监测对于其预测、处理和管理至关重要。虽然过去几十年中已经广泛使用了涉及卫星、现场观测和自动化水下监测站的几种方法，但它们不能以低成本和高精度完全提供浮游植物水华的高频和连续观测。因此，我们提出了一种新的基于地面的遥感系统（GRSS），可以以高频率实时监测内陆水域的叶绿素 a 浓度（Chla）。GRSS 主要由三个平台组成：光谱测量平台、数据处理平台和远程访问控制、显示和存储平台。GRSS 能够以 20 秒的高频率获得 400-1000nm 的遥感辐照度比（R(λ)）。使用从四个内陆水域采集的 481 对 GRSS R(λ)和原位 Chla 测量数据集，校准和验证了 8 种不同的 Chla 反演算法。结果表明，随机森林回归在推导 Chla 方面表现最好（R=0.95，均方根误差=13.40μg/L，平均相对误差=25.7%）。GRSS 成功捕获了 2021 年 8 月两次典型的浮游植物水华事件，Chla 从 20μg/L 到 325μg/L 的快速变化达到分钟级，突出了该 GRSS 在浮游植物水华高频监测中的关键作用。尽管嵌入到 GRSS 中的算法可能受到训练数据集大小的限制，但 GRSS 的高频、连续和实时数据采集能力可以有效弥补传统观测的局限性。初步应用表明，GRSS 可以在短时间内捕获浮游植物水华的快速变化，因此在浮游植物水华管理中将发挥关键作用。从更广泛的角度来看，这种方法可以扩展到其他载体，如飞机、船舶和无人机，以实现浮游植物水华的网络化监测。
