Real-Time and Continuous Tracking of Total Phosphorus Using a Ground-Based Hyperspectral Proximal Sensing System

Abstract

Total phosphorus (TP) is the main limiting factor of eutrophication for most inland waters globally. However, the combination of the limited temporal-spatial ranges of traditional manual sampling, poor spectral resolutions, and weather-vulnerable satellite observations, have yielded great data gaps in TP dynamics in short-lived, extreme episodic, or unpredictable pollution. Hence, a novel ground-based hyperspectral proximal sensing system (GHPSs) with a maximum observation frequency of 20 s and a spectral resolution of 1 nm between 400 and 900 nm was developed for automatic, real-time and continuous observation of TP. Focusing on the GHRSs, a TP machine learning model was developed and validated with ideal accuracy (R2 = 0.97, RMSE = 0.017 mg·L−1, MAPE = 12.8%) using 377 pairs of in situ TP measurements collected from Fuchunjiang Reservoir (FR), Liangxi River (LR), and Lake Taihu (LT). Second-scale TP results showed a low-value stable period followed by a sharp change period in LT during 29–31 October and 1–3 November, respectively. The exponential increase (R2 = 0.65, p −1 and 0.42 mg·L−1 were recorded in LT on 2 and 3 November, respectively. Simultaneously, a significant decrease (R2 = 0.57, p R2 > 0.99, slop = 0.98). Short and rapid TP changes were observed within one day in LT and LR based on GHPSs minute scale monitoring, which highlighted the importance of high frequency observations of TP. Several advantages of real-time, high accuracy and wide applicability to complex weather were highlighted for the GHPSs for TP monitoring compared to traditional equipment. Therefore, there are potential applications of the GHPSs in the integrated space-air-ground TP monitoring, as well as emergency monitoring and early-warning systems in the future, and it can raise our awareness of the dynamics and driving mechanisms of water quality for inland waters.

Keywords

• ground-based hyperspectral proximal sensing system
• total phosphorus
• real-time observation
• complex weather conditions
• integrated monitoring network