Abstract

Long-term and high-frequency observations are vital to reveal water quality dynamics and responses to climate change and human activities. However, the datasets collected from traditional in situ and satellite observations may miss the rapid dynamics of water quality in the short term due to low temporal-spatial monitoring frequency and cloudy or rainy weather. To address this shortage, innovative ground-based proximal sensing (GBPS) technology was proposed to monitor water quality and identify emergencies with a wavelength range of 400–1000 nm, a spectral resolution of 1 nm and a minimal observation interval of 30 s. The GBPS was equipped with a hyperspectral imager placed 4–5 m above the water surface to minimize the impacts of the atmosphere and clouds. In this study, combined with 583 water samples obtained from four field samplings, GBPS datasets were first applied to estimate the total suspended matter (TSM), Secchi disk depth (SDD) and beam attenuation coefficient at 550 nm (C(550)) in Taihu Lake (TL), Liangxi River (LR) and Funchunjiang Reservoir (FR). The results demonstrated good performance with the TSM (R2 = 0.83, RMSE = 8.35 mg/L, MAPE = 24.0%), SDD (R2 = 0.88, RMSE = 0.09 m, MAPE = 14.7%), and C(550) (R2 = 0.79, RMSE = 3.55 m-1, MAPE = 35.8%). The time series of TSM and C(550) at the second-minute level showed consistent changes, but they were opposite to those of SDD. Taking TSM as an example, the datasets captured two mutations in TL with an 853.6% increase in 65 min and a rapid change from 40.3 mg/L to 256.9 mg/L and then to 51.0 mg/L in 224 min on November 1 and 3, respectively. Meanwhile, a significant decreasing trend (r = −0.83, p < 0.01) in LR from November 7 to 9 and a periodic diurnal increasing trend of TSM in FR during November 11 to 13 (0.46 ≤ R2 ≤ 0.70, p < 0.01) were observed. GBPS, with the advantages of high-frequency observations and the applicability of complex weather conditions, compensates for the in situ, aircraft and satellite observation deficiencies. Therefore, GBPS allows us to capture more detailed water quality information and episodic events, which is an important part of an integrated air-space-ground monitoring system in the future.

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