Abstract
The chlorophyll-a (Chl-a) concentration of eutrophic lakes fluctuates significantly due to the disturbance of wind and anthropogenic activities on the water body. Consequently, estimation of the Chl-a concentration has become an immense challenge. Due to urgent demand and rapid development in high-resolution earth observation systems, it has become crucial to assess hyperspectral satellite imagery capabilities on inland water monitoring. The Orbita hyperspectral (OHS) satellite is the latest hyperspectral sensor with both high spectral and spatial resolution (2.5 nm and 10 m, respectively), which could provide great potential for remotely estimating the concentration of Chl-a for inland waters. However, there are still some deficiencies that are mainly manifested in the Chl-a concentration remote sensing retrieval model assessment and accuracy validation, as well as signal-to-noise ratio (SNR) estimation of OHS imagery for inland waters. Therefore, the radiometric performance of OHS imagery for water quality monitoring is evaluated in this study by comparing different atmospheric correction models and the SNR with several remote sensing images. Several crucial findings can be drawn: (1) the three-band model ((1/B15-1/B17)B19) developed by OHS imagery is most suitable for estimating the Chl-a concentration in Dianchi Lake, with the root-mean-square error (RMSE) and the mean absolute percentage error (MAPE) of 15.55 µg/L and 16.31%, respectively; (2) the applicability of the FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) atmospheric correction model for OHS imagery in a eutrophic plateau lake (Dianchi Lake) was better than the 6S (Second Simulation of Satellite Signal in the Solar Spectrum) model, and QUAC (Quick Atmospheric Correction) model, as well as the dark pixel method; (3) the SNR of the OHS imagery was similar to that of Hyperion imagery and was significantly higher than SNR of the HSI imagery; (4) the spatial resolution showed slight influence on the SNR of the OHS imagery. The results show that OHS imagery could be applied to remote sensing retrieval of Chl-a in eutrophic plateau lakes and presents a new tool for dynamic hyperspectral monitoring of water quality.
Highlights
Chlorophyll-a (Chl-a) is a crucial parameter which impacts the watercolor of inland lake water, and is a vital indicator measuring the eutrophication degree of lake water [1,2].The nutrients transported by Chl-a reduce the spread of light through a water column and affect the natural operation of the entire aquatic ecosystem
A new and customized Chl-a concentration retrieval algorithm based on measured spectral data and Orbita hyperspectral (OHS) imagery is proposed in this study
The model improved Chl-a estimation accuracy using the OHS imagery in extremely eutrophic plateau water bodies compared to the existing models proposed for clear open sea and turbid coastal waters
Summary
Chlorophyll-a (Chl-a) is a crucial parameter which impacts the watercolor of inland lake water, and is a vital indicator measuring the eutrophication degree of lake water [1,2].The nutrients transported by Chl-a reduce the spread of light through a water column and affect the natural operation of the entire aquatic ecosystem. Accurate prediction of Chl-a concentration and its spatiotemporal distribution pattern is of great significance to the protection of lake ecosystems and the improvement of water quality monitoring capabilities, and is the basis of water environment remote sensing monitoring and evaluation. Precise prediction of Chl-a concentration is the focus and challenge of watercolor remote sensing [3,4,5,6]. Dianchi Lake is the largest freshwater lake in Southwest China and the sixth-largest freshwater lake in China. It is an important drinking water source for residents, one of many important habitats for migratory birds, and an essential part of the wetland ecosystem
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