Abstract
Chlorophyll-a (chl-a) is an important parameter of water quality and its concentration can be directly retrieved from satellite observations. The Ocean and Land Color Instrument (OLCI), a new-generation water-color sensor onboard Sentinel-3A and Sentinel-3B, is an excellent tool for marine environmental monitoring. In this study, we introduce a new machine learning model, Light Gradient Boosting Machine (LightGBM), for estimating time-series chl-a concentration in Fujian’s coastal waters using multitemporal OLCI data and in situ data. We applied the Case 2 Regional CoastColour (C2RCC) processor to obtain OLCI band reflectance and constructed four spectral indices based on OLCI feature bands as supplementary input features. We also used root-mean-square error (RMSE), mean absolute error (MAE), median absolute percentage error (MAPE), and R2 as performance indicators. The results indicate that the addition of spectral indices can easily improve the prediction accuracy of the model, and normalized fluorescence height index (NFHI) has the best performance, with an RMSE of 0.38 µg/L, MAE of 0.22 µg/L, MAPE of 28.33%, and R2 of 0.785. Moreover, we used the well-known band ratio and three-band methods for chl-a estimation validation, and another two OLCI chl-a products were adopted for comparison (OC4Me chl-a and Inverse Modelling Technique (IMT) Neural Net chl-a). The results confirmed that the LightGBM model outperforms the traditional methods and OLCI chl-a products. This study provides an effective remote sensing technique for coastal chl-a concentration estimation and promotes the advantage of OLCI data in ocean color remote sensing.
Highlights
In the coastal regions, due to the impacts of climate change and intensive human activities, such as rainfall, sewage discharge, and overfishing, eutrophic and polluted water bodies are imported into coastal waters through surface runoff, threatening the already-deteriorating coastal water quality [1]
We proposed a new ensemble learning algorithm, Light Gradient Boosting Machine (LightGBM), by combining Ocean and Land Color Instrument (OLCI) data and in situ data to set up a LightGBMbased model in order to estimate the chl-a concentration of the coastal waters of Fujian (China)
To further evaluate the performance of the LightGBM method, we examined the band ratio (BR) algorithm based on near infrared (NIR)/Red and TB algorithm for comparison, which represent the empirical and semiempirical methods in turbid productive coastal waters, respectively, and both have been widely used
Summary
Due to the impacts of climate change and intensive human activities, such as rainfall, sewage discharge, and overfishing, eutrophic and polluted water bodies are imported into coastal waters through surface runoff, threatening the already-deteriorating coastal water quality [1]. Appropriate biomass is important for maintaining the balance of a healthy aquatic ecosystem. Chl-a has been used as a key indicator for evaluating water quality including eutrophication [4,5]. Monitoring chl-a concentration is a significant issue in coastal water management. Ocean color remote sensing technology has been used as a highly efficient means of estimating chl-a concentration owing to its advantages, such as large-scale and real-time observations. The coastal zone color scanner, the first ocean color sensor carried on the Nimbus-7 satellite launched by the National Aeronautics and Space Administration in
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