Abstract
Abstract. Recent studies have investigated the use of satellite imaging combined with machine learning for modelling the Chlorophyll-a (Chl-a) concentration of bodies of water. However, most of these studies use satellite data that lack the temporal resolution needed to monitor dynamic changes in Chl-a in productive lakes like Laguna Lake. Thus, the aim of this paper is to present the methodology for modelling the Chl-a concentration of Laguna Lake in the Philippines using satellite imaging and machine learning algorithms. The methodology uses images from the Himawari-8 satellite, which have a spatial resolution of 0.5–2 km and are taken every 10 minutes. These are converted into a GeoTIFF format, where differences in spatial resolution are resolved. Additionally, radiometric correction, resampling, and filtering of the Himawari-8 bands to exclude cloud-contaminated pixels are performed. Subsequently, various regression and gradient boosting machine learning algorithms are applied onto the train dataset and evaluated, namely: Simple Linear Regression, Ridge Regression, Lasso Regression, and Light Gradient Boosting Model (LightGBM). The results of this study show that it is indeed possible to integrate algorithms in Machine Learning in modelling the near real-time variations in Chl-a content in a body of water, specifically in the case of Laguna Lake, to an acceptable margin of error. Specifically, the regression models performed similarly with a train RMSE of 1.44 and test RMSE of 2.51 for Simple Linear Regression and 2.48 for Ridge and Lasso Regression. The linear regression models exhibited a larger degree of overfitting than the LightGBM model, which had a 2.18 train RMSE.
Highlights
Several studies have linked Chlorophyll-a (Chl-a) concentration with the trophic status of an aquatic ecosystem, which can be classified as oligotrophic, mesotrophic, or eutrophic (Sakamoto, 1966), (National Research Council (US) Committee on Water Quality Criteria, 1974), (Dobson et al, 1974), (Jones et al, 1979)
Blanco et al (2020) used regression analysis of Sentinel-3 OLCI images to produce algal classification maps, Syariz et al (2019) used neural networks trained on the same Sentinel-3 data to produce models that outperform existing 3-band and 2-band models in terms of accuracy, and Jalbuena et al (2019) inputted Landsat-8 data in the Bio-Optical Model Based tool for Estimating water quality and bottom properties from Remote sensing images (BOMBER) tool and processed this data through the Water Color Simulator (WASI) tool to produce Chl-a maps
The objective of this paper is to present the methodology for modelling the Chl-a concentration Laguna Lake in the Philippines using satellite imaging and machine learning algorithms
Summary
Several studies have linked Chlorophyll-a (Chl-a) concentration with the trophic status of an aquatic ecosystem, which can be classified as oligotrophic, mesotrophic, or eutrophic (Sakamoto, 1966), (National Research Council (US) Committee on Water Quality Criteria, 1974), (Dobson et al, 1974), (Jones et al, 1979). Recent studies have modeled Chl-a concentrations using imaging and machine learning. In 2014, Kim et al (2014) proposed machine learning approaches to coastal water quality monitoring using GOCI satellite data. The study of Li et al (2021) discussed the remote estimation of Chl-a based on artificial intelligence that can provide an effective and robust method to monitor the lake eutrophication on a macro-scale, and offer a better approach to elucidate the response of lake ecosystems to global change. Saberioon et al (2020) proposed that Sentinel-2A, when coupled with machine learning algorithms, could be employed as a reliable, inexpensive, and accurate instrument for monitoring the biophysical status of small inland waters like fishponds and sandpit lakes. Blanco et al (2020) used regression analysis of Sentinel-3 OLCI images to produce algal classification maps, Syariz et al (2019) used neural networks trained on the same Sentinel-3 data to produce models that outperform existing 3-band and 2-band models in terms of accuracy, and Jalbuena et al (2019) inputted Landsat-8 data in the Bio-Optical Model Based tool for Estimating water quality and bottom properties from Remote sensing images (BOMBER) tool and processed this data through the Water Color Simulator (WASI) tool to produce Chl-a maps
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