Chlorophyll-a Detection Algorithms at Different Depths Using In Situ, Meteorological, and Remote Sensing Data in a Chilean Lake

Abstract

In this study, we employ in situ, meteorological, and remote sensing data to estimate chlorophyll-a concentration at different depths in a South American freshwater ecosystem, focusing specifically on a lake in southern Chile known as Lake Maihue. For our analysis, we explored four different scenarios using three deep learning and traditional statistical models. These scenarios involved using field data (Scenario 1), meteorological variables (Scenario 2), and satellite data (Scenarios 3.1 and 3.2) to predict chlorophyll-a levels in Lake Maihue at three different depths (0, 15, and 30 m). Our choice of models included SARIMAX, DGLM, and LSTM, all of which showed promising statistical performance in predicting chlorophyll-a concentrations in this lake. Validation metrics for these models indicated their effectiveness in predicting chlorophyll levels, which serve as valuable indicators of the presence of algae in the water body. The coefficient of determination values ranged from 0.30 to 0.98, with the DGLM model showing the most favorable statistics in all scenarios tested. It is worth noting that the LSTM model yielded comparatively lower metrics, mainly due to the limitations of the available training data. The models employed, which use traditional statistical and machine learning models and meteorological and remote sensing data, have great potential for application in lakes in Chile and the rest of the world with similar characteristics. In addition, these results constitute a fundamental resource for decision-makers involved in the protection and conservation of water resource quality.

Spatio-Temporal Dynamics Coupling between Land Use/Cover Change and Water Quality in Dongjiang Lake Watershed Using Satellite Remote Sensing

“…The COD Mn index refers to the amount of oxidant consumed by the unit of reducing substances in the water body that needs to be oxidized under specified conditions [28]; the higher the value of COD Mn , the more the water bodies are polluted by organic and inorganic oxidizable substances [29]. COD refers to the amount of reducing substances in water samples that need to be oxidized and measured chemically, especially organic pollutants [30]; NH 3 -N is a nutrient in the water body that can lead to the phenomenon of water eutrophication and is the main oxygen-consuming pollutant in the water body; TP is the total amount of phosphorus in the water body, and excess phosphorus can cause eutrophication of the water body and disturb the balance of the water body [31]. Table 3 shows all the correlation coefficients between the reflectance of each band combination and measured data of DO, COD Mn , COD, NH 3 -N, and TP.…”