Regional Satellite Algorithms to Estimate Chlorophyll-a and Total Suspended Matter Concentrations in Vembanad Lake

Abstract

A growing coastal population is leading to increased anthropogenic pollution that greatly affects coastal and inland water bodies, especially in the tropics. The Sustainable Development Goal-14, ‘Life below water’ emphasises the importance of conservation and sustainable use of the ocean and its resources. Pollution management practices often include monitoring of water quality using in situ observations of chlorophyll-a (chl-a) and total suspended matter (TSM). Satellite technology, including the MultiSpectral Instrument (MSI) sensor onboard Sentinel-2, enables the continuous monitoring of these variables in inland waters at high spatial and temporal resolutions. To improve the monitoring of water quality in the tropical Vembanad-Kol-Wetland (VKW) system, situated on the southwest coast of India, we present two regionally tuned satellite algorithms developed to estimate chl-a and TSM concentrations. The new algorithms estimate the chl-a and TSM concentrations from the simulated reflectance values as a function of the inherent optical properties using a forward modelling approach. The model was parameterised using the National Aeronautics and Space Administration (NASA) bio-Optical Marine Algorithm Dataset (NOMAD) and in situ measurements collected in the VKW system. To assess model performance, results were compared with in situ measurements of chl-a and TSM and other existing satellite-based models of chl-a and TSM. For satellite application, two different atmospheric correction methods (ACOLITE and POLYMER) were tested and satellite matchups were used to validate the new chl-a and TSM algorithms following standard validation procedures. The results demonstrated that the new algorithms were in good agreement with in situ observations and outperform existing chl-a and TSM algorithms. The new regional satellite algorithms can be used to monitor water quality within the VKW system to support the sustainable management under natural (cyclones, floods, rainfall, and tsunami) and anthropogenic pressures (industrial effluents, agricultural practices, recreational activities, construction, and demolishing concrete structures) and help achieve Sustainable Development Goal 14.

Satellite Remote Sensing: A Tool to Support Harmful Algal Bloom Monitoring and Recreational Health Advisories in a California Reservoir

“…It applies the dark spectrum fitting method (DSF) for atmospheric correction, which determines the reflectance based on multiple dark targets in the image (Vanhellemont & Ruddick, 2018). This processor has been shown to be more effective for aquatic inland applications for S2 compared to other existing algorithms (Vanhellemont, 2019) and has been used in other inland aquatic studies (Bramich et al., 2021; Rodríguez‐Benito et al., 2020; Theenathayalan et al., 2022), including in California (Lee et al., 2021). Images were resampled to 20‐m and the default sunglint correction was applied.…”

Optimizing Optical Coastal Remote-Sensing Products: Recommendations for Regional Algorithm Calibration

“…Satellite scenes were further processed for atmospheric correction using two distinct methods, namely, POLYMER [16] and ACOLITE dark spectral fitting (DSF) [17]. This choice was based on previous comparisons of these two methods, showing that they have distinct spectral uncertainties [11] and performances for different final products [25]. In Patos Lagoon, previous works have applied ACOLITE [10,42] and POLYMER [34], but without a direct comparison of these methods.…”

“…An ideal scenario would be to use in situ radiometric data to directly evaluate atmospheric corrections and measure optical properties in the study area. Theenathayalan et al [25] studied Vembanad Lake and suggested the following possible path: the low availability of field data was overcome by using a simulated reflectance dataset for the study area based on a radiative transfer model. The data were used to develop regional algorithms for chlorophyll-a and SPM concentrations, which outperformed previously established algorithms.…”

“…These differences introduce specificity into the application of atmospheric corrections [11]. Thus, the same correction can be identified as yielding a better or worse performance depending on the region studied, band used, or final product derived [20,22,[24][25][26].…”

“…Analytical or semi-analytical algorithms often consider standard optical properties and are, therefore, regarded as a more global approach. Empirical algorithms, however, could yield better results when adjusted to the area of interest [25] but should not be applied in regions other than those used in calibration [9,27]. The adjusting of algorithms requires in situ radiometric data that do not contain uncertainties resulting from atmospheric correction and might not be available for the study area.…”