Space-time chlorophyll-a retrieval in optically complex waters that accounts for remote sensing and modeling uncertainties and improves remote estimation accuracy

Abstract

Remote sensing reflectance (Rrs) values measured by satellite sensors involve large amounts of uncertainty leading to non-negligible noise in remote Chlorophyll-a (Chl-a) concentration estimation. This work distinguished between two main stages in the case of estimating distributions of Chl-a within the Gulf of St. Lawrence (Canada). At the model building stage, the retrieval algorithm used both in-situ Chl-a measurements and the corresponding Moderate Resolution Imaging Spectroradiometer (MODIS) L2-level data estimated Rrs at 412, 443, 469, 488, 531, 547, 555, 645, 667, 678 nm at a 1 km spatial resolution during 2004-2013. Through the training and validation of various models and Rrs combinations of the considered eight techniques (including support vector regression, artificial neural networks, gradient boosting machine, random forests, standard CI-OC3M, multiple linear regression, generalized addictive regression, principal component regression), the support vector regression (SVR) technique was shown to have the best performance in Chl-a concentration estimation using Rrs at 412, 443, 488, 531 and 678 nm. The accuracy indicators for both the training (850) and the validation (213) datasets were found to be very good to excellent (e.g., the R² value varied between 0.7058 and 0.9068). At the space-time estimation stage, this work took a step forward by using the Bayesian maximum entropy (BME) theory to further process the SVR estimated Chl-a concentrations by incorporating the inherent spatiotemporal dependency of physical Chl-a distribution. A 56% improvement was achieved in the reduction of the mean uncertainty of the validation data decreased considerably (from 1.2222 to 0.5322 mg/m³). Then, this novel BME/SVR framework was employed to estimate the daily Chl-a concentrations in the Gulf of St. Lawrence during Jan 1-Dec 31 of 2017 (1 km spatial resolution). The results showed that the daily mean Chl-a concentration varied from 1.6630 to 3.3431 mg/m³, and that the daily mean Chl-a uncertainty reduction of the composite BME/SVR vs. the SVR estimation had a maximum reduction value of 1.0082 and an average reduction value of 0.6173 mg/m³. The monthly spatial Chl-a distribution covariances showed that the highest Chl-a concentration variability occurred during November and that the spatiotemporal Chl-a concentration pattern changed a lot during the period August to November. In conclusion, the proposed BME/SVR was shown to be a promising remote Chl-a retrieval approach that exhibited a significant ability in reducing the non-negligible uncertainty and improving the accuracy of remote sensing Chl-a concentration estimates.

Keywords: Bayesian maximum entropy; Chlorophyll-a; MODIS; Machine learning; Support vector regression; Uncertainty.