Remote retrieval of suspended particulate matter in inland waters: Image-based or physical atmospheric correction models?

Abstract

The objective of this paper was to compare the limits of three image-based atmospheric correction models (top of the atmosphere (ToA), dark object subtraction (DOS), and cosine of the sun zenith angle (COST)), and three physical models (atmospheric correction for flat terrain (ATCOR), fast line-of-sight atmospheric analysis of spectral hypercubes (FLAASH)), and ACOLITE) for retrieving suspended particulate matter (SPM) concentrations in inland water bodies using Landsat imagery. For SPM concentration estimates, all possible combinations of 2-band normalized ratios (2bNR ) were computed, and a stepwise regression was applied. The correlation analysis allowed highlighting that the red/blue 2bNR was the best spectral index to retrieve SPM concentrations in the case of image-based models, while the red/green 2bNR was the best in the case of physical models. Contrary to expectations, image-based atmospheric models outperformed the accuracy of physical models. The cross-validation results underlined the good performance of the DOS and COST models, with R2 > 0.83, NASH-criterion (Nash) > 0.83, bias = −0.01 mg/L, and RMSE < 0.27 mg/L. This outperformance was confirmed using blind test validation data, with an R2 > 0.86 and Nash > 0.58 for the DOS and COST models. The challenges and limitations involved in the remote monitoring of SPM spatial distribution in turbid productive waters using satellite data are discussed at the end of the paper.