Evaluation of five atmospheric correction algorithms for multispectral remote sensing data over plateau lake.

The precise correction of atmospheric effects is essential for the accurate analysis of inland water color from remote sensing (RS) data. This study systematically evaluates the efficacy of five atmospheric correction (AC) algorithms—Acolite, C2RCC, FLAASH, iCOR, and L2gen—when applied to Sentinel-2 Multi Spectral Instrument (MSI) and Sentinel-3 Ocean and Land Color Instrument (OLCI) imagery. The performance of these algorithms is scrutinized across different spectral bands and spatial resolutions, with a focus on their suitability for water body AC. Our validation approach primarily focuses on the spatial arrangement of sampling points and the timeliness of the collected data. The results reveal that Acolite demonstrates superior performance in the red band of MSI data, whereas C2RCC exhibits inconsistencies in the blue and green bands. FLAASH stands out in its handling of OLCI data, although iCOR exhibits sensitivity to resolution, resulting in over-correction in lower resolution scenarios. L2gen is noted for its consistent provision of concentrated data amplitude across the board. The findings, graphically represented through a radar chart, are pivotal for guiding the selection of optimal AC algorithms for the visible spectrum, thereby enhancing the accuracy of RS applications in environmental monitoring and research. Additionally, our study highlights the impact of sampling time differences on the AC spectrum, with variations of approximately 3% observed for a single sensor. This underscores the critical need for temporal consistency in field measurements. • Emphasizes differences in atmospheric correction results from satellites with varying spatial resolution. • Quantifies the impact of time differences between field measurements and satellite overpass on atmospheric correction. • Evaluates the performance of five atmospheric correction algorithms over plateau lake. [ABSTRACT FROM AUTHOR]