Satellite Surface Reflectance correction and completion methodology by using Principal Component Analysis

 Surface reflectance of remote sensing datasets contribute to various fields such as natural resources management (Liang et al., 2024), agricultural practices (Liu et al., 2020; Stratoulias et al., 2017), ecological monitoring (Liang et al., 2024), and climate studies (Liu et al., 2020), providing critical information about Earth's surface conditions and resources. Nonetheless, inaccuracies in the raw remote surface reflectance data, resulting from both internal sensor anomalies (Hu et al., 2012) and external atmospheric effects (Dash et al., 2011; Vermote et al., 2016), reveal that correction of these datasets is essential. Moreover, Surface Reflectance datasets of coastal and inland waters are significantly affected by cloud coverage (Wang & Chen, 2024) introducing noise (Qing et al., 2021) into the imagery and shadows. This study introduces a methodology to correct and fill in missing data from multispectral Level 2 Surface Reflectance daily time-series, by identifying logical errors and implementing Principal Component Analysis. The study successfully results in continuous two-decade surface reflectance dataset to assure its reliability and utility across various applications.

References
Dash, P., Walker, N. D., Mishra, D. R., Hu, C., Pinckney, J. L., & D’Sa, E. J. (2011). Estimation of cyanobacterial pigments in a freshwater lake using OCM satellite data. Remote Sensing of Environment, 115(12), 3409–3423. https://doi.org/10.1016/j.rse.2011.08.004
Hu, C., Lee, Z., & Franz, B. (2012). Chlorophyll a algorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference. Journal of Geophysical Research: Oceans, 117(1), 1–25. https://doi.org/10.1029/2011JC007395
Liang, S., Li, Y., Wei, H., Dong, L., Zhang, J., & Xiao, C. (2024). Research on Hyperspectral Surface Reflectance Dataset of Typical Ore Concentration Area in Hami Remote Sensing Test Field. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 10(1), 137–144. https://doi.org/10.5194/isprs-annals-X-1-2024-137-2024
Liu, J. L., Cheng, F. Y., Munger, W., Jiang, P., Whitby, T. G., Chen, S. Y., Ji, W. W., & Man, X. L. (2020). Precipitation extremes influence patterns and partitioning of evapotranspiration and transpiration in a deciduous boreal larch forest. Agricultural and Forest Meteorology, 287(January), 107936. https://doi.org/10.1016/j.agrformet.2020.107936
Qing, S., Cui, T., Lai, Q., Bao, Y., Diao, R., Yue, Y., & Hao, Y. (2021). Improving remote sensing retrieval of water clarity in complex coastal and inland waters with modified absorption estimation and optical water classification using Sentinel-2 MSI. International Journal of Applied Earth Observation and Geoinformation, 102, 102377. https://doi.org/10.1016/j.jag.2021.102377
Stratoulias, D., Tolpekin, V., de By, R. A., Zurita-Milla, R., Retsios, V., Bijker, W., Hasan, M. A., & Vermote, E. (2017). A workflow for automated satellite image processing: From raw VHSR data to object-based spectral information for smallholder agriculture. Remote Sensing, 9(10). https://doi.org/10.3390/rs9101048
Vermote, E., Justice, C., Claverie, M., & Franch, B. (2016). Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment, 185, 46–56. https://doi.org/10.1016/j.rse.2016.04.008
Wang, J., & Chen, X. (2024). A new approach to quantify chlorophyll-a over inland water targets based on multi-source remote sensing data. Science of the Total Environment, 906(July 2023), 167631. https://doi.org/10.1016/j.scitotenv.2023.167631