[1] SCHALLES J F, GITELSON A A, YACOBI Y Z et al. Estimation of Chlorophyll a from Time Series Measurements of High Spectral Resolution Reflectance in an Eutrophic Lake[J]. Journal of Phycology, 34, 383-390(1998).

[2] MEDINA-LOPEZ E. Machine Learning and the End of Atmospheric Corrections: A Comparison Between High-Resolution Sea Surface Salinity in Coastal Areas from Top and Bottom of Atmosphere Sentinel-2 Imagery[J]. Remote Sensing, 12, 2924(2020).

[3] GAO B C, MONTES M J, DAVIS C O et al. Atmospheric Correction Algorithms for Hyperspectral Remote Sensing Data of Land and Ocean[J]. Remote Sensing of Environment, 113, S17-S24(2009).

[7] [7] SHAH M, RAVAL M S, DIVAKARAN S. A Deep Learning Perspective to Atmospheric Crection of Satellite Images[C]IGARSS 20222022 IEEE International Geoscience Remote Sensing Symposium. Kuala Lumpur, Malaysia: IEEE, 2022: 346349. DOI: 10.1109IGARSS46834.2022.9884900.

[8] [8] BISHOP C M. Neural wks f Pattern Recognition[M]. New Yk: Oxfd University Press, 1995: DOI: 10.1093oso9780198538493.001.0001.

[9] BRAJARD J, SANTER R, CRÉPON M et al. Atmospheric Correction of MERIS Data for Case-2 Waters Using a Neuro-Variational Inversion[J]. Remote Sensing of Environment, 126, 51-61(2012).

[10] LI H, HE X A, BAI Y et al. Atmospheric Correction of Geostationary Satellite Ocean Color Data Under High Solar Zenith Angles in Open Oceans[J]. Remote Sensing of Environment, 249, 112022(2020).

[11] RUSIA P, BHATEJA Y, MISRA I et al. An Efficient Machine Learning Approach for Atmospheric Correction[J]. Journal of the Indian Society of Remote Sensing, 49, 2539-2548(2021).

[14] YANG J, HUANG X. 30 m Annual Land Cover and Its Dynamics in China from 1990 to 2019[J]. Earth System Science Data Discussions, 2021, 1-29(2021).

[15] [15] DANIELSON J J, GESCH D B. Global Multiresolution Terrain Elevation Data 2010(GMTED2010): GMTED2010[R]. Reston, VA: US Geological Survey, 2011.

[16] HOLBEN B N, TANRÉ D, SMIRNOV A et al. An Emerging Ground‐based Aerosol Climatology: Aerosol Optical Depth from AERONET[J]. Journal of Geophysical Research: Atmospheres, 106, 12067-12097(2001).

[17] ÅNGSTRÖM A. On the Atmospheric Transmission of Sun Radiation and on Dust in the Air[J]. Geografiska Annaler, 11, 156-166(1929).

[19] RIVERA J P, VERRELST J, GÓMEZ-DANS J et al. An Emulator Toolbox to Approximate Radiative Transfer Models With Statistical Learning[J]. Remote Sensing, 7, 9347-9370(2015).

[20] VERMOTE E F, TANRE D, DEUZE J L et al. Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: An Overview[J]. IEEE Transactions on Geoscience and Remote Sensing, 35, 675-686(1997).

[21] MAIN-KNORN M, PFLUG B, LOUIS J et al. Calibration and Validation Plan for the L2A Processor and Products of the Sentinel-2 Mission[J]. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 40, 1249-1255(2015).

[22] MAYER B, KYLLING A. The libRadtran Software Package for Radiative Transfer Calculations-Description and Examples of Use[J]. Atmospheric Chemistry and Physics, 5, 1855-1877(2005).

[23] [23] MAINKNN M, PFLUG B, LOUIS J, et al. Sen2C f Sentinel2[C]Image Signal Processing f Remote Sensing XXIII, SPIE, 2017: 3748. DOI: 10.111712.2278218.

[24] WILSON R T. Py6S: A Python Interface to the 6S Radiative Transfer Model[J]. Computers & Geosciences, 51, 166-171(2013).

[25] [25] IOFFE S, SZEGEDY C. Batch Nmalization: Accelerating Deep wk Training by Reducing Internal Covariate Shift[C]Proceedings of the 32nd International Conference on International Conference on Machine Learning, 2015, 37: 448456. DOI: 10.55553045118.3045167.

[26] [26] WANG Z, SIMONCELLI E P, BOVIK A C. Multiscale Structural Similarity f Image Quality Assessment[C]The Thrityseventh Asilomar Conference on Signals, Systems & Computers, 2003. Pacific Grove, CA, USA: IEEE, 2003: 13981402, DOI: 10.1109ACSSC.2003.1292216.