Applicability study of four atmospheric correction methods in the remote sensing of lake water color

References

• Adler-Golden SM, Matthew MW, Bernstein LS, et al. 1999. Atmospheric correction for shortwave spectral imagery based on MODTRAN4[C]. Imag Spectrom V SPIE. 3753:61–69.
   Google Scholar
• Bernardo N, Watanabe F, Rodrigues T, Alcântara E. 2017. Atmospheric correction issues for retrieving total suspended matter concentrations in inland waters using OLI/Landsat-8 image. Adv Space Res. 59(9):2335–2348. doi: 10.1016/j.asr.2017.02.017.
   Web of Science ®Google Scholar
• Bernstein LS, Jin X, Gregor B, et al. 2012. Quick atmospheric correction code: algorithm description and recent upgrades. Opt Eng. 51(11):111719–111719. doi: 10.1117/1.OE.51.11.111719.
   Web of Science ®Google Scholar
• Cai L, Bu J, Tang D, Zhou M, Yao R, Huang S. 2020. Geosynchronous satellite GF-4 observations of Chlorophyll-a distribution details in the Bohai Sea, China. Sensors. 20(19):5471. doi: 10.3390/s20195471.
   PubMed Web of Science ®Google Scholar
• Cheng CM, Wei YC, Li Y, Tu QG. 2020. The 6S image-by-image atmospheric correction of GF-1/WFV images of Taihu Lake water bodies. Remote Sens Technol Appl. 35(01):141–152.
   Google Scholar
• Ding F. 2018. Comparison and suitability evaluation of Taihu OLI images [D]. Shaanxi：Xi’an University of Science and Technology.
   Google Scholar
• Doxani G, Vermote EF, Roger J-C, Skakun S, Gascon F, Collison A, De Keukelaere L, Desjardins C, Frantz D, Hagolle O, et al. 2023. Atmospheric Correction Inter-comparison eXercise, ACIX-II Land: an assessment of atmospheric correction processors for Landsat 8 and Sentinel-2 over land. Remote Sens Environ. 285:113412. doi: 10.1016/j.rse.2022.113412.
   Web of Science ®Google Scholar
• Du T, Wang XH, Liang HY, et al. 2014. Comparative study on atmospheric correction model of lake chlorophyll A inversion. J Yun Univ (Nat Sci Ed). 36(02):241–248.
   Google Scholar
• Duan HT, Cao ZG, Shen M, Ma JG, Qi TC. 2022. Research progress and prospect of lake remote sensing research. J Rem Sens. 26(01):3–18.
   Google Scholar
• Guan H, Huang J, Li X, Zeng Y, Su W, Ma Y, Dong J, Niu Q, Wang W. 2022. An improved approach to estimating crop lodging percentage with Sentinel-2 imagery using machine learning. Int J Appl Earth Obs Geoinf. 113:102992. doi: 10.1016/j.jag.2022.102992.
   Web of Science ®Google Scholar
• Guo YK, Zeng F. 2012. Comparison of atmospheric correction of SPOT 5 images based on FLAASH and QUAC model. Survey Mapp Bul. 11:21–23.
   Google Scholar
• Hou XZ, Yi WN, Qiao YL, Huang HL, Cui WY, Du LL, Chen C. 2015. Atmospheric Correction Method for Remote Sensing Image Based on 6S Model. J Atmos Environ Optics. 10(01):63–68.
   Google Scholar
• Hu Y, Liu L, Liu L, Peng D, Jiao Q, Zhang H. 2014. A landsat-5 atmospheric correction based on MODIS atmosphere products and 6S model. IEEE J Sel Top Appl Earth Observ Rem Sens. 7(5):1609–1615. doi: 10.1109/JSTARS.2013.2290028.
   Web of Science ®Google Scholar
• Kaufman YJ, Sendra C. 1988. Algorithm for automatic atmospheric corrections to visible and near-IR satellite imagery. Int J Remote Sens. 9(8):1357–1381. doi: 10.1080/01431168808954942.
   Web of Science ®Google Scholar
• Lantzanakis G, Mitraka Z, Chrysoulakis N. 2017. Comparison of physically and image based atmospheric correction methods for Sentinel-2 satellite imagery[M]. Perspectives on atmospheric sciences. Cham: Springer; p. 255–261.
   Google Scholar
• Li ZQ, Chen XF, Ma LY, et al. 2018. Review of atmospheric correction of optical remote sensing satellites. J Nanjing Univ Inform Scie Technol (Nat Sci Ed). 10(01):6–15.
   Google Scholar
• Li HC, Kuang RY, Song ZH. 2021. Evaluation of poyang lake atmospheric correction method based on sentinel-2 image. Space Ret Rem Sens. 42(04):108–119.
   Google Scholar
• Liu G, Li Y, Lyu H, Wang S, Du C, Huang C. 2016. An improved land target-based atmospheric correction method for Lake Taihu. IEEE J Sel Top Appl Earth Observ Rem Sens. 9(2):793–803. doi: 10.1109/JSTARS.2015.2503800.
   Web of Science ®Google Scholar
• Luo K, Wei Y, Du J, Liu L, Luo X, Shi Y, Pei X, Lei N, Song C, Li J, et al. 2022. Machine learning-based estimates of aboveground biomass of subalpine forests using Landsat 8 OLI and Sentinel-2B images in the Jiuzhaigou National Nature Reserve, Eastern Tibet Plateau. J Res. 33(4):1329–1340. doi: 10.1007/s11676-021-01421-w.
   Google Scholar
• Martins V, Barbosa C, de Carvalho L, Jorge D, Lobo F, Novo E. 2017. Assessment of atmospheric correction methods for Sentinel-2 MSI images applied to Amazon floodplain lakes. Rem Sens. 9(4):322. doi: 10.3390/rs9040322.
   Google Scholar
• Mu C, Liu ZY, Meng D. 2021. Atmospheric correction of inland lake water bodies based on Sentinel-2 satellite data. Survey Map Bul. 09:9–14.
   Google Scholar
• Pan Y, Bélanger S, Huot Y. 2022. Evaluation of atmospheric correction algorithms over lakes for high-resolution multispectralimagery: implications of adjacency effect. Rem Sens. 14(13):2979. doi: 10.3390/rs14132979.
   Google Scholar
• Tian QL, Qin K, Chen XJ, Yu CF. 2021. Comparative study of the atmospheric correction method for WorldView-3 satellite images. World Nuclear Geol Sci. 38(02):229–236.
   Google Scholar
• Vermote EF,Tanre D,Deuze JL,Herman M,Morcette J-J. 1997. Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: an overview. IEEE Trans Geosci Remote Sensing. 35(3):675–686. doi: 10.1109/36.581987.
   Web of Science ®Google Scholar
• Wang SM, Dou HS. 1998. The lake annals of China [M]. Beijing: Science Press.
   Google Scholar
• Wang D, Ma R, Xue K, Loiselle S. 2019. The assessment of Landsat-8 OLI atmospheric correction algorithms for inland waters. Remote Sens. 11(2):169. doi: 10.3390/rs11020169.
   Google Scholar
• Xie EH, Wu JE, Yang K. 2022. Inversion of chlorophyll A mass concentration in Erhai Lake based on Sentinel-2 images. J Environ Eng. 16(9):3058–3069.
   Google Scholar
• Zheng W, Zeng ZY. 2004. Review of atmospheric correction methods of remote sensing images. Rem Sens Inform. 19(04):66–70.
   Google Scholar