|
|
|
| Fengyun-3D MERSI True Color Imagery Developed for Environmental Applications |
| Xiuzhen HAN1, Feng WANG2, and Yang HAN3 |
1. National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081;
2. Beijing Piesat Information Technology Co. Ltd., Beijing 100195;
3. Nanjing University of Information Science & Technology, Nanjing 210044 |
|
|
|
|
Abstract Many techniques were developed for creating true color images from satellite solar reflective bands, and the so-derived images have been widely used for environmental monitoring. For the newly launched Fengyun-3D (FY-3D) satellite, the same capability is required for its Medium Resolution Spectrum Imager-II (MERSI-II). In processing the MERSI-II true color image, a more comprehensive processing technique is developed, including the atmospheric correction, nonlinear enhancement, and image splicing. The effect of atmospheric molecular scattering on the total reflectance is corrected by using a parameterized radiative transfer model. A nonlinear stretching of the solar band reflectance is applied for increasing the image contrast. The discontinuity in composing images from multiple orbits and different granules is eliminated through the distance weighted pixel blending (DWPB) method. Through these processing steps, the MERSI-II true color imagery can vividly detect many natural events such as sand and dust storms, snow, algal bloom, fire, and typhoon. Through a comprehensive analysis of the true color imagery, the specific natural disaster events and their magnitudes can be quantified much easily, compared to using the individual channel data.
|
|
Received: 18 March 2019
Published Online: 28 October 2019
|
| Supported by: Supported by the National Key Research and Development Program of China (2018YFC1506500) |
|
Corresponding Authors:
Xiuzhen HAN
E-mail: hanxz@cma.gov.cn
|
|
|
|
Capuzzo, E., C. P. Lynam, J. Barry, et al., 2018: A decline in primary production in the North Sea over 25 years, associated with reductions in zooplankton abundance and fish stock recruitment. Global Change Biol., 24, e352–e364, doi: 10.1111/gcb.13916
Gao, B. C., M. J. Montes, Z. Ahmad, et al., 2000: Atmospheric correction algorithm for hyperspectral remote sensing of ocean color from space. Appl. Opt., 39, 887–896, doi: 10.1364/AO.39.000887
Greaves, J. R., and W. E. Shenk, 1985: The development of the geosynchronous weather satellite system. Monitoring Earth’s Ocean, Land, and Atmosphere from Space-Sensors, Systems, and Applications, A. Schnapf, Ed., AIAA, New York, 150–181.
Gumley, L., J. Descloitres, and J. Schmaltz, 2010: Creating Reprojected True Color MODIS Images: A Tutorial. [Available online at https://cdn.earthdata.nasa.gov/conduit/upload/946/MODIS_True_Color.pdf].
Hillger, D., C. Seaman, C. Liang, et al., 2014: Suomi NPP VIIRS imagery evaluation. J. Geophys. Res. Atmos., 119, 6440–6455, doi: 10.1002/2013JD021170
Kneizys, F. X., E. P. Shettle, W. O. Gallery, et al., 1980: Atmospheric Transmittance/Radiance: Computer Code LOWTRAN 5. Report AFGL-TR-80-067, Air Force Geophysics Lab., Hanscom AFB, MA.
Miller, S. D., T. L. Schmit, C. J. Seaman, et al., 2016: A sight for sore eyes: The return of true color to geostationary satellites. Bull. Amer. Meteor. Soc., 10, 1803–1816, doi: 10.1175/BAMS-D-15-00154.1
NOAA NESDIS Center for Satellite Applications and Research, 2012: GOES-R Advanced Baseline Imager (ABI) Algorithm Theoretical Basis Document for Suspended Matter/Aerosol Optical Depth and Aerosol Size Parameter. Aerosol Product Application Team of the AWG A Aerosols/Air Quality/Atmospheric Chemistry Team. Version 3.0, NOAA. (https://www.star.nesdis.noaa.gov/goesr/docs/ATBD/AOD.pdf)
Richter, R., 1996: A spatially adaptive fast atmospheric correction algorithm. Int. J. Remote Sens., 17, 1201–1214, doi: 10.1080/01431169608949077
Tanré, D., and M. Legrand, 1991: On the satellite retrieval of Saharan dust optical thickness over land: Two different approaches. J. Geophys. Res. Atmos., 96, 5221–5227, doi: 10.1029/90JD02607
Teillet, P. M., and G. Fedosejevs, 1995: On the dark target approach to atmospheric correction of remotely sensed data. Canadian J. Remote Sens., 21, 374–387, doi: 10.1080/07038992.1995.10855161
Vermote, E. F., D. Tanré, J. L. Deuzé, et al., 1997: Second simulation of the satellite signal in the solar spectrum, 6S: An overview. IEEE Trans. Geosci. Remote Sens., 35, 675–686, doi: 10.1109/36.581987
Xu, Y. L., R. S. Wang, S. W. Liu, et al., 2008: Atmospheric correction of hyperspectral data using MODTRAN model. Proceedings of SPIE 7123, Remote Sensing of the Environment: 16th National Symposium on Remote Sensing of China. SPIE, Beijing, China, 712306, doi: 10.1117/12.815552.
Zheng, W., C. Liu, Z. Y. Zeng, et al., 2007: A feasible atmosphe-ric correction method to TM image. J. China Univ. Mining Technol., 17, 112–115, doi: 10.1016/S1006-1266(07)60024-8
|
|
|
|
