Abstract
The surface vegetation condition has been operationally monitored from space for many years by the Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments. As these instruments are close to the end of their design life, the surface vegetation products are required by many users from the new satellite missions. The MEdium Resolution Spectral Imager-II (MERSI-II) onboard the Fengyun (FY) satellite (FY-3 series; FY-3D) is used to retrieve surface vegetation parameters. First, MERSI-II solar channel measurements at the red and near-infrared (NIR) bands at the top of atmosphere (TOA) are corrected to the surface reflectances at the top of canopy (TOC) by removing the contributions of scattering and absorption of molecules and aerosols. The normalized difference vegetation index (NDVI) at both the TOA and TOC is then produced by using the same algorithms as the MODIS and AVHRR. The MERSI-II enhanced VI (EVI) at the TOC is also developed. The MODIS technique of compositing the NDVI at various timescales is applied to MERSI-II to generate the gridded products at different resolutions. The MERSI-II VI products are consistent with the MODIS data without systematic biases. Compared to the current MERSI-II EVI generated from the ground operational system, the MERSI-II EVI from this study has a much better agreement with MODIS after atmospheric correction.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Baret, F., J. G. P. W. Clevers, and M. D. Steven, 1995: The robustness of canopy gap fraction estimates from red and near-infrared reflectances: A comparison of approaches. Remote Sens. Environ., 54, 141–151, doi: https://doi.org/10.1016/0034-4257(95)00136-O.
Blackburn, G. A., 1998: Quantifying chlorophylls and caroteniods at leaf and canopy scales: An evaluation of some hyperspectral approaches. Remote Sens. Environ., 66, 273–485, doi: https://doi.org/10.1016/S0034-4257(98)00059-5.
Gutman, G., and A. Ignatov, 1998: The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models. Int. J. Remote Sens., 19, 1533–1543, doi: https://doi.org/10.1080/014311698415333.
Han, X. Z., F. Wang, and Y. Han, 2019: Fengyun-3D MERSI true color imagery developed for environmental applications. J. Meteor. Res., 33, 914–944, doi: https://doi.org/10.1007/s13351-019-9048-7.
Huete, A., C. Justice, and W. Van Leeuwen, 1999: MODIS Vegetation Index (M0D13) Algorithm Theoretical Basis Document. University of Virginia, Charlottesville, VA, 15–61 pp.
Jacquemoud, S., C. Bacour, H. Poilvé, et al., 2000: Comparison of four radiative transfer models to simulate plant canopies reflectance: Direct and inverse mode. Remote Sens. Environ., 75, 471–181, doi: https://doi.org/10.1016/S0034-4457(00)00139-5.
Myneni, R. B., C. D. Keeling, C. J. Tucker, et al., 1997a: Increased plant growth in the northern high latitudes from 1981 to 1991. Nature, 386, 698–704, doi: https://doi.org/10.1038/386698a0.
Myneni, R. B., R. Ramakrishna, R. Nemani, et al., 1997b: Estimation of global leaf area index and absorbed PAR using radiative transfer models. IEEE Trans. Geosci. Remote Sens., 35, 1380–1393, doi: https://doi.org/10.1109/36.649788.
Rouse, J. W., R. H. Haas, J. A. Schell, et al., 1973: Monitoring vegetation systems in the Great Plains with ERTS. Third ERTS Symposium, NASA SP-351 I, 309–317 pp.
Saunders, R. W., and K. T. Kriebel, 1988: An improved method for detecting clear sky and cloudy radiances from AVHRR data. Int. J. Remote Sens., 9, 143–150, doi: https://doi.org/10.1080/01431168808954841.
Sims, D. A., A. F. Rahman, V. D. Cordova, et al., 2006: On the use of MODIS EVI to assess gross primary productivity of North American ecosystems. J. Geophys. Res. Biogeo., 111, G04015, doi: https://doi.org/10.1049/4006JG000164.
Stroppiana, D., S. Pinnock, and J. M. Gregoire, 2000: The global fire product: Daily fire occurrence from April 1994 to December 1993 derived from NOAA AVHRR data. Int. J. Remote Sens., 21, 1479–1488, doi: https://doi.org/10.1080/014311600210173.
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: https://doi.org/10.1109/36.581987.
Wang, J., X. G. Xu, S. G. Ding, et al., 2014: A numerical testbed for remote sensing of aerosols, and its demonstration for evaluating retrieval synergy from a geostationary satellite constellation of GEO-CAPE and GOES-R. J. Quant. Spectrosc. Radiat. Transfer, 146, 510–548, doi: https://doi.org/10.1016/j.jqsrt.2014,03.040.
Waterman, P. C., 1965: Matrix formulation of electromagnetic scattering. Proc. IEEE, 53, 805–814, doi: https://doi.org/10.1109/PROC.1965.4058.
Xu, N., X. H. Niu, X. Q. Hu, et al., 2018: Prelaunch calibration and radiometric performance of the advanced MERSI II on FengYun-3D. IEEE Trans. Geosci. Remote Sens., 66, 4866–4875, doi: https://doi.org/10.1109/TGRS.4018.4841847.
Zeng, X. B., R. E. Dickinson, A. Walker, et al., 2000: Derivation and evaluation of global 1-km fractional vegetation cover data for land modeling. J. Appl. Meteor., 39, 846–839, doi: https://doi.org/10.1175/1540-0450(4000)039<0846:DAEOGK>4.0.CO;4.
Author information
Authors and Affiliations
Corresponding author
Additional information
Supported by the National Key Research and Development Program of China (2018YFC1506500).
Rights and permissions
About this article
Cite this article
Han, X., Yang, J., Tang, S. et al. Vegetation Products Derived from Fengyun-3D Medium Resolution Spectral Imager-II. J Meteorol Res 34, 775–785 (2020). https://doi.org/10.1007/s13351-020-0027-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13351-020-0027-5