Global Land Cover Classification Based on Microwave Polarization and Gradient Ratio (MPGR)

  • Mukesh BooriEmail author
  • Ralph Ferraro
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


Microwave polarization and gradient ratio (MPGR) is an effective indicator for characterizing the land surface from sensors like EOS Advanced Microwave Scanning Radiometer (AMSR-E). Satellite-generated brightness temperatures (BT) are largely influenced by soil moisture and vegetation cover. The MPGR combines the microwave gradient ratio with polarization ratio to determine surface characteristics (i.e., bare soil/developed, ice, and water) and under cloud covered conditions when this information cannot be obtained using optical remote sensing data. This investigation uses the HDF Explorer, Matlab, and ArcGIS software to process the pixel latitude, longitude, and BT information from the AMSR-E imagery. This paper uses the polarization and gradient ratio from AMSR-E BT for 6.9, 10.7, 18.7, 23.8, 36.5, and 89.0 GHz to identify seventeen land cover types. A smaller MPGR indicates dense vegetation, with the MPGR increasing progressively for mixed vegetation, degraded vegetation, bare soil/developed, and ice and water. This information can help improve the characterization of land surface phenology for use in weather forecasting applications, even during cloudy and precipitation conditions which often interferes with other sensors.


AMSR-E MODIS MPGR Microwave remote sensing GIS Climate change CHAPTER 


  1. 1.
    Mao KB, Tang HJ, Zhang LX, Li MC, Guo Y, Zhao DZ (2008) A Method for retrieving soil moisture in Tibet region by utilizing microwave index from TRMM/TMI Data. Int J Remote Sens 29:2903–2923CrossRefGoogle Scholar
  2. 2.
    Fily M, Royer A, Goitab K, Prigentc C (2003) A simple retrieval method for land surface temperature and fraction of water surface determination from satellite microwave brightness temperatures in sub-arctic areas. Remote Sens Environ 85:328–338CrossRefGoogle Scholar
  3. 3.
    McFarland MJ, Miller RL, Neale CMU (1990) Land surface temperature derived from the SSM/I passive microwave brightness temperatures. IEEE Trans Geosci Remote Sens 28(5):839–845CrossRefGoogle Scholar
  4. 4.
    Becker F, Choudhury BJ (1988) Relative sensitivity of normalized difference vegetation index (NDVI) and microwave polarization difference index (MPDI) for vegetation and desertification monitoring. Remote Sens Environ 24:297–311CrossRefGoogle Scholar
  5. 5.
    Boori MS, Vozenilek V (2014) Assessing land cover change trajectories in Olomouc, Czech Republic. Int J Environ Ecol Geol Min Eng 8(8):540–546Google Scholar
  6. 6.
    Jackson TJ, Schmugge TJ (1991) Vegetation effects on the microwave emission of soils. Remote Sens Environ 36:203–212CrossRefGoogle Scholar
  7. 7.
    Calvet JC, Wigneron JP, Mougin E, Kerr YH, Brito LS (1994) Plant water content and temperature of the Amazon forest from satellite microwave radiometry. IEEE Trans Geosci Remote Sens 32:397–408CrossRefGoogle Scholar
  8. 8.
    Felde GW (1998) The effect of soil moisture on the 37 GHz microwave polarization difference index (MPDI). Int J Remote Sens 19:1055–1078CrossRefGoogle Scholar
  9. 9.
    Owe M, Richard DEJ, Walker J (2001) A methodology for surface soil moisture and vegetation optical depth retrieval using the microwave polarization difference index. IEEE Trans Geosci Remote Sens 39:1643–1654CrossRefGoogle Scholar
  10. 10.
    Choudhury BJ, Tucker CJ, Golus RE, Newcomb WW (1987) Monitoring vegetation using Nimbus-7 scanning multichannel microwave radiometer’s data. Int J Remote Sens 8:533–538CrossRefGoogle Scholar
  11. 11.
    Njoku EG, Chan SK (2006) Vegetation and surface roughness effects on AMSR-E land observations. Remote Sens Environ 100:190–199CrossRefGoogle Scholar
  12. 12.
    Boori MS, Vozenilek V, Burian J (2014) Land-cover disturbances due to tourism in Czech Republic. Advances in Intelligent Systems and Computing, vol. 303. Springer, Switzerland, pp 63–72. doi: 10.1007/978-3-319-08156-4-7
  13. 13.
    Paloscia S, Pampaloni P (1988) Microwave polarization index for monitoring vegetation growth. IEEE Trans Geosci Remote Sens 26:617–621CrossRefGoogle Scholar
  14. 14.
    Boori MS, Amaro VE (2011) A remote sensing approach for vulnerability and environmental change in Apodi valley region, Northeast Brazil. Int J Environ Earth Sci Eng 5(2):01–11Google Scholar
  15. 15.
    Boori MS, Amaro VE, Vital H (2010) Coastal ecological sensitivity and risk assessment: a case study of sea level change in Apodi River (Atlantic Ocean), Northeast Brazil. Int J Environ Earth Sci Eng 4(11):44–53Google Scholar
  16. 16.
    Clara SD, Jeffrey PW, Peter JS, Richard AM, Thomas RH (2009) An evaluation of AMSR-E derived soil moisture over Australia. Remote Sens Environ 113:703–710CrossRefGoogle Scholar
  17. 17.
    Chris D (2008) The contribution of AMSR-E 18.7 and 10.7 GHz measurements to improved boreal forest snow water equivalent retrievals. Remote Sens Environ 112:2701–2710CrossRefGoogle Scholar
  18. 18.
    Lubin D, Garrity C, Ramseier RO, Whritner RH (1997) Total sea ice concentration retrieval from the SSM/I 85.5 GHz channels during the Arctic summer. Remote Sens Environ 62:63–76CrossRefGoogle Scholar
  19. 19.
    Boori MS, Amaro VE (2010) Land use change detection for environmental management: using multi-temporal, satellite data in Apodi Valley of northeastern Brazil. Appl GIS Int J 6(2):1–15Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.National Research Council (NRC)College ParkUSA
  2. 2. NOAA/NESDIS/STAR/Satellite Climate Studies Branch and Cooperative Institute for Climate and Satellites (CICS), ESSICUniversity of MarylandCollege ParkUSA

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