Geoscience Reference
In-Depth Information
5 Conclusion
This study is an attempt to use AMSR-E BT data for retrieving land cover classes.
AMSR-E frequencies have relationship between land cover and MPGR values.
Results con
cation based on MPGR has the
potential to reveal more precise land surface features from AMSR-E remote sensing
data. Using a single day data, we classi
rm that the simpli
ed land cover classi
ed the land surface into 17 types based on
their MPGR values. Where all green/healthy vegetation comes near to 0.0 in
polarization ratio and bellow then 0.0 in gradient ratio. Normal vegetation falls till
0.05 and then higher values for degraded or low vegetation/bare soil and built
up. Highest values above 0.12 are for ice/water. This method can be used to target
speci
c locations based on ground observations, but needs additional investigation,
using data from different times of the year where the surface characteristics change.
In addition, applying these relationships to independent data to learn about their
stability also needs to be performed. Building an improved monitoring system for
meteorological applications should be a subject of further research.
References
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 - 2923
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
338
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
-
845
4. Becker F, Choudhury BJ (1988) Relative sensitivity of normalized difference vegetation index
(NDVI)
-
and microwave polarization difference
index (MPDI)
for vegetation and
311
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
deserti cation monitoring. Remote Sens Environ 24:297
-
546
6. Jackson TJ, Schmugge TJ (1991) Vegetation effects on the microwave emission of soils.
Remote Sens Environ 36:203
-
212
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 - 408
8. Felde GW (1998) The effect of soil moisture on the 37 GHz microwave polarization difference
index (MPDI). Int J Remote Sens 19:1055 - 1078
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
-
1654
10. Choudhury BJ, Tucker CJ, Golus RE, Newcomb WW (1987) Monitoring vegetation using
Nimbus-7 scanning multichannel microwave radiometer
-
538
11. Njoku EG, Chan SK (2006) Vegetation and surface roughness effects on AMSR-E land
observations. Remote Sens Environ 100:190
'
s data. Int J Remote Sens 8:533
-
199
-
Search WWH ::




Custom Search