Global Land Cover Classification Based on Microwave Polarization and Gradient Ratio (MPGR) - Geoinformatics for Intelligent Transportation - page 18

1 Introduction

Timely monitoring of natural disasters is important for minimizing economic losses
caused by floods, drought, etc. Access to large-scale regional land surface infor-

mation is critical to emergency management during natural disasters. Remote
sensing of land cover classi

fl

cation and surface temperature has become an
important research subject globally. Many methodologies use optical remote
sensing data (e.g. Moderate Resolution Imaging Spectro Radiometer — MODIS)
and thermal infrared satellite data to retrieve land cover classi

cation and surface
temperature. However, optical and thermal remote sensing data is greatly in fl uenced

by cloud cover, atmospheric water content, and precipitation, making it dif cult to

combine with microwave remote sensing data [ 1 ]. Thus, optical or thermal remote
sensing data cannot be used to retrieve surface temperature during active weather
conditions. However, microwave remote sensing can overcome these disadvan-
tages. Passive microwave emission penetrates non-precipitating clouds, providing a
better representation of land surface conditions under nearly all weather conditions.
Global data are available daily from microwave radiometers, whereas optical sen-
sors (e.g., Landsat TM, ASTER, and MODIS) are typically available globally only
as weekly products due to clouds. The coarse spatial resolution of passive micro-
wave sensors is not a problem for large scale studies of recent climate change [ 2 ].
For example McFarland et al. [ 3 ] showed that surface temperature for crop/range,
moist soils, and dry soils can be retrieved using linear regression models from the
Special Sensor Microwave/Imager (SSM/I) BT.
Microwave polarization ratio (PR; the difference between of the

rst two stokes
parameters (H- and V-polarization) divided by their sum) and gradient ratio (GR;
the difference of two Stokes Parameters either H or V with different frequency
divided by their sum) correspond with seasonal changes in vegetation water content
and leaf area index [ 4

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- 6 ]. The possibility of simultaneously retrieving effective

surface temperature with two additional parameters, vegetation characteristics and

soil moisture, has been demonstrated, mainly using simulated datasets [ 7 - 9 ]. The
MPGR is sensitive to the NDVI [ 4 , 10 ], as well as open water, soil moisture, and
surface roughness [ 11 ]. Paloscia and Pampaloni [ 12 ] used microwave radiometer to
monitor vegetation and demonstrate that the MPGR is very sensitive to vegetation
types (especially for water content in vegetation), and that microwave polarization
index increases exponential with increasing water stress index. The polarization
index also increases with vegetation growth [ 13 ]. Since microwave instruments can
obtain accurate surface measurements in conditions where other measurements are
less effective, MPGR has great potential for observing soil moisture, biological
inversion, ground temperature, water content in vegetation, and other surface
parameters [ 1 ]. This paper derives MPGR and uses it to discriminate different land
surface cover types, which is turn will help improve monitoring of weather, climate,
and natural disasters.

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