X-B8, 2012
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
SPECTRAL UNMIXING OF BLENDED REFLECTANCE
FOR DENSER TIME-SERIES MAPPING OF WETLANDS
Ryo Michishita * ^, Zhiben Jiang*, Bing Xu *"**
* College of Global Change and Earth System Science, Beijing Normal University
Beijing, 100875, China - zhibenjiang@ gmail.com
2 Department of Geography, University of Utah
260 S. Central Campus Dr. Rm. 270, Salt Lake City, Utah, 84112-9155, United States - ryo.michishita@geog.utah.edu
* School of Environment, Tsinghua University
Beijing, 100084, China - bingxu@tsinghua.edu.cn
Commission VIII, WG VIII/8
KEY WORDS: Classification, Environment, Generation, Land cover, Landsat, Multiresolution, Multispectral, Multitemporal
ABSTRACT:
The orbiting cycle and frequent cloud contamination have limited the applications of the moderate-resolution remotely sensed data
for detecting rapid land cover changes that are critical to the monitoring of wetlands. It is necessary to use multiple remotely sensed
data sources that have different spatial resolution and temporal frequency, because both spatial and temporal details are important in
understanding the mechanisms in wetland cover changes. This study examined the applicability of linear spectral mixture analysis to
the blended reflectance that was generated by incorporating the enhanced spatial and temporal adaptive reflectance fusion model
(ESTARFM). Nine TM and MODIS images of the Poyang Lake area, China acquired in 2004 and 2005 were used to blend the
reflectance. In order to account for the spectral variations in materials, we incorporated the multiple endmember spectral mixture
analysis (MESMA) in unmixing the blended reflectance. The average absolute differences between the land cover fractions derived
from the blended image and those from the observed image were calculated as well as correlation coefficients. Our results
demonstrated that MESMA could unmix the blended reflectance generated by ESTARFM. However, due to the existence of the
blended pixels with large difference in reflectance from the observed reflectance, the land cover fractions derived from the blended
reflectance did not match with those derived from the observed reflectance as well as expected. It is also suggested that the
comprehensiveness of the endmember spectral libraries was another factor influencing the agreement.
1. INTRODUCTION
Taking advantage of regular orbiting intervals and extensive
coverage, satellite remote sensing has been utilized as a
practical and economical means to monitor and inventory
different types of wetlands (Ozesmi and Bauer, 2002). Although
a wide variety of time-series remotely sensed data observed with
differing sensor designs have been used for the mapping of
wetland cover changes, previous studies have shown that
wetland mapping using optical remotely sensed data is not as
easy as the mapping of other ecosystems (Silva et al., 2008).
This is because the spectra of wetland vegetation species show a
high level of variability due to the species’ structural,
biochemical, and biophysical diversity, as well as the spectral
confusion among individual wetland components described
above (Adam et al. 2010). In addition, due to the tradeoff
between spatial resolution and temporal frequency, wetland
cover changes has not been monitored with spatial and temporal
details simultaneously using the imagery observed by single
remotely sensor. For a better understanding of the spatio-
temporal dynamics in all land cover components of wetland
ecosystems, it is necessary to overcome these difficulties.
In the goal of improving the accuracy of wetland mapping using
remotely sensed data, spectral mixture analysis (SMA) have
received more attention, due to their relative simplicity of use in
* Corresponding author
deriving physically interpretable information at subpixel level
(Roberts et al., 1993). SMA models mixed spectra in pixels of a
remotely sensed image as a combination of endmembers (EMs)
— pure spectra representing distinct land cover types (Adams et
al., 1993). In linear SMA, a spectrum within the instant field of
view of a sensor is determined by the sum of each EM spectrum
multiplied by its aerial coverage fraction and the residual error.
Although many studies have incorporated SMA in the mapping
of wetland vegetation and floodplain mapping, only a few
studies have also been conducted on SMA using multitemporal
remotely sensed data for the mapping of wetland land cover
changes. (He et al., 2010; Melendez-Pastor et al., 2010).
In order to increase temporal frequency of moderate-resolution
remotely sensed data, several blending techniques have been
developed and applied in some studies. Among them, The
spatial and temporal adaptive reflectance fusion model
(STARFM) (Gao et al., 2006) has been widely used. Recently,
Zhu et al. (2010) modified the original STARFM to overcome
the poor accuracy of STARFM in heterogeneous landscapes. A
few application studies of ESTARFM has proved that the
blended reflectance data is comparative to observed reflectance
data in chlorophyll index derivation and supervised
classification (Singh, 2011; Watts et al., 2011). However, no
studies have investigated on the applicability of SMA to the
blended reflectance data. In addition, Previous study has not
applied these blending techniques in wetland environment.
