How TerraSAR-X Quadpol data can help describing crop canopies? A comparison to
RapidEye multi-spectral vegetation indices.
S. Gebhardt1, A. Roth2, C. Kuenzer2
1University
of Wuerzburg, Institute of Geography, Germany c/o 2German Aerospace Center (DLR), German Remote Sensing
Data Center, 82234 Wessling, Germany – (steffen.gebhardt, achim.roth, claudia.kuenzer)@dlr.de
Abstract - For monitoring of crops in tropical regions
satellite SAR data are essential. Only few cloudless days
per year hinder the use of optical data. In the
microwave region of the electromagnetic spectrum,
intensity of energy scattered by vegetation is primarily a
function of canopy architecture, dielectric properties,
and cropping characteristics, while the visible and
infrared energy reflected by vegetation is related to
plant pigmentation, internal leaf structure, and
leaf/canopy moisture. The TerraSAR-X satellite
operates basic imaging modes with different swath
widths, resolutions and polarisations. Adjacent to that,
the Quadpol experimental mode delivers full
polarization capacity (HH, VV, VH and HV). This study
analyses the potential of TerraSAR-X Quadpol data for
estimating crop canopy parameters by comparing to a
RapidEye image of same region and time. We analyzed
to which extent different polarizations are correlated to
the respective vegetation index using pixel- and objectbased comparison approaches. The study highlights
potentials and limitations of high resolved X-band SAR
data and the full polarization capacity for estimating
plant physiological parameters in a region dominated
by rice cropping. The study region has been within the
Mekong Delta in Vietnam, one of the worlds most prone
areas to climate change potentially yielding in
increasing natural hazards and dearth prevention. Food
security is therefore a major task in Vietnam’s most
productive rice growing region. For doing so,
knowledge about actual acreage, cropping system and
yield estimation is required which for large areas can
only be assumed by satellite-based SAR remote sensing.
Keywords: Radar backscattering, Full polarization, Multispectral vegetation indices, TerraSAR-X, RapidEye
1.
INTRODUCTION
Vietnam is one the main rice producing countries in the
world with the Mekong Delta producing more than 90% of
Vietnam's export rice crop. The Delta is one of the world’s
most prone areas to climate change potentially yielding in
increasing natural hazards and dearth prevention (IPCC
2007; Plate 2007). These facts will most likely influence
the rice productivity which is dependent upon bountiful
water resources (Sakamoto, Van Nguyen et al. 2006). Food
security is therefore a major task in Vietnam’s most
productive rice growing region. For doing so, knowledge
about actual acreage, cropping system and yield estimation
is required. Mapping and estimation of rice yield is strongly
required for foresighted quantification and distribution of
yields. For the Mekong Delta the usage of remote sensing
data derived from optical satellite sensors is not acceptable
regarding the availability of cloudless data sets during only
20-30 days per year. To get reliable results about the
distribution of water and vegetation on the Earth`s surface
in the tropical region of Southern Vietnam the use of
satellite SAR data is essential.
In the microwave region of the electromagnetic spectrum,
the intensity of incident energy scattered by vegetation is
primarily a function of the canopy architecture, the
dielectric properties of the crop canopy, and the cropping
characteristics (McNairn, Champagne et al. 2009). With the
last decade, the number of publications on rice monitoring
and mapping using radar remote sensing data has increased.
Different studies have used multitemporal RADARSAT Cband data in HH polarization and found strong correlation
between backscatter values and plant height and age (Shao,
Fan et al. 2001; Chakraborty, Manjunath et al. 2005). Yang
et al. (2007) utilized ENVISAT ASAR alternating
polarization (ASAR APP) data and proved strong
correlation of the HH backscatters with plant water content
and VV and HH/VV values with LAI, plant dry biomass
and plant height. Other studies used multi-temporal ASARAPP data and proposed backscattering models to estimate
leaf area index (LAI) (Chen, Lin et al. 2006) or yield
prediction (Nguyen 2009). Little is known so far, to what
extent space born X-band SAR data may contribute to the
estimation of rice biological variables. Inoue et al. (2002)
used a multifrequency (Ka, Ku, X, C, and L) and fullpolarimetric (HH, HV, VV, and VH) scatterometer and
different incidence angles to investigate the relationship
between microwave backscatter signatures and rice canopy
growth variables. Higher frequency bands (Ka, Ku, and X)
were poorly correlated with LAI and biomass. However, a
close correlation with the fresh weight of heads, which
approximates the weight of grain, was found mostly at
higher frequencies.
The amount of visible and infrared (V-IR) energy reflected
by vegetation is directly related to plant pigmentation,
internal leaf structure, as well as leaf and canopy moisture.
Traditional remote sensing methods for yield estimation
rely on red and near-infrared (NIR) reflectance and crop
broadband vegetation indices, such as the Normalized
Difference Vegetation Index (NDVI) sensitive to
vegetation greenness and canopy scattering and related to
crop growth (Benedetti and Rossini 1993; Zarco-Tejada,
Ustin et al. 2005). However, NDVI data saturate at high
LAI values substantially below the LAI characteristic of
high-productivity crops. Haboudane et al. (2002) used the
combined TCARI/OSAVI index, which has been proven to
be robust to LAI and background influences, for
successfully estimating Chla+b in corn canopies. Eitel et al.
(2007) combined an index derived from the Modified
Chlorophyll Absorption Ratio Index (MCARI) with the
second Modified Triangular Vegetation Index (MTVI2) on
RapidEye (RE) data and obtained best regression
relationships with chlorophyll meter and flag leaf nitrogen
values in spring wheat. The authors have especially proven
that the availability of the red edge band of the RapidEye
sensor provides useful information on crop chlorophyll and
nitrogen estimation.
The present study aims to evaluate the potential of
TerraSAR-X (TSX) Quadpol data recorded on 2010-05-01
for estimating plant physiological parameters. We did not
utilize in situ measurements but defined a RapidEye multi-
spectral image from 2010-04-25 taken over the same region
as reference because of the proven relationship of multispectral vegetation indices with LAI and/or plant
biochemical constituent. We analyzed to which extent
different polarizations are correlated to the respective
vegetation index.
2. MATERIALS AND METHODS
The TerraSAR-X (TSX) satellite delivers X-Band SAR
data in different modes allowing to record images with
different swath widths, resolutions and polarisations
(Schreier, Dech et al. 2008). Basic products of the sensor
comprise single- and dual polarization images. Adjacent to
the basic imaging modes, the Quadpol experimental mode
delivers outstanding imaging results in HH, VV, VH and
HV polarization. In a first Quadpol experiment data were
acquired between April 11 and May 13, 2010. One of those
scenes was recorded on the 1st of May in 2010 over the
province of Can Tho in Mekong Delta of Vietnam (Figure
1). The ground resolution of the images reached 2.75
meters for an incidence angle of approximately 35°. The
false colour composite in HH-VV-HV band combination of
the utilized spatial scene subset is shown in Figure 1.
A RapidEye scene captured on April 25th 2010 has been
used as reference scene in the analysis (Figure 1). The
Multi-spectral imager is a pushbroom style imager, which
images the earth in 5 spectral bands ranging from 400850nm over an 80-km swath at 6.5m resolution at nadir
(Tyc, Tulip et al. 2005).
Figure 1. a) Overview
map of the Can Tho
province in the
Mekong Delta,
Vietnam and the
TerraSAR-X swath, b)
RapidEye subset from
2010-04-25, and c)
TerraSAR-X subset
from 2010-05-01
2.1. Pre-processing
A standard correction
for
atmospheric
influences has been
performed on the
RapidEye
dataset
utilizing the ATCOR2
algorithm
(Richter
1996).
Thereby,
standard parameters
for tropical maritime
land surfaces have
been used. Due to the
low
topographic
variation in the region
of interest with less
than 3 meters, the
incorporation of a
digital terrain model
for
atmospheric
correction has been
neglected.
With
the
given
spectral characteristics
of
the
RapidEye
sensor the typical vegetation indices NDVI, OSAVI,
MTVI2, MCARI and TCARI have been calculated. With
reference to Haboudane et al. (2002) the combined index of
TCARI/OSAVI has been calculated and also the
MCARI/MTVI2 as suggested by (Haboudane, Miller et al.
2002) and Eitel (Eitel, Long et al. 2007; Eitel, Long et al.
2008).
Absolute radiometric calibration has been performed on the
TSX image data. Therefore, in a first step the radar
brightness (Beta Naught) 0dB in dB is derived from the
image pixel values or digital numbers (DN) applying the
polarization specific calibration factor kS. Final radiometric
calibration has then been performed by Sigma Naught 0
calculation based on 0dB and local incidence angle image
(loc).
For speckle reduction the enhanced Lee (Le Toan, Laur et
al. 1989) filter has been chosen over other common speckle
reduction filters because it performed best in terms of
object and pixel value preservation.
2.2. Image segmentation and classification
A colour image segmentation has been performed utilizing
the false colour RapidEye composite with NIR-G-B band
combination. The algorithm comprises colour space
transformation of the input image and subsequent K-Means
clustering. In a first step, the false colour image in RGB
mode has been transformed to L*a*b* (CIELAB) colour
space revealing in a three band image with the respective L,
a, and b canals. The three coordinates of CIELAB represent
the lightness of the colour (L*), its position between
red/magenta and green (a*) and its position between yellow
and blue (b*). Since only a* and b* store actual colour
information, the respective clustering is based on these two
image components only.
Standard K-Means clustering was performed into six
clusters (classes) in three repetitions on the a*b* images.
The number of clusters has been derived empirically
whereby the chosen number revealed an appropriate result
in terms of object and class separability. The resulting
image of the segmentation exposed image segments
labelled with the respective class index. Based on that
object, re-labelling was performed yielding in an image
with all objects labelled/valued with a unique identifier.
2.3. Feature extraction
The individual objects median value for all image products
from the radar and optical data has been calculated over all
object pixels in the labelled segmentation image. The
median has been chosen over the mean in order to account
for extreme outliers especially in the SAR products. We
assume that the majority of objects pixels represent
backscatter values in accordance to a valid range of values
while the minority constitute outliers. As a result, the pixel
values of the respective object have been labelled with the
derived median value from each of the image products
(NDVI, TCARI/OSAVI, MCARI/MTVI2, HH, VV, VH,
and HV).
A total of 10,000 randomly generated sample points
distributed all over the analysis region have been created.
The beforehand calculated object mean pixel values from
the single images of NDVI, TCARI/OSAVI,
MCARI/MTVI2, HH, VV, VH, and HV have been
extracted for the respective sample point positions.
In order to select those sample points representing
vegetated rice fields, a cluster specific analysis of the
NDVI distribution of the samples over all 6 cluster classes
was performed. By doing so, the cluster specific minimum,
maximum, mean and standard deviation has been
calculated. Only the cluster class showing normal
distribution over the whole valid range of NDVI values has
been chosen representative for vegetated rice fields
yielding in a cluster class consisting of 660 sample points
out of the original 10,000. The respective natural class
observed in the images for those samples represented well
developed rice fields. The mean value ranged 0.87, the
standard deviation was 0.04 and the data ranged from 0.24
to 0.96. The mean and the standard deviation for this class
have been used as distribution parameters for class
description. Finally, outlier samples which NDVI values
differed from the class mean by more then the single
standard deviation of the 660 samples where excluded and
596 samples were conserved for further analysis.
2.4. Regression analysis
Simple correlation and regression analysis has been
performed on the 596 samples. Our hypotheses was to
indicate a direct relationship between the TerraSAR-X
radar information and the ones derived from the multispectral RapidEye. We expected increasing radar
backscatters will yield in increasing spectral indices values.
We assumed the optical data as reference and the radar data
as prediction variables. Therefore, Spearman's rank
correlation coefficient has been chosen over the Pearson
correlation. With the results of the correlation analysis
those TerraSAR-X and RapidEye based variables were
identified showing significant correlation. Those have been
furthermore undergoing linear regression analysis. The
linear models have been evaluated by means of R² and
RMSE measures.
3. RESULTS
Tables A shows the Spearman coefficients derived by
correlation analysis. NDVI shows no relationship with any
radar backscatter value, while the combined indices show
significant correlations. Strong correlation of either
TCARI/OSAVI or MCARI/MTVI2 versus VV, HV and
VH ranging from 0.80 to 0.82 and versus the HH/VV index
(0.65) can be observed.
Table A. Spearman correlation coefficients for sample
comparison using object-based features for 596 outlier
cleaned samples (NDVI: 0.83 – 0.92)
NDVI
MCARI/MTVI2
TCARI/OSAVI
HH
0.12
0.36
0.36
VV
0
0.81
0.82
HV
-0.04
0.8
0.8
VH
0.01
0.81
0.82
HH/VV
-0.1
0.65
0.65
With the given results we analysed to what extent radar
based information may explain those from the multispectral assuming a linear relationship between spectral
indices and radar backscattering. Linear regression analysis
was performed assuming the spectral indices as reference
and the radar backscattering values as prediction variable.
The R² and RMSE values for each linear regression model
were calculated (Table B). Figure 2 exemplarily shows the
scatter plot of the combined TCARI/OSAVI index vs. VV
backscatter values. As already revealed from the correlation
analysis, the TCARI/OSAVI vs. HH model showed no
relationship thus yielding in an R² of 0.053. However, all
other models predicted the spectral index very well
assuming this linear relationship with R² values greater then
0.6 for the VV, HV and VH models and approximately 0.5
for the HH/VV model. Almost the same results have been
obtained with the combined MCARI/MTVI2 index. No
correlation was found with the NDVI as reference.
Table B. R² and RMSE values for individual linear
regression models of the TCARI/OSAVI spectral index
versus all radar based variables using object-based features
for 596 outlier cleaned samples (NDVI: 0.83 – 0.92)
R²
RMSE
HH
0.05
0.46
VV
0.65
0.28
HV
0.61
0.30
VH
0.64
0.28
HH/VV
0.33
0.33
Figure 2. Scatter plot and linear regression model of the
TCARI/OSAVI spectral index versus VV radar backscatter
values using object-based features for 596 outlier cleaned
samples (NDVI: 0.83 – 0.92)
4. DISCUSSIONS
Significant correlations between radar backscattering
values in VV and cross-polarizations as also the HH/VV
index have only been derived from comparison with the
combined
spectral
indices
TCARI/OSAVI
and
MCARI/MTVI2. However, these could only be derived as
long as sample data representing pixels of high developed
and unique vegetation were utilized, as revealed by the
clustering based image segmentation. The respective NDVI
values were ranging from 0.83 to 0.91. This might indicate
a stronger background soil/water influence of low and
medium developed rice canopies, exhibiting lower density
and closure, on the radar backscatter values.
No correlation between radar backscattering over all
polarizations and the NDVI was observed. According to
Inoue et al. (Inoue, Kurosu et al. 2002) this indicates that
high-frequency microwaves may barely penetrate the
vegetation canopy. Therefore, the backscatter signatures
contain little information on volumetric features within the
canopy, such as LAI and biomass which have strong
correlation to NDVI. However, high correlations of the
combined spectral indices with VV and cross-polarization
backscatter values indicate a relation of X-band data and
plant cellular structure and –constituents (e.g. Chla+b,
nitrogen, or fresh weight) which have proven relationship
to the used combined spectral indices. This confirms the
study of Inoue et al. (Inoue, Kurosu et al. 2002) who found
HH polarization less well correlated with the fresh weight
of plant heads, while VV- and cross-polarizations were
highly correlated. The authors argued that this may occur
because the thin, vertical components (heads) at the upper
surface of the canopy are the major scatterers for highfrequency microwaves.
The applied methodology in image segmentation and
sample extraction aimed in identifying representative rice
samples. However, since the sample selection was mainly
based on the NDVI properties, samples may not
exclusively represent rice on ground. Even if rice is the
most dominant land use type in the region of interest, other
vegetation like fruit trees are present as well, thus
potentially sharing similar NDVI properties. This allows
the conclusions, that the observed relations of vegetation
parameters and X-band SAR backscatter values are
independent on the actual vegetation type.
We also conclude, with respect of revealed results, the full
polarization capability of TerraSAR-X quadpol mode does
not substantially contribute to higher relationships. Taking
into account the polarization modes available with the
standard dual polarized mode (HH/VV, or HH/HV or
VV/VH) one can conclude that especially the VV/VH
mode will transport most of the significant information.
However, one could do further analysis on multivariate
regression models to explore a potential benefit of all
polarizations in combination to vegetation parameter
estimation, however, for this study that would have led too
far.
The radar backscatter is dominated by geometric properties
of the observed objects and land surface. This means for
our case that size, shape and orientation of the rice plants
correlate with the backscattered signal. It can therefore be
assumed that such canopy architecture parameters can
potentially be derived from the SAR images as well.
Further investigation treat with this issue by employing
special SAR processing techniques like e.g. polarimetric
decomposition.
This study has not used any ground truth data but assumed
the spectral indices derived from the RapidEye dataset as
reference. It is not proven to what extent these are really
related to the values observable on the ground. However,
we argue that for a first test of TerraSAR-X capability for
rice vegetation parameter estimation the RapidEye scene
available for the same region at almost the same time is
beneficial. Future studies on the capabilities of both sensors
should utilize detailed and extensive field measures
continuously collected over the whole vegetation period.
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