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Title
Mapping without the sun
Author
Zhang, Jixian

the different radar polarization measurements has the different
sensitivities to the different surface properties. Especially, the
radar cross-polarization measurements are very sensitive to the
vegetation volume information since the surface backscattering
does not generate significant cross-polarization signal. As
shown in [8], the direct volume backscattering component of
the co-polarization in (5) can be directly estimated from cross
polarization signal, that is
^vV)^;(0)=3-*1(0) (6)
Where pp can be HH or VV. Then, using HV polarization of
PALS, volume term can be removed from total scatter.
Surface- Volume Interaction Contribution
Ulaby 191 set up an empirical model for are
dependent on incidence angle. When two acquisitions is
obtained, a formula can be written as
A($„SR)T BW
In SMEX02, PALS sensor has a constant incidence angle (~
45 ° ), so (9) can be expressed as
__0 l '
= (io)
The relationship between volumetric soil moisture contents and
T can be obtained from [13], namely:
ln(T) = -0.2312 *ln(mv) + 0.6874 (11)
[l-exp(-1.925rsec#)]-exp(-1.372r' 12 sec#) (7)
• exp[-0.84(fcs) 2 cos#] ■ r pp
Where r is optical thickness, co is single scatter albedo, ks is
normalised roughness factor, k has the value of 2n! a and s is
surface RMS height, r is Fresnel reflectivity, which is a
PP
function of incidence angle and dielectric constant, therefore, a
function of soil moisture.
Combining (10) and (11), the relative soil moisture change can
be expressed as:
m.
m.
V2_ _ (_il.) 0.2312-5(6»)
cr„
The parameter B(0) can be obtained from AIEM simulation
when incidence angle is 45° . In this paper, £(#) is 1.08, 0.90,
1.10 and 0.88 for L-W, L-HH, S-VV and S-HH, respectively.
Because cr° (#) is quite smaller than cr° (Q) and too many
unknows in (7), we have to do some assumptions for the above
parameters for the computation of olfQ), we believe these
assumptions will not bring too much errors in final results,
especially for VV polarization 191 . co is set a constant 0.06 for S-
band and 0.09 for L-band 1101 (These are just assumptions from
the literature), s is set 0.9 cm (which is field average), r is
PP
computed by setting soil moisture 0.15m 3 m' 3 (Approximately
field average). Combining with the r from part 3, be estimated for both HH and VV polarization.
Finally, can be obtained with (1) (5) (6) (7). In this
process, we must point out, the negative cr°(#)s are removed
from the database. Negatives can be the reason of wrong
estimation of r, volume contribution, or the wrong constant
setting in (7).
5. SOIL MOISTURE CHANGE RETRIEVAL AND
VALIDATION
In many studies 1 " 11121 , AIEM model is simulated with possible
combinations of dielectric constant and a wide range of surface
roughness, a simplified relation between the radar backscatter
and the reflectivity is developed for bare soil, the expression is
a°(0) = A(0,SR)- T B(9) (8)
Where the parameter A is dependent on incidence angle and
roughness, r is surface reflectivity at normal incidence. B is
In SMEX02, three depths soil moisture are recorded, 0~lcm,
0~6cm and l~6cm. Obviously, the toppest layer must be
detected by radar signal. The linear correlation between radar
backscatter at W polarization and soil moisture is computed,
the coefficient is shown in Table 2, from which we can see, the
correlation coefficient at 0~lcm for all four polarization is
higher than 0~6cm. Therefore in the validation, 0~lcm soil
moisture is used.
SM
ComLW
ComSVV
SoyLW
SoySW
0~ 1cm
0.788
0.691
0.858
0.821
0~6cm
0.621
0.513
0.793
0.734
Table.2 Linear correlation coefficient between radar and soil
moisture at two depths
The results are shown in Fig.2 for both soybean field and com
field, and at S-HH, S-VV, L-HH and L-VV polarization.
Generally, L-band has better results than S-band, and soybean
field has better results than com field, both are quite consistent
with our prediction. Compared with Table.2, for L-band, the
algorithm can improve the inversion accuracy (from 0.86, 0.79
to 0.91, 0.85, respectively), proved its usefulness. But for S-
band data, the correlation coefficients from the algorithm are
lower than linear regression (from 0.69, 0.82 to 0.58, 0.77,
repectively), this shows that the algorithm is incapable of being
used in dense vegetation area when wavelength is lower (than
L-band).
6. CONCLUSION
An algorithm is developed to estimate soil moisture change
combining optical and radar data from SMEX02. Canopy