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BackgroundThere is evidence that satellite-based Synthetic Aperture
Radar (SAR) sensors could provide a regional assessment of surface soil
moisture content ( | |||||
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| 1. | Investigate the sensitivity of ERS-2 C-band SAR backscatter measurements to soil moisture content in a semi-arid rangeland with sparse vegetation cover; and |
| 2. | Test an approach based on both optical (Landsat TM) and radar (ERS-2 SAR) measurements to improve regional estimates of soil moisture content. |
Approach
In this project, we designed an experiment to study the
link between ERS-2 SAR C-band backscatter to soil moisture, while minimizing
the influence of other conditions. That is, we focused our study on flat,
uniformly-vegetated sites, and planned to monitor the variations in soil
moisture and vegetation cover over time. By choosing flat sites, we avoided
the effects of topography; and by monitoring the sites over time (rather
than multiple sites over space), we minimized the influence of variations
in small-scale roughness conditions. Furthermore, by measuring vegetation
density on a monthly basis at each site, we were able to quantify changes
in vegetation that might influence SAR 
.
We requested 10 ERS-2 SAR scenes covering our study site throughout 1997. Dates of these overpasses were selected to correspond closely with the dates of overpasses of the Landsat-5 satellite. Landsat Thematic Mapper (TM) sensor measures surface reflected radiance in six wavelengths (from 0.45 to 2.35 µm) and measures surface temperature in a single spectral waveband covering 10.42 to 11.66 µm. Images from 6 months in 1997 are illustrated to below.
This figure is a graphic illustration of the basic approach
for the use of SAR/optical synergism for estimation of soil moisture content.
The effects of soil roughness were taken into account by taking the difference
between the SAR backscatter from a given image and the backscatter from
a "dry season" image (
).
The vegetation influence was corrected by using an empirical relationship
between () and green leaf area index
(GLAI), where the latter was derived from the optical data. Thus, the soil
moisture conditions are related to the length of the vertical arrow in the
Figure; where the soil moisture content of B is greatest and A least, with
C intermediate.
Experimental Site
Three sites were chosen in the Upper San Pedro River Basin (USPB) in southeast Arizona for investigation of the SAR/optical approach for monitoring surface soil moisture content. The sites were characterized by level terrain and uniform vegetation cover (over a 300 x 300 m area), and were named by the dominant vegetation type: Tobosa, Sacaton and Creosote. The Tobosa site is located in a swale which supports a mix of tobosa grass (Hilaria mutica) and creosote (Larrea tridentata) shrubs. The Sacaton site is dominated by big sacaton (Sporobolus wrightii) with some tobosa grass. The Creosote site is on a flat mesa and is characterized by scattered creosote shrubs (Larrea tridentata) with very few grasses or annual forbs.
Results
Due to fortunate weather conditions, we obtained an excellent
range of soil moisture conditions for our study. During the June SAR overpass,
the soil moisture conditions at all sites were extremely dry, and the late
summer greenup of the vegetation had not yet occurred. Consequently, we
designated it as the "dry" scene and subtracted the June SAR backscatter
(
) from the backscatter measured on all other dates to
account for the contribution of surface roughness to the SAR signal.
We derived a relation between () and
plant area index (PAI) using SAR () values
from the March image for the Creosote (C) and Sacaton (S) sites. There was
no significant variation in () associated
with the measured variation in PAI at the two sites.
It appeared that the differences in standing brown vegetation
biomass at these two sites were accounted for in the roughness correction.
Consequently, the values of () should
be directly related to soil moisture conditions at the site, with no need
for a PAI correction.
- As expected, the measured C-band SAR backscatter (, dB) was poorly correlated (
=0.21)
with surface soil moisture content (
) at the
three field sites (labeled C, S and T in the figure on top below). When
the data were corrected for differences in surface roughness and standing
brown vegetation biomass, there was a good correlation (=0.94) between ()
and at the three field sites (the figure on the bottom,
below).
- However, an unexpected result was discovered with data
from the February SAR scene. That is, the SAR backscatter related to the
moderately-wet soil conditions in February was less than that for extremely-dry
soil conditions in June (i.e., <0).
We suspect that there was an error in the sensor calibration on that date;
further investigation will be conducted regarding this anomalous result.
Using the relation between () and
derived from measurements at the three experimental sites,
regional maps of surface volumetric soil moisture were obtained from the
January and March SAR images. The maps of these two dates show a good contrast
between regional soil moisture conditions:
- 12 January: Due to recent snowmelt, the soil conditions at all three sites (and throughout the region) were near saturation.
- 23 March: Due to an exceptionally dry Spring, the regional soil conditions were very dry.
Concluding Remarks
These preliminary results are encouraging, though not entirely
conclusive. Despite the good relation between SAR backscatter () and volumetric soil moisture (),
the overall sensitivity of the SAR signal to changes in soil moisture was
low. The full set of ten ERS-2 SAR and Landsat TM images will allow further
investigation of this SAR/optical approach for mapping regional soil moisture.
The inclusion of a larger data set in this analysis will provide greater
understanding of and confidence in the final results. Also, the forthcoming
scenes will cover a period of increasing green vegetation at all three sites,
and allow analysis of the use of optical data to normalize the effects of
variations in green vegetation growth on the SAR signal.
Acknowledgements
We would like to acknowledge the valuable help we received from Wanmei
Ni, Chandra Holifield, Ross Bryant and others in collecting the multitude
of soil and vegetation samples. We also acknowledge financial support from
NASA Mission to Planet Earth (MTPE) and the European Space Agency that made
this work possible: NASA-S-41396-F, NASA NAGW-2425, NASA-W-18,997 and ESA-AO2.F115.
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