Soil Moisture Estimation From SIR-C Measurements

D.J. Chadwick and J.R. Wang

Goddard Space Flight Center

Code 975

Greenbelt, MD 20770 USA

1. Experiment Objective.

The main objective of our study is to explore the feasibility of estimating surface soil moisture from the multiple-frequency and multiple-polarization measurements provided by SIR-C/X-SAR. The approach was to first apply algorithms available in the literature to the SIR-C data and compare the results with ground truth data measured concurrently with the SIR-C data acquisition. Next, an effort was made to improve on the available retrieval algorithms, and an effort was made to develop a new algorithm which would be more robust in areas with vegetation. The test site is located in a well-maintained watershed near Chickasha, some 60 km southwest of Oklahoma City, Oklahoma.

2. Summary of the Experiment.

The SIR-C mission consisted of two different shuttle flights in April and October of 1994, each lasting about 10 days. Throughout the second flight in October 1994 most of the soils in our test site, the Little Washita River watershed (about 40 km by 25 km in size), remained dry. In addition, the last three days of that flight was devoted to interferometry and the test site was not covered by the SIR-C measurements. As a consequence, our analysis and research efforts have emphasized the data acquired during the April flight. A total of twelve SIR-C data takes over the test site, seven of them in mode 16X and remaining five in mode 11X, were acquired during the 10-day flight. Corner reflectors were placed in five widely separated locations for SIR-C/X-SAR calibrations. Ground truth of soil moisture was acquired within ±2 hours of the SIR-C overpasses, for a number of selected bare and vegetated fields in the test site. Samples of vegetation biomass and surface roughness were also obtained for several of these fields. These ground truth data sets were processed, organized, and reported in the Washita CD-ROM by Jackson et al (1996).

A moderate rain storm occurred in the test area just before the first SIR-C overpass on April 11, and prior to that the soils were fairly dry. There was no rainfall for nearly two weeks after April 11, and measurements during April 11-18 therefore covered a period of dry-down for the soils in the test area. The rainfall caused the average soil moisture in the test site to increase from about 10% to about 25% when the SIR-C passes over the area began. The average soil moisture decreased steadily during the April SIR-C mission to the pre-rain value of about 10%. The soil textures in the test site vary from silty loam to fine sand, with silty loam covering the northwestern and northeastern parts of the watershed, fine sandy loam distributed along the Little Washita River and upper-central part, and loamy fine sand and fine sand spreading over the southeastern and lower-central parts of the watershed. The percentage of sand in the soil ranges from 10% to 90% and the percentage of clay from 5% to 30%. There are a total of 43 rain gauges distributed fairly evenly throughout the watershed.

Results from a preliminary analysis showed that vegetation has a strong effect at both C and X bands, and the SIR-C data in these bands were not ideal for soil moisture estimation. Consequently, the L-band quad-polarized data were emphasized in the analysis and research efforts described below.

3. Estimation of Soil Moisture.

Two different algorithms (Dubois et al., 1995; Shi et al., 1995) developed recently for estimation of soil moisture have been applied to the SIR-C L-band data for the Little Washita study site. The algorithm of Dubois et al.(1995) was derived empirically based on the ground-level scatterometer measurements of Oh et al. (1992) and Wegmuller (1993). The algorithm of Shi et al. (1995) was based on an approximation to the integral equation method (IEM) developed by Fung et al. (1992). Both algorithms were derived for application to the L-band multiple-polarized measurements within the incidence angle range of 30_-50_. Six SIR-C passes over the test site satisfied these conditions. Three of them on April 11, 12, and 13 (incidence angles 28_, 42.3_, and 50.1_) were south-looking ascending passes, and the other three on April 15, 16, and 17 (incidence angles 42.4_, 36.2_, and 30.9_) were south-looking descending passes. The results of applying the two algorithms to these six passes of SIR-C L-band data were reported by Shi et al. (1997) and Wang et al. (1997). The soil moisture maps derived from both algorithms show the trend of drying-down consistent with the ground observations. The estimated soil moisture values (mv) were also compared with those sampled on the ground for selected bare and vegetated fields.

Figure 1 shows a scatter plot between the mv values estimated from the algorithm of Dubois et al.(1995) and those measured on the ground. Different symbols are used to represent data for a bare field, an alfalfa field, and other vegetated field to facilitate discussion. The estimated mv values for the alfalfa field are quite poor compared to the measured ones; five out of six times the algorithm overestimates mv, and there is no apparent correlation between the estimated and measured values. Results for the bare and other vegetated fields show a good correlation between the estimated and measured soil moisture. The standard deviation of the comparison is about 0.059 cm3/cm3 when all the fields are included. When the data points from the alfalfa field are excluded from the comparison, the standard deviation improves to 0.053 cm3/cm3.

Figure 2 shows a comparison of the mv values estimated from the algorithm of Shi et al. (1997) and those sampled from the fields. Again, different symbols are used to denote the mv values for the bare field, alfalfa field, and other vegetated fields. The data points from the bare field show some correlation, but those from the alfalfa and other vegetated fields show more scatter. The standard deviation is found to be 0.053 cm3/cm3 when all the fields are included in the comparison.

Both the empirically and theoretically based retrieval algorithms are derived from ground-level radar measurements and the IEM model only for bare soils with varying soil moisture and surface roughness parameters. Both of them are not expected to adequately handle the vegetated soils that comprise most of the Little Washita River watershed. The results shown in Figures 1 and 2 conform to the deficiency of these algorithms. An improvement to these algorithms or a new approach is needed because most land surfaces have some vegetation cover

4. A New Approach Under Exploration.

Radar signals from surface consist of three components: a direct backscatter from the surface (single reflection), a component that interacts with vegetation stalks before or after a specular reflection from the surface (double reflection), and a diffuse component from scattering by leaves or branches in a vegetation canopy. It is expected that the relative contribution from these components to the SAR signals would be different for bare and vegetated soils; and this relative contribution may also change with changes in mv. Theoretically, the ratio of single reflection (90_ incidence) to double reflection (true angle of incidence of SAR) backscatter should change with changing soil moisture (Ulaby et al., 1981). To explore this possibility, Cloude's decomposition method (Van Zyl, 1992) was applied to the SIR-C L-band data acquired on April 12 and 15 and the two reflection components were calculated for the fields with ground truth measurements. The data from these two days were acquired at an incidence angle of 42.3_. This choice of data sets removed incidence angle as a variable and the relative changes in the radar backscatter components could be analyzed with respect to soil moisture alone. Figures 3 and 4 summarize the results from these analyses.

Figure 3 shows the change in single to double reflection ratio as a function of change in mv for fields with ground truth measurements. The plot clearly shows that the single to double reflection ratio decreases in a near-linear fashion with mv. The consistent slope suggests that regardless of vegetation, there is a direct correspondence between the changes in the single to double reflection ratio and mv. In Figure 4, the values of the single to double reflection ratios are plotted as a function of mv., with each line segment representing the change in soil moisture between 12 and 15 April. Bare soil fields have the highest single/double reflection ratios, with generally decreasing ratios with increasing vegetation biomass. Figure 4 suggests that, for the single to double reflection ratio to be useful for estimation of mv, a method has to be developed to take into account the vegetation effect. Ranson et al. (1995) reported a method to estimate vegetation biomass by the ratio of backscattering coefficients at L and C bands, both at HV (horizontal transmit and vertical receive) polarization. The method could provide estimation of biomass up to 25 kg/m2, with a probable error at 95% confidence interval of 2 kg/m2. As the measured biomass values for the fields of our study area are all < 2.5 kg/m2 (Jackson et al., 1996), this probable error is quite significant. Nevertheless, the method provides the necessary means to normalize the vegetation effect displayed in Figure 4. Efforts to calculate biomass using the NDVI (Normalized Difference Vegetation Index) derived from the Landsat TM image obtained over the Little Washita River watershed near the time of SIR-C flight lead to larger errors in biomass estimation.

Combining the results displayed in Figure 4 and measured biomass values, one arrives at an equation for mv as:

mv = 0.026r + 0.11b - 0.14

where r is the ratio (in dB) of single to double reflection power at L band, VV polarization, and b is the dry biomass in kg/m2. This algorithm is currently being tested with the SIR-C data over our test site, and the results will be compared with those derived from the algorithms of Dubois et al. (1996) and Shi et al. (1997).

References.

Dubois, P. C., Van Zyl, J., and Engmen, E. T. (1995), Measuring soil moisture with imaging radar, IEEE Trans. Geosci. Remote Sens., 33(4), 915-926.

Jackson, T., Tang, L., Hsu, A., Wood, E., O'Neill, P., and Engman, E. (1996), Washita '94 SIR-C/X-SAR data sets, published in CD-ROM, Goddard Space Flight Center, Greenbelt, MD 20771.

Oh, Y., Sarabandi, K., and Ulaby, F. T. (1992), An empirical model and an inversion technique for radar scattering from bare soil surface. IEEE Trans. Geosci. Remote Sens., 30(2), 370-381.

Ranson K.J., Saatchi, S., and Sun, G. (1995), Boreal Forest Ecosystem Characterization with SIR-C/X-SAR, IEEE Trans. Geosci. Remote Sens., 33(4), 867-876.

Shi, J. C., Wang, J. R., Hsu, A. Y., O'Neill, P. E., and Engman, E. T. (1995), Estimation of bare surface soil moisture and surface roughness parameters using L-band SAR-measurements, IGARSS'95, Vol. I, 5-7-509.

Shi, J. C., Wang, J. R., Hsu, A. Y., O'Neill, P. E., and Engman, E. T. (1997), Estimation of bare surface soil moisture and surface roughness parameters using L-band SAR image data, IEEE Trans. Geosci. Remote Sens., in press.

Ulaby, F.T., Moore, R.K., and Fung, A.K., (1981), Microwave Remote Sensing: Active and Passive, vol. 1, Addison-Wesley pub., 456 pp.

Van Zyl, J.J. (1992), Application of Cloude's target decomposition theorem to polarimetric imaging radar data, Soc. Photo Optical Instr. Engin. 1748, 184-191.

Wang, J. R., Hsu, A. Y., Shi, J. C., O'Neill, P. E., and Engman, E. T. (1997), A comparison of soil moisture retrieval models using SIR-C measurements over the Little Washita River watershed, Remote Sens. Environ., in press.

Wegmuller, U. (1993), Active and passive microwave signature catalogue on bare soil (2-12 GHz), Inst. Applied Physics, Univ. Berne, Switzerland.