Quantitative Soil Moisture Extraction from Airborne ...

Canadian Journal of Remote Sensing Journal canadien de télédétection

ISSN: 0703-8992 (Print) 1712-7971 (Online) Journal homepage: http://www.tandfonline.com/loi/ujrs20

Quantitative Soil Moisture Extraction from Airborne SAR Data T.J. Pultz, R. Leconte, R.J. Brown & B. Brisco To cite this article: T.J. Pultz, R. Leconte, R.J. Brown & B. Brisco (1990) Quantitative Soil Moisture Extraction from Airborne SAR Data, Canadian Journal of Remote Sensing, 16:3, 56-62, DOI: 10.1080/07038992.1990.11487625 To link to this article: http://dx.doi.org/10.1080/07038992.1990.11487625

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QUANTITATIVE SOIL MOISTURE EXTRACTION FROM AIRBORNE SAR DATA by T.J. PULTZ R. LECONTE

R.J. BROWN Downloaded by [Natural Resources Canada] at 11:48 02 December 2015

B. BRISCO ,

,

RESUME Le taux d'humidite dans les sols est une information importante dans les etudes en hydrologie et en climatologie ainsi que dans les previsions relatives au rendement des cultures ou encore dans la planification agricole. Une serie d'experiences ont ete effectuees dans les Prairies canadiennes, en 1988, dans le cadre du programme de recherche a long terme mis en reuvre par le Centre canadien de teledetection (CCT) et visant a etablir une relation entre la retrodiffusion radar et les variations spatiales et temporelles de l'humidite des sols. Dans le present article, les auteurs etudient la retrodiffusion radar en fonction de l'humidite des sols, du type de culture et du developpement phenoloqique. Des donnees aeroportees d'un site temoin situe pres de Outlook, en Saskatchewan, ont ete acquises par le radar a antenne synthetique du CCT, dans la bande C, en juin et en aout 1988. Les images enreqistrees et traitees numeriquement ont ete etalonnees a l'aide de cibles ponctuelles de sections efficaces connues. Des mesures dielectriques des sols ont ete effectuees a l'aide d'une sonde dielectrique portative dans des champs dont la ruqosite de surface presentaient des caracteristiques similaires. Ces mesures ont ete utiiisees comme donnees d'entree dans un modele elabore par le CCT pour l'estimation de la teneur volumetrique des sols en eau. Les relations qui ont ete etablies entre l'humidite du sol sous des cultures de ble et de canola et la retrodiffusion radar sont decrites. Ces relations ont ete etablies a partir de donnees du radar a antenne synthetique etalonnees de facon relative, des teneurs volumetriques estimees du sol en eau obtenues a partir des mesures dielectriques du sol, des types de culture et des stades de developpement phenoioqique. L'analyse a revele, d'une part, une forte correlation entre la retrodiffusion radar et la teneur volumetrique du sol en eau tant sous les cultures de ble que sous les cultures de canola et, d'autre part, que cette correlation etait liee au type de culture et au developpement phenoloqique.

T.J. Pultz and B. Brisco are with Intera Kenting. 1525 Carling Avenue, Ottawa. Ontario KI Z 8R9. R. Leconte and R.J. Brown are with the Canada Centre for Remote Sensing. 2464 Sheffield Road, Ottawa. Ontario KIA OY7. ~ Canadian Remote Sensing Society I

56

Societe canadienne de teledetection

Canadian Journal of Remote Sensing/journal canadien de teledetection

SUMMARY Knowledge of the moisture content of soil is valuable for hydrology and climate studies, as well as for yield pred~ction or agricultura~ planning . As p,art of the long-term research plan at the Canada Centre for Remote SeY!szng. (CCRS) to e~tablzsh a r~lationshZp between radar backscatter and the spatial and temporal variations of SOli moisture, a senes of experiments were conducted in the Canadian prairies in 1988.

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This paper examines radar backscatter as a junction of soil moisture, plant type, and phenological development. Airborne data were acquired by the CCRS c-band SAR of a test site near Outlook, Saskatchewan, in June and August 1988. The digitally recorded and processed imagery were externally calibrated via point targets of known radar cross-section. Soil dielectric measurements were collected using a portable dielectric probe in fields with similar surface roughness characteristics. These measurements were used as input to a model developed by CCRS for estimating soil volumetric water content. This paper describes the development of relationships between soil moisture under wheat and canola canopies and radar backscatter. The relationships were developed using the relatively calibrated SAR, the estimate of soil volumetric water content derived from soil dielectric measurements, plant type, and phenological development. The analysis indicated a strong correlation between radar backscatter and volumetric soil moisture under both wheat and canola canopies and that the relationship is dependent on crop type and phenological development.

INTRODUCTION Knowledge of the soil water content and its variations in space and time is of importance for use in application models for predicting crop yield, plant stress, and watershed runoff. The ability of active microwave techniques to sense near-surface soil moisture has been, for some years, an area of considerable research interest (Dobson and Ulaby, 1986). In general, the studies have concentrated on non-vegetated soil surfaces and approached the problem as an optimization problem. Thus, the objective was to identify sensor parameters having maximal sensitivity to near-surface soil moisture. but also having minimal sensitivity to surface roughness and agricultural canopy cover. The resulting recommendations for a C-band radar (at about 5 GHz) operating at angles of incidence in the 10° to 20° range (Ulaby, 1974) have not been altered substantively by the findings of subsequent investigators. A major projected use of surface soil moisture observations is in determining the water balance at a desirable resolution over geographically large regions. However, the value of a remote sensing system is not entirely in producing a single soil moisture map for a specific time, but also in the temporal information that can be derived from a frequent series of these observations (Jackson and O'Neill, 1987; Brisco et al.. 1983). As part of the long-term research plan to establish a relationship between radar backscatter and the spatial and temporal variations of soil moisture, a series of experiments were conducted in the Canadian prairies in the summer of 1988. These experiments were part of the agricultural component of the Radar Data Development Program (RDDP) (Brown, 1990), the focus of this special issue. This paper describes an experiment conducted to examine radar backscatter as a function of soil moisture and plant phenological development.

SAR Data On June 22, 1988, and August 10, 1988, the CCRS X/C synthe~ic aperture radar (SAR) acquired C-HH (5.3 GHz) data over a test site near Outlook, Saskatchewan (51° 30' N, 106° 57' W). A detailed description of the system is given by Livingstone et ai. (1988). Comparison of the two SAR data sets necessitated calibration of the imagery. This was done using extern~ calibra~ion targets of kn.own radar cross-section (RCS). The test site was Instrumented With a

Vol. 16, No.3, October I octobre 1990

corner reflector during the June acquisition and an active radar calibrator (ARC) during the August acquisition. When properly deployed within an area characterized by a relatively low background cross-section, either calibration instrument will be imaged as a bright target that serves as a calibration reference for the establishment of a common grey scale (Brunfeldt and Ulaby, 1984; Hawkins et al., 1989). The SAR data were processed to one look (16-bits, power data) with a one-metre pixel spacing in range and azimuth. The approach used to relatively calibrate the August data with respect to the June data is described by Lukowski et al. (1989). Briefly, the multitemporal imagery consists of both imagery and background noise data. The background noise data is first subtracted from each image. To bring the two images to a common grey scale, the ratio of the power received from the calibration device to the target strength is determined for each image. A correction factor for each line at fixed range is used to multiply one image to obtain the common grey scale for the two. Each of the images also includes a sensitivity time control function (STC). The STC radiometrically balances the imagery across the swath by removing the combined effects of varying geometry, antenna pattern, terrain backscatter, and atmospheric attenuation. The image to be calibrated is then corrected to use the same STC as the reference image. By modelling the gain patterns used to acquire each of the images, a correction is performed to give the same antenna gain pattern as a function of slant range (compensating for differences in antenna pointing in elevanon). AS a resutt, data at the same slant range In the reterenced and calibrated images can be compared. Subsequently, the relatively calibrated l o-bit power August and June data were compressed to 8-bit using a linear transformation for analysis. The 8-bit power data were then filtered with a 3 by 3 median filter to reduce the speckle inherent in SAR data.

Site Description Ground data collected over the Outlook site included seeding date, fertilization rate, crop height and maturity, soil bulk density, soil textural composition, and 35 mm photographs acquired on the ground and from a light aircraft. Three centre pivot irrigated fields were selected for detailed analysis in this experiment, comprising two of canola and one of wheat. The characteristics of these fields are presented in Table 1. The fields were all planted using a concentric ring procedure and had row spacings of approximately

57


Figure 1. Irrigated Canola, June 22, 1988, Outlook, Saskatchewan. 20 ern and a furrow depth of 2 cm. At the time of the June flight. the irrigated canola was 40 to 70 cm high with 100 % ground cover (Figure 1), and the wheat was 20 cm in height with 60% ground cover (Figure 2). All three fields were being irrigated at the time of the SAR data collection in June. By August. the canola was 55 to 70 cm in height and ready for harvest, and the wheat had grown to 75 cm. All three fields had 100% ground cover by August, and irrigation activities had ceased.

Soil Moisture Estimation Concurrent with the SARoverflights , soil dielectric measurements were made at 10 to 20 sample locations per field using two portable dielectric probes (PDP) operating at 5.3 GHz. The PDPconsists of an open-ended coaxial cable . which contacts the material to be measured. a novel microwave reflectometer for measuring the reflection amplitude and phase from the probe. and a calculator for transforming the reflectometer data in the complex dielectric constant, E I -j E" (Brunfeldt, 1987). At each sample location, a hand tool was used to remove the top 0.25 to 0.5 ern of the soil surface to create a relatively flat location for probe tip placement. This was to ensure that a good contact of the probe tip with the soil was attained. If a good contact was not achieved. the resulting dielectric measurement was usually much lower than the average due to air voids in the sample area. Therefore, at each sample location seven dielectric measurements were acquired and subsequently visually scrutinized for outlying points . These outlying points were then removed and the remain ing points used to calculate an average dielectric value . The PDP provides information on a soil profile of about 1 to 2 em. To get depth profiles greater than this, it is necessary to make measurements at progressively deeper layers. Therefore, in addition

Table 1 Target Characteristics at the Time of SAR Data Collection In cid enc e Angle Date 22-Jun-88

10-Aug-88

58

Target

(0)

~

Height · (em)

Ground Cover

Range of Soil Mois ture

(% )

( %)

Wheat

55

20

60

10-30

Canola

57

40-70

100

6-30

Wheat

55

75

100

7- 11

Canola

57

55-70

100

10- 16

Figure 2. Irrigated Wheat, June 22, 1988, Outlook, Saskatchewan. to the surface measurements. a 2 cm layer of soil was removed. and dielectric measurements were obtained using the same methodology, thus providing an estimate of the soil dielectric properties for a surface layer (0 to 2 ern) and a sub-surface layer (2 to 4 em). Volumetric soil moisture (Mv) was determined using an empirical model developed by CCRS (Brisco et al., 1989) to convert the real part of the dielectric constant (E') to Mv as a function of soil texture and bulk density . This model was developed in a laboratory setting where it was found that Mvcould be estimated to + 1- .02 to 0.4. (This model has yet to be Vigorously tested in the field environment.) Unfortunately, because soil "grab" samples for moisture content analysis were not collected at the time of the SAR overflights. the absolute accuracy of the soil volumetric moisture con tent estimates cannot be assessed.

ANALYSIS For a given soil condition (roughness or texture). radar back scatter has been found to be linearly dependent on the volumetric moisture content in the upper 2 to 5 ern of soil (Dobson and Ulaby , 1986). In this study . linear correlation analysis was performed to relate the image grey level to the modelled volumetric moisture of the surface and sub-surface layers for each field. As stated. the absolute accuracy of the soil moisture estimates could not be assessed. However, because the model developed is linear. the calculation of the linear correlation coefficient (R) between the image grey scale level, and either the modelled volumetric moisture content or the measured dielectric constant, would result in the same R value . It was important that the physical location of the PDP sample site within the field identified with a geometric co-ordinate on the SAR image . To achie ve this, the location of sample sites was carefully mapped out within the field at the time of data collection. The presence of the centre pivot in these fields provided a convenient reference point, which aided in this process. Next. the image co-ordinate for the sample site had to be identified. First, the SAR image of the pivot fields were segmented. using an image analysis system. into 24 pie-shaped sections (training areas), each section being an arc of 15° starting from the orthogonal viewing angle . These sections were then split into two halves that consisted of an inner and an outer section. The split of the section into two halves was done to account for differences in the angular

Canadian Journal of Remote Sensing(Journal canadien de ttHedetection

ON

Table 2 Results of Regression Analysis Soil Profile Target

(em)

(R)

(%)

Level of Signifi· cance

Wheat

0-2

.804

8.209

0.0001

1.296

82.684

2-4

.949

4.449

0.0001

3.21

32.645

0-2

.687

4.459

0.0067

0.782

2-4

.798

3.695

0.0006

1.463

90.076

0-2

.637

8.386

0.0059

0.628

121.362

2-4

.720

7.403

0.0017

0.911

114.406

Canola Canola


Correlation Coefficient

Standard Error of MV Estimate

Slope

Intercept

106.076

velocity of the pivot. which could result in a greater soil moisture closer to the centre of the pivot due to the same amount of water being applied to a smaller area. Therefore. it was assumed that soil moisture over such a radial section would be relatively homogeneous. An average grey level was then extracted for each section. Observations of agricultural fields with truck-mounted and airborne scatterometers and seasat suggest that radar is most sensitive to azimuthal viewing geometry for angles within 15° of orthogonal to the row direction. In addition. this sensitivity decreases in an exponential fashion as the view angle becomes parallel to the row direction (Dobson and Ulaby, 1986). However. this response is unique to the combination of effects from the frequency. incidence angle. row spacing. and row height (Blanchard et al.. 1981). Due to the repeated irrigation cycles in the fields being investigated. the soil surfaces were quite smooth and there were no apparent row direction effects observed between the average grey level and the viewing angle for these sections. Next. the map of the PDP sample locations was overlaid onto the segmented SAR image. Each PDPsample location was then correlated with the mean of the section within which it fell. Finally. the average grey level value for the section was regressed against the modelled volumetric soil moisture for the two profiles specified.

=

32.645 + 3.21 (Mv)

with a linear correlation coefficient (R) of 0.949 over a volumetric soil moisture range of 10 to 30 %. This expression does not provide information on the effects of vegetation or surface roughness; rather it simply states that backscatter (ON) is strongly related to Mv (statistically significant correlation at a 0.001 level). However. the radar backscatter from a vegetated soil surface consists of three components: a soil surface component. a vegetation component. and a surface/vegetation interaction component (Dobson and Ulaby, 1986). In the case of the wheat field in June. the plants were in an early growth stage (15 to 20 cm in height); as such, the field was not uniformly covered with vegetation (40% bare soil cover). The relationship between radar response and soil moisture content for the soil surface component depends on the dielectric contrast across the air/soil interface. As well, one must consider the depth of the soil profile that should be used to determine the dielectric properties of the soil (penetration depth). In most studies on microwave measurements on bare soils, experimental relationships between soil moisture content and radar backscatter were conducted using mean volumetric water content measured from the soil surface to a depth of 5 or 10 em. In fact, the shape of the water content profile in this surface layer varies widely under natural conditions. If there is wide variation over an interval of one wavelength, correct evaluation of the reflection coefficient necessitates accurate knowledge of the moisture profile at depth intervals in the order of 0.1Arn- where Am is the wavelength in the medium (Ulaby and Batlivala, 1976).

255 "". . 22 . . . _

. . . . . .7·

'1M."

Intercept. ~·O••tt "'.72

204

ii

+ -t + +

158

+ .f,j..

+

+

~

DISCUSSION

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is

In general. two physical factors influence the rada~ backsca~er response. the geometry of the target. and the dielectnc prop.erttes of the target. For a particular land scene. several target ~anables may influence the physical properties. such as vegetation type. moisture. density. height. soil permittivity. and roughness. . The results of the regression analysis for the surface s~II moisture profile and the sub-surface profile are presented In Table 2. The measured digital number values versus the sub-surface volumetric soil moisture for wheat and canola, respectively. are presented in Figures 3 and 4. Due to the sm~l range in .soil moisture conditions encountered in August. regression analysis could only be performed on the June data. The results indicate str~ng co.rrelations between the calibrated digital numbers and SOil moisture despite having been obtained at an inciden~e angle of approximately 55° and having vegetation cove.r. Th~s. was rat~e~ u.n~x pected as the optimal angle of incidence identified for rrunirruzmg the sensitivity to surface roughness and vegetation canopy cover is in the 10° to 20° range (Ulaby, 1974). . The least squares linear regression of the sub-surface s~ll profile June data (15 samples) for the wheat field resulted In

Vol. 16. No.3. October /octobre 1990

102

51

o •

18

24

40

VOL~ SOIL IIOISTUIII! 12-4cm ...,Ihl

........ 22,1

.

+ AuvueI1O ..... Figure 3. Relation Between C-HH SARImage Digital Number and Sub-Surface Soil Moisture For Irrigated Canola, Outlook, Saskatchewan.

59

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lUIS

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...... 1111·

.........,

311

intercept·a•.MII

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R- .•••

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102

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Figure 5. Volumetric Soil Moisture at Two-Depth Profiles in an Irrigated Canola Field, June 22, 1988.

51

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24

3a

40

40

VOLU.ntlC SOIL IIIIOISTUII. la-4 em ....hl ",-all, 1

+

A.....t1O

35

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Figure 4. Relation Between C-HH SAR Image Digital Number and Sub-Surface Soil Moisture For Irrigated Wheat, Outlook, Saskatchewan.

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In general, dry conditions correspond to large soil penetration depths. As a result, taking into account a small soil profile in determining the soil dielectric properties may introduce non-negligible errors. Since wet soil conditions generally correspond to very small microwave penetration depths, using a large soil profile is probably unsatisfactory if a large water gradient exists (Bruckler et al., 1988). As spaceborne single channel and multi-channel SARs become a reality, this knowledge could be combined with meteorological data in an expert system to infer penetration depth and/or to select the frequency that provides the appropriate penetration depth for a particular application. The soil moisture (surface and sub-surface) at each ot the sample sites within a canola and a wheat field are presented in Figures 5 and 6. The sample locations are arranged in a time sequence such that location 1 is the most recently irrigated area, location 2 is the next most recently irrigated area, and locations 20 and 18 in Figures 5 and 6, respectively, are the areas that have not been irrigated for the longest period. From these figures it can be seen that recently irrigated locations have a fairly uniform moisture profile over the soil surface to 4 cm depth and that as the soil dries the moisture gradients in this surface layer increase. Higher correlations between the radar response and soil moisture for the sub-surface profile than for the surface profile were obtained. This is due to the fact that when the soil is wet, the surface and sub-surface layers have about the same moisture content, and it is really the surface layer that is driving the backscatter magnitude. However, when the soil is dry, the microwave energy penetrates the surface layer, and it is the sub-surface layer driving the radar backscatter magnitude. Hence, in both cases, the sub-surface

~


o

Figure 6. Volumetric Soil Moisture at Two-Depth Profiles in an Irrigated Wheat Field, June 22, 1988.

layer magnitude would correlate with the radar backscatter. This also occurs in canola fields with 100 % vegetation cover. In addition, the radar data were collected at a shallow incidence angle (,." 55°) rather than at the steep incidence angles (15° to 20°), which have been previously identified as optimal for minimizing the effects of vegetation. However, because of the variety of variables that influence radar return, several processes or a combination could be used to explain the interaction. First, the radar may be responding to changes in moisture content of the vegetation canopy, which follow the same soil moisture content trends within the irrigated field. Johannsen (1970) found that, for corn, the leaf moisture followed the same trend as soil moisture over a range of 4 to 22 % regardless of the leaf position or height of the leaf on the plant. Unfortunately, no plant moisture data were collected during this experiment. Second, the radar may be responding to the surface/vegetation interaction component. In

Canadian Journal of Remote Sensing!journal canadien de teledetection


this case. the target consisted of a scattering volume (vegetation) bounded on the top side by the canopy/air interface and on the bottom side by the soil/air interface. When energy from the radar enters the volume. both single and multiple scattering (depending on vegetation density) takes place. If the vegetation density is relatively low. a portion of the incident wave will reach the soil/air interface. When this interface is dry. the efficiency with which the energy is directed back to the radar is low. Therefore. in dry soil conditions very little energy that reaches the air/soil interface is actually backscattered. However. under wet soil conditions the target consists of a scattering volume bounded on one side by the canopy/air interface and on the other side by a reflective wet soil/air interface. Therefore. energy that reaches the wet soil/air interface is backscattered into the scattering volume where a portion is redirected toward the radar receiver. As a result, either one or a combination of these interactions may enhance the radar's sensitivity to changes in the soil moisture. Although relationships between radar backscatter and soil moisture in August could not be developed due to the limited range of soil moisture conditions, the data are presented in Figures 3 and 4. There were increases in grey level. for both the wheat and the canola targets, from those observed in June under similar moisture conditions. Therefore, the relationship developed for the June data is not applicable to the August data. During the period between the June and the August data acquisitions, both crops experienced significant changes. The wheat had gone from the emergent stage (60 % ground cover) to the senescent stage (100 % ground cover), and the canola had evolved from the vegetative growth stage to the senescent stage. Decreasing plant moisture is one of the measurable consequences of a ripening crop. Ulaby and Jedlicka (1984) found that, at 10.2 GHz. vertical polarization, and an angle of incidence of 56 0 , a soybean canopy may vary from appearing quasi-transparent to quasi-opaque, depending on its wet biomass. Therefore, the observed increase in backscatter may be explained, in part, by the lower attenuation properties of the canopy in August. which result in an increased return from the soil/air interface. However, while consistently decreasing plant moisture may be indicative of the maturation process. certainly other physical and morphological processes are occurrring. Therefore. increased backscatter may also be the result of reflections from heads or seed pods.

- precise water content information on the near-surface soil profile can be obtained using the portable dielectric probes and that this information is essential for determining the radar penetration depth; and - the PDP provides a means to access moisture gradients over very small soil profiles (1 to 2 ern), which is required for correct evaluation of the reflection coefficient. The moisture gradients can be assessed over any given depth profile by making measurements on progressively deeper layers.

ACKNOWLEDGEMENTS The authors thank Tom Lukowski, Paul Daleman, Richard Ford, and Paul McLean for their efforts in producing the relatively calibrated SAR data. Marguerite Trindade is also thanked for preparing the figures and Mike Manore for the constructive comments on a draft of this paper.

REFERENCES Blanchard, B.]., and Chang, ATe. 1983. Estimation of Soil Moisture from SEASAT Data. Water Resources Bulletin, Vol. 19. No.5, pp. 803-810. Brisco, B., Pultz. T.]., Brown, R.]., Topp, c.c., and Zebchuk, W.D. 1989. Dielectric Constant Measurement of Soil with Portable Dielectric Probes and TDR Techniques. Submitted to the Journal of Water Resources Research. Brisco, B., Ulaby, F.T., and Dobson, M.e. 1983. Spaceborne SAR Data for Land-Cover Classification and Change Detection. Digest of the 1983 International Geoscience and Remote Sensing Symposium, Vol. 1, San Francisco, June, pp. 739-745. Brown, R.]. 1990. Canadian Agricultural Research Program Overview. Canadian Journal of Remote Sensing, this issue.

CONCLUSIONS Airborne SAR images acquired over a test site near Outlook, Saskatchewan, on June 22 and August 10, 1988, were analyzed to determine the relationship between radar backscatter and volumetric soil moisture for irrigated wheat and canola fields and to identify issues that must be considered in further research. The analysis has shown a strong correlation between radar backscat~er and volumetric soil moisture under both wheat and canol a canopies and that this relationship depends on crop type and phen.ologi~al development. To establish the exact nature of .the relationship, backscatter models must be rigorously tested using data that provide sufficient physical characterization of the dielectric and geometric properties of soil and vegetation. . This experiment also established methodologle~ fo~ f~ture work. More specifically, the analysis yielded the following findings: _ relatively calibrated SAR can be obtained by accounting for ~ys~ tem variables and deploying targets of known radar cross-section: _ rapid in situ estimates of soil dielectr~c properties c.an be made using a portable dielectric probe, which ~erve .as Input to an empirical model to convert € to volumetnc moisture: I

Vol. 16, No.3, October /octobre 1990

Bruckler, L., Witono, H., and Stengel, P. 1988. Near Surface Soil Moisture Estimation from Microwave Measurements. Remote Sensing of Environment, 26: 101-121. Brunfeldt, D.R., and Ulaby, F.T. 1984. Active Reflector for Radar Calibration. IEEE Trans. Geos. and Remote Sensing, Vol. GE-22, No.2, pp. 165-169. Brunfeldt, D.R 1987. Theory and Design of a Field-Portable Dielectric Measurement System. Proceedings of the IGARSS '87, Ann Arbor, May 18-21, pp. 559-563. Dobson, M.e., and Ulaby, F.T. 1986. Active Microwave Soil Moisture Research. IEEE Trans. on Geos. and Remote Sensing, Vol. GE-24, No.1, pp. 23-36. Dobson, M.e., and Ulaby, F.T. 1986. Preliminary Evaluation of the SIR-B Response to Soil Moisture, Surface Roughness, and Crop Canopy Cover. IEEE Trans. on Geos. and Remote Sensing, Vol. GE-24, No.4, pp. 517-526. Hawkins, RK., Lukowski, T.I., Gray, A.L. and Livingstone, c.e. 1989. Calibration for Airborne SAR. Proceedings of IGARSS '89/12th Canadian Symposium on Remote Sensing, Vancouver, July 10-14, Vol. 1, pp. 238-242.

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jackson, T.j., and O'Neill, P. 1987. Temporal Observations of Surface

Soil Moisture Using a Passive Microwave Sensor. Remote Sensing of Environment, 21: 281-296. johannsen, C.j., and Baumgardner, M.F. 1971. Effect of Changing

Soil Moisture on Corn Leaf Moisture. Proceedings of the Indiana Academy of Science for 1970, Vol. 80, pp. 461-467. Livingstone, C.E. et al. 1988. CCRS CIXAirborne Synthetic Aperture Radar: An Rand D Toolfor the ERS-1 Time Frame. Proceedings of IEEE Nat. Radar Conf., Ann Arbor, Mi., April 20-21.

Ulaby, F.T. 1974. Radar Measurements ofSoil Moisture Content. IEEE

Trans. on Antennas and Propagation, Vol. AP-22, No.2, pp.257-265. Ulaby, F.T., and Bush, T.F. 1976. Monitoring Wheat Growth with

Radar. Photogrammetric Engineering and Remote Sensing, Vol. 42, No.4, pp. 557-568. Ulaby, F.T., and jedlicka. RP. 1984. Microwave DielectricProperties ofPlant Materials. IEEE Transactions on Geoscience and Remote Sensing, Vol. GE-22, pp. 406-414.

Lukowski, T.!., Hawkins, R.K., Brisco, B., Brown, R.]., Ford, R., and Daleman, P. 1989. The Saskatoon SAR Calibration Experiment.


Proceedings of IGARSS '89112th Canadian Symposium on Remote Sensing, Vancouver, july 10- 14, Vol. 1, pp. 254-257.

62

Canadian Journal of Remote Sensing/journal canadien de teledetection