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FRINGE '96 Workshop: ERS SAR Interferometry, 30 September - 2 October 1996
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FRINGE 96

On the Use of ERS SAR Interferometry for the Retrieval of Geo- and Bio-Physical Information

Maurice Borgeaud European Space Agency
ESTEC-XEP
P.O. Box 299
2200 AG Noordwijk
The Netherlands
Tel: +31-71-565-4830
Fax: +31-71-565-4999
Email: maurice@xe.estec.esa.nl
http://www.estec.esa.nl

Urs Wegmueller GAMMA Remote Sensing AG
Thunstrasse 130
3074 Muri BE
Switzerland
Tel: +41-31-951-7005
Fax: +41-31-951-7008
E-mail: gamma_rs@pingnet.ch
http://www.primenet.com/~gamma/gamma.html

Abstract

At the present time, most of the research activity in SAR interferometry is directed towards development and improvement of SAR interferometric techniques, mapping of scene topography (DEM), and displacement mapping with differential interferometry. Recently, it has been however shown that SAR interferometry has also a large potential for the retrieval algorithms for bio- and geophysical parameters.
Using only the coherence information derived from the complex correlation of two co-registered ERS SAR images, it is possible to retrieve additional information complementary to information contained in the amplitude value of the backscattering coefficient. Phase-unwrapping, one of the most critical steps of the interferometric processing chain, is not required for this type of analysis.
Applications of SAR interferometry for forest mapping, forest type discrimination, freezing, land-use classification, soil moisture monitoring, crop classification, crop density, crop growth and field development monitoring, monitoring of open water surfaces, and erosion are discussed. Examples from four different test sites (Bern, Flevoland, Middle Zeeland, and Death Valley) are shown using ERS-1 data collected during 3-day and 35-day repeat orbits as well as ERS-1/2 tandem data.
Keywords: SAR, interferometry, ERS, coherence, forest, land-use, crop, erosion

Introduction

In the last few years, SAR interferometry has become a very attractive technique to obtain extra information from SAR images. Not only the amplitude of the signal is considered, but its phase as well. In order to use this technique, two SAR images of the same region, acquired with slightly different sensor positions, are coherently combined together. SAR interferometry can be performed either using data collected by repeat-pass or single-pass sensors. The former implies the same antenna is used twice while the latter requires two distinct antennas to be flown aboard the aircraft or satellite.

This paper deals with repeat-pass SAR data acquired by ERS-1 during the 3-day (1991 and 1994) and 35-day repeat periods as well as during the ERS-1/2 tandem mission (1995). ERS-1, a satellite carrying a spaceborne C-band SAR, was built by ESA and launched in July 1991 while ERS-2 was launched in April 1995. Both satellites are identical (from the SAR point of view) and acquire data over the Earth with incidence angles varying between 19 deg and 26 deg with slant-range pixel spacing set to 7.9 m (for the single look complex SLC product generated by ESA).

A promising applications of SAR interferometry is to generate digital elevation maps (DEM) owing to the fact that the height information can be related to the phase difference between two SAR images. However, other applications of are emerging and this paper describes some of those for the retrieval of geo- and bio-physical information.

After a brief description on how the SAR data are processed, the paper lists the test sites which used for the study and then describes the potential of ERS SAR interferometry for several applications including forestry, agriculture, land-use classification, soil moisture monitoring, freezing, and erosion.

Processing of the SAR data

Interferometric processing of complex SAR data combines two single look complex (SLC) images into an interferogram. In a first step the two images are co-registered at sub-pixel accuracy. In the same step common band filtering of the azimuth and range spectra can be conducted, in order to include only those parts of the spectra which are common to the two images, and thereby optimizing the interferometric correlation and minimizing the effects of the baseline geometry on the interferometric correlation. Then the two images are cross correlated, i.e. the normalized complex interferogram is computed. The azimuth and range phase trends expected for a flat Earth are then removed from the interferogram. From this "flattened " interferogram and the two registered intensity images the multi-look interferometric correlation and backscatter intensities are estimated. The backscatter intensity and interferometric correlation estimators have to include a sufficiently large number of looks in order to reduce speckle noise and biased correlation estimation at low correlation levels. To optimize the trade off between spatial resolution and estimation accuracy estimators with adaptive window sizes were used. Notice that no phase unwrapping step if necessary for the estimation of the backscatter intensities and the interferometric correlation.

Test sites and data description

Several test sites have been used to investigate the potential of SAR interferometry for retrieving geo- and bio-physical information. Each of them is listed in Table 1 as well as the ERS data used and the bio/geo-physical parameter investigated.

Test site
Data
Season
Parameter retrieval
Bern (CH)
ERS-1
Nov. 91
Forest types, freezing
Bern (CH)
ERS-1/2 tandem
July 95
Forest/non-forest, forest types, land-use classification
Middle Zeeland (NL)
ERS-1
Jan.-Apr. 1994
Soil moisture, freezing
Flevoland (NL)
ERS-1
Sep.-Nov. 1991
Crop type, crop growth, field development
Flevoland (NL)
ERS-1/2 tandem
1995
Crop type, crop growth, field development
Death Valley USA)
ERS-1
Jan.-May 1993
Vegetation density, geometric change (erosion), mapping of surface types

Table 1: Test sites investigated

Applications of SAR interferometry

Some of the potential applications of SAR interferometry for the retrieval of bio- and geo-physical information, including forestry, agriculture, land-use classification, soil moisture monitoring, freezing, and erosion, are examined and examples are given in this section.

Forestry

It is difficult to identify forested areas using only single-frequency and single-polarization C-band amplitude SAR data as shown in Figure 1 . The city of Bern can be recognized at the bottom of the image while the lake of Bienne and Morat are displayed on the left of the image. However, it is almost impossible to differentiate forested from non-forested areas.

Figure 1: Part of the ERS-1 image (SLC format) over the Bern test site acquired on 24 November 1991 (full size image)

Using the coherence information between two ERS-1 images acquired 3 days apart (in this case: 24 and 27 November 1991), one can derive the so-called ``RGB" image to get a visual impression of the additional information that the coherence carries (Wegmueller 1995a, Wegmueller 1995b) . The red channel is proportional to the coherence, the green with the mean amplitude image, and the blue with the amplitude difference between the two images. Figure 2 shows the corresponding RGB image for the Bern test site.

Figure 2: RGB interferometric image over the Bern test site (full size image)

During the winter season, the coherence is quite high for agricultural fields (mainly bare soils) while forests exhibits a low coherence due to a three-day temporal decorrelation. Figure 3 shows the variation of backscattering coefficient as a function of the interferometric coherence, derived from ERS 3-day repeat-pass interferometry for different types of land surfaces.

Figure 3: Variation of backscattering coefficient as a function of the interferometric coherence for different types of land surface

It can be observed that both urban areas and sparse vegetation have a high backscattering coefficient and a high correlation. However, forested areas, while having a similar relatively high backscattering coefficient value, are described by a much lower coherence. Taking these differences into account, forested areas can easily be identified in green on Figure 2 using the RGB color scheme.

More recently, it was shown (Wegmueller 1996a) that it might also be possible to separate deciduous from coniferous trees using ERS SAR interferometry. From Figure 3 , it can be noted that the interferometric correlation is higher for deciduous trees than for coniferous while having an approximate constant backscattering coefficient. One should however not forget that this result is true only for winter time when deciduous trees have lost their leaves and hence are less sensitive to wind effects and is based on 3-day repeat-pass interferometry. In order to generalize this result, the different seasonal development of the two forest types (deciduous vs coniferous) was used. Deciduous forest sheds its leaves in fall, coniferous not. For the deciduous forest stands this results in increased interferometric correlation during winter time because the scatter contribution of more stable structures such as branches, stronger twigs and the soil is increased. The classification shown in Figure 4 was based on the interferometric correlation of the June 95, November 95 and April 96 ERS-1/2 tandem pairs over the Bern test site.

Figure 4: Forest type classification based on multi-temporal interferometric correlation for Bern test site. June 1995, Nov. 1995 and April 1996 ERS-1/2 tandem pairs were used for the classification.

The dark green areas correspond predominantly to coniferous forest, the bright green areas to deciduous forest. Blue corresponds to permanently very low correlation like water and lay-over. Grey-brown corresponds to higher correlation, i.e. urban areas and agricultural fields. The result was only validated for a few known forest stands. More quantitative validation is planned.

Agriculture

It has been recognized that ERS-SAR data can be very valuable for agricultural applications (Wooding 1995) , not only because of the all-day capability of microwave vs optical data, but also due to the fact that SAR data are very sensitive to moisture, soil roughness, and vegetation structure.

Additional, and most of time complementary, information can be derived from SAR interferometry. Several crops where observed during the Fall 1991 over the Flevoland test site by ERS-1. The temporal variation of the backscattering coefficient as well as the interferometric coherence is shown in Figure 5.

Figure 5: Temporal signatures of interferometric correlation and backscattering coefficient for several crops over Flevoland (full size image)

It can be seen that abrupt changes in the coherence for potatoes and wheat are not observed in the backscattering coefficient values. These losses of coherence can be attributed either to harvesting or mechanical preparations of the fields (e.g. ploughing).
In addition to the extraction of the signatures for selected fields, multi-temporal composites were generated. Figure 6 shows the multi-temporal image of the interferometric correlation combining the correlation of the 19 Sep. & 4 Oct. (red channel), 4 Oct. & 19 Oct. (green channel), and 19 Oct. & 9 Nov. (blue channel) interferograms. The fields appear in different colors according to its vegetation cover and farming activity occurring.

Figure 6: Multi-temporal coherence image over Flevoland in the Fall 1991 (full size image)

Using this technique, the area can also be classified in eight different classes depending on the value of the coherence between two dates. If the coherence is higher than 0.5, one assumes that no change has taken place during the time interval. On the other hand, if it is lower than 0.5, one assumes that some kind of "change" has occurred such as harvesting or mechanical preparations of the fields. Using the color table definition described in Table 2 , the classification of the Flevoland test site in eight different classes can be derived and is shown in Figure 7. Forest, water areas always exhibit change of correlation, therefore these areas are shown in black while bare soil fields (or spare vegetation covered fields) which are not mechanically cultivated during the whole period are displayed in white.

19 Sep. & 4 Oct.
4 Oct. & 19 Oct.
19 Oct. & 9 Nov.
Code
Color
change
change
change
000
Black
change
change
no change
001
Blue
change
no change
change
010
Green
change
no change
no change
011
Turquoise
no change
change
change
100
Red
no change
change
no change
101
Pink
no change
no change
change
110
Yellow
no change
no change
no change
111
White

Table 2: Color table definition according to a coherence greater (no change) or smaller (change) than 0.5 between two dates

Figure 7: Map of multi-temporal change over Flevoland in the Fall 1991 (full size image)

The temporal monitoring of the coherence may also give some information about the status of a crop. As shown in Figure 8 for the case of rapeseed (two different fields located in the Flevoland test site) during the growing season 1991-1992, the coherence decreases as the soil cover fraction increases. This can easily be explained due to the fact that as the vegetation increase, the coherence (measured by repeat-pass interferometry) decreases. With knowledge of the farming calendar, such information would allow the identification of the crop. For the specific case of the rape seed, it is possible to identify it as early as November 1991

Figure 8: Interferometric correlation as a function of rape seed soil cover fraction over the Flevoland test site

A promising approach would be to couple this information linked to the biomass content with crop growth models to predict the crop yield (Van Leeuwen, 1996) .

Land-use classification

Using SAR interferometry and combining coherently the data acquired on 24 November 1991 by data measured three days later (i.e. 27 November 1991), it is now possible (Wegmueller, 1996a) to derive land-use maps, as illustrated in Figure 9 . Land classes such as lakes, urban, forest, agriculture can be identified with a classification accuracy of 90%. Furthermore, lay-over areas are shown in yellow. The color table used is displayed in Figure 10 . This classification was made possible by combining the mean amplitude and differences between the data as well as the coherency image acquired on both days.

Figure 9: Landuse map using SAR interferometry technique with ERS-1 data acquired on 24 and 27 November 1991 over the Bern test site. Water (blue), urban (red), forest (dark green), agriculture (light green), and lay-over (yellow) are shown. (full size image)

Figure 10: Color table for Figure 9

Soil moisture monitoring

The scattering properties of a soil surface are dominated by its geometry and its permittivity or dielectric constant. The permittivity itself depends strongly on the soil moisture content because of the very high permittivity of liquid water. The main limitation in soil moisture retrieval is that the geometry of the soil as well as vegetation cover influence the backscattering coefficient, too. SAR interferometry can help to resolve the effects of the soil moisture, the soil roughness and the vegetation cover, which are very difficult to separate, otherwise (Wegmueller 1996b) . As discussed above vegetation can be identified by its lower interferometric correlation. This allows to identify bare and sparsely covered fields. In addition, high interferometric correlation indicates that no geometric change, that means also no surface roughness change, occurred between the acquisition of the image pair. Even if the surface roughness remains unknown, the fact that surface roughness change may be excluded allows to monitor soil moisture change more reliable. Of course, these ideas improve the situation primarily for those fields which are bare and without geometric change. Using a similar technique as described in Table 2 (setting a change/no change threshold for the coherence to 0.5), a multi-temporal image of the change of the interferometric correlation over the Middle Zeeland test site Figure 11 shows that such fields exist in relatively high number (identifiable by a white color), confirming that the presented ideas are be applicable in practise. The color table is the same as described in Table 2 , however different dates are used.

Figure 11: Map of multi-temporal change over Middle Zeeland in the winter 1994 (full size image)

Under the assumption of constant surface roughness, quite reliable soil moisture estimates may be obtained from the microwave backscattering coefficient as was shown in the past (Borgeaud, 1995) . The backscatter intensities of fields of high interferometric correlation allow to monitor near-surface soil moisture. Additional advantages of SAR interferometry with respect to soil moisture monitoring are the possibility to use the interferometric phase to retrieve topographic information, and to carry out geometric- and radiometric calibration of the data.

Freezing

The most drastic cases of permittivity change are freezing and thawing events. The liquid water content changes between almost zero for the frozen case to the usually very high values near saturation which are typically observed during winter season. As illustrated in Table 3 , the interferometric correlation (cc) and the backscatter change (delta sigma) in [dB] between the first and the second acquisition were extracted for three fields over the Middle Zeeland test site. The drastic decrease in the backscattering of more than 4 dB between 11 and 14 Feb at a high interferometric correlation (> 0.70) clearly indicates the freezing event.

Dates
Baseline [m]
Time interval [day]
In-situ info
Field 4 [cc]
Field 4 [delta sigma, dB]
Field 5 [cc]
Field 5 [delta sigma, dB]
Field 6 [cc]
Field 6 [delta sigma, dB]
5 & 11 Feb. 1994
30.1
6
not frozen
0.96
-0.8
0.97
-1.4
0.97
-0.4
11 & 14 Feb. 1994
136.8
3
frozen on 14 Feb.
0.76
-4.3
0.78
-4.7
0.73
-5.1
14 & 17 Feb. 1994
112.9
3
frozen both days
0.81
-0.2
0.80
+0.8
0.76
+0.5

Table 3: Coherence [cc] and change in backscattering coefficients [delta sigma] for three fields over the Middle Zeeland test site during the period 5-17 February 1994.

Vegetation density and erosion

Over semi-arid to arid sites such as the Death Valley and Amargosa Desert the interferometric correlation is high even after 35 days or longer. Besides radar system and processing related effects, decorrelation occurs from geometric change. In such areas mainly two sources of geometric change occur: changing vegetation (motion, growth, etc.) and erosion. Wind and rain change the shape of unstable sandy surfaces. As a result such surfaces can be distinguished from more stable surfaces with larger rocks. Figure 12 shows the interferometric correlation (color scale from 0.1 to 1.0). The plains of the Amargosa Desert are relatively unstable and affected by wind and rain erosion. On the other hand larger rocks are typically found in dry River beds and on the alluvial fans of the Panamint Range, Amargosa Range, and Yucca Mountain. An extreme example for erosion with very low correlation after 35 days are sand dunes as for example the Big Dune in the Amargosa Valley.

Figure 12: ERS repeat-pass ERS interferometry (35-day) over Death Valley (29 January & 5 March 1993) (full size image)

Conclusions

In this paper, techniques using SAR interferometry have been presented to retrieve several bio- and geo-physical parameters. Using only the coherence information derived from the complex correlation of two co-registered ERS SAR images, it is possible to retrieve additional information complementary to information contained in the amplitude value of the backscattering coefficient. Applications of SAR interferometry for forest mapping, forest type discrimination, freezing, land-use classification, soil moisture monitoring, crop classification, crop density, crop growth and field development monitoring, monitoring of open water surfaces, and erosion have been clearly demonstrated.

References

Borgeaud, Attema, Salgado-Gispert, Bellini, Noll, 1995:
Analysis of Bare Soil Surface Roughness Parameters with ERS-1 SAR Data, Symposium on Retrieval of Bio- and Geophysical Parameters from SAR Data for Land Applications , Toulouse, France, 10-13 October 1995.
Van Leeuwen, Bakker, van den Broek, Rijckenberg, Bouman, Clevers, 1996
Vegetation retrieval by combined microwave and optical remote sensing, ESA contract 11154/94 , Noordwijk, The Netherlands.
Wegmueller, Werner, Nuesch, Borgeaud, 1995a:
Forest Mapping Using ERS-1 SAR Interferometry, ESA Earth Observation Quarterly , No. 49.
Wegmueller, Werner, Nuesch, Borgeaud, 1995b:
Land-Surface Analysis Using ERS-1 SAR Interferometry, ESA Bulletin , No. 81, pp. 30-37.
Wegmueller, Werner, 1995c:
SAR interferometric signatures of forest, IEEE Transactions on Geoscience and Remote Sensing , Vol 33, No 5, pp 1153-1161.
Wegmueller, Urs, 1996a:
ERS SAR interferometry for land applications, ESA contract 11740/96 , in progress, Noordwijk, The Netherlands.
Wegmueller, Urs, 1996b:
The potential of ERS SAR interferometry for hydrology, Environmental Remote Sensing and Applications , Parlow (ed.), Balkema, Rotterdam, (ISBN 90 54 10 5984), pp. 319-324, in progress.

Wooding, Attema, Aschbacher, Borgeaud, Cordey, de Groof, Harms, Lichtenegger, Nieuwenhuis, Schmullius, Zumda, 1995

Satellite Radar in Agriculture: Experience with ERS-1, ESA SP-1185 Publication , Noordwijk, The Netherlands.

Keywords: ESA European Space Agency - Agence spatiale europeenne, observation de la terre, earth observation, satellite remote sensing, teledetection, geophysique, altimetrie, radar, chimique atmospherique, geophysics, altimetry, radar, atmospheric chemistry