- PI: Prof. PAOLO CANUTI - Dept. of Earth's Sciences, University of Florence.

Abstract - This report presents an overview of the experiments carried out on the Montespertoli supersite during the SIR-C/X-SAR missions and an analysis of the results obtained by the different groups of researchers working on this experiment. The measured quantities taken into consideration are the full polarimetric features at L- and C-bands and the VV polarized backscattering coefficient s° at X- band. The influence of frequency, polarization and incidence angle on the response of various surface types has been investigated and statistical relations between backscattering and soil moisture, surface roughness and forest biomass are discussed.

1. INTRODUCTION

A research activity, which aims at a better understanding of the information obtained from multifrequency polarimetric Synthetic Aperture Radar to be used in hydrology has been carried out in the framework of the SIR-C/X-SAR Project (Stofan et al. 1995).

A significant phase of this study consists of investigating the sensitivity of radar backscattering to some important parameters (soil moisture, surface roughness, vegetation cover and biomass) which are of primary importance in modelling the geophysical processes of the hydrological cycle. Although the detection of these parameters has been the subject of many investigations, carried out in past years with ground based and airborne sensors, only a very few preliminary investigations have been carried out using data collected with spaceborne sensors (e.g.: Evans et al. 1988, Touzi et al. 1992). A major problem in retrieving the hydrological parameters is that each of them affects in a different way the radar backscattering and separating the effects requires the use of appropriate multi-frequency polarimetric algorithms.

Multifrequency polarimetric SAR measurements carried out during SIR-C/X-SAR mission over the supersite of Montespertoli have been analyzed and compared with data collected at the same frequency and polarization with NASA/JPL AIRSAR and NASDA JERS-1 on different dates. The sensitivity of L-band HV pol to both arboreous and herbaceous vegetation biomass has ben confirmed; moreover, a good separation between bare and vegetated fields has been obtained from the ratio RL/RR at C band. A classification algorithm able to separate different surface classes has been also carried out using multifrequency-multipolarization images. Finally the soil moisture and the height standard deviation of surface roughness have been retrieved by means of the semiempirical model developed by Oh et al. (1992) and used to validate the soil erosion model (WEPP) on Pesa and Virginio basins.

2. EXPERIMENTAL ACTIVITIES

2.1. The experimental area

The site choosen for the experiment is the basin of the Pesa river, located in Tuscany, on the Tirrenian side of the Northern Appennines, on the hydrographycal left of the Arno river south of Florence. The basin mainly consists of rounded hills in Pliocene marine deposits, top are sometimes flat with gully edges (clay) or with steps where alternance of gravel, sand and clay occur. Geomorphology of Pesa basin is well related to the granulometric characteristics of the Pliocene marine sediments and the main erosion processes occur as landslides, sheet and rill erosion, often induced by human activity. Yearly runoff can be estimated of 3.19 m³/sec, maximum values can be registered in november-april, minimum values in august-september. The climate, typically mediterranean, is characterized by an arid period during summer and high rainfall intensities (100 mm/h) in autumn decreasing in winter (Canuti et al. 1992). The site is an agricultural area with few urbanization. In the last 20 years the hillsides have been estensivley remodeled to make vineyards accessible to mechanized farm equipment that requires slope uniformity. Croplands are mostly represented by vineyards, oliveyards, wheat and grass in hillside; corn, sunflower, sorghum, alfalfa and oilseed in the flatlands. Some woodlands are still present on the top of the hills. The Pesa river watershed has been the object of many studies in the field of the earth sciences and also of remote sensing techniques. On the test area some microwave remote sensing campaigns were carried out in 1988 (AGRISCATT'88) and 1991 (MAC'91). Such activities, concerning SAR, scatterometric and radiometric flights, were organized by ESA (Agriscatt'88), NASA/JPL (MAC'91) and CNR/IROE of Florence.

2.2. Ground truth measurements

An extensive mapping of the two areas of Pesa and Virginio has been done in April and then up-to-dated in October. During the SIR-C flight ground truth measurements have been carried out and the following main parameters of soil and vegetation involved in the hydrological cycle have been collected and stored in a database:

gravimetric and volumetric soil moisture content at different depths; soil roughness, i.e. standard deviation of the surface height and correlation length; dielectric constant of soils at L-band; soil texture; crop classification and phenological stage; row orientation; plant density and plant height; total number of leaves per plant; length and width of leaves; diameter of stems; leaf area index; water content of stems, leaves and ears/flowers. Air temperature and humidity, wind speed and direction, solar radiation, rainfalls have been collected in one meteorological station placed close to Cerbaia.

2.3. Calibration

Calibration activities, concerning check of absolute calibration, inbalance factor and phase, have been performed on the area using three thriedral corner reflectors of different sizes (one of 2.40 m by side for L-band calibration and two others of 1.80 m by side for C- and X-band calibration) (Van Zyl 1990, Zebker et al.1991). Some checks carried out for the first flight led to the conclusion that all data sets of SIR-C data have been well calibrated, whereas X-SAR data had to be calibrated. In order to guarantee a satisfactory comparison of data collected at different frequencies and dates on the whole dynamic range, a further check of calibration was performed by comparing the scattering characteristics of forests and reference bare soils whose features were controlled by ground truth. In fact on the Pesa area three fields of bare soil were worked with different techniques in order to achieve three types of surface roughness with a height standard deviation of about 1 cm (almost smooth), 2 cm and 3.5 cm (quite rough), respectively. These fields were used as cross reference between flights. Accurate measurements of soil moisture, surface roughness and dielectric constant have been carried out On these fields in order to test electromagnetic models for retrieving soil roughness.

2.4. Soil erosion models

Some models able to predict soil erosion have been tested on the area. However, the most suitable model allowing the use of remote sensing data resulted to be the WEPP model (Water Erosion Prediction Project) (Lane & Nearing 1989) available both in the slope profile and basin version. To verify the reliability of the data obtained with the model comparison between direct measurements of infiltration and erosion carried out by means of a rain simulator have been performed on the Pesa and Virginio areas.

3. SAR DATA

Spring and autumn are the most useful periods to study the soil erosion processes due to the presence of bare soils showing different surface roughnesses and relatively high values of soil moisture content. The supersite of Montespertoli was imaged several times at different incidence angles between 24° and 55° by the fully polarimetric L- and C-band SIR-C and the VV polarization X- band X-SAR during the two SIR-C/X-SAR missions. Seven data takes were performed in April (between the 12th and the 17th) and five in October (between the 3 rd and the 10th). Data takes at the most important incidence angles were in mode 16x (L- and C-bands fully polarimetric + X-band in VV pol.), the other in 11x (L- and C-bands in HH and HV polarization + X-band in VV pol.). Unfortunately, with respect to the planning of data takes, three of the most important data takes of October are missing (fully polarimetric at incidence angles ranging between 35 and 50 degrees) and another one scheduled as 16x has been transformed in 11x.Other SAR measurements had previously been carried out with the airborne NASA/JPL AIRSAR during the MAC-91 campaign in June and July 1991 and by means of ERS-1 and JERS-1 satellites on different dates in 1992 and 1994.

Table I: PROCESSED SAR DATA OVER MONTESPERTOLI TEST AREA

	Frequency	Polarization	Theta	Ground Resol. (m)	Dates
MAC'91	C, L, P	Quad-pol.	20°- 35° 50°	12.2 x 6.6	22-29/6/91 14/7/91
ERS-1	C	VV	23°	30 x 26.3	29/5/92 07/8/92 24/4/94
JERS-1	L	HH	35°	18.3x24.6	24/6/92 14/4/94
SIR-C/ X-SAR	C, L X	Quad-pol. VV	20°- 60°	25 x 20	April-Oct. 1994

A summary of data analyzed in this paper is reported in Table I. The main quantities taken into consideration are the linear and circular co- and cross- polar components of backscattering coefficient (s°) at L and C- band measured with SIR-C, and the copolar backscattering measured at X-band, VV pol. with X-SAR. In addition the available data set has been extended including polarimetric data collected at L- and C- band with NASA/JPL AIRSAR as well as copolar L - band (HH pol) available from NASDA JERS-1.

Backscattering coefficients from the selected areas have been extracted and average value of ° (in HH, VV and HV pol.) have been computed for each fields. Approximately 100 polygons corrispondent to selected fields have been identified. The ° values together with ground truth data concerning soil and vegetation parameters have been stored in a database.

4. SENSITIVITY TO HYDROLOGICAL PARAMETERS

The first analysis concerned the comparison of SIR-C data with those of AIRSAR (MAC'91) which is represented in figure 1. The diagram shows the average value of backscattering coefficient at L and C-band computed for the same terrain categories (forests, olivegroves, agricultural crops and bare soil). As expected there is a good agreement for forests which are the more stable targets in the area. Also bare soils show similar values, whilst the difference at C-band for agricultural crops (wheat and alfalfa) can be explained with the different phenological state of vegetation.

Previous research has shown that, at the present state of the art, there is a better chance of retrieving geophysical parameters from SAR data if a classification of the observed fields has been performed (Ferrazzoli et al. 1997). Moreover it has been pointed out that discrimination among broad terrain classes can be better accomplished with the use of low frequency data (P-band). On the other hand a discrimination between bare soil and vegetation can be achieved using C band data. Indeed, as it has already been noted (Baronti et al. 1995), s° at C-Band is sensitive to both small stems and leaves and, for example, the circular and linear cross-polarized components s°_RL and s°_HV allow a good separation of bare soil from vegetated areas. Bare soil has indeed a higher response to RL and VV (or HH) rather than to RR or HV components. As an example, figure 2, which represents s° _RR versus s° _RL, shows that the cluster of bare soils can be separated from the other vegetated fields where s° _RR » s° _RL. In addition SIR-C data have shown that three land categories can be separated using C- and L-band data at VV and HV polarization (figure 3). As a matter of fact the difference s°_HV-s°_VV has the highest value on forests and the lowest value on smooth bare soil both at L- and C-bands, whereas on crops and ploughed soils s°_HV-s°_VV is high at C-band and low at L- band. This behavior is well in agreement with the theoretical reasoning discussed in Ferrazzoli and Guerriero (1995) and Baronti et al. (1995). Indeed volume multiple scattering is generated by inclined cylinders of forest branches and crops stems and by dielectric discontinuities due to air bubbles in ploughed soils. On the contrary smooth bare soil generates prevailing surface scattering ( small s°_HV ).

4.1. Sensitivity to forest biomass

At the dates of the Shuttle mission only a small part of the agricultural area was covered by crops at an early growth stage and cannot be separated from rough bare soils. On the contrary the forests can be well identified since they present a high and stable value of backscattering coefficient. AIRSAR data had shown that L-band s°_HV is a suitable indicator of crop growth whereas forest biomass is better monitored at P-band (Baronti et al 1995, Ferrazzoli et al. 1997). However, SIR-C data have shown that at L- band too the sensitivity of backscattering to forest biomass is appreciable and that s°_HV, which is low for bare soils and is mainly associated to the presence of inclined stems and large leaves, is a better indicator of wood biomass than s°_HH, in that it changes on a wider range and with lower dispersion. An analysis of the correlation coefficients of the relation s° = A+B log (W) computed at L and C band for all polarizations and at X-band for VV pol. clearly indicate that the best sensor configuration, among those achievable from SIR-C/X-SAR, to separate dense from thin forests is L-Band HV (or RR or 45X) and q ~ 30°- 40° (Ferrazzoli et al.1997).

4.2. Sensitivity to Soil Moisture and Roughness

The sensitivity of the radar signal to soil moisture has been proven in many experiments carried out over the past years although the signal is influenced by surface roughness and vegetation cover as well. These spurious effects are minimized for observation at small incidence angle. However the quality of SAR images at incidence angle q close to nadir is affected by a relatively poor spatial resolution which makes difficult the exact identification of field borders.

Figure 4 represents the L-band, s°_HH at q= 26° collected with AIRSAR in 1991 and SIRC in April and October1994, on a certain number of bare or scarcely vegetated fields. The scattering of data appears at least partially related to surface roughness. The square correlation coefficient is r"= 0.624.

The relation between backscattering and soil parameters can be interpreted using the semi-empirical model developed by Oh et al. (1992). This model relates the parameters p = s°_HH/s°_VV and q = s°_HV/s°_VV to soil roughness, expressed by the normalized height standard deviation ks (k = 2p/l, l = electromagnetic wavelength), and to soil reflectivity G_o. These relations provide a good fit to the data when 0.1<ks<6 and 2.6<kl<19.7 (where l is the correlation length of the surface) and allow the retrieval of ks and G_o through an iterative procedure. The model seems to be able to satisfactorily simulate the backscattering from rough bare soils at least in the considered range of height standard deviation and correlation lengths. The comparison between measured and retrieved values of ks is shown in figure 5. In the diagram, only values of ks measured on bare soils have been considered (Paloscia et al. 1995). Once G_o has been retrieved the correspondent soil moisture value can be computed using the model developed by Dobson et al. (1985) which relates soil moisture to soil reflectivity, temperature and texture. In figure 6 the soil moisture measured on ground is compared with the soil moisture computed from experimental s°data through Oh and Dobson models. The results obtained with this approach appear satisfactory in spite of some overestimation of soil moisture.

5. THE EROSION MODEL

The most suitable physically-based hydrological model allowing the use of SAR data, seems to be the WEPP model (Water Erosion Prediction Project, developed by USDA (Lane & Nearing 1989)) which allows a continuous simulation both in time - in that it estimates the soil loss at time intervals - and in space - in that it provides a punctual estimate of erosion or/and deposition along the points of the profile.

The strategy for using SAR data in erosion models can follow this scheme: using both electromagnetic and statistical models, some geophysical parameters concerning soil and vegetation (such as leaf area index, soil cover, soil moisture and roughness) can be retrieved from SAR data, as has been partially shown in the previous sections. On the other hand, infomation on land use and crop classification can be obtained from SAR images as well. These outputs can be introduced into the hydrological model along with auxiliary data concerning climate, soil type, vegetation cover, agricultural practices, etc., in order to obtain the soil erosion and other parameters concerning the hydrological cycle.

In order to verify the reliability of the data obtained with the model, a comparison with direct measurements of infiltration, runoff and soil erosion carried out by means of a rain simulator have been performed on Pesa and Virginio basins (Canuti et al. 1985). From these experiments a satisfactory agreement between observed and computed soil loss has been obtained (Ballerini et al. 1993, Paloscia et al. 1995). One of the characteristics of this model, which makes it very interesting for the use of remote sensing data, is the capability of reconstructing the temporal variations of some soil and vegetation parameters starting from a known measured value and using a priori knowledge about meteorological conditions, soil properties and agricultural practices. The temporal behaviour of soil roughness of two bare fields with different surface roughnesses has been fairly well reconstructed using an initial value retrieved from SAR data at L band throughout the Oh model (Paloscia et al.1995). Table II shows the estimation of the surface roughness of three bare fields. The rather good agreement between the observed roughnesses and the values estimated with the Oh model allowed us to use these SAR estimations as input for the WEPP model.

CONCLUSIONS

From the analysis of data collected during the SIR-C/X-SAR mission over the supersite of Montespertoli the capability of multifrequency, multipolarization SAR images for discriminating different types of natural surfaces has been confirmed. It has been pointed out that discrimination between bare soil and vegetation can be achieved using circular and linear cross-polarized components of backscattering coefficient at C- band and that three land categories (forests, smooth bare soils and vegetated or ploughed soils) can be separated using C- and L-band data at VV and HV polarization. X- band data should be useful in discriminating among similar land classes with small structures mostly characterized by surface scattering.

In terms of the sensitivity of SAR data to the main parameters of soil and vegetation involved in the hydrological cycle, it has been shown that L band seems able to estimate forest biomass as well as moisture and surface roughness of bare soils. The latter parameters have been retrieved with a fairly good accuracy, from experimental data by means of an empirical model.

From the analysis of erosion model, reasonable results concerning water infiltration and overland flow and soil loss as well have been obtained. The first attempts to introduce geophysical parameters measured from SAR data in the model have been performed with satisfactory results. In particular, the surface roughness of two bare soils, retrieved by the inversion of the Oh model, has been modeled and used by the WEPP. The results obtained are very encouraging and suggest a continued analysis of SAR data in order to produce appropriate input for WEPP model.

ACKNOWLEDGMENTS

This work was supported in part by the Agenzia Spaziale Italiana

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FIGURE CAPTIONS

Figure 1 - The average value of s° at P- L- and C-band ( HH, HV and VV pol.) and X-band (VV pol.), q = 33°- 35°, computed for four homogeneous surface categories (forests:Y, olivegroves:O, cultivated fields: A, and bare soils: B). a = SIRC data (April 1994), b = AIRSAR data (June 1991)

Figure 2 - s°_RR versus s°_RL at C-band. Bare soils are separated from vegetated fields. Small captions refer to AIRSAR data and capital letters to SIR-C (labels are the same as in previous figures)

Figure 3 - s°_HV-VV (C-band) versus s°_HV-VV (L-band).Three classes can be separated: Forests (Y), bare soils (B) and agricultural fields (A=alfalfa, W=wheat, P=ploughed soils)

Figure 4 - s°_HH at L-band (q = 25°) as a function of the gravimetric soil moisture (SMCg) of different fields of bare soil. The line represents the best fit.

Figure 5 - The retrieved standard deviation of bare soils (ks), computed through the Oh model, as a function of the ground truth data

Figure 6 - The retrieved soil moisture of bare soils, computed through the Oh model, as a function of the ground truth data