Hydrological analysis of high resolution multifrequent, multipolarimetric and interferometric airborne SAR data

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1 Hydrological analysis of high resolution multifrequent, multipolarimetric and interferometric airborne SAR data VOLKER HOCHSCHILD, MARTIN HEROLD Institute for Geography, Department of Geoinformatics, Hydrology and Modelling, Friedrich- Schiller-University of Jena, Löbdergraben 32, D Jena, Germany Abstract Hydrological applications, i.e. physically-based, distributed hydrological modelling, require areal input for parametrization and validation of water cycle components. Active microwave remote sensing research shows the eligibility of multifrequent, multipolarimetric and interferometric SAR data to retrieve land use, topography, soil moisture and vegetation information. In this study data of the multifrequent, multipolarimetric and interferometric airborne Experimental Synthetic Aperture Radar (E-SAR) were evaluated and analysed to derive land use information, surface soil moisture and plant water content after a standardized scheme. Topographic information was derived using X-band single pass interferometry. Multipolarimetric L-Band data show the highest information content of hydrological relevant parameters. The hydrological parameters of landuse and vegetation features (plant water content, biomass, plant height) could be derived with satisfying accuracy. Problems were encountered during the soil moisture calculation as a result of the influence of vegetation on the L-band backscatter and the large incidence angles. INTRODUCTION During the last years, remote sensing has become an important method for the areal derivation of hydrological parameters for physically based, distributed drainage basin modelling (Mauser et al., 1997; Hochschild, 1999). The advantage of remote sensing is the availability of areal data in high spatial and temporal resolution (under consideration of terrestrial field measurements), which perform an appriciable contribution to the regionalisation of hydrological relevant entities for the parameterization, calibration and validation of hydrological models. 1

2 This study concerns the interpretation and evaluation of multifrequent, multipolarimetric and interferometric data from the airborne experimental SAR-system (E-SAR) of the Deutsche Forschungsanstalt für Luft- und Raumfahrt (DLR). Previous studies to derive hydrological surface parameters focus on land use or the determination of geo- and biophysical parameters (Ulaby et al., 1996). The largest potential show multifrequent and multipolarimetric data, which will play a major role in the coming years due to the launch of spaceborne systems like ENVISAT, LightSAR or ALOS (Ulaby, 1998). Accordingly this study aims for the application and evaluation of theoretically developed approaches and to point out problems for the model parameterization in MMS/PRMS or WASMOD. The data acquisition took place on the 26 th of June 1996 in the basin of the river Bröl in the Rhenish Slate Mountains and on the 30 th of April 1999 in the Zeulenroda basin (Thuringia) in Germany. Both times strips of 3 * 10 km were acquired in different polarisations (X-HH, C-HH, L-HH, L-HV, L-VV). The spatial resolution was 2.5 m and the average incidence angle was 45. Simultaneously, reference land use mapping and field measurements, including vegetation parameters (biomass, height, plant water content) and soil moisture (tensiometer and TDR sampling, 0-15 cm) were carried out. IMAGE PROCESSING METHODS The digital image processing was carried out according to the level approach of Ulaby et al. (1996); Ulaby (1998). He describes a hierarchical classification procedure developed particularly for multipolarimetric data. In the first level, areas of clearly different backscatter characteristics like tall vegetation (forest), short vegetation or bare soil were distinguished. In level II, a qualitative distinction between structural and land use classes took place according to the level I categories. Level III serves for the derivation of quantitative biophysical parameters. Fig. 1 shows the level approach and how it was used for this study. All preprocessed channels were used for an unsupervised level I classification. A division into farm- and grassland, forest/settlement and shadow/water was possible according to the backscatter characteristics. In level II the forest and settlement areas were divided due to the spectral and 2

3 textural characteristics of the L-bands and the forest class in a following step into deciduous and coniferous forests. The quantitative derivation of parameters in level III relates to the surface soil moisture and the vegetation parameters. Therefore the Principal Components Analysis (PCA) proved to be a suitable method, as mentioned several times before (Henebry, 1997; Neusch & Sties, 1998; Klenke & Hochschild, 1999). The PCs were calculated from the multipolarimetric L-bands. From visual interpretation and correlation with the field measurements of the surface soil moisture and vegetation parameters like the plant water content, vegetation height, and drymass a quantitative parameterization was tested (Fig. 2). X- band single pass interferometry data was processed by the DLR to provide a high resolution DTM of the Zeulenroda testsite. RESULTS OF THE DERIVATION OF HYDROLOGICAL PARAMETERS The hydrological relevance of land use, soil moisture and plant water content is well known. Land use contains spatial and temporal information about vegetation types, degree of imperviousness and solute transports. The soil moisture is a crucial parameter for the energy and water balance between earth surface and the atmosphere as well as for runoff generation. The plant water content yields information about the physiological condition of the plants and system losses due to mowing or harvesting of agricultural fields. The land use pattern was derived via integration of level II classes. Seven classes could be derived with an overall accuracy of 88.3 % when compared to a land use classification of Landsat TM data. As input for hydrological modelling the 7 classes were aggregated into 4 classes. They have been used for the derivation of Hydrological Response Units (HRU), areas of homogeneous hydrological dynamics (Flügel, 1995; Flügel, 1996). The best correlation between tensiometer point measurements and the microwave data resulted from the first PC of the multipolarimetric L-bands (farm- and grassland). It describes 88 % of the variation and it mainly contains the L-VV information (correlation 0.99). This polarisation was identified visually as a potential moisture sensitive channel. Fig. 2 (top left) shows the linear connection between volumetric soil moisture and the first PC. Fig. 3 shows the 3

4 surface soil moisture at a testsite from the microwave data (a), from the Multiple Flow Topographical Index (b) and the interpolated TDR-measurements (c). The moister drainage lines are visible in the microwave data, while the slopes are dryer. The coincidence between the three methods is good for estimating the wet and dry areas. At the Kiefer testsite there is a saturated zone in the middle of the slope, which is visible in the radar backscattering (Fig. 3a) and the TDR soil moisture sampling (Fig. 3c), but not in the mf-topographical index (Fig. 3b). All in all the soil moisture distribution from the microwave data is inhomogeneous and seems not to reflect a natural pattern. The short vegetation in the study area is mainly grass- and pastural land with homogeneous vegetation structure. The dielectrical characteristics of vegetation are to a high degree dependent on the plant water content. Therefore high correlations could be found between the second PC, that describes 12 % of the total variation, and the field measurements of the plant water content (L-HV 0.83, L-HH 0.49). Furthermore the drymass and the vegetation height could also be derived. All vegetation parameters could be regionalized in the testsite with high accuracy (Fig. 2). Differences of water content between various grassland types (mowed, not mowed) could be recognized clearly and will be used for water balance modelling (i.e. system water loss due to mowing or harvesting). ACKNOWLEDGEMENTS The study was funded by the German Research Association (DFG) under the project number HO1840/3-1 (HYDROSAR). Thanks also to the DLR team Hochfrequenztechnik for the acquisition and preprocessing of the data. 4

5 REFERENCES FLÜGEL, W.A. (1995): Delineating Hydrological Response Units by Geographical Information System Analyses for Regional Hydrological Modelling Using MMS/PRMS in the Drainage Basin of the River Bröl, Germany. Hydrological Processes, 9, FLÜGEL, W.A. (1996): Hydrological Response Units (HRU s) as Modelling Entities for Hydrological River Basin Simulation and their Methodological Potential for Modelling Complex Environmental Process Systems Results from the Sieg Catchment. Die Erde, 127, 43-62, Berlin. HENEBRY, G. M. (1997): Advantages of of Prinicpal Component Analysis for Land Cover Segmentation from SAR Image Series. Proceedings of the Third ERS Symposium, Florence, HOCHSCHILD, V. (1999): Parameterization of Hydrological Models: The Contribution of Remote Sensing to Water Resources Management. Proceedings of the MODSIM 99, International Congress on Modelling and Simulation, , Hamilton, New Zealand. KLENKE, M. & HOCHSCHILD, V. (1999): Reducing the Radiometric Terrain Effect in SAR Imagery by Means of Principal Components Analysis. Proceedings of the IGARSS'99, 2, , , Hamburg, Germany. MAUSER, W., BACH, H., DEMICRAN, A. EIBL, B., RIEGLER, G. & SCHNEIDER, K. (1997): The Contribution of Microwave Data to Distributed Hydrologic Modeling. Proceedings of the Third ERS Symposium, Florence, NEUSCH, T. & STIES, M. (1998): Experiments with Multi-frequency and Multi-polarization SAR Data for Hydrological Parameter Modelling. Proceedings of the ISPRS Symposium Resource and Environmental Monitoring, , Budapest. ULABY, F.T., DUBOIS, P.C. & VAN ZYL, J. (1996): Radar Mapping of Surface Soil Moisture. Journal of Hydrology, 184, ULABY, F. (1998): SAR Biophysical Retrievals: Lessons learned and Challenges to Overcome. Proceedings of the 2. International Workshop on Retrieval of Bio- and Geo-Physical Parameters from SAR Data for Land Applications, ESTEC, Noordwijk. 5

6 FIGURES: Fig. 1 Methods, techniques and accuracy of SAR data processing according to the level approach (mp = multipolarimetric). Preprocessed Data XHH, CHH, LHH, LHV, LVV Unsupervised Classification Level I Forest/ Urban Agriculture/ Grassland Shadow/ Water Classfication spectral/ texture L(mp) Classfication spectral X,C,L(mp) Level II Urban Coniferou s Forest Deciduou s Forest Agriculture Grassland mowed Grassland nonmowed Pastureland Principal Components L(mp) Regressions Level III Landuse Plant Water Content Drymass Vegetation Height Soil Moisture Accuracy Overall Accuracy 88.1 % Correlation with field data 0.98 Correlation with field data 0.97 Correlation with field data 0.96 Correlation with field data

7 Fig. 2 Correlation between the Principal Components one and two derived from the multipolarimetric L-band backscatter intensities and in situ measurements. PC1 and soil moisture (a), PC2 and plant water content (b), PC2 and vegetation height (c), PC2 and biomass (d). Backscatter Coefficient (db) Backscatter Coefficient (db) (a) -12, , ,5 Principal Component 1; R = 0, Soil Moisture (Vol. %) (c) -5 Principal Component 2; R = 0,96-7, , , Vegetation Height (kg/m²) Backscatter Coefficient (db) Backscatter Coefficient (db -5-7, ,5-15 Principal Component 2; R = 0,98-17, ,5 1 1,5 2 2,5 3 3,5 4 (d) -5-7, , ,5-20 (b) Plant Water Content (kg/m²) Principal Component 2; R = 0, Drymass (kg/m²) 7

8 Fig. 3 Comparison of surface soil moisture distribution as derived from radar backscatter (a), result of the Multiple Flow topographical Index (b) and interpolated from Time Domain Reflectrometry measurements (c), (Note: The varying values of the soil moisture scale result from different field methods). Testsite Kiefer Testsite Simon (a) (b) (c) Land use borders 8