Speaker
Dr
Maurizio Santoro
(GAMMA Remote Sensing)
Description
The availability of global datasets of synthetic aperture radar (SAR) data has boosted the development of algorithms in several thematic applications requiring large-scale classifications or quantitative mapping. From the Envisat mission (2002-2012), a decade of observations of the radar backscattered intensity acquired by the Advanced SAR (ASAR) sensor is available, with a spatial resolution between 30 m and 1,000 m. While the high resolution modes of ASAR were used for local studies, the coarse resolution modes were used more systematically to cover large areas. As a result, the archives of ASAR data are dense but are not spatially uniform. Availability of a large number of observations of the C-band SAR backscatter has been demonstrated to be beneficial to improve the performance of forest biomass retrieval, soil moisture estimation and wetlands detection (Pathe et al., 2009; Santoro et al., 2011; Bartsch et al., 2012). In such examples, estimates or classifications are derived from individual images and eventually combined to improve the original values. Multi-temporal metrics are another way to profit from the availability of multi-temporal observations either because the noise is reduced compared to the original data (Bartsch et al., 2008) or such metrics are better related to specific properties of a land cover class or land surface parameter (Quegan et al., 2000; Thiel et al., 2009).
With respect to the detection of open water bodies, we have reported on the reliability of the backscatter temporal variability and the minimum backscatter used as input in a rather straightforward approach based upon a number of thresholding rules (Santoro et al., 2014). When applied on a global scale to six years of observations by Envisat ASAR from the Wide Swath Mode, the thresholding rules were challenged by the heterogeneity of the Earth's landscape. The metrics were found to be similar for water bodies and specific surface types (e.g., sandy desert and snow fields) or events (snow melt, increased soil moisture) (Santoro et al., submitted). Recently, we integrated data from the Image Mode, Wide Swath and Global Monitoring archives over all land surfaces (Antarctica excluded) into a single database of SAR backscattering coefficients to allow an improved understanding of C-band time series of observations and develop further methods for improved characterization of parameters of the carbon and water cycle. With respect to the detection of water bodies, we are currently investigating the parameters of a linear model relating the SAR backscatter observations to the corresponding local incidence angle as additional parameter to discriminate water from other land surfaces.
In this presentation, we shall review observations of the SAR backscatter by Envisat ASAR and related metrics with the aim of a full characterization of water bodies with respect to other land surfaces. These investigations are possible thanks to the unique dataset of ASAR backscatter observations, which are publically available via the Grid Processing On Demand (G-POD) platform for the time period 2005-2012. In this presentation, we shall also present data products obtained by applying straightforward classification algorithms (threshold, classification tree) to the ASAR data, portraying the spatial distributions of water bodies at global scale.
References
Bartsch, A., Pathe, C., Scipal, K., Wagner, W., "Detection of permanent open water surfaces in central Siberia with ENVISAT ASAR wide swath data with special emphasis on the estimation of methane fluxes from tundra wetlands," Hydrology Research, vol. 39.2, pp. 89-100, 2008.
Bartsch, A., Trofaier, A. M., Hayman, G., Sabel, D., Schlaffer, S., Clark, D. B., Blyth, E., "Detection of open water dynamics with ENVISAT ASAR in support of land surface modelling at high latitudes," Biogeosciences, vol. 9, pp. 703-714, 2012.
Pathe, C., Wagner, W., Sabel, D., Doubkova, M., Basara, J. B., "Using ENVISAT ASAR Global Mode data for surface soil moisture retrieval over Oklahoma, USA," IEEE Transactions on Geoscience and Remote Sensing, vol. 47, 2, pp. 468-480, 2009.
Quegan, S., Le Toan, T., Yu, J. J., Ribbes, F., Floury, N., "Multitemporal ERS SAR analysis applied to forest mapping," IEEE Transactions on Geoscience and Remote Sensing, vol. 38, 2, pp. 741-753, 2000.
Santoro, M., Beer, C., Cartus, O., Schmullius, C., Shvidenko, A., McCallum, I., Wegmüller, U., Wiesmann, A., "Retrieval of growing stock volume in boreal forest using hyper-temporal series of Envisat ASAR ScanSAR backscatter measurements," Remote Sensing of Environment, vol. 115, 2, pp. 490-507, 2011.
Santoro, M., Wegmüller, U., "Multi-temporal Synthetic Aperture Radar metrics applied to map open water bodies," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, 8, pp. 3225-3238, 2014.
Santoro, M., Wegmuller, U., Lamarche, C., Bontemps, S., Defourny, P., Arino, O., "Strengths and weaknesses of multi-year ENVISAT ASAR backscatter measurements to map open water bodies globally," Remote Sensing of Environment, submitted.
Thiel, C., Cartus, O., Eckardt, R., Richter, N., Thiel, C., Schmullius, C., "Analysis of multi-temporal land observation at C-band," Proceedings of IGARSS’09, Capetown, 12-17 July, pp. III 318 - III 321, 2009.
Primary author
Dr
Maurizio Santoro
(GAMMA Remote Sensing)
Co-authors
Dr
Andreas Wiesmann
(GAMMA Remote Sensing)
Dr
Oliver Cartus
(GAMMA Remote Sensing)
Dr
Urs Wegmüller
(GAMMA Remote Sensing)
