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Scatterometry for Land Hydrology Science and its Applications


Affiliations
1 Earth, Ocean, Atmosphere, Planetary Sciences and Applications Area (EPSA), Space Applications Centre, ISRO, Ahmedabad 380 015, India
 

This study reports the potential of SCATSAT-1 scatterometer data for catchment-scale hydrological applications related with river water level estimation and flood detection. New approaches have been developed for estimation of river water levels and detection of surface flooding using Oceansat-II scatterometer (OSCAT) and SCATSAT-1 scatterometer-based highresolution backscatter and brightness temperature (BT) datasets respectively. Ku-band sigma-0 and BT data, Shuttle Radar Topography Mission Digital Elevation Model and observed hydrometric data have been used in this study. Catchments of gauging sites and their influencing areas were delineated using the topography, wetness conditions and land-cover variations. OSCAT time series of scatterometer image reconstruction data were used to develop model function between basin water index and ground-observed river-stage datasets. Subsequently, inverting these functions on SCATSAT-1 observations, river water levels for 2017 were estimated at different gauging sites. A study on the magnitude of each flooding event in terms of intensity, duration and extent of area affected was also carried out using the scatterometerbased BT data analysis. The study demonstrated that high temporal resolution scatterometer data has the potential to fill the gap of coarser temporal resolution altimeters (10–35 days) for river heights and Synthetic Aperture Radar Data (7–25 days) for surface flooding with the advantage of capturing extreme events.

Keywords

Backscattering Coefficient, Brightness Temperature, River Water Level, Scatterometers, Soil Wetness.
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  • Kumar, R. et al., Evaluation of Oceansat-2-derived Ocean surface winds using observations from global buoys and other scatterometers. IEEE Trans. Geosci. Remote Sensing, 2013, 51(5), 2571– 2576.

  • Naeimi, V., Leinenkugel, P., Sabel, D., Wagner, W., Apel, H. and Kuenzer, C., Evaluation of soil moisture retrieval from the ERS and MetOp scatterometers in the Lower Mekong basin. Remote Sensing, 2013, 5, 1603–1623.

  • Cui, Y. et al., Estimating snow water equivalent with backscattering at X and Ku band based on absorption loss. Remote Sensing, 2016, 8, doi:10.3390/rs8060505.

  • Turk, F. J., Sikhakolli, R., Kirstetter, P. and Durden, S. L., Exploiting over-land Oceansat-II scatterometer observations to capture short-period time-integrated precipitation. J. Hydrometeorol., 2015, 16, 2519–2535.

  • Remund, Q. P. and Long, D. G., Sea ice extent mapping using Ku-band scatterometer data. J. Geophys. Res.: Oceans, 1999, 104(C5), 11515–11527.

  • Ulaby, F. T., Moore, R. K. and Fung, A. K., In Microwave Remote Sensing, Fundamanetals and Radiometery, Vol. 1, Deedham MA, Artech House, 1981.

  • Ulaby, F. T., Moore, R. K. and Fung, A. K., In Microwave Remote Sensing, from Theory to Applications, Vol. 3, Deedham MA, Artech House, 1986.

  • Fung, A. K., In Microwave Scattering and Emission Models and their Applications, Norward MA, Artec House, 1994.

  • Misra, T. et al., Oceansat-II scatterometer: sensor performance evaluation, σ 0 analyses and estimation of biases. IEEE Trans. Geosci. Remote Sensing, 2014, 52(6), 3310–3315.

  • Ulaby F.T., Batlivala, P. P. and Dobson, M. C., Microwave backscatter dependence on surface roughness, soil moisture and soil texture, Part-I: bare soil. IEEE Trans. Geosci. Electron., 1978, GE-16, 286–295.

  • Birkett, C. M., Mertes, L. A. K., Dunne, T., Costa, M. H. and Jasinski, M. J., Surface water dynamics in the Amazon Basin: application of satellite radar altimetry. J. Geophys. Res., 2002, 107, LBA 26.

  • Brakenridge, G. R., Tracy, B. T. and Knox, J. C., Orbital remote sensing of a river flood wave. Int. J. Remote Sensing, 1998, 19, 1439–1445.

  • Townsend, P. A. and Foster, J. R., Assessing flooding and vegetation structure in forested wetlands using Radarsat SAR imagery. In IEEE International Geoscience Remote Sensing Symposium, IGARSS, Toronto, Canada, 2002, vol. 2, pp. 1171–1173.

  • Scipal, K., Scheffler, C. and Wagner, W., Soil moisture–runoff relation at the catchment scale as observed with coarse resolution microwave remote sensing. Hydrol. Earth Syst. Sci., 2005, 9, 173–183.

  • De Jeu, R., Wagner, W., Holmes, T. R. H., Dolman, A. J., Van De Giesen, N. C. and Friesen, J., Global soil moisture patterns observed by space borne microwave radiometers and scatterometers. Surv. Geophys., 2008, 29(4–5), 399–420.

  • Hirpa, F. A., Gebremichael, M. and Over, T. M., River flow fluctuation analysis: effect of watershed area. Water Resour. Res., 2010, 46, W12529.

  • Brakenridge, G. R., Knox, J. C., Magilligan, F. J. and Paylor, E., Radar remote sensing aids study of the Great flood of 1993. EOS, Trans. Am. Geophys. Union, 1994, 75(45), 521–528.

  • Gstaiger, V., Huth, J., Gebhardt, S., Wehrmann, T. and Kuenzer, C., Multi-sensoral and automated derivation of inundated areas using TerraSAR-X and ENVISAT-ASAR data. Int. J Remote Sensing, 2012, 33(22), 7291–7304.

  • Brakenridge, G. R., Anderson, E., Nghiem, S. V., Caquard, S. and Shabaneh, T. B., Flood warnings, flood disaster assessments and flood hazard reduction: the roles of orbital remote sensing. In Proceedings of 30th International Symposium on Remote Sensing of the Environment, Honolulu, Hawaii, 2002, p. 4.

  • Wagner, W., Noll, J., Borgeaud, M. and Rott, H., Monitoring soil moisture over the Canadian prairies with the ERS scatterometer. IEEE Trans. Geosci. Remote Sensing, 1999, 37, 206–216.

  • Moran, M. S., Hymer, D. C., Qi, J. and Sano, E. E., Soil moisture evaluation using multi-temporal synthetic aperture radar (SAR) in Semiarid Rangeland. Agric. For. Meteorol., 2000, 105, 69–80.

  • Paloscia, S. and Pampaloni, P., Experiment relationships between microwave emission and vegetation features. Int. J. Remote Sensing, 1985, 6, 315–323.

  • Singh, R. P. and Dadhwal, V. K., Comparison of space-based microwave polarization difference index and normalized difference vegetation index for crop growth monitoring. Indian J. Radio Space Phys., 2003, 32, 193–197.

  • Owe, M., de Jen, R. and Walker, J., Vegetation optical depth retrieval using the MPDI. IEEE Trans. Geosci. Remote Sensing, 2001, 39(8), 1643–1654.

  • Li, Y., Zhao, K., Zheng, X. and Ren, J., Analysis of microwave polarization difference index characteristics about different vegetation types in northeast of China. In International Conference on Remote Sensing, Environment and Transportation Engineering, Nanjing, China, 2013; doi:10.2991/rsete.2013.9.


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  • Scatterometry for Land Hydrology Science and its Applications

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Authors

P. K. Gupta
Earth, Ocean, Atmosphere, Planetary Sciences and Applications Area (EPSA), Space Applications Centre, ISRO, Ahmedabad 380 015, India
R. Pradhan
Earth, Ocean, Atmosphere, Planetary Sciences and Applications Area (EPSA), Space Applications Centre, ISRO, Ahmedabad 380 015, India
R. P. Singh
Earth, Ocean, Atmosphere, Planetary Sciences and Applications Area (EPSA), Space Applications Centre, ISRO, Ahmedabad 380 015, India
A. Misra
Earth, Ocean, Atmosphere, Planetary Sciences and Applications Area (EPSA), Space Applications Centre, ISRO, Ahmedabad 380 015, India

Abstract


This study reports the potential of SCATSAT-1 scatterometer data for catchment-scale hydrological applications related with river water level estimation and flood detection. New approaches have been developed for estimation of river water levels and detection of surface flooding using Oceansat-II scatterometer (OSCAT) and SCATSAT-1 scatterometer-based highresolution backscatter and brightness temperature (BT) datasets respectively. Ku-band sigma-0 and BT data, Shuttle Radar Topography Mission Digital Elevation Model and observed hydrometric data have been used in this study. Catchments of gauging sites and their influencing areas were delineated using the topography, wetness conditions and land-cover variations. OSCAT time series of scatterometer image reconstruction data were used to develop model function between basin water index and ground-observed river-stage datasets. Subsequently, inverting these functions on SCATSAT-1 observations, river water levels for 2017 were estimated at different gauging sites. A study on the magnitude of each flooding event in terms of intensity, duration and extent of area affected was also carried out using the scatterometerbased BT data analysis. The study demonstrated that high temporal resolution scatterometer data has the potential to fill the gap of coarser temporal resolution altimeters (10–35 days) for river heights and Synthetic Aperture Radar Data (7–25 days) for surface flooding with the advantage of capturing extreme events.

Keywords


Backscattering Coefficient, Brightness Temperature, River Water Level, Scatterometers, Soil Wetness.

References





DOI: https://doi.org/10.18520/cs%2Fv117%2Fi6%2F1014-1021