Abstract
Abstract. The radar signal recorded by earth observation (EO) satellites is sensitive to soil moisture and surface roughness, which both influence the onset of runoff. This paper focuses on inversion of these parameters using a multi-angular approach based on RADARSAT-1 data with incidence angles of 35° and 47° (in mode S3 and S7). This inversion was performed with three backscatter models: Geometrical Optics Model (GOM), Oh Model (OM), and Modified Dubois Model (MDM), which were compared to obtain the best configuration. Mean absolute errors of 1.23, 1.12, and 2.08 cm for roughness expressed in rms height and for dielectric constant, mean absolute errors of 2.46 – equal to 3.88 (m3 m−3) in volumetric soil moisture, – 4.95 – equal to 8.72 (m3 m−3) in volumetric soil moisture – and 3.31 – equal to 6.03 (m3 m−3) in volumetric soil moisture – were obtained for the MDM, GOM, and OM simulation, respectively. These results indicate that the MDM provided the most accurate data with minimum errors. Therefore, the latter inversion algorithm was applied to images, and the final results are presented in two different maps showing pixel and homogeneous zones for surface roughness and soil moisture.
Subject
General Earth and Planetary Sciences,General Engineering,General Environmental Science
Reference49 articles.
1. Álvarez-Mozos, J., Verhoest, N. E. C., Larrañaga, A., Casalí, J., and González-Audícana, M.: Influence of surface roughness spatial variability and temporal dynamics on the retrieval of soil moisture from SAR observations, Sensors, 9, 463–489, 2009.
2. Angles, J.: Séparation de l'humidité et de la rugosité dans le signal retrodiffusé des images RSO selon une approche multi-angle, Mémoire de Maîtrise, Dép de géographie et télédétection, Université de Sherbrooke, Canada, 82~pp., 2001.
3. Arnold, J. G. Allen, P. M., and Bernhardt, G.: A comprehensive surface-groundwater flow model, J. Hydrol., 142, 47–69, 1993.
4. Baghdadi, N., Gaultier, S., and King, C.: Retrieving surface roughness and soil moisture from synthetic aperture radar~(SAR) data using neural networks, Can. J. Remote Sens., 28, 701–711, 2002.
5. Baghdadi, N., Gherboudj, I., Zribi, M., Sahebi, M., King, C., and Bonn, F.: Semi-empirical calibration of the IEM backscattering model using radar images and moisture and roughness field measurements, Int. J. Remote Sens., 25, 3593–3623, 2004.
Cited by
31 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献