Abstract:
Soil moisture retrieval from SAR data presents two main sources of uncertainty: terrain heterogeneity and speckle noise. In this paper, these issues will be addressed by using a Bayesian approach. Such a Bayesian approach (1) needs only a forward model (no retrieval model required), (2) gives the optimal unbiased estimator for the soil moisture and its error and (3) can include as many error sources as required. Through numerical simulations, a standard Oh retrieval procedure and the Bayesian approach were tested for different number of looks (n = 3 and n = 64). The results indicate that for a large number of looks the region of validity of both approaches are similar. Furthermore, contrary to the Oh model retrieval procedure which is only valid in a bounded region of the (hh, vv, hv)-space, the Bayesian approach gives an estimation of soil moisture and its error for any combination of hh, vv and hv, so enlarging the region where the retrieval is possible. © 2011 IEEE.
Registro:
| Documento: |
Conferencia
|
| Título: | A Bayesian methodology for soil parameters retrieval from SAR images |
| Autor: | Barber, M.; Perna, P.; Bruscantinni, C.; Grings, F.; Karszenbaum, H.; Piscitelli, M.; Jacobo-Berlles, J. |
| Ciudad: | Vancouver, BC |
| Filiación: | Grupo de Teledetección Cuantitativa, Instituto de Astronomía Y Física del Espacio (IAFE), Argentina
Fac. de Agronomía, Univ. Nac. del Centro de la Pcia. de Buenos Aires (UNICEN), Argentina
Depto. de Computación, Fac. de Cs. Exactas Y Naturales (FCEN), Univ. de Buenos Aires (UBA), Argentina
|
| Palabras clave: | Bayesian retrieval approaches; radar remote sensing; Soil moisture; Bayesian approaches; Bayesian methodology; Bayesian retrieval; Error sources; Forward models; Radar remote sensing; Retrieval models; Retrieval procedures; SAR data; SAR Images; Soil moisture retrievals; Soil parameters; Sources of uncertainty; Speckle noise; Unbiased estimator; Bayesian networks; Moisture determination; Remote sensing; Soil moisture; Space optics; Synthetic aperture radar; Geologic models |
| Año: | 2011
|
| Página de inicio: | 1215
|
| Página de fin: | 1218
|
| DOI: |
http://dx.doi.org/10.1109/IGARSS.2011.6049417 |
| Título revista: | 2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011
|
| Título revista abreviado: | Dig Int Geosci Remote Sens Symp (IGARSS)
|
| CODEN: | IGRSE
|
| Registro: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_97814577_v_n_p1215_Barber |
Referencias:
- Verhoest, N.E.C., Lievens, H., Wagner, W., Alvarez-Mozos, J., Moran, M.S., Mattia, F., On the soil roughness parameterization problem in soil moisture retrieval of bare surfaces from synthetic aperture radar (2008) Sensors, 8, pp. 4213-4248. , Jul
- Callens, M., Verhoest, N.E.C., Davidson, M.W.J., Parameterization of tillage-induced single-scale soil roughness from 4-m profiles (2006) IEEE Transactions on Geoscience and Remote Sensing, 44 (4), pp. 878-888
- Haddad, Z.S., Dubois, P.D., Van Zyl, J.J., Bayesian estimation of soil parameters from radar backscatter data (1996) IEEE Transactions on Geoscience and Remote Sensing, 34 (1), pp. 76-82. , Jan
- Lee, J.S., Hoppel, K.W., Mango, S.A., Miller, A.R., Intensity and phase statistics of multilook polarimetric and interferometric sar imagery (1994) IEEE Transactions on Geoscience and Remote Sensing, 32 (5), pp. 1017-1028. , Sep
- Oh, Y., Quantitative retrieval of soil moisture content and surface roughness from multipolarized radar observations of bare soil surfaces (2004) IEEE Transactions on Geoscience and Remote Sensing, 42 (3), pp. 596-601. , Mar
- MacKay, D.J.C., (2003) Information Theory, Inference, and Learning Algorithms, , Cambridge University Press
- Wasserman, L., (2004) All of Statistics: A Concise Course in Statistical Inference, , Springer Texts in Statistics. SpringerA4 - Inst. Electr. Electron. Eng. Geosci.; Remote Sens. Soc. (IEEE GRSS)
Citas:
---------- APA ----------
Barber, M., Perna, P., Bruscantinni, C., Grings, F., Karszenbaum, H., Piscitelli, M. & Jacobo-Berlles, J.
(2011)
. A Bayesian methodology for soil parameters retrieval from SAR images. 2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011, 1215-1218.
http://dx.doi.org/10.1109/IGARSS.2011.6049417---------- CHICAGO ----------
Barber, M., Perna, P., Bruscantinni, C., Grings, F., Karszenbaum, H., Piscitelli, M., et al.
"A Bayesian methodology for soil parameters retrieval from SAR images"
. 2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011
(2011) : 1215-1218.
http://dx.doi.org/10.1109/IGARSS.2011.6049417---------- MLA ----------
Barber, M., Perna, P., Bruscantinni, C., Grings, F., Karszenbaum, H., Piscitelli, M., et al.
"A Bayesian methodology for soil parameters retrieval from SAR images"
. 2011 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011, 2011, pp. 1215-1218.
http://dx.doi.org/10.1109/IGARSS.2011.6049417---------- VANCOUVER ----------
Barber, M., Perna, P., Bruscantinni, C., Grings, F., Karszenbaum, H., Piscitelli, M., et al. A Bayesian methodology for soil parameters retrieval from SAR images. Dig Int Geosci Remote Sens Symp (IGARSS). 2011:1215-1218.
http://dx.doi.org/10.1109/IGARSS.2011.6049417