A Bayesian methodology for soil parameters retrieval from SAR images
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 op...
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todo:paper_97814577_v_n_p1215_Barber2023-10-03T16:43:23Z A Bayesian methodology for soil parameters retrieval from SAR images Barber, M. Perna, P. Bruscantinni, C. Grings, F. Karszenbaum, H. Piscitelli, M. Jacobo-Berlles, J. 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 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. Fil:Barber, M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Perna, P. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Grings, F. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Karszenbaum, H. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Jacobo-Berlles, J. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. CONF info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_97814577_v_n_p1215_Barber |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
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R-134 |
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Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
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 |
| spellingShingle |
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 Barber, M. Perna, P. Bruscantinni, C. Grings, F. Karszenbaum, H. Piscitelli, M. Jacobo-Berlles, J. A Bayesian methodology for soil parameters retrieval from SAR images |
| topic_facet |
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 |
| description |
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. |
| format |
CONF |
| author |
Barber, M. Perna, P. Bruscantinni, C. Grings, F. Karszenbaum, H. Piscitelli, M. Jacobo-Berlles, J. |
| author_facet |
Barber, M. Perna, P. Bruscantinni, C. Grings, F. Karszenbaum, H. Piscitelli, M. Jacobo-Berlles, J. |
| author_sort |
Barber, M. |
| title |
A Bayesian methodology for soil parameters retrieval from SAR images |
| title_short |
A Bayesian methodology for soil parameters retrieval from SAR images |
| title_full |
A Bayesian methodology for soil parameters retrieval from SAR images |
| title_fullStr |
A Bayesian methodology for soil parameters retrieval from SAR images |
| title_full_unstemmed |
A Bayesian methodology for soil parameters retrieval from SAR images |
| title_sort |
bayesian methodology for soil parameters retrieval from sar images |
| url |
http://hdl.handle.net/20.500.12110/paper_97814577_v_n_p1215_Barber |
| work_keys_str_mv |
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1807321059122216960 |