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Proceedings Paper

A sequential Bayesian procedure for integrating heterogeneous remotely sensed data for irrigation management
Author(s): Paolo Addesso; Roberto Conte; Maurizio Longo; Rocco Restaino; Gemine Vivone
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Paper Abstract

In irrigation management the estimation of the radiometric surface temperature is of fundamental importance in evaluating the spatial distribution of land surface evapotranspiration. However, obtaining both high spatial and temporal resolutions data is impossible for any real sensor. In this paper we propose and investigate the use of sequential Bayesian techniques for integrating heterogeneous data with complementary features. A validation is performed by means of images acquired from SEVIRI and MODIS sensors in the thermal channels IR 10:8 and 31, respectively.

Paper Details

Date Published: 19 October 2012
PDF: 10 pages
Proc. SPIE 8531, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIV, 85310C (19 October 2012); doi: 10.1117/12.974659
Show Author Affiliations
Paolo Addesso, Univ. degli Studi di Salerno (Italy)
Roberto Conte, Univ. degli Studi di Salerno (Italy)
Maurizio Longo, Univ. degli Studi di Salerno (Italy)
Rocco Restaino, Univ. degli Studi di Salerno (Italy)
Gemine Vivone, Univ. degli Studi di Salerno (Italy)


Published in SPIE Proceedings Vol. 8531:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XIV
Christopher M. U. Neale; Antonino Maltese, Editor(s)

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