A sequential Bayesian procedure for integrating heterogeneous remotely sensed data for irrigation management

Paolo Addesso; Roberto Conte; Maurizio Longo; Rocco Restaino; Gemine Vivone

doi:10.1117/12.974659

19 October 2012 A sequential Bayesian procedure for integrating heterogeneous remotely sensed data for irrigation management

Paolo Addesso, Roberto Conte, Maurizio Longo, Rocco Restaino, Gemine Vivone

Author Affiliations +

Proceedings Volume 8531, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIV; 85310C (2012) https://doi.org/10.1117/12.974659
Event: SPIE Remote Sensing, 2012, Edinburgh, United Kingdom

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.

Citation Download Citation

Paolo Addesso, Roberto Conte, Maurizio Longo, Rocco Restaino, and Gemine Vivone "A sequential Bayesian procedure for integrating heterogeneous remotely sensed data for irrigation management", Proc. SPIE 8531, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIV, 85310C (19 October 2012); https://doi.org/10.1117/12.974659

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