Translator Disclaimer
19 October 2012 A sequential Bayesian procedure for integrating heterogeneous remotely sensed data for irrigation management
Author Affiliations +
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.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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
PROCEEDINGS
10 PAGES


SHARE
Advertisement
Advertisement
Back to Top