Accurate estimation of physical quantities depends on the availability of High Resolution (HR) observations of the Earth surface. However, due to the unavoidable tradeoff between spatial and time resolution, the acquisition instants of HR data hardly coincides with those required by the estimation algorithms. A possible solution consists in constructing a synthetic HR observation at a given time k by exploiting Low Resolution (LR) and HR data acquired at different instants. In this work we recast this issue as a smoothing problem, thus focusing on cases in which observations acquired both before and after time k are available. The proposed approach is validated on a region of interest for the IRRISAT irrigation management project in which the surface thermal inertia estimation, requiring multiple HR images at specific instants, constitute a key step.
Enhancing TIR Image Resolution via Bayesian Smoothing for IRRISAT Irrigation Management Project
ADDESSO, PAOLO;LONGO, Maurizio;MONTONE, RITA;RESTAINO, Rocco;VIVONE, GEMINE
2013-01-01
Abstract
Accurate estimation of physical quantities depends on the availability of High Resolution (HR) observations of the Earth surface. However, due to the unavoidable tradeoff between spatial and time resolution, the acquisition instants of HR data hardly coincides with those required by the estimation algorithms. A possible solution consists in constructing a synthetic HR observation at a given time k by exploiting Low Resolution (LR) and HR data acquired at different instants. In this work we recast this issue as a smoothing problem, thus focusing on cases in which observations acquired both before and after time k are available. The proposed approach is validated on a region of interest for the IRRISAT irrigation management project in which the surface thermal inertia estimation, requiring multiple HR images at specific instants, constitute a key step.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
