The present document is delivered ad part of the deliverables of the Work Package 3 (WP3) of the DeepLIM project. The purpose of this document is to analyse the performance of the inversion models for Land Surface Temperature (LST) retrievals developed in the activity for the Use Case 1, both for the Temperature Emissivity Separation (TES) and the Generalized Split Window (GSW) models, and the Deep Learning-based Inversion Model (DLIM). In the last part, the application and results of the Shapley Value Theory are presented.
D6 - TIR Inversion Models assessment
Elisa Castelli;Enzo Papandrea;Alessio Di Roma
2021
Abstract
The present document is delivered ad part of the deliverables of the Work Package 3 (WP3) of the DeepLIM project. The purpose of this document is to analyse the performance of the inversion models for Land Surface Temperature (LST) retrievals developed in the activity for the Use Case 1, both for the Temperature Emissivity Separation (TES) and the Generalized Split Window (GSW) models, and the Deep Learning-based Inversion Model (DLIM). In the last part, the application and results of the Shapley Value Theory are presented.File in questo prodotto:
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