Use of Sar Based Regressors for Leaf Area Index (Lai) Spatial/Temporal Filling: a Machine Learning (Ml)-Based Outlook
- Resource Type
- Conference
- Authors
- Mastro, Pietro; Boschetti, Mirco; De Peppo, Margherita; Pepe, Antonio
- Source
- IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium Geoscience and Remote Sensing Symposium, IGARSS 2023 - 2023 IEEE International. :2113-2116 Jul, 2023
- Subject
- Aerospace
Components, Circuits, Devices and Systems
Fields, Waves and Electromagnetics
Geoscience
Signal Processing and Analysis
Training
Satellite constellations
Optical interferometry
Time series analysis
Geoscience and remote sensing
Machine learning
Gaussian processes
LAI
SAR
Interferometry
Machine Learning (ML)
Gaussian Processes (GPs)
- Language
- ISSN
- 2153-7003
This study investigates the efficacy of incoherent and coherent SAR descriptors for filling spatial and temporal gaps in optical-driven Leaf Area Index (LAI) time series. Within this context, an artificial intelligence (AI) algorithm based on Multi-Output Gaussian Process (MOGP) [1], [2] demonstrated its effectiveness in handling the different information derived from SAR signatures in a unified corpus. The study utilizes sequences of Sentinel-2 imagery to derive Leaf Area Index (LAI) maps, while Sentinel-1 observations over the same area are utilized to obtain SAR backscatter coefficients and interferometric coherence data. This comprehensive dataset is then employed as input for training the MOGP model. Experimental tests demonstrate the usefulness of the MOGP model in obtaining accurate LAI time series even during very cloudy periods.