USE OF SAR BASED REGRESSORS FOR LEAF AREA INDEX (LAI) SPATIAL/TEMPORAL FILLING: A MACHINE LEARNING (ML)-BASED OUTLOOK

被引：0

作者：

Mastro, Pietro ^{[1
]}

Boschetti, Mirco ^{[1
]}

De Peppo, Margherita ^{[1
]}

Pepe, Antonio ^{[1
]}

机构：

[1] Italian Natl Res Council, Inst Elect Sensing Environm IREA, 328 Diocleziano, I-80124 Naples, Italy

来源：

IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2023年

关键词：

LAI; SAR; Interferometry; Machine Learning (ML); Gaussian Processes (GPs); GREEN LAI; RADAR; RETRIEVAL; MISSIONS; SYSTEM;

D O I：

10.1109/IGARSS52108.2023.10281554

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

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.

引用

页码：2113 / 2116

页数：4

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