Enhancing Plant Area Index Retrieval Using Gaussian Process Regression from Dual-Polarimetric SAR Data.

Ghosh, Swarnendu Sekhar; Bhogapurapu, Narayanarao; Bhattacharya, Avik et Homayouni, Saeid ORCID: https://orcid.org/0000-0002-0214-5356 (2023). Enhancing Plant Area Index Retrieval Using Gaussian Process Regression from Dual-Polarimetric SAR Data. In: International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS), 27-29 janvier 2023, Hyderabad, India.

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Résumé

In this paper, a Gaussian Process Regression (GPR) model is implemented to retrieve the Plant Area Index (PAI) of wheat and canola. Backscatter information from Sentinel-l dualpol GRD SAR data and in-situ measurements collected during the Soil Moisture Active Passive Validation Experiment 2016 (SMAPVEX16-MB) Manitoba campaign were used to calibrate and validate the proposed GPR model. A recently proposed pseudo scattering entropy, H c derived from dual-pol GRD SAR data has been used along with backscatter information to investigate the improvement in retrieval accuracy. Including the pseudo entropy parameter in the feature, space showed an improvement of 4.28% and 3.66% in the correlation coefficient (ρ) for wheat and canola respectively. Similarly, a decrease in nRMSE by 4% for wheat and 4.76% for canola was observed during PAI retrieval.

Type de document:	Document issu d'une conférence ou d'un atelier
Mots-clés libres:	Gaussian Process Regression (GPR); Plant Area Index (PAI); Sentinel-l; Pseudo Entropy Parameter (Hc); SMAPVEX16-MB
Centre:	Centre Eau Terre Environnement
Date de dépôt:	06 févr. 2024 21:23
Dernière modification:	06 févr. 2024 21:23
URI:	https://espace.inrs.ca/id/eprint/14196

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