Physics-Driven Probabilistic Deep Learning for the Inversion of Physical Models With Application to Phenological Parameter Retrieval From Satellite Times Series-Reference-Cited by-同舟云学术

Physics-Driven Probabilistic Deep Learning for the Inversion of Physical Models With Application to Phenological Parameter Retrieval From Satellite Times Series

Published:2023 Issue: Volume:61 Page:1-23
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Zérah Yoël¹^ORCID,Valero Silvia¹^ORCID,Inglada Jordi¹^ORCID

Affiliation:

1. Centre d’Études Spatiales de la Biosphère (CESBIO), Centre National d’Études Spatiales (CNES), Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAe), Institut de Recherche pour le Développement (IRD), Université Paul Sabatier (UPS), Université de Toulouse, Toulouse, France

Funder

Natural Intelligence Toulouse Institute (ANITI) from the Université Fédérale Toulouse Midi-Pyrenées

ANR-JCJC DeepChange Project

Centre National d’Études Spatiales

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10156821.pdf?arnumber=10156821

Reference49 articles.

1. Informed machine learning—A taxonomy and survey of integrating prior knowledge into learning systems;von rueden;IEEE Trans Knowl Data Eng,2023

2. Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and -3

3. Long-term, medium spatial resolution annual land surface phenology with a Bayesian hierarchical model

4. Combining machine learning and simulation to a hybrid modelling approach: Current and future directions;von rueden;Advances in Intelligent Data Analysis XV,2020

5. Gaussian processes retrieval of crop traits in Google Earth Engine based on Sentinel-2 top-of-atmosphere data

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Physics-constrained deep learning for biophysical parameter retrieval from Sentinel-2 images: Inversion of the PROSAIL model;Remote Sensing of Environment;2024-10

2. A Deep-Learning Approach for SAR Tomographic Imaging of Forested Areas;IEEE Geoscience and Remote Sensing Letters;2023