Large Scale Forest Parameter Estimation Through a Deep Learning-Based Fusion of Sentinel-2 and Tandem-X Data-Reference-Cited by-同舟云学术

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Large Scale Forest Parameter Estimation Through a Deep Learning-Based Fusion of Sentinel-2 and Tandem-X Data

Published:2022-07-17 Issue: Volume: Page:
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Container-title:IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
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Author:

Carcereri Daniel¹,Rizzoli Paola¹,Ienco Dino²,Bueso-Bello Jose-Luis¹,Gonzalez Carolina¹,Puliti Stefano³,Brurzone Lorenzo⁴

Affiliation:

1. German Aerospace Center (DLR), Microwaves and Radar Institute

2. National Research Institute for Agriculture, Food and the Environment (INRAE),France

3. Norwegian Institute of Bioeconomy Research (NIBIO),Norway

4. Universitá degli studi di Trento (UNITN),Italy

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9883023/9883024/09884872.pdf?arnumber=9884872

Reference18 articles.

1. Vegetation characteristics and underlying topography from interferometric radar

2. Volume Decorrelation Effects in TanDEM-X Interferometric SAR Data

3. Comparison of Sentinel-2 and Landsat 8 imagery for forest variable prediction in boreal region

4. Model-Based Biomass Estimation of a Hemi-Boreal Forest from Multitemporal TanDEM-X Acquisitions

5. Country-wide high-resolution vegetation height mapping with Sentinel-2

Cited by 1 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. Above Ground Biomass Estimation By Multi-Source Data Based On Interpretable DNN Model;IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium;2023-07-16

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