Fine-Scale Inversion of Leaf Area Index and Chlorophyll Content Using Coupled 3D Radiative Transfer Model and Deep Learning-Reference-Cited by-同舟云学术

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Fine-Scale Inversion of Leaf Area Index and Chlorophyll Content Using Coupled 3D Radiative Transfer Model and Deep Learning

Published:2024-07-07 Issue: Volume: Page:5296-5299
ISSN:
Container-title:IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
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Author:

Zhao Xun¹,Zhang Limei²,Liu Shangbo¹,Qi Jianbo³,Huang Huaguo¹

Affiliation:

1. State Key Laboratory of Efficient Production of Forest Resources,Beijing,China

2. Yunnan Institute of Surveying and Mapping of Geology and Mineral Resources Co., Ltd,Kunming,China

3. Beijing Normal University,Faculty of Geographical Science,Beijing,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10640349/10640352/10642281.pdf?arnumber=10642281

Reference14 articles.

1. Utility of an image-based canopy reflectance modeling tool for remote estimation of LAI and leaf chlorophyll content at the field scale

2. Leaf chlorophyll content as a proxy for leaf photosynthetic capacity

3. Simple and robust methods for remote sensing of canopy chlorophyll content: a comparative analysis of hyperspectral data for different types of vegetation

4. Winter wheat chlorophyll content retrieval based on machine learning using in situ hyperspectral data

5. Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties – A review

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