Mapping mangrove functional traits from Sentinel-2 imagery based on hybrid models coupled with active learning strategies-Reference-Cited by-同舟云学术

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Mapping mangrove functional traits from Sentinel-2 imagery based on hybrid models coupled with active learning strategies

Published:2024-06 Issue: Volume:130 Page:103905
ISSN:1569-8432
Container-title:International Journal of Applied Earth Observation and Geoinformation
language:en
Short-container-title:International Journal of Applied Earth Observation and Geoinformation

Author:

Jia Mingming^ORCID,Guo Xianxian,Zhang Lin,Wang Mao,Wang Wenqing,Lu Chunyan,Zhao Chuanpeng,Zhang Rong^ORCID,Wang Ming,Yan Hengqi,Wang Zongming^ORCID,Verrelst Jochem^ORCID

Publisher

Elsevier BV

Reference52 articles.

1. Taxonomy and remote sensing of leaf mass per area (LMA) in humid tropical forests;Asner;Ecol. Appl.,2011

2. Evaluation of the PROSAIL model capabilities for future hyperspectral model environments: A review study;Berger;Remote Sens.,2018

3. A survey of active learning for quantifying vegetation traits from terrestrial earth observation data;Berger;Remote Sens.,2021

4. Quantifying mangrove leaf area index from Sentinel-2 imagery using hybrid models and active learning;Binh;Int. J. Remote Sens.,2022

5. Toward a semiautomatic machine learning retrieval of biophysical parameters. IEEE;Caicedo;J. Sel. Top. Appl. Earth Observ.,2014

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1. Local Peak Savitzky–Golay for Spatio-Temporal Reconstruction of Landsat NDVI Time Series: A Case Study Over the Qinghai–Tibet Plateau;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024

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