Continuous monitoring of grassland AGB during the growing season through integrated remote sensing: a hybrid inversion framework ...
Inverting grassland above-ground biomass (AGB) presents a significant challenge due to difficulties in characterizing leaf physiological states and obtaining accurate ground-truth data. This study introduces an innovative hybrid model for AGB inversion based on the AGB = leaf mass per area (LMA) * l...
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2024
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ftdatacite:10.6084/m9.figshare.25428432.v1 2024-04-28T08:18:12+00:00 Continuous monitoring of grassland AGB during the growing season through integrated remote sensing: a hybrid inversion framework ... Li, Hang Liu, Kai Yang, Banghui Wang, Shudong Meng, Yu Wang, Dacheng Liu, Xingtao Li, Long Li, Dehui Bo, Yong Li, Xueke 2024 https://dx.doi.org/10.6084/m9.figshare.25428432.v1 https://tandf.figshare.com/articles/journal_contribution/Continuous_monitoring_of_grassland_AGB_during_the_growing_season_through_integrated_remote_sensing_a_hybrid_inversion_framework/25428432/1 unknown Taylor & Francis https://dx.doi.org/10.6084/m9.figshare.25428432 https://dx.doi.org/10.1080/17538947.2024.2329817 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 Biotechnology Environmental Sciences not elsewhere classified Ecology FOS Biological sciences Biological Sciences not elsewhere classified Information Systems not elsewhere classified Plant Biology Journal contribution article-journal Text ScholarlyArticle 2024 ftdatacite https://doi.org/10.6084/m9.figshare.25428432.v110.6084/m9.figshare.2542843210.1080/17538947.2024.2329817 2024-04-02T11:40:25Z Inverting grassland above-ground biomass (AGB) presents a significant challenge due to difficulties in characterizing leaf physiological states and obtaining accurate ground-truth data. This study introduces an innovative hybrid model for AGB inversion based on the AGB = leaf mass per area (LMA) * leaf area index (LAI) paradigmn in the Ewenki Banner region of Inner Mongolia. The model integrates the PROSAIL radiative transfer model, machine learning regression, LEnKF data assimilation theory, multisource remote sensing, and meteorological data, following a four-step approach. Firstly, we establish LAI and LMA inversion models by combining the PROSAIL model with machine learning techniques. Secondly, data assimilation fuses the PROSAIL-derived LAI with MODIS-LAI. In the third phase, a Random Forest predictive model is developed for LMA estimation. Lastly, the accuracy of the hybrid model is assessed using empirical data. Precision evaluation with ground-truth samples demonstrates that the assimilated LAI and ... Text Ewenki DataCite Metadata Store (German National Library of Science and Technology) |
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DataCite Metadata Store (German National Library of Science and Technology) |
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Biotechnology Environmental Sciences not elsewhere classified Ecology FOS Biological sciences Biological Sciences not elsewhere classified Information Systems not elsewhere classified Plant Biology |
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Biotechnology Environmental Sciences not elsewhere classified Ecology FOS Biological sciences Biological Sciences not elsewhere classified Information Systems not elsewhere classified Plant Biology Li, Hang Liu, Kai Yang, Banghui Wang, Shudong Meng, Yu Wang, Dacheng Liu, Xingtao Li, Long Li, Dehui Bo, Yong Li, Xueke Continuous monitoring of grassland AGB during the growing season through integrated remote sensing: a hybrid inversion framework ... |
topic_facet |
Biotechnology Environmental Sciences not elsewhere classified Ecology FOS Biological sciences Biological Sciences not elsewhere classified Information Systems not elsewhere classified Plant Biology |
description |
Inverting grassland above-ground biomass (AGB) presents a significant challenge due to difficulties in characterizing leaf physiological states and obtaining accurate ground-truth data. This study introduces an innovative hybrid model for AGB inversion based on the AGB = leaf mass per area (LMA) * leaf area index (LAI) paradigmn in the Ewenki Banner region of Inner Mongolia. The model integrates the PROSAIL radiative transfer model, machine learning regression, LEnKF data assimilation theory, multisource remote sensing, and meteorological data, following a four-step approach. Firstly, we establish LAI and LMA inversion models by combining the PROSAIL model with machine learning techniques. Secondly, data assimilation fuses the PROSAIL-derived LAI with MODIS-LAI. In the third phase, a Random Forest predictive model is developed for LMA estimation. Lastly, the accuracy of the hybrid model is assessed using empirical data. Precision evaluation with ground-truth samples demonstrates that the assimilated LAI and ... |
format |
Text |
author |
Li, Hang Liu, Kai Yang, Banghui Wang, Shudong Meng, Yu Wang, Dacheng Liu, Xingtao Li, Long Li, Dehui Bo, Yong Li, Xueke |
author_facet |
Li, Hang Liu, Kai Yang, Banghui Wang, Shudong Meng, Yu Wang, Dacheng Liu, Xingtao Li, Long Li, Dehui Bo, Yong Li, Xueke |
author_sort |
Li, Hang |
title |
Continuous monitoring of grassland AGB during the growing season through integrated remote sensing: a hybrid inversion framework ... |
title_short |
Continuous monitoring of grassland AGB during the growing season through integrated remote sensing: a hybrid inversion framework ... |
title_full |
Continuous monitoring of grassland AGB during the growing season through integrated remote sensing: a hybrid inversion framework ... |
title_fullStr |
Continuous monitoring of grassland AGB during the growing season through integrated remote sensing: a hybrid inversion framework ... |
title_full_unstemmed |
Continuous monitoring of grassland AGB during the growing season through integrated remote sensing: a hybrid inversion framework ... |
title_sort |
continuous monitoring of grassland agb during the growing season through integrated remote sensing: a hybrid inversion framework ... |
publisher |
Taylor & Francis |
publishDate |
2024 |
url |
https://dx.doi.org/10.6084/m9.figshare.25428432.v1 https://tandf.figshare.com/articles/journal_contribution/Continuous_monitoring_of_grassland_AGB_during_the_growing_season_through_integrated_remote_sensing_a_hybrid_inversion_framework/25428432/1 |
genre |
Ewenki |
genre_facet |
Ewenki |
op_relation |
https://dx.doi.org/10.6084/m9.figshare.25428432 https://dx.doi.org/10.1080/17538947.2024.2329817 |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_doi |
https://doi.org/10.6084/m9.figshare.25428432.v110.6084/m9.figshare.2542843210.1080/17538947.2024.2329817 |
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1797582331943321600 |