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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ftsmithonianinsp:oai:figshare.com:article/25428432 2024-04-14T08:11:16+00:00 Continuous monitoring of grassland AGB during the growing season through integrated remote sensing: a hybrid inversion framework Hang Li Kai Liu Banghui Yang Shudong Wang Yu Meng Dacheng Wang Xingtao Liu Long Li Dehui Li Yong Bo Xueke Li 2024-03-18T10:20:11Z https://doi.org/10.6084/m9.figshare.25428432.v1 unknown https://figshare.com/articles/journal_contribution/Continuous_monitoring_of_grassland_AGB_during_the_growing_season_through_integrated_remote_sensing_a_hybrid_inversion_framework/25428432 doi:10.6084/m9.figshare.25428432.v1 CC BY 4.0 Biotechnology Ecology Plant Biology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified Grassland AGB PROSAIL machine learning data assimilation Inner Mongolia hybrid inversion Text Journal contribution 2024 ftsmithonianinsp https://doi.org/10.6084/m9.figshare.25428432.v1 2024-03-18T19:33:35Z 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 RF-predicted LMA yield the lowest prediction error for grassland AGB (RMSE = 0.0033 g/cm 2 MAE = 0.0028 g/cm 2 ). This model framework addresses the challenge of limited prior knowledge in the PROSAIL-AGB prediction model, thereby enhancing the prediction accuracy while maintaining its key advantages: providing continuous observations at high spatiotemporal resolutions without relying on measured sample data. Article in Journal/Newspaper Ewenki Smithsonian Institution: Figshare |
institution |
Open Polar |
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Smithsonian Institution: Figshare |
op_collection_id |
ftsmithonianinsp |
language |
unknown |
topic |
Biotechnology Ecology Plant Biology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified Grassland AGB PROSAIL machine learning data assimilation Inner Mongolia hybrid inversion |
spellingShingle |
Biotechnology Ecology Plant Biology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified Grassland AGB PROSAIL machine learning data assimilation Inner Mongolia hybrid inversion Hang Li Kai Liu Banghui Yang Shudong Wang Yu Meng Dacheng Wang Xingtao Liu Long Li Dehui Li Yong Bo Xueke Li Continuous monitoring of grassland AGB during the growing season through integrated remote sensing: a hybrid inversion framework |
topic_facet |
Biotechnology Ecology Plant Biology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified Grassland AGB PROSAIL machine learning data assimilation Inner Mongolia hybrid inversion |
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 RF-predicted LMA yield the lowest prediction error for grassland AGB (RMSE = 0.0033 g/cm 2 MAE = 0.0028 g/cm 2 ). This model framework addresses the challenge of limited prior knowledge in the PROSAIL-AGB prediction model, thereby enhancing the prediction accuracy while maintaining its key advantages: providing continuous observations at high spatiotemporal resolutions without relying on measured sample data. |
format |
Article in Journal/Newspaper |
author |
Hang Li Kai Liu Banghui Yang Shudong Wang Yu Meng Dacheng Wang Xingtao Liu Long Li Dehui Li Yong Bo Xueke Li |
author_facet |
Hang Li Kai Liu Banghui Yang Shudong Wang Yu Meng Dacheng Wang Xingtao Liu Long Li Dehui Li Yong Bo Xueke Li |
author_sort |
Hang Li |
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 |
publishDate |
2024 |
url |
https://doi.org/10.6084/m9.figshare.25428432.v1 |
genre |
Ewenki |
genre_facet |
Ewenki |
op_relation |
https://figshare.com/articles/journal_contribution/Continuous_monitoring_of_grassland_AGB_during_the_growing_season_through_integrated_remote_sensing_a_hybrid_inversion_framework/25428432 doi:10.6084/m9.figshare.25428432.v1 |
op_rights |
CC BY 4.0 |
op_doi |
https://doi.org/10.6084/m9.figshare.25428432.v1 |
_version_ |
1796308971943886848 |