Continuous monitoring of grassland AGB during the growing season through integrated remote sensing: a hybrid inversion framework ...

• Main Authors: Li, Hang, Liu, Kai, Yang, Banghui, Wang, Shudong, Meng, Yu, Wang, Dacheng, Liu, Xingtao, Li, Long, Li, Dehui, Bo, Yong, Li, Xueke
• Format: Text
• Language: unknown
• Published: Taylor & Francis 2024
• Subjects: Biotechnology; Environmental Sciences not elsewhere classified; Ecology; FOS Biological sciences; Biological Sciences not elsewhere classified; Information Systems not elsewhere classified; Plant Biology; Ewenki
• Online Access: https://dx.doi.org/10.6084/m9.figshare.25428432; 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

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 ...