دورية أكاديمية
Continuous monitoring of grassland AGB during the growing season through integrated remote sensing: a hybrid inversion framework
| العنوان: | Continuous monitoring of grassland AGB during the growing season through integrated remote sensing: a hybrid inversion framework |
|---|---|
| المؤلفون: | Hang Li, Kai Liu, Banghui Yang, Shudong Wang, Yu Meng, Dacheng Wang, Xingtao Liu, Long Li, Dehui Li, Yong Bo, Xueke Li |
| المصدر: | International Journal of Digital Earth, Vol 17, Iss 1 (2024) |
| بيانات النشر: | Taylor & Francis Group, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Mathematical geography. Cartography |
| مصطلحات موضوعية: | Grassland AGB, PROSAIL, machine learning, data assimilation, Inner Mongolia, hybrid inversion, Mathematical geography. Cartography, GA1-1776 |
| الوصف: | ABSTRACTInverting 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/cm2; MAE = 0.0028 g/cm2). 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 17538947 1753-8955 1753-8947 |
| Relation: | https://doaj.org/toc/1753-8947; https://doaj.org/toc/1753-8955 |
| DOI: | 10.1080/17538947.2024.2329817 |
| URL الوصول: | https://doaj.org/article/38af3cdb3f2d40659d2d9172756a6f22 |
| رقم الأكسشن: | edsdoj.38af3cdb3f2d40659d2d9172756a6f22 |
| قاعدة البيانات: | Directory of Open Access Journals |
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