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Remote Sens. 2019, 11(1), 56; https://doi.org/10.3390/rs11010056

Assimilating Multiresolution Leaf Area Index of Moso Bamboo Forest from MODIS Time Series Data Based on a Hierarchical Bayesian Network Algorithm

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1
State Key Laboratory of Subtropical Silviculture, Zhejiang A & F University, Hangzhou 311300, China
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Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, Zhejiang A & F University, Hangzhou 311300, China
3
School of Environmental and Resources Science, Zhejiang A & F University, Hangzhou 311300, China
These authors contributed equally to this study and shared first authorship.
*
Author to whom correspondence should be addressed.
Received: 27 November 2018 / Revised: 23 December 2018 / Accepted: 26 December 2018 / Published: 29 December 2018
(This article belongs to the Special Issue Leaf Area Index (LAI) Retrieval using Remote Sensing)
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Abstract

The highly accurate multiresolution leaf area index (LAI) is an important parameter for carbon cycle simulation for bamboo forests at different scales. However, current LAI products have discontinuous resolution with 1 km mostly, that makes it difficult to accurately quantify the spatiotemporal evolution of carbon cycle at different resolutions. Thus, this study used MODIS LAI product (MOD15A2) and MODIS reflectance data (MOD09Q1) of Moso bamboo forest (MBF) from 2015, and it adopted a hierarchical Bayesian network (HBN) algorithm coupled with a dynamic LAI model and the PROSAIL model to obtain high-precision LAI data at multiresolution (i.e., 1000, 500, and 250 m). The results showed the LAIs assimilated using the HBN at the three resolutions corresponded with the actual growth trend of the MBF and correlated significantly with the observed LAI with a determination coefficient (R2) value of >0.80. The highest-precision assimilated LAI was obtained at 1000-m resolution with R2 values of 0.91. The LAI assimilated using the HBN algorithm achieved better accuracy than the MODIS LAI with increases in the R2 value of 2.7 times and decreases in the root mean square error of 87.8%. Therefore, the HBN algorithm applied in this study can effectively obtain highly accurate multiresolution LAI time series data for bamboo forest. View Full-Text
Keywords: Moso bamboo forest; LAI; data assimilation; hierarchical Bayesian network; multiresolution Moso bamboo forest; LAI; data assimilation; hierarchical Bayesian network; multiresolution
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Xing, L.; Li, X.; Du, H.; Zhou, G.; Mao, F.; Liu, T.; Zheng, J.; Dong, L.; Zhang, M.; Han, N.; Xu, X.; Fan, W.; Zhu, D. Assimilating Multiresolution Leaf Area Index of Moso Bamboo Forest from MODIS Time Series Data Based on a Hierarchical Bayesian Network Algorithm. Remote Sens. 2019, 11, 56.

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