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Evaluation of optical and microwave-derived vegetation indices for monitoring aboveground biomass over China

Authors :
Zhongbing Chang
Lei Fan
Jean-Pierre Wigneron
Ying-Ping Wang
Xiaojun Li
Mengjia Wang
Xiangzhuo Liu
Huan Wang
Tianxiang Cui
Ling Yu
Jianping Wu
Xin Xiong
Shuo Zhang
Xuli Tang
Junhua Yan
Source :
Geo-spatial Information Science, Pp 1-16 (2024)
Publication Year :
2024
Publisher :
Taylor & Francis Group, 2024.

Abstract

The microwave-derived vegetation optical depth (VOD) products were used to monitor aboveground biomass (AGB) at regional to global scales, but the ability of VOD to monitor AGB in China is uncertain. This study evaluated the sensitivity of four VOD products (e.g. L-VOD, IB-VOD, LPDR-VOD, and Liu-VOD) and optical vegetation indices (VI) (e.g. NDVI, EVI, LAI, and tree cover from MODIS) to the AGB across China. Our results showed tree cover product has the highest spatial agreement with reference AGBs (indicated by the median correlation value of 0.85), followed by L-VOD (with a median correlation value of 0.80), which performs better than other VIs and VODs. Further comparisons between reference and estimated AGB computed using the fitted logistic regression showed that AGB estimations from tree cover and L-VOD outperformed the estimations from other VIs and VODs over most vegetation types (except forest), indicated by the higher median correlation value of 0.86 and 0.83 and lower RMSD of 23.9 and 27.3 Mg/ha, respectively. The good performance of tree cover could be partly due to that tree cover product is not independent from the reference AGBs. The good performance of L-VOD can be explained by its higher sensitivity to the vegetation characteristics of the entire canopy (including woody component), relative to other VODs and VIs. Among the six reference AGB products, Saatchi-WT and Saatchi-RF products were found to have the best correlations with VIs and VODs. This study demonstrates that microwave VODs, particularly L-VOD, are effective proxies for large-scale monitoring of vegetation AGB in China.

Details

Language :
English
ISSN :
10095020 and 19935153
Database :
Directory of Open Access Journals
Journal :
Geo-spatial Information Science
Publication Type :
Academic Journal
Accession number :
edsdoj.4686fcd19204f278571ec5f24edab84
Document Type :
article
Full Text :
https://doi.org/10.1080/10095020.2024.2311858