TCNIRv : topographically corrected near-infrared reflectance of vegetation for tracking gross primary production over mountainous areas
Chen, Riu (Southwest Jiaotong University. Faculty of Geosciences and Environmental Engineering)
Yin, Gaofei (Southwest Jiaotong University. Faculty of Geosciences and Environmental Engineering)
Zhao, Wei (Chinese Academy of Sciences. Institute of Mountain Hazards and Environment)
Xu, Baodong (Huazhong Agricultural University. Macro Agriculture Research Institute)
Zeng, Yelu (China Agricultural University. College of Land Science and Technology)
Liu, Guoxiang (Southwest Jiaotong University. Faculty of Geosciences and Environmental Engineering)
Verger, Aleixandre (Centre de Recerca Ecològica i d'Aplicacions Forestals)

Data:	2022
Resum:	The near-infrared reflectance of vegetation (NIRv) has been increasingly used as a proxy of gross primary production (GPP) across various temporal scales, ecosystems, and climate conditions. However, topography significantly distorts NIRv and GPP estimations over mountainous areas. We evaluated the topographic effects on NIRv and applied a path length correction (PLC) for improving its performance over mountainous areas. The proposed topographically corrected NIRv (referred to TCNIRv) was evaluated by multiple Landsat-8 operational land imager (OLI) images with concurrent in situ GPP measurements over the Lägeren mountainous forest area. TCNIRv reduced topographic effects in the original NIRv and it was comparable to the normalized difference vegetation index (NDVI) and the green normalized difference vegetation index (GNDVI), which are often deemed to be independent of topographic effects. In addition, TCNIRv better agreed with GPP than the other vegetation indices (VIs): coefficient of determination R2 = 0. 90 and root mean square error RMSE = 1. 40 g⋅ Cm -2⋅d -1 for TCNIRv compared to R2 = 0. 71 and RMSE = 2. 47 g⋅ Cm -2 ⋅d -1 for NIRv. The evaluation shows that TCNIRv is a reliable proxy of GPP, and because of its simplicity and physical soundness, it will facilitate vegetation monitoring over complex topography mountainous areas.
Ajuts:	European Commission 835541
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Llengua:	Anglès
Document:	Article ; recerca ; Versió acceptada per publicar
Matèria:	Gross primary production ; Near-infrared reflectance of vegetation ; Path length correction ; Topographic effects
Publicat a:	IEEE transactions on geoscience and remote sensing, Vol. 60 (2022) , art. 4409310, ISSN 1558-0644

DOI: 10.1109/TGRS.2022.3149655

Postprint 11 p, 1.2 MB

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