TCNIRv: Topographically corrected near-infrared reflectance of vegetation for tracking gross primary production over mountainous areas
2022
Chen, Rui | Yin, Gaofei | Zhao, Wei | Xu, Baodong | Zeng, Yelu | Liu, Guoxiang | Verger, Aleixandre
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.
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