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Can vegetation index track the interannual variation in gross primary production of temperate deciduous forests?

Fan Liu, Chuankuan Wang, Xingchang Wang

2021Ecological Processes29 citationsDOIOpen Access PDF

Abstract

Abstract Background Vegetation indices (VIs) by remote sensing are widely used as simple proxies of the gross primary production (GPP) of vegetation, but their performances in capturing the inter-annual variation (IAV) in GPP remain uncertain. Methods We evaluated the performances of various VIs in tracking the IAV in GPP estimated by eddy covariance in a temperate deciduous forest of Northeast China. The VIs assessed included the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), and the near-infrared reflectance of vegetation (NIRv) obtained from tower-radiometers (broadband) and the Moderate Resolution Imaging Spectroradiometer (MODIS), respectively. Results We found that 25%–35% amplitude of the broadband EVI tracked the start of growing season derived by GPP ( R 2 : 0.56–0.60, bias < 4 d), while 45% (or 50%) amplitudes of broadband (or MODIS) NDVI represented the end of growing season estimated by GPP ( R 2 : 0.58–0.67, bias < 3 d). However, all the VIs failed to characterize the summer peaks of GPP. The growing-season integrals but not averaged values of the broadband NDVI, MODIS NIRv and EVI were robust surrogates of the IAV in GPP ( R 2 : 0.40–0.67). Conclusion These findings illustrate that specific VIs are effective only to capture the GPP phenology but not the GPP peak, while the integral VIs have the potential to mirror the IAV in GPP.

Topics & Concepts

Enhanced vegetation indexNormalized Difference Vegetation IndexPrimary productionEnvironmental scienceEddy covarianceModerate-resolution imaging spectroradiometerTemperate deciduous forestVegetation (pathology)Growing seasonAtmospheric sciencesDeciduousLeaf area indexRemote sensingClimatologyVegetation IndexEcosystemGeographySatelliteEcologyGeologyAerospace engineeringEngineeringMedicinePathologyBiologyRemote Sensing in AgriculturePlant Water Relations and Carbon DynamicsRemote Sensing and LiDAR Applications