Estimating maize GPP using near-infrared radiance of vegetation
Liangyun Liu, Xinjie Liu, Jidai Chen, Shanshan Du, Yan Ma, Xiaojin Qian, Siyuan Chen, Dailiang Peng
Abstract
Gross primary production (GPP) is a fundamental variable in estimating crop yield and terrestrial-vegetation carbon cycling. This paper tested the near-infrared (NIR) radiance of vegetation (NIRVRad) for estimating GPP, where NIRVRad is defined as the product of observed NIR radiance and the normalized difference vegetation index (NDVI). Continuous observations of the canopy spectra and carbon flux of maize in 2017 and 2018 at two flux tower sites were used to evaluate the performance of NIRVRad for GPP estimation. The results show that NIRVRad well tracks the GPP at both half-hourly and daily timescales, with more accurate results than the parameterized LUE model employing the MOD17A2 GPP product, the solar-induced chlorophyll fluorescence (SIF), or the absorbed photosynthetically active radiation by vegetation (APAR). Furthermore, the results suggest that the temporal covariant relationship between NIR reflectance and LUE may be the real basis for the improved GPP estimation using NIRVRad. Therefore, NIRVRad, as a proxy for the canopy structure-radiation component of SIF, has potential to be a promising approach to estimating GPP by remote sensing.