NIRvP As a Remote Sensing Proxy for Measuring Gross Primary Production Across Different Biomes and Climate Zones: Performance and Limitations

The product of near-infrared radiation reflected by vegetation (NIRv) and PAR (NIRvP) is a promising proxy for the remote estimation of gross primary production (GPP). However, the efficiency of NIRvP in estimating the GPP and its limitations across multiple biomes and climate zones remain unclear. In this study, we aimed to evaluate the performance and limitations of NIRvP in estimating the GPP in comparison to absorbed photosynthetically active radiation (APAR), solar-induced chlorophyll fluorescence (SIF), and the MOD17A2H GPP product. Overall, the correlation between NIRvP and eddy covariance (EC) GPP was stronger than that of APAR, SIF, and MOD17A2H GPP across most biomes with usually similar seasonal variations in radiation, air temperature (TA), and precipitation. The near-​infrared (NIR) reflectance (ρNIR) and light use efficiency (LUE) exhibited a covarying relationship under these environmental conditions, which suggested that the ρNIR contributed positively to the NIRvP-GPP relationship under such climatic conditions. However, the performance of NIRvP was poor in some biomes and climate zones, which exhibited different variations in the seasonal patterns of radiation, TA, and precipitation. The resulting inconsistencies between ρNIR and LUE implied that the ρNIR contributed negatively to the NIRvP-GPP relationship in these regions. Altogether, the findings demonstrated that the NIRvP-GPP relationship was robust but attained a moderate overall relationship across ecosystems (R2 < 0.50) in the majority of biomes and climate zones. In addition, this study also elucidated the limitations of NIRvP as a GPP proxy in certain climate zones, which was attributed to the synergistic contributions of APAR and ρNIR in the NIRvP-GPP relationship.