Quantifying net primary productivity (NPP) is very important for understanding the global carbon cycle and assessing ecosystem carbon dynamics. But there are still uncertainties in the estimation of NPP. Using the winter wheat data from the experimental station in 2019, this study evaluated the ability of the near-infrared radiance of vegetation (NIRV,Rad) to estimate NPP on different time scales, and established an estimation model based on NIRV,Rad, where NIRV,Rad is defined as the product of normalized difference vegetation index (NDVI) and the near-infrared radiance. The results showed that the linear relationship between NIRV,Rad and NPP was better than the relationship of NPP with NDVI, enhanced vegetation index-2 (EVI2) and near-infrared reflectance of vegetation (NIRV,Ref) on each time scale (hourly, daily and growth period). The advantage of NIRV,Rad was more obvious on the hourly scale in particular, which showed that the R2 of NIRV,Rad and NPP reached 0.77, while the R2 of the correlation of NDVI, EVI2 and NIRV,Ref with NPP was 0.30, 0.16 and 0.14, respectively. There was a strong linear relationship between absorbed photosynthetically active radiation (APAR), net photosynthetic rate (Pn), leaf area index (LAI) and NIRV,Rad, which explained the good relationship between NIRV,Rad and NPP. Through a comparative analysis of various models, which showed that the NIRV,Rad model had the strongest ability to estimate NPP, and the R2 with the measured NPP reached 0.81. The accuracy of NIRV,Rad provide a new method for estimating NPP and provide a scientific basis for estimating NPP using high-resolution satellite remote sensing data on a regional scale
