Estimation of Terrestrial Net Primary Productivity in China from Fengyun-3D Satellite Data

Currently, the satellite data used to estimate terrestrial net primary productivity (NPP) in China are predominantly from foreign satellites, and very few studies have based their estimates on data from China’s Fengyun satellites. Moreover, despite their importance, the influence of land cover types and the normalized difference vegetation index (NDVI) on NPP estimation has not been clarified. This study employs the Carnegie–Ames–Stanford approach (CASA) model to compute the fraction of absorbed photosynthetically active radiation and the maximum light use efficiency suitable for the main vegetation types in China in accordance with the finer resolution observation and monitoring-global land cover (FROM-GLC) classification product. Then, the NPP is estimated from the Fengyun-3D (FY-3D) data and compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) NPP product. The FY-3D NPP is also validated with existing research results and historical field-measured NPP data. In addition, the effects of land cover types and the NDVI on NPP estimation are analyzed. The results show that the CASA model and the FY-3D satellite data estimate an average NPP of 441.2 g C m⁻² yr⁻¹ in 2019 for China’s terrestrial vegetation, while the total NPP is 3.19 Pg C yr⁻¹. Compared with the MODIS NPP, the FY-3D NPP is overestimated in areas of low vegetation productivity and is underestimated in high-productivity areas. These discrepancies are largely due to the differences between the FY-3D NDVI and MODIS NDVI. Compared with historical field-measured data, the FY-3D NPP estimation results outperformed the MODIS NPP results, although the deviation between the FY-3D NPP estimate and the in-situ measurement was large and may exceed 20% at the pixel scale. The land cover types and the NDVI significantly affected the spatial distribution of NPP and accounted for NPP deviations of 17.0% and 18.1%, respectively. Additionally, the total deviation resulting from the two factors reached 29.5%. These results show that accurate NDVI products and land cover types are important prerequisites for NPP estimation.