High spatiotemporal resolution satellite data are necessary for the retrieval of vegetation indexes, such as Normalized Difference Vegetation Index (NDVI), to be assimilated into the Carnegie-Ames-Stanford Approach (CASA) model for net primary productivity (NPP) estimation, especially in the growing season. However, current remotely sensed data cannot accurately monitor vegetation changes at high spatiotemporal resolution. To consider both temporal and spatial information, spatiotemporal fusion models have been developed to obtain the temporal information from high temporal resolution data (e.g., MODIS) together with the spatial information from high spatial resolution data (e.g., Landsat). In this paper, synthetic NDVI images with the spatial resolution of Landsat data and the temporal resolution of MODIS data were first produced using spatiotemporal fusion models. Next, phenological features were extracted from synthetic NDVI time series data to improve land cover classification accuracy. Finally, we evaluated the approach of assimilating the synthetic NDVI and land cover classification map into the CASA model for synthetic NPP estimation. The results revealed that the accuracy of the synthetic NPP was better than NPP estimation from non-fusion NDVI data, and improving the land cover classification accuracy could improve the accuracy of the synthetic NPP estimation. Furthermore, the monthly synthetic NPP showed a significant exponential relationship with the temperature, rainfall, and solar radiation of the current and previous month.

高时空分辨率的卫星数据对于检索植被指数（例如归一化植被指数（NDVI））是必要的，这些数据将被同化为卡内基-艾姆斯-斯坦福方法（CASA）模型以进行净初级生产力（NPP）估算，尤其是在生长季节。但是，当前的遥感数据无法以高时空分辨率准确地监测植被变化。为了考虑时间和空间信息，已经开发了时空融合模型以从高时间分辨率数据（例如，MODIS）获得时间信息以及从高空间分辨率数据（例如，Landsat）获得空间信息。在本文中，首先使用时空融合模型生成具有Landsat数据的空间分辨率和MODIS数据的时间分辨率的合成NDVI图像。接下来，从合成的NDVI时间序列数据中提取物候特征，以提高土地覆被分类的准确性。最后，我们评估了将合成NDVI和土地覆盖分类图吸收到CASA模型中以进行合成NPP估算的方法。结果表明，从非融合NDVI数据看，合成NPP的精度要好于NPP估计，提高土地覆盖分类精度可以提高合成NPP的估计精度。此外，月度合成NPP与当月和上月的温度，降雨量和太阳辐射呈显着的指数关系。