Accurate phenology information detection is the basis for other remote-sensing based agriculture applications. So far, there have been a lot of phenology estimation models based on remote-sensing data, but little attention was paid to microscopic mechanism of crops and the environmental factors. The main purpose of this chapter is to apply a new phenology detection model, which combined physical mechanism-based crop models with remote-sensing data to detect the critical phenological stages of corn in Northeast China (Jilin and Liaoning Provinces). Compared to the phenology observations from the agriculture meteorological stations, the corn phenology estimation accuracy in Northeast China using only MODIS data is much lower than that in the US field sites. The main reason might be the small size of single piece of cropland in northeastern China, which led to the mixed MODIS pixels. Accordingly, Landsat and MODIS data fusion methods were applied to get time-series images with Landsat-like spatial resolution and MODIS-like temporal resolution, and quantitative and qualitative validation was conducted to evaluate and verify the accuracy of the data fusion. The results show that data fusion of Landsat and MODIS improved the spatial resolution and decreased the inﬂuence of mixed pixels.
Part of the book: Recent Advances and Applications in Remote Sensing