# Harmonizing Multi-Source Remote Sensing Images for Summer Corn Growth Monitoring
## 5 Conclusions  
This paper presented a method for monitoring summer corn crop growth by harmonizing multi-source remote sensing images. We harmonized the high spatial and temporal resolution images using improved Kalman filter based on daily MOD09 GA reflectance images with the 250 m spatial resolution and GF-1 WFV images with 30-m-resolution. The growth status of summer corn in a designated research area was analyzed during part of 2014 in conjunction with MOD16 ET and LE product and meteorological data.  
We were able to draw a number of conclusions from our research. First, the high temporal and spatial resolution images reconstructed using the improved Kalman filter had higher precision than ESTARFM model only; it not only inherited the phenological trends in the MODIS data, but also preserved the spatial detail levels of the GF-1 datasets; moreover, the prediction accuracy of satellite images after corn silking is higher than that of early stage. Second, the vegetation indexes calculated from the multi-source image datasets, including NDVI, can be used to monitor the growth of crops at all stages when the analysis is combined with MOD16 ET and LE product and meteorological data. This includes slope and differences calculated from NDVI values. Third, before the milking stage, NDVI values for summer corn increased with the increase of temperature and precipitation. After the milking stage, NDVI first stabilized for a period of time and then decreased with the decrease of temperature and precipitation. The ET and LE data had a close relationship with the temperature and precipitation data.  
In conclusion, the harmonized multi-source remote sensing datasets can effectively reflect crop growth. The improved Kalman filter model can help resolve issues arising from incomplete satellite imaging datasets, particularly in areas with adverse climatic conditions. This monitoring method should be applied to other crops and to a broader range of source data, including that associated with diseases, insect pests, and final crop yield totals, in the future.  
Conceptualization, M.Z., W.S. and J.H.; methodology, W.S. and M.Z.; software, M.Z. and D.Z.; writing--original draft preparation, M.Z. and W.S.; writing--review and editing, X.Z. and Z.L.  
This study was funded by the National Natural Science Foundation of China under the project \"Growth process monitoring of corn by combining time series spectral remote sensing images and terrestrial laser scanning data\" (No. 41671433) and \"Estimating the leaf area index of corn in whole growth period using terrestrial LiDAR data\" (No. 41371327), Chinese Universities Scientific Fund \"Monitoring the quantity and quality using remote sensing and intelligence analysis of total factor for cropland\" (No. 2019 TC117) and \"Retrieval of biomass for summer corn after canopy is closed using remote sensing image\" (No. 2019 TC138) and Science and Technology Facilities Council of UK- Newton Agritech Programme (Project No. ST/N006798/1).  
We thank the journal's editors and anonymous reviewers for their kind comments and valuable suggestions to improve the quality of this paper.  
The authors declare no conflict of interest.