# Harmonizing Multi-Source Remote Sensing Images for Summer Corn Growth Monitoring
## 2 Materials and Methods
### Data Sources and Preprocessing  
GF-1 was the first satellite of China's high-resolution earth observation system and was successfully launched on 26 April 2013. The spatial resolution of GF-1 WFV images is 16 m, including four bands of blue, green, red and near-infrared, with revisiting period of four days. The GF-1 WFV images were downloaded from the China Centre for Resources Satellite Data and Application website ([[http://www.cresda.com/EN/](http://www.cresda.com/EN/)]([http://www.cresda.com/EN/](http://www.cresda.com/EN/))). The acquisition dates of studied GF-1 images were shown in Figure 2. Effected by the cloud, smog and raining, only eight GF-1 WFV images were available here in the whole corn growing period. Landsat 8 was successfully launched by NASA on 11 February 2013. This image could be downloaded freely from the USGS EarthExplorer website ([[http://earthexplorer.usgs.gov/](http://earthexplorer.usgs.gov/)]([http://earthexplorer.usgs.gov/](http://earthexplorer.usgs.gov/))). The imaging coverage size is 185 km by 185 km, with the revisiting period of 16 days. The published data indicate L1T and can be geometrically corrected for terrain form. In general, the datasets can be used directly without geometric correction. The preprocessing of GF-1 and Landsat 8 images included georeferencing, radiometric calibration, atmospheric correction, resampling and masking, which was done using the ENVI(r) v.5.2 software package (Exelis VIS, Boulder, CO, USA). First, radiometric calibration and atmospheric correction were done using FLAASH algorithm of ENVI(r) v.5.2. Then, the GF-1 images were georeferenced with Landsat 8 as the base map, with a resultant error of less than one pixel. Finally, the spatial resolution of the GF-1 images were resampled to 30 m using the nearest neighbor method and the datasets were cut down to represent only the research area. MODIS is an important optical sensor for observing global biological and physical processes. MODIS sensors were carried on the sun-synchronous polar orbit satellites Terra and Aqua, which were successfully launched in 1999 and 2002, respectively. The MOD09 GA reflectance product and MODIS Evapotranspiration Data Set (MOD16) [(49; 50)] were used in this study. For the MOD09 GA, there were seven bands ranging within blue, green, red, and near-infrared wavelengths at 459-479 nm, 545-565 nm, 620-670 nm, and 841-876 nm, similar to the band ranging of GF-1 images. There were 11 MODIS reflectance images in total in the whole corn growing season in the study area which were taken simultaneously on DOY 156, DOY 178, DOY 227, and DOY 252 with GF-1 images. For the MOD16, global evapotranspiration (ET) and latent heat flux (LE) datasets which we used were regular 1 km\({}^{2}\) land surface ET datasets for the global vegetated land areas at 8-day. Notice that, the 8-day ET data (mm/8 day) is the sum of ET during these 8-day time periods, and the 8-day LE data (10\({}^{4}\) J/m\({}^{2}\)/day) is the average daily LE over the corresponding time period [(51)]. The downloaded MODIS images are preprocessed using MRT (MODIS Reprojection Tool) tool. First, the data are organized using a  
Figure 1: The study area, and locations of the three counties in Hebei Province, including the distribution of agrometeorological stations. consistent day-numbering system. Then, the image sine projection format is converted to UTM 50N, for which the geographic coordinate system selected is WGS84. The output pixel size is 500 m by 500 m, and the \"nearest neighbor\" algorithm method was selected for downscaling to 30 m. A suitable cutting range for the study area was set, and the images were saved in GeoTIFF format. Finally, the files were saved with the above property settings, *.prm files were generated, and the command line was called to achieve data batch processing. The meteorological datasets and LAI data were collected from 20 National Agrometeorological Observation Stations in Hebei Province.