# Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring
## 4 Data Fusion by Platforms
### Aerial Platforms  
The development of an unmanned aerial vehicle (UAV) has improved the application of crop monitoring at fine scales [2; 3]. Particularly, the fusion of lightweight UAV with the red green blue (RGB) wavelengths is flexible, simple, and cost-efficient [163]. Its fusion with the digital photogrammetric method based on the structure from motion method (SfM) provides a detailed 3D canopy, which further leverages its cost-efficiency (Table 2) [164]. Although hindered by insufficient canopy penetration [165], this method provides point clouds with high density and precision when combined with the RGB and digital photogrammetry using the fusion method [166]. The fusion of UAV data with satellite imagery has been applied for crop volume and physiological stress estimation (Table 2) [167]. Moreover, its fusion with the satellite imagery, ranging system, and RGB data significantly develops the estimation accuracy of crop properties [168], as shown in Table 2. However, the UAV-based high-resolution canopy data with the satellite-based diverse spectral data are less tested and need further investigation [167]. The fusion of UAV and satellite imagery could develop the potentials of both platforms. The methods of modified hyperspherical color, FuzeGo, Gram-Schmidt, and criteria-based fusion (0.10 m) with the WorldView-2 satellite (1.85 m) provided good results. The criteria-based fusion is considered to be highly successful in preserving the original color of the images and delivered the highest spatial resolution of all the compared methods. However, the cost of WorldView-2 data hinders their application for many users. The fusion of UAV with the Sentinel-2A provides fine crop parametric data (Table 2) [169]. Although research shows that this type of fusion improves data quality, analysis of the resolution at a fine scale remains a challenge, and further development in this direction is needed for establishing high-quality fusion methods. The spatial resolution of a UAV depends on the type of sensor and height of the flight. During the tests, fusion of the high-spatial resolution needs a longer time to generate a large amount of data, which ultimately imply the need for more data saving space, transmission time, and processing resources. Many studies demonstrated the development of UAV using the new type of image data which includes the methodology for radiometric correction of images or development of the entire processing chain by integrating the photogrammetric and quantitative approaches [170]. Generally, UAV offers cheaper applications and higher spatial resolution with high flexibility [171]. Main technology is based on the multispectral systems, which facilitate single band for estimation of broadband vegetation indices [172]. Sentinel-2A data fusion with this system provides integrated benefits of both technologies (Table 2). The fusion of ultra-high-spatial-resolution texture information with the Sentinel-2A diverse spectral imagery produces data of the highest quality. [PERSON] et al. [173] analyzed the fusion of UAV and satellite data and reported that the fusion method provides good results in comparison with the single data methods. The free access to Sentinel-2A with high spatial (10 m) and spectral resolution would certainly improve its fusion with UAV data. The fusion methods of UAV with satellite data (Table 2) with deep analysis still remain. The type of data also impacts the fusion method, which further affects the crop monitoring values (Table 2). To obtain high-resolution imagery, the fusion of the lower-spatial-resolution data with the high-spatial-resolution panchromatic imagery is recommended. While the fusion of SPOT data with L-band PALSAR and C-band RADARSAT data acquires better land cover mapping to explore the separate functions of each wavelength. [PERSON] et al. [174] reported a new method for the fusion of high-resolution with moderate resolution images using the image decomposition technique to minimize color distortion.