# Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring
## 4 Data Fusion by Platforms
### Space-Borne Platforms  
Satellites  
Currently, field-based experiments and data collection and analysis are implemented for crop monitoring, but they are laborious, time-consuming, destructive, and are not reliable in large-scale tests [48]. Conversely, satellite monitoring has been extensively applied for crop monitoring. However, limited revisiting frequency and spatial resolution [148] weaken the application of satellite monitoring at fine spatial and temporal scales. Moreover, atmospheric conditions, soil background properties [149], lack of 3D canopy [150], and asymptotic saturation data [151] further restrict its application for crop monitoring, particularly among dense and diverse vegetation crops. In order to address the saturation issue of the spectral data, the fusion of two broad-band vegetation indices (Vis) with three spectral bands, narrow spectral bands, or red-edge spectral bands could be a potential solution for its improvement [8; 152; 153], as shown in Table 2. Moreover, the fusion of the microwave satellite-based metrics with a variety of VIs of three spectral bands have also been tested in reducing the saturation effects for dense and diverse vegetation coverage [154]. The fusion of satellite data with ranging systems has been used for crop monitoring. The fusion of WorldView-2 imagery with a terrestrial ranging system and the fusion of Gaofen-1 satellite system with the airborne ranging system recorded better accuracy in analyzing crop parameters (Table 2). The airborne ranging system has not been widely tested for crop research. This may be caused by its poor capacity of point density and canopy penetration when applied to dense vegetation in addition to its high costs and related system complications in data collection and analysis [45]. Image fusion is a compelling method because it allows the combination of information from multiple satellites with different spatial, spectral, and temporal resolutions to achieve image products with improved results (Table 2) [155]. Satellite applications benefit from the use of image fusion in many ways, such as higher spatial and temporal coverage of the source target along with improved reliability and robustness. Therefore, this study provides more information on data, analysis and fusion methods to improve crop monitoring results. Image fusion provides important spectral information, which is utilized for quantitative applications such as changes in reflectance over time. However, some image fusion methods provide high-quality reflectance data. Recently, image fusion methods based on spectral unmixing have shown a greater importance for delivering spectrally consistent merged images, while reducing the problem of pixel fusion (Table 2) [156]. The spectral unmixing technique results in the decomposition of mixed pixels into a collection of pure spectra and a set of fractional abundances that show the amount of each spectrum [157]. Mixed pixels collected from medium-resolution satellites, such as MODIS (250 m to 1 km) or MERIS (250 m), are analyzed with the spatial distribution of land covers at the best scale of Landsat (30 m) (Table 2). Similarly, the data fusion between Landsat and MERIS-FR was performed by [PERSON] et al. [158], [PERSON] et al. [159] and [PERSON] et al. [160] to develop the spatial resolution of MERIS. [PERSON] et al. [19] developed these methods by analyzing the spatial data of Landsat to improve the MERIS data (Table 2). These data are acquired by classifying the Landsat images to characterize the mixed pixels. The Sentinel-2 and Sentinel-1 satellites provide moderate real-time images with spatial (10-60 m) and temporal (1-5 days) resolution. Studies showed good performance of data for crop monitoring over different cropping systems and environmental conditions [161]. Moreover, latest research reported benefits of data fusion from Sentinel-2 with Sentinel-1, and SAR and Landsat for improving model quality and performance for crop assessment.