# Data Fusion in Agriculture: Resolving Ambiguities and Closing Data Gaps
## 3 Discussion
### Differences between Fusion Techniques  
Directly comparing the results yielded by different data fusion techniques is difficult for several reasons: the datasets used in the studies are different in terms of data distribution and characteristics of the experimental areas, assessment criteria are diverse, and constraints on the experimental setup usually make the results not generalizable. In addition, results reported in the literature are usually quite similar, but such similitude is often related to limitations in the representativeness of the datasets, and not to the methods themselves. Thus, instead of comparing the results yielded by different approaches, a qualitative comparison is carried out, with the classes of techniques and applications shown in Table 2 as basis. Almost all regression methods used in the context of data fusion are of the linear type. They attempt to fit a line that fits the data observed, thus providing a means for predicting values that are not present in the training datasets. In general, this type of technique can be used only when the different sources of data are of the same type or can yield features that are highly compatible. This kind of approach is still commonly used to derive soil properties, as the sensors used in this kind of application generate strings of numbers that can be easily made compatible, but it is seldom adopted in other applications. STARFM is a data fusion method created specifically to explore the temporal and spatial characteristics of satellite images in order to generate synthetic images that combine the high temporal and spatial resolutions provided by different sensors [130]. This technique and its derivatives [122; 131; 132] have been successfully applied for more than 15 years, being particular prevalent for fusing Landsat and MODIS images [20; 42; 53; 57; 58; 107; 108; 110; 116; 122; 126]. Despite the growth of machine learning and deep learning techniques, the fusion of satellite imagery is still dominated by this kind of approach. There are two main reasons for this. First, these techniques were specifically designed for this type of application and have been continuously perfected, achieving high levels of accuracy and robustness. Second, although machine learning and deep learning techniques can deliver good results, they are susceptible to regularization and overfitting issues that cannot be easily avoided with satellite images [133]. Geostatistical tools, such as kriging, cokriging, and Gaussian anamorphosis, are frequently used for the delineation of homogeneous zones in agricultural areas. This is accomplished by combining different types of variables into a map according to predefined criteria. This type of approach has some competition from machine learning-based models (e.g., K-means clustering), but the latter is usually applied only when digital images are employed in combination with soil measurements [28]. PCA aims at removing redundancies that are often found in data, retaining only the components that provide new information. Although PCA has been used in a wide variety of situations and types of data, in the case of data fusion, its use has been most applied when images are combined with spectroscopy data [77; 80], or as a preliminary step to prepare the data to be processed by the actual fusion models [35; 41]. In the context of data fusion, Kalman filters are almost exclusively applied to combine inertial and position measurements to aid in the navigation and positioning of autonomous vehicles. This is a well-established approach that is dominating this kind of application. Although some alternative techniques have been proposed [16; 98], there is not enough evidence in the literature to favor any given method over Kalman filters. Machine learning techniques have been applied to a wide variety of problems since (at least) the 1990s.