# Data Fusion in Agriculture: Resolving Ambiguities and Closing Data Gaps
## 3 Discussion
### Other Issues  
In its most basic definition, the term \"support\" refers to the size or volume associated with each data value [25]. Depending on the context, however, this term may encompass other factors. For example, in the field of geostatistical research, spatial support is related not only to the area and volume associated with the data, but also includes the shape and orientation of the spatial units involved in the measurements [27]. Differences in data representativeness can be very pronounced [95], and if not properly addressed, can cause the data fusion process to fail [25; 90]. As stated by [PERSON] et al. [27], \"at present, the advantages of using multi-sensor data cannot yet conceal the complexity of the problem encountered in combining disparate spatial data\". These authors added that while spatial data fusion has advanced greatly in the last years, the apparent progress can be deceiving as it is often built upon unrealistic assumptions regarding support differences. Given the complexity of the problem, more rigorous validations with more realistic data and assumptions are needed to enable the development of methods with real potential for practical applications.  
Overfitting is the phenomenon in which even small noisy variations in the data distribution are captured by the model, making predictions unreliable when applied to independent data [1]. This problem is particularly pervasive with small datasets with limited variability [105]. There are many techniques that can help preventing overfitting, such as data augmentation and cross-validation. Unfortunately, with a few exceptions [34; 35; 60; 77; 105], the problems of overfitting is often ignored, thus rendering the results unreliable.  
Data gap filling is usually applied to satellite data to compensate for cloud cover and other factors that cause data to be lost or unusable. This can be done either by using data from other satellites or employing data collected at other levels, especially proximal, but the latter does not always improves results [51]. Data gap filling can also refer to the improvement of spatial completeness for a more detailed representation of a given area [42]. Having multiple sensors, even if they are of different types, can also help to deal with situations in which one of the data sources become unavailable for a certain amount of time due to hardware or communication problems [14]. In these cases, even if the effectiveness of the system is diminished, useful information can still be produced until the problem is resolved.  
Owing to different time of acquisition, platform modes, and spectral bands of the source images, direct fusion leads to a loss of information and even the failure of fusion [68]. Currently, the most effective way to minimize this problem is by applying image registration, which has, as a main goal, to spatially match different images [19]. This step, which is often pointed out as the most important step in data fusion processes involving images [65], is essential to guarantee that the data present in different types of images are correctly fused into more complete and informative data [18]. This can be challenging, especially if the images being matched are obtained at different scales, at different bands of the spectrum, with different spatial resolutions, or are captured in areas with complex objects and topography. Additionally, the agricultural environment is highly dynamic, with plant canopies changing positions due to the wind or animals moving across the field,among other elements that may change between image captures [37]. To make matters even more complicated, significant differences between images may exist, especially if these are obtained by different acquisition methods [68]. In many cases, some kind of transformation capable of aligning the points in different images according to a given reference system is required [65].