# Data Fusion in Agriculture: Resolving Ambiguities and Closing Data Gaps
## 3 Discussion  
### Comparison of the Results Yielded by Fused and Individual Sources of Data  
The last columns of Tables 3-5 show the accuracies reported in each study considered in this article. There are a few important observations to be made before analysing those results. First, the value \"N/A\" is used in three situations: when accuracy values are not applicable (for example in review articles), when accuracy values are not available, and when the performance of the proposed models is evaluated using either qualitative criteria or metrics that cannot be summarized in a few numbers. Second, some studies report a wide range of accuracy values. This happens when different experimental setups are adopted and also when different variables are considered. This is particularly prevalent in studies dealing with soil variables, in which case, the effectiveness of models can vary significantly from variable to variable. Third, the application of data fusion in agriculture vary significantly, so does the way accuracies are assessed, which partially explains the wide differences observed in the tables. More importantly, even the results reported in studies dealing with similar applications cannot, in general, be directly compared, unless the exact same datasets were used in the experiments. Because of these limitations, the analysis in this section focuses on the effects of data fusion, and not on differences between studies. However, it is worth noting that not all studies explicitly compare results produced with and without data fusion. The impact of data fusion varies with the type of application. Studies that focused on the fusion of digital images invariably showed improvements with respect to the results obtained using single data sources [18; 19; 39; 84; 99; 106], although in some cases the improvement may not be substantial enough to justify the capture of additional data [21; 71]. The success of data fusion applied to digital images can be linked not only to the complementarities shown by different types of images, but also to the fact that the images can be easily made compatible using simple normalization operations when needed. As a result, proven techniques, such as deep learning, can be applied. In the case of agriculture, image data fusion has been particularly effective at the orbital level, both for artificially increasing the spatial resolution of sources with revisit frequencies [43; 57; 107; 108; 114; 53] and for com \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Reference** & **Application** & **Fusion Technique** & **Fused Data** & **Mean Accuracy** \\ \hline \hline
[122] & Building frequent landsat-like imagery & STARFM (\(\bot\)) & 2 & 0.63-0.99 \\ \hline
[58] & Evaportranspiration mapping & SADEAET (M) & 2 & N/A \\ \hline
[123] & Temporal land use mapping & Dynamic decision tree (M) & 2 & 0.86-0.96 \\ \hline
[124] & High-resolution leaf area index estimation & STDFA (\(\bot\)) & 2 & 0.98 \\ \hline
[125] & Monitoring cotton root rot & ISTDA (M) & 2 & 0.79-0.97 \\ \hline
[110] & Monitoring crop water content & Modified STARFM (\(\bot\)) & 2 & 0.44-0.85 \\ \hline
[105] & Soil moisture content estimation & Vector concatenation, followed by ANN (M) & 2, 6 & 0.39-0.93 \\ \hline
[126] & Impact of tile drainage on evapotranspiration & STARFM (\(\bot\)) & 2 & 0.23-0.91 \\ \hline
[127] & Estimation of leaf area index & CACAO method (\(\bot\)) & 2 & 0.88 \\ \hline
[106] & Mapping winter wheat in urban region & SVM (M), RF (M) & 2, 6 & 0.98 \\ \hline
[128] & Leaf area index estimation & ESTARM (\(\bot\)), linear regression model (M) & 2 & 0.37-0.95 \\ \hline
[71]1  & Vineyard monitoring & PLSR (M), SVR (M), RFR (M), ELR (M) & 2 & 0.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._pensating cloud cover using the information present in SAR images [45; 47; 51; 113]. In these cases, the improvement can exceed 50% [66].