# Data Fusion in Agriculture: Resolving Ambiguities and Closing Data Gaps
## 2 Literature Review
### Orbital Scale
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Reference** & **Application** & **Fusion Technique** & **Fused Data** & **Mean Accuracy** \\ \hline
[42] & Soil moisture mapping & ESTARFM (L) & 2 & 0.70-0.84 \\ \hline
[45] & Crop type mapping & 2D and 3D U-Net (L), SegNet (L), RF (L) & 2, 6 & 0.91-0.99 \\ \hline
[43] & Estimation of surface soil moisture & ESTARFM (L) & 2 & 0.55-0.92 \\ \hline
[26]\({}^{*}\) & Delineation of homogeneous zones & Kriging and other geostatistical tools & 2, 9 & N/A \\ \hline
[51]\({}^{*}\) & Estimation of crop phenological states & Particle filter scheme (L/M) & 2, 6, 10 & 0.93-0.96 \\ \hline
[53] & Evaportranspiration mapping at field scales & STARFM (L) & 2 & 0.92-0.95 \\ \hline
[31]\({}^{*}\) & In-field estimation of soil properties & RK (L), PLSR (L) & 3, 9 & \textgreater{}0.5 \\ \hline
[99] & Estimation of wheat grain nitrogen uptake & BK (L) & 2, 3 & N/A \\ \hline
[44] & Surface soil moisture monitoring & Linear regression analysis and Kriging (L/M) & 2, 15 & 0.51-0.84 \\ \hline
[46] & Crop discrimination and classification & Voting system (H) & 2, 6 & 0.96 \\ \hline
[9] & Review on multimodality and data fusion in RS & N/A & N/A & N/A \\ \hline
[47] & Crop Mapping & Pixelwise matching (H) & 2, 6 & 0.94 \\ \hline
[72] & Review on fusion between MODIS and Landsat & N/A & N/A & N/A \\ \hline
[107] & Mapping crop progress & STARFM (L) & 2 & 0.54-0.86 \\ \hline
[66]\({}^{b}\) & Generation of spectral—temporal response & Bayesian data imputation (L) & 2, 3 & 0.77-0.83 \\ \hline
[28]\({}^{*}\) & Delineation of management zones & K-means clustering (L) & 2, 9, 14 & N/A \\ \hline
[114] & Mapping irrigated areas & Decision tree (L) & 2 & 0.67-0.93 \\ \hline
[54] & Evaportranspiration mapping & Empirical exploration of band relationships (L) & 2, 4 & 0.20-0.97 \\ \hline
[67]\({}^{*}\) & Yield gap attribution in maize & Empirical equations (L) & 15 & 0.37-0.74 \\ \hline
[63] & Change detection and biomass estimation in rice & Graph-based data fusion (L) & 2 & 0.17-0.90 \\ \hline
[108] & Leaf area index estimation & STARFM (L) & 2 & 0.69-0.76 \\ \hline
[55] & Evaportranspiration estimates & STARFM (M) & 2 & N/A \\ \hline
[115] & Classification of agriculture drought & Optimal weighting of individual indices (M) & 2 & 0.80-0.92 \\ \hline
[56] & Mapping daily evapotranspiration & STARFM (L) & 2 & N/A \\ \hline
[20] & Mapping of cropping cycles & STARFM (L) & 2 & 0.88-0.91 \\ \hline
[116] & Evaportranspiration partitioning at field scales & STARFM (L) & 2 & N/A \\ \hline
[68]\({}^{*}\) & Review: image fusion technology in agriculture & N/A & N/A & N/A \\ \hline
[52]\({}^{b}\) & Crop monitoring & PLSR (M), RF (M), SVR (M), ELR (M) & 1, 2, 4 & 0.60-0.93 \\ \hline
[113] & Mapping of smallholder crop farming & XGBoost (L/M and H), RF (H), SVM (H), ANN (H), NB (H) & 2, 6 & 0.96-0.98 \\ \hline
[64]\({}^{*}\) & Estimation of biomass in grasslands & Simple quadratic combination (L/M) & 2, 15 & 0.66-0.88 \\ \hline
[40]\({}^{b}\) & Evaportranspiration estimation & MSDF-ET (L) & 1, 2, 4 & 0.68-0.77 \\ \hline
[117] & Semantic segmentation of land types & Majority rule (H) & 2 & 0.99 \\ \hline
[118] & Eucalyptus trees identification & Fuzzy information fusion (L) & 2 & 0.98 \\ \hline
[10]\({}^{*b}\) & Review: IoT and data fusion for crop disease & N/A & N/A & N/A \\ \hline
[69]\({}^{*}\) & Wheat yield prediction & CP-ANN (M), XY-fused networks (M), SKN (M) & 2, 7 & 0.82 \\ \hline
[112] & Drought monitoring & RF (M) & 2, 15 & 0.29-0.77 \\ \hline
[48] & Crop type classification and mapping & RF (L) & 2, 6, 13 & 0.37-0.94 \\ \hline
[119] & Time series data fusion & Environmental data acquisition module & 10 & N/A \\ \hline
[57] & Evaportranspiration prediction in vineyard & STARFM (L) & 2 & 0.77-0.81 \\ \hline
[109] & Daily NDVI product at a 30-m spatial resolution & GKSFM (M) & 2 & 0.88 \\ \hline
[49] & Crop classification & Committee of MLPs (L) & 2, 6 & 0.65-0.99 \\ \hline
[6] & Multisource classification of remotely sensed data & Bayesian formulation (L) & 2, 6 & 0.74 \\ \hline
[111] & Fractional vegetation cover estimation & Data fusion and vegetation growth models (L) & 2 & 0.83-0.95 \\ \hline
[120] & Land cover monitoring & FARMA (L) & 2, 6 & N/A \\ \hline
[121] & Crop ensemble classification & mosaicking (L), classifier majority voting (H) & 2 & 0.82-0.85 \\ \hline
[70]\({}^{*b}\) & Review: data fusion in agricultural systems & N/A & N/A \\ \hline
[50] & In-season mapping of crop type & Classification tree (M) & 2 & 0.93-0.99 \\ \hline \hline \end{tabular}
\end{table}