# Spectral Data-Driven Prediction of Soil Properties Using LSTM-CNN-Attention Model
## 3 Results  
### Descriptive Statistical Analysis  
To ensure that dataset partitioning did not introduce bias and that the test set accurately represented the diversity of the entire dataset, a detailed statistical and graphical analysis was performed. Table 3 summarizes the descriptive statistics of soil properties for the total dataset, training set, and test set. Metrics such as mean, standard deviation, skewness, and coefficient of variation (CV) reveal minimal differences between subsets, confirming that the LHS method effectively preserved the dataset's statistical characteristics.  
Figure 7 displays Kernel Density Estimation (KDE) plots for organic carbon (OC), nitrogen (N), calcium carbonate (CaCO\({}_{3}\)), and pH. The overlapping density curves for the total dataset, training set, and test set indicate consistent distributions, demonstrating that the test set adequately represents the variability of soil properties without introducing sampling bias. Figure 8 illustrates the results of Principal Component Analysis (PCA) on the spectral data, showing density plots for PC1 and PC2 across all subsets. The alignment of density curves for the total dataset, training set, and test set confirms that the partitioning method preserved the diversity of spectral features, ensuring representativeness in soil property prediction.  
The combined results of descriptive statistics and graphical analyses validate the use of LHS for dataset partitioning. The training and test sets are consistent with the total dataset in terms of both soil properties and spectral features, ensuring unbiased and reliable model evaluations.  
\begin{table}
\begin{tabular}{c c c} \hline
**RPD** & **Meaning** & **Level** \\ \hline RPD \(>\) 3 & Excellent Model & A \\
2.5 \(\leq\) RPD \(<\) 3.0 & Good Model & B \\
2.0 \(\leq\) RPD \(<\) 2.5 & Approximate Model & C \\ RPD \(<\) 2 & Unsatisfactory Model & D \\ \hline \end{tabular}
\end{table}
Table 2: Classification tiers of the performance-to-deviation ratio (RPD).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**Properties** & **Set** & **N** & **Min** & **Max** & **Q25** & **Median** & **Q75** & **Mean** & **Std** & **Skewness** & **CV * (\%)** \\ \hline OC (g-kg\({}^{-1}\)) & Total & 21,782 & 0.10 & 560.20 & 12.50 & 20.40 & 38.60 & 43.24 & 76.62 & 4.36 & 177.18 \\  & Training & 18,516 & 0.10 & 560.20 & 12.50 & 20.30 & 38.30 & 42.81 & 75.99 & 4.41 & 177.51 \\  & Test & 3266 & 0.20 & 555.50 & 12.60 & 21.20 & 39.80 & 45.71 & 80.08 & 4.11 & 175.19 \\ \hline N (g-kg\({}^{-1}\)) & Total & 21,782 & 0.00 & 38.50 & 1.30 & 2.00 & 3.30 & 3.10 & 3.67 & 3.91 & 118.32 \\  & Training & 18,516 & 0.00 & 37.60 & 1.30 & 2.00 & 3.30 & 3.08 & 3.64 & 3.91 & 118.09 \\  & Test & 3266 & 0.00 & 38.50 & 1.30 & 2.00 & 3.40 & 3.23 & 3.85 & 3.87 & 119.32 \\ \hline CaCO\({}_{3}\) (g-kg\({}^{-1}\)) & Total & 21,782 & 0.00 & 976.00 & 0.00 & 1.00 & 19.00 & 57.39 & 135.46 & 2.91 & 236.05 \\  & Training & 18,516 & 0.00 & 976.00 & 0.00 & 1.00 & 20.00 & 57.83 & 135.76 & 3.87 & 234.73 \\  & Test & 3266 & 0.00 & 962.00 & 0.00 & 0.00 & 16.00 & 54.85 & 133.79 & 3.11 & 243.90 \\ \hline pH(H\({}_{2}\)O) & Total & 21,782 & 3.17 & 10.37 & 4.92 & 6.07 & 7.45 & 6.13 & 1.35 & 0.01 & 21.95 \\  & Training & 18,516 & 3.17 & 10.37 & 4.93 & 6.08 & 7.45 & 6.14 & 1.35 & 0.00 & 21.92 \\  & Test & 3266 & 3.51 & 9.07 & 4.87 & 6.03 & 7.46 & 6.10 & 1.35 & 0.03 & 22.14 \\ \hline \end{tabular} \({}^{*}\)CV: coefficient of variation.  
\end{table}
Table 3: Summary statistics of soil properties for LUCAS.