# Spectral Data-Driven Prediction of Soil Properties Using LSTM-CNN-Attention Model
Traditionally, most studies have focused on linear models such as principal component regression (PCR), multiple linear regression (MLR), and least squares regression (PLSR) [11; 12; 13]. [PERSON] et al. [14] employed stepwise multiple linear regression, PCR, and PLSR to develop and evaluate the optimal prediction model for salinized soils in northern Shandong Province, China. However, the relationship between Visible-NIR spectroscopy and soil properties is often complex and nonlinear [15; 16], primarily due to significant heterogeneity in soil composition and the overlapping spectral reflectance of individual soil components [17; 18]. Machine learning techniques excel at modeling nonlinear relationships and managing large numbers of features and complex data structures effectively [19]. Consequently, machine learning models such as support vector machines (SVMs) and random forest (RF) have been increasingly adopted to address the nonlinear relationships between soil properties and Visible-NIR spectra [20; 21]. [PERSON] et al. [20] compared the predictive performance of partial least-squares regression with that of support vector machine regression for estimating soil organic matter. The results indicated that the support vector machine method demonstrated greater generalization capability and higher predictive accuracy. In recent years, the expansion of a data scale and the ongoing optimization of intelligent algorithms have driven the transition from machine learning to deep learning. Deep learning not only excels at uncovering complex nonlinear relationships between spectral data and soil properties but also has been shown to outperform traditional machine learning methods in predicting and mapping soil properties [22]. [PERSON] et al. [23] were the first to apply deep learning techniques to the spectral estimation of soil properties in 2015. [PERSON] et al. [24] compared a Convolutional Neural Network (CNN) model with PLSR and RF methods to evaluate their predictive ability in estimating soil phosphorus content. The results indicated that the deep learning model outperformed traditional machine learning methods in both accuracy and robustness. [PERSON] et al. [25] developed a CNN model using Vis-NIR spectral data from the LUCAS topsoil dataset, which significantly outperformed the PLSR model, particularly in predicting key soil properties such as organic carbon and calcium carbonate, achieving a higher ratio of percent deviation (RPD) values of 4.02 and 3.89, respectively. Although CNN models effectively learn local and abstract features from raw spectral data, they have limitations in capturing the inherent sequential dependencies within spectral data due to their sequential nature [26]. Recurrent Neural Networks (RNNs) are specifically designed for time series data by feeding the output back into the input, while Long Short-Term Memory (LSTM) networks excel at capturing long-term dependencies and leveraging correlations within time series spectral data [27; 28]. [PERSON] and [PERSON] [29] developed a hybrid framework that employed Principal Component Analysis (PCA) and Locality Preserving Projections (LPPs) for dimensionality reduction, combined with RNN variants such as LSTM and Gated Recurrent Unit (GRU). This approach outperformed CNN models in capturing both short-term and long-term dependencies within the LUCAS hyperspectral dataset. [PERSON] et al. [30] applied an LSTM-CNN model to predict soil organic matter using the Hebei Soil Spectral Library (HSSL), achieving high accuracy (R\({}^{2}\) = 0.96, RMSE = 1.66 g\(\cdot\)kg\({}^{-1}\)) by effectively extracting both spatial and temporal features from the spectral data. However, the aforementioned methods treat all spectral information equally, which can adversely affect the model's predictive accuracy when redundant or irrelevant information is incorporated [31]. Therefore, it is crucial to prioritize meaningful features while suppressing irrelevant ones to improve the model's predictive accuracy.