## 1 Introduction
(3) Existing methodologies predominantly adopt single-scale modeling, unable to simultaneously capture local spectral details and global trends, thereby resulting in fragmented cross-band relational information. Within the methodological framework of deep learning, approaches such as convolutional neural networks (CNN) and long short-term memory networks (LSTM) exhibit significant advantages compared with traditional machine learning paradigms. Specifically, deep learning circumvents limitations associated with manual feature engineering by leveraging autonomous feature learning mechanisms, effectively captures complex higher-order response relationships through deep nonlinear mappings, and enhances practical applicability via end-to-end data-driven modeling frameworks. From a theoretical perspective, these methods demonstrate superiority in feature representational capacity (representation learning), efficiency in processing high-dimensional data (dimensional invariance), multi-task generalization (parameter sharing), and robustness to noise (distributed representations), thereby providing a novel paradigm for modeling complex soil-spectral interactions. Empirically, the suitability and advantages of deep architectures have been validated by previous studies: for instance, [PERSON] et al. [29] employed an LSTM-basedframework to capture dependencies within spectral sequences; [PERSON] et al. [30] proposed a CNN-LSTM hybrid architecture for the joint extraction of spatial and temporal features; and [PERSON]'s group [31] developed a dual-branch CNN architecture effectively integrating heterogeneous features, achieving breakthroughs in various application scenarios. Addressing the core issues inherent in traditional methods--specifically the difficulties in processing high-dimensional data, insufficient nonlinear representation, and multi-scale fragmentation--this study introduces a novel deep-learning-based model termed as the multi-scale attention residual network (ReSE-AP Net). Building upon the residual convolutional neural network (ResNet) structure, the proposed model incorporates innovations across multiple dimensions. (1) Channel attention mechanism: By embedding a squeeze-and-excitation (SE) module, global average pooling is employed to capture statistical channel-wise feature responses, and a two-layer fully connected network dynamically calibrates feature channels, significantly enhancing the representation of critical spectral regions such as heavy-metal-sensitive bands. (2) Multi-scale feature pyramid construction: An atrous spatial pyramid pooling (ASPP) module based on dilated convolutions is designed to simultaneously capture the local spectral details and global spectral trends through parallel convolutional branches with varying receptive fields (dilation rates of 1, 2, and 4). (3) Hierarchical feature fusion: Employing residual skip-connections to facilitate cross-layer information interaction, local textural features from shallow layers (e.g., baseline reflectance fluctuations) and abstracted nonlinear spectral combinations from deep layers are integrated, creating a multi-granularity feature representation system. To validate the effectiveness of the proposed model, rigorous comparative experiments were conducted using the LUCAS2009 benchmark dataset. The experiments included traditional machine learning models (PLSR, Ridge, SVR, RF, XGBoost) and mainstream deep learning models (VGG, ResNet, temporal convolutional network (TCN), Transformer). Evaluation metrics employed were the coefficient of determination (R\({}^{2}\)) and root mean square error (RMSE). Results indicated that the ReSE-AP Net model significantly outperformed traditional machine learning methods across all elements, achieving improvements of 2.8-36.5% in R\({}^{2}\) and reductions of 14.2-69.2% in RMSE.