Dataset for: GLI-Fusion: A Low-Cost Strategy Integrating Geographical Location Identifiers with Residual Models to Enhance Soil Nitrogen Prediction using Near-Infrared Spectroscopy

This dataset contains the soil visible-near infrared (Vis-NIR) spectral data, laboratory-measured soil total nitrogen (TN) contents, and corresponding Geographical Location Identifiers (GLI) used in the aforementioned study. The dataset is designed to evaluate the performance of the GLI-Fusion deep learning framework, which leverages zero-cost spatial auxiliary features (GLI) to improve the Vis-NIR prediction accuracy of soil TN across different spatial scales. The repository includes data from two distinct spatial scales: a regional small-sample dataset (Nanhu, China) and a processed macro-scale dataset (LUCAS 2015, Europe). Data Files Overview: 1. Nanhu.xlsx Contents: It includes the sample ID, Geographical Location Identifier (GLI, representing the specific village of the sampling point), Vis-NIR spectral absorbance data (900–1700 nm, 227 bands), and the corresponding laboratory-measured soil total nitrogen (TN) content (g/kg). 2. Lucas2015.xlsx This file contains a processed subset of the public European LUCAS 2015 topsoil dataset, adapted to validate the cross-regional generalization of the GLI-Fusion model. Contents: To ensure comparability with the regional Nanhu dataset, the original high-resolution LUCAS spectra were linearly interpolated to match the specific 900–1700 nm wavelength range (retaining 227 bands). It also includes the corresponding physicochemical values, specifically soil TN content. 3. Lucas-GLI.xlsx This file contains the spatial auxiliary features for the LUCAS dataset. Contents: It provides the country-level Geographical Location Identifiers (GLI) for each sample in the LUCAS 2015 dataset. Usage Note: The row order in this file strictly corresponds to the sample sequence in Lucas2015.xlsx. Users can easily merge this GLI column with the spectral data in Lucas2015.xlsx via row-wise concatenation for multi-modal machine learning tasks.本数据集包含土壤可见-近红外（Vis-NIR）光谱数据、实验室测定的土壤总氮（TN）含量，以及前述研究中使用的对应地理位置标识符（GLI）。本数据集旨在评估GLI-Fusion深度学习框架的性能，该框架利用零成本空间辅助特征（GLI），以提升不同空间尺度下土壤总氮的可见-近红外光谱预测精度。 本仓库涵盖两种不同空间尺度的数据：中国南湖区域小样本数据集，以及经过处理的宏观尺度数据集（欧洲LUCAS 2015）。 数据文件概览： 1. Nanhu.xlsx 内容：包含样本ID、地理位置标识符（GLI，代表采样点所在的具体村落）、可见-近红外光谱吸光度数据（900–1700 nm，共227个波段），以及对应的实验室测定土壤总氮（TN）含量（单位：g/kg）。 2. Lucas2015.xlsx 本文件包含公开欧洲LUCAS 2015表层土壤数据集的经处理子集，用于验证GLI-Fusion模型的跨区域泛化能力。为保证与南湖区域数据集的可比性，原始高分辨率LUCAS光谱经线性插值匹配至900–1700 nm的特定波长范围（保留227个波段），同时包含对应的理化参数，具体为土壤总氮含量。 3. Lucas-GLI.xlsx 本文件包含LUCAS数据集的空间辅助特征，具体提供了LUCAS 2015数据集中每个样本的国家级地理位置标识符（GLI）。 使用说明：该文件的行顺序与Lucas2015.xlsx中的样本序列严格对应，研究人员可通过逐行拼接的方式，轻松将该GLI列与Lucas2015.xlsx中的光谱数据合并，以用于多模态机器学习任务。