Data · dataset · 2026
Dataset for: GLI-Fusion: A Low-Cost Strategy Integrating Geographical Location Identifiers with Residual Models to Enhance Soil Nitrogen Prediction using Near-Infrared Spectroscopy
Listed in Teesside University Research Data Repository
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.
Description
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.
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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.
Links
Where it is published
- DOI doi.org/10.17632/xbs8xnr76c.1 ↗
DOI / persistent id · from researchdata tees ac uk
Catalogue records · 1
- OAI-PMH record data.mendeley.com/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Adata… ↗
metadata API · from researchdata tees ac uk
Topics
- From keywords
- Chemistry · Earth & Environmental Science
Provenance · 1 source records, 7 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Teesside University Research Data Repository | oai:data.mendeley.com/xbs8xnr76c.1 | 15 h ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:chemistry | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| description | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/description |
| license | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/rights |
| publication_date | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| title | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/title |