Data · dataset · 2025
A Daily, Multi-scale (10m/2.5m) Multispectral and Vegetation Index Dataset of Chenba'erhu Banner, Inner Mongolia (2024)
Listed in ScienceDB
Fine-scale grassland monitoring is crucial for ecological conservation and sustainable animal husbandry, but has long been constrained by the spatio-temporal resolution trade-off of remote sensing data.
Description
This research produced a daily multi-scale remote sensing dataset covering the 2024 growing season (March to October) in Chenba'herhu Banner, Inner Mongolia. The dataset includes a 10 m product covering the entire banner and a 2.5 m enhanced product for the Bayankuren Town, both containing multispectral imagery and vegetation indices (NDVI, EVI).
Based on MODIS MCD43A4 and Sentinel-2 L2A products, this research reconstructed a continuous MODIS time series using the Whittaker smoother algorithm. Subsequently, a daily seamless 10 m dataset was generated through a "predict-and-update" chain spatio-temporal fusion strategy. Finally, using Bayan Kuren Town as a demonstration area, the 10 m imagery was enhanced to 2.5 m resolution using the SEN2SR super-resolution model.
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The quality assessment indicates high accuracy: the cross-validation for MODIS data reconstruction yielded an average coefficient of determination (R²) greater than 0.983. The indirect validation of the super-resolution model showed an average Structural Similarity Index (SSIM) above 0.94 and a Spectral Angle Mapper (SAM) below 0.07 rad. This dataset can support for research on fine-scale phenology monitoring, and ecosystem model optimization.
Links
Where it is published
- DOI doi.org/10.57760/sciencedb.j00001.01583 ↗
DOI / persistent id · from scidb cn
Catalogue records · 1
- OAI-PMH record scidb.cn/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=10.57760%2… ↗
metadata API · from scidb cn
Topics
- From keywords
- Earth & Environmental Science · Engineering · Humanities · Life Sciences · Social Science
- Inferred from text
- Crop and pasture production 74% · Satellite remote sensing 65%
Provenance · 1 source records, 11 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ScienceDB | 10.57760/sciencedb.j00001.01583 | 12 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[field].anzsrc:group:3004 | enrichment · scidb cn | taxonomy-embedding@1.0.0 | title+keywords+description (74%) |
| concepts[field].local:field:earth-environmental | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:engineering | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:humanities | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:social-science | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[modality].local:modality:remote-sensing | enrichment · scidb cn | keyword-concept-rules@1.0.0 | title+description (65%) |
| description | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/description |
| license_text | source · scidb cn | connector:scidb_cn@1.0.0 | |
| publication_date | source · scidb cn | connector:scidb_cn@1.0.0 | |
| title | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/title |