Data · dataset · 2026
The Chinese 10 m leaf area index of key growth stages of winter wheat in 2020-2025
Listed in ScienceDB
The retrieval of Leaf Area Index (LAI) from satellite imagery is crucial for monitoring global carbon cycles and food security.
Description
Methods for estimating LAI based on spectral reflectance and Vegetation Indices (VI), known as VI-LAI models, are widely used for LAI retrieval due to their convenience. However, the VI-LAI relationship can vary with changes in Leaf Chlorophyll Content (LCC), resulting in low generalizability of the corresponding VI-LAI models.
To address this issue, this study proposes a novel approach called the Difference Combination between Spectral Indices (DCSI), which combines existing VIs and derives a new index for generating LAI models, i.e., Sentinel-2 Modified Red Edge Position (S2MREP). The validation results based in-situ measurement data show that, the developed model appears high accuracy (R²=0.72, RMSE=0.90, RRMSE=23.61%) and robustness.
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It is against various confounding factors such as leaf chlorophyll content, canopy structure, and field background. With Google Earth Engine (GEE) cloud processing platform, the model can apply to generating national monthly winter wheat LAI maps.
Note
This dataset contains the monthly and daily spatial distribution of winter wheat LAI over the key growth stages (from tillering to maturity stages, i.e., from January to May), across China and from 2020 to 2025, with 10 m spatial resolution. Among them, the daily dataset is derived by using Sentinel-2 images retrieved on the same day and do not cover the entire country. Generally, the dataset could apply to the yield prediction and the analysis of fertilizer and water management.
For storage convenience, the values in the dataset have been scaled by a factor of 100. Users should divide the values by 100 during application.
Links
Where it is published
- DOI doi.org/10.57760/sciencedb.ecodb.00213 ↗
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
- Image 65% · Satellite remote sensing 65%
Provenance · 1 source records, 12 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ScienceDB | 10.57760/sciencedb.ecodb.00213 | 8 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · scidb cn | connector:scidb_cn@1.0.0 | |
| 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:image | enrichment · scidb cn | keyword-concept-rules@1.0.0 | title+description (65%) |
| 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 | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/rights |
| publication_date | source · scidb cn | connector:scidb_cn@1.0.0 | |
| title | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/title |