Data · dataset · 2026
Black Soil Texture Distribution Map (2021)
Listed in ScienceDB
This dataset presents the results of systematic sampling and high-resolution spatial prediction of topsoil texture (particle size fractions) for cultivated land in Northeast China.
Description
It includes 1,413 topsoil (0–20 cm) samples collected in April and October 2021 across the region, covering diverse soil types such as black soil, chernozem, meadow soil, and others, ensuring broad representativeness. Each sample was analyzed using a Malvern MS‑2000 laser particle size analyzer to determine the mass fractions of sand (0.05–2 mm), silt (0.002–0.05 mm), and clay (<0.002 mm), with the three fractions summing to 100%.
Based on the sample data and remote sensing variables, a Random Forest (RF) model was employed to spatially predict the particle size fractions, generating raster maps of sand, silt, and clay contents at 10 m resolution covering the entire study area, with a total data volume of approximately 48.17 GB. For the sand fraction, the validation R² was 0.77 and RMSE was 10.94%; for the silt fraction, R² was 0.75 and RMSE was 10.05%; and for the clay fraction, R² was 0.65 and RMSE was 2.27%.
Links
Where it is published
- DOI doi.org/10.57760/sciencedb.iga.001er ↗
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
- Computer Science & AI · Earth & Environmental Science · Engineering · Humanities · Life Sciences · Social Science
- Inferred from text
- Satellite remote sensing 65% · Soil sciences 69%
Provenance · 1 source records, 13 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ScienceDB | 10.57760/sciencedb.IGA.001er | 9 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].anzsrc:group:4106 | enrichment · scidb cn | taxonomy-embedding@1.0.0 | title+keywords+description (69%) |
| concepts[field].local:field:computer-science-ai | mapping · 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: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 |