Iowa State University Digital Repository
metadata only
A Submonthly Surface Water Classification Framework via Gap-Fill Imputation and Random Forest Classifiers of Landsat Imagery
earth-sciencesgeographyhydrologymachine-learningphysical-sciences-and-mathematicsremote-sensingsocial-and-behavioral-sciencesstatistics-and-probability
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Learning Regional Snow Water Equivalent and Snow Height Variations from Sentinel-1 InSAR Acquisitions#1128876
- Seasonal and annual tropical river pattern change detection using machine learning#246291
- Ensemble machine learning method for δ(18)O prediction in groundwater.#305104
- Machine learning-based seasonal SMAP soil moisture retrieval integrating MODIS drought indices: A case study of the Wujiang River Basin.#305074
- A hybrid deep learning framework for inferring unobserved subsurface water flow across the contiguous United States / Lennart Greiner ; Gutachter: Alexander Brenning, Shijie Jiang#1052369
Record · ID 326469
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive