Multi-source Dataset for Wheat Yield Modeling: Remote Sensing Indices, Climate Data, and Soil Characteristics in Kazakhstan Districts

Zenodo (CERN European Organization for Nuclear Research) 2026 Dataset Open access

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Authors: M.A. Alpysbay
DOI: 10.5281/zenodo.18847583
Cited by: 0

Abstract

This dataset contains 2,878 records from 16 regions of Kazakhstan. It integrates multi-temporal satellite vegetation indices (NDVI, EVI, SAVI, GNDVI), water indices (NDWI, NDDI), and climatic parameters (Temperature, Precipitation, Solar Radiation), along with soil characteristics and elevation. The data is structured for machine learning applications in agricultural yield forecasting.

Fields of Study & Subjects

Remote Sensing in Agriculture Smart Agriculture and AI Soil Moisture and Remote Sensing

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
3 March 2026
Language
EN
Document type
Dataset