AgroTwin: A Pixel-Level Agricultural Dataset for Crop Monitoring, Anomaly Detection, and Machine Learning (2020–2025)

Abstract

The dataset was developed using observations collected from agricultural fields of the North Kazakhstan Agricultural Experimental Station (North Kazakhstan AES), Kazakhstan, during the growing seasons from 2020 to 2025.. The dataset integrates multispectral observations derived from Sentinel-2, land surface temperature (LST) from MODIS, soil moisture data from SMAP, latent feature representations extracted using deep learning models, anomaly indicators, and monthly climate variables obtained from the NASA GLDAS dataset. The dataset is designed to support research in precision agriculture, crop monitoring, remote sensing, anomaly detection, machine learning, digital twin development, and geospatial analytics.

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