Sentinel-1 SAR, Harmonized Landsat Sentinel (HLS) Optical, and auxiliary data

Published: 6 October 2025| Version 1 | DOI: 10.17632/k59kb96955.1
Contributor:
Henry Osei

Description

Analysis-ready data to train a machine learning algorithm to estimate optical vegetation indices using Sentinel-1 SAR dual polarization features and auxiliary data such as Accumulate Growing Degree Days (AGDD). This a a vector-based geographic data in a tabular format, with each row representing a crop field that has a geographic location attached to it, and the columns represents the attributes such as the SAR features of the crop polygons. The column names for the SAR features are VV, VH, NRPB, Span, Ratio, RVI; NDVI and EVI for the optical vegetation indices; and AGDD, days-after-planting (DAP), and Digitial Elevation Model (DEM).

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Institutions

  • Bowling Green State University

Categories

Machine Learning, Synthetic Aperture Radar, Optical Remote Sensing, Agriculture

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