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The integration of the modern Machine Learning (ML) models into remotesensing and agriculture has expanded the scope of the application of satelliteimages in the agriculture domain. In this paper, we present how the accuracy ofcrop type identification improves as we move frommedium-spatiotemporal-resolution (MSTR) to high-spatiotemporal-resolution(HSTR) satellite images. We further demonstrate that high spectral resolutionin satellite imagery can improve prediction performance for low spatial andtemporal resolutions (LSTR) images. The F1-score is increased by 7% when usingmultispectral data of MSTR images as compared to the best results obtained fromHSTR images. Similarly, when crop season based time series of multispectraldata is used we observe an increase of 1.2% in the F1-score. The outcomemotivates further advancements in the field of synthetic band generation.

Crop Type Identification for Smallholding Farms: Analyzing Spatial, Temporal and Spectral Resolutions in Satellite Imagery