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

TL;DR

This study investigates how different spatial, temporal, and spectral resolutions of satellite imagery affect crop type identification accuracy, highlighting the benefits of high spectral resolution and seasonal data in improving predictions.

Contribution

It demonstrates the impact of spatial, temporal, and spectral resolutions on crop classification accuracy and suggests potential for synthetic band generation to enhance results.

Findings

F1-score increases by 7% with multispectral MSTR images

Seasonal multispectral data improves F1-score by 1.2%

High spectral resolution benefits low-resolution images

Abstract

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