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

arXiv: https://arxiv.org/abs/2205.03104 · HTML: https://mlsanity.com/paper/2205.03104/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 6 May 2022.

## Builds on (most cited first, 4 of 4)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-Attention](https://mlsanity.com/paper/1911.07757/) (2019)
- [SIPSA-Net: Shift-Invariant Pan Sharpening with Moving Object Alignment for Satellite Imagery](https://mlsanity.com/paper/2105.02400/) (2021)
- [Generation of the NIR spectral Band for Satellite Images with Convolutional Neural Networks](https://mlsanity.com/paper/2106.07020/) (2021)

## Abstract (arXiv)

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.