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

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Depanshu Sani, Sandeep Mahato, Parichya Sirohi, Saket Anand, Gaurav Arora, Charu Chandra Devshali, T. Jayaraman

Computer Vision and Pattern Recognition · Submitted 6 May 2022

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim 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.

how it is stated · asserted 0.99 read the record →

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arXiv ID2205.03104 (version 1)
Pith IDWPAF4JTT, Pith's permanent record of this paper and its review
CitationCharu Chandra Devshali, Depanshu Sani, Gaurav Arora, Parichya Sirohi, Saket Anand, Sandeep Mahato, T. Jayaraman. "Crop Type Identification for Smallholding Farms: Analyzing Spatial, Temporal and Spectral Resolutions in Satellite Imagery." arXiv:2205.03104 (2022). https://pith.science/paper/WPAF4JTT
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,933 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.16 · human participants 0.03 · controlled comparison 0.64 · randomized assignment 0.02 · states quantitative result 0.99 · compares with prior results 0.81 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

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.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 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. background 1.00 0.05 0.03 asserted 1.00
s1 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. contribution 0.95 0.81 0.41 asserted 0.99
s2 We further demonstrate that high spectral resolution in satellite imagery can improve prediction performance for low spatial and temporal resolutions (LSTR) images. result 0.51 0.87 0.22 asserted 0.99
s3 The F1-score is increased by 7% when using multispectral data of MSTR images as compared to the best results obtained from HSTR images. result 1.00 0.88 0.26 asserted 1.00
s4 Similarly, when crop season based time series of multispectral data is used we observe an increase of 1.2% in the F1-score. result 1.00 0.93 0.07 asserted 1.00
s5 The outcome motivates further advancements in the field of synthetic band generation. future work 0.84 0.15 0.08 asserted 0.59

What kind of paper the classifier reports this is: empirical study 1.00 · evidence from observation or experiment 0.60.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original 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.

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