Advancements in Crop Phenological Stage Mapping Using Satellite Imagery and Spectral Analysis: A Review

Shrikant R. Nikam; Sarika A. Kondekar

doi:10.2991/978-94-6239-791-0_19

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Advancements in Crop Phenological Stage Mapping Using Satellite Imagery and Spectral Analysis: A Review

Authors

Shrikant R. Nikam¹, Sarika A. Kondekar²^{, *}

¹Department of Computer Science and Application, School of Computer Sciences and Engineering, Sandip University, Nashik, Maharashtra, India

²Department of Computer Science and Application, School of Computer Sciences and Engineering, Sandip University, Nashik, Maharashtra, India

^*Corresponding author. Email: sarika.kondekar@sandipuniversity.edu.in

Corresponding Author

Sarika A. Kondekar

Available Online 9 October 2026.

DOI: 10.2991/978-94-6239-791-0_19 How to use a DOI?
Keywords: Crop phenology; NDVI; ML; Spectral Feature Extraction
Abstract: Crop phenology has become an important part of crop mapping, yield prediction and for better agricultural decisions for such as irrigation, fertilization and crop drought control. The satellite sensors such as sentinel-2 and landsat-8/9 can be used to obtained high resolution imagery that can be used for larger field area for crop analysis by using vegetation indices and time series analysis. The present study is systematic review approach for automated crop phenological stages based on satellite imagery and spectral signature. It discusses techniques like preprocessing and feature selection based on vegetation indices and temporal curves with help of different machine learning and deep learning techniques. This analysis indicates how the time-series data and smoothing methods are crucial in deriving dependable phenological measures like Start of Season (SOS), Peak of Season (POS), and End of Season (EOS). This study also highlights benefits and drawbacks of current methodological approach by giving future research direction for multi-source data integration with real time monitoring system with generalized models of various agricultural conditions.
Copyright: © 2026 The Author(s)
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the International Conference on Artificial Intelligence and Machine Learning for Agro-Ecosystems (ICAIML-AE 2026)
Series: Advances in Intelligent Systems Research
Publication Date: 9 October 2026
ISBN: 978-94-6239-791-0
ISSN: 1951-6851
DOI: 10.2991/978-94-6239-791-0_19 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Shrikant R. Nikam
AU  - Sarika A. Kondekar
PY  - 2026
DA  - 2026/10/09
TI  - Advancements in Crop Phenological Stage Mapping Using Satellite Imagery and Spectral Analysis: A Review
BT  - Proceedings of the International Conference on Artificial Intelligence and Machine Learning for Agro-Ecosystems (ICAIML-AE 2026)
PB  - Atlantis Press
SP  - 239
EP  - 249
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-791-0_19
DO  - 10.2991/978-94-6239-791-0_19
ID  - Nikam2026
ER  -

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