LAI calculation for Landsat, Hyperion, Sentinel using Time Series Analysis

Authors

  • K. Srinivasa Chakravarthy, G. Shyama Chandra Prasad

Keywords:

NDVI, LAI, Hyperion EO-1, Sentinel, Landsat

Abstract

Precision crop agriculture is an innovative approach that utilizes information technology to effectively manage crop variability through observation, measurement, and responsive actions. This paper focuses on the observation aspect of precision crop agriculture, specifically exploring the analysis of leaf area in agricultural land. Leaf Area Analysis is a technique employed to determine the total leaf area within a given ground area. It relies on the Leaf Area Index (LAI), a biophysical parameter that quantifies leaf area per unit of ground area. LAI is crucial in assessing vegetation processes like photosynthesis, transpiration, and energy balance. In this study, LAI is calculated using a statistical approach based on the dimensionless Normalized Difference Vegetation Index (NDVI), which compares visible and near-infrared reflectance of vegetation cover. The paper utilizes satellite data from Sentinel, Hyperion EO-1, and Landsat, consisting of various bands, with a specific focus on the red and near-infrared bands which have been taken from Earthexplorer for seasonal duration. The statistical approach employed for LAI calculation proves to be suitable for the Sentinel dataset due to its simplicity and effectiveness. Additionally, determining the percentage of vegetation cover is an important aspect of crop observation, providing insights into the greenness and growth variability across different regions and agricultural seasons within a defined timeframe. The proposed methodology verifies the increase in LAI from crop sown season to harvesting season and then decreases. Using various dataset over the same land area cross-verifies and confirms the LAI pattern change over the area..

Downloads

Download data is not yet available.

References

A. Juniansah, G. C. Tama, K. R. Febriani, M. N. Baharain, T. Kanekapu- tra, Y. S. Wulandari, and M. Kamal, “Mangrove leaf area index estimation using sentinel 2a imagery in teluk ratai, pesawaran lampung,” in IOP Conference series: earth and environmental science, vol. 165, no. 1. IOP Publishing, 2018, p. 012004.

B. Datt, T. R. McVicar, T. G. Van Niel, D. L. Jupp, and J. S. Pearlman, “Preprocessing eo-1 hyperion hyperspectral data to support the appli- cation of agricultural indexes,” IEEE Transactions on Geoscience and Remote Sensing, vol. 41, no. 6, pp. 1246–1259, 2003.

J. Liu, E. Pattey, and G. Je´go, “Assessment of vegetation indices for regional crop green lai estimation from landsat images over multiple growing seasons,” Remote Sensing of Environment, vol. 123, pp. 347– 358, 2012.

M. Kalacska, G. Sanchez-Azofeifa, T. Caelli, B. Rivard, and B. Boerlage, “Estimating leaf area index from satellite imagery using bayesian net- works,” IEEE Transactions on Geoscience and Remote Sensing, vol. 43, no. 8, pp. 1866–1873, 2005.

X. Guo, M. Wang, M. Jia, and W. Wang, “Estimating mangrove leaf area index based on red-edge vegetation indices: A comparison among UAV, WorldView-2 and Sentinel-2 imagery,” International Journal of Applied Earth Observation and Geoinformation, vol. 103, p. 102493, Dec. 2021.

F. Deng, J. Chen, S. Plummer, M. Chen, and J. Pisek, “Algorithm for global leaf area index retrieval using satellite imagery,” IEEE Transactions on Geoscience and Remote Sensing, vol. 44, no. 8, pp. 2219–2229, 2006.

M. Gonza´lez-Sanpedro, T. Le Toan, J. Moreno, L. Kergoat, and E. Rubio, “Seasonal variations of leaf area index of agricultural fields retrieved from landsat data,” Remote Sensing of Environment, vol. 112, no. 3, pp. 810– 824, 2008.

L. Pasolli, S. Asam, M. Castelli, L. Bruzzone, G. Wohlfahrt, M. Zebisch, and C. Notarnicola, “Retrieval of leaf area index in mountain grasslands in the alps from modis satellite imagery,” Remote Sensing of Environment, vol. 165, 08 2015.

D. Singh and S. Singh, “Leaf area index retrieval using hyperion eo-1 data-based vegetation indices in himalayan forest system,” p. 98800U, 04 2016

Downloads

Published

12.06.2024

How to Cite

K. Srinivasa Chakravarthy. (2024). LAI calculation for Landsat, Hyperion, Sentinel using Time Series Analysis. International Journal of Intelligent Systems and Applications in Engineering, 12(4), 2808 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6763

Issue

Vol. 12 No. 4 (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.