Monitoring Wheat Vegetation Health and Moisture-Related Spectral Variation Using Landsat 8-Derived Indices in Punjab

Authors

  • GURLEEN KAUR Department of Agronomy, School of Agriculture, Lovely Professional University, Phagwara, Punjab, India
  • SREETHU S. Department of Agronomy, School of Agriculture, Lovely Professional University, Phagwara, Punjab, India
  • VIKAS SHARMA Department of Soil Science and Agricultural Chemistry, School of Agriculture, Lovely Professional University, Phagwara, Punjab, India
  • VANDNA CHHABRA Department of Agronomy, School of Agriculture, Lovely Professional University, Phagwara, Punjab, India

DOI:

https://doi.org/10.54386/jam.v28i3.3274

Keywords:

Soil moisture stress, Wheat phenology, NDVI, LSWI, Landsat 8, Remote sensing

Abstract

Efficient monitoring of wheat growth and moisture-related stress is critical for improving irrigation management in Punjab, where groundwater depletion and climate variability threaten crop productivity. This study used Landsat 8 Surface Reflectance data processed in Google Earth Engine to assess vegetation health, phenological dynamics, and moisture-related spectral variation during the rabi seasons of 2022–23 and 2023–24 in Phagwara, Punjab. The Normalized Difference Vegetation Index (NDVI), Land Surface Water Index (LSWI), and False Colour Composite (FCC) were analysed at regional and selected field scales. FCC imagery clearly captured seasonal changes in vegetation cover and crop development. NDVI showed a consistent wheat growth pattern, increasing from sowing and early establishment to peak vegetative–reproductive stages, followed by decline during maturity due to senescence and canopy drying. Field-level NDVI trends corresponded well with observed phenological stages and crop growth indicators, including plant height, leaf area index, and relative water content under contrasting irrigation conditions. NDVI-based classification further distinguished wheat and non-wheat pixels within the selected study area. LSWI provided supplementary information on moisture-related spectral variation associated with rainfall, crop stage, and canopy condition. Overall, the integrated use of NDVI, FCC, and LSWI provides a practical framework for monitoring wheat growth and supporting irrigation decision-making.

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Published

07-09-2026

How to Cite

KAUR, G., S., S., SHARMA, V., & CHHABRA, V. (2026). Monitoring Wheat Vegetation Health and Moisture-Related Spectral Variation Using Landsat 8-Derived Indices in Punjab. Journal of Agrometeorology, 28(3), 324–335. https://doi.org/10.54386/jam.v28i3.3274

Issue

Vol. 28 No. 3 (2026): September

Section

Research Paper

Categories

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Copyright (c) 2026 GURLEEN KAUR, SREETHU S., VIKAS SHARMA, VANDNA CHHABRA

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