Salinity stress detection in rice crops using time series MODIS VI data

Ambica Paliwal* (Corresponding Author), Alice G. Laborte, A.D. Nelson, R.K. Singh

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

16 Citations (Scopus)
18 Downloads (Pure)


Soil salinity is one of the abiotic stresses that constrains rice crop growth in the extensive coastal regions of India. Monitoring soil condition on such a large scale is time-consuming and expensive and alternative approaches have been proposed. Crop condition can be used as a reference to detect salinity-affected soils in rice crops instead of direct detection of soil condition. Vegetation indices derived from high temporal resolution satellites like the Moderate Resolution Imaging Spectroradiometer (MODIS) have the potential to detect soil salinity in a cost- and time-efficient manner. This study analysed the temporal trend of salinity in the kharif (wet) season and the behaviour of rice crop phenological indicators under different levels of salinity. We tested the applicability of MODIS time series Enhanced Vegetation Index (EVI) data for soil salinity detection in rice crops in the coastal region of Odisha, India. We used smoothed time series profiles of MODIS EVI 8 day composite data from 2011 to 2015 to extract rice crop phenological parameters. A total of 350 soil samples were periodically collected from 80 sites during the kharif season. Results showed that soil salinity was negatively correlated with EVI (r = – 0.76). Among all the rice crop parameters, the seasonal integral (SI) and amplitude of the EVI signal were the most promising indicators of soil salinity and its intensity. We observed consistently low SI and amplitude values of EVI in homogeneous rice pixels at high saline levels from 2011 to 2015 (P > 0.050). Our study showed that the extraction of phenological parameters from MODIS EVI is a promising method for detecting soil salinity in rice growing areas at regional scales even at low salinity levels.
Original languageEnglish
Pages (from-to)8186-8202
Number of pages17
JournalInternational journal of remote sensing
Issue number21
Early online date10 Sept 2018
Publication statusPublished - 2 Nov 2019


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