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Soil salinity assessment using vegetation indices derived from Sentinel-2 multispectral data. application to Lezíria Grande, Portugal

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  • Ramos, Tiago B.
  • Castanheira, Nádia
  • Oliveira, Ana R.
  • Paz, Ana Marta
  • Darouich, Hanaa
  • Simionesei, Lucian
  • Farzamian, Mohammad
  • Gonçalves, Maria C.

Abstract

Lezíria Grande is an important agricultural area in Portugal, prone to waterlogging and salinity problems due to the influence of estuarine tides on groundwater dynamics. Simple, non-invasive, practical approaches are need for monitoring soil salinity in the region and preventing further degradation of soil resources. The objective of this study was to develop regression models for soil salinity assessment in Lezíria Grande based on the relationship between multi-year crop reflectance data derived from Sentinel-2 multispectral imagery and rootzone salinity. Nine vegetation indices (VI), computed from the annual averages of the spectral bands, were tested between 2017 and 2019. The multi-year maximum from each pixel was then used for correlating the VI with the ground-truth dataset. This dataset was composed of average values of the electrical conductivity of the soil saturation paste extract (ECe mean) measured in 80 sampling sites (0–1.5 m depth) located in four agricultural fields representative of the salinity gradient in the region. The Canopy Response Salinity Index (CRSI), which uses the blue (490 nm), green (560 nm), red (665 nm), and infrared (842 nm) bands, provided the strongest correlation with measured data (r=−0.787). Regression models further considered vegetation cover and soil type as explanatory variables, with predictions resulting in a coefficient of determination (R2) ranging from 0.63 to 0.91 and a root mean square error (RMSE) varying from 1.63 to 3.26 dS m−1. The use of remote sensing data for soil salinity assessment showed to be an interesting option to consider in future soil monitoring programs. Nevertheless, more detailed covariates are needed for improving salinity assessment models at the regional scale.

Suggested Citation

  • Ramos, Tiago B. & Castanheira, Nádia & Oliveira, Ana R. & Paz, Ana Marta & Darouich, Hanaa & Simionesei, Lucian & Farzamian, Mohammad & Gonçalves, Maria C., 2020. "Soil salinity assessment using vegetation indices derived from Sentinel-2 multispectral data. application to Lezíria Grande, Portugal," Agricultural Water Management, Elsevier, vol. 241(C).
    • Handle: RePEc:eee:agiwat:v:241:y:2020:i:c:s0378377420309008
    DOI: 10.1016/j.agwat.2020.106387
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    1. Khan, Nasir M. & Rastoskuev, Victor V. & Sato, Y. & Shiozawa, S., 2005. "Assessment of hydrosaline land degradation by using a simple approach of remote sensing indicators," Agricultural Water Management, Elsevier, vol. 77(1-3), pages 96-109, August.
    2. Minhas, P.S. & Ramos, Tiago B. & Ben-Gal, Alon & Pereira, Luis S., 2020. "Coping with salinity in irrigated agriculture: Crop evapotranspiration and water management issues," Agricultural Water Management, Elsevier, vol. 227(C).
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    Cited by:

    1. Jiawei Hui & Zhongke Bai & Baoying Ye & Zihao Wang, 2021. "Remote Sensing Monitoring and Evaluation of Vegetation Restoration in Grassland Mining Areas—A Case Study of the Shengli Mining Area in Xilinhot City, China," Land, MDPI, vol. 10(7), pages 1-18, July.
    2. Romeu Gerardo & Isabel P. de Lima, 2022. "Sentinel-2 Satellite Imagery-Based Assessment of Soil Salinity in Irrigated Rice Fields in Portugal," Agriculture, MDPI, vol. 12(9), pages 1-20, September.

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