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Soil salinity assessment using vegetation indices derived from Sentinel-2 multispectral data. application to Lezíria Grande, Portugal
Publication . Ramos, Tiago B.; Castanheira, Nádia; Oliveira, Ana R.; Paz, Ana Marta; Darouich, Hanaa; Simionesei, Lucian; Farzamian, Mohammad; Gonçalves, Maria C.
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
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Fundação para a Ciência e a Tecnologia
Programa de financiamento
3599-PPCDT
Número da atribuição
150010