ORIGINAL RESEARCH
Determining Salinity and Ion Soil Using Satellite Image Processing
 
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1
Facultad de Ingeniería Mochis, Universidad Autónoma de Sinaloa, Fuente de Poseidón y Ángel Flores s/n, Jiquilpan, Los Mochis, Sinaloa, México
2
Escuela de Ciencias Económicas y Administrativas, Universidad Autónoma de Sinaloa, San Joachín, Guasave, Sinaloa, México
3
Centro de Investigación Científica y de Educación Superior de Ensenada, Baja California (CICESE), Ensenada, México
4
Escuela de Biología, Universidad Autónoma de Sinaloa, Culiacán Sinaloa
CORRESPONDING AUTHOR
Víctor Manuel Peinado Guevara   

Escuela de Ciencias Económicas y Administrativas, Universidad Autónoma de Sinaloa, San Juachín, Guasave, 81049 Guasave, Mexico
Online publish date: 2018-12-12
Publish date: 2019-02-18
Submission date: 2017-11-13
Final revision date: 2017-12-29
Acceptance date: 2018-01-02
 
Pol. J. Environ. Stud. 2019;28(3):1549–1560
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ABSTRACT
Arid and semi-arid zones frequently present salinity problems in soils. The agriculture of the municipality of Ahome, Sinaloa has an agricultural region where its soils are characterized by problems of salinity and sodicity – conditions that reduce production. Salinity can be detected by implementing remote sensing techniques; there are ways to enhance the detection of satellite salinity through the use of diverse quantitative models, using the spectral signature of each of the components of the study area through algorithms named indices. For this study we used the normalized differential salinity index (NDSI) from a Landsat OLI image for the southern area of the city, which is related to the electrical conductivity (EC) of the soils (R = 0.90). At the same time, it is related to some anions and cations. As a result, it is possible to determine since the NDSI, the anion Cl– and Cations Na+, Ca++, and Mg++. We found a relationship between EC - Cl– (R = 0.94), EC - Na+ (R = 0.84), EC - Ca++ (R = 0.85), and EC-Mg++ (R = 0.86). The electrical conductivity in the field and laboratory, anions, cations, and NDSI index were filtered with the Kalman filter obtaining better fitter, eliminating dispersivity in the variable relations.
eISSN:2083-5906
ISSN:1230-1485