Determining Salinity and Ion Soil Using Satellite Image Processing

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.

Assessment of agricultural land salinization via soil analysis and remote sensing data: Case study in Pavlodar region, Kazakhstan

“…Several studies have found a positive correlation between NDSI and laboratory data. In studies (Avila Aceves et al 2019;Laiskhanov et al 2016), NDSI provided greater accuracy in the study area, so the obtained values were correlated with soil electrical Our results confirm the correlation and the normalized difference salinity index showed the highest correlation between the results of cations, SAR, ESP (Figure 3 and 4).…”

Assessment of the Impact of the Syrian Conflict on the Extent and Quality of Agricultural Land in the Sabkhat al-Jabbul Area (SJA)

“…Remote sensing technology is one of the most effective tools used for soil salinity detection. Researchers from India assessed the feasibility of using remote sensing technology to detect spatio-temporal patterns in soil salinity [14]. They explored different remote sensing satellite systems to delineate the soil salinity boundaries, (e.g.…”

“…They explored different remote sensing satellite systems to delineate the soil salinity boundaries, (e.g. Landsat-7 ETM+ and IRS data) by applying different indices, for example, Salinity Index (SI), Normalized Difference Salinity Index (NDSI), Brightness Index (BI), and Wetness Index (WI) [13]- [14].…”

“…Likewise, Aceves et al, (2018) used Landsat OLI image data to detect the soil salinity in and agricultural region in Ahome, Sinaloa, Mexico, using the normalized differential salinity index (NDSI) [14]. In response to salinity becoming a critical environmental problem affecting the agricultural production and food security in central and southern Iraq, Hasab et al, (2015) developed a new algorithm to retrieve salinity and normalized difference vegetation index (NDVI) from optical remote sensing Landsat-8 (OLI/TIRS) data [15].…”

Soil salinity mapping using Landsat 8 OLI data and regression modeling in the Great Hungarian Plain

“…Using spectral response extracted directly from sensors or after the application of spectral transformations, i.e., principal component analysis (PCA) [6,7], tasseled cap transformation [8], and spectral indices [9,10], have given promising results in terms of prediction accuracy. Many scholars emphasize the importance of spectral signature in remote sensing studies as a core concept in this field [11][12][13][14]. Digital soil mapping has been the focus of several research projects operated with different types of remotely sensed data, using statistical or geostatistical methods.…”