# SoilSaltIndex: Soil Salinity Indices Generation using Satellite Data

Authors - Nirmal Kumar and Nobin Chandra Paul

## Welcome to the SoilSaltIndex vignette

This vignette will guide users through the features and functionalities of the SoilSaltIndex package, which enables the generation of soil salinity indices using satellite data by utilizing multiple spectral bands. These indices are important in remote sensing applications for monitoring soil salinity, which impacts agricultural productivity and land management.

## Introduction

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The developed function is designed to facilitate the generation of soil salinity indices using satellite data, efficiently utilizing multiple spectral bands to identify and analyze salt-affected soils. The package uses bands such as Blue, Green, Red, Near-Infrared (NIR), and Shortwave Infrared (SWIR1, SWIR2) to compute a comprehensive set of 24 salinity indices. One of the key features of the function is its flexibility. Users can provide any combination of the required spectral bands, and the function will automatically calculate only the relevant indices based on the available data. This dynamic capability ensures that users can maximize the utility of their data without needing all spectral bands, making the package versatile and user-friendly. Outputs are provided in GeoTIFF file format, facilitating easy integration with GIS workflows.

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    ##Installation and loading the library of SoilSaltIndex R package
    # You can install the SoilSaltIndex package from CRAN using the following command:
    # install.packages("SoilSaltIndex")
    # Once installed, you can load the package using
    # library(SoilSaltIndex)

    ###  Generating Salinity Indices using spectral bands

    # Example:
    #'Example usage:
    #'Importing all the required spectral bands from extdata foldar
    library(SoilSaltIndex)
    library(raster)

    ## Loading required package: sp

    B <- raster::raster(system.file("extdata", "Blue.tif", package = "SoilSaltIndex"))
    G <- raster::raster(system.file("extdata", "Green.tif", package = "SoilSaltIndex"))
    R <- raster::raster(system.file("extdata", "Red.tif", package = "SoilSaltIndex"))
    NIR <- raster::raster(system.file("extdata", "NIR.tif", package = "SoilSaltIndex"))
    SW1 <- raster::raster(system.file("extdata", "SWIR1.tif", package = "SoilSaltIndex"))
    SW2 <- raster::raster(system.file("extdata", "SWIR2.tif", package = "SoilSaltIndex"))

    # Salinity Indices generation using all the bands
    Salt_Index1 <- Soil_Salinity_Indices(B=B, G=G, R=R, NIR=NIR, SW1=SW1, SW2=SW2)
    # Relevant Salinity indices generation using only SWIR1 AND SWIR 2 bands
    Salt_Index2 <- Soil_Salinity_Indices(SW1=SW1, SW2=SW2)
    # Relevant Salinity indices generation using only Blue, Green, Red and NIR bands
    Salt_Index3 <- Soil_Salinity_Indices(B=B, G=G, R=R, NIR=NIR)

# Salinity Indices Formulas

Here are the formulas for the salinity indices used in the package:

1. Normalized Difference Salinity Index (NDSI): \((R - NIR) / (R + NIR)\) ; (Reference: Major et al., 1990)
2. Salinity Index 1 (SI1): \(sqrt(G*R)\) ; (Reference: Khan et al., 2005)
3. Salinity Index 2 (SI2): \(sqrt(G^2 + R^2 + NIR^2)\) ; (Reference: Douaoui et al., 2006)
4. Salinity Index 3 (SI3): \(sqrt(G^2 + R^2)\) ; (Reference: Douaoui et al., 2006)
5. Salinity Index 4 (SI4): \(SW1 / SW2\) ; (Reference: INDP, 2002)
6. Salinity Index 5 (SI5): \((NIR - SW1) / (NIR + SW1)\) ; (Reference: INDP, 2002)
7. Salinity Index 6 (SI6): \((SW1 - SW2) / (SW1 + SW2)\) ; (Reference: INDP, 2002)
8. Salinity Index 7 (SI7): \(B / R\) ; (Ref:Abbas and Khan, 2007; Abbas et al., 2013)
9. Salinity Index 8 (SI8): \((B - R) / (B + R)\) ; (Ref:Abbas and Khan, 2007; Abbas et al., 2013)
10. Salinity Index 9 (SI9): \((G * R) / B\) ; (Ref:Abbas and Khan, 2007; Abbas et al., 2013)
11. Salinity Index 10 (SI10): \(sqrt(B * R)\) ; (Ref:Abbas and Khan, 2007; Abbas et al., 2013)
12. Salinity Index 11 (SI11): \((B * R) / G\) ; (Ref:Abbas and Khan, 2007; Abbas et al., 2013)
13. Salinity Index 12 (SI12): \((R * NIR) / G\) ; (Ref:Abbas and Khan, 2007; Abbas et al., 2013)
14. Salinity Index 13 (SI13): \(sqrt(R * NIR)\) ; (Ref:Dehni and Lounis, 2012)
15. Salinity Index 14 (SI14): \((R / NIR) * 100\) ; (Ref:Allbed et al., 2014)
16. Canopy Response Salinity Index (CRSI): \(sqrt(((R * NIR) - (G * B)) / ((R * NIR) + (G * B)))\) ; (Ref: Scudiero et al., (2014;2015))
17. Combined Spectral Response Index (COSRI): \(((B + G) / (R + NIR)) * ((NIR - R) / (NIR + R))\) ;(Ref: Fernandez-Buces et al., 2006)
18. NIR-SWIR Salinity Index (NSI): \((SW1 - SW2) / (SW1 - NIR)\) ; (Ref: Abuelgasim and Ammad, 2019)
19. ASTER Salinity Index (SI.ASTER): \((SW1 - NIR) / (SW1 + SW2)\) ; (Ref: Abuelgasim and Ammad, 2019)
20. Soil Salinity and Sodicity Index 1 (SSSI1): \(SW1 - SW2\) ; (Ref: Abuelgasim and Ammad, 2019)
21. Soil Salinity and Sodicity Index 2 (SSSI2): \((SW1 * SW2 - SW2 * SW2) / SW1\) ; (Ref: Abuelgasim and Ammad, 2019)
22. Vegetation Soil Salinity Index (VSSI): \(2 * G - 5 * (R + NIR)\) ; (Ref:Dehni and Lounis, 2015 )
23. Salinity Index Tripathi (SIT): \(NIR/SW1\) ; (Ref:Tripathi et al., 1997 )
24. Brightness Index (BI): \(sqrt(R^2+NIR^2)\) ; (Ref:Khan et al., 2005 )