**2. Characterization of soil salinity**

Mougenot et al. [12] argues that visible reflectance of leaves from plants growing on nonsalt affected leaves before plant maturation is higher than after maturity. In addition, visible reflectance of leaves from plants growing on salt affected soil is lower than the visible reflectance of plants growing on non-salt affected soils. Near-infrared reflectance increases without water stress due to a succulent (cell thickening) effect and increases in other cases. Spatial information on soil moisture can be accessed through bands in the near- and middle-infrared spectral bands; this is confirmed by [13]. The study showed that near- to middle-infrared indices are indicators for chlorosis in stressed crops normalized difference for TM bands 4 and 5. This new ratio is however dull to color variations and provides an indication of leaf water potential. In addition [14] showed that chlorotic canopies could be distinguished from healthy canopies, as biophysical response to a salinity can be seen in low fractional vegetation cover, low leaf-area index (LAI), high albedo, low surface roughness and high surface resistance compared with healthy crops. This is because healthy vegetation absorbs most of the visible light hitting it and reflects a large portion of the near infrared light. However, sparse vegetation (right) reflects more visible light and less near infrared light [15]. Traditionally, electrical conductivity (EC) measured in dS/m is used to determine the soil salinity on a small field with the aid of hand-held conductivity meter, while on a large scale it is measured and mapped using electromagnetic (EM) conductivity meter [15]. These approaches (traditional and geospatial) are used to quantify the density of plant growth on the earth visible radiation minus near-infrared radiation divided by near-infrared radiation plus visible radiation resulting into vegetation indices [16]. **Table 1** gave the criteria for classifying soil salinity.

#### **2.1. Geographical information system in soil salinity modeling**

During the last decade, there has been a proliferation of geospatial data in natural resource management including in the disciplines of forestry, fishery management, geology, geomorphology,


of such measurements to the nature and distribution of surface materials and atmospheric conditions. Remote sensing system operation involves the detection, collection and interpretation of data from distance by mean of sensors. The reflectance of electromagnetic radiations from the features at the earth surface is measured with the aid of a sensor, while the radiated energy is transmitted through space in waveform. For land resources survey using remote sensing, wavelengths between 0.4 and 2.4 nm are commonly used. Generally, the electromagnetic spectrum ranges from gamma rays, with wavelength of less than 0.03 nm, to radio energy with a wavelength of more than 30 cm. According to [12], the presence of salts at the terrain surface can be detected from remotely sensed data either directly on bare soils, with salt efflorescence and crust, or indirectly through vegetation type and growth as these are controlled or affected by salinity. Better understanding of the relationship can advance the use of remote sensing for soil studies between soil properties and surface reflectance. Salt affected soils in arid regions, especially when a salt crust whitish color is formed show a high reflectance. Effective application of remote sensing data requires one to have the technical expertise and understand the spectra characteristics of the particular features to be studied. In addition, an understanding of the behavior of different wavelength regions on different soil materials and surface conditions may increase the efficiency of the study of soil salinity based

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The change in soil color and the change in soil reflectance properties caused excess soil moisture, which can be easily detected by remote sensing. Plant response is one means of detecting poorly drained soils in California mainly because of a build-up of the water table. On the other hand, Salman [24] reported that as result of excess organic matter, soil color is generally darker in poorly drained areas than well-drained soils. The visible bands in Landsat-MSS data can be used to classify this color. According to [25] as cited in [26] pointed out that color infrared photography could indicate drainage problems by soil moisture saturation or plant stress. Shallow water tables exhibit a rise in surface moisture, which can be detected from visible reflectance and microwave emissivity. The information about drainage basin area and drainage pattern can be obtained from satellite imagery. Water logging and problems associated with drainage can be examined through GIS by identifying the drainage network and its characteristics in a basin, besides the report on the presence of high water table, high mor-

phology, soil color, plant stress and drainage water collection in lower spots [27].

salinity levels with errors between moderately saline and normal soils.

For relatively large areas, remote sensing is an essential tool for mapping and surveying saltaffected and waterlogged soils [28]. The understanding of the actual conditions at the earth surface makes it feasible to interpret the satellite images. However, because of lack of specific absorption bands and spectral confusion, it is complex to distinguish the degree of salinity through remote sensing approach [29] that separated different soil salinity level using Landsat imagery. Most authors are capable to differentiate only 2–3 classes (strong and medium) of

on remote sensing [23].

**2.3. Mapping of waterlogging**

**2.4. Application to large areas**

**Table 1.** Soil salinity classification.

hydrology, wildfire, and climate change [17]. Geographic Information System (GIS) enables the measurement and representation of geographic phenomena and thereafter transform this spatial information into various forms while interacting with social structures [18]. GIS has the benefit of its ability to interrelate spatially multiple types of information and data obtained from a range of sources. In addition based on the findings of [19], GIS technique has the capacity as an effective tool for spatial analyses and modeling of concentrations of air pollutants. GIS also has three basic key functions, which include database management, spatial analysis and visualization. Thus with these three components, this system (GIS) provides the capabilities of linking the concentration of the air pollutants with their geographic location [19]. From their study, it was also discovered that GIS technology could effectively represent the spatial relationships between sources and receptors as it can integrate with satellite remote sensing data in order to spatially analyze the relationship between the geographic location of air pollutants and the land use/land cover classes of the area. Models are representation of reality and they are created as a simplified, manageable view of reality, due to the inherent complexity of the earth and the diverse interactions in it. Models help to understand, describe, or predict how things work in the real world. Models are broadly classified into two namely representation and process models. Representation models are those that represent the objects in the landscape while those that attempt to simulate processes in the landscape are process models [20]. Representation models describe the objects in the landscape, such as buildings, streams, or forest while process models describe the interaction of the objects that are modeled in the representation model. There are different types of process models including suitability modeling, distance modeling and hydrological modeling [21].

#### **2.2. Remote sensing for soil salinity mapping**

The essence of remote sensing is the measuring and recording of the electromagnetic radiation emitted or reflected by the earth's surface [22]. For soil salinity investigation, this may be useful where salty soil, salt-affected vegetation, saline water, pond water and high water table area give contrasting reflectance with other landscape features so that they can be unambiguously distinguished. Remote Sensing (RS) is the art and science of obtaining information about an object, area or phenomenon through analysis of the data acquired by such device that is not in contact with the object, area or phenomenon under consideration. It is the measurement of object properties on Earth's surface using data acquired from aircraft and satellites. Rather than in situ, RS attempts to measure something at a distance. It involves measuring, recording and transmission of electromagnetic energy by the sensors mounted on aircraft or satellite reflected from or emitted by object from vantage point above the surface and relating of such measurements to the nature and distribution of surface materials and atmospheric conditions. Remote sensing system operation involves the detection, collection and interpretation of data from distance by mean of sensors. The reflectance of electromagnetic radiations from the features at the earth surface is measured with the aid of a sensor, while the radiated energy is transmitted through space in waveform. For land resources survey using remote sensing, wavelengths between 0.4 and 2.4 nm are commonly used. Generally, the electromagnetic spectrum ranges from gamma rays, with wavelength of less than 0.03 nm, to radio energy with a wavelength of more than 30 cm. According to [12], the presence of salts at the terrain surface can be detected from remotely sensed data either directly on bare soils, with salt efflorescence and crust, or indirectly through vegetation type and growth as these are controlled or affected by salinity. Better understanding of the relationship can advance the use of remote sensing for soil studies between soil properties and surface reflectance. Salt affected soils in arid regions, especially when a salt crust whitish color is formed show a high reflectance. Effective application of remote sensing data requires one to have the technical expertise and understand the spectra characteristics of the particular features to be studied. In addition, an understanding of the behavior of different wavelength regions on different soil materials and surface conditions may increase the efficiency of the study of soil salinity based on remote sensing [23].

#### **2.3. Mapping of waterlogging**

hydrology, wildfire, and climate change [17]. Geographic Information System (GIS) enables the measurement and representation of geographic phenomena and thereafter transform this spatial information into various forms while interacting with social structures [18]. GIS has the benefit of its ability to interrelate spatially multiple types of information and data obtained from a range of sources. In addition based on the findings of [19], GIS technique has the capacity as an effective tool for spatial analyses and modeling of concentrations of air pollutants. GIS also has three basic key functions, which include database management, spatial analysis and visualization. Thus with these three components, this system (GIS) provides the capabilities of linking the concentration of the air pollutants with their geographic location [19]. From their study, it was also discovered that GIS technology could effectively represent the spatial relationships between sources and receptors as it can integrate with satellite remote sensing data in order to spatially analyze the relationship between the geographic location of air pollutants and the land use/land cover classes of the area. Models are representation of reality and they are created as a simplified, manageable view of reality, due to the inherent complexity of the earth and the diverse interactions in it. Models help to understand, describe, or predict how things work in the real world. Models are broadly classified into two namely representation and process models. Representation models are those that represent the objects in the landscape while those that attempt to simulate processes in the landscape are process models [20]. Representation models describe the objects in the landscape, such as buildings, streams, or forest while process models describe the interaction of the objects that are modeled in the representation model. There are different types of process models including suitability modeling, distance modeling and hydrological modeling [21].

**Degree of salinity Salinity ECe (dS/m)**

Slight 4–8 Moderate 8.25 Strong ˃25

Source: [15].

**Table 1.** Soil salinity classification.

68 Multi-purposeful Application of Geospatial Data

The essence of remote sensing is the measuring and recording of the electromagnetic radiation emitted or reflected by the earth's surface [22]. For soil salinity investigation, this may be useful where salty soil, salt-affected vegetation, saline water, pond water and high water table area give contrasting reflectance with other landscape features so that they can be unambiguously distinguished. Remote Sensing (RS) is the art and science of obtaining information about an object, area or phenomenon through analysis of the data acquired by such device that is not in contact with the object, area or phenomenon under consideration. It is the measurement of object properties on Earth's surface using data acquired from aircraft and satellites. Rather than in situ, RS attempts to measure something at a distance. It involves measuring, recording and transmission of electromagnetic energy by the sensors mounted on aircraft or satellite reflected from or emitted by object from vantage point above the surface and relating

**2.2. Remote sensing for soil salinity mapping**

The change in soil color and the change in soil reflectance properties caused excess soil moisture, which can be easily detected by remote sensing. Plant response is one means of detecting poorly drained soils in California mainly because of a build-up of the water table. On the other hand, Salman [24] reported that as result of excess organic matter, soil color is generally darker in poorly drained areas than well-drained soils. The visible bands in Landsat-MSS data can be used to classify this color. According to [25] as cited in [26] pointed out that color infrared photography could indicate drainage problems by soil moisture saturation or plant stress. Shallow water tables exhibit a rise in surface moisture, which can be detected from visible reflectance and microwave emissivity. The information about drainage basin area and drainage pattern can be obtained from satellite imagery. Water logging and problems associated with drainage can be examined through GIS by identifying the drainage network and its characteristics in a basin, besides the report on the presence of high water table, high morphology, soil color, plant stress and drainage water collection in lower spots [27].

#### **2.4. Application to large areas**

For relatively large areas, remote sensing is an essential tool for mapping and surveying saltaffected and waterlogged soils [28]. The understanding of the actual conditions at the earth surface makes it feasible to interpret the satellite images. However, because of lack of specific absorption bands and spectral confusion, it is complex to distinguish the degree of salinity through remote sensing approach [29] that separated different soil salinity level using Landsat imagery. Most authors are capable to differentiate only 2–3 classes (strong and medium) of salinity levels with errors between moderately saline and normal soils.

#### **2.5. Techniques in monitoring soil salinity**

The classification of salt affected soils, assessment of the percentage of severity particularly in its early stage is important in terms of sustainable agricultural management [30]. Various approaches have been employed by researchers to analyze and monitor soil salinity. The three major techniques commonly used in soil salinity determination include traditional method, Electromagnetic Induction method and Remote Sensing and GIS method. The traditional or conventional methods used for detecting soil properties include ground-based geophysics and laboratory analysis methods [31]. Adeniran et al. [5] used this method to determine electrical conductivity of soil in Omi irrigation scheme by carrying out chemical analysis of the soil samples. The disadvantages of traditional method includes; time consuming, costly since dense sampling is required to adequately characterize the spatial variability of an area and demanding when considering large areas [32, 33]. Remote sensing methods are suitable for detecting, monitoring and controlling soil salinity. Researchers have used GIS and RS techniques to model, assessed, and investigate land use and land cover pattern, detect, map, monitor and forecast soil salinity on an irrigation scheme [34]. Ojo et al. [15] stated the advantages of remote sensing and GIS method includes; time saving, wide range of coverage, facilitation of faster and long term monitoring. Electromagnetic Induction (EMI) was first employed in agriculture to detect saline soils by measuring its electrical conductivity [35]. Electromagnetic approaches are reliable means used for rapid determination of soil salinity [36]. Spatially varying soil types and properties are identified easily and map out quickly with the application of EMI as it offers unique benefit over traditional methods.

Landsat TM data with the depth and mineralization rate of groundwater to create soil salinity map. In Israel, hyper-spectral airborne sensor data were processed to yield quantitative

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Landsat image consists of three separate instrument subsystem, each operating in a different spectral region, using a separate optical system [54]. These subsystems are the Visible and Near Infrared (VNIR), the Short Wave Infrared (SWIR), and the Thermal Infrared (TIR), respectively. Landsat data has 14 bands allocated in three spectral regions as VNIR (band 1, 2, 3) with 15 m resolution, SWIR (bands 4–9) with 30 m resolution and TIR (bands 10–14) with 90 m

With the launching of the Landsat satellite in 1972, researchers began to use satellite data for monitoring environmental activities in different parts of the world [56]. Landsat provides basic tools for working with satellite imagery as automated geo-referencing and cloud detection. Landsat consist of functions for radiometric normalization and various approaches to atmospheric correction. It also includes useful functions such as bare soil line and tasseled cap calculations [57]. The physiological condition of a crop is shown best at TM 5 and TM7. Landsat 5 (TM) that was launched on 01/03/1984 and Landsat 7 (ETM+) on 15/04/1999, each revisit a location every 16 days, and the two orbits are staggered for images to be taken every 8 days. Due to a hardware failure on 31/05/2003, Landsat 7 scenes are now missing 22% of the pixels also severe problem occurs toward the edges of an image. Band attributes are largely coherent, but ETM+ added an additional band. Landsat images haves been converted to integer digital numbers (DN) before distribution to facilitates storage and display. Atmospheric correction, topographic correction and conversion to radiance or reflectance may be required. Minimal processing may be needed if a single image or images widely separated in time are used to examine gross changes. However, careful correction is needed to examine detailed comparison of vegetation indices from multiple images. The most accurate atmospheric corrections need ground data collected during the satellite image capturing. For retrospective studies, this is impossible to obtain, and less-accurate image-based correction method must be used [58]. Band Wavelength and resolution for Landsat 5 Thematic Mapper (TM) and Landsat 7 Thematic Mapper (TM) is shown in **Tables 2** and **3** respectively. Panah and Goossens [23] claimed that thermal band of Landsat (TM) imagery is a good source of information that may have a vital role in soil salinity studies and in detecting gypsiferous soils in arid region. The reflective bands 1, 3, 4 and 7 are the best band composition for preparing the color composite

Land-use and land-cover change being one of the major driving forces of global ecological change, is vital to the sustainable development discussion. One of the most accurate methods

maps of soil salinity [53].

**2.7. Landsat image**

resolution [55].

images [59].

**2.9. Land use and land cover (LU/LC)**

**2.8. Landsat platform characteristics**

#### **2.6. Soil mapping**

There are varieties of methods to identify and map surface features using remotely sensed imagery. Techniques for mapping soil surface conditions, such as salinity and waterlogging are based on the presence or absence of spectral absorption features. Soil mapping include locating and identifying the various soils that occur, nature and properties, collecting information about soil location and recording this information on maps and in supporting documents to show their spatial distribution. Seghal et al. [37] applied Landsat MSS data for mapping salt affected soils in the frame of the reconnaissance soil map of Indian. Dwivedi [38] used Landsat MSS and TM data for more detailed mapping and monitoring of salt affected soils in the Indo-Gangetic alluvial plain. Landsat TM data have proved useful for mapping depositional environments on playas Tunisia [39]. Crowley [40] reported that gypsum and halite were likely to be the only evaporate phases detected and mapped on the Chott el Dyerid using TM data. Mehrjardi et al. [41] used Landsat TM+ taken in 2002 to map soil salinity in Ardakane Yazd by using an exponential model. They used band 3 of the images and soil salinity parameter in a regression analysis (R2 = 0.58) and reported a map accuracy of 0.87% and K coefficient equal to 0.47%. Various remote sensing data such as aerial photos, video, images, infrared thermography, visible and infrared multispectral, microwave and airborne geophysical data, is available for monitoring, classification and mapping out of saline soil [42]. According to [43] several authors have dealt with the study of soil salinization using satellite data, among them [12, 44–51]. In China, Peng [52] integrate Landsat TM data with the depth and mineralization rate of groundwater to create soil salinity map. In Israel, hyper-spectral airborne sensor data were processed to yield quantitative maps of soil salinity [53].
