**1. Introduction**

116 Remote Sensing – Applications

Pontara, R.C.P. (1998). *Análise do comportamento espectral dos filitos carbonosos para interpretação de imagens*. Brasilia University, Academic dissertation, Brasília, Brazil. Schroder, V.F.; Filippini-Alba, J.M. (2010). Classificaçção de imagens orbitais com auxílio da

Schroder, V.F.; Filippini-Alba, J.M. (2010). Potencialidade do uso de imagens orbitais para

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análise por componentes principais no município de Montenegro – RS, 2009. In:

detecção de mudanças temporais: estudo de caso no município de Montenegro –

Soil salt content is a key factor that determines soil chemical quality together with soil reaction, charge properties and nutrient reserves (Lal et al., 1999). An adequate salt supply is essential for an optimum development of photosynthetic mechanism and other biochemical processes in plants (Sitte et al., 1994). Soil salt content constitutes an environmental problem when salt accumulation generates drastic changes in soil physical and chemical properties, adversely affecting soil productivity and plant growth (Richards, 1954; Qadir et al., 2000).

Salinization affects about 30% of the irrigated land of the world, decreasing this area approximately 1-2% per year due to salt-affected land surfaces (FAO, 2002). In Europe, about 1-3 million hectares of the land are affected by salinization (European Commission, 2003), and most of these areas are situated in the Mediterranean basin. In Spain, about 18% of the 3.5 million hectares of irrigated land are severely affected or at serious risk of soil salinization (European Commission, 2002). Soil salinization is a frequent problem in arid and semiarid regions like Southeast Spain (Hernández Bastida et al., 2004). In these areas, agriculture with a great water requirement combined with high water tables and an adverse climate (increased occurrence of extreme drought events) have forced irrigation with poor quality water, causing processes of soil degradation and salinization, limiting crop growth and the production capacity (Pérez-Sirvent et al., 2003; Acosta et al., 2011).

Evaluating the spatial variability of basic soil properties in saline soils, and mapping spatial distribution patterns of these soil properties helps to make effective site-specific management decisions (Ardahanlioglu et al., 2003). Accordingly, remote sensing techniques and geographic information systems (GIS) have introduced a new era for soil resources assessment and monitoring in terms of information quality (Mermut and Eswaran, 2001). *A priori* knowledge of spectral characteristics of remotely sensed materials is fundamental to any valuable quantitative analysis (Ben-Dor et al., 1997). The variety of absorption processes occurring in the soil and their wavelength dependence allow us to derive information about the chemistry of the minerals composing it from the reflected or emitted light (Clark, 1999). Reflectance spectra of soils are attributed to numerous soil properties. There are no narrow absorption bands linked to soil salinity status, since it is

Mapping Soil Salinization of Agricultural Coastal Areas in Southeast Spain 119

extensive use. Therefore, it is necessary to continue investigating the application of multispectral image repositories as a tool to assist in the monitoring and management of

This study will evaluate the applicability of various remote sensing techniques for studying salinization processes in an agricultural coastal area. One of the greatest difficulties in the application of remote sensing techniques to the study area is the fragmentation of the territory by the existence of small plots and buildings that create a dispersed mixture of spectral signals to the scale of a moderate spatial resolution multispectral remote sensing image as those acquired by the Landsat Thematic Mapper sensor. This difficulty motivates the need to evaluate various techniques and methodological approaches to carry out this

Representative soils of the area were sampled and their properties were characterized at the laboratory by standard methods. Predominant land cover classes at the soil sampling plots and at additional land cover validation points were identified. Land cover is a fundamental variable that impacts on and links many parts of the human and physical environments (Foody, 2002) with a great influence on soil properties (Caravaca et al., 2002; Majaliwa et al., 2010; Biro et al., 2011). Both kinds of information in a GIS database were included. In this sense, the effect of land cover on soil properties was statistically evaluated. Then, multispectral images were employed for a hard land cover mapping with a supervised approach using the k-nearest-neighbour classifier. Accuracy assessment methods highlighted the need to employ a mixed pixel focus to deal with the particularities of the study area. Spectral unmixing techniques allowed the identification of representative spectral endmembers and the obtainment of their corresponding fraction images. Finally, fraction endmembers were employed to characterize land cover classes and to predict soil

The study area is located in a coastal zone of Southeast Spain, in the province of Alicante. It is located around 38.14°N and 0.73ºW, at the south of the cities of Elche and Alicante. The study area (Figure 1) comprises alluvial plains resulting from the accumulation of sediments from the Segura and Vinalopó rivers. During most of the Holocene (~10,000 years ago to present) the study area was a large lagoon (Blázquez, 2003). In the last centuries, the ancient lagoon was transformed into an irrigated agricultural land draining the wetland. Nowadays, this area is a mixture of small-size cities, coastal urban areas, scattered residential houses, irrigated crops and isolated and scattered wetlands. The perimeter of the study area was delimited according to natural or man-made features in order to enclose a large coastal plain area primarily occupied by irrigated agricultural activities. The study area lies in the north with the natural parks of *El Hondo* and the *Salinas de Santa Pola*. Both natural areas are wetlands included in the RAMSAR list of wetlands of international importance. The east and south boundaries are the *Sierra del Molar* and the *Segura River* respectively. Urban areas and sclerophyllous vegetation mainly occupy the Sierra del Molar, while the Segura River is the most important watercourse in southeast of Iberian Peninsula providing water for irrigation agriculture and to fill the reservoirs that currently comprise

study as necessary to help monitoring the processes of salinization.

properties with various statistical methods.

**2.1 Description of the study area** 

saline soils.

**2. Material and methods** 

determined by soil properties such as pH, electrical conductivity, salt content and exchangeable sodium percentage (Csillag et al., 1993; Farifteh et al., 2008). In this sense, soil reflectance is derived from the particular spectral behaviour of the heterogeneous combination of minerals, organic matter and soil water (Ben-Dor and Banin, 1994). Saltaffected soils cations (Na+, Mg2+, K+, and Ca2+) and anions (Cl-, SO42-, CO3 2- and HCO3-) can be detected by optical spectrometers since salt minerals have diagnostic spectral features occurring in the visible and near infrared (VNIR) and short-wave infrared (SWIR) spectral regions (Farifteh et al., 2008). Saline soils usually have evaporate minerals, which spectral features that can be explained by vibrational absorption due to water molecules chemically bound as part of the crystal structure (Howari et al., 2000). In this sense, the spectral differences of evaporates of single salt compounds are determinant of the type and mineralogy of the soils (Howari et al., 2000).

Remote sensing has been extensively employed in soil salinity studies. Data from aerial photography, videography, and optical, thermal, microwave or geophysical sensors has been used in soil salinity mapping (Metternich and Zinck, 2003). Perhaps, the most widely used remote sensing data in recent decades have been provided by multispectral (Landsat, SPOT, IRS, ASTER) or hyperspectral (DAIS, HyMap, AVIRIS, Hyperion) sensors in the spectral range approximately between 400 and 2500 nm. Researchers have frequently employed remote sensing data to map soil salinity with multispectral (Metternich and Zinck, 1997; Dwivedi et al., 2001; Melendez-Pastor et al., 2010a) and hyperspectral images (Dehaan and Taylor, 2002, 2003; Schmid et al., 2009, Ghrefat and Goodell, 2011). Pioneering studies in the 1970s employed air-borne and satellite-borne multispectral scanners to detect soil salinity, indicating the better capability of infrared bands over visible bands to locate saline soils and the low contribution of thermal bands to improve the delineation of saline areas (Richardson et al., 1976; Dalsted et al., 1979). Nowadays, imaging spectroscopy techniques are employed for the automatic detection of soil salinization with airborne or satellite sensor (Dehaan and Taylor, 2002, 2003; Dutkiewicz et al., 2009; Schmid et al., 2009; Weng et al., 2009; Melendez-Pastor et al., 2010a; Ghrefat and Goodell, 2011). Imaging spectroscopy deals with the mapping of ground materials by detecting and analysing reflectance/absorbance features in hyperspectral (or multispectral) images (Clark, 1999). Imaging spectroscopy adds a new dimension of remote sensing by expanding point spectrometry into a spatial domain and under field conditions, which is a very good approach for the study of soil properties (Ben-Dor et al., 2009).

The aim of this chapter is the application of remote sensing for the study of soil salinity of an agricultural area in southeast coast of the Iberian Peninsula. Different digital image processing techniques were applied to satellite multispectral images (Landsat TM). 'Conventional' hard classification techniques were combined with spectral mixture analysis and soil properties to achieve a better understanding of the soil salinization process in the study area.

Multispectral satellite images such as those obtained by the Landsat program provide low or free cost worldwide coverage for four decades. Moreover, salinization problems are concentrated in arid and semi-arid regions, often in developing countries with few economic resources. Although there are more advanced sensors that can provide a more precise quantification of the extent of soil salinity (e.g. hyperspectral), their high cost difficult its
