**3. Results and discussion**

The relationship between various soil properties and land cover classes was analysed. Two approaches to the study of land cover are presented. A first categorization based on discrete land cover classes and another based on mixture fractions. Finally, statistical models for predicting soil properties of interest in the study of soil salinity through the use of mixture fractions are presented.

### **3.1 Soil properties**

The study was focused on soil electrical conductivity (EC), pH and organic carbon (OC) (Table 2). These properties are important in chemical and biological quality of soils (Lal et al., 1999). Previous studies in semiarid areas combining remote sensing and soil analyses have indicated significant differences in these properties in different land cover classes (Biro et al.,


Table 2. Descriptive statistics (mean ± standard deviation) of soil properties based on land cover classes. The *p*-value and homogeneous subgroups (lower case letters; Tukey test, *P* < 0.05) resulting from the ANOVA test are included.

The possible existence of differences in soil properties based on the land cover classes was determined by the use of the analysis of variance (ANOVA). ANOVA is used to evaluate significant differences between means of independent variables. The observed variance of independent variables is partitioned into components by several explanatory variables (factors). Land cover class was the factor employed in the analysis. Post-hoc analysis was

Relationships between fraction endmembers and soil properties were studied by the principal component analysis (PCA). PCA is a technique of data dimensionality reduction that performs an orthogonal transformation to convert potentially correlated input variables into uncorrelated variables or principal components. The first components accumulate most

Regression analyses between soil properties and fraction endmembers were performed for quantitative estimation of soil properties. A linear regression analysis applying a stepwise method for variable entry and removal was the selected statistical technique. Model selection was based on the lower typical error of the estimation and minimum collinearity.

The relationship between various soil properties and land cover classes was analysed. Two approaches to the study of land cover are presented. A first categorization based on discrete land cover classes and another based on mixture fractions. Finally, statistical models for predicting soil properties of interest in the study of soil salinity through the use of mixture

The study was focused on soil electrical conductivity (EC), pH and organic carbon (OC) (Table 2). These properties are important in chemical and biological quality of soils (Lal et al., 1999). Previous studies in semiarid areas combining remote sensing and soil analyses have indicated significant differences in these properties in different land cover classes (Biro et al.,

Land covers EC (mS/cm) pH OC (%) Arable land 1.38 ± 1.01 *ab* 8.23 ± 0.26 *ab* 1.92 ± 0.85 *a* Permanent crops 0.70 ± 1.00 *b* 8.40 ± 0.26 *b* 1.29 ± 0.24 *a* Fallow/abandoned 3.52 ± 2.43 *ac* 8.14 ± 0.25 *ab* 1.46 ± 0.36 *a* Saltmarsh 3.98 ± 2.86 *c* 8.05 ± 0.07 *ab* 3.61 ± 0.81 *b* Palm groves 3.82 ± 2.42 *ac* 8.20 ± 0.20 *ab* 2.11 ± 1.00 *a* Marsh 4.71 ± 2.04 *c* 7.82 ± 0.09 *a* 5.24 ± 1.39 *c P*-value <0.001\*\*\* <0.001\*\*\* <0.001\*\*\*

Table 2. Descriptive statistics (mean ± standard deviation) of soil properties based on land cover classes. The *p*-value and homogeneous subgroups (lower case letters; Tukey test, *P* <

0.05) resulting from the ANOVA test are included.

of the variance and therefore, the most useful information about the variables.

**2.6 Statistical methods** 

performed using Tukey method.

**3. Results and discussion** 

fractions are presented.

**3.1 Soil properties** 

2011). Cultivated areas (i.e. arable land and permanent crops, homogenous subgroup *b*) have lower electrical conductivity values than natural or semi-natural vegetation. The construction of drainage systems at agricultural areas to encourage the leaching for salinity control has been a traditional amelioration strategy (Qadir et al., 2000). This fact explains that marshes and saltmarshes soils have higher EC values than the other land cover classes, since they are areas with poor drainage and temporally flooded. Salinity increases when farming finishes (i.e. fallow/abandoned) because irrigation water is not available to promote salts leaching.

The pH values were slightly alkaline but significantly different for marshes. Wetland soils are characterized by the permanent or seasonal inundation of the land, promoting anaerobic conditions and thus reduced redox conditions (high concentration of H+ which implies low pH)(Reddy et al., 2000). The organic carbon content was also different depending on the type of land cover. Arable land and permanent crops soils have organic carbon content ranging from 1.46 to 2.11% that is not very high (Pérez-Sirvent et al., 2003). Opposite, wetland soils (i.e. stable saltmarshes and saltmarshes) exhibited the highest organic carbon contents. Compared to upland areas, most wetland soils show an accumulation of organic matter by the higher rates of photosynthesis in wetlands than other ecosystems and the lower rates of decomposition due to anaerobic conditions (Reddy et al., 2000).

All soil properties are significantly correlated (P<0.01) according to the Pearson bivariate correlation test applied to the full dataset. Figure 3 shows two scatterplots of the land cover classes average values (error bars represent the standard deviation) of pH and organics carbon versus electrical conductivity (EC). EC is negatively correlated with pH (R=-0.61) and positively correlated with organic carbon (R=0.34), while pH and organic carbon are negatively correlated (R=-0.32). Two sets of distinct land uses mainly dependent on the EC values are distinguished: 1) active cultures: with low EC and OC values, and 2) natural vegetation and crops of low requirements (palm groves): with high EC and OC values, increasing as the land cover is more similar to the wetland. Palm groves are the most halotolerant crop and require little tillage.

#### **3.2 Land cover classification**

A land cover map was obtained with the *k*-nearest-neighbours algorithm (Figure 4). Optimum results were obtained with *k*=4. The area occupied by the land cover classes (hectares and percentage of the total area) was quantified (Table 3).

The study area is mainly agricultural but largely occupied by urban areas. Urban/manmade areas represent 14.4% of the study area. There is a clear distinction between the northeast portion (area between the two natural parks and the *Sierra del Molar*) with large fields and less presence of buildings, and the rest of the study area, with numerous buildings scattered, villages, small-size towns and smaller parcels. Dominant land cover classes are also different at these two sectors. Close to the natural parks, there are many saltmarshes (7.09% of the study area), marshes (1.95% of the study area), palm groves (2.21% of the study area) and arable land (45.76% of the area and mainly forage, barley and melons). The other sector has a massive presence of permanent crops (16.3% of the study area and mainly citrus trees such as orange and lemon trees).

Mapping Soil Salinization of Agricultural Coastal Areas in Southeast Spain 129

**Land cover classes Area (ha) Area (%)** 

Water 17.37 0.20 Arable land 4061.52 45.76 Permanent crops 1446.93 16.30 Fallow/abandoned 1074.51 12.11 Saltmarsh 629.01 7.09 Palm groves 196.02 2.21 Marsh 172.71 1.95 Man-made/urban 1277.73 14.40 *TOTAL 8875.80 100* 

Table 3. Area occupied by land cover classes according to the map obtained by k-nearest

The land cover map accuracy was evaluated with the data set of validation points. Overall accuracy was a 68%, and KHAT value was 0.56. According to Landis and Koch (1977),

Fig. 4. Land cover map of the study area.

neighbour.

Fig. 3. Scatterplots with the average values pH and organic carbon versus electrical conductivity for land cover classes (numbers in italics are the ID number of the class). X and Y bars represent one standard deviation.

The distribution of land cover classes can be explained by the characteristics of the soils. Generally, the closest soils to the wetland areas of the natural parks are more saline. These soils have a poor drainage due to its lower altitude and very high-water tables, largely due to the horizontal flow of water and salts from the nearby water bodies. Permanent crops class dominates in areas that are close to the towns, being better drained and less saline. Fallow/abandoned areas (12.11% of the study area) are spread throughout the study area as a result of the abandonment of farming on individual fields. However, abandoned land is more present in the proximal portion of the natural parks since the conditions of salinization of soils led to their abandonment.

Fig. 3. Scatterplots with the average values pH and organic carbon versus electrical

Y bars represent one standard deviation.

of soils led to their abandonment.

conductivity for land cover classes (numbers in italics are the ID number of the class). X and

The distribution of land cover classes can be explained by the characteristics of the soils. Generally, the closest soils to the wetland areas of the natural parks are more saline. These soils have a poor drainage due to its lower altitude and very high-water tables, largely due to the horizontal flow of water and salts from the nearby water bodies. Permanent crops class dominates in areas that are close to the towns, being better drained and less saline. Fallow/abandoned areas (12.11% of the study area) are spread throughout the study area as a result of the abandonment of farming on individual fields. However, abandoned land is more present in the proximal portion of the natural parks since the conditions of salinization

Fig. 4. Land cover map of the study area.


Table 3. Area occupied by land cover classes according to the map obtained by k-nearest neighbour.

The land cover map accuracy was evaluated with the data set of validation points. Overall accuracy was a 68%, and KHAT value was 0.56. According to Landis and Koch (1977),

Mapping Soil Salinization of Agricultural Coastal Areas in Southeast Spain 131

values of the shade fraction image are present in the wetland areas of the natural parks and in a triangular area in the middle-right boundary of the study area that corresponds with a small wetland. A white area in the right of the image corresponds with the Mediterranean Sea. High green vegetation fraction values area scattered through the study area. They correspond with active crops at the time of the image acquisition. Indeed, the white areas in the NPV image fraction correspond with bare soil and saltmarshes which vegetation is quite dry in summer and have a great spectral confusion with background soil. Urban areas were also associated with this fraction image. Finally, high values of the residual fraction are located in industrial areas, whose spectral signature was notably different respect to the

Fig. 6. Fraction images of shade/water, green vegetation (GV), non-photosynthetic vegetation (NPV) and the residual component of the linear spectral mixture analysis.

White/black polygon represents the boundary of the study area.

three endmembers of the unmixing model.

KHAT values ranging from 0.4 to 0.8 exhibit a moderate agreement. Inter-class confusion was detected analysing the error matrix. A portion of arable land (78% of producer's accuracy, 65% of user's accuracy) was wrongly classified as permanent crops, fallow/abandoned land or saltmarshes. A great portion of palm groves (21% of producer's accuracy and 75% of user's accuracy) was classified as arable land. The performance of the automatic classification for marshes (90% of producer's accuracy and 75% of user's accuracy) and water areas (100% of producer's accuracy and 80% of user's accuracy) was highly satisfactory. The performance of the KNN algorithm for our land cover classification approach was enough good and comparable with the accuracy obtained by Franco-Lopez et al. (2001) classifying a forest stand (52% of overall accuracy with *k*=10), and the results of the experiment carried on by Samaniego and Schulz (2009) classifying crop types (47% of overall accuracy with *k*=5).

#### **3.3 Spectral unmixing and land covers**

Spectral mixture analysis was applied to obtain fraction images of green vegetation (GV), non-photosynthetic vegetation (NPV) and shade endmembers. Spectral signatures of selected endmembers are highly distinctive (Figure 5). These endmembers had optimal spectral separability as measured with the transformed divergence method. GV endmember is associated with vigorous vegetation, NPV endmember is associated with bare soil and dry halophytic vegetation, and shade endmember is associated with water bodies and low illuminated areas.

Fig. 5. Plot showing the spectral signatures of selected green vegetation (GV), nonphotosynthetic vegetation (NPV) and shade endmembers.

Fraction images of the three endmembers and the residual fraction of the spectral mixture analysis were obtained (Figure 6). Values range from 0 for low high membership to the image fraction (black colour) to 1 for high membership (white colour). Fractions images are continuous variables that are graphically represented with a greyscale colour ramp. High

KHAT values ranging from 0.4 to 0.8 exhibit a moderate agreement. Inter-class confusion was detected analysing the error matrix. A portion of arable land (78% of producer's accuracy, 65% of user's accuracy) was wrongly classified as permanent crops, fallow/abandoned land or saltmarshes. A great portion of palm groves (21% of producer's accuracy and 75% of user's accuracy) was classified as arable land. The performance of the automatic classification for marshes (90% of producer's accuracy and 75% of user's accuracy) and water areas (100% of producer's accuracy and 80% of user's accuracy) was highly satisfactory. The performance of the KNN algorithm for our land cover classification approach was enough good and comparable with the accuracy obtained by Franco-Lopez et al. (2001) classifying a forest stand (52% of overall accuracy with *k*=10), and the results of the experiment carried on by Samaniego and Schulz (2009) classifying crop types (47% of

Spectral mixture analysis was applied to obtain fraction images of green vegetation (GV), non-photosynthetic vegetation (NPV) and shade endmembers. Spectral signatures of selected endmembers are highly distinctive (Figure 5). These endmembers had optimal spectral separability as measured with the transformed divergence method. GV endmember is associated with vigorous vegetation, NPV endmember is associated with bare soil and dry halophytic vegetation, and shade endmember is associated with water bodies and low

Fig. 5. Plot showing the spectral signatures of selected green vegetation (GV), non-

Fraction images of the three endmembers and the residual fraction of the spectral mixture analysis were obtained (Figure 6). Values range from 0 for low high membership to the image fraction (black colour) to 1 for high membership (white colour). Fractions images are continuous variables that are graphically represented with a greyscale colour ramp. High

photosynthetic vegetation (NPV) and shade endmembers.

overall accuracy with *k*=5).

illuminated areas.

**3.3 Spectral unmixing and land covers** 

values of the shade fraction image are present in the wetland areas of the natural parks and in a triangular area in the middle-right boundary of the study area that corresponds with a small wetland. A white area in the right of the image corresponds with the Mediterranean Sea. High green vegetation fraction values area scattered through the study area. They correspond with active crops at the time of the image acquisition. Indeed, the white areas in the NPV image fraction correspond with bare soil and saltmarshes which vegetation is quite dry in summer and have a great spectral confusion with background soil. Urban areas were also associated with this fraction image. Finally, high values of the residual fraction are located in industrial areas, whose spectral signature was notably different respect to the three endmembers of the unmixing model.

Fig. 6. Fraction images of shade/water, green vegetation (GV), non-photosynthetic vegetation (NPV) and the residual component of the linear spectral mixture analysis. White/black polygon represents the boundary of the study area.

Mapping Soil Salinization of Agricultural Coastal Areas in Southeast Spain 133

of total variance. PC1 was positively correlated with NPV fraction (factor loading = 0.988) and negatively correlated with GV fraction (factor loading = -0.902). PC1 might be used to separate vegetated from non-vegetated pixels. PC2 was positively correlated with electrical conductivity (factor loading = 0.778) and the shade fraction (factor loading = 0.604) and negatively correlated with the pH (factor loading = -0.785). PC2 might be used to

Salinization status seems to be related to the abundance of the shade fraction. This result could be explained by the presence of water at the soil profile, which is an evidence of poor drainage that could lead to salt accumulation. Thus, monitoring shade fraction values along a year could be an indirect method to detect the evolution of soil electrical conductivity with remote sensing. PC3 was positively correlated with the residual fraction of the spectral unmixing (factor loading = 0.853) and organic carbon content (factor loading = 0.711).

Fig. 8. Factor loadings plot for the measured soil properties and mixture fractions, and

average values factor scores plot for the land cover classes.

differentiate soil salinity status.

Evident negative correlations with the PC3 were not found.

Average values of the three fraction images for the land covers were computed and represented in a ternary diagram (Figure 7). Water land cover has high shade fraction values (>90%) and very low values for the GV and NPV fractions. Marshes have an important fraction of shade (>55%) and around 30% of the GV fraction. This mixture composition is highly indicative of the marshes structure with green *Phragmites australis* stands, growing on flooded or water-saturated soils. Shade fraction has a low contribution in the other land covers (<30%). Saltmarshes, permanent crops, arable land, fallow/abandoned and palm groves land cover classes have GV fraction values between 20-40% and NPV fraction values between 50-70%. This relative homogeneity in the mixture fractions values for different land cover classes could be attributed to the lower water availability in summer, that promotes a drying and browning of the vegetation and promotes spectral confusion. Melendez-Pastor et al. (2010b) previously observed this phenomenon in the study area. They also employed ternary diagrams, combining mixture fraction and land cover classes for a drought year and an average year. Soil or NPV fractions increase their contribution in a dry weather scenario (i.e. drought or summer) and the water and GV fractions have a lower contribution.

#### **3.4 Fraction endmembers to predict soil properties**

Mixture fraction values were statistically related to soil salinity. Principal component analysis provided valuable information about the relationship among soil properties and spectral mixture analysis fractions. The first three principal components accumulated 75.8%

Average values of the three fraction images for the land covers were computed and represented in a ternary diagram (Figure 7). Water land cover has high shade fraction values (>90%) and very low values for the GV and NPV fractions. Marshes have an important fraction of shade (>55%) and around 30% of the GV fraction. This mixture composition is highly indicative of the marshes structure with green *Phragmites australis* stands, growing on flooded or water-saturated soils. Shade fraction has a low contribution in the other land covers (<30%). Saltmarshes, permanent crops, arable land, fallow/abandoned and palm groves land cover classes have GV fraction values between 20-40% and NPV fraction values between 50-70%. This relative homogeneity in the mixture fractions values for different land cover classes could be attributed to the lower water availability in summer, that promotes a drying and browning of the vegetation and promotes spectral confusion. Melendez-Pastor et al. (2010b) previously observed this phenomenon in the study area. They also employed ternary diagrams, combining mixture fraction and land cover classes for a drought year and an average year. Soil or NPV fractions increase their contribution in a dry weather scenario

(i.e. drought or summer) and the water and GV fractions have a lower contribution.

Fig. 7. Ternary diagram of the average mixture fraction values for the land cover classes.

Mixture fraction values were statistically related to soil salinity. Principal component analysis provided valuable information about the relationship among soil properties and spectral mixture analysis fractions. The first three principal components accumulated 75.8%

**3.4 Fraction endmembers to predict soil properties** 

of total variance. PC1 was positively correlated with NPV fraction (factor loading = 0.988) and negatively correlated with GV fraction (factor loading = -0.902). PC1 might be used to separate vegetated from non-vegetated pixels. PC2 was positively correlated with electrical conductivity (factor loading = 0.778) and the shade fraction (factor loading = 0.604) and negatively correlated with the pH (factor loading = -0.785). PC2 might be used to differentiate soil salinity status.

Salinization status seems to be related to the abundance of the shade fraction. This result could be explained by the presence of water at the soil profile, which is an evidence of poor drainage that could lead to salt accumulation. Thus, monitoring shade fraction values along a year could be an indirect method to detect the evolution of soil electrical conductivity with remote sensing. PC3 was positively correlated with the residual fraction of the spectral unmixing (factor loading = 0.853) and organic carbon content (factor loading = 0.711). Evident negative correlations with the PC3 were not found.

Fig. 8. Factor loadings plot for the measured soil properties and mixture fractions, and average values factor scores plot for the land cover classes.

Mapping Soil Salinization of Agricultural Coastal Areas in Southeast Spain 135

to detect specific absorption bands of some salt types, and the spectra interfere with other soil chromophores (Mougenot et al., 1993). More research will help to improve the prediction of soil properties with remote sensing data for a fast assessment of soil status

This chapter provides an interesting case of study on the application of remote sensing to soil salinity. The land use and management greatly affect soil salinity and land cover mapping helps to delineate areas with different severity of salinization. The use of spectral mixture analysis in combination with land cover maps and soil properties data is a more advanced technique. Mixture fractions help to know the spectral behaviour of land cover and their constituents using a simple three endmembers model. In addition, mixture fractions can be used as predictors in regression models to predict the electrical conductivity of soils. The results of regression models were encouraging but require further research to improve them. Since mixture fractions are sensitive to spectral changes due to changes in ground surface, they may be particularly useful for mapping the severity of soil salinization processes over time with low coast satellite images. The combined use of soil properties analytics, land cover maps and spectral mixture analysis is feasible for monitoring saline

The authors acknowledge the Spanish Ministry of Science and Innovation for the financial support of the project '*Efectos de la aplicación de un compost de biosólido sobre la calidad de suelos con distinto grado de salinidad'* with the reference CGL2009-11194 that allowed this research.

Acosta, J.A.; Faz, A.; Jansen, B.; Kalbitz, K. & Martínez-Martínez, S. (2011). Assessment of

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Ben-Dor, E. & Banin, A. (1994). Visible and near-infrared (0.4-1.1 m) analysis of arid and

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salinity status in intensively cultivated soils under semiarid climate, Murcia, SE Spain. *Journal of Arid Environments*, Vol. 75, No. 11 (November 2011), pp. 1056-1066,

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soils and land management over large areas with a reduced cost.

over large areas and at low cost.

**4. Conclusions** 

**5. Acknowledgment** 

ISSN 0140-1963

Cambridge, UK

274, ISSN 0034-4257

**6. References** 

Previous studies assessed the relationships between PC factor loadings of soil properties and PC factor scores of land cover classes (Biro et al., 2011). We also included the mixture fraction values for soil plots in the principal component analysis. Soil properties and mixture fractions factor loadings and average factor scores for land cover classes of the first two components were plotted to explore their relationship (Figure 8). Factor loading values range from -1 to 1. Factor scores of land cover classes were also included in the plot. Land cover classes were differentiated from each other along PC1, mainly because of the high positive factor loading of the NPV fraction and high negative factor loading of the GV fraction. Also, land cover classes were differentiated from each other along PC2, mainly because of the high positive loadings of the shade fraction, electrical conductivity and organic carbon content, and high negative loading value of pH.

Finally, we tested the usefulness of mixture fractions to predict EC. Stepwise linear regression was employed to model relationship among EC and mixture fractions. EC variable was normalised with the natural logarithm, while the other variables were normal. Table 4 summarizes the main parameters of two linear regression models. Moderate adjustment was obtained from the regression with R=0.338 for the first model and R=0.408 for the second model. The ANOVA test (data not included in the table) indicated the usefulness of the models with a *p*-value <0.001 for both cases. Model 1 included GV fraction as the unique mixed fraction predictor variable, while model 2 included GV and also the shade fraction. In both models, the coefficient B has a negative value for the variable GV, suggesting an inverse relationship between the green covers and EC. By contrast, the regression coefficient B was positive for the shade fraction, indicating a direct relationship between the presence of shadows/water and EC. This latter observation corroborates the interpretation given in the principal component analysis on the usefulness of the shade fraction to predict soil salinity. Collinearity statistics revealed the absence of collinearity problems as we obtained tolerance values much greater than 0 and VIF values much lower than 15 (SPSS, 2009).


VIF: variable inflation factor

Table 4. Summary of results of linear regression models.

Regression models with mixture fractions did not show collinearity that could lead us to false predictions. A major constraint using proximal and remote sensing data for mapping salinity is related to the fact that there is a strong vertical, spatial and temporal variability of salinity in the soil profile (Mulder et al., 2011). Direct and precise estimation of the salt quantities is difficult by using satellite data with a low spectral resolution because these fail to detect specific absorption bands of some salt types, and the spectra interfere with other soil chromophores (Mougenot et al., 1993). More research will help to improve the prediction of soil properties with remote sensing data for a fast assessment of soil status over large areas and at low cost.

#### **4. Conclusions**

134 Remote Sensing – Applications

Previous studies assessed the relationships between PC factor loadings of soil properties and PC factor scores of land cover classes (Biro et al., 2011). We also included the mixture fraction values for soil plots in the principal component analysis. Soil properties and mixture fractions factor loadings and average factor scores for land cover classes of the first two components were plotted to explore their relationship (Figure 8). Factor loading values range from -1 to 1. Factor scores of land cover classes were also included in the plot. Land cover classes were differentiated from each other along PC1, mainly because of the high positive factor loading of the NPV fraction and high negative factor loading of the GV fraction. Also, land cover classes were differentiated from each other along PC2, mainly because of the high positive loadings of the shade fraction, electrical conductivity and

Finally, we tested the usefulness of mixture fractions to predict EC. Stepwise linear regression was employed to model relationship among EC and mixture fractions. EC variable was normalised with the natural logarithm, while the other variables were normal. Table 4 summarizes the main parameters of two linear regression models. Moderate adjustment was obtained from the regression with R=0.338 for the first model and R=0.408 for the second model. The ANOVA test (data not included in the table) indicated the usefulness of the models with a *p*-value <0.001 for both cases. Model 1 included GV fraction as the unique mixed fraction predictor variable, while model 2 included GV and also the shade fraction. In both models, the coefficient B has a negative value for the variable GV, suggesting an inverse relationship between the green covers and EC. By contrast, the regression coefficient B was positive for the shade fraction, indicating a direct relationship between the presence of shadows/water and EC. This latter observation corroborates the interpretation given in the principal component analysis on the usefulness of the shade fraction to predict soil salinity. Collinearity statistics revealed the absence of collinearity problems as we obtained tolerance values much greater than 0 and VIF values much lower

> **Unstandardized Coefficients**

> > **Sig.**

**B Std. Error Tolerance VIF** 

GV -1.081 0.279 0.000 1.000 1.000

GV -1.005 0.273 0.000 0.989 1.011 Shade 1.320 0.492 0.008 0.989 1.011

**Collinearity Statistics** 

organic carbon content, and high negative loading value of pH.

than 15 (SPSS, 2009).

VIF: variable inflation factor

**Model R R2 Adjusted R2 Predictors**

Table 4. Summary of results of linear regression models.

1 0.338 0.115 0.107 (Constant) 7.483 0.106 0.000

2 0.408 0.167 0.152 (Constant) 7.302 0.123 0.000

Regression models with mixture fractions did not show collinearity that could lead us to false predictions. A major constraint using proximal and remote sensing data for mapping salinity is related to the fact that there is a strong vertical, spatial and temporal variability of salinity in the soil profile (Mulder et al., 2011). Direct and precise estimation of the salt quantities is difficult by using satellite data with a low spectral resolution because these fail This chapter provides an interesting case of study on the application of remote sensing to soil salinity. The land use and management greatly affect soil salinity and land cover mapping helps to delineate areas with different severity of salinization. The use of spectral mixture analysis in combination with land cover maps and soil properties data is a more advanced technique. Mixture fractions help to know the spectral behaviour of land cover and their constituents using a simple three endmembers model. In addition, mixture fractions can be used as predictors in regression models to predict the electrical conductivity of soils. The results of regression models were encouraging but require further research to improve them. Since mixture fractions are sensitive to spectral changes due to changes in ground surface, they may be particularly useful for mapping the severity of soil salinization processes over time with low coast satellite images. The combined use of soil properties analytics, land cover maps and spectral mixture analysis is feasible for monitoring saline soils and land management over large areas with a reduced cost.

### **5. Acknowledgment**

The authors acknowledge the Spanish Ministry of Science and Innovation for the financial support of the project '*Efectos de la aplicación de un compost de biosólido sobre la calidad de suelos con distinto grado de salinidad'* with the reference CGL2009-11194 that allowed this research.

#### **6. References**


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**6** 

*1,2Russia 3USA* 

**Remote Sensing Based** 

**Modeling of Water and Heat** 

**Regimes in a Vast Agricultural Region** 

*1Water Problem Institute of Russian Academy of Sciences, Moscow 2State Research Center of Space Hydrometeorology Planeta, Moscow 3City College of City University of New York, New York, NY* 

A. Gelfan1, E. Muzylev1, A. Uspensky2, Z. Startseva1 and P. Romanov3

In many parts of the world, solution of the problem of water security is closely associated with possibility and predictability of access to soil water which accounts, globally, for approximately 60% of total rainfall. Soil water, so-called "green water", is fundamentally significant for maintaining sustainability of ecosystems and providing water for agriculture (in average, agriculture demands about 70% of global water use, and over 90% in some regions (Global Water Security, 2010). In addition, changes in the store of soil water due to evapotranspiration, infiltration of surface water and other water cycle processes strongly affect land-atmosphere interactions on intraseasonal to interannual timescales (Yang, 2004) and understanding physical mechanisms of these processes is central for effective modeling

Taking into account a vital role of "green water" both for ecosystems and agriculture, there is a need for physically based models allowing to describe land-surface processes in their interaction with atmosphere and to predict soil water availability under changing environment and climate. Processes of mass and energy exchange at the land surface, linking the atmosphere and soil water are of interest to several geophysical disciplines; particularly, the possibility of developing adequate models of these processes was studied intensively by hydrological and meteorological communities for a few decades. Recently, a huge contribution to our understanding these processes has been made by remote sensing community. Combination of these efforts results in intensive developing so called landsurface models (LSMs), considering as a key tool to predict successfully the likely future states of the terrestrial systems under anthropogenic pressure and climate change. There are a lot of reviews providing a detailed and comprehensive discussion of LSMs (e.g. Sellers et al., 1997; Pitman, 2003; Overgaard et al., 2006). Most of them are concentrated on the development of LSMs designed for use in weather and climate models. Below a review of LSMs will be done with emphasis on their availability to capture processes controlling soil water dynamics, and primarly, evapotranspiration. Additionally, focus will be on utilization

**1. Introduction** 

mesoscale atmospheric circulation.

of remotely sensed data in LSMs.

