Modeling Soil Salinity and Mapping Using Spectral Remote Sensing Data in the Arid and Semi-arid Region

Transcription

1 International Journal of Remote Sensing Applications (IJRSA) Volume 6, 2016 doi: /ijrsa Modeling Soil Salinity and Mapping Using Spectral Remote Sensing Data in the Arid and Semi-arid Region Majed Ibrahim GIS and Remote Sensing Department, Al albayt University, Jordan Abstract Arid and semi-arid region is one of the most areas exposed to the risk of salinization, where the soil salinity is the one of the severe global problems and environmental issues which faces the world; in addition, it has effects on land cover and productivity of the agriculture land leading to a decrease fertility and quality of the soil. This degradation occurs in agriculture practices (Human activities) or naturally, therefore it is important to detect the soil salinity at an early stage through mapping and monitor the salinization to support soil reclamation and increase effective and productivity of the soil that decreases the effect of salinization in soil quality and helps prevent increasing the impact of salinity in the future. This paper aims to study possibility of using an image of the spectral remote sensing data and field survey to sampling soil and measure salinity using significant parameter e.g, Sodium Absorption Ratio (SAR) to develop experimentally model allowing the mapping of soil salinity in the arid and semi-arid region. Keywords Remote Sensing; Soil Salinity; SAR; OLI; Spectral Indices Introduction Soil salinization is considered one of the most environmental issues affecting natural resources in the world [1] [2] [3], and it s also considered one of the global problems that lead to land degradation and desertification especially in the arid and semi-arid areas and wherever agriculture practiced. Soil salinity, as a form of soil degradation which leads to land degradation. often the soil salinity occurs naturally [4] [5], or human activities, mismanagement of agriculture practice due to along accumulation and presence of soluble salts in the surface or near surface soil [6]; on the other side, the salinization also affects other global soil degradation problems like decrease soil quality, increase soil erosion, soil dispersion. FAO and UNESCO reported as much as half of the world s existing irrigation schemes is more or less under the influence of secondary salinization and water logging [7] [8]. The secondary salinization caused by land mismanagement, with 58% of these in irrigated areas alone and nearly 20% of all irrigated land is salt affected [9] [10]; and one of the most sources salinity in the arid and semi-arid areas is over pumping of groundwater to irrigated agricultural land [11]. According FAO, the saline soil covered 397 million hectares of the total land area of the world [12] [13]. Europe, Latin America, Near East and North America, Africa and Asia are the most affected areas. Therefore, it s essential to quantify different soil salinities in different areas. Obviously, this problem has a great effect on quality and fertility of soil which in turns has a great impact on soil productivity [14] [15] [16]. Thus, there emerges impact on vegetation cover and agriculture land. Many related works with this field in the recent years have been directed toward monitoring and mapping soils salinity in the very salted areas especially in the arid and semi-arid area. In order to achieve the results of monitoring and mapping, it needs to collect soil data by traditional (laboratory analysis, field survey) which was considered problematic where it s unsuited and insufficient to assess of soil salinity phenomenon and is demanding high costs [15] [17]. Therefore, Remote Sensing has a good technique and powerful tool to keep historical and continuous record of the progression of this phenomenon. Remote Sensing gets the information about the target through energy reflected from the earth s surface based on this concept the spectral reflectance of 76

2 International Journal of Remote Sensing Applications (IJRSA) Volume 6, salt features as a direct indicator for soil salinity monitoring, detection and mapping [12]. Remote Sensing is widely used to detect and map soil salinity using multispectral data such as landsat (MSS, TM, ETM), System Pour I, Observation de la Terre (SPOT), IKONOS, QuickBird and the Indian Remote Sensing (IRS), as well as hyperspectral data such as EO-1 Hyperion and HyMap [18-24]. Several indexes are used to detect soil salinity such as brightness index (BI) and Salinity Index (SI) [25], Normalized Difference Salinity Index (NDSI) [26], Soil Adjusted Vegetation Index (SAVI) [27], Enhanced Vegetation Index (EVI) [28], Normalized Differential Vegetation Index (NDVI) [29], Salinity Index ((SI-1(2), SI-2 (2), SI-3(2)) []. The purpose of the study is the mapping of soil salinity in the arid and semi-arid area based on optical remote sensing data and field measurements of sodium absorption ratio (SAR). In this case, soil salinity is assessed using spectral indices previously cited, and principal components derived from Landsat ETM and OLI satellite image. Material and Methods Study Area and Data Used The study area is located in the north-eastern part of the Yarmouk basin in Jordan as shown in Figure 1. It has a semi-arid climate of the Mediterranean region with a limited amount of rainfall and high temperatures; it is characterized by a wet season from October to April and a dry season that lasts from May to September. Mean annual rainfall and temperatures are 350 mm and 18 C respectively, as measured at the Irbid station (north Jordan). The soil of this site is characterized by secondary salinization induced by irrigation. The choice of the image type referred from a satellite to another. For our study, we used the raw image Landsat 8 OLI acquired in March 2013 as shown in Table 2. Methodology FIG. 1 STUDY AREA The methodology applied in this research is illustrated in Fig. 2. The raw data of remote sensing were used to carry out some processes. Second of all, the spectral indices of soil salinity, the Normalized Difference Vegetation Index (NDVI) all derived data were compared with field measurements of soil SAR to extract an SAR estimation model allowing soil salinity mapping. 77

3 International Journal of Remote Sensing Applications (IJRSA) Volume 6, 2016 LANDSAT OLI Image Composite bands and band ratio analysis NDVI Spectral Indices of Soil Salinity Spectral Indices OLI Validation Soil Salinity estimation Lab Measurement SAR Map of Salinity affected soils FIG. 2 METHODOLOGY FLOWCHART Field Measurements of Sodium Absorption Ratio (SAR) Soil samples were collected during the field survey as shown in Fig. 3. The ground recognition and the sample selection test were carried out during one period being spread out over March Each sampling site was georeferenced and located on the ground by the use of a GPS into UTM projection (Zone 37 North, WGS 84). The full number of the 66 taken samples is distributed on all the plain (52 samples to correlation and 20 to validation), and their SAR was measured in the laboratory using physicochemical analysis. SAR is often used to measure soil and water salinity and a good indicator of soil quality. Table 1 shows the Soil-Quality Guideline for SAR which is used to classify the level of soil salinity. Before the interpolating of field measurements to obtain a SAR map, we studied the spatial structure of the variable using a variogram with the ArcGIS10.2 software. Finally, it was found that the variable is spatialisable. After several tests of deterministic and geostatistical methods, the universal kriging provided the best model. TABLE 1 SOIL QUALITY GUILELINE FOR SAR IN THE LAND USE [31] Parameters Rating Categories Good Fair Poor Unsuitable Landsat Data and Salinity Index SAR (Sodicity) < > 12 TABLE 2 SUMMARIZED INFORMATION FOR LANDSAT 8 Resolution (m) Wavelength (μm) Band description Band 1 blue Band 2 blue Band 3 green Band 4 red Band 5 near infrared Band 6 shortwave infrared Band 7 shortwave infrared Band 8 panchromatic Band 9 cirrus Band 10 thermal Infrared Band 11 thermal Infrared In order to achieve aim of the study, landsat 8 raw data have been used to carry out methodology. The Landsat 8 satellite carries a two-sensor payload, the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS), 88

4 International Journal of Remote Sensing Applications (IJRSA) Volume 6, which are summarized in Table 2. The data were downloaded from USGS website at dated 21-march-2013 (only available images for study area during studying period). The OLI and TIRS designs incorporate technical advancements that improve their performance over the previous Landsat sensors. Generally, the data acquired on 21-march-2013 were selected for the analysis due to coincide with the period of sample collection. The necessary pre-processing and corrections were applied to the selected data in order to accurate calculations. The other important step in accurate delineation of soil salinity is selected to be the best bands combination. Band combination is principally one of image enhancement techniques because it provides the maximum information with the minimum number of spectral intervals. This process could be easy using the OIF technique that helps to determine the primary axes of visual quality of the final color image (R-G-B combination) [8] [32] [33]. Through applying optimum index factor (OIF) to remote sensing data where they were found that the band combinations and serve well which aim to highlight on the salinity areas and to distinguish the surface properties. Moreover, band ratios of visible to near-infrared and between infrared bands will be used to detect soil salinity. Table 3 shows correlation coefficient for different bands combinations and their relative accuracies. TABLE 3 CORRELATION MATRIXES OF BANDS DATA AND SALINITY PARAMETER Bands B1 B2 B3 B4 B5 B6 B7 SAR B1 1 B B B B B B SAR Results and Discussion Assessment of Spectral Indices TABLE 4 CORRELATIONS BETWEEN THE SPECTRAL INDICIES OF SALINITY (LANDSAT 8) AND SAR OF SOIL Spectral indices R² Equation References S1* - Blue/Red 84-4 S2* - (Blue-R)/(Blue+R) S (Green*R)/Blue S4* S5 0.4 (Blue*Red)/Green S (Red*NIR)/Green SI3* SI2* - SI SI BI* - NDSI (R-NIR)/(R+NIR) 91-4 RVI* - NIR/R NDVI (NIR-R)/(NIR+R) 82 * There is error when using spectral bands of landsat 8 OLI The several indices computerized were evaluated and some of these gave good correlations. The index that gave the good correlation more than others is the SI1 and SI index with R² = and R² = respectively, with 79

5 International Journal of Remote Sensing Applications (IJRSA) Volume 6, 2016 significance p-value less than Generally, the spectral indices based on the visible radiation bands are more sensitive to changes of the SAR against those and near-infrared range spectral with the exception of the index SSI1 with R²=0.4 and significance p-value less than Table 4 shows the result of evaluation. Modelling Soil Salinity and Validation Results Firstly, the data were calibrated using several indices and samples which were collected to validation. Then the calibration was made using interpolation based on SAR values and a new modeling obtained by the field measurements. This latter approach provides another good estimation that the first two approaches. This is due to the high variability of salinity per unit of distance and the large pixel size of data ( meters). Table 5 presents the correlation coefficient of the SAR estimation in which it was obtained by the extracted values and sampling validation. With the exception of the index SSI1, and the equation of SSI1 can be listed as following: SSI: Spectral salinity index 1 Blue: spectral band SWIR2: Shortwave SSI1= TABLE 5 CORRELATION MATRIX BETWEEN SPECTRAL INDICIES OF BANDS Spectral indices S3 S5 S6 SI1 SI NDSI NDVI SSI1 S3 1 S S SI SI NDSI NDVI SSI On the other hand, the 20 samples validation appears where there are moderate positive correlation between spectral indices and measured SAR. And the index that gave the good correlation more than others with developed soil salinity index (SSI1) which is SI index with R² = 0.99 with significance p-value less than 0.05, which have good correlation with SAR. Thus, SSI1 index is able to estimate soil salinity as compared with other spectral indices. Finally, the spectral indices based on the visible and infrared range radiation bands through band combination, bands ration or other techniques to developed model to estimate some soil properties with soil parameter. Soil Salinity Mapping Using the Developed Model Cross-validation showed that the model calibrated by classes and based on the SI1 spectral index model is the most efficient in estimating the SAR, presenting a moderate coefficient of determination R² = in Fig. 3 A. In addition, the validation of this model is based on band data which were obtained in this study (SSI1), presenting a moderate coefficient of determination R² = 0.4 in Fig 3 B. Thereafter, the image obtained by best model of salinity and developed model has been classified to produce the salinity map Fig. 3 C/D. These maps are derived using the equation for the SAR model as shown in Table 1. Figures represent the whole area covered by the image. In addition to the irrigated fields, this area contains many other non-irrigated areas (protected area, urban areas, etc.). The soil salinity values for these non-irrigated area are fairly not realistic where the data were collected for these area. It was not affected by agricultural practices that lead to salinity, while the most of irrigated areas - cultivated areas are located in the salinity areas. This shows clearly after comparing the results of experimental model SI1 with that of SSI1. Generally, the resources of salinity are likely from pesticide, fertilizers and over-pumping of water to irrigate in agricultural land which mean mismanagement in agricultural practice which requires study and verification of agricultural practices and opens the field to research at the agricultural effects on soil quality. 80

6 International Journal of Remote Sensing Applications (IJRSA) Volume 6, D C FIG. 3 MODEL BASED ON SI AND SSI INDEX FOR ESTIMATING AD MAPING SOIL SALINITY; A: CROSS-VALIDATION RESULTS, B: VALIDATION USING OTHER SAMPLES, C: FINAL SOIL SALINITY MAP OF STUDY AREA USING SI1, D: FINAL SOIL SALINITY MAP OF THE STUDYAREA USING DEVELOPED MODEL SSI1 Conclusion It was concluded of this paper that it could use landsat 8 (OLI) through spectral indices as a good variable in the spatial variations to estimate and map salinity in arid and semi-arid land; on the other hand, based on the OIF, it could be selected to be the best band combinations to show salinity. Moreover, band ratios of visible to nearinfrared and between infrared bands will be used to detect soil salinity using landsat 8 (OLI). For this aim, the indices based on the visible spectral bands are more sensitive to the soil salinity and the SI index has a better correlation with soil salinity in our region than the others; and SSI1 index also has been well sensitive to the soil salinity in arid and semi-arid regions which were developed in this study. The Stepwise regression allowed finding the most correlated parameters with soil salinity. Calibration based on the largest unit of aggregation gives a more accurate model, because it reduces the effects of the high soil salinity variability. Otherwise, we need to expand the mesh of soil samples to get the best estimates. ACKNOWLEDGEMENT The author is grateful to Al- albyat University REFERENCES [1] Fernandez-Buces N., Siebe C,. Cramb. S, Palacio J. Mapping soil salinity using a com-bined spectral response index for bare soil and vegetation: A case study in the former lake Texcoco, Mexico. Journal of Arid Environments 65,

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