Soil salinity detection using RS data

Transcription

1 Soil salinity detection using RS data SADIA IQBAL 1 & NIKOS MASTORAKIS 2 1 Faculty of Agricultural Engineering & tecnology, University of Agriculture, Faisalabad- PAKISTAN sadia_iqbal_eng@yahool.com 2 Technical University of Sofia, Department of Industrial Engineering, Sofia BULGARIA mastor@wseas.org Abstract: - Soil salinity refers to the state of accumulation of soluble salts in the root zone to adversely affect the growth of most crops. It is a severe environmental degradation that impedes crop growth and production [1]. The conventional processes for the detection, monitoring and mapping of salt-affected soil are known to be difficult and dynamic.(206khan). Remote Sensing has the ability to predict soil salinity accurately. With the increasing population, the demand of increasing agricultural areas to increase food productivity is also increasing. This requires extending the existing irrigation system, thus making more and more agricultural areas vulnerable to soil salinity. A number of remote sensing data sources are available these days, which include, air borne and space borne systems. Satellite data has a great potential for monitoring salinization in both spatial and temporal extents [1]. The current paper reviews a number of different techniques and methods implemented worldwide, on remotely sensed data to measure the soil salinity. The paper also discusses the accuracies achieved by different methods in mapping soil salinity. Key-Words:- Remote Sensing, LandsatETM+, ASTER, band combination, salinity index, principle component analysis, regression method 1 Introduction Soil salinity refers to the state of accumulation of soluble salts in the root zone to adversely affect the growth of most crops [5]. It is one of the main environmental problems which affect extensive areas in the world. According to FAO and UNESCO, as much as half of the world s existing irrigation schemes is more or less under the influence of secondary salinization and water logging [5]. In general, the estimates of soilaffected areas are close to 1 billion hectares, which is about 7% of the earth s continental extent. This results in increasing impact on crop yields and agricultural production in both dry and irrigated areas due to poor land and water management and expansion of the agricultural frontier into marginal dry lands. On average, 20% of the world s irrigated lands are affected by salts, but this figure increases to more than 30% in countries such as Egypt, Iran and Argentina [6]. In the future, more dry lands will be put into agricultural production because of increasing population pressure. This will mainly be achieved with irrigation and will thus expand the salinization hazard. Furthermore, salinity also affects other major soil degradation phenomena such as soil dispersion, increased soil erosion, and engineering problems. When soil salinization is assessed in economic terms, reasons to be concerned about it become more apparent. For instance, the economic damage caused by secondary salinization was estimated at US$750 million per year for the Colorado. River Basin in the USA, US$300 million per year for the Punjab and Northwest Frontier Provinces in Pakistan, and US$208 million per year for the Murray Darling Basin in Australia [6]. To keep track of changes in salinity and anticipate further degradation, monitoring is needed so that proper and timely decisions can be made to modify the ISBN:

2 management practices or undertake reclamation advances in the application of remote sensing technology in mapping and monitoring degraded lands, especially in salt-affected soils, have shown great promise of enhanced speed, accuracy and cost-effectiveness. The approach to the problem of delineating saline soils using remote sensing data and GIS techniques has been proved in many recent studies to be most efficient [13]. The review is devoted to studying approaches used on the remotely sensed data to delineate the salt affected areas worldwide Table1: Total Accuracy of Soil Salinity Classification based on Different band combination 2 Methods and techniques used A number of image processing and image enhancement techniques are applied to carry out the analysis. When it comes to image processing, georeferencing and correction of geometric distortions are the two important processes essentially carried out as a part of data preprocessing. [1,10]. Selection of the best band combination is also an important step in accurate delineation of saline soil. Selection of band combinations and their accuracy vary from case to case. To find the best TM three band combination, [4] applied optimum index factor (OIF) to remote sensing data covering saltaffected areas in the Indo- Gangetic plain. It was found that the band combinations 1-3-5, and 3-5-7serve well for the purpose. Moreover, band ratios of visible to near-infrared and between infrared bands have proven to be better for identifying salts in soils and salt-stressed crops than individual bands [2,812]. used the ETM+ satellite images for soil salinity studies in the Nyshaboor Region of Iran. They were able to differentiate between medium and highly saline soils. Using different band combinations, they achieved different accuracies. The highest accuracy achieved, derived from 1, 3, 4, 7 combination of bands, was 80.5%. Table 2 shows different band combinations and their relative accuracies [1], using band data of the satellite IRS-1B LISS-II found the Band selection is dependent on the spectral reflectance as DN (digital number) of the selected pixels of land use classes. The salt affected soils have relatively higher reflectance compared with other land use. It is noticed that first threebands (B1, B2, and B3) are promising in developing relationship. The infrared band (B4) did not yield reliable information when compared with ground truth data. Determining the best salinity index is also important for accurate analysis. The index that gives the best results when compared with the ground data is selected. [1], developed 6 salinity indexes in their study namely S1 = B1/B3, S2 = (B1 - B3) / (B1 + B3), S3 = (B2 x B3) / B1, S4 = Sqrt (B1 x B3), S5 = (B1 x B3) / B2 and S6 = (B3 x B4) / B2. Only S3 was selected for its best correlation with ground data. ISBN:

3 Table 2: Correlation matrix of band data, salinity index and salinity parameter \zzy classification improved, or at least equalled, the crisp classification 3 Results and Discussions [5], while using ASTER data in empirical method, for fallow land, to find soil salinity developed two salinity indexes. Index 1: (ban4 - band5 / band4 + band 5), giving accurate detection for overall salinity in the bare agriculture soils. Index 2: (ban4 band3 / band4 + band 3), which has the potential to detect the chemical soil composition such as nitrogen or iron dioxides. The principal component analysis is carried out in terms of mean, standard deviation, correlation coefficient and variance / covariance matrices. The PCA is based on the computation of eigen vectors and eigen values [1]. Principle Component Analysis is used to achieve the stable brightness of PC1 and the stable greenness of PC2 allowing separation of saline from nonsaline soils, while the differential brightness in PC3 and the differential greenness in PC4 accounts for the changes occurring in surface salinity. [3] used a modified stepwise principal components analysis to assess the effectiveness of individual bands for discriminating salinity states from high-resolution spectra. Successful discrimination of saline and alkaline areas from remotely sensed data requires correct determination of information classes. In nature, salt contents vary in gradual manner, horizontally as well as vertically. Broad zones of gradual transition may be misrepresented because of the arbitrary assignment of sharp class boundaries. Fuzzy classification is based on the fuzzy set theory. Better results can be obtained regarding the distribution of salts and sodium, as compared to crisp classification. Expanding the concepts highlighted for soil classification to the issue of soil salinity, [17] proposes the determination of transitional fuzzy class boundaries, derived from continuous salinity classes that intergrade gradually, to better represent real-world situations. Fig. 7 illustrates the approach devised for determining fuzzy classes and the fuzzy classification applied to a JERS-1 SAR data image from Cochabamba, Bolivia. This method provided a reliable detection of salt-affected areas, with an overall accuracy of 81%[17]. In all cases, fu7 The studies show that mostly Landsat ETM+ data is used for salinity mapping. In some cases, only single bands are tested and the band with the best result is selected, like in the case of [10]. Out of thesix available bands, they used only band 3 in the regression model and exponential relation was found to be the best model, which had the highest correlation. The obtained model was applied to band 3 in ArcGIS environment and classified soil salinity map was produced Fig 1: Correlation between salinity (electrical conductivity) and digital numbers of band 3 Fig2: Resulting Salinity Map; [[9] ISBN:

4 [12], used different combinations of bands. Highest accuracy (80.5%) was derived when 1,3,4,7 combination bands with hybrid method. Using this method increases the classification accuracy in soils with high and medium. The results show that soil salinity classification is not very much accurate even by using the best band combination of false color and applying the classification method based on the training samples for each class. It also shows that separating no salinity soils from soils with low salinity is more difficult than separating soils with low salinity (accuracy = 55%) from high salinity (accuracy = 62%). Therefore it is concluded that there are more overlap between digital values of soils with no salinity and soils with low salinity. It is also concluded that mean values of reflection in all of the bands is higher in soils with high salinity than other soils. [12]concluded in their study that salt-affected soils reflect more incident energy in comparison with normal soils and other classes of the land use in the visible and near-infra spectrum of the satellite data. The images were classified using supervised maximum likelihood classification with an overall accuracy of more than 90%. The principal component analysis (PCA) and the salinity indices are found to be promising techniques for prediction of saline soils from satellite imageries. Fig3: The occurrence pattern of salt affected soils in the study area;[1] Studies conducted by [9] on the applications of Remote Sensing in Australia revealed that the identification of saline areas was most successful during peak vegetation growth. In other periods, phenomenon like low overgrazing, erosion or ploughing makes it difficult to distinguish fractional vegetation cover in salinized areas from the bare land [9]. [14],[9] concluded a different result. He concluded that salinity is best seen at the end of irrigation or the rainy season when the plots are bare [14].[15]used MSS images of premonsoon, post-monsoon and harvest seasons to map soil salinity in the Punjab, India. He concluded that the spectral curves of highly and moderately saline soils change considerably during the annual cycle, which significantly complicates the timecompositing procedure. There are some other problems in using RS in arid and semiarid areas with soil salinity. Many parameters such as gullies and non saline crust, salinity resistant plants, gravels and so on are responsible for interfering of spectrum values between different pixels with different soil saliniti. References 1. Abbas, A., & Khan, S. (2000). Using Remote Sensing Techniques for Appraisal of. NSW 2678, Australia. 2. Craig, J. C., Shih, S. F., Boman, B. J., & Carter, G. A. (1998). Detection of salinity stress in citrus trees using narrow-band multispectral imaging. ASAE paper no ASAE Annual International Meeting, Orlando, FL, USA, July pp. 3. Csillag, F., Pa sztor, L., & Biehl, L. (1993). Spectral band selection for the characterization of salinity status of soils. Remote Sensing of Environment, 43, Dwivedi.R.S., K. Sreenivas (1998). Delineation of salt-acted soils and waterlogged areas in the Indo-Gangetic plains using IRS- 1C LISS-III data. International Journal of Remote Sensing, 19:14, Fouad, A.-K. (2003). Soil Salinity Detection Using Satellite Remote Sensin. 6. Ghassemi, F., Jakeman, A. J., & Nix, H. A. (1995). Salinisation of land and water resources: human causes, extent, management and case studies. Canberra, Australia: The Australian National University, Wallingford, Oxon, UK: CAB International 7. Huete.A.,(2004).Remote Sensing for Natural Resources Management and Environmental Monitoring: Manual of remote sensing3 ed., Vol. 4. University of Arizona. 8. Iqbal.S & Iqbal.F (2010) satellite sensing for performance evaluation of irrigation system in ISBN:

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