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Analysis of salinization dynamics by remote sensing in Hetao Irrigation District of North China
Agricultural Water Management 97 (2010) 1952–1960
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Agricultural Water Management journal homepage: www.elsevier.com/locate/agwat
Analysis of salinization dynamics by remote sensing in Hetao Irrigation District of North China夽 Ruihong Yu a , Tingxi Liu b,∗ , Youpeng Xu c , Chao Zhu a , Qing Zhang d , Zhongyi Qu b , Xiaomin Liu b , Changyou Li b a
College of Environment and Resources, Inner Mongolia University, Hohhot, 010021 Inner Mongolia Autonomous Region, PR China College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot, 010018 Inner Mongolia Autonomous Region, PR China c School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing, 210093 Jiangsu Province, PR China d College of Life Sciences, Inner Mongolia University, Hohhot, 010021 Inner Mongolia Autonomous Region, PR China b
a r t i c l e
i n f o
Article history: Available online 15 April 2010 Keywords: Salinization Spatial-temporal analysis Remote sensing Hetao Irrigation District
a b s t r a c t Remote sensing can provide base information for documenting salinity change and for predicting its future evolution trend. The spatial and temporal distributions of soil salinization of Jiefangzha Irrigation Sub-district, the western part of Hetao Irrigation District of Inner Mongolia in northern China, were determined through analysis of satellite-based remote sensing images. Three Landsat TM/ETM+ satellite images taken during 14 years (1991 ∼ 2005) coupled with field observations were chosen as the basic data sources. Supervised classification and visual interpretation were used to analyze salinity classification and statistical method was applied to analyze the relationship between salinity and groundwater depth. From 1991 to 2005 the area of heavy saline land decreased from 191 to 136 km2 , or 3.9 km2 per year; the moderate saline land decreased from 318 to 284 km2 , or 2.5 km2 per year; the slight saline land decreased from 510 to 394 km2 , or 8.2 km2 per year. Therefore, soil salinization in Jiefangzha Irrigation Sub-district is decreasing in general. The electrical conductivity (EC) values measured from field have the following relationship with the reflectance composition obtained from LANDSAT Enhanced Thematic Mapper Plus (ETM+) data: EC = 5.653(band5 − band7)/(band5 + band7) + 0.246. In addition, an r2 value between EC values and groundwater depth is 0.72, which indicates groundwater depth is the major factor for the regional soil salinity control. The paper can serve as a theoretical reference for optimal allocation of irrigation water resource and salinization control in Hetao Irrigation District. © 2010 Elsevier B.V. All rights reserved.
1. Introduction Soil salinization caused by agricultural production is a severe environmental problem (Dehaan and Taylor, 2002; Verma et al., 1994; Masoud and Koike, 2006). Worldwide, soil salinization is now spreading at a speed of up to 2 million hectares a year, which offsets a large part of crop yield increased by expansion in irrigation area (Postel, 1999). Natural soil salinization, or primary soil salinization, is formed under long-term influence of various natural processes, while man-made soil salinization, or secondary salinization, is the result of salt accumulation in the soil profile
夽 This paper is supported by Research Program of Sciences at Universities of Inner Mongolia Municipality in China (NJ09010), Natural Science Foundation of Inner Mongolia Municipality in China (2009BS0604), National Natural Science Foundation of China (50669002), Launching Fund for the Scientific Research Program of the Higher-Level Faculty Member in Inner Mongolia University, China (E20080207) and National Undergraduate Innovative Test Program for Inner Mongolia University (081012608). ∗ Corresponding author. E-mail address: [email protected] (T. Liu). 0378-3774/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.agwat.2010.03.009
caused by extra water input from human activities such as irrigation (Szabolcs, 1989). The purpose of this study was to monitor the soil salinization of Hetao Irrigation District in northern Inner Mongolia of China using remote sensing and field observations. Hetao Irrigation District was selected as the study area because of its importance for the agricultural development of Inner Mongolia. To keep track of salinity change of the saline land and predict its future trend, spatial-temporal monitoring urgently needs to be implemented in the district. Many researchers (Qiao, 1996; Stasyuk, 2001; Saysel and Barlas, 2001; Shi et al., 2004; He et al., 2004; Pankova, 2007) have done various studies on soil salinization that mainly concentrated on salinization monitoring and modeling. These works all point out that a useful approach to monitor soil salinization is to acquire reliable and up-to-date monitoring information by remote sensing techniques and then make effective prediction with a system dynamic model if real-measured data and satellite images are available. Remote sensing data has a great potential for monitoring any dynamic processes, including soil salinization. Compared with field collection of soil salinity data (Robbins and Wiegand, 1990), remote sensing technique can save labor and time, and remote sensing
R. Yu et al. / Agricultural Water Management 97 (2010) 1952–1960
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Fig. 1. Location of the study areas.
also has the ability to predict soil salinity (Mougenot and Pouget, 1993). Remote sensing of surface features with aerial photography, videography, infrared thermometry, and multispectral scanners has been used intensively to identify and map salt-affected area (Robbins and Wiegand, 1990). Dwivedi and Rao (1992) noted that the digital analysis of multispectral data using the spectral response pattern of salt-affected soils may be plagued by misclassification, and in order to improve the detectability of these soils and other natural features using remote sensing requires the development of various image transforms. These transforms can not only enhance the detectability of these features, but also aid data compression, resulting in a substantial reduction in computational time and cost (Dwivedi and Rao, 1992). Furthermore, Metternicht and Zinck (1997) combined digital image classification with field observation of soil degradation and laboratory analysis to map salt and sodium-affected areas in the semiarid valleys of Cochabamba, Bolivia. Multispectral data acquired from platforms such as Landsat, SPOT and the Indian Remote Sensing (IRS) series of satellites have been found to be useful in detecting, mapping and monitoring salt-affected soils. Hetao Irrigation District is a seasonal soil frozen region, and the period from freezing to thawing lasts about six months. During the non-frozen period (irrigation period), soil salinity and groundwater levels rise and fall during the year under the influence of irrigation infiltration and phreatic evaporation. When the soil is frozen, the groundwater depth descends steadily and salt accumulates slowly in the upper portion of the soil profile. During thawing, the frozen layer thaws from both the upper and lower surfaces. Water released from the lower layer moves downward into the groundwater and groundwater level rises, while water released from the upper layer evaporates. The objectives of this paper are (1) to identify the spatialtemporal variability of soil salinity by combining remote sensing (RS) with field observations and (2) to qualify the relationship between soil salinization and groundwater depth. Analysis of salinization dynamics in Hetao Irrigation District can serve as a theoretical reference for optimal allocation of irrigation water resource and salinization control in the arid and cold area.
2. Materials and methods 2.1. Study site Located in the western part of Inner Mongolia Autonomous Region, Hetao Irrigation District is one of the three largest irrigation districts in China and an important food supply base. Jiefangzha Irrigation Sub-district, in the western part of Hetao Irrigation District, was selected as the study area (Fig. 1). The area has a typical continental climate, being very cold in winter with little snowfall and very dry in summer with little rainfall. Its mean annual precipitation is about 152 mm occurring mainly between July and August, and its mean annual evaporation is about 2300 mm based on 20 cm diameter evaporation pan. The elevation of the subdistrict is higher in the south-west and lower in the northwest with an elevation ranging from 1032 to 1050 m above sea level. Groundwater depth varies annually between 0.8 and 2.6 m. The soil texture is silty clay loam with severe salinization. The unfavorable topography, geomorphology, soil texture and irrigation of Hetao Irrigation District results in rise of groundwater level during the spring and summer and is the main cause of secondary salinization (Wang et al., 1993). 2.2. Data sources 2.2.1. Satellite image data The remote sensing data, including three sets of Landsat (Row 129/Path 32 of the WRS-2), Thematic Mapper (TM) images (dated 23 March 1991, 26 March 1998 and 29 March 2005), were obtained from China Remote Sensing Satellite Ground Station. The Landsat scenes were acquired under clear atmospheric conditions at the end of March when salt surface features are enhanced and the impact of soil salinity on surface attributes is intensified. 2.2.2. Field observation data Field observation data include soil electrical conductivity, groundwater depth, total alkalinity, total salinity, Ca2+ , Mg2+ , Na+ , K+ , HCO3 − , CO3 2− , Cl− , SO4 2− . The soil samples from 21 locations
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referenced data were then clipped to the study area. This task was carried out using ARCGIS for Windows operating system. 2.3.2. Selection of best band combination (Chavez et al., 1982) In essence, the best band combination is to analyze the information content of the composite bands and the affinity among the different bands. In other words, the composite bands with maximum information content and minimum affinity would be selected. The Optimum Index Factor (OIF) method which was put forward by Chavez et al. in 1982 mainly considers two factors: one is the maximum information content of the composite bands, because the larger the standard deviation, the more the information content derived from the composite bands; the other is the minimum affinity of the composite bands, because weak correlativity among the bands will lead to significant independence and less redundant data. The OIF is expressed as follows:
3 OIF =
i=1 i
3
j=1
(1)
|ccj |
where i is the standard deviation for band i, |CCj | is the absolute value of the correlation coefficient between any two of three bands, i represents any one of three bands, j represents any one of three bands but is not equal to i. The larger the OIF, the more the information can be derived from the composite bands.
Fig. 2. Observation sites of groundwater characteristics and soil electrical conductivity in Jiefangzha Irrigation Sub-district.
were obtained six times each year from 1990 to 2005, viz. before the autumn irrigation, after the autumn irrigation, before freezing, the maximum frozen soil depth, before thawing and after thawing. Samples were taken from five layers: 0–20, 20–40, 40–60, 60–80, and 80–100 cm depth. Soil samples sites are shown in Fig. 2. With water to soil ratio of 5:1, the soil electrical conductivity (EC) was measured with Walklab con 60 conductivity meter. The electrical conductivities were used to classify the degree of soil salinization as follows: (1) heavy saline soil: 0.8 < EC < 1.6; (2) moderate saline soil: 0.4 < EC < 0.8; (3) slight saline soil: 0.2 < EC < 0.4, and (4) non-saline soil: 0.0 < EC < 0.2 (Shirokova et al., 2000; Chi and Wang, 2009). A regional network of 57 (Fig. 2) test wells distributed in Jiefangzha Irrigation Sub-district was used for this study. Groundwater depth was measured once in every five days, i.e. on 1, 6, 11, 16, 21, 26 every month from 1991 to 2005, and groundwater observation sites are shown in Fig. 2. Ca2+ , Mg2+ and SO4 2− were measured with EDTA complexometry. HCO3− and CO3 2− were titrated with the double indicator method. Cl− was measured by AgNO3 liquor. Na+ and K+ were measured with subtraction method. Total alkalinity was measured by titration, and total salinity was measured with Walklab con 60 conductivity meter.
2.3.3. Histogram transformation (Feng, 1992) In order to display the saline soil information prominently, the other surface features can be compressed by stretching the gray range of saline soil. The gray histogram was used to reflect probability density function of the distribution of gray values in the TM images. Horizontal axis represents the gray values, and vertical axis represents the pixel numbers corresponding to gray values. Subsection linear intensified method is efficient for histogram transforms and can be expressed as follows:
e gik =
⎧ e e − g e )/(g − g )] · (g − g ) gi1 + [(gi2 ⎪ i2 i1 ik i1 i1 ⎪ ⎪ ⎨ e e e gi2 + [(gi3 − gi2 )/(gi3 − gi2 )] · (gik − gi2 )
⎪ · · ·· · · ⎪ ⎪ ⎩ ge e − ge + [(gin )/(gin − gi(n−1) )] · (gik − gi(n−1) ) i(n−1) i(n−1)
(2)
where gik represents gray value in the i band and the k pixel before e represents gray value in the i band and image enhancement. gik the k pixel after image enhancement. gi1 , gi2 , . . ., gin represent gray e , ge , . . . , ge range of the original satellite images in the i band. gi1 i2 in represent gray range of the enhanced satellite images in the i band. The aim of histogram transform is to extrude the information of saline soil and to shrink the other information of surface features. Therefore, the subsection linear intensified method was used in the paper:
0 256 × gik /(gin − gil )
When When
0 ≤ gik < gil or gin < gik ≤ 255 gil ≤ gik ≤ gin (3)
2.3. Processing of remote sensing data
e = gik
2.3.1. Image calibration For unbiased comparison of these multi-temporal data sets, the data should be referenced to a common coordinate grid. Therefore, an accurate image-to-image calibration by histogram normalization was applied (Chavez and Mackinnon, 1994), and the composite image of TM Mosaic with fine geometric correction was taken as the referenced image. Moreover, the data in 1991, 1998 and 2005 were geo-referenced to the UTM coordinate system, and the accuracy was controlled to 0.5 grids (1 grid = 30 m × 30 m). The geo-
where gil represents the minimum gray value of saline soil in the i band. gin represents the maximum gray value of saline soil in the i band. It is obvious that the selection of gil and gin is the key step for Eq. (3). Gray range of saline soil can be stretched from 0 to 255 using Eq. (3), while most of the other surface features of non-saline soil will be given 0. This method can differentiate the saline soil from non-saline soil and improve the classification accuracy.
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2.3.4. Principle component analysis (Dwivedi, 1996) Principle component analysis is a linear dimension reduced method. The new features are all linear function of the original features. Here, the linear transform is almost equal to the coordinate transform, which does not change the data self. With the transform, the new features having same numbers with the original ones can be obtained, and the key information of the original features might be included in the first few new features. Under the conditions of linearity, non-relativity and maximum variance, the new features can be calculated: (1) Calculating the co-variance matrix of original features of whole samples Snn : 1 (gik − gi ) · (gjk − gj ) n−1 n
Sij =
(4)
k=1
(2) Calculating all features of Snn (1 , 2 , . . ., n ) and corresponding feature vectors by Jacobi algorithm. (3) Defining the contribution ratio of variance in the i principle component: Fi =
i 1 + 2 + . . . + n
(5)
The contribution ratio of accumulated variance of the first h principle component is Lh =
1 + 2 + . . . + h 1 + 2 + . . . + n
(6)
In common, the numbers of principle components are determined by condition of Lh > 85%. 2.3.5. Image classification Influenced by the topography, geomorphology, soil texture, the groundwater depth, water chemical characteristics, irrigation mode and plants, the salinization degree of each site is different, so distinct soil types with different salinization extent have different percent of plant cover and different characteristics in the color LANDSAT images. Additionally, according to the salt capacity and salt type, we can also distinguish different levels of salt salinization: viz. heavy saline soil, moderate saline soil, slight saline soil and non-saline soil. Classification results of Jiefangzha Irrigation Sub-district were acquired by the integration of the maximum-likelihood supervised method (Foody et al., 1992) and visual translation. Moreover, the classified image was then sieved, clumped, and filtered before producing the final landcover map so as to improve the classification precision. 3. Results 3.1. Image processing results Based on the comparison of different band combination, OIF values of band7, 5, and 1 in 1991, 1998 and 2005 are all maximum, respectively 26.87, 24.95 and 30.00. Therefore, band7, 5, and 1 is selected as the best band combination in the three typical years. Furthermore, in terms of histogram transformation, the typical saline soils are sampled in the Jiefangzha Irrigation Sub-district so as to guarantee that saline soils with different level can be reflected exactly. In order to avoid the stretch of gray range originated from the intermix of surface features, we only used the pixel numbers with certain gray values that were more than 0.01% of total pixels, and the gray values are effective. According to this rule, the maximum and minimum gray values in the different bands are applied to Eq. (3), and the gray values after stretch are acquired. In addition, in order to reflect the information content of satellite images at the
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most degree, after the operation of principle component analysis for the 7 bands, the numbers of principle component were selected with the condition of Lh > 98%. Taken 1991 as example, F1 = 98.72%, F2 = 0.81%, F3 = 0.41%; L2 = 99.53%, L3 = 99.94%. Therefore, the first three principle components can be selected as the new features, which can compress 60% of data quantities and greatly increase the computable effectiveness. Compared with the original satellite images, the information of saline soil is almost retained and the other surface features are also integrated. The Landsat TM images in 1991, 1998 and 2005 are all selected at the end of March when salinization is severe, thus the distribution of saline soil can be identified more easily and clearly. With the actual distribution of saline soil as reference, the classified results are analyzed by integration of the supervised method and visual translation and shown in Fig. 3. 3.2. Temporal changes of salinization The dynamic change of soil salinization occurs in the heavy saline soil, moderate saline soil, slight saline soil and non-saline soil. Based on the classification of satellite images in 1991, 1998 and 2005, the statistical results of the different types of areas and their percentages of the total area are calculated using ENVI 4.2 software. Fig. 4a shows the different types of areas in percentages of total area and Fig. 4b shows the changes of different types of areas with year. The analysis results show that, from 1991 to 2005, the total area of saline land decreased from 1019 to 814 km2 , with an annual decreasing rate of 14.6 km2 , and the areas of heavy saline land, moderate saline land and slight saline land decreased by 55, 34 and 116 km2 , respectively. It can be seen that the area of slight saline land decreased most evidently among others, with an annual decreasing rate of 8.2 km2 /y. However, the non-saline land has a inverse trend to the saline land, increasing from 956 km2 in 1991 to 1161 km2 in 2005, with an annual increasing rate of 14.6 km2 /y. In addition, the changes of the different area types from 1991 to 2005 are also analyzed. From 1991 to 1998, the area of saline land remains more or less stable, and the saline land in percent of the total area remained the same with a value of 51.6%. However, the moderate saline land and heavy saline land present an increasing trend, changing from 16.1% to 20.7% and from 9.7% to 10.8%. The slight saline land presents a decreasing trend, decreased by 5.8%. Therefore, most of the decreased slight saline land between 1991 and 1998 converted into moderate or heavy saline land, and the salinization in 1998 is more severe than that in 1991. Simultaneously, the area of non-saline land is as same as that of the saline land which remains stable, and the non-saline area in percent of the total area remained the same with a value of 48.4%. From 1998 to 2005, the saline area decreased from 51.6% to 41.2%, with an annual decreasing rate of 0.7%/y. In detail, the heavy saline land is decreased from 10.8% to 6.9%, with an annual decreasing rate of 0.3%/y; moderate saline land is decreased from 20.7% to 14.3%, with an annual decreasing rate of 0.5%/y; Slight saline land remains stable and accounts for 19.9% of the total area. Therefore, with unchanged slight saline area, the areas of heavy saline land and moderate saline land decreased, while the non-saline area increased, which means that salinization in Jiefangzha Irrigation Sub-district of Hetao Irrigation District is improving. As shown in Fig. 4b, saline land area is biggest in 1991 compared with that in 1998 and 2005, and the area in 1998 is about the same as that in 1991. The areas of moderate saline land and heavy saline land are biggest in 1998 and smallest in 2005. With time goes by, the salinity in Jiefangzha Irrigation Sub-district of Hetao Irrigation District presents decreasing trend, especially after the application of soil improvement and water-saving reconstruction between 1998 and 2005.
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Fig. 3. Classified results of Landsat TM images in 1991, 1998 and 2005.
3.3. Spatial changes of saline land The characteristics of spatial changes of saline land can not only be discussed qualitatively through the landcover map, but can also be analyzed statistically by overlapping the landcover maps in the different periods. Fig. 3 shows the spatial distribution of saline land in 1991, 1998 and 2005. It can be seen clearly that the distribution of saline land in Jiefangzha Irrigation Sub-district presents an increasing trend from south-west to north-east, which accords with the total trend of Hetao Irrigation District. Because the terrain of Hetao Irrigation District is high in south-west and low in northeast, the salinity in north-east is more severe than south-west. In other words, most of moderate saline land and heavy saline land are distributed in north-east. After the qualitative analysis of Jiefangzha Irrigation Subdistrict, we would like to explain the transition of the different types of saline land in 1991, 1998 and 2005. Landcover maps for 1991, 1998 and 2005 were overlaid two at a time, and subsequent GIS analyses was performed by applying simple image differencing. Land salinization occurred during 1991–2005 period are detected and characterized. The transition matrixes are shown in Table 1. From the transition matrix between 1991 and 1998, we can see that: 110, 123 and 90 km2 of non-saline land changed into slight saline land, moderate saline land and heavy saline land. However, 190 km2 of slight saline land, 75 km2 of moderate saline land and 60 km2 of heavy saline land transformed into non-saline land.
Moreover, 101 and 31 km2 of slight saline land transformed into moderate saline land and heavy saline land, and 37 km2 of moderate saline land transformed into heavy saline land. On the other hand, 81 km2 of moderate saline land and 15 km2 of heavy saline land transformed into slight saline land, and 110 km2 of non-saline land transformed into slight saline land. Therefore, slight, moderate and heavy saline lands transformed each other, and non-saline land and saline land transformed each other. The two-type transformation resulted in the changes of the different types of areas. In total, from 1991 to 1998, heavy saline land and moderate saline land increased, while the slight saline land decreased. From the transition matrix between 1998 and 2005, we can conclude that: 134 km2 of slight saline land, 183 km2 of moderate saline land and 122 km2 of heavy saline land had transformed into non-saline land. 85 km2 of moderate saline and 26 km2 of heavy saline land transformed into slight saline land, and 32 km2 of heavy saline land transformed into moderate saline land. In addition, 115, 81 and 40 km2 of non-saline land transformed into slight, moderate and heavy saline land, respectively. 78 and 14 km2 of slight saline land transformed into moderate and heavy saline land. In total, the land transform mainly occurred between the saline land and nonsaline land. Some saline lands transformed into non-saline lands, which lead to the increase of non-saline lands and the decrease of saline lands. Moreover, the transformations among the different types of saline lands resulted in the decrease of moderate and heavy saline land. In a word, the salinization of Jiefangzha Irrigation Sub-district was decreased from 1998 to 2005.
Fig. 4. Changes of the different types of areas with years.
R. Yu et al. / Agricultural Water Management 97 (2010) 1952–1960
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Table 1 Transition matrix of different types of areas (km2 ) in 1991, 1998 and 2005.
1991
1998
1991
Classification
Slight
Moderate
Heavy
1998 Slight Moderate Heavy Non-saline Sum
188 81 15 110 394
101 125 61 123 410
31 37 55 90 213
190 75 60 633 958
510 318 191 956 1975
2005 Slight Moderate Heavy Non-saline Sum
168 85 26 115 394
78 93 32 81 284
14 49 33 40 136
134 183 122 722 1161
394 410 213 958 1975
2005 Slight Moderate Heavy Non-saline Sum
201 87 15 91 394
86 92 34 72 284
12 37 52 35 136
211 102 90 758 1161
510 318 191 956 1975
Combining the results of transition matrix during 1991–1998 and 1998–2005, the statistics from 1991 to 2005 show that: 211 km2 of slight saline land, 102 km2 of moderate saline land and 90 km2 of heavy saline land transformed into non-saline land. While only 91, 72 and 35 km2 of non-saline land transformed into slight, moderate and heavy saline land. In addition, 15 and 34 km2 of heavy saline land transformed into slight and moderate saline land, and 87 km2 of moderate saline land transformed into slight saline land. However, only 86 km2 of slight saline land transformed into moderate saline land, and 12 km2 of that transformed into heavy saline land. Therefore, the transition type between 1991 and 2005 is as same as that between 1998 and 2005. In conclusion, the slight, moderate and heavy saline land presented a decreasing trend from 1991 to 2005. Especially, the slight saline land was decreased obviously. 3.4. Relationship between LANDSAT TM data and field observation data The single variable correlative analysis of EC values measured on 26 March 1998 and 29 March 2005 and the reflectance data of band1, band5, band7 and (band5 − band7)/(band5 + band7) are carried through. The EC values on 23 March 1991 are utilized to assess the precision of estimates of EC values using the reflectance data. Comprehensive consideration on the correlation of the field data and the image reflectance, the field data have a good relationship with the reflectance composition (band5 − band7)/(band5 + band7) with a correlation of 0.845 (Fig. 5). The regression equation obtained using the reflectance data obtained on 23 March 1991 was: EC =
5.653(band5 − band7) + 0.246 (band5 + band7)
Non-saline
Sum
cover. The accumulation of soil salinity is a dynamic process, and all the factors are in one dynamic system. The analysis of the characteristics of different types of areas in 1991, 1998 and in 2005 showed that the changes of different types of areas are mainly related to meteorological factors, irrigation and drainage infrastructure, irrigation quota and time. The reasons for the large variation of soil salinization are that Jiefangzha Irrigation Sub-district is mixed with cultivated lands and waste lands. When the cultivated lands are located lower than the waste lands and water level is lower in the canal under a condition of inefficient drainage, the waste lands will become the water discharge areas. However, after the construction of hydraulic works, the water level of the canal is lifted up, so a large part of the waste lands are developed into cultivated lands, while the old cultivated lands will become the salt and water discharge areas under an efficient drainage condition. As a result, the transition of light saline soil into moderate and heavy saline soils increases. 4.1.1. Impacts of groundwater depth on soil salinization The groundwater dynamics of Jiefangzha Irrigation Sub-district belongs to vertical infiltration-evaporation type. The irrigation infiltration is the main inflow to the groundwater, and the groundwater evaporation is the main outflow in the sub-district. Therefore, the irrigation infiltration is the main reason for the rise of groundwater level, and the groundwater evaporation maintains the annual balance of groundwater level.
(7)
With a mean square error of calculated values of 0.052, assuming that the parameters that effect reflectance do not change with time, the regression equation can be used to the prediction of EC values of Jiefangzha Irrigation Sub-district. 4. Discussion 4.1. Analysis of reasons for spatial-temporal changes of soil salinity Soil salinization is caused by a lot of factors, such as groundwater depth, total dissolved salt, precipitation, soil texture and plant
Fig. 5. Relationship between EC and (band5 − band7)/(band5 + band7).
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Fig. 6. Dynamic changes of groundwater depth during freezing period from 1990 to 2005.
In Jiefangzha Irrigation Sub-district, because of the special characteristics of water and salinity movement and the influence of autumn irrigation, the salinity accumulation is closely related with the groundwater depth during freezing and thawing period. Therefore, the groundwater depth data from October to April from 1990 to 1991, 1997 to 1998, and 2004 to 2005 were selected for the analysis of groundwater dynamics. The fluctuation of groundwater depth during freezing period from 1990 to 1991, 1997 to 1998, 2004 to 2005 are shown in Fig. 6. Fig. 6 reflects the groundwater dynamics during the period of autumn irrigation from October to November and the soil freezing period from early November to the end of February next year. The dynamic change of groundwater exhibited the same trends from 1991 to 1992, from 1997 to 1998, and from 2004 to 2005, respectively. Autumn irrigation is soil storage irrigation after harvest for the crops next year, and its water diversion accounts for one-third of the total annual diversion from Yellow River. The groundwater level is lower in October at the beginning of the autumn irrigation period. After the autumn irrigation, the groundwater level starts to rise.
Because autumn irrigation area is bigger and the irrigation period lasts longer, the groundwater level increases greatly after irrigation. For example, the mean groundwater depth after the autumn irrigation is close to 0.6 m in 1997, and the groundwater level reached its peak in the whole year. In early November, as the temperature descends, the surface soil begins to freeze and the groundwater level descends slowly at a rate of 1–3 cm per day. At the end of Feb. when the drop of air temperature ceases, soil freezing is completed and the soil frozen layer reaches its maximum, meanwhile the groundwater level decreases to its lowest value of whole year. During this period, i.e. the freezing period (from early November to the end of February next year), upward groundwater movement in response to thermal gradients brings the salt to the soil frozen layer. From the early Mar., the frozen layer starts to thaw from both the upper and lower surfaces. Water released from the lower surface moves downward into the groundwater and groundwater levels rise. While water released from the upper surface evaporates and salt remained on the soil surface, resulting in the highest levels of salt content during an annual freeze–thaw cycle. At the end of May, the thawing of frozen layer is finished.
Fig. 7. Changes of EC values from 0 to 20 cm along with groundwater depth during freezing and thawing period.
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Table 2 Relationship matrix of groundwater chemical characteristics. Items 2+
Ca Mg2+ K+ + Na+ C1− SO4 2− CO3 2− HCO3 2− Total alkalinity Total salinity EC ln(EC)
Ca2+
Mg2+
K+ + Na+
C1−
SO4 2−
CO3 −
HCO3 −
Total alkalinity
Total salinity
EC
ln(EC)
1 0.77 0.62 0.78 0.71 −0.28 0.31 0.31 0.76 0.14 0.16
1 0.80 0.84 0.74 −0.18 0.41 0.40 0.92 0.09 0.13
1 0.94 0.54 −0.15 0.56 0.55 0.95 0.07 0.18
1 0.66 −0.20 0.51 0.48 0.96 0.03 0.11
1 −0.08 0.09 0.11 0.67 0.11 0.13
1 −0.17 −0.12 −0.19 0.00 0.07
1 0.99 0.58 0.40 0.43
1 0.57 0.46 0.49
1 0.15 0.23
1 0.97
1
Moreover, EC for the 0–20 cm soil layer with an r2 value of 0.67 follows an exponential relationship with groundwater depth (Fig. 7). The subtraction of EC values for the 0–20 cm soil layer after thawing from those before freezing also has a good relationship with groundwater depth, with an r2 value of 0.72. The correlative coefficient of later is bigger, which indicates that the soil salt accumulation in freezing period has a close relationship with the groundwater depth. Therefore, the groundwater depth is the major factor causing salt accumulation. The maximum groundwater level data after autumn irrigation from 1990 to 1991, 1997 to 1998, and 2004 to 2005, occurred in 1997 to 1998, so soil salinization in 1998 is most severe. The groundwater level from 1990 to 1991 is lower compared with that from 2004 to 2005. However, the soil salinization in 1991 is more severe compared with that in 2005. The reason is that the construction of irrigation and drainage works in an area of 2100 km2 financed by the World Bank started in 1990 and completed in 1996. In 1991, the construction irrigation and drainage works was not established completely, which led to the less salt accumulated in the topsoil. In addition, though the works was finished in 1998, a large irrigation in autumn 1997 resulted in salt accumulation in the topsoil in 1998. In the first half year of 2000, Surveying & Designing Institute of Water Conservancy & Hydropower of Inner Mongolia Autonomous Region compiled the ‘Planning report of continuous construction and water-saving transformation in Hetao Irrigation District of Yellow River Basin’, which accelerated the development of irrigation and drainage engineering. In 2005, with the improvement of water-saving engineering and irrigation and drainage construction, the soil salinization of Hetao Irrigation District was reduced obviously and the soil condition was also improved greatly.
SO4 2− are all collected and processed from 1990 to 1991, 1997 to 1998, 2004 to 2005. The correlative coefficient matrix of above factors is listed in Table 2. From Table 2, soil EC values from 0 to 20 cm have a close relationship with HCO3 − and total alkalinity. Therefore, groundwater depth, HCO3 − and total alkalinity are all key factors causing soil salinity. In addition, the changes of soil EC, groundwater depth and total alkalinity along with time are all shown in Fig. 8. From the Fig. 8, it can be concluded that the change of soil EC is consistent with that of the total alkalinity, but opposite with that of the groundwater depth. The findings are a validation to the above correlative results.
4.1.2. Impact of groundwater chemical characteristics on the soil salinization Soil electronic conductivity from o to 20 cm, groundwater total alkalinity, total salinity, Ca2+ , Mg2+ , Na+ , K+ , HCO3 − , CO3 2− , Cl− ,
Acknowledgements
5. Conclusions The combination of remote sensing with field observation proposed in this work is a useful tool to estimate the spatial and temporal dynamics of soil salinization. With this method, the salinity degree of Hetao Irrigation District is determined and the main reasons of its soil salinization are identified. The development of the remote sensing method is a priority for economical and environmental reasons, especially in the semiarid and arid areas where severe salinization has to be controlled. The remote sensing technique is an essential tool that is rarely applied in the management of irrigated agricultural systems in these regions. LANDSAT can be used to estimate EC values of soil of large irrigation fields due to high spatial and temporal resolution of images. It is concluded that remote sensing technique can be favorably applied for the evaluation of soil salinization dynamics in regional scale.
The authors wish to acknowledge the instructive suggestion and great help of chief editor J.D. Oster and Professor J.S. Yang (Institute of Soil Science, Chinese Academy of Sciences). Also, thanks to all those who provided data for the inventory. References
Fig. 8. Changes of soil EC, groundwater depth and total alkalinity along with time.
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Agricultural Water Management journal homepage: www.elsevier.com/locate/agwat
Analysis of salinization dynamics by remote sensing in Hetao Irrigation District of North China夽 Ruihong Yu a , Tingxi Liu b,∗ , Youpeng Xu c , Chao Zhu a , Qing Zhang d , Zhongyi Qu b , Xiaomin Liu b , Changyou Li b a
College of Environment and Resources, Inner Mongolia University, Hohhot, 010021 Inner Mongolia Autonomous Region, PR China College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot, 010018 Inner Mongolia Autonomous Region, PR China c School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing, 210093 Jiangsu Province, PR China d College of Life Sciences, Inner Mongolia University, Hohhot, 010021 Inner Mongolia Autonomous Region, PR China b
a r t i c l e
i n f o
Article history: Available online 15 April 2010 Keywords: Salinization Spatial-temporal analysis Remote sensing Hetao Irrigation District
a b s t r a c t Remote sensing can provide base information for documenting salinity change and for predicting its future evolution trend. The spatial and temporal distributions of soil salinization of Jiefangzha Irrigation Sub-district, the western part of Hetao Irrigation District of Inner Mongolia in northern China, were determined through analysis of satellite-based remote sensing images. Three Landsat TM/ETM+ satellite images taken during 14 years (1991 ∼ 2005) coupled with field observations were chosen as the basic data sources. Supervised classification and visual interpretation were used to analyze salinity classification and statistical method was applied to analyze the relationship between salinity and groundwater depth. From 1991 to 2005 the area of heavy saline land decreased from 191 to 136 km2 , or 3.9 km2 per year; the moderate saline land decreased from 318 to 284 km2 , or 2.5 km2 per year; the slight saline land decreased from 510 to 394 km2 , or 8.2 km2 per year. Therefore, soil salinization in Jiefangzha Irrigation Sub-district is decreasing in general. The electrical conductivity (EC) values measured from field have the following relationship with the reflectance composition obtained from LANDSAT Enhanced Thematic Mapper Plus (ETM+) data: EC = 5.653(band5 − band7)/(band5 + band7) + 0.246. In addition, an r2 value between EC values and groundwater depth is 0.72, which indicates groundwater depth is the major factor for the regional soil salinity control. The paper can serve as a theoretical reference for optimal allocation of irrigation water resource and salinization control in Hetao Irrigation District. © 2010 Elsevier B.V. All rights reserved.
1. Introduction Soil salinization caused by agricultural production is a severe environmental problem (Dehaan and Taylor, 2002; Verma et al., 1994; Masoud and Koike, 2006). Worldwide, soil salinization is now spreading at a speed of up to 2 million hectares a year, which offsets a large part of crop yield increased by expansion in irrigation area (Postel, 1999). Natural soil salinization, or primary soil salinization, is formed under long-term influence of various natural processes, while man-made soil salinization, or secondary salinization, is the result of salt accumulation in the soil profile
夽 This paper is supported by Research Program of Sciences at Universities of Inner Mongolia Municipality in China (NJ09010), Natural Science Foundation of Inner Mongolia Municipality in China (2009BS0604), National Natural Science Foundation of China (50669002), Launching Fund for the Scientific Research Program of the Higher-Level Faculty Member in Inner Mongolia University, China (E20080207) and National Undergraduate Innovative Test Program for Inner Mongolia University (081012608). ∗ Corresponding author. E-mail address: [email protected] (T. Liu). 0378-3774/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.agwat.2010.03.009
caused by extra water input from human activities such as irrigation (Szabolcs, 1989). The purpose of this study was to monitor the soil salinization of Hetao Irrigation District in northern Inner Mongolia of China using remote sensing and field observations. Hetao Irrigation District was selected as the study area because of its importance for the agricultural development of Inner Mongolia. To keep track of salinity change of the saline land and predict its future trend, spatial-temporal monitoring urgently needs to be implemented in the district. Many researchers (Qiao, 1996; Stasyuk, 2001; Saysel and Barlas, 2001; Shi et al., 2004; He et al., 2004; Pankova, 2007) have done various studies on soil salinization that mainly concentrated on salinization monitoring and modeling. These works all point out that a useful approach to monitor soil salinization is to acquire reliable and up-to-date monitoring information by remote sensing techniques and then make effective prediction with a system dynamic model if real-measured data and satellite images are available. Remote sensing data has a great potential for monitoring any dynamic processes, including soil salinization. Compared with field collection of soil salinity data (Robbins and Wiegand, 1990), remote sensing technique can save labor and time, and remote sensing
R. Yu et al. / Agricultural Water Management 97 (2010) 1952–1960
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Fig. 1. Location of the study areas.
also has the ability to predict soil salinity (Mougenot and Pouget, 1993). Remote sensing of surface features with aerial photography, videography, infrared thermometry, and multispectral scanners has been used intensively to identify and map salt-affected area (Robbins and Wiegand, 1990). Dwivedi and Rao (1992) noted that the digital analysis of multispectral data using the spectral response pattern of salt-affected soils may be plagued by misclassification, and in order to improve the detectability of these soils and other natural features using remote sensing requires the development of various image transforms. These transforms can not only enhance the detectability of these features, but also aid data compression, resulting in a substantial reduction in computational time and cost (Dwivedi and Rao, 1992). Furthermore, Metternicht and Zinck (1997) combined digital image classification with field observation of soil degradation and laboratory analysis to map salt and sodium-affected areas in the semiarid valleys of Cochabamba, Bolivia. Multispectral data acquired from platforms such as Landsat, SPOT and the Indian Remote Sensing (IRS) series of satellites have been found to be useful in detecting, mapping and monitoring salt-affected soils. Hetao Irrigation District is a seasonal soil frozen region, and the period from freezing to thawing lasts about six months. During the non-frozen period (irrigation period), soil salinity and groundwater levels rise and fall during the year under the influence of irrigation infiltration and phreatic evaporation. When the soil is frozen, the groundwater depth descends steadily and salt accumulates slowly in the upper portion of the soil profile. During thawing, the frozen layer thaws from both the upper and lower surfaces. Water released from the lower layer moves downward into the groundwater and groundwater level rises, while water released from the upper layer evaporates. The objectives of this paper are (1) to identify the spatialtemporal variability of soil salinity by combining remote sensing (RS) with field observations and (2) to qualify the relationship between soil salinization and groundwater depth. Analysis of salinization dynamics in Hetao Irrigation District can serve as a theoretical reference for optimal allocation of irrigation water resource and salinization control in the arid and cold area.
2. Materials and methods 2.1. Study site Located in the western part of Inner Mongolia Autonomous Region, Hetao Irrigation District is one of the three largest irrigation districts in China and an important food supply base. Jiefangzha Irrigation Sub-district, in the western part of Hetao Irrigation District, was selected as the study area (Fig. 1). The area has a typical continental climate, being very cold in winter with little snowfall and very dry in summer with little rainfall. Its mean annual precipitation is about 152 mm occurring mainly between July and August, and its mean annual evaporation is about 2300 mm based on 20 cm diameter evaporation pan. The elevation of the subdistrict is higher in the south-west and lower in the northwest with an elevation ranging from 1032 to 1050 m above sea level. Groundwater depth varies annually between 0.8 and 2.6 m. The soil texture is silty clay loam with severe salinization. The unfavorable topography, geomorphology, soil texture and irrigation of Hetao Irrigation District results in rise of groundwater level during the spring and summer and is the main cause of secondary salinization (Wang et al., 1993). 2.2. Data sources 2.2.1. Satellite image data The remote sensing data, including three sets of Landsat (Row 129/Path 32 of the WRS-2), Thematic Mapper (TM) images (dated 23 March 1991, 26 March 1998 and 29 March 2005), were obtained from China Remote Sensing Satellite Ground Station. The Landsat scenes were acquired under clear atmospheric conditions at the end of March when salt surface features are enhanced and the impact of soil salinity on surface attributes is intensified. 2.2.2. Field observation data Field observation data include soil electrical conductivity, groundwater depth, total alkalinity, total salinity, Ca2+ , Mg2+ , Na+ , K+ , HCO3 − , CO3 2− , Cl− , SO4 2− . The soil samples from 21 locations
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referenced data were then clipped to the study area. This task was carried out using ARCGIS for Windows operating system. 2.3.2. Selection of best band combination (Chavez et al., 1982) In essence, the best band combination is to analyze the information content of the composite bands and the affinity among the different bands. In other words, the composite bands with maximum information content and minimum affinity would be selected. The Optimum Index Factor (OIF) method which was put forward by Chavez et al. in 1982 mainly considers two factors: one is the maximum information content of the composite bands, because the larger the standard deviation, the more the information content derived from the composite bands; the other is the minimum affinity of the composite bands, because weak correlativity among the bands will lead to significant independence and less redundant data. The OIF is expressed as follows:
3 OIF =
i=1 i
3
j=1
(1)
|ccj |
where i is the standard deviation for band i, |CCj | is the absolute value of the correlation coefficient between any two of three bands, i represents any one of three bands, j represents any one of three bands but is not equal to i. The larger the OIF, the more the information can be derived from the composite bands.
Fig. 2. Observation sites of groundwater characteristics and soil electrical conductivity in Jiefangzha Irrigation Sub-district.
were obtained six times each year from 1990 to 2005, viz. before the autumn irrigation, after the autumn irrigation, before freezing, the maximum frozen soil depth, before thawing and after thawing. Samples were taken from five layers: 0–20, 20–40, 40–60, 60–80, and 80–100 cm depth. Soil samples sites are shown in Fig. 2. With water to soil ratio of 5:1, the soil electrical conductivity (EC) was measured with Walklab con 60 conductivity meter. The electrical conductivities were used to classify the degree of soil salinization as follows: (1) heavy saline soil: 0.8 < EC < 1.6; (2) moderate saline soil: 0.4 < EC < 0.8; (3) slight saline soil: 0.2 < EC < 0.4, and (4) non-saline soil: 0.0 < EC < 0.2 (Shirokova et al., 2000; Chi and Wang, 2009). A regional network of 57 (Fig. 2) test wells distributed in Jiefangzha Irrigation Sub-district was used for this study. Groundwater depth was measured once in every five days, i.e. on 1, 6, 11, 16, 21, 26 every month from 1991 to 2005, and groundwater observation sites are shown in Fig. 2. Ca2+ , Mg2+ and SO4 2− were measured with EDTA complexometry. HCO3− and CO3 2− were titrated with the double indicator method. Cl− was measured by AgNO3 liquor. Na+ and K+ were measured with subtraction method. Total alkalinity was measured by titration, and total salinity was measured with Walklab con 60 conductivity meter.
2.3.3. Histogram transformation (Feng, 1992) In order to display the saline soil information prominently, the other surface features can be compressed by stretching the gray range of saline soil. The gray histogram was used to reflect probability density function of the distribution of gray values in the TM images. Horizontal axis represents the gray values, and vertical axis represents the pixel numbers corresponding to gray values. Subsection linear intensified method is efficient for histogram transforms and can be expressed as follows:
e gik =
⎧ e e − g e )/(g − g )] · (g − g ) gi1 + [(gi2 ⎪ i2 i1 ik i1 i1 ⎪ ⎪ ⎨ e e e gi2 + [(gi3 − gi2 )/(gi3 − gi2 )] · (gik − gi2 )
⎪ · · ·· · · ⎪ ⎪ ⎩ ge e − ge + [(gin )/(gin − gi(n−1) )] · (gik − gi(n−1) ) i(n−1) i(n−1)
(2)
where gik represents gray value in the i band and the k pixel before e represents gray value in the i band and image enhancement. gik the k pixel after image enhancement. gi1 , gi2 , . . ., gin represent gray e , ge , . . . , ge range of the original satellite images in the i band. gi1 i2 in represent gray range of the enhanced satellite images in the i band. The aim of histogram transform is to extrude the information of saline soil and to shrink the other information of surface features. Therefore, the subsection linear intensified method was used in the paper:
0 256 × gik /(gin − gil )
When When
0 ≤ gik < gil or gin < gik ≤ 255 gil ≤ gik ≤ gin (3)
2.3. Processing of remote sensing data
e = gik
2.3.1. Image calibration For unbiased comparison of these multi-temporal data sets, the data should be referenced to a common coordinate grid. Therefore, an accurate image-to-image calibration by histogram normalization was applied (Chavez and Mackinnon, 1994), and the composite image of TM Mosaic with fine geometric correction was taken as the referenced image. Moreover, the data in 1991, 1998 and 2005 were geo-referenced to the UTM coordinate system, and the accuracy was controlled to 0.5 grids (1 grid = 30 m × 30 m). The geo-
where gil represents the minimum gray value of saline soil in the i band. gin represents the maximum gray value of saline soil in the i band. It is obvious that the selection of gil and gin is the key step for Eq. (3). Gray range of saline soil can be stretched from 0 to 255 using Eq. (3), while most of the other surface features of non-saline soil will be given 0. This method can differentiate the saline soil from non-saline soil and improve the classification accuracy.
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2.3.4. Principle component analysis (Dwivedi, 1996) Principle component analysis is a linear dimension reduced method. The new features are all linear function of the original features. Here, the linear transform is almost equal to the coordinate transform, which does not change the data self. With the transform, the new features having same numbers with the original ones can be obtained, and the key information of the original features might be included in the first few new features. Under the conditions of linearity, non-relativity and maximum variance, the new features can be calculated: (1) Calculating the co-variance matrix of original features of whole samples Snn : 1 (gik − gi ) · (gjk − gj ) n−1 n
Sij =
(4)
k=1
(2) Calculating all features of Snn (1 , 2 , . . ., n ) and corresponding feature vectors by Jacobi algorithm. (3) Defining the contribution ratio of variance in the i principle component: Fi =
i 1 + 2 + . . . + n
(5)
The contribution ratio of accumulated variance of the first h principle component is Lh =
1 + 2 + . . . + h 1 + 2 + . . . + n
(6)
In common, the numbers of principle components are determined by condition of Lh > 85%. 2.3.5. Image classification Influenced by the topography, geomorphology, soil texture, the groundwater depth, water chemical characteristics, irrigation mode and plants, the salinization degree of each site is different, so distinct soil types with different salinization extent have different percent of plant cover and different characteristics in the color LANDSAT images. Additionally, according to the salt capacity and salt type, we can also distinguish different levels of salt salinization: viz. heavy saline soil, moderate saline soil, slight saline soil and non-saline soil. Classification results of Jiefangzha Irrigation Sub-district were acquired by the integration of the maximum-likelihood supervised method (Foody et al., 1992) and visual translation. Moreover, the classified image was then sieved, clumped, and filtered before producing the final landcover map so as to improve the classification precision. 3. Results 3.1. Image processing results Based on the comparison of different band combination, OIF values of band7, 5, and 1 in 1991, 1998 and 2005 are all maximum, respectively 26.87, 24.95 and 30.00. Therefore, band7, 5, and 1 is selected as the best band combination in the three typical years. Furthermore, in terms of histogram transformation, the typical saline soils are sampled in the Jiefangzha Irrigation Sub-district so as to guarantee that saline soils with different level can be reflected exactly. In order to avoid the stretch of gray range originated from the intermix of surface features, we only used the pixel numbers with certain gray values that were more than 0.01% of total pixels, and the gray values are effective. According to this rule, the maximum and minimum gray values in the different bands are applied to Eq. (3), and the gray values after stretch are acquired. In addition, in order to reflect the information content of satellite images at the
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most degree, after the operation of principle component analysis for the 7 bands, the numbers of principle component were selected with the condition of Lh > 98%. Taken 1991 as example, F1 = 98.72%, F2 = 0.81%, F3 = 0.41%; L2 = 99.53%, L3 = 99.94%. Therefore, the first three principle components can be selected as the new features, which can compress 60% of data quantities and greatly increase the computable effectiveness. Compared with the original satellite images, the information of saline soil is almost retained and the other surface features are also integrated. The Landsat TM images in 1991, 1998 and 2005 are all selected at the end of March when salinization is severe, thus the distribution of saline soil can be identified more easily and clearly. With the actual distribution of saline soil as reference, the classified results are analyzed by integration of the supervised method and visual translation and shown in Fig. 3. 3.2. Temporal changes of salinization The dynamic change of soil salinization occurs in the heavy saline soil, moderate saline soil, slight saline soil and non-saline soil. Based on the classification of satellite images in 1991, 1998 and 2005, the statistical results of the different types of areas and their percentages of the total area are calculated using ENVI 4.2 software. Fig. 4a shows the different types of areas in percentages of total area and Fig. 4b shows the changes of different types of areas with year. The analysis results show that, from 1991 to 2005, the total area of saline land decreased from 1019 to 814 km2 , with an annual decreasing rate of 14.6 km2 , and the areas of heavy saline land, moderate saline land and slight saline land decreased by 55, 34 and 116 km2 , respectively. It can be seen that the area of slight saline land decreased most evidently among others, with an annual decreasing rate of 8.2 km2 /y. However, the non-saline land has a inverse trend to the saline land, increasing from 956 km2 in 1991 to 1161 km2 in 2005, with an annual increasing rate of 14.6 km2 /y. In addition, the changes of the different area types from 1991 to 2005 are also analyzed. From 1991 to 1998, the area of saline land remains more or less stable, and the saline land in percent of the total area remained the same with a value of 51.6%. However, the moderate saline land and heavy saline land present an increasing trend, changing from 16.1% to 20.7% and from 9.7% to 10.8%. The slight saline land presents a decreasing trend, decreased by 5.8%. Therefore, most of the decreased slight saline land between 1991 and 1998 converted into moderate or heavy saline land, and the salinization in 1998 is more severe than that in 1991. Simultaneously, the area of non-saline land is as same as that of the saline land which remains stable, and the non-saline area in percent of the total area remained the same with a value of 48.4%. From 1998 to 2005, the saline area decreased from 51.6% to 41.2%, with an annual decreasing rate of 0.7%/y. In detail, the heavy saline land is decreased from 10.8% to 6.9%, with an annual decreasing rate of 0.3%/y; moderate saline land is decreased from 20.7% to 14.3%, with an annual decreasing rate of 0.5%/y; Slight saline land remains stable and accounts for 19.9% of the total area. Therefore, with unchanged slight saline area, the areas of heavy saline land and moderate saline land decreased, while the non-saline area increased, which means that salinization in Jiefangzha Irrigation Sub-district of Hetao Irrigation District is improving. As shown in Fig. 4b, saline land area is biggest in 1991 compared with that in 1998 and 2005, and the area in 1998 is about the same as that in 1991. The areas of moderate saline land and heavy saline land are biggest in 1998 and smallest in 2005. With time goes by, the salinity in Jiefangzha Irrigation Sub-district of Hetao Irrigation District presents decreasing trend, especially after the application of soil improvement and water-saving reconstruction between 1998 and 2005.
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Fig. 3. Classified results of Landsat TM images in 1991, 1998 and 2005.
3.3. Spatial changes of saline land The characteristics of spatial changes of saline land can not only be discussed qualitatively through the landcover map, but can also be analyzed statistically by overlapping the landcover maps in the different periods. Fig. 3 shows the spatial distribution of saline land in 1991, 1998 and 2005. It can be seen clearly that the distribution of saline land in Jiefangzha Irrigation Sub-district presents an increasing trend from south-west to north-east, which accords with the total trend of Hetao Irrigation District. Because the terrain of Hetao Irrigation District is high in south-west and low in northeast, the salinity in north-east is more severe than south-west. In other words, most of moderate saline land and heavy saline land are distributed in north-east. After the qualitative analysis of Jiefangzha Irrigation Subdistrict, we would like to explain the transition of the different types of saline land in 1991, 1998 and 2005. Landcover maps for 1991, 1998 and 2005 were overlaid two at a time, and subsequent GIS analyses was performed by applying simple image differencing. Land salinization occurred during 1991–2005 period are detected and characterized. The transition matrixes are shown in Table 1. From the transition matrix between 1991 and 1998, we can see that: 110, 123 and 90 km2 of non-saline land changed into slight saline land, moderate saline land and heavy saline land. However, 190 km2 of slight saline land, 75 km2 of moderate saline land and 60 km2 of heavy saline land transformed into non-saline land.
Moreover, 101 and 31 km2 of slight saline land transformed into moderate saline land and heavy saline land, and 37 km2 of moderate saline land transformed into heavy saline land. On the other hand, 81 km2 of moderate saline land and 15 km2 of heavy saline land transformed into slight saline land, and 110 km2 of non-saline land transformed into slight saline land. Therefore, slight, moderate and heavy saline lands transformed each other, and non-saline land and saline land transformed each other. The two-type transformation resulted in the changes of the different types of areas. In total, from 1991 to 1998, heavy saline land and moderate saline land increased, while the slight saline land decreased. From the transition matrix between 1998 and 2005, we can conclude that: 134 km2 of slight saline land, 183 km2 of moderate saline land and 122 km2 of heavy saline land had transformed into non-saline land. 85 km2 of moderate saline and 26 km2 of heavy saline land transformed into slight saline land, and 32 km2 of heavy saline land transformed into moderate saline land. In addition, 115, 81 and 40 km2 of non-saline land transformed into slight, moderate and heavy saline land, respectively. 78 and 14 km2 of slight saline land transformed into moderate and heavy saline land. In total, the land transform mainly occurred between the saline land and nonsaline land. Some saline lands transformed into non-saline lands, which lead to the increase of non-saline lands and the decrease of saline lands. Moreover, the transformations among the different types of saline lands resulted in the decrease of moderate and heavy saline land. In a word, the salinization of Jiefangzha Irrigation Sub-district was decreased from 1998 to 2005.
Fig. 4. Changes of the different types of areas with years.
R. Yu et al. / Agricultural Water Management 97 (2010) 1952–1960
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Table 1 Transition matrix of different types of areas (km2 ) in 1991, 1998 and 2005.
1991
1998
1991
Classification
Slight
Moderate
Heavy
1998 Slight Moderate Heavy Non-saline Sum
188 81 15 110 394
101 125 61 123 410
31 37 55 90 213
190 75 60 633 958
510 318 191 956 1975
2005 Slight Moderate Heavy Non-saline Sum
168 85 26 115 394
78 93 32 81 284
14 49 33 40 136
134 183 122 722 1161
394 410 213 958 1975
2005 Slight Moderate Heavy Non-saline Sum
201 87 15 91 394
86 92 34 72 284
12 37 52 35 136
211 102 90 758 1161
510 318 191 956 1975
Combining the results of transition matrix during 1991–1998 and 1998–2005, the statistics from 1991 to 2005 show that: 211 km2 of slight saline land, 102 km2 of moderate saline land and 90 km2 of heavy saline land transformed into non-saline land. While only 91, 72 and 35 km2 of non-saline land transformed into slight, moderate and heavy saline land. In addition, 15 and 34 km2 of heavy saline land transformed into slight and moderate saline land, and 87 km2 of moderate saline land transformed into slight saline land. However, only 86 km2 of slight saline land transformed into moderate saline land, and 12 km2 of that transformed into heavy saline land. Therefore, the transition type between 1991 and 2005 is as same as that between 1998 and 2005. In conclusion, the slight, moderate and heavy saline land presented a decreasing trend from 1991 to 2005. Especially, the slight saline land was decreased obviously. 3.4. Relationship between LANDSAT TM data and field observation data The single variable correlative analysis of EC values measured on 26 March 1998 and 29 March 2005 and the reflectance data of band1, band5, band7 and (band5 − band7)/(band5 + band7) are carried through. The EC values on 23 March 1991 are utilized to assess the precision of estimates of EC values using the reflectance data. Comprehensive consideration on the correlation of the field data and the image reflectance, the field data have a good relationship with the reflectance composition (band5 − band7)/(band5 + band7) with a correlation of 0.845 (Fig. 5). The regression equation obtained using the reflectance data obtained on 23 March 1991 was: EC =
5.653(band5 − band7) + 0.246 (band5 + band7)
Non-saline
Sum
cover. The accumulation of soil salinity is a dynamic process, and all the factors are in one dynamic system. The analysis of the characteristics of different types of areas in 1991, 1998 and in 2005 showed that the changes of different types of areas are mainly related to meteorological factors, irrigation and drainage infrastructure, irrigation quota and time. The reasons for the large variation of soil salinization are that Jiefangzha Irrigation Sub-district is mixed with cultivated lands and waste lands. When the cultivated lands are located lower than the waste lands and water level is lower in the canal under a condition of inefficient drainage, the waste lands will become the water discharge areas. However, after the construction of hydraulic works, the water level of the canal is lifted up, so a large part of the waste lands are developed into cultivated lands, while the old cultivated lands will become the salt and water discharge areas under an efficient drainage condition. As a result, the transition of light saline soil into moderate and heavy saline soils increases. 4.1.1. Impacts of groundwater depth on soil salinization The groundwater dynamics of Jiefangzha Irrigation Sub-district belongs to vertical infiltration-evaporation type. The irrigation infiltration is the main inflow to the groundwater, and the groundwater evaporation is the main outflow in the sub-district. Therefore, the irrigation infiltration is the main reason for the rise of groundwater level, and the groundwater evaporation maintains the annual balance of groundwater level.
(7)
With a mean square error of calculated values of 0.052, assuming that the parameters that effect reflectance do not change with time, the regression equation can be used to the prediction of EC values of Jiefangzha Irrigation Sub-district. 4. Discussion 4.1. Analysis of reasons for spatial-temporal changes of soil salinity Soil salinization is caused by a lot of factors, such as groundwater depth, total dissolved salt, precipitation, soil texture and plant
Fig. 5. Relationship between EC and (band5 − band7)/(band5 + band7).
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Fig. 6. Dynamic changes of groundwater depth during freezing period from 1990 to 2005.
In Jiefangzha Irrigation Sub-district, because of the special characteristics of water and salinity movement and the influence of autumn irrigation, the salinity accumulation is closely related with the groundwater depth during freezing and thawing period. Therefore, the groundwater depth data from October to April from 1990 to 1991, 1997 to 1998, and 2004 to 2005 were selected for the analysis of groundwater dynamics. The fluctuation of groundwater depth during freezing period from 1990 to 1991, 1997 to 1998, 2004 to 2005 are shown in Fig. 6. Fig. 6 reflects the groundwater dynamics during the period of autumn irrigation from October to November and the soil freezing period from early November to the end of February next year. The dynamic change of groundwater exhibited the same trends from 1991 to 1992, from 1997 to 1998, and from 2004 to 2005, respectively. Autumn irrigation is soil storage irrigation after harvest for the crops next year, and its water diversion accounts for one-third of the total annual diversion from Yellow River. The groundwater level is lower in October at the beginning of the autumn irrigation period. After the autumn irrigation, the groundwater level starts to rise.
Because autumn irrigation area is bigger and the irrigation period lasts longer, the groundwater level increases greatly after irrigation. For example, the mean groundwater depth after the autumn irrigation is close to 0.6 m in 1997, and the groundwater level reached its peak in the whole year. In early November, as the temperature descends, the surface soil begins to freeze and the groundwater level descends slowly at a rate of 1–3 cm per day. At the end of Feb. when the drop of air temperature ceases, soil freezing is completed and the soil frozen layer reaches its maximum, meanwhile the groundwater level decreases to its lowest value of whole year. During this period, i.e. the freezing period (from early November to the end of February next year), upward groundwater movement in response to thermal gradients brings the salt to the soil frozen layer. From the early Mar., the frozen layer starts to thaw from both the upper and lower surfaces. Water released from the lower surface moves downward into the groundwater and groundwater levels rise. While water released from the upper surface evaporates and salt remained on the soil surface, resulting in the highest levels of salt content during an annual freeze–thaw cycle. At the end of May, the thawing of frozen layer is finished.
Fig. 7. Changes of EC values from 0 to 20 cm along with groundwater depth during freezing and thawing period.
R. Yu et al. / Agricultural Water Management 97 (2010) 1952–1960
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Table 2 Relationship matrix of groundwater chemical characteristics. Items 2+
Ca Mg2+ K+ + Na+ C1− SO4 2− CO3 2− HCO3 2− Total alkalinity Total salinity EC ln(EC)
Ca2+
Mg2+
K+ + Na+
C1−
SO4 2−
CO3 −
HCO3 −
Total alkalinity
Total salinity
EC
ln(EC)
1 0.77 0.62 0.78 0.71 −0.28 0.31 0.31 0.76 0.14 0.16
1 0.80 0.84 0.74 −0.18 0.41 0.40 0.92 0.09 0.13
1 0.94 0.54 −0.15 0.56 0.55 0.95 0.07 0.18
1 0.66 −0.20 0.51 0.48 0.96 0.03 0.11
1 −0.08 0.09 0.11 0.67 0.11 0.13
1 −0.17 −0.12 −0.19 0.00 0.07
1 0.99 0.58 0.40 0.43
1 0.57 0.46 0.49
1 0.15 0.23
1 0.97
1
Moreover, EC for the 0–20 cm soil layer with an r2 value of 0.67 follows an exponential relationship with groundwater depth (Fig. 7). The subtraction of EC values for the 0–20 cm soil layer after thawing from those before freezing also has a good relationship with groundwater depth, with an r2 value of 0.72. The correlative coefficient of later is bigger, which indicates that the soil salt accumulation in freezing period has a close relationship with the groundwater depth. Therefore, the groundwater depth is the major factor causing salt accumulation. The maximum groundwater level data after autumn irrigation from 1990 to 1991, 1997 to 1998, and 2004 to 2005, occurred in 1997 to 1998, so soil salinization in 1998 is most severe. The groundwater level from 1990 to 1991 is lower compared with that from 2004 to 2005. However, the soil salinization in 1991 is more severe compared with that in 2005. The reason is that the construction of irrigation and drainage works in an area of 2100 km2 financed by the World Bank started in 1990 and completed in 1996. In 1991, the construction irrigation and drainage works was not established completely, which led to the less salt accumulated in the topsoil. In addition, though the works was finished in 1998, a large irrigation in autumn 1997 resulted in salt accumulation in the topsoil in 1998. In the first half year of 2000, Surveying & Designing Institute of Water Conservancy & Hydropower of Inner Mongolia Autonomous Region compiled the ‘Planning report of continuous construction and water-saving transformation in Hetao Irrigation District of Yellow River Basin’, which accelerated the development of irrigation and drainage engineering. In 2005, with the improvement of water-saving engineering and irrigation and drainage construction, the soil salinization of Hetao Irrigation District was reduced obviously and the soil condition was also improved greatly.
SO4 2− are all collected and processed from 1990 to 1991, 1997 to 1998, 2004 to 2005. The correlative coefficient matrix of above factors is listed in Table 2. From Table 2, soil EC values from 0 to 20 cm have a close relationship with HCO3 − and total alkalinity. Therefore, groundwater depth, HCO3 − and total alkalinity are all key factors causing soil salinity. In addition, the changes of soil EC, groundwater depth and total alkalinity along with time are all shown in Fig. 8. From the Fig. 8, it can be concluded that the change of soil EC is consistent with that of the total alkalinity, but opposite with that of the groundwater depth. The findings are a validation to the above correlative results.
4.1.2. Impact of groundwater chemical characteristics on the soil salinization Soil electronic conductivity from o to 20 cm, groundwater total alkalinity, total salinity, Ca2+ , Mg2+ , Na+ , K+ , HCO3 − , CO3 2− , Cl− ,
Acknowledgements
5. Conclusions The combination of remote sensing with field observation proposed in this work is a useful tool to estimate the spatial and temporal dynamics of soil salinization. With this method, the salinity degree of Hetao Irrigation District is determined and the main reasons of its soil salinization are identified. The development of the remote sensing method is a priority for economical and environmental reasons, especially in the semiarid and arid areas where severe salinization has to be controlled. The remote sensing technique is an essential tool that is rarely applied in the management of irrigated agricultural systems in these regions. LANDSAT can be used to estimate EC values of soil of large irrigation fields due to high spatial and temporal resolution of images. It is concluded that remote sensing technique can be favorably applied for the evaluation of soil salinization dynamics in regional scale.
The authors wish to acknowledge the instructive suggestion and great help of chief editor J.D. Oster and Professor J.S. Yang (Institute of Soil Science, Chinese Academy of Sciences). Also, thanks to all those who provided data for the inventory. References
Fig. 8. Changes of soil EC, groundwater depth and total alkalinity along with time.
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