SoilUse and Management doi: 10.1111/sum.12059
Soil Use and Management, September 2013, 29, 445–456
Application of digital soil mapping methods for identifying salinity management classes based on a study on coastal central China Y. G U O 1 , Z. S H I 1 , H. Y. L I 2 & J. T R I A N T A F I L I S 3 1
Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China, 2School of Tourism and Urban Management, Jiangxi University of Finance and Economics, Nanchang 330013, China, and 3School of Biological, Earth and Environmental Sciences, The University of New South Wales, Sydney, NSW 2052, Australia
Abstract In coastal China, there is an urgent need to increase land for agriculture. One solution is land reclamation from coastal tidelands, but soil salinization poses a problem. Thus, there is need to map saline areas and identify appropriate management strategies. One approach is the use of digital soil mapping. At the first stage, auxiliary data such as remotely sensed multispectral imagery can be used to identify areas of low agricultural productivity due to salinity. Similarly, proximal sensing instruments can provide data on the distribution of soil salinity. In this study, we first used multispectral QuickBird imagery (Bands 1–4) to provide information about crop growth and then EM38 data to indicate relative salt content using measurements of apparent soil electrical conductivity (ECa) in the horizontal (ECh) and vertical (ECv) modes of operation. Second, we used a fuzzy k-means (FKM) algorithm to identify three salinity management zones using the normalized difference vegetation index (NDVI), ECh and ECv/ECh. The three identified classes were statistically different in terms of auxiliary and topsoil properties (e.g. soil organic matter) and more importantly in terms of the distribution of soil salinity (ECe) with depth. The resultant three classes were mapped to demonstrate that remote and proximally sensed auxiliary data can be used as surrogates for identifying soil salinity management zones.
Keywords: Digital soil mapping, soil salinity, management zones, fuzzy k-means, remote sensing
Introduction There is an urgent need to increase arable land for agricultural production and urban development in the coastal zones of China where there is a rapidly growing population. For example, in Zhejiang Province, the population is large (56 million) and the area is dominated by low hills and mountains (70.4%). One solution has been coastal reclamation. Over the last 40 yr, 400 000 ha has been reclaimed for agriculture and urban buffer zones under a series of reclamation programmes (Huang et al., 2008). However, soil salinization is a problem, particularly in the first 10–15 yr of agricultural production. There is need to map the saline areas and identify appropriate salinity management strategies. We need information about
Correspondence: Z. Shi. E-mail:
[email protected] Received November 2012; accepted after revision May 2013
topsoil and root zone salt content to assess the impact of salinization on agriculture. One cost-effective approach is to use digital soil mapping (DSM). The first requirement is the collection of easily obtainable auxiliary information. These include remote and/ or proximal sensed data (Triantafilis et al., 2009). In the former, multispectral data (e.g. SPOT-5 or Quickbird images) can be used to infer crop growth (L opez-Lozano et al., 2010) and vegetation vigour using an index such as normalized difference vegetation index (NDVI) (SalinasZavala et al., 2002; Pinter et al., 2003), and Pe~ nuelas et al. (1997) show that as salinity increases, NDVI decreases. This has special relevance to germination because plants are most susceptible at this growth stage. For characterizing subsoil salinity, electromagnetic (EM) induction data (e.g. EM38) are an option because many studies have demonstrated how apparent electrical conductivity (ECa) can be used to measure (Triantafilis et al., 2000) and then to map spatial
© 2013 The Authors. Journal compilation © 2013 British Society of Soil Science
445
446 Y. Guo et al. variability in salinity with depth (Eldeiry & Garcia, 2011; Li et al., 2013). Secondly, we need to define salinity management zones on an objective basis. There have been many approaches to delineating site-specific management zones, which include the use of the ISODATA method (Host et al., 1996), geostatistical techniques (Moral et al., 2011) and k-means clustering (Altdorf & Dietrich, 2012). For precision agriculture, crop yield (Boydell & McBratney, 2002; Jaynes et al., 2003), topographical features (Franzen et al., 2011) and soil fertility measures (Davatgar et al., 2012) have also been used to partition fields into homogeneous units for management. It has been demonstrated that the fuzzy k-means (FKM) cluster algorithm is useful for showing the continuous natures of landscapes. This includes the use of high-resolution remotely sensed data such as raw multispectral imagery from SPOT-5 (Li et al., 2007) and QuickBird (Song et al., 2009), spectral indices (Oldeland et al., 2010) and gamma-ray spectrometry (Triantafilis et al., 2013a). However, there are few studies that have combined highresolution remotely sensed imagery and proximal sensor data using the fuzzy k-means algorithm to identify soil management zones. The main objective of this research was to determine whether salinity management zones could be discerned using two different sets of auxiliary data. Specifically, we used remotely sensed data (QuickBird Bands 1–4) to derive NDVI because this might reflect the rice productivity and thereby indicate differences in topsoil salinity. In addition, we used EM instruments to discriminate between soil management zones based on differences in changes in profile salinity. We tested whether these zones characterized areas which could be managed in the same way for mitigating salinization and determined whether there were differences between them based on various properties (e.g. SOM and available N, P and K).
(a)
China
N Province/city boundary National boundary
(b)
120°40'0"E
Study area Shangyu City is in the southern coastal area of the Hangzhou Gulf (Figure 1a) and receives water from the Qiantang River. The city has an area of 26 061 ha (30°04′ 00″–30°13′ 47″ N, 120°38′ 32″–120°51′ 53″ E) and a subtropical climate. The average daily maximum and minimum monthly temperatures are 4 °C (January) and 28 °C (July), respectively. Average annual precipitation is 1440 mm, with the heaviest rainfall during two rainy seasons between March and June and also during September. Around Shangyu city, ca. 17 000 ha of coastal land have been reclaimed over the past 40 yr (Figure 1b). The soil is therefore derived from recent marine and fluvial deposits. The reclaimed land has been used for growing cotton, rice and horticultural crops (e.g. watermelons and grapes).
120°50'0"E 30°15'0"N
30°10'0"N
30°10'0"N
30°5'0"N
30°5'0"N
120°40'0"E
Materials and methods
120°45'0"E
30°15'0"N
120°45'0"E
120°50'0"E
Figure 1 Location of the study field with reference to (a) the Hangzhou Gulf and (b) reclaimed lands over the past 40 yr.
The land has also been used for aquaculture (e.g. prawns). The investigated fields were reclaimed in 1996. There were five fields with an area of 2.2 ha (Figure 2a). Three of the fields were slightly larger and were ca. 0.5 ha each whilst the two smaller fields were ca. 0.35 ha. The fields were separated by small embankments (bunds), which ensured flooded conditions within each of the study fields.
Auxiliary data collection, processing and harmonization Remotely sensed data were collected using the QuickBird (Digital Globe, Inc.) platform. QuickBird is a passive remote sensor that provides very high-resolution (i.e. 2.4 m)
© 2013 The Authors. Journal compilation © 2013 British Society of Soil Science, Soil Use and Management, 29, 445–456
Digital mapping of soil salinity
values by applying transformation coefficients as suggested by the provider (Digital Globe, Inc.). The data were subsequently used to calculate the NDVI. This is because the NDVI has been used as an indicator of vegetation vigour (Salinas-Zavala et al., 2002; Pinter et al., 2003). More significantly, Metternicht (2003) and Paine (2003) showed that salinization diminished crop yield and productivity. The NDVI was calculated from the nearinfrared and red wavelengths using the standard equation:
(a) Ridge line N Red:
Band 3 Green: Band 2 Blue: Band 1
NDVI ¼
Metres 0 5 10
20
30
40
(b) Ridge line
N
NDVI 0.21 – 0.45
447
0.58 – 0.60
0.45 – 0.54
0.60 – 0.61
0.54 – 0.58
0.61 – 0.62
Band4 Band3 : Band4 þ Band3
ð1Þ
Proximally sensed data were collected during November 2003 after the rice harvest. A 5-m grid (Figure 3a) was used to make 386 measurements of bulk apparent soil electrical conductivity (ECa, mS/m). The data were collected using a Geonics EM38 and in the horizontal (ECh) and vertical modes (ECv). This provided information about the root zone (0–0.75 m) and subsoil (0–1.5 m), respectively, and potentially about salt content. Georeferencing was provided by a Trimble Global Positioning System and with differential correction to within 2 m. We calculated the ratio of ECv and ECh (ECv/ECh) in order to provide information more relevant to the salt content. The remotely sensed QuickBird imagery and proximally sensed EM data were harmonized on a common grid by extracting NDVI data as a function of the respective longitudes and latitudes of the 386 ECa locations. This was carried out using the nearest neighbour algorithm available in ARCGIS 9.3 (ESRI, Inc.).
Soil sampling and laboratory analysis
Metres 0 5 10
20
30
40
Figure 2 Spatial distribution of (a) remotely sensed multispectral QuickBird data (b) Normalized Difference Vegetation Index (NDVI) calculated from (a).
multispectral data in the blue (Band1, 450–520 nm), green (Band2, 520–660 nm), red (Band3, 630–690 nm) and nearinfrared (Band4, 760–900 nm) wavelengths. The imagery in Figure 2a was acquired on 1 August 2003, which was a cloudless day with clear skies. We chose these data because in this part of China rice is approaching its peak reproductive stage in early to mid-August (Xiao et al., 2002). Thus, the vegetation cover measured by NDVI will best reflect crop growth, which is affected by salinity. The digital values for Bands 1–4 were converted to absolute radiance
Figure 3b shows the locations where the soil samples were collected systematically at ca. 20 m on a grid. The samples were collected without considering the spatial or frequency distribution of the NDVI and/or ECa data. A total of 47 soil sample locations were chosen. At each, a sample was obtained from the topsoil (0–0.20 m). The samples were airdried, sieved through a 2-mm sieve and then analysed. Analyses were carried out according to the procedures described by Bao (2007). In brief, salinity was determined using a 1 part soil to 5 part water suspension (EC1:5, mS/m). Soil organic matter (SOM) was determined colorimetrically after H2SO4-dichromate oxidation at 150 °C, with available nitrogen (AN) measured by the alkaline hydrolysis diffusion method. Available phosphorus (AP) was measured by the NH4F-HCl method, whilst available potassium (AK) was measured using the NH4OAC extraction method and analysed using a flame photometer. In addition, soil samples were taken from an initial depth of 0.05 m and then at every 0.10 m to a maximum of 1.05 m and analysed only for EC1:5. All EC1:5 measurements were converted to ECe
© 2013 The Authors. Journal compilation © 2013 British Society of Soil Science, Soil Use and Management, 29, 445–456
448 Y. Guo et al.
(a) N
Ridge line
EM38 survey locations
and proximally (ECh and ECv/ECh) sensed data and derived indices. The FKM method is described by Triantafilis et al. (2003). In brief, it calculates a measure of similarity between an individual i and a cluster c in multivariable space (Bezdek, 1981). The best outcome minimizes the objective function J(M,C): JðM; CÞ ¼
n X k X i¼1 c¼1
Metres 0 5 10
20
30
40
(b) N Ridge line Soil sampling locations
m/ic dic2 ðxi ; cc Þ
ð2Þ
where M = mic is a n 9 k matrix of membership values (where n is the number of objects), C = (ccv) is a k 9 p matrix of class centres (p denotes the number of variables), ccv (value of the centre of class c for variable v), xi = (xi1,…, xip)T is a vector representing the individual i, cc = (cc1,…, ccp)T is the vector representing the centre of class c, dic2 ðxi ; cc Þ is the square distance between xi and cc according to a chosen definition of distance, and the exponent / determines the degree of fuzziness. The distance function is used to measure similarity or dissimilarity between two individual observations and then later between two clusters. Various distance measures can be used (e.g. Mahalanobis or Diagonal) to optimize J(M,C). In this study, we used Euclidean as the distance metric. This is because this measure is useful for uncorrelated variables on the same scale when attributes are independent and the clusters have the general shape of spherical clouds. We also chose it as it accounts for (a) differences in variances and correlations among variables and (b) because it gives equal weight to all variables (Bezdek, 1981). It is defined as: vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi uc ¼ 1 uX dic ¼ t ðxi cc Þ2
ð3Þ
i¼1
Metres 0 5 10
20
30
40
Figure 3 Spatial distributions of (a) EM38 survey locations and (b) Soil sampling locations.
using the following relationship ECe = 0.3556 + 0.0921 9 EC1:5 as established from data collected in an adjoining field (Li et al., 2013).
Fuzzy k-means (FKM) clustering and validation The FKM clustering algorithm is an unsupervised clustering method that has been widely used in climatology, geology, suburban environmental studies and soil landscape mapping (Gorsevski et al., 2003). We used the FKM algorithm to determine the number of classes within our remote (NDVI)
We used a value of / = 1.3. We did this because many other authors have shown that for auxiliary data this value is appropriate. For example, Kitchen et al. (2005) found that a value of / = 1.3 was suitable in delineating productivity zones from ECa data and various other auxiliary data. This was also the case for the study by Fridgen et al. (2004) who found that / = 1.5 was useful in delineating management zones using only ECa. Triantafilis et al. (2009) reported that a value of / = 1.4 was suitable for identifying digitized soil classes using air photography and ECa at the field scale. In terms of small-scale soil classification, similar values of / (between 1.2 and 1.5) are recommended by Odeh et al. (1992) who achieved reasonable results when considering soil properties similar to those we used to validate our salinity management zones. The fuzziness performance index (FPI) and the modified partition entropy (MPE) can be used to determine a suitable value for k and as a function of /. FPI is a measure of the continuity between classes. An FPI of 1 represents a very
© 2013 The Authors. Journal compilation © 2013 British Society of Soil Science, Soil Use and Management, 29, 445–456
Digital mapping of soil salinity
fuzzy classification whilst a value approaching 0 indicates distinct classes with little shared membership. The MPE is a measure of the degree of disorganization created by partitioning the data into various classes. Values near 0 indicate that the classes are well structured. Values approaching 1 indicate that these classes are disorganized and exhibit large entropy. The least fuzzy and least disorganized number of classes (i.e. minimum) are considered optimal (Odeh et al., 1992). The FKM analysis was carried out using the FuzME program (Minasny & McBratney, 2002). We compared them in order to validate the FKM analysis and to determine whether the identified classes represented contiguous areas that had similar chemical properties as needed for soil management zones. We did this by carrying out a mean separation test to compare the class means of measured soil data (topsoil ECe, SOM, available N, P and K) at the sample locations. We chose the Tukey–Kramer multiple comparison procedure in JMP Version 10 (SAS Institute Inc., 2012) and limited Type 1 error to 0.05.
Class overlap, confusion index and defuzzification The confusion index (CI) is a measure of the degree of class overlap in attribute space (Burrough & McDonnell, 1998). The concept of ‘confusion index’ is a measure of how well each individual observation has been classified. The CI is used to translate combined maps of fuzzy memberships into easy to understand crisp zones. The CI is calculated by the following equation where mmaxi denotes the dominant membership value and mðmax 1Þi is the subdominant membership value for each observation: CI ¼ 1 ½mmaxi mðmax 1Þi
ð4Þ
If the calculated CI approaches zero, then the observation is more likely to belong to the dominant class, whilst if the CI approaches one, the difference between the dominant and subdominant classes is negligible, which creates confusion in classification for that particular observation. After the membership values have been calculated, defuzzification is applied to get a crisp numerical output value (Burrough et al., 2000). Each observation is assigned to a ‘hard class’ when membership is high, to an ‘intragrade’ when membership is intermediate and to an ‘extragrade’ when membership is low. For example, high membership means that an observation is more likely to belong to one class; intermediate membership means that observation might belong to two or more classes, and low membership means that observation belongs equally to all classes. We consider that a membership value of 0.4 or greater for any given class means that this observation belongs to that class.
449
Results and discussion Preliminary data analysis Table 1 gives the Pearson’s correlation coefficients for the various auxiliary data sets for the 386 survey locations. The correlation between ECh and NDVI is the best, but is not strong (0.50). The next best is between ECh and ECv/ECh, which is moderate (0.48). The NDVI and ECv/ECh are unrelated (0.16). Similar correlation coefficients are shown for the 47 soil sampling locations. This is particularly the case with respect to ECh and NDVI (0.39) and to a moderate extent ECh and ECv/ECh (0.47). This suggests that our sampling and survey locations are equivalent to each other in terms of auxiliary data, and the former is a fair reflection of the latter. Thus, the soil data available at the sampling locations can be used to validate the validity of our salinity management classes as derived from the FKM analysis of the auxiliary data collected at the survey locations. Table 2 gives the summary statistics for the 47 soil sample locations and the various laboratory measured soil properties. Of most interest is the topsoil ECe which has a minimum value of 3.03 dS/m, which is at the lower limit of salinity tolerance for rice. The average topsoil ECe (6.97 dS/ m) across the field is just over twice this value with the median (5.81 dS/m) just under. It is also worth noting the Pearson’s correlation coefficients between the remotely sensed NDVI and various soil analytical data. The greatest are between NDVI and available P (0.58), followed by available N (0.53) and then SOM (0.52). The largest positive coefficient is between ECh and topsoil ECe (0.89) with respect to the proximally sensed ECa, this suggests the EM38 in the horizontal mode is the most responsive to salinity. The greatest negative correlation exists between ECh and SOM (0.78) and available P (0.77). This indicates that salinity is having a negative effect on these soil variables. This is unsurprising for SOM as high salinity means reduced crop growth and therefore a lesser accumulation of organic matter.
Table 1 Pearson’s correlation coefficients between ECa (ECh) and ECv/ECh and NDVI of 386 survey locations and 47 soil sampling locations 386 survey locations Data ECh ECv/ECh NDVI
ECh
ECv/ECh
1 0.48 0.50
1 0.16
47 sampling locations
NDVI
ECh
ECv/ECh
NDVI
1
1 0.47 0.39
1 0.27
1
NVDI, normalized difference vegetation index.
© 2013 The Authors. Journal compilation © 2013 British Society of Soil Science, Soil Use and Management, 29, 445–456
450 Y. Guo et al. Table 2 Descriptive statistics of soil properties, including electrical conductivity of a saturated soil paste extract (ECe, dS/m); soil organic matter (SOM, g/kg); available nitrogen (AN, mg/kg); available phosphorus (AP, mg/kg); and available potassium (AK, mg/kg), collected at the 47 soil validation locations and Pearson’s correlation coefficients between soil properties and NDVI, ECh (mS/ m) and ECv/ECh
Basic statistics
ECe (dS/m)
SOM (g/kg)
AN (mg/kg)
AP (mg/kg)
(a) Ridge line
N
ECh (mS/m) 59.75–100
200–250
100–150
250–300
150–200
300–362
AK (mg/kg)
Soil chemical properties collected at the 47 soil sampling locations Mean 6.97 6.49 39.76 29.6 82.18 Median 5.81 6.34 37.56 28.71 80.23 Min 3.03 4.99 22.95 11.78 44.57 Max 16.01 9.09 60.95 51 138.41 SD 3.35 1.01 11.02 11.65 29.07 Skewness 0.72 0.39 0.2 0.02 0.35 Kurtosis 0.95 0.46 1.3 1.28 1.16 Pearson’s correlation coefficients between soil properties and ECh, ECv/ECh and NDVI NDVI 0.53 0.52 0.53 0.58 0.33 ECh 0.89 0.78 0.71 0.77 0.66 ECv/ECh 0.43 0.29 0.17 0.25 0.08
Metres 0 5 10
20
30
40
(b) Ridge line
N
ECv (mS/m)
NVDI, normalized difference vegetation index.
67.75–100
200–250
100–150
250–300
150–200
300–357.38
Spatial distribution of auxiliary and soil data Figure 2b shows the spatial distribution of the NDVI. The large values (NDVI > 0.58) characterize the western fields and in particular the two fields to the north. Conversely, the smallest (NDVI < 0.45) defines the areas in the south-eastern field. As shown in Figure 2a, these NDVI values indicate much reflectance, bare soil and where the rice was not growing well. Figure 4a shows the spatial distribution of ECh. The small (250 mS/m) values. Figure 4b shows the same spatial pattern but for ECv. The spatial distributions of two of the most important soil properties relevant to our study (topsoil ECe and SOM) are shown in Figure 5. Of interest is the larger ECe in the southeastern field. Here, salinity (15 dS/m) is at a level at which rice germination would be minimal. Salinity decreases generally with distance. Over 100 m, the ECe is sufficiently less so that germination and crop growth would be near optimal (200 mS/m) and NDVI smallest (0.4 qualified each of the 386 survey locations to membership of classes A, B or C. Class A had the largest number of members (204) and class C the smallest (63). Class B consisted of approximately half as many as A and twice that of C. The means of the auxiliary data within these classes show that class A has the largest NDVI (0.58) and smallest ECh (114 mS/m). Conversely, class C has the smallest NDVI (0.46) and largest ECh (276 mS/m). Figure 8a and b shows the calculated Tukey–Kramer comparison of means for the NDVI and ECh, respectively.
The results suggest success in identifying soil salinity management zones on the basis of the statistics and the contiguous nature of the classes. This is reinforced further when we consider the plot of estimated ECe versus depth and for each zone. Figure 9a shows these data and the results can be described using the following soil salinity ranges (Victorian Resources online, 2013): nonsaline (16 dS/m) saline. With respect to zone A, topsoil (i.e. 0–0.20 m) ECe is slightly to moderately saline, whilst subsoil ECe is highly saline. Similar increasing trends are apparent with the ECe profile distribution for zones B and C. Of these, profiles for zone C have on average the most highly saline top and extremely saline subsoil ECe. The ECe profiles for each class are generally consistent with crop growth and soil conditions. Overall, rice grown in zone A was not noticeably affected. However, towards and within zone C, there was a noticeable and progressive reduction in rice growth, density and vigour. The methodology is also validated by Figure 9b, which shows that the ECe profiles taken from sites that had a high membership in each zone (e.g. 0.999), and these are located centrally in each. The cause of the highly saline soil ECe in zone C is most likely due to the method of land reclamation. This is because zone C is at the lowest point in the field and is downstream of the area from which water had been pumped. Because of
© 2013 The Authors. Journal compilation © 2013 British Society of Soil Science, Soil Use and Management, 29, 445–456
Digital mapping of soil salinity
(a)
(b)
400
0.60
350
0.55
300 ECh (mS/m)
0.65
NDVI
0.50 0.45 0.40
250 200 150
0.35
100
0.30
50 0
0.25 A
B
C
Zones
A
All Pairs Tukey-Kramer 0.05
15
8
12
7
SOM (g/kg)
(d) 9
9 6 3
B
C
Zones
(c) 18
ECe (dS/m)
453
All Pairs Tukey-Kramer 0.05
6 5 4
0 A
B Zones
C
All Pairs Tukey-Kramer 0.05
3
A
B Zones
C
All Pairs Tukey-Kramer 0.05
Figure 8 One-way analysis of (a) normalized difference vegetation index (NDVI), (b) ECh (dS/m), (c) ECe (mS/m) and (d) soil organic matter (SOM) (g/kg).
this, the influence of any fluctuating shallow saline water tables would have been most pronounced in this depressed part of the field. This is the case during the wetter months, in particular April and September, and when the water table is within 1 m of the surface. The greater salinity and shallow water tables explain the small amounts of SOM and applied fertilizers, such as superphosphate and urea. In class C, the solution and leaching of fertilizers occur during periods of high and low water tables, respectively. In zone A, and in the northern part of the study area, the relative elevations are higher, water tables are deeper, and ECe is less in topsoil and subsoils ( 0.999) for the three soil salinity management zones versus depth.
Acknowledgements This article is based upon research funded by the Zhejiang Provincial Natural Science Foundation of China (No. R5100140), by National Natural Science Foundation of China (No. 40871100, No. 41101197), by the Science and Technology Project of Zhejiang Province (No. 2011C13010) and Ministry of Education, Humanities and social science project (No. 10YJC910002).
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© 2013 The Authors. Journal compilation © 2013 British Society of Soil Science, Soil Use and Management, 29, 445–456
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