Soil Science and Plant Nutrition

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Spatial estimation of surface soil texture using remote sensing data Kaihua Liao , Shaohui Xu , Jichun Wu & Qing Zhu To cite this article: Kaihua Liao , Shaohui Xu , Jichun Wu & Qing Zhu (2013) Spatial estimation of surface soil texture using remote sensing data, Soil Science and Plant Nutrition, 59:4, 488-500, DOI: 10.1080/00380768.2013.802643 To link to this article: http://dx.doi.org/10.1080/00380768.2013.802643

Published online: 13 Aug 2013.

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Date: 15 January 2017, At: 17:19

Soil Science and Plant Nutrition (2013), 59, 488–500

http://dx.doi.org/10.1080/00380768.2013.802643

ORIGINAL ARTICLE

Spatial estimation of surface soil texture using remote sensing data Kaihua LIAO1, Shaohui XU2, Jichun WU3 and Qing ZHU1 1

State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China, 2Department of Environmental Science, Qingdao University, Qingdao 266071, China and 3 Department of Hydrosciences, Nanjing University, Nanjing 210093, China

Abstract Understanding the spatial distribution and variability of soil texture is essential for land use planning and other activities related to agricultural management and environmental protection. This study was conducted to evaluate Landsat Enhanced Thematic Mapper (ETM) remote sensing data as auxiliary variables for spatial estimation of surface soil texture using a limited number of soil samples taken from a site located in the city of Pingdu, Shandong Province, China. Three methods of evaluating variability in surface soil texture were evaluated: (1) multiple stepwise regression (MSR) based on the relationship between surface soil sand, silt and clay contents and remote sensing data; (2) kriging of surface soil sand, silt and clay contents; (3) cokriging with remote sensing data. Correlation analysis showed that surface soil sand, silt and clay contents were significantly correlated with Landsat ETM digital number (DN) of six bands (Bands 1–5 and Band 7), and the DN of Band 7 explained most of the variability in soil sand, silt and clay contents. The DN of Band 7 was selected as auxiliary data for the estimation of surface soil texture. The cross-validation results indicated that both MSR and kriged estimates had low reliability due to the variations in landscape and the low-density sampling in the study area. Cokriging with remote sensing data significantly improves estimates of surface soil texture compared with MSR and kriging. Key words: soil texture, remote sensing, spatial estimation, multiple stepwise regression, geostatistics.

INTRODUCTION Soil texture is considered an important influential factor on many of the physical and chemical properties and behavior of soil, such as water storage, cation exchange capacity, soil fertility, internal drainage and sorption characteristics (Rawls et al. 1982; Manrique et al. 1991; Gawlik et al. 1999; Makabe et al. 2009). In terms of organic matter content, soil texture is often used for developing pedotransfer functions to predict soil hydraulic properties and cation exchange capacity (Wösten et al. 2001; Seybold et al. 2005). Furthermore, soil texture is

Correspondence: S. XU, Department of Environmental Science, Qingdao University, Qingdao 266071, China. Email: [email protected] and Q. ZHU, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China. Email: [email protected] Received 7 September 2012. Accepted for publication 1 May 2013. © 2013 Japanese Society of Soil Science and Plant Nutrition

also a pertinent property to quantify vulnerability to soil erosion (Le Bissonnais 1996; Warrington et al. 2009). Therefore, high-resolution maps of soil texture are essential for hydrological and ecological modeling, as well as other activities related to agricultural management and environmental protection (Hassink 1992; Oberthür et al. 1996; Zhao et al. 2009). Over the last two decades, both remote sensing images of bare soil and spectral reflectance of soil samples have been successfully used for accurate and rapid estimation of soil properties (Henderson et al. 1989; Agbu et al. 1990; Sudduth and Hummel 1991; Ben-Dor and Banin 1995; Ben-Dor et al. 1997, 1999; Chen et al. 2000; McCarty et al. 2002; Brown et al. 2006; Ben-Dor et al. 2008; Chen et al. 2008; D’Acqui et al. 2010). For example, Chen et al. (2000) used remotely sensed imagery to evaluate surface soil organic matter at landscape scale. Ben-Dor et al. (2008) combined active (using imaging spectroscopy) and passive (using ground penetrating radar and frequent domain electromagnetic) remote

Estimation of surface soil texture 489

sensing methods for assessing soil salinity in the field. Studies also have indicated that soil texture correlates significantly with soil reflectance in the visible and near-infrared region (400–2500 nm) (Al-Abbas et al. 1972; Suliman and Post 1988; Zhang et al. 1992; Sullivan et al. 2005). Therefore, soil reflectance measurements can be used for estimating soil texture by statistical methods such as stepwise linear regression and partial least squares. The relationship between reflectance and soil texture, however, degrades when going from the laboratory scale to the field and large scales, due to the variations in surface status including roughness, soil moisture and nature of pebbles (Lagacherie et al. 2008). Although soil texture can be estimated using remote sensing data such as Landsat Enhanced Thematic Mapper (ETM) imagery, this approach may yield unacceptable performance over large areas. Geostatistics is a powerful tool for characterizing and mapping the spatial distribution and variability of soil properties (Burgess and Webster 1980). Kriging is a basic geostatistical technique that provides the best linear unbiased estimation (BLUE) for a spatially dependent variable. Cokriging is another geostatistical method that extends kriging of a primary variable to secondary variables based on their correlations with the primary variable. It has been demonstrated that cokriging is superior to kriging for minimizing estimation variance (Vauclin et al. 1983; Istok et al. 1993; Wu et al. 2009). If auxiliary variables are not highly correlated to the primary variable, cokriging is only slightly superior to (or at least equal to) ordinary kriging (Shouse et al. 1990; Triantafilis et al. 2001). Yates and Warrick (1987) suggested that cokriging is superior to ordinary kriging for predictions for 0.5 or larger correlation coefficients between variables and when the auxiliary variables are oversampled. The spatial distribution of soil texture can be predicted by kriging interpolation. This method will produce satisfying results if enough sample points are available. Regretfully, laboratory measurements (e.g., pipette method) of the particle size distribution (PSD) are costly and time-consuming. Cokriging can also be used for predicting soil texture when it is highly correlated to easily measured auxiliary variables. Lagacherie et al. (2008) found that soil texture correlates with soil reflectance significantly over small to large scales with low variation in surface roughness. Compared with the measurement of the PSD, remote sensing data is much easier to obtain for detailed descriptions of large areas. Thus, remote sensing data such as Landsat ETM imagery may be useful auxiliary variables for the prediction of surface soil texture. While there have been several studies focusing on the spatial estimation of soil texture using laboratory soil reflectance data (Zhang et al. 1992) and satellite remote sensing data such as IKONOS (Sullivan et al.

2005) and AVHRR imageries (Odeh and McBratney 2000), to our knowledge little research has been conducted using Landsat ETM imagery. Therefore, the objectives of this study are to: (1) investigate the utilization of Landsat ETM remote sensing data as auxiliary variables for the estimation of surface soil texture, and (2) evaluate the performance of three methods (multivariate regression, kriging and cokriging) in assessing the variability of surface soil texture.

MATERIALS AND METHODS Descriptions of study area This study was conducted in agricultural regions of Pingdu City. The study area (36°28′–37°02′ N, 119°31′– 120°19′ E), covering 3166.54 km2, is located in the eastern part of Shandong Province, China (Fig. 1). Pingdu is located in a warm temperate climate zone with marine monsoons, hot rainy summers, and cold dry winters. The annual mean temperature is 12.3°C with an average annual sunshine of 2541 h. The study area is a typical dry-land farming area with average annual precipitation of 707 mm, over 70% of which occurs from July to September. The landscape of the area is generally sloping. The elevation ranges from more than 150 m above sea level in the northeast to less than 50 m in the south and center of the study area (Fig. 2). Also, the typical crop rotation for this region is winter wheat (Triticum aestivum L.) and summer maize (Zea mays L.) cropping. Parent materials in this area are mainly residuum and colluvium granite and gneiss, alluvial, and lacustrine deposits. Soils formed in these parent materials are classified as Luvisols, Fluvisols and Vertisols respectively, according to the World Reference Base for Soil Resources (WRB) (Food and Agriculture Organization of the United Nations 1998). These three soil types cover more than 96% of the area. The Luvisols are mainly distributed in the northeastern part of the study area with high elevation and steeper slope, while the Fluvisols are mainly distributed around rivers with low elevation and gentle slope, and Vertisols are also mainly distributed in the areas with low elevation and gentle slope, but far away from rivers (Fig. 1 and 2). Additionally, very few Lixisols and Solonchaks are also distributed in this region.

Soil sampling and analysis Based on the number of samples being proportional to each soil type, a total of 58 samples were randomly taken from topsoil (0–15 cm) in November 2007 in agricultural regions of Pingdu City, covering 3166.54 km2 (Fig. 1). Twenty-seven samples of Luvisols, 16 samples of Fluvisols and 15 samples of Vertisols were collected. When sampling, five surface soil cores were collected

490 K. Liao et al.

Figure 1 The soil types, soil sampling sites (58), and sampling locations (344; 3-km grid) on a Landsat Enhanced Thematic Mapper (ETM) image in the study area.

Figure 2 The elevation change within the study area.

with a hand auger at each sampling site. These five soil cores were fully mixed, and then approximately 1 kg of the mixed soil was packed in a bag and brought back to the laboratory for analysis. The coordinates of all sampling locations were recorded using a global positioning system (GPS) receiver. Soil samples were air-dried, passed through a 2-mm sieve and then analyzed in the laboratory for the contents of sand (0.05–2 mm), silt (0.002– 0.05 mm) and clay (0–0.002 mm) particles in the soil following the pipette method (Soil Science Society of China 2000). To predict soil texture in the study area using remote sensing data as auxiliary variables, we selected a Landsat

ETM image acquired on 7 November 2007. Landsat data are available for free download from the USGS Global Visualization Viewer website (U.S. Geological Survey 2010). Most values of the normalized difference vegetation index (NDVI), which is a commonly used vegetative index in remote sensing analysis, were smaller than 0.1 in the study area. This indicated that the agricultural fields were mostly bare at the time of Landsat data acquisition. To use remote sensing data as an auxiliary variable for the prediction of surface soil texture, we sampled 402 pixels; 344 pixels were sampled using a grid-based sampling scheme with 3-km grid spacing, and the other 58 pixels were sampled based on the corresponding locations of the 58 soil samples described above (Fig. 1).

Multivariate regression Multiple stepwise regression (MSR) was conducted for surface soil texture of the 58 soil samples (dependent variable) with digital numbers (DNs) of six bands (Bands 1–5 and Band 7) of the Landsat ETM image (independent variables). A backward method regression (Norusis 1994) was selected and the level for entry in the regression model was set at p < 0.10, while a 0.05 significance level was applied to retain the variables in the model. The DNs of six bands were taken as independent variables and the contents of sand, silt and clay particles were taken as dependent variables in the linear regression model.

Estimation of surface soil texture 491

The multiple linear regression model was fitted using the regress function of MATLAB software (The MathWorks Inc., USA).

in kriging, the following set of equations should be solved simultaneously: 8 I P > > > λi γðxi ; xj Þ  μ ¼ γðxi ; xÞ < > > > :

Kriging and cokriging Ordinary kriging was used in this study. It is a typical geostatistical approach that relies on the observed soil textures and their corresponding spatial positions to predict the soil textures at unsampled locations. Cokriging is an extension of the kriging method that integrates the information carried by a secondary variable related to the primary variable being estimated. Such spatial variables are also called co-regionalized and are spatially dependent. In this study, cokriging is expected to produce a more accurate estimation since the relationship between surface soil texture and remote sensing data (e.g., DN of Band 7) was considered in the interpolation. Both kriging and cokriging are viewed as unbiased prediction methods with minimum prediction variance. They respectively use the semivariogram and cross-semivariogram to quantify the spatial patterns of the dependent variable, both isotropically and anisotropically (Western et al. 2004). The estimator for the semivariogram and cross-semivariogram is: 1 X f½zu ðxi þ hÞ  zu ðxÞ½zv ðxi þ hÞ  zv ðxÞg 2NðhÞ i¼1 NðhÞ

γuv ðhÞ ¼

(1) where γuv is the semivariance (when u = v) with respect to random variable zu, h is the separation distance, and N(h) is the number of pairs of zu(xi) and zv(xi) in a given lagged distance interval of (h + dh). When u ≠ v, γuv is the cross-semivariogram, which is a function of h (Yates and Warrick 1987). In this study, anisotropy of variograms was not founded. Four variogram models (spherical, exponential, linear and Gaussian) were used to describe the semivariograms or cross-semivariograms and the best-fitted models were selected with the largest coefficient of determination (R2) between model predicted variances and the measured values of soil texture (Wang et al. 2002). The semivariogram and cross-semivariogram can be used for kriging and cokriging analyses. The general equation of kriging estimator is: z ðxp Þ ¼

I X

λi zðxi Þ

(2)

i¼1

where z*(xp) is the kriging estimate at location xp, z(xi) is the known value at location xi, and λi is the weight of sample z at xi. In order to achieve unbiased estimations

i¼1

I P

(3) λi ¼ 1

i¼1

where γ(xi, xj) is the value of the variogram corresponding to a vector with origin in xi and extremity in xj, and μ is the Lagrange coefficient. Also, the general form of the cokriging equation can be formulated as: ^zðxp Þ ¼

I X

ai zðxi Þ þ

J X

bj yðxj Þ

(4)

j¼1

i¼1

subject to: 8 I P > > > < ai ¼ 1 i¼1

J P > > > : bj ¼ 0

(5)

j¼1

in which ^zðxp Þ is the cokriging estimate at location xp, and ai and bj are weights of z (primary variable) at xi and y (secondary variable) at xj, respectively. The weights are calculated by solving the cokriging equations which can be found in geostatistical textbooks (Journel and Huijbregts 1978; Isaaks and Srivastava 1989). All the geostatistical computations were performed using GS+ version 9.0 (Gamma Design Software LLC., Plainwell, MI). ArcGIS 9.3 (ESRI, The Redlands, CA) was used for mapping and spatial analysis.

Evaluation criteria Due to the relatively small number of sites used in this study, a cross-validation technique was used to evaluate the predictive power of kriging and cokriging (Lagacherie et al. 2008). We removed one sample from the data set, adjusted the prediction model (e.g. kriging or cokriging) over the 57 remaining samples, and predicted the ‘leave out’ individual. We repeated this process until all samples had been removed individually. Estimated and measured soil texture were used to calculate the mean error (ME) and root mean square error (RMSE) which are defined based on Isaaks and Srivastava (1989): ME ¼

58 1 X ½zðui Þ  z ðui Þ 58 i¼1

(6)

492 K. Liao et al. vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u 58 u1 X RMSE ¼ t ½zðui Þ  z ðui Þ2 58 i¼1

Geostatistical analysis (7)

where z(ui) is the measured value of z at location ui and z*(ui) is the predicted value at the same location. The ME provides a measure of bias, and the RMSE provides a measure of accuracy.

RESULTS Correlation analysis In order to detect the relationships between surface soil texture and Landsat ETM DNs of six bands (Bands 1–5 and Band 7), the linear correlation analysis was applied, and the results are shown in Table 1. A positive correlation exists between soil sand content and DNs of six bands, and silt and clay content have negative correlation with DNs of six bands. The DN of Band 7 has the strongest absolute correlation with surface soil texture.

Multiple linear regression analysis The derivation of regression models for surface soil texture was performed for the 58 soil samples through multiple stepwise regression (MSR) using the DN values of six bands. Results indicated that only DN7 remained as a significant predict variable (p