Predict soil texture distributions using an artificial neural ... - DEINFO

c o m p u t e r s a n d e l e c t r o n i c s i n a g r i c u l t u r e 6 5 ( 2 0 0 9 ) 36–48

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Predict soil texture distributions using an artificial neural network model Zhengyong Zhao a , Thien Lien Chow b , Herb W. Rees b , Qi Yang a , Zisheng Xing b , Fan-Rui Meng a,∗ a b

Faculty of Forestry and Environmental Management, University of New Brunswick, Fredericton, New Brunswick, Canada E3B 6C2 Potato Research Centre, Agriculture and Agri-Food Canada, Fredericton, New Brunswick, Canada E3B 4Z7

a r t i c l e

i n f o

a b s t r a c t

Article history:

High-resolution soil maps are important for planning agriculture crop production,

Received 4 May 2007

forest management, hydrological analysis and environmental protection. However, high-

Received in revised form 8 July 2008

resolution soil maps are generally only available for small areas because obtaining these

Accepted 23 July 2008

maps through field survey is time consuming and expensive. The objective of this study was to develop an artificial neural network (ANN) model to predict soil texture (sand,

Keywords:

clay and silt contents) based on soil attributes obtained from existing coarse resolution

Soil texture

soil maps combined with hydrographic parameters derived from a digital elevation model

Clay

(DEM). The calibrated ANN model then can be used to produce high-resolution soil maps

Sand

in area with similar conditions without additional field surveys. The hydrographic param-

High-resolution soil map

eters derived from DEM were soil terrain factor, sediment delivery ratio and vertical slope

Artificial neural network

position. Field measured soil texture in the Black Brook Watershed (BBW) in northwest-

DEM

ern New Brunswick, Canada was used to train and test the ANN model. Results indicated that the Levenberg–Marquardt optimization algorithm was better than the commonly used training method based on the resilient back-propagation algorithm. The root mean square errors between model predictions and field determination were 4.0 for clay and 6.6 for sand contents. The relative overall accuracy (within ±5% of field measurement) was 88% for clay content and 81% for sand content. The trained ANN model has been tested in an experimental farm located in southeastern NB about 180 km from the Black Brook Watershed where the model was first calibrated. Results indicated that with proper training, the ANN model can be used in the areas where the model was calibrated (for interpolations), or other areas provided that the relative range of input parameters were similar to the region where the model was calibrated. Crown Copyright © 2008 Published by Elsevier B.V. All rights reserved.

1.

Introduction

High-resolution soil maps are essential for land-use planning and other activities related to forestry, agriculture and environment protection (Hassink, 1992; Oberthür et al., 1996). Soil texture is one of the important properties of soil maps and

∗

is defined as relative proportions of clay, sand and silt contents. Soil texture directly affects the porosity of soil, which in turn, determines its water-retention and flow characteristics, nutrient-holding capacity and long-term soil fertility. Heavy clay soils normally have higher percentage of smaller pores, higher water holding capacity at lower water potentials and

Corresponding author. Tel.: +1 506 453 4921; fax: +1 506 453 3538. E-mail address: [email protected] (F.-R. Meng). 0168-1699/$ – see front matter. Crown Copyright © 2008 Published by Elsevier B.V. All rights reserved. doi:10.1016/j.compag.2008.07.008


are often associated with poorly drained conditions with limited aeration for plant growth. On the other hand, sandy soils have relatively higher percentage of larger pores with lower water holding capacity under relatively dry conditions. Soil texture also determines the soil erodibility (Wischmeier and Smith, 1978) and thus, affects the risk of soil erosion. Field soil survey is the primary method of acquiring soil properties including soil texture and is normally done with point samples, obtained either systematically or randomly. The point data are usually interpolated to produce soil maps. Various interpolation methods have been used to produce soil maps, especially the kriging method. For example, Voltz and Webster (1990) predicted the clay content of soil based on the kriging method and the cubic spline method, using field data which were collected at 175 positions using a sampling scheme consisting of two interlaced 100 m × 100 m square grids. Voltz et al. (1997) found that three interpolation methods (kriging, inverse squared distance and nearest neighbour) combined with ordinary soil classification information achieved higher accuracy in mapping soil properties than using 1:100,000 scale soil classification maps alone. The major limitation of the interpolation method is the assumption that the spatial distributions and changes of the interpolated properties are continuous. Thus, these methods often require a large amount of data to produce accurate high-resolution soil maps. Various improved kriging methods have been developed in order to improve the interpolation accuracy with sparsely distributed sample points (McBratney et al., 2000, 2003). However, this method still requires substantial amounts of field samples to define the spatial autocorrelation. As an evolution, incorporating auxiliary information and/or combing other methods with kriging were used to overcome these problems. Oberthür et al. (1996) mapped soil texture classes using field texturing, particle size distribution and local knowledge (individual field or farm recommendations) by both conventional and kriging methods, with point data from 341 locations on a regular 750 m × 750 m grid plus 43 random locations. They found that local knowledge was valuable in the interpolation of soil texture with kriging methods and achieved an accuracy and resolution needed to support agronomic decisions at a local scale. Hybrid krigings have also been used to solve problems resulting from the lack of adequate samples, for instance cokriging (Wackernagel, 1994), regression-kriging (Goovaerts, 1997), kriging with external drift (Odeh and McBratney, 2000), and other schemes (Boucneau et al., 1998; McBratney et al., 2003). These improved methods are still based on interpolation which needs field data as inputs. The precision of the resultant map is still depended upon the density and distribution of original data points (Heuvelink and Bierkens, 1992; Thattai and Islam, 2000; McBratney et al., 2000). Although the accuracy of a soil map may be increased with increasing data points, intensive field surveys are expensive and time consuming. Due to high spatial variability of soil characteristics, large numbers of sampling points are required to generate an accurate high-resolution soil map. Furthermore, the accuracy is affected by the quality of the data, which, to a great extend, depends on the field experience of the soil surveyors (Webster, 1968; Bie and Beckett, 1971). As such, there is a need for a more efficient method to generate accurate high-resolution soil texture maps over larger areas at reasonable cost. Some

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researchers have used statistic models to do this job. For example, a soil moisture distribution map was produced using statistical methods at different scales, based on the spatial distribution of soil water content (Zhang and Berndtsson, 1991; Cassel et al., 2000; Fernández-Gálvez et al., 2005). Using a similar method, soil carbon maps (Fearnside and Barbosa, 1998; Cerri et al., 2003), soil drainage and soil structure maps (Bell et al., 1992) were drawn. Soil properties are closely related to geological formations and landscape positions. Various multiple regression models have been used to co-relate parameters derived from digital elevation model (DEM) with soil properties, and have reported a high degree of success (Bell et al., 1994; Oldak et al., 2002; Jain et al., 2005). The relationships between soil characteristics and other biophysical variables are rarely linear in nature. Artificial neural network (ANN) models can be used to overcome the non-linearity problem. The ANN is a form of artificial intelligence that was inspired by the studies of the human neuron and has been used to analyze biophysical data (Wasserman, 1989; Hewitson and Crane, 1994; Levine et al., 1996). ANN model has the ability to auto-analyze the relationships between multi-source inputs (including combinations of qualitative and quantitative data) by self-learning, and produce results without hypothesis. A number of studies have indicated that the ANN model can be used to establish relationships with linear or non-linear mapping functions to a desirable accuracy (Hornik, 1991; Xu and Wu, 2002; Somaratne et al., 2005). The ANN model has been successfully used to map soil profiles. Licznar and Nearing (2003) attempted to quantitatively predict soil loss from natural runoff plots with the ANN method. They found that correlation coefficients for predicted soil loss versus measured values were in the range of 0.7–0.9. Ramadam et al. (2005) presented two different multivariate calibration methods (PCA and back-propagation ANN) to predict soil properties (sand, silt, clay, nitrogen, organic carbon) using DNA data form microbial community. The objective of this study was to develop an ANN model that uses coarse resolution soil texture data and DEM derived parameters to generate high-resolution soil texture maps. Specific objectives included: (1) calculating hydrological parameters derived from high-resolution DEM, (2) selecting the optimum ANN model and assessing the model performance, and (3) testing the accuracy of the ANN model when used outside of the calibrating area. Field measured soil texture data from two experimental farm areas maintained by Agriculture and Agri-Food Canada (AAFC) were used to test the model.

2.

Materials and methods

2.1.

Study site

The study site used to train and validate the ANN model was located in the Black Brook Watershed (BBW) in northwestern New Brunswick (Fig. 1; 47◦ 05 –47◦ 09 N and 67◦ 43 –67◦ 48 W). Elevation in BBW ranges from 170 to 260 m above mean sea level. The total area of the watershed is approximately 1450 ha. The climate associated with the watershed is moderately cool boreal with approximately 120 frost-free days per

38


Fig. 1 – Sampling sites, calibrating and validation data sets of the Black Brook Watershed (BBW) and Agriculture Experimental Farm (AEF).

year (Environment Canada, 1982a,b). Average annual rainfall, snowfall, and daily temperatures are 730.7 mm, 306.7 cm, and 3.7 ◦ C, respectively. Most of the area is on a plateau that is characterized by gently rolling and undulating topography, with slopes of 1–6% in the upper portions and slopes of 4–9% in the central parts. In the lower portions, slopes are more strongly rolling at 5–16% (Mellerowicz et al., 1993). The geological material of the area is mostly Ordovician and/or Silurian calcareous and argillaceous sedimentary rocks (shale, slate, limestone) with some volcanic rocks. The major glacial influence on the area resulted from the Wisconsin ice sheet. Surface deposits are mostly glaciofluvial and morainal till containing mixed sand, silt gravel, and stones (Langmaid et al., 1976, 1980; Mellerowicz et al., 1993). The major land-use within the watershed is agriculture with approximately 1050 ha out of total area of 1450 ha is devoted to farming. The major crop is potato in rotation with barley, corn, and pasture. The Agriculture Experimental Farm (AEF) situated in the Saint John River Valley, southwestern New Brunswick (45◦ 55 15 N; 66◦ 36 30 W). Total area of the farm was 306.2 ha. Agriculture land accounts for 158.6 ha and woodlands accounts for 101.6 ha. The rest areas of the watershed were buildings, roads and recreational reserves. The underlying bedrock is sedimentary, red to gray sandstone and siltstones of Pennsylvania age, in the form of horizontally to gently inclined strata (Gadd, 1971; Rees and Fahmy, 1984). On the AEF, parent material composition and topography which played a major role in soil formation and biotic variations at the micro-level were normally associated with topography (Rees and Fahmy,

1984). Surface expression is generally undulating to rolling with some local relief that is typical of its valley-side location. Elevation ranges from 5 to 38 m above mean sea level. Compared with the Black Brook Watershed, the climate at the AEF is moderate due to the influence of the Atlantic Ocean. The annual average temperature is higher (5.4 ◦ C), and the frost-free period is longer (129 days).

2.2.

Soil sampling

The BBW is an experimental watershed established by Agriculture and Agri-Food Canada. Soils of the watershed were surveyed at a scale of 1:10,000. Soil profiles (total 465 points) were surveyed at a density of approximately one soil inspection for every 2 ha in agricultural land and 9 ha in forested land. A total of 442 polygons were mapped based on soil association, drainage and slope. A maximum of 15% inclusions of different soil types was allowed in any polygon. Six mineral soils and one organic soil were identified in the BBW: Grand Falls, Holmesville, Interval, Muniac, Siegas, Undine and St. Quentin. Forty six of the 442 polygons (Fig. 1) had detailed soil texture data. Measured clay content ranged from 6% to 28%, sand from 33% to 73%, and silt from 21% to 54%. In the AEF, about 450 survey points were located at either fixed intervals along transects run across the landscape, or at strategic points such as mid-slope, crests, and depressions. On average, there was one point per 0.4 ha in cleared agriculture land and one point per 1.4 ha in the forested area. The published soil map scale was 1:4800. The minimum soil polygon


size identified was equal to or greater than 2 ha. Of the 450 sampling points, 434 had valid soil texture records and valid coordination (Fig. 1). Field estimated clay content ranged from 7% to 75%, sand from 5% to 80%, and silt from 5% to 65%.

2.3.

Other information

Digital elevation model data in the BBW and AEF were obtained from Service New Brunswick. The NB provincial DEM was compiled by photogrammetry method from 1:35,000 aerial photographs taken in the late 1980s (NBGIC, 1995) and was used to derive slope steepness and slope length. The distance between irregular elevation points was 25–70 m apart. Irregular elevation points were interpolated into a grid map at a 10 m × 10 m grid using the conventional inversed distance weighted method. Coarse resolution soil data for the two study areas were obtained from the Soil Landscapes of Canada (Soil Landscapes of Canada Working Group, 2006; SLC 3.1 version). These maps were compiled on 1:1,000,000 scale base maps from existing soil survey data at various scales. The theoretical spatial resolution for the maps was approximately 100 m and the accuracy is unknown. Two soil types (Siegas and Holmesville) were identified within the BBW. The average texture values for the Siegas soil were 31% clay, 23% sand and 46% silt whereas 22% clay, 30% sand and 48% silt were typical for the Holmesville soil. In the AEF, the average texture values for the Interval soil are 24% clay, 26% sand and 50% silt whereas 10% clay, 70% sand and 20% silt were typical for the Oromocto soil.

2.4.

Artificial neural network

ANN was used because it could accommodate non-linear mapping with limited discontinuous points between input and output data without any hypothesis (Li, 1998). Backpropagation networks were trained with a back-propagation technique which adjusted the weight and bias values along

39

a negative gradient descent directed in an attempt to minimize the mean squared error (MSE) between the input and output vectors of the training set (Sigillito and Hutton, 1990). The model was ‘trained’ by adjusting model structure and coefficients according to different rules or algorithms. One presentation of the training data with the associated weight and bias adjustment was called an epoch (Levine et al., 1996). The MSE between the network outputs (predicted values) and the targeted values (measured values) was calculated through each epoch. Training was stopped when the MSE could not be reduced further. The input layer and output layer was connected by a hidden layer. The number of nodes of the hidden layer determined the complexity of the model. Two neighbouring layers were connected with links and each node in one layer was linked with all nodes of the next layer. All links between input layers and hidden layers composed the input weight matrix and all links between hidden layers and output layers composed the output weight matrix. Weight (w) which controls the propagation value (x) and the output value (o) from each node was modified using the value from the preceding layer according to Eq. (1).

o=f

−T +

wi xi

(1)

where T was a specific threshold (bias) value for each node. f was a non-linear sigmoid function, which increased monotonically. In this study, a model of back-propagation ANN was developed to estimate clay and sand contents based on the schematic diagram in Fig. 2. The output layer contained two nodes: clay and sand contents. The silt content was calculated using 100 − (sand + clay). The input layer had 6 nodes, including clay and sand contents based on coarse resolution soil data, and 4 variables derived from the DEM data. Two training algorithms were used in this study. The Levenberg–Marquardt algorithm (LM) was based on Levenberg–Marquardt optimiza-

Fig. 2 – Structure and flow of the artificial neural network model for predicting high-resolution soil texture maps.

40


tion theory (Fun, 1996). The resilient algorithm (RP) was a kind of rebound back-propagation algorithm (Reidmiller and Braun, 1993).

al., 2003).

2.5.

where ti is the travel time from cell i to the nearest channel in seconds. ˇ is a watershed-specific constant. Traveling time ti is defined by Eq. (4):

Input variables

Spatial variations of soil characteristics were determined by variations of soil forming factors. The selection and screening of input variables for the ANN model was based on the assumption that existing coarse resolution soil maps (1:1,000,000) should commonly capture average soil texture determined by geological formations and associated soil parent materials. For example, parent materials consisting of red siltstone will form fine-textured soils compared to sandy soils from sandstones. At the local level, soil texture should be modified by topography and hydrological processes. For example, fine particles such as clay and silt could be easily transported by surface runoff from upper slopes and subsequently deposited on lower slopes. This process was determined by landscape topography and modeled with the DEM (Beven and Kirkby, 1979; Martz and Garbrecht, 1992; Ren and Liu, 2000). Other researchers also reported that soil properties were closely associated with landscape position (Brubaker et al., 1993; Moore et al., 1993; Case et al., 2005; Meng et al., 2005). Theoretically, DEM related variables that have impacts on water flow and sediment transport and deposition should be related to the re-distribution of soil particles and soil texture. A quick screening of different DEM related variables were done with ANN model with single input variable and clay content. Soil terrain factor (STF), soil drainage (SD), sediment delivery ratio (SDR) and vertical slope position (VSP) data were included in the final ANN model. Generated maps are presented in Fig. 3. Each factor is described briefly as follows. The soil terrain factor was a modified hydrological similarity index (Ambroise et al., 1996; Meng et al., 1997; Scanlon et al., 2000). Soil terrain factors can be used to determine the transfer conditions of surface water. The map of soil terrain factors was created using Eq. (2),

STF = ln

(A + 1)Pclay (k + r)

2

(2)

where A is the flow accumulation in square metres. Pclay is the content of clay from the coarse resolution soil data in percent. k is a constant. r is the radius slope based on high-resolution DEM data slope. Soil drainage classes were the most important site factors reflecting the ability of water movement, which pertains to the length of time it takes for the water to be removed from the soil in relation to supply. Soil drainage maps were predicted based on the method presented by Meng et al. (2005). Six categories of soil drainage were determined in this study: rapidly, well, moderately well, imperfect, poor, and very poor. Sediment delivery ratio, the percent of sediment removed by surface water compared to the total amount of erosion occurring in the watershed, was calculated by Eq. (3). This ratio indicated the efficiency of sediment transport in the watershed and was largely influenced by topography and the flow distance to streams (Ferro and Minacapilli, 1995; Fernandez et

SDRi = exp(−ˇti )

ti =

Np lj j=1

(3)

(4)

vj

where Np is the total number of cells along the flow path from cell j to the nearest channel. Lj is the length of cell j in the flow path in metres. vj is flow velocity for the cell in metres per second, Flow velocity vj is calculated according to Eq. (5) (Haan et al., 1994); 1/2

vi = di si

(5)

where si is slope of the cell in percent. di is a coefficient for cell i dependent on surface roughness characteristics in metres per second. To obtain travel time ti , the flow length was calculated using HYDRO-tools extension in Arc View. An inverse velocity grid was used as a weighting factor (Schauble, 2004). The watershed parameter ˇ was estimated by numerically solving Eq. (6):

N SDRw =

exp[−ˇti ]l0.5 s2i ai i

N

i=1

l0.5 s2i ai i=1 i

(6)

where SDRw is the watershed average sediment delivery ratio, which was estimated with an empirical function similar to SDRw = kAc (Vanoni, 1975). Parameters k and c were fixed as 0.42 and −0.125 respectively, because they were a good general approximation between SDRw and SDR (Ouyang and Bartholic, 1997; Ferro et al., 1998). N is total number of cells over the watershed. ai is area of the cell in square metres. A is the area of the watershed in square kilometres. Vertical slope position was defined as the elevation difference between the upland and water surfaces. The map was calculated by overlaying the hydrographic layer with DEM to obtain the elevation of the water surfaces. The minimum elevation difference between the land surface and nearby water surface was calculated with the Forest Hydrology Tool (Meng et al., 1997, 2006; Zhao et al., 2006). Low VSP values indicated that the land surface was at a small height difference to nearby water surfaces, and may be a riparian area.

2.6.

Model calibration and validation

In the BBW, all 46 polygons which had the detailed quantitative soil texture data were used to calibrate and validate the model. These polygons were re-sampled into the grid data with 10 m resolution, based on the scale of the base map (1:35,000) and used as reference data. The reference data were randomly divided into calibration sets and validation sets. Calibration sets (total 14,196 points) were used to train the ANN model and validation sets (total 12,957 points) were used to check the


41

Fig. 3 – Hydrological parameter maps of soil terrain factor (STF), soil drainage (SD), soil delivery ratio (SDR) and vertical slope position (VSP) of the BBW.

42


model performance. All of the 434 data points of the AEF were used as an independent validation set to test the predictive capability of the ANN model outside the calibration area.

ROA and lower RMSE and ME was considered to be a successful model.

2.7.

3.

Results and discussions

3.1.

Model performance

Accuracy assessment

In this study, soil texture was determined by field assessment and described in categories. As such, relative proportions of clay, sand and silt were recorded as a range rather than a fixed value. Using Silt Loam (SiL) texture as an example, clay content could be from 10% to 30%, and sand from 20% to 50%. Model predictions were considered to be relatively accurate if the model predicted soil texture content was within a certain percent of the measured soil texture content (reference data). A new term relative overall accuracy (ROA) is defined to assess the relative accuracy of model predictions. For example, ROA ± 5% was calculated by counting all predictions within a 5% range of the measured soil texture content. The root mean square error (RMSE) and the mean error (ME) were also used to assess the model accuracy. The model prediction with higher

The performance of the ANN model trained with LM and RP methods was evaluated using combinations of the hidden layer nodes changed from 5 to 40, and training epochs changed from 25 to 250. Accuracy of model predictions using the LM and RP training methods in 100 training epochs to various net structures is shown in Table 1. Results indicated that the model trained with the LM algorithm had substantially higher ROA ± 5% and lower RMSE than the model trained with the RP algorithm with the same number of hidden layer nodes. The LM algorithmtrained ANN model had higher prediction accuracy. However, the LM method took much longer time to train an ANN model

Table 1 – Prediction accuracy of ANN model trained with LM and RP algorithms with 100 epochs and nodes of hidden layer changing from 5, 10, 15, 20, 25, 30, 35 and 40 Training method

Levenberg–Marquardt back-propagation (LM)

Net structurea

Training

Predicted (%)

Time (m)

MSE

6–5–2

2

29

6–10–2

4

26

6–15–2

6

25

6–20–2

8

24

6–25–2

11

24

6–30–2

13

24

6–35–2

17

24

6–40–2

21

23

6–5–2