Bruin, S., de

Mulder, V.L.

Schaepman, M.E.

English

Wageningen Yield 

The Second Global Workshop on Proximal Soil Sensing – Montreal 2011 
 
 
 
136 
Soil mapping at regional scale using ASTER and VNIR spectroscopy 
 
V.L. Mulder1*, S. de Bruin2, M.E. Schaepman12  
1Laboratory of Geo-Information Science and Remote sensing, Wageningen University, 
Droevendaalsesteeg 3, P.O. Box 47, 6700 AA Wageningen, The Netherlands 
2Remote Sensing Laboratories, University of Zürich, Winterthurerstrasse 190, 8057 Zürich, 
Switzerland 
titia.mulder@wur.nl 
 
Abstract 
 
The use of satellite data as a measure of spatial and spectral variability for soil mapping 
constitutes the link between proximal and remote sensing. This paper proposes a sparse 
sampling approach which makes use of constrained Latin Hypercube to determine the spatial 
and spectral variability in soil properties at a regional scale. The sampling approach was 
successful in representing major variability. In addition, the spectral similarity between field and 
laboratory spectra was high and therefore the field spectra are suitable for soil property 
analysis. Of course, vegetation influences the field spectra and therefore it is recommended to 
select spectra based on low NDVI-values.  
 
Keywords: Soil mapping, VNIR spectroscopy; Remote sensing; Regional scale 
 
Introduction 
 
Soil and terrain information is needed for policy-making, land resource management, and for 
monitoring the environmental impact of development. Global and regional models that address 
climate change, land degradation and hydrological processes need soil input parameters with 
complete area coverage. Especially in development countries information about solids is sparse 
while at the same time resources for data acquisition are limited (Mulder et al., 2011). 
Therefore, we propose a sparse sampling approach which utilizes remote sensing data to 
determine the spatial variability in soil properties on a regional scale. This approach would 
reduce the financial constraints on soil property mapping. This paper examines whether the 
sampled locations do represent the spectral variability found within remote sensing data. Also, 
field spectra and laboratory spectra are compared to find out if the former can substitute the 
latter in an attempt to further reduce data acquisition costs. The spectral similarity of the spectra 
was determined by the comparing spectral angles.  
 
Materials and Methods 
 
Sampling scheme 
The research area is located in Northern Morocco, centred at 34,0 N, -4,5W and covers an area 
of 15000 km2 with the Rif Mountains being the northern border and the Anti-Atlas Mountains 
being the southern border. An optimal sampling scheme was developed with use of constrained 
Latin Hypercube sampling (LHS) which is a stratified random procedure for sampling variables 
from their multivariate distributions, for more details we refer to McKay et al. (1979) and 
Minasny & McBratney (2006). The Latin Hypercube consisted of the first three principal 
components from ASTER imagery and the ASTER GDEM, representing variation in topography. 
Hence, the ASTER imagery was not used to provide the spectral resolution for soil analysis, but 
rather as a data source for spectral variability. 
The Second Global Workshop on Proximal Soil Sensing – Montreal 2011 
 
 
 
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To increase efficiency of the fieldwork, the Latin Hypercube sampling was constrained by the 
distance to the roads and steepness of slopes. Using simulated annealing, 100 sites were 
optimised to optimally sample the Latin Hypercube (Fox and Metla, 2005).  
On-site sampling 
On-site spectral measurements were taken with an ASD Fieldspec Pro FR spectroradiometer, 
covering the 350-2500 nm wavelength region. For these measurements, Valeri plot sampling 
(Rossello and Baret, 2007) on a 15*15 meter plot was used to estimate the full spectral 
variability. On the corners and the centre of the plot a mixed soil sample was taken of the top 5 
cm. The soil samples were dried at 70° C, sieved at 2mm and spectral measurements were 
taken under laboratory conditions with the same spectroradiometer and the contact probe.  
Spectral Angle Mapper (SAM) 
In order to determine the spectral similarity of the spectra the spectral angle was calculated 
between the field spectra and the laboratory spectra. This is achieved by treating them as 
vectors in a space with dimensionality equal to the number of spectral bands of the field spectra, 
this algorithm is called the Spectral Angle Mapper (SAM). A spectral angle smaller than 0.1 
radians was considered as the threshold indicating similarity. SAM is an feature-based analysis, 
which means that the presence of the absorption feature is taken into account but the depth of 
the absorption feature does not matter, only the angular difference (Kruse et al., 1993). This is 
ideal for comparing the different sets of spectra collected in the field and under laboratory 
conditions. However, vegetation might influence the absorption features, therefore two different 
analyses were performed. In the first analysis, the average of the field spectra taken with valeri-
plot sampling was used to calculate the spectral angle, including the measurements taken on 
more vegetated sites. This analysis is referred to as ‘Averaged field spectra’. The second 
analysis included the sample measured on-site with the lowest Normalized Difference 
Vegetation Index (NDVI, Tucker 1979) and is referred to as ‘Lowest NDVI spectra’.  
 
Results  
 
Constrained Latin Hypercube Sampling 
Owing to difficult field circumstances related to accessibility and topography, 73 sites could be 
sampled during the fieldwork campaign. Main limitations in the field were related to accessibility 
and topography. In the variable space from the DEM it can be seen that the largest gaps occur 
for higher altitude (Fig. 1). In order to solve this problem in the field, similar soil characterises 
were selected at lower altitudes when possible. Therefore, the variable space for the principal 
components is better sampled. However, the samples do cover the extend of the multivariate 
distribution which would indicate that the planned special and spectral information needed for 
soil mapping is collected during the field campaign. 
Similarity of the field and laboratory spectra 
In Figure 2 an example is presented with field and laboratory spectra and similarity is apparent 
since the overall shape of the spectrum and locations of absorption features are alike. The 
spectral signatures from the field were pre-processed; The atmospheric and bad bands were 
removed, resulting in a spectral range from 350 nm to 2460 nm; The spectra were smoothed 
over the remaining spectral range. The spectral angle was calculated between the pre-
processed spectra and the laboratory spectra.  
Figure 3 shows a medium strong relation (R2 = 0.5089) between the spectral angle and the NDV 
calculated from the field spectra. This suggests that the absorption features are influenced by 
vegetation. n Table 1, the summarizing statistics are presented for the two analyses on spectral 
similarity between field and laboratory spectra. This table shows that for both the ‘Averaged field 
spectra’ and the ‘Lowest NDVI spectra’ the average spectral angle was were smaller than 0.1 
radians. The maximum angles are larger than 0.1 radians which indicates that for some 
samples the similarity is too low, what would result in misclassification. The large angles might 
The Second Global Workshop on Proximal Soil Sensing – Montreal 2011 
 
 
 
138 
be contributed to the high coverage of non-photosynthetic vegetation and soil moisture 
conditions in-situ. Both the standard deviation and the number of samples with an angle larger 
than 0.1 radians is lower for the ‘Lowest NDVI spectra’.  
 
Figure 1: Distribution of the field samples over the defined intervals for the Latin Hypercube, 
which consists of Principal Component 1, 2 and 3 and the Digital Elevation Model (DEM). 
 
 
Table 1: Summarizing statistics for the calculated spectral angles for the two datasets. 
 Averaged field spectra Lowest NDVI spectra 
Average angle 0.089 0.077 
Minimum 0.012 0.020 
Maximum 0.396 0.333 
Standard deviation 0.063 0.053 
Average NDVI  0.220 0.160 
No. samples < 0.1rad 21 13 
Figure 2: Example spectral signature measured 
under field and laboratory conditions 
Figure 3: Relation between NDVI and  
 Spectral angle. 
The Second Global Workshop on Proximal Soil Sensing – Montreal 2011 
 
 
 
139
 
These results show that the spectral similarity between field and laboratory spectra decreases 
with increasing vegetation coverage. 
 
Conclusion and discussion 
 
In this paper we proposed a sparse sampling method, which made use of remote sensing data 
to determine the spatial variability in soil properties on a regional scale. Firstly, we have shown 
that using constrained Latin hypercube sampling a relatively small sample can represent the 
spatial and spectral variability within ASTER satellite images and a Digital Elevation Model 
(DEM). Secondly, we found large spectral similarity between field spectra and laboratory data 
and therefore the field spectra can be utilized for soil property analysis. Of course, green 
vegetation influences the field spectra and therefore it is recommended to select spectra based 
on low NDVI-values. The use of satellite data as a variability measure for a sparse sampling 
approach seems to be promising. The approach outlined in this paper could be used on finer 
scales as well. However, ASTER does not have the spatial and spectral resolution to detect the 
spatial and spectral variability at these scales. Therefore, higher resolution data would be 
required for the constrained Latin Hypercube approach. In our opinion, the use of satellite data 
as a measure of spatial and spectral variability for soil property mapping on a regional scale 
constitutes a useful link between proximal and remote sensing.  
 
Acknowledgements  
 
We thank INRA Maroc for their support with the fieldwork campaign and Remote Sensing 
Laboratories, University of Zürich for their assistance with the laboratory spectral 
measurements. We acknowledge financial support from the EU FP7 program under the e-
SOTER project (contract 211578). 
 
References 
 
Fox, G.A. and Metla, R., 2005. Soil Property Analysis using Principal Components Analysis, Soil 
Line, and Regression Models. Soil Sci. Soc. Am. J., 69(6): 1782-1788. 
Kruse, F.A. et al., 1993. The spectral image processing system (SIPS)--interactive visualization 
and analysis of imaging spectrometer data. Remote Sensing of Environment, 44(2-3): 
145-163. 
McKay, M.D., Beckman, R.J. and Conover, W.J., 1979. A Comparison of Three Methods for 
Selecting Values of Input Variables in the Analysis of Output from a Computer Code. 
Technometrics, 21(2): 239-245. 
Minasny, B. and McBratney, A.B., 2006. A conditioned Latin hypercube method for sampling in 
the presence of ancillary information. Computers & geosciences, 32(9): 1378-1388. 
Mulder, V.L., de Bruin, S., Schaepman, M.E. and Mayr, T.R., The use of remote sensing in soil 
and terrain mapping -- A review. Geoderma, In Press, Corrected Proof. 
Rossello, P. and Baret, F., 2007. VALidation of Land European Remote sensing Instruments. 
INRA, Davos. 
Tucker, C.J., 1979. Red and photographic infrared linear combinations for monitoring 
vegetation. Remote Sensing of Environment, 8(2): 127-150. 
 


A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code.

A conditioned Latin hypercube method for sampling in the presence of ancillary information.

Red and photographic infrared linear combinations for monitoring vegetation.

Soil Property Analysis using Principal Components Analysis, Soil Line, and Regression Models. Soil Sci.

The spectral image processing system (SIPS)--interactive visualization and analysis of imaging spectrometer data. Remote Sensing of Environment,

VALidation of Land European Remote sensing Instruments.

Soil mapping at regional scale using ASTER and VNIR spectroscopy

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Soil mapping at regional scale using ASTER and VNIR spectroscopy

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