McBratney, A.B.

Walvoort, D.J.J.

English

NARCIS 

Diffuse reflectance spectrometry as a proximal sensing tool for precision agriculture

Name not available

Wageningen Yield 

DIFFUSE REFLECTANCE SPECTROMETRY AS A PROXIMAL SENSING TOOL 
FOR PRECISION AGRICULTURE 
D. J. J. WALVOORT 
Wageningen University & Research Centre, Wageningen, The Netherlands 
E-mail: Dennis. Walvoort@oio.beng.wag-ur.nl 
A. B. M£BRATNEY 
Australian Centre for Precision Agriculture, The University of Sydney, Sydney, Australia 
E-mail: Alex.McBratney@acss.usyd.edu.au 
ABSTRACT 
The aim of this paper is to evaluate diffuse reflectance spectrometry as a data acquisition 
method for precision agriculture in general and variable rate nitrogen fertiliser application in 
particular. Partial least squares regression models were fitted to predict clay, carbon, and 
nitrogen contents of soil. Diffuse reflectance spectra (Vis-NIR region) were used as inputs for 
these models. Results indicate that clay and carbon contents can be predicted reasonably well. 
However, more work has still to be done to make the models more robust. 
INTRODUCTION 
A condition for the successful implementation of precision agriculture is the availability of 
high quality soil and crop data sampled at high resolution and minimal cost. Conventional 
sampling and laboratory methods are generally too expensive and time consuming for this 
purpose. Therefore, much research is aimed at developing alternative data acquisition 
methods that are fast, cheap and accurate. Proximal sensing, i.e., the application of sensors in 
close proximity to the property of interest, appears to be promising in this respect (Viscarra 
Rössel and M-Bratney, 1998; Walvoort et al, 2001; Viscarra Rössel et al, 2001). 
Potentially, diffuse reflectance spectrometry may provide a relatively fast and cheap proximal 
sensing method without the need for elaborate sample preparation. Dalai and Henry (1986) 
applied near infrared (NIR) diffuse reflectance spectrometry to predict soil moisture content, 
total nitrogen content and organic carbon content of soil. They concluded that NIR 
spectrometry could be successfully applied in soils with a narrow colour range and with 
moderate organic matter contents. Viscarra Rössel and N^Bratney (1998) applied NER 
spectrometry to predict the moisture content, the clay content, and the organic matter content 
of soil. They found significant response-surface models, except for organic matter content. 
Masserschmidt et al (1999) predicted soil organic matter content by analysing mid infrared 
diffuse reflectance soil spectra. Ehsani et al (1999) successfully applied NIR diffuse 
reflectance spectrometry to predict nitrate concentrations in soil. 
For soil nutrient management, the farmer is concerned about where and when to apply a 
specific fertiliser and in what amounts. The optimal application rate should maximise crop 
yield and crop quality subject to environmental and economic constraints. In addition to 
503 
information collected by means of proximal sensing, we also have to rely on mathematical 
models to compute this optimal rate. Processes like denitrification and nitrogen leaching are 
hard to quantify without models. Van Alphen (2001), for example, used a mechanistic model 
to optimise nitrogen fertiliser application rates for distinct management units. The approach 
he pursued not only predicts optimal fertiliser application rates but also forecasts the optimal 
application dates. For the farm he studied, four application dates were needed. 
As an extension of Van Alphen's (2001) approach, we may also consider the variation within 
management units. Proximal sensing may provide a means for this. Optimal fertiliser 
application rates not only depend on the amount of mineral N in the soil at the start of the 
growing season, but also on processes like mineralisation, denitrification, and leaching. To 
quantify these processes by means of mathematical models, properties like the amount of N 
(both mineral and organic), the soil organic matter content, and the clay content have to be 
known. In this paper, we will apply diffuse reflectance spectrometry as a proximal sensing 
method to predict these soil properties. 
MATERIALS AND METHODS 
Soil sampling 
Soil sampling was performed at West Creek, an 80 ha field near Moree, New South Wales, 
Australia. An aerial colour photograph of the bare soil surface showed distinct patterns of 
spatial variation. The soils encountered in this field are classified as red chromosols and grey 
and brown vertosols (Isbell, 1996). Soil samples were taken along a NW-SE transect at 59 
equidistant sampling points. 
Laboratory analysis 
After drying (air dry), grinding, and sieving (<2 mm), all soil samples were analysed for clay, 
carbon (C) and nitrogen (N) content. Clay content was measured by means of a hydrometer. C 
and N were measured using a LECO CHN-1000 analyser. A Varian Cary 500 scan 
spectrophotometer equipped with a Labsphere DRA-CA-50D diffuse reflectance accessory 
was used to collect diffuse reflectance spectra of the soil. The spectral range of interest was 
from 250 nm to 2450 nm. The spectral resolution was 1.1 nm for wavelengths up to 800 nm 
and 3 nm for wavelengths greater than 800 nm. 
Statistical methods 
Given a soil sample, we want to use its diffuse reflectance spectrum to predict soil property y. 
For that purpose we need a calibration model. Suppose we have taken n soil samples i=\..n. 
Each soil sample i is analysed for property y by means of a conventional (expensive, and/or 
time consuming) laboratory method. In addition, diffuse reflectance spectra x, = [JCI, X2,..., x j ' 
consisting of diffuse reflectances x are collected at k wavelengths. Let y -\y\, yi,..., y„]' be a 
vector of n measurements of soil property y, and let X = [xi, \2, ..., x„]' be a matrix of 
corresponding diffuse reflectance spectra. A calibration model can now be constructed by 
means of partial least squares regression (PLS). PLS decomposes X and y into factor scores 
(T) and factor loadings (P and q) according to: 
504 
X = TP'+E 
y = Tq + f 
X and y should be zero-centred prior to decomposition. Decomposition is performed in such a 
way that the first/? (< m'm(n,k)) bilinear factors explain most of the variation in X and y. The 
remaining bilinear factors resemble noise and can thus be ignored, hence the addition of 
residuals E and f. The resulting matrices and vectors generally have a much lower dimension 
than X and y. The estimator^ given a new spectrum x is then a linear combination of the 
factors scores and factor loadings of x. For more details on PLS see e.g., Martens and Naes 
(1989) and Denham (1995). For an application of PLS with respect to soil nitrate, see Ehsani 
et al. (1999). A paper on predicting soil organic matter content by PLS is written by 
Masserschmidt et al. (1999). 
Leave-one-out cross-validation (Martens and Nass, 1989) was applied to estimate the number 
of bilinear factors to retain in the model. This method consists of calibrating a model n times, 
each time leaving out one of the n observations. Each calibrated model is used to predict the 
removed observation and the error is computed as the difference between its observed and 
predicted value. A statistic like the Root Mean Squared Error of prediction (RMSE) is then 
computed to summarise the results. This procedure is repeated for models with different 
numbers of bilinear factors. The model with the lowest RMSE is usually selected. 
RESULTS AND DISCUSSION 
Fig. 1 shows the cumulative distributions of clay, C, and N content. It reveals only a slight 
variation in C, N, and clay content, despite a distinct variation in pedology. It should be noted 
that calibration models conditioned on reference data with low variability like the West Creek 
samples only have local validity. 
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FIGURE 1. Graphs of cumulative distributions of clay content (%), carbon content (%), and 
nitrogen content (%). 
Leave-one-out cross-validation was applied to find the number of bilinear factors to retain in 
the model. The results are given in Fig. 2. We selected 5 bilinear factors for clay content, 6 for 
C, and 11 for N. However, it can be argued that a smaller number of bilinear factors would 
yield more robust and parsimonious models at the expense of only a small increase in RMSE. 
More work has to be done on this issue. 
505 
Potential outlier wavelengths were identified by means of procedures described in Martens & 
Naes (1989, chapter 5). Outlier wavelengths were only removed when they were caused by 
artefacts of the spectrophotometer, i.e., changing sources, filters or gratings. 
clay content (%) C (%) N (%) 
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FIGURE 2. RMSE as a function of the number of bilinear factors retained in the model. 
Predictions are obtained by means of leave-one-out cross-validation. 
The resulting models were validated by predicting clay, N, and C contents of soil samples by 
using the diffuse reflectance spectra as inputs. The results are given in Fig. 3. The RMSE for 
clay content is 2.3%, the RMSE for C is 0.04% and the RMSE for N is 0.0008%. However, 
these predictions are too optimistic, because the observations are used for both calibration and 
validation. 
clay content (%) C(%) NIH 
40 45 
actual 
0.01 0.02 
actual 
FIGURE 3. Predicted versus observed clay, C, and N contents. Samples are used for 
calibration and validation. 
Independent validation can be achieved by means of leave-one-out cross-validation. In this 
way, validation is performed with samples not used for calibration. The results are given in 
Fig. 4. As can be expected, the RMSEs are greater, i.e., 2.9% for clay content, 0.06% for C 
and even 0.007%. for N. Although clay and C content only show a small increase in RMSE, 
the increase for N is quite significant. These results probably suggest that a larger calibration 
set is needed to obtain more robust calibration models. 
506 
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FIGURE 4. Predicted versus observed clay, C, and N contents. Predictions are obtained by 
means of leave-one-out cross-validation. 
CONCLUSIONS 
Calibration models were derived for predicting clay, C, and N contents in soil. The models 
were calibrated by means of partial least squares regression with diffuse reflectance soil 
spectra as inputs. Although the data set was limited in size, reasonable predictions could be 
obtained. However, in order to obtain more robust models, a larger data set is probably 
needed. Furthermore, the issue of finding the optimal number of bilinear factors to attain in 
the model has still to be addressed. 
ACKNOWLEDGEMENTS 
We would like to thank Matthew Clutterham for soil sampling and particle size analysis and 
Raphael Viscarra Rössel and two anonymous reviewers for their useful comments on an 
earlier draft of this paper. This research was supported by the Technology Foundation STW, 
i.e., the applied science division of NWO and the technology program of the Ministry of 
Economic Affairs of the Netherlands. 
REFERENCES 
Dalai, R.C., Henry, R.J. (1986). Simultaneous Determination of Moisture, Organic Carbon, 
and Total Nitrogen by Near Infrared Reflectance Spectrometry. Soil Science Society of 
America Journal 50: 120-123. 
Denham. M.C. (1995). Implementing Partial Least Squares. Statistics and Computing 5: 191-
202. 
Ehsani, M.R., Upadhyaya, S.K., Slaughter, D. Shafii, S., Pelletier, M. (1999). A NIR 
Technique for Rapid Determination of Soil Mineral Nitrogen. Precision Agriculture 1: 
217-234. 
Isbell, R.F. (1996). The Australian Soil Classification. CSIRO Publishing, Melbourne, 160 
pp. 
Masserschmidt, I. Cuelbas, C.J., Poppi, R.J., De Andrade, J.C., De Abreu, CA., Davanzo, 
C.U. (1999). Determination of Organic Matter in Soils by FTIR/Diffuse Reflectance 
and Multivariate Calibration. Journal of Che mome tries 13: 265-273. 
507 
Martens H., Naes, T. (1989). Multivariate Calibration. John Wiley & Sons, Chichester, 419 
pp. 
Van Alphen, B.J. (2001). Precision Nitrogen Fertilization: A Case Study for Dutch Arable 
Farming. In: Proceedings of the 5th International Conference on Precision Agriculture 
and Other Research Management (CD-ROM). Precision Agriculture Center, University 
of Minnesota, ASA/CSSA/SSSA. 
Viscarra Rössel R.A., M^ratney, A.B. (1998). Laboratory Evaluation of a Proximal Sensing 
Technique for Simultaneous Measurement of Clay and Water Content. Geoderma 85: 
19-39. 
Viscarra Rössel, R.A., Walvoort, D.J.J., M£Bratney, A.B., Janik, L.J., Skjemstad, J.O. (2001). 
Proximal Sensing of Soil pH and Lime Requirement by Mid Infrared Diffuse 
Reflectance Spectrometry. These proceedings. 
Walvoort, D.J.J., Bouma, J., de Gruijter, J.J., Peters, P.D. (2001). Using ISFETs for Proximal 
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Precision Agriculture and Other Research Management (CD-ROM). Precision 
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508 


A NIR Technique for Rapid Determination of Soil Mineral Nitrogen.

Determination of Organic Matter in Soils by FTIR/Diffuse Reflectance and Multivariate Calibration.

Implementing Partial Least Squares.

Laboratory Evaluation of a Proximal Sensing Technique for Simultaneous Measurement of Clay and Water Content.

Multivariate Calibration.

Precision Nitrogen Fertilization: A Case Study for Dutch Arable Farming. In:

Proximal Sensing of Soil pH and Lime Requirement by Mid Infrared Diffuse Reflectance Spectrometry. These proceedings.

Simultaneous Determination of Moisture, Organic Carbon, and Total Nitrogen by Near Infrared Reflectance Spectrometry.

The Australian Soil Classification.

Using ISFETs for Proximal Sensing in Precision Agriculture. In:

Diffuse reflectance spectrometry as a proximal sensing tool for precision agriculture

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