A MULTI SENSOR APPROACH FOR GENERATING IN

A MULTI SENSOR APPROACH FOR GENERATING IN
A MULTI SENSOR APPROACH FOR GENERATING IN -FIELD
PEDOLOGICAL VARIABILITY MAPS
F. Lahoche, C. Godard, T. Fourty, V. Lelandais, D. Lepoutre
Geosys.
20, impasse Couzinet
Toulouse, 31500
France
ABSTRACT
Predicting spatial variations of soil components of fer great promises in term of
precision farming and decreased pressure to the environment. This paper outlines
some results of a recent collaboration aimed at the development of non destructive methods to do this. The feasibility of coupling electromagneti c
inductance (Geonics EM38©), GPS RTK and radiometric sensors to predict soil
parameters (chemical, physical) variability was investigated. Simultaneous
measurements of radiometric, GPS RTK, and EM38 sensors have been taken over
different fields characteri zed by different soil properties and climatologic
conditions, and for which soil samplings were available. Linear predictive models
have been fitted on calibration data and applied on validation data in order to test
accuracy of predictions. Also, a compar ative study between electromagnetic
inductance and electrical resistance has been made in order to validate EM38
sensor data. This study allowed different conclusions:
§ MUCEP and EM38 give similar results on the variability mapping for one of
the two test fields. On the second field, very dry soil conditions probably
explained the poor MUCEP results (difficulty to measure very low resistance with
that sensor). On the contrary, EM38 keeps a good sensitivity and remains well
adapted for mapping variability ev en in dry conditions
§ The innovative coupling process between GPS, EMI and radiometric data
revealed to be very efficient and improved the overall prediction. Better results
for prediction have been found for clay than for other components (such as pH
and MgO). Correlation coefficients between predicted and real test data vary
between 0.7 and 0.9.
Keywords: in field variability, linear prediction model, multi sensor approach,
precision farming, induction
INTRODUCTION
In Precision farming mnagement mode, it has been ascertain that spatial
variability of soil parameters is key to sound management decisions.. Producing
accurate maps from soil sampling usually require intense field measurements and
scouting, thus rarely cost –effective . Several papers d eal with the variability
mapping with non destructive methods: yield sensors [Layrol et al., 2000], remote
sensing [Dicker et al., 1999] [ Varvel et al., 1999 ], radar images [Moran et al.,
1999] or geophysical sensors [Dabas et al., 2000] [Nemdhal et al., 2 001] [Sudduth
et al., 1999]. In this paper we propose a multi sensor approach to quantatively
predict soils parameters. In this study, the feasibility of coupling an
electromagnetic inductance EMI sensor (Geonics EM38©), high precision GPS
RTK and surface radiometric data to map field variability is investigated. Also, in
order to validate the quality of the EM38 EMI sensor, a comparison study has
been made with other geophysical sensors like MUCEP© and portable electrical
resistance sensors.
The paper is divided into two main parts. The first part presents briefly the
experiment sites and materials. The second part focuses on results and their
interpretation (first, the validation of EM38 EMI data by comparison with
MUCEP and portable electrode data; then, experimental results of predictive
models). Finally, a conclusion is given.
MATERIALS AND METHOD
The field trials were conducted at several locations in France and Spain and for
different kind of soil: clayey, silt -laden, sandy. The different experime nt fields are
illustrated and briefly described on figure 1 (general characteristics and nature of
the collected data on the site). For each field, EMI, GPS RTK, spectral image and
soil sampling have been collected. Additionally, MUCEP and portable electro de
measurements have be done for some fields as mentioned on figure 1.
1 Gaillac : limestone, various
texture.
2 Ondes : sandy and/or clayey
alluvium + MUCEP
3 Auzeville : clay and limestone,
deep soil
4 Baziège : clay and limestone, deep
soil
5 Calmont : stony soil, alluvium. +
portable electrodes
6 Bellvis : clay and limestone, not
stony
7 Vallmanya : clay and limestone,
stony + MUCEP
Fig. 1 : Overview of sites location.
Sites were selected over different soil types as indicated in Fig.1 and data
were gathered early in the growing season, on bare soil prior to planting in
early April 2001. EM38 and GPS are mounted on 2x4 quad (see Fig. 2a) and a
digital camera was set on a Unmanned Aerial Vehicle UAV (radio controlled)
(see Fig. 2b).
Fig. 2 : EMI EM38 sensor mounted on a 2x4 quad (fig. 2a) and UAV (radio controlled)
used for taking spectral images of the fields (fig 2b).
-
-
EMI measurements were continuously taken at two depths (75 cm, 150
cm) with a 5 meters grid sampling, with the EM38 sensor developed by
Geonics.
MUCEP data were collected and delivered by Geocarta. MUCEP
measures continuously resistance at 3 depths: 50, 100 and 150 cm.
Portable electrodes have been used for measuring electrical resistance at 7
depths (10, 20, 30, 45, 65, 80 et 105cm).
A digital camera was mou nted on an UAV. The UAV was piloted with the
help of a navigation software, based on video parameters and GPS
location transmissions on a digital map (see Fig 3). This software has been
developed by GEOSYS.
Fig. 3 : View of the help UAV navigation softw are developed by Geosys .
-
Topography of each site has been done in collaboration with Toposat. A
Real Time Kinematic RTK GPS receiver with a less than 1 cm accuracy
for X(latitude) , Y (longitude) and Z (elevation)
-
A grid soil sampling over the field was a lso performed with a density of
13 points per hectare at two depths (10 -30cm, 60-80cm).
Based on these data sets, we investigated the possibilities to predict soil
properties (physical, chemical, and physico -chemical). A fist step has been to
georeference all data set into a common geographical system. Then, the aim of
the study was to define which kind of inputs give the best prediction for the
output parameters: texture components, organic matter, chemical
components.… as summarized in figure 3.
MODEL INPUT ? ?
Microtopography
Aerial IR photography
RPV
Learning data set
Linear
model to
learn
MODEL OUTPUT
Texture parameters
(clay, sand, alluvium)
[g/kg]
Organic Matter [g/kg]
Electrical conductivity
EC
Resistance MUCEP
Adjusted
model
Validation data set :
accuracy ?
P, K, N, Ca, Mg [ppm]
pH
Fig. 3 : Scheme of the study.
RESULTS
Comparative study of sensors
EMI was measured with EM38 sensor. On particular fields, we added
resistance measurements (MUCEP or handy electrodes) for:
- cross validation of EM38,
- cost/quality comparison between sensors for furt her studies.
Resistance and electromagnetic inductance are two inverses phenomenon.
Consequently, we aim to find inverse relation f(x)=1/x between these two
data sets. MUCEP continuously measures resistance at 3 depths: 50, 100 and
150 cm. EM38 continuou sly measures conductivity at 2 depths: 75 and 150
cm. Finally, point resistance has been measured with portable electrodes at 7
depths (10, 20, 30, 45, 65, 80 et 105cm). We studied 3 cases:
- EM38 (75cm depth) vs. MUCEP (R1 50cm depth)
- EM38 (75cm depth) vs. MUCEP (R1 100cm depth)
- EM38 (75cm depth) vs. Point resistance (80cm depth)
EMI vs. handed ponctual resistance
Figure 4 displays obtained results on the Calmont site. We represent there the
relationship between EM38 data and resistance portable data.
Fig. 4 : Relationship between EMI EM38 data and portable resistance electrode data,
Calmont site.
Observed relations have for equations: (Y for handed resistance data, X for
EMI data)
Calmont, parcel A: Y= -55.62+1288.45 1/X
R=0.88
Calmont, parce l B: Y= -55.85+1780.68 1/X R=0.80
The high R correlations are coherent with the expected inverses behaviors of
these two sensors. This result is a first validation of the EMI data.
EMI vs MUCEP
Figure 5 displays obtained results on the Ondes and Vallman ya sites. We
represent there the relationship between EM38 data and resistance MUCEP
data. Histograms of the values are also given for each kind of data.
Fig. 5 : Relationships between EM38 EMI data and MUCEP resistance data , on two
sites, with histograms of data values.
Observed relations have for equations: (Y for MUCEP resistance data, X for
EMI data)
Ondes, parcelle A : Y= -6.02+492,95 1/X R=0.89
Ondes, parcelle B : Y= 3.97+118,23 1/X
R=0.64
The high R correlations validate the i nverse behaviors of these two sensors.
We can also infer of second validation of EMI data from this result. We also
note the bimodal character of EMI data on parcel B, which illustrates an
important within-field variability not detected with MUCEP data.
It is easy to see that obtained results are very bad for Vallmanya field: no
inverse relation, and no dynamic in MUCEP data as depicted on the
histogram. An explanation can be the extreme dryness of the field. MUCEP
sensor does not appear to be well adapted for measuring low resistance values,
as related by Geocarta who made the experiment over Vallmanya. On the
contrary, EM38 keeps a good sensitivity (illustrated by the good dynamic of
the histogram) and remains well adapted for mapping variability . This can be
explained by the fact that EM38 doesn’t have any contact with the surface,
contrary to resistance electrodes.
These two comparative studies between EMI EM38 data and both types of
resistance data confirm the quality of the data acquired with the EM38 s ensor
mounted on a 2x4 quad. Also our experiments showed a good sensitivity of
the EM38 sensor in difficult conditions.
Results of prediction
According to our methodology summarized on fig. 2, we have built a linear
predictive model between input data (EMI , GPS RTK and spectral data) and
output data (soil components). Models were defined on 2/3 of data set and
validation is applied on the remaining 1/3 data. These data sets (learning and
validation) were randomly generated. Fig 6 displays, the accuracy of
prediction for clay content over the Auzeville site with 4 different
configurations of input data (learning data set is in red, validation data set is in
blue). The figure presents:
- correlation R values between predicted and observed data
- best linear fit equations between predicted and observed values.
p
r
e
d
i
c
t
e
d
p
r
e
d
i
c
t
e
d
measured
measured
Input : EMI
Input : EMI and topography .
p
r
e
d
i
c
t
e
d
p
r
e
d
i
c
t
e
d
measured
measured
Input : EMI and spectral image
Input : EMI, spectral image and topography.
Fig. 6 : Relationships between predicted and real values of clay conte nt over Auzeville
site,with four input data configurations.
Every correlation between observed parameters and predicted data were
computed and are compiled in the following chart.. Figure 7 displays the
evolution of R-values of all variables (mean over al l sites) for four different
input data configurations.
0,9
0,8
0,7
0,6
0,5
0,4
0,3
0,2
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EMI
EMI + topography
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EMI + spectral
EMI + topography + spectral
Figure 7 : R-values between predicted and observed values for the validation data set
of all variables and four input data configuration (30cm depth, mean over all sites)
Figure 7 indicates that the innovative coupling process between GPS, EMI
and radiometric data revealed to be very efficient and improves the prediction
for several variables. In particular, prediction of pH improves from 0.4 to
around 0.75. Prediction remains inefficient for orga nic matter or limestone.
For these variables, linear predictive models are not useful, and more complex
models should be investigated, such as neural networks.
These prediction models will be used for generating pedological variability
maps with the same high spatial resolution as EMI, GPS RTK and spectral
data. Figure 8 displays a first obtained result over a parcel (predicted clay
content at 30cm depth from EMI, GPS RTK and spectral data).
Figure 8 : Example of predicted clay content over a field (Auz eville site, France).
Inputs of the model are EMI, GPS RTK and spectral data.
CONCLUSION
This paper presents a multi sensor approach for characterizing in -field
variability. Linear predictive models of soil properties (texture, chemical,
physicochemical) were fit on calibration data and applied on validation data in
order to test accuracy of predictions. In addition, a comparative study has been
made by varying the number of inputs for each model. This study
revealed that the innovative coupling process between GPS, EMI and
radiometric data is very efficient and improves the prediction compared to
predictions solely based on one type of data. Better results for prediction were
achieved for clay than for other components (such as pH and MgO). However,
it is worth noting that correlation coefficients between predicted and real test
data vary from 0.7 to 0.9.
Also, a comparative study has been made for cross validating the EMI data
with resistance sensors. MUCEP and EM38 give similar results for one of the
two test fields. On the second field, very dry soil conditions probably
explained the poor MUCEP results (difficult to measure very low resistance
with that sensor). On the contrary, EM38 keeps a good sensitivity and remains
well adapted for mapping varia bility even in dry conditions.
ACKNOWLEDGEMENTS
The work reported was funded by European Community EC.
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