Using Electromagnetic Induction Methods to Map Groundwater in Florida Citrus Soils
N
Observation wells
Elevation above msl (m)
Soil series
$
W
E
Revell grove
S
Survey boundary
36.5
BASINGER FINE SAND
36.5
$1
$2
$ 3$ 4
IMMOKALEE FINE SAND
$ 13
$ 15
$ 16
$ 12
$ 11
ONA
FINE SAND
$
10
35
$ 14
$5
38
$9 $8 $7 $6
$ 17 $ 18
$ 19
$ 20
$ 21
$ 22
$ 23
$ 24 $ 25 $ 26 $ 27
ZOLFO FINE SAND
JONATHAN SAND
$ 38
$ 37
$ 39
$ 36
$ 40
$ 35
$ 34
$ 33 $ 32
$ 31
36.5
$ 30
$ 29
35
$ 41 $ 42 $ 43
$ 44 $ 45
$ 46
$ 47
$ 48
$ 28
JONATHAN
SAND
$
$
49
50
SPARR FINE SAND
33.5
0
a)
100
200
300
400
500
600
700
N
W
800 Meters
$
E
Varner grove
S
Methods
Two variable field sites: Revell and Varner groves, located near
Bowling Green, Hardee Co., Florida (27.63089oN, 81.82467o W).
Revell grove has no artificial drainage and is planted with a
mixture of mature ‘Hamlin’ and ‘Valencia’ oranges at an elevation of
33.5 to 38.1 m above mean sea level (msl), with dimensions of 789
m E-W, 712 m N-S (Fig. 1).
Varner grove has artificial drainage (double beds and ditches) and
is planted with mature ‘Hamlin’ oranges at an elevation of 30.5 to
34.0 m above msl, with dimensions of 402 m E-W, 806 m N-S (Fig.
1). The horizontal distance between the two groves is 12.3 km.
Fifty water table observation wells were installed during the dry
season in each grove (Fig. 1).
Wells were1.5-m long, 10-cm diameter PVC pipe, perforated with
2.5-cm holes in the lower 1.1-m section and covered with nylon
drain sleeve to prevent soil entry.
Collected soil samples at 0-15, 15-30, 30-60, 60-90, 90-120, 120150 cm depths for soil electrical conductivity (EClab) measurements
in the laboratory.
 Ground conductivity measurements (ECa) were taken with an
EM38 electromagnetic soil profiler (Geonics Limited, Mississauga,
Ontario, Canada) at each well when the water table was measured.
Both horizontal dipole (EMh) and vertical dipole (EMv) orientations.
Motorized ground conductivity surveys were performed in each
grove with the EM38 instrument mounted vertically on a specially
constructed acrylic sled (Fig. 3).
EM38 was towed 5-m behind a four-wheel drive vehicle equipped
with a Trimble 106 Differential Global Positioning System (Trimble
Navigation Limited, Sunnyvale, CA).
Ground speed of the sled was about 5 m/s, which translates to an
ECa distance resolution of about 5 m at 1 Hz averaged data rate.
50 water table depths per grove and the corresponding ground
conductivities (ECa) were analyzed by regression with Genstat
statistical software (Genstat 5, Lawes Agricultural Trust,
Rothamsted, UK) to obtain calibrations for predicting water table
depths from ECa readings.
The accuracy of water table depths predicted from EMv was
estimated from the root mean square error (RMSE) of the
regressions.
Survey data files of DGPS and EMv readings were imported into
ArcView GIS software (ESRI, Redlands, CA) for calculation of
water table depth and mapping.
Regular gridded XYZ data surfaces were interpolated from the
calculated water table depth data using point kriging and eight
search sectors with the Surfer 7 software (Golden Software Inc.,
Golden, CO).
Observation wells
Elevation above msl (m)
Soil series
Survey boundary
IMMOKALEE FINE SAND
SMYRNA SAND
$ 60
$ 61
$ 80
$
81
PLACID FINE SAND/DEPRESSIONAL
$ 59
$
62
$ 79
29
$ 82
ONA FINE SAND
$ 58
$
63
$ 78
27.
5
$ 83
IMMOKALEE FINE SAND
POMELLO FINE SAND
$
$ 77
$ 84
$ 56
$ 65
$ 76
$ 85
32
57
$ 64
$ 55
$
66
$ 75
$
$ 54
$ 67
$ 74
$
$ 53
86
$ 95
$ 96
87
$
$ 97
94
32
30.5
ONA FINE SAND
Figure 2. Example of a Florida Spodosol
Results & Discussion
MANUAL MEASUREMENTS
Ground conductivities measured with the EM38 over five dates at
comparable water table depths were consistently higher in the
Varner grove than the Revell grove (Table 1; Fig. 4). These trends
could be attributed to the higher EClab values in the Revell grove
(Table 1).
Different measurement dates provided a wide temporal range of
water table conditions in the groves (Table 1) and yielded different
calibrations (Table 2; Fig. 4).
Correlations between water table depth and ECa ground
conductivities were consistently higher when measured in the
deeper sensing vertical dipole mode (EMv) than the horizontal
dipole mode (EMh).
A logarithmic regression resulted in a slightly better fit than a
linear regression, probably due to the nonlinear response of the
EM38 instrument (Table 2).
The accuracy of these regression calibrations for water table
depth prediction was estimated by the RMSE and ranged from 4.1
to 15.5 cm over the different dates and groves (Table 2).
Due to the higher soil solution conductivity (EClab) in the Varner
grove, the regression models for water table depth and EMv in the
two groves were significantly different even on the same day (Table
2; Fig. 4).
The semivariograms for EClab, EMv and water depth for both
groves were best fitted by the spherical model.
The semi-variance attained its maximum at a range of influence
of about 250 m in the Revell grove and 650 m in the Varner grove.
This indicated the presence of spatial dependence for soil and
water table depth parameters within both groves (Table 3).
AUTOMATED (SURVEY) MEASUREMENTS
The Revell grove survey collected 5438 georeferenced EMv
values, at a mean speed of 4.50 m/s, taking 108 min to complete
45.2 ha (25.1 ha/h). The Varner grove survey collected 3553
georeferenced EMv values, at a mean speed of 4.43 m/s, taking 83
min to complete 26.8 ha (19.4 ha/h).
The raw EMv survey data from the groves were used to predict
five water table depth classes (Fig. 5) using site-and-time-specific
calibrations (Table 2).
Final water table maps after interpolation by kriging and
smoothing showed large spatial variability similar to that observed
across the 100 manually measured wells (Fig. 6).
Two large zones of deeper water table depths roughly
corresponded to the location of the Jonathan sand soil series (Figs.
1 & 6).
In contrast, the Zolfo fine sand and Sparr fine sand corresponded
to the shallower water tables in Revell grove (Figs. 1 & 6).
In the Varner grove, deeper water tables were found in the
Pomello fine sand area, occupying most of the northern half of the
survey area, while the shallower water tables occurred in the
Immokalee fine sand (southern half), and Ona fine sand / Placid
fine sand zones (northeast corner) (Figs. 1 & 6).
precautions should be taken during surveys to avoid metal
objects such as cans, buried pipes, and adjacent buildings, pumps
or fences in the route because erroneous ECa readings were
obtained in their proximity. Electrolyte concentrations in the soil and
soil solutions could also greatly affect the EMI results, thus strongly
biasing the calculated water table depths.
In order to ensure the best possible water table prediction, we
therefore recommend that a site-specific calibration between EMv
and water table depth be conducted on each large contiguous
block of land at each given time only.
April 2002
July 2002
August 2002
September 2002
40
October 2002
80
120
160
a)
2.0
4.0
$ 73
$ 88
$
93
29
52
$
69
$ 72
$ 89
$ 92
$ 71
$ 91
$ 100
300
400
500
600
August 2002
September 2002
November 2002
80
120
a)
8.0
10.0
12.0
5.0
b)
10.0
700
Figure 1. Aerial photographs of a) the Revell grove and b) the Varner grove
showing locations of fifty observation wells, elevation contours, soil series,
and the survey boundaries for this study.
Figure 3. Acrylic sled with dust cover, EM38
instrument, and typical field survey
configuration with four-wheel drive vehicle.
20.0
100
200
300
400
500
600
700
Survey boundary
Water table (cm)
% 25 - 95
% 95 - 124
% 124 - 149
% 149 - 174
% 174 - 287
800 Meters
N
25.0
W
E
Varner grove
S
Survey boundary
Water table (cm)
%
36 - 56
%
56 - 64
%
64 - 72
%
72 - 81
%
81 - 95
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%%%%%%%%%%%
0
100
200
300
400
500
600
700
800 Meters
Figure 6. Interpolated water table depths predicted
from EMv ground conductivity measurements
collected by sled-mounted EM38 and DGPS in a) the
Revell and b) the Varner groves.
Summary
N
W
Revell grove
E
S
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Survey boundary
Water table (cm)
%
0 - 109
%
109 - 135
%
135 - 154
%
154 - 174
%
174 - 250
Efficient drainage is an important prerequisite for economic citrus production
on southern flatwoods soils in Florida because their shallow water tables can
limit root development, stunt tree growth and contribute to mortality. The
objective of this study was to develop a new precision agriculture application,
using ground conductivity measured with the EM38 electromagnetic soil
profiler for the estimation and mapping of shallow water table depths in
Florida’s citrus groves. Calibrations were developed and tested in five different
months and two sites to evaluate the spatial and temporal accuracy of water
table predictions. Three automated mobile surveys of water table depth were
then conducted in the same groves using a differential global positioning
system (DGPS) for georeferencing the ground conductivity data. The spatial
variability of water table depths was mainly influenced by soil type and the
temporal variability was influenced strongly by the seasonal rainfall pattern.
The vertical dipole (EMv) of the EM38 instrument was better than the
horizontal dipole (EMh) for estimating water table depths because of its greater
sensing depth. Accuracy, calculated as root mean square error (RMSE),
ranged from 4.1 to 15.5 cm on a given day.
Keywords: EM38, precision agriculture, GIS, DGPS, hydromorphic soils,
water table
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Selected References
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*
800 Meters
15.0
0
Ground conductivity (mS/ m)
500
600
700
800 Meters
Figure 5. Water table depths in the Revell grove predicted from
5438 EMv ground conductivity measurements collected by
sled-mounted EM38 and DGPS.
32
0 FINE
ZOLFO
SAND 200
ZOLFO
FINE 100
SAND
July 2002
b)
0
$ 90
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May 2002
40
Revell grove
E
S
0
$ 99
29
30.5
$ 70
W
Figure 4. Calibration curves developed for predicting
water table depth from EMv on five dates in a) the
Revell grove and b) the Varner grove.
$ 98
33.5
$ 51
6.0
Ground conductivity (mS/ m)
MYAKKA FINE SAND
$ 68
N
160
IMMOKALEE FINE SAND
$
b)
0
Water depth below soil surface (cm)
Florida citrus growers currently manage their groves as uniform
production units, but considerable variability in various grove
characteristics may exist (Fig. 1). Recent results show that soil
and groundwater variability are dominant factors affecting in-field
variability and profitability of citrus (Schumann et al., 2002).
Spodosols in south-central Florida have sand or fine-sand
textures, and silt plus clay content is less than 5% except in the
Bh (spodic) horizon (Fig. 2). These soils typically have shallow
water tables, caused by perching of water above the Bh horizon
(Hyde and Ford, 1989). Minimal vertical saturated flow capacity in
the Bh horizon causes ponding into the overlying E and A
horizons. Water logging may develop all the way to the surface
(Cox and McFarlane, 1995) and can damage the root systems of
citrus trees. OBJECTIVES:
1. Determine the best operating conditions of a commercial
electromagnetic induction (EMI) soil sensing device and calibrate
it for use in estimating shallow water table depths from apparent
conductivity (ECa).
2. Examine the spatial and temporal variability of soil ECa and
water table data to determine the accuracy and repeatability of
water tables measured by EMI.
3. Use the validated EMI instrument together with DGPS to make
automated in-field soil water table depth measurements which can
be mapped and used in precision agriculture for improving citrus
grove management and profitability through optimized drainage,
fertilization and irrigation.
A.W. Schumann* and Q. Zaman, University of Florida / IFAS, Citrus Research
and Education Center, 700 Experiment Station Road, Lake Alfred, FL
Water depth below soil surface (cm)
Introduction
Cox. J.W., and D.J. McFarlane. 1995. The causes of water logging in shallow
soils and their drainage in southwestern Australia. J. Hydrology. 167: 175-194.
Hyde, A.G., and R.D. Ford. 1989. Water table fluctuations in representative
Immokalee and Zolfo soils of Florida. Soil Sci. Soc. Am. J. 53:1475-1478.
Schumann, A.W., T.A. Wheaton, and J.D. Whitney. 2002. In-Field Soil Variability
Affecting Productivity of Florida Citrus Groves –– A Precision Agriculture
Application. ASA Southern Branch Annual Meeting, Orlando, FL.
Acknowledgements
Support for this research was received from USDA-IFAFS grant No. 2001-5210311323 Maintaining the Competitiveness of Tree Fruit Production Through
Precision Agriculture and a donation from Cargill Fertilizer. The authors would
also like to thank the assistance and contribution from Tom Pospichal, Kevin
Hostler, Adair Wheaton, Jodie Whitney, Gerald Perkins, Stuart Pocknee and
John Roegner during the field instrumentation and data collection phases of this
project.
AWS, 23 October, 2003