Soil moisture estimation
A method of calculating a soil moisture profile includes obtaining a capacitance measurement of a soil surface at a location, applying a preselected calibration to the capacitance measurement to obtain a top surface moisture estimate, obtaining local climatic and hydrological data correlated with the location, the local climatic and hydrological data, which includes at least local water input data, and calculating an estimate of the soil moisture at the location by applying a preselected evapotranspiration model to the top surface moisture estimate and the local climatic and hydrological data.
1. A method of calculating a soil moisture profile, comprising the steps of:
obtaining a capacitance measurement of a soil surface obtained by a contact sensor in contact with the soil surface, the contact sensor comprising at least one capacitor at a location;
applying a preselected calibration to the capacitance measurement to obtain a top surface moisture estimate;
obtaining local climatic and hydrological data correlated with the location, the local climatic and hydrological data comprising at least local water input data; and
calculating an estimate of the soil moisture at the location by applying a preselected evapotranspiration model to the top surface moisture estimate and the local climatic and hydrological data.
2. The method of claim 1 , wherein the evapotranspiration model comprises inputting the top surface moisture estimate and its variation over time t into an algorithm having the form:
∂
θ
surface
∂
t
=
B
/
(
ρ
W
d
1
)
(
P
G
-
E
G
)
-
B
2
/
τ
1
(
θ
surface
-
θ
depth
)
for
0
≤
θ
surface
≤
θ
S
a
t
∂
θ
depth
∂
t
=
B
2
/
(
ρ
w
d
2
)
(
P
G
-
E
G
-
E
T
R
)
-
B
3
/
τ
1
max
[
0
,
(
θ
depth
-
θ
F
C
)
]
for
0
≤
θ
depth
≤
θ
S
a
t
where ρ w is the density of water, d 1 is the depth of the surface soil, d 2 is the depth of interest corresponding to the desired soil moisture profile, P G is an input due to rainfall and irrigation, which is obtained from obtained climatic and hydrological data, E G is the soil evaporation and E TR represents the transpiration rates, τ 1 is a restore constant for one day, and B 1 , B 2 and B 3 are site specific parameters related to the particular soil type, and w FC is the field capacity of the soil which must be determined based on empirical testing.
3. The method of claim 1 , wherein the evapotranspiration model is a transient multiplayer soil moisture simulation model.
4. The method of claim 3 , wherein the transient multiplayer soil moisture simulation model is based on the FOA56 algorithm, for example of the form:
E
T
0
(
P
-
M
)
=
E
T
rad
(
P
-
M
)
+
E
T
a
e
r
o
(
P
-
M
)
=
{
0
.
4
0
8
Δ
(
R
n
-
G
)
Δ
+
γ
(
1
+
0
.
3
4
u
)
}
+
{
9
0
0
γ
[
Δ
+
γ
(
1
+
0
.
3
4
u
)
]
·
u
D
(
T
+
2
7
3
)
}
where the term ET rad (P−M) is the radiation term of the FAO-56 Penman-Monteith equation, and ET aero (P−M) is the aerodynamic component, R n is the net radiation at the surface (MJ·m −2 ·d −1 ); Δ is the slope of the saturation vapor pressure curve (kPa/° C.); γ is psychrometric coefficient (kPa/° C.); G is soil heat flux density (MJ·m −2 ·d −1 ) and u is wind speed at 2 m height (m·s −1 ).
5. The method of claim 1 , wherein the capacitance measurement is obtained by a contact sensor in contact with the soil surface, the contact sensor comprising at least one capacitor.
6. The method of claim 1 , further comprising the step of:
obtaining a surface temperature measurement at the location, and wherein the preselected evapotranspiration model incorporates the surface temperature measurement when estimating the soil moisture at the location.
7. The method of claim 6 , wherein the surface temperature measurement is obtained by a temperature sensor component of the contact sensor.
8. The method of claim 1 , further comprising the step of:
obtaining a selection of soil type from a plurality of predefined soil types, and wherein the preselected evapotranspiration model incorporates the selected soil type when estimating the soil moisture at the location.
9. The method of claim 8 , wherein the predefined soil types include two or more of: all soil types; clay or silty or loamy soil; and sandy soil.
10. The method of claim 1 , further comprising the steps of:
obtaining a calibration measurement being a capacitance measurement of air at the location;
modifying the capacitance measurement in accordance with the calibration measurement in order to calibrate the capacitance measurement.
11. The method of claim 1 , wherein the preselected calibration comprises applying the following calculation:
θ surface =α·( C soil −C air ) β
wherein θ is the top surface moisture estimate, C soil is capacitance measurement, C air is the capacitance of air at the location, and α are β are soil specific coefficients.
12. The method of claim 1 , wherein the local climatic and hydrological data are obtained, at least in part, from a local site non-specific weather station.
13. The method of claim 1 , wherein the local climatic and hydrological data are obtained, at least in part, from a local site specific weather station.
14. The method of claim 1 , wherein the method is implemented by a processing device, and the method include the processing device receiving the capacitance measurement via a data a data transfer.
15. The method of claim 14 , wherein the processing device is configured to receive the capacitance measurement via one or more of the following: wired data transfer; mobile broadband data transfer; and wireless data transfer.
16. A contact sensor comprising a housing on which is located a sensitive region, the sensitive region defined by a capacitance sensor located at least partially within the housing, the contact sensor further comprising circuitry configured to obtain capacitance measurements from the capacitance sensor and to store said measurements within a memory, wherein the circuitry is further configured to implement the method of claim 1 .
17. The contact sensor of claim 16 , wherein a sensitive region of the contact sensor is circular with a diameter of between 8 and 48 mm.
18. The method of claim 1 , wherein the evapotranspiration model is a model trained with a machine learning algorithm.
19. The method of claim 18 , wherein the machine learning algorithm is an artificial neural network.
20. The method of claim 18 , wherein the evapotranspiration model is trained using an open-loop feedback architecture.