RETRIEVAL OF P-BAND SOIL REFLECTIVITY FROM SIGNALS OF OPPORTUNITY

Abstract

A system and method for determining moisture content of soil, comprising providing bistatic radar configuration to measure soil reflectivity in UHF and S-band, cross-correlating between Sky-viewed and Earth-viewed signals and reflected signals in order to isolate the reflected signals, and correlating the isolated reflectesd signal to moisture content of the soil.

Claims

1. A method for determining moisture content of soil, comprising: providing bistatic radar configuration to measure soil reflectivity in UHF and S-band; cross-correlating between Sky-viewed and Earth-viewed signals and reflected signals in order to isolate the reflected signals; and correlating the isolated reflected signal to moisture content of the soil.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The above and other objects, features, and advantages of the present invention will become more apparent when taken in conjunction with the following description and drawings wherein identical reference numberals have been used, where possible, to designate identical features that are common to the figures, and wherein:

[0011] FIG. 1a is a graph showing soil moisture vs. reflectivity for P-band and S-band signals.

[0012] FIG. 1b is a graph showing reflectivity vs. soil moisture for P-band and S-band signals.

[0013] FIG. 2 shows a diagram of direct and reflected signals.

[0014] FIG. 3 is a graph showing soil moisture vs. depth.

[0015] FIG. 4a is a graph showing soil moisture vs. reflectivity for reverse polorization.

[0016] FIG. 4b is a graph showing soil moisture vs. reflectivity for same polarization.

[0017] FIG. 5a is a graph showing soil moisture vs. reflectivity for reverse circular polorization.

[0018] FIG. 5b is a graph showing soil moisture vs. reflectivity for same circular polarization.

[0019] FIG. 5c is a graph showing reflectivity vs. soil moisture for P and S bands.

[0020] FIG. 6 is a graph showing varying calibration implementations according to various aspects.

[0021] FIG. 7 is a graph showing delays vs. correlation according to various aspects.

[0022] FIG. 8 is a diagram illustrating a soil moisture measurement system according to various aspects.

[0023] FIG. 9 is a diagram showing a transfer switch configured to switch between Skyview and Earthview antennas according to various aspects.

[0024] FIG. 10 is a diagram showing a calibration sequence according to various embodiments.

[0025] FIG. 11 is a diagram showing details of a switching mode for the swap mode according to various aspects.

[0026] FIG. 12 is a diagram showing details of a switching mode for a reference or noise mode according to various aspects.

DETAILED DESCRIPTION

[0027] For the purposes of promoting an understanding of the principles of the present disclosure, reference will now be made to the embodiments illustrated in the drawings, and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of this disclosure is thereby intended.

[0028] A novel soil condition determination system is disclosed. This disclosure describes a new sensing technology for precision agriculture. It encompasses an instrument and related data processing to extract estimates of sub-surface soil moisture from measurements made on an airborne platform (which could include piloted or un-piloted vehicles).

[0029] This technology uses reflection of electromagnetic radiation from the surface of the Earth to measure the water content of the soil (soil moisture). The fundamental physical principle involved in this measurement is the reflectivity of the soil surface, the fraction of incident radiation reflected forward vs. that absorbed by the surface, depends on the amount of water in the soil. Reflectivity of water is higher than that of dry soil, so as the soil moisture increases, the power in the reflected signal would also increase.

[0030] The depth of penetration of an electromagnetic signal is approximately proportional to the wavelength. Satellite and airborne remote sensing uses microwave frequencies, typically in L-band (1.4 GHz) and above. At these frequencies, the signal penetrates only the top few cm of the soil, thus producing a direct measurement of only moisture within this thin layer on the top of the soil. Many problems in agricultural production will require knowledge of the water distribution in from the surface down to the “root zone”, which is approximately a meter below the surface. With present technology, using L-band and higher, this root-zone soil moisture (RZSM) can only be estimated by extrapolating the surface measurements using a model for the distribution of water in the soil.

[0031] The present technology for making this measurement uses either an active radar, to transmit the incident signal, from the same platform (airborne or satellite), at which the reflected signal is observed, also known as a monostatic configuration. The frequencies available for this application are only those allocated for scientific use (radio astronomy). These are severely limited and may be susceptible to interference from other transmitters at nearby frequencies. An alternative approach used for remote sensing of soil moisture makes use of measurement of the natural emission of microwave radiation from the soil surface, using a radiometer, a very sensitive receiver.

[0032] L-band is the lowest practical frequency for either of these two existing techniques, due to the presence of many communication transmitters operating at lower frequencies, and the required antenna size.

[0033] The system and method described herein makes use of signals transmitted from satellites for other purposes (typically communications) which are reflected from the soil surface. A specialized receiver compares the signal observed directly from the transmitting satellite with that reflected from the soil surface through the mathematical process of cross-correlation. Cross-correlation will provide a measurement of the reflectivity of the soil surface. By re-utilizing man-made signals already transmitted, vs. using natural emission or transmission of a dedicated signal, it opens the possibility of using any frequency used for communication or navigation. In the particular case of soil moisture sensing, this allows the use of frequencies below L-band, with longer wavelengths and thus deeper penetration. A large number of satellite communication transmitters operate in P-band (known as UHF/VHF in the communications field.). These include a frequency allocation to government use from 225-328.6 MHz. Commercial satellite transmissions are also allowed between 137 MHz and 138 MHz, and 148 to 150 MHz. At these frequencies, the penetration depth ranges from approximately 9 cm to 22 cm, providing better sensing of the soil below the surface and into a significant portion of the root-zone. It is not feasible to operate either an active radar or passive radiometer in space at these frequencies due to the required antenna size, the lack of a special allocation for scientific use, and the presence of interference from high-powered communications transmitters.

[0034] The system and method disclosed herein comprises an instrument and related data processing to extract an estimate of the volumentric soil moisture from reflections of P-band communication satellite signals. This system may be installed on light aircraft and un-piloted aerial vehicles (UAV's, “drones”), and used in surveying agricultural fields, to monitor the sub-surface moisture content in the soil for precision agriculture. For example, these measurements may be used to regulate irrigation, to more selectively and thereby efficiently provide water as needed for plant growth and reduce waste. The technology may also have applications in forestry and disaster preparation, in the prediction and management of drought, forest fire, or flood risk.

[0035] Unlike past experience with signals of opportunity, there are unique approaches necessary to work with these P-band signals from an airborne platform. These arise from the longer wavelengths involved, and the very low bandwidth of the transmitted data. These prevent the use of directional antennas, or time-delay to clearly separate the direct and reflected signals. The following features are of interest: [0036] 1) Multiple cross-correlation array to generate pairs for cross correlations between the sky-view and earth-view antennas in both Linear polarization components. [0037] 2) Formation of observables (Gammal and Gamma 2) from these cross-correlations given a functional relationship to surface reflectivity. [0038] 3) Calibration of the observables, using models or experimental data, to account for the cross-interference between the direct and reflected signals, visible in both the Earth and Sky-view antennas simultaneously [0039] 4) Antenna design for installation on side of aircraft, to provide maximum gain in the direction of the desired signal (Direct-Skyview, Reflected-Earth view) and maximum attenuation in the direction of the desired null (Reflected-Skyview, Direct-Earthview). [0040] 5) Kalman filter method for simultaneously estimating antenna parameters and surface reflectivity from the combined direct and reflected signals, using measurements of the cross-correlation pairs.

[0041] Data collected at L-band and higher frequencies, regardless of the instrument principle or geometry, will only be sensitive to moisture in the top few cm of the soil. Estimates of the sub-surface soil moisture can be obtained by fitting a hydrological model for the flow of water from the surface, to these measurements, using any number of data inversion methods, such as least squares, Kalman filters, or the simulated annealing. The “Level-4” data product from the SMAP mission is a model inversion of this type. The accuracy of these methods, of course, will depend upon the quality of the underlying physical models and their assumptions, such as the length scale over which homogenous properties can be assumed.

[0042] The system and method of the present disclosure offer the best direct measurement of sub-surface soil moisture available. It makes use of lower-frequency signals which are required to penetrate the soil, but which cannot be used for active or passive remote sensing due to interference, and the large antenna size. The signals of opportunity concept, which re-utilizes existing transmitter sources, will produce high signal to noise ratio measurements with low instrument power requirements. Resolution will be determined only by the frequency of the signal, under the assumption of a near-specular reflection, not the antenna size. Signals of opportunity measurements can also make use of the direct signal power for calibration. These features will enable the use of this instrument on small airborne platforms such as UAV's.

[0043] Airborne measurements provide higher resolution than satellite measurements, and can be targeted specifically to the areas of interest.

[0044] The soil moisture retrieval can be simplified as a reflectivity estimation problem. The soil reflectivity will be estimated from the correlations between Sky-viewed and Earth-viewed signals using dedicated antennas, RF filter, signal processing algorithms and antennas and receiver calibrations. As shown in FIGS. 1a and 1b, the P-Band (110) and S-Band (112) are practically on top of each other.

[0045] As shown in FIG. 2, the direct and reflected signals have the following structure, where α is the data signal, ω.sub.b is the baseband frequency, ω.sub.b is the frequency of the signals in space and τ stands for time delay:

[00001]$\begin{array}{c}{x}_D\left(t\right)=\sqrt{C_D}.Math.a\left(t-{\tau }_D\right).Math.e^{j.Math..Math.{\omega }_b.Math.t}.Math.e^{-j.Math..Math.{\omega }_e.Math.{\tau }_D}\\ x_R\left(t\right)=\sqrt{C_R}.Math.a\left(t-{\tau }_R\right).Math.e^{j.Math..Math.{\omega }_b.Math.t}.Math.e^{-j.Math..Math.{\omega }_e.Math.{\tau }_R}\end{array}.Math..Math.\Gamma =\frac{C_R}{C_D}$${\hat{\Gamma }}_1\approx \frac{R_{22}^n\left(0\right)-G_1.Math.{\sigma }_1^2}{R_{11}^n\left(0\right)-G_2.Math.{\sigma }_2^2}.Math.\frac{G_1}{G_2}.Math.\frac{G_{\mathrm{SD}}}{G_{\mathrm{ER}}}$${\hat{\Gamma }}_2\approx {\left(\frac{.Math.R_{12}^n\left({\tau }_{\mathrm{RD}}\right).Math.}{R_{11}^n\left(0\right)-G_2.Math.{\sigma }_2^2}\right)}^2.Math.\frac{G_1}{G_2}.Math.\frac{G_{\mathrm{SD}}}{G_{\mathrm{ER}}}$

The approaches to estimate reflectivity include calibration of channel gain, antenna gain, and channel noise. If the antenna gain along the opposite path (G.sub.SR and G.sub.ED) are not zeros, there is a bias of reflectivity estimation. To correct the bias due to the interference, an empirical calibration of Direct-Reflection interference can be performed.

[0046] The penetration depth (δ.sub.p) depends on the frequency (f) and dielectric constant of material.

[00002]${\delta }_p=\frac{\lambda }{4.Math.\pi .Math..Math.\mathrm{Im}\left[\sqrt{\mathrm{.Math.}}\right].Math.}=\frac{c}{4.Math.\pi .Math..Math.f.Math..Math.\mathrm{Im}\left[\sqrt{\mathrm{.Math.}}\right].Math.}$

[0047] FIG. 3 shows the relationship between the penetration depth and soil moisture. The texture of soil in the illustrated case is Sand 40%, Clay 20% and Slit 40%.

[0048] The dielectric constant (ε) of soil is a function of temperature, soil texture, salinity, and Soil moisture. Reflectivity is the function of dielectric constant and incident angle (θ) Γ.sub.lr and Γ.sub.ll are the reflectivity for the reverse and same circular polarization, respectively.

[00003]${\gamma }_{\mathrm{hh}}=\frac{\cos .Math..Math.\theta -\sqrt{\mathrm{.Math.}-{\sin }^2.Math.\theta }}{\cos .Math..Math.\theta +\sqrt{\mathrm{.Math.}-{\sin }^2.Math.\theta }}$${\gamma }_{\mathrm{vv}}=\frac{\mathrm{.Math.}.Math..Math.\cos .Math..Math.\theta -\sqrt{\mathrm{.Math.}-{\sin }^2.Math.\theta }}{\mathrm{.Math.}.Math..Math.\cos .Math..Math.\theta +\sqrt{\mathrm{.Math.}-{\sin }^2.Math.\theta }}$${\Gamma }_{\mathrm{lr}}={.Math.\frac{{\gamma }_{\mathrm{hh}}-{\gamma }_{\mathrm{vv}}}{2}.Math.}^2,{\Gamma }_{\mathrm{ll}}={.Math.\frac{{\gamma }_{\mathrm{hh}}+{\gamma }_{\mathrm{vv}}}{2}.Math.}^2$

The dielectric constant of soil depends on the soil moisture, and the reflectivity is a function of the dielectric constant. Therefore, the relationship between the soil moisture and reflectivity can be established when the soil texture, frequency, and salinity are known, as shown in FIGS. 4a and 4b, also illustrated in FIGS. 5a, 5b, and 5c.

[0049] There are three calibration methods including noise injection, reference load, and antenna swapping, as illustrated in FIG. 6.

The signals in the receiver are:

x.sub.1(t)=√{right arrow over (G.sub.1,D)}x.sub.D(t)+√{square root over (G.sub.1,R)}x.sub.R(t)+n.sub.1(t)

x.sub.2(t)=√{right arrow over (G.sub.2,D)}x.sub.D(t)+√{square root over (G.sub.2,R)}x.sub.R(t)+n.sub.2(t)

G.sub.1,D=G.sub.1G.sub.SD

G.sub.1,R=G.sub.1G.sub.SR

G.sub.2,D=G.sub.1G.sub.ED

G.sub.2,R=G.sub.2G.sub.ER

[0050] The auto- and cross-correlations between these signals can be modeled as:

R.sub.1,1(τ.sup.s)={[g.sub.1d.sup.2+g.sub.1r.sup.2]R.sub.a(τ.sup.s)+g.sub.1dg.sub.1r[R.sub.a(τ.sup.s−τ.sub.RD.sup.s)e.sup.jω.sup.e.sup.τ.sup.RD+R.sub.a(τ.sup.s+τ.sub.RD.sup.s)e.sup.−jω.sup.e.sup.τ.sup.RD]}φ.sub.b+σ.sub.1.sup.2(τ.sup.s)n.sub.11δ(τ.sup.s)

R.sub.1,2(τ.sup.s)={[g.sub.1dg.sub.2d+g.sub.1rg.sub.2r]R.sub.a(τ.sup.s)+g.sub.1dg.sub.2r[R.sub.a(τ.sup.s−τ.sub.RD.sup.s)e.sup.jω.sup.e.sup.τ.sup.RD+g.sub.1rg.sub.2dR.sub.a(τ.sup.s+τ.sub.RD.sup.s)e.sup.−jω.sup.e.sup.τ.sup.RD]}φ.sub.b+n.sub.12(τ.sup.s)

R.sub.2,2(τ.sup.s)={[g.sub.2d.sup.2+g.sub.2r.sup.2]R.sub.a(τ.sup.s)+g.sub.2dg.sub.2r[R.sub.a(τ.sup.s−τ.sub.RD.sup.s)e.sup.jω.sup.e.sup.τ.sup.RD+R.sub.a(τ.sup.s+τ.sub.RD.sup.s)e.sup.−jω.sup.e.sup.τ.sup.RD]}φ.sub.b+σ.sub.2.sup.2δ(τ.sup.s)n.sub.22δ(τ.sup.s)

with g.sub.ik=√{square root over (G.sub.l,kC.sub.k)}φ.sub.b=e.sup.jω.sup.e.sup.τ.sup.a

[0051] The auto- and cross-correlations model is non-linear and depends on several unknown parameters: the soil reflectivity, the receiver channels gains sand noises, the antennas gains and the space phase between the direct and reflected signals. These parameters will be estimated as states of an Extended Kalman Filter, based on the observation of four correlations lags.

[0052] Two methods to determine reflectivity estimates are provided, the ratio of the auto-correlations and the ratio of cross- to auto-correlation, have been defined, allowing soil moisture to be retrieved using established empirical models for the soil dielectric constant. Using synthetic signals having realistic noise power, a calibration function has been developed to correct these observables, accounting for the cross-channel interference.

[0053] In certain aspects, as shown in FIG., a communication satellite 1 generates a transmission signal, transmitted in wide range of directions. A line of sight signal 2 can be received by a receiving antenna 8 on an aerial platform 9 (e.g., a fixed wing airplane), while another signal 3 travels along the ray-path from the satellite to reflect from the top surface of soil 3 in a given area, or vegetation growth on top of the soil. The incident signals reaching the soil 3 penetrate different depths (between 3 and 4) and are reflected outwards accordingly (i.e., one reflection for one penetrating depth and another reflection for another penetrating depth), i.e., some portion of the signal penetrates deeper into the soil to reflect at multiple depths.

[0054] Penetration depth is approximately proportional to wavelength, so lower frequencies (larger wavelengths penetrate deeper). L-band (e.g. NASA SMOS or ESA SMAP instruments, operating at 1.4 GHz) penetrates to 2-5 cm. P-band (230-270 MHz) can penetrate approximately 6-8 times deeper, or roughly 12-40 cm. Soil moisture within the “root zone” the depths of plant roots, is most important for predicting agricultural production and understanding the absorption of water by plants. This is typically considered the top meter of the soil. Reflection for P-band wavelengths (˜1 meter) will generally be approximated as specular, such that the angle of incidence 5 (indicated by θ) equal to the angle of reflection 6. Reflectivity of the soil is strongly dependent upon the water content often expressed as “volumetric soil moisture”(volume of water)/(volume of soil). The functional relationship between soil moisture and reflectivity is well established from past experimental measurements and defined in empirical models. Models also depend upon soil composition. Reflected ray paths—with intensity proportional to the reflectivity at each layer. Total scattered power is the combination of that in the rays from multiple depths. Signals from both the direct 2 and reflected ray paths 7 are received by an antenna with 2 the beams identified as “sky-view” (antenna pointed toward the satellite) and “Earthview” (antenna pointed toward soil).

[0055] Antenna 8 can be mounted on any type of platform, including satellites, aircraft, unpiloted aerial vehicle (UAV's, e.g. “drones”), or fixed installations, such as tower.

[0056] The sky-view antenna can be a separate, physical antenna, or a “smart antenna” beam formed using an antenna array, using common techniques known in the field. Beam of antenna is oriented in the predicted direction of the direct signal. If the design allows such control, a null of the antenna is steered to the direction of the reflected ray path. The earth-view antenna can be similarly design as the sky-view, but with a beam directed to the reflected ray path and optionally a null directed to the reflected ray path.

[0057] A calibration source can be used to calibrate the system which could be a noise source at a controlled temperature, or a synthetic signal of know properties.

[0058] A transfer switch shown in FIG. 9 is configured to switch between Skyview and Earthview antennas to a receiver channels 1 and 2. The switch has 2 modes: a) Thru mode: Skyview coupled to channel 1 and Earthview coupled to channel 2; and b) Swap mode: Skyview coupled to Channel 2 and Earthview coupled to channel 1. Outputs of the transfer switch are sent to 2 receivers of identical design. The swap process with the transfer switch is used to calibrate the gain differences. A correlator in the receiver computes the autocorrelation of the Channel 1 and 2 and the cross-correlation between channels 1 and 2 using standard methods in digital signal processing. Correlation can be performed on a number of discrete “lags” between the channels. An estimated reflectivity (G) is computed from the outputs of the correlator as

[00004]$\Gamma ={.Math.\frac{R.Math..Math.12}{R.Math..Math.11}.Math.}^2$

[0059] Where R12 is the cross-correlation between channel 1 and channel 2 and R11 is the autocorrelation of channel 1. Switch 3 is turned to the calibration source for a small fraction (˜10%) of the data collection time. Indicated as “Ref” or “noise” in the following exemplary timeline. Transfer switch 4 is switched from “Thru” to “Swap” for equal periods of time as shown in FIG. 10. Data computing estimated reflectivity is obtained for each of these ties. Details of the switching mode for the swap mode is illustrated in FIG. 11. Details of the switching mode for the reference or noise mode is illustrates in FIG. 12.

[0060] Those having ordinary skill in the art will recognize that numerous modifications can be made to the specific implementations described above. The implementations should not be limited to the particular limitations described or the claim provided. Other implementations may be possible.