WO2022038623A1

WO2022038623A1 - System and method for remote quantification of electrical conductivity of soil

Info

Publication number: WO2022038623A1
Application number: PCT/IN2020/051018
Authority: WO
Inventors: Shoba Periasamy
Original assignee: Shoba Periasamy
Priority date: 2020-08-20
Filing date: 2020-12-11
Publication date: 2022-02-24

Abstract

The various embodiments of the present invention provide a system and method for identifying salination of soil by remotely determining the electrical conductivity (EC) of soil in vegetated and bare lands. The embodiments also provide a system and method for identifying salination of soil by remotely determining the imaginary part of the dielectric constant of the soil through C-band Synthetic-Aperture Radar (SAR). In the present invention, the three-dimensional density space is constructed with field-observed dielectric constant measurements of non-saline and saline soils. The present invention enables quantifying the soil Electrical Conductivity of the bare soils and the soils covered with crops of less than 0.5m height. The invention finds application in research groups working on agriculture related use-cases as the resultant product of the model is essential to carry out appropriate agricultural processes according to actual salinity level in the soil.

Description

SYSTEM AND METHOD FOR REMOTE QUANTIFICATION OF ELECTRICAL CONDUCTIVITY OF SOIL

The present invention is generally related to a system and method for remote identification of salination of soil. The present invention is particularly related to a system and method for identifying salination of soil by remotely determining the electrical conductivity of soil in vegetated and bare lands. The present invention is further related to a system and method for identifying salination of soil by remotely determining the imaginary part of the dielectric constant of the soil through C-band Synthetic-Aperture Radar (SAR).

BACKGROUND OF THE INVENTION

The accurate measurement of soil’s electrical conductivity (EC) over vegetated land is challenging in multispectral remote sensing due to the lack of surface penetration capacity. Synthetic Aperture Radar (SAR) imagery has great potential to investigate soil EC due to its penetration capacity and high sensitivity towards the dielectric properties of the medium. However, the theoretical and empirical models of SAR are site-specific and need too many in-situ observations to implement the model in the spatial domain. These practical limitations lead to the development of semi-empirical models over bare soils. Though these semi-empirical models were widely accepted and employed in various studies, some studies reported discrepancies between predicted and measured values.

Furthermore, the models have studied only the dependency of dielectric constant on soil moisture while ignoring the effect of soil EC on the dielectric constant. In Dobson model (1985), which is considered to be the most accurate model so far, the potential of the imaginary part was explored in describing the soil EC as experimental research work. However, the model does not address retrieving spatial products representing soil salinity at a finer resolution.

Hence, there exists a need for an effective system and method for remotely determining the electrical conductivity of soil in vegetated lands. There also exists a need for a method to identify the electrical conductivity of soil from the imaginary part of dielectric constant through remote methodologies such as a C-band Synthetic-Aperture Radar (SAR) with high prediction accuracy level for all ranges.

The above-mentioned shortcomings, disadvantages and problems are addressed herein and which will be understood by reading and studying the following specification.

OBJECT OF THE INVENTION

The primary object of the present invention is to provide a system and method for identifying salination of soil by remotely determining the electrical conductivity (EC) of soil in vegetated lands.

Another object of the present invention is to simulate the SAR product to act as a proxy for soil textural characteristics.

Another object of the present invention is to provide a system and method for identifying salination of soil by remotely determining the imaginary part of the dielectric constant of the soil through C-band Synthetic-Aperture Radar (SAR).

Yet another object of the present invention is to enable retrieving the soil EC with optimum accuracy level even when the field samples are not equally distributed in all ranges of values.

Yet another object of the present invention is to quantify the soil Electrical Conductivity of the bare soils and the soils covered with crops of less than 0.5m height.

Yet another object of the present invention is to enable carrying out essential and appropriate agricultural processes according to actual salinity level in the soil.

These and other objects and advantages of the present invention will become readily apparent from the following detailed description taken in conjunction with the accompanying drawings.

SUMMARY OF THE INVENTION

The various embodiments of the present invention provide a system and method for identifying salination of soil by remotely determining the electrical conductivity (EC) of soil in vegetated lands. The embodiments also provide a system and method for identifying salination of soil by remotely determining the imaginary part of the dielectric constant of the soil through C-band Synthetic-Aperture Radar (SAR).

According to one embodiment of the present invention, a system is provided for identifying salination of soil by remotely determining the electrical conductivity of soil in vegetated and bare lands. The system comprises a Synthetic-Aperture Radar (SAR) electronics module, a Synthetic-Aperture Radar (SAR) antenna module and an application platform module. The SAR electronics module further comprises a C-band signal generator, a processor module, a digitizer module, a Ground Range Detected (GRD) module and a communication module. The SAR antenna module further comprises an antenna module, a transmitter module, an acquisition modes module, a receiver module and a waveguide radiators module. The application platform module provides an architecture for processing and analysis for Earth Observation, and further comprises a pre-processing module and a calibrated backscattering module.

According to one embodiment of the present invention, the calibrated backscattering module further comprises a soil moisture information database, soil textural index database, a Spatial Simulation module for dielectric constant of non-saline soils, a Spatial Simulation module for dielectric constant of saline soils and a Spatial simulation module of Density Space Dielectric Model for Soil Electrical Conductivity.

According to one embodiment of the present invention, the communication module is configured to communicatively couple with a plurality of remote computing devices to determine the type of activities that are suitable on a piece of land based on preset data and observed and/or simulated data from the system. The communication module is also configured to render the observations and simulated data on a plurality of computing devices in user-intelligible form.

According to one embodiment of the present invention, a method is provided for identifying salination of soil by remotely determining the electrical conductivity of soil in vegetated lands. The method provides a Density Space Dielectric Model for Soil Electrical Conductivity (DSDM-SEC), wherein the DSDM-SEC is enabled by three-dimensional density space information obtained as Synthetic-Aperture Radar (SAR) data product in C-band frequency. The DSDM-SEC comprises the following steps: describing the available total soil moisture content (

) by investigating the scattering and penetration behavior of SAR signals of VV and VH polarizations with an interacting medium in two-dimensional scatterplot; determining soil moisture content (

) by decoupling the effect of vegetation moisture (

) from the

using DPSVI as a vegetation descriptor; providing an “Soil Textural Index (STI)” by mathematically investigating the water holding capacity of the soil in terms of available bound and free water in the soil; identifying models for dielectric constant of non-saline soil (

) and saline soil (

) from parameters such as field-observed dielectric constant measurements (

), STI, and

as three axial parameters in a three-dimensional density plot; providing an empirical Density Space Dielectric Model for Soil Electrical Conductivity and identifying Imaginary part of dielectric constant (

) the soil by scaling down the values of

from

.

According to one embodiment of the present invention, the empirical model provided to estimate the soil moisture further comprises the following: constructing a theoretical scatterplot to the shape of a triangle in which the possible positions of a plurality of features such as Water bodies (WB), Dense Vegetation (D-Veg), Sparse Vegetation (S-Veg), Moisture Soil (MS) and Bare Soil without moisture (BS) are located according the de-polarization characteristics of the SAR signal, and wherein the scattering behavior in the scatter plot was analyzed from VH polarization (y-axis) and VV polarization (x-axis); constructing the functional two-dimensional scatterplot between VH and VV polarization to cross verify the theoretical assumptions of the possible positions of the different features; and, representing the soil moisture content as a simulation of diagonal distance measure (from BS to WB and D-Veg) obtained from approximated triangle in the two-dimensional scatter plot.

According to one embodiment of the present invention, the empirical model “Soil Textural Index (STI)” is provided for mapping and representing the soil textural characteristics as a relative measure from varying moisture content according to sand and clay fraction in the soil under semi-saturated condition.

According to one embodiment of the present invention, identifying models for dielectric constant of non-saline soil (

) and saline soil (

) further includes: constructing a three-dimensional density space from inputs such as field-observed dielectric constant measurements (

), STI, and

as three axial parameters in which the z-axis (

) is considered as a conductivity domain, while the x- and y-axis (STI, and

) are considered as a polarization domain; examining the relationship of STI with

in representing dielectric constant in three-dimensional density space by keeping the values of the third dimension (

) as null, and wherein the model of 2-Dimensional Spatial Distance Measure (2D-SDM) is provided by simulating the gradient starting from the pixels having maximum

, and minimum STI values to the pixels distributed at the minimum range of

, and maximum range of STI to indicate the dielectric constant of the soil according to the polarization property; providing an elevation to the gradient of 2-Dimensional Spatial Distance Measure (2D-SDM) in the z-axis (

) by two different angles of optimization, namely optimization angle of saline soil (

) and non-saline soil (

) based on the mean values of maximum 2D-SDM and

of saline (

) and non-saline soils (

); and, identifying the 3D-SDM(NS) (3-Dimensional Spatial Distance Measure for Non-Saline soil) or

and 3D-SDM(S) (3-Dimensional Spatial Distance Measure for Saline soil) or

by applying an inverse cosine rule from dividing the resultant product of 2D-SDM by the cos of

and

to represent dielectric constant of non-saline soil (

) and saline soil (

).

According to one embodiment of the present invention, an empirical model DSDM-SEC (Density Space Dielectric Model for Soil Electrical Conductivity) is provided by subtracting the resultant products of dielectric constant of saline soil (

) from the corresponding product of non-saline soil (

).

According to one embodiment of the present invention, the model DSDM-SEC (Density Space Dielectric Model for Soil Electrical Conductivity) is dependent on soil textural characteristics under the semi-saturated condition.

According to one embodiment of the present invention, wherein the model DSDM-SEC (Density Space Dielectric Model for Soil Electrical Conductivity) is potential to quantify the soil Electrical Conductivity of the bare soils and soils covered with the crops of less than 0.5m height.

These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. It should be understood, however, that the following descriptions, while indicating the preferred embodiments and numerous specific details thereof, are given by way of illustration and not of limitation. Many changes and modifications may be made within the scope of the embodiments herein without departing from the spirit thereof, and the embodiments herein include all such modifications.

BRIEF DESCRIPTION OF THE DRAWINGS

The other objects, features and advantages will occur to those skilled in the art from the following description of the preferred embodiment and the accompanying drawings in which:

illustrates a system for identifying salination of soil by remotely determining the electrical conductivity of soil in vegetated lands, according to one embodiment of the present invention.

illustrates a calibrated backscattering module, according to one embodiment of the present invention.

illustrates a graphical plot that enables the determination of soil moisture content, according to one embodiment of the present invention.

illustrates a three-dimensional graphical plot that enables the determination of dielectric constant value of saline and non-saline soil, and dielectric loss according to one embodiment of the present invention.

illustrates an exemplary implementation of the present invention, according to one embodiment of the present invention.

Although the specific features of the present invention are shown in some drawings and not in others. This is done for convenience only as each feature may be combined with any or all of the other features in accordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

In the following detailed description, a reference is made to the accompanying drawings that form a part hereof, and in which the specific embodiments that may be practiced is shown by way of illustration. These embodiments are described in sufficient detail to enable those skilled in the art to practice the embodiments and it is to be understood that other changes may be made without departing from the scope of the embodiments. The following detailed description is therefore not to be taken in a limiting sense.

The various embodiments of the present invention provide a system and method for identifying salination of soil by remotely determining the electrical conductivity (EC) of soil in vegetated and bare lands. The embodiments also provide a system and method for identifying salination of soil by remotely determining the imaginary part of the dielectric constant of the soil through C-band Synthetic-Aperture Radar (SAR).

According to one embodiment of the present invention, an empirical simulation for identifying soil textural index (STI) is provided by investigating the scattering and penetration behavior of C-band SAR with the interacting surface features in the two-dimensional scatter plot. The spatial model to estimate the Electrical Conductivity of the soil from three-dimensional density space using C-band SAR has not been developed in any of the earlier inventions. In the present invention, the three-dimensional density space is constructed with field-observed dielectric constant measurements of non-saline (

) and saline soils (

), STI, and

as three axial parameters in which STI, and

represent the polarization domain and

and

represent conductivity domain. The 2D-SDM is given elevation in third-dimension by the angles of optimization to simulate 3D-SDM for saline and non-saline soil. Based on the promising partition found between the 3D-SDM (NS) or (

) and 3D-SDM (S) or (

) in the conductivity domain, the spatial simulation for the imaginary part of dielectric constant (

) is provided.

According to one embodiment of the present invention, a system is provided for identifying salination of soil by remotely determining the electrical conductivity (EC) of soil in vegetated and bare lands. The system comprises a Synthetic-Aperture Radar (SAR) electronics module, a Synthetic-Aperture Radar (SAR) antenna module and an application platform module. The SAR electronics module further comprises a C-band signal generator, a processor module, a digitizer module, a Ground Range Detected (GRD) module and a communication module. The SAR antenna module further comprises an antenna module, a transmitter module, an acquisition modes module, a receiver module and a waveguide radiators module. The application platform module provides an architecture for processing and analysis for Earth Observation, and further comprises a pre-processing module and a calibrated backscattering module. The application platform is operated in C-band frequency (5.36 GHz). The C-band signal is generated inside SAR electronic module and transmitted to the beam-forming network of SAR antenna module. The signal transmission and reception over the earth surface is carried out by SAR antenna module in four different modes such as Strip map, Interferometric Wide swath, Extra-wide swath and Wave mode. The signal transmission and reception of the SAR Antenna Module is controlled by waveguide radiators. i.e. the receiver module is turned off while transmission and transistor module is turn off while reception. The received signals in SAR Antenna Module in terms of phase and amplitude are transmitted again to SAR Electronic module to process like digitization, data compression and formatting. The process is resulted in the generation of various levels of data products among which the current invention concentrates on Ground Range Detected product (GRD) acquired in Interferometric Wide swath mode.

According to one embodiment of the present invention, an application platform module is provided. The calibrated backscattering products of VV and VH polarization are retrieved from GRD product (delivered in terms of amplitude and phase) by the sequence of steps performed in the application platform module. The steps include: pre-processing for radiometric calibration to retrieve sigma naught of VV and VH polarization; removing speckle from data products of VV and VH polarization; and, Range-Doppler terrain correction for VV and VH polarization to attain geographical orientation. The pixels influenced by effective scatters, and corner reflectors were identified and masked by terrain masking function to reduce the uncertainty in the modeling results due to the incoherent backscattering values induced by terrain variations. The effect of double bounce scattering in the backscattering values is eliminated in this step.

According to one embodiment of the present invention, an empirical model to arrive soil moisture content (

) is provided by investigating the scattering characteristics of the SAR signal in two-dimensional scatter plot. The empirical simulation is provided to retrieve “Soil Textural Index (STI)” by considering varying

as a crucial indicator. The semi-empirical simulations for

(dielectric constant of non-saline soil from SAR data) and

(dielectric constant of saline affected soil from SAR data) are invented by simulating the inputs such as field-observed dielectric constant measurements (

), STI, and

as three axial parameters in a three-dimensional density plot. Based on the promising partition observed between the trends of

and

in third dimension, the model

is invented to arrive

by scaling down the values of the product

from the corresponding values of

.

According to one embodiment of the present invention, soil moisture content is identified. The

and

stands for the optimized backscattering products of VV and VH polarization for incidence angle variations. The unique nature of the different surface features to influence the scattering and penetration characteristics of the SAR signal is examined by generating the scatterplot between

and

. The theoretical scatterplot (Fig. 3a) is approximated to the shape of a triangle in which the possible positions of the different features like Water bodies (WB), Dense Vegetation (D-Veg), Sparse Vegetation (S-Veg), Moisture Soil (MS) and Bare Soil without moisture (BS) are located according to the de-polarization and penetration characteristics of an illuminated SAR signal.

The pixels representing water bodies attain lower values in both the polarizations as the water surfaces induce additional forward scattering and thus reduces the backscattering return of the SAR signals. The ability of sandy soil to allow free water movement in it, often resulted in the semi-forward scattering, because of which the pixels representing semi-saturated sandy soil attains low

values next to water bodies. The values of

for the clay soil is expected to be slightly higher than sandy soil because under semi-saturated (53% moisture content) the water in the clay soil available in the form of bound water which allows the signal to attain penetration and less scattering return. The electromagnetic wave experiences scattering while interacting with bound water.

Since the depolarization rate of loose and dry-bare soil is very minimal, the pixels of bare soil surface would be expected to attain high values in VV polarization and low values in VH polarization because of which they should have been scattered at the lower right corner of the theoretical triangle (Fig. 3a). The pixels representing dense vegetation are identified in the upper left portion of the approximated triangle (Periasamy, 2018) as the rate of depolarization of an illuminated signal is more significant in densely arranged vegetation and the sensitivity of the VV polarization towards its high moisture content.

The Diagonal Measure (DM) (represented in grey line in Fig. 3a & 3b) emanating from the point z to the edge xy in the scatterplot was considered as a first derivative to be modeled to derive the soil moisture content. The point where the DM originates represents the bare soil (higher values of

and lower values of

) and the edge where the DM ends indicates water bodies and moisture-rich dense vegetation (lower values of

and higher values of

). Hence the highest value of DM indicates moisture-rich dense vegetation and water bodies, next to which the saturated sandy and clay soil were found. The lower values of DM represent bare soils with less moisture or no moisture, and when the distance of DM increases towards the lower-left portion of the scatterplot, it starts showing the increment in the moisture content. The DM generally gets saturated at its upper limit or pixels belong water bodies and dense vegetation. An empirical model was developed for DM in the scatterplot based on the Pythagoras theorem (Equation 1) to exemplify the total moisture content (

).

(1)

(2)
where

=

* DPSVI
The soil moisture content (

) is estimated by decoupling the effect of vegetation moisture (

) from the

using DPSVI as a vegetation descriptor (Equation 2).

According to one embodiment of the present invention, a method for identifying soil textural index (STI) is provided. The empirical simulation is proposed to retrieve the target parameter “Soil Textural Index ( STI)” by considering varying

as a crucial indicator. The simulation is done based on the assumption that the surface under investigation has the constant roughness value in the semi-saturated state, and the influence of the system parameter wavelength (

) is constant and the incidence angle is controlled by adopting optimization procedure. Hence, the significance of

in representing the soil textural properties is mathematically analyzed and the predicted relationship is shown in equation 3. The equation for STI was proposed in such a way to extract the normalized product ranging from 0 to 1 (Shafian and Maas, 2015) .

(3)
Where the

stands for the value of soil textural index,

is soil moisture value in the i ^th pixel of the corresponding imagery,

and

stands for the maximum and minimum value of soil moisture.
Three-dimensional Density space for Dielectric Constant

Semi-empirical models are provided to estimate

(dielectric constant of non-saline soil from SAR data) and

(dielectric constant of saline affected soil from SAR data) by simulating the inputs such as field-observed dielectric constant measurements (

), STI, and

as three axial parameters in a three-dimensional density plot. As mentioned earlier, with the average field readings on rms-height (0.27 cm) and correlation length (3.1 cm), the surface under investigation has been assumed to be smooth in a semi-saturated condition. Initially, the relationship of STI with

in representing dielectric constant has been examined in three-dimensional density space by keeping the values of the third dimension (

) as null (Fig. 4a). Based on the negative relationship that the pixels of STI have followed with corresponding pixels of

in representing dielectric constant, the model of 2-Dimensional Spatial Distance Measure ( 2D-SDM) is invented by simulating the gradient starting from the pixels having maximum

, and minimum STI values to the pixels distributed at the minimum range of

, and maximum range of STI (Fig. 4a). The equation for the gradient of 2D-SDM is developed in such a way that it increases with an increasing dielectric constant in the soil (Equation 4). The two-dimensional spatial distance measure could be considered as a preliminary theoretical simulation to indicate the dielectric constant of the soil according to the relationship of polarization property of a signal with the grain-size distribution of the soil.

(4)

In three-dimensional feature space, the products of STI, and

make up the x-axis and y-axis, and the corresponding values of

are taken up in the z-axis, respectively. From the feature space, the z-axis is considered as a conductivity domain, while the x- and y-axis are considered as a polarization domain. The

is shown an increasing trend in two different patterns when the property of conductivity is introduced in third-dimension of the existing polarization domain (Fig. 4b) representing the behavior of saline and non-saline soils.

To simulate the model for

(dielectric constant of non-saline soil from SAR data) and

(dielectric constant of saline soil from SAR data), the gradient of 2-dimensional spatial distance is given elevation in the z-axis (

) and non-saline soil (

). The pixels representing non-saline soil are decoupled from saline soil and represented in a separate three-dimensional density space with the corresponding STI and

values. The outliers are excluded from the sampling points, and the shape of the three-dimensional density space for non-saline soil is approximated to a shape of a right angle triangle in which pixels oriented along the line indicating 3-Dimensional Spatial Distance Measure (

) are considered as hypotenuse,

are considered as an adjacent side, and

are considered as an opposite side to

. Because of the constant of proportionality found between the pixels distributed along

, and

there is no much variation observed in

throughout its trend during the upliftment of the pixels from two-dimension to three-dimension. By analyzing the constant rate of the gradient from

to

,

is computed by taking the inverse tangent between the mean of maximum dielectric constant (

readings recorded in the field for non-saline soils and the maximum computed value of the two-dimensional spatial measure (

).

Once the angle of optimization is determined, a model for

(3-Dimensional Spatial Distance Measure for Non-Saline soil) is proposed by applying an inverse cosine rule from dividing the resultant product of

by the cos of

(Equ 5). The model

is proposed in such a way that it starts increasing the dielectric values of the non-saline pixels distributed in the SAR imagery as the three-dimensional spatial distance measure increases. The same procedure is employed for the pixels recorded as saline affected to find out the

, and to retrieve the

(3-Dimensional Spatial Distance Measure for Saline soil) (Fig 4d) (Equ 6 & 6a). The modeled products

and

are resulted in the imagery representing

and

.

(5)

(6)
NS in the equation stands for non-saline soil, and S stands for saline soil. An expanded form of

equations

5 and 6 is given in 5a and 6a.

(5a)

(6a)
Though the equations 5a and 6a seem to be the same, the model arrives different dielectric values for saline and non-saline soils due to the change in the angles of optimization

and

computed from the 3-dimensional density plot. This is due to the variations of the dielectric constant measurements for saline and non-saline soils recorded in the field. The mean value of

is 37 for non-saline soils, and 29 for saline affected soils. Note that

mentioned in the equations is an optimized product

.
DSDM-SEC (Density Space Dielectric Model for Soil Electrical Conductivity)

The

is recorded for both saline and non-saline soils, and when plotted those points against STI and

in the density space, we recorded two different sets of pixels alignment in the third dimension (Fig 4b). This demonstrated that the difference in the orientation was due to dielectric loss that occurred in saline soil. The simulated products

and

have shown promising partition in the third dimension that leads to the simple but effective way of extracting soil EC. With the two different paradigm shift observed for saline and non-saline soils in 3-dimensional density space, the model for extracting the imaginary of part of dielectric constant (

) is invented by scaling down the values of

(simulated from

) from the corresponding values of

(simulated from

) (Fig. 4b).

Since the model for

(Equ 6a) is simulated based on the dielectric loss due to ionic conductivity of samples and polarization behavior of SAR (Fig 4b), the resultant imagery could able to describe the relative permittivity (

). Though the model for

is simulated by investigating the samples of non-saline soils in polarization and conductivity domain of a three-dimensional scatter plot, the resultant product is influenced only by the presence of moisture content (polarization) but not by loss factor and hence is considered as an effective indicator of the real part of dielectric constant (

). In this study, we proposed a model DSDM-SEC (Density Space Dielectric Model for Soil Electrical Conductivity) for retrieving

based on the conception that the simulated dielectric constant of saline soil is relatively lesser than that of the non-saline soil and the potential difference of which could yield the dielectric loss factor (Fig. 4b). The model DSDM-SEC is simulated to arrive

by scaling down the product of

from

(Equ 7). The resultant product of

could act as an effective indicator of soil EC.

(7)
The proposed equation 7 is simple but effective method to retrieve an imaginary part of dielectric constant from the SAR imagery.

illustrates a system for identifying salination of soil by remotely determining the electrical conductivity of soil in vegetated and bare lands. The system comprises a Synthetic-Aperture Radar (SAR) electronics module 101, a Synthetic-Aperture Radar (SAR) antenna module 102 and an application platform module 103. The SAR electronics module further comprises a C-band signal generator 101a, a processor module 101b, a digitizer module 101c, a Ground Range Detected (GRD) module 101d and a communication module 101e. The SAR antenna module further comprises an antenna module 102a, a transmitter module 102b, an acquisition modes module102e, a receiver module 102c and a waveguide radiators module 102d. The application platform module provides an architecture for processing and analysis for Earth Observation, and further comprises a pre-processing module 104 and a calibrated backscattering module 105.

illustrates a calibrated backscattering module. The calibrated backscattering module further comprises a soil moisture information database 105a, soil textural index database 105b, a Spatial Simulation module 105c for dielectric constant of non-saline soils, a Spatial Simulation module 105d for dielectric constant of saline soils and a Spatial simulation module 105e of Density Space Dielectric Model for Soil Electrical Conductivity.

illustrates a graphical plot that enables the determination of soil moisture content.

illustrates a three-dimensional graphical plot that enables the determination of dielectric constant value for saline and non-saline soil and dielectric loss.

FIG. 5 illustrates an exemplary implementation of the present invention. The results of SAR derived

have shown optimum agreement with ground measurements (exclusive of water bodies) with a correlation coefficient equal to 0.86 and with the least RMSE (0.04) and Bias (0.005) values. When the values of SAR derived STI were plotted against the corresponding values of soil samples, we found that STI values were positively correlated (R ²=0.779) with the soil samples composed with a high percentage of sand like sandy soil, and sandy loam categories. The negative correlation has been recorded between the STI values and the soils with a relatively high percentage of clay fraction (R ²=-0.779). The statistical results showed that the

and

have maintained optimum correlation with the corresponding samples assigned for training and validation (R ²(

) = 0.839, 0.702; R ²(

) = 0.858, 0.707). The statistical significance of

in demonstrating soil EC values was examined for different soil categories (Fig 5). The statistical bias in the relationship has been found decreasing as the percentage of free water increases in the soil. Among four soil categories under investigation, sandy soil has attained good correlation (R2=0.883), least RMSE (0.618), and biased value (-0.112) with ground measurements. The mean values of the correlation coefficient between soil EC values and

were found to be higher (>R ²=0.7) with acceptable variance (

=0.005) for the soil patches uncovered with vegetation. The accuracy level remains approximately the same for the soil covered with the crops of less than 0.5m height.

Although the embodiments herein are described with various specific embodiments, it will be obvious for a person skilled in the art to practice the embodiments herein with modifications.

ADVANTAGES OF THE INVENTION

The various embodiments of the present invention provide a system and method for identifying salination of soil by remotely determining the electrical conductivity (EC) of soil in vegetated and bare lands. The embodiments also provide a system and method for identifying salination of soil by remotely determining the imaginary part of the dielectric constant of the soil through C-band Synthetic-Aperture Radar (SAR). Unlike multiple regression analysis, the present invention yields better results irrespective of the variations in the distribution of the samples. Hence, the invention is employed to retrieve the soil EC with optimum accuracy level even when the field samples are not equally distributed in all ranges of values. The implementation of the present invention does not depend on the different types of field samples and the corresponding spatial data products, which makes it more convenient to employ in soil salinity studies. The present invention enables quantifying the soil Electrical Conductivity of the bare soils and the soils covered with crops of less than 0.5m height. The invention finds application in research groups working on agriculture related use-cases as the resultant product of the model is essential to carry out appropriate agricultural processes according to actual salinity level in the soil.

The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such as specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments.

It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modifications. However, all such modifications are deemed to be within the scope of the claims.

Claims

A system for identifying salination of soil by remotely determining the electrical conductivity of soil in vegetated lands, the system comprising:
a Synthetic-Aperture Radar (SAR) electronics module, wherein the SAR electronics module further comprises a C-band signal generator, a processor module, a digitizer module, a Ground Range Detected (GRD) module and a communication module;
a Synthetic-Aperture Radar (SAR) antenna module, wherein the SAR antenna module further comprises an antenna module, a transmitter module, an acquisition modes module, a receiver module and a waveguide radiators module; and,
an application platform module, wherein the application platform module provides an architecture for processing and analysis for Earth Observation, and wherein the application platform module further comprises a pre-processing module and a calibrated backscattering module.
The system according to claim 1, wherein the calibrated backscattering module further comprises a soil moisture information database, soil textural index database, a Spatial Simulation module for dielectric constant of non-saline soils, a Spatial Simulation module for dielectric constant of saline soils and a Spatial simulation module of Density Space Dielectric Model for Soil Electrical Conductivity.
The system according to claim 1, wherein the communication module is configured to communicatively couple with a plurality of remote computing devices to determine the type of activities that are suitable on a piece of land based on preset data and observed and/or simulated data from the system, and wherein the communication module is also configured to render the observations and simulated data on a plurality of computing devices in user-intelligible form.
A method for identifying salination of soil by remotely determining the electrical conductivity of soil in vegetated lands, wherein the method provides a Density Space Dielectric Model for Soil Electrical Conductivity (DSDM-SEC), wherein the DSDM-SEC is enabled by three-dimensional density space information obtained as Synthetic-Aperture Radar (SAR) data product in C-band frequency, the DSDM-SEC comprises:
describing the available total moisture content (
) by investigating the scattering and penetration behavior of SAR signals of VV and VH polarizations with an interacting medium in two-dimensional scatterplot;
determining soil moisture content (
) by decoupling the effect of vegetation moisture (
) from the
using DPSVI as a vegetation descriptor;
providing an “Soil Textural Index (STI)” by mathematically investigating the water holding capacity of the soil in terms of available bound and free water in the soil;
identifying models for dielectric constant of non-saline soil (
) and saline soil (
) from parameters such as field-observed dielectric constant measurements (
), STI, and
as three axial parameters in a three-dimensional density plot; and,
providing an empirical Density Space Dielectric Model for Soil Electrical Conductivity and identifying Imaginary part of dielectric constant (
) the soil by scaling down the values of
from
;
The method according to claim 4, wherein the empirical model to estimate the soil moisture further comprises:
constructing a theoretical scatterplot to the shape of a triangle in which the possible positions of a plurality of features such as Water bodies (WB), Dense Vegetation (D-Veg), Sparse Vegetation (S-Veg), Moisture Soil (MS) and Bare Soil without moisture (BS) are located according the de-polarization characteristics of the SAR signal, and wherein the scattering behavior in the scatter plot was analyzed from VH polarization (y-axis) and VV polarization (x-axis);
constructing the functional two-dimensional scatterplot between VH and VV polarization to cross verify the theoretical assumptions of the possible positions of the different features; and,
representing the total moisture content as a simulation of diagonal distance measure (from BS to WB and D-Veg) obtained from the approximated triangle in the two-dimensional scatter plot.
representing the soil moisture content by reducing the influence of vegetation moisture from total moisture values by considering DPSVI as a vegetative descriptor.
The method according to claim 4, wherein the empirical model “Soil Textural Index (STI)”is provided for mapping and representing the soil textural characteristics as a relative measure from varying moisture content according to sand and clay fraction in the soil under semi-saturated condition.
The method according to claim 4, wherein identifying models for dielectric constant of non-saline soil (
) and saline soil (
) further includes:
constructing a three-dimensional density space from inputs such as field-observed dielectric constant measurements (
), STI, and
as three axial parameters in which the z-axis (
) is considered as a conductivity domain, while the x- and y-axis (STI, and
) are considered as a polarization domain;
examining the relationship of STI with
in representing dielectric constant in three-dimensional density space by keeping the values of the third dimension (
) as null, and wherein the model of 2-Dimensional Spatial Distance Measure (2D-SDM) is provided by simulating the gradient starting from the pixels having maximum
, and minimum STI values to the pixels distributed at the minimum range of
, and maximum range of STI to indicate the dielectric constant of the soil according to the polarization property;
providing an elevation to the gradient of 2-Dimensional Spatial Distance Measure (2D-SDM) in the z-axis (
) by two different angles of optimization, namely optimization angle of saline soil (
) and non-saline soil (
) based on the mean values of maximum 2D-SDM and
of saline (
) and non-saline soils (
); and,
identifying the 3D-SDM(NS) (3-Dimensional Spatial Distance Measure for Non-Saline soil) or
and 3D-SDM(S) (3-Dimensional Spatial Distance Measure for Saline soil) or
by applying an inverse cosine rule from dividing the resultant product of 2D-SDM by the cos of
and
to represent dielectric constant of non-saline soil (
) and saline soil (
).
The method according to claim 4, wherein an empirical model DSDM-SEC (Density Space Dielectric Model for Soil Electrical Conductivity) is provided by subtracting the resultant products of dielectric constant of saline soil (
) from the corresponding product of non-saline soil (
).
The method according to claim 4, wherein the model DSDM-SEC (Density Space Dielectric Model for Soil Electrical Conductivity) is dependent on soil textural characteristics under the semi-saturated condition.
The method according to claim 4, wherein the model DSDM-SEC (Density Space Dielectric Model for Soil Electrical Conductivity) is potential to quantify the soil Electrical Conductivity of the bare soils and soils covered with the crops of less than 0.5m height.