Description:FIELD OF THE INVENTION
This invention relates to Quantification of Clay and Sand Fractions from Synthetic Aperture Radar Data
BACKGROUND OF THE INVENTION
Previous studies primarily focused on estimating soil moisture using SAR data, with only limited research addressing the estimation of soil textural indices. Unlike those, the present study advances this field by directly estimating sandy and clay fractions using dual-polarimetric SAR data, enabling more detailed and accurate characterization of soil texture.
Optical remote sensing techniques (e.g., using Landsat, Sentinel-2, MODIS) are heavily dependent on surface visibility, which is often obstructed by cloud cover, dense vegetation, or atmospheric conditions. As a result, accurate soil texture mapping is seasonally limited and unreliable in tropical or monsoon climates. The invention utilizes C-band dual-polarimetric Synthetic Aperture Radar (SAR) data (e.g., Sentinel-1), which operates in the microwave domain and can penetrate cloud cover and partially through vegetation. This enables year-round, all-weather monitoring of soil texture, regardless of surface obstructions.
Earlier studies are available to assess soil textural index using optical remote sensing data and is difficult to investigate soil condition during dense vegetation condition.
A microwave remote sensing related studies focused on estimating soil moisture using SAR data, with only limited research addressing the estimation of soil textural indices.
No studies are available in microwave remote sensing techniques till date to estimate Clay and Sand fraction based on polarization techniques. This method is advantageous since it is capable of predicting Clay and Sand fraction in unfavourable environmental conditions and for large-scale study.
This invention is best described as a new process and analytical system, with the potential for integration into software platforms used for geospatial analysis. It is not a physical device but rather a computational framework and methodology combining:
• Image processing algorithms
• Soil texture estimation models
• Environmental model
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
Clay soil, with its tiny particles and high porosity, holds more moisture than sandy soil. Water's presence raises its dielectric constant, which causes significant backscatter in VV-polarized C-band transmissions from improved surface reflection. Wet clay also shows moderate cross-polarized backscatter since water retention causes some level of volume scattering inside the soil matrix. Clay soils can keep leftover moisture in their microstructures, so even when dry they still show a considerable backscatter reaction. By contrast, sandy soil, defined by coarse particles and low moisture retention, interacts differently with C-band SAR signals. Dry sandy soil generates very weak backscatter in both VV and VH polarisations since it lacks enough moisture to improve radar reflectivity and, therefore lower dielectric constant. But, when sand gets wet, backscatter rises; still, because of the fast water drainage and low volume scattering impacts, it stays lower than that of wet clay. Considering the textural behavior with soil moisture dual polarimetric soil textural classification index proposed to estimate clay and sand fraction (Eq.4).
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Clay soil, with its tiny particles and high porosity, holds more moisture than sandy soil. Water's presence raises its dielectric constant, which causes significant backscatter in VV-polarized C-band transmissions from improved surface reflection. Wet clay also shows moderate cross-polarized backscatter since water retention causes some level of volume scattering inside the soil matrix. Clay soils can keep leftover moisture in their microstructures, so even when dry they still show a considerable backscatter reaction. By contrast, sandy soil, defined by coarse particles and low moisture retention, interacts differently with C-band SAR signals. Dry sandy soil generates very weak backscatter in both VV and VH polarisations since it lacks enough moisture to improve radar reflectivity and, therefore lower dielectric constant. But, when sand gets wet, backscatter rises; still, because of the fast water drainage and low volume scattering impacts, it stays lower than that of wet clay. Considering the textural behavior with soil moisture dual polarimetric soil textural classification index proposed to estimate clay and sand fraction (Eq.4).
The clay content (Ci) was computed using the STI value, where a higher STI indicates higher clay content due to the soil’s greater ability to retain moisture. The (Eq.5) adjusts for clay content by multiplying STI by 100, dividing by 2, and adding a scaled maximum clay content term of 100. This ensures that the estimated clay content aligns with the known physical properties of clay soils. Based on the presumption that soil is made mostly of clay and sand, sandy soil content (Si) was calculated by deducting the projected clay content from 100% (Eq.6). A high STI number indicates more clay and less sand; a low STI value indicates less clay and more sand. This mathematical model effectively distinguishes between clay and sandy soils, helping to improve soil moisture estimation, irrigation planning, and crop management in varying soil environments.
DPSTCI = f (C_i,S_i ) (4)
Clay content (C_i) = ((〖STI〗_i*100)/2)+ (C_max/10) (5)
Sand content (S_i) = 100 - C_i (6)
Where,
Cmax is the maximum clay content i.e. Cmax – 100, and STIi is the soil textural index of ith pixel.
The model performed well on both clay and sandy soils in 2017. For clay, the correlation coefficient (r) was 0.89 and for sand it was 0.84, suggesting a high positive link between the simulated and field-based results. Relatively high R² values—0.8 for clay and 0.7 for sand—indicated that the model accounted for a notable amount of data variance. The RMSE and MAE were also lower than in previous years, indicating the model's precise forecasts. The sandy soil, however, had somewhat higher error values (RMSE: 7.32, MAE: 5.92) than clay (RMSE: 6.26, MAE: 5.12), suggesting superior performance in clay soil estimate.
The model showed the greatest prediction errors across both soil types moving to 2019. Although the correlation coefficients stayed high (0.9 for clay and 0.85 for sand), the RMSE values rose dramatically (10.26 for clay and 12.8 for sand), indicating a drop in prediction accuracy. The MAE values were also at their highest (9.05 for clay and 10.9 for sand), implying that the model regularly overestimated values. The high errors imply that agricultural practices influenced model performance this year, even if the R² values stayed consistent (0.82 for clay and 0.72 for sand), suggesting that the model still accounted for a fair proportion of the variance.
The model's performance in sandy soil had increased by 2021; in clay soil, it had somewhat worsened. The correlation coefficient in clay fell to 0.75 and the R² value to 0.71, indicating the model accounted for less variation in the data. Suggesting moderate accuracy, the error values remained very high (RMSE: 9.21, MAE: 7.41). On the other hand, the sandy soil performance got better as the R² value rose to 0.82, suggesting a better match. Though the Dual Polarimetric Soil Textural Classification Index (DPSCTI) caught more variation, the absolute prediction errors were still notable as shown by the high RMSE and MAE values (11.2 and 10.1, respectively).
The year 2023 saw a notable increase as both soil kinds exhibited the greatest general performance. The correlation coefficient reached its greatest point 0.92 for clay and 0.85 for sand showing a very good link between DPSCTI and actual values. The R² values were similarly high (0.86 for clay and 0.73 for sand), indicating that the model accounted for a significant part of the variation in soil properties. The error numbers also significantly decreased; for clay, they were 9.53 and for sand 7.86; for MAE they were 8.31 and 6.28, respectively. This implies that the model predicted more accurately, particularly in sandy soil, where the error values were the lowest observed throughout all years.
The model kept performing well in 2025, with a high correlation coefficient of 0.92 for clay and 0.86 for sand. Strong R² values (0.84 for clay and 0.75 for sand) confirmed that the model properly accounted for data variation. Furthermore, the error numbers were lowest relative to prior years, with RMSE values of 8.7 for clay and 7.68 for sand, and MAE values of 7.27 and 6.12, respectively. These numbers show that the model attained its maximum accuracy in 2025, thereby being the most dependable year for estimating soil texture. Figure 3 shows statistical validation of clay and sand content utilizing reference values derived from the wet sieving method, spanning the period from 2017 to 2025 at 2-year intervals.
NOVELTY:
A novel Soil Textural Classification Index (STI) derived from dual-polarimetric SAR backscatter data to estimate the relative clay and sand content in soil with high spatial accuracy.
A method of computing clay content (Cᵢ) from STI using the equation:
Cᵢ = ((STIᵢ × 100) / 2) + (Cₘₐₓ / 10)
where Cₘₐₓ = 100, enabling the mapping of moisture-retentive, fine-grained clay soils based on their microwave scattering behavior.
A derived method for computing sand content (Sᵢ) by subtracting the estimated clay content from 100%:
Sᵢ = 100 – Cᵢ
providing a complete dual-fraction analysis of soil texture using only SAR data.
An application of VV and VH polarizations in C-band SAR to distinguish soil texture types by leveraging their distinct backscatter responses due to moisture retention and dielectric behaviour.
, Claims:1. A method for quantifying clay and sand fractions in soil using Synthetic Aperture Radar (SAR) data, comprising the steps of:
(a) receiving dual-polarized C-band SAR data of the soil surface;
(b) calculating a Soil Textural Index (STI) based on backscatter characteristics in VV and VH polarisations; and
(c) estimating clay content (Ci) by applying a mathematical expression to the STI value, wherein a higher STI corresponds to higher clay content.
2. The method as claimed in claim 1, wherein the clay content (Ci) is computed by multiplying the STI by 100, dividing by 2, and adding a maximum clay content adjustment term of 100, thereby aligning with the physical moisture-retention behavior of clay soils.
3. The method as claimed in claim 1, wherein sandy soil content (Si) is derived by subtracting the estimated clay content (Ci) from 100%, based on the assumption that the soil is primarily composed of clay and sand.
4. The method as claimed in claim 1, wherein the STI value is derived from the differential backscatter behavior of clay and sandy soils in response to moisture content, with wet clay exhibiting higher backscatter due to improved surface reflection and volume scattering, and dry sand exhibiting low backscatter due to poor moisture retention.
5. The method as claimed in claim 1, wherein the system comprises a data processing unit adapted to analyze C-band SAR data, compute the STI, and estimate clay and sand content for the purpose of soil classification, irrigation planning, and crop management.