Description:FIELD OF THE INVENTION
This invention relates to Dual Polarimetric Radar Vegetation Index at C band to Estimate Vegetation Biomass Using Sentinel-1 SAR Data over Agricultural Patches
BACKGROUND OF THE INVENTION
Few studies are available on crop biomass in agricultural patches. But, earlier studies were conducted to predict the presence of biomass which is limited to saturated at higher biomass as well as to the dense vegetation.
The presence of soil and water in the agricultural field leads to misclassifying the pixel information in spatial data which reduces the accuracy and applicability of the model.
Existing studies are available using remote sensing techniques to date to estimate vegetation biomass and are limited to higher and denser vegetation. This method is advantageous since it is capable of estimating higher vegetation biomass and applies to large-scale studies.
Previous solutions are limited by their complexity and are primarily effective in estimating vegetation biomass only under sparse or dense conditions, yielding higher accuracy mainly when crop growth is optimal.
Known products:
A DPSVI index to estimate plant biomass based on depolarization theory from SAR-intensity products. This model is derived from the central theory of "soil vegetation edge" in 2-dimensional scattering constructed between the backscattering coefficients of VV and VH polarizations.
The DpRVI model is based on the eigenvalue (λ/span) spectrum and degree of depolarization. This index effectively investigates the plant growth dynamics for crops such as canola, soybean, and wheat.
Commercial Practice:
a) In spatial applications, the proposed approach is useful for image analysis with which the researchers will able to study crop biomass in the agricultural field.
b) It examines crop conditions to provide suitable remedial measures that enhance crop output through the optimisation of resource utilisation (fertilisers, water), hence indirectly bolstering the food security system.
c) Ecosystem health is indicated by estimations of biomass. Dense vegetation in healthy environments usually supports a greater number of species; lower biomass in degraded areas may indicate a decline in biodiversity.
d) Because biomass varies over time, scientists can track how well restoration initiatives are doing to restore degraded land.
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.
The backscattering behaviour of water, soil, and vegetation in the triangulation occurs at the hypotenuse (Grigorieva, 2013), particularly for vegetation which begins to increase as the backscattering values increase. Wherein the DPSVI model the tangent is inversely proportional to the high backscattering value between cross and co polarization, this results in an underestimation of vegetation backscatter levels at higher biomass and dense vegetation. To overcome the problem of saturation, the 2D scatterplot construct between the backscattering of varying target features and the lower left margin is occupied by the water content and starts to increase (σ_vv^soil-0.1) to the right side and the contribution of the soil starts to increase and their maximum value is (σ_vv^soil-2.9). Also, in the vertical faces of a triangle, the impact of water gets reduced with an increase of cross-polarization from (σ_vh^water- 0.025) and the contribution of vegetation to be the top margin of the triangle with maximum values of (σ_vh^veg- 0.225). Accordingly, the vertical line represents the vegetation and water behaviour, the bottom horizontal line represents the water and soil behaviour and the connecting lines between the two above lines form the soil vegetation interaction edge which provides the biomass value in the agricultural field.
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.
The backscattering behaviour of water, soil, and vegetation in the triangulation occurs at the hypotenuse (Grigorieva, 2013), particularly for vegetation which begins to increase as the backscattering values increase. Wherein the DPSVI model the tangent is inversely proportional to the high backscattering value between cross and co polarization, this results in an underestimation of vegetation backscatter levels at higher biomass and dense vegetation. To overcome the problem of saturation, the 2D scatterplot construct between the backscattering of varying target features and the lower left margin is occupied by the water content and starts to increase (σ_vv^soil-0.1) to the right side and the contribution of the soil starts to increase and their maximum value is (σ_vv^soil-2.9). Also, in the vertical faces of a triangle, the impact of water gets reduced with an increase of cross-polarization from (σ_vh^water- 0.025) and the contribution of vegetation to be the top margin of the triangle with maximum values of (σ_vh^veg- 0.225). Accordingly, the vertical line represents the vegetation and water behaviour, the bottom horizontal line represents the water and soil behaviour and the connecting lines between the two above lines form the soil vegetation interaction edge which provides the biomass value in the agricultural field.
Considering the effect of water, soil and vegetation accounts together in DPSVI leads to decrease a precision of biomass estimation. According to the backscattering study, the major factors to cause errors in DpRVI (Mandal et al., 2020), DPSVI (Periasamy, 2018) and ISVI (Krishnan et al., 2023) are soil and water. Comparing the effect of water and soil based on the behavioural study, the water has minimal value (σ_vv^water- 0.004) and the soil has a high impact resulting in higher values (σ_vv^soil-0.02) of the co-polarized signal. In cross-polarization, the water has minimal values (σ_vh^water- 0.06) and the soil also has lower values (σ_vh^soil- 0.09). Fig.1 shows the distribution of the different targeted features in a 2D scatterplot constructed between the σ_vh^i and the σ_vv^i.
Backscatter Vegetation Approach
The responses of σ_vh^ and σ_vv^ backscatter co-efficient differs depending on the target features such as water, soil, and vegetation namely density and geometry. A σ_vh^ backscatter co-efficient is sensitive to complex scattering mechanisms caused by multiple reflections within vegetation leaves, and branches, resulting in increased volume scattering at A (Fig. 2a). High σ_vh^ backscatter co-efficient indicates significant depolarization, typically observed in dense vegetation at the mature stage, while low σ_vh^ backscatter co-efficient at B suggests less depolarization, commonly associated with sparse vegetation, and areas with low biomass. Considering the σ_vh^ responses to differentiating crops and their parameters can be challenging in terms of statistical deficiency. In such cases, σ_vv^ backscatter co-efficient can capture density parameters and consider double-bounce effects, especially in environments with vertical structures like stems or trunks which is helpful in avoiding the problem of crops with uniform structures that exhibit distinct polarization signatures depending on row orientation and growth stage. A behavior of the σ_vv^ backscatter co-efficient is moderate and arises from surface scattering at B and interactions with vertical structures, whereas increased σ_vv^ backscatter co-efficient results from volumetric scattering within the canopy and double-bounce effects between trunks and the ground at C. Additionally advantageous of σ_vv^ backscatter co-efficient is influenced by moisture content, which varies with crop growth stages to improve the model accuracy. For low biomass, all polarizations, including σ_vh^ and σ_vv^ , exhibit weaker backscatter. In forests, tall trees with dense foliage cause higher σ_vh^ backscatter due to pronounced volumetric scattering. Also, the responses influenced by the agricultural managemental practices and soil conditions show similar responses to varying conditioned crops. To overcome the problem in effective vegetation response identification, (Fig. 2b) the density and geometry can be combined into a single vector plane to optimize reactions to the SAR backscattering coefficient (Eq. 1).
The responses of σ_vh^ and σ_vv^ backscatter co-efficient differs depending on the target features such as water, soil, and vegetation namely density and geometry. A σ_vh^ backscatter co-efficient is sensitive to complex scattering mechanisms caused by multiple reflections within vegetation leaves, and branches, resulting in increased volume scattering at A (Fig. 2a). High σ_vh^ backscatter co-efficient indicates significant depolarization, typically observed in dense vegetation at the mature stage, while low σ_vh^ backscatter co-efficient at B suggests less depolarization, commonly associated with sparse vegetation, and areas with low biomass.
Considering the σ_vh^ responses to differentiating crops and their parameters can be challenging in terms of statistical deficiency. In such cases, σ_vv^ backscatter co-efficient can capture density parameters and consider double-bounce effects, especially in environments with vertical structures like stems or trunks which is helpful in avoiding the problem of crops with uniform structures that exhibit distinct polarization signatures depending on row orientation and growth stage. A behavior of the σ_vv^ backscatter co-efficient is moderate and arises from surface scattering at B and interactions with vertical structures, whereas increased σ_vv^ backscatter co-efficient results from volumetric scattering within the canopy and double-bounce effects between trunks and the ground at C. Additionally advantageous of σ_vv^ backscatter co-efficient is influenced by moisture content, which varies with crop growth stages to improve the model accuracy. For low biomass, all polarizations, including σ_vh^ and σ_vv^ , exhibit weaker backscatter. In forests, tall trees with dense foliage cause higher σ_vh^ backscatter due to pronounced volumetric scattering. Also, the responses influenced by the agricultural managemental practices and soil conditions show similar responses to varying conditioned crops. To overcome the problem in effective vegetation response identification, (Fig. 2b) the density and geometry can be combined into a single vector plane to optimize reactions to the SAR backscattering coefficient (Eq. 1).
σ_veg^tot= σ_vv^i+ σ_vh^i (1)
Dual Polarimetric Vegetation Modeling
A response of the water content in the proposed empirical modeling origin at point B differs significantly due to the distinct scattering mechanisms and is to be 0 due to very less depolarization of the SAR signal. After that, the response is increased with an increase of the vegetation structure (Eq. 2) interms of geometry and density contributes to medium to higher depolarization from B’ to A (0.1 to 1). Point A is the maximum response of the DPBVI to represent a forest cover and the urban structures observed greater than 1 (Eq. 3). For the proposed theorem, the responses are sum into a single plane, the value of the AB = BC and is equal to 〖(σ_vh^ +σ_vv^ )〗^
Dual Polarimetric Vegetation Edge (DPVE) = 2 〖(σ_vh^ +σ_vv^ )〗^2 (2) Polarimetric Backscatter Vegetation Index = √2(〖σ_vh^ +σ_vv^ )〗^ (3)
For dual-polarization scenarios, the SAR backscattering responses account for the combined effects of signal emission and reception. These formulations provide a robust framework for analyzing vegetation characteristics using SAR data in semi-arid regions (Eq. 4). From the dual polarimetric backscatter vegetation index (DPBVI) at the C band, the lower biomass represents 0 and the higher biomass represents 1 as illustrated in Fig. 3.
DPBVI = 4√2 (σ_vh^i+σ_vv^i) (4)
σ_vh^i represent the backscattering coefficient of cross-polarization of the ith pixel, and the σ_vv^i is the backscattering coefficient of co-polarization of the ith pixel.
The Peanut, Sesamum Indicum and Oryza sativa were considered for the in-depth analysis illustrated in Fig.5. From a response of backscattering to the vegetation, an orientation positively responds to the vertically structured crops (ex. Oryza sativa) compared to the Peanut. An order of the orientation responses to the backscattering coefficient σ_VH^ is Oryza sativa (R2 - 0.30) > Sesamum Indicum (R2 - 0.09) ~ Peanut (R2 - 0.09). An orientation is a less positive relationship between the horizontal growing crops and the non-symmetrical crops. In density aspects, the positive responses to similar vegetation element structures and the variation in crop structural elements show a lesser correlation to the backscattering coefficient. An order of the dense crops responses to the backscattering coefficient is Peanut (R2 - 0.28) > Sesamum Indicum (R2 - 0.15) ~ Oryza sativa (R2 - 0.15). From Fig. 4, it was observed that the crop density has a less positive relationship with the vertically growing crops and the non-symmetrical crops due to the presence of seeds/pods.
When vegetation is completely hydrated, it can create significant volume scattering, increasing backscatter. However, if the soil is sufficiently wet, soil backscatter can dominate the signal, although the height of crops with higher biomass is often greater than 80 cm in the study's site to mitigate the impact of soil moisture-induced backscatter from vegetation. In terms of radar backscatter, different crops and plant species react to irrigation in different ways. Compared to Oryza sativa, dense crops like zea mays exhibit increased volume scattering and a noticeable rise in backscatter under irrigation observed from the field observation and the SAR data.
Due to variations in vegetation water content, and plant structure, the backscattering coefficient of vegetation varies dramatically under various irrigation environments. The irrigation process causes vegetation to absorb water, which raises the water content of the plants (leaf and stem moisture). This alters the plant's dielectric characteristics, which has an impact on radar backscatters, especially in higher frequency ranges.
A shorter and horizontal growth crops shows varying response to the DPBVI and is to discussed in this subsection. The vegetation ranges about 〖DPBVI〗_(0-0.1)^ the responses of the backscattering coefficient is σ_VH^ (0.001 to 0.02) and σ_VV (0.03 to 0.035) the presence of vegetation is sparse, the 〖DPBVI〗_(0.1-0.2)^ the responses of the backscattering coefficient is σ_VH^ (0.020 to 0.025) and σ_VV (0.04 to 0.06) the presence of vegetation is denser and well-grown, and the 〖DPBVI〗_(0.2-0.3)^ the responses of the backscattering coefficient is σ_VH^ (0.026 to 0.031) and σ_VV (0.06 to 0.1) due to the crops being closer and denser. The lack of moisture reduces the water content of the vegetation, which reduces volume scattering because of the density and development of the vegetation. The majority of the radar signals are surface scattering; thus the total backscatter value is smaller.
A medium and vertically varying vegetation elemental growth crops shows varying response to the DPBVI and is too discussed in this subsection. Vegetation ranges about 〖DPBVI〗_(0.3-0.4)^ the responses of the backscattering coefficient is σ_VH^ (0. 025 to 0.04) and σ_VV (0.11 to 0.14) the presence of vegetation in less orientation shows the lesser vegetation elements, the 〖DPBVI〗_(0.4-0.5)^ the responses of the backscattering coefficient is σ_VH^ (0.026 to 0.045) and σ_VV (0.13 to 0.17) the presence of vegetation is well-grown and increases the volume scattering as well as the surface scattering, and the 〖DPBVI〗_(0.5-0.6)^ the responses of the backscattering coefficient is σ_VH^ (0.033 to 0.06) and σ_VV (0.07 to 0.21) due to the crops being denser to increase the volume scattering. Moderately wet vegetation will exhibit more dielectric qualities, which will increase volume scattering, or the amount of the radar signal that bounces about inside the plant. Comparing this to dry moisture-conditioned crops, there is an increase in the backscatter signal, which results in higher biomass (Fig. 6).
Vegetation ranges about 〖DPBVI〗_(0.6-0.7)^ the responses of the backscattering coefficient is σ_VH^ (0.028 to 0.06) and σ_VV (0.21 to 0.24) the presence of vegetation is denser and less orientation with fewer vegetation elements, the 〖DPBVI〗_(0.7-0.8)^ the responses of the backscattering coefficient is σ_VH^ (0.04 to 0.1) and σ_VV (0.25 to 0.28) the presence of vegetation is higher increases the volume scattering by multiple scattering among vegetation elements and the 〖DPBVI〗_(0.8-0.9)^ the responses of the backscattering coefficient is σ_VH^ (0.04 to 0.11) and σ_VV (0.28 to 0.32) due to the crops being ready for the harvest stage as well as the height of the crops being close to 1m to increase the volume scattering and slight increase of the surface scattering. the 〖DPBVI〗_(0.9-1)^ the responses of the backscattering coefficient are σ_VH^ (0.06 to 0.16) and σ_VV (0.32 to 0.35) due to the crops being very denser and the height being about 1m and more to increase the volume scattering. The DPBVI's performance was evaluated by comparing it to Google Earth photos from various semi-arid terrain conditions. Fig. 6 and Fig. 7 demonstrates the DPBVI's usefulness and accuracy in capturing the features of semi-arid landscapes under a variety of topographic circumstances
Statistical analysis of a model typically refers to the evaluation of its performance and the overall effectiveness of the predicted data. When compared to widely use optical remote sensing vegetation indices, limitations occur in the interaction of optical signals with dense vegetation canopies and constraints to penetrate and properly depict the structure of dense ecosystems (NDVI > 0.8). In dense vegetation, the optical data predominantly engages with the upper canopy layers such as leaves, and branches, and as biomass grows, further reflectance from trunks or lower branches does not substantially enhance the reflectance. In places with elevated biomass the vegetation structural changes at the top of the crop up to 10 cm, and the selection of polarization can influence the radar's sensitivity to vegetation structure up to 5cm sufficient to quantify biophysical parameters. Volume scattering from vegetation is most effectively represented by cross-polarized signals (σ_VH^ - 0.04 to 0.15), however, its efficacy diminishes with increasing biomass. Co-polarized signals exhibit heightened sensitivity to surface scattering; at elevated biomass levels, co-polarization increases (σ_VV - 0.28 to 0.35), hence diminishing the efficacy of cross-polarization in biomass estimation. A DPBVI model can sense the saturated response from the vegetation and effectively predict the biomass as observed from Fig. 7 and R2 - 0.73 with in-situ biomass and R2 - 0.52 with NDVI. This enhanced performance can be attributed to the model's robust integration of vegetation backscatter characteristics and the inherent penetration capabilities of Synthetic Aperture Radar signals.
NOVELTY:
Backscatter-Based Vegetation Index Formulations - Introduces new formulations
Dual Polarimetric Vegetation Edge (DPVE) = 2 〖(σ_vh^ +σ_vv^ )〗^2
Polarimetric Backscatter Vegetation Index = √2(〖σ_vh^ +σ_vv^ )〗^
Dual Polarimetric Backscatter Vegetation Index = 4√2 (σ_vh^i+σ_vv^i)
, Claims:1. A method for estimating vegetation biomass using dual-polarimetric Synthetic Aperture Radar (SAR) data at C-band, comprising the steps of:
a) acquiring σ_vh and σ_vv backscatter coefficients from Sentinel-1 SAR data;
b) computing a Dual Polarimetric Backscatter Vegetation Index (DPBVI) defined as DPBVI = 4√2 (σ_vh + σ_vv); and
c) mapping vegetation biomass over agricultural patches based on the DPBVI values ranging from 0 to 1.
2. The method as claimed in claim 1, wherein the estimation accounts for different vegetation structures and moisture conditions by analyzing volumetric and surface scattering characteristics.
3. The method as claimed in claim 1, wherein the backscatter coefficients are combined to compute additional vegetation indices comprising:
a) Dual Polarimetric Vegetation Edge (DPVE); and
b) Polarimetric Backscatter Vegetation Index (PBVI).
4. The method as claimed in claim 1, wherein the backscatter behavior is represented in a 2D scatterplot triangle model to differentiate contributions from soil, water, and vegetation, enabling improved separation of vegetation signals.
5. The method as claimed in claim 1, wherein the vegetation biomass estimation using DPBVI demonstrates higher correlation with in-situ biomass (R² = 0.73) compared to optical vegetation indices such as NDVI (R² = 0.52), particularly under dense canopy conditions.