Description:FIELD OF THE INVENTION
This invention relates to Dual-Polarimetric SAR based Crop Moisture Stress Index for agricultural crops
BACKGROUND OF THE INVENTION
Many indices, such as the Falkenmark indicator, assume uniform per capita water demand, ignoring variations in water use efficiency, climatic conditions, and socio-economic factors.
Optical remote sensing indices are capable of measuring vegetation water stress index and are limited by atmospheric moisture. A current method overcomes this limitation.
Indices focusing solely on vegetation water content, soil moisture and irrigation water availability failed to account ecological crop water stress index potentially compromising long-term sustainability.
Earlier studies are available to assess vegetation water content using microwave remote sensing data. In optical remote sensing Vegetation stress index or Moisture Stress Index but it is difficult to unfavorable environmental conditions.
No studies are available in remote sensing techniques till date to estimate crop moisture stress index based on polarization techniques. This method is advantageous since it is capable of predicting crop moisture stress index 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.
Agro-tech companies use Water stress Index (WSI) to develop efficient irrigation systems.
Governments and NGOs apply WSI for regional water management and policy development.
Industries like manufacturing and food processing integrate WSI insights into sustainability assessments.
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.
Equation 3 utilizes backscatter measurements to compute the relative difference in moisture content between soil and plants. A positive Crop Moisture Distribution (CMD) signifies that soil moisture substantially exceeds vegetation moisture, frequently indicating arid conditions in the crops. A negative CMD indicates that vegetation moisture surpasses soil moisture, perhaps signalling excessive moisture or flooding. A CMSI score approaching 0 indicates equilibrium in moisture levels within both soil and vegetation, which is generally optimal for crop cultivation. This indicator facilitates the monitoring of crop water stress and supports informed decision-making in irrigation and crop management methods.
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:
Fig.1 Statistical Comparison of CMSI and DPCMSI Derived from Sentinel-1 and Sentinel-2 Data
Fig. 2 Statistical Validation of CMSI and DPCMSI from February to May 2024
Fig. 3 Spatial Distribution of CMSI and DPCMSI with Histogram Analysis for Crop Moisture Stress in Perambalur District
Fig. 4 Variation of the CMSI and DPCMSI across months February to May in 2024
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.
Estimation of Crop Moisture Stress Equation 3 utilizes backscatter measurements to compute the relative difference in moisture content between soil and plants. A positive Crop Moisture Distribution (CMD) signifies that soil moisture substantially exceeds vegetation moisture, frequently indicating arid conditions in the crops. A negative CMD indicates that vegetation moisture surpasses soil moisture, perhaps signalling excessive moisture or flooding. A CMSI score approaching 0 indicates equilibrium in moisture levels within both soil and vegetation, which is generally optimal for crop cultivation. This indicator facilitates the monitoring of crop water stress and supports informed decision-making in irrigation and crop management methods.
Crop Moisture Distibution = (M_v^soil- M_v^Veg)/(M_v^soil+M_v^Veg ) (3)
DPBVI is the relative moisture content in the crop, while a greater DPBVI indicates that the crops are under moisture stress, maybe due to insufficient irrigation circumstances, a lower DPBVI result indicates healthier, sufficiently irrigated crops with reduced moisture stress levels. Incorporating a factor of 2 helps to sufficiently scale the index, therefore guaranteeing that it ranges from +1 (indicating extreme moisture stress) to -1 (indicating ideal moisture availability). Consequently, the Crop moisture stress in Equation 4 obtained from the SAR-based DPBVI provides a direct and effective approach for assessing crop water stress. The index is especially beneficial in regions where optical imaging is obstructed by cloud cover or where regular, prompt moisture evaluations are essential for efficient crop management and irrigation strategies.
Crop Moisture Stress= 1-(2*DPBVI) (4)
A DPCMSI value less than -0.10 indicates no moisture stress on the crops. This suggests that the crops are not under water stress or shortage; they have enough moisture.
When DPCMSI falls between -0.1 and 0.03, it indicates minimal moisture stress—that is, a little water shortage that has no appreciable effect on crop health.
A DPCMSI score between 0.03 and 0.5 suggests considerable stress on crops. Although the crops are not in a condition of extreme stress, this range denotes a decrease in crop moisture content, so signalling a water deficit.
A DPCMSI result between +0.5 and +1.0 indicates extreme stress and suggests that water shortage significantly influences the crops. Within this range, insufficient precipitation for good development could cause the crops great drought stress.
DPCMSI = {█(-0.10< DPCMSI,No stress@-0.1≥DPCMSI≤0.03 Slight stress@0.03 ≥DPCMSI≤0.5, Moderate stress@+0.5 ≥DPCMSI≤+1.0,High stress )┤

NOVELTY:
Detection of both drought and waterlogging conditions with a single metric by Introducing relative moisture difference metric that quantifies moisture balance between soil and vegetation.
Crop Moisture Distibution = (M_v^soil- M_v^Veg)/(M_v^soil+M_v^Veg )
Development of a new empirical index that synthesizes VV and VH backscatter into a continuous stress classification model, adaptable to different crops and climate zones. This provides a direct classification schema for crop stress levels based on SAR. The stress classification thresholds are physically interpretable and suitable for automated monitoring systems.
Dual Polarimetric Moisture stress Index (DPCMSI)=[1-8√2(σ_vv+σ_vh)]
Application of a scaled Dual Polarimetric Crop Moisture Stress Index to represent moisture stress levels on a symmetric scale from +1 (extreme stress) to -1 (no stress).

, C , Claims:1. A method for estimating crop moisture stress in agricultural crops using dual-polarimetric Synthetic Aperture Radar (SAR) data, comprising:
a) acquiring dual-polarimetric SAR backscatter measurements;
b) computing the relative difference in moisture content between soil and vegetation using the said backscatter measurements;
c) calculating a Crop Moisture Distribution (CMD) value based on said relative difference;
d) interpreting the CMD value wherein:
e) a positive CMD indicates that soil moisture exceeds vegetation moisture, signifying arid conditions;
f) a negative CMD indicates that vegetation moisture exceeds soil moisture, signifying excessive moisture or flooding; and
g) a CMD value near zero indicates moisture equilibrium in soil and vegetation, representing optimal crop conditions.
2. The method as claimed in claim 1, wherein the computed CMD is further used to derive a Crop Moisture Stress Index (CMSI) indicating the degree of water stress in crops.
3. The method as claimed in claim 1, wherein the CMSI value is used as a decision-support tool in irrigation scheduling and crop management practices.
4. The method as claimed in claim 1, wherein the dual-polarimetric SAR backscatter measurements are acquired in VV and VH polarizations.
5. The method as claimed in claim 1, wherein the CMSI is calculated from the CMD using a defined mathematical relationship based on SAR backscatter properties.