Crop heading stage identification method and system based on radar time-series observation

Abstract

The present invention belongs to the technical field of spaceborne radar data processing, and particularly relates to a crop heading stage identification method and system based on radar time-series observation. The method comprises: obtaining a depolarization index by means of index calculation, implementing extraction of periodic parameters of a time-series depolarization index by means of harmonic decomposition, determining a dominant periodic component of the time-series depolarization index by means of weight analysis, identifying a crop heading stage by means of peak detection, and implementing crop heading stage mapping on the basis of a land use product. In the present invention, a dominant periodic component of a time-series depolarization index is determined by means of weight analysis, a crop heading stage is identified by means of peak detection, and crop heading stage mapping is implemented on the basis of a land use product. The present invention is applicable to large-area crop heading stage identification involving complex and diverse tillage practices and frequent cloud and mist meteorological interference, and solves the present three problems, namely, the lack of prior phenological information, fuzzy heading stage features and interference of non-heading-stage features, in the crop heading stage identification using radar data.

Description

A method and system for identifying the heading period of crops based on radar time series observation Technical Field

[0001] The present invention belongs to the technical field of spaceborne radar data processing, and in particular relates to a method and system for identifying the heading period of crops based on radar time series observation. Background Art

[0002] Heading refers to the physiological phenomenon of crop plants developing ears or inflorescences. It is a critical step in the crop growth cycle, marking the beginning of pollination and seed formation. Agricultural authorities closely monitor the heading date of crops to determine the optimal harvest time for maximum yield. The heading date is often related to crop variety, climatic conditions, and cultivation practices. Farmland can be planted with different crops in different seasons to maximize land productivity. In this case, farmland undergoes multiple cropping cycles in a year, resulting in multiple heading dates. Satellite remote sensing observations are an effective means of identifying the heading date of crops. Identifying the heading date of crops across different cropping cycles requires year-round remote sensing observation data. Optical satellites are traditional remote sensing platforms that can accurately detect changes in crop growth. However, optical satellites are highly susceptible to obstruction by clouds and fog, making year-round remote sensing observations difficult. Radar satellites, on the other hand, provide stable, high-quality imaging that is unaffected by meteorological interference, making them an important alternative data source for identifying the heading date of crops.

[0003] Time-series radar observations can be used to obtain time-series scattering from cultivated land, analyze changes in crop plant and soil properties, and establish a correlation between time-series scattering curve characteristics and crop heading date. Time-series feature discrimination is the most commonly used method. Based on prior crop phenological information, radar time-series observations are conducted throughout the growing season to construct a time-series scattering curve. Multi-order derivatives and local trend analysis are then used to detect curve features, enabling identification of the crop heading date. However, crop planting structures in agricultural areas are complex and diverse, and field management practices vary widely, making prior crop phenological information extremely difficult to obtain. Time-series scattering curve features also exhibit ambiguity regarding the heading date. While the peak of the time-series scattering curve is generally assumed to correspond to the heading date, high crop canopy density can cause the peak to shift in time. Furthermore, scattering coherent noise, weed growth, and human activity can sometimes cause pseudo-peaks in the time-series scattering curve. These non-heading date features can interfere with identification of the heading date. These three problems seriously restrict the operational application of radar time series observations in identifying the heading period of crops. Technical issues

[0004] Through the above analysis, the problems and defects of the existing technology are: the existing methods lack prior phenological information, the characteristics of the heading period are vague, and the interference of non-heading period characteristics remains to be solved. Technical Solutions

[0005] In response to the problems existing in the prior art, the present invention provides a method and system for identifying the heading period of crops based on radar time series observation.

[0006] The present invention is implemented as follows: a method for identifying the heading period of crops based on radar time series observation, obtaining a depolarization index through index calculation, extracting periodic parameters of the time series depolarization index through harmonic decomposition, determining the dominant periodic component of the time series depolarization index through weight analysis, identifying the heading period of crops through peak detection, and realizing crop heading period mapping in combination with land use products.

[0007] Furthermore, the method for identifying the heading period of crops based on radar time series observations specifically includes the following steps:

[0008] S1, indicator calculation:

[0009] Obtain the VH (Vertical-Horizontal) and VV (Vertical-Vertical) polarization data from the annual radar time series observations and calculate the depolarization index (DI) using the following formula:

[0010] DI =

[0011] S2, harmonic decomposition:

[0012] For the temporal depolarization index , t is the normalized annual cumulative day, and the following formula is used for harmonic decomposition:

[0013]

[0014] a represents the time series mean, represents the amplitude of the i-th order cosine term, Represents the phase of the i-th order cosine term. Use least squares fitting to obtain a, and The value of

[0015] S3, weight analysis:

[0016] The weight of the i-th order cosine term It is defined as follows:

[0017]

[0018] The maximum weight of the three cosine terms for:

[0019]

[0020] When the weight of the i-th order cosine term Equal to the maximum weight of the three cosine terms When, that is:

[0021]

[0022] The dominant periodic component is the i-th order cosine term;

[0023] S4, peak detection:

[0024] The first-order cosine term has one peak, and the peak date is Calculated by the following formula:

[0025]

[0026] The second-order cosine term has two peaks, the peak dates are and Calculated by the following formula:

[0027]

[0028] The third-order cosine term has three peaks, the peak dates are , and Calculated by the following formula:

[0029]

[0030] S5, Crop Heading Period Mapping:

[0031] Obtain land use products and extract cultivated land areas; determine the dominant periodic component pixel by pixel in the cultivated land area, and then determine the number and date of peaks; determine whether the cultivated land is in the crop heading period on a month-by-month basis, and realize crop heading period mapping.

[0032] Furthermore, in S3, harmonic decomposition regards the time series DI as a synthesis of a constant component and three-order cosine components. Then, each peak in the time series DI is a synthesis of the peaks of the three-order cosine components. The local trend of the peak can be approximately expressed as the slope from the trough to the peak. This slope can be expressed in the cosine component as the ratio of the amplitude to the order i, representing the weight of the cosine component. The cosine component with the largest weight describes the dominant periodicity of the time series DI.

[0033] Furthermore, in S4, the peak position of the cosine component depends on the phase and order i, and the center point of the cosine component is , which date corresponds to the peak of the second-order cosine component and the trough of the first-order and third-order cosine components; taking the center point date as the starting point and the integer multiples of half the period of the cosine component as the interval, the dates of the remaining peaks can be obtained through step operation.

[0034] Furthermore, in S5, the premise for identifying the heading period of crops is to determine the cultivated land area. The heading period of crops in the cultivated land area is obtained by performing indicator calculation, harmonic decomposition, weight analysis, and peak detection pixel by pixel. In order to better display the temporal distribution characteristics of the heading period of crops, a month-by-month mapping strategy is adopted to identify whether the cultivated land pixels are in the heading period of crops.

[0035] Another object of the present invention is to provide a crop heading period identification system based on radar time series observation using the crop heading period identification method based on radar time series observation, comprising:

[0036] Index calculation module: used to obtain VH (Vertical-Horizontal) and VV (Vertical-Vertical) polarization data from annual radar time series observations and calculate the depolarization index (DI);

[0037] Harmonic decomposition module: used to perform harmonic decomposition of the temporal depolarization index;

[0038] Weight analysis module: used for weight analysis;

[0039] Peak detection module: used for peak detection of third-order cosine terms;

[0040] Crop heading period mapping module: used to obtain land use products and extract cultivated land areas; determine the dominant periodic component pixel by pixel in the cultivated land area, and then determine the number and date of peaks; determine whether the cultivated land is in the crop heading period on a month-by-month basis to realize crop heading period mapping.

[0041] Another object of the present invention is to provide a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method for identifying the heading period of crops based on radar time series observation.

[0042] Another object of the present invention is to provide a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of the method for identifying the heading period of crops based on radar time series observation.

[0043] Another object of the present invention is to provide an information data processing terminal, which is used to implement the crop heading period identification system based on radar time series observation. Beneficial effects

[0044] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0045] First, the present invention successfully solves the important technical problems currently faced in identifying the heading period of crops using radar time series observations. In response to the problem of lack of prior phenological information, the present invention analyzes radar time series observations throughout the year, rather than just analyzing radar time series observations during the growing season of crops. In response to the problem of fuzzy heading period characteristics, the present invention introduces depolarization index and harmonic decomposition technology to stably express the heading period of crops as the peak of the time series curve. In response to the problem of interference from non-heading period characteristics, the present invention introduces weight analysis and peak detection technology to eliminate the influence of non-heading period characteristics by determining the dominant periodicity of the time series curve, and quickly corresponds the peak of the dominant periodic component to the heading period of crops. In summary, the present invention successfully solves the three core problems in the prior art, making the identification of the heading period of crops more efficient and accurate.

[0046] The present invention does not require the acquisition of prior phenological information of the study area, does not require window filtering of radar time series observations, and the peak characteristics of the time series curve during the heading period of crops are significant. There is no need to perform local trend analysis and pseudo-peak identification on the time series curve, and the peak date of the heading period of crops can be quickly obtained through phase calculation.

[0047] Second, the expected benefits and commercial value after the transformation of the technical solution of the present invention are as follows: the present invention can be used by business consulting companies to provide high-value services, such as farmland management consulting, smart agriculture monitoring and business strategy analysis; it can help agricultural operators to reasonably arrange farmland management activities according to heading period information, reduce fertilization, irrigation, and pest control costs, and improve crop yield and quality; it can help the food supply chain to more efficiently arrange the harvest and distribution of agricultural products, reduce resource waste and reduce environmental impact; it can help the insurance sector to more accurately assess and manage agricultural risks and formulate agricultural insurance policies in a timely manner; it can help agricultural management departments to more comprehensively estimate food production and supply time, prevent abnormal fluctuations in agricultural product market prices, and ensure food security.

[0048] The technical solution of the present invention fills a technological gap in the industry at home and abroad: The present invention proposes a solution for identifying the heading period of crops using radar time-series observation data throughout the year, solving the problem of difficulty in expanding the research area due to the lack of prior phenological information. The present invention proposes a new depolarization index to more accurately track crop growth, solving the problem of ambiguous crop heading period characteristics caused by using only single polarization data. The present invention proposes a new time-series data analysis approach, including steps such as harmonic decomposition, weight analysis, and peak detection, which solves the problem of interference from non-heading period characteristics caused by scattered coherent noise, weed growth, and human activities.

[0049] Third, the crop heading period identification method provided by this invention represents a major advancement in the application of remote sensing technology in precision agriculture. By analyzing and processing radar time-series data, this method can accurately identify the heading period, thereby effectively monitoring the growth and development of crops. The following summarizes the significant technological advancements brought about by this method:

[0050] 1. High-precision time recognition:

[0051] The depolarization index calculated using VH and VV polarization data provides accurate time series data for monitoring crop growth.

[0052] The application of harmonic decomposition extracts the periodic variations in the time-series depolarization index, which is crucial for capturing and understanding the crop growth cycle.

[0053] 2. Efficiency of data analysis:

[0054] Least squares fitting is a mathematically effective method for processing observed data to find the best function match and accurately estimate periodic parameters.

[0055] The introduction of weight analysis can identify the main periodic components that affect the identification of heading date and improve the reliability of the identification method.

[0056] 3. Accurate heading date detection:

[0057] Peak detection is an accurate means of judging the key stages of crop growth, helps determine the heading period, and plays an important role in crop growth monitoring and prediction.

[0058] The use of multi-order cosine terms enables the detection of multiple growth peaks occurring throughout the year, adapting to different crops and diverse growing conditions.

[0059] 4. Land use data integration:

[0060] Combining land use products can more accurately distinguish between cultivated and non-cultivated land, and identify the heading period only in relevant areas, which improves the efficiency and accuracy of the analysis.

[0061] Plot-level heading date mapping is crucial for understanding regional agricultural production patterns and optimizing resource allocation.

[0062] Practical application value:

[0063] Agricultural Management:

[0064] Keep abreast of crop growth conditions and provide a scientific basis for agricultural production management, such as irrigation, fertilization, pest and disease control, etc.

[0065] Harvest Forecast:

[0066] Heading period identification can be used to estimate crop harvest time and yield, which has an important impact on food security and market supply.

[0067] Agricultural insurance:

[0068] Providing accurate crop growth data for agricultural insurance, reducing the risks incurred by insurance companies when making claims.

[0069] Agricultural Policy Development:

[0070] The government can better plan food reserves and agricultural support policies based on time-series data on crop growth.

[0071] Climate Change Research:

[0072] Long-term observational crop growth data can be used to study the impact of climate change on agriculture.

[0073] In summary, the crop heading period identification method based on radar time-series observations improves the accuracy of crop growth monitoring and provides an efficient tool for agricultural production management, which is of great significance for improving the sustainability of agricultural production, increasing food yield and quality, and reducing environmental impact. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0075] 1 is a flow chart of a method for identifying the heading period of crops based on radar time series observations according to an embodiment of the present invention;

[0076] FIG2 is a structural diagram of a crop heading period identification system based on radar time series observation provided by an embodiment of the present invention;

[0077] FIG3 is a diagram of a time series depolarization index provided by an embodiment of the present invention;

[0078] FIG4 is a harmonic decomposition diagram provided by an embodiment of the present invention;

[0079] FIG5 is a diagram of weight analysis and peak detection provided by an embodiment of the present invention;

[0080] FIG6 is a diagram for identifying the heading period of crops provided by an embodiment of the present invention. Modes for Carrying Out the Invention

[0081] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0082] In view of the problems existing in the prior art, the present invention provides a method and system for identifying the heading period of crops based on radar time series observation. The present invention is described in detail below with reference to the accompanying drawings.

[0083] Example 1: Precision monitoring of rice heading period

[0084] 1. Data Collection:

[0085] Synthetic aperture radar (SAR) was used to obtain VH and VV polarimetric time series data over the rice-growing area throughout the year from different satellite platforms.

[0086] 2. Calculation of depolarization index:

[0087] The DI calculation formula is applied to process the original radar data to obtain the depolarization index series related to the crop growth status.

[0088] 3. Harmonic decomposition:

[0089] Harmonic decomposition of the depolarization index time series data was performed to capture the rice growth cycle.

[0090] 4. Weight analysis and identification of periodic components:

[0091] Perform a weight analysis to determine which first-order cosine term best represents the growth cycle of rice.

[0092] 5. Peak detection and heading period prediction:

[0093] The heading date of rice is predicted using the peak detection algorithm based on the dominant periodic component.

[0094] 6. Application and verification of results:

[0095] Land use data were combined to divide cultivated land into regions, and then peak dates were applied within these regions to identify the heading date.

[0096] Ground survey data were used to verify the heading date prediction results obtained from radar time series observations.

[0097] Example 2: Monitoring the Growth Cycle of Wheat Crops

[0098] 1. Data Collection:

[0099] VH and VV polarimetric data of wheat-growing areas were obtained using radar satellites, such as Sentinel-1, at multiple time points.

[0100] 2. Depolarization Index Extraction:

[0101] Using radar data, the depolarization index series is calculated according to the formula.

[0102] 3. Harmonic decomposition and period analysis:

[0103] Harmonic decomposition of the depolarization index was performed to extract periodic changes consistent with the wheat growth cycle.

[0104] 4. Determination of important periodic components:

[0105] By analyzing the weights of different period items, we can determine which one best reflects the critical period of wheat growth.

[0106] 5. Peak identification during heading period:

[0107] Based on the determined periodic components, the peak detection technology is used to predict the wheat heading date.

[0108] 6. Mapping and Decision Support:

[0109] Land use data were used for mapping to determine the spatial distribution of heading date.

[0110] The results are used to guide farming activities, such as irrigation and fertilization plans, to optimize the production process.

[0111] The specific implementation schemes in these two embodiments provide a complete process from data collection to result application, which can accurately monitor the growth cycle of different crops and provide strong support for agricultural production and management decisions. The embodiment of the present invention provides a method for identifying the heading period of crops based on radar time series observation. The method obtains the depolarization index through index calculation, extracts the periodic parameters of the time series depolarization index through harmonic decomposition, determines the dominant periodic component of the time series depolarization index through weight analysis, identifies the heading period of crops through peak detection, and realizes crop heading period mapping by combining land use products.

[0112] As shown in FIG1 , the method for identifying the heading period of crops based on radar time series observations specifically includes the following steps:

[0113] S1, indicator calculation:

[0114] Obtain the VH (Vertical-Horizontal) and VV (Vertical-Vertical) polarization data from the annual radar time series observations and calculate the depolarization index (DI) using the following formula:

[0115] DI =

[0116] S2, harmonic decomposition:

[0117] For the temporal depolarization index , t is the normalized annual cumulative day, and the following formula is used for harmonic decomposition:

[0118]

[0119] a represents the time series mean, represents the amplitude of the i-th order cosine term, Represents the phase of the i-th order cosine term. Use least squares fitting to obtain a, and The value of

[0120] S3, weight analysis:

[0121] The weight of the i-th order cosine term It is defined as follows:

[0122]

[0123] The maximum weight of the three cosine terms for:

[0124]

[0125] When the weight of the i-th order cosine term Equal to the maximum weight of the three cosine terms When, that is:

[0126]

[0127] The dominant periodic component is the i-th order cosine term;

[0128] S4, peak detection:

[0129] The first-order cosine term has one peak, and the peak date is Calculated by the following formula:

[0130]

[0131] The second-order cosine term has two peaks, the peak dates are and Calculated by the following formula:

[0132]

[0133] The third-order cosine term has three peaks, the peak dates are , and Calculated by the following formula:

[0134]

[0135] S5, Crop Heading Period Mapping:

[0136] Obtain land use products and extract cultivated land areas; determine the dominant periodic component pixel by pixel in the cultivated land area, and then determine the number and date of peaks; determine whether the cultivated land is in the crop heading period on a month-by-month basis, and realize crop heading period mapping.

[0137] Calculation Principle: VH polarization represents the vibration of electromagnetic waves in both the vertical and horizontal directions, while VV polarization represents the vibration of electromagnetic waves in the vertical direction. VH polarization is sensitive to target characteristics such as directionality and roughness, while VV polarization is more sensitive to the vertical characteristics of ground objects. The sum of VH and VV represents the total radar echo energy, and the depolarization index (DI) is the ratio of VH to the total echo energy. DI represents the proportion of the vertically transmitted signal that is converted into a horizontal vibration signal by ground objects. This proportion is highest during the heading period of crops, resulting in a peak in the DI during this period.

[0138] Harmonic decomposition principle: Arable land can undergo multiple crop planting cycles within a year. For each crop planting cycle, the DI undergoes a cosine-like process, from low (germination) to high (heading) to low (harvest). This process can be described by the cosine components in harmonic decomposition. Generally, the time from germination to harvest is over 100 days, and a maximum of three crop planting cycles can occur in a year. Therefore, three orders of cosine components are used in harmonic decomposition. Cosine components can only represent the fluctuation pattern of the DI, not its magnitude. Therefore, a constant component is used to represent the annual mean of the DI.

[0139] Weighted Analysis Principle: Harmonic decomposition considers the time series DI as a composite of a constant component and three-order cosine components. Therefore, each peak in the time series DI is the composite of the peaks of the three-order cosine components. The local trend of a peak can be approximated as the slope from trough to peak. This slope, expressed in the cosine component as the ratio of amplitude to order i, represents the weight of the cosine component. The cosine component with the largest weight describes the dominant periodicity of the time series DI.

[0140] Peak detection principle: The peak position of the cosine component depends on the phase and order i. The center point of the cosine component is , which corresponds to the peak of the second-order cosine component and the trough of the first-order and third-order cosine components. Using the central date as the starting point and intervals of integer multiples of half the cosine component period, the dates of the remaining peaks can be obtained through step operations.

[0141] Principles of Crop Heading Period Mapping: Crop heading period identification presupposes the identification of cultivated land areas. Currently, a large number of land use products provide spatial distribution information on cultivated land. Through pixel-by-pixel indicator calculation, harmonic decomposition, weight analysis, and peak detection, the heading period of crops in cultivated land areas can be determined. To better demonstrate the temporal distribution of the crop heading period, a month-by-month mapping strategy is adopted to identify whether cultivated land pixels are in the heading period.

[0142] As shown in FIG2 , the crop heading period identification system based on radar time series observation provided by an embodiment of the present invention includes:

[0143] Index calculation module: used to obtain VH (Vertical-Horizontal) and VV (Vertical-Vertical) polarization data from annual radar time series observations and calculate the depolarization index (DI);

[0144] Harmonic decomposition module: used to perform harmonic decomposition of the temporal depolarization index;

[0145] Weight analysis module: used for weight analysis;

[0146] Peak detection module: used for peak detection of third-order cosine terms;

[0147] Crop heading period mapping module: used to obtain land use products and extract cultivated land areas; determine the dominant periodic component pixel by pixel in the cultivated land area, and then determine the number and date of peaks; determine whether the cultivated land is in the crop heading period on a month-by-month basis to realize crop heading period mapping.

[0148] A crop heading period identification method based on radar time series observation and harmonic analysis. The development environment of this embodiment is GEE (Google Earth Engine) and the programming language is JavaScript.

[0149] Step 1. Retrieve the Sentinel-1 data for Changxing County, Huzhou City in 2021 and use the image.pow and image.divide functions to construct a time series depolarization index.

[0150] Step 2. Use the image.cos and image.linearRegression functions to obtain the unknown coefficients a, Ai, and i of each cosine component to achieve harmonic decomposition of the temporal depolarization index.

[0151] Step 3. Use the image.multiply and reduce('sum') functions to obtain the weights of each cosine component, use the reduce('max') and image.eq functions to determine the dominant periodic component, and use the image.abs and image.add functions to detect peaks and calculate peak dates.

[0152] Step 4: Retrieve the ESA WorldCover2021 land use product and use the image.mask function to extract cultivated land areas. Use the image.gte and image.lte functions to determine the dominant periodic component and calculate the peak date pixel by pixel within the cultivated land area. Use the image.and and image.or functions to identify the heading date of crops.

[0153] This embodiment processes the time-series Sentinel-1 radar data and ESA WorldCover2021 data. Figure 3 is a time-series depolarization index diagram of the embodiment, Figure 4 is a harmonic decomposition diagram of the embodiment, Figure 5 is a weight analysis and peak detection diagram of the embodiment, and Figure 6 is a crop heading period identification diagram of the embodiment.

[0154] The problems and defects of existing technologies are: lack of prior phenological information, fuzzy heading period characteristics, and interference from non-heading period characteristics. The crop planting structure in agricultural planting areas is complex and diverse, and the field management plans vary greatly, making it extremely difficult to obtain prior phenological information on crops. The characteristics of the time series scattering curve during the heading period of crops are also ambiguous. It is generally believed that the peak of the time series scattering curve corresponds to the heading period of crops, but when the crop canopy density is high, the peak characteristics are time-shifted. In addition, scattered coherent noise, weed growth, and human activities sometimes cause pseudo-peaks to appear in the time series scattering curve. These non-heading period characteristics will interfere with the identification of the heading period of crops. These three problems seriously restrict the operational application of radar time series observations in the identification of the heading period of crops. It can be seen from the examples that the present invention realizes the identification of the heading period of crops in Changxing County, Huzhou City in 2021, and the accuracy test result reaches 76.19%. The method provided by the present invention effectively tracks the remote sensing time series characteristics of crop growth without relying on prior phenological information, evaluates the periodic composition of multiple rounds of crop planting time series data, determines the dominant periodicity of the annual planting pattern, and identifies the peaks by combining the crop growth law and cosine component phase information, realizing the identification of the heading period of crops in the context of complex and diverse farming practices and frequent cloud and fog meteorological interference. It can promote the commercial application of radar data and provide information support for stable and increased grain production.

[0155] An application embodiment of the present invention provides a computer device, which includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor executes the steps of a method for identifying the heading period of crops based on radar time series observations.

[0156] An application embodiment of the present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of a method for identifying the heading period of crops based on radar time series observation.

[0157] An application embodiment of the present invention provides an information data processing terminal, which is used to implement a crop heading period identification system based on radar time series observation.

[0158] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware portion can be implemented using dedicated logic; the software portion can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. Those skilled in the art will appreciate that the above-mentioned devices and methods can be implemented using computer-executable instructions and / or contained in processor control code, for example, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The devices and modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, or can be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0159] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with this technical field within the technical scope disclosed by the present invention and within the spirit and principles of the present invention should be covered by the scope of protection of the present invention.

Claims

1. A method for identifying the heading period of crops based on radar time series observation, characterized in that: The depolarization index is obtained through indicator calculation, the periodic parameters of the time series depolarization index are extracted through harmonic decomposition, the dominant periodic component of the time series depolarization index is determined through weight analysis, the heading period of crops is identified through peak detection, and the heading period mapping of crops is achieved in combination with land use products.

2. The method for identifying heading period of crops based on radar time series observation according to claim 1, characterized in that: The method for identifying heading period of crops based on radar time series observation specifically comprises the following steps: S1, indicator calculation: The VH and VV polarization data of annual radar time series observations are obtained, and the depolarization index is calculated using the following formula: OF = S2, harmonic decomposition: For the temporal depolarization index , t is the normalized annualized day, and the following formula is used for harmonic decomposition: a represents the time series mean, represents the amplitude of the i-th order cosine term, represents the phase of the i-th order cosine term. Using least squares fitting, we can obtain a, and The value of S3, weight analysis: The weight of the i-th order cosine term Defined as: The maximum weight of the three cosine terms for: When the weight of the i-th order cosine term Equal to the maximum weight of the three cosine terms When, that is: The dominant periodic component is the i-th order cosine term; S4, peak detection: The first-order cosine term has one peak, the peak date is Calculated by the following formula: The second-order cosine term has two peaks, the peak dates are and Calculated by the following formula: The third-order cosine term has three peaks, the peak dates are , and Calculated by the following formula: S5, Crop heading period mapping: Obtain land use products and extract cultivated land areas; determine the dominant periodic component pixel by pixel in the cultivated land area, and then determine the number and date of peaks; determine whether the cultivated land is in the heading period of crops month by month, and realize crop heading period mapping.

3. The method for identifying heading period of crops based on radar time series observation according to claim 2, characterized in that: In S3, harmonic decomposition regards the time series DI as the synthesis of a constant component and three-order cosine components. Then each peak in the time series DI is the synthesis of the peaks of three-order cosine components; the local trend of the peak can be approximately expressed as the slope from the trough to the peak, which can be expressed in the cosine component as the ratio of the amplitude and the order i, representing the weight of the cosine component. The cosine component with the largest weight describes the dominant periodicity of the time series DI.

4. The method for identifying heading period of crops based on radar time series observation according to claim 2, characterized in that: In S4, the peak position of the cosine component depends on the phase and order i, and the center point of the cosine component is , which date corresponds to the peak of the second-order cosine component and the trough of the first-order and third-order cosine components; taking the central point date as the initial point and the integer multiples of the half period of the cosine component as the interval, the dates of the remaining peaks can be obtained through step calculation.

5. The method for identifying heading period of crops based on radar time series observation according to claim 2, characterized in that: In S5, the premise of identifying the heading period of crops is to determine the cultivated land area. The heading period of crops in the cultivated land area is obtained by performing indicator calculation, harmonic decomposition, weight analysis and peak detection pixel by pixel. In order to better show the temporal distribution characteristics of the heading period of crops, a month-by-month mapping strategy is adopted to identify whether the cultivated land pixels are in the heading period of crops.

6. A crop heading period recognition system based on radar time series observation using the crop heading period recognition method based on radar time series observation as claimed in any one of claims 1 to 5, characterized in that: include: Index calculation module: used to obtain VH and VV polarization data of annual radar time series observations and calculate depolarization index; Harmonic decomposition module: used to perform harmonic decomposition of the temporal depolarization index; Weight analysis module: used for weight analysis; Peak detection module: used for peak detection of third-order cosine terms; Crop heading period mapping module: used to obtain land use products and extract cultivated land areas; In the cultivated land area, the dominant periodic component is determined pixel by pixel, and then the number and date of peaks are determined; whether the cultivated land is in the heading period of crops is judged month by month, and the mapping of the heading period of crops is realized.

7. A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method for identifying heading period of crops based on radar time series observation as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to execute the steps of the method for identifying heading period of crops based on radar time series observation as described in any one of claims 1 to 5.

9. An information data processing terminal, used for implementing the crop heading period identification system based on radar time series observation as claimed in claim 6.

Images