Method for monitoring key growth period of rice based on time sequence SAR (Synthetic Aperture Radar) and accumulated temperature

Abstract

The invention relates to a rice key growth period monitoring method based on time sequence SAR and cumulative temperature, and the method comprises the steps: obtaining an annual time sequence VH curve and an annual time sequence VV curve corresponding to the time sequence SAR of rice in a target region, carrying out the filtering smoothing processing of the annual time sequence VH curve, so as to obtain a smooth curve corresponding to the annual time sequence VH curve, and carrying out the filtering smoothing processing of the smooth curve; the rice sowing time is determined according to the smooth curve; extracting accumulated temperature information from a rice sowing time to a rice booting-heading day of a target area, and constructing multi-scale information of an annual time sequence VV curve; and extracting tillering stage information and fruit development and maturation stage information in the key growth period of the rice according to the multi-scale information, and extracting booting-heading stage information in the key growth period of the rice in the target area based on the multi-scale information and the accumulated temperature information. Therefore, the problems of insufficient utilization of crop growth period information, insufficient consideration of climatic factors and the like in an existing rice monitoring method are solved.

Description

Technical Field

[0001] The present application relates to the field of agricultural remote sensing technology, and in particular to a method for monitoring the key growth period of rice based on time-series SAR and cumulative temperature. Background Art

[0002] Optical and SAR remote sensing images have been widely used in rice growth period monitoring. Since multiple bands in optics are sensitive to plant pigments and leaf area characteristics, the existing technology combines different spectral bands to extract the growth period. In previous applications, the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Chlorophyll Index-Red Edge (CLI) and other indicators are usually used to monitor the growth period. red-edge ) and other optical vegetation indices to extract the growth period of rice, and achieved good results. However, since the optical vegetation index is easily saturated, it is difficult to distinguish the later growth status of crops. At present, because SAR can penetrate clouds and is sensitive to rice canopy structure, moisture content and biomass, it can extract more information for the key growth period of crops. Therefore, the rice growth period monitoring algorithm based on SAR data has been rapidly developed.

[0003] Due to the dynamic response of radar backscatter coefficient to different structures of crop growth stages, the information contained in time series SAR deserves attention. How to extract growth period related information from time series signals is the focus of research. At present, a large number of studies have developed effective time series data information extraction methods, mainly through time domain filtering to reduce local noise, and extracting growth period by establishing the relationship between time domain feature points and crop growth stage. However, such methods often ignore the duration of different growth periods and the response of crops to climate. Similar feature points in different growth stages are easy to confuse. Therefore, in addition to the description of feature points of time domain curves, it is urgent to develop other key features of the growth period to solve the mismatch between satellite inversion indicators and ground observations.

[0004] In summary, existing rice monitoring methods have problems such as insufficient utilization of crop growth period information and insufficient consideration of climate factors, which need to be solved urgently. Summary of the invention

[0005] The present application provides a method for monitoring the key growth period of rice based on time-series SAR and cumulative temperature, so as to solve the problems existing in the existing rice monitoring methods, such as insufficient use of crop growth period information and insufficient consideration of climate factors.

[0006] The first aspect of the present application provides a method for monitoring a critical growth period of rice based on time-series SAR and cumulative temperature, comprising the following steps: obtaining an annual time-series VH curve and an annual time-series VV curve corresponding to the time-series SAR of rice in a target area, filtering and smoothing the annual time-series VH curve to obtain a smooth curve corresponding to the annual time-series VH curve, and determining the rice sowing period according to the smooth curve; extracting cumulative temperature information from the rice sowing period to the rice booting-heading date in the target area, and constructing multi-scale information of the annual time-series VV curve; extracting tillering period information and fruit development and maturity period information in the critical growth period of rice according to the multi-scale information, and extracting booting-heading period information in the critical growth period of rice in the target area based on the multi-scale information and the cumulative temperature information.

[0007] Optionally, in one embodiment of the present application, the filtering and smoothing processing is performed on the annual time series VH curve to obtain a smooth curve corresponding to the annual time series VH curve, and the rice sowing period is determined according to the smooth curve, including: performing SG filtering and smoothing processing on the annual time series VH curve to generate a smooth curve corresponding to the annual time series VH curve; calculating the minimum value in the smooth curve, and determining the rice sowing period according to the minimum point corresponding to the minimum value.

[0008] Optionally, in one embodiment of the present application, the extracting the cumulative temperature information from the rice sowing period to the rice booting-heading date of the target area includes: determining the rice booting-heading period based on the rice sowing period and the rice booting-heading date of the target area; determining the growth reference temperature of the rice, and obtaining the daily minimum temperature and the daily maximum temperature during the rice booting-heading period, so as to calculate the daily average temperature based on the daily minimum temperature and the daily maximum temperature; calculating the growing degree days based on the daily average temperature, the daily minimum temperature, the daily maximum temperature and the growth reference temperature, and calculating the cumulative temperature information based on the growing degree days.

[0009] Optionally, in one embodiment of the present application, constructing the multi-scale information of the annual time series VV curve includes: performing time-frequency analysis on the annual time series VV curve based on a preset multi-resolution analysis strategy to separate different frequency components of the annual time series VV curve; constructing multi-scale information corresponding to the different frequency components according to a preset wavelet basis center frequency, convolution scale and image time resolution, wherein the multi-scale information includes noise, local changes, seasonal changes and long-term trends.

[0010] Optionally, in one embodiment of the present application, the tillering period information and the fruit development and maturity period information in the key growth period of rice are extracted according to the multi-scale information, and the booting-heading period information in the key growth period of rice in the target area is extracted based on the multi-scale information and the cumulative temperature information, including: determining the growth period characteristics of rice through the multi-scale information, and obtaining the maximum value of the seasonal change of the rice after the sowing date, so as to extract the tillering period information according to the growth period characteristics of the rice and the maximum value; obtaining the characteristic points of the local changes and the The positive and negative derivatives of seasonal changes are obtained, and the growth indicators corresponding to the periodic fluctuations of the seasonal changes are determined according to the positive and negative derivatives, so as to extract the fruit maturity information through the growth indicators and the characteristic points; the mean and standard deviation corresponding to the cumulative temperature information are calculated, and a normal distribution function is constructed according to the mean and the standard deviation; the probability of the booting-heading period is calculated through the normal distribution function, and the local change extreme point in the local change when the probability of the booting-heading period is the largest is obtained, so as to determine the booting-heading period information according to the local change extreme point.

[0011] The second aspect of the present application provides a rice key growth period monitoring device based on time series SAR and cumulative temperature, including: a filtering module, used to obtain an annual time series VH curve and an annual time series VV curve corresponding to the time series SAR of rice in a target area, and filter and smooth the annual time series VH curve to obtain a smooth curve corresponding to the annual time series VH curve, and determine the rice sowing period according to the smooth curve; a construction module, used to extract the cumulative temperature information from the rice sowing period to the rice booting-heading date in the target area, and construct multi-scale information of the annual time series VV curve; an extraction module, used to extract tillering period information and fruit development and maturity period information in the key growth period of rice according to the multi-scale information, and extract the booting-heading period information in the key growth period of rice in the target area based on the multi-scale information and the cumulative temperature information.

[0012] Optionally, in one embodiment of the present application, the filtering module includes: a generation unit, used to perform SG filtering and smoothing processing on the annual time series VH curve to generate a smooth curve corresponding to the annual time series VH curve; a first calculation unit, used to calculate the minimum value in the smooth curve, and determine the rice sowing period according to the minimum point corresponding to the minimum value.

[0013] Optionally, in one embodiment of the present application, the construction module includes: a first determination unit, used to determine the rice heading-heading period based on the rice sowing period and the rice heading-heading date of the target area; a second determination unit, used to determine the growth reference temperature of the rice, and obtain the daily minimum temperature and the daily maximum temperature during the rice heading-heading period, so as to calculate the daily average temperature based on the daily minimum temperature and the daily maximum temperature; a second calculation unit, used to calculate the growing degree days based on the daily average temperature, the daily minimum temperature, the daily maximum temperature and the growth reference temperature, and calculate the cumulative temperature information based on the growing degree days.

[0014] Optionally, in one embodiment of the present application, the construction module also includes: a separation unit, used to perform time-frequency analysis on the annual time series VV curve based on a preset multi-resolution analysis strategy to separate different frequency components of the annual time series VV curve; an establishment unit, used to construct multi-scale information corresponding to the different frequency components according to a preset wavelet basis center frequency, convolution scale and image time resolution, wherein the multi-scale information includes noise, local changes, seasonal changes and long-term trends.

[0015] Optionally, in one embodiment of the present application, the extraction module includes: a first acquisition unit, used to determine the growth period characteristics of the rice through the multi-scale information, and obtain the maximum value of the seasonal changes of the rice after the sowing date, so as to extract the tillering period information according to the growth period characteristics of the rice and the maximum value; a second acquisition unit, used to obtain the characteristic points of the local changes and the positive and negative derivatives of the seasonal changes, and determine the growth indicators corresponding to the periodic fluctuations of the seasonal changes according to the positive and negative derivatives, so as to extract the fruit development and maturity period information through the growth indicators and the characteristic points; a construction unit, used to calculate the mean and standard deviation corresponding to the cumulative temperature information, and construct a normal distribution function according to the mean and the standard deviation; a third calculation unit, used to calculate the probability of the booting-heading period through the normal distribution function, and obtain the local change extreme point in the local change when the probability of the booting-heading period is maximum, so as to determine the booting-heading period information according to the local change extreme point.

[0016] The third aspect of the present application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for monitoring the critical growth period of rice based on time series SAR and cumulative temperature as described in the above embodiment.

[0017] The fourth aspect of the present application provides a computer-readable storage medium, which stores a computer program. When the program is executed by a processor, it implements the above-mentioned rice key growth period monitoring method based on time series SAR and cumulative temperature.

[0018] The fifth aspect of the present application provides a computer program product, including a computer program, which is executed to implement the above-mentioned rice key growth period monitoring method based on time series SAR and cumulative temperature.

[0019] Therefore, the embodiments of the present application have the following beneficial effects:

[0020] The embodiment of the present application can obtain the annual time series VH curve and annual time series VV curve corresponding to the time series SAR of rice in the target area, and filter and smooth the annual time series VH curve to obtain the smooth curve corresponding to the annual time series VH curve, and determine the rice sowing period according to the smooth curve; extract the cumulative temperature information from the rice sowing period to the rice heading date in the target area, and construct the multi-scale information of the annual time series VV curve; extract the tillering period information and fruit development and maturity period information in the key growth period of rice according to the multi-scale information, and extract the heading-heading period information in the key growth period of rice in the target area based on the multi-scale information and the cumulative temperature information. The present application uses the multi-scale information in the wavelet change, so as to effectively reduce the influence of noise in the time series curve, and at the same time enhance the utilization of agricultural meteorological information, thereby improving the accuracy of growth period monitoring. Thus, the existing rice monitoring methods solve the problems of insufficient utilization of crop growth period information and insufficient consideration of climate factors.

[0021] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0023] Figure 1 A flowchart of a method for monitoring the key growth period of rice based on time series SAR and cumulative temperature according to an embodiment of the present application;

[0024] Figure 2 A schematic diagram of a multi-resolution analysis technology structure provided for an embodiment of the present application;

[0025] Figure 3 A schematic diagram of an algorithm for extracting the tillering stage and the fruit development and maturity stage provided in one embodiment of the present application;

[0026] Figure 4 A schematic diagram of an algorithm for extracting the booting and heading period provided for one embodiment of the present application;

[0027] Figure 5 A schematic diagram of the execution logic of a method for monitoring a key growth period of rice based on time series SAR and cumulative temperature provided in one embodiment of the present application;

[0028] Figure 6 This is an example diagram of a device for monitoring the key growth period of rice based on time series SAR and cumulative temperature according to an embodiment of the present application;

[0029] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0030] Among them, 10-a rice key growth period monitoring device based on time series SAR and cumulative temperature; 100-a filtering module, 200-a building module, 300-an extraction module; 701-a memory, 702-a processor, 703-a communication interface. DETAILED DESCRIPTION

[0031] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0032] The following describes the rice key growth period monitoring method based on time series SAR and cumulative temperature of the embodiment of the present application with reference to the accompanying drawings. In view of the problems mentioned in the above background technology, the present application provides a rice key growth period monitoring method based on time series SAR and cumulative temperature. In the method, the annual time series VH curve and the annual time series VV curve corresponding to the time series SAR of rice in the target area are obtained, and the annual time series VH curve is filtered and smoothed to obtain the smooth curve corresponding to the annual time series VH curve, and the rice sowing period is determined according to the smooth curve; the cumulative temperature information from the rice sowing period to the rice booting-heading date in the target area is extracted, and the multi-scale information of the annual time series VV curve is constructed; the tillering period information and the fruit development and maturity period information in the key growth period of rice are extracted according to the multi-scale information, and the booting-heading period information in the key growth period of rice in the target area is extracted based on the multi-scale information and the cumulative temperature information. The present application uses the multi-scale information in the wavelet change, so as to effectively reduce the influence of noise in the time series curve, while enhancing the utilization of agricultural meteorological information, and improving the accuracy of growth period monitoring. This solves the problems existing in existing rice monitoring methods, such as insufficient use of crop growth period information and insufficient consideration of climate factors.

[0033] Specifically, Figure 1 A flow chart of a method for monitoring the critical growth period of rice based on time-series SAR and cumulative temperature provided in an embodiment of the present application.

[0034] like Figure 1 As shown, the rice key growth period monitoring method based on time series SAR and cumulative temperature includes the following steps:

[0035] In step S101, the annual time series VH curve and the annual time series VV curve corresponding to the time series SAR of rice in the target area are obtained, and the annual time series VH curve is filtered and smoothed to obtain a smooth curve corresponding to the annual time series VH curve, and the rice sowing period is determined according to the smooth curve.

[0036] The embodiments of the present application can first obtain the annual time-series VH curve and the annual time-series VV curve corresponding to the time-series SAR of rice in the target area, and perform filtering and smoothing operations on the annual time-series VH curve to generate a smooth curve corresponding to the annual time-series VH curve, so as to determine the rice planting period through the smooth curve.

[0037] Optionally, in one embodiment of the present application, the annual time series VH curve is filtered and smoothed to obtain a smooth curve corresponding to the annual time series VH curve, and the rice sowing period is determined based on the smooth curve, including: performing SG filtering and smoothing on the annual time series VH curve to generate a smooth curve corresponding to the annual time series VH curve; calculating the minimum value in the smooth curve, and determining the rice sowing period based on the minimum point corresponding to the minimum value.

[0038] It should be noted that the embodiment of the present application can perform SG filtering and smoothing processing on the annual time series VH curve to generate a smooth curve, and use the minimum value point of the smooth curve as the sowing period of rice.

[0039] Therefore, the embodiments of the present application can be based on SG filtering to effectively reduce random noise in the data through polynomial fitting, making the signal easier to analyze.

[0040] In step S102, the cumulative temperature information from the rice sowing period to the rice booting-heading date in the target area is extracted, and multi-scale information of the annual time series VV curve is constructed.

[0041] After obtaining the rice sowing period, the embodiment of the present application can further perform statistics on the cumulative temperature of the rice heading-heading period based on the rice heading-heading date mapped on the spot; thereafter, the embodiment of the present application uses discrete wavelet transform as the basic framework to describe the multi-scale information in the annual time series VV signal, mainly through the convolution scale, image time resolution and the center frequency of the mother wavelet, so as to define signals of different scales as long-term trends, seasonal changes and local changes and other information.

[0042] Optionally, in one embodiment of the present application, the cumulative temperature information from the rice sowing period to the rice booting-heading date in the target area is extracted, including: determining the rice booting-heading period based on the rice sowing period and the rice booting-heading date in the target area; determining the rice growth reference temperature, and obtaining the daily minimum temperature and the daily maximum temperature during the rice booting-heading period, to calculate the daily average temperature based on the daily minimum temperature and the daily maximum temperature; calculating the growing degree days based on the daily average temperature, the daily minimum temperature, the daily maximum temperature and the growth reference temperature, and calculating the cumulative temperature information based on the growing degree days.

[0043] It should be noted that the embodiment of the present application can calculate the cumulative temperature of rice samples based on the rice booting-heading date mapped on the spot according to formulas (1) and (2). Considering the impact of low temperature on crops, the embodiment of the present application can use 10°C as the reference temperature T for rice growth. base , the average temperature or the lowest temperature of the day (T min ) is lower than this level, crops are considered to stop growing, and the daily average temperature is changed from the daily maximum temperature T max The daily minimum temperature T min The mean of is defined as:

[0044]

[0045] Among them, GDD (Growing Degree Days) represents the accumulated heat; AGDD (Accumulate Growing Degree Days) represents the total accumulated temperature from the sowing day to the booting-heading period.

[0046] Therefore, the embodiment of the present application ensures the uniqueness of the timing characteristic points in the middle stage of rice growth by introducing the cumulative temperature information, thereby effectively improving the accuracy of growth period monitoring.

[0047] Optionally, in one embodiment of the present application, multi-scale information of the annual time series VV curve is constructed, including: based on a preset multi-resolution analysis strategy, performing time-frequency analysis on the annual time series VV curve to separate different frequency components of the annual time series VV curve; constructing multi-scale information corresponding to different frequency components according to a preset wavelet basis center frequency, convolution scale and image time resolution, wherein the multi-scale information includes noise, local changes, seasonal changes and long-term trends.

[0048] Due to the vertical structure of rice, the extinction coefficients between different polarizations are different, which can easily cause the VV channel to attenuate more significantly.

[0049] In order to effectively extract the change information of the timing curve, such as Figure 2As shown, the embodiment of the present application uses discrete wavelet transform as the basic framework to describe the long-term trend, seasonal changes and local changes in the annual time series VV. The mathematical expression process of wavelet transformation is as follows:

[0050] (1) The annual time series VV signal f(x) is subjected to time-frequency analysis through discrete wavelet transformation. The mathematical process can be expressed as:

[0051]

[0052] Among them, Φ(t) is the mother wavelet, Φ a,b (t) represents the wavelet basis function. By scaling and translating the mother wavelet Φ(t), a series of wavelet basis functions can be generated. The wavelet basis function is mainly convolved with the time series signal f(t) to separate the different frequency components in the time series data. In the discrete wavelet transform, a represents the scale parameter of the wavelet basis function, and b represents the displacement parameter.

[0053] (a,b)=(2 j ,2 j k)(5)

[0054] Among them, j is defined by the level of convolution and k is the position index.

[0055] In order to perform multi-scale analysis, discrete wavelet transformation is implemented in the pyramid algorithm; multi-scale analysis decomposes the signal into different scale components through the filter associated with the mother wavelet. In the j-th convolution, the original signal is reconstructed by the low-frequency component and the high-frequency component, as shown in formula (6):

[0056]

[0057] Among them, D j (t) represents the high frequency component, A j (t) represents the low-frequency component.

[0058] (2) In order to select the appropriate scale to monitor different growth stages, it is necessary to associate the scale of the signal with the frequency, as shown in Table 1. The mathematical process is expressed as follows:

[0059]

[0060] Among them, p is the periodic fluctuation of different frequency components; a is the scale, which is related to the convolution level; Vt is the image temporal resolution; v c is the center frequency of the mother wavelet. Since the DB4 basis has a large vanishing moment, it makes different components in the time series smoother in the convolution. In this embodiment of the application, the DB4 basis can be used as the mother wavelet; secondly, the VV time series curve is similar to the DB4 basis, which is conducive to the reconstruction of the signal.

[0061] Table 1

[0062]

[0063] Therefore, the embodiments of the present application utilize multi-resolution analysis technology to decompose the time-series SAR into information of different scales, such as long-term trends, seasonal changes, and local changes, as key features for extracting the growth period, thereby effectively solving the problem of easy confusion of characteristic points of the key growth period of rice in the time-series curve.

[0064] In step S103, the tillering period information and the fruit development and maturity period information in the key growth period of rice are extracted based on the multi-scale information, and the booting-heading period information in the key growth period of rice in the target area is extracted based on the multi-scale information and the accumulated temperature information.

[0065] Furthermore, the embodiments of the present application can select information of appropriate scale to describe the characteristics of the growth period according to the duration of different growth periods; it can be understood that the tillering period belongs to the nutritional period and lasts for a long time, and the embodiments of the present application can extract it through the characteristic points of seasonal changes; in addition, since the fruit development and maturity period lasts for a short time and belongs to the late stage of crop growth, the embodiments of the present application can characterize the key nodes starting from the late stage of crop development through trend changes in seasonal changes, and extract the fruit development and maturity period based on the characteristic points of local changes.

[0066] Those skilled in the art can understand that the booting-heading period belongs to the middle stage of crop growth and lasts for a short time. Therefore, the embodiments of the present application can use the extreme points in the local changes as the characteristic points of the booting-heading period, and based on the mean and standard deviation of the cumulative temperature of the booting-heading period of the rice samples, construct a normal distribution function to represent the probability that the characteristic point is the booting-heading period. When the probability of the characteristic point being in the booting-heading period in the cumulative temperature distribution is the largest, the extreme points in the local changes can be identified as the booting-heading period.

[0067] Therefore, the embodiments of the present application can reduce the impact of noise in the time series curve through the multi-scale information in the wavelet change, and at the same time enhance the utilization of the growth period duration information, thereby realizing the extraction of the growth period in the later stage of rice growth without samples.

[0068] Optionally, in one embodiment of the present application, tillering period information and fruit development and maturity period information in the critical growth period of rice are extracted based on multi-scale information, and booting-heading period information in the critical growth period of rice in the target area is extracted based on multi-scale information and cumulative temperature information, including: determining the growth period characteristics of rice through multi-scale information, and obtaining the maximum value of seasonal changes of rice after the sowing date, so as to extract tillering period information according to the growth period characteristics and the maximum value of rice; obtaining characteristic points of local changes and positive and negative derivatives of seasonal changes, and determining growth indicators corresponding to periodic fluctuations of seasonal changes according to the positive and negative derivatives, so as to extract fruit development and maturity period information through growth indicators and characteristic points; calculating the mean and standard deviation corresponding to the cumulative temperature information, and constructing a normal distribution function according to the mean and standard deviation; calculating the probability of booting-heading period through the normal distribution function, and obtaining the local change extreme point in the local change when the probability of booting-heading period is maximum, so as to determine the booting-heading period information according to the local change extreme point.

[0069] It should be understood by those skilled in the art that, as a semi-aquatic crop, the backscattering characteristics of rice are mainly affected by surface water in its initial growth stage. During this period, the scattering mechanism is mainly surface scattering caused by the water surface, so the backscattering coefficient is low; by the tillering stage of rice, the leaves are fully developed, and the backscattering coefficient is comparable to that of typical vegetation; then, as the stem grows, the vertical structure causes a significant attenuation of the VV intensity, and this attenuation process continues until the plant's booting-heading stage. Therefore, the local maximum after the sowing date is related to the tillering stage of rice.

[0070] The tillering period belongs to the vegetative period of crops and lasts for a long time. The vertical stems of rice cause the VV intensity to decay greatly within the season. In discrete wavelet changes, seasonal changes can be observed as continuous changes in the time series curve. Therefore, the embodiment of the present application can further extract the tillering period based on the maximum value of seasonal changes after the sowing date. In order to filter out abnormal local maxima, the embodiment of the present application takes into account the continuous attenuation of the VV channel, such as Figure 3 As shown in Figure 1, the local maximum value t1 after the sowing date is defined as the tillering period based on formula (8).

[0071] t>sowing date∧f'(t-1)>0∧f'(t)<0∧f'(t+1)<0 (8)

[0072] Among them, f'(t) represents the degree of change of the local change curve in the t phase, which is defined by the right derivative; f'(t-1) and f'(t+1) respectively represent the degree of change of the local change curve in adjacent phases. The above formula mainly defines the local maximum through the positive and negative changes of the derivative and the continuous change trend.

[0073] Secondly, the fruit development and maturity period belongs to the late stage of crop growth and lasts for a relatively short time; Figure 3 As shown, the embodiment of the present application proposes a new crop growth descriptor to characterize the node where the late growth starts, and defines the periodic fluctuation of seasonal changes as a growth indicator based on the positive and negative derivatives observed in seasonal changes. The period always covers the vegetative stage and the reproductive stage of the crop. As shown in Formula 9, t2 is defined as the starting phase for searching for feature points in the late growth stage:

[0074] t>t1∧f'(t-2)<0∧f'(t-1)<0∧f'(t)>0 (9)

[0075] Among them, f'(t) represents the degree of change of the local change curve in the t phase, which is defined by the right derivative; f'(t-1) and f'(t-2) respectively represent the degree of change of the local change curve in the first two phases. The above formula mainly defines the starting phase of the search for characteristic points in the late growth period through the tillering period t1 and the positive and negative changes of the derivative.

[0076] In rice canopy monitoring, the local maximum of VV observed in the late stage of rice growth is related to the fruit development and maturity stage, when the rice canopy develops most vigorously. The embodiments of the present application can use t2 as the starting point to eliminate the influence of local extreme values ​​in the crop nutrition and reproductive periods. In local changes, the first local maximum is defined as the fruit development and maturity stage, which is mainly because local changes are more sensitive to events of short duration.

[0077] In addition, the booting-heading period belongs to the reproductive period and lasts for a short time. Therefore, the embodiments of the present application can use the local minimum value as a necessary condition for the booting-heading period in the local changes; however, the local changes include not only the short-term changes of rice, but also other accidental events such as the change of water surface roughness in the early stage of plant growth; in order to reduce the uncertainty caused by similar feature points, the embodiments of the present application can calculate the cumulative temperature by reanalyzing the daily maximum temperature and daily minimum temperature in the product ERA5-Land Daily Aggregated, and construct a normal distribution function based on the mean u and standard deviation σ of the cumulative temperature of the booting-heading period of the rice sample, as shown in formula (10), to represent the probability of the booting-heading period. Figure 4 As shown, when the probability of the booting-heading stage represented by the cumulative temperature is the largest, the extreme point in the local variation is identified as the booting-heading stage.

[0078]

[0079] Where x is the cumulative temperature corresponding to the extreme point in the local change; P is the probability of the booting-heading period represented by the cumulative temperature.

[0080] Therefore, the embodiments of the present application define the growth characteristics of rice by combining multi-scale information to characterize the time nodes of the rice growth period, and introduce cumulative temperature information to ensure the uniqueness of the characteristic points of the time series curve in rice growth. Finally, the characteristic points in seasonal changes and local changes are used to extract different growth periods, thereby reducing the impact of noise in the time series curve through multi-scale information in wavelet changes, while enhancing the utilization of agricultural meteorological information and improving the accuracy of growth period monitoring.

[0081] The following application describes the execution logic of the rice key growth period monitoring method based on time series SAR and cumulative temperature in conjunction with the accompanying drawings.

[0082] Figure 5 FIG. 1 is a schematic diagram of the execution logic of the rice key growth period monitoring method based on time series SAR and cumulative temperature of the present application. Figure 5 As shown, the execution process of the rice key growth period monitoring method based on time series SAR and cumulative temperature of the present application is as follows:

[0083] S501: Perform SG filtering and smoothing on the annual time series VH curve, and use the minimum value point of the smooth curve as the rice sowing period;

[0084] S502: Based on the extraction of rice sowing period, and based on the rice heading-heading date mapped on the spot, the cumulative temperature characteristics of the rice heading-heading period are counted;

[0085] S503: Based on multi-resolution analysis technology, characterize the periodic changes of different scales in the VV time series curve;

[0086] S504: extracting rice tillering period and fruit development and maturity period through multi-scale information;

[0087] S505: Extracting the heading period of rice based on the multi-scale information and the accumulated temperature information.

[0088] According to the rice key growth period monitoring method based on time series SAR and cumulative temperature proposed in the embodiment of the present application, the annual time series VH curve and annual time series VV curve corresponding to the time series SAR of rice in the target area are obtained, and the annual time series VH curve is filtered and smoothed to obtain the smooth curve corresponding to the annual time series VH curve, and the rice sowing period is determined according to the smooth curve; the cumulative temperature information from the rice sowing period to the rice booting-heading date in the target area is extracted, and the multi-scale information of the annual time series VV curve is constructed; the tillering period information and the fruit development and maturity period information in the key growth period of rice are extracted according to the multi-scale information, and the booting-heading period information in the key growth period of rice in the target area is extracted based on the multi-scale information and the cumulative temperature information. The present application uses the multi-scale information in the wavelet change, so as to effectively reduce the influence of noise in the time series curve, and at the same time enhance the utilization of agricultural meteorological information, thereby improving the accuracy of growth period monitoring.

[0089] Next, a device for monitoring the critical growth period of rice based on time series SAR and cumulative temperature according to an embodiment of the present application will be described with reference to the accompanying drawings.

[0090] Figure 6 It is a block diagram of a device for monitoring a critical growth period of rice based on time-series SAR and cumulative temperature according to an embodiment of the present application.

[0091] like Figure 6 As shown, the rice key growth period monitoring device 10 based on time series SAR and cumulative temperature includes: a filtering module 100, a building module 200 and an extraction module 300.

[0092] Among them, the filtering module 100 is used to obtain the annual time-series VH curve and the annual time-series VV curve corresponding to the time-series SAR of rice in the target area, and filter and smooth the annual time-series VH curve to obtain a smooth curve corresponding to the annual time-series VH curve, and determine the rice sowing period according to the smooth curve.

[0093] The construction module 200 is used to extract the cumulative temperature information from the rice sowing period to the rice booting-heading date in the target area, and to construct the multi-scale information of the annual time series VV curve.

[0094] The extraction module 300 is used to extract tillering period information and fruit development and maturity period information in the key growth period of rice based on multi-scale information, and to extract booting-heading period information in the key growth period of rice in the target area based on multi-scale information and cumulative temperature information.

[0095] Optionally, in one embodiment of the present application, the filtering module 100 includes: a generating unit and a first calculating unit.

[0096] The generating unit is used to perform SG filtering and smoothing processing on the annual time series VH curve to generate a smooth curve corresponding to the annual time series VH curve.

[0097] The first calculation unit is used to calculate the minimum value in the smooth curve and determine the rice sowing period according to the minimum point corresponding to the minimum value.

[0098] Optionally, in one embodiment of the present application, the construction module 200 includes: a first determination unit, a second determination unit and a second calculation unit.

[0099] The first determination unit is used to determine the rice heading-heading period based on the rice sowing period and the rice heading-heading date in the target area.

[0100] The second determination unit is used to determine the growth reference temperature of rice and obtain the daily minimum temperature and the daily maximum temperature during the rice booting-heading period to calculate the daily average temperature based on the daily minimum temperature and the daily maximum temperature.

[0101] The second calculation unit is used to calculate the growing degree day based on the daily average temperature, the daily minimum temperature, the daily maximum temperature and the growth reference temperature, and calculate the cumulative temperature information according to the growing degree day.

[0102] Optionally, in one embodiment of the present application, the construction module 200 further includes: a separation unit and a creation unit.

[0103] Among them, the separation unit is used to perform time-frequency analysis on the annual time series VV curve based on a preset multi-resolution analysis strategy to separate different frequency components of the annual time series VV curve.

[0104] A unit is established to construct multi-scale information corresponding to different frequency components according to a preset wavelet basis center frequency, convolution scale and image time resolution, wherein the multi-scale information includes noise, local changes, seasonal changes and long-term trends.

[0105] Optionally, in one embodiment of the present application, the extraction module 300 includes: a first acquisition unit, a second acquisition unit, a construction unit and a third calculation unit.

[0106] Among them, the first acquisition unit is used to determine the rice growth period characteristics through multi-scale information, and obtain the maximum value of the seasonal change of rice after the sowing date, so as to extract the tillering period information according to the rice growth period characteristics and the maximum value.

[0107] The second acquisition unit is used to obtain the characteristic points of local changes and the positive and negative derivatives of seasonal changes, and determine the growth indicators corresponding to the periodic fluctuations of seasonal changes according to the positive and negative derivatives, so as to extract the fruit maturity information through the growth indicators and characteristic points.

[0108] The construction unit is used to calculate the mean and standard deviation corresponding to the cumulative temperature information, and construct a normal distribution function based on the mean and standard deviation.

[0109] The third calculation unit is used to calculate the probability of the booting-heading period through a normal distribution function, and obtain the local change extreme point in the local change when the probability of the booting-heading period is maximum, so as to determine the booting-heading period information according to the local change extreme point.

[0110] It should be noted that the above explanation of the embodiment of the rice key growth period monitoring method based on time series SAR and cumulative temperature is also applicable to the rice key growth period monitoring device based on time series SAR and cumulative temperature of this embodiment, which will not be repeated here.

[0111] According to the rice key growth period monitoring device based on time series SAR and cumulative temperature proposed in the embodiment of the present application, it includes a filtering module for obtaining the annual time series VH curve and the annual time series VV curve corresponding to the time series SAR of rice in the target area, and filtering and smoothing the annual time series VH curve to obtain the smooth curve corresponding to the annual time series VH curve, and determining the rice sowing period according to the smooth curve; a construction module for extracting the cumulative temperature information from the rice sowing period to the rice booting-heading date in the target area, and constructing the multi-scale information of the annual time series VV curve; an extraction module for extracting the tillering period information and the fruit development and maturity period information in the key growth period of rice according to the multi-scale information, and extracting the booting-heading period information in the key growth period of rice in the target area based on the multi-scale information and the cumulative temperature information. The present application uses the multi-scale information in the wavelet change to effectively reduce the influence of noise in the time series curve, while enhancing the utilization of agricultural meteorological information and improving the accuracy of growth period monitoring.

[0112] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0113] A memory 701 , a processor 702 , and a computer program stored in the memory 701 and executable on the processor 702 .

[0114] When the processor 702 executes the program, the method for monitoring the key growth period of rice based on time series SAR and cumulative temperature provided in the above embodiment is implemented.

[0115] Furthermore, the electronic device further comprises:

[0116] The communication interface 703 is used for communication between the memory 701 and the processor 702 .

[0117] The memory 701 is used to store computer programs that can be executed on the processor 702 .

[0118] The memory 701 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0119] If the memory 701, the processor 702 and the communication interface 703 are implemented independently, the communication interface 703, the memory 701 and the processor 702 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0120] Optionally, in a specific implementation, if the memory 701, the processor 702 and the communication interface 703 are integrated on a chip, the memory 701, the processor 702 and the communication interface 703 can communicate with each other through an internal interface.

[0121] The processor 702 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0122] The embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for monitoring the critical growth period of rice based on time-series SAR and cumulative temperature.

[0123] The embodiment of the present application also provides a computer program product, including a computer program, which, when executed, is used to implement the above-mentioned rice key growth period monitoring method based on time series SAR and cumulative temperature.

[0124] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0125] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0126] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0127] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0128] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0129] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0130] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0131] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for monitoring the key growth period of rice based on time series SAR and cumulative temperature, characterized in that: The following steps are involved: Obtaining an annual time-series VH curve and an annual time-series VV curve corresponding to the time-series SAR of rice in the target area, filtering and smoothing the annual time-series VH curve to obtain a smooth curve corresponding to the annual time-series VH curve, and determining the rice sowing period according to the smooth curve; Extracting the cumulative temperature information from the rice sowing period to the rice booting-heading date in the target area, and constructing the multi-scale information of the annual time series VV curve; The tillering period information and the fruit development and maturity period information in the key growth period of rice are extracted according to the multi-scale information, and the booting-heading period information in the key growth period of rice in the target area is extracted based on the multi-scale information and the cumulative temperature information.

2. The method according to claim 1, characterized in that The filtering and smoothing process is performed on the annual time series VH curve to obtain a smooth curve corresponding to the annual time series VH curve, and the rice sowing period is determined according to the smooth curve, including: Performing SG filtering and smoothing processing on the annual time series VH curve to generate a smooth curve corresponding to the annual time series VH curve; The minimum value in the smooth curve is calculated, and the rice sowing period is determined according to the minimum point corresponding to the minimum value.

3. The method according to claim 2, characterized in that The step of extracting the cumulative temperature information from the rice sowing period to the rice booting-heading period in the target area includes: Determining the rice heading-heading period based on the rice sowing period and the rice heading-heading date in the target area; Determining the growth reference temperature of the rice, and obtaining the daily minimum temperature and the daily maximum temperature during the rice booting-heading period, so as to calculate the daily average temperature according to the daily minimum temperature and the daily maximum temperature; Growing degree days are calculated based on the daily average temperature, the daily minimum temperature, the daily maximum temperature, and the growth reference temperature, and the cumulative temperature information is calculated based on the growing degree days.

4. The method according to claim 3, characterized in that The multi-scale information of constructing the annual time series VV curve includes: Based on a preset multi-resolution analysis strategy, a time-frequency analysis is performed on the annual time series VV curve to separate different frequency components of the annual time series VV curve; The multi-scale information corresponding to the different frequency components is constructed according to the preset wavelet basis center frequency, convolution scale and image time resolution, wherein the multi-scale information includes noise, local changes, seasonal changes and long-term trends.

5. The method according to claim 4, characterized in that The step of extracting tillering period information and fruit development and maturity period information in the key growth period of rice according to the multi-scale information, and extracting booting-heading period information in the key growth period of rice in the target area based on the multi-scale information and the accumulated temperature information, comprises: Determine the growth period characteristics of rice through the multi-scale information, and obtain the maximum value of the seasonal change of the rice after the sowing date, so as to extract the tillering period information according to the growth period characteristics of rice and the maximum value; Acquire the characteristic points of the local changes and the positive and negative derivatives of the seasonal changes, and determine the growth indicators corresponding to the periodic fluctuations of the seasonal changes according to the positive and negative derivatives, so as to extract the fruit development maturity information through the growth indicators and the characteristic points; Calculating a mean value and a standard deviation corresponding to the accumulated temperature information, and constructing a normal distribution function according to the mean value and the standard deviation; The probability of the booting-heading period is calculated by the normal distribution function, and the local change extreme point in the local change when the probability of the booting-heading period is the largest is obtained, so as to determine the booting-heading period information according to the local change extreme point.

6. A rice key growth period monitoring device based on time series SAR and cumulative temperature, characterized in that: include: A filtering module is used to obtain an annual time series VH curve and an annual time series VV curve corresponding to the time series SAR of rice in the target area, and filter and smooth the annual time series VH curve to obtain a smooth curve corresponding to the annual time series VH curve, and determine the rice sowing period according to the smooth curve; A construction module is used to extract the cumulative temperature information from the rice sowing period to the rice booting-heading date in the target area, and to construct the multi-scale information of the annual time series VV curve; The extraction module is used to extract the tillering period information and the fruit development and maturity period information in the key growth period of rice according to the multi-scale information, and extract the booting-heading period information in the key growth period of rice in the target area based on the multi-scale information and the cumulative temperature information.

7. The device according to claim 6, characterized in that The filtering module comprises: A generating unit, configured to perform SG filtering and smoothing processing on the annual time series VH curve to generate a smooth curve corresponding to the annual time series VH curve; The first calculation unit is used to calculate the minimum value in the smooth curve, and determine the rice sowing period according to the minimum point corresponding to the minimum value.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for monitoring the critical growth period of rice based on time series SAR and cumulative temperature as described in any one of claims 1 to 5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the method for monitoring the key growth period of rice based on time series SAR and cumulative temperature as described in any one of claims 1 to 5.

10. A computer program product, comprising a computer program, characterized in that The computer program is executed to implement the method for monitoring the critical growth period of rice based on time-series SAR and cumulative temperature as described in any one of claims 1 to 5.

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