Rice key growth period monitoring method based on timing SAR and cumulative temperature

Abstract

The application relates to a rice key growth period monitoring method based on time sequence SAR and accumulated temperature, wherein the method comprises the following steps: acquiring year time sequence VH curves and year time sequence VV curves corresponding to time sequence SAR of rice in a target area, performing filtering and smoothing processing on the year time sequence VH curves to obtain a smooth curve corresponding to the year time sequence VH curves, and determining a rice sowing period according to the smooth curve; extracting accumulated temperature information from the rice sowing period to a rice booting to heading date of the target area, and constructing multi-scale information of the year time sequence VV curves; extracting tillering period information and fruit development and maturation period information in the key growth period of the rice according to the multi-scale information, and extracting booting to heading period 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 that crop growth period information is not fully utilized and climate factors are not considered in the existing rice monitoring method are solved.

Description

TECHNICAL FIELD

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

[0002] Optical and SAR remote sensing images have been widely used in rice growth period monitoring. Since multiple bands in the optical spectrum are sensitive to plant pigments and leaf area characteristics, the prior art combines different spectral bands to extract the growth period. In previous applications, normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), red edge chlorophyll index (CI red-edge ), and other optical vegetation indices can be used to extract the growth period of rice and achieve good results. However, optical vegetation indices are prone to saturation, making it difficult to distinguish the late growth state of crops. Currently, SAR can penetrate clouds and is sensitive to rice canopy structure, water content, and biomass, providing more information for crop key growth period extraction. Therefore, rice growth period monitoring algorithms based on SAR data have developed rapidly.

[0003] Due to the dynamic response of radar backscatter coefficient to different structures of crop growth stages, the information contained in time-series SAR is worth attention. How to extract growth period related information from time-series signals is the focus of research. Currently, a large number of studies have developed effective time-series data information extraction methods, mainly through time domain filtering to reduce local noise and through establishing the relationship between time domain feature points and crop growth stages to extract the growth period. However, this type of method often ignores the duration of different growth periods and the response of crops to climate, and similar feature points in different growth stages are easily confused. Therefore, in addition to the description of time domain curve feature points, other key features of the growth period are urgently needed to solve the mismatch between satellite inversion indicators and ground observations.

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

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

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

[0007] Optionally, in an embodiment of the present application, the filtering and smoothing processing on the annual time sequence VH curve to obtain a smooth curve corresponding to the annual time sequence VH curve, and determining the rice sowing period according to the smooth curve, comprises: performing S-G filtering and smoothing processing on the annual time sequence VH curve to generate a smooth curve corresponding to the annual time sequence VH curve; calculating the minimum value in the smooth curve, and determining the rice sowing period according to the minimum value point corresponding to the minimum value.

[0008] Optionally, in an embodiment of the present application, the extracting the cumulative temperature information between the rice sowing period and the rice booting date of the rice in the target area comprises: determining the rice booting date based on the rice sowing period and the rice booting date of the rice in the target area; determining the growth reference temperature of the rice, and obtaining the daily minimum temperature and the daily maximum temperature in the rice booting period to calculate the daily average temperature according to the daily minimum temperature and the daily maximum temperature; calculating the growth length day 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 according to the growth length day.

[0009] Optionally, in an embodiment of the present application, the constructing the multi-scale information of the annual time sequence VV curve comprises: performing time-frequency analysis on the annual time sequence VV curve based on a preset multi-resolution analysis strategy to separate different frequency components of the annual time sequence VV curve; constructing the 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 comprises noise, local change, seasonal change and long-term trend.

[0010] Optionally, in an embodiment of the present application, the extracting the tillering stage information and the fruit development and maturation stage information in the key growth period of rice according to the multi-scale information, and simultaneously extracting the booting stage information in the key growth period of rice in the target area based on the multi-scale information and the cumulative temperature information, comprises: determining a rice growth period feature through the multi-scale information, and obtaining a maximum value of seasonal change after sowing day of the rice, so as to extract the tillering stage information according to the rice growth period feature and the maximum value; obtaining a feature point of local change and a positive and negative derivative of seasonal change, and determining a growth indicator corresponding to a periodic fluctuation of the seasonal change according to the positive and negative derivative, so as to extract the fruit development and maturation stage information through the growth indicator and the feature point; calculating a mean value and a standard deviation corresponding to the cumulative temperature information, and constructing a normal distribution function according to the mean value and the standard deviation; calculating a booting stage probability through the normal distribution function, and obtaining a local change extreme point in the local change when the booting stage probability is maximum, so as to determine the booting stage information according to the local change extreme point.

[0011] The second aspect embodiment of the present application provides a rice key growth period monitoring device based on time sequence SAR and cumulative temperature, comprising: a filtering module configured to obtain an annual time sequence VH curve and an annual time sequence VV curve corresponding to a time sequence SAR of rice in a target area, and perform filtering and smoothing processing on the annual time sequence VH curve to obtain a smooth curve corresponding to the annual time sequence VH curve, and determine a rice sowing period according to the smooth curve; a construction module configured to extract cumulative temperature information between the rice sowing period and a booting stage date of rice in the target area, and construct multi-scale information of the annual time sequence VV curve; and an extraction module configured to extract tillering stage information and fruit development and maturation stage information in a key growth period of rice according to the multi-scale information, and simultaneously extract booting stage 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 an embodiment of the present application, the filtering module comprises: a generation unit configured to perform S-G filtering and smoothing processing on the annual time sequence VH curve to generate a smooth curve corresponding to the annual time sequence VH curve; and a first calculation unit configured to calculate a minimum value in the smooth curve, and determine the rice sowing period according to a minimum value point corresponding to the minimum value.

[0013] Optionally, in an embodiment of the present application, the constructing module comprises: a first determining unit configured to determine a booting-to-heading period of the rice based on the rice sowing date and a booting-to-heading date of the target area; a second determining unit configured to determine a growth base temperature of the rice, and acquire a daily minimum temperature and a daily maximum temperature in the booting-to-heading period of the rice, so as to calculate a daily average temperature according to the daily minimum temperature and the daily maximum temperature; and a second calculating unit configured to calculate a growth length day based on the daily average temperature, the daily minimum temperature, the daily maximum temperature and the growth base temperature, and calculate the cumulative temperature information according to the growth length day.

[0014] Optionally, in an embodiment of the present application, the constructing module further comprises: a separating unit configured to separate different frequency components of the annual time-series VV curve based on a preset multi-resolution analysis strategy, so as to perform time-frequency analysis on the annual time-series VV curve; and a establishing unit configured to construct multi-scale information corresponding to the different frequency components according to a preset wavelet basis center frequency, a convolution scale and an image time resolution, wherein the multi-scale information comprises noise, local variation, seasonal variation and long-term trend.

[0015] Optionally, in an embodiment of the present application, the extracting module comprises: a first acquiring unit configured to determine a rice growth period feature by using the multi-scale information, and acquire a maximum value of the seasonal variation after the sowing day, so as to extract the tillering period information according to the rice growth period feature and the maximum value; a second acquiring unit configured to acquire a feature point of the local variation and a positive and negative derivative of the seasonal variation, and determine a growth indicator corresponding to a periodic fluctuation of the seasonal variation according to the positive and negative derivative, so as to extract the fruit development and maturation period information by using the growth indicator and the feature point; a constructing unit configured to calculate a mean value and a standard deviation corresponding to the cumulative temperature information, and construct a normal distribution function according to the mean value and the standard deviation; and a third calculating unit configured to calculate a booting-to-heading period probability by using the normal distribution function, and acquire a local variation extreme point in the local variation when the booting-to-heading period probability is maximum, so as to determine the booting-to-heading period information according to the local variation extreme point.

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

[0017] The fourth aspect of the embodiments of the present application provides a computer readable storage medium storing a computer program, which is executed by a processor to implement the method for monitoring the key growth period of rice based on the timing SAR and the cumulative temperature.

[0018] The fifth aspect of the embodiments of the present application provides a computer program product comprising a computer program, which is executed to implement the method for monitoring the key growth period of rice based on the timing SAR and the cumulative temperature.

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

[0020] The embodiments of the present application can obtain the annual timing VH curve and the annual timing VV curve corresponding to the timing SAR of the rice in the target area, and perform filtering and smoothing processing on the annual timing VH curve to obtain a smoothed curve corresponding to the annual timing VH curve, and determine the rice planting period according to the smoothed curve; extract the cumulative temperature information from the rice booting to the heading date of the rice in the target area, and construct the multi-scale information of the annual timing VV curve; extract the tillering period information and the fruit development and maturation period information in the key growth period of rice according to the multi-scale information, and extract the booting to 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 embodiments of the present application can effectively reduce the influence of noise in the timing curve by using the multi-scale information in wavelet change, and at the same time, enhance the utilization of agricultural meteorological information, and improve the accuracy of the growth period monitoring. Therefore, the problems of insufficient utilization of crop growth period information and insufficient consideration of climate factors in the existing rice monitoring method are solved.

[0021] Additional aspects and advantages of the present application will be made apparent by the following description and the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0022] The above and / or additional aspects and advantages of the present application will become apparent and be readily understood by considering the following detailed description, from which the embodiments of the present application can be realized, taken 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 the timing SAR and the cumulative temperature according to an embodiment of the present application is shown in FIG. 1;

[0024] Figure 2 A multi-resolution analysis technology structure diagram according to an embodiment of the present application is shown in FIG. 2;

[0025] Figure 3 A tillering period and fruit development and maturation period extraction algorithm diagram according to an embodiment of the present application is shown in FIG. 3;

[0026] Figure 4 An inflorescence emergence and heading stage extraction algorithm schematic diagram provided for an embodiment of the present application;

[0027] Figure 5 An execution logic schematic diagram of a rice key growth period monitoring method based on timing SAR and cumulative temperature provided for an embodiment of the present application;

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

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

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

[0031] The embodiments of the present application are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference signs represent the same or similar elements or elements with the same or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary and are intended to explain the present application, and cannot be understood as a limitation of the present application.

[0032] The rice key growth period monitoring method based on timing SAR and cumulative temperature of the embodiments of the present application is described below with reference to the accompanying drawings. In view of the problems mentioned in the above background art, the present application provides a rice key growth period monitoring method based on timing SAR and cumulative temperature, in which the annual timing VH curve and the annual timing VV curve corresponding to the timing SAR of the rice in the target area are obtained, and the annual timing VH curve is filtered and smoothed to obtain a smoothed curve corresponding to the annual timing VH curve, and the rice planting period is determined according to the smoothed curve; the cumulative temperature information from the rice booting to the heading date of the rice in the target area is extracted, and the multi-scale information of the annual timing VV curve is constructed; the tillering period information and the fruit development and maturation period information in the key growth period of the rice are extracted according to the multi-scale information, and the inflorescence emergence and heading period information in the key growth period of the rice in the target area is extracted based on the multi-scale information and the cumulative temperature information. The present application extracts the multi-scale information in the wavelet change, thereby effectively reducing the influence of noise in the timing curve, enhancing the utilization of agricultural meteorological information, and improving the accuracy of growth period monitoring. Thus, the problems of insufficient utilization of crop growth period information and insufficient consideration of climate factors in the existing rice monitoring method are solved.

[0033] Specifically,Figure 1 A flowchart of a rice key growth period monitoring method based on timing SAR and cumulative temperature provided by an embodiment of the present application.

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

[0035] In step S101, the year timing VH curve and the year timing VV curve corresponding to the timing SAR of the rice in the target area are obtained, and the year timing VH curve is filtered and smoothed to obtain a smoothed curve corresponding to the year timing VH curve, and the smoothed curve is used to determine the rice planting period.

[0036] The embodiment of the present application can first obtain the year timing VH curve and the year timing VV curve corresponding to the timing SAR of the rice in the target area, and perform filtering and smoothing operation on the year timing VH curve to generate a smoothed curve corresponding to the year timing VH curve, so as to determine the rice planting period through the smoothed curve.

[0037] Optionally, in an embodiment of the present application, the year timing VH curve is filtered and smoothed to obtain a smoothed curve corresponding to the year timing VH curve, and the smoothed curve is used to determine the rice planting period, including: performing S-G filtering and smoothing on the year timing VH curve to generate a smoothed curve corresponding to the year timing VH curve; calculating the minimum value in the smoothed curve, and determining the rice planting period according to the minimum value point corresponding to the minimum value.

[0038] It should be noted that the embodiment of the present application can perform S-G filtering and smoothing on the year timing VH curve to generate a smoothed curve, and use the minimum value point of the smoothed curve as the planting period of the rice.

[0039] Therefore, the embodiment of the present application can effectively reduce random noise in the data through polynomial fitting based on S-G filtering, so that the signal is easy to analyze.

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

[0041] After obtaining the rice planting period, further, the embodiment of the present application can statistically analyze the cumulative temperature of the booting-to-heading period of the rice based on the field survey of the booting-to-heading date of the rice; then, the embodiment of the present application uses discrete wavelet transform as the basic framework to describe the multi-scale information in the year timing VV signal, mainly through convolution scale, image time resolution and center frequency of mother wavelet, so as to define the signals of different scales as long-term trend, seasonal change and local change and other information.

[0042] ​Optionally, in an embodiment of the present application, the cumulative temperature information of the rice from the rice seeding date to the target area's booting-to-heading date is extracted, including: determining the booting-to-heading period of the rice based on the rice seeding date and the target area's booting-to-heading date; determining the growth base temperature of the rice, and obtaining the daily minimum temperature and the daily maximum temperature in the booting-to-heading period of the rice to calculate the daily average temperature according to the daily minimum temperature and the daily maximum temperature; calculating the growth length day based on the daily average temperature, the daily minimum temperature, the daily maximum temperature and the growth base temperature, and calculating the cumulative temperature information according to the growth length day.

[0043] It should be noted that, in an embodiment of the present application, the cumulative temperature of the rice sample is calculated according to the formula (1) and (2) based on the field survey of the booting-to-heading date of the rice, and the base temperature T of 10°C of the rice growth is used to consider the influence of low temperature on crops. base When the daily average temperature or the daily minimum temperature (T min ) is lower than this level, the crop is considered to stop growing, and the daily average temperature is defined by the average of the daily maximum temperature T max and the daily minimum temperature T min .

[0044]

[0045] Wherein, GDD (Growing Degree Days) represents the accumulated heat; AGDD (Accumulate Growing Degree Days) represents the total cumulative temperature from the seeding date to the booting-to-heading period.

[0046] Therefore, in an embodiment of the present application, the cumulative temperature information is introduced to ensure the uniqueness of the timing feature point in the middle growth period of the rice, and the accuracy of the growth period monitoring is effectively improved.

[0047] Optionally, in an embodiment of the present application, the multi-scale information of the annual timing VV curve is constructed, including: performing time-frequency analysis on the annual timing VV curve based on a preset multi-resolution analysis strategy to separate different frequency components of the annual timing VV curve; and 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 change, seasonal change and long-term trend.

[0048] Due to the vertical structure of the rice, the extinction coefficients are different between different polarizations, which easily causes more obvious attenuation in the VV channel.

[0049] In order to effectively extract the change information of the timing curve, the VV curve is decomposed into different frequency components, and the multi-scale information of the VV curve is constructed. Figure 2As shown, the embodiments of the present application take discrete wavelet transform as a basic framework to describe the long-term trend, seasonal change and local change in the annual time series VV. The mathematical expression process of wavelet change is as follows:

[0050] (1) The annual time series VV signal f(x) is analyzed by discrete wavelet transform, and the mathematical process can be expressed as:

[0051]

[0052] Wherein, Φ(t) is the mother wavelet, Φ a,b (t) represents the wavelet basis function, and a series of wavelet basis functions can be generated by scaling and shifting the mother wavelet Φ(t); the wavelet basis function is mainly convolved with the time series signal f(t) to separate 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] Wherein, j is defined by the level of convolution, and k is the position index.

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

[0056]

[0057] Wherein, D j (t) represents the high frequency component, and A j (t) represents the low frequency component.

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

[0059]

[0060] Wherein, p is the periodic fluctuation of different frequency components; a is the scale, which is related to the convolution level; Vt is the image time resolution; v c is the center frequency of the mother wavelet. Since the DB4 basis has a large vanishing moment, the different components in the time series are relatively smooth in convolution, and the embodiments of the present application can use DB4 basis as the mother wavelet; secondly, the VV time series curve has similarity with DB4 basis, which is conducive to the reconstruction of the signal.

[0061] Table 1

[0062]

[0063] Therefore, the embodiment of the present application can effectively solve the problem that the key feature points of the key growth period of rice are easy to be confused by decomposing the time series SAR into information of different scales such as long-term trend, seasonal change and local change as key features for extracting the growth period by using the multi-resolution analysis technology.

[0064] In step S103, the tillering period information and the fruit development and maturation period information in the key growth period of rice are extracted according to the multi-scale information, and the jointing-emergence 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.

[0065] Further, the embodiment of the present application can select information of a suitable scale to describe the feature of the growth period according to the duration of different growth periods. It can be understood that the tillering period belongs to the vegetative period and has a long duration, and the embodiment of the present application can extract the tillering period by the feature points of the seasonal change. In addition, since the fruit development and maturation period has a short duration and belongs to the later stage of crop growth, the embodiment of the present application can represent the key node at which the later stage of crop growth starts by the trend change in the seasonal change, and extract the fruit development and maturation period based on the feature points of the local change.

[0066] It can be understood by those skilled in the art that the jointing-emergence period belongs to the middle stage of crop growth and has a short duration, and therefore, the embodiment of the present application can take the extreme point in the local change as the feature point of the jointing-emergence period, and construct a normal distribution function to represent the probability that the feature point is the jointing-emergence period based on the mean value and the standard deviation of the cumulative temperature of the jointing-emergence period of the rice sample. When the feature point has the maximum probability of the jointing-emergence period in the cumulative temperature distribution, the extreme point in the local change can be identified as the jointing-emergence period.

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

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

[0069] As a semi-aquatic crop, the backscattering characteristics of rice are mainly affected by the surface water in the initial growth stage, and the scattering mechanism is mainly surface scattering caused by the water surface in this period, so the backscattering coefficient is low; tillering stage, the leaves are fully developed, and the backscattering coefficient is comparable to that of typical vegetation; then, with the growth of the stem, the vertical structure causes significant attenuation of the VV intensity, and this attenuation process continues to the booting stage of the plant. Therefore, the local maximum after the sowing date is related to the tillering stage of rice.

[0070] The tillering stage belongs to the nutrient stage of crops, and the duration is relatively long, and the vertical stem of rice causes a large seasonal attenuation of the VV intensity. In discrete wavelet change, the seasonal change can observe the continuous change of the time series curve, therefore, the embodiment of the present application can further extract the tillering stage based on the maximum value of the seasonal change after the sowing date; in order to filter out abnormal local maximum, the embodiment of the present application considers the continuous attenuation of the VV channel, as shown in formula (8), the local maximum t1 after the sowing date is defined as the tillering stage based on formula (8). Figure 3

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

[0072] Wherein, f’(t) represents the change degree of the local change curve at t time phase, which is defined by the right derivative; f’(t-1) and f’(t+1) respectively represent the change degree of the local change curve at adjacent time phases, and the above formula mainly defines the local maximum value through the positive and negative change of the derivative and the continuous change trend.

[0073] Secondly, the fruit development and maturation stage belongs to the later stage of crop growth, and the duration is relatively short; as shown in​Figure 3 As shown, the embodiment of the present application proposes a new crop growth descriptor to represent the node of late growth start, and defines the periodic fluctuation of seasonal change as a growth indicator according to the positive and negative derivatives observed in the seasonal change, which always covers the vegetative stage and reproductive stage of the crop, as shown in formula 9, t2 is defined as the starting phase of the late growth feature point search:

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

[0075] Wherein, f'(t) represents the degree of change of the local change curve at 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 at the previous two phases, and the above formula mainly defines the starting phase of the late growth feature point search through the tillering stage t1 and the positive and negative changes of the derivative.

[0076] In the monitoring of rice canopy, the local maximum value of VV observed in the late growth of rice is related to the fruit development and maturation period, at which time the rice canopy develops most vigorously, and the embodiment of the present application can take t2 as the starting point to eliminate the influence of the local maximum value of the crop vegetative stage and reproductive stage. In the local change, the first local maximum value is defined as the fruit development and maturation period, which is mainly due to the sensitivity to short duration events in the local change.

[0077] In addition, the booting to heading stage belongs to the reproductive stage and has a short duration, so the embodiment of the present application can take the local minimum value as a necessary condition for the booting to heading stage in the local change; however, the local change not only includes the short-term change of rice, but also contains other accidental events such as the roughness change of water surface in the early stage of plant growth; in order to reduce the uncertainty brought by similar feature points, the embodiment 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 based on the mean value u and standard deviation σ of the cumulative temperature of the booting to heading stage of the rice sample, a normal distribution function is constructed, as shown in formula (10), to represent the probability of the booting to heading stage. As Figure 4 As shown, when the probability of the booting to heading stage represented by the cumulative temperature is maximum, the extreme point in the local change is identified as the booting to heading stage.

[0078]

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

[0080] Therefore, the embodiment of the present application defines the growth characteristics of rice by combining multi-scale information to depict the time nodes of rice growth period, and introduces cumulative temperature information to ensure the uniqueness of the feature points of the timing curve in the growth of rice. Finally, the different growth periods are extracted through the feature points in the seasonal changes and local changes, thereby reducing the influence of noise in the timing curve through the multi-scale information in the wavelet change, enhancing the utilization of agricultural meteorological information, and improving the accuracy of growth period monitoring.

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

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

[0083] S501: Perform S-G filtering smoothing on the annual timing VH curve, and take the minimum value point of the smoothed curve as the rice sowing period;

[0084] S502: On the basis of the extracted rice sowing period, the cumulative temperature characteristics of the rice booting-heading period are counted based on the field survey of the rice booting-heading dates;

[0085] S503: Based on the multi-resolution analysis technology, the periodic changes of different scales in the VV timing curve are depicted;

[0086] S504: The rice tillering period and the fruit development and maturation period are extracted through multi-scale information;

[0087] S505: Based on the multi-scale information and the cumulative temperature information, the rice booting-heading period is extracted.

[0088] ​According to the method for monitoring key growth periods of rice based on time-series SAR and accumulated temperature provided in the embodiments of the present application, the year time-series VH curve and the year time-series VV curve corresponding to the time-series SAR of the rice in a target area are obtained, the year time-series VH curve is filtered and smoothed to obtain a smoothed curve corresponding to the year time-series VH curve, and the rice sowing period is determined according to the smoothed curve; the accumulated temperature information between the rice booting and heading dates of the rice in the target area from the rice sowing period is extracted, and the multi-scale information of the year time-series VV curve is constructed; the tillering period information and the fruit development and maturation period information in the key growth periods of the rice are extracted according to the multi-scale information, and the booting and heading period information in the key growth periods of the rice in the target area is extracted based on the multi-scale information and the accumulated temperature information. The multi-scale information in the wavelet change is used in the embodiments of the present application, so that the influence of noise in the time-series curve can be effectively reduced, the utilization of agricultural meteorological information is improved, and the precision of the growth period monitoring is improved.

[0089] Secondly, the device for monitoring key growth periods of rice based on time-series SAR and accumulated temperature according to the embodiments of the present application is described with reference to the drawings.

[0090] Figure 6 FIG. 1 is a block schematic diagram of the device for monitoring key growth periods of rice based on time-series SAR and accumulated temperature according to the embodiments of the present application.

[0091] As shown in FIG. 2, the device for monitoring key growth periods of rice based on time-series SAR and accumulated temperature 10 comprises a filtering module 100, a construction module 200 and an extraction module 300. Figure 6 The filtering module 100 is configured to obtain the year time-series VH curve and the year time-series VV curve corresponding to the time-series SAR of the rice in a target area, filter and smooth the year time-series VH curve to obtain a smoothed curve corresponding to the year time-series VH curve, and determine the rice sowing period according to the smoothed curve.

[0092] The construction module 200 is configured to extract the accumulated temperature information between the rice booting and heading dates of the rice in the target area from the rice sowing period, and construct the multi-scale information of the year time-series VV curve.

[0093] The extraction module 300 is configured to extract the tillering period information and the fruit development and maturation period information in the key growth periods of the rice according to the multi-scale information, and extract the booting and heading period information in the key growth periods of the rice in the target area based on the multi-scale information and the accumulated temperature information.

[0094] Optionally, in an embodiment of the present application, the filtering module 100 comprises a generation unit and a first calculation unit.

[0095]

[0096] ​The generating unit is configured to perform S-G filtering and smoothing on the annual time-series VH curve to generate a smoothed curve corresponding to the annual time-series VH curve.

[0097] The first calculating unit is configured to calculate a minimum value in the smoothed curve, and determine the rice seeding period according to a minimum value point corresponding to the minimum value.

[0098] Optionally, in an embodiment of the present application, the constructing module 200 comprises a first determining unit, a second determining unit and a second calculating unit.

[0099] The first determining unit is configured to determine a booting-to-heading period of the rice based on the rice seeding period and booting-to-heading dates of the target area.

[0100] The second determining unit is configured to determine a growth base temperature of the rice, and acquire daily minimum temperature and daily maximum temperature in the booting-to-heading period of the rice, so as to calculate a daily average temperature according to the daily minimum temperature and the daily maximum temperature.

[0101] The second calculating unit is configured to calculate a growth length day based on the daily average temperature, the daily minimum temperature, the daily maximum temperature and the growth base temperature, and calculate cumulative temperature information according to the growth length day.

[0102] Optionally, in an embodiment of the present application, the constructing module 200 further comprises a separating unit and a establishing unit.

[0103] The separating unit is configured to perform time-frequency analysis on the annual time-series VH curve based on a preset multi-resolution analysis strategy, so as to separate different frequency components of the annual time-series VH curve.

[0104] The establishing unit is configured 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 comprises noise, local variation, seasonal variation and long-term trend.

[0105] Optionally, in an embodiment of the present application, the extracting module 300 comprises a first acquiring unit, a second acquiring unit, a constructing unit and a third calculating unit.

[0106] The first acquiring unit is configured to determine a rice growth period feature through the multi-scale information, and acquire a maximum value of seasonal variation of the rice after a seeding day, so as to extract tillering period information according to the rice growth period feature and the maximum value.

[0107] The second acquiring unit is configured to acquire a feature point of the local variation and positive and negative derivatives of the seasonal variation, and determine a growth indicator corresponding to a periodic fluctuation of the seasonal variation according to the positive and negative derivatives, so as to extract fruit development and maturation period information through the growth indicator and the feature point.

[0108] The construction unit is configured to calculate a mean value and a standard deviation corresponding to the accumulated temperature information, and construct a normal distribution function according to the mean value and the standard deviation.

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

[0110] It should be noted that the foregoing description of the embodiment of the method for monitoring the key growth period of rice based on the time-series SAR and the accumulated temperature is also applicable to the embodiment of the device for monitoring the key growth period of rice based on the time-series SAR and the accumulated temperature, which will not be described here again.

[0111] The device for monitoring the key growth period of rice based on the time-series SAR and the accumulated temperature provided by the embodiment of the present application comprises a filtering module configured to obtain an annual time-series VH curve and an annual time-series VV curve corresponding to the time-series SAR of the rice in a target region, and perform filtering and smoothing processing on the annual time-series VH curve to obtain a smoothed curve corresponding to the annual time-series VH curve, and determine the sowing period of the rice according to the smoothed curve; a construction module configured to extract accumulated temperature information between the heading-to-heading date of the rice in the target region from the sowing period of the rice, and construct multi-scale information of the annual time-series VV curve; and an extraction module configured to extract tillering period information and fruit development and maturation period information in the key growth period of the rice according to the multi-scale information, and extract heading-to-heading period information in the key growth period of the rice in the target region based on the multi-scale information and the accumulated temperature information. The multi-scale information in the wavelet change is used in the present application, so that the influence of noise in the time-series curve can be effectively reduced, the utilization of agricultural meteorological information is enhanced, and the precision of the growth period monitoring is improved.

[0112] Figure 7 The structure schematic diagram of the electronic device provided by the embodiment of the present application is shown. The electronic device can comprise:

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

[0114] The processor 702 executes the program to implement the method for monitoring the key growth period of rice based on the time-series SAR and the accumulated temperature provided in the foregoing embodiments.

[0115] Further, the electronic device further comprises:

[0116] The communication interface 703 is configured to communicate between the memory 701 and the processor 702.

[0117] The memory 701 is configured to store the computer program executable on the processor 702.

[0118] The memory 701 can include a high-speed RAM memory, and can also include a 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 complete communication between 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, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For convenience of representation, Figure 7 Only one thick line is used in the figure to represent the bus, but it 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 complete communication between each other through an internal interface.

[0121] The processor 702 can be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement one or more embodiments of the present application.

[0122] The embodiments of the present application also provide a computer readable storage medium, which stores a computer program, and the program is executed by a processor to implement the above-mentioned method for monitoring key growth periods of rice based on timing SAR and cumulative temperature.

[0123] The embodiments of the present application also provide a computer program product, which includes a computer program, and the computer program is executed to implement the above-mentioned method for monitoring key growth periods of rice based on timing SAR and cumulative temperature.

[0124] In the description of the application, reference to "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" means that a particular feature, structure, material, or characteristic being described is included in at least one embodiment or example of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment or example. Furthermore, the described specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples. In addition, the usage of "N" means at least two, for example, two, three or the like, unless explicitly stated otherwise.

[0125] Furthermore, the terms "first", "second", or the like, are used merely as a designation of certain elements or features of the application, and do not imply or connote relative importance or a specific order of precedence. Thus, features defined with "first", "second", etc. can include at least one of the features, either explicitly or implicitly.

[0126] Any process or method descriptions or blocks in flow charts or otherwise described herein represent embodiments of modules, segments, or portions of code which include one or more executable instructions for implementing specific logic functions or steps, and alternate implementations are possible. The preferred embodiments of this application are preferably practiced in conjunction with a computer system capable of carrying out the functions described herein, although the application is not limited to being implemented by any particular computer system. The machine-executable instructions can be stored on one or more machine-readable media, which can include any available storage media or memory element. Any of the machine-readable media can be a computer- readable storage medium or memory element. Some examples of such computer- readable storage media or memory elements include primary storage, secondary storage, removable storage, and non-removable storage.

[0127] The logic and / or steps represented in the flowcharts and / or described herein, for example, can be considered as a sequence of instructions to implement logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device, such as a computer-based system, processor- based system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For purposes of this specification, a "computer-readable medium" can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer-readable medium can be a computer- readable storage medium or a computer-readable signal medium. The computer- readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include the following: an electrical connection having one or more wires (electrical connections), a portable computer diskette (a magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium can even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via, for example, optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.

[0128] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the above embodiments, the N steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware and in another embodiment, any of the following technologies, known in the art, or their combinations can be used: discrete logic circuitry having logic gates for implementing logic functions on data signals, application specific integrated circuits having appropriate combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), etc.

[0129] Those skilled in the art can understand that all or part of the steps carried out by the above-mentioned embodiments can be completed by programs instructing related hardware, and the programs can be stored in a computer-readable storage medium. When the programs are executed, one or a combination of the steps of the method embodiments is included.

[0130] In addition, each of the functional units in the various embodiments of the present application can be integrated in one processing module, or each of the units can be physically present separately, or two or more units can be integrated in one module. The integrated module can be realized in the form of hardware or in the form of a software functional module. When the integrated module is realized in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer readable storage medium.

[0131] The storage medium mentioned above can 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 should be understood that the above embodiments are exemplary and should not be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

Claims

1. A method for monitoring key growth stages of rice based on timing SAR and cumulative temperature, characterized in that, The method comprises the following steps: obtaining year time series VH curve and year time series VV curve corresponding to time series SAR of rice in a target area, and performing filtering and smoothing processing on the year time series VH curve to obtain a smoothed curve corresponding to the year time series VH curve, and determining a rice planting period according to the smoothed curve; extracting cumulative temperature information between the rice planting period and rice booting date in the target area, and constructing multi-scale information of the year time series VV curve; extracting tillering period information and fruit development and maturation period information in a key growth period of rice according to the multi-scale information, and extracting booting period information in the key growth period of rice in the target area based on the multi-scale information and the cumulative temperature information; wherein the extraction of the tillering period information and the fruit development and maturation period information in the key growth period of rice according to the multi-scale information, and the extraction of the booting period information in the key growth period of rice in the target area based on the multi-scale information and the cumulative temperature information, comprises: determining rice growth period characteristics through the multi-scale information, and obtaining a maximum value of seasonal change in the multi-scale information after the planting date, so as to extract the tillering period information according to the rice growth period characteristics and the maximum value; obtaining feature points of local change and positive and negative derivatives of the seasonal change in the multi-scale information, and determining a growth indicator corresponding to periodic fluctuation of the seasonal change according to the positive and negative derivatives, so as to extract the fruit development and maturation period information through the growth indicator and the feature points; calculating a mean value and a standard deviation corresponding to the cumulative temperature information, and constructing a normal distribution function according to the mean value and the standard deviation; calculating a booting period probability through the normal distribution function, and obtaining a local change extreme point in the local change when the booting period probability is maximum, so as to determine the booting period information according to the local change extreme point.

2. The method of claim 1, wherein, the filtering and smoothing processing on the year time series VH curve to obtain a smoothed curve corresponding to the year time series VH curve, and the determination of the rice planting period according to the smoothed curve, comprises: performing S-G filtering and smoothing processing on the year time series VH curve to generate a smoothed curve corresponding to the year time series VH curve; calculating a minimum value in the smoothed curve, and determining the rice planting period according to a minimum value point corresponding to the minimum value.

3. The method of claim 2, wherein, the extraction of the cumulative temperature information between the rice planting period and the rice booting date in the target area, comprises: determining a rice booting period based on the rice planting period and the rice booting date in the target area; determining a growth base temperature of the rice, and obtaining a daily minimum temperature and a daily maximum temperature in the rice booting period, so as to calculate a daily average temperature according to the daily minimum temperature and the daily maximum temperature; calculating a growth length day based on the daily average temperature, the daily minimum temperature, the daily maximum temperature and the growth base temperature, and calculating the cumulative temperature information according to the growth length day.

4. The method of claim 3, wherein, The multi-scale information of the year-time VV curve includes: Based on a preset multi-resolution analysis strategy, the year-time VV curve is subjected to time-frequency analysis to separate different frequency components of the year-time VV curve; According to a preset wavelet basis center frequency, convolution scale and image time resolution, multi-scale information corresponding to the different frequency components is constructed, wherein the multi-scale information includes noise, local change, seasonal change and long-term trend.

5. A device for monitoring key growth stages of rice based on timing SAR and cumulative temperature, characterized in that, It includes: The filtering module is used for obtaining the year-time VH curve and the year-time VV curve corresponding to the time-series SAR of the target area, and filtering and smoothing processing is performed on the year-time VH curve to obtain a smoothed curve corresponding to the year-time VH curve, and the rice planting period is determined according to the smoothed curve; The construction module is used for extracting the cumulative temperature information from the rice planting period to the rice booting-heading date of the target area, and constructing the multi-scale information of the year-time VV curve; The extraction module is used for extracting the tillering period information and the fruit development and maturation 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 extraction module includes: The first acquisition unit is used for determining the rice growth period characteristics through the multi-scale information, and acquiring the maximum value of the seasonal change in the multi-scale information after the planting date, so as to extract the tillering period information according to the rice growth period characteristics and the maximum value; The second acquisition unit is used for acquiring the feature points of the local change and the positive and negative derivatives of the seasonal change in the multi-scale information, and determining the growth indicator corresponding to the periodic fluctuation of the seasonal change according to the positive and negative derivatives, so as to extract the fruit development and maturation period information through the growth indicator and the feature points; The construction unit is used for calculating the mean and standard deviation corresponding to the cumulative temperature information, and constructing a normal distribution function according to the mean and the standard deviation; The third calculation unit is used for calculating the booting-heading period probability through the normal distribution function, and acquiring the local change extreme point in the local change when the booting-heading period probability is maximum, so as to determine the booting-heading period information according to the local change extreme point.

6. The apparatus of claim 5, wherein, The filtering module includes: The generation unit is used for performing S-G filtering and smoothing processing on the year-time VH curve to generate a smoothed curve corresponding to the year-time VH curve; The first calculation unit is used for calculating the minimum value in the smoothed curve, and determining the rice planting period according to the minimum value point corresponding to the minimum value.

7. An electronic device, comprising: It includes: The memory, the processor and the computer program stored on the memory and executable on the processor, the processor executes the program to realize the key growth period monitoring method of rice based on time-series SAR and cumulative temperature according to any one of claims 1-4.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to realize the key growth period monitoring method of rice based on time-series SAR and cumulative temperature according to any one of claims 1-4.

9. A computer program product comprising a computer program, characterized in that, The computer program is executed for implementing the method for monitoring the key growth period of rice based on timing SAR and cumulative temperature according to any one of claims 1-4.

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