Method for determining number of vegetation growing seasons

Abstract

The invention discloses a vegetation growth season number determination method, and the method comprises the steps: carrying out the fitting of a vegetation GPP time sequence of a target region, and obtaining a data set composed of the GPP values of a wave crest and a wave trough; filtering the adjacent wave crests and the adjacent wave troughs based on a preset filtering rule, removing the wave troughs of which the GPP values are greater than the wave trough threshold value, removing the wave crest data which do not meet the preset requirement from the data set, and removing the corresponding wave crests; and filtering the adjacent wave crests and the adjacent wave troughs based on a preset filtering rule, and filtering the wave trough GPP value according to the time distance between the wave crest and the left and right adjacent wave troughs and the wave trough GPP value. According to the method, the comprehensive relevance between the wave crest and the adjacent wave trough is considered, the wave crest and the wave trough are filtered, the data which does not meet the preset requirement is gradually removed from the data set, and the final remaining wave crest and wave trough data better conform to the actual situation, so that the judgment of the number of the growing seasons is more accurate.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vegetation phenology prediction, and more particularly, to a method for determining the number of vegetation growing seasons. BACKGROUND

[0002] The vegetation growing season refers to the period of time in which the vegetation actively performs photosynthesis in a year. Accurate determination of the number, start and end time, and duration of the vegetation growing season is of great significance for understanding carbon cycle of the ecological system, agricultural production planning, and response to climate change. Gross primary productivity (GPP) is a key indicator for measuring the photosynthetic carbon fixation capacity of vegetation. The time series dynamics of GPP can directly reflect the periodic changes of vegetation growth, decline, and dormancy. Therefore, analyzing the number of vegetation growing seasons based on the time series of GPP has become an important means of current ecological remote sensing and phenology research.

[0003] In the prior art, the number of vegetation growing seasons is mainly determined based on simple statistics and intuitive judgment of the time series of GPP. The usual practice is to first perform preliminary smoothing on the GPP data of the target region within a period of time to reduce the interference of random noise, set a fixed GPP threshold value, and identify the period higher than the threshold value as the vegetation growing season, and the period lower than the threshold value as the non-growing season. Alternatively, a sliding window method is used to calculate the average value of the GPP data within the window. When the average value exceeds a predetermined average threshold value, it is determined that the growing season is entered, and when the average value is lower than the average threshold value, it is determined that the non-growing season is entered. In addition, some methods are to observe the fluctuations of the GPP time series curve by artificial observation, and determine the start and end time of the growing season by experience, and then count the number of growing seasons.

[0004] However, the existing technology has the following defects: the filtering logic for extreme points is too simple, only relying on time interval or simple GPP difference constraint, without fully considering the comprehensive correlation between the wave peak and the adjacent wave trough, which may retain false secondary wave peaks or wave troughs, thereby increasing the error of the determination result of the number of growing seasons. At the same time, the filtering of the wave trough only focuses on the GPP threshold value or a single time condition, without considering the synergistic relationship between the wave trough and the adjacent wave peak, which leads to the real main wave trough being mistakenly removed, or the secondary wave trough interfering with the effective identification of the wave peak, and finally affecting the accuracy of the number of growing seasons. SUMMARY

[0005] In view of at least one defect or improvement demand of the prior art, the present application provides a vegetation growing season number determination method, device, equipment and storage medium, to solve the problem that the prior art does not fully consider the comprehensive correlation between the wave peak and the adjacent wave trough, resulting in that a false secondary wave peak or wave trough is retained, and the growing season number is incorrectly increased, and the filtering of the wave trough does not combine the synergistic relationship between the wave trough and the adjacent wave peak, resulting in that a real main wave trough is mistakenly removed, and finally affecting the accuracy of the growing season number.

[0006] To achieve the above-mentioned purpose, according to a first aspect of the present application, a vegetation growing season number determination method is provided, comprising: fitting the vegetation GPP time series of the target area, and identifying all wave peaks and wave troughs, to obtain a data set composed of GPP values of the wave peaks and wave troughs; filtering the adjacent wave peaks and wave troughs based on a preset filtering rule, and removing the wave troughs with GPP values greater than a wave trough threshold, until the wave troughs and wave peaks in the GPP time series appear alternately, removing the wave peak data that does not meet the preset requirements from the data set, and removing the corresponding wave peak; filtering the adjacent wave peaks and wave troughs based on a preset filtering rule, until each wave trough and wave peak remaining in the GPP time series appears alternately, and filtering the wave trough GPP values according to the time distance between the wave peak and the two adjacent wave troughs on the left and right and the wave trough GPP values; filtering the adjacent wave peaks and wave troughs based on a preset filtering rule, until each wave peak and wave trough remaining in the GPP time series appears alternately, and determining the number of vegetation growing seasons according to the time distance between the wave peak and the two adjacent wave troughs on the left and right.

[0007] In a possible implementation, fitting the vegetation GPP time series of the target area, and identifying all wave peaks and wave troughs, to obtain a data set composed of GPP values of the wave peaks and wave troughs, further comprises: obtaining the vegetation GPP time series of the target area within a preset time, and fitting the GPP time series by using a cubic spline method; identifying all wave peaks and wave troughs on the fitted GPP time series by using a moving window with a preset time length, to obtain a data set composed of GPP values of the wave peaks and wave troughs.

[0008] In a possible implementation, filtering the adjacent wave peaks and wave troughs based on a preset filtering rule further comprises: obtaining the GPP values of the two adjacent wave peaks and comparing them, and removing the wave peak with a smaller GPP value; obtaining the GPP values of the two adjacent wave troughs and comparing them, and removing the wave trough with a larger GPP value.

[0009] In one possible implementation, removing peak data that does not meet the preset requirements from the dataset, and removing the corresponding peaks, also includes: Determine whether the GPP value of each peak is reasonable, remove unreasonable peak GPP values ​​from the dataset, and remove the corresponding peaks. Determine if the first data point in the dataset is the GPP value of the peak. If the first data point is the GPP value of the peak, remove the first data point and the corresponding peak.

[0010] In one possible implementation, determining whether the GPP value of each peak is reasonable also includes: Get the time distance |Δt between the current peak and its adjacent peak. _peak |Δt|, the time distance between a wave crest and the two nearest troughs _peak_trough (left) |and|Δt _peak_trough (right) | and the GPP difference between the peak and the two nearest troughs |ΔGPP _peak_trough (left) |and|ΔGPP _peak_trough (right) |; If the time distance is |Δt _peak |Greater than the first preset time and time distance|Δt _peak_trough (left) |and|Δt _peak_trough (right) Both are greater than the second preset time and the GPP difference |ΔGPP _peak_trough (left) |and|ΔGPP _peak_trough (right) If all values ​​are greater than the preset threshold, then the GPP value of the current peak is reasonable.

[0011] In one possible implementation, filtering the valley GPP value based on the time distance between the peak and the two adjacent valleys and the valley GPP value also includes: If the time distance between two adjacent troughs to the left and right of a peak is less than the length of the moving window, then the trough with the larger GPP value is removed from the dataset.

[0012] In one possible implementation, determining the number of vegetation growing seasons based on the time distance between a wave crest and two adjacent troughs also includes: The time distance between two adjacent troughs of the same peak is defined as a vegetation growing season. Determining all vegetation growing seasons yields the number of vegetation growing seasons.

[0013] According to a second aspect of the invention, an apparatus for determining the number of vegetation growing seasons is also provided, comprising: The data processing module is configured to fit the vegetation GPP time series of the target area, identify all peaks and troughs, and obtain a dataset consisting of GPP values ​​of peaks and troughs. a wave peak filtering module configured to filter adjacent wave peaks and adjacent wave troughs respectively based on preset filtering rules, and remove wave troughs with GPP values greater than a wave trough threshold until wave troughs and wave peaks in the GPP time sequence appear alternately, remove wave peak data in the data set that does not meet preset requirements, and remove corresponding wave peaks; a wave trough filtering module configured to filter adjacent wave peaks and adjacent wave troughs respectively based on preset filtering rules until every wave trough and wave peak remaining in the GPP time sequence appears alternately, and filter wave trough GPP values according to time distances between wave peaks and two wave troughs adjacent to the left and right and the wave trough GPP values; a number determining module configured to filter adjacent wave peaks and adjacent wave troughs respectively based on preset filtering rules until every wave peak and wave trough remaining in the GPP time sequence appears alternately, and determine the number of vegetation growing seasons according to time distances between wave peaks and two wave troughs adjacent to the left and right.

[0014] According to a third aspect of the present application, there is also provided a vegetation growing season number determining device comprising at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program which, when executed by the processing unit, causes the processing unit to perform the steps of any of the above vegetation growing season number determining methods.

[0015] According to a fourth aspect of the present application, there is also provided a storage medium storing a computer program executable by a vegetation growing season number determining device, which, when running on the vegetation growing season number determining device, causes the vegetation growing season number determining device to perform the steps of any of the above vegetation growing season number determining methods.

[0016] Overall, compared with the prior art, the above technical solutions of the present application can achieve the following beneficial effects: The method for determining the number of vegetation growth seasons provided by the application can better retain the real trough and peak information and make the determination of the number of growth seasons more accurate by combining the synergistic relationship between the trough and the adjacent peak and fully considering the comprehensive correlation between the peak and the adjacent trough instead of considering each peak or trough in isolation. Through the operation of filtering the adjacent peak and the adjacent trough based on the preset filtering rule for multiple rounds, the data that does not meet the preset requirement is gradually removed from the data set, so that the data is continuously optimized, and the final remaining peak and trough data are more in line with the actual situation and are easier to accurately determine the number of growth seasons. When filtering the trough, not only is the trough GPP value preliminarily processed based on the preset filtering rule, but also is further filtered according to the time distance between the peak and the two troughs adjacent to the left and right and the trough GPP value, so that the effectiveness of the trough is more scientifically judged and the accuracy of the filtering is improved, thereby improving the accuracy of the determination of the number of growth seasons. The number of vegetation growth seasons is determined according to the time distance between the peak and the two troughs adjacent to the left and right, so that the time relationship between the peak and the trough is fully considered, the determination of the number of growth seasons is more reasonable and scientific, and conforms to the actual situation of vegetation growth. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required to be used in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0018] Figure 1 The flowchart of an embodiment of the method for determining the number of vegetation growth seasons provided by the present application is shown in the figure. Figure 2 The curve diagram of an embodiment of the time sequence of the growth season of the farmland site provided by the present application is shown in the figure. Figure 3 The structure diagram of an embodiment of the device for determining the number of vegetation growth seasons provided by the present application is shown in the figure. Figure 4 The structure diagram of the device for determining the number of vegetation growth seasons provided by the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of the present application more clear, the present application will be further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application. In addition, the technical features involved in each embodiment of the present application described below can be combined with each other as long as they do not conflict with each other.

[0020] The terms "first", "second", "third", and the like in the description and claims of the present application and the above drawings are used to distinguish different objects, and are not used to describe a particular order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units is not limited to the listed steps or units, but can optionally include steps or units not listed, or can optionally include other steps or units inherent to the process, method, product or device.

[0021] The present application provides a vegetation growing season number determination method, device, equipment and storage medium, which are described below respectively.

[0022] Please refer to Figure 1 , Figure 1 The flowchart of an embodiment of the vegetation growing season number determination method provided by the present application is shown in the figure. In one specific embodiment of the present application, a vegetation growing season number determination method is disclosed, which comprises: S101, fitting the vegetation GPP time series of the target area, and identifying all the peaks and troughs to obtain a data set composed of GPP values of the peaks and troughs; S102, filtering the adjacent peaks and adjacent troughs based on the preset filtering rules respectively, and removing the troughs with GPP values greater than the trough threshold until the troughs and peaks in the GPP time series appear alternately, removing the peak data that does not meet the preset requirements from the data set, and removing the corresponding peaks; S103, filtering the adjacent peaks and adjacent troughs based on the preset filtering rules respectively until each remaining trough and peak in the GPP time series appears alternately, and filtering the trough GPP values according to the time distance between the peak and the two adjacent troughs on the left and right and the trough GPP values; S104, filtering the adjacent peaks and adjacent troughs based on the preset filtering rules respectively until each remaining peak and trough in the GPP time series appears alternately, and determining the number of vegetation growing seasons according to the time distance between the peak and the two adjacent troughs on the left and right.

[0023] In the above embodiment, first, the time series data of the total primary productivity (GPP) of the vegetation in a target area for a period of time (such as one year) is obtained from a flux tower network or a remote sensing data source. These data may contain noise and disturbances, so they need to be preprocessed. Cubic Spline (CS) is used to fit the GPP time series to smooth the data and eliminate short-term fluctuations, highlight long-term trends, and the fitted curve should be able to better reflect the periodic changes of vegetation GPP.

[0024] A peak is defined as a point where the GPP value reaches a maximum in a local region, and a trough is defined as a point where the GPP value reaches a minimum in a local region. On the fitted GPP time series, a fixed-length moving window (e.g., 30 days) is used to identify all peaks and troughs. The identified peaks and troughs and their corresponding GPP values form a dataset.

[0025] The adjacent peaks and troughs are filtered based on preset filtering rules. Then, a trough threshold can be set according to actual needs to remove troughs with GPP values greater than the threshold, thereby filtering out troughs that may be caused by abnormal climate conditions or data errors. This step is repeated until the peaks and troughs in the GPP time series appear alternately, i.e., each peak is immediately adjacent to a trough on both sides. According to the preset rationality judgment rules of the peaks, the peak data and its corresponding peak that do not meet the requirements are removed from the dataset, and the remaining peaks can truly reflect the growth status of the vegetation.

[0026] The adjacent peaks and troughs are filtered again based on preset filtering rules to ensure that each remaining peak and trough in the GPP time series appears alternately, eliminating potential false peaks and troughs. For each peak, the time distance (|Δt _ trough|) and GPP difference (|ΔGPP _ peak_trough|) between the left and right adjacent two troughs are calculated to further remove unreasonable trough GPP values.

[0027] The filtering step is repeated again to ensure that each remaining peak and trough in the GPP time series appears alternately. For each peak, the left and right adjacent two troughs are determined, and the number of growth seasons of the vegetation is determined according to the time distance (i.e., the length of the growth season) between the two troughs. If the time distance between the two troughs meets the preset growth season length range (e.g., more than one month), it is considered that the peak represents a complete growth season, and the number of all peaks that meet the conditions is counted, i.e., the number of growth seasons of the vegetation.

[0028] Compared with the prior art, the method for determining the number of vegetation growth seasons provided by the embodiment can better retain the real trough and peak information, and make the determination of the number of growth seasons more accurate, by filtering the troughs and peaks in combination with the synergistic relationship between the troughs and adjacent peaks, and fully considering the comprehensive correlation between the peaks and adjacent troughs, instead of regarding each peak or trough in isolation.

[0029] In some embodiments of the application, the vegetation GPP time series of the target region is fitted, and all peaks and troughs are identified to obtain a data set composed of GPP values of the peaks and troughs, and the method further comprises: obtaining the vegetation GPP time series of the target region in a preset time, and fitting the GPP time series by using a cubic spline method; all peaks and troughs are identified on the fitted GPP time series by using a moving window with a preset time length, and a data set composed of GPP values of the peaks and troughs is obtained.

[0030] In the above embodiment, the total gross primary productivity (GPP) time series data of the target region in a preset time range is obtained from reliable observation sites such as flux tower networks and satellite remote sensing data sources, and should have sufficient time resolution (such as daily or weekly) to ensure that the seasonal changes of vegetation growth can be accurately captured. It should be noted that the preset time range should be reasonably set according to the research purpose and the type of vegetation. For example, for annual crops, a complete growth year can be selected as the time range; for perennial vegetation, multiple growth years may need to be selected to capture interannual variations.

[0031] The cubic spline method is a commonly used curve fitting method, which approximates the original data points by constructing piecewise cubic polynomials while ensuring the continuity and smoothness of the curve at the nodes. It can well handle time series data with nonlinear characteristics. The smooth curve generated by the cubic spline method can reflect the long-term trend of vegetation GPP, while eliminating the influence of short-term fluctuations and noise.

[0032] The length of the moving window should be reasonably set according to the growth cycle of the vegetation and the characteristics of the data. For vegetation with obvious seasonal changes, a window of one month or longer can be selected; for vegetation with a short growth cycle, a shorter window may be needed.

[0033] On the fitted GPP time series, a moving window of a preset length is used to slide point by point to calculate the maximum and minimum values of GPP within the window. When the window slides to a certain position, if the GPP value at that position is greater than the GPP values at all other positions within the window, it is considered to be a wave crest; conversely, if the GPP value at that position is less than the GPP values at all other positions within the window, it is considered to be a wave trough.

[0034] Integrate the GPP values of all identified wave crests and wave troughs and their position information in the time series into a dataset for subsequent filtering and judgment of the number of growth seasons of the vegetation. After constructing the dataset, the accuracy of the dataset can be verified by visual inspection or comparison with the original data to ensure that there are no missed or incorrect wave crest and wave trough identifications.

[0035] In some embodiments of the present application, the adjacent wave crests and adjacent wave troughs are filtered based on preset filtering rules, which further include: Obtain the GPP values of the two adjacent wave crests and compare them, and remove the wave crest with the smaller GPP value; Obtain the GPP values of the two adjacent wave troughs and compare them, and remove the wave trough with the larger GPP value.

[0036] In the above embodiments, the GPP values of the two adjacent wave crests are obtained, and those wave crests with relatively small GPP values are identified and removed. In actual application scenarios, wave crests with small GPP values often represent temporary fluctuations in productivity or measurement errors, rather than significant changes in ecosystem productivity levels. Therefore, by removing these smaller wave crests, data noise can be effectively reduced, making subsequent analysis more focused on changes in productivity that have ecological significance.

[0037] Similarly, for two adjacent troughs, their GPP values are compared and the larger one is removed, and the GPP values of the two troughs are obtained and compared. Different from the peak processing, in the comparison of the troughs, the trough with the larger GPP value is removed. This is because, in most cases, the trough with the relatively larger GPP value may not be the real growth trough of the vegetation, but an abnormal point generated by data noise or measurement error. By removing these larger troughs, it can be ensured that the retained data more accurately reflects the real productivity level of the ecosystem.

[0038] In some embodiments of the present application, the peak data not meeting the preset requirement is removed from the data set, and the corresponding peak is also removed, and the method further comprises: determining whether the GPP value of each peak is reasonable, removing the unreasonable peak GPP value from the data set, and removing the corresponding peak; by this operation, the isolated peak without left trough support at the beginning of the time series is avoided to interfere with the determination of the growth season.

[0039] determining whether the first data in the data set is a peak GPP value, and if the first data is a peak GPP value, removing the first data and removing the corresponding peak.

[0040] In some embodiments of the present application, determining whether the GPP value of each peak is reasonable further comprises: obtaining the time distance |Δt _peak | between the current peak and the adjacent peak _peak_trough (left) | and the time distance |Δt _peak_trough (right) | between the peak and the two nearest troughs _peak_trough (left) | and the GPP difference |ΔGPP _peak_trough (right) | between the peak and the two nearest troughs If the time distance |Δt _peak | is greater than the first preset time, the time distance |Δt _peak_trough (left) | and |Δt _peak_trough (right) | are both greater than the second preset time, and the GPP difference |ΔGPP _peak_trough (left) | and |ΔGPP _peak_trough (right) | are both greater than the preset threshold value, then the GPP value of the current peak is reasonable.

[0041] As a preferred embodiment, the first preset time, the second preset time and the preset threshold value need to be preset according to the vegetation type, climate characteristics and data time resolution of the target region, for example: for annual growth cycle vegetation, the first preset time is 30 days, the second preset time is 15 days, and the preset threshold value is 0.13 of the maximum GPP value. It can be understood that the specific parameters can be set according to the actual needs, and the present application does not make further limitation.

[0042] In some embodiments of the present application, the filtering of the trough GPP values according to the time distance between the peak and the two troughs adjacent to the left and right of the peak and the trough GPP values further comprises: If the time distance between the two troughs adjacent to the left and right of the peak is less than the length of the moving window, the trough with a larger GPP value is removed from the data set.

[0043] In the above embodiments, the time distance reflects the distribution of the peak and the trough in the time dimension, which can reflect the periodicity and trend characteristics of the data change, and the GPP value of the trough directly reflects the productivity level of the ecosystem at that moment. By comparing the time distance between the two troughs adjacent to the left and right of the peak with the length of the moving window, if the time distance between the two troughs adjacent to the left and right of the peak is less than the length of the moving window, it means that the two troughs are relatively dense in time, and there may be data anomalies or noise interference. In order to simplify the data structure, reduce redundant information and improve data quality, the trough with a larger GPP value is removed from the data set.

[0044] In some embodiments of the present application, the determination of the number of vegetation growing seasons according to the time distance between the peak and the two troughs adjacent to the left and right of the peak further comprises: The time distance between the two troughs adjacent to the left and right of the same peak is a vegetation growing season, and the number of vegetation growing seasons is determined by all the vegetation growing seasons.

[0045] In the above embodiments, the vegetation will show obvious periodic change characteristics in its growth process, and on the relevant data curve of the vegetation time series, it is usually represented by the alternating appearance of peaks and troughs. The peak often represents the period of vigorous growth of vegetation and high productivity, for example, the stage of lush vegetation and active photosynthesis in summer; while the trough corresponds to the period of slow growth of vegetation and low productivity, such as the stage of vegetation dormancy and weakened photosynthesis in winter. Therefore, the time distance between the two troughs adjacent to the left and right of the same peak can reasonably define a complete vegetation growing cycle, that is, a vegetation growing season. By counting the number of such growing seasons, the change of the growth cycle of the vegetation in a period of time can be clearly understood.

[0046] Please refer to Figure 2 , Figure 2 The curve diagram of an embodiment of the time series of the growing season of the farmland site provided by the present application is shown in the figure, and a specific embodiment of the present application gives the time series of the growing season of CH-Oe2 (a farmland site) in 2007, 2008 and 2009 after being processed by the determination method of the number of vegetation growing seasons provided by the present application, as shown in Figure 2 The red dots and blue dots show reasonable peaks and troughs by the determination method of the vegetation growing season provided by the present application.

[0047] In order to better implement the method for determining the number of vegetation growth seasons in the embodiments of the present application, on the basis of the method for determining the number of vegetation growth seasons, please refer to Figure 3 , Figure 3 The structural schematic diagram of an embodiment of the vegetation growth season number determination device provided by the present application, the embodiment of the present application provides a vegetation growth season number determination device 300, which comprises: The data processing module 310 is configured to fit the GPP time series of the vegetation of the target region, identify all the wave crests and wave troughs, and obtain a data set composed of the GPP values of the wave crests and wave troughs; The wave crest filtering module 320 is configured to filter the adjacent wave crests and wave troughs based on the preset filtering rules respectively, remove the wave troughs with GPP values greater than the wave trough threshold, and until the wave troughs and wave crests appear alternately in the GPP time series, remove the wave crest data that does not meet the preset requirements from the data set, and remove the corresponding wave crest; The wave trough filtering module 330 is configured to filter the adjacent wave crests and wave troughs based on the preset filtering rules respectively, until each wave trough and wave crest remaining in the GPP time series appears alternately, and filter the wave trough GPP values according to the time distance between the wave crest and the two adjacent wave troughs on the left and right and the wave trough GPP values; The number determination module 340 is configured to filter the adjacent wave crests and wave troughs based on the preset filtering rules respectively, until each wave crest and wave trough remaining in the GPP time series appears alternately, and determine the number of vegetation growth seasons according to the time distance between the wave crest and the two adjacent wave troughs on the left and right.

[0048] Please refer to Figure 4 , Figure 4 The structural schematic diagram of the vegetation growth season number determination device 400 provided by the embodiment of the present application. Based on the above-mentioned method for determining the number of vegetation growth seasons, the present application also correspondingly provides a vegetation growth season number determination device 400. The vegetation growth season number determination device 400 can be a mobile terminal, a desktop computer, a notebook computer, a palm computer, a server and other computing devices. The vegetation growth season number determination device 400 comprises a processor 410, a memory 420 and a display 430. Figure 4 Only part of the components of the vegetation growth season number determination device is shown, but it should be understood that all the shown components are not required to be implemented, and more or less components can be alternatively implemented.

[0049] The memory 420 may, in some embodiments, be an internal storage unit of the vegetation growing season number determination device 400, such as a hard disk or a memory of the vegetation growing season number determination device 400. The memory 420 may, in other embodiments, also be an external storage device of the vegetation growing season number determination device 400, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, and the like, which are equipped on the vegetation growing season number determination device 400. Further, the memory 420 may also include both an internal storage unit and an external storage device of the vegetation growing season number determination device 400. The memory 420 is used to store application software and various data installed on the vegetation growing season number determination device 400, such as program codes installed on the vegetation growing season number determination device 400. The memory 420 may also be used to temporarily store data that has been output or is to be output. In an embodiment, the memory 420 stores a vegetation growing season number determination program 440, which can be executed by the processor 410 to implement the vegetation growing season number determination method of the embodiments of the present application.

[0050] The processor 410 may, in some embodiments, be a central processing unit (CPU), a microprocessor, or other data processing chip, which is used to run program codes or process data stored in the memory 420, such as to execute the vegetation growing season number determination method.

[0051] The display 430 may, in some embodiments, be an LED display, a liquid crystal display, a touch liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, and the like. The display 430 is used to display information of the vegetation growing season number determination device 400 and to display a visualized user interface. The components of the vegetation growing season number determination device 400, i.e., the processor 410, the memory 420, and the display 430, communicate with each other through a system bus.

[0052] In an embodiment, the steps in the above vegetation growing season number determination method are implemented when the processor 410 executes the vegetation growing season number determination program 440 in the memory 420.

[0053] The embodiments also provide a computer readable storage medium, which stores a vegetation growing season number determination program, and the vegetation growing season number determination program, when executed by a processor, implements the following steps: fitting the time series of vegetation GPP of the target region, and identifying all the peaks and troughs to obtain a data set composed of GPP values of the peaks and troughs; filtering the adjacent peaks and troughs respectively based on preset filtering rules, and removing the troughs with GPP values greater than the trough threshold until the troughs and peaks in the GPP time series appear alternately, removing the peak data that does not meet the preset requirements from the data set, and removing the corresponding peaks; filtering the adjacent peaks and troughs respectively based on preset filtering rules until each trough and peak remaining in the GPP time series appears alternately, and filtering the trough GPP values according to the time distance between the peak and the two adjacent troughs on the left and right and the trough GPP values; filtering the adjacent peaks and troughs respectively based on preset filtering rules until each peak and trough remaining in the GPP time series appears alternately, and determining the number of vegetation growing seasons according to the time distance between the peak and the two adjacent troughs on the left and right.

[0054] In summary, the method for determining the number of vegetation growing seasons provided by the application, when filtering the peaks and troughs, considers the synergistic relationship between the troughs and adjacent peaks, fully considers the comprehensive correlation between the peaks and adjacent troughs, and does not consider each peak or trough in isolation, which can better preserve the true trough and peak information and make the determination of the number of growing seasons more accurate. Through multiple rounds of filtering of adjacent peaks and troughs based on preset filtering rules, data that does not meet the preset requirements is gradually removed from the data set, so that the data is continuously optimized, the remaining peak and trough data is more in line with the actual situation, and the number of growing seasons is more easily and accurately determined. When filtering the troughs, not only are they preliminarily processed based on preset filtering rules, but also the trough GPP values are further filtered according to the time distance between the peak and the two adjacent troughs on the left and right and the trough GPP values, so that the effectiveness of the troughs is more scientifically judged through multi-dimensional and refined filtering, the accuracy of the filtering is improved, and the accuracy of the determination of the number of growing seasons is improved. The number of vegetation growing seasons is determined according to the time distance between the peak and the two adjacent troughs on the left and right, which fully considers the time relationship between the peaks and troughs, makes the determination of the number of growing seasons more reasonable and scientific, and is in line with the actual situation of vegetation growth.

[0055] The application further provides a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the steps of the method. The computer readable storage medium can include, but is not limited to, any type of disk, including a floppy disk, an optical disk, a DVD, a CD-ROM (Compact Disc Read-Only Memory), a microdrive, and a magneto-optical disk, a ROM (Read-Only Memory), a RAM (Random Access Memory), an EPROM (Erasable Programmable Read-Only Memory), an EEPROM (Electrically Erasable Programmable Read-Only Memory), a DRAM (Dynamic Random Access Memory), a VRAM (Video Random Access Memory), a flash memory device, a magnetic or optical card, a nanosystem (including a molecular memory IC), or any type of medium or device suitable for storing instructions and / or data.

[0056] It should be noted that, for the foregoing method embodiments, in order to simply describe, they are all expressed as a combination of a series of actions, but those skilled in the art should know that the application is not limited to the order of the actions described, because according to the application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions and modules involved are not necessarily necessary for the application.

[0057] In the above embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0058] In several embodiments provided by the application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely schematic. The division of the units is merely a logical function division. In actual implementation, another division manner can be adopted. For example, a plurality of units or components can be combined or integrated into another system, or some features can be omitted or not executed. In addition, the coupling or direct coupling or communication connection between the units or components shown or discussed can be indirect coupling or communication connection through some service interfaces. The coupling or communication connection can be electrical or in other forms.

[0059] The units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, i.e. may be located in one place, or may also be distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment of the present application.

[0060] In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0061] The integrated unit, if realized in the form of a software functional unit and sold or used as an independent product, can be stored in a computer readable memory. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a memory and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: a U disk, a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0062] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by a program instructing relevant hardware, and the program can be stored in a computer readable memory, which can include a flash disk, a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a magnetic disk or an optical disk, etc.

[0063] The above is only an exemplary embodiment of the present disclosure, and cannot limit the scope of the present disclosure. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure are still within the scope of the present disclosure. Those skilled in the art will easily think of embodiments of the present disclosure after considering the specification and practicing the disclosure herein. The present application is intended to cover any variations, uses or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or conventional techniques in the art that are not described in the present disclosure. The specification and examples are only considered as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

[0064] Any technical features in the above embodiments can be combined, and for brevity, not every combination of the technical features in the above embodiments is described, however, any combination of the technical features should be considered to be within the scope of the present disclosure, as long as the combination does not result in a contradiction.

[0065] Those skilled in the art easily understand that the above description is only the preferred embodiments of the present application, and is not intended to limit the present application, and any modification, equivalent replacement and improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for determining the quantity of vegetation during the growing season, characterized in that, include: Fit the vegetation GPP time series of the target area, identify all peaks and troughs, and obtain a dataset composed of GPP values ​​of peaks and troughs; Based on preset filtering rules, adjacent peaks and adjacent troughs are filtered separately, and troughs with GPP values ​​greater than the trough threshold are removed until troughs and peaks alternate in the GPP time series. Peak data that does not meet the preset requirements are removed from the dataset, and the corresponding peaks are also removed. Adjacent peaks and valleys are filtered according to preset filtering rules until each remaining valley and peak in the GPP time series alternates. The valley GPP value is filtered according to the time distance between the peak and the two adjacent valleys and the valley GPP value. Based on preset filtering rules, adjacent peaks and troughs are filtered separately until each remaining peak and trough in the GPP time series appears alternately. The number of vegetation growing seasons is determined based on the time distance between a peak and its two adjacent troughs.

2. The method for determining the number of vegetation growing seasons as described in claim 1, characterized in that, The process of fitting the vegetation GPP time series of the target area and identifying all peaks and troughs to obtain a dataset composed of GPP values ​​of peaks and troughs also includes: Obtain the vegetation GPP time series of the target area within a preset time period, and fit the GPP time series using the cubic spline method; Using a moving window of preset duration, all peaks and troughs are identified on the fitted GPP time series, resulting in a dataset composed of GPP values ​​of peaks and troughs.

3. The method for determining the number of vegetation growing seasons as described in claim 1, characterized in that, The filtering of adjacent peaks and adjacent troughs based on preset filtering rules also includes: Obtain the GPP values ​​of two adjacent peaks and compare them, then remove the peak with the smaller GPP value; Obtain the GPP values ​​of two adjacent valleys and compare them, then remove the valley with the larger GPP value.

4. The method for determining the number of vegetation growing seasons as described in claim 1, characterized in that, The step of removing peak data that does not meet the preset requirements from the dataset, and removing the corresponding peaks, further includes: Determine whether the GPP value of each peak is reasonable, remove unreasonable peak GPP values ​​from the dataset, and remove the corresponding peaks. Determine whether the first data in the dataset is the GPP value of the peak. If the first data is the GPP value of the peak, remove the first data and the corresponding peak.

5. The method for determining the number of vegetation growing seasons as described in claim 4, characterized in that, The determination of whether the GPP value of each peak is reasonable also includes: Get the time distance |Δt between the current peak and its adjacent peak. _peak |Δt|, the time distance between a wave crest and the two nearest troughs _peak_trough (left) |and|Δt _peak_trough (right) | and the GPP difference between the peak and the two nearest troughs |ΔGPP _peak_trough (left) |and|ΔGPP _peak_trough (right) |; If the time distance is |Δt _peak |Greater than the first preset time and time distance|Δt _peak_trough (left) |and|Δt _peak_trough (right) Both are greater than the second preset time and the GPP difference |ΔGPP _peak_trough (left) |and|ΔGPP _peak_trough (right) If all values ​​are greater than the preset threshold, then the GPP value of the current peak is reasonable.

6. The method for determining the number of vegetation growing seasons as described in claim 2, characterized in that, The filtering of valley GPP values ​​based on the time distance between the peak and the two adjacent valleys and the valley GPP value also includes: If the time distance between two adjacent troughs to the left and right of a peak is less than the length of the moving window, then the trough with the larger GPP value is removed from the dataset.

7. The method for determining the number of vegetation growing seasons as described in claim 1, characterized in that, The method of determining the number of vegetation growing seasons based on the time distance between the peak and the two adjacent troughs also includes: The time distance between two adjacent troughs of the same peak is defined as a vegetation growing season. Determining all vegetation growing seasons yields the number of vegetation growing seasons.

8. A device for determining the quantity of vegetation during the growing season, characterized in that, include: The data processing module is configured to fit the vegetation GPP time series of the target area, identify all peaks and troughs, and obtain a dataset consisting of GPP values ​​of peaks and troughs. The peak filtering module is configured to filter adjacent peaks and adjacent valleys based on preset filtering rules, and remove valleys with GPP values ​​greater than the valley threshold, until valleys and peaks alternate in the GPP time series, remove peak data that do not meet the preset requirements from the dataset, and remove the corresponding peaks. The valley filtering module is configured to filter adjacent peaks and valleys based on preset filtering rules until each remaining valley and peak in the GPP time series alternates. The valley GPP value is filtered according to the time distance between the peak and the two adjacent valleys and the valley GPP value. The quantity determination module is configured to filter adjacent peaks and adjacent troughs based on preset filtering rules until each remaining peak and trough in the GPP time series alternates, and determine the number of vegetation growing seasons based on the time distance between the peak and the two adjacent troughs.

9. A device for determining the quantity of vegetation during the growing season, characterized in that, It includes at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program that, when executed by the processing unit, causes the processing unit to perform the steps of the method for determining the number of vegetation growing seasons according to any one of claims 1 to 7.

10. A storage medium, characterized in that, It stores a computer program executable by a device for determining the number of vegetation growing seasons, which, when run on the device for determining the number of vegetation growing seasons, causes the device for determining the number of vegetation growing seasons to perform the steps of the method for determining the number of vegetation growing seasons according to any one of claims 1 to 7.

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