Lodging damage quantitative estimation method based on remote sensing time sequence and growth period model

Abstract

The invention discloses a lodging damage quantitative estimation method based on a remote sensing time sequence and growth period model, and relates to the technical field of data processing, and the method comprises the steps: S1, collecting remote sensing time sequence data, and carrying out the preprocessing; s2, analyzing a change rule of the remote sensing time sequence data during vegetation lodging, and constructing instantaneous lodging intensity; s3, analyzing the vegetation lodging duration based on the instantaneous lodging intensity, and constructing a lodging cumulative loss index; s4, analyzing the senescence change characteristics of the vegetation based on the lodging cumulative loss index, and constructing a calibration factor; and S5, adaptively adjusting parameters in the double-logic function time sequence model based on the calibration factor, and quantitatively estimating the lodging damage. According to the method, the problem that the real amplitude of the damage cannot be accurately captured when the local steep drop curve of the lodging occurrence stage is fitted due to the fact that the original double-logic function time sequence is poor in adaptability to sudden local change is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a quantitative estimation method for lodging damage based on remote sensing time series and growth period model. BACKGROUND

[0002] The quantitative estimation of vegetation lodging damage aims to accurately quantify the impact of lodging events on the planting area, severity and even the final yield of vegetation, which can provide important and objective data support for insurance and other institutions to conduct post-disaster rapid assessment, develop compensation schemes and guide post-disaster recovery production. Lodging is one of the important agricultural disasters that affect the yield and quality of vegetation. The traditional ground survey method is time-consuming, labor-intensive, subjective and difficult to popularize in a large area. The use of remote sensing time series technology can quickly, objectively and non-contactly estimate the lodging damage of vegetation in a large area.

[0003] In the prior art, the double logistic function time series fitting model is often used for quantitative analysis of vegetation growth period model because it can effectively fit the single-season growth period curve of vegetation, has high parameterization degree and clear physical meaning. However, the lodging damage of vegetation has the unique characteristics of strong randomness of occurrence time, sudden process and local degree, which is manifested as a local sharp drop in the middle of the vegetation growth period curve. Since the original double logistic function time series fitting model uses fixed function form and global parameters for fitting, it has poor adaptability to such sudden local changes, which results in that it cannot accurately capture the true magnitude of the damage when fitting the local sharp drop curve in the lodging occurrence stage, and further causes low calculation accuracy, which cannot meet the requirements of accurate estimation of local sudden lodging damage.

[0004] Therefore, the present application provides a quantitative estimation method for lodging damage based on remote sensing time series and growth period model. SUMMARY

[0005] The present application aims to provide a quantitative estimation method for lodging damage based on remote sensing time series and growth period model to solve the problems in the background art.

[0006] To achieve the above-mentioned purpose, the present application provides the following technical solution: a quantitative estimation method for lodging damage based on remote sensing time series and growth period model, comprising the following steps: Step S1, collecting remote sensing time series data and preprocessing; Step S2, analyzing the change rule of the remote sensing time series data when the vegetation lodges, and constructing an instantaneous lodging intensity; Step S3, analyzing the duration of the vegetation lodging based on the instantaneous lodging intensity, and constructing a lodging cumulative loss index; Step S4, analyzing the aging change characteristics of the vegetation based on the lodging cumulative loss index, and constructing a calibration factor; Step S5: Adaptively adjust the parameters in the dual logic function time series model based on the calibration factor, and quantitatively estimate the lodging damage.

[0007] A further improvement of the present invention is that the remote sensing time-series data includes the normalized differential vegetation index of vegetation throughout the growth period obtained by a medium- and high-resolution multispectral satellite sensor as the remote sensing time-series data. The missing values ​​in the remote sensing time-series data are filled by regression imputation method, and the remote sensing time-series data after filling the missing values ​​is smoothed by moving average method to obtain the preprocessed remote sensing time-series data.

[0008] A further improvement of this invention is that the process of constructing the instantaneous collapse intensity specifically includes: taking time t as an example, the time window consisting of N times before time t is recorded as the monitoring window. The sequence of remote sensing time-series data within the monitoring window is denoted as the landslide monitoring sequence, and the monitoring window that is adjacent to but does not overlap with the monitoring window at time t is denoted as the nearest neighbor window. .

[0009] A further improvement of this invention is that the formula for calculating the instantaneous collapse strength is expressed as: in This represents the instantaneous collapse intensity of the remote sensing time series data at time t. This represents the maximum value in the landslide monitoring sequence at time t. This represents the remote sensing time series data at time t, and N represents the monitoring window. The amount of remote sensing time series data within, Indicates neighboring windows The value of the i-th element in the lodging monitoring sequence. This represents the median of the collapse monitoring sequence in the nearest neighbor window at time t.

[0010] A further improvement of this invention is that the construction process of the lodging cumulative loss index is as follows: collect historical remote sensing time-series data of vegetation without lodging in the manner described in step S1, use it as input to the dual logic function time-series model, and output the remote sensing time-series curve of vegetation without lodging, i.e., the remote sensing time-series curve of vegetation during the normal growth period, denoted as the growth period curve; use the remote sensing time-series data at time t as input to the growth period curve to obtain the predicted value of the remote sensing time-series data of vegetation without lodging.

[0011] A further improvement of this invention is that the formula for calculating the cumulative lodging loss index is expressed as follows: in The index represents the cumulative lodging loss at time t, and N represents the number of remote sensing time-series data points within the monitoring window. representing the monitoring window the theoretical value of the remote sensing time series data at the jth moment in the no-lodging period, representing the monitoring window the actual value of the remote sensing time series data at the jth moment in the no-lodging period, representing the monitoring window the instantaneous lodging intensity at the jth moment in the no-lodging period, representing the monitoring window the maximum value of the instantaneous lodging intensity at all moments in the no-lodging period.

[0012] The application further improves in that the calibration factor is constructed based on the lodging cumulative loss index, and is used for calibrating the senescence parameter after lodging occurs, and the construction process of the calibration factor is as follows: the lodging cumulative loss index of the remote sensing time series data at each moment in the historical vegetation no-lodging period is calculated according to steps S1 to S3, and all the lodging cumulative loss indexes at the historical moments are taken as the input of the Otsu threshold segmentation method, and the segmentation threshold value of the lodging cumulative loss index is output.

[0013] The application further improves in that the calculation formula of the calibration factor is represented as: wherein represents the calibration factor at the tth moment, represents the lodging cumulative loss index at the tth moment, represents the segmentation threshold value of the lodging cumulative loss index, and exp() represents the exponential function with the natural constant as the base.

[0014] The application further improves in that the specific process of adjusting the parameter by the calibration factor includes: setting the senescence rate parameter and the senescence starting time parameter according to the calibration factor, and is represented as: and ; wherein represents the senescence rate parameter at the tth moment in the double logistic function time series model, represents the calibration factor at the tth moment, and respectively represent the maximum value and the minimum value of the senescence rate parameter.

[0015] The application further improves in that the specific step of step S5 includes: taking the actually collected remote sensing time series data as the input of the updated double logistic function time series model, and outputting the predicted value of the remote sensing time series data to obtain the prediction curve, and calculating the area surrounded by the growth period curve and the prediction curve above the growth period curve by the mathematical integration method, as the total loss amount of the photosynthetic product accumulation caused by lodging, and taking it as the result of the quantitative estimation of the lodging damage.

[0016] Compared with the prior art, the present application has the beneficial effects of: 1、The present application firstly reflects the change of remote sensing time series data in the short term by comparing the maximum value of remote sensing time series data in the monitoring window with the current value, and constructing the instantaneous lodging intensity by normalizing the stability of the adjacent window, thereby improving the sensitivity of lodging event identification; 2、Secondly, the growth period curve is fitted according to the change rule of remote sensing time series data in the normal growth period, the instantaneous lodging intensity is taken as the weight, and the lodging cumulative loss index is constructed by combining the difference between the theoretical value and the actual value of the growth period curve, thereby reflecting the cumulative damage of the vegetation, and making the damage evaluation result more close to the actual yield loss; 3、Finally, the calibration factor is constructed based on the lodging cumulative loss index, and the adjustment intensity of the parameters in the double logistic function time series model is evaluated; based on the calibration factor, the senescence rate parameter and the senescence starting time parameter in the double logistic function time series model are adaptively adjusted, the remote sensing time series data is accurately predicted, and the remote sensing time series curve is obtained. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 The present application is a quantitative estimation method of lodging damage based on remote sensing time series and growth period model. DETAILED DESCRIPTION

[0018] The technical scheme of the present application will be described in detail below with the help of the accompanying drawings and specific embodiments. It should be understood that the embodiments and specific features in the embodiments are detailed descriptions of the technical scheme of the present application, and are not limitations of the technical scheme of the present application. In the case of no conflict, the technical features in the embodiments and the embodiments can be combined with each other.

[0019] The term "and / or", only describes the association relationship of the associated objects, which means that there can be three kinds of relationships, for example, A and / or B, which can represent the three cases of A alone, A and B together, and B alone.

[0020] Embodiment 1 Figure 1 The present embodiment discloses a quantitative estimation method of lodging damage based on remote sensing time series and growth period model flow chart, and the steps are as follows: Step S1, collecting remote sensing time series data and preprocessing.

[0021] In order to quantitatively estimate the lodging damage, remote sensing time series data needs to be collected, and the collection method is: through a medium-high resolution multispectral satellite sensor such as Sentinel-2 or Landsat series, the vegetation index sequence of the vegetation in the entire growth period is obtained. Among them, the time interval for collecting the vegetation index in the application is 5 days, and because there are many types of vegetation indexes, the normalized difference vegetation index is the most commonly used and mature index for monitoring vegetation health and photosynthesis, so the normalized difference vegetation index is selected as the remote sensing time series data in the application.

[0022] Because the process of collecting or transmitting remote sensing time series data is prone to noise interference, there may be missing values and noise, so the remote sensing time series data needs to be preprocessed, specifically: the missing values in the remote sensing time series data are filled by regression filling method, and the remote sensing time series data after filling the missing values is smoothed by moving average method.

[0023] The preprocessed remote sensing time series data is output.

[0024] Step S2, analyze the change rule of remote sensing time series data when the vegetation is lodging and construct the instantaneous lodging intensity.

[0025] The vegetation lodging is different from the natural slow maturation process, and the lodging damage has a burst, such as a strong wind or a rainstorm during the peak growth period of the vegetation, which can cause the lodging of the vegetation, resulting in a dramatic change in the canopy structure in a short time, causing the leaves and stems to change from upright to lying or tilting. This structural change will cause the canopy reflectivity, especially the near-infrared reflectivity, to sharply decrease in the observation direction, resulting in a significant and persistent local decrease in the remote sensing time series data. The parameters in the existing double-logic function time series fitting model are globally optimized based on the entire growth cycle, and it is difficult to accurately identify the local dramatic change in the short lodging period, resulting in the smoothing of the remote sensing time series data by the model fitting curve and the decrease in the model accuracy.

[0026] Based on the above analysis, the application constructs the instantaneous lodging intensity to reflect the change of the remote sensing time series data in a short period, which is convenient for subsequent adjustment of the parameters in the double-logic function time series fitting model. The construction process of the instantaneous lodging intensity is as follows: Taking t time as an example, the time window composed of the N time points before t time is denoted as a monitoring window The sequence composed of the remote sensing time series data in the monitoring window is denoted as a lodging monitoring sequence, and the monitoring window adjacent to the monitoring window at t time and not overlapping is denoted as a neighbor window . Among them, the value of N in the application is 6, which can be selected according to the situation.

[0027] Based on the above processing steps, the calculation method of the instantaneous lodging intensity in the application is as follows: in This represents the instantaneous collapse intensity of the remote sensing time series data at time t. This represents the maximum value in the landslide monitoring sequence at time t. This represents the remote sensing time series data at time t, and N represents the monitoring window. The amount of remote sensing time series data within, Indicates neighboring windows The value of the i-th element in the lodging monitoring sequence. This represents the median of the collapse monitoring sequence in the nearest neighbor window at time t.

[0028] When vegetation collapses, the vegetation index drops suddenly, meaning the remote sensing time-series data decreases rapidly, corresponding to the numerator. Rapidly increasing, denominator term Rapidly decrease, making The number of items increases; because when vegetation begins to lodging, the time period within the nearest window has not yet shown lodging, therefore the median of the lodging monitoring sequence in the nearest window is still close to the remote sensing time series data when lodging has not yet occurred, corresponding to... The magnitude is relatively small. Therefore, in summary, when vegetation begins to lodging, the calculated instantaneous lodging intensity will increase significantly, making it easier to identify the timing of the lodging.

[0029] Step S3: Construct a cumulative lodging loss index based on the analysis of the duration of vegetation lodging based on the instantaneous lodging intensity.

[0030] When quantitatively estimating vegetation lodging damage, different vegetation types have different genes. Some vegetation types are resistant to lodging and can recover quickly after extreme environments such as strong winds or heavy rain, resulting in less damage. However, some vegetation types that do not have lodging-resistant genes recover slowly or not at all after extreme environments such as strong winds or heavy rain. This can cause remote sensing time series data to show persistently low values, meaning that the remote sensing time series data may show a long period of non-consistency with historical variation patterns, resulting in greater damage.

[0031] Based on the above analysis, this application constructs a cumulative lodging loss index based on instantaneous lodging intensity to reflect the cumulative damage to vegetation. The construction process of the cumulative lodging loss index is as follows: Following the same method as step S1, historical remote sensing time-series data of vegetation without lodging is collected and used as input to a dual-logistic function time-series model. The output is a remote sensing time-series curve of the vegetation without lodging, i.e., the remote sensing time-series curve of the vegetation within its normal growth period, denoted as the growth period curve. The remote sensing time-series data at time t is used as input to the growth period curve to obtain the predicted value of the remote sensing time-series data without lodging. The dual-logistic function time-series model is a well-known technique and will not be elaborated upon here.

[0032] Based on the above processing steps, the calculation method of the lodging cumulative loss index in the present application is as follows: wherein represents the lodging cumulative loss index at time t, N represents the number of remote sensing time series data in the monitoring window, represents the theoretical value of the remote sensing time series data at the jth time in the monitoring window under no lodging, represents the actual value of the remote sensing time series data at the jth time in the monitoring window , represents the instantaneous lodging intensity at the jth time in the monitoring window , represents the maximum value of the instantaneous lodging intensity at all times in the monitoring window .

[0033] When the vegetation appears serious lodging loss, the actual value of the remote sensing time series data continuously is lower than the theoretical value , so that the relative damage is significantly increased, and the calculated in combination with the instantaneous lodging intensity is weighted to the relative damage

[0034] , so that the calculated lodging cumulative loss index can better reflect the cumulative impact of lodging, and further evaluate the growth potential loss of the vegetation when lodging occurs.

[0035] In physiological ecology, the long-term impact of lodging is the interference with the life cycle process of vegetation, that is, when the lodging damage is serious, the recovery ability of the vegetation is limited, and the recovery time is earlier than the normal In the time series model, the aging stage of the vegetation is controlled by two core parameters, namely the aging rate parameter and the aging starting time parameter , which respectively control the steepness and the starting time point of the decline of the double logistic function. The traditional double logistic function model is a fixed global parameter, which cannot effectively identify the remote sensing time series data after lodging. The lodging cumulative loss index obtained by the above steps reflects the cumulative damage of the vegetation, therefore, the calibration factor is constructed based on the lodging cumulative loss index in the present application, which is used to calibrate the aging parameters after lodging, and the construction process of the calibration factor is as follows: The accumulated loss index of the remote sensing time series data at each time when the historical vegetation is not flat is calculated according to the above steps, and all the accumulated loss indexes of the historical time are taken as the input of the Otsu threshold segmentation method, and the output is the segmentation threshold of the accumulated loss index. The Otsu threshold segmentation method is a known technology, and will not be described here.

[0036] Based on the above processing steps, the calibration factor is calculated as follows: Wherein represents the calibration factor at time t, represents the accumulated loss index at time t, represents the segmentation threshold of the accumulated loss index, exp() represents the exponential function with the natural constant as the base, and 1 in the denominator is to limit the value of the calibration factor to .

[0037] If the damage caused by lodging to the vegetation is small, that is, the value of is small, at this time is close to , and the calculated is close to 0.5; if the damage caused by lodging to the vegetation increases, in order to improve the sensitivity of the double-logic function time series model to the influence of lodging, the is exponentially processed, that is, when the damage caused by lodging to the vegetation increases, after the exponential processing, the calibration factor will be significantly increased, and the adjustment intensity of the parameters in the double-logic function time series model in the subsequent steps is greater.

[0038] Step S5, based on the calibration factor, the parameters in the double-logic function time series model are adaptively adjusted, and the lodging damage is quantitatively estimated.

[0039] The calibration factor obtained by the above steps reflects the adjustment intensity of the parameters in the double-logic function time series model, that is, when the vegetation appears obvious lodging, the parameters in the original double-logic function time series model cannot accurately predict the remote sensing time series data of the vegetation, at this time, the calibration factor is needed to adjust the parameters, and then the remote sensing time series data is accurately predicted and the remote sensing time series curve is obtained, and the lodging damage is quantitatively estimated based on the remote sensing time series curve and the growth period curve.

[0040] Wherein the specific process of adjusting the parameters by the calibration factor is as follows: The greater the value of the calibration factor, the faster the remote sensing time series data changes with time, the more serious the deviation from the normal growth condition of the growth period, the more serious the lodging damage, and the faster the passive decline process of the vegetation, so the senescence rate parameter , that is, the senescence rate parameter positively correlated with the calibration factor; the greater the value of the calibration factor, the earlier the time of lodging, i.e. the earlier the passive decline of the vegetation begins, i.e. the senescence initiation time parameter negatively correlated with the calibration factor.

[0041] In this embodiment, the calculation methods of the senescence rate parameter and the senescence initiation time parameter are respectively as follows: wherein represents the senescence rate parameter at time t in the double logistic function time series model, represents the calibration factor at time t, and respectively represent the maximum value and the minimum value of the senescence rate parameter, which are respectively taken as 1 and 0.2 in this application, represents the senescence initiation time parameter at time t in the double logistic function time series model, and respectively represent the maximum value and the minimum value of the senescence initiation time parameter, i.e. the latest senescence initiation time and the earliest senescence initiation time under normal conditions, which can be obtained through the latest senescence initiation time and the earliest senescence initiation time in the historical growth period curve.

[0042] wherein the specific process for quantitatively estimating the lodging damage based on the remote sensing time series curve and the growth period curve is as follows: The double logistic function time series model is updated through the above steps, the actually collected remote sensing time series data are taken as the input of the updated double logistic function time series model, the output is the predicted value of the remote sensing time series data, the predicted curve is obtained, the area surrounded by the growth period curve and the predicted curve is calculated through the method of mathematical integration, the area represents the total amount of loss of photosynthetic product accumulation caused by lodging, which is taken as the result of the quantitative estimation of the lodging damage, and the quantitative estimation of the lodging damage based on the remote sensing time series and the growth period model is realized.

[0043] The threshold value and the weight and other setting values can be set by default according to the present application, or can be set by the person skilled in the art.

[0044] Those skilled in the art will appreciate that embodiments of the present application can be provided as methods, systems, or computer program products. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage media, etc.) having computer-usable program code embodied in the medium.

[0045] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 one or more flow or blocks

[0046] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 one or more flow or blocks

[0047] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 one or more flow or blocks

[0048] The embodiments of the present application described above are merely intended to illustrate the present application, but not to limit the present application. The skilled in the art can make many modifications and improvements without departing from the spirit and scope of the present application, which should be protected as long as they fall within the scope of the present application.

Claims

1. A method for quantitatively estimating lodging damage based on remote sensing time series and growth period models, characterized in that: Includes the following steps: Step S1: Collect remote sensing time-series data and perform preprocessing; Step S2: Analyze the variation pattern of the remote sensing time series data when vegetation falls, and construct the instantaneous fall intensity; Step S3: Analyze the duration of vegetation lodging based on the instantaneous lodging intensity, and construct a cumulative lodging loss index; Step S4: Analyze the aging characteristics of vegetation based on the lodging cumulative loss index and construct calibration factors; Step S5: Adaptively adjust the parameters in the dual logic function time series model based on the calibration factor, and quantitatively estimate the lodging damage.

2. The method for quantitative estimation of lodging damage based on remote sensing time series and growth period model according to claim 1, characterized in that: The remote sensing time-series data includes the normalized differential vegetation index (NDVI) of vegetation throughout its growth period, obtained by a medium-to-high resolution multispectral satellite sensor. Missing values ​​in the remote sensing time-series data are filled using a regression imputation method, and the remote sensing time-series data after filling missing values ​​is smoothed using a moving average method to obtain preprocessed remote sensing time-series data.

3. The method for quantitative estimation of lodging damage based on remote sensing time series and growth period model according to claim 2, characterized in that: The process of constructing the instantaneous collapse intensity specifically includes: taking time t as an example, the time window consisting of N times before time t is recorded as the monitoring window. The sequence of remote sensing time-series data within the monitoring window is denoted as the landslide monitoring sequence, and the monitoring window that is adjacent to but does not overlap with the monitoring window at time t is denoted as the nearest neighbor window. .

4. The method for quantitative estimation of lodging damage based on remote sensing time series and growth period model according to claim 3, characterized in that: The formula for calculating the instantaneous collapse strength is as follows: in This represents the instantaneous collapse intensity of the remote sensing time series data at time t. This represents the maximum value in the landslide monitoring sequence at time t. This represents the remote sensing time series data at time t, and N represents the monitoring window. The amount of remote sensing time series data within, Indicates neighboring windows The value of the i-th element in the lodging monitoring sequence. This represents the median of the collapse monitoring sequence in the nearest neighbor window at time t.

5. The method for quantitative estimation of lodging damage based on remote sensing time series and growth period model according to claim 4, characterized in that: The construction process of the lodging cumulative loss index is as follows: Collect historical remote sensing time-series data of vegetation without lodging in the manner described in step S1, use it as input to the dual-logic function time-series model, and output the remote sensing time-series curve of vegetation without lodging, that is, the remote sensing time-series curve of vegetation in the normal growth period, which is denoted as the growth period curve; use the remote sensing time-series data at time t as input to the growth period curve to obtain the predicted value of the remote sensing time-series data without lodging.

6. The method for quantitative estimation of lodging damage based on remote sensing time series and growth period model according to claim 5, characterized in that: The formula for calculating the cumulative loss index due to lodging is as follows: in The index represents the cumulative lodging loss at time t, and N represents the number of remote sensing time-series data points within the monitoring window. Indicates monitoring window The theoretical value of the remote sensing time series data at time j in the absence of lodging. Indicates monitoring window The actual value of the remote sensing time series data at time j. Indicates monitoring window The instantaneous collapse intensity at the j-th moment. Indicates monitoring window The maximum instantaneous collapse intensity at all moments within the timeframe.

7. The method for quantitative estimation of lodging damage based on remote sensing time series and growth period model according to claim 6, characterized in that: The calibration factor is constructed based on the lodging cumulative loss index and is used to calibrate the aging parameters after lodging. The construction process of the calibration factor is as follows: calculate the lodging cumulative loss index of remote sensing time series data at each time when the historical vegetation was not lodging according to steps S1 to S3, use the lodging cumulative loss index of all historical time as input of Otsu threshold segmentation method, and output the segmentation threshold of the lodging cumulative loss index.

8. The method for quantitative estimation of lodging damage based on remote sensing time series and growth period model according to claim 7, characterized in that: The formula for calculating the calibration factor is as follows: in This represents the calibration factor at time t. This represents the cumulative loss index due to lodging at time t. The threshold value for the cumulative loss exponent due to lodging is exp(), which represents an exponential function with the natural constant as its base.

9. The method for quantitative estimation of lodging damage based on remote sensing time series and growth period model according to claim 8, characterized in that: The specific process of adjusting the parameters using the calibration factor includes: setting the aging rate parameter according to the calibration factor. and aging onset time parameter , is represented as: and ;in This represents the aging rate parameter at time t in a dual-logic function time-series model. This represents the calibration factor at time t. and These represent the maximum and minimum values ​​of the aging rate parameter, respectively.

10. The method for quantitative estimation of lodging damage based on remote sensing time series and growth period model according to claim 9, characterized in that: The specific steps of step S5 include: using the actual collected remote sensing time series data as the input of the updated dual logic function time series model, outputting the predicted value of the remote sensing time series data, obtaining the prediction curve, and calculating the area enclosed by the growth period curve and the prediction curve through mathematical integration, which is taken as the total loss of photosynthetic product accumulation caused by lodging, and using it as the result of quantitative estimation of lodging damage.

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