A method for determining crop harvest time based on flux constraints and metabolic stagnation

Abstract

This invention discloses a method for determining crop harvest time based on flux constraints and metabolic stagnation, belonging to the interdisciplinary field of agricultural remote sensing and ecological metabolic dynamics. The method first uses multi-year ground-based micrometeorological data to calibrate the absolute boundary and core physical constraint parameters of crop metabolic stagnation; then, it acquires photosynthetic fluorescence and temperature data from satellite remote sensing to construct a multi-source remote sensing dataset; subsequently, it constructs a two-dimensional dynamic phase space of heat and metabolism with cumulative growth days as the horizontal axis and chlorophyll fluorescence as the vertical axis to plot the physiological dynamics evolution trajectory of crops; finally, it combines historical flux prior constraints and applies a variational decoupling function containing Lagrange multipliers for extreme value optimization, accurately locking the dry matter accumulation cessation point, achieving deep decoupling between physiological lifespan and environmental driving forces, eliminating false ripening noise, and determining the optimal harvest time window. This solves the technical problems of existing methods being susceptible to environmental interference leading to false ripening misjudgment and the difficulty of extrapolating single-point flux observations to a wide-area space.

Description

Technical Field

[0001] This invention relates to the interdisciplinary field of agricultural remote sensing and ecological metabolic dynamics, specifically to a method for determining crop harvest time based on flux constraints and metabolic stagnation. Background Technology

[0002] Accurately determining the maturity and harvest period of crops is of significant economic and ecological importance for optimizing the scheduling of agricultural machinery and preventing excessive grain dehydration and shedding, as well as mold growth in the field. From a biological perspective, the core indicator of crop physiological maturity is the complete cessation of grain dry matter accumulation and the reduction of canopy photosynthesis (carbon assimilation) intensity to a level that can only sustain the crop's own respiration.

[0003] Currently, the methods used in the industry to determine crop maturity are mainly divided into two categories: one is the threshold method based on the time series fitting of traditional vegetation indices, and the other is the agricultural meteorological model method driven by accumulated temperature.

[0004] The first type of method mainly uses optical remote sensing to obtain long-term greenness curves such as the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI). Then, it uses Logistic curve fitting to find the inflection point of the declining segment of the curve or sets a fixed threshold intercept to determine crop maturity. However, this method has significant drawbacks: the external changes in leaf greenness (structural senescence) and the internal changes in carbon assimilation (metabolic senescence) are not synchronized, exhibiting a significant time lag. Moreover, during extreme weather events such as drought and high temperatures, crop leaf chlorophyll decreases rapidly, causing a sudden and sharp drop in traditional vegetation indices. This type of method can easily misjudge this abnormal situation as normal crop maturity, leading to premature harvesting.

[0005] The second type of method mainly relies on ground weather stations to collect data on the effective accumulated temperature of crops from sowing. When the accumulated temperature reaches the preset accumulated temperature constant for the crop variety, the crop is considered mature. The problem with this method is that it completely ignores the actual growth conditions of the crops and cannot reflect the spatial differences between different plots. For example, different soil moisture conditions in different plots can lead to early or late maturity of crops. Using this method to determine the maturity period of crops over a large area results in extremely low accuracy.

[0006] In summary, existing large-scale remote sensing methods generally lack observational constraints centered on the core maturity condition of "crop cessation of photosynthetic metabolism," resulting in imprecise physical principles for maturity determination and susceptibility to interference from external environmental factors. Furthermore, while eddy covariance flux towers can accurately measure crop photosynthesis, these devices only enable single-point scale observations. Current technologies lack physical methods to convert historical flux observation data from single-point scales into data applicable to regional-scale remote sensing analysis. Therefore, there is an urgent need for a kinetic discrimination method that transcends the superficial appearance of crop greenness and directly reflects the intrinsic metabolic nature of crops, enabling accurate determination of crop harvest dates. Summary of the Invention

[0007] The purpose of this invention is to address the shortcomings of existing technologies by providing a crop harvest date determination method based on flux constraints and metabolic stagnation. This method aims to solve two major problems in existing technologies: first, optical vegetation indices are not sensitive enough to detecting crop photosynthetic metabolic stagnation, easily leading to false ripening due to extreme environmental influences; second, single-point data from traditional micrometeorological flux observations are difficult to directly apply to large-scale area harvest date determination. This invention provides a crop harvest date determination method based on flux constraints and metabolic stagnation, achieving accurate and efficient harvest date determination.

[0008] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0009] A method for determining crop harvest date based on flux constraints and metabolic stagnation includes the following steps:

[0010] Step S1: Determine the absolute boundary of metabolic stagnation at historical flux points. This is done using actual ground micrometeorological observation data covering less than three complete growth cycles of the target crop, including historical carbon flux data including net ecosystem exchange. The three complete growth cycles are preferably 5-10 years. The critical parameters related to photosynthetic metabolism when the crop truly reaches physiological maturity are extracted. This means that the critical accumulated temperature threshold and critical fluorescence radiation value for metabolic stagnation are determined, providing a unified and accurate physical benchmark for subsequent large-scale remote sensing inversion analysis.

[0011] Step S2: Multi-source satellite-based remote sensing spatiotemporal data assimilation and feature mapping, to obtain continuous time-series data of sunlight-induced chlorophyll fluorescence inversion that can reflect crop metabolic status within the target area, as well as parameters that can reflect the heat-driven conditions for crop growth, to complete the integration of multi-dimensional data, and to achieve unified alignment of data spatial coordinates and time dimensions, generating a multi-dimensional joint data set containing the above two parameters.

[0012] Step S3: Reconstruct the dynamic trajectory of the two-dimensional phase space of heat and metabolism, eliminate the interference factors of interannual fluctuations caused by calendar time and time axis deviations caused by extreme climate, construct a two-dimensional phase space with the cumulative growth degree days as the horizontal axis and the sunlight-induced chlorophyll fluorescence inversion time series data as the vertical axis, and draw the crop growth dynamic evolution trajectory in this space, transforming the problem of crop maturity determination into an analysis problem focusing only on the physiological dynamic characteristics of crops.

[0013] Step S4: The metabolic stagnation point, constrained by flux priors, is decoupled. In the reconstructed two-dimensional phase space of heat and metabolism, partial derivative features reflecting the crop's metabolic decline are extracted for the dynamic evolution trajectory. A joint extreme value optimization function, including the critical fluorescence radiation value of metabolic stagnation, partial derivative features, and critical accumulated temperature threshold of metabolic stagnation, is introduced. Based on the reference of historical flux data and the extreme value optimization calculation method, the optimal harvest time window for the complete cessation of crop dry matter accumulation is accurately found, while effectively eliminating interference from crop false ripening signals caused by environmental stress.

[0014] Furthermore, step S1 includes:

[0015] Step S11: Extraction of absolute boundaries of metabolic arrest;

[0016] Step S12: Calibration of rigid physical constraint parameters;

[0017] Step S2 includes:

[0018] Step S21: Acquisition of satellite-based remote sensing and meteorological data;

[0019] Step S22: Daily cumulative calculation of growth degree;

[0020] Step S3 includes:

[0021] Step S31: Eliminate calendar time coordinates;

[0022] Step S32: Draw the intrinsic evolutionary manifold;

[0023] Step S4 includes:

[0024] Step S41: Extraction of phase space dynamic features;

[0025] Step S42: Decoupling of joint extreme value optimization and pseudo-ripeness.

[0026] Furthermore, step S11 specifically includes:

[0027] Metabolic stagnation absolute boundary extraction was used to collect multi-year historical net ecosystem exchange volume from the eddy covariance flux tower within the target crop planting area. The data was broken down into ecosystem respiration using a carbon flux decomposition algorithm. and total primary productivity By combining historical field measurements of crop grain dehydration time and dry matter accumulation cessation time, the absolute boundary of crop metabolic cessation is determined, that is, the finding... Decreased to the point where it could only offset the crop's maintenance respiration The specific time point when the net photosynthetic increase is zero.

[0028] Furthermore, step S12 specifically includes:

[0029] Rigid physical constraint parameter calibration: Extract the two core physical constraint parameters corresponding to the absolute boundary of metabolic cessation, and the critical accumulated temperature threshold for metabolic cessation. and the critical fluorescence radiation value of metabolic cessation , will this and This information is fixed and serves as a reference for subsequent large-scale regional data calculation and analysis.

[0030] Furthermore, step S21 specifically includes:

[0031] Satellite-based remote sensing and meteorological data acquisition: This involves acquiring satellite remote sensing data on solar-induced chlorophyll fluorescence in the target area for the current season, as well as spatial grid data of daily average temperature from satellite remote sensing. The satellite remote sensing data on solar-induced chlorophyll fluorescence is... Its inversion time series data is Time-series radiance data characterizing the physiological and metabolic activities of crop canopy; among which, The value of the value can dynamically reflect the electron transport rate inside the crop canopy, characterize the actual metabolic status of the crop in the actual photosynthetic process, and thus reflect the actual photosynthetic capacity of the crop.

[0032] Furthermore, step S22 specifically includes:

[0033] The cumulative growth time and joint tensor construction were performed on the various satellite data obtained above. The spatial coordinates and calendar time axis were aligned at the pixel level, and the starting time of each pixel from the sowing date was calculated. arrive

[0034] Calculation deadline Daily cumulative growth The calculation formula is as follows:

[0035]

[0036] in, The sowing date is the starting time. To calculate the deadline, and The spatial grid data of daily average temperature from satellite remote sensing is the first... Daily maximum and minimum temperatures, This is the basic temperature threshold for the growth of this crop;

[0037] Based on the calculation results, generate time-series data containing growth degree days. and satellite remote sensing time series data of sunlight-induced chlorophyll fluorescence inversion A pixel-level multi-dimensional joint data set.

[0038] Furthermore, step S31 specifically includes:

[0039] Calendar time coordinates are eliminated, abandoning the traditional use of calendar time. The time-series data analysis method uses the horizontal axis as the abscissa and constructs a separate two-dimensional phase space of heat and metabolism for each pixel in the target area, using the heat conditions and metabolic status of crop growth as the analysis dimensions.

[0040] Furthermore, step S32 specifically includes:

[0041] The intrinsic evolutionary manifold is plotted in the aforementioned constructed two-dimensional phase space of heat and metabolism, with... As the x-axis, with Plot the intrinsic dynamic evolution manifold of crop growth, i.e., the dynamic evolution trajectory, with the vertical axis as the ordinate. :

[0042]

[0043] in, It can eliminate interference from interannual fluctuations caused by calendar time and time axis deviations caused by extreme climate, and truly reflect the actual law that crops' metabolic capacity naturally declines under the dual effects of heat conditions and their own physiological lifespan.

[0044] Furthermore, step S41 specifically includes:

[0045] Phase space dynamics feature extraction, based on the above-drawn crop growth dynamics evolution trajectory. For the descending segment, calculate the first partial derivative of the trajectory with respect to the accumulated temperature. and second-order partial derivatives The first-order partial derivative The second-order partial derivative reflects the rate of metabolic decline in crops. It reflects the acceleration of crop metabolic decline.

[0046] Furthermore, step S42 specifically includes:

[0047] Joint Extremum Optimization and Decoupling from Pseudo-Factoriousness: Introducing a joint extremum optimization function with Lagrange multipliers, the formula is as follows:

[0048]

[0049] in, The optimal harvest date to be sought; First item This is a state approximation constraint term used to ensure that the current crop metabolic state matches the critical state of metabolic stagnation as determined by historical flux. ; Second item The first term is the decay rate constraint, used to ensure that the rate of crop metabolic decay at the judgment time is close to zero, i.e., photosynthesis completely stops; the third term... This is a unidirectional accumulated temperature penalty term, used for crop growth days. The critical accumulated temperature threshold has not yet been reached. At the same time, a penalty value is generated to avoid misjudging false wilting of crops caused by diseases or extreme weather. and To adjust the weighting coefficients, a nonlinear optimization calculation is performed by minimizing the objective function, ultimately yielding a unique global optimum, which represents the optimal harvest date for the crop. .

[0050] Compared with the prior art, the present invention, employing the above technical solution, has the following technical effects:

[0051] (1) This invention provides a crop harvesting period determination method based on flux constraints and metabolic stagnation. It eliminates the need for the use of the easily affected optical reflectance vegetation index and instead directly adopts the daylight-induced chlorophyll fluorescence (SIF) signal, which reflects the actual photosynthetic electron transport rate of crops. This signal only responds when crops undergo actual photosynthetic assimilation, directly reflecting the true metabolic status of crops and eliminating the core characteristic of crop physiological maturity—the increase in grain dry matter—which can be directly identified and determined through calculation.

[0052] (2) This invention provides a crop harvesting period discrimination method based on flux constraints and metabolic stagnation, which innovatively constructs a two-dimensional phase space of heat and metabolism, and sets a one-way accumulated temperature penalty term in combination with historical data. When crops encounter sudden drought or other extreme weather, resulting in abnormal yellowing of leaves and a sharp drop in SIF signal, since the accumulated growth temperature has not yet reached the critical threshold calibrated by historical flux, the joint extreme value optimization function will calculate a high penalty value to prevent the state from being judged as mature, thereby avoiding the misjudgment of "false maturity" from the perspective of calculation logic, and preventing the beneficial effects of crop yield reduction or even crop failure caused by premature harvesting.

[0053] (3) This invention provides a crop harvest date determination method based on flux constraints and metabolic stagnation. It establishes a rigorous conversion method between satellite remote sensing data and ground-based measured data, so that the high-precision single-point historical observation data obtained by the eddy flux tower is no longer isolated single-point data, but is refined into the core reference basis for guiding regional-scale phase space calculations, that is, considering the critical fluorescence radiation value and the critical accumulated temperature threshold. This global constraint based on ground-based measured data can ensure that the large-scale regional harvest date data obtained through satellite remote sensing inversion fully conforms to the actual micro-meteorological laws in the field, and greatly improves the accuracy of regional-scale harvest date determination. Attached Figure Description

[0054] Figure 1 This is a schematic diagram of a crop harvesting period determination method based on flux constraints and metabolic stagnation according to the present invention.

[0055] Figure 2 This is a schematic diagram of the source data processing and pixel dataset construction process of the present invention;

[0056] Figure 3 This is a schematic diagram of the multi-source data processing and pixel dataset construction process of the present invention. Detailed Implementation

[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] A method for determining crop harvest time based on flux constraints and metabolic stagnation, such as... Figure 1 This includes the following steps:

[0059] Step S1: Determine the absolute boundary of metabolic stagnation at historical flux points. This is done using actual ground micrometeorological observation data covering less than three complete growth cycles of the target crop, including historical carbon flux data including net ecosystem exchange. The three complete growth cycles are preferably 5-10 years. The critical parameters related to photosynthetic metabolism when the crop truly reaches physiological maturity are extracted. This means that the critical accumulated temperature threshold and critical fluorescence radiation value for metabolic stagnation are determined, providing a unified and accurate physical benchmark for subsequent large-scale remote sensing inversion analysis.

[0060] Step S2: Multi-source satellite-based remote sensing spatiotemporal data assimilation and feature mapping, to obtain continuous time-series data of sunlight-induced chlorophyll fluorescence inversion that can reflect crop metabolic status within the target area, as well as parameters that can reflect the heat-driven conditions for crop growth, to complete the integration of multi-dimensional data, and to achieve unified alignment of data spatial coordinates and time dimensions, generating a multi-dimensional joint data set containing the above two parameters.

[0061] Step S3: Reconstruct the dynamic trajectory of the two-dimensional phase space of heat and metabolism, eliminate the interference factors of interannual fluctuations caused by calendar time and time axis deviations caused by extreme climate, construct a two-dimensional phase space with the cumulative growth degree days as the horizontal axis and the sunlight-induced chlorophyll fluorescence inversion time series data as the vertical axis, and draw the crop growth dynamic evolution trajectory in this space, transforming the problem of crop maturity determination into an analysis problem focusing only on the physiological dynamic characteristics of crops.

[0062] Step S4: The metabolic stagnation point, constrained by flux priors, is decoupled. In the reconstructed two-dimensional phase space of heat and metabolism, partial derivative features reflecting the crop's metabolic decline are extracted for the dynamic evolution trajectory. A joint extreme value optimization function, including the critical fluorescence radiation value of metabolic stagnation, partial derivative features, and critical accumulated temperature threshold of metabolic stagnation, is introduced. Based on the reference of historical flux data and the extreme value optimization calculation method, the optimal harvest time window for the complete cessation of crop dry matter accumulation is accurately found, while effectively eliminating interference from crop false ripening signals caused by environmental stress.

[0063] Furthermore, step S1 includes:

[0064] Step S11: Extraction of absolute boundaries of metabolic arrest;

[0065] Step S12: Calibration of rigid physical constraint parameters;

[0066] Step S2, as follows Figure 2 ,include:

[0067] Step S21: Acquisition of satellite-based remote sensing and meteorological data;

[0068] Step S22: Daily cumulative calculation of growth degree;

[0069] Step S3, as follows Figure 3 ,include:

[0070] Step S31: Eliminate calendar time coordinates;

[0071] Step S32: Draw the intrinsic evolutionary manifold;

[0072] Step S4 includes:

[0073] Step S41: Extraction of phase space dynamic features;

[0074] Step S42: Decoupling of joint extreme value optimization and pseudo-ripeness.

[0075] Furthermore, step S11 specifically includes:

[0076] Metabolic stagnation absolute boundary extraction was used to collect multi-year historical net ecosystem exchange volume from the eddy covariance flux tower within the target crop planting area. The data was broken down into ecosystem respiration using a carbon flux decomposition algorithm. and total primary productivity By combining historical field measurements of crop grain dehydration time and dry matter accumulation cessation time, the absolute boundary of crop metabolic cessation is determined, that is, the finding... Decreased to the point where it could only offset the crop's maintenance respiration The specific time point when the net photosynthetic increase is zero.

[0077] Furthermore, step S12 specifically includes:

[0078] Rigid physical constraint parameter calibration: Extract the two core physical constraint parameters corresponding to the absolute boundary of metabolic cessation, and the critical accumulated temperature threshold for metabolic cessation. and the critical fluorescence radiation value of metabolic cessation , will this and This information is fixed and serves as a reference for subsequent large-scale regional data calculation and analysis.

[0079] Furthermore, step S21 specifically includes:

[0080] Satellite-based remote sensing and meteorological data acquisition: This involves acquiring satellite remote sensing data on solar-induced chlorophyll fluorescence in the target area for the current season, as well as spatial grid data of daily average temperature from satellite remote sensing. The satellite remote sensing data on solar-induced chlorophyll fluorescence is... Its inversion time series data is Time-series radiance data characterizing the physiological and metabolic activities of crop canopy; among which, The value of the value can dynamically reflect the electron transport rate inside the crop canopy, characterize the actual metabolic status of the crop in the actual photosynthetic process, and thus reflect the actual photosynthetic capacity of the crop.

[0081] Furthermore, step S22 specifically includes:

[0082] The cumulative growth time and joint tensor construction were performed on the various satellite data obtained above. The spatial coordinates and calendar time axis were aligned at the pixel level, and the starting time of each pixel from the sowing date was calculated. arrive

[0083] Calculation deadline Daily cumulative growth The calculation formula is as follows:

[0084]

[0085] in, The sowing date is the starting time. To calculate the deadline, and The daily average temperature spatial grid data from satellite remote sensing are for the first time. Daily maximum and minimum temperatures, This is the basic temperature threshold for the growth of this crop;

[0086] Based on the calculation results, generate time-series data containing growth degree days. and satellite remote sensing time series data of sunlight-induced chlorophyll fluorescence inversion A multi-dimensional, pixel-level joint data set.

[0087] Furthermore, step S31 specifically includes:

[0088] Calendar time coordinates are eliminated, abandoning the traditional use of calendar time. The time-series data analysis method uses the horizontal axis as the abscissa and constructs a separate two-dimensional phase space of heat and metabolism for each pixel in the target area, using the heat conditions and metabolic status of crop growth as the analysis dimensions.

[0089] Furthermore, step S32 specifically includes:

[0090] The intrinsic evolutionary manifold is plotted in the aforementioned constructed two-dimensional phase space of heat and metabolism, with... As the x-axis, with Plot the intrinsic dynamic evolution manifold of crop growth, i.e., the dynamic evolution trajectory, with the vertical axis as the ordinate. :

[0091]

[0092] in, It can eliminate interference from interannual fluctuations caused by calendar time and time axis deviations caused by extreme climate, and truly reflect the actual law that crops' metabolic capacity naturally declines under the dual effects of heat conditions and their own physiological lifespan.

[0093] Furthermore, step S41 specifically includes:

[0094] Phase space dynamics feature extraction, based on the above-drawn crop growth dynamics evolution trajectory. For the descending segment, calculate the first partial derivative of the trajectory with respect to the accumulated temperature. and second-order partial derivatives The first-order partial derivative The second-order partial derivative reflects the rate of metabolic decline in crops. It reflects the acceleration of crop metabolic decline.

[0095] Furthermore, step S42 specifically includes:

[0096] Joint Extremum Optimization and Decoupling from Pseudo-Factoriousness: Introducing a joint extremum optimization function with Lagrange multipliers, the formula is as follows:

[0097]

[0098] in, The optimal harvest date to be sought; First item This is a state approximation constraint term used to ensure that the current crop metabolic state matches the critical state of metabolic stagnation as determined by historical flux. ; Second item The first term is the decay rate constraint, used to ensure that the rate of crop metabolic decay at the judgment time is close to zero, i.e., photosynthesis completely stops; the third term... This is a unidirectional accumulated temperature penalty term, used for crop growth days. The critical accumulated temperature threshold has not yet been reached. At the same time, a penalty value is generated to avoid misjudging false wilting of crops caused by diseases or extreme weather. and To adjust the weighting coefficients, a nonlinear optimization calculation is performed by minimizing the objective function, ultimately yielding a unique global optimum, which represents the optimal harvest date for the crop. .

[0099] Those skilled in the art should understand that, unless otherwise specified, the meanings of the technical and scientific terms used herein are consistent with the general understanding of the relevant technical field. Furthermore, terms defined in general dictionaries should be understood in the context of the technical background in this field and should not be interpreted in an overly idealized or formalistic manner divorced from practical application scenarios.

[0100] The above embodiments have described in detail the main concept, technical solution, and technical effects of the present invention. It should be noted that the above content is merely illustrative and not intended to limit the scope of protection of the present invention. Any equivalent modifications, substitutions, or optimizations based on the present invention without departing from its core principles are within the scope of the present invention.

Claims

1. A method for determining crop harvest time based on flux constraints and metabolic stagnation, characterized in that, Includes the following steps: Step S1: Determine the absolute boundary of metabolic stagnation at historical flux points. This is done using actual ground micrometeorological observation data covering less than three complete growth cycles of the target crop, including historical carbon flux data including net ecosystem exchange. The three complete growth cycles are preferably 5-10 years. The critical parameters related to photosynthetic metabolism when the crop truly reaches physiological maturity are extracted. This means that the critical accumulated temperature threshold and critical fluorescence radiation value for metabolic stagnation are determined, providing a unified and accurate physical benchmark for subsequent large-scale remote sensing inversion analysis. Step S2: Multi-source satellite-based remote sensing spatiotemporal data assimilation and feature mapping, to obtain continuous time-series data of sunlight-induced chlorophyll fluorescence inversion that can reflect crop metabolic status within the target area, as well as parameters that can reflect the heat-driven conditions for crop growth, to complete the integration of multi-dimensional data, and to achieve unified alignment of data spatial coordinates and time dimensions, generating a multi-dimensional joint data set containing the above two parameters. Step S3: Reconstruct the dynamic trajectory of the two-dimensional phase space of heat and metabolism, eliminate the interference factors of interannual fluctuations caused by calendar time and time axis deviations caused by extreme climate, construct a two-dimensional phase space with the cumulative growth degree days as the horizontal axis and the sunlight-induced chlorophyll fluorescence inversion time series data as the vertical axis, and draw the crop growth dynamic evolution trajectory in this space, transforming the problem of crop maturity determination into an analysis problem focusing only on the physiological dynamic characteristics of crops. Step S4: The metabolic stagnation point, which is constrained by flux prior, is decoupled. In the reconstructed two-dimensional phase space of heat and metabolism, partial derivative features reflecting the metabolic decline of crops are extracted for the dynamic evolution trajectory. Furthermore, a joint extreme value optimization function is introduced, which includes the critical fluorescence radiation value of metabolic cessation, the partial derivative characteristics, and the critical accumulated temperature threshold of metabolic cessation. Based on the reference of historical flux data and the extreme value optimization calculation method, the optimal harvest time window for the complete cessation of crop dry matter accumulation is accurately found, while effectively eliminating the interference of crop false ripening signals caused by environmental stress.

2. The crop harvest date determination method based on flux constraints and metabolic stagnation according to claim 1, characterized in that, Step S1 includes: Step S11: Extraction of absolute boundaries of metabolic arrest; Step S12: Calibration of rigid physical constraint parameters; Step S2 includes: Step S21: Acquisition of satellite-based remote sensing and meteorological data; Step S22: Daily cumulative calculation of growth degree; Step S3 includes: Step S31: Eliminate calendar time coordinates; Step S32: Draw the intrinsic evolutionary manifold; Step S4 includes: Step S41: Extraction of phase space dynamic features; Step S42: Decoupling of joint extreme value optimization and pseudo-ripeness.

3. The crop harvest date determination method based on flux constraints and metabolic stagnation according to claim 2, characterized in that, Step S11 specifically includes: Metabolic stagnation absolute boundary extraction was used to collect multi-year historical net ecosystem exchange volume from the eddy covariance flux tower within the target crop planting area. The data was broken down into ecosystem respiration using a carbon flux decomposition algorithm. and total primary productivity By combining historical field measurements of crop grain dehydration time and dry matter accumulation cessation time, the absolute boundary of crop metabolic cessation is determined, that is, the finding... Decreased to the point where it could only offset the crop's maintenance respiration The specific time point when the net photosynthetic increase is zero.

4. The crop harvest date determination method based on flux constraints and metabolic stagnation according to claim 2, characterized in that, Step S12 specifically includes: Rigid physical constraint parameter calibration: Extract the two core physical constraint parameters corresponding to the absolute boundary of metabolic cessation, and the critical accumulated temperature threshold for metabolic cessation. and the critical fluorescence radiation value of metabolic cessation , will this and This information is fixed and serves as a reference for subsequent large-scale regional data calculation and analysis.

5. The crop harvest date determination method based on flux constraints and metabolic stagnation according to claim 2, characterized in that, Step S21 specifically includes: Satellite-based remote sensing and meteorological data acquisition: This involves acquiring satellite remote sensing data on solar-induced chlorophyll fluorescence in the target area for the current season, as well as spatial grid data of daily average temperature from satellite remote sensing. The satellite remote sensing data on solar-induced chlorophyll fluorescence is... Its inversion time series data is Time-series radiance data characterizing the physiological and metabolic activities of crop canopy; among which, The value of the value can dynamically reflect the electron transport rate inside the crop canopy, characterize the actual metabolic status of the crop in the actual photosynthetic process, and thus reflect the actual photosynthetic capacity of the crop.

6. The crop harvest date determination method based on flux constraints and metabolic stagnation according to claim 2, characterized in that, Step S22 specifically includes: The cumulative growth time and joint tensor construction were performed on the various satellite data obtained above. The spatial coordinates and calendar time axis were aligned at the pixel level, and the starting time of each pixel from the sowing date was calculated. arrive Calculation deadline Daily cumulative growth The calculation formula is as follows: in, The sowing date is the starting time. To calculate the deadline, and The daily average temperature spatial grid data from satellite remote sensing are for the first time. Daily maximum and minimum temperatures, This is the basic temperature threshold for the growth of this crop; Based on the calculation results, generate time-series data containing growth degree days. and satellite remote sensing time series data of sunlight-induced chlorophyll fluorescence inversion A multi-dimensional, pixel-level joint data set.

7. The crop harvest date determination method based on flux constraints and metabolic stagnation according to claim 2, characterized in that, Step S31 specifically includes: Calendar time coordinates are eliminated, abandoning the traditional use of calendar time. The time-series data analysis method uses the horizontal axis as the abscissa and constructs a separate two-dimensional phase space of heat and metabolism for each pixel in the target area, using the heat conditions and metabolic status of crop growth as the analysis dimensions.

8. The crop harvest date determination method based on flux constraints and metabolic stagnation according to claim 2, characterized in that, Step S32 specifically includes: The intrinsic evolutionary manifold is plotted in the aforementioned constructed two-dimensional phase space of heat and metabolism, with... As the x-axis, with Plot the intrinsic dynamic evolution manifold of crop growth, i.e., the dynamic evolution trajectory, with the vertical axis as the ordinate. : in, It can eliminate interference from interannual fluctuations caused by calendar time and time axis deviations caused by extreme climate, and truly reflect the actual law that crops' metabolic capacity naturally declines under the dual effects of heat conditions and their own physiological lifespan.

9. The crop harvest date determination method based on flux constraints and metabolic stagnation according to claim 2, characterized in that, Step S41 specifically includes: Phase space dynamics feature extraction, based on the above-drawn crop growth dynamics evolution trajectory. For the descending segment, calculate the first partial derivative of the trajectory with respect to the accumulated temperature. and second-order partial derivatives The first-order partial derivative The second-order partial derivative reflects the rate of metabolic decline in crops. It reflects the acceleration of crop metabolic decline.

10. The crop harvest date determination method based on flux constraints and metabolic stagnation according to claim 2, characterized in that, Step S42 specifically includes: Joint Extremum Optimization and Decoupling from Pseudo-Factoriousness: Introducing a joint extremum optimization function with Lagrange multipliers, the formula is as follows: in, The optimal harvest date to be sought; First item This is a state approximation constraint term used to ensure that the current crop metabolic state matches the critical state of metabolic stagnation as determined by historical flux. ; Second item The first term is the decay rate constraint, used to ensure that the rate of crop metabolic decay at the judgment time is close to zero, i.e., photosynthesis completely stops; the third term... This is a unidirectional accumulated temperature penalty term, used for crop growth days. The critical accumulated temperature threshold has not yet been reached. At the same time, a penalty value is generated to avoid misjudging false wilting of crops caused by diseases or extreme weather. and To adjust the weighting coefficients, a nonlinear optimization calculation is performed by minimizing the objective function, ultimately yielding a unique global optimum, which represents the optimal harvest date for the crop. .

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