A method and apparatus for near real-time remote sensing estimation of crop agronomic phenology

Abstract

The application discloses a kind of near real-time remote sensing estimation method and device for crop agronomic phenophase, which comprises the following steps: obtaining historical remote sensing image and target remote sensing image of target area;According to the historical remote sensing image, a plurality of time curve model sets of different lengths starting from the starting time are constructed;According to the target remote sensing image, a target time curve is constructed, the target time curve is translated and stretched, the best matching time curve model is adaptively determined, and the corresponding translation and stretching factor and the agronomic phenophase information of the target crop are calculated.The above technical scheme is used, the user can combine historical remote sensing image and ground phenology statistical survey data to establish crop time curve model;And through model adaptive matching, the time curve model matched with the target time curve under the current non-complete growth cycle is obtained, and then the near real-time estimation of crop agronomic phenophase in target remote sensing image is realized, to meet the use demand of user.

Description

Technical Field

[0001] This invention relates to the field of agricultural remote sensing application technology, and in particular to a near real-time remote sensing estimation method and apparatus for crop agronomic phenological stages. Background Technology

[0002] Vegetation phenology can be obtained from changes in individual plants or from spectral variations in vegetation observed at the pixel level using remote sensing data. For specific species, such as crops, agronomists have defined agronomic phenological periods based on their physiological characteristics. Estimating crop agronomic phenology is crucial for understanding crop physiological processes and accurately obtaining important information such as crop yield.

[0003] Traditional methods for estimating crop agronomic phenological stages mainly fall into three categories: those based on agronomic statistical data, those based on agro-meteorological statistical data, and those based on crop models. These methods, due to their high time, manpower, and material costs, as well as the limitations of the crop models themselves, struggle to achieve high-precision estimations of regional crops, thus affecting users' control over crop agronomic phenological stages. Existing remote sensing estimation methods for agronomic phenological stages can only be applied to the estimation and extraction of phenological information after the crop growing season, and cannot achieve near real-time estimation of phenological information during the growing season. Summary of the Invention

[0004] In view of the above problems, embodiments of the present invention are proposed to provide a near real-time remote sensing estimation method and a near real-time remote sensing estimation device for crop agronomic phenology that overcomes or at least partially solves the above problems.

[0005] To address the aforementioned problems, this invention discloses a near-real-time remote sensing estimation method for crop agronomic phenological stages, the method comprising:

[0006] Acquire remote sensing images of the target area, including historical remote sensing images and target remote sensing images;

[0007] Based on the historical remote sensing images, several sets of historical remote sensing data of target crops are obtained. The historical remote sensing data includes time series data of remote sensing vegetation index. After data processing of the time series data of remote sensing vegetation index, a time series curve model is constructed. The data processing includes translation, stretching and matching.

[0008] Acquire agronomic phenological data of the target crop, and construct several sets of time series curve models with different time lengths starting from a preset start time in combination with the time series curve model;

[0009] Target remote sensing data is obtained from the target remote sensing image, including the target remote sensing vegetation index data under the current incomplete cycle of the target crop, and a target time series curve is constructed.

[0010] The near-real-time target time-series curve under the incomplete growth cycle is translated and stretched, and a time-series curve model matching the target time-series curve is determined to obtain the translation and stretching factor corresponding to the target time-series curve.

[0011] The phenological information of the target crop is determined based on the translational stretching factor.

[0012] Optionally, after obtaining several sets of historical remote sensing data of the target crop based on the historical remote sensing images, or obtaining target remote sensing data of the target crop based on the target remote sensing images, the method further includes:

[0013] The historical remote sensing data and the target remote sensing data are subjected to cloud removal, radiometric correction, and filtering.

[0014] Optionally, both the historical remote sensing data and the target remote sensing data include vegetation indices, including the Normalized Difference Vegetation Index (NDVI) and the Wide Dynamic Range Vegetation Index (WDRVI).

[0015] NDVI=(ρ nir -ρ red ) / (ρ nir +ρ red ),

[0016] WDRVI={[(α-1)+(α+1)×NDVI] / [(α+1)+(α-1)×NDVI]+(1-α) / (1+α)}×100,

[0017] Where, ρ nir and ρ red α represents the surface reflectance values ​​in the near-infrared and red light bands, and α is the weighting coefficient.

[0018] Optionally, the step of constructing a set of several time series curve models with different time lengths starting from a preset start time, in conjunction with the time series curve model, includes:

[0019] The remote sensing vegetation index data of the target crop is obtained based on the historical remote sensing imagery, and the remote sensing vegetation index data includes the masking data of the target crop for the corresponding year; the masking data of the target crop is obtained in near real-time based on the target imagery.

[0020] Time series data of the mean WDRVI of all target crops in the target area, calculated based on the masking data of the target crops and the phenological statistical survey data;

[0021] The historical WDRVI mean time series curves within the target area corresponding to the phenological statistical survey data are translated, stretched, and matched to obtain the multi-year mean. The phenological statistical survey data are then marked on the time series curve model to construct a set of time series curve models of different lengths starting from the starting time.

[0022] Optionally, the step of translating and stretching the target time series curve and determining the time series curve model that matches the target time series curve includes:

[0023] After obtaining the target time series curve, the target time series curve is translated and stretched along the X and Y axes to obtain several translated and stretched time series curves. The distance between each time series curve model in the time series curve model set and the translated and stretched target time series curve is calculated. The translation and stretching parameters with the smallest distance to each time series curve model are determined by the least squares method. The time series curve model with the smallest distance to the target time series curve is then compared and determined as the time series curve model that matches the target time series curve.

[0024] Optionally, the translation and stretching parameters that minimize the distance to the time series curve model are determined using the least squares method; that is, the translation and stretching parameters are optimized to minimize the distance between the two curves.

[0025]

[0026] The formula for handling translational stretching is:

[0027]

[0028] x represents time, h(x) represents the target time series curve, g(X) represents the target time series curve after translation and stretching transformation, and xscale and yscale are the translation and stretching factors. peak The time when the target time-series curve reaches its peak value.

[0029] Optionally, the calculation formula for determining the phenological information of the target crop based on the translational stretching factor is as follows:

[0030] X est = xscale × (X0 + tshift),

[0031] Where X0 represents the time of the target crop agronomic phenological period predefined in the time-series curve model based on regional phenological survey statistics, X est The agronomic phenological period time is estimated using this invention.

[0032] This invention also discloses a near-real-time remote sensing estimation device for crop agronomic phenological stages, the device comprising:

[0033] The remote sensing image acquisition module is used to acquire remote sensing images of the target area, including historical remote sensing images and target remote sensing images;

[0034] The historical time-series curve acquisition module is used to obtain several sets of historical remote sensing data of target crops based on the historical remote sensing images. The historical remote sensing data includes time-series data of remote sensing vegetation index. After data processing of the time-series data of remote sensing vegetation index, a time-series curve model is constructed. The data processing includes translation, stretching and matching.

[0035] The time-series curve model construction module is used to construct a time-series curve model of the target crop, and to construct a set of time-series curve models with different time lengths starting from a preset start time in combination with the time-series curve model.

[0036] The target time-series curve acquisition module is used to obtain target remote sensing data based on the target remote sensing image. The target remote sensing data includes target remote sensing vegetation index data under the current incomplete cycle of the target crop, and constructs the target time-series curve.

[0037] The time-series curve adaptive matching module is used to translate and stretch the near real-time target time-series curve under the incomplete growth cycle, and determine the time-series curve model that matches the target time-series curve, and obtain the translation and stretching factor corresponding to the target time-series curve.

[0038] The phenological information calculation module is used to determine the phenological information of the target crop based on the translational stretching factor.

[0039] This invention also discloses a computer-readable storage medium storing a computer program that can run on a processor. When the computer program is executed by the processor, it implements the steps of the above-described near-real-time remote sensing estimation method for crop agronomic phenology.

[0040] This invention discloses a near-real-time remote sensing estimation method for crop agronomic phenology. The method includes: acquiring historical remote sensing images of the target area and a target remote sensing image; obtaining several sets of historical remote sensing vegetation index time-series data based on the historical remote sensing images, and constructing several time-series curve model sets of different lengths starting from a given time point, combined with statistically surveyed crop agronomic phenology data; obtaining target remote sensing vegetation index data based on the target remote sensing image, and constructing a target time-series curve based on the target remote sensing vegetation index data; translating and stretching the target time-series curve, adaptively determining the best-matching time-series curve model, and calculating the corresponding translation / stretching factor and the agronomic phenology information of the target crop. Using this technical solution, users can combine historical remote sensing images and ground phenological statistical survey data to establish crop time-series curve models; and through adaptive model matching, obtain a time-series curve model that matches the near-real-time target time-series curve under the current incomplete growth cycle, thereby achieving near-real-time intra-season estimation of crop agronomic phenology in the target remote sensing image, meeting user needs. Attached Figure Description

[0041] Figure 1 This is a flowchart illustrating the steps of a near-real-time remote sensing estimation method for crop agronomic phenology provided in an embodiment of the present invention.

[0042] Figure 2 This is a schematic diagram of several phenological stages of corn provided in an embodiment of the present invention;

[0043] Figure 3 This is a schematic diagram of a corn time series curve model provided in an embodiment of the present invention;

[0044] Figure 4 This is a schematic diagram illustrating the process of constructing a crop time-series curve model according to an embodiment of the present invention.

[0045] Figure 5 This is a comparison chart verifying the estimation results of the embodiments of the present invention and the MCD12 product data. Detailed Implementation

[0046] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0047] Figure 1 This invention provides a flowchart of a near-real-time remote sensing estimation method for crop agronomic phenological stages, comprising the following steps:

[0048] Step 101: Acquire remote sensing images of the target area, including historical remote sensing images and target remote sensing images;

[0049] Remote sensing technology has three important characteristics: speed, macroscopicity, and dynamicity. It can quickly acquire information about a large area of ​​the Earth's surface and plays a significant role in large-scale crop growth monitoring and yield forecasting.

[0050] The target area can contain crops such as soybeans, wheat, rice, and corn. In agronomy, crops are classified into different agronomic phenological stages based on their physiological processes. For example, corn has different agronomic phenological stages, including emergence, jointing, tasseling, and grain-filling. This invention provides a near-real-time remote sensing method for estimating the time (date) when a crop reaches its corresponding agronomic phenological stage.

[0051] Step 102: Obtain several sets of historical remote sensing data of the target crop based on the historical remote sensing images. The historical remote sensing data includes time series data of remote sensing vegetation index. After data processing of the time series data of remote sensing vegetation index, a time series curve model is constructed. The data processing includes translation, stretching and matching.

[0052] After obtaining several sets of historical remote sensing data for the target crop based on historical remote sensing images, atmospheric correction, radiometric correction, cloud removal, and filtering can be performed on the historical remote sensing data. After cloud removal, some less obvious clouds are still difficult to identify and remove, affecting the data. In this embodiment, Savitzky-Golay filtering can be used for smoothing to reconstruct the WDRVI time series curve.

[0053] Vegetation indices are calculated based on the historical remote sensing data. These indices include the Normalized Difference Vegetation Index (NDVI) and the Wide Dynamic Range Vegetation Index (WDRVI).

[0054] NDVI=(ρ nir -ρ red ) / (ρ nir +ρ red ),

[0055] WDRVI={[(α-1)+(α+1)×NDVI] / [(α+1)+(α-1)×NDVI]+(1-α) / (1+α)}×100,

[0056] Where, ρ nir and ρ red α represents the surface reflectance values ​​in the near-infrared and red light bands, and α is the weighting coefficient.

[0057] Step 103: Obtain agronomic phenological data of the target crop, and construct a set of several time series curve models with different time lengths starting from a preset start time in combination with the time series curve model.

[0058] The time series data of the WDRVI mean of all target crops in the target area corresponding to the phenological statistical survey data are calculated based on the mask data of the target crops. The multi-year WDRVI mean time series curves in the target area corresponding to the phenological statistical survey data are then translated, stretched, and matched to obtain the multi-year mean. The phenological statistical survey data are then marked on the time series curve model, constructing several sets of time series curve models of different lengths starting from a starting time. It should be noted that those skilled in the art can set the starting time according to their actual needs. For example, the starting time can be one month before the average planting time of the crop, and the ending time can be any time before the average harvest time of the crop. This application does not specifically limit the specific time length.

[0059] Step 104: Obtain target remote sensing data based on the target remote sensing image. The target remote sensing data includes target remote sensing vegetation index data under the current incomplete cycle of the target crop, and construct the target time series curve.

[0060] After obtaining the target remote sensing image, atmospheric correction, radiometric correction, cloud removal, and filtering are performed on the target remote sensing data, and the WDRVI index is calculated. After cloud removal, some less obvious clouds are still difficult to identify and remove, affecting the data. In this embodiment, Savitzky-Golay filtering can be used for smoothing to reconstruct the WDRVI time series curve.

[0061] Step 105: Translate and stretch the near real-time target time-series curve under the incomplete growth cycle, and determine the time-series curve model that matches the target time-series curve to obtain the translation and stretching factor corresponding to the target time-series curve.

[0062] After obtaining the target time series curve, the target time series curve is translated and stretched along the X and Y axes to obtain several sets of translated and stretched time series curves. The translation and stretching parameters that minimize the distance between the target time series curve model and each time series curve model in the set are determined by the least squares method. That is, the translation and stretching parameters are optimized to minimize the distance between two curves.

[0063]

[0064] The time series curve model that minimizes the distance to the target time series curve is then selected as the time series curve model that matches the target time series curve. The formula for translation and stretching is as follows:

[0065]

[0066] x represents time, h(x) represents the target time series curve, g(X) represents the target time series curve after translation and stretching transformation, and xscale and yscale are the translation and stretching factors. peak The time when the target time-series curve reaches its peak value.

[0067] Step 106: Determine the agronomic phenological information of the target crop based on the translational stretching factor.

[0068] The formula for calculating the phenological information of the target crop based on the translational stretching factor is as follows:

[0069] X est = xscale × (X0 + tshift),

[0070] Where X0 is the time of the agronomic phenological period predefined on the time-series curve model based on regional phenological survey statistics, X est This invention estimates the agronomic phenological period of a target crop. It's important to note that during the early stages of crop growth, the vegetation index continuously increases. Therefore, before estimating the peak vegetation index, matching is performed only through X-axis translation and stretching, i.e., yscale is set to 1. After the crop reaches the peak vegetation index, normalization is first performed in the Y-axis direction, followed by X-axis stretching and translation for matching.

[0071] Using the above technical solution, users can process the target remote sensing image and historical remote sensing image, match the processed time series curves, and obtain a time series curve model that matches the target time series curve under the current incomplete growth cycle. This enables near real-time estimation of crop agronomic phenology in the target remote sensing image, meeting the user's needs.

[0072] In some embodiments, data from the U.S. Corn Belt from 2001 to 2019 are used as an example, including six states: Iowa, Indiana, Illinois, eastern Nebraska, southern Minnesota, and northwestern Ohio. Figure 2 Taking data from the Moderate-resolution Imaging Spectroradiometer (MODIS) sensor as an example, bands 1 (620nm-670nm) and 2 (841nm-876nm) of the 8-day synthesized MODIS surface reflectance product MOD09Q1, representing the red and near-infrared bands respectively, were used to construct the WDRVI spectral index. The time-series WDRVI of the maize pixel is calculated using the following formula:

[0073] WDRVI={[(α-1)+(α+1)×NDVI] / [(α+1)+(α-1)×NDVI]+(1-α) / (1+α)}×100

[0074] NDVI=(ρ nir -ρ red ) / (ρ nir +ρ red ), where α is 0.1.

[0075] Secondly, MODIS remote sensing data for the six states were obtained by cropping the vector files of these six states, and then corn pixels were extracted using the near real-time classification mask data of the target crop.

[0076] In some embodiments, phenological statistical survey data are state-level phenological progress reports, sourced from USDA state-level crop phenological progress reports. This example primarily focuses on five key agronomical phenological stages of maize (VE, R1, R4, R5, R6). Figure 3 Therefore, this example calculates the state-average WDRVI time series data and marks these five key agronomic phenological periods on the WDRVI time series to obtain a time series curve model. The time series curve model set is a set of function curves with a fixed step size (8 days) and variable length, and the key agronomic phenological periods of the target crop are marked.

[0077] It is important to note that during the early stages of crop growth, the vegetation index is on an upward trend. Therefore, in this example, matching is performed only through translation and stretching of the X-axis before estimating the R1 stage, i.e., yscale is set to 1. After maize reaches the R1 stage, normalization is first performed in the Y-axis direction, and then matching is performed through stretching and translation of the X-axis.

[0078] This example validates the phenological estimation results using the root mean square error (RMSE), and also compares the accuracy with MODIS's global vegetation phenology product MCD12. Figure 5 The results show that the phenological estimation of maize by this invention at the field and regional scales is highly consistent with the phenological data reported by NASS, and is also more accurate than the MCD12 phenological product.

[0079] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0080] This invention also discloses a near-real-time remote sensing estimation device for crop agronomic phenological stages, the device comprising:

[0081] The remote sensing image acquisition module is used to acquire remote sensing images of the target area, including historical remote sensing images and target remote sensing images;

[0082] The historical time-series curve acquisition module is used to obtain several sets of historical remote sensing data of target crops based on the historical remote sensing images. The historical remote sensing data includes time-series data of remote sensing vegetation index. After data processing of the time-series data of remote sensing vegetation index, a time-series curve model is constructed. The data processing includes translation, stretching and matching.

[0083] The time-series curve model construction module is used to construct a time-series curve model of the target crop, and to construct a set of time-series curve models with different time lengths starting from a preset start time in combination with the time-series curve model.

[0084] The target time-series curve acquisition module is used to obtain target remote sensing data based on the target remote sensing image. The target remote sensing data includes target remote sensing vegetation index data under the current incomplete cycle of the target crop, and constructs the target time-series curve.

[0085] The time-series curve adaptive matching module is used to translate and stretch the near real-time target time-series curve under the incomplete growth cycle, and determine the time-series curve model that matches the target time-series curve, and obtain the translation and stretching factor corresponding to the target time-series curve.

[0086] The phenological information calculation module is used to determine the phenological information of the target crop based on the translational stretching factor.

[0087] Using the above technical solution, users can process the target remote sensing image and historical remote sensing image, match the processed time series curves, and obtain a time series curve model that matches the near real-time target time series curve under incomplete growth cycle. This enables near real-time remote sensing estimation of crop agronomic phenology in the target remote sensing image, meeting the user's needs.

[0088] On the other hand, the present invention may provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described near-real-time remote sensing estimation method for crop agronomic phenology.

[0089] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0090] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, embodiments of the present invention can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of the present invention can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0091] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0092] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0093] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0094] Although preferred embodiments of the present invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present invention.

[0095] Finally, it should be noted that in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, data, or terminal device that includes said element.

[0096] The present invention provides a detailed description of a near-real-time remote sensing estimation method and a near-real-time remote sensing estimation device for crop agronomic phenology. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, those skilled in the art will recognize that there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for near real-time remote sensing estimation of crop agronomic phenological stages, characterized in that, The method comprises: acquiring remote sensing images of a target area, the remote sensing images comprising historical remote sensing images and target remote sensing images; obtaining historical remote sensing data of a plurality of groups of target crops from the historical remote sensing images, the historical remote sensing data comprising remote sensing vegetation index time series data, performing data processing on the remote sensing vegetation index time series data to construct a time series curve model, the data processing comprising translation, stretching and matching; acquiring agronomic phenological period data of the target crops, and constructing a plurality of time series curve model sets of different time lengths starting from a preset starting time in combination with the time series curve model; obtaining target remote sensing data from the target remote sensing images, the target remote sensing data comprising target remote sensing vegetation index data of the target crops in a non-complete growth cycle, and constructing a target time series curve; performing translation and stretching on the near-real-time target time series curve in the non-complete growth cycle, and determining a time series curve model matched with the target time series curve to obtain a translation and stretching factor corresponding to the target time series curve; determining the phenological information of the target crops according to the translation and stretching factor.

2. The method of claim 1, wherein, After obtaining the historical remote sensing data of a plurality of groups of target crops from the historical remote sensing images or obtaining the target remote sensing data of the target crops from the target remote sensing images, the method further comprises: performing cloud removal processing, radiation correction and filtering processing on the historical remote sensing data and the target remote sensing data.

3. The method of claim 1, wherein, The historical remote sensing data and the target remote sensing data both comprise normalized difference vegetation index (NDVI) and wide dynamic range vegetation index (WDRVI), NDVI = (p nir - p red ) / (p nir + p red ), WDRVI = { [(α-1) + (α+1) × NDVI] / [(α+1) + (α-1) × NDVI] + (1-α) / (1+α)} × 100, wherein ρ nir and ρ red are surface reflectance values in the near-infrared and red light bands, and α is a weighting coefficient.

4. The method of claim 3, wherein, The combination of the time series curve model to construct a plurality of time series curve model sets of different time lengths starting from a preset starting time comprises: obtaining remote sensing vegetation index data of the target crops from the historical remote sensing images; calculating time series data of the mean WDRVI of all target crops in the target area in each year according to the vegetation index data of the target crops and the phenological statistical survey data; labeling the phenological statistical survey data on the time series curve model, and constructing a plurality of time series curve model sets of different lengths.

5. The method of claim 1, wherein, The translation and stretching of the near-real-time target time series curve in the non-complete growth cycle and the determination of the time series curve model matched with the target time series curve comprise: after obtaining the target time series curve, performing translation and stretching processing of the X-axis and the Y-axis on the near-real-time target time series curve in the non-complete growth cycle, calculating the distance between each time series curve model in the time series curve model set and the target time series curve after the translation and stretching processing, respectively, determining the translation and stretching parameters with the minimum distance to each time series curve model, and comparing to determine the time series curve model with the minimum distance to the target time series curve as the time series curve model matched with the target time series curve.

6. The method of claim 5, wherein, The shift and stretch parameters that minimize the distance between the timing curve model and the target timing curve are determined by a least square method, so that the distance between the timing curve model and the target timing curve is minimized: The processing formula of the shift and stretch is: x is time, h(x) is the target timing curve, g(x) is the target timing curve after translation and stretching transformation, xscale and yscale are translation and stretching factors, X peak is the time when the target timing curve reaches the peak value.

7. The method of claim 5, wherein, The calculation formula of the phenological information of the target crop according to the shift and stretch factors is: X est = xscale * (X0 + tshift), wherein X0 is a time of a target crop agronomic phenophase predefined on the time series curve model according to regional phenological survey statistical data, X est is a time of an agronomic phenophase estimated by the time series curve model method.

8. A device for near real-time remote sensing estimation of crop agronomic phenological phases, characterized by, The device comprises: A remote sensing image acquisition module is configured to acquire remote sensing images of a target region, wherein the remote sensing images comprise historical remote sensing images and target remote sensing images. A historical timing curve acquisition module is configured to obtain historical remote sensing data of target crops in several groups according to the historical remote sensing images, wherein the historical remote sensing data comprise remote sensing vegetation index timing data, and a timing curve model is constructed after data processing of the remote sensing vegetation index timing data, wherein the data processing comprises shift, stretch and matching. A timing curve model construction module is configured to construct a timing curve model of target crops, and to construct several timing curve model sets of different time lengths starting from a preset starting time in combination with the timing curve model. A target timing curve acquisition module is configured to obtain target remote sensing data according to the target remote sensing images, wherein the target remote sensing data comprise target remote sensing vegetation index data of target crops in a current incomplete growth period, and a target timing curve is constructed. A timing curve adaptive matching module is configured to shift and stretch a near-real-time target timing curve in the incomplete growth period, and to determine a timing curve model that matches the target timing curve, so as to obtain shift and stretch factors corresponding to the target timing curve. A phenological information calculation module is configured to determine phenological information of the target crops according to the shift and stretch factors.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, the program can run on the processor, and the computer program is executed by the processor to realize the steps of the method in any one of claims 1-7. The computer readable storage medium stores a computer program, the program can run on the processor, and the computer program is executed by the processor to realize the steps of the method in any one of claims 1-7.

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