Method, device, terminal equipment and storage medium for monitoring crop harvest time

Abstract

The application relates to the field of satellite remote sensing, in particular to a crop harvesting time monitoring method and device, terminal equipment and a storage medium. The monitoring method comprises the following steps: acquiring a first ground reflectivity product of a crop to-be-identified area at a first resolution when passing through a first time period of each day, and acquiring a second ground reflectivity product of the crop to-be-identified area at a second resolution when passing through a second time period of each day; obtaining a daily index image of each day based on the first ground reflectivity product and the second ground reflectivity product; obtaining a daily index difference matrix of each day based on the daily index image and a preset difference value rule; and predicting a crop harvesting time based on all the daily index difference matrices. The application can efficiently, accurately, and at low cost, acquire a crop harvesting process in a large range.

Description

Technical Field

[0001] This application relates to the field of satellite remote sensing, and in particular to a method, apparatus, terminal equipment, and storage medium for monitoring crop harvest time. Background Technology

[0002] With the continuous development of science and technology, its application is becoming increasingly widespread. Improving crop yield is a topic that people are constantly exploring. Currently, improving crop yield includes improving varieties, enhancing soil quality, strengthening farmland management, and ensuring timely harvesting. Harvesting crops too early or too late will significantly reduce yield. However, traditional monitoring of crop harvest time mainly relies on field observations or statistical reporting, making it difficult to grasp the crop harvesting progress at a macroscopic regional scale. This poses challenges to coordinating the transportation and allocation of agricultural machinery, scientifically responding to adverse weather conditions, and accurately guiding grain harvesting and drought-resistant sowing. Moreover, even with current common remote sensing monitoring methods, it is impossible to accurately and timely obtain crop harvest time. Summary of the Invention

[0003] In view of the above problems, this application proposes a method, device, terminal equipment and storage medium for monitoring crop harvest time.

[0004] This application proposes a method for monitoring crop harvest time, applied to a remote sensing data processing terminal, including:

[0005] Acquire the first surface reflectance product of the crop identification area at a first resolution when the satellite passes over during the first time period of each day, and acquire the second surface reflectance product of the crop identification area at a second resolution when the satellite passes over during the second time period of each day.

[0006] Based on the first surface reflectance product and the second surface reflectance product, a daily index image is obtained.

[0007] Based on all the daily index images, the change time series curve corresponding to each pixel in the daily index image is obtained;

[0008] Based on the changing time-series curve, the crop harvest time of the crop to be identified area corresponding to each pixel is obtained.

[0009] Furthermore, in the above-mentioned method for monitoring crop harvest time, obtaining the daily index image based on the first surface reflectance product and the second surface reflectance product includes:

[0010] Based on the first surface reflectance product, the second surface reflectance product, the first preset center wavelength, and the second preset center wavelength, the first surface reflectance image and the second surface reflectance image corresponding to the first time period, as well as the third surface reflectance image and the fourth surface reflectance image corresponding to the second time period, are obtained respectively.

[0011] Based on the first surface reflectance image, the second surface reflectance image, the third surface reflectance image, the fourth surface reflectance image, and the preset index formula, the first index image corresponding to the first time period of each day and the second index image corresponding to the second time period are calculated respectively.

[0012] The first index image and the second index image are processed to be cloud-free according to preset image rules to obtain the daily index image for each day. The index image is an index image reflecting the cellulose content of crops.

[0013] Furthermore, in the above-mentioned method for monitoring crop harvest time, the step of obtaining a first surface reflectance image, a second surface reflectance image, a third surface reflectance image, and a fourth surface reflectance image based on the first surface reflectance product, the second surface reflectance product, the first preset center wavelength, and the second preset center wavelength, respectively, includes:

[0014] The first surface reflectance product is preprocessed with the first preset center wavelength and the second preset center wavelength respectively to obtain the first surface reflectance image and the second surface reflectance image respectively.

[0015] The second surface reflectance product is preprocessed with the first preset center wavelength and the second preset center wavelength respectively to obtain a third surface reflectance image and a fourth surface reflectance image;

[0016] The preprocessing process described above includes sequential band extraction, image stitching, projection transformation, and resampling.

[0017] Furthermore, in the above-mentioned method for monitoring crop harvest time, the preset image rules include:

[0018] If a pixel in the first index image is cloudy, while a pixel at the same position in the second index image is cloudless, then the pixel at the same position in the second index image is taken as the pixel at the same position in the daily index image for that day.

[0019] If a pixel in the first index image is cloudless, while a pixel at the same position in the second index image is cloudy, then the pixel at the same position in the first index image is taken as the pixel at the same position in the daily index image for that day.

[0020] If a pixel in the first index image and a pixel at the same position in the second index image both have clouds, then the pixel at the same position in the daily index image of that day is selected according to the preset selection rules.

[0021] If a pixel in the first index image and a corresponding pixel in the second index image are both cloudless, then the index values ​​corresponding to the pixels at the same position in the first and second index images are compared, and the pixel with the smaller index value is taken as the pixel at the same position in the daily index image for that day.

[0022] Furthermore, in the above-mentioned method for monitoring crop harvest time, the preset index formula is:

[0023]

[0024] Wherein, NDTI is the normalized differential cellulose index of the crop, Band1 is the first surface reflectance corresponding to a certain pixel in the surface reflectance image of the first preset center wavelength, and Band2 is the second surface reflectance corresponding to a certain pixel in the surface reflectance image of the second preset center wavelength.

[0025] Furthermore, in the above-mentioned method for monitoring crop harvest time, after obtaining the daily index image and before obtaining the change time-series curve corresponding to each pixel in the daily index image, the method further includes:

[0026] The pixels in the daily index image are interpolated one by one according to the filtering algorithm formula to obtain the time-series index dataset, which includes each new daily index image.

[0027] Furthermore, in the above-mentioned method for monitoring crop harvest time, the filtering algorithm formula is as follows:

[0028]

[0029] Among them, NDTI t (j) is the filtered normalized differential cellulose index on day t, NDTI j+i This is the i-th original NDTI value, where m is the upper limit of the sliding window size, -m is the lower limit of the sliding window size, and C... i is the correlation coefficient of the i-th NDTI value in the sliding window, N refers to the number of convolutions, and j is the coefficient of the original NDTI dataset.

[0030] Another embodiment of this application also provides an apparatus for obtaining crop harvest time, comprising:

[0031] The acquisition unit is used to acquire the first surface reflectance product of the crop identification area at a first resolution when the satellite passes over during the first time period of each day, and to acquire the second surface reflectance product of the crop identification area at a second resolution when the satellite passes over during the second time period of each day.

[0032] The first calculation unit is used to obtain a daily index image based on the first surface reflectance product and the second surface reflectance product.

[0033] The second calculation unit is used to obtain the change time series curve corresponding to each pixel in the daily index image based on all the daily index images;

[0034] The output unit is used to obtain the crop harvest time of the crop to be identified area corresponding to each pixel based on the changing time-series curve.

[0035] Another embodiment of this application proposes a satellite including a storage unit and a processing unit. The storage unit stores a computer program, and the processing unit executes the steps of the crop harvest time monitoring method described above by calling the computer program stored in the storage unit.

[0036] Another embodiment of this application provides a computer-readable storage medium storing a computer program adapted for loading by a processor to perform the steps of the crop harvest time monitoring method described above.

[0037] The embodiments of this application have the following beneficial effects:

[0038] This application proposes a method for monitoring crop harvest time. By using optical remote sensing to monitor the crop harvest process, it can efficiently, accurately, cost-effectively, and over a wide area acquire information on the crop harvest process when the crop harvest window is short and adverse weather is frequent. This provides support for coordinating the transfer and allocation of agricultural machinery, scientifically responding to adverse weather conditions, and accurately guiding grain harvesting and rain-resistant sowing. Attached Figure Description

[0039] To more clearly illustrate the technical solutions of this application, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of this application and should not be considered as a limitation on the scope of protection of this application. In the various drawings, similar components are numbered similarly.

[0040] Figure 1 A first flowchart of a method for monitoring crop harvest time according to some embodiments of this application is shown;

[0041] Figure 2A second flowchart illustrating a method for monitoring crop harvest time according to some embodiments of this application is shown;

[0042] Figure 3 A third flowchart of a method for monitoring crop harvest time according to some embodiments of this application is shown;

[0043] Figure 4 A schematic diagram of the structure of a crop harvest time monitoring device according to some embodiments of this application is shown. Detailed Implementation

[0044] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0045] The components of the embodiments of this application described and illustrated in the accompanying drawings can be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of this application provided in the drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0046] In the following, the terms “comprising,” “having,” and their cognates, which may be used in various embodiments of this application, are intended only to indicate a particular feature, number, step, operation, element, component, or combination thereof, and should not be construed as excluding, firstly, the presence of one or more other features, numbers, steps, operations, elements, components, or combinations thereof, or adding the possibility of one or more features, numbers, steps, operations, elements, components, or combinations thereof.

[0047] Furthermore, the terms "first," "second," and "third" are used only to distinguish descriptions and should not be interpreted as indicating or implying relative importance.

[0048] Unless otherwise specified, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which the various embodiments of this application pertain. Terms (such as those defined in commonly used dictionaries) shall be interpreted as having the same meaning as in their contextual meaning in the relevant technical field and shall not be construed as having an idealized or overly formal meaning, unless clearly defined in the various embodiments of this application.

[0049] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0050] Generally, harvesting too early, before the crop has completed its grain filling and is not mature enough, leads to reduced grain yield. Harvesting too late increases the risk of encountering adverse weather conditions such as rainfall, thus reducing yield. Accurately grasping the crop harvesting process is particularly important for agricultural and rural departments and meteorological departments to guide timely harvesting during the harvest season. This can avoid yield reductions caused by adverse weather factors, help improve harvesting efficiency, and ensure maximum grain production.

[0051] Given that remote sensing data has advantages such as wide coverage, high timeliness, low cost, and repeatability, if appropriate algorithms can be used to enable satellites to perform long-term monitoring and yield estimation of crops, this would greatly improve work efficiency and the accuracy of obtaining crop maturity time.

[0052] Therefore, this application proposes a method for monitoring crop harvest time to solve the above problems.

[0053] Please refer to Figure 1 This is a schematic flowchart of a crop harvest time monitoring method proposed in an embodiment of this application. Exemplarily, this crop harvest time monitoring method is applied to a remote sensing data processing terminal, wherein the remote sensing data can be obtained by monitoring crops via satellite.

[0054] Next, the monitoring method for crop harvest time in this application will be described in detail.

[0055] In some implementations, such as Figure 1 As shown, the method for monitoring crop harvest time, applied to a remote sensing data processing terminal, may include:

[0056] S110, acquire the first surface reflectance product of the crop identification area at a first resolution when the satellite passes over during the first time period of each day, and acquire the second surface reflectance product of the crop identification area at a second resolution when the satellite passes over during the second time period of each day.

[0057] Specifically, when the satellite passes over the area to be identified for crop measurement, it will observe and acquire relevant data. Because atmospheric conditions differ significantly between morning and afternoon, the same area needs to be observed in two separate phases. The first phase is the morning phase, and the second phase is the afternoon phase. Of course, the morning / afternoon phases can be further customized. For example, the morning phase could be 0:00–12:00, and the afternoon phase could be 12:00–24:00. This is just an example; other custom time periods are possible and not limited here. Resolution is a measure of the sensor imaging system's ability to discern details in the output image. The first and second resolutions can be low to medium resolution, typically around 100-1000m, but not limited to this range. The first and second resolutions can be equal or unequal, depending on the specific circumstances. Surface reflectance products include surface reflectance files, observation angle files, quality control files, and time specification files, which can be found in existing technologies.

[0058] S210, based on the first and second surface reflectance products, obtain the daily index image for each day.

[0059] Specifically, the center wavelength refers to the wavelength corresponding to the center position of the full width at half maximum (FWHM) of the spectrum measured at rated power. Preferably, for general crops, the chlorophyll content drops to its lowest level and the cellulose content is relatively high during the ripening period, resulting in a yellow color. The center wavelength is mainly selected to reflect the cellulose content of the crop. Therefore, the first preset center wavelength can be selected as 1600 nm, and the second preset center wavelength can be selected as 2100 nm. Of course, different crops may use different center wavelengths, and other corresponding optimal center wavelengths can be selected based on the characteristics of the crop at ripening. This is not limited here. Then, the corresponding surface reflectance images are obtained according to the first and second preset center wavelengths. The first and second surface reflectance values ​​corresponding to each pixel are calculated according to the preset index formula to obtain the normalized differential cellulose index corresponding to each pixel. The preset image rule specifies how to obtain the daily index image for each day. The first surface reflectance value is the surface reflectance obtained with the first preset center wavelength, and the second surface reflectance value is the surface reflectance obtained with the second preset center wavelength.

[0060] In some implementations, such as Figure 2 As shown, in the method for monitoring crop harvest time, based on the first surface reflectance product, the second surface reflectance product, the first preset center wavelength, the second preset center wavelength, the preset index formula, and the preset image rules, the daily index image is obtained, including:

[0061] S211, based on the first surface reflectance product, the second surface reflectance product, the first preset center wavelength, and the second preset center wavelength, the first surface reflectance image, the second surface reflectance image, the third surface reflectance image, and the fourth surface reflectance image are obtained respectively.

[0062] Specifically, surface reflectance products can obtain surface reflectance images of multiple bands, but in this embodiment, only surface reflectance images of the first preset center wavelength and the second preset center wavelength need to be obtained.

[0063] In some embodiments, in the method for monitoring crop harvest time, obtaining a first surface reflectance image, a second surface reflectance image, a third surface reflectance image, and a fourth surface reflectance image based on a first surface reflectance product, a second surface reflectance product, a first preset center wavelength, and a second preset center wavelength includes:

[0064] The first surface reflectance product is preprocessed with the first preset center wavelength and the second preset center wavelength respectively to obtain the first surface reflectance image and the second surface reflectance image. The second surface reflectance product is then preprocessed with the first preset center wavelength and the second preset center wavelength respectively to obtain the third surface reflectance image and the fourth surface reflectance image. The preprocessing process includes band extraction, image stitching, projection transformation and resampling in sequence.

[0065] Specifically, such as Figure 3 As shown, the preprocessing steps sequentially include band extraction, image stitching, projection transformation, and resampling. Each step can utilize corresponding well-known methods.

[0066] S212, based on the first surface reflectance image, the second surface reflectance image, the third surface reflectance image, the fourth surface reflectance image and the preset index formula, the first index image and the second index image for each day are obtained.

[0067] Specifically, the normalized differential cellulose index corresponding to a pixel can be obtained by calculating the first and second surface reflectances corresponding to a certain pixel according to a preset index formula. Finally, the normalized differential cellulose index corresponding to each pixel in the surface reflectance image is obtained. Based on the normalized differential cellulose index corresponding to each pixel, the corresponding index image can be obtained.

[0068] It is conceivable that a first index image can be obtained based on the first and second surface reflectance images; and a second index image can be obtained based on the third and fourth surface reflectance images.

[0069] In some implementation methods, the preset index formula in some crop harvest time monitoring methods is:

[0070]

[0071] Wherein, NDTI is the normalized differential cellulose index of the crop, Band1 is the first surface reflectance corresponding to a pixel in the surface reflectance image with the first preset center wavelength, and Band2 is the second surface reflectance corresponding to a pixel in the surface reflectance image with the second preset center wavelength.

[0072] Specifically, if the surface reflectance of the same pixel is 0.45 at the first preset center wavelength and the spectral reflectance of the same pixel is 0.35 at the second preset center wavelength, then the normalized differential cellulose index corresponding to the pixel is 0.125.

[0073] It should be noted that the first and second surface reflectances in the same preset index formula correspond to pixels at the same location, but are obtained using different preset center wavelengths, so different surface reflectances will be obtained.

[0074] S213. Based on the first index image, the second index image, and the preset image rules for each day, a daily index image is obtained. The index image is an index image reflecting the cellulose content of the crop.

[0075] Specifically, since the first index image and the second index image sometimes differ significantly and are affected by climate changes, it is necessary to convert the first index image and the second index image according to certain rules to obtain the daily index image for that day.

[0076] In some implementations, the preset image rules in the crop harvest time monitoring method include:

[0077] If a pixel in the first index image has clouds, while a pixel at the same position in the second index image does not have clouds, then the pixel at the same position in the second index image is taken as the pixel at the same position in the daily index image for that day.

[0078] If a pixel in the first index image is cloudless, while a corresponding pixel in the second index image is cloudy, then the corresponding pixel in the first index image is taken as the corresponding pixel in the daily index image for that day.

[0079] If a pixel in the first index image and a corresponding pixel in the second index image both have clouds, then the corresponding pixel in the daily index image for that day is selected according to the preset selection rules.

[0080] If a pixel in the first index image and a corresponding pixel in the second index image are both cloudless, then the index values ​​corresponding to the pixels at the same position in the first and second index images are compared, and the pixel with the smaller index value is taken as the pixel at the same position in the daily index image for that day.

[0081] Specifically, since the climate varies greatly from moment to moment in the morning and afternoon, it is impossible to predict when there will be clouds. When there are clouds, the area to be measured will be blocked, which will affect the acquisition of satellite data and cause certain inaccuracies in the results. Therefore, in order to make the results more accurate, the morning and afternoon of each day are judged separately, and the pixels without clouds are taken as the pixels at the same position in the daily index image of that day.

[0082] The preset selection rules include, but are not limited to, the following four:

[0083] The first method involves selecting pixels from the first index image as the corresponding pixels at the same positions in the daily index image.

[0084] The second method involves selecting pixels from the second index image as the corresponding pixels at the same positions in the daily index image.

[0085] The third method involves comparing the degree of cloud cover in two pixels. If the degree is different, the pixel with a lower degree of cloud cover can be selected as the pixel corresponding to the same position in the daily index image.

[0086] The fourth method involves calculating the average index of pixels at the same location for the previous n days and the following m days, and using this average as the index of pixels at the same location in the daily index image. Here, n and m can be equal or unequal.

[0087] For example, if the pixel in the first row and first column of the first index image is cloudy, and the pixel in the first row and first column of the second index image is cloudless, then the pixel in the first row and first column of the second index image is selected as the pixel in the first row and first column of the daily index image for that day. Therefore, the entire process selects each pixel in each index image one by one according to the preset image rules, and finally obtains a complete daily index image.

[0088] S310, based on all daily index images, obtain the change time series curve corresponding to each pixel in the daily index image.

[0089] S410, based on the changing time-series curve, obtains the crop harvest time of the crop to be identified area corresponding to each pixel.

[0090] Specifically, if crops are not harvested, the time-series curve formed by pixels at the same position in the daily index images over several consecutive days will not show significant changes. Therefore, the index values ​​corresponding to two adjacent points on the curve will not change drastically. However, if crops in a certain area are harvested on a certain day, the pixel values ​​in the daily index images for that area and the harvest day will decrease sharply compared to the pixel values ​​at the same position in the daily index images of the previous day or several days prior, resulting in a very large and obvious difference. To determine the harvest time, it is necessary to calculate the difference between pixels at the same position in adjacent daily index images. The preset difference rule is to calculate the difference between pixels at the same position in adjacent daily index images. For example, the pixel value in the first column and first row of the daily index image of the second day is subtracted from the pixel value in the first column and first row of the daily index image of the first day. The difference obtained is the pixel value in the first column and first row of the newly generated index difference image. Following this method, the calculation is performed sequentially for each pixel in the daily index images, ultimately obtaining the adjacent daily index difference image. The daily index difference for day n is obtained from the daily index image of day n and the daily index image of day n-1, where n is an integer greater than 1. Here, the index is the normalized differential cellulose index.

[0091] Finally, all daily index differences are compared, and the dates corresponding to the daily index differences that meet the criteria are taken as the harvest time. The daily index difference that meets the criteria is generally the largest difference among all differences.

[0092] It should also be noted that the daily index image corresponds to multiple pixels, and each pixel corresponds to a sub-region of the satellite image area. Not every sub-region will have corresponding crops. Therefore, although the time series curve corresponding to each pixel will be obtained in the end, not all time series curves will have the corresponding crop harvest time. Only the time series curve of the pixel corresponding to the sub-region with crops will have the corresponding harvest time.

[0093] In addition, because each sub-region with crops has a different harvest time, there will be multiple harvest times in the end.

[0094] In some implementations, the method for monitoring crop harvest time, after obtaining the daily index image and before obtaining the time-series curve of change corresponding to each pixel in the daily index image, further includes:

[0095] The pixels in the daily index image are interpolated one by one according to the filtering algorithm formula to obtain the time-series index dataset, which includes each new daily index image.

[0096] Specifically, due to incomplete cloud removal or the influence of image noise or outliers, temporal interpolation is required. Then, based on the newly obtained daily index image, the temporal curve of the change corresponding to each pixel in the daily index image is obtained.

[0097] In some implementations, the filtering algorithm formula in the crop harvest time monitoring method is as follows:

[0098]

[0099] NDTI t (j) is the filtered normalized differential cellulose index on day t, NDTI j+i This is the i-th original NDTI value, where m is the upper limit of the sliding window size, -m is the lower limit of the sliding window size, and C... i is the correlation coefficient of the i-th NDTI value in the sliding window, N refers to the number of convolutions, and j is the coefficient of the original NDTI dataset. Furthermore, the filtering algorithm in this embodiment can be optimized based on existing filtering algorithms.

[0100] This application proposes a method for monitoring crop harvest time. By using optical remote sensing to monitor the crop harvest process, it can efficiently, accurately, cost-effectively, and over a wide area acquire information on the crop harvest process when the crop harvest window is short and adverse weather is frequent. This provides support for coordinating the transfer and allocation of agricultural machinery, scientifically responding to adverse weather conditions, and accurately guiding grain harvesting and rain-resistant sowing.

[0101] Another embodiment of this application also proposes a device 500 for obtaining crop harvest time, such as... Figure 4 As shown, the device 500 includes:

[0102] The acquisition unit 510 is used to acquire the first surface reflectance product of the crop identification area at a first resolution when the satellite passes over during the first time period of each day, and to acquire the second surface reflectance product of the crop identification area at a second resolution when the satellite passes over during the second time period of each day.

[0103] The first calculation unit 520 is used to obtain daily index images based on the first and second surface reflectance products.

[0104] The second calculation unit 530 is used to obtain the change time series curve corresponding to each pixel in the daily index image based on all daily index images.

[0105] The output unit 540 is used to obtain the crop harvest time of the crop identification area corresponding to each pixel based on the changing time-series curve.

[0106] Another embodiment of this application proposes a satellite, including a storage unit and a processing unit. The storage unit stores a computer program, and the processing unit executes the steps of the crop harvest time monitoring method described above by calling the computer program stored in the storage unit.

[0107] Another embodiment of this application provides a computer-readable storage medium storing a computer program adapted for loading by a processor to perform the steps of the crop harvest time monitoring method described above.

[0108] It is understood that the method steps in this embodiment correspond to the crop harvest time monitoring method in the above embodiments. The optional methods for monitoring crop harvest time described above are also applicable to this embodiment and will not be described again here.

[0109] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that, in alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0110] In addition, the functional modules or units in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0111] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a smartphone, personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0112] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for monitoring crop harvest time, applied to a remote sensing data processing terminal, characterized in that, include: Acquire the first surface reflectance product of the crop identification area at a first resolution when the satellite passes over during the first time period of each day, and acquire the second surface reflectance product of the crop identification area at a second resolution when the satellite passes over during the second time period of each day. Based on the first surface reflectance product and the second surface reflectance product, a daily index image is obtained. Based on all the daily index images, the change time series curve corresponding to each pixel in the daily index image is obtained; Based on the changing time-series curve, the crop harvest time of the crop identification area corresponding to each pixel is obtained; The step of obtaining the daily index image based on the first surface reflectance product and the second surface reflectance product includes: The first surface reflectance product is preprocessed with a first preset center wavelength and a second preset center wavelength to obtain a first surface reflectance image and a second surface reflectance image, respectively. The second surface reflectance product is preprocessed using the first preset center wavelength and the second preset center wavelength respectively to obtain a third surface reflectance image and a fourth surface reflectance image; the preprocessing process includes sequentially performing band extraction, image stitching, projection conversion and resampling. Based on the first surface reflectance image, the second surface reflectance image, the third surface reflectance image, the fourth surface reflectance image, and the preset index formula, the first index image corresponding to the first time period of each day and the second index image corresponding to the second time period are calculated respectively. The first index image and the second index image are combined without clouds according to preset image rules to obtain the daily index image for each day. The index image reflects the cellulose content of the crop. The preset image rules include: If a pixel in the first index image is cloudy, while a pixel at the same position in the second index image is cloudless, then the pixel at the same position in the second index image is taken as the pixel at the same position in the daily index image for that day. If a pixel in the first index image is cloudless, while a pixel at the same position in the second index image is cloudy, then the pixel at the same position in the first index image is taken as the pixel at the same position in the daily index image for that day. If a pixel in the first index image and a pixel at the same position in the second index image both have clouds, then the pixel at the same position in the daily index image of that day is selected according to the preset selection rules. If a pixel in the first index image and a corresponding pixel in the second index image are both cloudless, then the index values ​​corresponding to the pixels at the same position in the first and second index images are compared, and the pixel with the smaller index value is taken as the pixel at the same position in the daily index image for that day.

2. The method for monitoring crop harvest time according to claim 1, characterized in that, The preset index formula is: ； Wherein, NDTI is the normalized differential cellulose index of the crop, Band1 is the first surface reflectance corresponding to a certain pixel in the surface reflectance image of the first preset center wavelength, and Band2 is the second surface reflectance corresponding to a certain pixel in the surface reflectance image of the second preset center wavelength.

3. The method for monitoring crop harvest time according to claim 1, characterized in that, After obtaining the daily index image for each day, and before obtaining the change time-series curve corresponding to each pixel in the daily index image, the method further includes: The pixels in the daily index image are interpolated one by one according to the filtering algorithm formula to obtain the time-series index dataset, which includes each new daily index image.

4. The method for monitoring crop harvest time according to claim 3, characterized in that, The filtering algorithm formula is as follows: ； Among them, NDTI t (j) is the filtered normalized differential cellulose index on day t, NDTI j+i This is the i-th original NDTI value, where m is the upper limit of the sliding window size, -m is the lower limit of the sliding window size, and C... i is the correlation coefficient of the i-th NDTI value in the sliding window, N refers to the number of convolutions, and j is the coefficient of the original NDTI dataset.

5. A device for obtaining crop harvest time, characterized in that, include: The acquisition unit is used to acquire the first surface reflectance product of the crop identification area at a first resolution when the satellite passes over during the first time period of each day, and to acquire the second surface reflectance product of the crop identification area at a second resolution when the satellite passes over during the second time period of each day. The first calculation unit is used to obtain a daily index image based on the first surface reflectance product and the second surface reflectance product. The second calculation unit is used to obtain the change time series curve corresponding to each pixel in the daily index image based on all the daily index images; The output unit is used to obtain the crop harvest time of the crop to be identified area corresponding to each pixel based on the change time-series curve; The first calculation unit is used to obtain a daily index image based on the first surface reflectance product and the second surface reflectance product, including: The first surface reflectance product is preprocessed with a first preset center wavelength and a second preset center wavelength to obtain a first surface reflectance image and a second surface reflectance image, respectively. The second surface reflectance product is preprocessed using the first preset center wavelength and the second preset center wavelength respectively to obtain a third surface reflectance image and a fourth surface reflectance image; the preprocessing process includes sequentially performing band extraction, image stitching, projection conversion and resampling. Based on the first surface reflectance image, the second surface reflectance image, the third surface reflectance image, the fourth surface reflectance image, and the preset index formula, the first index image corresponding to the first time period of each day and the second index image corresponding to the second time period are calculated respectively. The first index image and the second index image are combined without clouds according to preset image rules to obtain the daily index image for each day. The index image reflects the cellulose content of the crop. The preset image rules include: If a pixel in the first index image is cloudy, while a pixel at the same position in the second index image is cloudless, then the pixel at the same position in the second index image is taken as the pixel at the same position in the daily index image for that day. If a pixel in the first index image is cloudless, while a pixel at the same position in the second index image is cloudy, then the pixel at the same position in the first index image is taken as the pixel at the same position in the daily index image for that day. If a pixel in the first index image and a pixel at the same position in the second index image both have clouds, then the pixel at the same position in the daily index image of that day is selected according to the preset selection rules. If a pixel in the first index image and a corresponding pixel in the second index image are both cloudless, then the index values ​​corresponding to the pixels at the same position in the first and second index images are compared, and the pixel with the smaller index value is taken as the pixel at the same position in the daily index image for that day.

6. A terminal device, characterized in that, It includes a storage unit and a processing unit. The storage unit stores a computer program, and the processing unit executes the steps of the crop harvest time monitoring method as described in any one of claims 1 to 4 by calling the computer program stored in the storage unit.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program adapted for loading by a processor to perform the steps of the crop harvest time monitoring method as described in any one of claims 1 to 4.

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