Cloud-free remote sensing image synthesis method, system, equipment, medium and product for presenting drought characteristics of vegetation

Abstract

The invention discloses a cloudless remote sensing image synthesis method, system, device, medium and product presenting vegetation drought characteristics, and relates to the field of remote sensing image synthesis, the method comprises the following steps: obtaining remote sensing surface reflectance images of a target monitoring area at different time points; preprocessing all the remote sensing surface reflectance images to obtain preprocessed remote sensing surface reflectance images; based on the surface water index, the cloud / cloud shadow distance and the cloud and mist influence of each pixel, determining an overall quality evaluation value of each pixel of the preprocessed remote sensing surface reflectance image; based on the total quality evaluation value of each pixel, determining a cloudless remote sensing synthetic image of the target monitoring area; the pixel at any position in the cloudless remote sensing synthetic image is the pixel with the maximum total quality evaluation value at the corresponding position in the preprocessed remote sensing surface reflectance images at different time points. According to the method, vegetation drought characteristics are represented through the surface water indexes, so that cloud-free and seamless remote sensing image synthesis which highlights vegetation drought characteristic information is realized.

Description

Technical Field

[0001] The present application relates to the field of remote sensing image synthesis, and in particular to a cloudless remote sensing image synthesis method, system, equipment, medium and product that presents drought characteristics of vegetation. Background Art

[0002] Due to atmospheric effects and field-of-view limitations of optical satellite sensors, it is difficult to obtain cloud-free images covering large areas. To improve image utilization efficiency, researchers have proposed cloud-free remote sensing image synthesis algorithms, primarily to address issues such as cloud impact, aerosol contamination, viewing angle effects, and data utilization. The resulting cloud-free, seamless, and clear, high-quality images provide an important and complete image information foundation for subsequent remote sensing applications. Common pixel-based cloud-free image synthesis methods include maximum NDVI (Normalized Difference Vegetation Index) synthesis, time series harmonic analysis, mean or median synthesis, and best available pixel synthesis. The Best Available Pixel (BAP) synthesis method uses a weighted rule to score all pixels in multi-temporal input images. For pixels in the same location, the pixel with the highest score is identified as the optimal pixel for pixel-level image synthesis.

[0003] However, when targeting specific monitoring needs, such as monitoring drought characteristics of vegetation, current research does not consider the drought characteristics of the ground objects when synthesizing images. All input data are mixed together when synthesizing images, resulting in the subsequent drought monitoring applications losing some vegetation drought characteristic information, weakening the monitoring ability of drought characteristics. Summary of the Invention

[0004] The purpose of this application is to provide a cloud-free remote sensing image synthesis method, system, equipment, medium and product that presents vegetation drought characteristics, which can achieve cloud-free, seamless remote sensing image synthesis that highlights vegetation drought characteristic information.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a cloud-free remote sensing image synthesis method for presenting vegetation drought characteristics, comprising:

[0007] Acquire remote sensing surface reflectance images of a target monitoring area at different time points; the remote sensing surface reflectance images include a plurality of pixels;

[0008] Preprocessing all the remote sensing surface reflectance images to obtain preprocessed remote sensing surface reflectance images;

[0009] Determining an overall quality evaluation value for each pixel of the preprocessed remote sensing surface reflectance image based on a surface water index, cloud / cloud shadow distance, and cloud and fog influence of each pixel;

[0010] Based on the overall quality evaluation value of each pixel, a cloud-free remote sensing synthetic image of the target monitoring area is determined; the pixel at any position in the cloud-free remote sensing synthetic image is the pixel with the largest overall quality evaluation value at the corresponding position in the preprocessed remote sensing surface reflectance image at different time points.

[0011] Optionally, when the remote sensing surface reflectance images at different time points are from the same series of satellite images, preprocessing is performed on all the remote sensing surface reflectance images to obtain preprocessed remote sensing surface reflectance images, specifically including:

[0012] All the remote sensing surface reflectance images are subjected to cloud removal / cloud shadow processing to obtain pre-processed remote sensing surface reflectance images.

[0013] Optionally, when the remote sensing surface reflectance images at different time points are multi-source satellite images, preprocessing all the remote sensing surface reflectance images to obtain preprocessed remote sensing surface reflectance images specifically includes:

[0014] Multi-source data spectral coordination, spatial matching and cloud / cloud shadow removal processing are performed on all the remote sensing surface reflectance images to obtain pre-processed remote sensing surface reflectance images.

[0015] Optionally, based on the surface water index, cloud / cloud shadow distance and cloud and fog influence of each pixel, an overall quality evaluation value of each pixel of the pre-processed remote sensing surface reflectance image is determined, and the method further includes:

[0016] Calculates the surface water index, cloud / cloud shadow distance, and cloud and fog influence for each pixel.

[0017] Optionally, calculate the surface water index, cloud / cloud shadow distance, and cloud and fog impact for each pixel, including:

[0018] Using the formula Calculate the surface water index of each pixel; where LSWI is the surface water index of the current pixel; NIR is the surface reflectance value of the current pixel in the near-infrared band; SWIR1 is the surface reflectance value of the current pixel in the short-wave infrared band;

[0019] Using the formula Calculate the cloud / cloud shadow distance for each pixel; where S CloudDist is the cloud / cloud shadow distance of the current pixel; D i D is the distance between the current pixel and the nearest cloud / cloud image pixel; req is the threshold distance; Dmin The minimum distance between the current pixel and the nearest cloud / cloud image pixel;

[0020] Using the formula Calculate the cloud and fog influence of each pixel; where S HOT is the cloud and fog influence of the current pixel; BLUE is the surface reflectance value of the blue band; RED is the surface reflectance value of the red band; HOT is the cloud and fog optimization conversion function.

[0021] Optionally, based on the surface water index, cloud / cloud shadow distance, and cloud and fog influence of each pixel, determining the overall quality evaluation value of each pixel of the pre-processed remote sensing surface reflectance image, specifically including:

[0022] Using formula S TOTAL =7×S LSWI +2×S CloudDist +S HOT Determine the overall quality evaluation value of each pixel of the pre-processed remote sensing surface reflectance image; wherein, S TOTAL is the overall quality evaluation value of the current pixel; S LSWI is the water content of the current pixel, S LSWI =1-LSWI;S CloudDist is the cloud / cloud shadow distance of the current pixel; S HOT is the cloud and fog influence of the current pixel.

[0023] In a second aspect, the present application provides a cloud-free remote sensing image synthesis system for presenting vegetation drought characteristics, comprising:

[0024] An image acquisition module is used to acquire remote sensing surface reflectance images of the target monitoring area at different time points; the remote sensing surface reflectance images include multiple pixels;

[0025] A data processing module, configured to preprocess all of the remote sensing surface reflectance images to obtain preprocessed remote sensing surface reflectance images;

[0026] An evaluation value calculation module is used to determine the overall quality evaluation value of each pixel of the pre-processed remote sensing surface reflectance image based on the surface water index, cloud / cloud shadow distance and cloud and fog influence of each pixel;

[0027] An image synthesis module is used to determine a cloud-free remote sensing synthetic image of the target monitoring area based on the overall quality evaluation value of each pixel; the pixel at any position in the cloud-free remote sensing synthetic image is the pixel with the largest overall quality evaluation value at the corresponding position in the preprocessed remote sensing surface reflectance image at different time points.

[0028] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement any of the above-mentioned cloud-free remote sensing image synthesis methods that present vegetation drought characteristics.

[0029] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-mentioned cloud-free remote sensing image synthesis methods that present vegetation drought characteristics.

[0030] In a fifth aspect, the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements any of the above-mentioned cloud-free remote sensing image synthesis methods that present vegetation drought characteristics.

[0031] According to the specific embodiments provided in this application, this application has the following technical effects:

[0032] The present application provides a cloud-free remote sensing image synthesis method, system, equipment, medium and product for presenting vegetation drought characteristics, obtaining remote sensing surface reflectance images of a target monitoring area at different time points; the remote sensing surface reflectance images include multiple pixels; all remote sensing surface reflectance images are preprocessed to obtain preprocessed remote sensing surface reflectance images; based on the surface water index, cloud / cloud shadow distance and cloud and fog influence of each pixel, the overall quality evaluation value of each pixel in the preprocessed remote sensing surface reflectance image is determined; based on the overall quality evaluation value of each pixel, a cloud-free remote sensing synthetic image of the target monitoring area is determined; the pixel at any position in the cloud-free remote sensing synthetic image is the pixel with the largest overall quality evaluation value at the corresponding position in the preprocessed remote sensing surface reflectance images at different time points. The present application characterizes vegetation drought characteristics through the surface water index, thereby achieving cloud-free, seamless remote sensing image synthesis that highlights vegetation drought characteristic information. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0034] Figure 1 A flowchart of a method for synthesizing cloud-free remote sensing images showing vegetation drought characteristics provided by one embodiment of the present application;

[0035] Figure 2These are the cloud-free synthetic remote sensing image effects of different image synthesis methods in test areas A and B;

[0036] Figure 3 Surface water index effect maps calculated for different image synthesis methods;

[0037] Figure 4 Statistical graphs of surface water index in test area A using different image synthesis methods;

[0038] Figure 5 Statistical graphs of surface water index in test area B using different image synthesis methods;

[0039] Figure 6 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0041] The cloudless remote sensing image synthesis method for presenting vegetation drought characteristics in this application makes the synthesized remote sensing image cloudless, seamless and highlights the vegetation drought characteristic information, which can provide solid and reliable data support for practical applications such as ecological protection planning and forest health status assessment.

[0042] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0043] In an exemplary embodiment, Figure 1 As shown, a cloud-free remote sensing image synthesis method for presenting vegetation drought characteristics is provided, including the following steps:

[0044] S1: Acquire remote sensing surface reflectance images of the target monitoring area at different time points; the remote sensing surface reflectance images include multiple pixels.

[0045] S2: Preprocessing all the remote sensing surface reflectance images to obtain preprocessed remote sensing surface reflectance images.

[0046] As an optional implementation manner, when the remote sensing surface reflectance images at different time points are from the same series of satellite images, preprocessing is performed on all the remote sensing surface reflectance images to obtain preprocessed remote sensing surface reflectance images, specifically including:

[0047] All the remote sensing surface reflectance images are subjected to cloud removal / cloud shadow processing to obtain pre-processed remote sensing surface reflectance images.

[0048] As an optional implementation manner, when the remote sensing surface reflectance images at different time points are multi-source satellite images, preprocessing all the remote sensing surface reflectance images to obtain preprocessed remote sensing surface reflectance images specifically includes:

[0049] Multi-source data spectral coordination, spatial matching and cloud / cloud shadow removal processing are performed on all the remote sensing surface reflectance images to obtain pre-processed remote sensing surface reflectance images.

[0050] In this embodiment, if the input multi-temporal remote sensing images (remote sensing surface reflectance images at different time points) are from the same satellite image series (such as Landsat, Sentinel, or Gaofen), only cloud / cloud shadow removal processing is required using cloud / cloud shadow mask data products. If the input multi-temporal remote sensing images are from multiple sources, preprocessing is required, including multi-source data spectral coordination, spatial matching, and cloud / cloud shadow removal. Spectral coordination of multi-source data is typically performed using linear relationships for accuracy, spatial matching is achieved using pixel-level sampling and georeferencing of same-name points, and clouds and cloud shadows are removed using cloud / cloud shadow mask data products. Finally, clear pixels are retained for subsequent cloud-free synthesis calculations.

[0051] S3: Determine an overall quality evaluation value for each pixel of the preprocessed remote sensing surface reflectance image based on the surface water index, cloud / cloud shadow distance, and cloud and fog influence of each pixel.

[0052] As an optional implementation manner, based on the surface water index, cloud / cloud shadow distance and cloud and fog influence of each pixel, an overall quality evaluation value of each pixel of the pre-processed remote sensing surface reflectance image is determined, and the method also includes:

[0053] Calculates the surface water index, cloud / cloud shadow distance, and cloud and fog influence for each pixel.

[0054] As an optional implementation, S3 specifically includes:

[0055] Using formula S TOTAL =7×S LSWI +2×S CloudDist +S HOT Determine the overall quality evaluation value of each pixel of the pre-processed remote sensing surface reflectance image; wherein, S TOTAL is the overall quality evaluation value of the current pixel; S LSWI is the water content of the current pixel, S LSWI =1-LSWI;S CloudDistis the cloud / cloud shadow distance of the current pixel; S HOT is the cloud and fog influence of the current pixel.

[0056] In this embodiment, parameters such as the Land Surface Water Index (LSWI), cloud / cloud shadow distance, and cloud and fog impact are set as indicators for evaluating pixels, with a weight ratio of 7:2:1 respectively. The three indicators are added together to obtain the overall quality evaluation value of each pixel.

[0057] (1) LSWI is an index used to detect the moisture content of ground objects. It uses near-infrared and short-wave infrared data to calculate the moisture content of ground objects. The calculation formula is as follows:

[0058]

[0059] S LSWI =1-LSWI(2)

[0060] Among them, NIR is the surface reflectance value of the near infrared band, and SWIR1 is the surface reflectance value of the short wave infrared band. The value range of LSWI is usually between [-1, 1]. The larger the value, the higher the moisture content, and the smaller the value, the lower the moisture content, that is, the drier it is. LSWI The water content of the ground.

[0061] (2) The cloud / cloud shadow distance shows the degree to which the target pixel is affected by the cloud / cloud shadow. The farther the distance, the clearer the pixel, and the closer the distance, the blurrier the pixel. The calculation formula is as follows:

[0062]

[0063] Among them, D i D is the distance between the target pixel (current pixel) and the nearest neighbor cloud / cloud image pixel; req is the threshold distance, and meter-level resolution images usually set D req =200, Sentinel-2 image setting D req =100, Landsat series image setting D req =50;D min D i The minimum value of D min =0.

[0064] (3) Cloud and fog impact reflects the degree to which a pixel is affected by haze and thin clouds, and is evaluated based on the haze optimized transformation function (HOT). The calculation formula is as follows:

[0065] HOT=BLUE-0.5RED-0.08(4)

[0066]

[0067] Among them, BLUE and RED represent the surface reflectance values ​​of the blue band and red band respectively.

[0068] (4) Overall quality evaluation value of pixel. The above three evaluation parameters are weighted with a ratio of 7:2:1, and then the three indicators are added together to obtain the overall quality evaluation value of each pixel, as shown in formula (6).

[0069] S TOTAL =7×S LSWI +2×S CloudDist +S HOT (6)

[0070] S4: Based on the overall quality evaluation value of each pixel, determine the cloud-free remote sensing synthetic image of the target monitoring area; the pixel at any position in the cloud-free remote sensing synthetic image is the pixel with the largest overall quality evaluation value at the corresponding position in the preprocessed remote sensing surface reflectance image at different time points.

[0071] In this embodiment, the pixels with the highest quality evaluation value are used for image synthesis to obtain a cloud-free remote sensing image that highlights the drought characteristics of vegetation, as shown in formula (7).

[0072]

[0073] Where I(i,j) is the surface reflectance value of the pixel in the i-th row and j-th column of the cloud-free remote sensing synthetic image, i and j represent the i-th row and j-th column of the image respectively, I max(STOTAL) (i, j) represents the surface reflectance value of the pixel with the highest quality evaluation value in the i-th row and j-th column in the preprocessed remote sensing surface reflectance image at different time points.

[0074] Existing cloud-free remote sensing image synthesis algorithms mostly use the mean / median method, while BAP primarily considers cloud-free image synthesis during the vegetation growing season. Both methods lag behind the proposed method in monitoring vegetation drought characteristics. To explore the proposed method's ability to capture vegetation drought characteristics, we conducted analysis and verification across different test areas. We compared the proposed method's synthetic images with those from BAP and median synthetic images in terms of visual quality and quantitative evaluation.

[0075] For test area A, Sentinel-2 images (cloud cover <70%) taken between March 15, 2024 and April 15, 2024 were used to synthesize cloud-free images. For test area B, Sentinel-2 images (cloud cover <70%) taken between April 1, 2024 and May 31, 2024 were used to synthesize cloud-free images. Figure 2 Visually, it can be seen that the results produced by the three different image synthesis methods are quite different. Among them, the cloud-free remote sensing image synthesized by the method of this application can better reflect the characteristics of vegetation drought during this period. The surface water index (LSWI) is calculated based on the synthesized cloud-free image. For different test areas, the surface water content presented by the method of this application is the lowest. Compared with the BAP algorithm and the median synthesis algorithm, it has obvious advantages in presenting drought information. Figure 3 As shown in the figure, High is the highest value of the surface water index and Low is the lowest value of the surface water index.

[0076] From the quantitative results, the statistical values ​​of different cloud-free synthesis methods (BAP synthesis, median synthesis and the method of this application) in different test areas are as follows: Figure 4 and Figure 5 As shown. For different test areas, the maximum, minimum and mean values ​​of the surface moisture content calculated by the method of the present application are the smallest compared with other algorithms. In test area A, the overall mean of the surface moisture content calculated by the method of the present application is -0.221756, while the overall means of the BAP synthesis method and the median synthesis are -0.00075 and -0.069425 respectively; in test area B, the overall mean of the surface moisture content calculated by the method of the present application is -0.171539, while the overall means of the BAP synthesis method and the median synthesis are -0.146753 and -0.135246 respectively. The results show that the moisture content contained in the images synthesized by the method of the present application is relatively small, and it better reflects the drought information of the surface.

[0077] The cloud-free remote sensing image synthesis method for presenting vegetation drought characteristics in this application sets parameters such as the surface water index, cloud / cloud shadow distance, and cloud and fog impact as indicators for evaluating pixels, and increases the weight of drought information in the surface water index. This makes the synthesized remote sensing image cloudless, seamless, and highlights the vegetation drought characteristic information, which can provide solid and reliable data support for practical applications such as ecological protection planning and forest health status assessment.

[0078] Based on the same inventive concept, the present application also provides a cloud-free remote sensing image synthesis system for implementing the aforementioned cloud-free remote sensing image synthesis method for displaying vegetation drought characteristics. The solution provided by this system is similar to the solution described in the aforementioned method. Therefore, the specific limitations of the cloud-free remote sensing image synthesis system for displaying vegetation drought characteristics provided below can be found in the limitations of the cloud-free remote sensing image synthesis method for displaying vegetation drought characteristics above, and will not be repeated here.

[0079] In an exemplary embodiment, a cloud-free remote sensing image synthesis system for presenting vegetation drought characteristics is provided, comprising:

[0080] The image acquisition module is used to acquire remote sensing surface reflectance images of the target monitoring area at different time points; the remote sensing surface reflectance images include multiple pixels.

[0081] The data processing module is used to preprocess all the remote sensing surface reflectance images to obtain preprocessed remote sensing surface reflectance images.

[0082] The evaluation value calculation module is used to determine the overall quality evaluation value of each pixel of the pre-processed remote sensing surface reflectance image based on the surface water index, cloud / cloud shadow distance and cloud and fog influence of each pixel.

[0083] An image synthesis module is used to determine a cloud-free remote sensing synthetic image of the target monitoring area based on the overall quality evaluation value of each pixel; the pixel at any position in the cloud-free remote sensing synthetic image is the pixel with the largest overall quality evaluation value at the corresponding position in the preprocessed remote sensing surface reflectance image at different time points.

[0084] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the above-mentioned cloud-free remote sensing image synthesis method showing vegetation drought characteristics when executing the computer program.

[0085] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, which, when executed by a processor, implements the above-mentioned method for synthesizing cloud-free remote sensing images that present vegetation drought characteristics.

[0086] In an exemplary embodiment, a computer program product is provided, including a computer program, which, when executed by a processor, implements the above-mentioned method for synthesizing cloud-free remote sensing images that present vegetation drought characteristics.

[0087] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 6As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a cloud-free remote sensing image synthesis method that presents vegetation drought characteristics is implemented.

[0088] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0089] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0090] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0091] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0092] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0093] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A cloud-free remote sensing image synthesis method showing vegetation drought characteristics, characterized by: include: Acquire remote sensing surface reflectance images of a target monitoring area at different time points; the remote sensing surface reflectance images include a plurality of pixels; Preprocessing all the remote sensing surface reflectance images to obtain preprocessed remote sensing surface reflectance images; Determining an overall quality evaluation value for each pixel of the preprocessed remote sensing surface reflectance image based on a surface water index, cloud / cloud shadow distance, and cloud and fog influence of each pixel; Based on the overall quality evaluation value of each pixel, a cloud-free remote sensing synthetic image of the target monitoring area is determined; the pixel at any position in the cloud-free remote sensing synthetic image is the pixel with the largest overall quality evaluation value at the corresponding position in the preprocessed remote sensing surface reflectance image at different time points.

2. The cloud-free remote sensing image synthesis method for presenting vegetation drought characteristics according to claim 1, characterized in that: When the remote sensing surface reflectance images at different time points are from the same series of satellite images, preprocessing is performed on all the remote sensing surface reflectance images to obtain preprocessed remote sensing surface reflectance images, specifically including: All the remote sensing surface reflectance images are subjected to cloud / cloud shadow removal.

3. The cloud-free remote sensing image synthesis method for presenting vegetation drought characteristics according to claim 1, characterized in that: When the remote sensing surface reflectance images at different time points are multi-source satellite images, preprocessing is performed on all the remote sensing surface reflectance images to obtain preprocessed remote sensing surface reflectance images, specifically including: Multi-source data spectral coordination, spatial matching and cloud / cloud shadow removal processing are performed on all the remote sensing surface reflectance images to obtain pre-processed remote sensing surface reflectance images.

4. The cloud-free remote sensing image synthesis method for presenting vegetation drought characteristics according to claim 1, characterized in that: Determine the overall quality evaluation value of each pixel of the pre-processed remote sensing surface reflectance image based on the surface water index, cloud / cloud shadow distance and cloud and fog influence of each pixel, which also includes: Calculates the surface water index, cloud / cloud shadow distance, and cloud and fog influence for each pixel.

5. The cloud-free remote sensing image synthesis method for presenting vegetation drought characteristics according to claim 4, characterized in that: Calculates the surface water index, cloud / cloud shadow distance, and cloud and fog impact for each pixel, including: Using the formula Calculate the surface water index of each pixel; where LSWI is the surface water index of the current pixel; NIR is the surface reflectance value of the current pixel in the near-infrared band; SWIR1 is the surface reflectance value of the current pixel in the short-wave infrared band; Using the formula Calculate the cloud / cloud shadow distance for each pixel; where S CloudDist is the cloud / cloud shadow distance of the current pixel; D i D is the distance between the current pixel and the nearest cloud / cloud image pixel; req is the threshold distance; D min The minimum distance between the current pixel and the nearest cloud / cloud image pixel; Using the formula Calculate the cloud and fog impact of each pixel; where S HOT is the cloud and fog influence of the current pixel; BLUE is the surface reflectance value of the blue band; RED is the surface reflectance value of the red band; HOT is the cloud and fog optimization conversion function.

6. The cloud-free remote sensing image synthesis method for presenting vegetation drought characteristics according to claim 1, characterized in that: Based on the surface water index, cloud / cloud shadow distance and cloud and fog influence of each pixel, the overall quality evaluation value of each pixel of the pre-processed remote sensing surface reflectance image is determined, specifically including: Using formula S TOTAL =7×S LSWI +2×S CloudDist +S HOT Determine the overall quality evaluation value of each pixel of the pre-processed remote sensing surface reflectance image; wherein, S TOTAL is the overall quality evaluation value of the current pixel; S LSWI is the water content of the current pixel, S LSWI =1-LSWI;S CloudDist is the cloud / cloud shadow distance of the current pixel; S HOT is the cloud and fog influence of the current pixel.

7. A cloud-free remote sensing image synthesis system showing vegetation drought characteristics, characterized by: include: An image acquisition module is used to acquire remote sensing surface reflectance images of the target monitoring area at different time points; the remote sensing surface reflectance images include multiple pixels; A data processing module, configured to preprocess all of the remote sensing surface reflectance images to obtain preprocessed remote sensing surface reflectance images; An evaluation value calculation module is used to determine the overall quality evaluation value of each pixel of the pre-processed remote sensing surface reflectance image based on the surface water index, cloud / cloud shadow distance and cloud and fog influence of each pixel; An image synthesis module is used to determine a cloud-free remote sensing synthetic image of the target monitoring area based on the overall quality evaluation value of each pixel; the pixel at any position in the cloud-free remote sensing synthetic image is the pixel with the largest overall quality evaluation value at the corresponding position in the preprocessed remote sensing surface reflectance image at different time points.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the cloud-free remote sensing image synthesis method presenting vegetation drought characteristics according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the cloud-free remote sensing image synthesis method for presenting vegetation drought characteristics according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the cloud-free remote sensing image synthesis method for presenting vegetation drought characteristics according to any one of claims 1 to 6 is implemented.

Images