A method for reconstructing chlorophyll a concentration data based on multi-source satellite remote sensing data

Abstract

The present application provides a method for reconstructing chlorophyll a concentration data based on multi-source satellite remote sensing data, including the steps of: preprocessing the original image data corresponding to the spatial and temporal remote sensing monitoring, identifying and marking missing value pixels and non-missing value pixels, and performing outlier tests to identify and mark outlier pixels; using a filtering algorithm to replace the data of the outlier pixels in the original image; performing reconstruction processing on the preprocessed original image data by using the data empirical orthogonal function decomposition method; performing outlier tests on the data of the reconstructed image to identify and mark outlier pixels, and using a filtering algorithm to replace the data of the outlier pixels in the reconstructed image; reprocessing the data of the reconstructed image to obtain spatially and temporally complete chlorophyll a concentration data. This method can fill in the gaps and reconstruct the satellite remote sensing observation values of chlorophyll a concentration, and achieve continuous coverage monitoring of global chlorophyll a concentration in the time and space domains.

Description

Technical Field

[0001] The present invention relates to the technical field of remote sensing monitoring, and particularly relates to a method for reconstructing chlorophyll a concentration data based on multi-source satellite remote sensing data. Background Art

[0002] Ocean color data is crucial for monitoring and understanding water body optics, biological, and ecological processes, and it is also an important data source for ocean models. Chlorophyll a, which has an important impact on ocean color, is an important indicator for monitoring water body eutrophication and primary productivity. Traditional in-situ ocean observations are limited by spatio-temporal scales and cannot obtain large-area continuous monitoring of ocean color data. Satellite remote sensing provides a means for global water body monitoring and can provide chlorophyll a concentration product data for large-scale and long-term observations.

[0003] Since 2008, the Medium Resolution Spectral Imager (MERSI) on the Fengyun-3 (FY-3) meteorological satellite has provided observation data of global ocean color products. Relying on a single instrument often cannot achieve continuous coverage observations of the global sea area space every day. Therefore, the Visible Infrared Imaging Radiometer Suite (VIIRS) instruments on the US Suomi National Polar-orbiting Partnership (SNPP) and NOAA-20 satellites fuse multiple instruments to achieve high spatial coverage of the observation object globally every day and provide the required ocean color product observation data.

[0004] In practical applications, affected by factors such as instrument scan width, zenith angle, solar flare, and cloud cover, the sea surface observation data in some sea areas has large-area and irregular missing values due to instrument and meteorological conditions, which has a serious impact on the later analysis and research of ocean color remote sensing observation data. Summary of the Invention

[0005] In order to solve the problem of missing data caused by the influence of existing ocean color monitoring by observation instruments and meteorological conditions, it is necessary to adopt a data reconstruction method to fill in the missing data, while ensuring the calculation speed of data processing to meet the accuracy requirements, so as to fill the data missing caused by factors such as high zenith angle of satellite sensors, solar flares, etc. and inversion algorithm limitations, and achieve continuous spatial coverage of global chlorophyll a concentration monitoring, so that the reconstructed chlorophyll a concentration product data can reveal the large-scale and mesoscale ocean color spatial distribution characteristics and the seasonal variation characteristics of chlorophyll a concentration, and provide effective data support for the remote sensing quantitative monitoring of global chlorophyll. The present application provides a method for reconstructing chlorophyll a concentration data based on multi-source satellite remote sensing data, which can reconstruct the chlorophyll a concentration observation values and supplement the missing data caused by the limitations of sensor high zenith angle, solar flare, and inversion algorithm, so as to achieve continuous coverage monitoring of global chlorophyll a concentration in the time and space domains.

[0006] The technical solution adopted by this application to solve its technical problems is: a method for reconstructing chlorophyll a concentration data based on multi-source satellite remote sensing data, characterized by including the steps:

[0007] S1. Preprocess the original image data of the corresponding space and time monitored by remote sensing, identify and mark the missing value pixels and non-missing value pixels, and perform outlier tests to identify and mark the outlier pixels;

[0008] S2. Adopt a filtering algorithm to replace the data of the outlier pixels in the original image;

[0009] S3. Reconstruct the preprocessed original image data by using the data empirical orthogonal function decomposition method;

[0010] S4. Perform outlier tests on the reconstructed image data to identify and mark the outlier pixels, and adopt a filtering algorithm to replace the data of the outlier pixels in the reconstructed image;

[0011] S5. Reprocess the reconstructed image data to obtain complete chlorophyll a concentration data in space and time.

[0012] In a specific implementation, the median filtering algorithm adopted in step S2 includes the steps:

[0013] T1. Set a data reference range for the outlier pixels;

[0014] T2. Calculate the median P m of the non-missing value pixels within the reference range, the standard deviation P n of the non-missing value pixels, the weight w m of the non-missing value pixels, and the weight w n of the missing value pixels; n f ;

[0015] T3. Replace the outlier pixel with P f , where:

[0016] P f = w n P n + w m P m ; w n + w m = 1.

[0017] In a specific implementation, the data reference range of the outlier pixels in step T1 is 8 pixels around the outlier pixel.

[0018] In a specific implementation, the outlier pixels in step S2 include the cloud coverage edge positions identified and marked in the original image by the edge detection method.

[0019] In a specific implementation, the empirical orthogonal function decomposition method for data in S3 includes the following steps:

[0020] E1. Calculate the mean value of the valid data in the m-row and n-column data matrix X composed of the original images in the corresponding space and time after preprocessing 0 to obtain the matrix Select valid original values in X to form the original cross-validation point set X ; c-v

[0021] E2. Mark the data positions corresponding to the missing pixels in X and the data positions selected as the original cross-validation point set X c-v in X as the missing point NaN;

[0022] E3. Assign the value 0 to the data positions marked as the missing point NaN in X so that its initial value is an unbiased estimate value, and let the initial value of the characteristic mode parameter P be 1;

[0023] E4. According to the value of the characteristic mode parameter P, perform singular value decomposition on the matrix X: X = USV T , where U is the spatial characteristic mode, S is the singular value matrix, and V is the temporal characteristic mode;

[0024] E5. Calculate the reconstruction value of the missing point where i and j are the row and column positions of the missing point in the matrix X, respectively, (u t ) i is the i-th element in the t-th column of the spatial characteristic mode U, (v t ) j is the j-th element in the t-th column of the temporal characteristic mode V, ρ t is the corresponding singular value;

[0025] E6. Evaluate the accuracy of the original values and their corresponding reconstruction values in the original cross-validation point set X c-v ;

[0026] E7. Repeat steps E4 - E6, and respectively evaluate the accuracy of the original values and their corresponding reconstruction values in the original cross-validation point set X max when the characteristic mode parameter P = 2, 3,..., k c-v and obtain the characteristic mode parameter corresponding to the highest accuracy evaluation where k max is the maximum number of iterations, k max ≤ min(m, n);

[0027] E8. Calculate the reconstruction value of the missing point in the matrix X when the characteristic mode parameter ; Replace the data at the positions corresponding to the missing points in the replacement matrix X, and restore the data of the selected original cross-validation point set X in X to the original values, so as to obtain the matrix X1, and calculate the reconstruction value of the original image data matrix X c-v of the data, so as to obtain the matrix X1, and calculate the reconstruction value of the original image data matrix X 0

[0028] In a specific implementation, in step E6, the indexes used for accuracy evaluation include the correlation coefficient CC, the root mean square error RMSE, and the bias Bias; where:

[0029]

[0030]

[0031]

[0032] In the formula, N represents the total number of data in the original cross-validation point set X c-v in, respectively represent the original value and the reconstruction value of the i-th data in the original cross-validation point set X c-v in, respectively represent the mean value of the original values and the mean value of the reconstruction values in the original cross-validation point set X c-v in.

[0033] In a specific implementation, in step S5, the reprocessing method for the reconstructed image data includes multi-point smoothing processing and land-sea masking processing.

[0034] In a specific implementation, the system of the chlorophyll a concentration data reconstruction method based on multi-source satellite remote sensing data includes:

[0035] A preprocessing module, configured to obtain multi-source satellite remote sensing data and perform preprocessing, and identify and process the data of abnormal pixels in the original image;

[0036] A reconstruction module, configured to reconstruct the preprocessed original image data by using the data empirical orthogonal function decomposition method;

[0037] A reconstructed data verification module, configured to perform outlier verification and processing on the reconstructed image data;

[0038] A reprocessing module, configured to perform relevant processing on the reconstructed image data to obtain spatially and temporally complete chlorophyll a concentration data.

[0039] In a specific implementation, the device of the chlorophyll a concentration data reconstruction method based on multi-source satellite remote sensing data includes:

[0040] One or more processors; ​

[0041] A storage device for storing one or more programs, which when executed by one or more processors cause the one or more processors to implement the method for reconstructing chlorophyll a concentration data based on multi-source satellite remote sensing data.

[0042] In a specific embodiment, a program is stored in a computer-readable storage medium, and when the program is executed by a processor, the method for reconstructing chlorophyll a concentration data based on multi-source satellite remote sensing data is implemented.

[0043] The advantages of the embodiments of the present application are as follows:

[0044] The method for reconstructing chlorophyll a concentration data based on multi-source satellite remote sensing data uses the data empirical orthogonal function decomposition method to process satellite monitoring data, completes the reconstruction of missing pixel information, and can quantitatively evaluate the accuracy of the reconstructed data, filling the data missing caused by inversion algorithm limitations such as high satellite sensor zenith angles and solar flares, and achieving continuous coverage of global chlorophyll a concentration in space and time; by fusing the ocean color monitoring data of two types of sensors, it provides more perfect and effective data support for the remote sensing quantitative monitoring of global chlorophyll a. Reconstructing and processing by fusing the microwave data of the FY-3 polar orbiting meteorological satellite and VIIRS data can significantly improve the accuracy of the reconstructed data of chlorophyll a concentration compared with the original data. Brief Description of the Drawings

[0045] Figure 1 It is a schematic flowchart of a method for reconstructing chlorophyll a concentration data based on multi-source satellite remote sensing data of the present invention;

[0046] Figure 2 It is a schematic diagram of the chlorophyll a concentration monitored by instruments MERSI and VIIRS respectively in a method for reconstructing chlorophyll a concentration data based on multi-source satellite remote sensing data of the present invention;

[0047] Figure 3 It is a schematic diagram of the reconstruction of the original image data of the chlorophyll a concentration monitored by instrument MERSI in a method for reconstructing chlorophyll a concentration data based on multi-source satellite remote sensing data of the present invention;

[0048] Figure 4 It is a schematic diagram of the reconstruction of the original image data of the chlorophyll a concentration monitored by instrument VIIRS in a method for reconstructing chlorophyll a concentration data based on multi-source satellite remote sensing data of the present invention;

[0049] Figure 5 It is a schematic diagram of a system for a method for reconstructing chlorophyll a concentration data based on multi-source satellite remote sensing data of the present invention;

[0050] Figure 6Schematic diagram of the device for a method of reconstructing chlorophyll a concentration data based on multi-source satellite remote sensing data of the present invention. Detailed implementation manners

[0051] In this embodiment of the application, by providing a method for reconstructing chlorophyll a concentration data based on multi-source satellite remote sensing data, the problem of data missing values caused by the influence of observation instruments and meteorological conditions in existing ocean color monitoring is solved. The general idea is as follows:

[0052] Please refer to Figure 1 , the present invention provides a method for reconstructing chlorophyll a concentration data based on multi-source satellite remote sensing data, including steps S1 - S5:

[0053] S1. Preprocess the original image data corresponding to the spatial and temporal remote sensing monitoring, identify and mark the missing value pixels and non-missing value pixels, and perform outlier tests to identify and mark the abnormal pixels.

[0054] Through the preprocessing of the chlorophyll a concentration image data of satellite remote sensing monitoring in step S1, the abnormal pixels are identified and marked for the next step of processing, improving the accuracy of data processing.

[0055] S2. Adopt a filtering algorithm to replace the data of the abnormal pixels in the original image.

[0056] Process the abnormal pixels in the original image to avoid their influence on the accuracy of data reconstruction. Specifically, in this embodiment, in step S2, a median filtering algorithm is adopted, which specifically includes the steps:

[0057] T1. Set a data reference range for the abnormal pixels;

[0058] T2. Calculate the median P m , the standard deviation P n , the weight w m of the non-missing value pixels, and the weight w n of the missing value pixels;

[0059] T3. Replace the abnormal pixel with P f , where:

[0060] P f = w n P n + w m P m ; w n + w m = 1.

[0061] In this example, by introducing the median filtering algorithm, the abnormal pixels existing in the data set are replaced with the median. The data reference range of the abnormal pixels can preferably be the 8 pixels surrounding the abnormal pixel in the image. Through this method, the abnormal pixels at the cloud coverage edge position marked by the edge detection method can be replaced to ensure the subsequent data processing accuracy.

[0062] S3. Reconstruct the preprocessed original image data by using the empirical orthogonal function decomposition method of data.

[0063] To solve the problem of missing data values caused by the influence of observation instruments and meteorological conditions through data reconstruction by the empirical orthogonal function decomposition method of data, please continue to refer to Figure 1 , when adopting the empirical orthogonal function decomposition method of data in step S3, the following method steps can be adopted:

[0064] E1. Calculate the mean value of the valid data in the m-row and n-column data matrix X composed of the preprocessed original image of the corresponding space and time 0 to obtain the matrix Select the valid original values in X to form the original cross-validation point set X ; c-v ;

[0065] E2. Mark the data positions corresponding to the missing pixels in X and the data positions in X selected as the original cross-validation point set X c-v as the missing points NaN;

[0066] E3. Assign the value 0 to the data positions marked as missing points NaN in X to make its initial value an unbiased estimate value, and let the initial value of the characteristic mode parameter P be 1;

[0067] E4. According to the value of the characteristic mode parameter P, perform singular value decomposition on the matrix X: X = USV T , where U is the spatial characteristic mode, S is the singular value matrix, and V is the temporal characteristic mode;

[0068] E5. Calculate the reconstructed values of the missing points where i and j are the row and column positions of the missing point in the matrix X respectively, (u t ) i is the i-th element in the t-th column of the spatial characteristic mode U, (v t ) j is the j-th element in the t-th column of the temporal characteristic mode V, ρ t is the corresponding singular value;

[0069] E6. Evaluate the accuracy of the original values and their corresponding reconstructed values in the original cross-validation point set X c-v ;

[0070] E7. Repeat steps E4 - E6 for the characteristic modal parameters P = 2, 3, …, k max for the original cross - validation point set X c-v at this time, evaluate the accuracy between the original values and their corresponding reconstructed values in it, and obtain the characteristic modal parameter corresponding to the highest accuracy evaluation where k max is the maximum number of iterations, and k max ≤ min(m, n);

[0071] E8. Calculate the reconstructed values of the missing points in matrix X at the characteristic modal parameter to replace the data at the corresponding positions of the missing points in matrix X, and restore the data selected as the original cross - validation point set X c-v to their original values, so as to obtain matrix X1, and calculate the reconstructed values 0 of the original image data matrix X

[0072] The multi - source data interpolation and reconstruction method based on the data empirical orthogonal function decomposition method is a method that can infer the missing value points in the spatio - temporal field without prior values and adaptively. This method reconstructs the missing data in the space through the main modes of the empirical orthogonal function, and the most important modes generated by the optimal truncation are used to obtain a dynamic fitting image that reflects the overall state of the data and the time development trend. For the specific process, please refer to Figure 1 , by establishing a spatio - temporal matching data set for the remotely sensed chlorophyll a concentration data Daily Chl - a(d) at time d, constructing a two - dimensional data matrix for the space and time domains of the original image data, and then extracting a certain proportion, such as 1%, of the effective original values as the original cross - validation point set to evaluate the accuracy of the reconstructed values calculated by the data empirical orthogonal function decomposition method under different characteristic modal parameters, obtaining the optimal characteristic modal parameter value, and then realizing the reconstruction and filling of the missing parts of the pre - processed original image data at the optimal characteristic modal parameter value, so as to realize the continuous coverage monitoring of the global chlorophyll a concentration in the time and space domains. There can be multiple accuracy evaluation methods. Specifically, in step E6, the indicators used for accuracy evaluation can include the correlation coefficient CC, the root mean square error RMSE, and the bias Bias; where:

[0073]

[0074]

[0075]

[0076] In the above formula, N represents the total number of data in the original cross - validation point set X c-v and respectively represent the original cross - validation point set X c-vThe original value and the reconstructed value of the i-th data in respectively represent the original cross-validation point set X c-v the mean value of the original values and the mean value of the reconstructed values in. In this example Figure 1 as shown, the specific accuracy evaluation index adopted is the root mean square error RMSE. Through the iterative calculation process of each characteristic mode parameter, the original cross-validation point set X c-v the minimum root mean square error RMSE between the original value and its corresponding reconstructed value in P and the corresponding characteristic mode parameters Then, the original image data is reconstructed under this characteristic mode parameter Furthermore, the root mean square error can be used to evaluate this reconstruction result.

[0077] S4. Perform an outlier test on the reconstructed image data to identify and mark outlier pixels, and use a filtering algorithm to replace the data of the outlier pixels in the reconstructed image.

[0078] Further perform an outlier test on the reconstructed image to replace the outlier pixels, so as to further ensure the accuracy and reliability of the reconstructed data.

[0079] S5. Reprocess the reconstructed image data to obtain spatially and temporally complete chlorophyll a concentration data.

[0080] The reprocessing method of the reconstructed image data includes multi-point smoothing processing and land-sea masking processing; by performing multi-point smoothing processing and land-sea masking processing on the reconstructed image data, the data accuracy is ensured and the required image data is obtained.

[0081] This embodiment also provides a system for a chlorophyll a concentration data reconstruction method based on multi-source satellite remote sensing data, including a preprocessing module, a reconstruction module, a reconstructed data verification module, and a reprocessing module. The preprocessing module is used to obtain multi-source satellite remote sensing data and perform preprocessing, and identify and process the data of the outlier pixels in the original image; the reconstruction module is used to reconstruct the preprocessed original image data by using the data empirical orthogonal function decomposition method; the reconstructed data verification module is used to perform an outlier test on the reconstructed image data and perform processing; the reprocessing module is used to perform relevant processing on the reconstructed image data to obtain spatially and temporally complete chlorophyll a concentration data. Specifically, please refer to Figure 5 The preprocessing module may specifically include a daily Fengyun polar-orbiting meteorological satellite chlorophyll a concentration data preprocessing module, a daily VIIRS chlorophyll a concentration data preprocessing module, a unified spatio-temporal scale module. Through the processing of the data empirical orthogonal function data reconstruction module and the reconstructed data verification module, and further reprocessing by the thematic map production module, spatially and temporally complete chlorophyll a concentration data is obtained.

[0082] Please refer to Figure 6 Figure 6 , this embodiment also provides a device for a method of reconstructing chlorophyll a concentration data based on multi-source satellite remote sensing data, including one or more processors and a storage device. The storage device is used to store one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method of reconstructing chlorophyll a concentration data based on multi-source satellite remote sensing data.

[0083] The components of the device may include, but are not limited to: one or more processors or processing units, a memory, and a bus connecting different system components (including the memory and the processing unit).

[0084] The bus represents one or more of several types of bus architectures, including a memory bus or a memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. By way of example, these architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

[0085] The device for the method of reconstructing chlorophyll a concentration data based on multi-source satellite remote sensing data typically includes a variety of computer system readable media. These media can be any available media accessible by the device for the method of reconstructing chlorophyll a concentration data based on multi-source satellite remote sensing data, including volatile and non-volatile media, removable and non-removable media.

[0086] The memory may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory. The device for the method of reconstructing chlorophyll a concentration data based on multi-source satellite remote sensing data may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system may be used to read and write non-removable, non-volatile magnetic media (commonly referred to as a "hard disk drive"), may provide a disk drive for reading and writing removable non-volatile disks (such as a "floppy disk"), and an optical disk drive for reading and writing removable non-volatile optical disks (such as a CD-ROM, DVD-ROM, or other optical media). In these cases, each drive may be connected to the bus through one or more data media interfaces. The memory may include at least one program product having a set (such as at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0087] A program / util utility having a set (at least one) of program modules can be stored, for example, in a memory. Such program modules include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. The program modules generally perform the functions and / or methods in the embodiments described in the present invention.

[0088] The device of the method for reconstructing chlorophyll a concentration data based on multi-source satellite remote sensing data can also communicate with one or more external devices (such as a keyboard, a pointing device, a display, etc.), and can also communicate with one or more devices that enable a user to interact with the device of the method for reconstructing chlorophyll a concentration data based on multi-source satellite remote sensing data, and / or communicate with any device (such as a network card, a modem, etc.) that enables the device of the method for reconstructing chlorophyll a concentration data based on multi-source satellite remote sensing data to communicate with one or more other computing devices. Such communication can be carried out through an input / output (I / O) interface. Moreover, the device of the method for reconstructing chlorophyll a concentration data based on multi-source satellite remote sensing data can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter. The network adapter communicates with other modules of the device of the method for reconstructing chlorophyll a concentration data based on multi-source satellite remote sensing data through a bus, and other hardware and / or software modules can be combined with the device of the method for reconstructing chlorophyll a concentration data based on multi-source satellite remote sensing data, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0089] The processing unit executes various functional applications and data processing by running the programs stored in the memory, for example, implementing the processing method of stack splitting provided in any embodiment of the present invention. That is: obtaining multi-source satellite remote sensing data and performing preprocessing, identifying and processing the data of abnormal pixels in the original image; reconstructing the preprocessed original image data by using the data empirical orthogonal function decomposition method; performing outlier detection and processing on the reconstructed image data; performing relevant processing on the reconstructed image data to obtain spatially and temporally complete chlorophyll a concentration data.

[0090] Meanwhile, this embodiment may also include a computer-readable storage medium, in which a program is stored, and when the program is executed by a processor, the method for reconstructing chlorophyll a concentration data based on multi-source satellite remote sensing data is implemented.

[0091] The computer storage medium of the embodiments of the present invention may adopt any combination of one or more computer-readable media. The computer-readable media may be computer-readable signal media or computer-readable storage media. The computer-readable storage media, for example, may be, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination of the above. More specific examples (a non-exhaustive list) of the computer-readable storage media include: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this document, the computer-readable storage media may be any tangible medium that contains or stores a program, which can be used by or in combination with an instruction execution system, device, or component.

[0092] The computer-readable signal media may include data signals propagated in a baseband or as part of a carrier wave, which carry computer-readable program codes. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal media may also be any computer-readable media other than the computer-readable storage media, which can send, propagate, or transmit a program for use by or in combination with an instruction execution system, device, or component.

[0093] The program codes contained on the computer-readable media may be transmitted by any appropriate media, including but not limited to wireless, wire, optical fiber cable, RF, etc., or any suitable combination of the above. The computer program codes for performing the operations of the present invention may be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program codes may be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0094] In this embodiment, in combination with the Fengyun polar-orbiting meteorological satellite, for the global daily grid product data of chlorophyll a concentration of VIIRS and MERSI, the empirical orthogonal function method of data can be used to perform the fusion processing of global multi-source ocean color products, complete the reconstruction of the missing pixel information of a single instrument product, and be able to quantitatively evaluate the accuracy of the reconstructed data. Please refer to Figure 2 the schematic diagram of the chlorophyll a concentration obtained by the remote sensing monitoring instruments MERSI and VIIRS on the satellite, Figure 3 is the schematic diagram of the reconstruction of the original image data of the chlorophyll a concentration monitored by the instrument MERSI by the data missing measurement reconstruction method of this application, Figure 4 is the schematic diagram of the reconstruction of the original image data of the chlorophyll a concentration monitored by the instrument VIIRS by the data missing measurement reconstruction method of this application. The results show that through this method, the complementary processing of the daily chlorophyll a concentration product data can be realized, the missing data caused by the high zenith angle of the sensor, solar flare and inversion algorithm limitations can be filled, and the continuous coverage of the global chlorophyll a concentration data in the spatial and temporal domains can be achieved; the reconstructed chlorophyll a concentration product data can reveal the large-scale and mesoscale ocean color spatial distribution characteristics, as well as the obvious seasonal variation characteristics of the chlorophyll a concentration. By fusing the ocean color product data of the two types of sensors, it can provide effective data support for the remote sensing quantitative monitoring of global chlorophyll a. For example, in this application, the microwave of the Fengyun satellite and VIIRS data can be reconstructed to obtain the chlorophyll a concentration product data with global coverage and all-weather monitoring. The obtained chlorophyll a concentration product data has a more complete spatial coverage and the accuracy rate can be increased by 30% to 50% compared with the original data.

[0095] In summary, the chlorophyll a concentration data reconstruction method based on multi-source satellite remote sensing data of the present invention can reconstruct the chlorophyll a concentration observation values, supplement the missing data caused by the limitations of the sensor high zenith angle, solar flare and inversion algorithm, so as to realize the continuous coverage monitoring of the global chlorophyll a concentration in the time and space domains, and solve the problem of data missing values caused by the influence of observation instruments and meteorological conditions.

[0096] Finally, it should be noted that: Obviously, the above embodiments are only examples for clearly explaining the present invention, and are not limitations on the implementation manners. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to enumerate all the implementation manners here. And the obvious changes or modifications derived therefrom are still within the protection scope of the present invention.

Claims

1. A method for reconstructing chlorophyll a concentration data based on multi-source satellite remote sensing data, characterized in that, Including the steps: S1. Preprocess the original image data of corresponding space and time by remote sensing monitoring, identify and mark the missing value pixels and non-missing value pixels, and conduct outlier tests to identify and mark the outlier pixels; S2. Adopt a filtering algorithm to replace the data of the outlier pixels in the original image; S3. Reconstruct the preprocessed original image data by using the data empirical orthogonal function decomposition method; S4. Conduct outlier tests on the reconstructed image data to identify and mark the outlier pixels, and adopt a filtering algorithm to replace the data of the outlier pixels in the reconstructed image; S5. Reprocess the reconstructed image data to obtain the spatially and temporally complete chlorophyll a concentration data; Among them, the median filtering algorithm adopted in step S2 includes the steps: T1. Set the data reference range for the outlier pixels; T2. Calculate the median P of non-missing pixels within the reference range m , standard deviation of non-missing pixels P n , the weight of non-missing pixels w m , the weight of missing pixels w n ; T3, with P f Replace the abnormal pixel, where: P f = w n P n + w m P m ; w n + w m = 1; Among them, the data empirical orthogonal function decomposition method in S3 includes the steps: E1. Calculate the mean of the valid data in the m-row and n-column data matrix X composed of the preprocessed original images in the corresponding space and time 0 to obtain the matrix Select valid original values from X to form the original cross-validation point set X ; c-v ; E2. Mark the data positions corresponding to the missing pixels in X and the data positions in X that are selected as the original cross-validation point set X c-v as missing points NaN; E3. Assign the value of 0 to the data position marked as missing point NaN in X to make its initial value an unbiased estimated value, and set the initial value of the characteristic mode parameter P to 1; E4. Perform singular value decomposition on matrix X according to the P value of the characteristic mode parameter: X = USV T , where U is the spatial characteristic mode, S is the singular value matrix, and V is the temporal characteristic mode; E5. Reconstructed value of missing points where i and j are the row and column positions of the missing point in matrix X respectively, (u t ) i is the i-th element in the t-th column of the spatial feature mode U, (v t ) j is the j-th element in the t-th column of the temporal feature mode V, and ρ t is the corresponding singular value; E6. Evaluate the accuracy of the original values and their corresponding reconstructed values in the original cross-validation point set X c-v ; E7. Repeat steps E4 - E6 to separately evaluate the accuracy of the original values and their corresponding reconstructed values in the original cross - validation point set X for the characteristic mode parameters P = 2, 3, …, k max when c-v and obtain the characteristic mode parameter corresponding to the highest accuracy evaluation where k max is the maximum number of iterations, and k max ≤ min(m, n); E8. Calculate the characteristic mode parameters The reconstructed value of the missing point in matrix X To replace the data at the corresponding position of the missing point in matrix X, and restore the data selected as the original cross-validation point set X c-v in X to the original value, so as to obtain matrix X1, and calculate the reconstructed value of the original image data matrix X 0 ​ 2. The chlorophyll a concentration data reconstruction method based on multi-source satellite remote sensing data according to claim 1, wherein In step T1, the data reference range of the outlier pixels is the 8 pixels around the outlier pixels.

3. The method for reconstructing chlorophyll a concentration data based on multi-source satellite remote sensing data according to claim 2, characterized in that In step S2, the outlier pixels include the cloud cover edge positions identified and marked in the original image by the edge detection method.

4. The chlorophyll a concentration data reconstruction method based on multi-source satellite remote sensing data according to claim 3, characterized in that In step E6, the indexes adopted for accuracy evaluation include the correlation coefficient CC, the root mean square error RMSE, and the bias Bias; among them: where \(N\) represents the total number of data in the original cross-validation point set \(X\) c-v , respectively represent the original value and the reconstructed value of the \(i\)-th data in the original cross-validation point set \(X\) c-v , respectively represent the mean of the original values and the mean of the reconstructed values in the original cross-validation point set \(X\). c-v ​ 5. A method for reconstructing chlorophyll a concentration data based on multi-source satellite remote sensing data according to claim 4, characterized in that In step S5, the reprocessing method for the reconstructed image data includes multi-point smoothing processing and land-sea masking processing.

6. A system for a chlorophyll a concentration data reconstruction method based on multi-source satellite remote sensing data as described in claim 5, characterized in that, Including: A preprocessing module, configured to obtain multi-source satellite remote sensing data and conduct preprocessing, and identify and process the data of the outlier pixels in the original image; A reconstruction module, configured to reconstruct the preprocessed original image data by using the data empirical orthogonal function decomposition method; A reconstructed data inspection module, configured to conduct outlier tests on the reconstructed image data and conduct processing; A reprocessing module, configured to conduct relevant processing on the reconstructed image data to obtain the spatially and temporally complete chlorophyll a concentration data.

7. An apparatus for a method of reconstructing chlorophyll a concentration data based on multi-source satellite remote sensing data as claimed in claim 5, characterized in that, Including: One or more processors; A storage device, configured to store one or more programs, and when the one or more programs are executed by the one or more processors, enable the one or more processors to implement the chlorophyll a concentration data reconstruction method based on multi-source satellite remote sensing data as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, A program is stored in the computer-readable storage medium, and when the program is executed by a processor, the chlorophyll a concentration data reconstruction method based on multi-source satellite remote sensing data as described in any one of claims 1-5 is implemented.

Images