Remote sensing method, device and equipment for inorganic nitrogen concentration in target sea area and medium

Abstract

The application provides a target sea area inorganic nitrogen concentration remote sensing inversion method, device, equipment and medium, relates to remote sensing image processing technical field. The method comprises the following steps: acquiring remote sensing image data of a target sea area, and the remote sensing image data comprises remote sensing reflectivity data of a plurality of visible light bands; calculating a multispectral apparent visual wavelength according to the remote sensing reflectivity of the plurality of visible light bands; performing deviation correction according to the multispectral apparent visual wavelength to calculate a hyperspectral apparent visual wavelength; performing inorganic nitrogen concentration remote sensing inversion according to the hyperspectral apparent visual wavelength to obtain the inorganic nitrogen concentration. The application can effectively improve the normal patrol supervision ability of the marine ecological environment of the nearshore sea area, helps to achieve the beautiful bay construction target as soon as possible, and has very important scientific significance and practical application value.

Description

Technical Field

[0001] This application relates to the field of remote sensing image processing technology, and in particular to a method, apparatus, equipment and medium for remote sensing inversion of inorganic nitrogen concentration in a target sea area. Background Technology

[0002] Remote sensing technology, with its unique advantages such as macroscopic scale, speed, real-time capability, and spatial continuity, is widely used in various fields such as resources and environment, and socio-economic development. In particular, at the macroscopic scale, remote sensing technology has great advantages in monitoring changes in the ecological environment.

[0003] Water color is an "apparent indicator" of water bodies that is visible to the human eye, while water quality is an "intrinsic indicator" of water bodies that cannot be directly perceived by the human eye. In nearshore waters, both are related to factors such as land-based runoff input and resuspension of bottom sediments. Therefore, there is a certain inevitable correlation between water color and water quality. This opens up a new approach and direction for remote sensing detection of water quality parameters (such as nitrogen and phosphorus) using water color parameters.

[0004] Apparent visible wavelength (AVW) can effectively quantify water color characteristics. Using it as a key indicator, it can provide a new technical approach for remote sensing inversion of inorganic nitrogen concentration in nearshore waters, showing significant application potential. Compared to indicator factors such as chromaticity angle, dominant wavelength, water color index, and traditional band ratios, AVW exhibits higher sensitivity for inorganic nitrogen concentration inversion. Currently, no research has been reported on remote sensing inversion of inorganic nitrogen concentration in nearshore waters based on AVW. Summary of the Invention

[0005] This application provides a method, apparatus, equipment, and medium for remote sensing inversion of inorganic nitrogen concentration in a target sea area. It aims to address the lack of existing technical solutions for remote sensing inversion of nearshore water inorganic nitrogen concentration based on apparent visible wavelengths, and the problem that systematic biases in the calculation results of apparent visible wavelengths caused by the discretization of multispectral remote sensing data affect inversion accuracy. This application calculates the multispectral apparent visible wavelengths by analyzing the remote sensing reflectance of multiple visible light bands in remote sensing image data. Then, based on different satellite data sources, it uses corresponding bias correction equations to correct the multispectral apparent visible wavelengths to hyperspectral apparent visible wavelengths, eliminating systematic biases between multispectral and hyperspectral data. Finally, it uses a quantitative inversion model between hyperspectral apparent visible wavelengths and inorganic nitrogen concentration to calculate the inorganic nitrogen concentration. This enables large-scale, rapid, and high-precision remote sensing monitoring of inorganic nitrogen concentration in the target sea area, providing reliable technical support for nearshore water quality assessment and routine ecological environment inspection and supervision.

[0006] In a first aspect, this application provides a remote sensing inversion method for inorganic nitrogen concentration in a target sea area, the method comprising: Acquire remote sensing image data of the target sea area, wherein the remote sensing image data includes remote sensing reflectance data of multiple visible light bands; Calculate the multispectral apparent visible wavelength based on the remote sensing reflectance of the multiple visible light bands; Based on the deviation correction performed on the multispectral apparent visible wavelength, the hyperspectral apparent visible wavelength is calculated. The inorganic nitrogen concentration is obtained by remote sensing inversion based on the hyperspectral apparent visible wavelength.

[0007] In one possible design, the multispectral apparent visible wavelength is calculated based on the remote sensing reflectance of the multiple visible light bands using the following formula: In the formula, Indicates the apparent visible wavelengths of the multispectral array; , , , , , , , , , , e represents the remote sensing reflectance of the first to eleventh bands in the visible light band.

[0008] In one possible design, when the remote sensing image data is Sentinel-3A OLCI data, based on the multispectral apparent visible wavelength... To perform bias correction, the hyperspectral apparent visible wavelength is calculated using the following bias correction equation: In the formula, Indicates the apparent visible wavelength of the hyperspectral spectrum.

[0009] In one possible design, when the remote sensing image data is Sentinel-3B OLCI data, based on the multispectral apparent visible wavelength... To perform bias correction, the hyperspectral apparent visible wavelength is calculated using the following bias correction equation: In the formula, Indicates the apparent visible wavelength of the hyperspectral spectrum.

[0010] In one possible design, based on the hyperspectral apparent visible wavelength... Inorganic nitrogen concentration was remotely inverted using the following model: In the formula, This indicates the concentration of inorganic nitrogen.

[0011] In one possible design, after obtaining the inorganic nitrogen concentration, the method further includes: drawing a remote sensing thematic map of the inorganic nitrogen concentration in the target sea area based on the inorganic nitrogen concentration.

[0012] In one possible design, when drawing a remote sensing thematic map of inorganic nitrogen concentration in the target sea area based on the inorganic nitrogen concentration, invalid value areas are represented in white; inorganic nitrogen concentration is represented by a color box to indicate the level of inorganic nitrogen concentration; the remote sensing thematic map is also marked with a north arrow, scale bar, coordinate system, data source, and imaging date information.

[0013] Secondly, this application provides a remote sensing inversion device for inorganic nitrogen concentration in a target sea area, the device comprising: The data acquisition module is configured to acquire remote sensing image data of the target sea area, the remote sensing image data including remote sensing reflectance data of multiple visible light bands; The first wavelength calculation module is configured to calculate the multispectral apparent visible wavelength based on the remote sensing reflectance of the multiple visible light bands. The second wavelength calculation module is configured to perform deviation correction based on the multispectral apparent visible wavelength and calculate the hyperspectral apparent visible wavelength. The inorganic nitrogen concentration inversion module is configured to perform remote sensing inversion of inorganic nitrogen concentration based on the hyperspectral apparent visible wavelength to obtain the inorganic nitrogen concentration.

[0014] Thirdly, embodiments of this application provide an electronic device, including: at least one processor and a memory; the memory stores computer execution instructions; the at least one processor executes the computer execution instructions stored in the memory, causing the at least one processor to execute the remote sensing inversion method for inorganic nitrogen concentration in a target sea area as described in the first aspect and various possible designs of the first aspect.

[0015] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions. When a processor executes the computer-executable instructions, it implements the remote sensing inversion method for inorganic nitrogen concentration in the target sea area as described in the first aspect and various possible designs of the first aspect.

[0016] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the remote sensing inversion method for inorganic nitrogen concentration in a target sea area as described in the first aspect and various possible designs of the first aspect.

[0017] The remote sensing inversion method, apparatus, equipment, and medium for inorganic nitrogen concentration in the target sea area provided in this application have at least the following beneficial effects: This application proposes for the first time a remote sensing inversion method for inorganic nitrogen concentration in target sea areas based on apparent visible wavelength. By constructing a deviation correction equation between multispectral and hyperspectral apparent visible wavelengths, it effectively eliminates the system calculation bias caused by band discretization settings in multispectral remote sensing data. This allows for the acquisition of apparent visible wavelength calculation results with near-hyperspectral accuracy based on multispectral satellite data, significantly improving the calculation accuracy of apparent visible wavelength without relying on hyperspectral satellite data. Furthermore, based on the high sensitivity between apparent visible wavelength and inorganic nitrogen concentration, this application constructs a quantitative remote sensing inversion model for inorganic nitrogen concentration by combining synchronous field measurement data. This model can realize the inversion from remote sensing reflectance to multispectral apparent wavelength. The complete data processing chain, from visible wavelength to hyperspectral apparent visible wavelength and finally to inorganic nitrogen concentration, demonstrates a higher sensitivity of apparent visible wavelength to inorganic nitrogen concentration compared to traditional indicators such as chromaticity angle, dominant wavelength, water color index, and band ratio. This effectively improves inversion accuracy and better reflects the spatial distribution gradient of inorganic nitrogen concentration in the target sea area, which gradually decreases from nearshore to offshore. This application only requires conventional multispectral remote sensing image data to achieve large-scale, rapid, and routine monitoring of inorganic nitrogen concentration in the target sea area, eliminating the need for on-site point-by-point sampling and testing. This significantly reduces monitoring costs and improves monitoring efficiency, providing a reliable technical means for water quality assessment and routine patrol and supervision of the marine ecological environment in the target sea area. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0019] Figure 1 A flowchart illustrating a remote sensing inversion method for inorganic nitrogen concentration in a target sea area, provided in this application embodiment; Figure 2 △AVW and AVW provided in the embodiments of this application multi The fifth-order polynomial fitting curve of / 100; where (a) is the bias fitting result of Sentinel-3A OLCI satellite data; and (b) is the bias fitting result of Sentinel-3B OLCI satellite data. Figure 3 lgDIN and AVW provided for embodiments of this application hyper The linear fitting curve of / 100; Figure 4 Remote sensing thematic map of inorganic nitrogen concentration in the target sea area provided for embodiments of this application (April 11, 2026); Figure 5Remote sensing thematic map of inorganic nitrogen concentration in the target sea area provided for embodiments of this application (May 30, 2026); Figure 6 This is a structural diagram of a remote sensing inversion device for inorganic nitrogen concentration in a target sea area, provided in an embodiment of this application.

[0020] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concepts of this application to those skilled in the art through reference to specific embodiments. Detailed Implementation

[0021] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0022] The collection, storage, use, processing, transmission, provision, and disclosure of relevant data and information in the technical solution of this application all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0023] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0024] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0025] This application provides a remote sensing inversion method for inorganic nitrogen concentration in a target sea area, such as... Figure 1 As shown, the remote sensing inversion method for inorganic nitrogen concentration in the target sea area includes the following steps S10-S50.

[0026] S10: Acquire remote sensing image data of the target sea area, including remote sensing reflectance data of multiple visible light bands.

[0027] For example, in this embodiment, the remote sensing image data of the target sea area is downloaded from the Copernicus Data Space Ecosystem website (https: / / dataspace.copernicus.eu / ) as Sentinel-3 OLCI Level-2 WFR remote sensing image data.

[0028] S20: Calculate the multispectral apparent visible wavelength based on the remote sensing reflectance of multiple visible light bands.

[0029] In some embodiments, based on the Sentinel-3 OLCI Level-2 WFR image obtained in step S10, the multispectral apparent visible wavelength is calculated using the reflectance of the first to 11 visible light bands in the image, as shown in the following formula: In the formula, Indicates the apparent visible wavelengths of the multispectral array; , , , , , , , , , , e represents the remote sensing reflectance of the first to eleventh bands in the visible light band.

[0030] S30: Based on the deviation correction of the multispectral apparent visible wavelength, the hyperspectral apparent visible wavelength is calculated.

[0031] The multispectral apparent visible wavelengths were calculated. Next, bias correction is required, and then the apparent visible wavelength of the hyperspectral spectrum is calculated. .

[0032] In some embodiments, when the downloaded image is Sentinel-3A OLCI data, the correction formula is as follows: In some embodiments, when the downloaded image is Sentinel-3B OLCI data, the correction formula is as follows: In the formula, AVW hyper Indicates the apparent visible wavelength of the hyperspectral spectrum.

[0033] Due to the discrete nature of multispectral data bands and their location settings, the calculated results for the apparent visible wavelength will exhibit a systematic bias compared to the integral results of hyperspectral data. This systematic bias ΔAVW can be expressed as AVW hyper-AVW multi , and AVW hyper =AVW multi +△AVW. In this embodiment, 3578 sets of field-measured seawater hyperspectral remote sensing reflectance data were collected (used for fitting the deviation calculation equation, independent of the collection area). First, based on the band response function of the Sentinel-3A&B OLCI sensor, the equivalent remote sensing reflectance of bands 1-11 was calculated. Then, the hyperspectral apparent visible wavelength AVW was calculated using the field-measured hyperspectral remote sensing reflectance and the OLCI multispectral equivalent remote sensing reflectance data, respectively. hyper With multispectral apparent visible wavelengths (AVW) multi The difference between the two is △AVW, and (AVW) multi Using ( / 100) as the independent variable, a fifth-order polynomial fitting can be used to obtain the equation for calculating the ΔAVW deviation (see fitting curve). Figure 2 ), and AVW multi The summation yields the formula for calculating the apparent visible wavelength of the hyperspectral spectrum, as shown in the formula above.

[0034] Specifically, Figure 2 △AVW and AVW provided in the embodiments of this application multi The graphs show the fifth-order polynomial fitting curves with a value of 100, where (a) corresponds to the bias fitting result for Sentinel-3A OLCI satellite data, and (b) corresponds to the bias fitting result for Sentinel-3B OLCI satellite data. The x-axis of both sub-graphs is AVW. multi / 100, in nm, with a value range of 4.0 to 6.5; the vertical axis is ΔAVW, in nm, used to characterize the systematic deviation between the hyperspectral apparent visible wavelength and the multispectral apparent visible wavelength.

[0035] Figure 2 (a) contains scatter plot data corresponding to 3578 sets of measured samples. Each set of data was obtained by subtracting the apparent visible wavelength from the field-measured hyperspectral remote sensing reflectance of seawater and the Sentinel-3A OLCI equivalent multispectral remote sensing reflectance. The scatter plots generally decrease with increasing abscissa value, with the densest distribution in the abscissa range of 4.5–6.2, corresponding to ΔAVW values ​​ranging from approximately -32 nm to 15 nm, exhibiting a clear nonlinear negative correlation. The fitted curve in the figure uses a fifth-order polynomial form, and the corresponding fitted expression is: y =-1297.36515+1742.30202 x -883.53502 x 2 +216.9183 x3 -25.87989 x 4 +1.19774 x 5 ; In the formula, x Normalized multispectral apparent visible wavelength (AVW) multi / 100, where y is the systematic deviation △AVW. Figure 2 The coefficient of determination R of the fitting result in (a) 2 The value is 0.9089, representing the number of samples involved in the fitting. n =3578, with high fitting accuracy, accurately reflecting the variation of system deviation with multispectral apparent visible wavelength.

[0036] Figure 2 (b) also includes scatter plot data corresponding to 3578 samples. Each set of data was obtained by subtracting the apparent visible wavelengths calculated from the field-measured hyperspectral remote sensing reflectance of seawater and the Sentinel-3B OLCI equivalent multispectral remote sensing reflectance. The overall trend of the scatter plots is similar to... Figure 2 Consistent with (a), it continuously decreases as the horizontal axis value increases, corresponding to a value range of ΔAVW of -32nm to 18nm. The corresponding fifth-order polynomial fitting expression is: y =-2991.39929+3309.19625 x -1460.13016 x 2 +322.4822 x 3 -35.49847 x 4 +1.54677 x 5 ; In the formula, x Normalized multispectral apparent visible wavelength (AVW) multi / 100, y Let ΔAVW be the system deviation. Figure 2 The coefficient of determination R of the fitting result in (b) 2 The value is 0.9064, representing the number of samples involved in the fitting. n =3578, which shows a good fit.

[0037] Depend on Figure 2 It can be seen that the multispectral apparent visible wavelength calculation results of the Sentinel-3A OLCI and Sentinel-3B OLCI satellite sensors have a stable systematic bias compared with the hyperspectral apparent visible wavelength, and the variation law of this systematic bias can be fitted with high precision by a fifth-order polynomial.

[0038] S40: The inorganic nitrogen concentration is obtained by remote sensing inversion based on the apparent visible wavelength of the hyperspectral image.

[0039] In some embodiments, the hyperspectral apparent visible wavelength (AVW) is calculated. hyper Subsequently, remote sensing inversion of inorganic nitrogen concentration was performed. The remote sensing inversion model of inorganic nitrogen concentration in the target sea area is as follows: In the formula, This indicates the concentration of inorganic nitrogen.

[0040] Compared to indicator factors such as chromaticity angle, dominant wavelength, water color index, and traditional band ratios, apparent visible wavelength exhibits higher sensitivity to inorganic nitrogen concentration inversion. Considering the high numerical magnitude of apparent visible wavelength, which may interfere with the stability of the model fitting coefficients, it was reduced in size (i.e., divided by 100, denoted as AVW). hyper / 100). Furthermore, due to the large numerical range of inorganic nitrogen concentrations, direct fitting is easily affected by a few high-concentration samples, leading to inaccurate model fitting coefficients. Therefore, a logarithmic transformation was performed. In this embodiment, based on 15 sets of synchronous data on inorganic nitrogen concentration and hyperspectral apparent visible wavelength measured in the target sea area on April 11, 2026, a linear function was selected to compare lgDIN and AVW. hyper / 100 was quantitatively fitted (fitting curves are shown in [reference]). Figure 3 The remote sensing inversion model of inorganic nitrogen concentration in the target sea area can be obtained by anti-logarithmic transformation.

[0041] Specifically, Figure 3 lgDIN and AVW provided for embodiments of this application hyper The linear fitting curve is 100. The horizontal axis is AVW. hyper / 100, in nm, with a value range of 5.15~5.50; the vertical axis is lgDIN, a commonly used logarithmic value representing the concentration of inorganic nitrogen, with a value range of -0.6~0.3.

[0042] Figure 3 The dataset contains scatter plots corresponding to 15 sets of measured samples. Each set of data was taken from inorganic nitrogen concentration and hyperspectral remote sensing reflectance data simultaneously observed in the target sea area on April 11, 2026. The apparent visible wavelengths of the hyperspectral array were calculated and reduced in size, then mapped one-to-one with the logarithmic values ​​of the inorganic nitrogen concentration. The scatter plots show a gradual upward trend with increasing abscissa, exhibiting a clear positive correlation, indicating that higher values ​​of the apparent visible wavelengths of the hyperspectral array correspond to higher inorganic nitrogen concentrations in the water.

[0043] Figure 3The fitted line is in the form of a univariate linear function, and the corresponding fitting expression is: y =2.0887 x -11.395; In the formula, x Normalized hyperspectral apparent visible wavelength (AVW) hyper / 100, y lgDIN is a commonly used logarithm of the inorganic nitrogen concentration. The coefficient of determination for this fitting result is... R 2 The value is 0.9205, representing the number of samples involved in the fitting. n The value is 15, indicating high fitting accuracy. It can accurately reflect the linear variation between the logarithmic value of inorganic nitrogen concentration and the normalized hyperspectral apparent visible wavelength.

[0044] Depend on Figure 3 It is evident that a stable quantitative response relationship exists between the apparent visible wavelength of the hyperspectral image and the inorganic nitrogen concentration. The linear model constructed after logarithmic transformation and reduction exhibits good fitting performance. By performing an antilogarithmic transformation on the above linear expression, a remote sensing inversion model for the inorganic nitrogen concentration in the target sea area can be obtained. This model can be directly used for remote sensing estimation of inorganic nitrogen concentration in large-scale nearshore sea areas, providing effective technical support for dynamic monitoring of nearshore water quality and ecological environment supervision.

[0045] S50: Draw a remote sensing thematic map of inorganic nitrogen concentration in the target sea area based on the inorganic nitrogen concentration.

[0046] In some embodiments, after obtaining the remote sensing inversion result of inorganic nitrogen concentration through step S40, a remote sensing thematic map is further drawn through step S50. Invalid value areas are represented in white; inorganic nitrogen concentration is represented by a color box to indicate the level of inorganic nitrogen concentration. Specific color boxes can be found in [reference needed]. Figure 4 and Figure 5 As shown; additional information such as north arrow, scale, coordinate system, data source, and imaging date should also be labeled.

[0047] The feasibility and progressiveness of the method in this application will be further illustrated by two examples below.

[0048] Example 1: First, download the Sentinel-3 OLCI Level-2 WFR remote sensing image data "S3B_OL_2_WFR____20260411T022433_20260411T022733_20260412T081615_0180_118_374_2160_MAR_O_NT_004.SEN3" for the target sea area on April 11, 2026 from the Copernicus Data Spatial Ecosystem website (https: / / dataspace.copernicus.eu / ). After decompression, calculate the multispectral apparent visible wavelength using SNAP software. Then, use ENVI software to perform bias calculations to obtain the hyperspectral apparent visible wavelength, and further calculate the inorganic nitrogen concentration remote sensing results using the inorganic nitrogen concentration remote sensing inversion model. Finally, import the inorganic nitrogen concentration remote sensing results into ArcGIS software to create a remote sensing thematic map of inorganic nitrogen concentration in the target sea area on April 11, 2026, as shown below. Figure 4 As shown, the inorganic nitrogen concentration in the target sea area gradually decreases from nearshore to offshore, exhibiting a good gradient change characteristic.

[0049] Example 2: First, download the Sentinel-3 OLCI Level-2 WFR remote sensing image data "S3A_OL_2_WFR____20260530T015513_20260530T015813_20260531T071319_0179_140_060_2340_MAR_O_NT_004.SEN3" for the target sea area on May 30, 2026 from the Copernicus Data Spatial Ecosystem website (https: / / dataspace.copernicus.eu / ). After decompression, calculate the multispectral apparent visible wavelength using SNAP software. Then, use ENVI software to perform bias calculations to obtain the hyperspectral apparent visible wavelength, and further calculate the inorganic nitrogen concentration remote sensing results using the inorganic nitrogen concentration remote sensing inversion model. Finally, import the inorganic nitrogen concentration remote sensing results into ArcGIS software to create a remote sensing thematic map of inorganic nitrogen concentration in the target sea area on May 30, 2026, as shown below. Figure 5 As shown, it can also be observed that the concentration of inorganic nitrogen in the target sea area gradually decreases from nearshore to offshore, exhibiting a good gradient change characteristic.

[0050] This application also provides a remote sensing inversion device for inorganic nitrogen concentration in a target sea area, used to implement the methods described in any of the above embodiments, such as... Figure 6 As shown, the device includes: The data acquisition module 601 is configured to acquire remote sensing image data of the target sea area, the remote sensing image data including remote sensing reflectance data of multiple visible light bands.

[0051] The first wavelength calculation module 602 is configured to calculate the multispectral apparent visible wavelength based on the remote sensing reflectance of the plurality of visible light bands.

[0052] The second wavelength calculation module 603 is configured to perform deviation correction based on the multispectral apparent visible wavelength and calculate the hyperspectral apparent visible wavelength.

[0053] The inorganic nitrogen concentration inversion module 604 is configured to perform remote sensing inversion of inorganic nitrogen concentration based on the hyperspectral apparent visible wavelength to obtain the inorganic nitrogen concentration.

[0054] This application provides an electronic device. The electronic device may include a processor and a memory, wherein the processor and the memory can communicate; exemplarily, the processor and the memory communicate via a communication bus.

[0055] The processor executes computer execution instructions stored in memory, causing the processor to perform the scheme in the above embodiments. The processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0056] The communication bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The system bus can be divided into address bus, data bus, control bus, etc. Transceivers are used to enable communication between database access devices and other computers (e.g., clients, read-write libraries, and read-only libraries). Memory may include random access memory (RAM) and may also include non-volatile memory.

[0057] The electronic device provided in this application embodiment can be the terminal device described in the above embodiments.

[0058] This application also provides a computer-readable storage medium storing computer instructions. When the computer instructions are executed on a computer, the computer performs the technical solution of the remote sensing inversion method for inorganic nitrogen concentration in the target sea area described in the above embodiments.

[0059] This application also provides a computer program product, which includes a computer program stored in a computer-readable storage medium. At least one processor can read the computer program from the computer-readable storage medium. When the at least one processor executes the computer program, it can implement the technical solution of the remote sensing inversion method for inorganic nitrogen concentration in the target sea area described in the above embodiments.

[0060] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or modules, and may be electrical, mechanical, or other forms.

[0061] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to implement the solution of this embodiment according to actual needs.

[0062] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The unit composed of the above modules can be implemented in hardware or in the form of hardware plus software functional units.

[0063] The integrated modules described above, implemented as software functional modules, can be stored in a computer-readable storage medium. These software functional modules, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods of the various embodiments of this application.

[0064] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the application can be directly manifested as being executed by a hardware processor, or executed by a combination of hardware and software modules within the processor.

[0065] The memory may include high-speed RAM, and may also include non-volatile storage (NVM), such as at least one disk storage device, and may also be a USB flash drive, external hard drive, read-only memory, disk or optical disc, etc.

[0066] Buses can be Industry Standard Architecture (ISA) buses, Peripheral Component Interconnect (PCI) buses, or Extended Industry Standard Architecture (EISA) buses, etc. Buses can be categorized into address buses, data buses, control buses, etc.

[0067] The aforementioned storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0068] An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Alternatively, the storage medium can be an integral part of the processor. The processor and storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and storage medium can exist as discrete components in an electronic control unit or main control device.

[0069] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0070] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A remote sensing inversion method for inorganic nitrogen concentration in a target sea area, characterized in that, The method includes: Acquire remote sensing image data of the target sea area, wherein the remote sensing image data includes remote sensing reflectance data of multiple visible light bands; Calculate the multispectral apparent visible wavelength based on the remote sensing reflectance of the multiple visible light bands; Based on the deviation correction performed on the multispectral apparent visible wavelength, the hyperspectral apparent visible wavelength is calculated. The inorganic nitrogen concentration is obtained by remote sensing inversion based on the hyperspectral apparent visible wavelength.

2. The remote sensing inversion method for inorganic nitrogen concentration in a target sea area according to claim 1, characterized in that, Based on the remote sensing reflectance of the multiple visible light bands, the multispectral apparent visible wavelengths are calculated using the following formula: In the formula, Indicates the apparent visible wavelengths of the multispectral array; , , , , , , , , , , e represents the remote sensing reflectance of the first to eleventh bands in the visible light band.

3. The remote sensing inversion method for inorganic nitrogen concentration in a target sea area according to claim 1, characterized in that, When the remote sensing image data is Sentinel-3A OLCI data, according to the multispectral apparent visible wavelength... To perform bias correction, the hyperspectral apparent visible wavelength is calculated using the following bias correction equation: In the formula, Indicates the apparent visible wavelength of the hyperspectral spectrum.

4. The remote sensing inversion method for inorganic nitrogen concentration in a target sea area according to claim 1, characterized in that, When the remote sensing image data is Sentinel-3B OLCI data, according to the multispectral apparent visible wavelength... To perform bias correction, the hyperspectral apparent visible wavelength is calculated using the following bias correction equation: In the formula, Indicates the apparent visible wavelength of the hyperspectral spectrum.

5. The remote sensing inversion method for inorganic nitrogen concentration in a target sea area according to claim 1, characterized in that, Based on the hyperspectral apparent visible wavelength Inorganic nitrogen concentration was remotely inverted using the following model: In the formula, This indicates the concentration of inorganic nitrogen.

6. The remote sensing inversion method for inorganic nitrogen concentration in a target sea area according to claim 1, characterized in that, After obtaining the inorganic nitrogen concentration, the method further includes: drawing a remote sensing thematic map of the inorganic nitrogen concentration in the target sea area based on the inorganic nitrogen concentration.

7. The remote sensing inversion method for inorganic nitrogen concentration in a target sea area according to claim 6, characterized in that, When drawing a remote sensing thematic map of inorganic nitrogen concentration in the target sea area based on the inorganic nitrogen concentration, invalid value areas are represented in white; inorganic nitrogen concentration is represented by a color box to indicate the level of inorganic nitrogen concentration; the remote sensing thematic map also includes a north arrow, scale bar, coordinate system, data source, and imaging date information.

8. A remote sensing inversion device for inorganic nitrogen concentration in a target sea area, characterized in that, The device includes: The data acquisition module is configured to acquire remote sensing image data of the target sea area, the remote sensing image data including remote sensing reflectance data of multiple visible light bands; The first wavelength calculation module is configured to calculate the multispectral apparent visible wavelength based on the remote sensing reflectance of the multiple visible light bands. The second wavelength calculation module is configured to perform deviation correction based on the multispectral apparent visible wavelength and calculate the hyperspectral apparent visible wavelength. The inorganic nitrogen concentration inversion module is configured to perform remote sensing inversion of inorganic nitrogen concentration based on the hyperspectral apparent visible wavelength to obtain the inorganic nitrogen concentration.

9. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes the computer execution instructions stored in the memory to implement the remote sensing inversion method for inorganic nitrogen concentration in the target sea area as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the remote sensing inversion method for inorganic nitrogen concentration in a target sea area as described in any one of claims 1-7.

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