A consistency correction method and device based on multi-source satellite inversion of water color index

Abstract

The present invention proposes a consistency correction method and device based on multi-source satellite inversion of water color index. The method comprises: converting the surface reflectance of the multi-source satellite sensor of the target water area into remote sensing reflectance, performing water body range extraction on the multi-source satellite sensor to determine the water body range, and removing multi-category interference pixels from the image of the surface reflectance within the water body range to obtain an effective remote sensing reflectance; calculating the chromaticity angle inverted by the multi-source satellite sensor based on the effective remote sensing reflectance and the CIE chromaticity system; performing spectral response correction on the chromaticity angle inverted by the multi-source satellite sensor using a polynomial spectral correction model constructed based on a water body optical simulation data set; performing systematic deviation correction on the chromaticity angle of the multi-source satellite sensor after the spectral response correction using a cross-calibration correction model established based on synchronous observation data of the multi-source satellite sensors; and determining the water color index of the target water area based on the chromaticity angle after the systematic deviation correction.

Description

Technical Field

[0001] The present application relates to the field of satellite remote sensing data processing, and more specifically, to a consistency correction method and device based on multi-source satellite inversion of water color index. Background Art

[0002] Water color is an important indicator of the optical properties of water bodies and a key climatic variable in aquatic ecosystems. Changes in water color are primarily caused by the interaction of sunlight with components such as chlorophyll-a, chromatic dissolved organic matter (CDOM), and total suspended matter (TSM). The spatiotemporal variations in these components not only affect water color but also indicate the ecological and environmental status of the water body, such as eutrophication, pollutant inputs, and suspended particulate matter concentrations. Therefore, the Forel-Ule Index (FUI), as a quantitative indicator of water color, has been widely used in remote sensing monitoring and ecological assessment of water environments.

[0003] Water color index monitoring in water bodies is limited by high altitude, complex terrain, and field monitoring conditions. Traditional ground-based observation methods struggle to achieve large-scale, high-frequency dynamic monitoring of lake water quality. In recent years, satellite remote sensing technology has advanced its large-scale and long-term observation capabilities. Currently, independent satellite sensors are commonly used to monitor the aquatic environment in water bodies. However, these independent satellite sensors are limited by factors such as observation time, spatial resolution, and coverage, making them inadequate for continuous monitoring of long-term changes in water color. Summary of the Invention

[0004] The present application provides a consistency correction method and device for the water color index inverted by multi-source satellites. By performing spectral response correction and system deviation correction on the water body chromaticity angle inverted by multi-source satellite sensors calculated by the effective remote sensing reflectance and the CIE chromaticity system, and obtaining the water color index based on the corrected chromaticity angle, the consistency of the correction results is improved, the inverted data of multi-source satellite sensors can be collaboratively applied, the applicability of multi-source satellite sensor data is improved, the consistency of the FUI inverted by multi-source satellite data is improved, the data discontinuity problem caused by sensor differences is solved, and high-precision collaborative inversion can be provided for long-term monitoring of water color in inland lakes.

[0005] In a first aspect, an embodiment of the present application provides a consistency correction method based on multi-source satellite inversion of water color index, the method comprising:

[0006] Obtain the surface reflectance of the target waters in the visible light band, near infrared band, and shortwave infrared band of multi-source satellite sensors;

[0007] Converting the surface reflectivity of the multi-source satellite sensor into a remote sensing reflectivity, performing water body range extraction on the multi-source satellite sensor to determine the water body range corresponding to the multi-source satellite sensor, and performing multi-category interference pixel removal on the image of the surface reflectivity within the water body range to obtain an effective remote sensing reflectivity of the image after removing the interference pixels;

[0008] Based on the effective remote sensing reflectance and the CIE colorimetric system, respectively calculating the chromaticity angles inverted by the multi-source satellite sensor;

[0009] Constructing a polynomial spectral correction model based on a water body optical simulation data set, and performing spectral response correction on the chromaticity angle inverted by the multi-source satellite sensor based on the polynomial spectral correction model;

[0010] Establishing a cross-calibration correction model using the synchronous observation data of the multi-source satellite sensor, and performing system deviation correction on the chromaticity angle of the multi-source satellite sensor after spectral response correction based on the cross-calibration correction model;

[0011] The water color index of the target water area is determined based on the chromaticity angle after system deviation correction.

[0012] In a possible implementation, converting the surface reflectivity of the multi-source satellite sensor into remote sensing reflectivity includes:

[0013] Based on the correction method of the minimum surface reflectance in the near-infrared band and the short-wave infrared band, a remote sensing reflectance calculation formula is determined, and based on the remote sensing reflectance calculation formula, the surface reflectance of the multi-source satellite sensor is converted into remote sensing reflectance and residual noise is corrected; wherein, the remote sensing reflectance calculation formula is:

[0014]

[0015] in, Represents remote sensing reflectance, NIR represents near infrared band, SWIR represents short wave infrared band, Indicates the minimum surface reflectance in the NIR and SWIR bands, Represents the surface reflectivity.

[0016] In a possible implementation, extracting the water body range of the multi-source satellite sensor to determine the water body range corresponding to the multi-source satellite sensor includes:

[0017] For a Landsat type satellite sensor, a preset water body range data set is obtained, and a candidate water body range of the target water area is determined from the water body range data set;

[0018] For MODIS type satellite sensors, data based on the SWIR band of the satellite sensor is obtained, and the water body range of the multi-source satellite sensor is extracted based on the data based on the SWIR band and an adaptive threshold method to obtain output data of the SWIR band, and the candidate water body range of the target water area is determined based on the output data of the SWIR band;

[0019] The water body range of each type of satellite sensor is determined by moving the water body boundary of the candidate water body range corresponding to each type of satellite sensor inward by a preset distance.

[0020] In a possible implementation, removing multiple categories of interference pixels from the image of the surface reflectance within the water body includes:

[0021] Generating a quality assessment band of the multi-source satellite sensor based on a cloud mask processing algorithm, and removing first-category interference pixels within the water body based on the quality assessment band; wherein the first-category interference pixels include at least clouds, cloud shadows, ice, and snow;

[0022] Based on the remote sensing reflectance in the shortwave infrared spectral band of each satellite sensor and a preset shortwave infrared band threshold, the second type of interference pixels within the water body are identified and removed; wherein the second type of interference pixels include water surface flares.

[0023] In a possible implementation, the calculating, based on the effective remote sensing reflectance and the CIE colorimetric system, the chromaticity angles inverted by the multi-source satellite sensors respectively includes:

[0024] Acquire multiple band values ​​of the multi-source satellite sensor in the visible light band;

[0025] When the number of wavelength bands of the plurality of wavelength band values ​​matches the number of tristimulus values ​​of the CIE colorimetry system, converting the effective remote sensing reflectance of the visible light band into tristimulus values ​​X, Y, and Z of the CIE colorimetry system based on a first preset coefficient;

[0026] When the number of bands in the plurality of band values ​​does not match the number of tristimulus values ​​of the CIE colorimetry system, converting the effective remote sensing reflectance of the visible light band of each satellite sensor into tristimulus values ​​X, Y, and Z of the CIE colorimetry system respectively by using a linear interpolation method based on a second preset coefficient;

[0027] The chromaticity coordinates x and y are calculated according to the tristimulus values ​​X, Y and Z, and a new coordinate system is established based on the equal energy white point in the two-dimensional chromaticity diagram, and the chromaticity angle of the multi-source satellite sensor is calculated according to the new coordinate system.

[0028] In one possible implementation, constructing a polynomial spectral correction model based on a water optical simulation dataset, and performing spectral response correction on the chromaticity angle inverted by the multi-source satellite sensor based on the polynomial spectral correction model, includes:

[0029] Based on the hyperspectral reflectance provided by the water body optical simulation dataset and the spectral response function of the multi-source satellite sensor, simulating the hyperspectral reflectance as the reflectance of the satellite sensor after conversion in the corresponding visible light band;

[0030] Calculating a first chromaticity angle according to the hyperspectral reflectance, and calculating second chromaticity angles corresponding to the multi-source satellite sensors according to the converted reflectances of the multi-source satellite sensors in their respective visible light bands, and constructing a polynomial spectral correction model for the multi-source satellite sensors according to the first chromaticity angle and the second chromaticity angle;

[0031] Based on the polynomial spectral correction model, spectral response correction is performed on the chromaticity angles respectively inverted by the multi-source satellite sensors to obtain the chromaticity angles of the multi-source satellite sensors after spectral response correction.

[0032] In one possible implementation, establishing a cross-calibration correction model using the synchronous observation data of the multi-source satellite sensor, and performing systematic deviation correction on the chromaticity angle of the multi-source satellite sensor after spectral response correction based on the cross-calibration correction model includes:

[0033] Resampling the target multispectral image of the multi-source satellite sensor based on a preset time window and a preset spatial resolution, constructing a pixel window of a preset size with the resampled sampling point as the center, calculating the coefficient of variation of the pixel window in the visible light band, and selecting valid pixels whose coefficient of variation meets a preset threshold;

[0034] constructing a point-to-point matching dataset of multi-source satellite sensors based on the selected valid pixels, and establishing a cross-calibration correction model between each other satellite sensor in the multi-source satellite sensors and the designated satellite sensor based on the matching dataset;

[0035] Based on the cross-calibration correction model, the system deviation correction is performed on the chromaticity angles of other matched satellite sensors after spectral response correction.

[0036] In a second aspect, an embodiment of the present application further provides a consistency correction device based on multi-source satellite inversion of water color index, the device comprising:

[0037] an acquisition module, configured to acquire a target multispectral image acquired by multiple satellite sensors in respective multiple spectral bands within a preset time period, and extract surface reflectance from the target multispectral image; the target multispectral image is a multispectral image of a target water area;

[0038] A calculation module is used to convert the surface reflectance of each satellite sensor into a remote sensing reflectance suitable for the water body based on the absorption characteristics of the specified bands in multiple different spectral bands of the water body with different characteristics, and to invert the chromaticity angle of the water body based on the effective remote sensing reflectance of each satellite sensor;

[0039] a correction module, configured to correct the chromaticity angles respectively inverted by the multi-source satellite sensors based on multi-dimensional system deviation information of the multi-source satellite sensors to obtain a corrected effective chromaticity angle;

[0040] The determination module is used to determine the water color index of the target water area by combining the effective chromaticity angle corrected by the multi-source satellite sensor.

[0041] In the third aspect, an embodiment of the present application also provides an electronic device, comprising: a processor, a storage medium and a bus, wherein the storage medium stores machine-readable instructions executable by the processor. When the electronic device is running, the processor and the storage medium communicate through the bus, and the processor executes the machine-readable instructions to perform the steps of the consistency correction method based on multi-source satellite inversion of water color index as described in any one of the first aspects.

[0042] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the consistency correction method based on multi-source satellite inversion of water color index as described in any one of the first aspects are executed.

[0043] The present application provides the above-mentioned consistency correction method and device based on the multi-source satellite inversion water color index. By performing spectral response correction and system deviation correction on the water body chromaticity angle inverted by the multi-source satellite sensor calculated by the effective remote sensing reflectance and the CIE chromaticity system, and obtaining the water color index based on the corrected chromaticity angle, the consistency of the correction result is improved, the inversion data of the multi-source satellite sensor is ensured to be collaboratively applied, the applicability of the multi-source satellite sensor data is improved, the consistency of the FUI inverted by the multi-source satellite data is improved, and the data discontinuity problem caused by sensor differences is solved. It can provide high-precision collaborative inversion for long-term monitoring of the water color of inland lakes. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0045] Figure 1 A flowchart of a first method for consistency correction of water color index based on multi-source satellite inversion provided by an embodiment of the present application is shown;

[0046] Figure 2 A flow chart of a second method for consistency correction of water color index based on multi-source satellite inversion provided in an embodiment of the present application is shown;

[0047] Figure 3 A flowchart of a third method for consistency correction of water color index based on multi-source satellite inversion provided in an embodiment of the present application is shown;

[0048] Figure 4 A flowchart of a fourth method for consistency correction of water color index based on multi-source satellite inversion provided in an embodiment of the present application is shown;

[0049] Figure 5 A schematic structural diagram of a consistency correction device based on multi-source satellite inversion of water color index provided in an embodiment of the present application is shown;

[0050] Figure 6 A schematic structural diagram of an electronic device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the drawings in the present application only serve the purpose of illustration and description and are not used to limit the scope of protection of the present application. In addition, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate the operations implemented according to some embodiments of the present application. It should be understood that the operations of the flowcharts can be implemented out of sequence, and steps without logical context can be reversed or implemented simultaneously. In addition, those skilled in the art, under the guidance of the contents of this application, can add one or more other operations to the flowchart, or remove one or more operations from the flowchart.

[0052] In addition, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present application.

[0053] It should be noted that the term "comprising" will be used in the embodiments of the present application to indicate the existence of the features declared thereafter, but does not exclude the addition of other features.

[0054] In the field of water ecological environment monitoring based on satellite remote sensing, monitoring is mainly based on satellite sensors. Currently, monitoring is based on a single satellite sensor, which is limited by factors such as observation time, spatial resolution and coverage, and it is difficult to meet the needs of continuous monitoring of long-term changes in lake water color. Based on this, an embodiment of the present application provides a consistency correction method and device for water color index inversion based on multi-source satellites, by performing spectral response correction and system deviation correction on the water body chromaticity angle inverted by multi-source satellite sensors calculated by effective remote sensing reflectance and CIE chromaticity system, and obtaining the water color index based on the corrected chromaticity angle, thereby improving the consistency of the correction results, ensuring that the inversion data of multi-source satellite sensors can be collaboratively applied and improving the applicability of multi-source satellite sensor data, improving the consistency of multi-source satellite data inversion FUI, solving the data discontinuity problem caused by sensor differences, and providing high-precision collaborative inversion for long-term monitoring of inland lake water color.

[0055] like Figure 1 As shown, an embodiment of the present application provides a consistency correction method based on multi-source satellite inversion of water color index, the method comprising:

[0056] S101. Obtain the surface reflectance of the target water area in the respective visible light band, near infrared band, and shortwave infrared band of multi-source satellite sensors.

[0057] S102. Convert the surface reflectance of the multi-source satellite sensor into remote sensing reflectance, perform water body range extraction on the multi-source satellite sensor to determine the water body range corresponding to the multi-source satellite sensor, and remove multi-category interference pixels from the image of the surface reflectance within the water body range to obtain the effective remote sensing reflectance of the image after removing the interference pixels.

[0058] S103. Based on the effective remote sensing reflectance and the CIE colorimetric system, respectively calculate the chromaticity angles inverted by the multi-source satellite sensors.

[0059] S104: construct a polynomial spectral correction model based on the water optical simulation data set, and perform spectral response correction on the chromaticity angle inverted by the multi-source satellite sensor based on the polynomial spectral correction model.

[0060] S105 , establishing a cross-calibration correction model using the synchronous observation data of the multi-source satellite sensors, and performing system deviation correction on the chromaticity angle of the multi-source satellite sensors after spectral response correction based on the cross-calibration correction model.

[0061] S106. Determine the water color index of the target water area based on the chromaticity angle after system deviation correction.

[0062] In the above-mentioned consistency correction method based on the multi-source satellite inversion water color index, the spectral response correction and system deviation correction are performed on the water body chromaticity angle inverted by the multi-source satellite sensor calculated by the effective remote sensing reflectance and the CIE chromaticity system, and the water color index is obtained based on the corrected chromaticity angle, which improves the consistency of the correction results, ensures that the inversion data of the multi-source satellite sensors can be used collaboratively, improves the applicability of the multi-source satellite sensor data, improves the consistency of the FUI inverted by the multi-source satellite data, solves the data discontinuity problem caused by sensor differences, and can provide high-precision collaborative inversion for long-term monitoring of water color in inland lakes.

[0063] The above exemplary embodiments are described below respectively:

[0064] S101. Obtain the surface reflectance of the target water area in the respective visible light band, near infrared band, and shortwave infrared band of multi-source satellite sensors.

[0065] In the embodiment of the present application, the target water area may be a typical lake, such as Lake A, Lake B, Lake C, Lake D, and Lake E in a well-known region. Multi-source satellite sensors include multiple different types of satellite sensors, such as Landsat-type sensors and MODIS-type sensors. Each type of sensor also includes multiple versions of sensors, such as Landsat-type sensors including Landsat TM, ETM+, and Landsat OLI. Each satellite sensor corresponds to multiple different spectral bands, and the spectral bands of different satellite sensors are generally different, namely, the visible light band (in this embodiment, the visible light band can use the RGB band), the near-infrared band, and the short-wave infrared band.

[0066] In this embodiment, the multi-source satellite sensors include MODIS, Landsat 5 / TM, Landsat 7 / ETM+, and Landsat 8 / OLI. Each of these multi-source satellite sensors has three bands: visible light (RGB), near-infrared, and short-wave infrared. Landsat TM and ETM have central wavelengths of 485nm, 560nm, 660nm, 830nm, and 1650nm, for a total of five bands; Landsat OLI has central wavelengths of 485nm, 569nm, 660nm, 840nm, and 1650nm, for a total of five bands; and MODIS has central wavelengths of 469nm, 555nm, 645nm, 859nm, 1240nm, and 1640nm, for a total of six bands.

[0067] In an embodiment of the present application, each satellite sensor collects multispectral images in its own multiple different spectral bands, namely, the visible light band, the near-infrared band, and the short-wave infrared band. After obtaining the multispectral images, the surface reflectance (SR) in the multispectral images is extracted. The surface reflectance here is data after radiation calibration.

[0068] In the calibration method of the embodiment of the present application, an application system (which can be located in a terminal device or a server) re-inputs surface reflectance data from multiple satellite sources, including visible light, near-infrared, and short-wave infrared bands, from MODIS, Landsat 5 / TM, Landsat 7 / ETM+, and Landsat 8 / OLI. Specifically, the surface reflectance data for LandsatTM and ETM+ are input for five bands with central wavelengths of 485nm, 560nm, 660nm, 830nm, and 1650nm; the surface reflectance data for Landsat OLI are input for five bands with central wavelengths of 485nm, 569nm, 660nm, 840nm, and 1650nm; and the surface reflectance data for MODIS are input for six bands with central wavelengths of 469nm, 555nm, 645nm, 859nm, 1240nm, and 1640nm.

[0069] S102: Convert the surface reflectance from the multi-source satellite sensor to remote sensing reflectance, perform water range extraction on the multi-source satellite sensor to determine the water range corresponding to the multi-source satellite sensor, and remove multi-category interference pixels from the surface reflectance image within the water range to obtain the effective remote sensing reflectance of the image after removing the interference pixels. Specifically, although the surface reflectance data from Landsat and MODIS have been atmospherically corrected, their correction methods are not designed specifically for water bodies. Therefore, when calculating remote sensing reflectance, it is necessary to further remove skylight and residual aerosol scattering noise from the surface reflectance.

[0070] In the embodiments of the present application, water bodies have physical and optical characteristics; optical characteristics include water transparency, color, and light transmittance; physical characteristics include suspended matter concentration and turbidity. The designated wavebands are the near-infrared and short-wave infrared bands. Water bodies with different characteristics have different absorption characteristics for remote sensing reflectance (i.e., water-offset reflectance) in the near-infrared and short-wave infrared bands. Since pure water has strong absorption in the near-infrared and short-wave infrared bands, water-offset radiation within this spectrum is assumed to be zero. For relatively clean water, the near-infrared water-offset reflectance can be considered to be zero, while the short-wave infrared water-offset reflectance of relatively turbid water is approximately zero. Therefore, the minimum reflectance in the near-infrared or short-wave infrared bands is considered to be the water surface reflection of skylight.

[0071] Optionally, based on the correction method of the minimum surface reflectance in the near-infrared band and the short-wave infrared band, a remote sensing reflectance calculation formula is determined, and based on the remote sensing reflectance calculation formula, the surface reflectance of the multi-source satellite sensor is converted into remote sensing reflectance and the residual noise is corrected.

[0072] Therefore, this paper uses a simple correction method for the minimum near-infrared and short-wave infrared reflectance to convert surface reflectance data into remote sensing reflectance, thereby eliminating residual noise and improving data quality. In practice, remote sensing reflectance correction is performed by subtracting the minimum value of the near-infrared and short-wave infrared bands. This also eliminates abnormally high noise values ​​in the short-wave infrared band in clean water bodies that may be caused by noise. The data is then divided by 𝜋 to convert it into remote sensing reflectance.

[0073] Specifically, the remote sensing reflectivity calculation formula is as follows:

[0074] ;

[0075] in Represents remote sensing reflectance, NIR (Near-Infrared) represents the near-infrared band, SWIR (Shortwave Infrared) represents the shortwave infrared band, Indicates the minimum surface reflectance in the NIR and SWIR bands, that is, the minimum surface reflectance in the near-infrared and short-wave infrared bands. Represents the surface reflectivity.

[0076] In addition, each satellite sensor inverts the surface reflectivity of the target water area. In practice, the target water area also has abnormal interference, and water body range identification is required to eliminate abnormal interference data from the surface reflectivity within the water body range, so as to obtain effective remote sensing reflectivity.

[0077] Here, when extracting the water body range of multi-source satellite sensors to determine the water body range corresponding to the multi-source satellite sensors, different types of satellite sensors determine the water body range in different ways; for Landsat type satellite sensors, a preset water body range data set is obtained, and the candidate water body range of the target water area is determined from the water body range data set; for MODIS type satellite sensors, SWIR band-based data of this type of satellite sensor is obtained, and the water body range of the multi-source satellite sensor is extracted based on the SWIR band-based data and an adaptive threshold method to obtain SWIR band output data, and the candidate water body range of the target water area is determined based on the SWIR band output data; the water body boundary of the candidate water body range corresponding to each type of satellite sensor is moved inward by a preset distance to determine the water body range of each type of satellite sensor.

[0078] Specifically, for water body extent extraction based on satellite data, Landsat data uses the permanent water bodies in the annual water extent dataset v1.4 released by the EU Joint Research Centre (JRC) as the water body extent; MODIS data uses an adaptive thresholding method based on the SWIR band to extract water body extents. Furthermore, to avoid land proximity effects and optically shallow water near water body boundaries, the boundaries of each water body extent corresponding to each type of satellite sensor are eroded inward by a preset distance, such as 1000 meters, to determine the water body extent corresponding to multiple satellite sensors.

[0079] Optionally, when removing multi-category interference pixels from surface reflectance images within water bodies, the C function of mask (CFMASK) algorithm is used to generate quality assessment (QA) bands for multiple satellite sensors. First-category interference pixels within water bodies are removed based on the QA bands. Second-category interference pixels within water bodies are identified and removed based on the remote sensing reflectance in the mid- and short-wave infrared spectral bands of each satellite sensor and a preset short-wave infrared band threshold. First-category interference pixels include at least clouds, cloud shadows, ice, and snow; second-category interference pixels include water surface flares.

[0080] It should be noted that the water surface flare phenomenon caused by specular reflection will cause the brightness values ​​of all bands of remote sensing images to increase abnormally. This phenomenon is particularly significant in remote sensing images with high spatial resolution, increasing the uncertainty of water color inversion. Based on the shortwave infrared (OLI: 1650nm / 2230nm) threshold, the water surface flare area is identified and removed. Taking Landsat OLI image as an example, when , it is determined to be the flare area and removed.

[0081] Here, each type of sensor has a Q / A band. By identifying the data in this band, it is possible to determine multiple categories of interference pixels in the surface reflectance image within the water body identified by each satellite sensor, such as clouds, cloud shadows, ice, snow, and water surface flare. These multiple categories of interference pixels are then removed from the surface reflectance image within the water body to obtain the effective remote sensing reflectance of the image after removing the interference pixels. S103: Based on the effective remote sensing reflectance and the CIE colorimetric system, the chromaticity angles retrieved by the multi-source satellite sensors are calculated.

[0082] In the embodiment of the present application, the chromaticity angles inverted by multi-source satellite sensors are calculated separately based on the above-mentioned effective remote sensing reflectance and the CIE (International Commission on Illumination) colorimetry system for subsequent processing.

[0083] S104: constructing a polynomial spectral correction model based on the water body optical simulation data set, and performing spectral response correction on the chromaticity angle inverted by the multi-source satellite sensor based on the polynomial spectral correction model.

[0084] In this embodiment of the present application, the multi-dimensional system deviation information includes deviation information caused by differences in spectral response functions and deviation information caused by differences in data processing methods (such as atmospheric correction algorithms). Therefore, correction models are constructed based on these two types of deviation information. The chromaticity angles retrieved from the multi-source satellite sensors are then subjected to spectral response correction based on the constructed correction models to obtain the corrected effective chromaticity angles. Here, a polynomial spectral correction model is constructed based on a water optical simulation dataset. The chromaticity angles retrieved from the multi-source satellite sensors are subjected to spectral response correction (a first correction) based on the polynomial spectral correction model to obtain the chromaticity angles after spectral response correction.

[0085] S105 , establishing a cross-calibration correction model using the synchronous observation data of the multi-source satellite sensors, and performing system deviation correction on the chromaticity angle of the multi-source satellite sensors after spectral response correction based on the cross-calibration correction model.

[0086] In an embodiment of the present application, when dealing with deviation information caused by differences in data processing methods (such as atmospheric correction algorithms), a cross-calibration correction model is established based on the synchronous observation data of multi-source satellite sensors. The chromaticity angle of the above-mentioned multi-source satellite sensors after spectral response correction is further corrected for system deviation through the cross-calibration correction model to obtain the final chromaticity angle after system deviation correction.

[0087] S106. Determine the water color index of the target water area based on the chromaticity angle after system deviation correction.

[0088] Here, for the same target water area, the effective chromaticity angle after system bias correction of the multi-source satellite sensors can be combined to jointly determine the water color index (FUI) of the target water area. Specifically, when any satellite sensor is needed, the water color index (FUI) of the water body is calculated using the chromaticity angle after system bias correction of that satellite sensor, and the water color index (FUI) of the target water area is determined based on the water color indices (FUI) of the multiple satellite sensors.

[0089] Specifically, the effective chromaticity angle after system deviation correction can be used to find the water color index FUI of the water body through a pre-established chromaticity angle lookup table, which includes the mapping relationship between chromaticity angle and water color index FUI, thereby realizing the accurate inversion of the water color index FUI of the target water area based on multi-source optical satellite data.

[0090] Table 1 Chromaticity angle α and FUI lookup table

[0091]

[0092] Further, such as Figure 2 As shown, in the consistency correction method based on multi-source satellite inversion of water color index provided in the embodiment of the present application, the chromaticity angle inverted by the multi-source satellite sensor is calculated based on the effective remote sensing reflectance and the CIE colorimetric system, including:

[0093] S201: Acquire multiple band values ​​of multi-source satellite sensors in the visible light band.

[0094] In the embodiment of the present application, the band settings of different sensors within the visible light range are determined.

[0095] S202 : When the number of wavelength bands of the plurality of wavelength band values ​​matches the number of tristimulus values ​​of the CIE colorimetry system, convert the effective remote sensing reflectance of the visible light band into tristimulus values ​​X, Y, and Z of the CIE colorimetry system based on a first preset coefficient.

[0096] In an embodiment of the present application, different methods are used to convert satellite visible light band data into tristimulus values ​​X, Y, and Z of the CIE (International Commission on Illumination) colorimetry system according to the band settings of different satellite sensors in the visible light range. That is, different methods are used according to whether the number of bands of the multiple band values ​​matches the number of tristimulus values ​​of the CIE colorimetry system. When the number of bands of the multiple band values ​​matches the number of tristimulus values ​​of the CIE colorimetry system, that is, when the number of bands in the multiple band values ​​is three, the effective remote sensing reflectance of the visible light band is converted into the tristimulus values ​​X, Y, and Z of the CIE colorimetry system based on the first preset coefficient.

[0097] When the number of bands in multiple band values ​​matches the number of tristimulus values ​​in the CIE colorimetry system, the sensor is considered a three-band sensor. For three-band sensors, MOD09, Landsat TM, and Landsat ETM+ satellite remote sensing data are converted using the RGB conversion method from visible light band water reflectance data to CIE tristimulus values ​​X, Y, and Z in the CIE-XYZ colorimetry system. The formula is as follows (the coefficients in the following formula correspond to the first preset coefficients):

[0098] ;

[0099] in, They are the remote sensing reflectance values ​​of the R, G, and B bands of MOD09, Landsat TM, and Landsat ETM+ remote sensors respectively.

[0100] S203. When the number of bands in the multiple band values ​​does not match the number of tristimulus values ​​of the CIE colorimetry system, convert the effective remote sensing reflectance of the visible light band of each satellite sensor into tristimulus values ​​X, Y, and Z of the CIE colorimetry system using a linear difference method based on a second preset coefficient.

[0101] In an embodiment of the present application, when it is determined that the number of bands in the multiple band values ​​does not match the number of tristimulus values ​​of the CIE colorimetry system, that is, the number of bands in the multiple band values ​​is not three, the effective remote sensing reflectance of the visible light band of each satellite sensor is converted into the tristimulus values ​​X, Y and Z of the CIE colorimetry system by linear interpolation based on the second preset coefficient.

[0102] When the number of bands in the multiple band values ​​does not match the number of tristimulus values ​​in the CIE colorimetry system, the sensor may include four bands. For a sensor including four bands, such as Landsat 8 OLI, its visible light band includes four bands. Therefore, linear interpolation is used to convert the color space into tristimulus values. The formula is as follows (the coefficients in the following formula correspond to the second preset coefficients):

[0103] ;

[0104] in, They are the remote sensing reflectance values ​​of the four visible light bands of Landsat OLI.

[0105] S204: Calculate chromaticity coordinates x and y based on the tristimulus values ​​X, Y, and Z, establish a new coordinate system based on the equal-energy white point in the two-dimensional chromaticity diagram, and calculate the chromaticity angle of the multi-source satellite sensor based on the new coordinate system.

[0106] In this step, the chromaticity coordinates x and y are further calculated based on the calculated tristimulus values. A new coordinate system (x', y') is established based on the equal energy white point (1 / 3, 1 / 3) in the two-dimensional chromaticity diagram. The chromaticity angle is calculated according to the chromaticity angle formula. :

[0107]

[0108] Further, such as Figure 3 As shown, in the consistency correction method based on multi-source satellite inversion of water color index provided in an embodiment of the present application, a polynomial spectral correction model is constructed based on a water body optical simulation data set, and spectral response correction is performed on the chromaticity angle inverted by the multi-source satellite sensor based on the polynomial spectral correction model, including:

[0109] S301. Based on the hyperspectral reflectance provided by the water optical simulation dataset and the spectral response function of the multi-source satellite sensor, the hyperspectral reflectance is simulated as the reflectance of the satellite sensor after conversion in the corresponding visible light band.

[0110] In the embodiment of the present application, in view of the differences in the band settings of satellite sensors in the visible light range, the present invention performs spectral response correction for different remote sensors, thereby improving the consistency of multi-source satellite sensor data.

[0111] The hyperspectral reflectance data Rrs(λ) provided by the global surface water measurement dataset, i.e., the water optical simulation dataset, is obtained. The above hyperspectral reflectance data Rrs(λ) can simulate the real color of the water body. The above hyperspectral reflectance data is combined with the spectral response function of the satellite sensor to convert (i.e., simulate) the hyperspectral reflectance data Rrs(λ) into the reflectance of the corresponding satellite band (including the RGB band).

[0112] Here, based on the hyperspectral reflectance data Rrs (λ) provided by the water optical simulation dataset and combined with the relative spectral response function (RSR) of the sensor, the hyperspectral reflectance is simulated as the converted reflectance of the visible light band of multi-source remote sensing data (Landsat TM, Landsat ETM+, Landsat OLI, and MODIS).

[0113]

[0114] Where Rrs_band is the converted reflectance, that is, the simulated satellite reflectance in the corresponding visible light band, Rrs(λ) is the hyperspectral data of water surface reflectance, RSR(λ) is the relative spectral response function of the satellite sensor, and λ1 and λ2 are the upper and lower limit wavelengths of the satellite band.

[0115] S302: Calculate a first chromaticity angle based on the hyperspectral reflectance, calculate second chromaticity angles corresponding to the multi-source satellite sensors based on the converted reflectances of the multi-source satellite sensors in their respective visible light bands, and construct a polynomial spectral correction model for the multi-source satellite sensors based on the first chromaticity angle and the second chromaticity angle.

[0116] In this step, a first chromaticity angle is calculated based on the original hyperspectral reflectance data Rrs(λ); a second chromaticity angle is calculated based on the converted reflectance simulated by each satellite sensor; a polynomial formula for the deviation between the two chromaticity angles is constructed by comparing the two chromaticity angles to obtain a spectral correction model for each satellite sensor. The chromaticity angle inverted by each satellite sensor is calculated using this spectral correction model to obtain a corrected chromaticity angle. This correction method allows the data from multiple satellite sensors to more closely approximate the chromaticity angle values ​​of the hyperspectral data, thereby avoiding chromaticity angle deviation caused by spectral response differences and ensuring consistency among multiple satellites.

[0117] Specifically, by comparing the chromaticity angles before and after simulation, a polynomial correction formula for the deviation between the two is constructed. Based on this formula, the chromaticity angles calculated by each sensor are corrected to avoid deviations in chromaticity angle calculations caused by differences in spectral response, thereby improving the comparability and consistency of multi-source data. The corresponding formulas for multi-source satellite sensors (i.e., spectral correction models) are as follows:

[0118] ;

[0119] Where b = α multi / 100,α multi The chromaticity angles calculated using the global surface Rrs(λ) dataset simulate the TM, ETM, OLI, and MODIS bands. Using the above correction formula, the chromaticity angles calculated by each sensor are corrected, effectively eliminating deviations in chromaticity angle calculations caused by differences in spectral response and enhancing the comparability and consistency of multi-source data.

[0120] S303 , performing spectral response correction on the chromaticity angles respectively inverted by the multi-source satellite sensors based on a polynomial spectral correction model to obtain the chromaticity angles of the multi-source satellite sensors after spectral response correction.

[0121] For each satellite sensor in the multi-source satellite sensor, the inverted chromaticity angle of the satellite sensor is corrected based on the correction model matched with the satellite sensor to obtain the effective chromaticity angle of the multi-source satellite sensor after spectral response correction.

[0122] In the embodiment of the present application, taking into account the inconsistency caused by the "different spectral response functions" between the satellite sensors, a spectral correction model is constructed for each satellite sensor, and the chromaticity angle calculated by the matched sensors is corrected through the above spectral correction model, thereby effectively eliminating the chromaticity angle calculation deviation caused by the spectral response difference and enhancing the comparability and consistency of multi-source data.

[0123] Further, such as Figure 4 As shown, in the consistency correction method based on multi-source satellite inversion of water color index provided in an embodiment of the present application, the synchronous observation data of the multi-source satellite sensor is used to establish a cross-calibration correction model, and the chromaticity angle of the multi-source satellite sensor after spectral response correction is corrected based on the cross-calibration correction model is corrected for the system deviation, including:

[0124] S401. Resample the target multispectral image of the multi-source satellite sensor based on a preset time window and a preset spatial resolution, construct a pixel window of a preset size with the resampled sampling point as the center, calculate the coefficient of variation of the pixel window in the visible light band, and select valid pixels whose coefficient of variation meets a preset threshold.

[0125] In an embodiment of the present application, a preset time window and preset spatial resolution are determined based on the usage and attribute characteristics of each satellite sensor. Here, the preset time window is to find images from different satellite sensors on the same day. For example, Landsat and MODIS have a time difference in satellite transit. Therefore, the coordination between Landsat and MODIS is based on setting a time window of ±3 hours. The preset spatial resolution is to unify the spatial resolutions of different satellite sensors. For example, Landsat and MODIS have a spatial resolution of 30 meters, which will result in inconsistent spatial resolutions. Therefore, they are all unified to 500 meters. According to the above preset time window and preset spatial resolution, each satellite sensor is resampled.

[0126] During the resampling process, different satellite sensors will have spatial heterogeneity. Therefore, it is necessary to eliminate pixels that will produce spatial heterogeneity (which can also be understood as eliminating the corresponding data). Therefore, a pixel window of preset size (for example, a 3×3 MODIS pixel window) is constructed with each resampled sampling point as the center. Then, the coefficient of variation of the red R, green G, and blue B bands within the pixel window is calculated and pixels with a coefficient of variation less than a preset threshold (for example, 0.2) are screened out. The coefficient of variation (cv) of this RGB band is calculated. If the cv value is less than 0.2, the remote sensing reflectance value and chromaticity angle value at this sampling point are used to avoid the impact of spatial heterogeneity on the synchronization point data.

[0127] S402: construct a point-to-point matching dataset of the multi-source satellite sensors based on the selected valid pixels, and establish a cross-calibration correction model between each other satellite sensor in the multi-source satellite sensors and the designated satellite sensor based on the matching dataset.

[0128] In the embodiment of the present application, MODIS is used as the designated satellite sensor among the multi-source satellite sensors, and the linear fitting parameters for each other satellite sensor in the multi-source satellite sensor are determined based on the difference information between the inverted chromaticity angle of each other satellite sensor and the designated satellite sensor. Based on the inverted chromaticity angle and the fitting parameters of each other satellite sensor, a cross-calibration correction model, i.e., a cross-calibration linear correction model, is determined.

[0129] S403: Perform system deviation correction on the chromaticity angles of other matched satellite sensors after spectral response correction based on the cross-calibration correction model.

[0130] Here, each other satellite sensor in the multi-source satellite sensor corresponds to a cross-calibration linear correction model, and the chromaticity angle inverted by the other satellite sensor is corrected for the systematic bias based on the cross-calibration linear correction model to obtain the effective chromaticity angle after the systematic bias correction.

[0131] In this specific example, using the MODIS inversion results as the benchmark data, a chromaticity angle cross-calibration linear correction model is established to further correct the systematic deviation of the chromaticity angle inverted by other satellite sensors (it should be noted that the chromaticity angle here can be the chromaticity angle of other satellite sensors after spectral response correction). The above chromaticity angle cross-calibration linear correction model is as follows:

[0132]

[0133] in, is the chromaticity angle of each sensor after spectral response correction. ' represents the chromaticity angles of TM, ETM+, and OLI obtained after cross-calibration correction based on linear fitting. It should be noted that the coefficients and intercepts in the above formula represent the difference information between each other satellite sensor and the specified satellite sensor after fitting.

[0134] The consistency correction method based on multi-source satellite inversion of water color index provided in the embodiment of the present application has the following beneficial effects:

[0135] 1) Multi-source Data Usability: Suitable for collaborative processing of diverse sensors (such as Landsat TM / ETM+ / OLI, and MODIS), and compatible with visible, near-infrared, and shortwave infrared data, the system can process FUI inversion for a wide range of lake water types (from clear to eutrophic), adapting to diverse water quality conditions. Water color parameters can be extracted using only visible light bands (red, green, and blue), supporting the mining of historical remote sensing data from Landsat, MODIS, and other sources, enabling large-scale, long-term water color monitoring.

[0136] 2) Spectral response difference correction: Based on a global surface water hyperspectral dataset and the sensor relative spectral response function (RSR), we simulate the reflectance differences of different satellite bands and construct a polynomial correction model to eliminate the chromaticity angle calculation deviation caused by differences in the band center wavelength and bandwidth.

[0137] 3) Cross-calibration to eliminate systematic errors: Synchronous imagery (such as Landsat and MODIS ±3-hour window data) is used to construct a point-to-point matching dataset. Chromaticity angle cross-calibration is performed using a linear regression model to reduce systematic errors caused by differences in radiometric calibration and atmospheric correction methods.

[0138] 4) Standardized FUI Conversion Mechanism: Calculates chromaticity angles based on the CIE chromaticity space and uses a standard FUI lookup table (covering the correspondence between 21 chromaticity angles and FUI) to achieve uniform conversion of color indices, ensuring comparability between inversion results from different satellite data.

[0139] 5) Full-process optimization and quality control: Integrated preprocessing steps such as noise removal (NIR-SWIR minimum correction), flare identification (SWIR threshold <0.1), water masking (JRC permanent water dataset + boundary erosion), and cloud / ice filtering (QA band) improve input data quality.

[0140] This application addresses the problem of systematic bias in the FUI inversion results from multi-source satellite data and proposes a method for correcting the consistency of lake water color index based on multi-source optical satellite data. Compared with the existing technology, the main technical advantages of this invention are as follows:

[0141] (1) Proposing a normalized correction model to improve the consistency of FUI inversion results from multiple satellite sources: In existing methods, different satellites have different spectral response functions and data processing methods, which leads to systematic deviations in FUI inversion results and affects the spatiotemporal consistency of the data. By constructing a normalized correction model, the FUI results from different satellites are standardized, reducing the systematic errors between sensors and improving the comparability of the data, making it suitable for long-term monitoring of lake water color.

[0142] (2) Optimizing chromaticity angle calculation to improve FUI inversion accuracy: Chromaticity angle is a key parameter in FUI calculation and is significantly affected by spectral band settings and atmospheric correction methods. This paper adopts an improved chromaticity angle calculation method and combines the spectral characteristics of multi-source satellite data to optimize chromaticity angle correction, thereby reducing FUI calculation errors caused by band differences between different sensors and improving inversion accuracy.

[0143] (3) Applicable to multi-source satellite data, improving spatiotemporal coverage: The method of the present invention is applicable to multiple satellite sensors such as Landsat and MODIS, and can be extended to other optical remote sensing data to achieve cross-sensor data fusion. Compared with single satellite data, this method significantly improves the spatiotemporal coverage of lake water color monitoring, making the FUI inversion results temporally coherent and spatially more applicable, making it suitable for analyzing long-term water color changes.

[0144] Based on the same inventive concept, the embodiment of the present application also provides a consistency correction device based on the multi-source satellite inversion water color index corresponding to the consistency correction method based on the multi-source satellite inversion water color index. Since the principle of solving the problem by the device in the embodiment of the present application is similar to the above-mentioned consistency correction method based on the multi-source satellite inversion water color index in the embodiment of the present application, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated.

[0145] Reference Figure 5 As shown, a consistency correction device based on multi-source satellite inversion of water color index provided in an embodiment of the present application is provided, and the device includes:

[0146] An acquisition module 501 is used to obtain the surface reflectance of the target water area in the respective visible light band, near infrared band, and shortwave infrared band from multiple satellite sensors;

[0147] Processing module 502 is configured to convert the surface reflectance of the multi-source satellite sensor into a remote sensing reflectance, perform water range extraction on the multi-source satellite sensor to determine the water range corresponding to the multi-source satellite sensor, and remove multi-category interference pixels from the image of the surface reflectance within the water range to obtain an effective remote sensing reflectance of the image after removing the interference pixels;

[0148] A calculation module 503 is configured to calculate the chromaticity angles inverted by the multi-source satellite sensor based on the effective remote sensing reflectance and the CIE colorimetric system;

[0149] A first correction module 504 is configured to construct a polynomial spectral correction model based on the water optical simulation dataset, and perform spectral response correction on the chromaticity angle inverted by the multi-source satellite sensor based on the polynomial spectral correction model;

[0150] A second correction module 505 is configured to establish a cross-calibration correction model using the synchronous observation data of the multi-source satellite sensor, and perform system deviation correction on the chromaticity angle of the multi-source satellite sensor after spectral response correction based on the cross-calibration correction model;

[0151] The determination module 506 is configured to determine the water color index of the target water area based on the chromaticity angle after the system bias correction.

[0152] In a possible implementation, converting the surface reflectivity of the multi-source satellite sensor into remote sensing reflectivity includes:

[0153] Based on the correction method of the minimum surface reflectance in the near-infrared band and the short-wave infrared band, a remote sensing reflectance calculation formula is determined, and based on the remote sensing reflectance calculation formula, the surface reflectance of the multi-source satellite sensor is converted into remote sensing reflectance and residual noise is corrected; wherein, the remote sensing reflectance calculation formula is:

[0154]

[0155] in, Represents remote sensing reflectance, NIR represents near infrared band, SWIR represents short wave infrared band, Indicates the minimum surface reflectance in the NIR and SWIR bands, Represents the surface reflectivity.

[0156] In a possible implementation, extracting the water body range of the multi-source satellite sensor to determine the water body range corresponding to the multi-source satellite sensor includes:

[0157] For a Landsat type satellite sensor, a preset water body range data set is obtained, and a candidate water body range of the target water area is determined from the water body range data set;

[0158] For MODIS type satellite sensors, data based on the SWIR band of the satellite sensor is obtained, and the water body range of the multi-source satellite sensor is extracted based on the data based on the SWIR band and an adaptive threshold method to obtain output data of the SWIR band, and the candidate water body range of the target water area is determined based on the output data of the SWIR band;

[0159] The water body range of each type of satellite sensor is determined by moving the water body boundary of the candidate water body range corresponding to each type of satellite sensor inward by a preset distance.

[0160] In a possible implementation, removing multiple categories of interference pixels from the image of the surface reflectance within the water body includes:

[0161] Generating a quality assessment band of the multi-source satellite sensor based on a cloud mask processing algorithm, and removing first-category interference pixels within the water body based on the quality assessment band; wherein the first-category interference pixels include at least clouds, cloud shadows, ice, and snow;

[0162] Based on the remote sensing reflectance in the shortwave infrared spectral band of each satellite sensor and a preset shortwave infrared band threshold, the second type of interference pixels within the water body are identified and removed; wherein the second type of interference pixels include water surface flares.

[0163] In a possible implementation, the calculating, based on the effective remote sensing reflectance and the CIE colorimetric system, the chromaticity angles inverted by the multi-source satellite sensors respectively includes:

[0164] Acquire multiple band values ​​of the multi-source satellite sensor in the visible light band;

[0165] When the number of wavelength bands of the plurality of wavelength band values ​​matches the number of tristimulus values ​​of the CIE colorimetry system, converting the effective remote sensing reflectance of the visible light band into tristimulus values ​​X, Y, and Z of the CIE colorimetry system based on a first preset coefficient;

[0166] When the number of bands in the plurality of band values ​​does not match the number of tristimulus values ​​of the CIE colorimetry system, converting the effective remote sensing reflectance of the visible light band of each satellite sensor into tristimulus values ​​X, Y, and Z of the CIE colorimetry system respectively by using a linear interpolation method based on a second preset coefficient;

[0167] The chromaticity coordinates x and y are calculated according to the tristimulus values ​​X, Y and Z, and a new coordinate system is established based on the equal energy white point in the two-dimensional chromaticity diagram, and the chromaticity angle of the multi-source satellite sensor is calculated according to the new coordinate system.

[0168] In one possible implementation, constructing a polynomial spectral correction model based on a water optical simulation dataset, and performing spectral response correction on the chromaticity angle inverted by the multi-source satellite sensor based on the polynomial spectral correction model, includes:

[0169] Based on the hyperspectral reflectance provided by the water body optical simulation dataset and the spectral response function of the multi-source satellite sensor, simulating the hyperspectral reflectance as the reflectance of the satellite sensor after conversion in the corresponding visible light band;

[0170] Calculating a first chromaticity angle according to the hyperspectral reflectance, and calculating second chromaticity angles corresponding to the multi-source satellite sensors according to the converted reflectances of the multi-source satellite sensors in their respective visible light bands, and constructing a polynomial spectral correction model for the multi-source satellite sensors according to the first chromaticity angle and the second chromaticity angle;

[0171] Based on the polynomial spectral correction model, spectral response correction is performed on the chromaticity angles respectively inverted by the multi-source satellite sensors to obtain the chromaticity angles of the multi-source satellite sensors after spectral response correction.

[0172] In one possible implementation, establishing a cross-calibration correction model using the synchronous observation data of the multi-source satellite sensor, and performing systematic deviation correction on the chromaticity angle of the multi-source satellite sensor after spectral response correction based on the cross-calibration correction model includes:

[0173] Resampling the target multispectral image of the multi-source satellite sensor based on a preset time window and a preset spatial resolution, constructing a pixel window of a preset size with the resampled sampling point as the center, calculating the coefficient of variation of the pixel window in the visible light band, and selecting valid pixels whose coefficient of variation meets a preset threshold;

[0174] constructing a point-to-point matching dataset of multi-source satellite sensors based on the selected valid pixels, and establishing a cross-calibration correction model between each other satellite sensor in the multi-source satellite sensors and the designated satellite sensor based on the matching dataset;

[0175] Based on the cross-calibration correction model, the system deviation correction is performed on the chromaticity angles of other matched satellite sensors after spectral response correction.

[0176] The present application provides the above-mentioned consistency correction device based on the multi-source satellite inversion water color index, which performs spectral response correction and system deviation correction on the water body chromaticity angle inverted by the multi-source satellite sensor calculated by the effective remote sensing reflectance and the CIE chromaticity system, and obtains the water color index based on the corrected chromaticity angle, thereby improving the consistency of the correction results, ensuring that the inversion data of the multi-source satellite sensor can be used collaboratively and improving the applicability of the multi-source satellite sensor data, improving the consistency of the FUI inverted by the multi-source satellite data, solving the data discontinuity problem caused by sensor differences, and providing high-precision collaborative inversion for long-term monitoring of the water color of inland lakes.

[0177] like Figure 6As shown, an electronic device 600 provided in an embodiment of the present application includes: a processor 601, a memory 602 and a bus, wherein the memory 602 stores machine-readable instructions executable by the processor 601. When the electronic device is running, the processor 601 communicates with the memory 602 through the bus, and the processor 601 executes the machine-readable instructions to perform the steps of the above-mentioned consistency correction method based on multi-source satellite inversion of water color index.

[0178] Specifically, the above-mentioned memory 602 and processor 601 can be general-purpose memories and processors, which are not specifically limited here. When the processor 601 runs the computer program stored in the memory 602, it can execute the above-mentioned consistency correction method based on the multi-source satellite inversion water color index.

[0179] Corresponding to the above-mentioned consistency correction method based on the multi-source satellite inversion water color index, an embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by the processor, the steps of the above-mentioned consistency correction method based on the multi-source satellite inversion water color index are executed.

[0180] The above-mentioned electronic device and storage medium provided in real time by the present application correct the water body chromaticity angle inverted by the multi-source satellite sensor through the multi-dimensional system deviation information of the multi-source satellite sensor, and obtain the water color index based on the corrected chromaticity angle, thereby improving the consistency of the correction results, ensuring that the inversion data of the multi-source satellite sensor can be used collaboratively, and improving the applicability of the multi-source satellite sensor data.

[0181] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system and device described above can refer to the corresponding process in the method embodiment, and will not be repeated in this application. In the several embodiments provided in this application, it should be understood that the disclosed system, device and method can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0182] The modules described as separate components may or may not be physically separate, and 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 elements. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0183] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0184] If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, or the portion that contributes to the prior art, or the portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The aforementioned storage media include various media that can store program code, such as USB flash drives, mobile hard drives, ROM, RAM, magnetic disks, or optical disks.

[0185] The above are only specific embodiments of the present application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A consistency correction method based on multi-source satellite inversion of water color index, characterized in that: The method comprises: Step S1, obtaining the surface reflectance of the target water area in the visible light band, near infrared band, and shortwave infrared band of the multi-source satellite sensors; Step S2: converting the surface reflectivity of the multi-source satellite sensor into a remote sensing reflectivity, performing water body range extraction on the multi-source satellite sensor to determine the water body range corresponding to the multi-source satellite sensor, and performing multi-category interference pixel removal on the image of the surface reflectivity within the water body range to obtain an effective remote sensing reflectivity of the image after removing the interference pixels; Step S3, based on the effective remote sensing reflectance and the CIE colorimetric system, respectively calculating the chromaticity angles inverted by the multi-source satellite sensor; Step S4: constructing a polynomial spectral correction model based on the water body optical simulation data set, and performing spectral response correction on the chromaticity angle inverted by the multi-source satellite sensor based on the polynomial spectral correction model; Step S5: establishing a cross-calibration correction model using the synchronous observation data of the multi-source satellite sensor, and performing system deviation correction on the chromaticity angle of the multi-source satellite sensor after spectral response correction based on the cross-calibration correction model; Step S6: determining the water color index of the target water area based on the chromaticity angle after system deviation correction.

2. The consistency correction method based on multi-source satellite inversion of water color index according to claim 1 is characterized in that: In step S2, converting the surface reflectivity of the multi-source satellite sensor into remote sensing reflectivity includes: Based on the correction method of the minimum surface reflectance in the near-infrared band and the short-wave infrared band, a remote sensing reflectance calculation formula is determined, and based on the remote sensing reflectance calculation formula, the surface reflectance of the multi-source satellite sensor is converted into remote sensing reflectance and residual noise is corrected; wherein, the remote sensing reflectance calculation formula is: in, Represents remote sensing reflectance, NIR represents near infrared band, SWIR represents short wave infrared band, Indicates the minimum surface reflectance in the NIR and SWIR bands, Represents the surface reflectivity.

3. The consistency correction method based on multi-source satellite inversion of water color index according to claim 2 is characterized in that: In step S2, performing water body range extraction on the multi-source satellite sensor to determine the water body range corresponding to the multi-source satellite sensor includes: For a Landsat type satellite sensor, a preset water body range data set is obtained, and a candidate water body range of the target water area is determined from the water body range data set; For MODIS type satellite sensors, data based on the SWIR band of the satellite sensor is obtained, and the water body range of the multi-source satellite sensor is extracted based on the data based on the SWIR band and an adaptive threshold method to obtain output data of the SWIR band, and the candidate water body range of the target water area is determined based on the output data of the SWIR band; The water body range of each type of satellite sensor is determined by moving the water body boundary of the candidate water body range corresponding to each type of satellite sensor inward by a preset distance.

4. The consistency correction method based on multi-source satellite inversion of water color index according to claim 3 is characterized in that: In step S2, removing multiple categories of interference pixels from the image of the surface reflectance within the water body includes: Generating a quality assessment band of the multi-source satellite sensor based on a cloud mask processing algorithm, and removing first-category interference pixels within the water body based on the quality assessment band; wherein the first-category interference pixels include at least clouds, cloud shadows, ice, and snow; Based on the remote sensing reflectance in the shortwave infrared spectral band of each satellite sensor and a preset shortwave infrared band threshold, the second type of interference pixels within the water body are identified and removed; wherein the second type of interference pixels include water surface flares.

5. The consistency correction method based on multi-source satellite inversion of water color index according to claim 1 is characterized in that: In step S3, the chromaticity angles inverted by the multi-source satellite sensors are calculated based on the effective remote sensing reflectance and the CIE chromaticity system, including: Acquire multiple band values ​​of the multi-source satellite sensor in the visible light band; When the number of wavelength bands of the plurality of wavelength band values ​​matches the number of tristimulus values ​​of the CIE colorimetry system, converting the effective remote sensing reflectance of the visible light band into tristimulus values ​​X, Y, and Z of the CIE colorimetry system based on a first preset coefficient; When the number of bands in the plurality of band values ​​does not match the number of tristimulus values ​​of the CIE colorimetry system, converting the effective remote sensing reflectance of the visible light band of each satellite sensor into tristimulus values ​​X, Y, and Z of the CIE colorimetry system respectively by using a linear interpolation method based on a second preset coefficient; The chromaticity coordinates x and y are calculated according to the tristimulus values ​​X, Y and Z, and a new coordinate system is established based on the equal energy white point in the two-dimensional chromaticity diagram, and the chromaticity angle of the multi-source satellite sensor is calculated according to the new coordinate system.

6. The consistency correction method based on multi-source satellite inversion of water color index according to claim 1 is characterized in that: In step S4, constructing a polynomial spectral correction model based on the water optical simulation data set, and performing spectral response correction on the chromaticity angle inverted by the multi-source satellite sensor based on the polynomial spectral correction model include: Based on the hyperspectral reflectance provided by the water body optical simulation dataset and the spectral response function of the multi-source satellite sensor, simulating the hyperspectral reflectance as the reflectance of the satellite sensor after conversion in the corresponding visible light band; Calculating a first chromaticity angle according to the hyperspectral reflectance, and calculating second chromaticity angles corresponding to the multi-source satellite sensors according to the converted reflectances of the multi-source satellite sensors in their respective visible light bands, and constructing a polynomial spectral correction model for the multi-source satellite sensors according to the first chromaticity angle and the second chromaticity angle; Based on the polynomial spectral correction model, spectral response correction is performed on the chromaticity angles respectively inverted by the multi-source satellite sensors to obtain the chromaticity angles of the multi-source satellite sensors after spectral response correction.

7. The consistency correction method based on multi-source satellite inversion of water color index according to claim 6 is characterized in that: In step S5, establishing a cross-calibration correction model using the synchronous observation data of the multi-source satellite sensor, and performing system deviation correction on the chromaticity angle of the multi-source satellite sensor after spectral response correction based on the cross-calibration correction model include: Resampling the target multispectral image of the multi-source satellite sensor based on a preset time window and a preset spatial resolution, constructing a pixel window of a preset size with the resampled sampling point as the center, calculating the coefficient of variation of the pixel window in the visible light band, and selecting valid pixels whose coefficient of variation meets a preset threshold; constructing a point-to-point matching dataset of multi-source satellite sensors based on the selected valid pixels, and establishing a cross-calibration correction model between each other satellite sensor in the multi-source satellite sensors and the designated satellite sensor based on the matching dataset; Based on the cross-calibration correction model, the system deviation correction is performed on the chromaticity angles of other matched satellite sensors after spectral response correction.

8. A consistency correction device based on multi-source satellite inversion of water color index, characterized in that: The device comprises: An acquisition module is used to obtain the surface reflectance of the target water area in the respective visible light band, near infrared band, and shortwave infrared band from multi-source satellite sensors; a processing module, configured to convert the surface reflectance of the multi-source satellite sensor into a remote sensing reflectance, perform water body range extraction on the multi-source satellite sensor to determine the water body range corresponding to the multi-source satellite sensor, and perform multi-category interference pixel removal on the image of the surface reflectance within the water body range to obtain an effective remote sensing reflectance of the image after the interference pixels are removed; A calculation module, configured to calculate the chromaticity angles inverted by the multi-source satellite sensor based on the effective remote sensing reflectance and the CIE chromaticity system; The first correction module is used to construct a polynomial spectral correction model based on the water optical simulation data set, and perform spectral response correction on the chromaticity angle inverted by the multi-source satellite sensor based on the polynomial spectral correction model; the second correction module is used to establish a cross-calibration correction model using the synchronous observation data of the multi-source satellite sensor, and perform systematic deviation correction on the chromaticity angle of the multi-source satellite sensor after the spectral response correction based on the cross-calibration correction model; The determination module is used to determine the water color index of the target water area based on the chromaticity angle after system deviation correction.

9. An electronic device, characterized in that: include: A processor, a storage medium and a bus, wherein the storage medium stores machine-readable instructions executable by the processor. When the electronic device is running, the processor and the storage medium communicate via the bus, and the processor executes the machine-readable instructions to perform the steps of the consistency correction method based on multi-source satellite inversion of water color index as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, executes the steps of the consistency correction method based on multi-source satellite inversion of water color index as described in any one of claims 1 to 7.

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