Consistency correction method and device based on multi-source satellite inversion water color index

Abstract

The invention provides a consistency correction method and device based on a multi-source satellite inversion water color index, and the method comprises the steps: converting the surface reflectance of a multi-source satellite sensor of a target water area into remote sensing reflectance, and carrying out the water body range extraction of the multi-source satellite sensor, so as to determine the water body range, carrying out multi-class interference pixel removal on the image of the surface reflectance in the water body range to obtain effective remote sensing reflectance; calculating a chromaticity angle inverted by the multi-source satellite sensor based on the effective remote sensing reflectivity and a CIE chromaticity system; performing spectral response correction on the chromaticity angle inverted by the multi-source satellite sensor based on a polynomial spectral correction model constructed by the water optical simulation data set; performing system deviation correction on the chromaticity angle of the multi-source satellite sensor after spectral response correction by a cross calibration correction model established based on the synchronous observation data of the multi-source satellite sensor; and determining the water color index of the target water area based on the chromaticity angle after system deviation correction.

Description

Technical Field

[0001] This application relates to the field of satellite remote sensing data processing. Specifically, it relates to a method and device for consistency correction of water color index retrieved from multi-source satellites. Background Art

[0002] The water color of water bodies in water areas is an important indicator reflecting the optical properties of water bodies and is also one of the key climate variables of water area ecosystems. The change of water color is mainly caused by the interaction between components such as chlorophyll-a, chromophoric dissolved organic matter (CDOM), and total suspended matter (TSM) in water and sunlight. The spatio-temporal changes of these components not only affect the water color but also indicate the ecological environment status of water areas, such as the degree of water eutrophication, pollutant input, and suspended particulate matter concentration. Therefore, the Forel-Ule Index (FUI), as an index for quantifying water color, has been widely used in water environment remote sensing monitoring and ecological assessment.

[0003] The monitoring of the water color index of water areas is restricted by high altitude, complex terrain, and field monitoring conditions. Traditional ground observation methods are difficult to achieve large-scale and high-frequency dynamic monitoring of lake water quality. In recent years, satellite remote sensing technology has its large-scale and long-time series observation capabilities. Currently, independent satellite sensors are usually used for water environment monitoring in water areas. However, independent satellite sensors are restricted by factors such as observation time, spatial resolution, and coverage, and it is difficult to meet the continuous monitoring requirements for the long-term changes of water color in water areas. Summary of the Invention

[0004] This application provides a method and device for consistency correction of water color index retrieved from multi-source satellites. By performing spectral response correction and systematic deviation correction on the water body chromaticity angle retrieved by multi-source satellite sensors calculated from 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, ensuring that the retrieved data of multi-source satellite sensors can be used collaboratively and improving the applicability of multi-source satellite sensor data, enhancing the consistency of multi-source satellite data in retrieving FUI, solving the problem of discontinuous data caused by sensor differences, and providing high-precision collaborative inversion for long-time series monitoring of the water color of inland lakes.

[0005] In a first aspect, an embodiment of this application provides a method for consistency correction of water color index retrieved from multi-source satellites, the method including: Obtain the surface reflectance of multi-source satellite sensors of a target water area in their respective visible light bands, near-infrared bands, and short-wave infrared bands; Convert the surface reflectance of the multi-source satellite sensor into remote sensing reflectance, extract the water body range of the multi-source satellite sensor to determine the water body range corresponding to the multi-source satellite sensor, and remove multi-category interfering 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 interfering pixels; Based on the effective remote sensing reflectance and the CIE chromaticity system, calculate the chromaticity angles retrieved by the multi-source satellite sensor respectively; Construct a polynomial spectral correction model based on the water body optical simulation dataset, and perform spectral response correction on the chromaticity angles retrieved by the multi-source satellite sensor based on the polynomial spectral correction model; 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 angles of the multi-source satellite sensor after spectral response correction based on the cross-calibration correction model; Determine the water color index of the target water area based on the chromaticity angles after systematic deviation correction.

[0006] In one possible implementation manner, the conversion of the surface reflectance of the multi-source satellite sensor into remote sensing reflectance includes: Based on the correction method of the minimum surface reflectance in the near-infrared band and the short-wave infrared band, determine the remote sensing reflectance calculation formula, and convert the surface reflectance of the multi-source satellite sensor into remote sensing reflectance and correct the residual noise based on the remote sensing reflectance calculation formula; where the remote sensing reflectance calculation formula is:

[0007] Where, represents the remote sensing reflectance, NIR represents the near-infrared band, SWIR represents the short-wave infrared band, represents the minimum surface reflectance of the NIR and SWIR bands, represents the surface reflectance.

[0008] In one possible implementation manner, the extraction of the water body range of the multi-source satellite sensor to determine the water body range corresponding to the multi-source satellite sensor includes: For Landsat-type satellite sensors, obtain a preset water body range dataset, and determine the candidate water body range of the target water area from the water body range dataset; For MODIS-type satellite sensors, obtain the data based on the SWIR band of this type of satellite sensor, extract the water body range of the multi-source satellite sensor according to the data based on the SWIR band and using the adaptive threshold method to obtain the output data of the SWIR band, and determine the candidate water body range of the target water area based on the output data of the SWIR band; Move inward a preset distance from the water body boundaries of the candidate water body ranges corresponding to each type of satellite sensor to determine the water body ranges of each type of satellite sensor.

[0009] In one possible implementation, the removing of multi-class interference pixels from the image of the surface reflectance within the water body range includes: Generating a quality assessment band for the multi-source satellite sensors based on a cloud masking processing algorithm, and removing the first type of interference pixels within the water body range based on the quality assessment band; wherein, the first type of interference pixels at least includes clouds, cloud shadows, ice, and snow; Identifying and removing the second type of interference pixels within the water body range based on the remote sensing reflectance in the short-wave infrared spectral band of each satellite sensor and a preset short-wave infrared band threshold; wherein, the second type of interference pixels includes specular reflections on the water surface.

[0010] In one possible implementation, the calculating of the chromaticity angles inverted by the multi-source satellite sensors respectively based on the effective remote sensing reflectance and the CIE chromaticity system includes: Obtaining multiple band values of the multi-source satellite sensors within the visible light band; When the number of bands of the multiple band values matches the number of tristimulus values of the CIE chromaticity system, converting the effective remote sensing reflectance in the visible light band into the tristimulus values X, Y, and Z of the CIE chromaticity system based on a first preset coefficient; When the number of bands in the multiple band values does not match the number of tristimulus values of the CIE chromaticity system, respectively converting the effective remote sensing reflectance in the visible light band of each satellite sensor into the tristimulus values X, Y, and Z of the CIE chromaticity system by using linear interpolation based on a second preset coefficient; Calculating chromaticity coordinates x and y according to the tristimulus values X, Y, and Z, establishing a new coordinate system with the equal-energy white point in the two-dimensional chromaticity diagram as the reference, and calculating the chromaticity angles of the multi-source satellite sensors according to the new coordinate system.

[0011] In one possible implementation, the constructing of a polynomial spectral correction model based on the water body optical simulation dataset and the spectral response correction of the chromaticity angles inverted by the multi-source satellite sensors based on the polynomial spectral correction model includes: Based on the hyperspectral reflectance provided by the water body optical simulation dataset and the spectral response function of the multi-source satellite sensors, simulating the hyperspectral reflectance as the reflectance after conversion of the satellite sensor in the corresponding visible light band; Calculate the first chromaticity angle according to the hyperspectral reflectance, and calculate the second chromaticity angles corresponding to the multi-source satellite sensors respectively according to 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 according to the first chromaticity angle and the second chromaticity angles; Perform spectral response correction on the chromaticity angles inversely retrieved by the multi-source satellite sensors respectively based on the polynomial spectral correction model to obtain the chromaticity angles of the multi-source satellite sensors after spectral response correction.

[0012] In a possible implementation manner, the establishing a cross-calibration correction model by using the synchronous observation data of the multi-source satellite sensors, and performing systematic deviation correction on the chromaticity angles of the multi-source satellite sensors after spectral response correction based on the cross-calibration correction model includes: Based on a preset time window and a preset spatial resolution, resample the target multispectral images of the multi-source satellite sensors, construct a pixel window with a preset size centered on the sampling points of the resampling, calculate the coefficient of variation of the pixel window in the visible light band, and select valid pixels whose coefficient of variation meets the preset threshold; Based on the selected valid pixels, construct a point-to-point matching data set of the multi-source satellite sensors, and based on the matching data set, establish a cross-calibration correction model between each other satellite sensor and the specified satellite sensor in the multi-source satellite sensors; Perform systematic deviation correction on the chromaticity angles of the other satellite sensors after spectral response correction that are matched based on the cross-calibration correction model.

[0013] In a second aspect, an embodiment of the present application further provides a consistency correction device for inverting water color indices based on multi-source satellites, and the device includes: An acquisition module, configured to acquire target multispectral images collected by multi-source satellite sensors in their respective multiple different spectral bands within a preset time period, and extract the surface reflectance in the target multispectral images; the target multispectral images are multispectral images of a target water area; A calculation module, configured to convert the surface reflectance of each satellite sensor into a remote sensing reflectance suitable for water bodies according to the absorption characteristics of a specified band in multiple different spectral bands of water bodies with different characteristics, and invert the chromaticity angle of the water body based on the effective remote sensing reflectance of each satellite sensor; A correction module, configured to correct the chromaticity angles inversely retrieved by the multi-source satellite sensors respectively based on the multi-dimensional systematic deviation information of the multi-source satellite sensors to obtain corrected effective chromaticity angles; A determination module, configured to determine the water color index of the target water area in combination with the corrected effective chromaticity angles of the multi-source satellite sensors.

[0014] In a third aspect, an embodiment of the present application further provides an electronic device, including: a processor, a storage medium, and a bus. The storage medium stores machine-readable instructions executable by the processor. When the electronic device runs, the processor communicates with the storage medium through the bus, and the processor executes the machine-readable instructions to perform the steps of the consistency correction method for inverting water color index based on multi-source satellites according to any one of the first aspects.

[0015] 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 run by a processor, it performs the steps of the consistency correction method for inverting water color index based on multi-source satellites according to any one of the first aspects.

[0016] The present application provides the above-mentioned consistency correction method and device for inverting water color index based on multi-source satellites. By performing spectral response correction and systematic deviation correction on the water body chromaticity angle inverted by multi-source satellite sensors calculated from 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, ensuring that the inversion data of multi-source satellite sensors can be synergistically applied and improving the applicability of multi-source satellite sensor data, enhancing the consistency of multi-source satellite data inversion of FUI, solving the problem of discontinuous data caused by sensor differences, and providing high-precision collaborative inversion for long-term monitoring of the water color of inland lakes. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] To more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings required for the embodiments. It should be understood that the following drawings only show some embodiments of the present application, so they should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0018] Figure 1 Shows a flowchart of the first consistency correction method for inverting water color index based on multi-source satellites provided by the embodiment of the present application; Figure 2 Shows a flowchart of the second consistency correction method for inverting water color index based on multi-source satellites provided by the embodiment of the present application; Figure 3 Shows a flowchart of the third consistency correction method for inverting water color index based on multi-source satellites provided by the embodiment of the present application; Figure 4 Shows a flowchart of the fourth consistency correction method for inverting water color index based on multi-source satellites provided by the embodiment of the present application; Figure 5 The figure shows a schematic structural diagram of a consistency correction device for multi-source satellite-inverted water color index provided by an embodiment of the present application; Figure 6 The figure shows a schematic structural diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0019] To make the objectives, 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 with reference to the accompanying drawings in the embodiments of the present application. It should be understood that the accompanying drawings in the present application are only for the purposes of illustration and description, and are not used to limit the protection scope of the present application. In addition, it should be understood that the schematic drawings are not drawn to actual scale. The flowcharts used in the present application illustrate the operations implemented according to some embodiments of the present application. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical context relationships may be reversed or implemented simultaneously. In addition, those skilled in the art may add one or more other operations to the flowchart or remove one or more operations from the flowchart under the guidance of the content of the present application.

[0020] In addition, the described embodiments are only some of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application usually described and illustrated in the accompanying drawings here may be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application claimed, but merely represents the selected embodiments of the present application. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative efforts fall within the protection scope of the present application.

[0021] It should be noted that the term "including" will be used in the embodiments of the present application to indicate the existence of the subsequently stated features, but does not exclude the addition of other features.

[0022] In the field of monitoring water ecological environment based on satellite remote sensing, it is mainly based on satellite sensors for monitoring. Currently, it is all based on single satellite sensor monitoring, which is restricted by factors such as observation time, spatial resolution, and coverage range, and it is difficult to meet the continuous monitoring requirements for the long-term changes in lake water color. Based on this, the embodiments of the present application provide a consistency correction method and device for multi-source satellite inversion water color index. By performing spectral response correction and systematic deviation correction on the water body chromaticity angles inverted by multi-source satellite sensors calculated from effective remote sensing reflectance and the CIE chromaticity system, and obtaining the water color index based on the corrected chromaticity angles, the consistency of the correction results is improved, ensuring that the inversion data of multi-source satellite sensors can be synergistically applied and improving the applicability of multi-source satellite sensor data, enhancing the consistency of multi-source satellite data inversion FUI, solving the problem of discontinuous data caused by sensor differences, and providing high-precision collaborative inversion for long-term time-series monitoring of inland lake water color.

[0023] As Figure 1 shown, the embodiments of the present application provide a consistency correction method for multi-source satellite inversion water color index, and the method includes: S101. Obtain the surface reflectance of multi-source satellite sensors of the target water area in their respective visible light bands, near-infrared bands, and short-wave infrared bands.

[0024] S102. Convert the surface reflectance of the multi-source satellite sensors into remote sensing reflectance, extract the water body range of the multi-source satellite sensors to determine the water body range corresponding to the multi-source satellite sensors, and remove multi-category interfering pixels from the images of the surface reflectance within the water body range to obtain the effective remote sensing reflectance of the image after removing the interfering pixels.

[0025] S103. Based on the effective remote sensing reflectance and the CIE chromaticity system, calculate the chromaticity angles inverted by the multi-source satellite sensors respectively.

[0026] S104. Construct a polynomial spectral correction model based on the water body optical simulation data set, and perform spectral response correction on the chromaticity angles inverted by the multi-source satellite sensors based on the polynomial spectral correction model.

[0027] S105. Establish a cross-calibration correction model using the synchronous observation data of the multi-source satellite sensors, and perform systematic deviation correction on the chromaticity angles of the multi-source satellite sensors after spectral response correction based on the cross-calibration correction model.

[0028] S106. Determine the water color index of the target water area based on the chromaticity angles after systematic deviation correction.

[0029] In the above consistency correction method for retrieving water color indices from multi-source satellites, the spectral response correction and systematic deviation correction are performed on the water body chromaticity angles retrieved by multi-source satellite sensors calculated from the effective remote sensing reflectance and the CIE chromaticity system. Based on the corrected chromaticity angles, the water color indices are obtained, which improves the consistency of the correction results, ensures the collaborative application of the retrieved data from multi-source satellite sensors, improves the applicability of the data from multi-source satellite sensors, enhances the consistency of the retrieved FUI from multi-source satellite data, solves the problem of discontinuous data caused by sensor differences, and can provide high-precision collaborative retrieval for long-term monitoring of the water color of inland lakes.

[0030] The above exemplary embodiments will be described separately as follows: S101. Obtain the surface reflectance of the multi-source satellite sensors of the target water area in their respective visible light bands, near-infrared bands, and short-wave infrared bands.

[0031] 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. The multi-source satellite sensors include multiple different types of satellite sensors. For example, Landsat type sensors and MODIS type sensors, and each type of sensor also includes multiple versions of sensors. For example, the Landsat type sensors include Landsat TM, ETM+, and Landsat OLI; among them, each satellite sensor corresponds to multiple different spectral bands, and the spectral bands between different satellite sensors are usually 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.

[0032] In the embodiment of the present application, the multi-source satellite sensors include: MODIS, Landsat 5 / TM, Landsat 7 / ETM+, Landsat 8 / OLI; the above multi-source satellite sensors each have three bands: visible light (RGB) band, near-infrared band, and short-wave infrared band. The central wavelengths of Landsat TM and ETM are 485nm, 560nm, 660nm, 830nm, and 1650nm, a total of 5 bands; the central wavelengths of Landsat OLI are 485nm, 569nm, 660nm, 840nm, and 1650nm, a total of 5 bands; the central wavelengths of MODIS are 469nm, 555nm, 645nm, 859nm, 1240nm, and 1640nm, a total of 6 bands.

[0033] In the embodiments of the present application, each satellite sensor collects multispectral images in 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 (Surface Reflectance Data, SR) in the multispectral images is extracted. Here, the surface reflectance is the data after radiometric calibration.

[0034] In the calibration method of the embodiments of the present application, a system (which can be placed in a terminal device or in a server) re-enters multi-source satellite surface reflectance including the visible light band, the near-infrared band, and the short-wave infrared band of MODIS, Landsat 5 / TM, Landsat 7 / ETM+, and Landsat 8 / OLI. Specifically, Landsat TM and ETM+ input the surface reflectance of 5 bands with central wavelengths of 485 nm, 560 nm, 660 nm, 830 nm, and 1650 nm; Landsat OLI inputs the surface reflectance of 5 bands with central wavelengths of 485 nm, 569 nm, 660 nm, 840 nm, and 1650 nm; MODIS inputs the surface reflectance of 6 bands with central wavelengths of 469 nm, 555 nm, 645 nm, 859 nm, 1240 nm, and 1640 nm.

[0035] S102. Convert the surface reflectance of the multi-source satellite sensor into remote sensing reflectance, extract the water body range of the multi-source satellite sensor to determine the water body range corresponding to the multi-source satellite sensor, and remove multi-category interfering 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 interfering pixels. Specifically, although the surface reflectance data of Landsat and MODIS have been atmospherically corrected, their correction methods are not specifically designed for water bodies. Therefore, when calculating the remote sensing reflectance, it is necessary to further remove the skylight and residual aerosol scattering noise in the surface reflectance.

[0036] In the embodiments of the present application, water bodies have physical characteristics and optical characteristics; the optical characteristics include the transparency, chromaticity, and light transmittance of water; the physical characteristics include the suspended sediment concentration and turbidity. The specified bands are the near-infrared band and the short-wave infrared band; the absorption characteristics of the remote sensing reflectance (i.e., the reflectance from water leaving the surface) of water bodies with different characteristics in the near-infrared band and the short-wave infrared band are different. Since pure water has strong absorption in the near-infrared and short-wave infrared bands, it is assumed that the radiation from water leaving the surface within this spectral range is 0. For relatively clean water bodies, it can be considered that the near-infrared reflectance from water leaving the surface is 0, while for relatively turbid water bodies, the reflectance from water leaving the surface in the short-wave infrared band is approximately 0. Therefore, the minimum value of the reflectance in the near-infrared or short-wave infrared band is regarded as the skylight water surface reflection.

[0037] Optionally, a correction method based on the minimum surface reflectance in the near-infrared band and the shortwave infrared band is used to determine the remote sensing reflectance calculation formula, 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.

[0038] Therefore, the present invention adopts a simple correction method of the minimum near-infrared and shortwave infrared band reflectances to convert the surface reflectance data into remote sensing reflectance to eliminate the residual noise and improve the data quality. During the actual operation process, the remote sensing reflectance is corrected by subtracting the minimum value of the near-infrared and the shortwave infrared, which also excludes the abnormally high noise values that may be caused by noise in the shortwave infrared band in clean water bodies, and then divided by π to convert it into remote sensing reflectance.

[0039] Specifically, the remote sensing reflectance calculation formula is as follows: ; where represents the remote sensing reflectance, NIR (Near-Infrared) represents the near-infrared band, SWIR (Shortwave Infrared) represents the shortwave infrared band, represents the minimum surface reflectance of the NIR and SWIR bands, that is, the minimum surface reflectance of the near-infrared and shortwave infrared bands, represents the surface reflectance.

[0040] In addition, each satellite sensor retrieves the surface reflectance of the target water area. In practice, there are also abnormal interferences in the target water area, and it is necessary to identify the water body range to eliminate the abnormal interference data from the surface reflectance within the water body range, so as to obtain the effective remote sensing reflectance.

[0041] Here, when extracting the water body range of the multi-source satellite sensor to determine the water body range corresponding to the multi-source satellite sensor, 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, the data based on the SWIR band of this type of satellite sensor is obtained, and according to the data based on the SWIR band and using the adaptive threshold method, the water body range of the multi-source satellite sensor is extracted to obtain the 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 boundaries of the candidate water body ranges corresponding to each type of satellite sensor are moved inwards by a preset distance to determine the water body ranges of each type of satellite sensor.

[0042] Specifically, in the extraction of water body range based on satellite data, the Landsat data uses the permanent water bodies in the annual water body range dataset v1.4 released by the Joint Research Centre (JRC) of the European Union as the water body range; the MODIS data uses the adaptive threshold method based on the SWIR band to extract the water body range. In addition, to avoid the land proximity effect and the optical shallow water near the water body boundary, the boundary of each water body range corresponding to each type of satellite sensor is eroded inward by a preset distance, such as 1000 meters, to determine the water body range corresponding to the multi-source satellite sensors.

[0043] Optionally, when removing multi-category interfering pixels from the image of the surface reflectance within the water body range, a quality assessment (QA) band of the multi-source satellite sensors is generated based on the cloud masking processing (C function of Mask, CFMASK) algorithm, and the first type of interfering pixels within the water body range are removed based on the quality assessment band; based on the remote sensing reflectance in the short-wave infrared spectral band of each satellite sensor and a preset short-wave infrared band threshold, the second type of interfering pixels within the water body range are identified and removed. Among them, the first type of interfering pixels at least includes clouds, cloud shadows, ice, and snow; the second type of interfering pixels includes specular reflections on the water surface.

[0044] It should be noted that due to the specular reflection causing the specular reflection phenomenon on the water surface, the brightness values of all bands of the remote sensing image will increase abnormally, and this phenomenon is particularly significant in high-spatial-resolution remote sensing images, increasing the uncertainty of water color inversion. The specular reflection area is identified and removed based on the short-wave infrared (OLI: 1650nm / 2230nm) threshold. Taking the Landsat OLI image as an example, when it is determined as the flare area and removed.

[0045] Here, each type of sensor has a Q / A band. By identifying the data of this band, it is possible to determine the multi-category interfering pixels in the image of the surface reflectance within the water body range identified by each satellite sensor, such as clouds, cloud shadows, ice, snow, and specular reflections on the water surface. The above multi-category interfering pixels are removed 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 interfering pixels. S103. Based on the effective remote sensing reflectance and the CIE chromaticity system, the chromaticity angles inverted by the multi-source satellite sensors are calculated respectively.

[0046] In the embodiment of the present application, according to the above effective remote sensing reflectance and the CIE (International Commission on Illumination) chromaticity system, the chromaticity angles inverted by the multi-source satellite sensors are calculated respectively for subsequent processing.

[0047] S104. Construct a polynomial spectral correction model based on the water body 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.

[0048] In the embodiments of the present application, the above-mentioned multi-dimensional systematic 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 respectively based on the deviation information in these two aspects, and the chromaticity angle inverted by the multi-source satellite sensor is spectrally response corrected based on the constructed correction models to obtain the corrected effective chromaticity angle. Here, a polynomial spectral correction model is constructed according to the water body optical simulation dataset, and the chromaticity angle inverted by the multi-source satellite sensor is spectrally response corrected (the first correction) according to the polynomial spectral correction model to obtain the chromaticity angle after spectral response correction.

[0049] S105. 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 spectral response correction based on the cross-calibration correction model.

[0050] In the embodiments of the present application, when dealing with the deviation information caused by differences in data processing methods (such as atmospheric correction algorithms), a cross-calibration correction model is established according to the synchronous observation data of the multi-source satellite sensor, and the chromaticity angle of the above-mentioned multi-source satellite sensor after spectral response correction is further systematically deviation corrected through the cross-calibration correction model to obtain the final chromaticity angle after systematic deviation correction.

[0051] S106. Determine the water color index of the target water area based on the chromaticity angle after systematic deviation correction.

[0052] Here, for the same target water area, the effective chromaticity angle after systematic deviation correction of the multi-source satellite sensor system can be combined to jointly determine the water color index FUI of the target water area. Specifically, when any satellite sensor is required, the chromaticity angle after systematic deviation correction of the satellite sensor is used to calculate the water color index FUI of the water body, and the water color index FUI of the target water area is determined based on the water color index FUI of multiple satellite sensors.

[0053] Specifically, through the pre-established chromaticity angle look-up table for the effective chromaticity angle after systematic deviation correction, which includes the mapping relationship between the chromaticity angle and the water color index FUI, the water color index FUI of the water body can be found, thus realizing the accurate inversion of the water color index FUI of the target water area based on multi-source optical satellite data.

[0054] Table 1 Chromaticity angle α and FUI look-up table

[0055] Further, as Figure 2 shown, in the consistency correction method for retrieving water color indices from multi-source satellites provided in the embodiments of the present application, calculating the chromaticity angles retrieved by multi-source satellite sensors based on the effective remote sensing reflectance and the CIE chromaticity system includes: S201. Obtain multiple band values of the multi-source satellite sensor within the visible light band.

[0056] In the embodiments of the present application, determine the band settings of different sensors within the visible light range.

[0057] S202. When the number of bands of the multiple band values matches the number of tristimulus values of the CIE chromaticity system, convert the effective remote sensing reflectance of the visible light band into the CIE chromaticity system's tristimulus values X, Y, and Z based on the first preset coefficient.

[0058] In the embodiments of the present application, according to the band settings of different satellite sensors within the visible light range, different methods are respectively used to convert the satellite visible light band data into the CIE (International Commission on Illumination) chromaticity system's tristimulus values X, Y, and Z, that is, different methods are adopted according to whether the number of bands of the multiple band values matches the number of tristimulus values of the CIE chromaticity system. When the number of bands of the multiple band values matches the number of tristimulus values of the CIE chromaticity system, that is, when the number of bands in the multiple band values is three, convert the effective remote sensing reflectance of the visible light band into the CIE chromaticity system's tristimulus values X, Y, and Z based on the first preset coefficient.

[0059] When the number of bands of the multiple band values matches the number of tristimulus values of the CIE chromaticity system, at this time, it is a sensor including three bands; for a sensor including three bands: convert the MOD09, Landsat TM, and Landsat ETM+ satellite remote sensing data into the CIE tristimulus values X, Y, and Z of the CIE-XYZ chromaticity system by using the RGB conversion method for the water-leaving reflectance data in the visible light band. The formula is as follows (the coefficients in the following formula correspond to the first preset coefficient): ; where are respectively the remote sensing reflectance values of the R, G, and B three bands of the MOD09, Landsat TM, and Landsat ETM+ remote sensors.

[0060] S203. When the number of bands in the multiple band values does not match the number of tristimulus values of the CIE chromaticity system, convert the effective remote sensing reflectance of the visible light band of each satellite sensor into the CIE chromaticity system's tristimulus values X, Y, and Z respectively by using the linear interpolation method based on the second preset coefficient.

[0061] In the embodiments of the present application, when it is determined that the number of bands in multiple band values does not match the number of tristimulus values of the CIE chromaticity system, that is, when 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 chromaticity system by using linear interpolation based on the second preset coefficient.

[0062] When the number of bands in the multiple band values does not match the number of tristimulus values of the CIE chromaticity system, it may be a sensor including four bands at this time; for a sensor including four bands: for Landsat 8 OLI, its visible light band contains four bands. Therefore, the linear interpolation method is used to convert the color space into tristimulus values, and the formula is as follows (the coefficients in the following formula correspond to the second preset coefficient): ; where are the remote sensing reflectance values of the four visible light bands of Landsat OLI respectively.

[0063] S204. Calculate the chromaticity coordinates x and y according to the tristimulus values X, Y, and Z, establish a new coordinate system with the equal-energy white point in the two-dimensional chromaticity diagram as the reference, and calculate the chromaticity angle of the multi-source satellite sensor according to the new coordinate system.

[0064] In this step, according to the calculated tristimulus values, the chromaticity coordinates x and y are further calculated, a new coordinate system (x', y') is established with the equal-energy white point (1 / 3, 1 / 3) in the two-dimensional chromaticity diagram as the reference, and the chromaticity angle is calculated according to the chromaticity angle formula : A

[0065] Further, as Figure 3 shown, in the method for consistency correction of water color index inversion based on multi-source satellites provided in the embodiments of the present application, the polynomial spectral correction model is constructed based on the water body optical simulation data set, and the spectral response correction of the chromaticity angle inverted by the multi-source satellite sensor is performed based on the polynomial spectral correction model, including: S301. Based on the hyperspectral reflectance provided by the water body optical simulation data set and the spectral response function of the multi-source satellite sensor, simulate the hyperspectral reflectance as the reflectance after conversion of the satellite sensor in the corresponding visible light band.

[0066] In the embodiments of the present application, due to 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 the data of multi-source satellite sensors.

[0067] Obtain the hyperspectral reflectance data Rrs(λ) provided by the measured dataset of surface water bodies globally or the water body optical simulation dataset. The above hyperspectral reflectance data Rrs(λ) can simulate the true color of the water body; combining the above hyperspectral reflectance data with the spectral response function of the satellite sensor can convert (i.e., simulate) the above hyperspectral reflectance data Rrs(λ) into the reflectance of the corresponding satellite bands (including the RGB band).

[0068] Here, based on the hyperspectral reflectance data Rrs(λ) provided by the water body optical simulation dataset and combining with the relative spectral response function (RSR) of the sensor, the hyperspectral reflectance is simulated as the conversion reflectance of the visible light bands of multi-source remote sensing data (Landsat TM, Landsat ETM+, Landsat OLI, MODIS).

[0069]

[0070] Among them, Rrs_band is the conversion reflectance, that is, the simulated reflectance of the satellite in the corresponding visible light band. Rrs(λ) is the hyperspectral data of the 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.

[0071] S302. Calculate the first chromaticity angle according to the hyperspectral reflectance, and calculate the second chromaticity angle corresponding to each multi-source satellite sensor according to the converted reflectance of each multi-source satellite sensor in its respective visible light band, and construct a polynomial spectral correction model for the multi-source satellite sensor according to the first chromaticity angle and the second chromaticity angle.

[0072] In this step, for the original hyperspectral reflectance data Rrs(λ), calculate the first chromaticity angle based on this hyperspectral reflectance data Rrs(λ); for the converted reflectance after simulation of each satellite sensor, calculate the second chromaticity angle; construct a polynomial formula for the deviation between the two by comparing the above two chromaticity angles to obtain the spectral correction model for each satellite sensor, and calculate the chromaticity angle inverted by each satellite sensor through the above spectral correction model to obtain the corrected chromaticity angle. Here, through the above correction method, the data of the multi-source satellite sensor can be made closer to the chromaticity angle value of the hyperspectral data, thereby avoiding the chromaticity angle deviation caused by the spectral response difference and making the multi-source satellites have consistency.

[0073] Specifically, by comparing the chromaticity angles before and after simulation, construct a polynomial correction formula for the deviation between the two. Based on this formula, correct the chromaticity angles calculated by each sensor to avoid the chromaticity angle calculation deviation caused by the spectral response difference and improve the comparability and consistency between the multi-source data. The formulas (i.e., spectral correction models) corresponding to each multi-source satellite sensor are as follows: ; where b = α multi / 100, and α multi is the chromaticity angle calculated by simulating the TM, ETM, OLI, and MODIS bands respectively based on the globally measured Rrs(λ) dataset on the earth's surface. Through the above correction formula, the chromaticity angles calculated by each sensor are corrected, effectively eliminating the chromaticity angle calculation deviation caused by the spectral response difference, and enhancing the comparability and consistency of multi-source data.

[0074] S303. Perform spectral response correction on the chromaticity angles inversely retrieved by multi-source satellite sensors based on the polynomial spectral correction model to obtain the chromaticity angles of the multi-source satellite sensors after spectral response correction.

[0075] For each satellite sensor in the multi-source satellite sensors, correct the inversely retrieved chromaticity angle of the satellite sensor based on the correction model matched with the satellite sensor to obtain the effective chromaticity angles of the multi-source satellite sensors after spectral response correction.

[0076] In the embodiments of the present application, considering the inconsistency caused by the "different spectral response functions" between satellite sensors, by constructing the spectral correction model of each satellite sensor and using the above spectral correction model to correct the chromaticity angles calculated by the matched sensors, the chromaticity angle calculation deviation caused by the spectral response difference is effectively eliminated, and the comparability and consistency of multi-source data are enhanced.

[0077] Further, as Figure 4 shown, in the consistency correction method for retrieving water color index based on multi-source satellites provided in the embodiments of the present application, the method of establishing a cross-calibration correction model using the synchronous observation data of the multi-source satellite sensors and performing systematic deviation correction on the chromaticity angles of the multi-source satellite sensors after spectral response correction based on the cross-calibration correction model includes: S401. Resample the target multispectral images of the multi-source satellite sensors based on a preset time window and a preset spatial resolution, construct a pixel window of a preset size centered on the resampled sampling points, calculate the coefficient of variation of the pixel window in the visible light band, and select the effective pixels whose coefficient of variation meets the preset threshold.

[0078] In the embodiments of the present application, according to the usage conditions and respective attribute characteristics of each satellite sensor, a preset time window and a preset spatial resolution are determined. Here, the preset time window is to find the images of different satellite sensors on the same day. For example, Landsat and MODIS. Since there is a time difference in satellite overpass, the cooperation situation between Landsat and MODIS is based on setting the time window to ±3 hours. The preset spatial resolution is to unify the spatial resolutions of different satellite sensors. For example, for Landsat and MODIS, since the spatial resolution of Landsat is 30 meters, it 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, resampling is performed on each satellite sensor.

[0079] During the resampling process, spatial heterogeneity will occur in different satellite sensors. Therefore, it is necessary to remove the pixels that will generate spatial heterogeneity (which can also be understood as removing the corresponding data). Therefore, with each resampled sampling point as the center, a pixel window of a preset size is constructed (for example, a 3×3 MODIS pixel window). Then, the coefficient of variation in the red (R), green (G), and blue (B) bands within this pixel window is calculated, and the pixels with a coefficient of variation less than a preset threshold (for example, 0.2) are screened out. The coefficient of variation (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 influence of spatial heterogeneity on the synchronous point data.

[0080] S402. Based on the selected valid pixels, a point-to-point matching data set of multi-source satellite sensors is constructed, and based on the matching data set, a cross-calibration correction model is established between each other satellite sensor and the specified satellite sensor in the multi-source satellite sensors.

[0081] In the embodiments of the present application, MODIS in the multi-source satellite sensors is used as the specified satellite sensor. Based on the difference information between the retrieved chromaticity angle of each other satellite sensor in the multi-source satellite sensors and the specified satellite sensor, the linear fitting parameters for each other satellite sensor are determined, and based on the retrieved chromaticity angle and fitting parameters of each other satellite sensor, a cross-calibration correction model is determined, that is, a cross-calibration linear correction model.

[0082] S403. Based on the cross-calibration correction model, systematic deviation correction is performed on the chromaticity angles of the other satellite sensors after spectral response correction that are matched.

[0083] Here, each of the other satellite sensors in the multi-source satellite sensors corresponds to a cross-calibration linear correction model. Based on this cross-calibration linear correction model, systematic deviation correction is performed on the chromaticity angle inverted by the other satellite sensors to obtain an effective chromaticity angle after systematic deviation correction.

[0084] In a specific example, taking the MODIS inversion result as the reference data, a cross-calibration linear correction model based on the chromaticity angle is established to further perform systematic deviation correction on 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-mentioned chromaticity angle cross-calibration linear correction model is as follows:

[0085] Among them, is the chromaticity angle of each sensor after spectral response correction. ' is the chromaticity angle 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 characterize the difference information between each other satellite sensor and the specified satellite sensor after fitting.

[0086] The above-mentioned consistency correction method for inverting water color indices based on multi-source satellites provided by the embodiments of the present application has the following beneficial effects: 1) Universality of multi-source data: It is applicable to the collaborative processing of different sensors (such as Landsat TM / ETM+ / OLI, MODIS), compatible with visible light, near-infrared, and short-wave infrared band data, can process the FUI inversion of various types of lake waters (from clear to eutrophic), and adapt to various water quality conditions. Only the visible light red, green, and blue three bands are required to extract water color parameters, support the mining of historical remote sensing data such as Landsat and MODIS, and realize large-scale and long-term water color monitoring.

[0087] 2) Spectral response difference correction: Based on the global surface water hyperspectral dataset and the sensor relative spectral response function (RSR), the reflectance differences of different satellite bands are simulated, and a polynomial correction model is constructed to eliminate the chromaticity angle calculation deviation caused by the differences in band center wavelengths and bandwidths.

[0088] 3) Cross-calibration to eliminate systematic errors: Use synchronous images (such as Landsat and MODIS ±3-hour window data) to construct a point-to-point matching dataset, and perform chromaticity angle cross-calibration through a linear regression model to reduce the systematic errors caused by the differences in radiometric calibration and atmospheric correction methods.

[0089] 4) Standardized FUI conversion mechanism: Calculate the chromaticity angle based on the CIE chromaticity space, and achieve unified conversion of color indices through a standard FUI lookup table (covering the correspondence between 21 levels of chromaticity angles and FUI), ensuring the comparability of inversion results of different satellite data.

[0090] 5) Full-process optimization and quality control: Integrate preprocessing steps such as noise rejection (near-infrared - short-wave infrared minimum value correction), flare identification (SWIR threshold < 0.1), water body masking (JRC permanent water body dataset + boundary erosion), cloud / snow filtering (QA band), etc., to improve the quality of input data.

[0091] This application aims at the problem of systematic deviation in the FUI inversion results of multi-source satellite data, and proposes a consistency correction method for lake water color indices based on multi-source optical satellite data. Compared with the prior art, the main technical advantages of the present invention are as follows: (1) Propose a normalization correction model to improve the consistency of FUI inversion results of multi-source satellites: In existing methods, the spectral response functions and data processing methods of different satellites are different, resulting in systematic deviations in FUI inversion results, which affect the spatio-temporal consistency of data. The present invention constructs a normalization correction model to standardize the FUI results of different satellites, reduce the systematic error between sensors, improve the comparability of data, and make it applicable to the long-term monitoring of lake water body colors.

[0092] (2) Optimize the chromaticity angle calculation to improve the FUI inversion accuracy: The chromaticity angle is a key parameter in FUI calculation and is greatly affected by spectral band settings and atmospheric correction methods. The present invention adopts an improved chromaticity angle calculation method and combines the spectral characteristics of multi-source satellite data for chromaticity angle optimization and correction, thereby reducing the FUI calculation error caused by band differences between different sensors and improving the inversion accuracy.

[0093] (3) Applicable to multi-source satellite data and improve spatio-temporal coverage: The method of the present invention can be applied to various 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 spatio-temporal coverage of lake water color monitoring, making the FUI inversion results coherent in time and having better applicability in space, and being applicable to the analysis of water color changes in long time series.

[0094] Based on the same inventive concept, the embodiments of this application also provide a consistency correction device for inverting water color indices based on multi-source satellites corresponding to the consistency correction method for inverting water color indices based on multi-source satellites. Since the principle of solving problems by the device in the embodiments of this application is similar to the above-mentioned consistency correction method for inverting water color indices based on multi-source satellites in the embodiments of this application, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be elaborated.

[0095] Referring to Figure 5 as shown, a consistency correction device based on multi-source satellite retrieved water color index provided by an embodiment of the present application, the device includes: An acquisition module 501, configured to acquire the surface reflectance of multi-source satellite sensors in respective visible light bands, near-infrared bands, and short-wave infrared bands of a target water area; A processing module 502, configured to convert the surface reflectance of the multi-source satellite sensors into remote sensing reflectance, extract the water body range of the multi-source satellite sensors to determine the water body range corresponding to the multi-source satellite sensors, 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 interference pixels; A calculation module 503, configured to calculate the chromaticity angles retrieved by the multi-source satellite sensors respectively based on the effective remote sensing reflectance and the CIE chromaticity system; A first correction module 504, configured to construct a polynomial spectral correction model based on a water body optical simulation data set, and perform spectral response correction on the chromaticity angles retrieved by the multi-source satellite sensors based on the polynomial spectral correction model; A second correction module 505, configured to establish a cross-calibration correction model using the synchronous observation data of the multi-source satellite sensors, and perform systematic deviation correction on the chromaticity angles of the multi-source satellite sensors after spectral response correction based on the cross-calibration correction model; A determination module 506, configured to determine the water color index of the target water area based on the chromaticity angles after systematic deviation correction.

[0096] In a possible implementation manner, the conversion of the surface reflectance of the multi-source satellite sensors into remote sensing reflectance includes: Based on the correction method of the minimum surface reflectance in the near-infrared band and the short-wave infrared band, determine the remote sensing reflectance calculation formula, and convert the surface reflectance of the multi-source satellite sensors into remote sensing reflectance and correct the residual noise based on the remote sensing reflectance calculation formula; wherein, the remote sensing reflectance calculation formula is:

[0097] wherein, represents the remote sensing reflectance, NIR represents the near-infrared band, SWIR represents the short-wave infrared band, represents the minimum surface reflectance of the NIR and SWIR bands, represents the surface reflectance.

[0098] 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: 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.

[0099] In a possible implementation, 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.

[0100] 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: 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.

[0101] In a possible implementation manner, constructing a polynomial spectral correction model based on the water body optical simulation data set, and performing spectral response correction on the chromaticity angles inverted by the multi-source satellite sensors based on the polynomial spectral correction model includes: Based on the hyperspectral reflectance provided by the water body optical simulation data set and the spectral response functions of the multi-source satellite sensors, simulating the hyperspectral reflectance as the reflectance converted by the satellite sensors in the corresponding visible light bands; Calculating a first chromaticity angle according to the hyperspectral reflectance, and calculating second chromaticity angles corresponding to the multi-source satellite sensors respectively according to the reflectances converted by 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 angles; Performing spectral response correction on the chromaticity angles inverted by the multi-source satellite sensors respectively based on the polynomial spectral correction model to obtain the chromaticity angles of the multi-source satellite sensors after spectral response correction.

[0102] In a possible implementation manner, establishing a cross-calibration correction model by using the synchronous observation data of the multi-source satellite sensors, and performing systematic deviation correction on the chromaticity angles of the multi-source satellite sensors after spectral response correction based on the cross-calibration correction model includes: Based on a preset time window and a preset spatial resolution, resampling the target multispectral images of the multi-source satellite sensors, constructing a pixel window with a preset size centered on the sampling points of the resampling, calculating the coefficient of variation of the pixel window in the visible light bands, and selecting valid pixels whose coefficients of variation meet the preset threshold; Based on the selected valid pixels, constructing a point-to-point matching data set of the multi-source satellite sensors, and establishing a cross-calibration correction model between each other satellite sensor and a specified satellite sensor in the multi-source satellite sensors based on the matching data set; Performing systematic deviation correction on the chromaticity angles of the other satellite sensors after spectral response correction that are matched based on the cross-calibration correction model.

[0103] The present application provides the above-mentioned consistency correction device for multi-source satellite-inverted water color index. By performing spectral response correction and systematic deviation correction on the water body chromaticity angle inverted by multi-source satellite sensors calculated from 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, ensuring that the inversion data of multi-source satellite sensors can be collaboratively applied and enhancing the applicability of multi-source satellite sensor data. The consistency of the multi-source satellite data-inverted FUI is enhanced, solving the problem of discontinuous data caused by sensor differences, and enabling high-precision collaborative inversion for long-term monitoring of the water color of inland lakes.

[0104] As Figure 6 shown, an electronic device 600 provided by an embodiment of the present application includes: a processor 601, a memory 602, and a bus. The memory 602 stores machine-readable instructions executable by the processor 601. When the electronic device runs, communication between the processor 601 and the memory 602 is carried out through the bus, and the processor 601 executes the machine-readable instructions to perform the steps of the above-mentioned consistency correction method for multi-source satellite-inverted water color index.

[0105] Specifically, the above-mentioned memory 602 and processor 601 can be general-purpose memory and processor, and no specific limitation is made here. When the processor 601 runs the computer program stored in the memory 602, it can execute the above-mentioned consistency correction method for multi-source satellite-inverted water color index.

[0106] Corresponding to the above-mentioned consistency correction method for multi-source satellite-inverted water color index, an embodiment of the present application also provides a computer-readable storage medium. A computer program is stored on the computer-readable storage medium, and when the computer program is run by a processor, it executes the steps of the above-mentioned consistency correction method for multi-source satellite-inverted water color index.

[0107] The above-mentioned electronic device and storage medium provided in real time by the present application correct the water body chromaticity angle inverted by multi-source satellite sensors through the multi-dimensional systematic deviation information of the multi-source satellite sensors, and obtain the water color index based on the corrected chromaticity angle, improving the consistency of the correction result, ensuring that the inversion data of multi-source satellite sensors can be collaboratively applied, and enhancing the applicability of multi-source satellite sensor data.

[0108] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems and devices described above can refer to the corresponding processes in the method embodiments, and will not be elaborated herein. In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there can be other division methods in actual implementation. For another 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 couplings, direct couplings, or communication connections shown or discussed with each other can be through some communication interfaces. The indirect couplings or communication connections of the devices or modules can be in electrical, mechanical, or other forms.

[0109] The modules described as separate components may or may not be physically separated. The components shown as modules may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0110] In addition, in each embodiment of the present application, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0111] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this 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 causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical discs that can store program codes.

[0112] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A consistency correction method based on multi-source satellite retrieved water color index, characterized in that The method includes: Step S1: Obtain the surface reflectance of the multi-source satellite sensors in the target water area in their respective visible light bands, near-infrared bands, and short-wave infrared bands; Step S2: Convert the surface reflectance of the multi-source satellite sensors into remote sensing reflectance, extract the water body range of the multi-source satellite sensors to determine the water body range corresponding to the multi-source satellite sensors, and remove multi-category interfering 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 interfering pixels; Step S3: Calculate the chromaticity angles retrieved by the multi-source satellite sensors respectively based on the effective remote sensing reflectance and the CIE chromaticity system; Step S4: Construct a polynomial spectral correction model based on the water optical simulation dataset, and perform spectral response correction on the chromaticity angles retrieved by the multi-source satellite sensors based on the polynomial spectral correction model; Step S5: Establish a cross-calibration correction model using the synchronous observation data of the multi-source satellite sensors, and perform systematic deviation correction on the chromaticity angles of the multi-source satellite sensors after spectral response correction based on the cross-calibration correction model; Step S6: Determine the water color index of the target water area based on the chromaticity angles after systematic deviation correction.

2. The consistency correction method based on multi-source satellite retrieved water color index according to claim 1, characterized in that, In the step S2, the conversion of the surface reflectance of the multi-source satellite sensors into remote sensing reflectance includes: Based on the correction method of the minimum surface reflectance in the near-infrared band and the short-wave infrared band, determine the remote sensing reflectance calculation formula, and convert the surface reflectance of the multi-source satellite sensors into remote sensing reflectance and correct the residual noise based on the remote sensing reflectance calculation formula; wherein, the remote sensing reflectance calculation formula is: Among them, represents the remote sensing reflectance, NIR represents the near-infrared band, and SWIR represents the shortwave infrared band. represents the minimum surface reflectance of the NIR and SWIR bands. represents the surface reflectance.

3. The consistency correction method based on multi-source satellite-inverted water color index according to claim 2, wherein In the step S2, the extraction of the water body range of the multi-source satellite sensors to determine the water body range corresponding to the multi-source satellite sensors includes: For Landsat-type satellite sensors, obtain a preset water body range dataset, and determine the candidate water body range of the target water area from the water body range dataset; For MODIS-type satellite sensors, obtain the data of this type of satellite sensors based on the SWIR band, extract the water body range of the multi-source satellite sensors according to the data based on the SWIR band and using the adaptive threshold method to obtain the output data of the SWIR band, and determine the candidate water body range of the target water area based on the output data of the SWIR band; Move inward a preset distance from the water body boundaries of the candidate water body ranges corresponding to each type of satellite sensor to determine the water body ranges of each type of satellite sensor.

4. The consistency correction method based on multi-source satellite retrieved water color index according to claim 3, wherein In the step S2, the removal of multi-category interfering pixels from the image of the surface reflectance within the water body range includes: Generate a quality assessment band of the multi-source satellite sensors based on the cloud masking processing algorithm, and remove the first type of interfering pixels within the water body range based on the quality assessment band; wherein, the first type of interfering pixels at least includes clouds, cloud shadows, ice, and snow; Based on the remote sensing reflectance in the short-wave infrared spectral band of each satellite sensor and a preset short-wave infrared band threshold, the second type of interfering pixels within the water body range are identified and removed; wherein, the second type of interfering pixels includes specular reflections on the water surface.

5. The consistency correction method based on multi-source satellite-inverted water color index according to claim 1, wherein In the step S3, calculating the chromaticity angles inverted by the multi-source satellite sensors respectively based on the effective remote sensing reflectance and the CIE chromaticity system includes: Obtaining multiple band values of the multi-source satellite sensors within the visible light band; When the number of bands of the multiple band values matches the number of tristimulus values of the CIE chromaticity system, converting the effective remote sensing reflectance in the visible light band into the tristimulus values X, Y, and Z of the CIE chromaticity system based on a first preset coefficient; When the number of bands in the multiple band values does not match the number of tristimulus values of the CIE chromaticity system, converting the effective remote sensing reflectance in the visible light band of each satellite sensor into the tristimulus values X, Y, and Z of the CIE chromaticity system respectively by using linear interpolation based on a second preset coefficient; According to the tristimulus values X, Y, and Z, calculating the chromaticity coordinates x, y, and establishing a new coordinate system with the equal-energy white point in the two-dimensional chromaticity diagram as the reference, and calculating the chromaticity angle of the multi-source satellite sensors according to the new coordinate system.

6. The consistency correction method based on multi-source satellite retrieved water color index according to claim 1, characterized in that In the step S4, constructing a polynomial spectral correction model based on the water optical simulation dataset and performing spectral response correction on the chromaticity angles inverted by the multi-source satellite sensors based on the polynomial spectral correction model includes: Based on the hyperspectral reflectance provided by the water optical simulation dataset and the spectral response function of the multi-source satellite sensors, simulating the hyperspectral reflectance as the reflectance converted by the satellite sensor 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 respectively according to the reflectances converted by 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 angles; Performing spectral response correction on the chromaticity angles inverted by the multi-source satellite sensors respectively based on the polynomial spectral correction model to obtain the chromaticity angles of the multi-source satellite sensors after spectral response correction.

7. The consistency correction method for multi-source satellite-inverted water color index according to claim 6, characterized in that In the step S5, establishing a cross-calibration correction model by using the synchronous observation data of the multi-source satellite sensors and performing systematic deviation correction on the chromaticity angles of the multi-source satellite sensors after spectral response correction based on the cross-calibration correction model includes: Based on a preset time window and a preset spatial resolution, resampling the target multispectral images of the multi-source satellite sensors, constructing a pixel window of a preset size with the resampled sampling points as the center, calculating the coefficient of variation of the pixel window in the visible light band, and selecting effective pixels whose coefficient of variation meets the preset threshold; Based on the selected valid pixels, construct a point-to-point matching dataset for multi-source satellite sensors, and based on the matching dataset, establish a cross-calibration correction model between each other satellite sensor and the specified satellite sensor in the multi-source satellite sensors; Based on the cross-calibration correction model, perform systematic deviation correction on the chromaticity angles of the other satellite sensors after spectral response correction that are matched.

8. A consistency correction device for multi-source satellite-inverted water color index, characterized in that The device includes: An acquisition module, configured to acquire the surface reflectance of multi-source satellite sensors in their respective visible light bands, near-infrared bands, and short-wave infrared bands for a target water area; A processing module, configured to convert the surface reflectance of the multi-source satellite sensors into remote sensing reflectance, extract the water body range of the multi-source satellite sensors to determine the water body range corresponding to the multi-source satellite sensors, and remove multi-category interfering pixels from the images of the surface reflectance within the water body range to obtain the effective remote sensing reflectance of the image after removing interfering pixels; A calculation module, configured to calculate the chromaticity angles inverted by the multi-source satellite sensors respectively based on the effective remote sensing reflectance and the CIE chromaticity system; A first correction module, configured to construct a polynomial spectral correction model based on a water body optical simulation dataset, and perform spectral response correction on the chromaticity angles inverted by the multi-source satellite sensors based on the polynomial spectral correction model; a second correction module, configured to establish a cross-calibration correction model using the synchronous observation data of the multi-source satellite sensors, and perform systematic deviation correction on the chromaticity angles of the multi-source satellite sensors after spectral response correction based on the cross-calibration correction model; A determination module, configured to determine the water color index of the target water area based on the chromaticity angles after systematic deviation correction.

9. An electronic device, characterized in that, Includes: A processor, a storage medium, and a bus. The storage medium stores machine-readable instructions executable by the processor. When the electronic device runs, the processor communicates with the storage medium through the bus, and the processor executes the machine-readable instructions to perform the steps of the consistency correction method for inverting water color index based on multi-source satellites as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, and when the computer program is run by the processor, it performs the steps of the consistency correction method for inverting water color index based on multi-source satellites as described in any one of claims 1 to 7.

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