A water body sediment concentration inversion method and device based on satellite remote sensing images

Abstract

The application provides a water body sediment concentration inversion method and device based on satellite remote sensing images, and the method comprises the following steps: pre-processing satellite remote sensing images of a measured water body to generate a remote sensing reflectance above water surface; performing optical classification on the measured water body based on the remote sensing reflectance above water surface to generate a first type of water body, a second type of water body, a third type of water body and a fourth type of water body; performing backscattering coefficient inversion on the remote sensing reflectance above water surface to determine the backscattering coefficient of the first type of water body, the backscattering coefficient of the second type of water body, the backscattering coefficient of the third type of water body and the backscattering coefficient of the fourth type of water body; and performing sediment concentration remote sensing inversion on the backscattering coefficient of the first type of water body, the backscattering coefficient of the second type of water body, the backscattering coefficient of the third type of water body and the backscattering coefficient of the fourth type of water body to generate water body sediment concentration. The method effectively realizes high-precision backscattering coefficient inversion at different reference wave bands, and improves the inversion precision.

Description

Technical Field

[0001] This invention relates to the field of water sediment content inversion technology, and in particular to a method and apparatus for inverting water sediment content based on satellite remote sensing images. Background Technology

[0002] Sediment content in water bodies is closely related to the biological processes of river ecosystems, the development of floodplains, and riverbed deposition, making it crucial for understanding changes in deposition and erosion within aquatic ecosystems. Traditional methods of measuring sediment content involve manually collecting water samples and measuring them in a laboratory. While this method offers high accuracy, it is time-consuming, labor-intensive, and costly, making it unsuitable for long-term, large-scale sediment monitoring. Satellite remote sensing technology, due to its non-contact and spatially continuous nature, offers unparalleled advantages for sediment monitoring in large watersheds.

[0003] Existing methods for retrieving water sediment concentration are often spatially limited and lack versatility. These models are highly dependent on training data and their generalizability is insufficient for areas without available data. For large-scale, complex water bodies, the optical signals of suspended sediment exhibit piecewise responses to concentration variations, meaning that water bodies with different sediment concentration ranges require separate inversion methods and cannot be treated uniformly. Therefore, a coupled inversion method applicable to multiple water body types is needed.

[0004] For large-scale, complex water bodies, existing optical classification methods mainly rely on thresholding and clustering. These methods are easily affected by atmospheric correction accuracy, sensor band settings, and observation conditions, resulting in numerous uncertainties and unstable classification results. Therefore, there is a need to propose a water body optical classification method that is suitable for complex water bodies and resistant to environmental interference.

[0005] To address the current issue of low accuracy in sediment concentration retrieval in complex water bodies, existing semi-analytical retrieval methods can improve this problem to some extent. These methods introduce the absorption coefficient *a* and backscattering coefficient *bbp* as intermediate variables. Relying on the analytical relationship of the water body radiative transfer equation, *a* and *bbp* are calculated from the remotely sensed reflectance *Rrs* above the water surface, and then the sediment concentration is extrapolated based on empirical relationships. However, existing semi-analytical methods have two problems: ① Most rely on the 443nm (nanometer) band, while wide-spectrum Landsat sensors such as Landsat 5 / 7 and the Environmental Series lack the 443nm band, making it impossible to calculate *a* and *bbp* using this method. ② Existing methods often use 555nm and 655nm as reference bands, and then extrapolate *bbp* to a longer band (such as 859nm) based on empirical formulas such as slope. This causes error propagation during the calculation process, resulting in low accuracy of the calculated *bbp* (859nm). Summary of the Invention

[0006] Therefore, the technical solution of this invention mainly addresses the shortcomings of existing remote sensing inversion models for sediment content, such as limited applicable sensors and types of water bodies, low inversion accuracy, and low model versatility, thereby providing a method and device for inverting water sediment content based on satellite remote sensing images.

[0007] In a first aspect, embodiments of the present invention provide a method for inverting water sediment content based on satellite remote sensing imagery, comprising:

[0008] Collect satellite remote sensing images of the water body to be measured, preprocess the satellite remote sensing images of the water body to be measured, and generate the remote sensing reflectance above the water surface;

[0009] Based on the remote sensing reflectance above the water surface, the measured water body is optically classified to generate Class I, Class II, Class III and Class IV water bodies;

[0010] Backscattering coefficients were inverted to determine the backscattering coefficients of the first type of water body, the second type of water body, the third type of water body, and the fourth type of water body.

[0011] The sediment content of the water body is generated by remote sensing inversion of the backscattering coefficients of the first, second, third and fourth types of water bodies.

[0012] This invention provides a method for inverting water sediment concentration based on satellite remote sensing imagery. The method employs a classification-based approach followed by inversion. Specifically, it performs rapid and effective optical classification of large-scale complex water bodies, then inverts the backscattering coefficients of different wavebands for each category, and finally calculates the final sediment concentration from these backscattering coefficients. The rapid and effective optical classification of large-scale complex water bodies is highly universal, with a simple process, and effectively classifies the complex water bodies (i.e., the measured water bodies) into four types with different sediment concentration ranges. Furthermore, the method of inverting the backscattering coefficients of different wavebands for different categories of water bodies solves the problems of "reliance on the 443nm waveband" and "low accuracy of inversion results in the 859nm waveband" in existing technologies, effectively achieving high-precision backscattering coefficient inversion across different reference wavebands. Finally, considering the complex optical characteristics of water bodies, a multi-water-body-type coupled inversion method addresses the problem of spectral segmentation response under different sediment concentration ranges, making the sediment concentration remote sensing inversion model more robust.

[0013] In conjunction with the first aspect, in one possible implementation, satellite remote sensing images of the water body being measured are preprocessed to generate remote sensing reflectance above the water surface, including:

[0014] Cloud masking, atmospheric correction, flare masking, and water body extraction were performed on satellite remote sensing images to determine the top atmospheric radiance, correction parameters, direct solar irradiance, and diffuse sky irradiance.

[0015] The reflectance of the air-water interface and the direct solar irradiance are obtained. Based on the irradiance value of the top atmospheric layer, correction parameters, direct solar irradiance, diffuse sky irradiance, and the reflectance of the air-water interface, the remote sensing reflectance above the water surface is calculated.

[0016] In conjunction with the first aspect, in another possible implementation, the remote sensing reflectance above the water surface includes:

[0017] Remote sensing reflectance in the blue light band, green light band, red light band, and near-infrared band.

[0018] In conjunction with the first aspect, in another possible implementation, the measured water body is optically classified based on the remote sensing reflectance above the water surface to generate Class I, Class II, Class III, and Class IV water bodies, including:

[0019] The first stimulus value, the second stimulus value, and the third stimulus value were calculated based on the remote sensing reflectance of the blue light band, the green light band, the red light band, and the near-infrared band, respectively.

[0020] The chromaticity angle is determined based on the first stimulus value, the second stimulus value, and the third stimulus value;

[0021] Based on the chromaticity angle, the Freyr color index is determined using a lookup table;

[0022] The Freyer water color index is compared with a preset threshold, and the water body to be tested is classified into Class I, Class II, Class III and Class IV based on the comparison results.

[0023] In conjunction with the first aspect, in another possible implementation, backscattering coefficient inversion is performed on the remote sensing reflectance above the water surface to determine the backscattering coefficients of the first type of water body, the second type of water body, the third type of water body, and the fourth type of water body, including:

[0024] Based on the remote sensing reflectance above the water surface, the backscattering albedo of different bands is determined using a radiative transfer model within the water body; among which, the backscattering albedo of different bands includes the backscattering albedo of the blue light band, the backscattering albedo of the green light band, the backscattering albedo of the red light band, and the backscattering albedo of the near-infrared band.

[0025] The backscattering coefficients of Class I, Class II, Class III, and Class IV water bodies were calculated based on the remote sensing reflectance of the blue band, green band, red band, and near-infrared band, and the backscattering albedo of different bands.

[0026] In conjunction with the first aspect, in another possible implementation, based on the remotely sensed reflectance above the water surface, the backscattering albedo of different bands is determined using a radiative transfer model within the water body, including:

[0027] Determine the remote sensing reflectance below the water surface based on the remote sensing reflectance above the water surface;

[0028] The calibration coefficients were obtained, and the backscattering albedo of different bands was determined based on the calibration coefficients and the remote sensing reflectance below the water surface.

[0029] In conjunction with the first aspect, in another possible implementation, the backscattering coefficients of the first type of water body, the second type of water body, the third type of water body, and the fourth type of water body are calculated based on the remote sensing reflectance of the blue light band, the green light band, the red light band, the near-infrared band, and the backscattering albedo of different bands.

[0030] The total absorption coefficient of the green light band was determined based on the remote sensing reflectance of the blue light band, the green light band, and the red light band; wherein, the total absorption coefficient of the green light band is the sum of the absorption coefficients of all substances except water.

[0031] The absorption coefficient and backscattering coefficient of pure water in the green light band were obtained. Based on the backscattering albedo of the green light band, the total absorption coefficient of the green light band, the absorption coefficient of pure water in the green light band, and the backscattering coefficient of pure water, the backscattering coefficient of the first type of water body was determined.

[0032] The total absorption coefficient of the red light band is determined based on the remote sensing reflectance of the blue light band and the remote sensing reflectance of the red light band; wherein, the total absorption coefficient of the red light band is the sum of the absorption coefficients of all substances except water.

[0033] The absorption coefficient and backscattering coefficient of pure water in the red light band were obtained. Based on the backscattering albedo, total absorption coefficient, absorption coefficient and backscattering coefficient of pure water in the red light band, the backscattering coefficient of Class II water body and Class III water body were determined.

[0034] The total absorption coefficient of the near-infrared band is determined based on the remote sensing reflectance of the near-infrared band and the remote sensing reflectance of the green band; wherein, the total absorption coefficient of the near-infrared band is the sum of the absorption coefficients of all substances except water.

[0035] The absorption coefficient and backscattering coefficient of pure water in the near-infrared band were obtained. Based on the backscattering albedo, total absorption coefficient, absorption coefficient and backscattering coefficient of pure water in the near-infrared band, the backscattering coefficient of Class IV water body was determined.

[0036] Secondly, embodiments of the present invention also provide a water sediment content inversion device based on satellite remote sensing imagery, comprising:

[0037] The preprocessing module is used to acquire satellite remote sensing images of the water body being measured, preprocess the satellite remote sensing images of the water body being measured, and generate the remote sensing reflectance above the water surface.

[0038] The classification module is used to perform optical classification of the measured water body based on the remote sensing reflectance above the water surface, generating Class I, Class II, Class III and Class IV water bodies;

[0039] The first inversion module is used to invert the backscattering coefficient of the remote sensing reflectance above the water surface, and to determine the backscattering coefficients of the first type of water body, the second type of water body, the third type of water body, and the fourth type of water body.

[0040] The second inversion module is used to perform remote sensing inversion of sediment content on the backscattering coefficients of the first type of water body, the second type of water body, the third type of water body, and the fourth type of water body, and generate the sediment content of the water body.

[0041] Thirdly, embodiments of the present invention also disclose an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to cause the at least one processor to perform the steps of a water sediment content inversion method based on satellite remote sensing images as described in the first aspect or any optional embodiment of the first aspect.

[0042] Fourthly, embodiments of the present invention also disclose a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of a method for inverting water sediment content based on satellite remote sensing images as described in the first aspect or any optional embodiment of the first aspect. Attached Figure Description

[0043] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0044] Figure 1 A flowchart of a method for retrieving water sediment content based on satellite remote sensing imagery provided in an embodiment of the present invention;

[0045] Figure 2 A schematic diagram of the water sediment content inversion method based on satellite remote sensing imagery provided in an embodiment of the present invention;

[0046] Figure 3 A schematic diagram illustrating the model inversion effect provided in an embodiment of the present invention;

[0047] Figure 4 A flowchart of S101 provided in an embodiment of the present invention;

[0048] Figure 5 A flowchart of S102 provided in an embodiment of the present invention;

[0049] Figure 6 A schematic diagram illustrating the classification effect of the water body optical classification method provided in an embodiment of the present invention;

[0050] Figure 7 A flowchart of S103 provided in an embodiment of the present invention;

[0051] Figure 8 A flowchart of S1031 provided in an embodiment of the present invention;

[0052] Figure 9 A flowchart of S1032 provided in an embodiment of the present invention;

[0053] Figure 10 A block diagram of a water sediment content inversion device based on satellite remote sensing imagery provided in an embodiment of the present invention;

[0054] Figure 11 This is a specific example diagram of an electronic device in an embodiment of the present invention. Detailed Implementation

[0055] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] In the description of this invention, it should be noted that the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, unless otherwise explicitly specified and limited, the terms "installed," "connected," and "linked" should be interpreted broadly. For example, they can refer to fixed connections, mechanical connections, or electrical connections; they can also refer to direct connections or indirect connections through an intermediate medium; they can also refer to the internal connection of two components; and they can be wireless or wired connections. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0057] This invention provides a method for inverting water sediment content based on satellite remote sensing imagery, such as... Figure 1-2 As shown, it includes:

[0058] S101. Collect satellite remote sensing images of the water body to be measured, preprocess the satellite remote sensing images of the water body to be measured, and generate the remote sensing reflectance above the water surface.

[0059] Specifically, the remote sensing reflectance above the water surface includes: remote sensing reflectance in the blue light band, remote sensing reflectance in the green light band, remote sensing reflectance in the red light band, and remote sensing reflectance in the near-infrared band.

[0060] Furthermore, image preprocessing work is carried out on the satellite remote sensing images of the water body being measured, including cloud masking, atmospheric correction, flare masking, water body extraction, and remote sensing reflectance calculation.

[0061] S102. Based on the remote sensing reflectance above the water surface, perform optical classification of the measured water body to generate Class I water body, Class II water body, Class III water body and Class IV water body.

[0062] S103. Perform backscattering coefficient inversion on the remote sensing reflectance above the water surface to determine the backscattering coefficients of the first type of water body, the second type of water body, the third type of water body, and the fourth type of water body.

[0063] Specifically, the backscattering coefficients at 555nm, 655nm, and 859nm were calculated using the three-color optical level analysis method (QAA-RGB), the improved three-color optical level analysis method including QAA-RGB-Modified (QRM), and the QAA-z859 method.

[0064] S104. Perform remote sensing inversion of sediment content on the backscattering coefficients of the first type of water body, the second type of water body, the third type of water body, and the fourth type of water body to generate the sediment content of the water body.

[0065] Specifically, for each type of water body after optical classification, remote sensing inversion of sediment content is performed to generate the water body sediment content; among them, the calculation formula for the water body sediment content SS1 of the first type of water body is as follows:

[0066] SS1 = 0.01 * 346.09 * bpp555 (1)

[0067] In the above formula, bbb555 represents the backscattering coefficient of the first type of water body.

[0068] Furthermore, the formula for calculating the sediment content SS2 of the second type of water body is as follows:

[0069] SS2 = -0.54 * 23.18 * bpp655 0.66 (2)

[0070] In the above formula, bbb655 represents the backscattering coefficient of the second type of water body (or the backscattering coefficient of the third type of water body).

[0071] Furthermore, the formula for calculating the sediment content (SS3) of Class III water bodies is as follows:

[0072] SS3 = -2.72 * 163.94 * bpp655 0.76 (3)

[0073] Furthermore, the formula for calculating the sediment content (SS4) of Class IV water bodies is as follows:

[0074] SS4 = 2.98 * 173.09 * bpp859 (4)

[0075] In the above formula, bbb859 represents the backscattering coefficient of the fourth type of water body.

[0076] This embodiment proposes a method for inverting water sediment concentration based on satellite remote sensing imagery. It employs a method of first classifying the water body before inverting the concentration. Specifically, it performs rapid and effective optical classification of large-scale complex water bodies, then inverts the backscattering coefficients of different wavebands for different water body categories, and finally calculates the final sediment concentration from the backscattering coefficients. The rapid and effective optical classification of large-scale complex water bodies has high universality, a simple process, and effectively classifies complex water bodies (i.e., the water body being measured) into four types with different sediment concentration ranges. Furthermore, the method of inverting the backscattering coefficients of different wavebands for different water body categories solves the problems of "reliance on the 443nm waveband" and "low accuracy of inversion results in the 859nm waveband" in existing technologies, effectively achieving high-precision backscattering coefficient inversion across different reference wavebands. Finally, as... Figure 3 As shown, the multi-water-body-type coupled inversion method addresses the problem of spectral segmented response under different sediment concentration ranges, thus making the sediment concentration remote sensing inversion model more robust, in response to the complex optical properties of water bodies.

[0077] As an optional embodiment of the present invention, such as Figure 4 As shown, S101 above, which involves preprocessing the satellite remote sensing image of the water body to generate the remote sensing reflectance above the water surface, includes:

[0078] S1011. Perform cloud masking, atmospheric correction, flare masking, and water extraction on the above-mentioned satellite remote sensing images to determine the top atmospheric radiance, correction parameters, direct solar irradiance, and diffuse sky irradiance.

[0079] Specifically, cloud masking is performed using the image's own quality control bands; atmospheric correction employs the 6S (The Second Simulation of Satellite Signal in the Solar Spectrum) method, a theoretical model for atmospheric transmission, with required parameters obtained from empirical knowledge, image parameters, and synchronously observed MODIS (Moderate-resolution Imaging Spectroradiometer) images; for example, aerosol types are selected from continental, urban, combustible, and marine types based on actual conditions; aerosol optical thickness is obtained from synchronously observed MCD19A2 (a Level 2 product of the land aerosol optical depth grid) and MOD08 products (a Level 3 standard atmospheric data product, containing raster atmospheric products with a 1 km spatial resolution); water vapor column concentration is obtained from the National Center for Environmental Forecasting-National Center for Atmospheric Research (NCEP-NCAR); and elevation data is obtained from the Digital Elevation Dataset, etc.

[0080] Furthermore, the flare masking process includes: comparing the flare index of the flare region with a set threshold (usually -0.5), and then removing the flare region based on the comparison result. The flare region is calculated using the flare index (SGI) formula shown below:

[0081]

[0082] In the above formula, B7 and B3 are the atmospheric top radiance values ​​of the seventh and third bands, respectively. The Landsat 8 band setting is used as an example here. For other sensors, it can be changed to the band near 2000nm and the green light band, respectively.

[0083] Furthermore, the water body extent is extracted using the modified normalized interpolated water body index (Equation 2), wherein the calculation formula for the modified normalized interpolated water body index MNDWI is as follows:

[0084] MNDWI=(B Green -B MIR ) / (B Green +B MIR (6)

[0085] Among them, B Green and B MIR These represent the water surface remote sensing reflectance in the green light band and the mid-infrared band, respectively.

[0086] S1012. Obtain the air-water interface reflectance and direct solar irradiance, and calculate the remote sensing reflectance above the water surface based on the above-mentioned atmospheric top radiance value, the above-mentioned correction parameters, the above-mentioned direct solar irradiance, the above-mentioned diffuse sky irradiance and the above-mentioned air-water interface reflectance.

[0087] Specifically, the formula for calculating the remote sensing reflectance Rrs above the water surface is as follows:

[0088]

[0089] In the above formula, r sky E represents the air-water interface reflectance, taken as 0.0245. dir E represents direct solar irradiance. dif Let ρ be the diffuse irradiance of the sky, and ρ be the surface reflectance. The formula for calculating the surface reflectance ρ is as follows:

[0090]

[0091] In the above formula, L is the radiance of the top atmosphere, and x a x b x c These are the correction parameters obtained after 6S atmospheric correction.

[0092] As an optional embodiment of the present invention, such as Figure 5 As shown, S102 above refers to the optical classification of the measured water body based on the remote sensing reflectance above the water surface, generating Class I, Class II, Class III, and Class IV water bodies, including:

[0093] S1021. Calculate the first stimulus value, the second stimulus value, and the third stimulus value based on the above-mentioned blue light band remote sensing reflectance, the above-mentioned green light band remote sensing reflectance, the above-mentioned red light band remote sensing reflectance, and the above-mentioned near-infrared band remote sensing reflectance, respectively.

[0094] Specifically, the formula for calculating the first stimulus value X is as follows:

[0095] X = 11.053 * Rrs b1 +6.950*Rrs b2 +51.135*Rrs b3 +34.457*Rrs b4 (9)

[0096] In the above formula, Rrs b1 Rrs represents the remote sensing reflectance in the blue light band. b2 Rrs represents the remote sensing reflectance in the green light band. b3 Rrs represents the remote sensing reflectance in the red band. b4 This indicates the reflectance of near-infrared remote sensing.

[0097] Furthermore, the formula for calculating the second stimulus value Y is as follows:

[0098] Y = 1.320 * Rrs b1 +21.053*Rrs b2 +66.023*Rrs b3 +18.034*Rrs b4 (10)

[0099] Furthermore, the formula for calculating the third stimulus value Z is as follows:

[0100] Z = 58.038 * Rrs b1 +34.931*Rrs b2 +2.606*Rrs b3 +0.016*Rrs b4 (11)

[0101] S1022. Determine the chromaticity angle based on the first stimulus value, the second stimulus value, and the third stimulus value.

[0102] Specifically, the chromaticity coordinates (x, y) are calculated based on the first stimulus value X, the second stimulus value Y, and the third stimulus value Z, and the calculation formula is as follows:

[0103] x=X / (X+Y+Z) (12)

[0104] y = Y / (X + Y + Z) (13)

[0105] Furthermore, the chromaticity angle α is calculated based on the chromaticity coordinates (x, y). The formula for calculating α is as follows:

[0106]

[0107] S1023. Based on the above chromaticity angles, the Freyr color index is determined using a lookup table.

[0108] Specifically, the FUI index (i.e., the Flyre color index) corresponding to the chromaticity angle α is found using the FUI-α lookup table, which is shown in Table 1 below:

[0109] Table 1:

[0110]

[0111]

[0112] S1024. Compare the above-mentioned Freyr color index with the preset threshold, and based on the comparison result, classify the water body to be tested into the above-mentioned Class I water body, Class II water body, Class III water body and Class IV water body.

[0113] Specifically, such as Figure 6 As shown, the water bodies to be measured are divided into four categories according to FUI: Category I (FUI < 7), Category II (7 ≤ FUI ≤ 14 and Rrs483 > Rrs655), Category III (7 ≤ FUI ≤ 14 and Rrs483 < Rrs655), and Category IV (FUI ≥ 15). Here, Rrs483 represents the remote sensing reflectance in the 483nm band (i.e., the blue light band), and Rrs655 represents the remote sensing reflectance in the 655nm band (i.e., the red light band). Figure 6 SPM indicates the sand content, with the unit being mg / L (milligrams per liter).

[0114] In the above optional embodiments, the selected FUI inversion factor reasonably characterizes the water color and has a high correlation with the sediment content in the water. It can effectively classify the water body into four types with different sediment content ranges. Moreover, the FUI index has the ability to resist atmospheric interference and sensor observation geometry and is less affected by external conditions. It is the preferred classification index in sediment content remote sensing observation.

[0115] As an optional embodiment of the present invention, such as Figure 7 As shown, S103 above, which involves inverting the backscattering coefficient of the remote sensing reflectance above the water surface to determine the backscattering coefficients of the first type of water body, the second type of water body, the third type of water body, and the fourth type of water body, includes:

[0116] S1031. Based on the aforementioned remote sensing reflectance above the water surface, the backscattering albedo of different bands is determined using a water body radiative transfer model; wherein, the backscattering albedo of the aforementioned different bands includes the backscattering albedo of the blue light band, the backscattering albedo of the green light band, the backscattering albedo of the red light band, and the backscattering albedo of the near-infrared band.

[0117] S1032. Based on the above-mentioned blue light band remote sensing reflectance, the above-mentioned green light band remote sensing reflectance, the above-mentioned red light band remote sensing reflectance, the above-mentioned near-infrared band remote sensing reflectance and the above-mentioned backscattering albedo of different bands, calculate the backscattering coefficient of the above-mentioned first type of water body, the above-mentioned second type of water body, the above-mentioned third type of water body and the above-mentioned fourth type of water body.

[0118] As an optional embodiment of the present invention, such as Figure 8 As shown, S1031 above, which is based on the remote sensing reflectance above the water surface, uses a water body radiative transfer model to determine the backscattering albedo in different bands, includes:

[0119] S10311. Determine the remote sensing reflectance below the water surface based on the above-mentioned remote sensing reflectance above the water surface.

[0120] Specifically, the formula for calculating the remote sensing reflectance (rrs) below the water surface is as follows:

[0121] rrs=Rrs / (0.52+1.7*Rrs) (15)

[0122] S10312. Obtain calibration coefficients, and determine the backscattering albedo of the different wavebands based on the calibration coefficients and the remote sensing reflectance below the water surface.

[0123] Specifically, the formulas for calculating the backscattering albedo u in different wavebands are as follows:

[0124]

[0125] In the above formula, g0 and g1 represent calibration coefficients, where g0 is 0.089 and g1 is 0.125.

[0126] As an optional embodiment of the present invention, such as Figure 9As shown, S1032 above, which calculates the backscattering coefficients of the first type of water body, the second type of water body, the third type of water body, and the fourth type of water body based on the remote sensing reflectance of the blue light band, the green light band, the red light band, the near-infrared band, and the backscattering albedo of the different bands, includes:

[0127] S10321. Determine the total absorption coefficient of the green light band based on the above-mentioned blue light band remote sensing reflectance, the above-mentioned green light band remote sensing reflectance and the above-mentioned red light band remote sensing reflectance; wherein, the above-mentioned total absorption coefficient of the green light band is the sum of the absorption coefficients of all substances except water.

[0128] Specifically, for Class I water bodies, the backscattering coefficient (bbp555) at 555 nm was calculated using the QAA-RGB method; the formula for calculating the total absorption coefficient anw555 in the green light band is shown below:

[0129]

[0130] S10322. Obtain the pure water absorption coefficient and pure water backscattering coefficient in the green light band, and determine the backscattering coefficient of the first type of water body based on the backscattering albedo of the green light band, the total absorption coefficient of the green light band, the pure water absorption coefficient and pure water backscattering coefficient in the green light band.

[0131] Specifically, the formula for calculating the backscattering coefficient bbb555 (i.e., the backscattering coefficient in the green light band) of the first type of water body is as follows:

[0132]

[0133] In the above formula, u555 represents the backscattering albedo of the green light band (i.e., the 555nm band), aw555 represents aw of the green light band, aw is the absorption coefficient of pure water, i.e., a-water, bbw555 represents bbw of the green light band, bbw is the backscattering coefficient of pure water, i.e., b-backscattering-water, where aw555 is 0.06 and bbw555 is 0.0009.

[0134] S10323. Determine the total absorption coefficient of the red light band based on the above-mentioned blue light band remote sensing reflectance and the above-mentioned red light band remote sensing reflectance; wherein, the above-mentioned total absorption coefficient of the red light band is the sum of the absorption coefficients of all substances except water.

[0135] Specifically, for Class II and III water bodies, the QRM method is used to calculate the backscattering coefficient (bbp655) in the 655nm band (i.e., the red light band); the formula for calculating the total absorption coefficient anw655 in the red light band is as follows:

[0136]

[0137] S10324. Obtain the pure water absorption coefficient and pure water backscattering coefficient in the red light band, and determine the backscattering coefficient of the second type of water body and the backscattering coefficient of the third type of water body based on the backscattering albedo of the red light band, the total absorption coefficient of the red light band, the pure water absorption coefficient and the pure water backscattering coefficient in the red light band.

[0138] Specifically, the formula for calculating the backscattering coefficient bbb655 of the second type of water body is as follows:

[0139]

[0140] In the above formula, u655 represents the backscattering albedo of the red light band (i.e., the 655nm band), aw655 represents the aw of the red light band, and bbw655 represents the bbw of the red light band. Among them, aw655 is 0.37 and bbw655 is 0.0005.

[0141] Furthermore, the calculation steps for the backscattering coefficient of the third type of water body are the same as those for the calculation steps for the backscattering coefficient of the second type of water body.

[0142] S10325. Determine the total absorption coefficient of the near-infrared band based on the above-mentioned near-infrared band remote sensing reflectance and the above-mentioned green band remote sensing reflectance; wherein, the above-mentioned total absorption coefficient of the near-infrared band is the sum of the absorption coefficients of all substances except water.

[0143] Specifically, for Class IV water bodies, the QAA-z859 method is used to calculate the backscattering coefficient (bbp859) in the 859nm band (i.e., the near-infrared band). The formula for calculating the total absorption coefficient anw859 in the near-infrared band is as follows:

[0144]

[0145] S10326. Obtain the pure water absorption coefficient and pure water backscattering coefficient in the near-infrared band, and determine the backscattering coefficient of the fourth type of water body based on the backscattering albedo of the near-infrared band, the total absorption coefficient of the near-infrared band, the pure water absorption coefficient and pure water backscattering coefficient of the near-infrared band.

[0146] Specifically, the formula for calculating the backscattering coefficient bbb655 of the second type of water body is as follows:

[0147]

[0148] In the above formula, u859 represents the backscattering albedo of the near-infrared band (i.e., the 859nm band), aw859 represents the aw of the near-infrared band, and bbw8595 represents the bbw of the near-infrared band, where aw859 is 4.37 and bbw8595 is 0.0001.

[0149] In the above optional embodiments, a BBP655 inversion method based on the remote sensing reflectance of the blue band and the red band is proposed. This solves the problem of the traditional BBP655 inversion method relying on the 443nm band. The backscattering coefficient can achieve good accuracy and is applicable to Landsat sensors that lack the 443nm band, making the method more versatile. Furthermore, the BBP859 inversion method solves the problem of existing BBP859 inversion methods relying on the slope. It transforms the currently commonly used indirect calculation of BBP859 in the red band into direct calculation of BBP859 at the 859nm reference band, improving its inversion accuracy. Finally, high-precision remote sensing estimation of sediment content in lakes and rivers is achieved through the four universal blue, green, red, and near-infrared bands, realizing high-efficiency, large-scale, and high-timeliness global water sediment content monitoring.

[0150] This invention also discloses a water sediment content inversion device based on satellite remote sensing imagery, such as... Figure 10 As shown, it includes:

[0151] The preprocessing module 101 is used to acquire satellite remote sensing images of the water body to be measured, preprocess the satellite remote sensing images of the water body to be measured, and generate the remote sensing reflectance above the water surface; for details, please refer to the relevant description of S101 in the above method embodiment.

[0152] The classification module 102 is used to perform optical classification of the measured water body based on the remote sensing reflectance above the water surface, and generate Class I water body, Class II water body, Class III water body and Class IV water body; for details, please refer to the relevant description of S102 in the above method embodiment.

[0153] The first inversion module 103 is used to invert the backscattering coefficient of the remote sensing reflectance above the water surface to determine the backscattering coefficients of the first type of water body, the second type of water body, the third type of water body, and the fourth type of water body; for details, please refer to the relevant description of S103 in the above method embodiment.

[0154] The second inversion module 104 is used to perform remote sensing inversion of sediment content on the backscattering coefficients of the first type of water body, the second type of water body, the third type of water body, and the fourth type of water body to generate the sediment content of the water body; for details, please refer to the relevant description of S104 in the above method embodiment.

[0155] This invention provides a water sediment concentration inversion device based on satellite remote sensing imagery. It employs a method of first classifying the water body under test and then inverting the result. Specifically, it performs rapid and effective optical classification of large-scale complex water bodies, then inverts the backscattering coefficients of different wavebands for different categories of water bodies, and finally calculates the final sediment concentration from the backscattering coefficients. The rapid and effective optical classification of large-scale complex water bodies has high universality, a simple process, and effectively divides the complex water body (i.e., the water body under test) into four types with different sediment concentration ranges. Furthermore, the method of inverting the backscattering coefficients of different wavebands for different categories of water bodies solves the problems of "reliance on the 443nm waveband" and "low accuracy of inversion results in the 859nm waveband" in existing technologies, effectively achieving high-precision backscattering coefficient inversion across different reference wavebands. Finally, the multi-class coupled inversion method has good universality for sediment concentration inversion of large-scale complex water bodies.

[0156] As an optional embodiment of the present invention, the preprocessing module 101 includes: a processing submodule, used to perform cloud masking, atmospheric correction, flare masking, and water body extraction on the satellite remote sensing image, and determine the top atmospheric radiance, correction parameters, direct solar irradiance, and diffuse sky irradiance; and a first calculation submodule, used to obtain the air-water interface reflectance and direct solar irradiance, and calculate the remote sensing reflectance above the water surface based on the top atmospheric radiance value, the correction parameters, the direct solar irradiance, the diffuse sky irradiance, and the air-water interface reflectance.

[0157] As an optional embodiment of the present invention, the classification module 102 includes: a second calculation submodule, used to calculate a first stimulus value, a second stimulus value, and a third stimulus value based on the remote sensing reflectance of the blue light band, the remote sensing reflectance of the green light band, the remote sensing reflectance of the red light band, and the remote sensing reflectance of the near-infrared band, respectively; a first determination submodule, used to determine a chromaticity angle based on the first stimulus value, the second stimulus value, and the third stimulus value; a second determination submodule, used to determine the Freyr color index using a lookup table based on the chromaticity angle; and a comparison submodule, used to compare the Freyr color index with a preset threshold, and classify the water body to be tested into the first category of water body, the second category of water body, the third category of water body, and the fourth category of water body based on the comparison result.

[0158] As an optional embodiment of the present invention, the first inversion module 103 includes: a third determining submodule, used to determine the backscattering albedo of different bands based on the remote sensing reflectance above the water surface using a radiative transfer model within the water body; wherein the backscattering albedo of the different bands includes the backscattering albedo of the blue light band, the backscattering albedo of the green light band, the backscattering albedo of the red light band, and the backscattering albedo of the near-infrared band; and a third calculation submodule, used to calculate the backscattering coefficients of the first type of water body, the second type of water body, the third type of water body, and the fourth type of water body based on the remote sensing reflectance of the blue light band, the green light band, the red light band, the near-infrared band, and the backscattering albedo of the different bands.

[0159] As an optional embodiment of the present invention, the third determining submodule includes: a first determining unit, used to determine the remote sensing reflectance below the water surface based on the remote sensing reflectance above the water surface; and a second determining unit, used to obtain calibration coefficients and determine the backscattering albedo of the different wavebands based on the calibration coefficients and the remote sensing reflectance below the water surface.

[0160] As an optional embodiment of the present invention, the third calculation submodule includes: a third determining unit, configured to determine the total absorption coefficient of the green light band based on the remote sensing reflectance of the blue light band, the remote sensing reflectance of the green light band, and the remote sensing reflectance of the red light band; wherein the total absorption coefficient of the green light band is the sum of the absorption coefficients of all substances except water; a fourth determining unit, configured to obtain the pure water absorption coefficient and the pure water backscattering coefficient of the green light band, and determine the backscattering coefficient of the first type of water body based on the backscattering albedo of the green light band, the total absorption coefficient of the green light band, the pure water absorption coefficient of the green light band, and the pure water backscattering coefficient; a fifth determining unit, configured to determine the total absorption coefficient of the red light band based on the remote sensing reflectance of the blue light band and the remote sensing reflectance of the red light band; wherein the total absorption coefficient of the red light band is the sum of the absorption coefficients of all substances except water; a sixth The system comprises: a seventh determining unit, used to obtain the pure water absorption coefficient and pure water backscattering coefficient in the red light band, and to determine the backscattering coefficients of the second type of water body and the third type of water body based on the backscattering albedo of the red light band, the total absorption coefficient of the red light band, the pure water absorption coefficient and the pure water backscattering coefficient of the red light band; a eighth determining unit, used to obtain the pure water absorption coefficient and pure water backscattering coefficient in the near-infrared band, and to determine the backscattering coefficient of the fourth type of water body based on the backscattering albedo of the near-infrared band, the total absorption coefficient of the near-infrared band, the pure water absorption coefficient and the pure water backscattering coefficient of the near-infrared band.

[0161] In addition, embodiments of the present invention also provide an electronic device, such as... Figure 11 As shown, the electronic device may include a processor 110 and a memory 120, wherein the processor 110 and the memory 120 may be connected via a bus or other means. Figure 11 For example, the connection is via a bus. Furthermore, the electronic device also includes at least one interface 130, which can be a communication interface or other interface; this embodiment does not impose any limitations on this.

[0162] The processor 110 can be a central processing unit (CPU). The processor 110 can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations of the above types of chips.

[0163] The memory 120, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the video synthesis method in this embodiment of the invention. The processor 110 executes various functional applications and data processing by running the non-transitory software programs, instructions, and modules stored in the memory 120, thereby implementing a water sediment content inversion method based on satellite remote sensing imagery in the above method embodiment.

[0164] The memory 120 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created by the processor 110, etc. Furthermore, the memory 120 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 120 may optionally include memory remotely located relative to the processor 110, and these remote memories may be connected to the processor 110 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0165] In addition, at least one interface 130 is used for communication between the electronic device and external devices, such as communication with a server. Optionally, at least one interface 130 can also be used to connect peripheral input / output devices, such as a keyboard or display screen.

[0166] The one or more modules are stored in the memory 120, and when executed by the processor 110, they perform actions such as... Figure 1 The embodiment shown is a method for inverting water sediment content based on satellite remote sensing imagery.

[0167] For specific details regarding the aforementioned electronic devices, please refer to the relevant documentation. Figure 1 The relevant descriptions and effects in the illustrated embodiments are for understanding purposes only and will not be repeated here.

[0168] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), etc.; the storage medium can also include combinations of the above types of memory.

[0169] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A method for inverting water sediment content based on satellite remote sensing imagery, characterized in that, include: Satellite remote sensing images of the water body to be measured are acquired, and the satellite remote sensing images of the water body to be measured are preprocessed to generate the remote sensing reflectance above the water surface. The remote sensing reflectance above the water surface includes: blue light band remote sensing reflectance, green light band remote sensing reflectance, red light band remote sensing reflectance and near-infrared band remote sensing reflectance; Based on the remote sensing reflectance above the water surface, the measured water body is optically classified to generate Class I water body, Class II water body, Class III water body and Class IV water body; Backscattering coefficient inversion is performed on the remote sensing reflectance above the water surface to determine the backscattering coefficients of the first type of water body, the second type of water body, the third type of water body, and the fourth type of water body; The sediment content of the water body is generated by remote sensing inversion of the backscattering coefficients of the first type of water body, the second type of water body, the third type of water body, and the fourth type of water body. The process of optically classifying the measured water body based on the remote sensing reflectance above the water surface to generate Class I, Class II, Class III, and Class IV water bodies includes: The first stimulus value, the second stimulus value, and the third stimulus value are calculated based on the remote sensing reflectance of the blue light band, the remote sensing reflectance of the green light band, the remote sensing reflectance of the red light band, and the remote sensing reflectance of the near-infrared band, respectively. The chromaticity angle is determined based on the first stimulus value, the second stimulus value, and the third stimulus value; Based on the chromaticity angle, the Freyr color index is determined using a lookup table; The Freyr color index is compared with a preset threshold, and the water body to be tested is classified into the first type of water body, the second type of water body, the third type of water body, and the fourth type of water body based on the comparison result; The backscattering coefficient inversion of the remote sensing reflectance above the water surface to determine the backscattering coefficients of the first type of water body, the second type of water body, the third type of water body, and the fourth type of water body includes: Based on the remote sensing reflectance above the water surface, the backscattering albedo of different bands is determined using a water body radiative transfer model; wherein, the backscattering albedo of different bands includes the backscattering albedo of the blue light band, the backscattering albedo of the green light band, the backscattering albedo of the red light band, and the backscattering albedo of the near-infrared band. Based on the remote sensing reflectance of the blue light band, the remote sensing reflectance of the green light band, the remote sensing reflectance of the red light band, the remote sensing reflectance of the near-infrared band, and the backscattering albedo of the different bands, the backscattering coefficients of the first type of water body, the second type of water body, the third type of water body, and the fourth type of water body are calculated.

2. The method for inverting water sediment content based on satellite remote sensing imagery according to claim 1, characterized in that, The preprocessing of the satellite remote sensing image of the water body to generate the remote sensing reflectance above the water surface includes: The satellite remote sensing images were processed with cloud masking, atmospheric correction, flare masking, and water extraction to determine the top atmospheric radiance, correction parameters, direct solar irradiance, and diffuse sky irradiance. The air-water interface reflectance and direct solar irradiance are obtained. Based on the atmospheric top radiance value, the correction parameters, the direct solar irradiance, the diffuse sky irradiance, and the air-water interface reflectance, the remote sensing reflectance above the water surface is calculated.

3. The method for inverting water sediment content based on satellite remote sensing imagery according to claim 1, characterized in that, The determination of backscattering albedo in different bands based on the remotely sensed reflectance above the water surface and using a water body radiative transfer model includes: Determine the remote sensing reflectance below the water surface based on the remote sensing reflectance above the water surface. Obtain calibration coefficients, and determine the backscattering albedo of the different wavebands based on the calibration coefficients and the remote sensing reflectance below the water surface.

4. The method for inverting water sediment content based on satellite remote sensing imagery according to claim 1, characterized in that, The calculation of the backscattering coefficients of the first type of water body, the second type of water body, the third type of water body, and the fourth type of water body based on the remote sensing reflectance of the blue band, the green band, the red band, the near-infrared band, and the backscattering albedo of different bands includes: The total absorption coefficient of the green light band is determined based on the remote sensing reflectance of the blue light band, the remote sensing reflectance of the green light band, and the remote sensing reflectance of the red light band; wherein, the total absorption coefficient of the green light band is the sum of the absorption coefficients of all substances except water; Obtain the pure water absorption coefficient and pure water backscattering coefficient in the green light band, and determine the backscattering coefficient of the first type of water body based on the backscattering albedo of the green light band, the total absorption coefficient of the green light band, the pure water absorption coefficient and pure water backscattering coefficient in the green light band. The total absorption coefficient of the red light band is determined based on the remote sensing reflectance of the blue light band and the remote sensing reflectance of the red light band; wherein, the total absorption coefficient of the red light band is the sum of the absorption coefficients of all substances except water; Obtain the pure water absorption coefficient and pure water backscattering coefficient in the red light band, and determine the backscattering coefficient of the second type of water body and the backscattering coefficient of the third type of water body based on the backscattering albedo of the red light band, the total absorption coefficient of the red light band, the pure water absorption coefficient and the pure water backscattering coefficient in the red light band. The total absorption coefficient of the near-infrared band is determined based on the remote sensing reflectance of the near-infrared band and the remote sensing reflectance of the green band; wherein, the total absorption coefficient of the near-infrared band is the sum of the absorption coefficients of all substances except water. The absorption coefficient and backscattering coefficient of pure water in the near-infrared band are obtained. Based on the backscattering albedo of the near-infrared band, the total absorption coefficient of the near-infrared band, the absorption coefficient of pure water in the near-infrared band, and the backscattering coefficient of pure water in the near-infrared band, the backscattering coefficient of the fourth type of water body is determined.

5. A water sediment content inversion device based on satellite remote sensing imagery, characterized in that, include: The preprocessing module is used to acquire satellite remote sensing images of the water body to be measured, preprocess the satellite remote sensing images of the water body to be measured, and generate the remote sensing reflectance above the water surface. The remote sensing reflectance above the water surface includes: blue light band remote sensing reflectance, green light band remote sensing reflectance, red light band remote sensing reflectance and near-infrared band remote sensing reflectance; The classification module is used to perform optical classification of the measured water body based on the remote sensing reflectance above the water surface, and generate Class I water body, Class II water body, Class III water body and Class IV water body; The first inversion module is used to invert the backscattering coefficient of the remote sensing reflectance above the water surface, and determine the backscattering coefficients of the first type of water body, the second type of water body, the third type of water body, and the fourth type of water body. The second inversion module is used to perform remote sensing inversion of sediment content on the backscattering coefficients of the first type of water body, the second type of water body, the third type of water body, and the fourth type of water body to generate the sediment content of the water body. The aforementioned classification module includes: a second calculation submodule, used to calculate a first stimulus value, a second stimulus value, and a third stimulus value based on the remote sensing reflectance of the blue light band, the remote sensing reflectance of the green light band, the remote sensing reflectance of the red light band, and the remote sensing reflectance of the near-infrared band, respectively; a first determination submodule, used to determine the chromaticity angle based on the first stimulus value, the second stimulus value, and the third stimulus value; a second determination submodule, used to determine the Freyr water color index using a lookup table based on the chromaticity angle; and a comparison submodule, used to compare the Freyr water color index with a preset threshold, and classify the tested water body into the first type of water body, the second type of water body, the third type of water body, and the fourth type of water body based on the comparison result. The first inversion module includes: a third determining submodule, used to determine the backscattering albedo of different bands based on the remote sensing reflectance above the water surface using a water body radiative transfer model; wherein the backscattering albedo of different bands includes the backscattering albedo of the blue light band, the backscattering albedo of the green light band, the backscattering albedo of the red light band, and the backscattering albedo of the near-infrared band; and a third calculation submodule, used to calculate the backscattering coefficients of the first type of water body, the second type of water body, the third type of water body, and the fourth type of water body based on the remote sensing reflectance of the blue light band, the remote sensing reflectance of the green light band, the remote sensing reflectance of the red light band, the remote sensing reflectance of the near-infrared band, and the backscattering albedo of the different bands.

6. An electronic device, characterized in that, It includes a processor and a memory, wherein the memory is coupled to the processor; The memory stores computer-readable program instructions that, when executed by the processor, implement the method as described in any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 4.

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