A rural suspected black and odorous water body identification method based on high-resolution images

Abstract

The application discloses a rural suspected black and odorous water body identification method based on high-resolution images, and belongs to the field of remote sensing image information extraction. The method comprises the following steps: obtaining and preprocessing high-resolution remote sensing images; collecting different types of water bodies in the field and determining related chemical indicators; extracting and analyzing the remote sensing reflectivity of red, green, blue and near-infrared bands of different types of ground objects; preliminary screening based on the combination of identification index threshold values; constructing a water quality parameter inversion model based on water body chemical indicator determination and spectral characteristics, and then inverting the water quality parameters of the water bodies in the preliminary screening results to further determine the rural suspected black and odorous water bodies. The application uses the identification index and the water quality parameter inversion model based on high-resolution remote sensing images to identify the rural suspected black and odorous water bodies, and in practical application, the working efficiency of rural black and odorous water body investigation, supervision and treatment can be effectively improved, and the application has certain theoretical, practical significance and popularization and application value.

Description

Technical Field

[0001] This invention belongs to the field of remote sensing image information extraction, specifically relating to a method for identifying suspected black and odorous water bodies in rural areas based on high-resolution images. Background Technology

[0002] The investigation of black and odorous water bodies is a preliminary step in the remediation process, and its results are crucial to the entire remediation effort. Because black and odorous water bodies (especially those in rural areas) are often characterized by their wide and complex distribution, small area, and large number, traditional investigation methods have significant limitations. Remote sensing technology, with its advantages of large-scale coverage, multi-temporal capabilities, and easy data acquisition, has been widely applied in water body analysis and water environment monitoring.

[0003] Current remote sensing identification methods for black and odorous water bodies mainly rely on the differences in spectral characteristics between black and odorous water bodies and ordinary water bodies to construct identification indices and set empirical thresholds for water body identification; or they identify black and odorous water bodies based on colorimetric indicators, taking into account the "black" color characteristic of the water. However, different black and odorous water bodies are affected by external factors such as pollutant type, concentration, and surrounding environment, resulting in significant differences in their water body characteristics, and general black and odorous water body identification methods have poor robustness. Therefore, this invention proposes a remote sensing identification method for suspected black and odorous water bodies in rural areas based on high-resolution imagery to address the problems existing in the prior art. Summary of the Invention

[0004] The purpose of this invention is to solve the problems existing in the current survey of black and odorous water bodies in rural areas. Based on high-resolution image data, and by comprehensively using identification index and water quality parameter inversion model, a method for identifying suspected black and odorous water bodies in rural areas is provided.

[0005] The specific technical solution adopted in this invention is as follows:

[0006] This invention provides a method for identifying suspected black and odorous water bodies in rural areas based on high-resolution imagery, as detailed below:

[0007] S1: Acquire high-resolution remote sensing images and perform preprocessing operations on them; the preprocessing operations include four steps: radiometric calibration, atmospheric correction, orthorectification, and geometric fine correction.

[0008] S2: Select different types of water bodies for on-site sampling and determination of relevant chemical indicators;

[0009] S3: Extract the remote sensing reflectance of different types of land cover in the red, green, blue and near-infrared bands, and perform spectral feature analysis;

[0010] S4: Based on the remote sensing reflectance and spectral characteristics in S3, calculate the identification index and determine the corresponding threshold, and make preliminary identification of suspected black and odorous water bodies based on the combination of index thresholds;

[0011] S5: Based on the results of S2, extract the remote sensing reflectance of the corresponding water body from the high-resolution remote sensing image after preprocessing in S1, and construct a water quality parameter inversion model by combining the corresponding water body chemical index measurement results.

[0012] S6: Based on the water quality parameter inversion model constructed in S5, perform water quality parameter inversion on the water bodies in the preliminary identification results of suspected black and odorous water bodies obtained in S4, and further determine the spatial distribution of suspected black and odorous water bodies in rural areas.

[0013] Preferably, in step S1, the high-resolution remote sensing image is a Gaofen-1 image and / or a Gaofen-2 image; wherein, the spatial resolution of the Gaofen-1 image is 2m, and the spatial resolution of the Gaofen-2 image is 1m.

[0014] Preferably, in the preprocessing operation of S1, radiometric calibration uses the ENVI China-made satellite extension tool; atmospheric correction uses the FLAASH model; orthorectification uses the RPC Orthorectification tool in ENVI; and geometric fine correction uses the Tianditu image from the National Geographic Information Public Service Platform as the reference image.

[0015] Preferably, in step S2, the different types of water bodies include two categories: general water bodies and black and odorous water bodies. Based on the specific distribution of general water bodies and black and odorous water bodies, the location information of the field sampling points is initially determined. Based on the field sampling point information, a field survey is conducted to collect water samples and record the coordinate latitude and longitude information. Subsequently, the transparency and dissolved oxygen indexes are measured at the field survey site, and the ammonia nitrogen index is measured in the laboratory.

[0016] Preferably, S3 is as follows:

[0017] Remote sensing reflectance in red, green, blue, and near-infrared bands was extracted for different types of land cover, including black and odorous water bodies, general water bodies, vegetation, and urban areas. Subsequently, the spectral characteristics of each land cover were analyzed, and the differences and similarities in spectral reflectance among different land cover types were compared.

[0018] Preferably, the identification index in S4 includes NIR, NDWI, NDBWI, and BOI, and the specific calculation formula is as follows:

[0019] NIR = Rrs(NIR);

[0020] NDWI=(Rrs(G)-Rrs(NIR)) / (Rrs(G)+Rrs(NIR));

[0021] NDBWI=(Rrs(G)-Rrs(R)) / (Rrs(G)+Rrs(R));

[0022] BOI=(Rrs(G)-Rrs(R)) / (Rrs(B)+Rrs(G)+Rrs(R));

[0023] In the formula, Rrs(B), Rrs(G), Rrs(R), and Rrs(NIR) are the remote sensing reflectances of the blue, green, red, and near-infrared bands of the high-resolution image, respectively. Referring to the spectral characteristics of different types of land cover in S3, the thresholds of each identification index are determined. The determined thresholds should, as far as possible, exclude other land cover types while retaining suspected black and odorous water bodies. Subsequently, the combination of the four thresholds is used for preliminary screening of suspected black and odorous water bodies.

[0024] Preferably, S5 is as follows:

[0025] Based on the results of S2, the remote sensing reflectance values ​​of each band of the corresponding water body in the preprocessed high-resolution image of S1 are extracted, and correlation analysis is performed between the original value, ratio and normalized difference index and the water body chemical index measurement results in S2.

[0026] The ratio is in the form y = b i / b j The normalized difference index is in the form of y = (b i -b j ) / (b i +b j In the formula, b i and b j Let i and j represent the remote sensing reflectance of the i-th and j-th bands of the high-resolution image, respectively; select the reflectance combinations with significant correlation as the input features of the model to create sample sets of black and odorous water bodies and general water bodies.

[0027] Training and test sets are randomly generated, with a sample size ratio of 7:3. Inversion models are constructed for the water quality parameters in S2. The built-in type of the model is set to Support Vector MachineRegression, and the kernel function type is set to radial. The optimal penalty coefficient cost, kernel parameters gamma, and epsilon are found using cross-validation grid search. The hyperparameters of the obtained model are adjusted based on feedback from the test set to avoid overfitting.

[0028] As a preferred option, the water quality parameter inversion results obtained in S6 are referenced in the "Guidelines for the Treatment of Black and Odorous Water Bodies in Rural Areas (Trial)" to further determine the spatial distribution of suspected black and odorous water bodies in rural areas.

[0029] Compared with the prior art, the present invention has the following advantages:

[0030] This invention proposes a method for identifying suspected black and odorous water bodies in rural areas based on high-resolution imagery, combining an identification index and a water quality parameter inversion model. This method ultimately yields spatial distribution information of these suspected black and odorous water bodies. By comprehensively utilizing an identification index and a water quality parameter inversion model, this invention enhances the operability and transferability of the black and odorous water body identification method. Regarding the identification results, this method effectively improves the accuracy and efficiency of identifying suspected black and odorous water bodies in rural areas. The spatial distribution information obtained through remote sensing identification of suspected black and odorous water bodies in rural areas using this method can effectively assist in on-site investigations and regulatory remediation of these water bodies, possessing significant theoretical and practical value and potential for widespread application. Attached Figure Description

[0031] Figure 1 This is a flowchart of the method of the present invention.

[0032] Figure 2 This is a line graph showing the remote sensing reflectance of different land features in the example.

[0033] Figure 3 The box plots show the identification indices of different land features in the embodiments; where (a) is NIR, (b) is NDWI, (c) is NDBWI, and (d) is BOI.

[0034] Figure 4 The example shows the identification results of suspected black and odorous water bodies in a rural area of ​​a certain city. The non-black and odorous water bodies in the figure are those initially identified as black and odorous water bodies in S4 based on the combination of identification index thresholds, but further judged as non-black and odorous water bodies based on the water quality parameter inversion results in S6. Detailed Implementation

[0035] The present invention will be further described and illustrated below with reference to the accompanying drawings and specific embodiments. The technical features of each embodiment of the present invention can be combined accordingly, provided that there is no mutual conflict.

[0036] like Figure 1 As shown, this invention provides a method for identifying suspected black and odorous water bodies in rural areas based on high-resolution imagery. The method is as follows:

[0037] S1: Acquire high-resolution remote sensing images and perform preprocessing operations. The preprocessing operations here include four steps: radiometric calibration, atmospheric correction, orthorectification, and geometric fine correction.

[0038] In practical use, the high-resolution remote sensing imagery is Gaofen-1 and / or Gaofen-2, with Gaofen-1 having a spatial resolution of 2m and Gaofen-2 having a spatial resolution of 1m. This step utilizes platforms such as ENVI for preprocessing. In this process, radiometric calibration uses the ENVI China-made satellite extension tool; atmospheric correction uses the FLAASH model; orthorectification uses the RPC Orthorectification tool in ENVI; and geometric fine correction uses the "National Geographic Information Public Service Platform (Tianditu)" as the reference image.

[0039] S2: Select different types of water bodies, conduct on-site sampling and determine relevant chemical indicators.

[0040] In practical application, different types of water bodies are categorized into two types: general water bodies and black and odorous water bodies. The classification criteria refer to the "Guidelines for the Treatment of Black and Odorous Water Bodies in Rural Areas (Trial Implementation)". Based on the specific distribution of general and black and odorous water bodies in the field, the location information of the field sampling points is initially determined. A field survey is then conducted based on the field sampling point information, water samples are collected, and the coordinates (latitude and longitude) are recorded. Subsequently, transparency and dissolved oxygen levels are measured at the field survey site, and ammonia nitrogen levels are measured in the laboratory.

[0041] S3: Extract the remote sensing reflectance of red, green, blue and near-infrared bands of different types of land features and perform spectral feature analysis.

[0042] In practical applications, the remote sensing reflectance of different land features in the red, green, blue, and near-infrared bands is extracted. The land feature types include major land feature types such as black and odorous water bodies, general water bodies, vegetation, and towns. The spectral characteristics of each land feature type are analyzed, and the similarities and differences in spectral reflectance among different land feature types are compared.

[0043] S4: Based on the remote sensing reflectance and spectral characteristics in S3, calculate the identification index and determine the corresponding threshold, and perform preliminary identification of suspected black and odorous water bodies based on the combination of index thresholds.

[0044] In practical use, the identification indices include NIR, NDWI, NDBWI, and BOI, and the specific calculation formulas are as follows:

[0045] NIR = Rrs(NIR);

[0046] NDWI=(Rrs(G)-Rrs(NIR)) / (Rrs(G)+Rrs(NIR));

[0047] NDBWI=(Rrs(G)-Rrs(R)) / (Rrs(G)+Rrs(R));

[0048] BOI=(Rrs(G)-Rrs(R)) / (Rrs(B)+Rrs(G)+Rrs(R));

[0049] In the formula, Rrs(B), Rrs(G), Rrs(R), and Rrs(NIR) are the remote sensing reflectances of the blue, green, red, and near-infrared bands of the high-resolution image, respectively. Referring to the spectral characteristics of different types of land cover in S3, the thresholds of each identification index are determined. The determined thresholds should, as far as possible, exclude other land cover types while retaining suspected black and odorous water bodies. Subsequently, the combination of the four thresholds is used for preliminary screening of suspected black and odorous water bodies.

[0050] S5: Based on the results of S2, extract the remote sensing reflectance of the corresponding water body from the high-resolution remote sensing image after preprocessing in S1, and combine it with the corresponding water body chemical index measurement results to construct a water quality parameter inversion model.

[0051] In practical applications, the water quality parameter inversion model is constructed as follows:

[0052] Based on the results of S2, the remote sensing reflectance values ​​of each band of the corresponding water body in the preprocessed high-resolution image of S1 are extracted, and correlation analysis is performed between the original value, ratio and normalized difference index and the water body chemical index measurement results in S2.

[0053] The ratio is in the form y = b i / b j The normalized difference index is in the form of y = (b i -b j ) / (b i +b j In the formula, b i and b j Let i and j represent the remote sensing reflectance of the i-th and j-th bands of the high-resolution image, respectively; select the reflectance combinations with significant correlation as the input features of the model to create sample sets of black and odorous water bodies and general water bodies.

[0054] Training and test sets are randomly generated, with a sample size ratio of 7:3. Inversion models are constructed for the water quality parameters in S2. The built-in type of the model is set to Support Vector Machine Regression (SVR), and the kernel function type is set to radial, implemented using the RStudio platform. The optimal penalty coefficient cost, kernel parameters gamma, and epsilon are found using the cross-validation grid search method. The search range for parameter cost is set to [1, 100] with a step size of 5, the search range for parameter gamma is set to [0.01, 1.00] with a step size of 0.01, and the search range for parameter epsilon is set to [0.01, 0.10] with a step size of 0.01. The hyperparameters of the obtained model are adjusted based on feedback from the test set to avoid overfitting.

[0055] S6: Based on the water quality parameter inversion model constructed in S5, perform water quality parameter inversion on the water bodies in the preliminary identification results of suspected black and odorous water bodies obtained in S4, and further determine the spatial distribution of suspected black and odorous water bodies in rural areas.

[0056] In practical use, based on the water quality parameter inversion results obtained in S6, and referring to the "Guidelines for the Treatment of Black and Odorous Water Bodies in Rural Areas (Trial Implementation)," the spatial distribution of suspected black and odorous water bodies in rural areas can be further determined.

[0057] Example

[0058] A city (110°45′40″-112°31′07″E, 27°12′31″-28°14′27″N) was selected as the study area. Using Gaofen-1 and Gaofen-2 remote sensing images from 2019-2020, suspected black and odorous water bodies in rural areas of this region were identified. The specific identification method is as follows:

[0059] Step 1) Data Acquisition and Preprocessing: Acquire high-resolution image datasets of the study area and preprocess the image datasets. The specific preprocessing steps are as follows: perform radiometric calibration using the ENVI China-made satellite extension tool; perform atmospheric correction using the ENVI FLAASH model; perform orthorectification using the ENVI RPC Orthorectification tool; and perform geometric fine correction using the "National Geographic Information Public Service Platform (Tianditu)" as the reference image.

[0060] Step 2) Water Sampling and Analysis: Referring to the "Guidelines for the Treatment of Black and Odorous Water Bodies in Rural Areas (Trial Implementation)," a total of 31 sampling points were selected in the study area, including 14 black and odorous water bodies and 17 general water bodies. Field investigations were conducted to collect water samples (three sampling points were set up at each location: upstream, midstream, and downstream), and the coordinate latitude and longitude information of the water bodies were recorded. Transparency and dissolved oxygen were measured at the field investigation site, and ammonia nitrogen was measured in the laboratory.

[0061] Step 3) Remote sensing reflectance extraction and spectral analysis: Extract the remote sensing reflectance of different land cover types within the study area in the red, green, blue, and near-infrared bands. Land cover types include six categories: black and odorous water bodies, general water bodies, vegetation, cultivated land, roads, and buildings. Analyze the spectral characteristics of each type of land cover and compare the similarities and differences in spectral reflectance among different land cover types. For example... Figure 2 As shown, the reflectivity of water gradually decreases with increasing wavelength. Among the four bands, the near-infrared band has the lowest reflectivity, which is also the most obvious difference between water bodies and other land features. Compared with ordinary water bodies, black and odorous water bodies have lower reflectivity in the blue light band and relatively higher reflectivity in the red light band and near-infrared band.

[0062] Step 4) Preliminary identification of suspected black and odorous water bodies in rural areas: Calculate the identification indices of different land features, including NIR, NDWI, NDBWI, and BOI. The specific calculation formulas are as follows:

[0063] NIR = Rrs(NIR)

[0064] NDWI=(Rrs(G)-Rrs(NIR)) / (Rrs(G)+Rrs(NIR))

[0065] NDBWI=(Rrs(G)-Rrs(R)) / (Rrs(G)+Rrs(R))

[0066] BOI=(Rrs(G)-Rrs(R)) / (Rrs(B)+Rrs(G)+Rrs(R))

[0067] In the formula, Rrs(B), Rrs(G), Rrs(R), and Rrs(NIR) represent the remote sensing reflectance of the blue, green, red, and near-infrared bands of the high-resolution image, respectively. Within this study area, the main land cover type is vegetation; therefore, identification indices for three types of land cover are calculated: vegetation, general water bodies, and black and odorous water bodies. Refer to step 3) for the differences in spectral reflectance between different land cover types and the calculation results of the identification indices. Figure 3 Using the NIR and NDWI indices, water bodies and vegetation can be effectively distinguished; using the NDBWI and BOI indices, black and odorous water bodies can be effectively distinguished from ordinary water bodies. Figure 3For the boundary positions of the middle box plots, the thresholds for each recognition index are determined as follows: NIR < 0.12, -0.35 < NDWI < 0.05, 0.11 < NDBWI < 0.19, 0.08 < BOI < 0.135. The combination of the four thresholds is used for the preliminary identification of suspected black and odorous water bodies.

[0068] Step 5) Construction of the water quality inversion model: According to the field sampling results of the water bodies in S2, the remote sensing reflectance values of the corresponding bands in the high-resolution images after preprocessing in S1 are extracted, and their correlation analyses are respectively carried out with the water quality parameter values in S2 in the forms of original values, ratios, and normalized difference indices. The form of the ratio is y = b i / b j , and the form of the normalized difference index is y = (b i -b j ) / ( b i + b j ), where b i and b j respectively represent the remote sensing reflectances of the i-th and j-th bands of the high-resolution images; the band combination forms with significant correlations are selected as the input features of the model to make the sample sets of black and odorous water bodies and general water bodies.

[0069] Randomly generate the training set and the test set, and the ratio of the sample numbers of the training set and the test set is 7:3. Based on the R language, inversion models are respectively constructed for the three water quality parameters of transparency, dissolved oxygen, and ammonia nitrogen. Set the built-in model types to be all SupportVector Machine Regression (SVR), and set the kernel function type of the model to be radial; use the cross-validation grid search method to find the optimal penalty coefficient cost, kernel parameter gamma, and epsilon. Set the search range of the parameter cost to be [1, 100] with a step size of 5, the search range of the parameter gamma to be [0.01, 1.00] with a step size of 0.01, and the search range of the parameter epsilon to be [0.01, 0.10] with a step size of 0.01; adjust the hyperparameters of the obtained model through the feedback of the test set to avoid model overfitting.

[0070] In this embodiment, the cost of the inversion model for transparency is 1, gamma is 0.5, and epsilon is 0.1; the cost of the inversion model for dissolved oxygen is 10, gamma is 1, and epsilon is 0.01; the cost of the inversion model for ammonia nitrogen is 10, gamma is 0.1, and epsilon is 0.01. The R 2 of the transparency inversion model in the test set is 0.60, the R 2 of the dissolved oxygen inversion model in the test set is 0.54, and the R 2 of the ammonia nitrogen inversion model in the test set is 0.60.

[0071] Step 6) Identification of suspected black and odorous water bodies in rural areas: Based on the water quality parameter inversion model established in Step 5), water quality parameters are inverted for the water bodies in the preliminary identification results of suspected black and odorous water bodies in rural areas obtained in Step 4). The identification results are judged according to the "Guidelines for the Treatment of Black and Odorous Water Bodies in Rural Areas (Trial Implementation)". The results are as follows: Figure 4 In the figure, non-black and odorous water bodies are those judged as black and odorous by the threshold, and the water quality inversion results show that they are not black and odorous. Rural water bodies suspected of being black and odorous are those judged as black and odorous by both the threshold and water quality inversion results. The verification accuracy reached 74.2%, and the distribution of black and odorous water bodies exhibited significant spatial heterogeneity. This result can be used in the investigation of black and odorous water bodies, improving the efficiency of the investigation.

[0072] This invention uses a comprehensive identification index and water quality parameter inversion model based on high-resolution remote sensing images to identify suspected black and odorous water bodies in rural areas. In practical applications, it can effectively improve the efficiency of investigation, supervision and treatment of black and odorous water bodies in rural areas, and has certain theoretical and practical significance and application value.

[0073] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the invention. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the invention. Therefore, all technical solutions obtained through equivalent substitution or transformation fall within the protection scope of the present invention.

Claims

1. A method for identifying suspected black and odorous water bodies in rural areas based on high-resolution imagery, characterized in that, Specifically as follows: S1: Acquire high-resolution remote sensing images and perform preprocessing operations on them; the preprocessing operations include four steps: radiometric calibration, atmospheric correction, orthorectification, and geometric fine correction. S2: Select different types of water bodies for on-site sampling and determination of relevant chemical indicators; S3: Extract the remote sensing reflectance of different types of land cover in the red, green, blue and near-infrared bands, and perform spectral feature analysis; S4: Based on the remote sensing reflectance and spectral characteristics in S3, calculate the identification index and determine the corresponding threshold, and make preliminary identification of suspected black and odorous water bodies based on the combination of index thresholds; S5: Based on the results of S2, extract the remote sensing reflectance of the corresponding water body from the high-resolution remote sensing image after preprocessing in S1, and construct a water quality parameter inversion model by combining the corresponding water body chemical index measurement results. S6: Based on the water quality parameter inversion model constructed in S5, perform water quality parameter inversion on the water bodies in the preliminary identification results of suspected black and odorous water bodies obtained in S4, and further determine the spatial distribution of suspected black and odorous water bodies in rural areas. S5 is specifically as follows: Based on the results of S2, the remote sensing reflectance values ​​of each band of the corresponding water body in the preprocessed high-resolution image of S1 are extracted, and correlation analysis is performed between the original value, ratio and normalized difference index and the water body chemical index measurement results in S2. The ratio is in the form of The normalized difference index is in the form of In the formula, and Let i and j represent the remote sensing reflectance of the i-th and j-th bands of the high-resolution image, respectively; select the reflectance combinations with significant correlation as the input features of the model to create sample sets of black and odorous water bodies and general water bodies. Training and test sets are randomly generated, with a sample size ratio of 7:

3. Inversion models are constructed for the water quality parameters in S2. The built-in type of the model is set to Support Vector Machine Regression, and the kernel function type is set to radial. The optimal penalty coefficient cost, kernel parameters gamma, and epsilon are found using cross-validation grid search. The hyperparameters of the obtained model are adjusted based on feedback from the test set to avoid overfitting.

2. The method for identifying suspected black and odorous water bodies in rural areas based on high-resolution imagery according to claim 1, characterized in that, In S1, the high-resolution remote sensing image is Gaofen-1 image and / or Gaofen-2 image; wherein, the spatial resolution of Gaofen-1 image is 2m, and the spatial resolution of Gaofen-2 image is 1m.

3. The method for identifying suspected black and odorous water bodies in rural areas based on high-resolution imagery according to claim 1, characterized in that, In the preprocessing operation of S1, radiometric calibration uses the ENVI China-made satellite extension tool; atmospheric correction uses the FLAASH model; orthorectification uses the RPC Orthorectification tool in ENVI; and geometric fine correction uses the Tianditu image from the National Geographic Information Public Service Platform as the reference image.

4. The method for identifying suspected black and odorous water bodies in rural areas based on high-resolution imagery according to claim 1, characterized in that, In S2, different types of water bodies include general water bodies and black and odorous water bodies. Based on the specific distribution of general water bodies and black and odorous water bodies, the location information of field sampling points is initially determined. Field surveys are conducted based on the field sampling point information to collect water samples and record the coordinate latitude and longitude information. Subsequently, transparency and dissolved oxygen indicators are measured at the field survey site, and ammonia nitrogen indicators are measured in the laboratory.

5. The method for identifying suspected black and odorous water bodies in rural areas based on high-resolution imagery according to claim 1, characterized in that, S3 is specifically as follows: Remote sensing reflectance in red, green, blue, and near-infrared bands was extracted for different types of land cover, including black and odorous water bodies, general water bodies, vegetation, and urban areas. Subsequently, the spectral characteristics of each land cover were analyzed, and the differences and similarities in spectral reflectance among different land cover types were compared.

6. The method for identifying suspected black and odorous water bodies in rural areas based on high-resolution imagery according to claim 1, characterized in that, The identification indices in S4 include NIR, NDWI, NDBWI, and BOI, and the specific calculation formulas are as follows: ； ； ； ； In the formula, Rrs(B), Rrs(G), Rrs(R), and Rrs(NIR) are the remote sensing reflectances of the blue, green, red, and near-infrared bands of the high-resolution image, respectively. Referring to the spectral characteristics of different types of land cover in S3, the thresholds of each identification index are determined. The determined thresholds should, as far as possible, exclude other land cover types while retaining suspected black and odorous water bodies. Subsequently, the combination of the four thresholds is used for preliminary screening of suspected black and odorous water bodies.

7. The method for identifying suspected black and odorous water bodies in rural areas based on high-resolution imagery according to claim 1, characterized in that, The water quality parameter inversion results obtained in S6 are used to further determine the spatial distribution of suspected black and odorous water bodies in rural areas, referring to the "Guidelines for the Treatment of Black and Odorous Water Bodies in Rural Areas (Trial)".

Images