Remote sensing interpretation and verification method for black and odorous water body

Abstract

The invention relates to a black and odorous water body remote sensing interpretation and verification method. The method comprises the following steps: acquiring high-resolution remote sensing image data, administrative boundary data and DEM data of a target area; preprocessing the high-resolution remote sensing image data to form a surface reflectance remote sensing image of the target area; collecting reference data for checking the suspected black and odorous water body; screening the suspected black and odorous water body points to form point elements of the suspected black and odorous water body points; drawing surface elements of the suspected black and odorous water body by utilizing a high-resolution remote sensing image; adding the information of the suspected black and odorous water body points into an attribute table of the suspected black and odorous water body points, and making an information document of the suspected black and odorous water body points according to the attribute table; performing artificial remote sensing interpretation on the suspected black and odorous water body at each suspected black and odorous water body point, and performing field inspection; making a suspected black and odorous water body list, and forming a black and odorous water body remote sensing monitoring result of the target area. According to the method, the large-scale black and odorous water body can be quickly checked and identified, and corresponding treatment measures can be carried out.

Description

Technical Field

[0001] The invention relates to a method for verifying polluted water quality, in particular to a method for remote sensing interpretation and verification of black and odorous water bodies. Background Art

[0002] With the acceleration of urbanization, the problem of black and odorous water bodies has become increasingly serious, causing serious impacts on the ecological environment and the quality of life of residents in urban and rural areas. It is of great significance to investigate and control them. As an efficient, rapid and large-scale water monitoring method, remote sensing technology has unique advantages in the identification of black and odorous water bodies. At present, with the further improvement of the environment of large rivers and urban water bodies, large-scale black and odorous water bodies have been basically eliminated, while a large number of smaller and more hidden water bodies are still polluted. Optical remote sensing images with sub-meter high spatial resolution can be used to identify and find the location of black and odorous water bodies.

[0003] At present, the automation technology of identifying black and odorous water bodies based on high-resolution remote sensing images is booming. The band ratio method is widely used in the identification of black and odorous water bodies with high-resolution remote sensing images. This method uses the characteristic that the band ratio of black and odorous water bodies in remote sensing images is different from that of normal water bodies for automatic identification. Although it is convenient and fast, it has a very big disadvantage in the production process, that is, the water body boundary needs to be strictly and carefully circled before the black and odorous index is calculated. This will make it very difficult to screen black and odorous water bodies across the province. Deep learning technology is currently beginning to be gradually applied to the identification of black and odorous water bodies with high-resolution remote sensing images, mainly using high-resolution remote sensing images for black and odorous water body target identification or type identification. At present, the number of black and odorous water body samples based on high-resolution remote sensing is very small, and no mature multi-type black and odorous water body deep learning model has been found, and the training time and process are slow. In the black and odorous water body investigation tasks with tight schedules or the emergency temporary tasks of government departments, this method is still immature. Summary of the invention

[0004] The purpose of the present invention is to provide a method for remote sensing interpretation and verification of black and odorous water bodies, so as to solve the defect of the band ratio method that is not suitable for production projects, and to solve the problem that the training process of deep learning technology is slow and cannot make rapid emergency response to black and odorous water body inspection tasks with tight construction period or urgent temporary tasks of government departments.

[0005] The object of the present invention is achieved in that: A method for remote sensing interpretation and verification of black and odorous water bodies, comprising the following steps: S1. Obtain high-resolution remote sensing image data, administrative boundary data and DEM data of the target area.

[0006] S2. Preprocess the high-resolution remote sensing image data, including radiation correction, atmospheric correction, orthorectification, and image fusion, to form a surface reflectance remote sensing image of the target area.

[0007] S3. Collect information and data related to the investigation of suspected black and odorous water bodies in the target area.

[0008] S4. Screen suspected black and odorous water bodies based on the collected information and data to form elements of suspected black and odorous water bodies.

[0009] S5. Add the information of suspected black and odorous water body points, including the city, district, county name, town name, village name, image phase, longitude and latitude, sequence number and serial number, to the attribute table of suspected black and odorous water body points.

[0010] S6. Use the attribute table of suspected black and odorous water body points to create an information document for each suspected black and odorous water body point.

[0011] S7. Combined with the surface reflectance remote sensing images of the target area and the information documents of the suspected black and odorous water bodies, manual remote sensing interpretation of the suspected black and odorous water bodies at each suspected black and odorous water body point is carried out, and field verification is carried out simultaneously to determine whether the interpretation of the suspected black and odorous water bodies is correct.

[0012] S8. Prepare a list of suspected black and odorous water bodies and form remote sensing monitoring results of black and odorous water bodies in the target area.

[0013] Furthermore, the preprocessing of high-resolution remote sensing image data includes the following sub-steps: S1-1 obtains the absolute radiation calibration coefficient file of the land observation satellite on-orbit. According to the remote sensing image satellite type, sensor type and shooting time, the corresponding gain value and offset value are obtained from the absolute radiation calibration coefficient file of the land observation satellite on-orbit, and the radiation correction calculation is performed on the high-resolution remote sensing image data: L = Gain × DN + Bias Among them, L is the equivalent radiance at the entrance pupil of the satellite payload channel, Gain is the calibration coefficient gain value, DN is the observed value of the high-resolution remote sensing image, and Bias is the offset value of the high-resolution remote sensing image.

[0014] S1-2 obtains the parameters of the target area including the longitude and latitude of the remote sensing image center, aerosol type, underlying surface type, 550nm aerosol optical thickness, working area altitude and satellite sensor orbit altitude from the XML configuration file of the high-resolution remote sensing image, and uses the 6S model to carry out atmospheric correction.

[0015] S1-3 performs orthorectification on the atmospherically corrected panchromatic data and multispectral data, inputs the workspace DEM file, and outputs the orthorectified panchromatic band raster file and multispectral raster file.

[0016] S1-4 performs image fusion on the corrected panchromatic band raster file and the multispectral raster file to form a surface reflectance remote sensing image of the target area.

[0017] Furthermore, the method of making the attribute table includes the following sub-steps: S4-1 uses the spatial link tool to link the city, district, county, town, and image time phases of suspected black and odorous water point elements to the attribute table.

[0018] S4-2 Use the Add XY tool to calculate the longitude and latitude of each suspected black and odorous water point element.

[0019] S4-3 uses the Calculate Field tool to assign attribute information including sequence number and serial number to each suspected black and odorous water point element.

[0020] S4-4 obtains the village name from the reverse geocoding interface of the electronic map, inputs the longitude and latitude of the attribute table, sets the corresponding offset according to the location of the target area, extracts the village name from the captured content, and saves it in the attribute table.

[0021] Furthermore, the method of preparing the information document of the suspected black and odorous water body point includes the following sub-steps: S6-1 Draws the surface elements of suspected black and smelly water bodies at the points of suspected black and smelly water bodies on the high-resolution remote sensing images of the target area.

[0022] S6-2 adds scale and image time phase text to the ArcGIS mapping page based on the suspected black and odorous water body point elements and suspected black and odorous water body surface elements, uses Python to obtain the extent of each surface element, and outputs the image thumbnail of each suspected black and odorous water body surface element.

[0023] S6-3 fills the attribute information of the point elements of each suspected black and odorous water body point and the image thumbnail of the corresponding surface element into the document template to form an information document for each suspected black and odorous water body point.

[0024] S6-4 exports the locations of suspected black and smelly water bodies as kmz files and imports them into the mobile phone’s map software for planning field routes.

[0025] Furthermore, the field inspection in step S7 includes visual inspection, water quality testing and drone photography.

[0026] The present invention uses POI points of interest to find out whether there is black and smelly phenomenon in the water bodies around key water pollution discharge enterprises, uses WTMS online image sources to view clearer historical images from multi-period images, and uses public opinion information to find water pollution sites that have occurred in the target area recently. These reference materials provide sufficient judgment information for the remote sensing interpretation process and improve the comprehensiveness of remote sensing interpretation of suspected black and smelly water bodies. The present invention uses Python to greatly simplify the process of remote sensing interpretation and data formation, allowing remote sensing interpreters to focus all their energy on the screening process of suspected black and smelly water bodies. The present invention uses Memento software or developed mobile phone APP to greatly simplify the data filling process in field site verification. The present invention uses various reference materials that are helpful for the investigation of suspected black and smelly water bodies, which can provide sufficient judgment information for the remote sensing interpretation process. The use of Python, Memento software or developed mobile phone APP can greatly simplify the process of data collation and field site verification, achieving the purpose of rapid investigation and identification of black and smelly bodies in a large area, enabling the government to fully and actively grasp the status of black and smelly water bodies in the area and quickly carry out corresponding treatment measures.

[0027] The present invention can save remote sensing interpreters from a large amount of data sorting work and focus on remote sensing interpretation work. Therefore, black and odorous water bodies can be located from high-resolution remote sensing images at the provincial level in a very short time, solving the increasingly urgent ecological environment verification tasks, quickly discovering and reporting black and odorous water bodies at the provincial level, so that administrative departments can fully and actively grasp the status of black and odorous water bodies within their jurisdiction, and quickly take corresponding treatment measures to prevent the expansion of black and odorous water pollution. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a flowchart of the identification method of the present invention. DETAILED DESCRIPTION

[0029] Glossary: ​​High-resolution remote sensing images refer to remote sensing images with high spatial resolution.

[0030] The present invention will be further described below in conjunction with the accompanying drawings.

[0031] like Figure 1 As shown, the remote sensing interpretation and verification method of black and odorous water bodies of the present invention comprises the following steps: 1. On the "Land Observation Satellite Data Service" website, retrieve sub-meter high-resolution remote sensing image data according to the target area and time. Select GF7-BWD (0.8m panchromatic and 3.2m multispectral) and GF2-PMS (1m panchromatic and 4m multispectral) as satellite sensors. Select clear cloudless or lightly clouded images with less than 10% cloud cover. Collect administrative boundary data of the target area from the Tiantu official website, including administrative boundaries at the provincial, municipal, and county levels. Collect SRTM DEM data with a resolution of 30m within the target area from the USGS website.

[0032] 2. After the high-resolution remote sensing image data is collected, use Python's gdal library to write remote sensing image batch preprocessing code to automatically complete preprocessing steps such as radiation correction, atmospheric correction, orthorectification, and image fusion to obtain image fusion data. Use the "Register Raster" tool in ArcGIS to automatically perform geometric precision correction on the image fusion data to form a surface reflectance remote sensing image of the target area. Use the Watchdog library to monitor the data processing process. When the surface reflectance remote sensing image in tif format is monitored, delete the remote sensing data compression package to ensure that the computer has sufficient space to receive and process data in a rolling manner. Preprocessing of surface reflectance remote sensing images includes the following sub-steps: (1) Obtain the absolute radiation calibration coefficient file of domestic land observation satellites from the China Resources Satellite Application Center. According to the remote sensing image satellite type, sensor type and shooting time, obtain the corresponding gain value and offset value from the calibration coefficient file, and perform radiation correction calculation on the high-resolution remote sensing image data: L = Gain DN + Bias Among them, L is the equivalent radiance at the entrance pupil of the satellite payload channel, Gain is the calibration coefficient gain value, DN is the observed value of the high-resolution remote sensing image, and Bias is the offset value of the high-resolution remote sensing image.

[0033] (2) From the XML configuration file of the high-resolution remote sensing image, obtain the central latitude and longitude of the remote sensing image of the target area, aerosol type, underlying surface type, 550nm aerosol optical depth, working area altitude and satellite sensor orbit altitude, and use the 6S model to carry out atmospheric correction.

[0034] (3) Use the gdal library in Python to orthorectify the panchromatic data and multispectral data after atmospheric correction, input the workspace DEM file, and output the orthorectified panchromatic band raster file and multispectral raster file.

[0035] (4) Using the gdal library in Python, the results of the previous step are fused to form a remote sensing image of the surface reflectance of the target area. The Gram-Schmidt fusion algorithm has a better image effect.

[0036] 3. Collect various information materials that are helpful for the investigation of suspected black and odorous water bodies, such as POI points of interest related to water pollution, WTMS online image source materials and public opinion information. The process of collecting various reference materials includes the following steps: (1) Collect and establish a list of industry keywords related to water pollution, such as "breeding", "paper industry", "chemical industry", "textile industry", "pharmaceutical industry", "leather industry", "agricultural chemicals", "food processing", "oil refining", etc., as well as various synonyms derived from these words, such as "pig farm", "papermaking", "printing and dyeing", etc. Improve the keyword list as detailed as possible, use Python's requests, json, shapefile, xlrd and other libraries, and batch crawl points of interest from map websites such as Gaode and Baidu according to the names of districts and counties. The points of interest include the company name, address, type, and longitude and latitude locations. Convert the points of interest into POI point vectors, which can be more targeted and directional when checking suspected black and odorous water bodies in ArcGIS.

[0037] (2) Search for all the locations of "pig farms" and "cattle farms" in the target area from the pages of livestock trading websites such as Muyi.com, browse the website to understand the list layout of these locations, use Python's requests library and BeautifulSoup library to obtain web page content and parse HTML, extract address information and perform data cleaning to ensure the correctness of the address format, such as removing extra spaces and line breaks. Use online geocoding services such as Google Maps Geocoding API, OpenStreetMap Nominatim API, etc. to convert address information into longitude and latitude, and load it into ArcGIS to be more targeted and directional when investigating suspected black and smelly water bodies.

[0038] (3) Collect links to online image sources such as Star Map Earth and Tiandi Map, and use WTMS technology to load online slice images into the ArcGIS platform. Although the phase of this type of image is not as new as that of remote sensing images, it has higher clarity and can assist in identifying suspected black and smelly water bodies and their surrounding environment.

[0039] (4) Retrieve water pollution cases related to the target area from search websites and short video platforms, search for keywords such as "water pollution in XX district", and find out whether there is any news and public opinion information related to water pollution in the recent period. If there is, convert the address in the public opinion information into longitude and latitude, form a public opinion information point vector and load it into the ArcGIS platform. Or use Python's requests, BeautifulSoup and other libraries to analyze the structure of multiple target websites, write crawler code, extract structured information such as the time, location and pollution details of water pollution, and use the Geocoding API tool to convert the location name into longitude and latitude, save it as a public opinion information point vector and load it into the ArcGIS platform.

[0040] Fourth, set up three levels of point element templates in the ArcGIS platform, and combine the collected reference information to screen suspected black and odorous water bodies to form suspected black and odorous water body point elements. The screening of suspected black and odorous water bodies includes the following sub-steps: (1) Before remote sensing interpretation, interpreters need to master the relevant knowledge of black and odorous water bodies and remote sensing interpretation signs. According to the interpreters' understanding of black and odorous water bodies, the degree of suspicion is determined into three levels, where level 1 indicates a confirmed suspected black and odorous water body, level 2 indicates a possible suspected black and odorous water body, and level 3 indicates a general suspected black and odorous water body. Point feature templates of three levels are created.

[0041] (2) Add administrative area boundaries, POI point vectors, public opinion information point vectors, high-resolution remote sensing images, and WTMS online layers to the ArcGIS platform in sequence.

[0042] (3) Use the "fishnet" tool to make a grid according to the scope of the work area. The scale of each grid can be freely set. It is recommended to be between 1:2000 and 1:5000. Use the data-driven page tool to screen the screen grid by grid to see if there is any suspected black and smelly water body. A large scale covers a small area and can see the ground objects more clearly, but the number of grids is large; a small scale covers a large area and has a small number of grids, but it is difficult to identify smaller water bodies and is easy to miss. Therefore, the appropriate scale can be set according to the staffing and work period.

[0043] (4) After the interpretation is completed, the suspected black and odorous water point elements are reviewed to determine whether the level is appropriate.

[0044] 5. Use multiple tools in ArcGIS to add the city, county, town, village, image phase, longitude, latitude, sequence number and serial number of the suspected black and odorous water body to the attribute table of the suspected black and odorous water body. The creation of the attribute table includes the following sub-steps: (1) Use the spatial linking tool to batch link city names, district and county names, township names, and image time phases to the attribute table.

[0045] (2) Use the Add XY tool to batch calculate the longitude and latitude of point features.

[0046] (3) Use the Calculate Field tool to automatically assign attributes such as sequence numbers and serial numbers to point features in batches.

[0047] (4) Using Python libraries such as xlrd, xlwt, and requests, we obtain the village name from the reverse geocoding interface of Amap, where the location parameter of the interface inputs the longitude and latitude of the attribute table. The longitude and latitude here are Mars coordinates, and the corresponding offset needs to be set according to the location of the target area. The output parameter is set to json, the extension parameter is set to all, the radius parameter is set to 10000, and the poitype parameter is set to 190108. The village name is extracted from the captured content and saved in the attribute table.

[0048] 6. Use the attribute table of suspected black and odorous water points to prepare detailed information documents for each suspected black and odorous water point in preparation for field inspection. The preparation of information documents includes the following steps: (1) Draw the surface elements of suspected black and odorous water bodies at the points of suspected black and odorous water bodies on the high-resolution remote sensing images of the target area.

[0049] (2) Based on the suspected black and odorous water body point features and suspected black and odorous water body surface features, dynamically updateable scales and image phase texts are added to the ArcGIS mapping page. Python is used to obtain the extent of each suspected black and odorous water body surface feature. The scale corresponding to the range is multiplied by 2 to determine the output range, and the image thumbnails of each suspected black and odorous water body surface feature are batch output.

[0050] (3) Using Python libraries such as python-docx and xlrd, we set up corresponding document templates and filled the attribute information and image thumbnails of the suspected black and odorous water body point elements into the document templates in batches to form an information document for each suspected black and odorous water body, which is convenient for information comparison during field inspection.

[0051] (4) Use the layer to KMZ tool to export the suspected black and odorous water body points as kmz files and import them into the mobile phone's Aowei interactive map software to facilitate field route planning. You can also develop a corresponding website platform and mobile phone APP to import the attribute information and corresponding image thumbnails of the suspected black and odorous water body point elements into the website platform, and use the mobile phone APP for information retrieval, viewing, positioning, navigation and field verification.

[0052] 7. Based on the suspected black and odorous water body locations in the Aowei interactive map software on the mobile phone, combined with the surface reflectivity remote sensing images of the target area and the information documents of the suspected black and odorous water body locations, artificial remote sensing interpretation of the suspected black and odorous water bodies at each suspected black and odorous water body location is carried out, and field on-site verification is carried out simultaneously to determine whether the interpretation of the suspected black and odorous water bodies is correct.

[0053] The field inspection is carried out using the method of "visual inspection + water quality testing + drone photography", that is, visual inspection is used first. If the water environment is found to be in poor condition or the water is black, smelly and has a fishy smell, it is directly determined to be a black and smelly water body; if visual inspection is difficult, water quality testing is used for judgment; if the water body is difficult to reach, a drone is used for low-altitude photography.

[0054] The order of water quality index testing is ammonia nitrogen, dissolved oxygen and redox potential, transparency. According to the indicator range determined by the "Guidelines for the Treatment of Rural Black and Smelly Water Bodies" issued by the Ministry of Ecology and Environment and the "Guidelines for the Treatment of Urban Black and Smelly Water Bodies" issued by the Ministry of Housing and Urban-Rural Development, the water quality index is determined in order to determine whether it exceeds the standard. If it exceeds the standard, it can be directly determined as a black and smelly water body; if it does not exceed the standard, continue to test the next indicator.

[0055] Use Memento mobile software to create a field information collection form; use text box type fields to record data such as serial number, detailed location and detailed description; use radio button type fields to record water body type (pond, ditch, river, other), color (light green, yellow-green, gray, gray-black, yellow, colorless), whether there is a sewage outlet (yes, no) and whether it is a black and smelly water body (yes, no) and other data; use check box type fields to record pollution problems (industrial sewage, domestic sewage, domestic garbage, livestock manure) and odor (rotten egg smell, fishy smell, pungent smell, no obvious smell) and other data; use real number type fields to record ammonia nitrogen value, transparency value, dissolved oxygen value and redox potential value and other data; use coordinate type fields to record the latitude and longitude of the field point and the nearest POI information; use date and time type fields to record the survey time; use photo type fields to record field photos, etc. According to the degree of common use, put the most commonly used functions on the first page and the secondary record fields on the second page to increase the efficiency of field data collection. The collected results will be automatically synchronized to the cloud server, or the developed mobile phone APP can be used to collect on-site information.

[0056] 8. Export the data recorded in the Memento software or the developed mobile phone APP as a table file, and modify and improve it to form a black and odorous water body database. The photos collected in the field are divided into mobile phone photos and drone photos. If the photos are taken with software such as watermark cameras, the photos will have watermarks such as time and location. However, for photos that are not taken with watermark cameras or drones, the PIL library in Python can be used to extract the shooting time (DateTimeOriginal), shooting location (GPSLongitude, GPSLatitude) and other information in the EXIF ​​metadata of the photos without watermark information. If the shooting location information is not extracted from the EXIF ​​information of the photo, the location information recorded in the black and odorous water body database will be extracted to replace the shooting location information. The aforementioned reverse geocoding interface will be used to convert the longitude and latitude positions into location description information, and the above information will be printed on the photos in batches to enhance the readability of the photos. After the database and photos are sorted, use Python libraries such as python-docx and xlrd to make a list of black and odorous water bodies and form the remote sensing monitoring results of black and odorous water bodies in the target area.

Claims

1. A method for remote sensing interpretation and verification of black and odorous water bodies, characterized in that: The following steps are involved: S1. Obtain high-resolution remote sensing image data, administrative boundary data and DEM data of the target area; S2. Preprocess the high-resolution remote sensing image data, including radiation correction, atmospheric correction, orthorectification, and image fusion, to form a surface reflectance remote sensing image of the target area; S3. Collect information and data on the investigation of suspected black and odorous water bodies in the target area; S4. Screen suspected black and odorous water points based on the collected information and data to form suspected black and odorous water point elements; S5. Add the information of the suspected black and odorous water body point, including the city, county, town, village name, image phase, longitude and latitude, sequence number and serial number, to the attribute table of the suspected black and odorous water body point; S6. Using the attribute table of suspected black and odorous water body points, prepare information documents for each suspected black and odorous water body point; S7. Combine the surface reflectance remote sensing images of the target area and the information documents of the suspected black and odorous water body points, conduct manual remote sensing interpretation of the suspected black and odorous water body at each suspected black and odorous water body point, and simultaneously conduct field verification to determine whether the interpretation of the suspected black and odorous water body is correct; S8. Prepare a list of suspected black and odorous water bodies and form remote sensing monitoring results of black and odorous water bodies in the target area.

2. The remote sensing interpretation and verification method of black and odorous water according to claim 1 is characterized in that: The preprocessing of high-resolution remote sensing image data includes the following sub-steps: S1-1 obtains the absolute radiation calibration coefficient file of the land observation satellite on-orbit. According to the remote sensing image satellite type, sensor type and shooting time, the corresponding gain value and offset value are obtained from the absolute radiation calibration coefficient file of the land observation satellite on-orbit, and the radiation correction calculation is performed on the high-resolution remote sensing image data: L = Gain × DN + Bias Wherein, L is the equivalent radiance at the entrance pupil of the satellite payload channel, Gain is the gain value of the calibration coefficient, DN is the observed value of the high-resolution remote sensing image, and Bias is the offset value of the high-resolution remote sensing image; S1-2 Obtain the parameters of the target area including the latitude and longitude of the remote sensing image center, aerosol type, underlying surface type, 550nm aerosol optical thickness, work area altitude and satellite sensor orbit altitude from the XML configuration file of the high-resolution remote sensing image, and use the 6S model to carry out atmospheric correction: S1-3 orthorectifies the panchromatic data and multispectral data after atmospheric correction, inputs the workspace DEM file, and outputs the orthorectified panchromatic band raster file and multispectral raster file; S1-4 performs image fusion on the corrected panchromatic band raster file and the multispectral raster file to form a surface reflectance remote sensing image of the target area.

3. The method for remote sensing interpretation and verification of black and odorous water bodies according to claim 1 is characterized in that: The method for making the attribute table includes the following sub-steps: S4-1 Use the spatial link tool to link the city, county, township, and image time phase of the suspected black and odorous water point element to the attribute table; S4-2 Use the Add XY tool to calculate the longitude and latitude of each suspected black and odorous water point element; S4-3 Use the Calculate Field tool to assign attribute information including sequence number and serial number to each suspected black and odorous water point element; S4-4 obtains the village name from the reverse geocoding interface of the electronic map, inputs the longitude and latitude of the attribute table, sets the corresponding offset according to the location of the target area, extracts the village name from the captured content, and saves it in the attribute table.

4. The method for remote sensing interpretation and verification of black and odorous water bodies according to claim 1 is characterized in that: The preparation method of the information document of suspected black and odorous water body points includes the following sub-steps: S6-1 Draw the suspected black and odorous water body surface elements at the suspected black and odorous water body points on the high-resolution remote sensing image of the target area; S6-2 Add scale and image time phase text to the ArcGIS mapping page based on the suspected black and odorous water body point elements and suspected black and odorous water body surface elements, use Python to obtain the extent of each surface element, and output the image thumbnail of each suspected black and odorous water body surface element; S6-3 fills the attribute information of the point element of each suspected black and odorous water body point and the image thumbnail of the corresponding area element into the document template to form an information document of each suspected black and odorous water body point; S6-4 exports the locations of suspected black and smelly water bodies as kmz files and imports them into the mobile phone’s map software for planning field routes.

5. The method for remote sensing interpretation and verification of black and odorous water bodies according to claim 1 is characterized in that: The field inspection in step S7 includes visual inspection, water quality testing and drone photography.

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