A surface water quality monitoring method based on remote sensing water color index change

Abstract

A surface water quality monitoring method based on remote sensing water color index change comprises the following steps: S1: calculating the surface reflectivity of each year's regional remote sensing image to obtain the first remote sensing image and the corresponding regional surface reflectivity in each quarter; S2: extracting the water surface of the target water area in each year's region according to the first remote sensing image in each quarter region to generate the water surface range vector data of the target water area in each quarter region; S3: obtaining the water color index value grid image of the target water area in each quarter according to the first remote sensing image in each quarter region and the water surface range vector data of the target water area; S4: obtaining the water color index mean value of the target water area in multiple years according to the water color index value grid image of the target water area in each quarter of each year; S5: obtaining the spatial change grid of the water color index of the target water area in each quarter according to the water color index value grid image of the target water area in each quarter; and S6: obtaining the relative change value in multiple years according to the water color index mean value of the target water area in multiple years.

Description

Technical Field

[0001] This invention belongs to the field of remote sensing and environment, and specifically relates to a method for monitoring surface water quality based on changes in remote sensing water color index. Background Technology

[0002] Water quality monitoring plays a vital role in maintaining a healthy aquatic environment. In water pollution prevention and control, water quality monitoring is a necessary means and crucial link in improving water environment management and protection, and it constitutes a significant portion of environmental monitoring.

[0003] Inland water bodies are closely related to human production and daily life. In my country, the biggest problem with water resources is the lack of surface water quality monitoring, which makes it difficult to meet the needs of key areas for obtaining water quality information. Specifically, the number of water quality monitoring stations is too small to comprehensively obtain water quality information; the sampling data in water quality monitoring is insufficient, making it difficult to obtain data for key areas. In short, the cost of actual measurement data is high and it is difficult to provide timely feedback.

[0004] In recent years, with the development of satellite remote sensing, related research has begun to utilize satellite remote sensing data to extract water color parameters, represented by the water color index, for water quality studies. Studies have found that the water color index has a good correlation with the nutrient status of water bodies, the concentration of total suspended solids, and water transparency, and can indicate the overall water quality, especially the degree of turbidity. Therefore, in recent years, the water color index has been elevated to one of the important comprehensive water quality parameters. The lower the color level, the clearer the water and the better the water quality; the higher the color level, the more turbid the water and the worse the water quality. The water color index is developed based on the traditional Forel-Ule index (FUI) in oceanography. FUI uses discrete values ​​(1-21), achieving full coverage from blue to brown, and can be effectively used for water bodies with different turbidity levels. FUI extraction based on remote sensing images began in 2012, and to date, water color studies have been conducted on large lakes and oceans based on remote sensing. However, there are currently no feasible technical means to use satellite remote sensing imagery for water quality monitoring. Summary of the Invention

[0005] This invention provides a surface water quality monitoring method based on changes in remotely sensed water color index. It extracts the distribution of multi-year water color index values ​​of a target water area from satellite remote sensing imagery, constructs a quantitative spatiotemporal local variation data of the water color index, effectively reflects the spatiotemporal changes in water quality, and thus achieves the effect of water quality monitoring.

[0006] To achieve the above objectives, the present invention provides a method for monitoring surface water quality based on changes in remotely sensed water color index, comprising:

[0007] S1: Acquire regional remote sensing images containing the complete target water area in multiple years, calculate the surface reflectance of the regional remote sensing image in each year, and obtain the first remote sensing image of the region in each quarter and the corresponding regional surface reflectance.

[0008] S2: Based on the first remote sensing image of each quarter region, extract the water surface of the target water area in each year region and generate vector data of the water surface range of the target water area in each quarter region;

[0009] S3: Based on the first remote sensing image of the region in each quarter and the vector data of the water surface area of ​​the target water area, the raster image of the water color index value of the target water area in each quarter is obtained through the satellite-based water color index;

[0010] S4: Based on the raster images of the water color index values ​​of the target water area in each quarter of each year, the average water color index of the target water area over many years is calculated by means of the average value.

[0011] S5: For each quarterly raster image of the water color index of the target water area, obtain the spatial variation raster of the water color index of each quarter by using the local variation index of the water color index.

[0012] S6: The relative change over many years is obtained by using the local change index of the water color index to obtain the average water color index of the target water area over many years.

[0013] In one embodiment of the invention, the intervals between multiple years are equal.

[0014] In one embodiment of the present invention, in step S1, for quarters without remote sensing images, the corresponding year is skipped.

[0015] Step S1 includes:

[0016] Preprocessing is performed on the regional remote sensing images for each quarter, including atmospheric correction and cloud removal. The corresponding regional surface reflectance is calculated for each preprocessed regional remote sensing image for each quarter.

[0017] For each quarterly regional remote sensing image after preprocessing, the images are stitched together according to the average surface reflectance of the corresponding region to obtain the first remote sensing image of each quarter and the corresponding regional surface reflectance.

[0018] In one embodiment of the present invention, step S3 involves obtaining raster images of the target water body for each quarter based on the first remote sensing image of the region in each quarter and the corresponding regional surface reflectance, using a remote sensing-based water color index. This includes:

[0019] Using the surface reflectance raster image of the target water area for the quarter, the chromatic angle raster image for each quarter is obtained through a remote sensing-based chromatic angle algorithm;

[0020] Each raster in the chromatic angle raster image of each quarter is classified according to the chromatic angle value using 21 values ​​in the water color index lookup table to obtain a water color index raster image containing the target water area for each quarter.

[0021] In one embodiment of the present invention, step S2 involves extracting the surface area of ​​the target water body within each quarterly region based on the first remote sensing image and the corresponding regional surface reflectance, thereby generating vector data of the surface area of ​​the target water body within each quarterly region, including:

[0022] For the first remote sensing image of the region in each quarter and the corresponding regional surface reflectance, a raster image with a water surface range value of 1 is obtained by using a dynamic surface water range algorithm.

[0023] In one embodiment of the present invention, step S3 involves obtaining the water color index raster images of the target water area for each quarter by using a mask, based on a raster image of the target water area with a water surface range value of 1 within each quarter and a raster image of the water color index for each quarter containing the target water area. This includes:

[0024] Using a raster image of the target water area with a water surface range value of 1 within each quarterly region, the range with a value of 1 is selected as the mask image;

[0025] Based on the raster images of the water color index for each quarter containing the target water area, the raster images of the water color index for each quarter are obtained by cropping through a mask image.

[0026] In one embodiment of the present invention, step S4 involves calculating the average water color index of the target water area over many years based on the quarterly raster images of the water color index of the target water area, including:

[0027] Using the raster images of the water color index of the target water area in each quarter, the average water color index of the target water area in each quarter is calculated by the raster mean.

[0028] By using the average water color index of the target water area in each quarter, the average water color index of multiple quarters each year is calculated to obtain the average water color index of the target water area over many years.

[0029] In one embodiment of the present invention, based on the quarterly raster images of the water color index of the target water area and the multi-year average water color index of the target water area, the local variation index of the water color index is used to obtain the quarterly raster images of the local variation of the water color index of the target water area and the annual water color index variation values ​​of the target water area, including:

[0030] The local variation index of water color is constructed as shown in the following formula (1):

[0031]

[0032] Using the raster images of the water color index of the target water area in each quarter, and through the local change index of the water color index, the local change raster images of the water color index of the target water area in each quarter are obtained. At this time, in formula (1), This refers to the FUI local change value of the x-th grid cell, used to explore local changes relative to the entire space. x This represents the FUI value of the x-th grid cell, where i is a constant (1), and j represents the number of grid cells within the target water area. max and FUI min These represent the maximum and minimum values ​​of the grid within the target water area, respectively;

[0033] Using the average water color index of the target water area over many years, and through the local variation index of the water color index, the annual variation value of the water color index of the target water area is obtained. At this time, in formula (1), This refers to the FUI local change index over year x, used to explore changes in a local area relative to the entire study period. x Let FUI represent the FUI value for year x, where i represents the start year of the study and j represents the end year. max and FUI min These represent the maximum and minimum values ​​within the research period, respectively.

[0034] The surface water quality monitoring method based on remote sensing water color index changes provided by this invention has the following beneficial technical effects:

[0035] (1) This invention utilizes continuous remote sensing images, combines remote sensing technology with geographic information technology, and innovatively constructs the FUI local change index to quantitatively analyze the multi-year and spatial changes in surface water color.

[0036] (2) This invention utilizes continuous remote sensing imagery. Compared to previous methods that primarily relied on surface water quality parameters, this method is based on remote sensing-based analysis of water color changes. Historical remote sensing imagery stores a wealth of information, facilitating the investigation of historical changes in surface water color. By comparing changes over many years, changes in water color can be detected promptly, thus providing strong data support for surface water quality monitoring. Fully utilizing remote sensing imagery reduces labor costs and overcomes the limitations of areas difficult to measure manually, thereby enabling large-scale surface water quality detection. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is an overall flowchart of a surface water quality monitoring method based on changes in remotely sensed water color index, according to an embodiment of the present invention.

[0039] Figure 2a This is a surface reflectance image containing the Guanting Reservoir obtained in an embodiment of the present invention;

[0040] Figure 2b This is a raster image obtained in this embodiment of the invention, reflecting the distribution of the water color index (FUI) inverted from the actual water surface of Guanting Reservoir in summer and autumn.

[0041] Figure 2c The image obtained in this embodiment of the invention reflects the distribution of FUI changes in Guanting Reservoir over many years and in different seasons.

[0042] Figure 2d This is a time variation of the FUI of Guanting Reservoir over many years obtained in an embodiment of the present invention. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. 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.

[0044] This invention provides a method for monitoring surface water quality based on changes in remotely sensed water color index, comprising:

[0045] S1: Acquire regional remote sensing images containing the complete target water area in multiple years, calculate the surface reflectance of the regional remote sensing image in each year, and obtain the first remote sensing image of the region in each quarter and the corresponding regional surface reflectance.

[0046] Here, the intervals between multiple years are generally taken to be equal.

[0047] S2: Based on the first remote sensing image of each quarter region, extract the water surface of the target water area in each year region and generate vector data of the water surface range of the target water area in each quarter region;

[0048] S3: Based on the first remote sensing image of the region in each quarter and the vector data of the water surface area of ​​the target water area, the raster image of the water color index value of the target water area in each quarter is obtained through the satellite-based water color index;

[0049] S4: Based on the raster images of the water color index values ​​of the target water area in each quarter of each year, the average water color index of the target water area over many years is calculated by means of the average value.

[0050] S5: For each quarterly raster image of the water color index of the target water area, obtain the spatial variation raster of the water color index of each quarter by using the local variation index of the water color index.

[0051] S6: The relative change over many years is obtained by using the local change index of the water color index to obtain the average water color index of the target water area over many years.

[0052] In one embodiment of the present invention, in step S1, for quarters without remote sensing images, the corresponding year is skipped.

[0053] Step S1 includes:

[0054] Preprocessing is performed on the regional remote sensing images for each quarter, including atmospheric correction and cloud removal. The corresponding regional surface reflectance is calculated for each preprocessed regional remote sensing image for each quarter.

[0055] For each quarterly regional remote sensing image after preprocessing, the images are stitched together according to the average surface reflectance of the corresponding region to obtain the first remote sensing image of each quarter and the corresponding regional surface reflectance.

[0056] In one embodiment of the present invention, step S3 involves obtaining raster images of the target water body for each quarter based on the first remote sensing image of the region in each quarter and the corresponding regional surface reflectance, using a remote sensing-based water color index. This includes:

[0057] Using the surface reflectance raster image of the target water area for the quarter, the chromatic angle raster image for each quarter is obtained through a remote sensing-based chromatic angle algorithm;

[0058] Each raster in the chromatic angle raster image of each quarter is classified according to the chromatic angle value using 21 values ​​in the water color index lookup table to obtain a water color index raster image containing the target water area for each quarter.

[0059] In one embodiment of the present invention, step S2 involves extracting the surface area of ​​the target water body within each quarterly region based on the first remote sensing image and the corresponding regional surface reflectance, thereby generating vector data of the surface area of ​​the target water body within each quarterly region, including:

[0060] For the first remote sensing image of the region in each quarter and the corresponding regional surface reflectance, a raster image with a water surface range value of 1 is obtained by using a dynamic surface water range algorithm.

[0061] In one embodiment of the present invention, step S3 involves obtaining the water color index raster images of the target water area for each quarter by using a mask, based on a raster image of the target water area with a water surface range value of 1 within each quarter and a raster image of the water color index for each quarter containing the target water area. This includes:

[0062] Using a raster image of the target water area with a water surface range value of 1 within each quarterly region, the range with a value of 1 is selected as the mask image;

[0063] Based on the raster images of the water color index for each quarter containing the target water area, the raster images of the water color index for each quarter are obtained by cropping through a mask image.

[0064] In one embodiment of the present invention, step S4 involves calculating the average water color index of the target water area over many years based on the quarterly raster images of the water color index of the target water area, including:

[0065] Using the raster images of the water color index of the target water area in each quarter, the average water color index of the target water area in each quarter is calculated by the raster mean.

[0066] By using the average water color index of the target water area in each quarter, the average water color index of multiple quarters each year is calculated to obtain the average water color index of the target water area over many years.

[0067] In one embodiment of the present invention, based on the quarterly raster images of the water color index of the target water area and the multi-year average water color index of the target water area, the local variation index of the water color index is used to obtain the quarterly raster images of the local variation of the water color index of the target water area and the annual water color index variation values ​​of the target water area, including:

[0068] The local variation index of water color is constructed as shown in the following formula (1):

[0069]

[0070] Using the raster images of the water color index of the target water area in each quarter, and through the local change index of the water color index, the local change raster images of the water color index of the target water area in each quarter are obtained. At this time, in formula (1), This refers to the FUI local change value of the x-th grid cell, used to explore local changes relative to the entire space. x This represents the FUI value of the x-th grid cell, where i is a constant (1), and j represents the number of grid cells within the target water area. max and FUI min These represent the maximum and minimum values ​​of the grid within the target water area, respectively;

[0071] Using the average water color index of the target water area over many years, and through the local variation index of the water color index, the annual variation value of the water color index of the target water area is obtained. At this time, in formula (1), This refers to the FUI local change index over year x, used to explore changes in a local area relative to the entire study period. x Let FUI represent the FUI value for year x, where i represents the start year of the study and j represents the end year. max and FUI min These represent the maximum and minimum values ​​within the research period, respectively.

[0072] like Figure 1 This is an overall flowchart of a surface water quality monitoring method based on changes in remotely sensed water color index according to an embodiment of the present invention. The specific details of this embodiment are as follows:

[0073] 1) Acquire regional remote sensing images containing the complete target water area from multiple years, calculate the surface reflectance of the regional remote sensing image for each year, and obtain remote sensing images containing the surface reflectance of the target water area for each season over many years. Adjacent years are spaced at equal intervals.

[0074] 2) Using the remote sensing images of the surface reflectance of the target water area obtained in step 1) for each season over many years, extract the water surface of the target water area in each season and generate a raster image of the water surface range of the target water area in each season of each year as the mask range of the water surface.

[0075] In this embodiment, the specific steps are as follows:

[0076] 2.1) Based on the GEE platform, download JRC-GSW data, extract the maximum water surface vector image of the study area, and add a 1000-meter buffer as the first mask file of the water area of ​​the study area.

[0077] 2.2) Using the largest vector image of the study area obtained in step 2.1), the surface reflectance image of the study area is masked by a masking algorithm. The DSWE algorithm is applied, and the value of the raster covered by the water surface is 1 after recoding, so as to obtain the classification raster image of the true water surface range in each season.

[0078] 2.3) Using the raster images of the DSWE algorithm calculation results for each season of the year obtained in step 2.2), perform calculations, retain the raster with a value of 1, and set the rest to 0, to obtain the raster image of the water surface range of the target water area in each season of the year.

[0079] 3) Using the raster images of the target water area in each season of each year obtained in step 2), mask the remote sensing images containing the surface reflectance of the target water area in each season, and apply the FUI algorithm to obtain the multi-year FUI value distribution raster images of the target water area in each season; the specific steps are as follows:

[0080] Using the raster data of the water surface range of the target water area obtained in each season of each year (step 2), as a mask, the FUI calculation formula is applied to calculate only the area with a mask raster of 1 for the remote sensing image containing the surface reflectance of the target water area, and finally obtain the FUI value distribution raster image of the target water area.

[0081] 4) Using the FUI value distribution raster images of the target water area in each season of each year obtained in step 3), the FUI local variation index is used to obtain the multi-year FUI local variation distribution raster images and multi-year variation data of the target water area in each season; the specific steps are as follows:

[0082] 4.1) Using the FUI value distribution raster images of the target water area in each season of the year obtained in step 3), the local variation index of water color index is spatially applied, that is, the difference between each raster value and the raster mean value within the target water area is divided by the difference between the maximum and minimum raster values ​​of the image, to obtain the local variation distribution raster images of the FUI of the target water area in each season of the year.

[0083] 4.2) Using the raster images of the FUI value distribution of the target water area in each season obtained in step 3), the average FUI value for each year is calculated by means. The time application of the local variation index of water color index is then used, that is, the FUI value of a certain year is subtracted from the multi-year FUI average value, and then divided by the maximum and minimum annual average FUI values ​​to obtain the multi-year FUI value variation data of the target water area.

[0084] In this embodiment, the GEE cloud platform is used to process the raster data of the water surface range of the target water area in each season of each year. The water surface vector data of the target water area in each season of each year is input, and a 1000-meter buffer is used as a mask for water surface extraction. The resulting real water surface range raster data for each season is then masked for water color inversion. Using the FUI distribution raster data for each season, the constructed FUI local variation index is applied to obtain the distribution of FUI changes over multiple years for the corresponding season. The portions of FUI values ​​greater than 0 and less than 0 in the same season over multiple years are overlaid and statistically analyzed to obtain the spatial distribution of changes. The annual average FUI value for each year is calculated by averaging all FUI raster images for each year. The FUI local variation index is then applied to obtain the temporal variation data of FUI over multiple years.

[0085] In one specific embodiment of the present invention, the Guanting Reservoir is selected as the area to be studied. The main part of the Guanting Reservoir spans Huailai County, Zhangjiakou City, Hebei Province and Yanqing District, Beijing, and the water surface coverage has not exceeded 100 km in the past 21 years. 2The study area was analyzed using nearly 23 years of remote sensing data (SR series images from Landsat 7ETM+ and Landsat 8OLI) from the GEE platform, with each seasonal area located on the same image. Figure 2 shows the effectiveness of this embodiment using a surface water quality monitoring method based on changes in the remote sensing water color index proposed in this invention.

[0086] Figure 2a The surface reflectance images of Guanting Reservoir obtained in this embodiment of the invention consist of 46 images from 1999 to 2021, including summer and autumn seasons.

[0087] Figure 2b This is a raster image obtained in this embodiment of the invention, reflecting the distribution of the Water Color Index (FUI) inverted from the actual water surface of Guanting Reservoir during the summer and autumn of 2021. In the image, the color from dark blue to reddish brown indicates that the FUI increases from small to large. The smallest FUI indicates a darker blue actual water color, while the largest FUI indicates a reddish brown actual water color.

[0088] Figure 2c This is a raster image obtained in this embodiment of the invention, reflecting the distribution of FUI changes in Guanting Reservoir over many years and in different seasons. The value of each raster represents the magnitude of the FUI change over the corresponding range over many years. In the image, black indicates a positive change over many years, and gray indicates a negative change over many years. Areas with positive FUI changes are areas where the water quality deteriorates.

[0089] Figure 2d This chart shows the long-term variation of the FUI (Frequency Identification Value) of Guanting Reservoir obtained in this embodiment of the invention. The chart illustrates the 23-year water color change of Guanting Reservoir, showing that the FUI value increased before 2013 and decreased after 2013. This indicates that the water quality deteriorated in 2013 and improved after 2013.

[0090] The surface water quality monitoring method based on remote sensing water color index changes provided by this invention has the following beneficial technical effects:

[0091] (1) This invention utilizes continuous remote sensing images, combines remote sensing technology with geographic information technology, and innovatively constructs the FUI local change index to quantitatively analyze the multi-year and spatial changes in surface water color.

[0092] (2) This invention utilizes continuous remote sensing imagery. Compared to previous methods that primarily relied on surface water quality parameters, this method is based on remote sensing-based analysis of water color changes. Historical remote sensing imagery stores a wealth of information, facilitating the investigation of historical changes in surface water color. By comparing changes over many years, changes in water color can be detected promptly, thus providing strong data support for surface water quality monitoring. Fully utilizing remote sensing imagery reduces labor costs and overcomes the limitations of areas difficult to measure manually, thereby enabling large-scale surface water quality detection.

[0093] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of one embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing the present invention.

[0094] Those skilled in the art will understand that the modules in the apparatus of the embodiments can be distributed in the apparatus of the embodiments as described in the embodiments, or they can be located in one or more devices different from this embodiment with corresponding changes. The modules of the above embodiments can be combined into one module, or they can be further divided into multiple sub-modules.

[0095] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for monitoring surface water quality based on changes in remotely sensed water color index, characterized in that, include: S1: Acquire regional remote sensing images containing the complete target water area in multiple years, calculate the surface reflectance of the regional remote sensing image in each year, and obtain the first remote sensing image of the region in each quarter and the corresponding regional surface reflectance. S2: Based on the first remote sensing image of the region in each quarter, extract the water surface of the target water area in the region for each year and generate vector data of the water surface range of the target water area in the region for each quarter; S3: Based on the first remote sensing image of the region in each quarter and the vector data of the water surface area of ​​the target water area, the raster image of the water color index value of the target water area in each quarter is obtained through the satellite-based water color index; S4: Based on the raster images of the water color index values ​​of the target water area in each quarter of each year, the average water color index of the target water area over many years is calculated by means of the average value. S5: For each quarterly raster image of the water color index of the target water area, obtain the spatial variation raster of the water color index of the target water area in each quarter through the local variation index of the water color index; S6: The relative change over many years is obtained by using the local variation index of the water color index to calculate the average water color index of the target water area over many years. In step S4, based on the quarterly raster images of the target water area, the average water color index over many years is calculated, including: Using the raster images of the water color index of the target water area in each quarter, the average water color index of the target water area in each quarter is calculated by the raster mean. By using the average water color index of the target water area for each quarter, the average water color index for multiple quarters each year is calculated to obtain the multi-year average water color index of the target water area. Based on the quarterly raster images of the water color index of the target water area and the multi-year average water color index of the target water area, the local variation index of the water color index is used to obtain the quarterly raster images of the local variation of the water color index of the target water area and the annual water color index variation values ​​of the target water area, including: The local variation index of water color is constructed as shown in the following formula (1): ； Using the raster images of the water color index of the target water area in each quarter, and through the local change index of the water color index, the local change raster images of the water color index of the target water area in each quarter are obtained. At this time, in formula (1), This refers to the local variation value of the x-th raster, used to explore local changes relative to the entire space. This represents the FUI value of the x-th grid cell, where i is a constant (1) and j represents the number of grid cells representing the target water area. and These represent the maximum and minimum values ​​of the grid within the target water area, respectively; Using the average water color index of the target water area over many years, and through the local variation index of the water color index, the annual variation value of the water color index of the target water area is obtained. At this time, in formula (1), This refers to the FUI local change index over year x, used to explore changes in a local area relative to the entire study period. Let represent the FUI value for year x, where i represents the start year of the study, and j represents the end year. and These represent the maximum and minimum values ​​within the research period, respectively.

2. The surface water quality monitoring method according to claim 1, characterized in that, The intervals between multiple years are equal.

3. The surface water quality monitoring method according to claim 1, characterized in that, In step S1, for quarters without remote sensing imagery, the corresponding year is skipped. Step S1 includes: Preprocessing is performed on the regional remote sensing images for each quarter, including atmospheric correction and cloud removal. The corresponding regional surface reflectance is calculated for each preprocessed regional remote sensing image for each quarter. For each quarterly regional remote sensing image after preprocessing, the images are stitched together according to the average surface reflectance of the corresponding region to obtain the first remote sensing image of each quarter and the corresponding regional surface reflectance.

4. The surface water quality monitoring method according to claim 3, characterized in that, In step S3, based on the first remote sensing image of the region in each quarter and the corresponding regional surface reflectance, a water color index raster image of each quarter containing the target water body is obtained using the remote sensing-based water color index, including: Using the surface reflectance raster image of the target water area for the quarter, the chromatic angle raster image for each quarter is obtained through a remote sensing-based chromatic angle algorithm; Each raster in the chromatic angle raster image of each quarter is classified according to the chromatic angle value using 21 values ​​in the water color index lookup table to obtain a water color index raster image containing the target water area for each quarter.

5. The surface water quality monitoring method according to claim 1, characterized in that, In step S2, based on the first remote sensing image of each quarter region and the corresponding regional surface reflectance, the water surface area of ​​the target water body within each quarter region is extracted, generating vector data of the water surface range of the target water body within each quarter region, including: For the first remote sensing image of the region in each quarter and the corresponding regional surface reflectance, a raster image with a water surface range value of 1 is obtained by using a dynamic surface water range algorithm.

6. The surface water quality monitoring method according to claim 5, characterized in that, In step S3, based on the raster image of the target water area with a water surface range value of 1 within each quarter and the water color index raster image of each quarter containing the target water area, a mask is used to obtain the water color index raster image of the target water area for each quarter, including: Using a raster image of the target water area with a water surface range value of 1 within each quarterly region, the range with a value of 1 is selected as the mask image; Based on the raster images of the water color index for each quarter containing the target water area, the raster images of the water color index for each quarter are obtained by cropping through a mask image.

Images