A method and system for remotely extracting a plume region caused by a heavy rainfall process

Abstract

The application discloses a method and system for remotely sensing plume area caused by heavy rainfall process, and belongs to the technical field of environmental science and monitoring technology. A heavy rainfall event is identified; a plume area remote sensing index calculation formula is created, and a plume area remote sensing extraction index PI caused by the heavy rainfall process in a current region is calculated; a PI threshold inequality is constructed; the PI threshold inequality and water body remote sensing reflectivity are used to determine a PI threshold range; the plume area remote sensing extraction index PI and the PI threshold range are applied to other secondary independent heavy rainfall events in the current region, and the current region is remotely sensed to obtain a corresponding plume area. In the construction process of the extraction index, the threshold range can be flexibly set, different rainfall conditions and water body types can be adapted, and the limitation caused by the fixed suspended matter concentration range is avoided. The method can not only identify turbid water, but also effectively process water turbidity changes under different intensity rainfall conditions.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of environmental science and monitoring technology, and specifically relates to a method and system for remotely sensing and extracting plume areas caused by strong rainfall processes. BACKGROUND

[0002] With the advancement of climate change and urbanization, the frequency of strong rainfall events has increased globally. Strong rainfall can cause a large amount of surface water to flow into water bodies, especially in areas such as lake inlets, bringing a large amount of suspended solids, pollutants, and nutrients, forming high-turbidity areas (plume areas). Plume areas are usually characterized by a rapid increase in suspended solids concentration in water bodies, with significant changes in water quality, which has important implications for water body ecosystems and human activities such as water quality monitoring, water resource utilization, etc.

[0003] Traditional water body monitoring methods, such as field sampling and laboratory analysis, often have limited spatial coverage, long time periods, and high costs, making it difficult to meet the needs of large-scale, real-time monitoring. Remote sensing technology has become an important tool in water body environmental monitoring because it can provide efficient, continuous monitoring data in a large area and short time.

[0004] Currently, methods for extracting water turbidity areas based on remote sensing data have made some progress, but most existing technologies mainly focus on monitoring static water bodies or long time scales, and have not effectively addressed the real-time monitoring of dynamic water body changes (such as plume areas) caused by strong rainfall processes. During strong rainfall, the suspended solids concentration and water body structure change significantly, especially in a short period of time, so there is a lack of high-precision, real-time extraction methods for this process.

[0005] In addition, existing methods usually rely on fixed suspended solids concentration ranges to identify plume areas. However, the suspended solids concentration of plume areas caused by strong rainfall events varies greatly due to differences in rainfall intensity, rainfall duration, and terrain, making it difficult for fixed-range extraction methods to adapt to changes under different rainfall intensities. For example, under light rain and heavy rain, the suspended solids concentration in water bodies changes significantly, and the diffusion and settling speed of suspended solids in water bodies also differs. Therefore, how to accurately distinguish plume areas from surrounding water bodies under conditions of large differences in rainfall intensity remains an important challenge in remote sensing water body monitoring. SUMMARY

[0006] The present application provides a method and system for remotely sensing and extracting plume areas caused by strong rainfall processes to solve the technical problems in the background art.

[0007] The application realizes the method by the following technical solutions: a method for remotely sensing plume area caused by strong rainfall process, comprising the following steps:

[0008] Collecting effective satellite images of a current region corresponding to a weather station, obtaining accumulated rainfall of the previous N days of the effective satellite images, and identifying a strong rainfall event according to the accumulated rainfall;

[0009] Creating a plume area remote sensing index calculation formula to calculate a plume area remote sensing extraction index PI caused by the strong rainfall process of the current region;

[0010] Building a PI threshold inequality based on the determined strong rainfall event, and determining a PI threshold range by using the PI threshold inequality and water body remote sensing reflectivity;

[0011] Applying the plume area remote sensing extraction index PI and the PI threshold range to other independent strong rainfall events of the current region , remotely sensing the current region to obtain a corresponding plume area .

[0012] In further embodiments, the following steps are further included:

[0013] Based on the plume area , the current region is divided into a high turbidity area and a non-high turbidity area, and a verification model is built;

[0014] The verification model is used to verify the effectiveness and optimize the plume area remote sensing extraction index PI and the PI threshold range.

[0015] In further embodiments, the strong rainfall event identification method is as follows:

[0016] The following formula is used to calculate the accumulated rainfall of the previous N days : ; in the formula, is the rainfall of the i-th day;

[0017] If , it is determined as a strong rainfall event, and Q is a rainfall threshold.

[0018] In further embodiments, the expression form of the plume area remote sensing index calculation formula is as follows:

[0019] ;

[0020] In the formula, , and respectively represent the wavelength of the green light band, the red light band and the near-infrared band, , and​ respectively the green band , the red band and the near-infrared band of remote sensing reflectance.

[0021] In further embodiments, the PI threshold inequality is constructed as follows:

[0022] The selection of pixel remote sensing reflectance data from the strong rainfall events is performed to distinguish the non-turbid region, the turbid region before rainfall and the turbid region after rainfall.

[0023] The remote sensing reflectance data of all pixels in the non-turbid region, the turbid region before rainfall and the turbid region after rainfall are extracted to obtain three data sets, which are respectively: clean water spectrum data set I , turbid water spectrum data set II before rainfall and turbid water spectrum data set III after rainfall.

[0024] Box plots of the three data sets in different bands are drawn, and the PI threshold inequality is obtained by analysis.

[0025] In further embodiments, the PI threshold inequality is expressed as:

[0026] Clean water spectrum data set I : or ; in the formula, is the plume region remote sensing extraction index PI of the clean water spectrum data set I , , , and is the green band remote sensing reflectance mean value, the near-infrared band remote sensing reflectance mean value, the red band remote sensing reflectance minimum value and the maximum value of the clean water spectrum data set I .

[0027] Turbid water spectrum data set II before rainfall: or ; in the formula, is the plume region remote sensing extraction index PI of the turbid water spectrum data set II before rainfall , , and is the green band remote sensing reflectance mean value, the near-infrared band remote sensing reflectance mean value, the red band remote sensing reflectance minimum value and the maximum value of the turbid water spectrum data set II before rainfall

[0028] Turbid water spectrum data set III after rainfall: and In the formula, The index PI was extracted from the plume region of the post-rainfall spectral dataset III for turbid water bodies. , , and The mean remote sensing reflectance in the green band, the mean remote sensing reflectance in the near-infrared band, and the minimum and maximum remote sensing reflectance in the red band of the spectral dataset III after rainfall in turbid water bodies are given.

[0029] Correspondingly, the process for determining the PI threshold range is as follows:

[0030] Spectral datasets of clean water bodies were obtained using the PI threshold inequality. The range of clean water index given in the spectral datasets II and III of turbid water bodies before and after rainfall. Turbidity water body index range before rainfall The range of plume index after rainfall in turbid water bodies ;

[0031] The clean water index ranges were respectively... Turbidity water body index range before rainfall Inverting the water body yields the range of the plume index after inversion. The range of the plume index before and after rainfall in turbid water bodies after reversal. Based on the range of the plume index after rainfall in the turbid water body Taking the intersection yields the range of the composite index. Obtain the range of the composite index. minimum boundary value and maximum boundary value ;

[0032] Therefore, the PI threshold range is: .

[0033] In a further embodiment, the plume region The extraction steps are as follows:

[0034] Obtain other independent heavy rainfall events Effective observation images were used to calculate heavy rainfall events using the remote sensing index calculation formula for plume regions. Remote sensing extraction index of plume region ;

[0035] If the remote sensing extraction index of the plume area If it falls within the PI threshold range, then it is a plume region. The water body corresponding to the area of the effective observation image is a turbid zone water body, and other areas are non-turbid zone water bodies.

[0036] In a further embodiment, the construction process of the verification model is as follows:

[0037] Based on the high turbidity zone and the non-high turbidity zone divided out, the fourth quantile value TSM Q4 of the turbid zone water body and the first quantile value TSM Q1 of the non-turbid zone water body are calculated, respectively.

[0038] If TSM Q4 > TSM Q1 , it indicates that the plume zone remote sensing extraction index PI is effective, otherwise it is invalid.

[0039] A system for remotely extracting a plume zone caused by a heavy rainfall process is used to implement the method described above, comprising:

[0040] A first module is configured to collect effective satellite images of a current region corresponding to a weather station, obtain the cumulative rainfall of the previous N days of the effective satellite images, and identify a heavy rainfall event according to the cumulative rainfall.

[0041] A second module is configured to create a plume zone remote sensing index calculation formula to calculate the plume zone remote sensing extraction index PI caused by the heavy rainfall process in the current region.

[0042] A third module is configured to construct a PI threshold inequality based on the heavy rainfall event that has been determined, and determine the PI threshold range using the PI threshold inequality and the water body remote sensing reflectivity.

[0043] A fourth module is configured to apply the plume zone remote sensing extraction index PI and the PI threshold range to other independent heavy rainfall events in the current region to remotely extract the corresponding plume zone in the current region.

[0044] The beneficial effects of the present application are:

[0045] (1) Flexible index threshold setting: In the construction process of the extraction index, the present application can adapt to different rainfall conditions and water body types by flexibly setting the threshold range, avoiding the limitations brought by the fixed suspended matter concentration range. This method not only can identify turbid zone water bodies, but also can effectively handle the water body turbidity changes under different intensity rainfall conditions.

[0046] (2) Reduce monitoring cost: Compared with the traditional on-site sampling and laboratory analysis method, the present application can greatly reduce the monitoring cost through the remote sensing data extraction method, reduce the manual intervention and sampling workload, and realize large-scale and high-frequency water quality monitoring.

[0047] (3) Wide application prospects: This method is suitable for monitoring various inland lakes and reservoirs, and has good popularization. Through processing of remote sensing image data, real-time and accurate data support can be provided for water quality monitoring, water resource management and ecological protection.

[0048] (4) Enhancing water body management decision support: Through efficient plume zone extraction and water turbidity monitoring, this invention provides strong technical support for water quality protection, pollution source tracking and ecological restoration, and can provide scientific basis for water management departments to help make more accurate water environment governance decisions. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1 is a method flowchart for extracting plume zones caused by heavy rainfall processes in Example 1.

[0050] Figure 2 is an extraction schematic diagram of three data sets in Example 1.

[0051] Figure 3 is a box plot of the corresponding wave bands of the three data sets in Example 1.

[0052] Figure 4 is a comparison chart for verifying the effectiveness of the plume zone remote sensing extraction index PI in Example 1. DETAILED DESCRIPTION

[0053] The present invention will be further described below in conjunction with the drawings and examples in the specification.

[0054] Example 1

[0055] This example takes Qiandao Lake as an example. Large surface runoff caused by heavy rainfall will carry a large amount of particulate matter, nutrients, pollutants and heavy metals into the Xin'anjiang Reservoir, thereby increasing the frequency and scale of algal outbreaks. Therefore, this example discloses a method for remotely extracting plume zones caused by heavy rainfall processes, as shown in Figure 1 , comprising the following steps:

[0056] Collect effective satellite images of the corresponding weather station in the current region, obtain the cumulative rainfall of the previous N days of the effective satellite images, and identify a heavy rainfall event according to the cumulative rainfall;

[0057] Create a plume zone remote sensing index calculation formula to calculate the plume zone remote sensing extraction index PI caused by heavy rainfall processes in the current region;

[0058] Build a PI threshold inequality based on the heavy rainfall event that has been determined; determine the PI threshold range using the PI threshold inequality and the water body remote sensing reflectance;

[0059] applying the plume area remote sensing extraction index PI and the PI threshold range to other sub-independent heavy rainfall events of the current region , and the corresponding plume area is obtained by remote sensing extraction of the current region .

[0060] In a further embodiment, the weather station corresponding to the current region can be the nearest weather station to Qiandao Lake, and the effective satellite image is a remote sensing image obtained by HJ-1A / B satellite under cloudless conditions. The acquisition event of the remote sensing image is determined as the current time, and the cumulative rainfall of the previous N days is obtained at the time point of the current time. In this embodiment, N is 7, that is, the cumulative rainfall of the previous 7 days (statistical according to the daily rainfall of each day) is obtained.

[0061] Correspondingly, the identification method of the heavy rainfall event is as follows:

[0062] The cumulative rainfall of the previous N days is calculated by the following formula : ; in the formula, is the rainfall of the i-th day;

[0063] If , it is determined as a heavy rainfall event, and Q is the rainfall threshold.

[0064] Taking , for example, if the cumulative rainfall of the previous 7 days of the effective satellite image is , it is determined as a heavy rainfall event; otherwise, it is a non-heavy rainfall event.

[0065] In a further embodiment, considering that the turbid water body of the plume area will lift the remote sensing reflectance of the red light band position, the expression form of the plume area remote sensing index calculation formula of this embodiment is as follows:

[0066] ;

[0067] In the formula, , and respectively represent the wavelength of the green light band, the red light band and the near-infrared band, , and are the remote sensing reflectance of the green light band , the red light band and the near-infrared band .

[0068] Among them, the green light band is the green light of HJ-1A / B, ​; the wavelength of the red light band is 660nm, and the wavelength of the near-infrared band is 835nm, so the plume zone remote sensing index calculation formula is further represented as:

[0069] .

[0070] Based on this, the construction process of the PI threshold inequality is as follows:

[0071] From the pixel remote sensing back emission rate data determined as a strong rainfall event, the non-turbid region, the turbid region before rainfall and the turbid region after rainfall are distinguished; taking other 10 independent strong rainfall events in Qiandao Lake as an example.

[0072] Combining Figure 2 , the remote sensing back emission rate data of all pixels in the non-turbid region, the turbid region before rainfall and the turbid region after rainfall are extracted to obtain three data sets, which are: clean water spectrum data set , turbid water spectrum data set II before rainfall and turbid water spectrum data set III after rainfall;

[0073] Each data set contains the remote sensing reflectivity of all pixels in the green light band, the red light band and the near-infrared band in the corresponding region. The visible light and near-infrared band remote sensing reflectivity data of all pixels in the non-turbid region, the turbid region before rainfall and the turbid region after rainfall are counted, and the box plots of the three data sets in different bands are drawn as Figure 3 , and the PI threshold inequality is obtained.

[0074] Based on the PI threshold inequality, the determination process of the PI threshold range is as follows:

[0075] The PI threshold inequality is used to obtain the clean water spectrum data set , the turbid water spectrum data set II before rainfall and the turbid water spectrum data set III after rainfall given the clean water index interval range , the turbid water index interval range before rainfall and the turbid water plume index interval range after rainfall .

[0076] The clean water index interval range , the turbid water index interval range before rainfall are negated to obtain the clean water plume index interval range after negation and the turbid water plume index interval range before rainfall after negation , and the turbid water plume index interval range after rainfall is taken as the intersection to obtain the comprehensive index interval range , and the comprehensive index interval range minimum boundary value and maximum boundary value ;

[0077] Therefore, the PI threshold range is: In this embodiment, the comprehensive index range is... The methods for obtaining it are as follows: .

[0078] To achieve the above process, this embodiment further elaborates on the PI threshold inequality. First, Figure 3 (A) in the dataset represents the spectral data of clean water bodies. Box diagram, Figure 3 (B) in the figure is a box plot of the spectral dataset II of turbid water bodies before rainfall. Figure 3 (C) in the figure is a box plot of the spectral dataset III after rainfall in turbid water bodies.

[0079] Furthermore, for clean water spectral datasets For the spectral dataset II of turbid water bodies before rainfall, PI can be geometrically represented as Figure 3 (A) and Figure 3 The negative value of line segment 'BO' in (B); for the box plot of the post-rainfall spectral dataset III for turbid water bodies, PI can be geometrically represented as Figure 3 In (C), line segment 'BO' is used. Based on the spectral distribution range of turbid water pixels in different datasets, the PI threshold inequality is constructed as follows:

[0080] Clean water spectral dataset : or In the formula, For clean water spectral datasets The remote sensing extraction index PI of the plume region , , and For clean water spectral datasets The mean remote sensing reflectance in the green band, the mean remote sensing reflectance in the near-infrared band, and the minimum and maximum remote sensing reflectance in the red band were measured. The clean water index range in this embodiment can be determined using the above method. The range of the plume index after reversing the corresponding clean water body The inverse is the range of the clean water index. Other intervals.

[0081] Pre-rainfall spectral dataset II for turbid water bodies: or In the formula, PI is the plume area remote sensing extraction index of the turbid water before rainfall for the spectral data set II before rainfall, , , and are the average remote sensing reflectance of the green band, the average remote sensing reflectance of the near-infrared band, the minimum and maximum remote sensing reflectance of the red band of the turbid water before rainfall for the spectral data set II before rainfall. The turbid water before rainfall index interval range in this embodiment can be determined by the above scheme and the corresponding plume turbid water before rainfall after inversion index interval range .

[0082] Spectral data set III after rainfall of turbid water: and ; in the formula, PI is the plume area remote sensing extraction index of the turbid water after rainfall for the spectral data set III after rainfall, , , and are the average remote sensing reflectance of the green band, the average remote sensing reflectance of the near-infrared band, the minimum and maximum remote sensing reflectance of the red band of the turbid water after rainfall for the spectral data set III after rainfall. The plume index interval range in this embodiment can be determined by the above scheme .

[0083] Combining the above description, the intersection of the three interval ranges is processed to obtain the minimum boundary value and the maximum boundary . Further, the PI threshold range in this embodiment is specifically expressed as follows: .

[0084] Based on the plume area remote sensing extraction index PI and the PI threshold range, the extraction steps of the plume area are as follows:

[0085] Obtain the effective observation image of other secondary independent heavy rainfall event , and use the plume area remote sensing index calculation formula to obtain the plume area remote sensing extraction index of the heavy rainfall event ;

[0086] If the plume area remote sensing extraction index belongs to the PI threshold range, it is the plume area , and the water body of the region corresponding to the effective observation image is the turbid area water body, and the other regions are the non-turbid area water body.

[0087] In another embodiment, it further includes: based on the plume area Divide the current region into a high turbidity area and a non-high turbidity area, and construct a verification model;

[0088] Use the verification model to verify and optimize the plume area remote sensing extraction index PI and the PI threshold range.

[0089] Furthermore, the construction process of the verification model is as follows:

[0090] Based on the divided high turbidity area and non-high turbidity area, calculate the fourth quartile value TSM of the water body in the turbidity area Q4 and the first quartile value TSM of the water body in the non-turbidity area Q1 ;

[0091] If TSM Q4 > TSM Q1 , it means that the plume area remote sensing extraction index PI is valid, otherwise it is invalid.

[0092] Combined with Figure 4 Further elaboration, the water bodies in the non-turbidity area and the turbidity area are distinguished by the measured total suspended solids concentration. From the extraction results, the total suspended solids concentration of the water body in the turbidity area (5.63 ± 1.51 mg / L) is significantly higher than that of the water body in the non-turbidity area (2.34 ± 0.73 mg / L). In addition, in 4 independent heavy rainfall events, the first quartile value TSM of the water body in the non-turbidity area Q1 is less than the fourth quartile value TSM of the water body in the turbidity area Q4 , indicating that the PI threshold (-0.062 < PI < 0.016) can effectively distinguish the water bodies in the turbidity area and the non-turbidity area, and the remote sensing extraction index is effective.

[0093] Furthermore, for the results verified as invalid, this embodiment also includes the following optimization methods:

[0094] Amplify the determined heavy rainfall events and obtain the corresponding effective satellite images to get the amplified sample data; such as the corresponding amplified clean water body spectral dataset , the pixel numbers of the spectral dataset II of the turbid water body before rainfall and the spectral dataset III of the turbid water body after rainfall. And perform quality control on the extended spectral dataset, removing abnormal pixels such as cloud, shadow, and shoreline interference to ensure the representativeness and stability of the reflectance data.

[0095] Based on the amplified sample data, reconstruct the PI threshold inequality, and use the reconstructed PI threshold inequality to determine the PI threshold range to obtain a new PI threshold range, that is, update the minimum boundary value of the comprehensive index interval range and the maximum boundary value of the comprehensive index interval range , the comprehensive index interval range of .

[0096] Remote sensing extraction of other independent heavy rainfall events using new PI threshold range and TSM Q4 TSM Q1 Verification, if passed, the new PI threshold range is retained; if still not passed, repeat updating sample data until a stable and effective PI threshold range is formed.

[0097] Embodiment 2

[0098] The embodiment discloses a system for remote sensing extraction of plume area caused by heavy rainfall process, for implementing the method described in embodiment 1, comprising:

[0099] The first module is configured to collect effective satellite images of the corresponding weather station in the current region, obtain the cumulative rainfall of the previous N days of the effective satellite images, and identify a heavy rainfall event according to the cumulative rainfall;

[0100] The second module is configured to create a plume area remote sensing index calculation formula, and calculate the plume area remote sensing extraction index PI caused by the heavy rainfall process in the current region;

[0101] The third module is configured to construct a PI threshold inequality based on the heavy rainfall event that has been determined, and determine the PI threshold range using the PI threshold inequality and the water body remote sensing reflectance;

[0102] The fourth module is configured to apply the plume area remote sensing extraction index PI and the PI threshold range to other independent heavy rainfall events in the current region Remote sensing extraction of the corresponding plume area in the current region ;

[0103] The fifth module is configured to divide the current region into a high turbidity area and a non-high turbidity area based on the plume area Remote sensing extraction index PI and PI threshold range

[0104] The sixth module is configured to use the verification model to verify and optimize the effectiveness of the plume area remote sensing extraction index PI and the PI threshold range.

Claims

1. A method of remotely extracting a plume zone caused by a heavy rainfall event, characterized in that, The method comprises the following steps: Collecting effective satellite images of a corresponding weather station in a current region, obtaining accumulated rainfall of the effective satellite images in the previous N days, and identifying a heavy rainfall event according to the accumulated rainfall; Creating a plume area remote sensing index calculation formula to calculate a plume area remote sensing extraction index PI caused by a heavy rainfall process in the current region; Building a PI threshold inequality based on the heavy rainfall event that has been determined; Determining a PI threshold range by using the PI threshold inequality and water body remote sensing reflectivity; applying the plume zone remote sensing extraction index PI and the PI threshold range to other sub-independent heavy rainfall events of the current region , and performing remote sensing extraction on the current region to obtain a corresponding plume zone ; The expression form of the plume area remote sensing index calculation formula is as follows: ； wherein, , and represent the wavelengths of the green, red and near-infrared wavebands, respectively, , and are the remote sensing reflectances of the green , red and near-infrared wavebands, respectively. The building process of the PI threshold inequality is as follows: Selecting pixel remote sensing reflectivity data from the heavy rainfall event that has been determined, and distinguishing non-turbid areas, turbid areas before rainfall, and turbid areas after rainfall; The remote sensing emissivity data of all pixels in the non-turbid region, the turbid region before rainfall and the turbid region after rainfall are extracted to obtain three data sets, i.e. a clean water body spectrum data set , a turbid water body spectrum data set II before rainfall and a turbid water body spectrum data set III after rainfall. Drawing box plots of three data sets in different bands, and obtaining a PI threshold inequality by analysis; The process for determining the PI threshold range is as follows: The PI threshold inequality is used to obtain the spectral dataset of clean water bodies. The range of clean water index given in the spectral datasets II and III of turbid water bodies before and after rainfall. Turbidity water body index range before rainfall The range of plume index after rainfall in turbid water bodies ; respectively, and taking complement of the clean water body index interval range , the turbid water body before rainfall index interval range , to obtain the clean water body after complement plume index interval range and the turbid water body before rainfall after complement plume index interval range , combined with the turbid water body after rainfall plume index interval range , to obtain the comprehensive index interval range , and the minimum boundary value and the maximum boundary value of the comprehensive index interval range are obtained; then, the PI threshold range is: .

2. A method of remotely extracting a plume zone caused by a heavy rainfall event according to claim 1, wherein, The method further comprises the following steps: Based on the plume region The current region is divided into high turbidity area and non-high turbidity area, and a verification model is constructed; Verifying and optimizing the plume area remote sensing extraction index PI and the PI threshold range by using the verification model.

3. The method of claim 1, wherein the method further comprises: The identification method of the heavy rainfall event is as follows: The accumulated rainfall of the previous N days is calculated using the following formula : ; where is the rainfall on day n; and is the accumulated rainfall. If then a heavy rainfall event is determined, Q being a rainfall threshold value.

4. The method of claim 1, wherein the method further comprises: The plume zone The extraction step is as follows: Obtaining effective observation images of other secondary independent heavy rainfall events , and calculating a plume area remote sensing extraction index of the heavy rainfall event by using a plume area remote sensing index calculation formula ; plume region remote sensing extraction index if the plume region remote sensing extraction index belongs to the pi threshold range, the plume region is determined if the water body corresponding to the region of the effective observation image is a turbid region water body, and other regions are non-turbid region water bodies.

5. The method of claim 2, wherein the method further comprises: The building process of the verification model is as follows: Based on the divided high-turbidity area and non-high-turbidity area, the fourth quantile value TSM of the turbid area water body is calculated Q4 and the first quantile value TSM of the non-turbid area water body Q1 ; If TSM Q4 > TSM Q1 , it means that the plume zone remote sensing extraction index PI is valid, otherwise it is invalid and needs to be further optimized.

6. The method of claim 2, wherein the method further comprises: The method further comprises the following optimization method: Enlarging the heavy rainfall event that has been determined, and obtaining corresponding effective satellite images to obtain sample data after enlargement; Rebuilding a PI threshold inequality based on the sample data after enlargement, and determining a PI threshold range by using the rebuilt PI threshold inequality to obtain a new PI threshold range; Using the new PI threshold range to extract other independent heavy rainfall events and perform TSM Q4 TSM Q1 Verify, if passed, keep the new PI threshold range setting; if still not passed, repeat updating sample data until a stable and effective PI threshold range is formed.

7. A system for remote sensing extraction of plume regions caused by heavy rainfall processes for implementing the method according to any one of claims 1 to 6, characterized in that, The method comprises: A first module is configured to collect effective satellite images of a corresponding weather station in a current region, obtain accumulated rainfall of the effective satellite images in the previous N days, and identify a heavy rainfall event according to the accumulated rainfall; A second module is configured to create a plume area remote sensing index calculation formula to calculate a plume area remote sensing extraction index PI caused by a heavy rainfall process in the current region; A third module is configured to build a PI threshold inequality based on the heavy rainfall event that has been determined; A PI threshold range is determined by using the PI threshold inequality and water body remote sensing reflectivity; a fourth module configured to apply the plume zone remote sensing extraction index PI and the PI threshold range to other sub-independent heavy rainfall events of the current region a plume zone corresponding to the current region is obtained by remote sensing extraction .

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