Method and system for improving satellite remote sensing inversion precision by fusing hyperspectral proximity

Abstract

The invention discloses a method and system for improving satellite remote sensing inversion precision by fusing hyperspectral proximity, and belongs to the technical field of water environment and water ecology monitoring. Acquiring a water body hyperspectral near-sensing reflectivity spectral data set, a satellite remote sensing surface reflectance data set and a water environment actual measurement data set, and performing preprocessing; a synchronous data set containing # imgabs0 # and water quality and a synchronous data set containing # imgabs1 # and water quality are constructed; based on the synchronous data of the # imgabs2 # and the water quality, synchronous data of the # imgabs3 # and the water quality are generated, and synchronous data of the # imgabs4 # and the # imgabs5 # are obtained; the # imgabs6 # is normalized to obtain # imgabs7 #, and the quantitative conversion relation between the # imgabs7 # and the # imgabs8 # is constructed band by band to eliminate errors; generating a fused satellite-ground data set, and constructing a training water quality parameter inversion model; and updating corresponding methods and data sets in real time by repeating specified steps, and optimizing the model. According to the method, the inversion result is displayed and downloaded according to the query instruction, the satellite spectrum precision and the satellite-ground synchronous data set scale are improved, and the method is used for high-precision water environment monitoring.

Description

Technical Field

[0001] The present invention belongs to the technical field of water environment and water ecology monitoring, and specifically relates to a method and system for integrating hyperspectral near-field sensing to improve satellite remote sensing inversion accuracy. Background Art

[0002] Traditional water quality monitoring mainly relies on manual collection and laboratory measurements, which is time-consuming and labor-intensive, and the timeliness cannot meet the needs of large-scale water quality monitoring, especially for rapidly changing water bodies. Satellite remote sensing is widely used in water environment inversion monitoring due to its large scale, periodicity, and cost-effectiveness. However, due to the influence of weather and incomplete atmospheric correction, the accuracy of satellite spectra still needs to be further improved. In addition, since satellite remote sensing has the characteristics of large-scale instantaneous collection, but the water quality of rivers and lakes has highly dynamic changes, it is difficult to obtain quasi-synchronous satellite-ground data that is consistent in time and space. Usually, 3 satellite data before and after the satellite passes are used. h Matching with ground data.

[0003] In addition, the accuracy and generalization of water quality remote sensing inversion models depend on the number and diversity of satellite-ground quasi-synchronous data sets, while the geosynchronous data containing a large amount of diversity is limited by the number of ground sampling personnel and costs, making it difficult to obtain a large number of strictly diverse synchronous water samples. In summary, the use of satellite remote sensing inversion to monitor water quality currently still has shortcomings such as low atmospheric correction accuracy, inconsistent satellite-ground synchronization time, and limited data and diversity of strictly satellite-ground synchronous data sets. Summary of the invention

[0004] Purpose of the invention: To solve the above problems, the present invention provides a method and system for improving the accuracy of satellite remote sensing inversion by fusing hyperspectral near-field sensing.

[0005] The present invention adopts the following technical solution: a method for improving the accuracy of satellite remote sensing inversion by integrating hyperspectral near-field sensing, comprising the following steps: Step 1: Obtain a water body hyperspectral near-sensing reflectance spectral dataset, a satellite remote sensing surface reflectance dataset, and a water environment measured dataset, and use corresponding data preprocessing methods for preprocessing; Step 2: Determine the best matching window and construct a synchronous data set using the time matching principle; the synchronous data set includes: Synchronous data with water quality, Synchronous data with water quality; wherein, is the preprocessed satellite remote sensing surface reflectance data, is the preprocessed water body hyperspectral near-sensing reflectance spectral data, For the band; Step 3: Create a hyperspectral conversion multispectral data simulation method based on the Corresponding to the synchronous data generation of water quality Synchronize data with water quality and get and The synchronization data, is the simulated satellite remote sensing surface reflectivity; Step 4: Based on and The preprocessed satellite remote sensing surface reflectance data Normalization processing is performed to obtain new satellite remote sensing surface reflectivity data , the new satellite remote sensing surface reflectivity data Compared with the simulated satellite remote sensing surface reflectivity Conduct comparative analysis and build band by band and Quantitative conversion relationship between them to eliminate errors; Step 5: Synchronous data with water quality and Combined with the synchronous data of water quality to generate a fused satellite-ground data set; With the synchronized data of water quality and the fused satellite-ground dataset as input features and the measured dataset of water environment as output features, the water quality parameter inversion model is constructed and trained; Step 6. Repeat steps 3 to 5, update the hyperspectral conversion multispectral data simulation method, the satellite spectral accuracy band improvement method and the fusion satellite-ground data set in real time, continuously optimize the water quality parameter inversion model, and display and download the inversion results according to the query instruction until the user's end query instruction is received.

[0006] In a further embodiment, the data preprocessing step of the water body hyperspectral near-sensing reflectance spectral dataset includes: The water body hyperspectral near-sensing reflectance spectral dataset is obtained by continuously collecting data in various weather conditions. , calculate the water body hyperspectral near-sensing reflectance spectrum for each band With 420-830 nm Average value of near-sensing reflectance of inner hyperspectral The ratio of the normalized hyperspectral near-sensing reflectance corresponding to the band is obtained. : ; Refer to the spectral data of synchronous ground object spectrum Perform radiation correction, the calculation formula is as follows: ; In the formula, is the radiation gain value of the land-based hyperspectral proximity sensor, is the radiation deviation value of the land-based hyperspectral proximity sensor.

[0007] In a further embodiment, the data preprocessing step of the satellite remote sensing surface reflectivity data set includes: Obtain satellite remote sensing surface reflectance dataset , extract the quality band qa 60, get the pixel value of the cloud mask file based on the bit operation principle : Using bitwise operations and quality bands qa 60 Generate opaque cloud mask files and cirrus cloud mask files respectively, using and The () function combines the opaque cloud mask file and the cirrus cloud mask file to generate a cloud mask file, thereby obtaining the pixel value of the mask file ; The following formula is used to obtain the preprocessed satellite remote sensing surface reflectance data: : , is the surface reflectivity from satellite remote sensing.

[0008] In a further embodiment, the process of determining the best matching window is as follows: The sum of the coefficients of determination of all bands is defined as S , used to filter out the optimal time matching window, the calculation formula is as follows: ; in, , Band The coefficient of determination of S The time matching window at the maximum value is the best matching window.

[0009] In a further embodiment, the formula of the hyperspectral conversion multispectral data simulation method is embodied as follows: , ; In the formula, is the preprocessed water body hyperspectral near-sensing reflectance spectral data, For Spectral response function of the satellite sensor at .

[0010] In a further embodiment, the preprocessed satellite remote sensing surface reflectivity data The calculation formula for normalization is as follows: ; in, , Indicates that the band is 443 nm Satellite remote sensing surface reflectivity data, and so on.

[0011] In a further embodiment, the and The quantitative conversion relationship between them is expressed as follows: ; in, is the linear regression slope, indicating and The gain between is a constant.

[0012] In a further embodiment, the step of eliminating the error comprises: Build a linear regression based on the slope The error analysis principle is based on the error analysis principle. and The error situation between them, and carry out targeted optimization according to the error situation; The error analysis principle is: ; When the above-mentioned overestimation or underestimation occurs, the correction accuracy of the satellite sensor towards the water body is further optimized.

[0013] In a further embodiment, the water quality parameter inversion model adopts a machine learning model, and the machine learning model includes at least: a deep neural network model, a random forest model, a support vector machine model, an extreme gradient tree model, a Gaussian regression model and a backward transfer neural network model.

[0014] The system for improving satellite remote sensing inversion accuracy by fusing hyperspectral near sensing is used to implement the method for improving satellite remote sensing inversion accuracy by fusing hyperspectral near sensing as described above, including: The near-sensing hyperspectral sensor, satellite sensor and water quality detector are configured to obtain a water body hyperspectral near-sensing reflectance spectral dataset, a satellite remote sensing surface reflectance dataset and a water environment measured dataset, respectively; The first module is configured to select corresponding data processing methods to perform data preprocessing on the water body hyperspectral near-sensing reflectance spectral dataset, satellite remote sensing surface reflectance dataset and water environment measured dataset respectively; The second module is configured to determine the best matching window and construct a synchronous data set by using a time matching principle; the synchronous data set includes: Synchronous data with water quality, Synchronous data with water quality; wherein, is the preprocessed satellite remote sensing surface reflectance data, is the preprocessed water body hyperspectral near-sensing reflectance spectral data, For the band; The third module is configured to create a hyperspectral conversion multispectral data simulation method based on the Corresponding to the synchronous data generation of water quality Synchronize data with water quality and get and The synchronization data, is the simulated satellite remote sensing surface reflectivity; The fourth module is set up based on and The preprocessed satellite remote sensing surface reflectance data Normalization processing is performed to obtain new satellite remote sensing surface reflectivity data , the new satellite remote sensing surface reflectivity data Compared with the simulated satellite remote sensing surface reflectivity Conduct comparative analysis and build band by band and Quantitative conversion relationship between them to eliminate errors; The fifth module is configured to convert the Synchronous data with water quality and Combined with the synchronous data of water quality to generate a fused satellite-ground data set; With the synchronized data of water quality and the fused satellite-ground dataset as input features and the measured dataset of water environment as output features, the water quality parameter inversion model is constructed and trained; The sixth module is configured to repeat steps 3 to 5, update the hyperspectral conversion multispectral data simulation method, the satellite spectral accuracy band improvement method and the fusion satellite-ground data set in real time, and continuously optimize the water quality parameter inversion model; The terminal display module is configured to display and download the inversion results according to the query instruction until receiving the user's query end instruction.

[0015] Beneficial effects of the invention: The invention shows users a method for improving satellite water quality remote sensing inversion based on near-ground hyperspectral data, provides an optimal time window for matching satellite and measured water quality data, and ensures that the spectrum obtained by the user is completely one-to-one corresponding to the measured water quality.

[0016] The present invention expands the scale of satellite and ground synchronous measured data through simple and easy-to-operate hyperspectral near-sensing data simulation, improves the diversity and complexity of modeling data, ensures the stability and robustness of the model, reduces the difficulty for users to obtain satellite-ground synchronous data sets, and improves model accuracy.

[0017] The present invention can update, query, count, upload and download water quality information, ensure that users obtain water quality information at multiple time and space scales, and assist users in making decisions. Compared with the problems of information lag, spatiotemporal discreteness, and lack of long-term and multi-space change characteristics in traditional manual monitoring, this method provides a more convenient and efficient method and system.

[0018] The present invention supports interactive query of a web page interface, and supports and assists users in obtaining water quality information of their interest without being disturbed by other information. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a flow chart of the method for fusing hyperspectral near-field sensing to improve satellite remote sensing inversion accuracy in Example 1.

[0020] Figure 2 It is a response function diagram of the hyperspectral conversion multispectral data simulation of Example 1.

[0021] Figure 3 The synchronization of Example 1 and Analytical diagram of consistency between .

[0022] Figure 4 Example 1 and Quantitative conversion relationship diagram between .

[0023] Figure 5 The chlorophyll of Example 1 a Schematic diagram of the inversion model. DETAILED DESCRIPTION

[0024] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0025] Example 1 Inland water bodies are complex and diverse. In order to improve the accuracy of satellite spectra, enhance the consistency of satellite-ground spectra time matching, and expand the number and diversity of synchronized data, this embodiment provides a method for integrating hyperspectral near-sensing to improve the accuracy of satellite remote sensing inversion (hereinafter referred to as this method), such as Figure 1 The following steps are shown: Step 1: Obtain water body hyperspectral near-sensing reflectance spectral dataset, satellite remote sensing surface reflectance dataset and water environment measured dataset, and select corresponding data preprocessing methods for preprocessing.

[0026] Step 2: Determine the best matching window and construct a synchronous data set using the time matching principle; the synchronous data set includes: Synchronous data with water quality, Synchronous data with water quality; wherein, is the preprocessed satellite remote sensing surface reflectance data, is the preprocessed water body hyperspectral near-sensing reflectance spectral data, For the band; Step 3: Create a hyperspectral conversion multispectral data simulation method based on the Corresponding to the synchronous data generation of water quality Synchronize data with water quality and get and The synchronization data, is the simulated satellite remote sensing surface reflectivity; Step 4: Based on and The preprocessed satellite remote sensing surface reflectance data Normalization processing is performed to obtain new satellite remote sensing surface reflectivity data , the new satellite remote sensing surface reflectivity data Compared with the simulated satellite remote sensing surface reflectivity Conduct comparative analysis and build band by band and Quantitative conversion relationship between them to eliminate errors; Step 5: Synchronous data with water quality and Combined with the synchronous data of water quality to generate a fused satellite-ground data set; With the synchronized data of water quality and the fused satellite-ground dataset as input features and the measured dataset of water environment as output features, the water quality parameter inversion model is constructed and trained; Step 6. Repeat steps 3 to 5, update the hyperspectral conversion multispectral data simulation method, the satellite spectral accuracy band improvement method and the fusion satellite-ground data set in real time, continuously optimize the water quality parameter inversion model, and display and download the inversion results according to the query instruction until the user's end query instruction is received.

[0027] The following describes step 1 to step 6 one by one. In this embodiment, the water body hyperspectral near-sensing reflectance spectral dataset, satellite remote sensing surface reflectance dataset and water environment measured dataset all include basic data information, such as the longitude and latitude of the collection point, the collection time (accurate to minutes), the type of water body collected and weather conditions, etc. Further, the weather conditions include: sunny, cloudy, overcast and rainy, and also include: wind speed, etc.

[0028] Based on the above basic data information, the water body hyperspectral near-sensing reflectance spectral dataset is obtained by near-sensing hyperspectral sensors at 2 to 10 meters above the water surface. mThe ratio of the irradiance on the water surface up to the irradiance on the water surface down is obtained by collecting the data from the height of the water surface. Among them, the near-sensing hyperspectral sensor includes at least: a ground object spectrometer and a land-based hyperspectral near-sensing monitoring instrument ( GBPS ) two monitoring devices. The satellite remote sensing surface reflectance dataset is a multi-band dataset covering visible light to near-infrared bands collected by the satellite-borne platform, which has been corrected by radiation, geometry and atmosphere. L Level 2 surface reflectance. Finally, the water environment measured data set includes at least chlorophyll a , total suspended matter concentration, transparency, diffuse attenuation coefficient and optical activity parameters such as colored soluble organic matter absorption coefficient.

[0029] For further example, the satellite sensor of this embodiment selects Sentinel-2 The near-sensing hyperspectral sensor uses a land-based hyperspectral near-sensing monitor, and the water quality parameter is selected as chlorophyll a .

[0030] Based on the above description, considering the differences in the acquisition environment and acquisition parameters of the water body hyperspectral near-sensing reflectance spectral dataset, the satellite remote sensing surface reflectance dataset, and the water environment measured dataset, this embodiment uses different data preprocessing methods for the water body hyperspectral near-sensing reflectance spectral dataset, the satellite remote sensing surface reflectance dataset, and the water environment measured dataset. Specifically, the data preprocessing steps of the water body hyperspectral near-sensing reflectance spectral dataset include: The water body hyperspectral near-sensing reflectance spectral dataset is obtained by continuously collecting data in various weather conditions. In order to avoid the inconsistency of the hyperspectral near-sensing reflectance in complex light environments caused by the sun’s altitude, light intensity, and water surface waves, it is necessary to analyze the hyperspectral near-sensing reflectance spectrum of the water body. Therefore, the water body hyperspectral near-sensing reflectance spectrum of each band is calculated With 420-830 nm Average value of near-sensing reflectance of inner hyperspectral The ratio of the normalized hyperspectral near-sensing reflectance corresponding to the band is obtained. : ; In order to further improve the accuracy and reliability of the hyperspectral near-sensing band, the spectral data of the synchronous ground object spectrum are used to Perform radiation correction, the calculation formula is as follows: ; In the formula, is the radiation gain value of the land-based hyperspectral proximity sensor, is the radiation deviation value of the land-based hyperspectral proximity sensor. In this embodiment, The value of is 1.086, The value of is 0.0054.

[0031] It is worth mentioning that the average value of the hyperspectral near-sensing reflectance mentioned above is The calculation formula is as follows: ; In the formula, For band 420 nm The water body hyperspectral near-sensing reflectance, and so on .

[0032] Correspondingly, the data preprocessing steps of satellite remote sensing surface reflectance dataset include: Obtain satellite remote sensing surface reflectance dataset , extract the quality band qa 60, get the pixel value of the cloud mask file based on the bit operation principle : Using bitwise operations and quality bands qa 60 Generate opaque cloud mask files and cirrus cloud mask files respectively, using and The () function combines the opaque cloud mask file and the cirrus cloud mask file to generate a cloud mask file, thereby obtaining the pixel value of the mask file .

[0033] In a further embodiment, the opaque cloud mask file is generated by shifting the binary bit operation number 1 to the left by 10 bits, and then Bit 10th digit (the 11th digit from right to left) and qa The band value of 60 (after conversion to binary bit operation) Bit The 10th bit (the 11th number from the right to the left) is compared: if the two are the same, it means that there is cloud in the pixel and it is thick cloud, then the pixel value of the pixel is assigned to 1; if the two are different, the pixel value of the pixel is assigned to 0, and finally an opaque cloud mask file is generated.

[0034] Correspondingly, the steps for generating the cirrus cloud mask file are as follows: shift the binary bit operation number 1 to the left by 11 bits, and then Bit 11th digit (the 12th digit from right to left) and qa The band value of 60 (after conversion to binary bit operation) Bit The 11th bit (the 12th number from the right to the left) is compared: if the two are the same, it means that there is cloud in the pixel and it is thin cloud, then the pixel value of the pixel is assigned to 1; if the two are different, then the pixel value of the pixel is assigned to 0, and finally a cirrus cloud mask file is generated.

[0035] The opaque cloud mask file and cirrus cloud mask file obtained by the above method are used and() function is used to merge and obtain the cloud mask file, and the corresponding assignment is used to obtain the pixel value of the mask file The specific code is expressed as: qa 60. bitwiseAnd (1<<10). eq (0). and ( qa 60. bitwiseAnd (1<<11). eq (0)).

[0036] The following formula is used to obtain the preprocessed satellite remote sensing surface reflectance data: : , is the surface reflectivity from satellite remote sensing.

[0037] Furthermore, the data preprocessing method for the water environment measured data set includes: cleaning the abnormal data below zero. Specifically, the chlorophyll measured in the laboratory a Negative and zero values ​​in the concentration were deleted as outliers. In addition, in order to further eliminate data anomalies caused by irregularities in the artificial sampling process, the chlorophyll concentration was compared with the on-site sampling photos and the laboratory measurements. a Concentration comparison, when the bloom intensity increases chlorophyll a If the height is obviously too low, it should be removed.

[0038] Traditionally, satellite sensors are usually used to measure ±3 h ~±7 d However, since water quality is in a dynamic state due to the influence of climate and river basin runoff, the inconsistency between satellite data and ground numerical acquisition time may cause the water body spectrum to not match the satellite spectrum, which will directly affect the accuracy and stability of the water quality inversion model.

[0039] Furthermore, the time windows are extracted based on the transit time of the satellite sensor as a reference. , , , , , , The water body hyperspectral near-sensing reflectance is: h Indicates hours, d Represents day. Matching is performed according to spatial and spectral criteria, and calculations are performed under different time matching windows. The coefficient of determination of the band , the calculation results are shown as follows: ; in, .

[0040] Therefore, it is necessary to further optimize the best matching window for satellite-ground matching. The process of determining the best matching window using the time matching principle in step 2 is as follows: The sum of the determination coefficients of all bands is defined as S , used to filter out the optimal time matching window, the calculation formula is as follows: ; in, , Band The coefficient of determination of S The time matching window at the maximum value is the best matching window.

[0041] Chlorophyll a As an example, the best matching window obtained based on the above method is obtained With chlorophyll a 77 pairs of synchronous data, With chlorophyll a 1736 pairs of synchronization data.

[0042] Furthermore, given that satellite multispectral data and hyperspectral near-sensing reflectance are different in band position, band width and wavelength range. Figure 2 , in step three, a method for simulating hyperspectral conversion to multispectral data is created, which is specifically as follows: , ; In the formula, is the preprocessed water body hyperspectral near-sensing reflectance spectral data, For Based on the above example and combined with the simulation method, 46 pairs of satellite sensor spectral response functions can be obtained. and of synchronization data.

[0043] For the 46 pairs and The synchronous data of and There are significant differences, such as Figure 3 As shown. Among them, Figure 3 In ( a )and( b ) are and In 443~783 nm The reflectivity spectrum between .

[0044] Therefore, it is necessary to pre-process the satellite remote sensing surface reflectivity data For further processing, the processing method of this embodiment is as follows: ; in, , Indicates that the band is 443 nm Satellite remote sensing surface reflectivity data, and so on.

[0045] The new satellite remote sensing surface reflectivity data obtained after processing Compared with the simulated satellite remote sensing surface reflectivity By comparison and analysis, it was found that the spectral shapes of the two are basically the same.

[0046] Furthermore, band by band construction and The quantitative conversion relationship between and The quantitative conversion relationship between them is expressed as follows: ; in, is the linear regression slope, indicating and The gain between is a constant. Combined Figure 4 , Figure 4 In ( a )to( h ) in 443 nm ,490 nm ,560 nm ,665 nm ,705 nm ,743 nm and 783 nm Department and Scatter plot of the quantitative conversion relationship between Figure 4 middle( i ) as an example.

[0047] Correspondingly, the steps to eliminate the error include: establishing a linear regression slope The error analysis principle is based on the error analysis principle. and The error situation between them, and carry out targeted optimization according to the error situation; The error analysis principle is: ; When the above-mentioned overestimation or underestimation occurs, the correction accuracy of the satellite sensor facing the water body is further optimized; further examples are given, respectively. For example, the quantitative conversion relationship of each band is expressed as follows: ; The results show that the different bands in this embodiment slop are all lower than 1 and are not equal, indicating Sentinel -2 has different degrees of overestimation in different bands, which may be related to Sentinel -2 The official atmospheric correction algorithm was developed for land and has insufficient correction when facing water bodies.

[0048] In order to maximize Sentinel -2 The accuracy of the data band after conversion is selected The bands above 0.5 are converted to generate Sentinel -2 Image standardization process and method, which includes 443 bands nm ,490 nm ,665 nm ,705 nm And the ratio of 705 / 665.

[0049] In a further embodiment, the water quality parameter inversion model described in step 5 adopts a machine learning model, and the machine learning model at least includes: a deep neural network model, a random forest model, a support vector machine model, an extreme gradient tree model, a Gaussian regression model, and a backward transfer neural network model. Further description, the input features are divided into a training set and a test set in a ratio of 7:3, and are input into the machine learning model in turn, and the chlorophyll is compared and selected. a Inversion model. The spectral band contains at least 443 nm ,490 nm ,665 nm ,705 nm And the ratio of 705 / 665.

[0050] At the same time, the following methods can be used to verify the machine learning model, such as calculating the model's coefficient of determination, mean absolute percentage error ( MAPE ) and the root mean square error ( RMSE Then, the remaining 30% The synchronized data of water quality and the fused satellite-ground data set were used as the model validation set and input into the above-built model to calculate the coefficient of determination, mean absolute percentage error ( MAPE ) and the root mean square error ( RMSE ). According to the average absolute percentage error in the training data set and the validation data set ( MAPE ) and the root mean square error ( RMSE ) The smallest model was selected for the water quality parameter Chlorophyll a The inversion model of the two synchronous data sets chlorophylla The accuracy of the model is verified to see if there is an improvement effect. In this embodiment, the chlorophyll based on the fusion of satellite and ground data sets a The accuracy of the model Chlorophyll data synchronized with water quality a The model accuracy is about 22.5% higher, proving that fusing hyperspectral near-sensing data can significantly improve Sentinel -2 Remote sensing inversion of chlorophyll a The accuracy, such as Figure 5 As shown. Among them, Figure 5 In ( a )and( b ) are based on Synchronize with water quality data to build and verify chlorophyll a Inversion model scatter plot, Figure 5 In ( c )and( d ) are respectively based on the construction and verification of chlorophyll based on the fusion satellite-ground dataset a Scatter plot of the inversion model.

[0051] Example 2 In order to implement the method for improving satellite remote sensing inversion accuracy by fusing hyperspectral near sensing as described in Example 1, this embodiment discloses a system for improving satellite remote sensing inversion accuracy by fusing hyperspectral near sensing, including: The near-sensing hyperspectral sensor, satellite sensor and water quality detector are configured to obtain a water body hyperspectral near-sensing reflectance spectral dataset, a satellite remote sensing surface reflectance dataset and a water environment measured dataset, respectively.

[0052] The first module is configured to select corresponding data processing methods to preprocess the water body hyperspectral near-sensing reflectance spectral dataset, satellite remote sensing surface reflectance dataset and water environment measured dataset respectively.

[0053] The second module is configured to determine the best matching window and construct a synchronous data set by using a time matching principle; the synchronous data set includes: Synchronous data with water quality, Synchronous data with water quality; wherein, is the preprocessed satellite remote sensing surface reflectance data, is the preprocessed water body hyperspectral near-sensing reflectance spectral data, For the band.

[0054] The third module is configured to create a hyperspectral conversion multispectral data simulation method based on the Corresponding to the synchronous data generation of water quality Synchronize data with water quality and get and The synchronization data, is the simulated satellite remote sensing surface reflectivity.

[0055] The fourth module is set up based on and The preprocessed satellite remote sensing surface reflectance data Normalization processing is performed to obtain new satellite remote sensing surface reflectivity data , the new satellite remote sensing surface reflectivity data Compared with the simulated satellite remote sensing surface reflectivity Conduct comparative analysis and build band by band and The quantitative conversion relationship between them can eliminate errors.

[0056] The fifth module is configured to convert the Synchronous data with water quality and Combined with the synchronous data of water quality to generate a fused satellite-ground data set; The water quality parameter inversion model is constructed and trained with the synchronized data of water quality and the fused satellite-ground dataset as input features and the measured water environment dataset as output features.

[0057] The sixth module is configured to repeat steps three to five, and to update in real time the hyperspectral conversion to multispectral data simulation method, the satellite spectral accuracy band enhancement method, and the fusion of satellite and ground data sets to continuously optimize the water quality parameter inversion model.

[0058] The terminal display module is configured to display and download the inversion results according to the query instruction until receiving the user's query end instruction.

[0059] Specifically, it is used to display and update the linear method of band conversion between satellite data and hyperspectral data, as well as the chlorophyll after fusing hyperspectral near-sensing data. a Inversion model accuracy and image results. Receive user query requirements on different time scales, count and display water quality spatiotemporal change images and time series change trend characteristics; the different time scales include at least four time scales: daily, monthly, seasonal and inter-annual. Receive user query requirements for different regional scales, count and display water quality images and time series changes at different time scales; the different regional scales include at least lake bays and open water areas. Receive user download and upload instructions, and construct links to download water quality change graphs and tables at different time scales and regional scales according to the instructions until the query signal ends.

Claims

1. A method for improving satellite remote sensing inversion accuracy by integrating hyperspectral near-field sensing, characterized in that: The following steps are involved: Step 1: Obtain a water body hyperspectral near-sensing reflectance spectral dataset, a satellite remote sensing surface reflectance dataset, and a water environment measured dataset, and use corresponding data preprocessing methods for preprocessing; Step 2: Determine the best matching window and construct a synchronous data set using the time matching principle; the synchronous data set includes: Synchronous data with water quality, Synchronous data with water quality; wherein, is the preprocessed satellite remote sensing surface reflectance data, is the preprocessed water body hyperspectral near-sensing reflectance spectral data, For the band; Step 3: Create a hyperspectral conversion multispectral data simulation method based on the Corresponding to the synchronous data generation of water quality Synchronize data with water quality and get and The synchronization data, is the simulated satellite remote sensing surface reflectivity; Step 4: Based on and The preprocessed satellite remote sensing surface reflectance data Normalization processing is performed to obtain new satellite remote sensing surface reflectivity data , the new satellite remote sensing surface reflectivity data Compared with the simulated satellite remote sensing surface reflectivity Conduct comparative analysis and build band by band and Quantitative conversion relationship between them to eliminate errors; Step 5: Synchronous data with water quality and Combined with the synchronous data of water quality to generate a fused satellite-ground data set; With the synchronized data of water quality and the fused satellite-ground dataset as input features and the measured dataset of water environment as output features, the water quality parameter inversion model is constructed and trained; Step 6. Repeat steps 3 to 5, update the hyperspectral conversion multispectral data simulation method, the satellite spectral accuracy band improvement method and the fusion satellite-ground data set in real time, continuously optimize the water quality parameter inversion model, and display and download the inversion results according to the query instruction until the user's end query instruction is received.

2. The method for improving satellite remote sensing inversion accuracy by fusing hyperspectral near-field sensing according to claim 1 is characterized in that: The data preprocessing steps of the water body hyperspectral near-sensing reflectance spectral dataset include: The water body hyperspectral near-sensing reflectance spectral dataset is obtained by continuously collecting data in various weather conditions. , calculate the water body hyperspectral near-sensing reflectance spectrum for each band With 420-830 nm Average value of near-sensing reflectance of inner hyperspectral The ratio of the normalized hyperspectral near-sensing reflectance corresponding to the band is obtained. : ； Refer to the spectral data of synchronous ground object spectrum Perform radiation correction, the calculation formula is as follows: ； In the formula, is the radiation gain value of the land-based hyperspectral proximity sensor, is the radiation deviation value of the land-based hyperspectral proximity sensor.

3. The method for improving satellite remote sensing inversion accuracy by fusing hyperspectral near-field sensing according to claim 1 is characterized in that: The data preprocessing steps of the satellite remote sensing surface reflectivity data set include: Obtain satellite remote sensing surface reflectance dataset , extract the quality band qa 60, get the pixel value of the cloud mask file based on the bit operation principle : Using bitwise operations and quality bands qa 60 Generate opaque cloud mask files and cirrus cloud mask files respectively, using and The () function combines the opaque cloud mask file and the cirrus cloud mask file to generate a cloud mask file, thereby obtaining the pixel value of the mask file ; The following formula is used to obtain the preprocessed satellite remote sensing surface reflectance data: : , is the surface reflectivity from satellite remote sensing.

4. The method for improving satellite remote sensing inversion accuracy by fusing hyperspectral near-field sensing according to claim 1 is characterized in that: The process of determining the best matching window is as follows: The sum of the coefficients of determination of all bands is defined as S , used to filter out the optimal time matching window, the calculation formula is as follows: ； in, , Band The coefficient of determination of S The time matching window at the maximum value is the best matching window.

5. The method for improving satellite remote sensing inversion accuracy by fusing hyperspectral near-field sensing according to claim 1 is characterized in that: The formula of the hyperspectral conversion multispectral data simulation method is as follows: ， ； In the formula, is the preprocessed water body hyperspectral near-sensing reflectance spectral data, For Spectral response function of the satellite sensor at .

6. The method for improving satellite remote sensing inversion accuracy by fusing hyperspectral near-field sensing according to claim 1 is characterized in that: The preprocessed satellite remote sensing surface reflectivity data The calculation formula for normalization is as follows: ； in, , Indicates that the band is 443 nm Satellite remote sensing surface reflectivity data, and so on.

7. The method for improving satellite remote sensing inversion accuracy by fusing hyperspectral near-field sensing according to claim 1 is characterized in that: Said and The quantitative conversion relationship between them is expressed as follows: ； in, is the linear regression slope, indicating and The gain between is a constant.

8. The method for improving satellite remote sensing inversion accuracy by fusing hyperspectral near-field sensing according to claim 7 is characterized in that: The error elimination step comprises: Build a linear regression based on the slope The error analysis principle is based on the error analysis principle. and The error situation between them is optimized according to the error situation; the error analysis principle is: ； When the above-mentioned overestimation or underestimation occurs, the correction accuracy of the satellite sensor towards the water body is further optimized.

9. The method for improving satellite remote sensing inversion accuracy by fusing hyperspectral near-field sensing according to claim 1 is characterized in that: The water quality parameter inversion model adopts a machine learning model, which includes at least: a deep neural network model, a random forest model, a support vector machine model, an extreme gradient tree model, a Gaussian regression model and a backward transmission neural network model.

10. A system for improving satellite remote sensing inversion accuracy by fusing hyperspectral near sensing, used to implement the method for improving satellite remote sensing inversion accuracy by fusing hyperspectral near sensing as described in any one of claims 1 to 9, characterized in that: include: The near-sensing hyperspectral sensor, satellite sensor and water quality detector are configured to obtain a water body hyperspectral near-sensing reflectance spectral dataset, a satellite remote sensing surface reflectance dataset and a water environment measured dataset, respectively; The first module is configured to select corresponding data processing methods to perform data preprocessing on the water body hyperspectral near-sensing reflectance spectral dataset, satellite remote sensing surface reflectance dataset and water environment measured dataset respectively; The second module is configured to determine the best matching window and construct a synchronous data set using the time matching principle; The synchronization data set includes: Synchronous data with water quality, Synchronous data with water quality; wherein, is the preprocessed satellite remote sensing surface reflectance data, is the preprocessed water body hyperspectral near-sensing reflectance spectral data, For the band; The third module is configured to create a hyperspectral conversion multispectral data simulation method based on the Corresponding to the synchronous data generation of water quality Synchronize data with water quality and get and The synchronization data, is the simulated satellite remote sensing surface reflectivity; The fourth module is set up based on and The preprocessed satellite remote sensing surface reflectance data Normalization processing is performed to obtain new satellite remote sensing surface reflectivity data , the new satellite remote sensing surface reflectivity data Compared with the simulated satellite remote sensing surface reflectivity Conduct comparative analysis and build band by band and Quantitative conversion relationship between them to eliminate errors; The fifth module is configured to convert the Synchronous data with water quality and Combined with the synchronous data of water quality to generate a fused satellite-ground data set; With the synchronized data of water quality and the fused satellite-ground dataset as input features and the measured dataset of water environment as output features, the water quality parameter inversion model is constructed and trained; The sixth module is configured to repeat steps 3 to 5, update the hyperspectral conversion multispectral data simulation method, the satellite spectral accuracy band improvement method and the fusion satellite-ground data set in real time, and continuously optimize the water quality parameter inversion model; The terminal display module is configured to display and download the inversion results according to the query instruction until receiving the user's query end instruction.

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