Ocean offshore dissolved oxygen estimation method, system and equipment based on remote sensing data

Abstract

The invention discloses an ocean nearshore dissolved oxygen estimation method, system and equipment based on remote sensing data. The method comprises the following steps: S1, acquiring measured data of a dissolved oxygen concentration site of a sea area to be measured, ocean remote sensing product data and ocean numerical mode HYCOM reanalysis data; s2, performing data reconstruction on missing data of four elements including chlorophyll, granular organic carbon, suspended solids and sea surface temperature in the marine remote sensing product data by adopting a DINCAE algorithm to obtain complete marine remote sensing product data; s3, performing correlation analysis and variance expansion factor analysis on the five ocean elements related to the dissolved oxygen, selecting characteristic variables, and constructing a remote sensing estimation model of the dissolved oxygen; and S4, inverting the sea surface dissolved oxygen concentration of the sea area to be measured by adopting the optimal dissolved oxygen remote sensing estimation model, and drawing the spatial distribution of the dissolved oxygen concentration. According to the invention, the problem that the dissolved oxygen as a non-optical active substance has no characteristic response on a remote sensing spectrum and cannot be inversed directly by satellite remote sensing is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite remote sensing image processing, and in particular to a method, system and equipment for estimating nearshore dissolved oxygen in the ocean based on remote sensing data. Background Art

[0002] Dissolved oxygen (DO) refers to molecular oxygen dissolved in water. Dissolved oxygen in seawater is a fundamental parameter for assessing marine water quality and ecosystems. It is a crucial factor in sustaining the life of marine plants and animals, promoting marine physical, biochemical processes, and maintaining the material and energy balance between the ocean and land. Atmospheric oxygen enters the water column through surface exchange. Photosynthesis by plants and plankton in seawater releases oxygen, making it the primary source of dissolved oxygen in the ocean. Respiration by aquatic organisms, decomposition of organic matter, and the redox reactions of pollutants consume dissolved oxygen. When the rate of oxygen consumption in seawater exceeds the rate of reoxygenation, DO can drop significantly. Global warming, anthropogenic marine emissions, and eutrophication are driving a downward trend in dissolved oxygen in coastal waters. Dissolved oxygen concentrations below 3 mg / L are typically considered hypoxia, and below 2 mg / L are considered anoxic. These conditions frequently occur in coastal estuaries. Hypoxia can damage marine habitats, alter species composition and community structure, reduce biodiversity, and reduce the space available for species competition. It also impairs the self-regulating capacity of marine ecosystems.

[0003] Current surveys of dissolved oxygen in seawater primarily rely on conventional ship observations. Standard methods for measuring dissolved oxygen are iodine titration or electrochemical probes. Field surveys widely utilize multi-parameter water quality meters, which can measure water temperature, pH, dissolved oxygen, conductivity, chlorophyll concentration, and other parameters. These instruments are easy to use and offer rapid measurements. While this method offers high measurement accuracy, it suffers from limited observation points and insufficient spatial representation of the data. This is particularly true in coastal waters, where water quality frequently fluctuates. Conventional methods are unable to rapidly, efficiently, and comprehensively monitor, manage, and predict water quality parameters like dissolved oxygen on a large scale.

[0004] With the continuous development of satellite ocean remote sensing technology, optically active parameters of the ocean surface such as phytoplankton chlorophyll, suspended matter, soluble organic matter (CDOM), particulate organic carbon (POC), sea surface temperature, sea surface salinity, etc. can be effectively observed. The Ocean Biology Processing Group (OBPG) of NASA has released long-term series of ocean color remote sensing data at different levels on a global scale from multiple satellites, providing important basic data for the evaluation, management and prediction of the marine ecological environment.

[0005] Leveraging the advantages of satellite remote sensing technology, such as speed, periodicity, and wide coverage, some researchers are attempting to use multi-band satellite remote sensing imagery to construct models for estimating dissolved oxygen in water and invert its concentration. However, because dissolved oxygen is a non-optical parameter, unlike chlorophyll and other substances that exhibit strong absorption, reflection, and scattering characteristics in remote sensing spectra, this approach, which ignores the optical mechanisms of dissolved oxygen remote sensing and simply uses multi-band remote sensing data for dissolved oxygen estimation, is inaccurate and difficult to generalize. Summary of the Invention

[0006] Purpose of the invention: The purpose of the present invention is to provide a method, system and equipment for estimating dissolved oxygen in the nearshore ocean based on remote sensing data, which realizes the remote sensing inversion of satellite data of dissolved oxygen as a non-optical substance and improves the accuracy of remote sensing inversion of dissolved oxygen in the nearshore ocean.

[0007] Technical solution: A method for estimating dissolved oxygen in coastal areas of the ocean based on remote sensing data, comprising the following steps: S1, obtain the measured data of dissolved oxygen concentration in the sea area to be measured, marine remote sensing product data, and marine numerical model HYCOM reanalysis data; S2, using the DINCAE algorithm to reconstruct the missing data of the four elements of chlorophyll, particulate organic carbon, suspended matter and sea surface temperature in the ocean remote sensing product data, and obtain the complete reconstructed ocean remote sensing product data; S3, based on the longitude and latitude coordinates of the dissolved oxygen observation station, extract the corresponding sea surface salinity element from the HYCOM reanalysis data, and extract the corresponding four ocean elements (chlorophyll, particulate organic carbon, suspended matter, and sea surface temperature) from the reconstructed ocean remote sensing product data to obtain sample data matching the dissolved oxygen station. Correlation analysis and variance inflation factor analysis were performed on dissolved oxygen and the five extracted elements to extract characteristic variables for constructing the dissolved oxygen remote sensing estimation model. Using dissolved oxygen concentration as the dependent variable and the five ocean element values ​​as characteristic variables, different machine learning methods were used to construct the dissolved oxygen remote sensing estimation model. The accuracy of the dissolved oxygen remote sensing estimation was evaluated to select the optimal dissolved oxygen remote sensing estimation model. S4, use the best dissolved oxygen remote sensing estimation model to invert the sea surface dissolved oxygen concentration in the measured sea area and map the spatial distribution of dissolved oxygen concentration.

[0008] Furthermore, in step S2, the specific steps for reconstructing the missing data of each ocean element remote sensing product are as follows: S21, the calculation steps of the DINCAE algorithm input parameters are as follows: First, the average value m is calculated for all images of each ocean element in the same season. ij , then the average value of each day’s image is subtracted from the original image concentration value to obtain the abnormal value p of each day’s image. ijt, again calculate the inverse of the error variance of the outlier image for each day ; Finally, multiply the image outlier value of each day by the inverse of the error variance to complete the scaling of the outlier image of each day , the expression is as follows: , , , , Where, subscripts i and j represent the row and column numbers of the image, respectively; subscript t represents the image data of the tth day in a season; n is the total number of days in a season; m represents the seasonal average value of the image; and DN represents the original image value. represents the error variance, Var( ) represents the function of finding the error variance of the image, and p' represents the outlier value of the image after scaling by the inverse of the error variance; The outlier image after scaling by the inverse of the error variance on the t-th day, the inverse value of the error variance on the t-th day, the outlier image after scaling by the inverse of the error variance on the t-1-th day, the inverse value of the error variance on the t-1-th day, the outlier image after scaling by the inverse of the error variance on the t+1-th day, the inverse value of the error variance on the t+1-th day, the scaled longitude data, the scaled latitude data, the cosine value of the annual accumulation day, and the sine value of the annual accumulation day are selected as the input parameters of the DINCAE algorithm; S22, the images of each ocean element in the first 50 days of each season are used as training samples, and the images of the remaining days are used as test samples, and the convolutional neural network is iteratively trained and tested, and the parameters of the encoding layer, fully connected layer and decoding layer in the network structure are set; wherein, the input parameters of the convolutional neural network are the images of each ocean element in each season, and the output result is the ocean element outlier data D after being scaled by the inverse of the expected error variance ij1 and the natural logarithm of the inverse of the expected error variance D ij2 ; S23, using the output result of step S22, reconstruct the data of the daily sub-images of the ocean elements in the same season, and then splice all the sub-images to obtain a complete image after data reconstruction.

[0009] Furthermore, in step S23, the steps for reconstructing the data of the daily sub-graphs of the ocean elements in the same season are as follows: S231, calculate the reconstruction error variance , the formula is: , Where, and is the error parameter; S232, calculate and reconstruct the outliers of ocean elements , the formula is: , S232, calculate and reconstruct ocean element values , the formula is: , Where m ij is the seasonal average value during the preprocessing of ocean element data, where the ocean element values ​​are chlorophyll concentration, particulate organic carbon concentration, suspended matter concentration, and sea surface temperature.

[0010] Furthermore, in step S3, the HYCOM sea surface salinity data and the reconstructed marine remote sensing product data are temporally and spatially matched, the outliers are removed from the temporally and spatially matched data, and the training set and test set are divided into training set and test set according to a 3:1 ratio. The support vector regression, random forest and XGBoost algorithms are used to establish the dissolved oxygen remote sensing model respectively; the three evaluation indicators of correlation coefficient R, root mean square error RMSE and mean absolute error MAE are used to screen out the best dissolved oxygen remote sensing estimation model according to the model training and test results.

[0011] A system for estimating dissolved oxygen in coastal areas of the ocean based on remote sensing data, for implementing any of the above-mentioned methods for estimating dissolved oxygen in coastal areas of the ocean, comprising: The sample data input module is used to input the dissolved oxygen concentration data of the nearshore ocean. The dissolved oxygen data includes the longitude and latitude information of the observation site, the observation date and time, and the dissolved oxygen concentration. It is used to input MODIS ocean remote sensing product data and HYCOM reanalysis data. The sample data processing module is used to generate ocean element concentration data that is not available in the remote sensing product data, reconstruct the missing data in the ocean remote sensing product data, and extract the sea surface salinity data corresponding to the station from the HYCOM reanalysis data. Based on the coordinates of the dissolved oxygen observation station, the sample data corresponding to the dissolved oxygen is extracted from the reconstructed ocean remote sensing product data and the HYCOM sea surface salinity data, and all sample data outliers are eliminated. The dissolved oxygen remote sensing estimation model construction module is used to analyze the correlation and multicollinearity between dissolved oxygen and various marine elements and select characteristic variables of dissolved oxygen. Different machine learning methods are used to construct the dissolved oxygen remote sensing estimation model and determine the optimal dissolved oxygen remote sensing estimation model based on the error indicators calculated from the training and test sets. The dissolved oxygen remote sensing inversion module uses the best dissolved oxygen remote sensing estimation model, combined with the reconstructed ocean remote sensing product data and HYCOM sea surface salinity data, to invert the dissolved oxygen concentration in the nearshore ocean and draw a spatial distribution map of dissolved oxygen in the nearshore ocean.

[0012] A computer device comprising: Hardware system, used to run various programs in the application system; Platform system for installing remote sensing image processing software; An application system for executing any of the above-mentioned methods for estimating marine nearshore dissolved oxygen based on remote sensing data, comprising a sample data input program, a sample data processing program, a dissolved oxygen remote sensing estimation model building program, and a dissolved oxygen remote sensing inversion program.

[0013] Compared with the prior art, the present invention has the following significant effects: 1. This paper fully utilizes remote sensing product data of ocean optical activity parameters (chlorophyll, particulate organic carbon, suspended matter, sea surface temperature, and sea surface salinity) that are closely related to dissolved oxygen. Through comparative analysis of three machine learning methods, it constructs an optimal model for remote sensing estimation of dissolved oxygen in coastal oceans. This solves the problem that dissolved oxygen, as a non-optical substance, has no characteristic response in the remote sensing spectrum and cannot be directly inverted using satellite remote sensing. 2. To address the problem of missing satellite optical remote sensing image data caused by cloudy and rainy weather over the ocean, the present invention introduces a data interpolation convolutional autoencoder algorithm in deep learning methods to reconstruct missing data of ocean remote sensing products. Compared with other data reconstruction methods, this method can effectively extract nonlinear relationships between data, retain small-scale information of data, improve the accuracy of missing data reconstruction, and promote the application efficiency of ocean remote sensing product data in the field of marine science. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 is a flow chart of the present invention; Figure 2 The MODIS image of the study area and the distribution map of water quality monitoring stations in the embodiment of the present invention; Figure 3 1. Comparison diagram of missing data before and after reconstruction of remote sensing product data in an embodiment of the present invention, wherein (a) is a schematic diagram of missing data before reconstruction, and (b) is a schematic diagram of missing data after reconstruction; Figure 4 This is a heat map of the correlation between dissolved oxygen and various ocean elements in an embodiment of the present invention; Figure 5 The figure shows a scatter plot of the measured and predicted values ​​of the support vector remote sensing model for dissolved oxygen in an embodiment of the present invention, wherein (a) is a scatter plot of the measured and predicted values ​​during model training, and (b) is a scatter plot of the measured and predicted values ​​during model testing; Figure 6 The figure shows a scatter plot of the measured and predicted values ​​of the random forest remote sensing model for dissolved oxygen in an embodiment of the present invention, wherein (a) is a scatter plot of the measured and predicted values ​​during model training, and (b) is a scatter plot of the measured and predicted values ​​during model testing; Figure 7The figure shows a scatter plot of the measured and predicted values ​​of the dissolved oxygen XGBoost remote sensing model in an embodiment of the present invention, wherein (a) is a scatter plot of the measured and predicted values ​​during model training, and (b) is a scatter plot of the measured and predicted values ​​during model testing; Figure 8 This is a spatial distribution map of dissolved oxygen remote sensing inversion in an embodiment of the present invention; Figure 9 This is a schematic diagram of the functional modules of the system in an embodiment of the present invention; Figure 10 Schematic diagram of the composition of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0015] The present invention will be described in further detail below with reference to the accompanying drawings and specific implementations.

[0016] To address the problems of existing technologies, the solubility of oxygen in seawater is affected by seawater temperature and salinity. Oxygen is released through photosynthesis by phytoplankton, consumed by the decay and decomposition of dead phytoplankton and organic matter in the water, and consumed by the redox processes of nutrients (such as inorganic nitrate and ammonium nitrogen, phosphates, and silicates). Nutrients are typically adsorbed in suspended particulate matter in the water. Therefore, this paper proposes to utilize ocean optical remote sensing product data, reconstruct missing data from remote sensing product data, combine it with measured dissolved oxygen concentration data in the nearshore ocean, and employ machine learning methods to construct an optimal remote sensing model for nearshore dissolved oxygen and invert the nearshore dissolved oxygen concentration.

[0017] The present invention proposes to utilize remote sensing product data of five elements, namely, ocean optical activity parameters closely related to dissolved oxygen, namely, phytoplankton chlorophyll, particulate organic carbon, suspended matter, sea surface temperature, and sea surface salinity. On the basis of reconstructing the missing data of the remote sensing product data, combined with the dissolved oxygen concentration data measured in the nearshore ocean, three machine learning methods, namely support vector model, random forest model, and extreme gradient boosting (XGBoost) model, are used to construct remote sensing estimation models for dissolved oxygen in nearshore ocean waters, respectively. The optimal model for remote sensing estimation of dissolved oxygen is selected based on the accuracy index rate obtained during model training and testing, so as to realize the inversion of dissolved oxygen concentration in nearshore waters from ocean remote sensing product data.

[0018] The present invention provides a method for estimating dissolved oxygen in coastal oceans based on remote sensing data. In order to verify the method proposed in the present invention, the Bohai Sea, the Yellow Sea and the East China Sea were used as research areas. The publicly available dissolved oxygen concentration data from coastal water quality monitoring in spring, summer and autumn 2020, the ocean color remote sensing products of the TERRA satellite MODIS (Moderate-resolution Imaging Spectroradiometer), and the surface salinity data of the Ocean Numerical Model (HYCOM) were used. A machine learning method was used to construct an optimal model for remote sensing estimation of dissolved oxygen in coastal ocean waters, and the spatial distribution of dissolved oxygen concentration in the study area was inverted. The implementation process is as follows: Figure 1 As shown, the following steps are included: Step 1: Acquisition of sample data; The sample data obtained in the study area mainly include dissolved oxygen measured data in coastal waters, ocean color remote sensing product data, and ocean salinity data. The implementation steps are as follows: Step 11, collection of dissolved oxygen data in nearshore waters; The dissolved oxygen data in this example comes from the national coastal waters environmental monitoring network. Coastal water quality monitoring stations are generally arranged using a grid method, taking into account typical and representative sea areas such as important fishing grounds, bays, estuaries, environmental functional areas, nature reserves, and waste dumping areas. The distribution of water quality monitoring stations in the study area is shown in [1]. Figure 2 Water quality monitoring in coastal waters is generally conducted 2-3 times a year, with monitoring periods in spring (March-May), summer (July-August), and autumn (October-November). The accuracy of each survey vessel's navigation positioning point is controlled within a certain error range based on the distance from shore, ensuring that each monitoring point is essentially consistent and the data is comparable. Dissolved oxygen concentration is determined by iodine titration using on-site water samples collected. This method provides high data accuracy. In this example, a total of 2,404 dissolved oxygen concentration data were collected from the study area's coastal waters during the spring, summer, and autumn of 2020.

[0019] Step 12: Collection of ocean color remote sensing product data; The earliest ocean remote sensing product data on the official website of NASA's Ocean Biology Processing Group (OBPG) is the Nimbus-7 Coastal Zone Color Scanner (CZCS) data from October 30, 1978. To date, a total of 16 satellites with multiple sensors have released ocean remote sensing products. For example, the TERRA and AQUA satellites both carry the same MODIS sensor, and the PACE satellite carries three ocean remote sensing sensors: HARP2, OCI, and SPEXONE. These satellite remote sensing data levels range from Level 0 for raw images to Level 4, the highest level for ocean products, meeting the needs of different users for global ocean remote sensing data. In this example, the TERRA satellite MODIS sensor Level 2 ocean remote sensing product data with the same observation date as the dissolved oxygen was used. It generally includes chlorophyll (Chl-a) concentration, particulate organic carbon (POC) concentration, sea surface temperature (SST), and remote sensing reflectance (R) with 10 channels at central wavelengths of 412, 443, 469, 488, 531, 547, 555, 645, 667, and 678 nm. rs ) data. Data without ocean features, such as suspended matter concentration, can be generated from remote sensing reflectance using relevant algorithms. Marine remote sensing product data has a spatial resolution of 1 km and a temporal resolution of once per day.

[0020] Step 13: Collection of ocean salinity data; Remote sensing of sea surface salinity is primarily performed using microwave radiometers with a center frequency of 1.4 GHz in the L-band. Currently, satellites equipped with this sensor for ocean salinity remote sensing include the European Space Agency's (ESA) SMOS satellite, the Aquarius / SAC-D satellite launched jointly by NASA and the Argentinian Space Agency, and NASA's SMAP satellite. Current satellite-based remote sensing of sea surface salinity has a low spatial resolution, typically between 40 and 200 km. Furthermore, these satellite-based salinity products suffer from significant data gaps and have limited coverage in Chinese waters. Therefore, this embodiment uses the 0-m depth salinity data from the ocean numerical model HYCOM to replace the satellite-based salinity data. This model assimilates observations from satellite altimeters, temperature and depth instruments (CTDs), emissive bathythermographs (XBTs), and Argo floats. It can generate reanalysis data for ocean temperature, salinity, and current fields at depths of 0 to 5000 m, with a horizontal resolution of 0.08° and a temporal resolution of 3 hours. This high-precision data offers significant performance advantages among many ocean models.

[0021] Step 2: Generation of ocean remote sensing product data; The ocean optical activity parameters contained in many satellite ocean remote sensing product data are not the same, but they all contain remote sensing reflectivity data of each channel of the ocean remote sensing sensor. For the missing ocean parameters, they can be generated according to relevant algorithms.

[0022] Step 21, remote sensing inversion of chlorophyll concentration; Chlorophyll is the primary pigment in marine phytoplankton, and its concentration indicates the abundance of phytoplankton in the water. Chlorophyll has absorption peaks in the blue and red bands and a reflection peak in the green band. Remote sensing reflectance data from these three bands can be used to construct the Ocean Color Index (OCI) to infer chlorophyll concentration. The MODIS sensor's chlorophyll remote sensing algorithm performs calculations for three scenarios: The first one is for ocean chlorophyll concentrations below 0.25 mg / m 3 situation; First, the water color index (CI) is constructed using the remote sensing reflectance of the three bands of MODIS green, red, and blue light. Then, the chlorophyll concentration is calculated using CI, which is called the CI algorithm. The water color index CI can be expressed as: , Chlorophyll (Chl) concentration can be calculated as: , Where R rs represents the remote sensing reflectivity, λ g ,λ r and λ b They represent the green (555nm), red (670nm) and blue (443nm) bands of MODIS respectively.

[0023] The second type is for marine chlorophyll concentrations above 0.35 mg / m 3 situation; The ratio of MODIS blue-green band remote sensing reflectance is used as the remote sensing variable, and a fourth-order polynomial function is used to calculate the chlorophyll concentration, which is called OC. x The algorithm is expressed as follows: , Where a0, a1, a2, a3, and a4 are polynomial coefficients, which are 0.26294, -2.64669, 1.28364, 1.08209, and -1.76828, respectively.

[0024] The third type is for marine chlorophyll concentrations between 0.25 and 0.35 mg / m 3 The situation between; CI algorithm and OC xThe chlorophyll concentration obtained by the algorithm is weighted calculated: , Where t1 and t2 are the CI algorithm and OC algorithm, respectively. x The critical values ​​of chlorophyll concentration of the algorithm are t1=0.25 and t2=0.35 in this embodiment.

[0025] In the process of chlorophyll concentration remote sensing inversion, CI algorithm and OC algorithm are used respectively. x The algorithm generates two data sets of chlorophyll concentration. The two data sets are then fused, i.e., the CI algorithm retains the values ​​less than 0.25 mg / m 3 and OC x Greater than 0.35 mg / m in the algorithm 3 pixel, and then the two data sets were concentrated at 0.25 to 0.35 mg / m 3 The pixel values ​​between the two are weighted and the chlorophyll concentration remote sensing product dataset is finally obtained.

[0026] Step 22, remote sensing inversion of particulate organic carbon concentration; For the MODIS sensor, the concentration of marine particulate organic carbon still uses the remote sensing reflectance ratio of the blue and green bands as the remote sensing variable, and adopts the power function form of remote sensing inversion. The algorithm is as follows: , Where R rs represents the remote sensing reflectivity, λ g ,λ b They represent the green light (555nm) and blue light (443nm) bands of MODIS respectively.

[0027] Step 23, remote sensing inversion of suspended matter concentration; For the study area, a regional algorithm for retrieving suspended matter (TSM) concentrations using the three bands of MODIS sensor red (645 nm), green (555 nm), and blue (488 nm) can be expressed as: , Where R rs represents the remote sensing reflectivity, λ r ,λ g and λ b They represent the red (645nm), green (555nm) and blue (488nm) bands of MODIS respectively.

[0028] Step 24, remote sensing inversion of sea surface temperature; The sea surface temperature (SST) inversion from MODIS images is mainly carried out using the data of its 31st and 32nd thermal infrared channels (with central wavelengths of 11.091 and 12.032 μm, respectively).

[0029] First, the image numerical signal values ​​(DN) of the two thermal infrared channels of the MODIS original image need to be converted into radiance (L). The calculation formula is: , Where L is the radiance, the unit is w / (m 2 ·μm·Sr); radiance_scales and radiance_offsets are the calibration coefficients converted into radiance, which can be obtained from MODIS image attribute data.

[0030] Then, the radiance (L) is calculated according to Planck's law to obtain the brightness temperature (T b ): , Where c1 and c2 are the two coefficients in Planck’s law, and c1 is 1.191×10 8 wm -2 μm 4 Sr -1 , c2 takes the value of 1.439×10 4 μm·K; λ is the wavelength of the thermal infrared band, in micrometers (μm).

[0031] Finally, the sea surface temperature (SST) is inverted from the brightness temperature data of the two thermal infrared channels of MODIS. The algorithm is as follows: , Where, T b31 is the brightness temperature data of MODIS channel 31, T b3132 is the brightness temperature difference between the 31st and 32nd channels, θ is the MODIS observation zenith angle, sec() represents the secant function; k1, k2, k3 and k4 are empirical coefficients, and their values ​​are shown in Table 1. In Table 1, T b32 It is the brightness temperature data of MODIS channel 32.

[0032] Table 1 Empirical coefficients obtained based on atmospheric correction of sounding data

[0033] Step 25, calculation of sea surface salinity; HYCOM salinity reanalysis data is stored as integers. When converted to actual salinity values, linear conversion is required. The calculation formula is: , Where SSS_0 is the salinity at 0 m depth in psu; DN is the integer number in the HYCOM salinity reanalysis data; Scale and Offset are the scale factor and offset, respectively. In this implementation, Scale is 0.001 and Offset is 20.

[0034] Step 3: Reconstruction of missing data of marine remote sensing products; The embodiment of the present invention uses remote sensing product data from April to May in spring, July to August in summer, and October to November in autumn in the study area in 2020, mainly including four element data: chlorophyll, particulate organic carbon, suspended matter, and sea surface temperature. The first three elements are obtained by inverting the remote sensing reflectivity of the MODIS red, green, and blue bands. The data missing rate is relatively high, with an average of 91.30% and a minimum missing rate of 76.56%. In rainy weather, the data missing rate is as high as 100%. The sea surface temperature is obtained by inverting the 31st and 32nd thermal infrared bands of MODIS data. The missing rate is relatively low, with an average of 74.83%, which is comparable to the global sea surface temperature missing rate of 75%.

[0035] This paper uses a deep learning method, the Data Interpolating Convolutional Auto-Encoder (DINCAE) algorithm, to reconstruct missing data from marine remote sensing products. The DINCAE algorithm combines the advantages of convolutional neural networks (CNNs) and autoencoders (AEs), maximizing the preservation of richer small-scale feature information during image processing. Using the DINCAE algorithm to reconstruct missing data from marine remote sensing products involves three steps: data preprocessing, algorithm training, and data reconstruction.

[0036] Step 31, preprocessing of ocean remote sensing product data; The preprocessing of marine remote sensing product data mainly includes the segmentation of remote sensing product image data and the calculation of DINCAE algorithm input parameters.

[0037] Step 311, segmenting the remote sensing product image data; The remote sensing products of the study area are 2004 rows × 1621 columns of images. The computer computing power is very large when running the DINCAE algorithm. In order to speed up the operation of the convolutional neural network, the daily image of each ocean element is divided into 250 rows × 250 columns of sub-images. Each sub-image has 10 rows and 10 columns of overlap to facilitate the splicing of the reconstructed image. The original image is divided into 63 sub-images in total.

[0038] Step 312, calculation of DINCAE algorithm input parameters; Considering the differences in the concentrations of four elements such as ocean chlorophyll in different seasons, the training and data reconstruction of the DINCAE algorithm for each element are carried out in different seasons. First, the average value m is calculated for all images of each ocean element in the same season. ij , then the average value of each day’s image is subtracted from the original image concentration value to obtain the abnormal value p of each day’s image. ijt , again calculate the inverse of the error variance of the outlier image for each day Finally, multiply the image outlier value of each day by the inverse of the error variance to complete the scaling of the outlier image of each day. The calculation formula is as follows: , , , , Where, subscripts i and j represent the row and column numbers of the image, respectively; subscript t represents the image data of the tth day in a season; n is the total number of days in a season; m represents the seasonal average value of the image; and DN represents the original image value. Represents the error variance, Var( ) represents the function of finding the error variance of the image, and p' represents the outlier value of the image after scaling by the inverse of the error variance.

[0039] Calculation of other auxiliary parameters (longitude, latitude, and data date): Since the global longitude ranges from -180° to 180° and the latitude ranges from -90° to 90°, the image longitude is divided by 180° and the latitude is divided by 90°, and scaled to data from -1 to 1 respectively; the image date is calculated as the annual cumulative day (d), that is, January 1, 2020 is 1, and December 31, 2020 is 366, and then the sine value sin(d / 365.25) and cosine value cos(d / 365.25) of the annual cumulative day are calculated respectively, and an image with 250 rows × 250 columns of equal value is generated.

[0040] Therefore, the 10 input parameters of the DINCAE algorithm are: the outlier image scaled by the inverse of the error variance on the t-th day, the inverse value of the error variance on the t-th day, the outlier image scaled by the inverse of the error variance on the t-1-th day, the inverse value of the error variance on the t-1-th day, the outlier image scaled by the inverse of the error variance on the t+1-th day, the inverse value of the error variance on the t+1-th day, the scaled longitude data, the scaled latitude data, the cosine value of the annual accumulation day, and the sine value of the annual accumulation day.

[0041] Step 32, training of the DINCAE algorithm; The images of each ocean feature in the first 50 days of each season are used as training samples, and the images of the remaining 11-12 days are used as test samples. Figure 10The convolutional neural network in the DINCAE algorithm is iteratively trained with an array of size 250×250×50. The number of iterations is set to 1000, and the reconstruction result is output every 10 iterations, along with calculation and test samples. The convolutional neural network structure consists of five parts: input layer, encoding layer, fully connected layer, decoding layer, and output layer.

[0042] Input layer: mainly receives training sample data sets and test sample data sets; Coding layer: It consists of 4 convolutional layers and 4 pooling layers. The number of filters in the convolutional layers is set to 16, 24, 36, and 54, respectively, and the convolution kernel size is set to (3, 3). The pooling layer uses the maximum pooling operation with a pooling size of (2, 2) and a pooling step of (2, 2).

[0043] Fully connected layer: After the last convolutional layer of the encoding layer, there are two fully connected layers, which are used to perform nonlinear combination of the extracted features. The number of neurons in the two fully connected layers is 1 / 5 and the total number of features output by the last pooling layer, respectively. After each fully connected layer, there is a dropout layer (Dropout). The dropout rate of the dropout layer is set to 0.3 during training, so that the neurons in the fully connected layer are randomly inactivated at this rate to prevent overfitting of the algorithm.

[0044] Decoding layer: Consists of four interpolation layers and four convolutional layers. Interpolation is performed using the nearest neighbor method. The number of filters in the convolutional layers is set to 36, 24, 16, and 2, respectively, and the convolution kernel size is set to (3, 3). A skip connection layer is placed after each interpolation layer, connecting the output of each pooling layer to the interpolation layer to capture small-scale information.

[0045] Output layer: Outputs the result of DINCAE algorithm training, which is the result of the last convolutional layer of the decoding layer. It is a 250×250×2 array, which is the ocean feature outlier data scaled by the inverse of the expected error variance (D ij1 ) and the natural logarithm of the inverse of the expected error variance (D ij2 ), where i and j represent the row and column numbers of the image.

[0046] Other parameters in the convolutional neural network training process: the activation function of the convolution layer uses Leaky-RELU, the learning rate is 0.001, and the regularization parameter is 10 -8 .

[0047] Step 33, reconstructing ocean remote sensing data based on DINCAE algorithm; The reconstruction of ocean element remote sensing data is based on the two parameters D output by DINCAE algorithm training. ij1 and D ij2 It can be implemented in three steps: Step 331, reconstruct error variance Calculation of Reconstruction error variance Can be obtained by D ij2 Calculation formula is: , Where, and is the error parameter, For each ocean parameter, 10 is taken; For particulate organic carbon, 10 -5 , chlorophyll, suspended matter, sea surface temperature, and sea surface salinity are 10 -3 The maximum and minimum functions are introduced here to avoid the denominator from having a value of 0 or a floating-point overflow.

[0048] Step 332: Reconstruct ocean feature outliers Calculation of Ocean feature outliers By D ij1 and reconstruction error variance Calculation formula is: , Step 333: Reconstruct ocean element values Calculation of , Where m ij is the seasonal average value during the preprocessing of ocean feature data, where the ocean feature values ​​represent chlorophyll concentration, particulate organic carbon concentration, suspended matter concentration or sea surface temperature.

[0049] After completing the data reconstruction of the 63 sub-images, the 63 sub-images are spliced ​​together to obtain the complete reconstructed ocean remote sensing product data. Taking the sea surface temperature element as an example, Figure 3 Comparison chart of sea surface temperature remote sensing products before and after reconstruction.

[0050] Step 4: Construction of a remote sensing estimation model for dissolved oxygen in coastal areas of the ocean; The construction of a remote sensing estimation model for dissolved oxygen nearshore in the ocean requires the collection of sample data, the selection of model characteristic parameters, the construction of a model based on machine learning methods, and the determination of the optimal model. The implementation steps are as follows: Step 41, modeling the spatiotemporal matching of sample data; Because ocean dissolved oxygen is point data, while reconstructed ocean remote sensing products and HYCOM sea surface salinity are grid-based data, spatial matching is required. The coordinates of the dissolved oxygen monitoring station are mapped to the grid containing the two grid-based data. The values ​​of the surrounding 3×3 grids, centered on this grid, are averaged to match the dissolved oxygen concentration. Temporal matching is performed by matching the date of the dissolved oxygen observation to the reconstructed ocean remote sensing products and HYCOM sea surface salinity data from the same day. Outliers are removed from the spatiotemporally matched data within a range of plus or minus three standard deviations of the mean concentration for each element. After quality control, the matched data were randomly sampled in a 3:1 ratio to construct the training and test sets for the dissolved oxygen estimation model, resulting in 1452 training sets and 485 test sets.

[0051] Step 42, selection of model characteristic parameters; The present invention uses dissolved oxygen (DO) concentration as the dependent variable, and chlorophyll (Chl) concentration, particulate organic carbon (POC) concentration, sea surface temperature (SST), sea surface salinity (SSS) and suspended solids (TSM) concentration as characteristic variables to select characteristic parameters of the dissolved oxygen remote sensing estimation model. Since different characteristic variables will directly affect the accuracy of the model, when the correlation between the characteristic variables and the dependent variables is low, it is difficult for the constructed model to have a good fitting effect; when the correlation is too high, the model will become flat, resulting in low model efficiency. In order to obtain a high-precision and high-efficiency model, it is necessary to screen the characteristic variables of the model, calculate the Pearson correlation coefficients between the characteristic variables and the characteristic variables, and between the characteristic variables and the dependent variables, and perform significance tests (such as Figure 4 As shown in the figure, *** indicates that the correlation significance P value is less than 0.001, ** indicates that the P value is less than 0.01, and * indicates that the P value is less than 0.05). The variance inflation factor (VIF) between the dependent variable and each characteristic variable is calculated. Finally, according to the correlation significance test P value less than 0.05 and the variance inflation factor less than 5, it is used as the optimal characteristic parameter for constructing the dissolved oxygen remote sensing estimation model. Table 2 lists the statistical results between dissolved oxygen and each characteristic variable. Figure 4 As shown in Table 2, each characteristic variable has a good correlation with dissolved oxygen, and the correlation significance reaches the 0.05 level. The variance inflation factor of each characteristic variable is less than 5, indicating that there is no multicollinearity problem between dissolved oxygen and each characteristic variable. The five characteristic variables can be used to construct a dissolved oxygen remote sensing estimation model.

[0052] Table 2 Statistics of dissolved oxygen and various characteristic variables

[0053] Step 43, constructing a remote sensing estimation model for dissolved oxygen in the nearshore ocean; Based on the statistical results of each characteristic variable and dissolved oxygen, three machine learning methods (support vector regression, random forest and XGBoost algorithm) were used to establish dissolved oxygen remote sensing estimation models, with dissolved oxygen concentration as the dependent variable and chlorophyll (Chl) concentration, particulate organic carbon (POC) concentration, sea surface temperature (SST), sea surface salinity (SSS) and suspended matter (TSM) concentration as independent variables.

[0054] (1) Construction of a dissolved oxygen remote sensing estimation model based on support vector regression; Support vector regression (SVR) maximizes the regression model's margin while allowing some samples to violate a certain error range to improve model robustness. During the SVR process, one function and two parameters significantly influence the model's results: the kernel function, the penalty coefficient C, and the kernel coefficient gamma. The kernel function determines the accuracy and universality of the SVR model. Commonly used kernel functions include the linear kernel function, the polynomial kernel function, the radial basis kernel function, and the sigmoid kernel function. This paper, through experiments with four kernel functions, selects the radial basis kernel function, which exhibits superior smoothing performance, for use in constructing a SVR remote sensing model for dissolved oxygen estimation.

[0055] The default value of the penalty coefficient C is 1.0. The C value controls the complexity of the model and its tolerance to errors. The larger the C value, the better the model fits the training data, that is, the model is less likely to tolerate errors in the training set, which may lead to model overfitting. Conversely, a smaller C value makes the model more tolerant of errors, which helps improve the model's generalization ability. The present invention takes the C value as [0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0] respectively, and uses a trial number method to test all training sets. The optimal C value is selected by calculating the correlation coefficient R and the root mean square error RMSE between the measured dissolved oxygen concentration and the SVR model predicted value.

[0056] The kernel coefficient gamma defines the range of influence of the kernel function, which affects the model's fit to the training data. A large gamma value causes the model to focus only on sample points close to the support vectors, potentially leading to overfitting of the training data. A small gamma value increases the range of the support vectors, potentially leading to underfitting. This paper uses a trial-and-error method to test all training sets against the measured dissolved oxygen concentrations and the RMSE (Residual Value Error) between the predicted values ​​and the SVR model to select the optimal gamma value.

[0057] By testing all training sets, the optimal parameters for constructing the dissolved oxygen support vector regression remote sensing estimation model were determined to be the radial basis kernel function, the penalty coefficient C was 2.0, and the kernel coefficient gamma was 1.0. The scatter plots of the measured and predicted values ​​during model training and testing are shown in Figure 2. Figure 5 shown.

[0058] (2) Construction of a dissolved oxygen remote sensing estimation model based on random forest; Random forest is an ensemble learning model based on the bagging strategy. It improves the model's accuracy and generalization ability by training a large number of decision trees and combining their predictions. The number of decision trees in the model and the number of features in each decision tree are two key parameters. The present invention sets the number of decision trees to be {50, 100, 150, 200, 250, 300, 350, 400, 450, 500, 600, 700, 800, 900, 1000}, the set of feature values ​​of each decision tree to be {1, 2, 3, 4, 5, 6}, adopts a 5-fold cross-validation method for the training sample data set, continuously optimizes the number of decision trees and the feature value of each decision tree from different combinations to improve the model accuracy, and determines that the optimal number of decision trees is 350 and the feature value of each decision tree is 3, constructs a random forest remote sensing estimation model for dissolved oxygen, and the scatter plots of the measured values ​​and the predicted values ​​during model training and testing are as shown in Figure 2. Figure 6 shown.

[0059] (3) Construction of dissolved oxygen remote sensing estimation model based on XGBoost; The XGBoost algorithm is based on gradient-based decision tree optimization, introduces regularization terms and second-order derivative information to improve the performance of the model, and has the advantages of fast processing speed and high accuracy in the model regression process. The present invention adopts the XGBoost algorithm to construct a dissolved oxygen remote sensing estimation model. In model construction, the model parameters are set: the set of decision trees is {50, 100, 150, 200, 250, 300, 350, 400, 450, 500, 600, 700, 800, 900, 1000}, the set of learning rates is {0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0}, the set of maximum tree depth is {1, 2, 3, 4, 5, 6}, the regularization parameter and the leaf node number weight coefficient are 1 and 0 respectively. The 5-fold cross-validation method was used for the training set. The decision parameters were optimized from different combinations of the number of decision trees, the maximum depth of the tree, and the learning rate to improve the model accuracy. The optimal number of decision trees was determined to be 100, the maximum depth of each decision tree was 10, and the learning rate was 0.15. The XGBoost remote sensing estimation model for dissolved oxygen was constructed. The scatter plots of the measured and predicted values ​​during model training and testing are shown in Figure 2. Figure 7 shown.

[0060] Step 44, selecting the optimal model for remote sensing estimation of dissolved oxygen in coastal oceans; For the dissolved oxygen remote sensing estimation models constructed using the three machine learning methods, three evaluation metrics, namely correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE), were calculated using the measured values ​​in the training and test sets and the model's predicted values. The results are shown in Table 3. The optimal model for dissolved oxygen remote sensing estimation was determined by comparing the accuracy metrics of the three models during training and testing. As shown in Table 3, the XGBoost model had the highest correlation coefficient, the lowest root mean square error, and the lowest MAE during model training, indicating that the XGBoost model achieved the highest training accuracy. However, the random forest model achieved the highest accuracy during model testing. To optimize the remote sensing inversion of dissolved oxygen in coastal areas, the random forest method was ultimately determined to be the optimal model for dissolved oxygen remote sensing estimation.

[0061] Table 3 Accuracy indicators of different dissolved oxygen remote sensing estimation models during training and testing

[0062] Step 5: remote sensing inversion of dissolved oxygen concentration in the study area; The missing data of remote sensing products in the study area were reconstructed to obtain complete chlorophyll concentration images, particulate organic carbon concentration images, sea surface temperature images and suspended matter concentration images. Combined with the HYCOM 0m sea surface salinity image, the dissolved oxygen random forest remote sensing estimation model established by the present invention was used to invert the sea surface dissolved oxygen concentration in the study area, and the spatial distribution of dissolved oxygen concentration was plotted as follows: Figure 8 shown.

[0063] The present invention provides a system for estimating dissolved oxygen in coastal areas of the ocean based on remote sensing data. Figure 9 As shown, including: Sample data input module: mainly used for inputting dissolved oxygen concentration data of nearshore ocean. Dissolved oxygen data includes the longitude and latitude information of the observation site, the observation date and time, and the dissolved oxygen concentration. The data format can be text (txt) or spreadsheet (csv, xls) file; used for inputting MODIS ocean remote sensing product data and HYCOM reanalysis data. Both types of data are downloaded from the Google Earth Engine (GEE) platform by writing Javascript programs for specific areas and times.

[0064] Sample data processing module: mainly used to generate ocean element concentration data that is not available in remote sensing product data, reconstruct missing data in ocean remote sensing product data, extract surface depth salinity data corresponding to the site from HYCOM reanalysis data, extract sample data corresponding to dissolved oxygen from the reconstructed ocean remote sensing product data and HYCOM sea surface salinity data according to the coordinates of the dissolved oxygen observation site, and eliminate all abnormal values ​​in the sample data.

[0065] Dissolved oxygen remote sensing estimation model construction module: mainly used to analyze the correlation and multicollinearity between dissolved oxygen and five marine elements such as chlorophyll, select characteristic variables of dissolved oxygen, use a variety of typical machine learning methods to build a dissolved oxygen remote sensing estimation model, and determine the optimal dissolved oxygen remote sensing estimation model based on the error indicators calculated from the training set and test set.

[0066] Dissolved oxygen remote sensing inversion module: It is mainly used to invert the dissolved oxygen concentration in the nearshore ocean and draw the spatial distribution map of dissolved oxygen in the nearshore ocean based on the established dissolved oxygen optimal remote sensing estimation model, combined with the reconstructed ocean remote sensing product data and HYCOM sea surface salinity data.

[0067] The present invention provides a computer device, such as Figure 10 As shown, including: Hardware system: The computer hardware system should be equipped with an operating system version of Windows 10 or above, an Intel / AMD 64-bit processor, 32GB of memory, an NVIDIA GPU card with 8GB of memory, and a CUDA computing capability of 3.5 or above, so as to achieve better computing efficiency when using the DINCAE algorithm to reconstruct missing data of remote sensing images.

[0068] Platform system: The computer operating environment should be installed with remote sensing image processing software ENVI / IDL 5.3 or above, Python 3.6.8, netcdf4 1.4.2, numpy 1.15.4, Tensorflow 1.15, R 4.42, etc. to ensure that the data processing of the embodiment of the present invention can be carried out normally.

[0069] Application system: mainly includes all application programs developed in the embodiments of the present invention, including sample data acquisition program, sample data processing program, dissolved oxygen (DO) remote sensing estimation model construction program and dissolved oxygen remote sensing inversion program.

Claims

1. A method for estimating dissolved oxygen in coastal areas of the ocean based on remote sensing data, characterized in that: The steps are as follows: S1, obtain the measured data of dissolved oxygen concentration in the sea area to be measured, marine remote sensing product data, and marine numerical model HYCOM reanalysis data; S2, using the DINCAE algorithm to reconstruct the missing data of the four elements of chlorophyll, particulate organic carbon, suspended matter and sea surface temperature in the ocean remote sensing product data, and obtain the complete reconstructed ocean remote sensing product data; S3, based on the longitude and latitude coordinates of the dissolved oxygen observation station, extract the corresponding sea surface salinity element from the HYCOM reanalysis data, and extract the corresponding four ocean elements (chlorophyll, particulate organic carbon, suspended matter, and sea surface temperature) from the reconstructed ocean remote sensing product data to obtain sample data matching the dissolved oxygen station. Correlation analysis and variance inflation factor analysis were performed on dissolved oxygen and the five extracted elements to extract characteristic variables for constructing the dissolved oxygen remote sensing estimation model. Using dissolved oxygen concentration as the dependent variable and the five ocean element values ​​as characteristic variables, different machine learning methods were used to construct the dissolved oxygen remote sensing estimation model. The accuracy of the dissolved oxygen remote sensing estimation was evaluated to select the optimal dissolved oxygen remote sensing estimation model. S4, use the best dissolved oxygen remote sensing estimation model to invert the sea surface dissolved oxygen concentration in the measured sea area and map the spatial distribution of dissolved oxygen concentration.

2. The method for estimating dissolved oxygen in coastal areas of the ocean based on remote sensing data according to claim 1, characterized in that: In step S2, the specific steps for reconstructing the missing data of each ocean element remote sensing product are as follows: S21, the calculation steps of the DINCAE algorithm input parameters are as follows: First, the average value m is calculated for all images of each ocean element in the same season. ij , then the average value of each day’s image is subtracted from the original image concentration value to obtain the abnormal value p of each day’s image. ijt , again calculate the inverse of the error variance of the outlier image for each day ; Finally, multiply the image outlier value of each day by the inverse of the error variance to complete the scaling of the outlier image of each day , the expression is as follows: ， ， ， ， Where, subscripts i and j represent the row and column numbers of the image, respectively; subscript t represents the image data of the tth day in a season; n is the total number of days in a season; m represents the seasonal average value of the image; and DN represents the original image value. represents the error variance, Var( ) represents the function of finding the error variance of the image, and p' represents the outlier value of the image after scaling by the inverse of the error variance; The outlier image after scaling by the inverse of the error variance on the t-th day, the inverse value of the error variance on the t-th day, the outlier image after scaling by the inverse of the error variance on the t-1-th day, the inverse value of the error variance on the t-1-th day, the outlier image after scaling by the inverse of the error variance on the t+1-th day, the inverse value of the error variance on the t+1-th day, the scaled longitude data, the scaled latitude data, the cosine value of the annual accumulation day, and the sine value of the annual accumulation day are selected as the input parameters of the DINCAE algorithm; S22, the images of each ocean element in the first 50 days of each season are used as training samples, and the images of the remaining days are used as test samples, and the convolutional neural network is iteratively trained and tested, and the parameters of the encoding layer, fully connected layer and decoding layer in the network structure are set; wherein, the input parameters of the convolutional neural network are the images of each ocean element in each season, and the output result is the ocean element outlier data D after being scaled by the inverse of the expected error variance ij1 and the natural logarithm of the inverse of the expected error variance D ij2 ; S23, using the output result of step S22, reconstruct the data of the daily sub-images of the ocean elements in the same season, and then splice all the sub-images to obtain a complete image after data reconstruction.

3. The method for estimating dissolved oxygen in coastal areas of the ocean based on remote sensing data according to claim 2, characterized in that: In step S23, the steps for reconstructing the data of the daily sub-maps of ocean elements in the same season are as follows: S231, calculate the reconstruction error variance , the formula is: ， Where, and is the error parameter; S232, calculate and reconstruct the outliers of ocean elements , the formula is: ， S232, calculate and reconstruct ocean element values , the formula is: ， Where m ij is the seasonal average value during the preprocessing of ocean element data, where the ocean element values ​​are chlorophyll concentration, particulate organic carbon concentration, suspended matter concentration, and sea surface temperature.

4. The method for estimating dissolved oxygen in coastal areas of the ocean based on remote sensing data according to claim 2, characterized in that: In step S3, the HYCOM sea surface salinity data and the reconstructed marine remote sensing product data are temporally and spatially matched, and the outliers are removed from the temporally and spatially matched data. The training set and test set are divided into training set and test set in a ratio of 3:

1. The support vector regression, random forest and XGBoost algorithms are used to establish the dissolved oxygen remote sensing model respectively. The three evaluation indicators of correlation coefficient R, root mean square error RMSE and mean absolute error MAE are used to screen the best dissolved oxygen remote sensing estimation model according to the model training and test results.

5. A system for estimating dissolved oxygen in coastal oceans based on remote sensing data, for implementing the method for estimating dissolved oxygen in coastal oceans based on remote sensing data as claimed in any one of claims 1 to 4, characterized in that: include: The sample data input module is used to input the dissolved oxygen concentration data of the nearshore ocean. The dissolved oxygen data includes the longitude and latitude information of the observation site, the observation date and time, and the dissolved oxygen concentration. It is used to input MODIS ocean remote sensing product data and HYCOM reanalysis data. The sample data processing module is used to generate ocean element concentration data that is not available in the remote sensing product data, reconstruct the missing data in the ocean remote sensing product data, and extract the sea surface salinity data corresponding to the station from the HYCOM reanalysis data. Based on the coordinates of the dissolved oxygen observation station, the sample data corresponding to the dissolved oxygen is extracted from the reconstructed ocean remote sensing product data and the HYCOM sea surface salinity data, and all sample data outliers are eliminated. The dissolved oxygen remote sensing estimation model construction module is used to analyze the correlation and multicollinearity between dissolved oxygen and various marine elements and select characteristic variables of dissolved oxygen. Different machine learning methods are used to construct the dissolved oxygen remote sensing estimation model and determine the optimal dissolved oxygen remote sensing estimation model based on the error indicators calculated from the training and test sets. The dissolved oxygen remote sensing inversion module uses the best dissolved oxygen remote sensing estimation model, combined with the reconstructed ocean remote sensing product data and HYCOM sea surface salinity data, to invert the dissolved oxygen concentration in the nearshore ocean and draw a spatial distribution map of dissolved oxygen in the nearshore ocean.

6. A computer device, characterized in that: include: Hardware system, used to run various programs in the application system; Platform system for installing remote sensing image processing software; An application system for executing the method for estimating dissolved oxygen in coastal areas of the ocean based on remote sensing data as described in any one of claims 1 to 4, comprising a sample data input program, a sample data processing program, a dissolved oxygen remote sensing estimation model construction program, and a dissolved oxygen remote sensing inversion program.

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