Multi-scale fusion method and system based on satellite sea surface temperature

Abstract

The invention discloses a multi-scale fusion method and system based on satellite sea surface temperature, and relates to the technical field of satellite remote sensing, and the method comprises the steps: obtaining a sea surface temperature data set; performing data preprocessing on the sea surface temperature data set to obtain a processed sea surface temperature data set; based on the actually measured sea surface temperature, performing quality precision evaluation on each data in the processed sea surface temperature data set; the evaluation indexes of quality precision evaluation are composed of average deviation, absolute deviation, root-mean-square error, standard deviation and correlation coefficients; according to the quality precision evaluation result of each piece of data, deviation correction is carried out on each piece of data in the processed sea surface temperature data set based on a segmented regression method, and a corrected sea surface temperature data set is obtained; and according to the corrected sea surface temperature data set, a multi-scale optimal interpolation method is adopted to construct a multi-scale fusion model. According to the invention, sea surface temperature multi-scale information fusion can be realized.

Description

Technical Field

[0001] The present application relates to the field of satellite remote sensing technology, and in particular to a multi-scale fusion method and system based on satellite sea surface temperature. Background Art

[0002] There are two main ways to obtain sea surface temperature data: on-site field measurements and satellite remote sensing observations. Traditionally, on-site field measurements rely on conventional observation systems such as survey ships, coastal stations and buoy sites, but the number of observation points of these methods is limited, resulting in a small amount of data collected. In contrast, ocean satellite remote sensing observations, with their all-weather, near-real-time, wide coverage of observation range and long-term repeated observation capabilities, effectively supplement the insufficiency of on-site measured data. Satellite remote sensing mainly relies on infrared and microwave sensors to monitor sea surface temperature. Through remote sensing technology, large-scale synchronous observations can be achieved, which not only significantly reduces the cost of sampling data, but also increases the observation speed and has a high temporal and spatial resolution. In recent years, satellite ocean remote sensing technology has become one of the key means of detecting sea surface temperature, and it can provide a large amount of ocean data.

[0003] The most widely used and effective method for ocean field measured data and satellite remote sensing data is data fusion processing to generate sea temperature analysis products with high temporal and spatial resolution. However, these methods have some limitations when processing satellite remote sensing data, such as the loss of some important information during the fusion process. Summary of the invention

[0004] The purpose of this application is to provide a multi-scale fusion method and system based on satellite sea surface temperature, which can accurately and effectively extract multi-scale information from satellite remote sensing observations of sea surface temperature and realize multi-scale information fusion of sea surface temperature.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a multi-scale fusion method based on satellite sea surface temperature, comprising:

[0007] A sea surface temperature data set is obtained; data in the sea surface temperature data set is obtained by a variety of satellite sensors.

[0008] The sea surface temperature data set is preprocessed to obtain a processed sea surface temperature data set.

[0009] Based on the measured sea surface temperature, a quality accuracy evaluation is performed on each data in the processed sea surface temperature data set; the evaluation index of the quality accuracy evaluation is composed of mean deviation, absolute deviation, root mean square error, standard deviation and correlation coefficient.

[0010] According to the quality accuracy evaluation results of each data, based on the piecewise regression method, deviation correction is performed on each data in the processed sea surface temperature data set to obtain a corrected sea surface temperature data set.

[0011] According to the corrected sea surface temperature dataset, a multi-scale fusion model is constructed using the multi-scale optimal interpolation method.

[0012] Optionally, the data in the sea surface temperature dataset include Fengyun satellite data, ocean satellite data, iQuam measured data, Argo buoy data, OISST data and OSTIA data.

[0013] Optionally, performing data preprocessing on the sea surface temperature dataset to obtain a processed sea surface temperature dataset specifically includes:

[0014] For the Fengyun satellite data, the sea mark temperature data, satellite zenith angle and quality mark in the Fengyun satellite data are transposed or rotated, and the invalid land values, other invalid data, sea mark temperature points without valid zenith angle data, and data with a difference of more than 5°C in the OISST monthly average data are eliminated according to the mask data to obtain the processed Fengyun satellite data.

[0015] For the ocean satellite data, the sea surface temperature data in the ocean satellite data are transposed, and the invalid land values ​​are eliminated according to the mask data, the remaining invalid data points are filled with NaN, and the data with the OISST monthly average data difference greater than 5°C are obtained to obtain the processed ocean satellite data.

[0016] For the iQuam measured data, the daily average values ​​of the latitude and longitude data, quality mark data, water depth data, observation time data, and sea surface temperature data in the iQuam measured data are solved to obtain the processed iQuam measured data.

[0017] For the Argo float data, the Argo data with a depth range of 5-6m from the sea surface are selected as the processed Argo float data.

[0018] For the OISST data, the sea mark temperature data and longitude and latitude information in the OISST data are transposed or rotated, and the invalid sea mark temperature data are eliminated according to the mask information to obtain the processed OISST data.

[0019] For OSTIA data, the sea mark temperature data and latitude and longitude information in the OSTIA data are transposed or rotated, and invalid sea mark temperature data are eliminated according to the mask information to obtain the processed OSTIA data.

[0020] Optionally, the formula expressions of the mean deviation, absolute deviation, root mean square error, standard deviation and correlation coefficient in the evaluation indicators are:

[0021]

[0022]

[0023]

[0024]

[0025]

[0026] Among them, Bias is the average bias, Abs_Bias is the absolute bias, RMSE is the root mean square error, STDE is the standard deviation, R is the correlation coefficient, S i ,I i They represent the sea mark temperature data value inverted from infrared data and the measured sea mark temperature data of the i-th matching point, respectively, and n is the total number of matching points between satellite and measured data; All matching points S i ,I i The mean of .

[0027] Optionally, according to the quality accuracy evaluation result of each data, based on the piecewise regression method, deviation correction is performed on each data in the processed sea surface temperature data set to obtain a corrected sea surface temperature data set, specifically including:

[0028] According to the formula T xz =b0+b1T s +b2(T s -T c ), and bias correction is performed on each data in the sea surface temperature data set.

[0029] Among them, T xz is the corrected sea surface temperature, b0~b2 are regression coefficients, T s and (T s -T c ) represent the sea surface temperature inversion value and the sea surface temperature anomaly value, respectively.

[0030] Optionally, a multi-scale fusion model is constructed based on the corrected sea surface temperature dataset using a multi-scale optimal interpolation method, specifically including:

[0031] Obtain a background field error covariance matrix; the background field error covariance matrix is ​​composed of a diagonal matrix D composed of background field errors and a horizontal correlation matrix ρ of the predicted background field.

[0032] Based on the background field error covariance matrix and the corrected sea surface temperature dataset, a spatial correlation function model is constructed.

[0033] When fusing sea surface temperature data, the background error correlation function of the observation points of different data is calculated.

[0034] The correlation coefficient between the observation points is calculated based on the correlation distribution parameter fitting formula of the background error.

[0035] Optionally, the background field error covariance matrix is ​​expressed as:

[0036] B=D 0.5 ρD 0.5 .

[0037] Among them, D is the diagonal matrix composed of background field errors, ρ is the horizontal correlation matrix of the predicted background field, and the matrix R is calculated using the same processing method as the background field error covariance matrix B.

[0038] Optionally, when the sea surface temperature data is fused, the background error correlation function of the observation points of different data is calculated, specifically including:

[0039] According to the formula Calculate the background error correlation function for different data observation points.

[0040] Among them, R ij represents the correlation coefficient, S i , S j They represent the SST values ​​of stations i and j respectively, and 〈〉 represents the vector average.

[0041] Optionally, the correlation coefficient between the observation points is calculated according to the correlation distribution parameter fitting formula of the background error, specifically including:

[0042] According to the correlation distribution parameter fitting formula Calculates the correlation coefficient between observations.

[0043] Among them, ρ ij represents the correlation coefficient between points i and j, r1 and r2 represent the distances in the latitude and longitude directions, and λ1 and λ2 represent the latitudes, respectively.

[0044] In the second aspect, the present application provides a multi-scale fusion system based on satellite sea surface temperature, comprising

[0045] The data acquisition module is used to acquire a sea surface temperature data set; the data in the sea surface temperature data set is acquired by a variety of satellite sensors.

[0046] The preprocessing module is used to perform data preprocessing on the sea surface temperature data set to obtain a processed sea surface temperature data set.

[0047] An evaluation module is used to perform quality accuracy evaluation on each data in the processed sea surface temperature data set based on the measured sea surface temperature; the evaluation index of the quality accuracy evaluation is composed of mean deviation, absolute deviation, root mean square error, standard deviation and correlation coefficient.

[0048] The correction module is used to perform deviation correction on each data in the processed sea surface temperature data set based on the quality accuracy evaluation result of each data and based on the segmented regression method to obtain a corrected sea surface temperature data set.

[0049] The fusion module is used to construct a multi-scale fusion model based on the corrected sea surface temperature dataset using a multi-scale optimal interpolation method.

[0050] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0051] The present application provides a multi-scale fusion method and system based on satellite sea surface temperature. First, a sea surface temperature data set is collected and then preprocessed. Next, the actual measured sea surface temperature data is used to evaluate the quality and accuracy of each data point in the preprocessed data set. The evaluation indicators include mean deviation, absolute deviation, root mean square error, standard deviation and correlation coefficient. According to these evaluation results, a piecewise regression method is used to correct the deviation of each data point in the data set to obtain a corrected sea surface temperature data set. Finally, using this corrected data set, a model is constructed by a multi-scale optimal interpolation method to achieve multi-scale fusion of sea surface temperature information. The present application can use satellite sea surface data as the input source, establish a model system from data quality control, deviation correction and data fusion, and construct a multi-scale data fusion model, which can effectively extract multi-scale information in satellite remote sensing observation of sea surface temperature. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0053] Figure 1 A flowchart of a multi-scale fusion method based on satellite sea surface temperature is provided in one embodiment of the present application.

[0054] Figure 2 A structural schematic diagram of a fusion method based on domestic satellite sea surface temperature provided in one embodiment of the present application.

[0055] Figure 3A deviation correction flow chart is provided for an embodiment of the present application.

[0056] Figure 4 A schematic diagram of a satellite sea surface temperature fusion process provided in one embodiment of the present application.

[0057] Figure 5 A schematic diagram of the structure of a multi-scale fusion system based on satellite sea surface temperature provided in one embodiment of the present application. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0059] More than two-thirds of the earth's surface is ocean, and the specific heat capacity of seawater is large. More than 70% of solar radiation is absorbed by the ocean every year and is mainly stored in the surface layer of the ocean. The ocean surface is the interface where the ocean and the atmosphere are in direct contact. As one of the basic parameters of the ocean surface, sea surface temperature (SST) is a crucial parameter for studying the exchange of ocean-atmosphere matter and energy. SST can intuitively characterize the changes in the thermal and dynamic conditions of the ocean and the process of sea-air interaction. It is a basic parameter of thermodynamic circulation at different scales, and is also an important indicator parameter of physical oceanography such as ocean fronts, ocean water masses, and ocean currents. SST is also one of the main factors affecting long-term weather processes and global climate change. Many climate phenomena and meteorological disasters are closely related to it, such as El Nino-La Nina, hurricanes, and severe tropical storms. SST directly affects the life processes of marine organisms, such as population distribution, reproduction, and migration, and plays an important role in determining the living environment of different fish and the development of marine fisheries. For example, in marine fisheries, each type of fish has its most suitable temperature range for survival, so SST can be used to forecast fish conditions, which plays an important guiding role in the development of marine fisheries. In particular, the location of central fishing grounds and the analysis of development conditions require fast, accurate, and wide-coverage sea surface temperature structure products. At the same time, various marine science studies and other commercial marine applications have increasingly higher requirements for sea surface temperature products. In summary, how to provide high temporal and spatial resolution and high-precision sea surface temperature structure products is of great significance to studying the ocean and responding to global climate change.

[0060] There are two main ways to obtain sea surface temperature data: on-site field measurement and satellite remote sensing observation. Traditional on-site field measurements are mainly obtained by conventional observation systems such as survey ships, coastal stations, and buoy stations. The observation points are very limited, the amount of data obtained is small, and the data quality is not satisfactory due to the limitations of the observation instrument level or other human factors. In recent years, with the continuous improvement of temperature measurement instrument accuracy technology and the continuous development of new sea temperature measurement equipment platforms, such as the application of new equipment such as Argo buoys and underwater gliders, the quality and quantity of on-site measured data have been greatly improved. However, compared with actual needs, the amount of SST data obtained through on-site field measurements is still very limited, and the measurement cost is high, the collection speed is slow, and the time synchronization and spatial continuity are not ideal. Compared with on-site field measurement methods, ocean satellite remote sensing observations have the advantages of all-weather, near real-time, wide observation coverage, and long-term repeated observations, which can effectively make up for the shortcomings of on-site measured data. Satellite remote sensing observations of sea surface temperature mainly rely on infrared and microwave sensors. Among them, the high temporal and spatial resolution of the observation results is the advantage of infrared sensors, but its disadvantage is that it is easily affected by aerosols, atmospheric cloud conditions, etc., which leads to a low inverted SST; microwaves have extremely strong penetrating properties, so microwave sensors can generally conduct observations throughout the day and in all weather conditions, but the spatial resolution of the observation results is slightly worse than that of infrared sensors.

[0061] In satellite remote sensing observations, controllable observation conditions and diverse observation data ensure the multi-source nature of the observed remote sensing data, including multi-phase, multi-band, multi-polarization, and multi-platform. In research and application, it is best to understand, learn, and apply the multi-source nature of satellite remote sensing data from different perspectives. Striving to discover errors from observation data, correct and eliminate errors, and thus hopefully improve the quality of satellite remote sensing data, is one of the tasks we need to continuously strengthen exploration.

[0062] Remote sensing can be used to conduct synchronous observations over a large scale, which significantly reduces the cost of sampling data, is faster, and has higher temporal and spatial resolution. In recent years, satellite ocean remote sensing technology has become one of the main means of detecting sea surface temperature. A large amount of ocean data can be obtained through satellite remote sensing, and its total amount is dozens or hundreds of times the total amount of field observation data over the past hundred years. How to make the most of these massive data has always been a problem worth studying.

[0063] The most common and effective method for ocean field measurements and satellite remote sensing data is to combine their strengths and weaknesses, make the best use of their strengths, perform data fusion processing, and make the most of their advantages to form sea temperature analysis products with high temporal and spatial resolution. Data fusion is a dynamic process that requires the continuous integration of new observation data into the model under the conditions of considering the background field and observation field errors of the data, as well as the distribution of data in time series and geographic space. With the continuous integration of new observation data into the model, the simulation prediction trajectory of the model can be gradually optimized and corrected, so that the model data is constantly approaching the real data trajectory, and finally the model prediction accuracy is improved, and the consistency between the model data and the measured data is better. In the development of data fusion, a variety of data processing methods have been developed, including step-by-step correction algorithm, hybrid analysis algorithm, optimal interpolation algorithm, Kriging method, variational method and Kalman filter algorithm, etc. Each method has its own characteristics, advantages and disadvantages. When choosing a research method, different methods can be selected for research according to specific target requirements.

[0064] SST is an extremely important parameter for the ocean, and how to obtain high temporal and spatial resolution, high spectral resolution, and high-precision sea surface temperature products is very important. At the same time, the independently developed Fengyun (FY-3) and Ocean (HY-2) series satellites can provide infrared and microwave remote sensing observations of global SST. Using independently developed satellite data to produce high-precision SST reanalysis products and promote the development of domestic sea surface temperature data fusion is a problem worthy of attention and research.

[0065] The problems that need to be solved in multi-source remote sensing data fusion are the research on the fusion theoretical framework, algorithms and models of remote sensing data with multiple sensors, different spectral and spatiotemporal resolutions, and the establishment of a performance evaluation index system for fusion results. At present, there are more and more studies on multi-source data fusion, but most of them are carried out for specific application fields. According to specific problems, each of them establishes corresponding fusion principles to achieve its own so-called best solution. However, there is no consistent evaluation criterion for the evaluation of fusion effect, and it is impossible to accurately measure the final fusion degree of the fusion product.

[0066] In general, the inversion technology of ocean remote sensing data has made great progress, and the products have been applied to a certain extent. However, compared with similar foreign satellites, the sea surface temperature products generated by domestic satellite sensors are not mature enough, and their international recognition and application rate are not enough, which is also the gap with foreign satellite sensors. At the same time, although many scholars have carried out data fusion research, there is still a lack of research on domestic autonomous satellite remote sensing data, which limits the further promotion and application of autonomous satellite data. This application uses multi-scale assimilation technology and domestic satellite data as the input source to establish a model system from data quality control, deviation correction and data fusion, build a data fusion system, and produce multi-scale satellite sea surface temperature fusion products, which can provide technical and data support for remote sensing data application and scientific research.

[0067] The purpose of this application is to provide a multi-scale fusion method and system based on satellite sea surface temperature, which can accurately and effectively extract multi-scale information from satellite remote sensing observations of sea surface temperature and realize multi-scale information fusion of sea surface temperature.

[0068] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0069] Embodiment 1

[0070] like Figure 1 This embodiment provides a multi-scale fusion method based on satellite sea surface temperature, including:

[0071] Step 101: Acquire a sea surface temperature dataset; data in the sea surface temperature dataset is acquired by a variety of satellite sensors.

[0072] Step 102: performing data preprocessing on the sea surface temperature dataset to obtain a processed sea surface temperature dataset.

[0073] Step 103: Based on the measured sea surface temperature, a quality accuracy evaluation is performed on each data in the processed sea surface temperature data set; the evaluation index of the quality accuracy evaluation is composed of mean deviation, absolute deviation, root mean square error, standard deviation and correlation coefficient.

[0074] Step 104: According to the quality accuracy evaluation results of each data, based on the piecewise regression method, deviation correction is performed on each data in the processed sea surface temperature data set to obtain a corrected sea surface temperature data set.

[0075] Step 105: Based on the corrected sea surface temperature dataset, a multi-scale optimal interpolation method is used to construct a multi-scale fusion model.

[0076] In some embodiments, when executing step 101, the specific steps may be as follows:

[0077] The data in the acquired sea surface temperature dataset include Fengyun satellite data, ocean satellite data, iQuam measured data, Argo buoy data, OISST data and OSTIA data.

[0078] In some embodiments, when executing step 102, the specific steps may be as follows:

[0079] For the Fengyun satellite data, the sea mark temperature data, satellite zenith angle and quality mark in the Fengyun satellite data are transposed or rotated, and the invalid land values, other invalid data, sea mark temperature points without valid zenith angle data, and data with a difference of more than 5°C in the OISST monthly average data are eliminated according to the mask data to obtain the processed Fengyun satellite data.

[0080] For the ocean satellite data, the sea surface temperature data in the ocean satellite data are transposed, and the invalid land values ​​are eliminated according to the mask data, the remaining invalid data points are filled with NaN, and the data with the OISST monthly average data difference greater than 5°C are obtained to obtain the processed ocean satellite data.

[0081] For the iQuam measured data, the daily average values ​​of the latitude and longitude data, quality mark data, water depth data, observation time data, and sea surface temperature data in the iQuam measured data are solved to obtain the processed iQuam measured data.

[0082] For the Argo float data, the Argo data with a depth range of 5-6m from the sea surface are selected as the processed Argo float data.

[0083] For the OISST data, the sea mark temperature data and longitude and latitude information in the OISST data are transposed or rotated, and the invalid sea mark temperature data are eliminated according to the mask information to obtain the processed OISST data.

[0084] For OSTIA data, the sea mark temperature data and latitude and longitude information in the OSTIA data are transposed or rotated, and invalid sea mark temperature data are eliminated according to the mask information to obtain the processed OSTIA data.

[0085] Specifically, before using the single-satellite sensor data, this embodiment needs to have a good understanding of it, clarify the characteristics of the data, perform certain preprocessing and quality control on the abnormal values, fill values, and invalid values ​​of the data, and then match and count them with the measured data to evaluate and analyze the accuracy of SST.

[0086] Therefore, preprocessing is used to focus on learning and understanding the basic information of a single star, and to separate and read out different elements in the file. Quality control is based on the separated sea and land masks, quality marks and other necessary control screening conditions, delete the fill values ​​in the SST data, and identify, judge and eliminate the outliers, and finally retain the data with good quality that meets the requirements.

[0087] (1) Fengyun satellite data preprocessing and quality control:

[0088] Since the SST in the daytime is easily warmed by sunlight, resulting in a diurnal thermocline, and there is no warming at night, the temperature difference between day and night may be large, so the daytime and nighttime data are processed separately. The FY-3CVIRR data first reads the SST, satellite zenith angle and quality mark in the study range of the file, and transposes or rotates the data to obtain a grid with the upper left corner (100.025°E, 49.975°N) and the lower right corner (159.975°E, 0.025°N), and the grid resolution is 0.041667°. The valid range of SST values ​​is -2℃-35℃, and the scaling factor for conversion to actual SST is 0.01. The binary quality mark is converted into decimal, and data with the last two digits of 00, 01, and 10 are selected, namely excellent, good, and poor data. Invalid land values ​​are eliminated according to the mask data, and NaN is filled in for the remaining invalid data or SST points without valid zenith angle data. Then, data with a difference of more than 5℃ from the OISST monthly average data are eliminated. The data processing methods for day and night are the same, and finally the preliminary satellite data set is obtained.

[0089] FY-3C MWRI data needs to read the SST and quality mark of the study range in the file first, and transpose the data. The upper left corner of the grid is (100.125°E, 49.875°N), the lower right corner is (159.875°E, 0.125°N), and the grid resolution is 0.25°. The effective range of SST values ​​is -2℃-35℃, and the scale factor for conversion to actual SST is 0.01. Data with quality marks of 50 and 51, that is, excellent and poor data, are selected. Invalid values ​​of land are removed according to mask data, and NaN is filled in for the remaining invalid data points. Data with a difference of more than 5℃ from the monthly average data of OISST are removed. The data processing methods of descending orbit (daytime) and ascending orbit (nighttime) are the same, and finally the preliminary satellite data set is obtained. (Fengyun Data User Manual).

[0090] (2) Ocean satellite data preprocessing and quality control:

[0091] HY-2B RM data needs to read the SST of the study range in the file first, and transpose the data. The upper left corner of the grid is (100.125°E, 49.875°N), the lower right corner is (159.875°E, 0.125°N), and the grid resolution is 0.25°. The valid range of SST values ​​is -2℃-35℃, and the scale factor for conversion to actual SST is 0.01. All valid observation data are selected, and invalid land values ​​are removed according to mask data. The remaining invalid data points are filled with NaN, and then the data with a difference of more than 5℃ from the OISST monthly average data are removed. The data processing methods of descending orbit (daytime) and ascending orbit (nighttime) are the same, and finally the preliminary satellite data set is obtained.

[0092] (3) iQuam measured data preprocessing and quality control:

[0093] As mentioned above, this embodiment uses three types of measured data from iQuam's coastal anchored buoys, Argo buoys and ship surveys, and the data has been quality-checked by the system. For Argo buoy data, extract the latitude and longitude, quality mark, water depth, observation time, sea surface temperature and other information, select SST data with a quality mark of up to 5, and the SST effective range is -2℃-40℃, and read and save them during the day and night respectively; coastal anchored buoys and ship surveys are also read and saved during the day and night respectively. Then, each set of data is tested for repeated observations at the same location on the same day. If there are multiple observations, take the average of the multiple observations to represent the observation value of the point. Then set the selection order of measured data to Argo, coastal anchored buoy, and ship survey data, that is, when there are two or more measured data at the same location on the same day, use them in the above order, otherwise, select the only valid observation value of the point, and finally obtain the measured SST data set.

[0094] (4) Argo float data preprocessing:

[0095] Since the amount of SST data of the Argo float at 0-1m is small and cannot meet the research needs, referring to the previous practices, this embodiment selects Argo data with a depth range of 5-6m, removes the values ​​of SST range exceeding -2℃-40℃, and generates a data set for standby use.

[0096] (5) OISST data reading and preprocessing:

[0097] The OISST data needs to first read the latitude and longitude information and SST of the study range in the file, and transpose the data. The upper left corner of the grid is (100.125°E, 49.875°N), the lower right corner is (159.875°E, 0.125°N), and the grid resolution is 0.25°. Invalid SST is eliminated based on mask information, and the 30-year daily average climate state value from 1989 to 2018 and the monthly average value from June to December 2019 are calculated to generate a data set.

[0098] (6) OSTIA data reading and preprocessing:

[0099] The OSTIA data first reads the latitude, longitude and SST data of the study range in the file, and transposes the data to obtain the upper left corner of the grid (100.025°E, 49.975°N) and the lower right corner (159.975°E, 0.025°N). Invalid SST is eliminated based on mask information, and the monthly average values ​​from June to September 2019 are calculated to generate a data set for backup.

[0100] In some embodiments, when executing step 103, the specific steps may be as follows:

[0101] Accuracy evaluation requires the selection of appropriate time and space windows, matching the measured iQuam data with satellite data, using statistical methods, calculating certain evaluation indicators, and analyzing the differences between satellite data and measured SST, so as to achieve a certain evaluation effect on satellite data.

[0102] When the single-star data is matched with the measured data in time and space, the time nodes separating day and night are 06:00-18:00 in the daytime period and 18:00-06:00 in the nighttime period.

[0103] The evaluation indicators are the following five items, namely, mean deviation (Bias), absolute deviation (Abs_Bias), root mean square error (RMSE), standard deviation (STDE) and correlation coefficient (R), and their calculation formulas are:

[0104]

[0105]

[0106]

[0107]

[0108]

[0109] Among them, Bias is the average bias, Abs_Bias is the absolute bias, RMSE is the root mean square error, STDE is the standard deviation, R is the correlation coefficient, S i ,I i They represent the sea mark temperature data value inverted from infrared data and the measured sea mark temperature data of the i-th matching point, respectively, and n is the total number of matching points between satellite and measured data; All matching points S i ,I i The mean of .

[0110] In some embodiments, when executing step 104, the specific steps may be as follows:

[0111] The piecewise regression method adopted in this embodiment is to correct the deviation of SST, and its error-related variables are analyzed from the SST inversion algorithm.

[0112] The VIRR sea surface temperature business product is to establish a linear relationship model between the brightness temperature at the central wavelength of 10.8μm (CH4) and 12μm (CH5) and the measured SST. The calculation formula is as follows:

[0113] T x =a0+a1T 11 +a2(T 11 -T 12 )+a3(T 11 -T 12 )(secθ-1).

[0114] Where: T x is the sea mark temperature retrieved from remote sensing, T 11 , T 12 They represent the brightness temperature at the central wavelength of 10.8 μm and the central wavelength of 12 μm respectively, a0-a3 represent the regression coefficients, and θ represents the observation zenith angle.

[0115] In the infrared sensor sea mark temperature inversion principle, the product results are mainly related to the thermal infrared brightness temperature at 10.8μm and 12μm and the observation zenith angle θ. However, the inverted sea mark temperature product no longer contains brightness temperature data, so the inverted sea surface temperature T is used for bias correction. S Instead of the original brightness temperature CH4, the climatological daily mean sea mark temperature (T C ) replaces CH5, where T C It is obtained by averaging the daily analysis products of OISST for 30 years (1989-2018) with a resolution of 25 km. In the VIRR bias correction, the bilinear interpolation method is required to convert T CThe grid resolution is converted to 5 km, which matches the infrared SST. Then, a sample data set (MD_all) for the entire study area is established for daytime and nighttime. MD_all contains the temporal and spatial information of sensors in the entire study area and the corresponding measured SST, T S 、T C and θ, the deviation correction calculation formula is as follows:

[0116] T xz =b0+b1T s +b2(T s -T c )+b3(T s -T c )(sec θ-1).

[0117] Where: T xz is the corrected SST, b0~b3 are regression coefficients, T xz It can be regarded as [T s , T s -T c , (T s -T c )(secθ-1)] is estimated by the linear combination of these three regression operators, which represent the original inverted SST, SST anomalies, and variables related to the observed zenith angle, respectively.

[0118] When performing bias correction, the estimation of the regression coefficient requires appropriate matching samples. This application first divides the study area into four sub-areas according to latitude. For each area, this application uses time-related samples from the 15 days before the experiment to generate a training dataset for bias correction pixels (Local Matchup Datasets, MD local ).

[0119] R=[T s , T s -T c , (T s -T c )(secθ-1)] T .

[0120] D local =<(R- <r> )(R- <r>) T >.

[0121] Where: R represents the regression operator in vector form, D local is the covariance of the regression operator, and 〈·〉 is the average of the vector. SST estimation error δT xz and the corresponding error variance V(δT xz ) can be expressed as:

[0122]

[0123] Where: c is the estimated regression coefficient, ρ represents the estimated standard deviation of the estimation error, which can be seen as R and <r>Distance in multidimensional space, for each independent sub-region, by selecting each pixel (i, j) corresponding to MD local The dataset {ρ local }|With a single ρ i，j The samples whose difference is less than a certain threshold S are regarded as the optimal associated matching samples (MD optimal ). In this paper, the initial S is set to 0.1, when the optimal sample size N optimal If it is less than 50, S is increased by 0.01 until the optimal matching point is all matching points. optimal ), the regression coefficient of each pixel is estimated by the least squares method, and finally the calculated regression coefficient is combined with the T corresponding to each pixel. S 、T C and θ, the bias-corrected sea surface temperature T can be obtained xz .

[0124] The principle of microwave data deviation correction is exactly the same as that of infrared correction. The difference is that microwave does not have the term related to the satellite zenith angle. The microwave sea surface temperature deviation correction formula is as follows:

[0125] T xz =b0+b1T s +b2(T s -T c ).

[0126] Where: T xz , the physical meanings of b0~b2 are the same as above, T xz Can be used as a regression operator T s and (T s -T c ), T s and (T s -T c ) represent the SST inversion value and SST anomaly value respectively. s , T s -T c ] T Except for the difference in the infrared data deviation correction formula, the rest of the processing methods, threshold settings, and processes are exactly the same as those for infrared SST deviation correction, so I will not describe them here in detail. Figure 3 As shown, Figure 3 Noptical is the optimal number of samples, and Nlocal is the number of local samples.

[0127] In some embodiments, when executing step 104, the specific steps may be as follows:

[0128] This application uses a multi-scale optimal interpolation method. The analysis value of the spatial grid point is obtained by adding the modified SST increment to the background field value of the point. The revised value increment is obtained by weighted summation of the deviations between the valid observation values ​​and the background field values ​​around the spatial grid point. The weight coefficient here satisfies the condition of minimizing the analysis error of the grid point, rather than weights determined empirically or based on distance. Multi-scale information in the observation data is proposed through multiple iterations. The expression is as follows:

[0129]

[0130] Where: the subscripts m and s represent the positions of the spatial grid points and observation grid points respectively; the superscripts a, b, and o represent the analysis value, background field value, and observation value respectively. When the spatial grid resolutions of the observation points, background field points, and grid points are different, the bilinear interpolation operator is used to interpolate the grid point resolution, then:

[0131]

[0132] H1 and H2 are bilinear interpolation operators. After data preprocessing, the weight coefficient matrix (gain matrix) K can be derived by solving equation (5-2):

[0133] K=BH T (HBH T +R)- 1 .

[0134] Where, the superscript T is the matrix transpose; B is the background field error covariance matrix, and R is the observation error covariance matrix.

[0135] When determining the covariance matrix B of the background error, the following two basic assumptions are often made: (1) B is constant; (2) the horizontal correlation of the background field error decreases exponentially with the increase of the horizontal distance. Then:

[0136] B=D 0.5 ρD 0.5 .

[0137] Where: D is the diagonal matrix composed of background field errors, ρ is the horizontal correlation matrix of the predicted background field, and the matrix R is calculated using the same processing method as the background field error covariance matrix B.

[0138] When SST data are fused, it is assumed that the SST background error is anisotropic and the correlation decay rate in the longitude direction is higher than that in the latitude direction. The background error correlation function represents the error correlation between grid points i and j, as shown in the following formula:

[0139]

[0140] In the formula, R ij represents the correlation coefficient, S i , S j Represent the SST values ​​of sites i and j respectively, and <> represents the vector average.

[0141] In this paper, the correlation distribution of background error uses the following formula for parameter fitting:

[0142]

[0143] In the formula, ρ ij represents the correlation coefficient between points i and j, r1 and r2 represent the distances in the latitude and longitude directions respectively, λ1 and λ2 represent the correlation scales in the latitude and longitude directions respectively. In this paper, λ1=200km and λ2=150km are selected.

[0144] Since the distribution of observation points is uneven, when calculating the weight coefficient K, (HBH T +R) -1 The inversion of often fails. Therefore, to avoid the inversion process, multiply both sides of equation (13) by (HBH T + R), we get:

[0145] K(HBH T + R)=BH T .

[0146] Transpose the matrices on both sides to get:

[0147] (HBH T +R) T K T =HB T .

[0148] After the above transformation processing, the problem of solving K is transformed into solving its transposed matrix, which can avoid the error in matrix inversion. However, since the observation matrix is ​​sparse and the distribution of elements in the matrix is ​​irregular, the transposed matrix may still be ill-conditioned. To address this practical problem, this paper uses the singular value decomposition (SVD) method to calculate the least squares solution of the linear equations. The grid size of the fusion analysis field in this application is 500×600. When performing the optimal interpolation, it faces problems such as insufficient computer memory and insufficient SST data in some areas. In order to effectively solve the above problems, the grid-point interpolation method is finally adopted. Point-by-point interpolation uses the idea of ​​taking points as the basic calculation unit to seek relevant points within the relevant radius, calculate the correlation between them, obtain the fusion value of the point, and then expand them row by row and column by column in space to calculate the analysis value of the analysis grid.

[0149] The singular value decomposition method is as follows:

[0150]

[0151] Among them, S m×n is a diagonal matrix. The SVD method can effectively eliminate abnormal solutions and meet the accuracy of equation solution.

[0152] After the data has been quality controlled and processed as above, the satellite sea surface temperature melting process is as follows: Figure 4 shown.

[0153] Embodiment 2

[0154] like Figure 5 As shown, this embodiment provides a multi-scale fusion system based on satellite sea surface temperature, including:

[0155] The data acquisition module 501 is used to acquire a sea surface temperature data set; the data in the sea surface temperature data set is acquired by a variety of satellite sensors.

[0156] The preprocessing module 502 is used to perform data preprocessing on the sea surface temperature dataset to obtain a processed sea surface temperature dataset.

[0157] The evaluation module 503 is used to perform quality accuracy evaluation on each data in the processed sea surface temperature data set based on the measured sea surface temperature; the evaluation index of the quality accuracy evaluation is composed of mean deviation, absolute deviation, root mean square error, standard deviation and correlation coefficient.

[0158] The correction module 504 is used to perform deviation correction on each data in the processed sea surface temperature data set based on the quality accuracy evaluation result of each data and based on the piecewise regression method to obtain a corrected sea surface temperature data set.

[0159] The fusion module 505 is used to construct a multi-scale fusion model based on the corrected sea surface temperature data set by using a multi-scale optimal interpolation method.

[0160] In summary, this application has the following technical effects:

[0161] 1) This application can comprehensively realize the quality control and deviation correction of domestic autonomous satellite remote sensing sea surface temperature. Due to the influence of uncontrollable factors such as bad weather conditions and equipment aging, the obtained satellite remote sensing data is very different from the real SST. In order to more comprehensively understand the accuracy of autonomous satellite remote sensing sea surface temperature observation, the outliers, fill values, and invalid values ​​of the data must be pre-processed and quality controlled before using the single-star sensor data, and then matched with the measured data for statistics to evaluate and analyze the accuracy of SST. This application separates different elements, deletes the fill values ​​in the SST data based on the separated sea and land masks, quality marks, and other necessary control screening conditions, and identifies, judges, and removes the outliers, and finally retains the data with better quality that meets the requirements. In addition, the SST inversion model does not fully and correctly reflect the relationship between the measured brightness temperature and the actual SST, and the infrared is susceptible to incomplete cloud detection and microwaves are susceptible to aerosols and land radiation during the inversion process. The inversion product accuracy is often not high, and outliers are very likely to occur. This application adopts a segmented sample regression method to process the estimated error in segments, uses different matching samples according to the error size interval, calculates different regression coefficients by the least squares method, and performs deviation correction. This method can effectively eliminate the sensor statistical error in the sea surface temperature product based on the matched optimal measured samples.

[0162] 2) The method based on this application can effectively extract multi-scale information from satellite sea surface temperature and realize multi-scale information fusion of sea surface temperature. Satellite remote sensing sea surface temperature data from different sources have different sampling resolutions and accuracies. This application proposes a multi-scale optimal interpolation method, which extracts multi-scale information from sea surface temperature by setting multiple search radius cycles, and sets influence weights according to the observation accuracy of different satellites, so as to realize multi-scale information fusion of sea surface temperature and improve the effective resolution and accuracy of sea surface temperature fusion products.

[0163] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0164] In this embodiment, specific examples are used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application; at the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.< / r> < / r> < / r>

Claims

1. A multi-scale fusion method based on satellite sea surface temperature, characterized in that: The multi-scale fusion method based on satellite sea surface temperature comprises: Acquire a sea surface temperature data set; data in the sea surface temperature data set is acquired by a variety of satellite sensors; Performing data preprocessing on the sea surface temperature data set to obtain a processed sea surface temperature data set; Based on the measured sea surface temperature, a quality accuracy evaluation is performed on each data in the processed sea surface temperature data set; the evaluation index of the quality accuracy evaluation is composed of mean deviation, absolute deviation, root mean square error, standard deviation and correlation coefficient; According to the quality accuracy evaluation results of each data, based on the piecewise regression method, each data in the processed sea surface temperature data set is subjected to deviation correction to obtain a corrected sea surface temperature data set; According to the corrected sea surface temperature dataset, a multi-scale fusion model is constructed using the multi-scale optimal interpolation method.

2. The multi-scale fusion method based on satellite sea surface temperature according to claim 1, characterized in that: The sea surface temperature data set includes Fengyun satellite data, ocean satellite data, iQuam measured data, Argo buoy data, OISST data and OSTIA data.

3. The multi-scale fusion method based on satellite sea surface temperature according to claim 2, characterized in that: The sea surface temperature dataset is subjected to data preprocessing to obtain a processed sea surface temperature dataset, specifically comprising: For the Fengyun satellite data, the sea mark temperature data, satellite zenith angle and quality mark in the Fengyun satellite data are transposed or rotated, and the invalid land values, other invalid data, sea mark temperature points without valid zenith angle data, and data with a difference of more than 5°C in the monthly average data of OISST are eliminated according to the mask data to obtain the processed Fengyun satellite data; For ocean satellite data, the sea surface temperature data in the ocean satellite data is transposed, and invalid land values ​​are removed according to the mask data, and the remaining invalid data points are filled with NaN and the data with the difference of OISST monthly average data greater than 5°C are obtained to obtain the processed ocean satellite data; For the iQuam measured data, the daily average values ​​of the latitude and longitude data, quality mark data, water depth data, observation time data, and sea surface temperature data in the iQuam measured data are solved to obtain the processed iQuam measured data; For Argo float data, the Argo data with a depth range of 5-6m from the sea surface were selected as the processed Argo float data; For OISST data, the sea mark temperature data and latitude and longitude information in the OISST data are transposed or rotated, and invalid sea mark temperature data are eliminated according to the mask information to obtain the processed OISST data; For OSTIA data, the sea mark temperature data and latitude and longitude information in the OSTIA data are transposed or rotated, and invalid sea mark temperature data are eliminated according to the mask information to obtain the processed OSTIA data.

4. The multi-scale fusion method based on satellite sea surface temperature according to claim 3 is characterized in that: The formula expressions of the mean deviation, absolute deviation, root mean square error, standard deviation and correlation coefficient in the evaluation indicators are as follows: Among them, Bias is the average bias, Abs_Bias is the absolute bias, RMSE is the root mean square error, STDE is the standard deviation, R is the correlation coefficient, S i ,I i They represent the sea mark temperature data value inverted from infrared data and the measured sea mark temperature data of the i-th matching point, respectively, and n is the total number of matching points between satellite and measured data; All matching points S i ,I i The mean of .

5. The multi-scale fusion method based on satellite sea surface temperature according to claim 1, characterized in that: According to the quality accuracy evaluation results of each data, based on the piecewise regression method, the deviation of each data in the processed sea surface temperature data set is corrected to obtain a corrected sea surface temperature data set, specifically including: According to the formula T xz =b0+b1T s +b2(T s -T c ), performing bias correction on each data in the sea surface temperature data set; Among them, T xz is the corrected sea surface temperature, b0, b1, b2 are regression coefficients, T s and (T s -T c ) are the sea surface temperature inversion value and the sea surface temperature anomaly value, respectively.

6. The multi-scale fusion method based on satellite sea surface temperature according to claim 5, characterized in that: According to the corrected sea surface temperature dataset, a multi-scale fusion model is constructed using the multi-scale optimal interpolation method, including: Obtaining a background field error covariance matrix; the background field error covariance matrix is ​​composed of a diagonal matrix D composed of background field errors and a horizontal correlation matrix ρ of the predicted background field; Based on the background field error covariance matrix and the corrected sea surface temperature dataset, a spatial correlation function model is constructed; When fusing sea surface temperature data, calculate the background error correlation function of observation points of different data; The correlation coefficient between the observation points is calculated based on the correlation distribution parameter fitting formula of the background error.

7. The multi-scale fusion method based on satellite sea surface temperature according to claim 6, characterized in that: The formula expression of the background field error covariance matrix is: B=D 0.5 ρD 0.5 ; Among them, D is the diagonal matrix composed of background field errors, ρ is the horizontal correlation matrix of the predicted background field, and the matrix R is calculated using the same processing method as the background field error covariance matrix B.

8. The multi-scale fusion method based on satellite sea surface temperature according to claim 7, characterized in that: When fusing sea surface temperature data, the background error correlation function of the observation points of different data is calculated, including: According to the formula Calculate the background error correlation function of the observation points of different data; Among them, R ij represents the correlation coefficient, S i , S j Represent the sea surface temperature values ​​of stations i and j respectively, and 〈〉 represents the vector average.

9. The multi-scale fusion method based on satellite sea surface temperature according to claim 8, characterized in that: According to the correlation distribution parameter fitting formula of the background error, the correlation coefficient between the observation points is calculated, including: According to the correlation distribution parameter fitting formula Calculate the correlation coefficient between observation points; Among them, ρ ij represents the correlation coefficient between points i and j, r1 and r2 represent the distances in the latitude and longitude directions, and λ1 and λ2 represent the latitudes, respectively.

10. A multi-scale fusion system based on satellite sea surface temperature, characterized in that: include: A data acquisition module is used to acquire a sea surface temperature data set; the data in the sea surface temperature data set is acquired by a variety of satellite sensors; A preprocessing module, used for performing data preprocessing on the sea surface temperature data set to obtain a processed sea surface temperature data set; An evaluation module, for performing a quality accuracy evaluation on each data in the processed sea surface temperature data set based on the measured sea surface temperature; the evaluation index of the quality accuracy evaluation is composed of mean deviation, absolute deviation, root mean square error, standard deviation and correlation coefficient; A correction module, for performing deviation correction on each data in the processed sea surface temperature data set based on the quality accuracy evaluation result of each data and based on the piecewise regression method, to obtain a corrected sea surface temperature data set; The fusion module is used to construct a multi-scale fusion model based on the corrected sea surface temperature dataset using a multi-scale optimal interpolation method.

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