All-day sea ice monitoring method, device and equipment based on FY4B stationary satellite

Abstract

The invention provides an all-day sea ice monitoring method, device and equipment based on an FY4B stationary satellite, and relates to the technical field of sea ice monitoring, and the method comprises the steps: obtaining the load data collected by the FY4B stationary satellite at a plurality of historical moments for a research region, carrying out the preprocessing of the load data, and obtaining a pre-processed load data; obtaining corresponding remote sensing physical quantities of longitude and latitude grid points in the ocean and non-cloud region in the research region at historical moments; based on the remote sensing physical quantity, performing multi-condition sea ice identification on the longitude and latitude grid points to determine historical sea ice identification results corresponding to the longitude and latitude grid points at historical moments; and for any moment in the to-be-predicted all-day time, constructing a historical time period matched with the moment based on a plurality of historical moments, and determining a target sea ice identification result corresponding to the longitude and latitude grid point at the moment according to a historical sea ice identification result corresponding to the longitude and latitude grid point at the historical time period. The sea ice monitoring timeliness can be obviously improved.

Description

Technical Field

[0001] The present invention relates to the technical field of sea ice monitoring, and in particular to a method, device and equipment for all-day sea ice monitoring based on a FY4B geostationary satellite. Background Art

[0002] At present, using remote sensing to monitor sea ice has become a major trend. However, whether in scientific research or business applications, the commonly used remote sensing data include MODIS, AVHRR, SAR, Landsat and HJ-1A, etc. However, when using the above remote sensing data to realize sea ice monitoring, there is a problem of insufficient timeliness of sea ice monitoring. Summary of the invention

[0003] In view of this, the purpose of the present invention is to provide a method, device and equipment for all-day sea ice monitoring based on the FY4B geostationary satellite, which can significantly improve the timeliness of sea ice monitoring.

[0004] In a first aspect, an embodiment of the present invention provides a method for all-day sea ice monitoring based on a FY4B geostationary satellite, comprising:

[0005] Obtain the payload data collected by the FY4B geostationary satellite for the study area at multiple historical moments, and preprocess the payload data to obtain the remote sensing physical quantities corresponding to the latitude and longitude grid points in the ocean and non-cloud areas within the study area at historical moments;

[0006] Based on remote sensing physical quantities, multi-condition sea ice identification is performed on longitude and latitude grid points to determine the historical sea ice identification results corresponding to the longitude and latitude grid points at historical moments;

[0007] For any moment in the whole day to be predicted, a historical period matching the moment is constructed based on multiple historical moments, and the target sea ice identification result corresponding to the longitude and latitude grid point at the moment is determined according to the historical sea ice identification result corresponding to the longitude and latitude grid point in the historical period.

[0008] In one embodiment, the load data is preprocessed to obtain remote sensing physical quantities corresponding to the latitude and longitude grid points in the ocean and non-cloud area in the study area at historical times, including:

[0009] Determine the load data corresponding to the latitude and longitude grid points in the study area at historical times;

[0010] Perform radiometric calibration on the load data corresponding to the longitude and latitude grid points to determine the remote sensing physical quantities corresponding to the longitude and latitude grid points at historical moments;

[0011] The load data after radiation calibration are processed by sea-land separation and cloud removal to extract the ocean and non-cloud area from the study area, and obtain the remote sensing physical quantities corresponding to the longitude and latitude grid points in the ocean and non-cloud area at historical moments.

[0012] In one embodiment, the remote sensing physical quantity includes reflectivity and brightness temperature data of multiple channels; based on the remote sensing physical quantity, multi-condition sea ice identification is performed on the longitude and latitude grid points to determine the historical sea ice identification results corresponding to the longitude and latitude grid points at historical moments, including:

[0013] Based on the reflectivity of multiple channels corresponding to the longitude and latitude grid points, the normalized differential ice and snow index corresponding to the longitude and latitude grid points at the historical moment is determined to determine whether the longitude and latitude grid points meet the first condition;

[0014] and, based on the reflectivity of the single channel corresponding to the longitude and latitude grid point, determining whether the longitude and latitude grid point satisfies the second condition;

[0015] and, based on the brightness temperature data of multiple channels corresponding to the longitude and latitude grid points, determining the sea ice surface temperature corresponding to the longitude and latitude grid points at the historical moment, so as to judge whether the longitude and latitude grid points meet the third condition;

[0016] According to the judgment results of the first condition, the second condition and the third condition, the historical sea ice identification results corresponding to the latitude and longitude grid points at the historical moment are determined.

[0017] In one embodiment, according to the judgment results of the first condition, the second condition, and the third condition, the historical sea ice identification result corresponding to the latitude and longitude grid point at the historical moment is determined;

[0018] According to the judgment results of the first condition, the second condition, and the third condition, the number of conditions satisfied by the longitude and latitude grid points is determined, and based on the number of conditions, a numerical value is assigned to the historical sea ice identification result corresponding to the longitude and latitude grid points at the historical moment;

[0019] Among them, the historical sea ice identification results are used to numerically represent the probability that the category of the longitude and latitude grid points belongs to sea ice. The numerical value is positively correlated with the conditional quantity, and the probability that the category of the longitude and latitude grid points belongs to sea ice is positively correlated with the numerical value.

[0020] In one implementation, constructing a historical period matching the moment based on multiple historical moments includes:

[0021] This moment is used as the end moment of the historical period, and based on the preset size and end moment of the time sliding window, the start moment of the historical period is determined; wherein the time interval between the start moment and the end moment is equal to the size of the time sliding window.

[0022] In one embodiment, determining the target sea ice identification result corresponding to the longitude and latitude grid point at the moment according to the historical sea ice identification result corresponding to the longitude and latitude grid point in the historical period includes:

[0023] The historical sea ice identification results corresponding to the longitude and latitude grid points in the historical period are accumulated to obtain the accumulated sea ice identification results corresponding to the longitude and latitude grid points;

[0024] According to the threshold interval of the accumulated sea ice identification results, the target sea ice identification results corresponding to the longitude and latitude grid points at that moment are determined.

[0025] In a second aspect, an embodiment of the present invention further provides a 24-hour sea ice monitoring device based on a FY4B geostationary satellite, comprising:

[0026] The preprocessing module is used to obtain the payload data collected by the FY4B geostationary satellite for the study area at multiple historical moments, and preprocess the payload data to obtain the remote sensing physical quantities corresponding to the latitude and longitude grid points in the ocean and non-cloud area in the study area at historical moments;

[0027] The historical sea ice identification module is used to perform multi-condition sea ice identification on longitude and latitude grid points based on remote sensing physical quantities to determine the historical sea ice identification results corresponding to the longitude and latitude grid points at historical moments;

[0028] The current sea ice identification module is used to construct a historical period matching any moment in the whole day to be predicted based on multiple historical moments, and determine the target sea ice identification result corresponding to the longitude and latitude grid point at that moment according to the historical sea ice identification result corresponding to the longitude and latitude grid point in the historical period.

[0029] In one embodiment, the preprocessing module is specifically used for:

[0030] Determine the load data corresponding to the latitude and longitude grid points in the study area at historical times;

[0031] Perform radiometric calibration on the load data corresponding to the longitude and latitude grid points to determine the remote sensing physical quantities corresponding to the longitude and latitude grid points at historical moments;

[0032] The load data after radiation calibration are processed by sea-land separation and cloud removal to extract the ocean and non-cloud area from the study area, and obtain the remote sensing physical quantities corresponding to the longitude and latitude grid points in the ocean and non-cloud area at historical moments.

[0033] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising a processor and a memory, wherein the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement any one of the methods provided in the first aspect.

[0034] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement any one of the methods provided in the first aspect.

[0035] The embodiments of the present invention provide a method, device and equipment for all-day sea ice monitoring based on the FY4B geostationary satellite. First, the payload data collected by the FY4B geostationary satellite for the study area at multiple historical moments are obtained, and the payload data are preprocessed to obtain the remote sensing physical quantities corresponding to the longitude and latitude grid points in the ocean and non-cloud area in the study area at the historical moments; then, based on the remote sensing physical quantities, multi-condition sea ice identification is performed on the longitude and latitude grid points to determine the historical sea ice identification results corresponding to the longitude and latitude grid points at the historical moments; finally, for any moment in the whole day to be predicted, a historical time period matching the moment is constructed based on multiple historical moments, and according to the historical sea ice identification results corresponding to the longitude and latitude grid points in the historical time period, the target sea ice identification results corresponding to the longitude and latitude grid points at the moment are determined. The above method uses the payload data collected by the FY4B geostationary satellite to extract the ocean and non-cloud area in the study area and determine the remote sensing physical quantities corresponding to the longitude and latitude grid points in the area at historical moments. Multi-condition sea ice identification is adopted to determine the historical sea ice identification results corresponding to the longitude and latitude grid points at historical moments based on the remote sensing physical quantities. According to the historical sea ice identification results, all-day and moment-by-moment sea ice monitoring is realized. The embodiment of the present invention proposes a sea ice monitoring algorithm suitable for FY4B high time resolution. On the basis of moment-by-moment monitoring, real-time monitoring of sea ice confidence is proposed to quantify the possibility of sea ice existence in the monitoring area, which significantly improves the timeliness of sea ice monitoring.

[0036] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0037] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 A schematic flow chart of a method for all-day sea ice monitoring based on a FY4B geostationary satellite provided in an embodiment of the present invention;

[0040] Figure 2 An overall flow chart of a method for all-day sea ice monitoring based on a FY4B geostationary satellite provided in an embodiment of the present invention;

[0041] Figure 3 A schematic structural diagram of a 24-hour sea ice monitoring device based on a FY4B geostationary satellite provided in an embodiment of the present invention;

[0042] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described in combination with the embodiments below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] At present, the use of remote sensing to monitor sea ice has become a major trend. However, the remote sensing data commonly used in both scientific research and business applications include MODIS (Moderate-resolution Imaging Spectroradiometer), AVHRR (Advanced Very High Resolution Radiometer), SAR (Synthetic Aperture Radar), Landsat and HJ-1A, etc., which are not timely enough for sea ice monitoring. The high temporal resolution of geostationary satellites makes it possible to monitor sea ice over a large area and in real time. The Fengyun-4B (FY-4B) launched in 2021 is the first operational satellite of my country's new generation of geostationary meteorological satellites, the Fengyun-4 series. The development of the sea ice monitoring algorithm for this payload can not only effectively make up for the current lack of polar orbit observation timeliness, but also improve the stability of domestic satellite sea ice monitoring business applications. Based on this, the present invention provides a method, device and equipment for all-day sea ice monitoring based on the FY4B geostationary satellite, which can significantly improve the timeliness of sea ice monitoring.

[0045] To facilitate understanding of this embodiment, firstly, a method for all-day sea ice monitoring based on a FY4B geostationary satellite disclosed in an embodiment of the present invention is described in detail, see Figure 1 The flowchart of a method for all-day sea ice monitoring based on the FY4B geostationary satellite is shown, and the method mainly includes the following steps S102 to S106:

[0046] Step S102, obtain the payload data collected by the FY4B geostationary satellite for the study area at multiple historical moments, and preprocess the payload data to obtain the remote sensing physical quantities corresponding to the latitude and longitude grid points in the ocean and non-cloud area in the study area at historical moments.

[0047] Among them, the FY4B geostationary satellite is equipped with AGRI (Advanced Geostationary Radiometric Imager). The data collected by AGRI is the payload data, which can also be called FY4B band data. The band data contains multi-channel DN (Digital Number, remote sensing image pixel brightness value) data; remote sensing physical quantities include reflectivity and brightness temperature data of multiple channels. In one example, for the payload data corresponding to each longitude and latitude grid point at multiple historical moments, radiation calibration, sea-land separation, cloud removal and other pre-processing are performed on it, so that the ocean and non-cloud area can be extracted and the remote sensing physical quantities corresponding to the longitude and latitude grid points in the area at historical moments can be determined.

[0048] Step S104, based on remote sensing physical quantities, multi-condition sea ice identification is performed on the longitude and latitude grid points to determine the historical sea ice identification results corresponding to the longitude and latitude grid points at historical moments.

[0049] Among them, multi-condition sea ice identification at least includes NDSI (Normalized Difference Snow Index) normalized difference ice and snow index identification, single channel threshold identification, IST (Ice Surface Temperature) sea ice surface temperature identification; historical sea ice identification results are used to numerically represent the probability that the category of the longitude and latitude grid point belongs to sea ice, and the numerical value is positively correlated with the number of identification conditions passed by the longitude and latitude grid point, and the probability that the category of the longitude and latitude grid point belongs to sea ice is positively correlated with the numerical value. In one example, for each longitude and latitude grid point, based on the remote sensing physical quantity corresponding to it at the historical moment, the NDSI normalized difference ice and snow index identification, single channel threshold identification, and IST sea ice surface temperature identification are respectively performed on it, and the number of identification conditions passed by it is obtained, and based on the number of identification conditions, the corresponding historical sea ice identification results of the longitude and latitude grid point at the historical moment are assigned.

[0050] Step S106, for any moment in the whole day to be predicted, a historical time period matching the moment is constructed based on multiple historical moments, and the target sea ice identification result corresponding to the longitude and latitude grid point at the moment is determined according to the historical sea ice identification result corresponding to the longitude and latitude grid point in the historical time period.

[0051] Among them, the end time of the historical period is the current time to be predicted, and the time interval between the start time and the end time of the historical period is equal to the size of the preset time sliding window; the target sea ice identification result is used to represent the probability that the category of the longitude and latitude grid point belongs to sea ice through a numerical value, and the numerical value is positively correlated with the cumulative value of the historical sea ice identification results corresponding to the longitude and latitude grid point in the historical period, and the probability that the category of the longitude and latitude grid point belongs to sea ice is positively correlated with the numerical value. In one example, the historical sea ice identification results of the previous N moments (including the current moment) of the current moment can be subjected to sliding confidence partitioning to determine the target sea ice identification result corresponding to the current moment.

[0052] The all-day sea ice monitoring method based on the FY4B geostationary satellite provided in the embodiment of the present invention utilizes the payload data collected by the FY4B geostationary satellite to extract the ocean and non-cloud area in the study area and determine the remote sensing physical quantities corresponding to the longitude and latitude grid points in the area at historical moments. Multi-condition sea ice identification is adopted to determine the historical sea ice identification results corresponding to the longitude and latitude grid points at historical moments based on the remote sensing physical quantities. According to the historical sea ice identification results, all-day and moment-by-moment sea ice monitoring is realized. The embodiment of the present invention proposes a sea ice monitoring algorithm suitable for FY4B high time resolution. On the basis of moment-by-moment monitoring, real-time monitoring of sea ice confidence is proposed to quantify the possibility of sea ice existence in the monitoring area, which significantly improves the timeliness of sea ice monitoring.

[0053] For ease of understanding, the present invention provides a specific implementation of a method for all-day sea ice monitoring based on the FY4B geostationary satellite, which requires the use of data: FY4B AGRI (AGRI: Advanced Geostationary Radiometric Imager, hereinafter referred to as FY4B) payload data, FY4B AGRI CLM cloud detection products provided by the Satellite Meteorological Center, and administrative boundaries of land and sea. Figure 2 The overall flow chart of a method for all-day sea ice monitoring based on the FY4B geostationary satellite is shown in FIG. The main steps of the method are:

[0054] 1) Extract FY4B data and transform the projection of the study area to determine the load data corresponding to the latitude and longitude grid points in the study area at historical times. In one embodiment, the row and column numbers corresponding to each latitude and longitude grid point in the nominal projection data are calculated based on the latitude and longitude grids in the study area, and the DN value of the latitude and longitude grid point projection in the study area can be indexed from the nominal projection data based on the calculated row and column numbers.

[0055] 2) FY4B band radiometric calibration: Radiometric calibration is performed on the load data corresponding to the longitude and latitude grid points to determine the remote sensing physical quantities corresponding to the longitude and latitude grid points at historical moments. In one embodiment, radiometric calibration is performed on the FY4B band data to map the DN value of each band to remote sensing physical quantities, which include reflectivity and brightness temperature data of multiple channels.

[0056] 3) Perform sea-land separation and cloud removal processing on FY4B band data: Perform sea-land separation and cloud removal processing on the load data after radiation calibration processing to extract the ocean and non-cloud area from the study area, and obtain the remote sensing physical quantities corresponding to the longitude and latitude grid points in the ocean and non-cloud area at the historical moment. In one embodiment, the sea-land administrative boundary and the CLM cloud detection product provided by the satellite meteorological center are used to perform sea-land separation and cloud removal processing on the FY4B band data, wherein the land area is assigned an invalid value of NaN, the ocean and cloud area is assigned an invalid value of NaN, and the ocean and non-cloud area is assigned a value of 0, thereby extracting the remote sensing physical quantities corresponding to the longitude and latitude grid points in the ocean and non-cloud area at the historical moment.

[0057] 4) Use multi-condition judgment method to identify sea ice. Multi-condition sea ice identification includes: NDSI normalized difference ice and snow index identification, single channel threshold identification, IST sea ice surface temperature identification. Specifically:

[0058] In one example, the NDSI normalized difference snow and ice index identification (recorded as the first condition S1) is as follows: based on the reflectivity of multiple channels corresponding to the longitude and latitude grid points, the normalized difference snow and ice index corresponding to the longitude and latitude grid points at the historical moment is determined to determine whether the longitude and latitude grid points meet the first condition.

[0059] Specifically, the expression of NDSI normalized difference snow and ice index identification (first condition S1) is as follows:

[0060]

[0061] Where B3 and B5 represent the reflectance of channel 3 (central wavelength: 0.825 microns) and channel 5 (central wavelength: 1.61 microns) of the FY4B band data, respectively, and NDSI is the ratio of the difference and sum of the reflectance of the two channels. α1 is the NDSI identification threshold. When the NDSI value is greater than α1, the first condition S1 is met.

[0062] In one example, the single-channel threshold determination (recorded as the second condition S2) is as follows: based on the reflectivity of the single channel corresponding to the longitude and latitude grid point, determine whether the longitude and latitude grid point meets the second condition.

[0063] Specifically, the expression of single-channel threshold recognition (second condition S2) is as follows:

[0064] B2>ɑ2;

[0065] Where B2 represents the reflectance data of channel 2 (central wavelength: 0.65 microns) of FY4B satellite data. ɑ2 represents the reflectance identification threshold of band 2. ɑ2 is the single channel identification threshold. When B2>α2, the second condition S2 is met.

[0066] In one example, the IST sea ice surface temperature identification (recorded as the third condition S3) is as follows: based on the brightness temperature data of multiple channels corresponding to the longitude and latitude grid points, the sea ice surface temperature corresponding to the longitude and latitude grid points at the historical moment is determined to determine whether the longitude and latitude grid points meet the third condition.

[0067] Specifically, the expression of IST sea ice surface temperature identification (referred to as the third condition S3) is as follows:

[0068]

[0069] Where T 13 and T 14 They represent the brightness temperature data of channel 13 (central wavelength: 10.80 microns) and channel 14 (central wavelength: 12.00 microns) of the FY4B band data; ψ represents the sensor scanning angle. The coefficient ω is determined by T 13 The temperature threshold is determined by the sensor zenith angle, R is the radius of the earth's equator, and A is the theoretical altitude of the satellite. α3 is the IST recognition threshold. When IST<α3, the third condition S3 is met.

[0070] On this basis, the historical sea ice identification results corresponding to the longitude and latitude grid points at historical moments can be determined according to the judgment results of the first, second and third conditions.

[0071] Specifically, according to the judgment results of the first condition, the second condition, and the third condition, the number of conditions satisfied by the longitude and latitude grid point is determined, and a numerical value is assigned to the historical sea ice identification result corresponding to the longitude and latitude grid point at the historical moment based on the number of conditions. In one embodiment, if the longitude and latitude grid point does not satisfy the first condition, the second condition, and the third condition, the historical sea ice identification result is assigned a value of 0; if the longitude and latitude grid point satisfies any one of the first condition, the second condition, and the third condition, the historical sea ice identification result is assigned a value of 1; if the longitude and latitude grid point satisfies any two of the first condition, the second condition, and the third condition, the historical sea ice identification result is assigned a value of 2; if the longitude and latitude grid point satisfies the first condition, the second condition, and the third condition, the historical sea ice identification result is assigned a value of 3.

[0072] For example, taking the i-th latitude and longitude grid point of the image at time t1 as an example, if one of the above conditions is met, the value is assigned to 1; if any two of the above conditions are met, the value is assigned to 2; if all of the above conditions are met, the value is assigned to 3; if none of the above conditions are met, the value is assigned to 0. The expression is as follows:

[0073]

[0074] in, That is, the historical sea ice identification result corresponding to the i-th longitude and latitude grid point at time t1, False(S1,S2,S3) means that the longitude and latitude grid point does not meet the first, second and third conditions, True(S1)&or True(S2)or True(S3) means that the longitude and latitude grid point meets any one of the first, second and third conditions, True(S1,S2)&or True(S2,S3)or True(S1,S3) means that the longitude and latitude grid point meets any two of the first, second and third conditions, True(S1,S2,S3) means that the longitude and latitude grid point meets the first, second and third conditions.

[0075] 5) The sea ice confidence is divided into sliding groups based on the identification results within the last 24 hours.

[0076] First, determine the historical period that matches the current moment. The process is as follows: take this moment as the end moment of the historical period, and determine the start moment of the historical period based on the size and end moment of the preset time sliding window; wherein the time interval between the start moment and the end moment is equal to the size of the time sliding window.

[0077] Then, the target sea ice identification result corresponding to the longitude and latitude grid point at that moment is determined. The process is as follows: the historical sea ice identification results corresponding to the longitude and latitude grid points in the historical period are accumulated to obtain the cumulative sea ice identification results corresponding to the longitude and latitude grid points; according to the threshold interval of the cumulative sea ice identification results, the target sea ice identification result corresponding to the longitude and latitude grid point at that moment is determined.

[0078] For example, the sea ice of the 24 hours before the current moment (including the current moment) is identified and assigned a value (0, 1, 2, 3). For the sea ice identification at the current moment, the idea of ​​sliding confidence division based on the sea ice identification of the past 24 hours is used as the final identification result:

[0079] The i-th latitude and longitude grid point is between t1 and t 24 Take the moment as an example (denoted as ), represents the historical sea ice identification result corresponding to the i-th latitude and longitude grid point at time j, t 24That is the present moment.

[0080] The latitude and longitude grid point at the current time t 24 The corresponding cumulative sea ice identification result is recorded as The historical sea ice identification results corresponding to the latitude and longitude grid points within 24 hours are accumulated, and the expression is as follows: in, Represents the historical sea ice identification result corresponding to the i-th longitude and latitude grid point at time j.

[0081] The i-th latitude and longitude grid point at the current time t 24 The corresponding target sea ice identification result is recorded as M i , the expression is as follows:

[0082]

[0083] For the current moment M i , if the value of the i-th longitude and latitude grid point is 0 at all times within 24 hours, that is Then M i =0; if Then M i =1; if Then M1=2; if Then M1 = 3. The possibility of sea ice existence in the study area is quantified as 0, 1, 2, 3.

[0084] In summary, the embodiments of the present invention propose a sea ice monitoring algorithm with high temporal resolution suitable for FY4B. On the basis of moment-by-moment monitoring, real-time monitoring of sea ice confidence is proposed to quantify the possibility of sea ice existence in the monitoring area, thereby significantly improving the timeliness of sea ice monitoring.

[0085] Based on the above embodiments, the present invention provides a 24-hour sea ice monitoring device based on the FY4B geostationary satellite. Figure 3 The structure diagram of a 24-hour sea ice monitoring device based on the FY4B geostationary satellite is shown in FIG. 1 . The device mainly includes the following parts:

[0086] The preprocessing module 302 is used to obtain the payload data collected by the FY4B geostationary satellite for the study area at multiple historical moments, and preprocess the payload data to obtain the remote sensing physical quantities corresponding to the latitude and longitude grid points in the ocean and non-cloud area in the study area at the historical moments;

[0087] The historical sea ice identification module 304 is used to perform multi-condition sea ice identification on the longitude and latitude grid points based on remote sensing physical quantities to determine the historical sea ice identification results corresponding to the longitude and latitude grid points at historical moments;

[0088] The current sea ice identification module 306 is used to construct a historical time period matching any moment in the whole day to be predicted based on multiple historical moments, and determine the target sea ice identification result corresponding to the longitude and latitude grid point at the moment according to the historical sea ice identification result corresponding to the longitude and latitude grid point in the historical time period.

[0089] The all-day sea ice monitoring device based on the FY4B geostationary satellite provided in the embodiment of the present invention utilizes the payload data collected by the FY4B geostationary satellite to extract the ocean and non-cloud area in the study area and determine the remote sensing physical quantities corresponding to the longitude and latitude grid points in the area at historical moments, adopts multi-condition sea ice identification, and determines the historical sea ice identification results corresponding to the longitude and latitude grid points at historical moments based on the remote sensing physical quantities, and realizes all-day and moment-by-moment sea ice monitoring according to the historical sea ice identification results. The embodiment of the present invention proposes a sea ice monitoring algorithm suitable for FY4B high time resolution, and on the basis of moment-by-moment monitoring, proposes real-time monitoring of sea ice confidence, quantifies the possibility of sea ice existence in the monitoring area, and significantly improves the timeliness of sea ice monitoring.

[0090] In one implementation, the preprocessing module 302 is specifically configured to:

[0091] Determine the load data corresponding to the latitude and longitude grid points in the study area at historical times;

[0092] Perform radiometric calibration on the load data corresponding to the longitude and latitude grid points to determine the remote sensing physical quantities corresponding to the longitude and latitude grid points at historical moments;

[0093] The load data after radiation calibration are processed by sea-land separation and cloud removal to extract the ocean and non-cloud area from the study area, and obtain the remote sensing physical quantities corresponding to the longitude and latitude grid points in the ocean and non-cloud area at historical moments.

[0094] In one embodiment, the remote sensing physical quantity includes reflectivity and brightness temperature data of multiple channels; the historical sea ice identification module 304 is specifically used to:

[0095] Based on the reflectivity of multiple channels corresponding to the longitude and latitude grid points, the normalized differential ice and snow index corresponding to the longitude and latitude grid points at the historical moment is determined to determine whether the longitude and latitude grid points meet the first condition;

[0096] and, based on the reflectivity of the single channel corresponding to the longitude and latitude grid point, determining whether the longitude and latitude grid point satisfies the second condition;

[0097] and, based on the brightness temperature data of multiple channels corresponding to the longitude and latitude grid points, determining the sea ice surface temperature corresponding to the longitude and latitude grid points at the historical moment, so as to judge whether the longitude and latitude grid points meet the third condition;

[0098] According to the judgment results of the first condition, the second condition and the third condition, the historical sea ice identification results corresponding to the latitude and longitude grid points at the historical moment are determined.

[0099] In one embodiment, the historical sea ice identification module 304 is specifically used to:

[0100] According to the judgment results of the first condition, the second condition, and the third condition, the number of conditions satisfied by the longitude and latitude grid points is determined, and based on the number of conditions, a numerical value is assigned to the historical sea ice identification result corresponding to the longitude and latitude grid points at the historical moment;

[0101] Among them, the historical sea ice identification results are used to numerically represent the probability that the category of the longitude and latitude grid points belongs to sea ice. The numerical value is positively correlated with the conditional quantity, and the probability that the category of the longitude and latitude grid points belongs to sea ice is positively correlated with the numerical value.

[0102] In one implementation, the current sea ice identification module 306 is specifically used to:

[0103] This moment is used as the end moment of the historical period, and based on the preset size and end moment of the time sliding window, the start moment of the historical period is determined; wherein the time interval between the start moment and the end moment is equal to the size of the time sliding window.

[0104] In one implementation, the current sea ice identification module 306 is specifically used to:

[0105] The historical sea ice identification results corresponding to the longitude and latitude grid points in the historical period are accumulated to obtain the accumulated sea ice identification results corresponding to the longitude and latitude grid points;

[0106] According to the threshold interval of the accumulated sea ice identification results, the target sea ice identification results corresponding to the longitude and latitude grid points at that moment are determined.

[0107] The device provided in the embodiment of the present invention has the same implementation principle and technical effects as those of the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference may be made to the corresponding contents in the aforementioned method embodiment.

[0108] An embodiment of the present invention provides an electronic device. Specifically, the electronic device includes a processor and a storage device. The storage device stores a computer program, and when the computer program is executed by the processor, it executes the method described in any one of the above-mentioned implementation methods.

[0109] Figure 4A structural diagram of an electronic device provided in an embodiment of the present invention, the electronic device 100 includes: a processor 40, a memory 41, a bus 42 and a communication interface 43, wherein the processor 40, the communication interface 43 and the memory 41 are connected via the bus 42; the processor 40 is used to execute an executable module stored in the memory 41, such as a computer program.

[0110] The memory 41 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 43 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0111] The bus 42 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0112] Among them, the memory 41 is used to store programs, and the processor 40 executes the program after receiving the execution instruction. The method executed by the device for flow process definition disclosed in any embodiment of the above-mentioned embodiment of the present invention can be applied to the processor 40 or implemented by the processor 40.

[0113] The processor 40 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 40. The above processor 40 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in the embodiments of the present invention can be directly embodied as a hardware decoding processor to execute, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 41, and the processor 40 reads the information in the memory 41 and completes the steps of the above method in combination with its hardware.

[0114] The computer program product of the readable storage medium provided in the embodiment of the present invention includes a computer-readable storage medium storing program code, and the instructions included in the program code can be used to execute the methods described in the previous method embodiments. The specific implementation can be referred to the previous method embodiments, which will not be repeated here.

[0115] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0116] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for all-day sea ice monitoring based on FY4B geostationary satellite, characterized in that: include: Obtain the payload data collected by the FY4B geostationary satellite for the study area at multiple historical moments, and preprocess the payload data to obtain the remote sensing physical quantities corresponding to the latitude and longitude grid points in the ocean and non-cloud area in the study area at the historical moments; Based on the remote sensing physical quantity, multi-condition sea ice identification is performed on the latitude and longitude grid points to determine the historical sea ice identification results corresponding to the latitude and longitude grid points at the historical moment; For any moment in the whole day to be predicted, a historical time period matching the moment is constructed based on multiple historical moments, and according to the historical sea ice identification results corresponding to the latitude and longitude grid points in the historical time period, the target sea ice identification results corresponding to the latitude and longitude grid points at the moment are determined.

2. The all-day sea ice monitoring method based on the FY4B geostationary satellite according to claim 1 is characterized in that: The load data is preprocessed to obtain remote sensing physical quantities corresponding to the latitude and longitude grid points in the ocean and non-cloud area in the study area at the historical moment, including: Determine the load data corresponding to the latitude and longitude grid points in the study area at the historical moment; Performing radiation calibration processing on the load data corresponding to the latitude and longitude grid points to determine the remote sensing physical quantity corresponding to the latitude and longitude grid points at the historical moment; The load data after radiation calibration is subjected to land and sea separation processing and cloud removal processing to extract ocean and non-cloud areas from the study area, and obtain remote sensing physical quantities corresponding to the latitude and longitude grid points in the ocean and non-cloud areas at the historical moment.

3. The all-day sea ice monitoring method based on the FY4B geostationary satellite according to claim 1 is characterized in that: The remote sensing physical quantity includes reflectivity and brightness temperature data of multiple channels; based on the remote sensing physical quantity, multi-condition sea ice identification is performed on the latitude and longitude grid points to determine the historical sea ice identification results corresponding to the latitude and longitude grid points at the historical moment, including: Based on the reflectivity of the multiple channels corresponding to the latitude and longitude grid points, determine the normalized differential ice and snow index corresponding to the latitude and longitude grid points at the historical moment, so as to determine whether the latitude and longitude grid points meet the first condition; and, based on the reflectivity of the single channel corresponding to the latitude and longitude grid point, determining whether the latitude and longitude grid point satisfies a second condition; and, based on the brightness temperature data of the multiple channels corresponding to the latitude and longitude grid points, determining the sea ice surface temperature corresponding to the latitude and longitude grid points at the historical moment, so as to determine whether the latitude and longitude grid points meet the third condition; According to the judgment results of the first condition, the second condition and the third condition, the historical sea ice identification result corresponding to the longitude and latitude grid point at the historical moment is determined.

4. The all-day sea ice monitoring method based on the FY4B geostationary satellite according to claim 3 is characterized in that: Determine the historical sea ice identification result corresponding to the latitude and longitude grid point at the historical moment according to the judgment results of the first condition, the second condition, and the third condition; Determine the number of conditions satisfied by the latitude and longitude grid point according to the judgment results of the first condition, the second condition, and the third condition, and assign a numerical value to the historical sea ice identification result corresponding to the latitude and longitude grid point at the historical moment based on the number of conditions; Among them, the historical sea ice identification results are used to characterize the probability that the category of the longitude and latitude grid points belongs to sea ice through the numerical value, the numerical value is positively correlated with the conditional quantity, and the probability that the category of the longitude and latitude grid points belongs to sea ice is positively correlated with the numerical value.

5. The all-day sea ice monitoring method based on the FY4B geostationary satellite according to claim 1 is characterized in that: Constructing a historical period matching the moment based on the multiple historical moments, including: This moment is used as the end moment of the historical period, and based on the size of a preset time sliding window and the end moment, the start moment of the historical period is determined; wherein the time interval between the start moment and the end moment is equal to the size of the time sliding window.

6. The all-day sea ice monitoring method based on the FY4B geostationary satellite according to claim 5 is characterized in that: Determining the target sea ice identification result corresponding to the latitude and longitude grid point at the moment according to the historical sea ice identification result corresponding to the latitude and longitude grid point in the historical period includes: Accumulating the historical sea ice identification results corresponding to the latitude and longitude grid points in the historical period to obtain the accumulated sea ice identification results corresponding to the latitude and longitude grid points; According to the threshold interval of the accumulated sea ice identification result, the target sea ice identification result corresponding to the longitude and latitude grid point at the moment is determined.

7. A 24-hour sea ice monitoring device based on FY4B geostationary satellite, characterized in that: include: A preprocessing module is used to obtain the payload data collected by the FY4B geostationary satellite for the study area at multiple historical moments, and preprocess the payload data to obtain the remote sensing physical quantities corresponding to the latitude and longitude grid points in the ocean and non-cloud area in the study area at the historical moments; A historical sea ice identification module, used to perform multi-condition sea ice identification on the latitude and longitude grid points based on the remote sensing physical quantity, so as to determine the historical sea ice identification result corresponding to the latitude and longitude grid points at the historical moment; The current sea ice identification module is used to construct a historical time period matching any moment in the whole day to be predicted based on multiple historical moments, and determine the target sea ice identification result corresponding to the latitude and longitude grid point at the moment according to the historical sea ice identification result corresponding to the latitude and longitude grid point in the historical time period.

8. The all-day sea ice monitoring device based on the FY4B geostationary satellite according to claim 7 is characterized in that: The preprocessing module is specifically used for: Determine the load data corresponding to the latitude and longitude grid points in the study area at the historical moment; Performing radiation calibration processing on the load data corresponding to the latitude and longitude grid points to determine the remote sensing physical quantity corresponding to the latitude and longitude grid points at the historical moment; The load data after radiation calibration is subjected to land and sea separation processing and cloud removal processing to extract ocean and non-cloud areas from the study area, and obtain remote sensing physical quantities corresponding to the latitude and longitude grid points in the ocean and non-cloud areas at the historical moment.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the method according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the method according to any one of claims 1 to 6.

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