A matching verification method for spaceborne lidar based on spatial variability and flow field

Abstract

The present invention discloses a spaceborne lidar matching verification method based on spatial variability and flow field, comprising: using a multi-scale grid to calculate the spatial variability of water color remote sensing data for each month; traversing the grid points to search for the maximum grid size with a spatial variability less than a threshold; calculating a spatial matching window and setting a time matching window based on the maximum grid size; searching for spaceborne lidar data using the maximum spatial matching window and the set time matching window based on the measurement time and position of the in-situ data, and correcting the position of the in-situ data hourly according to the corresponding ocean current speed; searching for the corresponding spatial matching window based on the corrected position, and matching it with the spaceborne lidar data; and performing quality control on the matched spaceborne data to form a spaceborne lidar-in-situ data matching pair. The present invention can achieve matching between spaceborne data and in-situ data, and improve the rationality of spatial matching window selection.

Description

Technical Field

[0001] The present invention belongs to the technical field of laser radar, and in particular relates to a space-borne laser radar matching and calibration method based on spatial variability and flow field. Background Art

[0002] Spaceborne lidar is an active remote sensing instrument with advantages such as continuous day and night observation and global-scale detection, playing a vital role in ocean observation. It can invert ocean optical parameters such as particle backscatter coefficient and attenuation coefficient, and further estimate parameters such as chlorophyll concentration, phytoplankton biomass, and particulate organic carbon content, making it crucial for studying marine ecosystems. In situ data, typically precise data obtained through field measurements, is highly accurate and reliable, and can be used to validate remote sensing data. However, its spatial and temporal distribution is uneven and its coverage is limited.

[0003] Chinese Patent Publication No. CN103020478A discloses a method for verifying the authenticity of ocean color remote sensing products. This method uses multiple spatial matching window sizes to calculate spatial variability and rank the matching results, but does not address how to select the optimal spatial matching window based on spatial variability. Chinese Patent Publication No. CN112699204A discloses a method and apparatus for determining a spatial matching window. This method uses a preset database to calculate a relative error sequence and determine the spatial matching window, but does not address the calculation of spatial variability or data matching methods. Therefore, the methods described in these inventions cannot be directly applied to matching spaceborne lidar with in-situ data.

[0004] Existing methods for matching spaceborne lidar with in-situ data typically employ a fixed temporal and spatial matching window. Satellite data falling within the matching window is collected, centered around the in-situ measurement. The average of the in-situ and satellite data is then used as a matching pair to complete the data matching. However, data from different regions exhibit significant spatial variability, and a fixed matching window cannot be applied to all regions. This can result in too few matching pairs or significant errors. Furthermore, due to the time lag between spaceborne lidar and in-situ data, ocean currents can cause measurement points to shift, creating a certain degree of uncertainty. Summary of the Invention

[0005] The present invention provides a spaceborne lidar matching verification method based on spatial variability and flow field. The method uses water color remote sensing data to evaluate the spatial variability of ocean parameters, thereby obtaining the size of the spatial matching window. The latitude and longitude of the in-situ data are corrected according to the ocean current velocity to achieve matching between the spaceborne lidar and the in-situ data.

[0006] A spaceborne lidar matching verification method based on spatial variability and flow field includes the following steps:

[0007] S1: Use multiple grid sizes to calculate the spatial variability of water color remote sensing data for each month;

[0008] S2: Traverse each grid point and search for the maximum grid size corresponding to a spatial variation that is less than the set threshold;

[0009] S3: Calculate the spatial matching window matrix based on the maximum grid size and set the temporal matching window to a fixed value;

[0010] S4: Based on the measurement time and longitude and latitude of the in-situ data, the largest spatial matching window and time matching window are used to search the spaceborne lidar data to find the corresponding hourly ocean current speed, and gradually correct the position of the in-situ data;

[0011] S5: Find the corresponding spatial matching window based on the measurement time and longitude and latitude of the corrected in-situ data, and match the spaceborne lidar data with the in-situ data based on the spatial matching window;

[0012] S6: Perform quality control on the matched spaceborne lidar data to form a spaceborne lidar-in-situ data matching pair.

[0013] The specific process of step S1 is:

[0014] Get the monthly average water color remote sensing grid data, and for each grid point (x, y), select the nearby n i ×n i Grid data, n i The values ​​of are recorded from small to large as n1, n2, ..., n q ; Calculate the spatial variability of each grid point:

[0015]

[0016] Where m is month and std is n i ×n i The standard deviation of all data in the grid, mean is n i ×n i The average value of all data in the grid.

[0017] The specific process of step S2 is:

[0018] For month m0 and grid point (x0, y0), the corresponding spatial variability is CV(x0, y0, m0, n i ), set a fixed threshold, find the value that satisfies CV(x0,y0,m0,n i ) is smaller than the maximum subscript i of threshold, denoted as i0, and the corresponding maximum grid size is According to the above rules, all grid points of each month are traversed to obtain the maximum grid size matrix N(x, y, m).

[0019] The specific process of step S3 is as follows:

[0020] Based on the obtained maximum grid size matrix N(x,y,m), calculate the spatial matching window of all grid points in each month:

[0021]

[0022] Where d is the spatial resolution of water color remote sensing grid data;

[0023] Set the time matching window size to t.

[0024] The specific process of step S4 is as follows:

[0025] The measurement time t for obtaining in-situ data insitu and longitude and latitude (lat insitu ,lon insitu );

[0026] Set the spatial matching window s and use the Haversine algorithm to calculate the longitude and latitude (lat insitu ,lon insitu ) and the surface spot position of the spaceborne lidar (lat lidar ,lon lidar ) and search for spaceborne lidar data that satisfies D0<s;

[0027] According to the time matching window t and the spaceborne lidar measurement time t lidar , find the data that meets the spatial matching window s and satisfies |t insitu -t lidar |<t spaceborne lidar data;

[0028] According to the measurement time t of the in-situ data insitu and longitude and latitude (lat insitu ,lon insitu ), find the corresponding ocean current speed

[0029] If t insitu <t lidar , set timestamp t0,t insitu ,t1,t2,…,t p ,t lidar ,t p+1 , where t1, t2, …, t p Between t insitu and t lidarAll consecutive integer hours between t0 and t insitu The maximum whole hour moment, t p+1 is greater than t lidar The minimum whole hour of the moment; t u-1 Time to t u Time, longitude and latitude (lat u ,lon u )(u=1,2…,p) average ocean current velocity;

[0030] If t insitu >t lidar , set timestamp t0,t lidar ,t1,t2,…,t p ,t insitu ,t p+1 , where t1, t2, …, t p Between t insitu and t lidar All consecutive integer hours between t0 and t lidar The maximum whole hour moment, t p+1 is greater than t insitu The minimum whole hour of the moment; t u-1 Time to t u Time, longitude and latitude (lat u ,lon u ) at the mean ocean current velocity;

[0031] If t insitu =t lidar , directly output the latitude and longitude of the original data;

[0032] According to the ocean current speed and time difference, the displacement distance is calculated, and then according to the direction of the ocean current speed, the latitude and longitude of the original data are updated as (lat u ,lon u ), where: (lat1,lon1)=(lat insitu ,lon insitu );

[0033] Finally, the output of the original data latitude and longitude is: (lat correct ,lon correct )=(lat p ,lon p );

[0034] If the k tracks of the lidar are within the time matching window, according to the average measurement time t of each track, lidarb and longitude and latitude (lat lidarb ,lonlidarb ), correct k orbits respectively to obtain multiple corrected in-situ data points: (lat correctb ,lon correctb )(b=1,2,…,k).

[0035] The specific process of step S5 is as follows:

[0036] According to the measurement time t of the corrected in-situ data insitu and longitude and latitude (lat correctb ,lon correctb ) in the moment

[0037] Find the spatial matching window r of the corresponding month and the nearest grid point in the matrix R(x,y,m) b (b=1,2,…,k);

[0038] Further, according to the spatial matching window r b , calculated using the Haversine algorithm (lat correctb ,lon correctb ) and the surface spot position of the spaceborne lidar (lat lidarb ,lon lidarb ) between b (b=1,2,…,k), find the b <r b Space-borne lidar data.

[0039] The specific process of step S6 is as follows:

[0040] Based on the quality control flags, spaceborne lidar data with signal saturation, cloud, or land conditions are excluded; if more than 50% of the data is excluded, the matching of this data is skipped;

[0041] If the amount of data remaining after screening according to quality control markers is not less than 50%, calculate the mean a and standard deviation σ of the remaining data, and retain the data between a-1.5σ and a+1.5σ;

[0042] Calculate the average value a of the remaining spaceborne lidar data again f and standard deviation σ f , calculate the coefficient of variation:

[0043]

[0044] If CV f >0.15, skip this data matching; if CV f <0.15, the in-situ data measurement value a insitu Compared with the average value of space-borne lidar data a fAs a pair of matching data, complete this matching;

[0045] If the satellite-borne lidar data of multiple orbits all meet CV f <0.15, then calculate the average value of their total data a f , the in-situ data measurement value a insitu Compared with the average value of space-borne lidar data a f As a pair of matching data, this matching is completed.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] The present invention provides an improved spaceborne lidar matching verification method based on spatial variability and flow field. Different spatial matching window sizes are selected based on spatial variability, which solves the problem that most existing methods use a fixed matching window, resulting in inconsistent matching results in different areas. It also takes into account the movement of measurement points caused by ocean currents. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a flow chart of a spaceborne lidar matching and calibration method based on spatial variability and flow field according to the present invention.

[0049] Figure 2 This is a statistical distribution diagram of the spatial variability of MODIS data calculated using multiple grid sizes in an example of the present invention. DETAILED DESCRIPTION

[0050] The present invention will be described in further detail below with reference to the accompanying drawings and examples. It should be noted that the following examples are intended to facilitate understanding of the present invention and do not have any limiting effect on the present invention.

[0051] The present invention takes the matching between the particle backscattering coefficients measured by the space-borne lidar CALIOP and the in-situ float Argo as an example. Figure 1 , specifically including the following steps:

[0052] Step 1: Use multiple grid sizes to calculate the spatial variability of water color remote sensing data for each month.

[0053] In this example, the monthly average MODIS particle backscattering coefficient grid data is used. For each grid point (x, y), the nearby n i ×n i Grid data, n i The values ​​of are n1=3, n2=5, n3=7, n4=11, n5=21 and n6=41. Calculate the spatial variability of each grid point:

[0054]

[0055] Where m is month and std is n i ×n i The standard deviation of all data in the grid, mean is n i ×n i The average value of all data in the grid. Figure 2 As shown in the figure, the spatial variability calculated using MODIS particle backscatter coefficient data from March to May 2019 is statistically analyzed. The spatial variability increases with the increase of grid size.

[0056] Step 2: traverse each grid point and search for the maximum grid size corresponding to a spatial variability that is less than a set threshold.

[0057] For month m0 and grid point (x0, y0), the corresponding spatial variability is CV(x0, y0, m0, n i ). Set the fixed threshold to threshold=0.02 and find the value that satisfies CV(x0,y0,m0,n i ) is smaller than the maximum subscript i of threshold, denoted as i0, and the corresponding maximum grid size is According to the above rules, all grid points of each month are traversed to obtain the maximum grid size matrix N(x, y, m).

[0058] Step 3: Calculate the spatial matching window matrix based on the maximum grid size and set the temporal matching window to a fixed value.

[0059] Based on the obtained maximum grid size matrix N(x,y,m), calculate the spatial matching window of all grid points in each month:

[0060]

[0061] Where d is the spatial resolution of the water color remote sensing grid data. In this example, the spatial resolution of the MODIS data is 4 kilometers.

[0062] Set the time matching window size to a fixed value t, which can be 3 hours, 12 hours, 24 hours, or 48 hours.

[0063] Step 4: Based on the measurement time and longitude and latitude of the in-situ data, use the largest spatial matching window and time matching window to search the spaceborne lidar data, find the corresponding hourly ocean current speed, and gradually correct the position of the in-situ data.

[0064] The measurement time t for obtaining Argo in-situ data insitu and longitude and latitude (lat insitu ,lon insitu ).

[0065] Set the spatial matching window s and use the Haversine algorithm to calculate the longitude and latitude (lat insitu ,lon insitu ) and the surface spot position of the spaceborne lidar (lat lidar ,lon lidar ) and search for spaceborne lidar data that satisfies D0<s;

[0066] According to the time matching window t and the spaceborne lidar measurement time t lidar , find the data that meets the spatial matching window s and satisfies |t insitu -t lidar |<t spaceborne lidar data;

[0067] According to the measurement time t of the in-situ data insitu and longitude and latitude (lat insitu ,lon insitu ), find the corresponding ocean current speed

[0068] If t insitu <t lidar , set timestamp t0,t insitu ,t1,t2,…,t p ,t lidar ,t p+1 , where t1, t2, …, t p Between t insitu and t lidar All consecutive integer hours between t0 and t insitu The maximum whole hour moment, t p+1 is greater than t lidar The minimum whole hour of the moment; t u-1 Time to t u Time, longitude and latitude (lat u ,lon u )(u=1,2…,p) average ocean current velocity;

[0069] If t insitu >t lidar , set timestamp t0,t lidar ,t1,t2,…,t p ,t insitu ,t p+1 , where t1, t2, …, t p Between t insitu and t lidar All consecutive integer hours between t0 and t lidar The maximum whole hour moment, tp+1 is greater than t insitu The minimum whole hour of the moment; t u-1 Time to t u Time, longitude and latitude (lat u ,lon u ) at the mean ocean current velocity;

[0070] If t insitu =t lidar , directly output the latitude and longitude of the original data;

[0071] According to the ocean current speed and time difference, the displacement distance is calculated, and then according to the direction of the ocean current speed, the latitude and longitude of the original data are updated as (lat u ,lon u ), where: (lat1,lon1)=(lat insitu ,lon insitu );

[0072] Finally, the output of the original data latitude and longitude is: (lat correct ,lon correct )=(lat p ,lon p );

[0073] If the k tracks of the lidar are within the time matching window, according to the average measurement time t of each track, lidarb and longitude and latitude (lat lidarb ,lon lidarb ), correct k orbits respectively to obtain multiple corrected in-situ data points: (lat correctb ,lon correctb )(b=1,2,…,k).

[0074] Step 5: Find the corresponding spatial matching window based on the measurement time and longitude and latitude of the corrected in-situ data, and match the spaceborne lidar data with the in-situ data based on the spatial matching window.

[0075] According to the measurement time t of the corrected in-situ data insitu and longitude and latitude (lat correctb ,lon correctb ) in the moment

[0076] Find the spatial matching window r of the corresponding month and the nearest grid point in the matrix R(x,y,m) b (b=1,2,…,k);

[0077] Further, according to the spatial matching window r b , calculated using the Haversine algorithm (latcorrectb ,lon correctb ) and the surface spot position of the spaceborne lidar (lat lidarb ,lon lidarb ) between b (b=1,2,…,k), find the b <r b Space-borne lidar data.

[0078] Step 6: Perform quality control on the matched spaceborne lidar data to form a spaceborne lidar-in-situ data matching pair.

[0079] Based on the quality control flags, spaceborne lidar CALIOP data with signal saturation, cloud, land, etc. are excluded. If more than 50% of the data is excluded, the matching of this data is skipped.

[0080] If the amount of data remaining after screening according to quality control markers is not less than 50%, calculate the mean a and standard deviation σ of the remaining data, and retain the data between a-1.5σ and a+1.5σ.

[0081] Calculate the average value a of the remaining spaceborne lidar data again f and standard deviation σ f , calculate the coefficient of variation:

[0082]

[0083] If CV f >0.15, skip this data matching; if CV f <0.15, the in-situ data measurement value a insitu Compared with the average value of space-borne lidar data a f As a pair of matching data, this matching is completed.

[0084] If the satellite-borne lidar data of multiple orbits all meet CV f <0.15, then calculate the average value of their total data a f , the in-situ data measurement value a insitu Compared with the average value of space-borne lidar data a f As a pair of matching data, this matching is completed.

[0085] The embodiments described above provide a detailed description of the technical solutions and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, supplements and equivalent substitutions made within the scope of the principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A spaceborne lidar matching verification method based on spatial variability and flow field, characterized in that: The following steps are involved: S1: Use multiple grid sizes to calculate the spatial variability of water color remote sensing data for each month; S2: Traverse each grid point and search for the maximum grid size corresponding to a spatial variation that is less than the set threshold; S3: Calculate the spatial matching window matrix based on the maximum grid size and set the temporal matching window to a fixed value; S4: Based on the measurement time and longitude and latitude of the in-situ data, the largest spatial matching window and time matching window are used to search the spaceborne lidar data to find the corresponding hourly ocean current speed, and gradually correct the position of the in-situ data; S5: Find the corresponding spatial matching window according to the measurement time and longitude and latitude of the corrected in-situ data, and match the spaceborne lidar data with the in-situ data according to the spatial matching window; S6: Perform quality control on the matched spaceborne lidar data to form a spaceborne lidar-in-situ data matching pair.

2. The spaceborne lidar matching verification method based on spatial variability and flow field according to claim 1 is characterized in that: The specific process of step S1 is: Get the monthly average water color remote sensing grid data, and for each grid point (x, y), select the nearby n i ×n i Grid data, n i The values ​​of are recorded from small to large as n1, n2, ..., n q ; Calculate the spatial variability of each grid point: Where m is month and std is n i ×n i The standard deviation of all data in the grid, mean is n i ×n i The average value of all data in the grid.

3. The spaceborne lidar matching verification method based on spatial variability and flow field according to claim 2 is characterized in that: The specific process of step S2 is as follows: For month m0 and grid point (x0, y0), the corresponding spatial variability is CV(x0, y0, m0, n i ), set a fixed threshold, find the value that satisfies CV(x0,y0,m0,n i ) is smaller than the maximum subscript i of threshold, denoted as i0, and the corresponding maximum grid size is According to the above rules, all grid points of each month are traversed to obtain the maximum grid size matrix N(x, y, m).

4. The spaceborne lidar matching verification method based on spatial variability and flow field according to claim 3 is characterized in that: The specific process of step S3 is as follows: Based on the obtained maximum grid size matrix N(x,y,m), calculate the spatial matching window of all grid points in each month: Where d is the spatial resolution of water color remote sensing grid data; Set the time matching window size to t.

5. The spaceborne lidar matching verification method based on spatial variability and flow field according to claim 1 is characterized in that: The specific process of step S4 is as follows: The measurement time t for obtaining in-situ data insitu and longitude and latitude (lat insitu ,lon insitu ); Set the spatial matching window s and use the Haversine algorithm to calculate the longitude and latitude (lat insitu ,lon insitu ) and the surface spot position of the spaceborne lidar (lat lidar ,lon lidar ) and search for spaceborne lidar data that satisfies D0<s; According to the time matching window t and the spaceborne lidar measurement time t lidar , find the data that meets the spatial matching window s and satisfies |t insitu -t lidar |<t spaceborne lidar data; According to the measurement time t of the in-situ data insitu and longitude and latitude (lat insitu ,lon insitu ), find the corresponding ocean current speed If t insitu <t lidar , set timestamp t0,t insitu ,t1,t2,…,t p ,t lidar ,t p+1 , where t1, t2, …, t p Between t insitu and t lidar All consecutive integer hours between t0 and t insitu The maximum whole hour moment, t p+1 is greater than t lidar The minimum whole hour of the moment; t u-1 Time to t u Time, longitude and latitude (lat u ,lon u ) is the average ocean current velocity at , u=1,2…,p; If t insitu >t lidar , set timestamp t0,t lidar ,t1,t2,…,t p ,t insitu ,t p+1 , where t1, t2, …, t p Between t insitu and t lidar All consecutive integer hours between t0 and t lidar The maximum whole hour moment, t p+1 is greater than t insitu The minimum whole hour of the moment; t u-1 Time to t u Time, longitude and latitude (lat u ,lon u ) at the mean ocean current velocity; If t insitu =t lidar , directly output the latitude and longitude of the original data; According to the ocean current speed and time difference, the displacement distance is calculated, and then according to the direction of the ocean current speed, the latitude and longitude of the original data are updated as (lat u ,lon u ), where: (lat1,lon1)=(lat insitu ,lon insitu ); Finally, the output of the original data latitude and longitude is: (lat correct ,lon correct )=(lat p ,lon p ); If the k tracks of the lidar are within the time matching window, according to the average measurement time t of each track, lidarb and longitude and latitude (lat lidarb ,lon lidarb ), correct k orbits respectively to obtain multiple corrected in-situ data points: (lat correctb ,lon correctb )b=1,2,…,k。 6. The spaceborne lidar matching verification method based on spatial variability and flow field according to claim 1, characterized in that: The specific process of step S5 is as follows: According to the measurement time t of the corrected in-situ data insitu and longitude and latitude (lat correctb ,lon correctb ) Find the spatial matching window r of the corresponding month and the nearest grid point in the matrix R(x,y,m) b , b=1,2,…,k; Further, according to the spatial matching window r b , calculated using the Haversine algorithm (lat correctb ,lon correctb ) and the surface spot position of the spaceborne lidar (lat lidarb ,lon lidarb ) between b , find the b <r b Space-borne lidar data.

7. The spaceborne laser radar matching verification method based on spatial variability and flow field according to claim 1 is characterized in that: The specific process of step S6 is as follows: Based on the quality control flags, spaceborne lidar data with signal saturation, cloud, or land conditions are excluded; if more than 50% of the data is excluded, the matching of this data is skipped; If the amount of data remaining after screening according to quality control markers is not less than 50%, calculate the mean a and standard deviation σ of the remaining data, and retain the data between a-1.5σ and a+1.5σ; Calculate the average value a of the remaining spaceborne lidar data again f and standard deviation σ f , calculate the coefficient of variation: If CV f >0.15, skip this data matching; if CV f <0.15, the in-situ data measurement value a insitu Compared with the average value of space-borne lidar data a f As a pair of matching data, complete this matching; If the satellite-borne lidar data of multiple orbits all meet CV f <0.15, then calculate the average value of their total data a f , the in-situ data measurement value a insitu Compared with the average value of space-borne lidar data a f As a pair of matching data, this matching is completed.

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