CN113252183A - Processing method of 89GHz data for Antarctic ice cover surface snow melting detection - Google Patents
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Abstract
The invention provides a processing method of 89GHz data for the surface snow melting detection of an Antarctic ice cover, which comprises the following steps: s1, screening out affected 89GHz data by utilizing the relation that the ratio of polarization ratios of 89GHz data and 36GHz data of AMSR-2 is stable under the sunny and cloudless weather conditions; s2, fitting the sample data selected from the five-year data to obtain a functional relation between 36GHz data and unaffected 89GHz data, and correcting the affected 89GHz data; and S3, applying the corrected 89GHz data to the antarctic ice cover surface snow melt detection based on a single-channel threshold method proposed by Mote 1993. By the method for processing the 89GHz data for the Antarctic ice cover surface snow melt detection, the 89GHz data which is high in spatial resolution and easy to be interfered by external environments such as clouds, water vapor and the like can be applied to the polar ice cover surface snow melt detection, so that the detection precision and the detection effect of the Antarctic ice cover surface snow melt are improved.
Description
Technical Field
The invention relates to the technical field, in particular to a processing method of 89GHz data for surface snow melting detection of an Antarctic ice cover.
Background
The Antarctic ice cover has important influence on global sea level rise and climate environment change, and the ice cover surface snow melting information with high spatial resolution has important significance for researching global climate change. At present, the space resolution of the ice cover surface snow melting detection result based on the low-frequency data of the microwave radiometer is low, and accurate freeze-thaw change cannot be obtained, under the condition, AMSR-E89 GHz data with the space resolution at least twice of that of other microwave frequency bands becomes a main data source of high-space-resolution microwave remote sensing, however, 89GHz data is easily influenced by atmospheric water vapor.
Therefore, the method for accurately and objectively acquiring the snow melting information on the surface of the Antarctic ice cover with high spatial resolution has great significance for analyzing global climate change. At present, a lot of work has been done on ice cover surface snow melt detection research based on low-frequency data of a microwave radiometer, but the low-frequency data has low spatial resolution, and an accurate snow melt detection result is difficult to obtain.
Disclosure of Invention
In view of the above, the present invention provides a processing method for 89GHz data used for antarctic ice cover surface snow melt detection, which can apply 89GHz data with high spatial resolution and easy to be interfered by external environments such as cloud and water vapor to the antarctic ice cover surface snow melt detection, thereby improving the detection accuracy and detection effect of the antarctic ice cover surface snow melt detection.
In order to solve the technical problems, the invention adopts the technical scheme that: a processing method of 89GHz data for the surface snow melting detection of an Antarctic ice cover,
s1, screening out affected 89GHz data by utilizing the relation that the ratio of polarization ratios of 89GHz data and 36GHz data of AMSR-2 is stable under the sunny and cloudless weather conditions;
s2, fitting the sample data selected from the five-year data to obtain a functional relation between 36GHz data and unaffected 89GHz data, and correcting the affected 89GHz data;
and S3, applying the corrected 89GHz data to the antarctic ice cover surface snow melt detection based on a single-channel threshold method proposed by Mote 1993.
Further, before ice cover surface snow melt detection is carried out by utilizing 89GHz data of AMSR-2, affected pixel points are screened by adopting a method provided by Iwamoto and the like, then the affected pixel points are corrected, and finally ice cover surface snow melt detection is carried out on the corrected 89GHz data by using a threshold value method.
Further, the screening process of the affected 89GHz data is as follows: selecting 89GHz and 36GHz sample data in clear weather, calculating the ratio of the polarization ratios of 89GHz and 36GHz data, drawing a polarization ratio scatter diagram of 89GHz and 36GHz data, fitting an 89GHz data screening model with large interference, if the data screening model is satisfied, indicating that the data is 89GHz data which is not interfered or has small interference, and if the data screening model is not satisfied, indicating that the data is 89GHz data with large interference;
the screening and correction of the affected data was performed using PR, with the polarization ratio equation (1) as follows:
wherein TBV、TBHVertical polarization and horizontal polarization brightness temperatures of AMSR-2 data respectively;
on the basis of a polarization ratio scatter diagram, the abscissa PR36Equally dividing into several intervals, and calculating the longitudinal coordinate PR in each interval89And then a least squares best fit curve is drawn using the mean minus twice the standard deviation in each interval, which is approximated by a quadratic equation, and can be expressed as (2):
PR89＝a(PR36)2+bPR36+c (2)
whereinPR89Polarization ratio, PR, for 89GHz data36For the polarization ratio of 36GHz data, a, b and c are respectively three parameters of the formula, and the values are-0.9095, 0.5404 and-0.0052.
Further, the correction process of the affected data is as follows: because the 36GHz data is stable and is weakly influenced by external environments such as cloud, water vapor and the like, the 36GHz data can be used for correcting the influence of the external environments such as the cloud, the water vapor and the like on the 89GHz data, and PR (physical resonance) under the sunny and cloudless weather condition is selected in large quantity89Data and PR36And (3) carrying out fitting calculation on the sample points of the data to find out a relational expression representing 89GHz data and 36GHz data, and finding out that the unitary quartic model can achieve the fitting effect through multiple experiments, wherein the fitted correction model is shown as a formula (3):
P'＝a1P4+a2P3+a3P2+a4P+a5 (3)
wherein P is the resampled PR36Data, P' is modified PR89Data, a1、a2、a3、a4And a55 parameters of a correction formula, wherein the parameters are 65257, -6351.5, 200.43, -1.6492 and 0.0068 respectively;
and (4) correcting the 89GHz data which is screened out and is influenced by external environments such as cloud, water vapor and the like by using the formula (3).
Further, the method for obtaining the freezing and thawing threshold value of the surface of the ice cover comprises the following steps: by using a single-channel threshold method proposed by Mote1993, selecting characteristic points from the corrected 89GHz data, then making a microwave brightness temperature time sequence of the characteristic points, and finally selecting the difference between the average value of the characteristic points in winter and the average value in summer as a freeze-thaw threshold of the ice cover surface, wherein the threshold selected in the application is 0.00526.
Compared with the prior art, the invention has the beneficial effects that: the processing method of 89GHz data for the Antarctic ice cover surface snow melt detection is basically feasible in the aspect of Antarctic ice cover surface snow melt detection, and because 89GHz data has higher spatial resolution, compared with ice cover surface snow melt detection algorithms of other frequency bands, the processing method has better details and more accurate results.
Drawings
FIG. 1 is a flow chart of the 89GHz affected data screening method of the present invention;
FIG. 2 is a scatter plot of 89GHz data PR and horizontally polarized freeze-thaw samples according to the present invention;
FIG. 3 shows PR of the present invention36And PR89A scatter diagram, wherein a solid curve is considered as a boundary influenced by external environments such as water vapor, and data points lower than the curve need to be corrected;
FIG. 4 is a schematic diagram of the detection result of snow melting on the surface of the Antarctic ice cover in January based on 89GHz data;
FIG. 5 is a schematic diagram of the detection result of snow melting on the surface of the Antarctic ice cover in January based on XPGR algorithm;
FIG. 6 is a schematic illustration of the ice-covered surface snow melt area of the 31-day Langshield princess coast in January 2017 in accordance with the present invention;
FIG. 7 is a site profile of a selected automated weather station of the present invention;
FIG. 8 is a schematic of the daily average temperature change for selected sites of the present invention;
FIG. 9 is a schematic diagram of an automated weather station for verifying the accuracy of ice cover surface snow melt detection results in accordance with the present invention;
FIG. 10 is a 31-day ice cover surface snow melt detection result of the method and XPGR algorithm of the present invention at the Bear Peninsula, Evans Knell and Thurston Island sites;
FIG. 11 is a schematic view of the investigation region of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1-11, a processing method of 89GHz data for the surface snow melting detection of the south pole ice cover,
s1, screening out affected 89GHz data by utilizing the relation that the ratio of polarization ratios of 89GHz data and 36GHz data of AMSR-2 is stable under the sunny and cloudless weather conditions;
s2, fitting the sample data selected from the five-year data to obtain a functional relation between 36GHz data and unaffected 89GHz data, and correcting the affected 89GHz data;
and S3, applying the corrected 89GHz data to the antarctic ice cover surface snow melt detection based on a single-channel threshold method proposed by Mote 1993.
The single-channel threshold method proposed in Mote1993 in step S3 is specifically disclosed in the literature: mote T L, Anderson M R, Kuivinen KC, et al, Passive microwave-derived and temporal variations of summers melt on the Greenland sheet [ J ]. Annals of Glaciology,1993,17(17): 233-.
Screening of affected data: the influence of external environments such as cloud and water vapor on 36GHz data is not large, and the influence on 89GHz data is large. Under the condition of clear and cloudy weather, the ratio of the polarization ratio of 89GHz data to 36GHz data is stable (Iwamoto, 2013), but when external environmental factors such as cloud, water vapor and the like exist, the ratio of the polarization ratio of 89GHz data to 36GHz data can be reduced, and the reduction degree is related to the influence of the external environments such as cloud, water vapor and the like. Based on this, the present application proposes a method for screening affected 89GHz data. The basic flow is shown in FIG. 1.
Because cloud and fog interference can be eliminated under the sunny and cloudless weather condition, and the influence on the data is reduced, a group of 36GHz data and 89GHz data under the sunny and cloudless weather condition are selected as sample data from data in 2015 to 2019 by combining MODIS data, and then the ratio of polarization ratios is calculated respectively. Finally, a polarization ratio scattergram of 89GHz and 36GHz sample data in sunny and cloudless weather is drawn, as shown in fig. 3. Wherein the polarization ratio equation (1) is as follows:
wherein TBV、TBHVertical polarization and horizontal polarization brightness temperatures, respectively, for AMSR-2 data.
FIG. 2 is a scatter plot of 89GHz data PR and horizontally polarized freeze-thaw samples.
FIG. 3 shows PR36And PR89In the scatter diagram, a solid curve is considered as a boundary affected by the external environment such as steam, and data points lower than the curve need to be corrected.
The scatter diagram shows PR89And PR36The relationship between them, based on the polarization ratio scattergram, the abscissa PR36Equally dividing into several intervals, and calculating the longitudinal coordinate PR in each interval89Mean and standard deviation of (d). A least squares best fit curve is then plotted using the mean minus two times the standard deviation in each interval, which is approximated by a quadratic equation, and can be expressed as equation (2):
PR89＝a(PR36)2+bPR36+c (2)
wherein PR89Polarization ratio, PR, for 89GHz data36The polarization ratio is 36GHz data. a, b and c are three parameters of the formula respectively, and the values of the three parameters are-0.9095, 0.5404 and-0.0052.
The relation curve is applied to 89GHz data, and 89GHz data influenced by external environments such as cloud, water vapor and the like and the remaining unaffected data are screened out pixel by pixel.
Correction of affected data: because 36GHz data is relatively stable and is weakly influenced by external environments such as cloud, steam and the like, the 36GHz data can be used for correcting the 89GHz data by the influence of the external environments such as the cloud, the steam and the like. By selecting PR under sunny and cloudless weather conditions in large quantities89Data and PR36Sample points of the data are then used for fitting calculation to find a data representing 89GHz and 36GHzAccording to the relation. Multiple experiments show that the one-element four-time model can achieve the fitting effect, and the fitted correction model is shown as a formula (3).
P′＝a1P4+a2P3+a3P2+a4P+a5 (3)
Wherein P is the resampled PR36Data, P' is modified PR89Data, a1、a2、a3、a4And a55 parameters of a correction formula, wherein the parameters are 65257, -6351.5, 200.43, -1.6492 and 0.0068 respectively;
and (4) correcting the 89GHz data which is screened out and is influenced by external environments such as cloud, water vapor and the like by using the formula (3).
The method for obtaining the freeze-thaw threshold of the surface of the ice cover comprises the following steps: according to previous research results and experience, the surface temperature of most regions of the Antarctic iceland is kept below zero all the year round, and the regions which experience melting every year are basically concentrated in marginal regions. According to the method, a single-channel threshold method proposed in Mote1993 is utilized, the characteristic points are selected from the 89GHz data after correction, and the research shows that the surface is melted, so that the selected characteristic point area is basically covered by ice and snow, no or few bare rocks exist, the terrain is required to be not large in fluctuation, and the error is reduced. And then making a microwave brightness temperature time sequence of the characteristic points. And finally, selecting the difference between the average value of the characteristic points in the microwave brightness temperature in winter and the average value in summer as the freezing and thawing threshold of the surface of the ice cover. The threshold chosen for this application is 0.00526.
And then, comparing the processed 89GHz data with a freeze-thaw threshold of the surface of the ice cover, and further obtaining a snow-thawing result of the surface of the ice cover: if the 89GHz data is smaller than the freeze-thaw threshold of the ice cover surface, the ice and snow on the ice cover surface at the position are not thawed; and if the 89GHz data is larger than the freeze-thaw threshold of the ice cover surface, the ice and snow on the ice cover surface at the position are melted.
And (3) result and verification:
and (3) comparing and analyzing results with XPGR algorithm: in order to more intuitively confirm the freeze-thaw detection precision of high-frequency data, the application takes the data of january in 2017 as an example, and compares the data with a detection result of an XPGR algorithm. The XPGR algorithm mainly utilizes the combination of the vertical polarization luminance temperature of the microwave radiometer at 37GHz and the horizontal polarization luminance temperature at 19GHz to carry out ice cover freezing and thawing detection on the difference of the response of dry snow and wet snow, the algorithm comprehensively utilizes the difference of frequency and polarization mode in the snow emissivity and the water content in the snow, melting information can be reflected more clearly, and the model stability is good. The formula of XPGR is shown below (4):
wherein, Tb19HIs 19GHz horizontally polarized light temperature, Tb37VThe vertical polarization brightness temperature is 37 GHz.
As shown in FIGS. 4 and 5, the present application respectively shows the total thawing duration detection results of 31 days in january of the method and the XPGR algorithm, which have a certain spatial difference in duration from the results of the XPGR algorithm due to the relatively high spatial resolution of the 89GHz data, but in general, the thawing trends of the two are substantially the same, but the detection results of the 89GHz data appear more finely divided, which indicates that the detection results of the 89GHz data are more easily reflected by the slight changes of freezing and thawing. In the aspect of local areas, the melt areas obtained by the method and the XPGR algorithm are respectively counted in 31 days by selecting the Lanschild princess coast, and as shown in FIG. 6, the freeze-thaw trends of the Lanschild princess coast and the XPGR algorithm are basically consistent through comparison.
And (3) ground temperature data verification, namely selecting the data of the Antarctic automatic weather station in the same period for verification in order to further verify the accuracy of the method. The selected automatic weather station data is near-ground air temperature data recorded every 3 hours. Studies suggest that the melting state occurs only when the near-surface temperature is above 0 ℃. Since the inner area of the Antarctic ice cover keeps a low temperature all year round even in summer and basically no melting phenomenon occurs, an automatic meteorological station close to the Antarctic coastline is mainly selected. The distribution of the selected site locations for the present application is shown in fig. 11.
As shown in fig. 9, the average temperature of 31 days a month in january at 6 selected sites is cross-verified with the results obtained by the present method and XPGR algorithm, which shows that the present algorithm has a high accuracy, wherein the average accuracy of XPGR algorithm at six sites is 74%, and the average accuracy of the present method at 6 sites is 91%. The curve in fig. 8 presents a very unstable condition, mainly because the curve is the daily average temperature calculated from the temperature data once in 3 hours, whereas AMSR-2 data only passes through the south pole twice a day, potentially missing the freeze-thaw phenomenon due to the large diurnal temperature difference in the south pole area.
The verification shows that 89GHz data processed by the method is basically feasible in the aspect of antarctic ice cover surface snow melt detection, and the 89GHz data has higher spatial resolution, so that compared with ice cover surface snow melt detection algorithms of other frequency bands, the method has better details and more accurate results.
Fig. 10 shows the detection results of snow melting on the surface of the ice cover of 31 days at the sites of Bear Peninsula, Evans Knoll and Thurston Island by the method and the XPGR algorithm, a, c and e are the detection results of the method at the three sites respectively, b, d and f are the detection results of the XPGR algorithm at the three sites respectively, the quasi-star at the center of the graph represents the position of the site, the gray area represents non-melting, and the black gradient area represents melting duration.
From FIG. 8, it can be seen that the Bear Peninsula site is not melted in the whole January, and the result a obtained by the method of the present application is not melted, but the result b of the XPGR algorithm is melted for 9 days; the EvansKnell site is not actually melted, the result c obtained by the method accords with the actual condition, but the result d of the XPGR algorithm shows that the station is melted for 7 days; the hurston Island site is actually thawed for one day, the result e obtained by the method of the application shows thawing for 5 days, but the result f of the XPGR algorithm shows thawing for 22 days. In contrast, the method has better freeze thawing detection precision. In addition, as can be seen from the comparison between fig. 10 and the above, the ice cover surface snow melting detection result obtained from the low-frequency data may cause the freeze-thaw mixed image element to appear due to the low resolution, while the 89GHz data may reflect the more subtle freeze-thaw changes due to the higher resolution, so that the situation may be improved when the 89GHz data is used.
The application provides a processing method for 89GHz data used for Antarctic ice cover surface snow melt detection based on AMSR-2 89GHz data and 36GHz data, and obtains the Antarctic ice cover surface snow melt detection result of 31 days 1 month in 2017 on the basis, and the result is cross-compared with an XPGR algorithm result (threshold value is-0.01711) and Antarctic automatic weather station temperature data using SSM/I data, and the result shows that: the method has higher precision and can reflect slight freeze-thaw changes.
The AMSR series microwave radiometer used in the method has continuous data of recent decades, is beneficial to expanding the research method, and discusses the snow melting condition of the surface of the Antarctic ice cover in a longer period. In addition, when the data is selected and sampled in the research, the influence of atmospheric water vapor such as cloud and fog needs to be avoided, and under the condition, in the future work, the accuracy of the sample data needs to be improved by combining various meteorological data.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (5)
1. A processing method of 89GHz data for Antarctic ice cover surface snow melting detection is characterized by comprising the following steps: the method comprises the following steps:
s1, screening out affected 89GHz data by utilizing the relation that the ratio of polarization ratios of 89GHz data and 36GHz data of AMSR-2 is stable under the sunny and cloudless weather conditions;
s2, fitting the sample data selected from the five-year data to obtain a functional relation between 36GHz data and unaffected 89GHz data, and correcting the affected 89GHz data;
and S3, applying the corrected 89GHz data to the antarctic ice cover surface snow melt detection based on a single-channel threshold method proposed by Mote 1993.
2. The processing method of 89GHz data for Antarctic ice cover surface snow melt detection according to claim 1, characterized by: before the 89GHz data of AMSR-2 is used for ice cover surface snow melt detection, affected pixel points are screened, then the affected pixel points are corrected, and finally a threshold value method is used for performing ice cover surface snow melt detection on the corrected 89GHz data.
3. The processing method of 89GHz data for Antarctic ice cover surface snow melt detection according to claim 1, characterized by: the screening process for the affected 89GHz data was: selecting 89GHz and 36GHz sample data in clear weather, calculating the ratio of the polarization ratios of 89GHz and 36GHz data, drawing a polarization ratio scatter diagram of 89GHz and 36GHz data, fitting an 89GHz data screening model with large interference, if the data screening model is satisfied, indicating that the data is 89GHz data which is not interfered or has small interference, and if the data screening model is not satisfied, indicating that the data is 89GHz data with large interference;
the screening and correction of the affected data was performed using PR, with the polarization ratio equation (1) as follows:
wherein TBV、TBHVertical polarization and horizontal polarization brightness temperatures of AMSR-2 data respectively;
on the basis of a polarization ratio scatter diagram, the abscissa PR36Equally dividing into several intervals, and calculating the longitudinal coordinate PR in each interval89And then a least squares best fit curve is drawn using the mean minus twice the standard deviation in each interval, which is approximated by a quadratic equation, and can be expressed as (2):
PR89＝a(PR36)2+bPR36+c (2)
wherein PR89Polarization ratio, PR, for 89GHz data36For the polarization ratio of 36GHz data, a, b and c are respectively three parameters of the formula, and the values are-0.9095, 0.5404 and-0.0052.
4. The processing method of 89GHz data for Antarctic ice cover surface snow melt detection according to claim 1, characterized by: the correction process of the affected data is as follows: the method utilizes 36GHz data to correct 89GHz data for the influence of external environments such as cloud, water vapor and the like, and PR (physical random access) is selected in a large quantity under the sunny and cloudless weather condition89Data and PR36And (3) carrying out fitting calculation on the sample points of the data, and finding out a relation expression representing 89GHz data and 36GHz data, wherein the fitted correction model is shown as the formula (3):
P'＝a1P4+a2P3+a3P2+a4P+a5 (3)
wherein P is the resampled PR36Data, P' is modified PR89Data, a1、a2、a3、a4And a55 parameters of a correction formula, wherein the parameters are 65257, -6351.5, 200.43, -1.6492 and 0.0068 respectively;
and (4) correcting the screened 89GHz data influenced by external environments such as cloud, water vapor and the like by using a formula (3).
5. The processing method of 89GHz data for Antarctic ice cover surface snow melt detection according to claim 1, characterized by: the method for obtaining the freeze-thaw threshold value of the surface of the ice cover comprises the following steps: and selecting characteristic points from the corrected 89GHz data by using a single-channel threshold method proposed by Mote1993, then making a microwave lighting temperature time sequence of the characteristic points, and finally selecting the difference between the winter average value and the summer average value of the microwave lighting temperature of the characteristic points as the freeze-thaw threshold of the surface of the ice cover.
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| CN106872466A (en) * | 2016-12-31 | 2017-06-20 | 中国科学院遥感与数字地球研究所 | A kind of lake ice freeze thawing monitoring method and system based on dynamic Decomposition of Mixed Pixels method |
| CN112818851A (en) * | 2020-09-02 | 2021-05-18 | 河南工业大学 | Method for detecting icebound lake based on FY-3MWRI data |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN106872466A (en) * | 2016-12-31 | 2017-06-20 | 中国科学院遥感与数字地球研究所 | A kind of lake ice freeze thawing monitoring method and system based on dynamic Decomposition of Mixed Pixels method |
| CN112818851A (en) * | 2020-09-02 | 2021-05-18 | 河南工业大学 | Method for detecting icebound lake based on FY-3MWRI data |
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| XINGDONG WANG等: "Snowmelt detection of Antarctic ice sheet based on AMSR-2 89 GHz channels", 《EARTH AND SPACE OPEN ARCHIVE》 * |
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