CN104635242A - Sand storm monitoring method based on multi-source satellite remote sensing data - Google Patents

Sand storm monitoring method based on multi-source satellite remote sensing data Download PDF

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CN104635242A
CN104635242A CN201510084424.5A CN201510084424A CN104635242A CN 104635242 A CN104635242 A CN 104635242A CN 201510084424 A CN201510084424 A CN 201510084424A CN 104635242 A CN104635242 A CN 104635242A
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sand
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CN104635242B (en
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罗敬宁
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Abstract

The invention relates to the technical field of atmospheric environment monitoring and particularly relates to a sand storm monitoring method based on multi-source satellite remote sensing data. The sand storm monitoring method for monitoring sand storm more accurately comprises the following steps: obtaining satellite detecting initial data; calculating a ratio of a near-infrared albedo to a visible light albedo in the initial data, and calculating a difference value between an infrared brightness temperature and a thermal infrared brightness temperature in the initial data; performing brightness temperature calculating of two wavebands of an infrared splitting window on the initial data; if the calculated results of the ratio, the difference value and the brightness temperature calculated results of the infrared splitting window are within corresponding regions, indicating the identified results as a sand storm region.

Description

A kind of Dust Storm Monitoring method based on Multi-sensor satellite remote sensing
Technical field
The present invention relates to atmosphere environment supervision technical field, particularly about a kind of Dust Storm Monitoring method of the satellite remote sensing date based on how far.
Background technology
Sandstorm is a kind of comparatively common spontaneous phenomenon caused by special geographical environment and meteorological condition, the arid and semi-arid regions mainly occurring in desert and close on, north African, the Middle East, the Central Asia and South Asia, East Asia, North America, Australia are main sandstorm generating region, the world, western China is the part in sandstorm district, the Central Asia, and northern territory is the part in sandstorm district, East Asia.The frequent of sandstorm is one of important symbol of environment deterioration, and sandstorm is a kind of meteorological disaster, is also serious ecological environment problem.According to meteorology definition, dust and sand weather is generally divided into 4 intensity ranks such as strong chromatic number, sandstorm, sand and floating dust.
Advanced satellite remote sensing platform, be the important means of Dust Storm Monitoring, ground based observa tion limits by space-time condition, cannot form all standing, quick, uniform Dust Storm Monitoring.Current, the satellite platform with multi light spectrum hands data retrieval capabilities has No. one, Chinese wind and cloud (FY-1) series, Chinese FY-2 (FY-2) series, No. three, Chinese wind and cloud (FY-3) series, U.S. NOAA series, U.S. EOS/MODIS series, European METOP series etc.These satellite platforms, the monitoring for sandstorm provides abundant data resource, has mostly possessed the spectral band of applicable Dust Storm Monitoring, can realize the accurate identification of sandstorm information and quantitatively calculate.
At present, the sandstorm recognition methods based on satellite remote sensing comprises: infrared division window difference arithmetic, its thinking providing sand and dust quantitative judge comparatively early; Utilize 3.7 μm of middle-infrared bands for the special reflected radiation characteristic of sand and dust, in conjunction with the sand and dust recognition methods of multi-wavelength data; Based on the look-up table method etc. that satellite image strengthens.Above method can partly solve sand and dust identification problem, but all has obvious limitation, lacks real quantitative computing power, well can not adapt to omnibearing dust detection demand.
Summary of the invention
The present invention considers deficiency and the defect of dust detection method in prior art just, propose the comprehensive dust detection using remote sensing method towards Global coverage, round-the-clock, multiple dimensioned, multi-source data, be applicable to the consistance monitoring of global range, there is good adaptability for strong chromatic number, general sand and dust process and weak floating dust, can Real-Time Monitoring be applied to.Meanwhile, according to sand and dust recognition result, give the computing method of dust intensity index, make the Quantitative Monitoring of sand and dust become possibility.
Embodiments provide a kind of Dust Storm Monitoring method based on satellite remote sensing date, comprise,
Obtain the primary data of satellite sounding;
Calculate the ratio of near infrared and visible albedo in primary data, and calculate the infrared difference with the bright temperature of thermal infrared in primary data;
The bright temperature of described primary data being carried out to infrared division window two wave bands calculates;
If described ratio, difference and infrared division window bright temperature result of calculation are all in corresponding interval, then sand and dust recognition result is sand and dust district.
A further aspect of a kind of Dust Storm Monitoring method based on satellite remote sensing date according to the embodiment of the present invention, also comprise among the primary data obtaining satellite monitoring, resampling and interpolation processing are carried out to Multi-sensor satellite remote sensing, temporal-spatial fusion process is carried out to overlapping region, uses the RS data that sun altitude cosine formula filtering sun altitude is greater than 70 °.
Another further aspect of a kind of Dust Storm Monitoring method based on satellite remote sensing date according to the embodiment of the present invention, calculating the ratio of near infrared and visible albedo in primary data, and calculate in primary data infrared with the difference of the bright temperature of thermal infrared before also comprise, using the bright temperature of Thermal infrared bands to carry out basic threshold value and sentence knowledge, is possible sand and dust district when the bright temperature of Thermal infrared bands is greater than threshold values.
Another further aspect of a kind of Dust Storm Monitoring method based on satellite remote sensing date according to the embodiment of the present invention, calculating the ratio of near infrared and visible albedo in primary data, and calculate in primary data infrared with the difference of the bright temperature of thermal infrared in also comprise:
The infrared difference with the bright temperature of thermal infrared in calculating:
DV mir＝T 3.7-T 11；
Wherein, T 3.7be the brightness temperature of 3.7 mu m wavebands, T 11be the brightness temperature of 11 mu m wavebands, as difference DV mirtime in first is interval, it is possible sand and dust district;
Calculate the ratio of near infrared and visible albedo:
RV nir＝R 1.6/R 0.85；
Wherein R 1.6be 1.6 mu m waveband albedos, R 0.85be 0.85 mu m waveband albedo, as ratio R V nirwhen second is interval, it is possible sand and dust district.
Another further aspect of a kind of Dust Storm Monitoring method based on satellite remote sensing date according to the embodiment of the present invention, described first interval is also divided into the first sub-range and the second sub-range, and described second interval is also divided into the 3rd sub-range and the 4th sub-range.
Another further aspect of a kind of Dust Storm Monitoring method based on satellite remote sensing date according to the embodiment of the present invention, the infrared difference with the bright temperature of thermal infrared in first calculating, calculate the ratio of near infrared and visible albedo again, when described difference belongs to the second sub-range, described ratio belongs to the 4th sub-range, be then possible strong sand and dust district; When described difference belongs to the first sub-range, described ratio belongs to the 3rd sub-range or belongs to the 4th sub-range, be then possible weak sand and dust district.
Another further aspect of a kind of Dust Storm Monitoring method based on satellite remote sensing date according to the embodiment of the present invention, first calculate the ratio of near infrared and visible albedo, the infrared difference with the bright temperature of thermal infrared in calculating again, when described ratio belongs to the 4th sub-range, described difference belongs to the second sub-range, be then possible strong sand and dust district; When described ratio belongs to the 3rd sub-range, described difference belongs to the first sub-range, be then possible Ruo sand and dust district, described difference belongs to the second sub-range, be then possible weak sand and dust district.
Another further aspect of a kind of Dust Storm Monitoring method based on satellite remote sensing date according to the embodiment of the present invention, the bright temperature calculating described primary data being carried out to infrared division window two wave bands comprises further,
When for possible strong sand and dust district, the ratio calculation of described primary data being carried out to the bright temperature of infrared division window is:
RV fir = e ( ( T 12 - 1.0 ) / T 11 - 1.0 ) * 10.0 ;
Wherein T 11be the brightness temperature of 11 mu m wavebands, T 12be the brightness temperature of 12 mu m wavebands, then RV firwhen belonging to the 3rd interval, then it is sand and dust district;
When for possible Ruo sand and dust district and weak sand and dust district, the mathematic interpolation described primary data being carried out to the bright temperature of infrared division window is:
DV fir＝T 12-T 11；
Wherein T 11be the brightness temperature of 11 mu m wavebands, T 12be the brightness temperature of 12 mu m wavebands, when being possible Ruo sand and dust district DV firwhen belonging to the 5th sub-range, be then sand and dust district, when being possible weak sand and dust district DV firwhen belonging to the 6th sub-range, then it is sand and dust district.
Another further aspect of a kind of Dust Storm Monitoring method based on satellite remote sensing date according to the embodiment of the present invention, after primary data is calculated, described difference belongs to the second sub-range and described ratio belongs to the 4th sub-range, described infrared division window result of calculation belong to the 3rd interval, be then sand and dust district, wherein, T 11be the brightness temperature of 11 mu m wavebands, T 12it is the brightness temperature of 12 mu m wavebands;
After calculating primary data, described difference belongs to the first sub-range and described ratio belongs to the 3rd sub-range, described infrared division window DV fir=T 12-T 11checkout result belong to the 5th sub-range, be then sand and dust district, wherein, T 11be the brightness temperature of 11 mu m wavebands, T 12it is the brightness temperature of 12 mu m wavebands;
After calculating primary data, described difference belongs to the first sub-range and described ratio belongs to the 4th sub-range, described infrared division window DV fir=T 12-T 11checkout result belong to the 6th sub-range, be then sand and dust district.
Another further aspect of a kind of Dust Storm Monitoring method based on satellite remote sensing date according to the embodiment of the present invention, also comprises after obtaining sand and dust recognition result, calculates dust intensity index:
DDI = ( ( e ( R 1.6 - 0.1 ) - 1.0 ) * 10.0 ) * e ( ( T 12 - 1.0 ) / T 11 - 1.0 ) * 10.0 ;
Wherein R 1.6be 1.6 mu m waveband albedos, T 11be the brightness temperature of 11 mu m wavebands, T 12be the brightness temperature of 12 mu m wavebands, DDI represents dust intensity index.
By the method for the embodiment of the present invention, sandstorm can be judged more accurately, and Sand index accurately can be provided, fully utilize multiwave satellite remote sensing date, towards the dust and sand weather of global range, multi-spatial scale, varying strength, propose comprehensive sand and dust recognition methods, effectively can solve the difficult problems such as the dynamic monitoring of sand and dust, GLOTRAC global tracking, multiscale space quantitative test.
Accompanying drawing explanation
In order to be illustrated more clearly in the technical scheme in the embodiment of the present invention, below the accompanying drawing used required in describing embodiment is briefly described, apparently, accompanying drawing in the following describes is only some embodiments of the present invention, for those of ordinary skill in the art, under the prerequisite not paying creative work, other accompanying drawing can also be obtained according to these accompanying drawings.In the accompanying drawings:
Figure 1 shows that the process flow diagram of a kind of Dust Storm Monitoring method based on satellite remote sensing date of the embodiment of the present invention;
Figure 2 shows that the process flow diagram of a kind of Dust Storm Monitoring method based on Multi-sensor satellite remote sensing of the embodiment of the present invention;
Figure 3 shows that the process flow diagram of a kind of Dust Storm Monitoring method based on Multi-sensor satellite remote sensing of the embodiment of the present invention.
Embodiment
For making the object of the embodiment of the present invention, technical scheme and advantage clearly understand, below in conjunction with accompanying drawing, the embodiment of the present invention is described in further details.At this, schematic description and description of the present invention is for explaining the present invention, but not as a limitation of the invention.
Be illustrated in figure 1 the process flow diagram of a kind of Dust Storm Monitoring method based on satellite remote sensing date of the embodiment of the present invention.
Comprise step 101, obtain the primary data of satellite sounding.
Step 102, calculates the ratio of near infrared and visible albedo in primary data, and calculates the infrared difference with the bright temperature of thermal infrared in primary data.
Step 103, the bright temperature of described primary data being carried out to infrared division window two wave bands calculates.
Step 104, if the result of calculation of described ratio, difference and infrared division window is all in corresponding interval, then sand and dust recognition result is sand and dust district.
Sequencing is not limited to the calculating of two in above-mentioned steps 102.
As one embodiment of the present of invention, also comprise among described step 101, resampling and interpolation processing are carried out to Multi-sensor satellite remote sensing, temporal-spatial fusion process is carried out to overlapping region.
As one embodiment of the present of invention, above-mentioned carry out temporal-spatial fusion process after also comprise, use the data that sun altitude cosine formula filtering sun altitude is greater than 70 °.
As one embodiment of the present of invention, also comprised before step 102, using the bright temperature of Thermal infrared bands to carry out basic threshold value and sentence knowledge, is possible sand and dust district when the bright temperature of Thermal infrared bands is greater than threshold values.
As one embodiment of the present of invention, also comprise in a step 102, the infrared difference with the bright temperature of thermal infrared in calculating:
DV mir＝T 3.7-T 11；
Wherein, T 3.7be the brightness temperature of 3.7 mu m wavebands, T 11be the brightness temperature of 11 mu m wavebands, as difference DV mirtime in first is interval, it is possible sand and dust district.Such as value 20K<DV mir<70K is possible sand and dust district, and namely the first interval is (20K, 70K), can also be divided in this first interval the first sub-range (20K, 30K], the second sub-range (30K, 70K).The division in described sub-range can be not quite similar, and does not repeat them here.
Calculate the ratio of near infrared and visible albedo:
RV nir＝R 1.6/R 0.85；
Wherein R 1.6be 1.6 mu m waveband albedos, R 0.85be 0.85 mu m waveband albedo, as ratio R V nirwhen second is interval, it is possible sand and dust district.Such as value 0.8<RV nir<1.5 is possible sand and dust district, and namely the second interval is (0.8,1.5), can also be divided in this second interval the 3rd sub-range (0.8,1], the 4th sub-range (1,1.5).The division in described sub-range can be not quite similar, and does not repeat them here.
As one embodiment of the present of invention, also comprise in a step 102, the infrared difference with the bright temperature of thermal infrared in first calculating, calculate the ratio of near infrared and visible albedo again, when described difference belongs to the second sub-range, described ratio belongs to the 4th sub-range, be then possible strong sand and dust district; When described difference belongs to the first sub-range, described ratio belongs to the 3rd sub-range, be then possible Ruo sand and dust district, described ratio belongs to the 4th sub-range, be then possible weak sand and dust district.
As one embodiment of the present of invention, also comprise in a step 102, first calculate the ratio of near infrared and visible albedo, the infrared difference with the bright temperature of thermal infrared in calculating again, when described ratio belongs to the 4th sub-range, described difference belongs to the second sub-range, be then possible strong sand and dust district; When described ratio belongs to the 3rd sub-range, described difference belongs to the first sub-range, be then possible Ruo sand and dust district, described difference belongs to the second sub-range, be then possible weak sand and dust district.
As one embodiment of the present of invention, also comprise in step 103, when for possible strong sand and dust district, the ratio calculation of described primary data being carried out to infrared division window two bright temperature of wave band is:
RV fir = e ( ( T 12 - 1.0 ) / T 11 - 1.0 ) * 10.0 ;
Wherein T 11be the brightness temperature of 11 mu m wavebands, T 12be the brightness temperature of 12 mu m wavebands, then RV firwhen belonging to the 3rd interval, be then sand and dust district, such as described 3rd interval is (1,1.5), i.e. 1<RV firit is sand and dust district during <1.5;
When for possible Ruo sand and dust district and weak sand and dust district, the mathematic interpolation described primary data being carried out to infrared division window two bright temperature of wave band is:
DV fir＝T 12-T 11；
Wherein T 11be the brightness temperature of 11 mu m wavebands, T 12be the brightness temperature of 12 mu m wavebands, when being possible Ruo sand and dust district DV firwhen belonging to the 5th sub-range, be then sand and dust district, when being possible weak sand and dust district DV firwhen belonging to the 6th sub-range, be then sand and dust district, such as the 4th interval is (0.5K, 5K), wherein also divide have the 5th sub-range (0.5K, 1.5K], the 6th sub-range (1.5K, 5K), when being possible Ruo sand and dust district DV firbelong to (0.5K, 1.5K], be then sand and dust district, when being possible weak sand and dust district DV firbelonging to (1.5K, 5K), is then sand and dust district.The division in described sub-range can be not quite similar, and does not repeat them here.
As one embodiment of the present of invention, after calculating primary data, described difference belongs to the second sub-range and described ratio belongs to the 4th sub-range, described infrared division window result of calculation belong to the 3rd interval, be then sand and dust district, wherein, T 11be the brightness temperature of 11 mu m wavebands, T 12it is the brightness temperature of 12 mu m wavebands;
After calculating primary data, described difference belongs to the first sub-range and described ratio belongs to the 3rd sub-range, described infrared division window DV fir=T 12-T 11checkout result belong to the 5th sub-range, be then sand and dust district, wherein, T 11be the brightness temperature of 11 mu m wavebands, T 12it is the brightness temperature of 12 mu m wavebands;
After calculating primary data, described difference belongs to the first sub-range and described ratio belongs to the 4th sub-range, described infrared division window DV fir=T 12-T 11checkout result belong to the 6th sub-range, be then sand and dust district.
As one embodiment of the present of invention, after calculating primary data, described ratio belongs to the 4th sub-range and described difference belongs to the second sub-range, described infrared division window result of calculation belong to the 3rd interval, be then sand and dust district, wherein, T 11be the brightness temperature of 11 mu m wavebands, T 12it is the brightness temperature of 12 mu m wavebands;
After calculating primary data, described ratio belongs to the 3rd sub-range and described difference belongs to the first sub-range, described infrared division window DV fir=T 12-T 11checkout result belong to the 5th sub-range, be then sand and dust district, wherein, T 11be the brightness temperature of 11 mu m wavebands, T 12it is the brightness temperature of 12 mu m wavebands;
After calculating primary data, described ratio belongs to the 4th sub-range and described difference belongs to the first sub-range, described infrared division window DV fir=T 12-T 11checkout result belong to the 6th sub-range, be then sand and dust district.
As one embodiment of the present of invention, also comprise after step 104, calculate dust intensity index:
DDI = ( ( e ( R 1.6 - 0.1 ) - 1.0 ) * 10.0 ) * e ( ( T 12 - 1.0 ) / T 11 - 1.0 ) * 10.0 ;
Wherein R 1.6be 1.6 mu m waveband albedos, T 11be the brightness temperature of 11 mu m wavebands, T 12be the brightness temperature of 12 mu m wavebands, DDI represents dust intensity index, is the dimensionless number between a 1-100, and it is stronger to be worth larger expression dust intensity.
As one embodiment of the present of invention, for the sand and dust recognition result of step 104, adopt small component minimizing technology, complete noise, judge the Transformatin in point or region by accident; Meanwhile, due to the impact of cloud, landform or other factors, there will be some orifice region in the Connected component inside of recognition result, the present invention adopts structural elements matrix and recognition result to carry out morphological operations, removes these orifice region.
By the method for the invention described above embodiment, sandstorm can be judged more accurately, and Sand index accurately can be provided, fully utilize multiwave satellite remote sensing date, towards the dust and sand weather of global range, multi-spatial scale, varying strength, propose comprehensive sand and dust recognition methods, effectively can solve the difficult problems such as the dynamic monitoring of sand and dust, GLOTRAC global tracking, multiscale space quantitative test.
Be illustrated in figure 2 the process flow diagram of a kind of Dust Storm Monitoring method based on Multi-sensor satellite remote sensing of the embodiment of the present invention.
Comprise step 201, read the Multi-sensor satellite remote sensing file of multiple format, resolve the complex data structures of often kind of data, remotely-sensed data is converted to real physical quantity, obtain the memory object entity that can carry out unifying subsequent treatment.
Step 202, carries out space projection process to remotely-sensed data, first according to locating information, sets up the longitude and latitude look-up table of view field; Then adopt shortest path near stratum exhaust algorithm, carry out space interpolation process; The last longitude and latitude table of comparisons completed according to interpolation, carries out projection transform by each wave band data.
Step 203, judges whether data comprise multiple observation time and have overlapping region, if need temporal-spatial fusion process, then carry out step 204 and process; If do not needed, then directly carry out step 205 and process.
Step 204, the data processed for needing overlapping region, adopt minimum satellite vehicle zenith angle priority algorithm to carry out Data Fusion.
Step 205, after obtaining the consistent data of time-space registration, uses sun altitude cosine formula, carries out correcting process for visible and near infrared data, and the data that sun altitude is greater than 70 ° will be left in the basket.
Step 206, starts the data of circular treatment each effective pixel, carries out sand and dust identifying processing, read each pixel visible ray, near infrared, in the physical quantity of the wave band such as infrared, 2 infrared division windows form primary data.
Step 207, starts sand and dust identifying processing, and first use the bright temperature of Thermal infrared bands to carry out basic threshold value and sentence knowledge, the pixel that bright temperature is less than 290K as tentatively sentencing knowledge, can enter next step sand and dust and sentencing knowledge; Bright temperature is greater than the pixel of 290K, then eliminating is possible of sand and dust, and two-value data is designated as 0, proceeds to next pixel process.Threshold values 290K is in this step the empirical value of inventor, can adopt other bright temperature value, and the 290K in this step just in order to using an example as the explanation of scheme implementation, should not do to limit and understand.
In this step, due to sand and dust mostly show as in the character of warm property landscape object of low albedo, use Detection Using Thermal Infrared Channel to carry out basic threshold value and sentence knowledge, main target determines that preliminary sand and dust and earth's surface demarcate, reduce sand and dust identification range, improve accuracy of identification, this is even more important in desert area.Threshold value uses satellite 11.0 μm of Thermal infrared bands data, carries out long-time statistical analysis, determines that the boundary on sand and dust and earth's surface concentrates near 290K.
Step 208, according in infrared with thermal infrared dual channel difference (DV mir) do and sentence knowledge, work as 30K<DV mirduring <70K, be then just judged to strong sand and dust, enter next step and sentence knowledge; Work as 20K<DV mirduring <=30K, be then just judged to weak sand and dust, enter next step and sentence knowledge; Work as DV mirnot when these two interval, then sentence and know for non-sand and dust, two-value data is designated as 0, proceeds to next pixel process.
In this step, in infrared 3.7 mu m wavebands to sand and dust, especially strong sand and dust, have obvious reflection.In actual use, adopt the difference of 3.7 mu m wavebands and 11 mu m wavebands as sentencing knowledge foundation, computing formula is as follows:
DV mir＝T 3.7-T 11
Wherein T 3.7be the brightness temperature of 3.7 mu m wavebands, T 11be the brightness temperature of 11 mu m wavebands, satisfy condition 20.0K<DV mir<70.0K, sentences knowledge condition as sand and dust.
Step 209, for 30K<DV mirthe strong sand and dust district of <70K, uses further near infrared and visible ray ratio to carry out sentencing knowledge, when ratio satisfies condition 1<RV nirduring <1.5, may be sand and dust district further, enter next step and sentence knowledge, otherwise, two-value data is designated as 0, proceeds to next pixel process.
Near infrared channels goes for sand and dust identification, especially strong sand and dust district, and near infrared and visible channel have notable difference, and introduce the ratio of two passages as strong sand and dust identification main criterion, computing formula is as follows:
RV nir＝R 1.6/R 0.85
Wherein R 1.6be 1.6 mu m waveband albedos, R 0.85it is 0.85 mu m waveband albedo.
Step 210, subsequent steps 209, uses further infrared division window ratio e index to carry out sentencing knowledge, when satisfy condition RVfir>1 time, then sentencing and knowing this pixel is sand and dust district, and two-value data is designated as 1, completes this pixel identification; If do not meet, two-value data is designated as 0, enters next pixel and processes.
2 attenuations by absorption of wave band to sand and dust of infrared division window are variant, can as the criterion of sand and dust identification, and especially for weak sand and dust district, infrared division window wave section compares other wave band, have the reflection of better otherness.Strong sand and dust are sentenced to the Ratio index known and use the bright temperature of infrared division window 2 wave band, as sand and dust identical criterion, computing formula is as follows:
RV fir = e ( ( T 12 - 1.0 ) / T 11 - 1.0 ) * 10.0 ;
Wherein T 11be the brightness temperature of 11 mu m wavebands, T 12be the brightness temperature of 12 mu m wavebands, then RV fir>1 is as the criterion of sand and dust identification.
Step 211, for 20K<DV mirthe weak sand and dust district of <30K, uses near infrared and visible ray ratio to carry out sentencing knowledge further, this ratio is divided into two kinds of situations, that is: 0.8<RV nir<=1 and 1<RV nir<1.5, enters next step respectively and sentences knowledge, if do not met this two conditions, then sentences and knows for non-sand and dust, two-value data is designated as 0, proceeds to next pixel process.
Step 212, for 0.8<RV nirthe situation of <1, uses infrared division window difference to carry out sentencing knowledge, as the 0.5K<DV that satisfies condition further firduring <=1.5K, then sentencing and knowing this pixel is sand and dust district, and two-value data is designated as 1, completes this pixel identification; If do not meet, two-value data is designated as 0, enters next pixel and processes.
More weak sand and dust are used to the difference of the bright temperature of infrared division window 2 wave band, as sand and dust identical criterion, computing formula is as follows:
DV fir＝T 12-T 11
Wherein T 11be the brightness temperature of 11 mu m wavebands, T 12be the brightness temperature of 12 mu m wavebands, then 0.5K<DV fir<5K is as the criterion of sand and dust identification.
Step 213, for 1<RV nirthe situation of <1.5, uses infrared division window difference to carry out sentencing knowledge, as the 1.5K<DV that satisfies condition further firduring <5K, then sentencing and knowing this pixel is sand and dust district, and two-value data is designated as 1, completes this pixel identification; If do not meet, two-value data is designated as 0, enters next pixel and processes.
Step 214, after the sand and dust identification of all effective pixels that circulated, finally obtain a sand and dust identification two-value data, 1 is represented as sand and dust district, and 0 indicates without sand and dust.
Step 215, according to sand and dust identification data, reads near infrared albedo and the bright temperature of infrared division window 2 wave band of sand and dust district pixel, adopts dust intensity computation model, obtain the dust intensity index of each sand and dust pixel.
DDI = ( ( e ( R 1.6 - 0.1 ) - 1.0 ) * 10.0 ) * e ( ( T 12 - 1.0 ) / T 11 - 1.0 ) * 10.0 ;
Wherein R 1.6be 1.6 mu m waveband albedos, T 11be the brightness temperature of 11 mu m wavebands, T 12be the brightness temperature of 12 mu m wavebands, DDI represents dust intensity index, is the dimensionless number between a 1-100, and it is stronger to be worth larger expression dust intensity.
In the prior art, dust intensity index can be calculated by optical thickness, set up the table of comparisons to contrast with the optical thickness collected, also can obtain dust intensity index, the computing method of a kind of new dust intensity index that above-mentioned steps provides for the present inventor.
Step 216, the zonule that some are isolated is there will be in sand and dust recognition result, these zonules may be noise, erroneous judgement point or region, the present invention adopts soiline-alkali plants and small component minimizing technology, define a structural elements matrix and each pixel carries out computing, thus sentence the zonule of knowing and isolating, and remove in the result.
Step 217, equally, based on the continuous distribution characteristic of sand and dust, but due to the impact of cloud, landform or other factors, there will be some orifice region in the Connected component inside of recognition result, the present invention designs an algorithm model based on morphological image, uses rational structural elements matrix and recognition result image to carry out morphological operations, remove these orifice region, ensure the continuous distribution characteristic of sand and dust.
Step 218, corrects after process through result, and sand and dust recognition result can export according to science data and image two kinds of modes, and the intensity index of each sand and dust pixel of science data record, may be used for follow-up analysis and application; Image comprises sand and dust color composite image, sand and dust recognition image, dust intensity index image etc.
Figure 3 shows that the process flow diagram of a kind of Dust Storm Monitoring method based on Multi-sensor satellite remote sensing of the embodiment of the present invention.
Basic step and Fig. 2 similar, the key distinction is sand and dust recognition sequence, Fig. 3 completes after the basic threshold value of Thermal infrared bands sentences knowledge, near infrared and visible ray ratio is first used to carry out sentencing knowledge, and then infraredly in using do sand and dust with thermal infrared dual channel difference and sentence knowledge, Main change is shown in step 308-313, and step 301-307 is identical with step 214-218 with step 201-207 with step 314-318, and same section no longer repeats in the present embodiment.
Step 308, according near infrared and visible ray ratio (RV nir) carry out sentencing knowledge, this ratio is divided into two kinds of situations, that is: 0.8<RV nir≤ 1 and 1<RV nir<1.5, enters next step respectively and sentences knowledge, if do not met this two conditions, then sentences and knows for non-sand and dust, two-value data is designated as 0, proceeds to next pixel process.
Step 309, for 1<RV nirthe strong sand and dust district of <1.5, infrared with thermal infrared dual channel difference (DV in using further mir) do and sentence knowledge, when difference satisfies condition 30K<DV mirduring <70K, may be sand and dust district further, enter next step and sentence knowledge, otherwise, two-value data is designated as 0, proceeds to next pixel process.
Step 310, subsequent steps 209, uses infrared division window ratio e index to carry out sentencing knowledge, as the RV that satisfies condition further firduring >1, then sentencing and knowing this pixel is sand and dust district, and two-value data is designated as 1, completes this pixel identification.
Step 311, for 0.8<RV nirthe weak sand and dust district of≤1, infrared with thermal infrared dual channel difference (DV in using further mir) carry out sentencing knowledge, for weak sand and dust district, this difference (DV mir) two kinds of situations, that is: 20K<DV can be divided into mir≤ 30K and 30K<DV mir<70K, enters next step respectively and sentences knowledge, if do not met this two conditions, then sentences and knows for non-sand and dust, two-value data is designated as 0, proceeds to next pixel process.
Step 312, for 20K<DV mirthe situation of≤30K, uses infrared division window difference to carry out sentencing knowledge, as the 0.5K<DV that satisfies condition further firduring≤1.5K, then sentencing and knowing this pixel is sand and dust district, and two-value data is designated as 1, completes this pixel identification.
Step 313, for 30K<DV mirthe situation of <70K, uses infrared division window difference to carry out sentencing knowledge, as the 1.5K<DV that satisfies condition further firduring <5K, then sentencing and knowing this pixel is sand and dust district, and two-value data is designated as 1, completes this pixel identification.
By the method for the invention described above embodiment, sandstorm can be judged more accurately, and Sand index accurately can be provided, fully utilize multiwave satellite remote sensing date, towards the dust and sand weather of global range, multi-spatial scale, varying strength, propose comprehensive sand and dust recognition methods, effectively can solve the difficult problems such as the dynamic monitoring of sand and dust, GLOTRAC global tracking, multiscale space quantitative test.By method, the Apparatus and system of the invention described above embodiment.
Above-described specific embodiment; object of the present invention, technical scheme and beneficial effect are further described; be understood that; the foregoing is only specific embodiments of the invention; the protection domain be not intended to limit the present invention; within the spirit and principles in the present invention all, any amendment made, equivalent replacement, improvement etc., all should be included within protection scope of the present invention.

Claims (9)

1., based on a Dust Storm Monitoring method for satellite remote sensing date, it is characterized in that comprising,
Obtain the primary data of satellite sounding;
Calculate the ratio of near infrared and visible albedo in primary data, and calculate the infrared difference with the bright temperature of thermal infrared in primary data;
The bright temperature of described primary data being carried out to infrared division window two wave bands calculates;
If described ratio, difference and infrared division window bright temperature result of calculation are all in corresponding interval, then recognition result is sand and dust district.
2. a kind of Dust Storm Monitoring method based on satellite remote sensing date according to claim 1, it is characterized in that, also comprise among the primary data obtaining satellite sounding, resampling and interpolation processing are carried out to Multi-sensor satellite remote sensing, temporal-spatial fusion process is carried out to overlapping region, uses the data that sun altitude cosine formula filtering sun altitude is greater than 70 °.
3. a kind of Dust Storm Monitoring method based on satellite remote sensing date according to claim 1, it is characterized in that, calculating the ratio of near infrared and visible albedo in primary data, and calculate in primary data infrared with the difference of the bright temperature of thermal infrared before also comprise, using the bright temperature of Thermal infrared bands to carry out basic threshold value and sentence knowledge, is possible sand and dust district when the bright temperature of Thermal infrared bands is greater than threshold values.
4. a kind of Dust Storm Monitoring method based on satellite remote sensing date according to claim 1, it is characterized in that, calculating the ratio of near infrared and visible albedo in primary data, and calculate in primary data infrared with the difference of the bright temperature of thermal infrared in also comprise:
The infrared difference with the bright temperature of thermal infrared in calculating:
DV mir＝T 3.7-T 11；
Wherein, T 3.7be the brightness temperature of 3.7 mu m wavebands, T 11be the brightness temperature of 11 mu m wavebands, as difference DV mirtime in first is interval, it is possible sand and dust district;
Calculate the ratio of near infrared and visible albedo:
RV nir＝R 1.6/R 0.85；
Wherein R 1.6be 1.6 mu m waveband albedos, R 0.85be 0.85 mu m waveband albedo, as ratio R V nirwhen second is interval, it is possible sand and dust district.
5. a kind of Dust Storm Monitoring method based on satellite remote sensing date according to claim 4, it is characterized in that, described first interval is also divided into the first sub-range and the second sub-range, and described second interval is also divided into the 3rd sub-range and the 4th sub-range.
6. a kind of Dust Storm Monitoring method based on satellite remote sensing date according to claim 5, it is characterized in that, the infrared difference with the bright temperature of thermal infrared in first calculating, calculate the ratio of near infrared and visible albedo again, when described difference belongs to the second sub-range, described ratio belongs to the 4th sub-range, be then possible strong sand and dust district; When described difference belongs to the first sub-range, described ratio belongs to the 3rd sub-range, be then possible Ruo sand and dust district, described ratio belongs to the 4th sub-range, be then possible weak sand and dust district.
7. a kind of Dust Storm Monitoring method based on satellite remote sensing date according to claim 5, it is characterized in that, first calculate the ratio of near infrared and visible albedo, the infrared difference with the bright temperature of thermal infrared in calculating again, when described ratio belongs to the 4th sub-range, described difference belongs to the second sub-range, be then possible strong sand and dust district; When described ratio belongs to the 3rd sub-range, described difference belongs to the first sub-range, be then possible Ruo sand and dust district, described difference belongs to the second sub-range, be then possible weak sand and dust district.
8. a kind of Dust Storm Monitoring method based on satellite remote sensing date according to claim 6 or 7, is characterized in that, the bright temperature of described primary data being carried out to infrared division window two wave bands calculates, and comprises further,
When for possible strong sand and dust district, carrying out infrared division window ratio calculation to described primary data is:
RV fir = e ( ( T 12 - 1.0 / T 11 1.0 ) * 10.0 ;
Wherein T 11be the brightness temperature of 11 mu m wavebands, T 12be the brightness temperature of 12 mu m wavebands, then RV firwhen belonging to the 3rd interval, then it is sand and dust district;
When for possible Ruo sand and dust district and weak sand and dust district, carrying out infrared division window mathematic interpolation to described primary data is:
DV fir＝T 12-T 11；
Wherein T 11be the brightness temperature of 11 mu m wavebands, T 12be the brightness temperature of 12 mu m wavebands, when being possible Ruo sand and dust district DV firwhen belonging to the 5th sub-range, be then sand and dust district, when being possible weak sand and dust district DV firwhen belonging to the 6th sub-range, then it is sand and dust district.
9. a kind of Dust Storm Monitoring method based on satellite remote sensing date according to claim 1, is characterized in that, also comprise after obtaining sand and dust recognition result, calculates dust intensity index:
DDI = ( ( e ( R 1.6 - 0.1 ) - 1.0 ) * 10.0 ) * e ( ( T 12 - 1.0 / t 11 - 1.0 ) * 10.0 * 10.0 ;
Wherein R 1.6be 1.6 mu m waveband albedos, T 11be the brightness temperature of 11 mu m wavebands, T 12be the brightness temperature of 12 mu m wavebands, DDI represents dust intensity index.
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CN106951702A (en) * 2017-03-15 2017-07-14 内蒙古工业大学 A kind of sandstorm data analysing method based on data mining
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CN114220024A (en) * 2021-12-22 2022-03-22 内蒙古自治区气象信息中心(内蒙古自治区农牧业经济信息中心)(内蒙古自治区气象档案馆) Static satellite sandstorm identification method based on deep learning
CN114220024B (en) * 2021-12-22 2023-07-18 内蒙古自治区气象信息中心(内蒙古自治区农牧业经济信息中心)(内蒙古自治区气象档案馆) Static satellite sand storm identification method based on deep learning

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