CN115901553A - Sand and dust monitoring method based on Himapari-8 satellite remote sensing data - Google Patents
Sand and dust monitoring method based on Himapari-8 satellite remote sensing data Download PDFInfo
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Abstract
The invention discloses a sand and dust monitoring method based on Himapari-8 satellite remote sensing data, and mainly relates to the technical field of atmospheric environment monitoring. The method comprises the following steps: acquiring scanning data of a Himapari-8 satellite full wave band; extracting brightness temperature data with the wavelength of 3.9 mu m in a channel, setting a threshold value to be 300K, and taking the threshold value larger than the threshold value as a condition for entering the next judgment; calculating the brightness temperature difference value of the wavelength between the 3.9 mu m channel and the 11.2 mu m channel, setting the threshold value to be 20K, and taking the difference value larger than the threshold value as the condition for entering the next judgment; the difference in luminance temperature between the 11 μm channel and the 12 μm channel at the wavelength was calculated, and a threshold value of 0K was set, and the difference greater than the threshold value was taken as a final condition for identifying dust. The invention has the beneficial effects that: the method greatly improves the time resolution and is beneficial to real-time dynamic monitoring of the dust.
Description
Technical Field
The invention relates to the technical field of atmospheric environment monitoring, in particular to a sand and dust monitoring method based on Himawari-8 satellite remote sensing data.
Background
The sand-dust weather is a general name of a weather phenomenon that dust and sand on the ground are drawn into the air by wind to cause air turbidity. The sand-dust weather is one of main disastrous weather in northern areas of China, influences traffic and working life of people and harms human health; the sand-dust weather prone area in the north of China is wide, the monitoring and early warning capability of foundation disasters is limited, and in the face of the weak point of the meteorological disaster prevention system, satellite remote sensing can well play a role; the development of sand monitoring has important significance for early warning of sand disasters and evaluation of ecological environment influence, and has profound influence on human social life and global climate change.
The work of monitoring the weather of the sand storm by using the satellite remote sensing technology is carried out at home and abroad, and in earth observation instruments of weather, resources and environmental satellites in the United states, european Union, japan and China, many of the earth observation instruments carry observation channels sensitive to sand aerosol, and the instruments can be used for monitoring the atmospheric sand. However, the satellite commonly used for monitoring the dust is mainly polar orbit satellite, and due to the long revisit period and the long image acquisition time interval, the dynamic monitoring of the dust is greatly limited.
Disclosure of Invention
The invention aims to provide a sand and dust monitoring method based on Himawari-8 satellite remote sensing data, which greatly improves the time resolution and is beneficial to real-time dynamic monitoring of sand and dust.
In order to achieve the purpose, the invention is realized by the following technical scheme:
a sand and dust monitoring method based on Himawari-8 satellite remote sensing data comprises the following steps:
acquiring scanning data of a Himapari-8 satellite full wave band;
extracting brightness temperature data with the wavelength of 3.9 mu m in a channel, setting a threshold value to be 300K, and taking the threshold value larger than the threshold value as a condition for entering the next judgment;
calculating the brightness temperature difference value of the wavelength between the 3.9 mu m channel and the 11.2 mu m channel, setting the threshold value to be 20K, and taking the difference value larger than the threshold value as the condition for entering the next judgment;
the difference in luminance temperature between the 11 μm channel and the 12 μm channel at the wavelength was calculated, and a threshold value of 0K was set, and the difference greater than the threshold value was taken as a final condition for identifying dust.
If the final condition is identified as sand dust, calculating a sand dust index D1, wherein the calculation formula is as follows:
wherein: r 1.6 The reflectance was measured in the near infrared 1.6 μm band.
Scanning data of the Himapari-8 satellite full-wave band are acquired at intervals of 10 minutes, and a sand dust dynamic monitoring graph is established based on a sand dust index D1 acquired by the scanning data.
Compared with the prior art, the invention has the beneficial effects that:
by the method, the sand and dust are monitored based on Himapari-8 satellite data, and the sand and dust area can be effectively extracted by comparing with an OMI AI product, so that the sand and dust range and the strength are basically consistent, and the requirement of business monitoring can be met. The method has the advantages that the sand dust is extracted based on the stationary satellite Himapari-8, and compared with a polar orbit satellite, the time resolution is greatly improved, and real-time dynamic monitoring of the sand dust is facilitated. The method is simple and effective, has single data source and uses less software.
Drawings
Fig. 1 is a flow chart of dust monitoring.
FIG. 2 is a picture of a Himapari-8 true color image taken on 3/30/2020.
Fig. 3 is the 7-band luminance temperature threshold extraction result.
FIG. 4 shows the difference extraction results of the mid-red 7 band outer and thermal infrared 14 bands.
FIG. 5 shows the extraction result of the band difference between the thermal infrared splitting window channels 14 and 15 according to the present invention.
FIG. 6 is a distribution diagram of the sand dust intensity in the Mongolian area within 3/30 of 2020.
Fig. 7 is a diagram of the invention for monitoring sand dust all day at 5/6/2021.
Fig. 8 is a dynamic sand dust monitoring diagram of 5/6/2021.
FIG. 9 compares the results of Hiwari-8 (left) and OMI (right) dust extraction according to the present invention.
Detailed Description
The invention will be further illustrated with reference to the following specific examples. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Further, it should be understood that various changes or modifications of the present invention can be made by those skilled in the art after reading the teaching of the present invention, and these equivalents also fall within the scope defined by the present application.
Unless otherwise specified, the instruments, reagents, materials and the like used in the following examples are conventional instruments, reagents, materials and the like known in the art and are commercially available. Unless otherwise specified, the experimental methods, detection methods, and the like described in the following examples are conventional experimental methods, detection methods, and the like in the prior art.
Himapari-8 (Chinese name: sunflower-8) meteorological satellite developed by Mitsubishi electric corporation, which was funded by the Japan weather hall, was successfully launched using Mitsubishi heavy industry H-IIA-25 rocket in 10, 7 days of 2014 in Japan. As a static meteorological satellite, himapari-8 operates synchronously with the rotation of the earth by about 35800 kilometers above the equator of the earth, is static relative to the earth, generates a synchronous effect, can observe a fixed area of one third of the earth surface, and can carry out continuous meteorological observation on the same target area. The time resolution of 10 minutes, up to 500 meters of space resolution, the ability of carrying the sensor of the sunflower No. 8 meteorological satellite is greatly improved, red, blue and green light can be identified, a color image can be displayed, the brown color is used for representing sand and dust, the sand and dust can be distinguished from clouds, and the method is very favorable for observation and forecast. The main performance parameters of the sunflower-8 satellite are as follows:
emission time: 10 and 7 days in 2014
Emission quality: about 3500kg
And (3) overall length: about 8 meters (wingspan with solar cell)
A platform: DS-2000
Service life: for more than 8 years (including parallel observation)
The observation range is as follows: visible light: 0.5-1 km;
near infrared: 1-2 km;
infrared: 2km
Scanning frequency: full-disc scanning: 1 time/10 minutes;
in the Japanese world: 1 time/2.5 minutes;
the excitation observation range is as follows: 1 times/2.5 minutes
The scheme is mainly based on the aerosol spectral characteristics of the sand dust particles, the sand dust particles can emit long-wave radiation and absorb long-wave radiation on the ground, and meanwhile, the absorption and scattering of solar radiation can jointly influence the earth radiation balance and energy balance, and the difference of spectral characteristics is shown. The radiation characteristics of airborne dust particles are closely related to the size, shape, texture and concentration of the particles, with particle size being an important factor in determining the scattering characteristics of the dust. The short-band extinction contribution comes primarily from scattering by the particles, the long-band extinction contribution comes primarily from absorption by the particles, and the extinction contribution of coarse particles is significantly greater than that of fine particles.
TABLE 1 Himapari-8 satellite band design
The above table 1 shows the main technical parameters of the Himapari-8 satellite. The total number of the wave bands is 16, the wave bands comprise 3 visible light wave bands, 3 near infrared wave bands and 10 infrared wave bands, and the spatial resolution is 500m, 1000m and 2000m respectively. The middle infrared channel (3.9 mu m) mainly contains radiation and reflection information of ground objects, and the channel is superposed with more dust and sand reflection information and has obvious reflection on dust and sand, so that the middle infrared channel can be used as a main basis for dust and sand identification in dust and sand inversion. The brightness temperature difference of 8-12 μm in the thermal infrared atmospheric window region can reduce the influence on the ground, and can be used for better researching the sand storm. And (3) performing statistical analysis on the extracted typical feature, and respectively calculating difference values by adopting a 3.9-micron waveband and thermal infrared wavebands, wherein the difference values of the brightness temperature of the dust in the 7 th waveband and the brightness temperatures of the 14 and 15 wavebands are obviously different from other features through analysis, and the difference values can be used as identification bases.
The main identification method is as follows (see fig. 1 sand monitoring flow chart):
acquiring scanning data of a Himapari-8 satellite full waveband, wherein the scanning data comprises wavebands 1-16;
extracting the brightness temperature corresponding to the 7 th wave band, setting 300K as the brightness temperature threshold of the 3.9 mu m channel, judging as non-dust when the brightness temperature threshold is less than or equal to 300K, further subtracting the brightness temperature data of the 7 th wave band from the brightness temperature data of the 14 th wave band when the brightness temperature threshold is greater than 300K to obtain a first difference value, judging as non-dust when the first difference value is less than or equal to 20K, further subtracting the brightness temperature data of the 14 th wave band from the brightness temperature data of the 15 th wave band when the first difference value is greater than 20K to obtain a second difference value, judging as non-dust when the second difference value is greater than or equal to 0K, and calculating a dust index D1 when the second difference value is less than 0K, wherein the formula is as follows:
wherein: r1.6 is the reflectivity measured in the near infrared 1.6 μm wave band (5 channels). And establishing a sand and dust dynamic monitoring graph based on the sand and dust index.
Example 1: concrete case for identifying sand-dust weather based on data scanned by Himapari-8 in 3/30/2020
1. Data import
The software adopted by the technology is Arcgis10.3, and Himapari-8 AHI images of 3 months and 28 days in 2021 and 6 days in 5 months are selected as data.
The Himapari-8 data storage format is.nc, arcgis10.3 can be opened directly. The data of 1, 2, 3 (not bottom graph, the above three bands may not be added), 5, 7, 14 and 15 bands to be used are turned on in Arcgis.
2. Basic threshold discrimination
The middle infrared 3.9 μm channel has obvious reflection to the dust, the 3.9 μm channel is used as the main identification basis, and the identification conditions are as follows:
BT 7 ＞300K (1)
wherein: BT (BT) 7 The luminance temperature of the 3.9 μm channel; 300K is the brightness temperature threshold of the channel with the diameter of 3.9 μm, and the threshold is taken as the primary condition for dust identification.
3. Difference between mid-infrared and thermal infrared channels
3.1 remote sensing spectral characteristic analysis on the sand dust and other ground objects shows that the difference value of a middle infrared 3.9 mu m channel and a thermal infrared channel has obvious difference. In practical application, the difference value combination between the channels of 3.9 μm and 11.2 μm is used as the main basis for dust identification, and the calculation formula is as follows:
BT 7 -BT 14 ＞20K (2)
wherein: BT (BT) 7 Is the luminance temperature, BT, of the 3.9 μm channel 14 The luminance temperature of the 11.2 μm channel. An empirical threshold method 20K is adopted as a main condition for sand dust identification.
3.2 Himapari-8 satellite data contains thermal infrared split window channels near 11 and 12 μm bands, which can eliminate partial cloud effect, and dry dust has different absorption attenuation to 11 and 12 μm radiation, so when BT is used 7 -BT 14 Binding BT less than threshold 14 -BT 15 Greater than 0 is the final condition for identifying dust.
BT 14 -BT 15 ＞0K (3)
4. Calculation of dust Strength index
The 1.6 mu m near-infrared band has obvious reflection on sand dust and can better describe the characteristics of sand dust storm. Compared with the visible light wave band, the wave band is less interfered by atmospheric molecules and particle aerosol, has higher stability in dust monitoring, and finds that the linear consistent relation exists between the dust intensity and the reflectivity of 1.6 mu m through the research of multiple satellite data, thereby being capable of well solving the standard homogenization of dust monitoring. Dust strength (dust index, DI) calculation formula:
wherein: r 1.6 Reflectance measured for the near infrared 1.6 μm band (5 channels).
5. Dynamic observation of dust and sand
The appearance and the fading of the sand and dust weather are a continuous process, the distribution and the intensity of the sand and dust in a certain period are extracted based on the Himapari-8 satellite image, however, the Himapari-8 is used as a static orbit satellite, the ultrahigh time resolution of one image is obtained within 10 minutes, and the dynamic monitoring of the sand and dust is enabled to play an incomparable advantage. Starting at 6 th 8 th 5 th 2021, one sand dust entering from Mongolia affects multiple areas such as inner Mongolia, ningxia, shanxi, gansu, shanxi and Jingjin Ji in China, and a sand dust all-day dynamic monitoring map is manufactured based on Himapari-8 hour-level data according to the method.
6. Evaluation of accuracy
According to the reflection characteristics of sand particles, a combined threshold value method of the brightness and temperature difference values of the intermediate infrared channel and the thermal infrared channel is provided, the occurrence condition of sand can be accurately judged, and the method is simple and reliable. Through the comparative analysis of the sand monitoring results of the long-time sequence images, the influence range and the strength of the sand can be accurately captured in real time. In order to verify the feasibility of the sand-dust identification algorithm, the inversion result is verified by taking an ultraviolet Aerosol Index (AI) product inverted by OMI as reference. The AI index is a key parameter for identifying sand aerosols, with good consistency in east asian continent, and data provided daily can cover almost the globe. Fig. 8 is a comparison of the sand-dust monitoring result at the time of 11 beijing at 5.6.2021 and the OMI sand-dust result, which shows that the algorithm can effectively extract the sand-dust region, and the extracted result has better space-time consistency with the OMI AI product. And the extraction result of Himapari-8 satellite sand dust is verified and analyzed by using an OMI AI product, the range and the strength of the sand dust are basically consistent, and the requirement of business monitoring can be met.
Claims (3)
1. A sand and dust monitoring method based on Himapari-8 satellite remote sensing data is characterized by comprising the following steps:
acquiring scanning data of a Himapari-8 satellite full wave band;
extracting brightness temperature data with the wavelength of 3.9 mu m in a channel, setting a threshold value to be 300K, and taking the threshold value larger than the threshold value as a condition for entering the next judgment;
calculating the brightness temperature difference value of the wavelength between the 3.9 mu m channel and the 11.2 mu m channel, setting the threshold value to be 20K, and taking the difference value larger than the threshold value as the condition for entering the next judgment;
the luminance temperature difference between the 11 μm channel and the 12 μm channel was calculated, and a threshold value of 0K was set, and the difference greater than the threshold value was taken as a final condition for identifying dust.
2. The sand and dust monitoring method based on Himapari-8 satellite remote sensing data as claimed in claim 1, wherein if the final condition is sand and dust, the sand and dust index D1 is calculated, and the calculation formula is as follows:
wherein: r 1.6 The reflectance was measured in the near infrared 1.6 μm band.
3. The sand and dust monitoring method based on the Himapwari-8 satellite remote sensing data as claimed in claim 2, wherein scanning data of the Himapwari-8 satellite full wave band are acquired once every 10 minutes, and a sand and dust dynamic monitoring graph is established based on a sand and dust index D1 obtained by the scanning data.
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| CN116449331B (en) * | 2023-06-20 | 2023-08-15 | 成都远望科技有限责任公司 | Dust particle number concentration estimation method based on W-band radar and meteorological satellite |
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