CN110489505B - Method for identifying low cloud and large fog by dynamic threshold value method - Google Patents

Method for identifying low cloud and large fog by dynamic threshold value method Download PDF

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CN110489505B
CN110489505B CN201910718515.8A CN201910718515A CN110489505B CN 110489505 B CN110489505 B CN 110489505B CN 201910718515 A CN201910718515 A CN 201910718515A CN 110489505 B CN110489505 B CN 110489505B
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CN110489505A (en
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胡斯勒图
尚华哲
彭中
马润
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Institute of Remote Sensing and Digital Earth of CAS
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Abstract

The application discloses a method for identifying low cloud and heavy fog by using a dynamic threshold value method, which solves the problem that the prior art cannot detect the low cloud and heavy fog in a large range and with high time resolution. And extracting satellite wave band data, and constructing an input data set of a low cloud and heavy fog identification algorithm. A dynamic threshold is set. And carrying out first threshold judgment on the input data, and identifying cloud and low-cloud large-fog pixels. The method adopts common optical satellite data, so that the data is easy to acquire, the observation range is larger, and the resolution ratio is higher.

Description

Method for identifying low cloud and large fog by dynamic threshold value method
Technical Field
The application relates to the field of satellite remote sensing low cloud and heavy fog detection, in particular to a method for identifying low cloud and heavy fog by using a dynamic threshold value method.
Background
Fog is a disastrous weather phenomenon, and along with rapid development of social economy and continuous improvement of life quality of people, the harm of the fog is more and more prominent. Fog, particularly dense fog, has a significant impact on visibility, which severely compromises marine, aviation and land traffic safety. Recent studies have found that changes in the frequency, extent and characteristics of fog can affect the radiation balance of the earth-atmosphere system. As a simple example, an increase in anthropogenic pollutant emissions decreases the effective particle radius of the droplets, increasing the optical thickness of the fog, and thus the fog's reflectivity to sunlight is increased, possibly counteracting the greenhouse effect. The main means of past fog monitoring is a meteorological observation station on the ground, and the technical means of utilizing satellite remote sensing to monitor fog is relatively few. However, the conventional monitoring method is limited by the distribution of observation sites and observation time, especially for monitoring a wide range of fog. The satellite data is utilized to monitor the fog, so that the method has the advantages of wide coverage range, rich information content, high time resolution, strong objective authenticity, reliable information source, low cost investment and the like, and any conventional monitoring means cannot replace the conventional monitoring method. For example, a visible light infrared radiometer (AHI) carried by sunflower-8 is taken as the most advanced new-generation meteorological observation sensor in the world, thereby not only greatly improving the meteorological observation capability, but also greatly shortening the global observation time. AHI has 16 bands including visible light, 3 bands (blue, green, red); near infrared, 3 bands; infrared, 10 bands.
The current common method for remote sensing monitoring of low-cloud and heavy fog comprises the steps of identifying cloud and fog information by means of scale characteristics and distribution conditions of cloud and fog top particles, analyzing and separating collected cloud and fog pixel textures on the basis of spectral and texture structural characteristics of fog on satellite images, extracting fog region information by means of a set threshold value, and analyzing visible light and infrared spectral characteristics of the cloud and fog by means of a spectral analysis method through an atmospheric radiation transmission theory to provide some cloud and fog identification and classification indexes. The methods are limited by satellite data time, range and resolution, so that the method has low universality on cloud identification of a specific area.
Disclosure of Invention
The invention provides a method for identifying low cloud and heavy fog by using a dynamic threshold value method, which solves the problem that the prior art cannot detect the low cloud and heavy fog in a large range and high time resolution.
The embodiment of the application provides a method for identifying low cloud and heavy fog by using a dynamic threshold method, which comprises the following steps:
when the SOZ is less than or equal to 72 degrees and the DEM is less than 200m, the first dynamic threshold =0.24; when the SOZ is less than or equal to 72 degrees and the DEM is more than or equal to 200m, the first dynamic threshold =0.20; when the SOZ > 72 °, the first dynamic threshold = -0.014 × SOZ +1.267; the second dynamic threshold = -0.0078 × SOZ +0.6764.
Condition 1: r is 2.3 The second dynamic threshold value is greater than, and DEM is less than 3000;
condition 2: r is 0.64 > first dynamic threshold, and BT 11.2 ＜270；
Condition 3: BT (BT) 8.6 –BT 7.3 Less than or equal to 21.415 or less than or equal to-0.2654 of NDSI.
Pixels which meet either of the conditions 1 or 2 and also meet the condition 3 are identified as clouds, and the others are identified as low-cloud and large-fog; the SOZ is a solar zenith angle, the DEM is an altitude, the NDSI is a snow cover index, the R is a reflectivity, and the BT is a brightness temperature.
Further, the method also comprises the following steps:
and judging a second threshold value for the pixel identified as the cloud, and identifying the cloud and clear sky.
DEM > 0 and NDSI > 0.36 and BT 7.3 –BT 11.2 < -5 > and BT 11.2 –BT 3.9 When > -9.4, the time is identified as cloud, and the other is identified as clear sky; or, when R 0.86 /R 1.6 <0.99 and DEM>And 500, identifying as clear sky.
Further, the method also comprises the following steps:
and judging a third threshold value for the pixels identified as low cloud and heavy fog, and identifying clear sky, cloud and low cloud and heavy fog.
BT 11.2 –BT 12.3 R is less than or equal to 1.035 and more than 0.186 0.46 Is less than or equal to 0.259 and DEM is more than 500, and is identified as clear sky; or, when BT 11.2 BT not less than 270 3.9 /R 0.64 When the cloud number is more than or equal to 1.5, the cloud is identified as low cloud and heavy fog, and the other clouds are identified as cloud.
Further, the satellite band data is from a stationary satellite.
Preferably, the satellite band data is from an AHI sensor carried by geostationary satellite sunflower 8.
The embodiment of the application adopts at least one technical scheme which can achieve the following beneficial effects:
the method adopts common optical satellite data, so that the data is easy to acquire, the observation range is larger, and the resolution ratio is higher.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a flow chart of a method for identifying low cloud and fog using dynamic thresholding;
FIG. 2 is a flow diagram of a method for identifying low cloud and fog using dynamic thresholding to further identify clear sky and cloud flow;
FIG. 3 is a flow chart of a method of identifying low cloud fog using a dynamic threshold method to further identify low cloud fog and clouds;
FIG. 4 is a flow chart of a method for identifying low cloud and fog by a dynamic threshold method, which is used for playing the game of low cloud and fog, clouds and clear sky.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without making any creative effort belong to the protection scope of the present application.
The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.
Example 1
FIG. 1 is a flow chart of a method for identifying low cloud and fog by a dynamic threshold method.
A method for identifying low cloud and fog by dynamic thresholding, comprising the steps of:
step 101, extracting satellite wave band data, and constructing an input data set of a low cloud and heavy fog identification algorithm.
In step 101, firstly, satellite band data is extracted, a low cloud and heavy fog identification basic database is established, then, data of the low cloud and heavy fog identification basic database is processed, and an input data set of a low cloud and heavy fog identification algorithm is established.
The satellite waveband data of the low cloud and heavy fog identification basic database comprises 17 characteristic wavebands such as a visible light waveband, a near infrared waveband, an infrared waveband and a solar zenith angle SOZ, wherein the characteristic wavebands comprise a reflectivity R, a brightness temperature value BT and an altitude DEM.
The satellite band data come from various stationary satellites, and preferably come from AHI sensors carried by stationary satellite sunflower 8.
For example, the 17 characteristic bands specifically include: r 0.47 (albedo_01，0.47um)、R 0.51 (albedo_02，0.51um)、R 0.64 (albedo_03，0.64um)、R 0.86 (albedo_04，0.86um)、R 1.6 (albedo_05，1.6um)、R 2.3 (albedo_06，2.3um)、BT 3.9 (tbb_07，3.9um)、BT 6.2 (tbb_08，6.2um)、BT 6.9 (tbb_09，6.9um)、BT 7.3 (tbb_10，7.3um)、BT 8.6 (tbb_11，8.6um)、BT 9.6 (tbb_12，9.6um)、BT 10.1 (tbb_13，10.1um)、BT 11 。2(tbb_14，11.2um)、BT 12.4 (tbb_15，12.4um)、BT 13.3 (tbb _16, 13.3 um) and SOZ. But also the altitude DEM. The albedo and tbb are names of the sunflower 8 satellite data.
The preprocessing comprises processes of projection transformation, radiometric calibration, band operation and the like.
And calculating a vegetation index NDCI, a snowcover index NDSI, a data difference operation result of partial wave bands and a reflectance ratio of the participating wave bands by using an input data set of the low cloud and heavy fog identification algorithm.
For example, the dynamic thresholding method to identify low cloud and heavy fog datasets requires the following: calculating a vegetation index NDVI and a snow cover index NDSI; the calculation of the difference of the bands comprises: (tbb _14, 11.2 um) and (tbb _07, 3.9um), (tbb _10, 7.3um) and (tbb _14, 11.2 um), (tbb _14, 11.2 um) and (tbb _15, 12.4 um), (tbb _11,8.6 um) and (tbb _10, 7.3um), (tbb _16, 13.3) and (tbb _14, 11.2 um); the calculation of the band reflectivity ratio comprises the following steps: (albedo _03, 0.64um) and (albedo _04, 0.86um), (albedo _04, 0.86um) and (albedo _05, 1.6um), (tbb _07, 3.9um) and (albedo _03, 0.64um), (tbb _11, 8.6um) and (tbb _14, 11.2 um); the bands requiring dynamic threshold setting include: r 0.64 (albedo-03, 0.64um) and R 2.3 (albedo_06，2.3um)。
The NDVI is calculated by (R) 0.86 -R 0.64 )/(R 0.86 +R 0.64 ) NDSI is calculated as (R) 0.51 -R 1.6 )/(R 0.51 +R 1.6 ). Wave (wave)Difference calculation of the segments: BT (BT) 11.2 –BT 3.9 ,BT 11.2 –BT 12.3 ，BT 10.8 –BT 3.9 (ii) a And (3) calculating the reflectivity ratio of the participating wave bands: r 0.86 /R 0.64 BT Brightness temperature 3.9 /R 0.64 . The thresholds for DEM are classified into 4 categories set at 150 meters, 500 meters, 800 meters and 3000 meters.
And 102, setting a dynamic threshold value.
In step 102, the bands requiring dynamic threshold setting include: r 0.64 And R 0.86 Wherein R is 0.64 The calculated threshold is represented by a first dynamic threshold, R 0.86 The calculated threshold value is represented by a second dynamic threshold value, and the calculating method comprises the following steps: when the SOZ is less than or equal to 72 degrees and the DEM is less than 200m, the first dynamic threshold =0.24; when the SOZ is less than or equal to 72 degrees and the DEM is more than or equal to 200m, the first dynamic threshold =0.20; when the SOZ > 72 °, the first dynamic threshold = -0.014 × SOZ +1.267; the second dynamic threshold = -0.0078 × SOZ +0.6764. R is the reflectivity, and SOZ is the solar zenith angle.
And 103, judging a first threshold value of the input data, and identifying cloud and low-cloud and large-fog pixels.
The threshold data of the first threshold comprises R 0.64 、R 2.3 、BT 11.2 、BT 8.6 –BT 7.3 And a DEM.
The first discriminant includes:
R 2.3 the DEM is less than 3000, and the condition is defined as 1;
R 0.64 > first dynamic threshold, and BT 11.2 < 270, defined as Condition 2;
BT 8.6 –BT 7.3 condition 3 is defined as ≦ 21.415 or NDSI ≦ -0.2654.
Pixels meeting one of the conditions 1 and 2 and meeting the condition 3 are identified as clouds, and the other pixels are identified as low clouds and heavy fog; the NDSI is the snowquilt index, BT is the brightness temperature, and DEM is the altitude.
And 104, outputting a low-cloud and large-fog identification result space distribution map.
The programming language for drawing the low cloud and fog recognition result can be Python, also can be Idl, and can also be R language or Java and other programming languages, the Python language has rich function call interfaces, satellite data reading is easy, data processing is advantageous, and the preferred programming language for drawing the low cloud and fog recognition result is Python.
The method for drawing the low cloud and fog identification space distribution map can generate a JPG picture format, a PDF format, a PNG or TIF picture format, and the like, which is not limited herein.
It should be noted that the formats of the satellite band data and the low cloud and fog detection data are NETCDF format, HDF format and HSD format. Since most satellites use NETCDF in binary format, the satellite band data and low cloud and fog detection data are preferably in NETCDF format.
Example 2
Fig. 2 is a flow chart of a method for identifying low cloud and heavy fog by a dynamic threshold method to further identify clear sky and cloud flow.
Step 101, extracting satellite wave band data and constructing an input data set of a low cloud and heavy fog identification algorithm.
And 102, setting a dynamic threshold, wherein the threshold calculated by R0.64 is represented by a first dynamic threshold, and the threshold calculated by R0.86 is represented by a second dynamic threshold.
And 103, judging a first threshold value of the input data, and identifying cloud and low-cloud heavy fog pixels.
Step 105, carrying out second threshold judgment on the pixels identified as the cloud, and identifying the cloud and clear sky;
the threshold data of the second threshold comprises: NDSI, BT 7.3 –BT 11.2 、BT 11.2 –BT 3.9 、R 0.86 /R 1.6 And a DEM;
the second determination equation is: DEM > 0 and NDSI > 0.36 and BT 7.3 –BT 11.2 < -5 > and BT 11.2 –BT 3.9 When > -9.4, the time is identified as cloud, and the other is identified as clear sky; or, when R is 0.86 /R 1.6 <0.99 and DEM>And 500, identifying as clear sky.
And 104, outputting a low-cloud and large-fog identification result space distribution map.
Example 3
FIG. 3 is a flow chart of a method for identifying low cloud and fog using a dynamic threshold method to further identify low cloud and fog.
Step 101, extracting satellite wave band data, and constructing an input data set of a low cloud and heavy fog identification algorithm.
And 102, setting a dynamic threshold, wherein the threshold calculated by R0.64 is represented by a first dynamic threshold, and the threshold calculated by R0.86 is represented by a second dynamic threshold.
And 103, judging a first threshold value of the input data, and identifying cloud and low-cloud and large-fog pixels.
And 106, judging a third threshold value of the pixel identified as the low cloud and the heavy fog, and identifying the cloud and the low cloud and the heavy fog.
The threshold data of the third threshold comprises: BT (BT) 11.2 –BT 12.3 、R 0.46 、BT 11.2 And BT 3.9 /R 0.64 ；
The third discriminant is: BT (BT) 11.2 –BT 12.3 R is less than or equal to 1.035 and more than 0.186 0.46 Is less than or equal to 0.259 and DEM is more than 500, and is identified as clear sky; or, when BT 11.2 BT not less than 270 3.9 /R 0.64 When the cloud number is more than or equal to 1.5, the cloud is identified as low cloud and heavy fog, and the other cloud is identified as cloud.
And 104, outputting a low cloud and large fog recognition result space distribution map.
Example 4
FIG. 4 is a flow chart of a method for identifying low cloud and fog by a dynamic threshold method, which is used for playing the game of low cloud and fog, clouds and clear sky.
Step 101, extracting satellite wave band data and constructing an input data set of a low cloud and heavy fog identification algorithm.
And 102, setting a dynamic threshold, wherein the threshold calculated by R0.64 is represented by a first dynamic threshold, and the threshold calculated by R0.86 is represented by a second dynamic threshold.
And 103, judging a first threshold value of the input data, and identifying cloud and low-cloud and large-fog pixels.
Step 105, carrying out second threshold judgment on the pixels identified as the cloud, and identifying the cloud and clear sky;
and 106, judging a third threshold value of the pixel identified as the low cloud and the heavy fog, and identifying the cloud and the low cloud and the heavy fog.
And 104, outputting a low cloud and large fog recognition result space distribution map.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims (5)

1. A method for identifying low cloud and large fog by dynamic threshold value is characterized by comprising the following steps:
extracting satellite wave band data, and constructing an input data set of a low cloud and heavy fog identification algorithm;
setting a dynamic threshold value:
when the SOZ is less than or equal to 72 degrees and the DEM is less than 200m, the first dynamic threshold =0.24; when the SOZ is less than or equal to 72 degrees and the DEM is more than or equal to 200m, the first dynamic threshold =0.20; when the SOZ > 72 °, the first dynamic threshold = -0.014 × SOZ +1.267; a second dynamic threshold = -0.0078 × SOZ +0.6764;
condition 1,R 2.3 The second dynamic threshold value is greater than, and DEM is less than 3000;
condition 2,R 0.64 Is greater than a first dynamic threshold value, and BT11.2 is less than 270;
under the condition of 3,BT8.6-BT7.3 is less than or equal to 21.415 or NDSI is less than or equal to-0.2654;
pixels which meet either of the conditions 1 or 2 and also meet the condition 3 are identified as clouds, and other pixels are identified as low-cloud and heavy-fog pixels; the SOZ is a solar zenith angle, the DEM is an altitude, the NDSI is a snowquilt index, the R is a reflectivity, and the BT is a brightness temperature.
2. The method for dynamic threshold recognition of low cloud and fog as claimed in claim 1, further comprising the steps of:
judging the pixels identified as the cloud:
DEM > 0 and NDSI > 0.36 and BT 7.3 –BT 11.2 < -5 > and BT 11.2 –BT 3.9 When > -9.4, the time is identified as cloud, and the other is identified as clear sky; or, when R is 0.86 /R 1.6 <0.99 and DEM>And 500, identifying as clear sky.
3. The method for dynamic threshold recognition of low cloud and fog as claimed in claim 1, further comprising the steps of:
judging the pixels identified as low cloud and heavy fog;
BT 11.2 –BT 12.3 is less than or equal to 1.035, R is more than 0.186 and less than or equal to 0.259, DEM is more than 500, and the product is identified as clear sky; or, when BT 11.2 BT not less than 270 3.9 /R 0.64 When the cloud number is more than or equal to 1.5, the cloud is identified as low cloud and heavy fog, and the other clouds are identified as cloud.
4. The method of claim 1, wherein the satellite band data is from a geostationary satellite.
5. The method of claim 4, wherein the satellite band data is from an AHI sensor carried by a sunflower 8 geostationary satellite.
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