CN113822381A - Sea cloud detection method based on sea temperature difference threshold - Google Patents
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Abstract
The invention discloses a sea cloud detection method based on a sea temperature difference threshold, which comprises the following steps: dividing the global earth surface into 7 types, adopting 12 cloud detection classifiers, and assuming that the cloud detection conditional probabilities under all classification characteristics are mutually independent to obtain a global Bayesian cloud detection result; aiming at clear sky ocean pixels obtained by Bayesian cloud detection, calculating the sea temperature of the polar orbit meteorological satellite MERSI inversion by using the split window brightness temperature, the satellite zenith angle and the regression coefficient according to a formula; and setting a temperature threshold according to the characteristics of the MERSI instrument, inverting sea temperature and high-precision analysis field sea temperature space-time matching, and performing projection interpolation, and then performing pixel-by-pixel dynamic comparison to perform further cloud detection confirmation. The sea cloud detection method based on the sea temperature difference threshold value adopts naive Bayes cloud detection, the calculation is simple and rapid, the cloud detection probability distribution obtained by the Bayes method is smoother compared with the threshold cloud detection method, and the accuracy of the cloud detection on the sea is improved.
Description
Technical Field
The invention relates to the technical field of meteorological remote sensing, in particular to a sea cloud detection method based on a sea temperature difference threshold value.
Background
The Fengyun No. three D star (FY 3D) is a second generation polar region orbit sun synchronous meteorological satellite in China, a Medium Resolution Spectral Imager (Medium Resolution Spectral Imager-II, MERSI II) is used as a key visible light infrared band imaging instrument, and the number of imaging bands is 25, and the central wavelength Spectral range of the bands is 0.412-12.0 mu m. Wherein, a medium wave infrared channel with a center wavelength of 3.8 μm and a long wave infrared channel with a center wavelength of 10.8 μm and 12.0 μm, i.e. a split window channel, can be used to estimate Sea Surface Temperature (SST). The inversion accuracy of FY3D/MERSI SST is highly dependent on the performance, positioning accuracy and calibration accuracy of MERSI instruments, and the accuracy of cloud detection products which are upstream products of the MERSI instruments. SST algorithms and cloud detection algorithms are inseparable, and the main goal of sea temperature/cloud detection algorithms is to ensure that cloud detection provides reliable "confident cloud" pixel identification, so that SST algorithms do not have to consider cloud detection.
Cloud detection plays an extremely important role in remote sensing application, and the quality of the cloud detection has a great influence on the inversion of quantitative products. SST products need high-precision cloud detection information, and can obtain accurate SST inversion under the condition of clear sky and no cloud. Since radiation entering a satellite is often polluted by cloud, cloud detection becomes one of the key challenges facing remote sensing inversion of SST from infrared satellites in order to obtain accurate SST.
The patent CN104820250B discloses a processing method for detecting cloud on the sea of a polar orbit meteorological satellite VIRR, which utilizes the climate change rule of sea temperature and the matching statistical information of a long-time sequence of a VIRR satellite detecting instrument, determines an applicable sea temperature regression coefficient according to the actual spectral characteristics, the satellite-borne running condition and the calibration updating condition of a satellite remote sensing instrument, sets a temperature threshold value, and judges whether the sea pixel is in cloud or clear air through quantitative calculation on a sea target observed by the satellite VIRR, but the processing method has the defect that the calculation process is too complex.
Disclosure of Invention
Aiming at the technical problems in the related art, the invention provides an offshore cloud detection method based on a sea temperature difference threshold, which can overcome the defects in the prior art.
In order to achieve the technical purpose, the technical scheme of the invention is realized as follows:
a sea cloud detection method based on a sea temperature difference threshold value comprises the following steps:
s1 data reading and preprocessing, namely reading L1B and GEO data of a polar orbiting meteorological satellite MERSI, and performing physical quantity conversion; reading NWP, OISST and other data, and performing radiation transmission calculation;
s2 naive Bayesian cloud detection, wherein the global earth surface is divided into 7 types, 12 cloud detection classifiers are adopted, and the cloud detection conditional probabilities under all classification characteristics are assumed to be mutually independent to obtain a global Bayesian cloud detection result;
s3, constructing a long-time sequence matching data set according to the characteristics of the MERSI instrument, and performing statistical analysis to obtain regression coefficients a 0-a 3 required by sea temperature inversion;
s4 sea temperature dynamic inversion, calculating polar orbit meteorological satellite MERSI inversion sea temperature according to the formula:
T S =a 0+a 1 T 4+a 2 (T 4-T 5)+a 3(T 4-T 5)(secθ-1)
wherein, TSRepresenting sea temperature, T, of polar orbit meteorological satellite MERSI inversion4Represents a channel brightness temperature of 10.3 to 11.3 μm, T5Expressing the channel brightness temperature of 11.5-12.5 mu m, expressing the satellite zenith angle by theta, and performing clear sky ocean pixel sea temperature inversion according to the Bayesian cloud detection result;
s5, the sea temperature of the high-precision analysis field is read, the sea temperature of the high-precision analysis field is inverted by the MERSI, the space-time matching and the projection interpolation of the sea temperature are carried out, and the pixel-by-pixel sea temperature difference is obtained by processing:
δ= T S - T a ，
wherein, delta represents the pixel-by-pixel sea temperature difference, TaRepresenting the sea temperature of a high-precision analysis field;
s6, based on cloud detection of a sea temperature difference threshold value, cloud pollution can cause deviation of polar orbit meteorological satellite MERSI inversion sea temperature and clear air sea temperature, and further cloud detection confirmation is carried out through pixel-by-pixel dynamic comparison of polar orbit meteorological satellite MERSI inversion sea temperature and high-precision analysis field sea temperature;
s7, outputting detection results according to pixels, wherein the detection results comprise a confident cloud (0), a possible cloud (1), a possible clear sky (2) and a confident clear sky (3).
Further, the channel light temperature is obtained by reading the L1B channel radiation value, the count value, the channel wavelength and the calibration table and performing physical quantity conversion.
Further, the satellite zenith angle is obtained by reading the GEO file.
Further, the naive bayes cloud detection step comprises: and reading the lookup table, calculating the numerical value of each classifier, and performing cloud probability calculation according to a naive Bayes formula to obtain a global Bayes cloud detection result.
Further, when sea temperature is inverted through a polar orbit meteorological satellite MERSI in S6 and sea temperature is subjected to pixel-by-pixel dynamic comparison in a high-precision analysis field, if the delta is greater than Th, the pixel is a cloud pixel, wherein Th is a sea temperature difference threshold.
The invention has the beneficial effects that: the sea cloud detection method based on the sea temperature difference threshold value adopts naive Bayes cloud detection, the calculation is simple and rapid, and compared with the threshold cloud detection method, the cloud detection probability distribution obtained by the Bayes method is smoother; meanwhile, the change rule of the sea temperature and the statistical information of the MERSI of the satellite detection instrument are utilized to set a temperature threshold value, and cloud or clear sky confirmation is carried out on the basis of Bayesian cloud detection, so that the accuracy of cloud detection on the ocean is improved, and the accuracy of sea temperature inversion is improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a flowchart of an offshore cloud detection method based on a sea temperature difference threshold according to an embodiment of the present invention.
Detailed Description
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 that can be derived by one of ordinary skill in the art from the embodiments given herein are intended to be within the scope of the present invention.
As shown in fig. 1, the offshore cloud detection method based on the sea temperature difference threshold according to the embodiment of the present invention includes the following steps:
s1 data reading and preprocessing, namely reading L1B and GEO data of a polar orbiting meteorological satellite MERSI, and performing physical quantity conversion and angle calculation; reading data such as numerical weather forecast (NWP), Optimal Interpolation Sea Surface Temperature (OISST) and the like, performing space-time matching processing, and performing radiation transmission calculation to obtain the clear air brightness of the infrared channel atmospheric top and the clear air reflectivity of the visible light channel;
s2 naive Bayesian cloud detection, wherein the global earth surface is divided into 7 types, 12 cloud detection classifiers are adopted, and the cloud detection conditional probabilities under all classification characteristics are assumed to be mutually independent to obtain a global Bayesian cloud detection result;
s3, constructing a long-time sequence matching data set according to the characteristics of the MERSI instrument, and performing statistical analysis to obtain regression coefficients a 0-a 3 required by sea temperature inversion;
s4 sea temperature dynamic inversion, calculating polar orbit meteorological satellite MERSI inversion sea temperature according to the formula:
T S =a 0+a 1 T 4+a 2 (T 4-T 5)+a 3(T 4-T 5)(secθ-1)
wherein, TSRepresenting sea temperature, T, of polar orbit meteorological satellite MERSI inversion4Represents a channel brightness temperature of 10.3 to 11.3 μm, T5Expressing the channel brightness temperature of 11.5-12.5 mu m, expressing the satellite zenith angle by theta, and performing clear sky ocean pixel sea temperature inversion according to the Bayesian cloud detection result;
s5, the sea temperature of the high-precision analysis field is read, the sea temperature of the high-precision analysis field and the polar orbit meteorological satellite MERSI are inverted to the sea temperature for time-space matching and projection interpolation, and the pixel-by-pixel sea temperature difference delta =is obtained through processing T S - T a Wherein T isaRepresenting the sea temperature of a high-precision analysis field;
s6, based on cloud detection of a sea temperature difference threshold value, cloud pollution can cause deviation of polar orbit meteorological satellite MERSI inversion sea temperature and clear air sea temperature, and further cloud detection confirmation is carried out through pixel-by-pixel dynamic comparison of polar orbit meteorological satellite MERSI inversion sea temperature and high-precision analysis field sea temperature;
s7, outputting the ocean cloud detection results according to pixels, wherein the detection results comprise a confident cloud (0), a possible cloud (1), a possible clear sky (2) and a confident clear sky (3).
Wherein the naive Bayesian cloud detection comprises: and reading the lookup table, calculating the numerical value of each classifier, and performing cloud probability calculation according to a naive Bayes formula to obtain a global Bayes cloud detection result. The channel brightness temperature is obtained by reading an L1B channel radiation value, a counting value, a channel wavelength and a calibration table to perform physical quantity conversion, and the satellite zenith angle is obtained by reading a GEO file.
Sea temperature is inverted through a polar orbit meteorological satellite MERSI, and sea temperature is dynamically compared with a high-precision analysis field by picture elements, when delta is greater than Th, the picture elements are cloud picture elements, and Th is a sea temperature difference threshold value.
Example 1
1) Reading information such as an L1B channel radiation value, a counting value, a channel wavelength, a calibration table and the like, and performing physical quantity conversion to obtain information such as channel brightness temperature, reflectivity and the like; reading the GEO file to obtain longitude, latitude, sun zenith angle, sun azimuth angle, satellite zenith angle and satellite azimuth angle, and calculating to obtain information such as relative azimuth angle, sun flare angle, zenith angle cosine value and the like;
2) time matching is carried out on numerical weather forecast (NWP) data and L1B observation data, and pixel-by-pixel spatial interpolation processing is carried out according to the geographic positioning (GEO) file spatial range; time matching of the Optimal Interpolation Sea Surface Temperature (OISST) and the Canada meteorological bureau analysis field sea temperature (CMC) and L1B observation data, and pixel-by-pixel interpolation according to the GEO space range; static auxiliary data such as elevation, sea-land modules, emissivity, coast mask, earth surface type, emissivity and the like are subjected to space matching to obtain auxiliary data in a GEO space range;
3) calculating a clear sky infrared channel Radiation Transmission Mode (RTM) to obtain clear sky brightness temperatures of 3.8um, 6.7um, 11um and 12um channel atmospheric tops; calculating a clear sky reflectivity Radiation Transmission Mode (RTM) to obtain the clear sky reflectivities of 0.65um, 1.60um and 3.8um atmospheric tops;
4) naive Bayesian cloud detection, which is to divide the global earth surface into 7 types, adopt 12 cloud detection classifiers, assume that the cloud detection conditional probabilities under each classification characteristic are mutually independent (assume that the conditional probabilities of the classification characteristics used for the classifiers are mutually independent under the condition determined by classification), and obtain the global Bayesian cloud detection result; the method specifically comprises the following steps: reading a lookup table, calculating the numerical value of each classifier, and carrying out cloud probability calculation according to a naive Bayes formula to obtain a global Bayes cloud detection result;
5) and (3) performing real-time sea temperature inversion, reading a sea temperature regression coefficient, and calculating the sea temperature of the ocean pixels according to a high-precision regression equation:
T S =a 0+a 1 T 4+a 2 (T 4-T 5)+a 3(T 4-T 5)(secθ-1)
wherein, TSRepresenting sea temperature, T, of polar orbit meteorological satellite MERSI inversion4Represents a channel brightness temperature of 10.3 to 11.3 μm, T5Represents a channel brightness temperature of 11.5 to 12.5 μm, a0、a1、a2、a3Expressing regression coefficients, theta representing satellite zenith angles, based on cloud detectionPerforming sea temperature inversion on the clear sky ocean pixels;
6) high-precision analysis of field sea temperature and satellite inversion sea temperature space-time matching, projection interpolation and processing to obtain pixel-by-pixel sea temperature difference delta = T S - T a Wherein T isaRepresenting the sea temperature of a high-precision analysis field;
7) based on cloud detection of a sea temperature difference threshold value, cloud pollution can cause deviation of polar orbit meteorological satellite MERSI inversion sea temperature and clear sky sea temperature, further cloud detection confirmation is carried out through pixel-by-pixel dynamic comparison of polar orbit meteorological satellite MERSI inversion sea temperature and high-precision analysis field sea temperature, when delta is greater than Th, a pixel is a cloud pixel, and Th is a sea temperature difference threshold value.
In summary, according to the technical scheme of the invention, the processing method for detecting the marine clouds of the resolution ratio spectral imager in the polar orbit meteorological satellite is provided by adopting naive Bayes cloud detection, setting the temperature threshold value by utilizing the change rule of the sea temperature and the statistical information of the MERSI of the satellite detection instrument, and confirming whether the clouds exist or the clear sky is carried out on the basis of the Bayes cloud detection, so that the accuracy of the marine cloud detection is improved, and high-precision marine cloud detection data is provided for the sea temperature inversion.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (5)
1. A sea cloud detection method based on a sea temperature difference threshold value is characterized by comprising the following steps:
s1 data reading and preprocessing, namely reading L1B and GEO data of a polar orbiting meteorological satellite MERSI, and performing physical quantity conversion; reading NWP and OISST data, and performing radiation transmission calculation;
s2 naive Bayesian cloud detection, wherein the global earth surface is divided into 7 types, 12 cloud detection classifiers are adopted, and the cloud detection conditional probabilities under all classification characteristics are assumed to be mutually independent to obtain a global Bayesian cloud detection result;
s3, according to the characteristics of the MERSI instrument, constructing a long-time sequence matching data set, and carrying out statistical analysis to obtain a regression coefficient a required by sea temperature inversion0、a1、a2、a3；
S4 sea temperature dynamic inversion, calculating polar orbit meteorological satellite MERSI inversion sea temperature according to the formula:
T S =a 0+a 1 T 4+a 2 (T 4-T 5)+a 3(T 4-T 5)(secθ-1)
wherein, TSRepresenting sea temperature, T, of polar orbit meteorological satellite MERSI inversion4Represents a channel brightness temperature of 10.3 to 11.3 μm, T5Expressing the channel brightness temperature of 11.5-12.5 mu m, expressing the satellite zenith angle by theta, and performing clear sky ocean pixel sea temperature inversion according to the Bayesian cloud detection result;
s5, the sea temperature of the high-precision analysis field is read, the sea temperature of the high-precision analysis field is inverted by the MERSI, the space-time matching and the projection interpolation of the sea temperature are carried out, and the pixel-by-pixel sea temperature difference is obtained by processing:
δ= T S - T a ，
wherein, delta represents the pixel-by-pixel sea temperature difference, TaRepresenting the sea temperature of a high-precision analysis field;
s6, based on cloud detection of a sea temperature difference threshold value, performing further cloud detection confirmation by inverting sea temperature and high-precision analysis field sea temperature pixel-by-pixel dynamic comparison through a polar orbit meteorological satellite MERSI;
s7, outputting detection results according to image elements, wherein the detection results comprise confident clouds, possible clear sky and confident clear sky.
2. The sea cloud detection method based on the sea temperature difference threshold value according to claim 1, wherein the channel brightness temperature is obtained by reading an L1B channel radiation value, a counting value, a channel wavelength and a calibration table and performing physical quantity conversion.
3. The sea cloud detection method based on the sea temperature difference threshold value according to claim 1, wherein the satellite zenith angle is obtained by reading a GEO file.
4. The sea cloud detection method based on sea temperature difference threshold of claim 1, wherein the naive bayes cloud detection step comprises: and reading the lookup table, calculating the numerical value of each classifier, and performing cloud probability calculation according to a naive Bayes formula to obtain a global Bayes cloud detection result.
5. The sea cloud detection method based on the sea temperature difference threshold value according to claim 1, wherein when sea temperature is inverted through polar orbit meteorological satellite MERSI in S6 and sea temperature is dynamically compared with high-precision analysis field sea temperature pixel by pixel, if | δ | > Th, the pixel is a cloud pixel, wherein Th is the sea temperature difference threshold value.
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