CN117216504B - Solar average earth surface temperature remote sensing estimation method for polar orbit satellite - Google Patents
Solar average earth surface temperature remote sensing estimation method for polar orbit satellite Download PDFInfo
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Abstract
The invention belongs to the technical field of remote sensing application, and relates to a polar orbit satellite daily average surface temperature remote sensing estimation method, which comprises the following steps: acquiring multi-source data; preprocessing multi-source data; constructing a weight regression model and a coefficient lookup table of the weight regression model, wherein the weight regression model comprises daily average surface temperature data of various polar satellites; filling the instantaneous earth surface temperature under the cloud of the polar orbit satellite based on the same difference value between ERA5-Land earth surface temperature data and polar orbit satellite earth surface temperature data under clear sky observation and cloud observation in one day; and selecting the matched weight regression model and coefficients of the weight regression model pixel by pixel, and calculating the average surface temperature of the day. The method solves the problem of poor applicability of the current average daily surface temperature estimation model; on the premise of ensuring the precision, the instantaneous surface temperature under the polar orbit satellite cloud is simply and rapidly filled, and the spatial continuity of the daily surface temperature is improved.
Description
Technical Field
The invention belongs to the technical field of remote sensing application, and particularly relates to a solar average surface temperature remote sensing estimation method of polar orbit satellites.
Background
The surface temperature is an important biophysical parameter affecting the surface energy balance and water circulation, and has important significance for related researches such as agriculture, geography, meteorology and the like. With the rapid development of satellite remote sensing technology, it is possible to obtain large-scale ground surface temperature data efficiently and accurately. In particular, a series of global high-resolution surface temperature products are developed by utilizing polar orbit satellite thermal infrared image measurement data. However, the polar orbit satellite acquires instantaneous earth surface temperature, which is difficult to reflect the continuous change condition of earth surface temperature, and the daily average earth surface temperature can estimate the earth surface temperature, earth surface air temperature and earth surface vapor emission, which are key indexes for monitoring global climate change for a long time. Therefore, estimating the average daily surface temperature from limited daily instantaneous observations of polar satellites is of great practical importance.
Currently, studies for estimating the average earth surface temperature using polar orbit satellite instantaneous earth surface temperature data mainly have two problems:
(1) The estimation models are poor in applicability, at present, four times daily instantaneous observation data of Aqua and Terra polar satellites are usually utilized in academic circles, daily surface temperature is estimated by constructing a linear regression model or a surface temperature daily cycle model, however, the estimation models are only suitable for Aqua and Terra polar satellites after 2000, the applicability is poor, on one hand, the transit time of different polar satellites is different, and the constructed linear regression model is only suitable for the polar satellites at the transit time; on the other hand, a single polar orbit satellite can only observe twice a world every day, and cannot meet the requirement of at least four observations of a daily earth surface temperature cycle model;
(2) The produced daily surface temperature product is spatially discontinuous. The thermal infrared remote sensing is easily affected by cloud, so that the polar orbit satellite instantaneous surface temperature data is missing in a large range, and in addition, the existing daily surface temperature estimation model is based on clear sky instantaneous observation, so that the current polar orbit satellite daily surface temperature product has a large range of missing values and is discontinuous in space.
Disclosure of Invention
In order to solve the problems of poor applicability and discontinuous product space of the current daily surface temperature estimation model, the invention provides a polar orbit satellite daily surface temperature remote sensing estimation method, which comprises the following steps:
acquiring multi-source data; the multi-source data comprise ground station observation data, ERA5-Land surface temperature data and polar orbit satellite surface temperature data;
preprocessing multi-source data;
constructing a weight regression model and a coefficient lookup table of the weight regression model, wherein the weight regression model comprises daily average surface temperature data of various polar satellites;
filling the instantaneous earth surface temperature under the cloud of the polar orbit satellite based on the same difference value between ERA5-Land earth surface temperature data and polar orbit satellite earth surface temperature data under clear sky observation and cloud observation in one day;
and selecting a matched weight regression model and coefficients of the weight regression model pixel by pixel based on the filled instantaneous surface temperature under the polar orbit satellite cloud, and calculating the daily surface temperature.
On the basis of the technical scheme, the invention can be improved as follows.
Further, the multi-source telemetry data includes:
ground site observation data distributed throughout the world in various climatic backgrounds and land cover types;
surface temperature data for each hour and month in the long time sequence;
polar orbit satellite instantaneous surface temperature data.
Further, preprocessing the multi-source data, including:
calculating the instantaneous surface temperature of the station according to the station long-wave uplink radiation data and the station long-wave downlink radiation data;
calculating the daily average surface temperature of the station according to the instantaneous surface temperature of the station;
resampling the ERA5-Land surface temperature data by using a Python programming language and a plurality of open source libraries to unify the resolution of the ERA5-Land surface temperature data and the polar orbit satellite surface temperature data;
cloud detection and abnormal point elimination are carried out on the polar orbit satellite instantaneous surface temperature data.
Further, calculating a site instantaneous surface temperature, comprising:
is provided withFor site instantaneous surface temperature, +.>For uplink long wave radiation, < >>For downstream long wave radiation, < >>Is the surface broadband emissivity +.>For the stefin-boltzmann constant, then:
；
calculating the daily surface temperature of the site according to the instantaneous surface temperature of the site, comprising:
is provided withFor site average surface temperature, < >>Site surface temperature for each time of day, +.>For the number of valid observations of a site in a day, then:
。
further, according to whether the day-to-night transit time of the polar orbit satellite is blocked by cloud, respectively constructing weight regression models of daily surface temperature data of various polar orbit satellites for each polar orbit satellite, including:
is provided withIs the Japanese surface temperature of polar orbit satellite, < >>For the daytime clear sky instantaneous surface temperature of polar orbit satellite, < >>Cloud instantaneous surface temperature in daytime for polar satellites, < >>For the night clear sky instantaneous surface temperature of polar orbit satellite, < > the night clear sky instantaneous surface temperature>Cloud instantaneous surface temperature for polar satellites at night, < ->、/>And->The weight regression model coefficient;
if the two observations are clear sky observations, the formula of the average day surface temperature of the polar orbit satellite is as follows:
；
if the daytime is clear air observation and the night is cloud observation, the formula of the average day surface temperature of the polar orbit satellite is as follows:
；
if the daytime is cloudy observation and the night is clear sky observation, the formula of the average day surface temperature of the polar orbit satellite is as follows:
；
if both day and night observations are cloud observations, the formula of the average day surface temperature of the polar orbit satellite is as follows:
。
further, constructing a coefficient lookup table of the weight regression model, comprising:
screening the site instantaneous surface temperature and the daily surface temperature which are matched in time according to the daily day and night transit time of the polar orbit satellite;
judging whether the instantaneous surface temperature of the screened site is covered by cloud according to the polar orbit satellite quality control file;
and taking the instantaneous surface temperature and the daily surface temperature of the station after cloud detection as training samples, acquiring weight regression model coefficients of four daily surface temperatures of each polar orbit satellite, which are covered by the cloud and are not covered by the cloud, at the moment of day and night transit, and constructing a coefficient lookup table.
Further, filling the instantaneous surface temperature under the polar orbit satellite cloud, comprising:
a. the day and night two observations of the polar orbit satellite comprise clear sky observations and cloudy observations, and the instantaneous surface temperature under the clouds of the polar orbit satellite is filled based on the same difference value of ERA5-Land surface temperature data and polar orbit satellite surface temperature data in one day under the clear sky observations and the cloudy observations;
is provided withIs at->The temperature difference value of the sunny ground surface in the ground surface temperature data of the polar orbit satellite and the ERA5-Land at the moment,is a polar orbit satellite->Sunny ground surface temperature at moment +.>Is +.in ERA5-Land surface temperature data>Surface temperature at time,/->Is a polar orbit satellite->Cloud subsurface temperature after time filling, +.>Is +.in ERA5-Land surface temperature data>The earth surface temperature at the moment;
；
；
b. the polar orbit satellite day and night two observations are cloud observations, and the two polar orbit satellite subsurface instantaneous surface temperatures are filled on the assumption that the difference between the ERA5-Land instantaneous surface temperature and the moon average surface temperature at the corresponding moment is the same as the difference between the polar orbit satellite instantaneous surface temperature data and the moon average surface temperature at the corresponding moment;
is provided withIs ERA5-Land day +.>Surface temperature and->The difference in the surface temperature of the month at the moment,is ERA5-Land day +.>Surface temperature at time,/->Is +.in ERA5-Land surface temperature data>Time of day, month average surface temperature,/->Is a polar orbit satellite->The temperature of the earth surface of the sunny month at moment, +.>Is a polar orbit satellite->Effective observation times in time month, +.>For the instantaneous surface temperature of polar satellites, +.>Is a polar orbit satellite->And (3) the temperature of the subsurface of the cloud after time filling, namely:
；
；
wherein,。
the beneficial effects of the invention are as follows: according to the invention, when estimating the average earth surface temperature, the earth site observation data distributed on various climatic backgrounds and land coverage types around the world are utilized to simulate the clear sky or the instantaneous earth surface temperature under the cloud at the transit time of the polar orbit satellite, and the problem of poor applicability of the current average earth surface temperature estimation model is solved by constructing a weight regression model and a coefficient lookup table of the weight regression model, wherein the weight regression model comprises the average earth surface temperatures of various polar orbit satellites; the invention is based on the difference of the instantaneous or month-average surface temperature observed under ERA5-Land and polar orbit satellite cloud and the difference under clear sky observation condition, and on the premise of ensuring the precision, the instantaneous surface temperature under polar orbit satellite cloud is simply and rapidly filled, and the spatial continuity of the day-average surface temperature is improved.
Drawings
Fig. 1 is a schematic diagram of a polar orbit satellite daily surface temperature remote sensing estimation method according to an embodiment of the invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
As an embodiment, as shown in fig. 1, to solve the above technical problem, the present embodiment provides a method for remote sensing estimation of solar average surface temperature of polar satellites, including:
acquiring multi-source data; the multi-source data comprise ground station observation data, ERA5-Land surface temperature data and polar orbit satellite surface temperature data;
preprocessing multi-source data;
constructing a weight regression model and a coefficient lookup table of the weight regression model, wherein the weight regression model comprises daily average surface temperature data of various polar satellites;
filling the instantaneous earth surface temperature under the cloud of the polar orbit satellite based on the same difference value between ERA5-Land earth surface temperature data and polar orbit satellite earth surface temperature data under clear sky observation and cloud observation in one day;
and selecting a matched weight regression model and coefficients of the weight regression model pixel by pixel based on the filled instantaneous surface temperature under the polar orbit satellite cloud, and calculating the daily surface temperature.
On the basis of the technical scheme, the invention can be improved as follows.
Optionally, the multi-source telemetry data includes:
ground site observation data distributed throughout the world in various climatic backgrounds and land cover types;
surface temperature data for each hour and month in the long time sequence;
polar orbit satellite instantaneous surface temperature data.
Optionally, preprocessing the multi-source data includes:
calculating the instantaneous surface temperature of the station according to the station long-wave uplink radiation data and the station long-wave downlink radiation data;
calculating the daily average surface temperature of the station according to the instantaneous surface temperature of the station;
resampling the ERA5-Land surface temperature data by using a Python programming language and a plurality of open source libraries to unify the resolution of the ERA5-Land surface temperature data and the polar orbit satellite surface temperature data;
cloud detection and abnormal point elimination are carried out on the polar orbit satellite instantaneous surface temperature data.
Optionally, calculating the site instantaneous surface temperature includes:
is provided withFor site instantaneous surface temperature, +.>For uplink long wave radiation, < >>For downstream long wave radiation, < >>Is the surface broadband emissivity +.>For the stefin-boltzmann constant, then:
；
calculating the daily surface temperature of the site according to the instantaneous surface temperature of the site, comprising:
is provided withFor site average surface temperature, < >>Site surface temperature for each time of day, +.>For the number of valid observations of a site in a day, then:
。
optionally, according to whether the day-to-night transit time of the polar orbit satellite is blocked by cloud, respectively constructing weight regression models of daily surface temperature data of various polar orbit satellites for each polar orbit satellite, including:
is provided withIs the Japanese surface temperature of polar orbit satellite, < >>For the daytime clear sky instantaneous surface temperature of polar orbit satellite, < >>Cloud instantaneous surface temperature in daytime for polar satellites, < >>For the night clear sky instantaneous surface temperature of polar orbit satellite, < > the night clear sky instantaneous surface temperature>Cloud instantaneous surface temperature for polar satellites at night, < ->、/>And->The weight regression model coefficient;
if the two observations are clear sky observations, the formula of the average day surface temperature of the polar orbit satellite is as follows:
；
if the daytime is clear air observation and the night is cloud observation, the formula of the average day surface temperature of the polar orbit satellite is as follows:
；
if the daytime is cloudy observation and the night is clear sky observation, the formula of the average day surface temperature of the polar orbit satellite is as follows:
；
if both day and night observations are cloud observations, the formula of the average day surface temperature of the polar orbit satellite is as follows:
。
optionally, constructing a coefficient lookup table of the weight regression model includes:
screening the site instantaneous surface temperature and the daily surface temperature which are matched in time according to the daily day and night transit time of the polar orbit satellite;
judging whether the instantaneous surface temperature of the screened site is covered by cloud according to the polar orbit satellite quality control file;
and taking the instantaneous surface temperature and the daily surface temperature of the station after cloud detection as training samples, acquiring weight regression model coefficients of four daily surface temperatures of each polar orbit satellite, which are covered by the cloud and are not covered by the cloud, at the moment of day and night transit, and constructing a coefficient lookup table.
Optionally, filling the instantaneous surface temperature under the polar orbit satellite cloud comprises:
a. the day and night two observations of the polar orbit satellite comprise clear sky observations and cloudy observations, and the instantaneous surface temperature under the clouds of the polar orbit satellite is filled based on the same difference value of ERA5-Land surface temperature data and polar orbit satellite surface temperature data in one day under the clear sky observations and the cloudy observations;
is provided withIs at->The temperature difference value of the sunny ground surface in the ground surface temperature data of the polar orbit satellite and the ERA5-Land at the moment,is a polar orbit satellite->Sunny ground surface temperature at moment +.>Is +.in ERA5-Land surface temperature data>Surface temperature at time,/->Is a polar orbit satellite->Cloud subsurface temperature after time filling, +.>Is +.in ERA5-Land surface temperature data>The earth surface temperature at the moment;
；
；
b. the polar orbit satellite day and night two observations are cloud observations, and the two polar orbit satellite subsurface instantaneous surface temperatures are filled on the assumption that the difference between the ERA5-Land instantaneous surface temperature and the moon average surface temperature at the corresponding moment is the same as the difference between the polar orbit satellite instantaneous surface temperature data and the moon average surface temperature at the corresponding moment;
is provided withIs ERA5-Land day +.>Surface temperature and->The difference in the surface temperature of the month at the moment,is ERA5-Land day +.>Surface temperature at time,/->Is +.in ERA5-Land surface temperature data>Time of day, month average surface temperature,/->Is a polar orbit satellite->The temperature of the earth surface of the sunny month at moment, +.>Is a polar orbit satellite->Effective observation times in time month, +.>For the instantaneous surface temperature of polar satellites, +.>Is a polar orbit satellite->And (3) the temperature of the subsurface of the cloud after time filling, namely:
；
；
wherein,。
according to the invention, when estimating the average earth surface temperature, the earth site observation data distributed on various climatic backgrounds and land coverage types around the world are utilized to simulate the clear sky or the instantaneous earth surface temperature under the cloud at the transit time of the polar orbit satellite, and the problem of poor applicability of the current average earth surface temperature estimation model is solved by constructing a weight regression model and a coefficient lookup table of the weight regression model, wherein the weight regression model comprises the average earth surface temperatures of various polar orbit satellites; the invention is based on the difference of the instantaneous or month-average surface temperature observed under ERA5-Land and polar orbit satellite cloud and the difference under clear sky observation condition, and on the premise of ensuring the precision, the instantaneous surface temperature under polar orbit satellite cloud is simply and rapidly filled, and the spatial continuity of the day-average surface temperature is improved.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. A solar average earth surface temperature remote sensing estimation method of polar orbit satellites is characterized by comprising the following steps:
acquiring multi-source data; the multi-source data comprise ground station observation data, ERA5-Land surface temperature data and polar orbit satellite surface temperature data;
preprocessing multi-source data;
constructing a weight regression model and a coefficient lookup table of the weight regression model, wherein the weight regression model comprises daily average surface temperature data of various polar satellites; according to whether the day-to-day transit time of the polar orbit satellites is blocked by cloud, respectively constructing weight regression models of daily surface temperature data of various polar orbit satellites for each polar orbit satellite, wherein the weight regression models comprise: is provided withIs the daily surface temperature of the polar orbit satellite,for the daytime clear sky instantaneous surface temperature of polar orbit satellite, < >>For polar satellites that have cloud instantaneous surface temperatures during the day,for the night clear sky instantaneous surface temperature of polar orbit satellite, < > the night clear sky instantaneous surface temperature>For polar satellites with cloud instantaneous surface temperatures at night,、/>and->The weight regression model coefficient;
if the two observations are clear sky observations, the formula of the average day surface temperature of the polar orbit satellite is as follows:
；
if the daytime is clear air observation and the night is cloud observation, the formula of the average day surface temperature of the polar orbit satellite is as follows:
；
if the daytime is cloudy observation and the night is clear sky observation, the formula of the average day surface temperature of the polar orbit satellite is as follows:
；
if both day and night observations are cloud observations, the formula of the average day surface temperature of the polar orbit satellite is as follows:
；
constructing a coefficient lookup table of a weight regression model, comprising: screening the site instantaneous surface temperature and the daily surface temperature which are matched in time according to the daily day and night transit time of the polar orbit satellite; judging whether the instantaneous surface temperature of the screened site is covered by cloud according to the polar orbit satellite quality control file; taking the instantaneous surface temperature and the daily surface temperature of the station after cloud detection as training samples, acquiring weight regression model coefficients of four daily surface temperatures of each polar orbit satellite, which are covered by the cloud and are not covered by the cloud, at the moment of day and night transit, and constructing a coefficient lookup table;
filling the instantaneous earth surface temperature under the cloud of the polar orbit satellite based on the same difference value between ERA5-Land earth surface temperature data and polar orbit satellite earth surface temperature data under clear sky observation and cloud observation in one day;
and selecting a matched weight regression model and coefficients of the weight regression model pixel by pixel based on the filled instantaneous surface temperature under the polar orbit satellite cloud, and calculating the daily surface temperature.
2. The method for remotely sensing and estimating the solar average surface temperature of a polar orbit satellite according to claim 1, wherein the multi-source data comprises:
ground site observation data distributed throughout the world in various climatic backgrounds and land cover types;
ERA5-Land surface temperature data is surface temperature data of hour-by-hour and month average;
polar orbit satellite instantaneous surface temperature data.
3. The polar orbit satellite daily surface temperature remote sensing estimation method according to claim 1, wherein preprocessing the multi-source data comprises:
calculating the instantaneous surface temperature of the station according to the station long-wave uplink radiation data and the station long-wave downlink radiation data;
calculating the daily average surface temperature of the station according to the instantaneous surface temperature of the station;
resampling the ERA5-Land surface temperature data by using a Python programming language and a plurality of open source libraries to unify the resolution of the ERA5-Land surface temperature data and the polar orbit satellite surface temperature data;
cloud detection and abnormal point elimination are carried out on the polar orbit satellite instantaneous surface temperature data.
4. A polar orbit satellite daily surface temperature remote sensing estimation method according to claim 3, wherein calculating the site instantaneous surface temperature comprises:
is provided withFor site instantaneous surface temperature, +.>For uplink long wave radiation, < >>For downstream long wave radiation, < >>Is the surface broadband emissivity +.>For the stefin-boltzmann constant, then:
；
calculating the daily surface temperature of the site according to the instantaneous surface temperature of the site, comprising:
is provided withFor site average surface temperature, < >>Site surface temperature for each time of day, +.>For the number of valid observations of a site in a day, then:
。
5. the method for remotely estimating the solar average surface temperature of the polar orbit satellite according to claim 1, wherein the filling the instantaneous surface temperature under the polar orbit satellite cloud comprises the following steps:
a. the day and night two observations of the polar orbit satellite comprise clear sky observations and cloudy observations, and the instantaneous surface temperature under the clouds of the polar orbit satellite is filled based on the same difference value of ERA5-Land surface temperature data and polar orbit satellite surface temperature data in one day under the clear sky observations and the cloudy observations;
is provided withIs at->The temperature difference value of the sunny ground surface in the ground surface temperature data of the polar orbit satellite and the ERA5-Land at the moment,is a polar orbit satellite->Sunny ground surface temperature at moment +.>Is +.in ERA5-Land surface temperature data>Surface temperature at time,/->Is a polar orbit satellite->Cloud subsurface temperature after time filling, +.>Is +.in ERA5-Land surface temperature data>The earth surface temperature at the moment;
；
；
b. the polar orbit satellite day and night two observations are cloud observations, and the two polar orbit satellite subsurface instantaneous surface temperatures are filled on the assumption that the difference between the ERA5-Land instantaneous surface temperature and the moon average surface temperature at the corresponding moment is the same as the difference between the polar orbit satellite instantaneous surface temperature data and the moon average surface temperature at the corresponding moment;
is provided withIs ERA5-Land day +.>Surface temperature and->The difference in the surface temperature of the month at the moment,is ERA5-Land day +.>Surface temperature at time,/->Is +.in ERA5-Land surface temperature data>Time of day, month average surface temperature,/->Is a polar orbit satellite->The temperature of the earth surface of the sunny month at moment, +.>Is a polar orbit satellite->Effective observation times in time month, +.>For the instantaneous surface temperature of polar satellites, +.>Is a polar orbit satellite->And (3) the temperature of the subsurface of the cloud after time filling, namely:
；
；
wherein,。
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20110096301A (en) * | 2010-02-22 | 2011-08-30 | 공주대학교 산학협력단 | Method for calculating land surface temperature |
| CN105184076A (en) * | 2015-09-02 | 2015-12-23 | 安徽大学 | Space-time integrated fusion method for remote sensing earth surface temperature data |
| CN115688356A (en) * | 2022-06-28 | 2023-02-03 | 中国农业科学院农业资源与农业区划研究所 | Polar orbit satellite earth surface temperature month difference estimation method based on month temperature cycle model |
| CN116450700A (en) * | 2023-03-09 | 2023-07-18 | 中国农业科学院农业资源与农业区划研究所 | Polar orbit satellite earth surface temperature time normalization method and device and electronic equipment |
| CN116611352A (en) * | 2023-07-20 | 2023-08-18 | 中国农业科学院农业资源与农业区划研究所 | General method and system for earth surface temperature time comparability correction of polar orbit satellite |
-
2023
- 2023-11-08 CN CN202311478155.1A patent/CN117216504B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20110096301A (en) * | 2010-02-22 | 2011-08-30 | 공주대학교 산학협력단 | Method for calculating land surface temperature |
| CN105184076A (en) * | 2015-09-02 | 2015-12-23 | 安徽大学 | Space-time integrated fusion method for remote sensing earth surface temperature data |
| CN115688356A (en) * | 2022-06-28 | 2023-02-03 | 中国农业科学院农业资源与农业区划研究所 | Polar orbit satellite earth surface temperature month difference estimation method based on month temperature cycle model |
| CN116450700A (en) * | 2023-03-09 | 2023-07-18 | 中国农业科学院农业资源与农业区划研究所 | Polar orbit satellite earth surface temperature time normalization method and device and electronic equipment |
| CN116611352A (en) * | 2023-07-20 | 2023-08-18 | 中国农业科学院农业资源与农业区划研究所 | General method and system for earth surface temperature time comparability correction of polar orbit satellite |
Non-Patent Citations (3)
| Title |
|---|
| A generalized method for retrieving global daily mean land surface temperature from polar-orbiting thermal infrared sensor instantaneous observations;Jia-Hao Li et al.;《INTERNATIONAL JOURNAL OF REMOTE SENSING》;全文 * |
| 极轨卫星热红外地表温度日变化模拟;陈颖;段四波;冷佩;陈媛媛;韩晓静;;遥感信息(第06期);全文 * |
| 航空遥感地表温度时间归一化;朱琳清;周纪;刘绍民;李国全;;遥感学报(第02期);全文 * |
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