CN113010994A - Regional evapotranspiration monitoring method based on surface evapotranspiration remote sensing - Google Patents
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Abstract
The invention discloses a method for monitoring regional evapotranspiration based on surface evapotranspiration remote sensing, which comprises the following steps of: s1, acquiring earth surface temperature, vegetation coverage and multi-temporal multi-resolution satellite remote sensing image data of a specific area; s2, constructing a two-stage feature space of earth surface temperature and vegetation coverage; s3, extracting the component temperature of vegetation and bare soil; s4, correcting the model parameters by adopting the actual observation result; s5, establishing a surface impedance model of the underlying surface; s6, expanding and determining the regional surface evapotranspiration; s7, calculating and outputting vegetation transpiration, soil evaporation and ground surface evapotranspiration; and S8, extracting the parameters of the existing remote sensing and meteorological database models in the specific area through the measured specific area data extraction module, and inputting the parameters and the empirical coefficients obtained in the steps into the models to obtain the value of the evapotranspiration of the measuring area. The monitoring method of the invention is verified through actual measurement in a plurality of areas, the evapotranspiration monitoring method is effective, and has the advantages of less input parameters, simplicity, flexibility, easy operation and the like.
Description
Technical Field
The invention relates to the technical field of remote sensing application, in particular to a method for monitoring regional evapotranspiration based on surface evapotranspiration remote sensing.
Background
At present, global water resources are increasingly scarce, and in order to reasonably utilize and distribute the water resources, the evaporation water consumption condition under different vegetation cover and land utilization conditions needs to be deeply understood. The evapotranspiration comprises vegetation interception evaporation, vegetation transpiration, soil evaporation and water surface evaporation, is a main component of regional water balance and energy balance, has extremely important function in the water circulation and energy circulation processes, and is an important link of the ecological process and the hydrological process. Therefore, the method can accurately and timely acquire the surface evapotranspiration data of the specific area, and plays an important role in the fields of agriculture, hydrology, forests, ecology and the like.
At present, the on-site determination method of the evapotranspiration is mainly based on the traditional hydrometeorology method, and mainly comprises the direct determination of the dehydration rate of the underlying surface (the atmospheric sphere takes the surface of the water and the land of the earth as the lower boundary, called the underlying surface of the atmosphere) and the water vapor transmission rate in the atmosphere. Generally speaking, due to the influence of uneven underlying surface, the measured values of the related measuring points have poor representativeness to the surface evapotranspiration conditions in a large range, and the cost for laying the measuring points is high, so that a practical observation network is difficult to form.
Compared with the traditional hydrometeorology method, the method for monitoring the surface evapotranspiration through remote sensing data has the characteristics of continuous space and dynamic time change. Multispectral information of the remote sensing data can provide parameters closely related to the surface energy balance process and the surface coverage condition. The evaluation of the evapotranspiration of the non-uniform underlying surface under the regional scale by using a remote sensing means has become an important research direction in the field of remote sensing application.
Currently, there are two main methods for estimating surface evapotranspiration at the regional scale: firstly, sensible heat is obtained by combining the radiation temperature of the remote sensing surface with the air temperature and a series impedance formula, and evapotranspiration is expressed by energy balance remainder terms; secondly, on the basis of a Penman-Monteith model (P-M model for short) recommended by Food and Agricultural Organization (FAO) of the United nations, the surface impedance in the P-M model is calculated by using ground temperature-vegetation index or other modes, and the evapotranspiration calculation is directly carried out. The former is considered to be highly accurate, while the latter is considered to be easy to apply because of its simpler parameters. Under the influence of cloud or other atmospheric factors, the available visible light and thermal infrared data cannot be obtained by remote sensing in non-fine weather, and the obtained land surface parameters are discontinuous actually, so that the remote sensing fine data and meteorological observation data are fully utilized. However, monitoring surface evapotranspiration through remote sensing data is an indirect evaporation measurement method, and involves many links and a large amount of data, and there are some practical technical problems to be solved, such as how to obtain spatially consistent and temporally continuous remote sensing data, how to provide the remote sensing data with calculation parameters required by a calculation method and a model, how to reduce uncertain errors by monitoring an intermediate process and performing model calibration by combining with ground data, and the like.
Disclosure of Invention
The invention aims to provide a method for monitoring regional evapotranspiration based on remote ground surface evapotranspiration sensing, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: a method for monitoring regional evapotranspiration based on remote ground surface evapotranspiration sensing specifically comprises the following steps:
s1, acquiring earth surface temperature, vegetation coverage and multi-temporal multi-resolution satellite remote sensing image data of a specific area by using a remote sensing data product;
s2, constructing a two-stage feature space of the earth surface temperature and the vegetation coverage, and performing remote sensing inversion on key parameters of the ground, wherein the key parameters comprise earth surface albedo, net radiant quantity, earth surface temperature, vegetation coverage, leaf surface index and momentum roughness;
s3, extracting the component temperatures of the vegetation and the bare soil by utilizing the characteristic space of the earth surface temperature and the vegetation coverage, and calculating the evaporation ratio of the components of the vegetation and the bare soil by combining the characteristic space of the earth surface temperature and the vegetation coverage and a Priestley-Taylor formula;
s4, combining the micro-scale SEBA model with the macro-scale SEBS model to serve as a remote sensing ground gas exchange model, and correcting model parameters by adopting an actual observation result;
s5, establishing an underlying surface impedance model based on the Penman-Monteith model, and estimating continuous day-by-day surface impedance by using the surface impedance of a fine day;
s6, solving the daily evapotranspiration by applying a Penman-Monteith model, and determining the regional ground surface evapotranspiration through time expansion;
s7, calculating the available energy of the vegetation component and the available energy of the bare soil component according to a radiation balance equation, and calculating and outputting vegetation transpiration, soil evaporation and ground surface transpiration through a ground surface transpiration two-source mode;
and S8, extracting the parameters of the existing remote sensing and meteorological database models in the specific area through the measured specific area data extraction module, and inputting the parameters and the empirical coefficients obtained in the steps into the models to obtain the value of the evapotranspiration of the measuring area.
Further, in the step S1, the MODIS remote sensing data product is used, and according to the quality file of the MODIS product, the decimal system is converted into the binary system, so as to realize the automatic filtering of the low-quality and invalid MODIS ground surface temperature and vegetation index data; and then, acquiring the earth surface temperature and the vegetation index by using a data product conversion formula.
Further, in step S1, enter ASTER/TM and AVHRR/MODIS as satellite remote sensing image data sources.
Further, in calculating the net radiation amount, the coefficient of direct radiation is 0.45 and the coefficient of scattered radiation is 0.1.
Further, when calculating the momentum roughness, weighting the geometrical roughness of the vegetation, the topographic relief and the non-vegetation covered surface respectively, and introducing radar data when calculating the geometrical roughness of the non-vegetation covered surface.
Further, in step S6, a representative high-resolution evapotranspiration image is selected in a predetermined time period; if no representative high resolution evapotranspiration image exists, selecting from adjacent time periods with similar vegetation coverage; if the representative high-resolution evapotranspiration image cannot be determined, the adjacent images before and after are selected and weighted according to the time distance to form the representative high-resolution evapotranspiration image of the time period.
Compared with the prior art, the invention has the following beneficial effects:
1) the invention fully utilizes the ground and high-altitude meteorological data to describe the characteristic parameters of the boundary layer ground-air exchange. When the earth surface energy balance model is applied, the precision of the model is improved, and therefore the accuracy of the underlying surface impedance is improved.
2) The invention combines the SEBA of the micro scale and the SEBS model of the macro scale, exerts the respective characteristics of the models and obtains good balance on the space-time resolution of the remote sensing data source.
3) The invention is based on the two-stage characteristic space mode of the earth surface temperature and the vegetation coverage, considers the response speed and the response degree difference existing between the vegetation and the bare soil radiation temperature, can improve the inversion precision of the vegetation and the bare soil component temperature, further improves the monitoring effect of earth surface evapotranspiration, is expected to provide more accurate earth surface evapotranspiration estimation and separation method models for the application requirements of crop irrigation water demand estimation, agricultural drought monitoring, crop yield prediction and the like, and is also beneficial to improving the level of quantitative remote sensing in China and supporting the scientific research progress of related fields.
4) And the remote sensing model often causes data loss because the weather condition can not obtain clear images. The method strengthens a time scale extension method in remote sensing application, surface impedance parameters and meteorological parameters obtained by remote sensing inversion are input into a P-M model, daily meteorological data and discontinuous remote sensing data are combined to finally obtain a continuous evapotranspiration product day by day, and the provided remote sensing evapotranspiration data can reflect the evapotranspiration space-time distribution rule of a research area at the same time, so that the method has great application value.
Detailed Description
The following will clearly and completely describe the technical solutions 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 embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
A method for monitoring regional evapotranspiration based on remote ground surface evapotranspiration sensing specifically comprises the following steps:
s1, acquiring earth surface temperature, vegetation coverage and multi-temporal multi-resolution satellite remote sensing image data of a specific area by using a remote sensing data product;
s2, constructing a two-stage feature space of the earth surface temperature and the vegetation coverage, and performing remote sensing inversion on key parameters of the ground, wherein the key parameters comprise earth surface albedo, net radiant quantity, earth surface temperature, vegetation coverage, leaf surface index and momentum roughness;
s3, extracting the component temperatures of the vegetation and the bare soil by utilizing the characteristic space of the earth surface temperature and the vegetation coverage, and calculating the evaporation ratio of the components of the vegetation and the bare soil by combining the characteristic space of the earth surface temperature and the vegetation coverage and a Priestley-Taylor formula;
s4, combining the micro-scale SEBA model with the macro-scale SEBS model to serve as a remote sensing ground gas exchange model, and correcting model parameters by adopting an actual observation result;
s5, establishing an underlying surface impedance model based on the Penman-Monteith model, and estimating continuous day-by-day surface impedance by using the surface impedance of a fine day;
s6, solving the daily evapotranspiration by applying a Penman-Monteith model, and determining the regional ground surface evapotranspiration through time expansion;
s7, calculating the available energy of the vegetation component and the available energy of the bare soil component according to a radiation balance equation, and calculating and outputting vegetation transpiration, soil evaporation and ground surface transpiration through a ground surface transpiration two-source mode;
and S8, extracting the parameters of the existing remote sensing and meteorological database models in the specific area through the measured specific area data extraction module, and inputting the parameters and the empirical coefficients obtained in the steps into the models to obtain the value of the evapotranspiration of the measuring area.
In this embodiment, in step S1, the MODIS remote sensing data product is used, and according to the quality file of the MODIS product, the decimal system is converted into the binary system, so as to implement automatic filtering of the low-quality and invalid MODIS ground temperature and vegetation index data; and then, acquiring the earth surface temperature and the vegetation index by using a data product conversion formula.
In this embodiment, in step S1, enter into the satellite remote sensing image data sources as enter into the process of processing the satellite remote sensing image data sources.
In this embodiment, the coefficient of direct radiation is 0.45 and the coefficient of scattered radiation is 0.1 when calculating the net radiation dose.
In this embodiment, in calculating the momentum roughness, the geometric roughness of the vegetation, the terrain relief, and the non-vegetation covered subsurface is weighted, and radar data is introduced in calculating the geometric roughness of the non-vegetation covered subsurface.
In this embodiment, in step S6, a representative high-resolution evapotranspiration image is selected in a predetermined time period; if no representative high resolution evapotranspiration image exists, selecting from adjacent time periods with similar vegetation coverage; if the representative high-resolution evapotranspiration image cannot be determined, the adjacent images before and after are selected and weighted according to the time distance to form the representative high-resolution evapotranspiration image of the time period.
The invention fully utilizes the ground and high altitude meteorological data to describe the characteristic parameters of the boundary layer ground air exchange. When the earth surface energy balance model is applied, the precision of the model is improved, so that the accuracy of the surface impedance of the underlying surface is improved; the method combines the SEBA of the micro scale and the SEBS of the macro scale, exerts respective characteristics of the models, obtains good balance on the space-time resolution of a remote sensing data source, simultaneously considers the difference of response speed and response degree of vegetation and bare soil radiation temperature based on a two-stage characteristic space mode of surface temperature and vegetation coverage, can improve the inversion precision of the temperature of the vegetation and bare soil components, further improves the monitoring effect of surface evapotranspiration, is expected to provide a more accurate method model for estimating and separating the surface evapotranspiration for application requirements of crop irrigation water demand estimation, agricultural drought monitoring, crop yield prediction and the like, is also favorable for improving the level of quantitative remote sensing in China and supporting the scientific research progress of related fields, and the remote sensing model often causes data loss because clear images cannot be obtained under the weather condition. The method strengthens a time scale extension method in remote sensing application, surface impedance parameters and meteorological parameters obtained by remote sensing inversion are input into a P-M model, daily meteorological data and discontinuous remote sensing data are combined to finally obtain a continuous evapotranspiration product day by day, and the provided remote sensing evapotranspiration data can reflect the evapotranspiration space-time distribution rule of a research area at the same time, so that the method has great application value.
The applicant verifies that the method for monitoring the evapotranspiration in the region is effective through actual measurement verification in a plurality of regions.
In the description of the present invention, it is to be understood that the terms "coaxial," "bottom," "one end," "top," "middle," "other end," "upper," "one side," "top," "inner," "front," "center," "two ends," and the like, are used merely for convenience in describing and simplifying the description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be considered as limiting.
Furthermore, the terms "first", "second", "third", "fourth" are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated, whereby the features defined as "first", "second", "third", "fourth" may explicitly or implicitly include at least one such feature.
In the present invention, unless otherwise expressly specified or limited, the terms "mounted," "disposed," "connected," "secured," "screwed" and the like are to be construed broadly, e.g., as meaning fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; the terms may be directly connected or indirectly connected through an intermediate, and may be communication between two elements or interaction relationship between two elements, unless otherwise specifically limited, and the specific meaning of the terms in the present invention will be understood by those skilled in the art according to specific situations.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. A method for monitoring regional evapotranspiration based on remote ground surface evapotranspiration sensing is characterized by comprising the following steps: the method specifically comprises the following steps:
s1, acquiring earth surface temperature, vegetation coverage and multi-temporal multi-resolution satellite remote sensing image data of a specific area by using a remote sensing data product;
s2, constructing a two-stage feature space of the earth surface temperature and the vegetation coverage, and performing remote sensing inversion on key parameters of the ground, wherein the key parameters comprise earth surface albedo, net radiant quantity, earth surface temperature, vegetation coverage, leaf surface index and momentum roughness;
s3, extracting the component temperatures of the vegetation and the bare soil by utilizing the characteristic space of the earth surface temperature and the vegetation coverage, and calculating the evaporation ratio of the components of the vegetation and the bare soil by combining the characteristic space of the earth surface temperature and the vegetation coverage and a Priestley-Taylor formula;
s4, combining the micro-scale SEBA model with the macro-scale SEBS model to serve as a remote sensing ground gas exchange model, and correcting model parameters by adopting an actual observation result;
s5, establishing an underlying surface impedance model based on the Penman-Monteith model, and estimating continuous day-by-day surface impedance by using the surface impedance of a fine day;
s6, solving the daily evapotranspiration by applying a Penman-Monteith model, and determining the regional ground surface evapotranspiration through time expansion;
s7, calculating the available energy of the vegetation component and the available energy of the bare soil component according to a radiation balance equation, and calculating and outputting vegetation transpiration, soil evaporation and ground surface transpiration through a ground surface transpiration two-source mode;
and S8, extracting the parameters of the existing remote sensing and meteorological database models in the specific area through the measured specific area data extraction module, and inputting the parameters and the empirical coefficients obtained in the steps into the models to obtain the value of the evapotranspiration of the measuring area.
2. The method for monitoring the regional evapotranspiration based on remote ground surface evapotranspiration sensing according to claim 1, wherein the method comprises the following steps: in the step S1, the MODIS remote sensing data product is utilized, and according to the quality file of the MODIS product, the automatic filtration of the low-quality and invalid MODIS earth surface temperature and vegetation index data is realized through decimal to binary conversion; and then, acquiring the earth surface temperature and the vegetation index by using a data product conversion formula.
3. The method for monitoring the regional evapotranspiration based on remote ground surface evapotranspiration sensing according to claim 1, wherein the method comprises the following steps: in the step S1, ASTER/TM and AVHRR/MODIS are used as satellite remote sensing image data sources.
4. The method for monitoring the regional evapotranspiration based on remote ground surface evapotranspiration sensing according to claim 1, wherein the method comprises the following steps: in calculating the net radiation dose, the coefficient for direct radiation is 0.45 and the coefficient for scattered radiation is 0.1.
5. The method for monitoring the regional evapotranspiration based on remote ground surface evapotranspiration sensing according to claim 1, wherein the method comprises the following steps: and when calculating the momentum roughness, respectively weighting the geometrical roughness of the vegetation, the topographic relief and the non-vegetation covered surface, and introducing radar data when calculating the geometrical roughness of the non-vegetation covered surface.
6. The method for monitoring the regional evapotranspiration based on remote ground surface evapotranspiration sensing according to claim 1, wherein the method comprises the following steps: in step S6, a representative high-resolution evapotranspiration image is selected in a predetermined time period; if no representative high resolution evapotranspiration image exists, selecting from adjacent time periods with similar vegetation coverage; if the representative high-resolution evapotranspiration image cannot be determined, the adjacent images before and after are selected and weighted according to the time distance to form the representative high-resolution evapotranspiration image of the time period.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113984212A (en) * | 2021-10-27 | 2022-01-28 | 中国气象科学研究院 | Agricultural irrigated area extraction method and system |
| CN114676588A (en) * | 2022-04-12 | 2022-06-28 | 中国科学院地理科学与资源研究所 | Method for estimating transpiration index of soil evaporation and vegetation transpiration in drainage basin |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101551459A (en) * | 2008-10-15 | 2009-10-07 | 北京天宏金睛信息技术有限公司 | Method for monitoring regional evapotranspiration on the basis of remote sensing |
| CN101957360A (en) * | 2010-09-10 | 2011-01-26 | 北京大学 | Method and system for measuring surface evapotranspiration quantity |
| CN105628618A (en) * | 2015-12-21 | 2016-06-01 | 中国矿业大学(北京) | Method for detecting earth surface evapotranspiration based on remote sensing earth surface temperature and vegetation coverage two-stage space |
| CN111999251A (en) * | 2020-08-14 | 2020-11-27 | 中国水利水电科学研究院 | Remote sensing model method for regional vegetation transpiration and soil evaporation inversion based on thermal infrared remote sensing |
-
2021
- 2021-01-21 CN CN202110083979.3A patent/CN113010994A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101551459A (en) * | 2008-10-15 | 2009-10-07 | 北京天宏金睛信息技术有限公司 | Method for monitoring regional evapotranspiration on the basis of remote sensing |
| CN101957360A (en) * | 2010-09-10 | 2011-01-26 | 北京大学 | Method and system for measuring surface evapotranspiration quantity |
| CN105628618A (en) * | 2015-12-21 | 2016-06-01 | 中国矿业大学(北京) | Method for detecting earth surface evapotranspiration based on remote sensing earth surface temperature and vegetation coverage two-stage space |
| CN111999251A (en) * | 2020-08-14 | 2020-11-27 | 中国水利水电科学研究院 | Remote sensing model method for regional vegetation transpiration and soil evaporation inversion based on thermal infrared remote sensing |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113984212A (en) * | 2021-10-27 | 2022-01-28 | 中国气象科学研究院 | Agricultural irrigated area extraction method and system |
| CN113984212B (en) * | 2021-10-27 | 2023-06-27 | 中国气象科学研究院 | Agricultural irrigation area extraction method and system |
| CN114676588A (en) * | 2022-04-12 | 2022-06-28 | 中国科学院地理科学与资源研究所 | Method for estimating transpiration index of soil evaporation and vegetation transpiration in drainage basin |
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