CN112270293B - Daily paddy field CH 4 Remote sensing estimation method of flux - Google Patents
Daily paddy field CH 4 Remote sensing estimation method of flux Download PDFInfo
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Abstract
The invention relates to a daily paddy field CH 4 Remote sensing estimation method of flux, CH measured day by day in a certain time scale in rice growth period 4 Flux E is a dependent variable, in CH 4 LST, SWI, LAI, GPP, ET five remote sensing indexes in remote sensing data of the same time scale and the same longitude and latitude position are taken as independent variables for flux measurement, and paddy field CH is constructed 4 Remote sensing estimation model one of flux: then in the rice field of the area to be estimated, through CH 4 Flux observer monitors rice field CH of a certain time scale in rice growth period 4 Flux, simultaneous acquisition and CH 4 The method comprises the steps that (1) daily remote sensing data of a flux observer at the same time scale and the same longitude and latitude position are respectively obtained to obtain LST, SWI, LAI, GPP, ET five remote sensing indexes, and the LST, SWI, LAI, GPP, ET five remote sensing indexes are respectively substituted into two sides of an equation of a first remote sensing estimation model in the first step to determine an unknown coefficient in the estimation model, so that a second remote sensing estimation model with a known coefficient is determined; rice field CH in rice growth period for region to be estimated 4 Remote sensing estimation of flux.
Description
Technical Field
The invention relates to the technical field of environmental monitoring and evaluation, in particular to a paddy field CH 4 A remote sensing estimation method of flux.
Background
Since the industrial revolution, greenhouse gases (e.g., CO 2 、CH 4 And N 2 O, etc.) content is drastically increased, which is an important cause of global warming. Wherein CH is 4 Has a heating potential of about CO 2 21 times of (3). The paddy field is a typical farmland ecological system, which is CH in the atmosphere 4 Is one of the important sources of (a) the present invention. It is estimated that about 10% to 20% of CH is in the atmosphere 4 Is derived from paddy fields. Thus, understand rice field CH 4 The change characteristic of flux provides important scientific basis for slowing down climate warming.
In general, ground monitoring is to know the CH of paddy field 4 One of the important ways of flux change law. For example, the current research mainly adopts a static box-gas chromatograph method for rice field CH 4 Flux is monitored, but this method is time consuming, laborious and only allows access to the paddy CH at a specific point in time 4 The actual measurement result of flux causes discontinuous measurement result on time scale, thereby being unfavorable for deep understanding of rice field CH 4 The law of variation of the flux. In addition, a small amount of researches on related vorticity technology based on micro-aeropictography method realize the implementation of the CH of the paddy field 4 Continuous flux monitoring is performed so as to solve the problem of discontinuous monitoring of the original static box-gas chromatograph, but flux monitoring cost based on the related vorticity technology is higher, and large-scale development of rice field CH is difficult 4 Flux monitoring experiments.
Because the remote sensing data has better space-time continuity, the remote sensing method is utilized to realize paddy field CH 4 The quantitative estimation and continuous monitoring of flux have wide application prospect. However, the existing estimation method cannot systematically consider the external environment condition and the rice growth process to the rice field CH 4 The effect of flux is such that rice field CH 4 There is a large uncertainty in the quantitative estimation of flux. Furthermore, because the existing estimation method generally lacks the rationality, continuous monitoring on a higher time scale (such as a daily scale) is difficult, which not only limits the applicability of the estimation method, but also is not beneficial to the deep understanding of the paddy field CH 4 The law of variation of the flux. Therefore, how to build reliable paddy field CH by using limited ground monitoring data 4 Flux remote sensing estimation model for realizing paddy field CH 4 Continuous monitoring of flux is one of the important issues to be addressed in this field.
Disclosure of Invention
The invention aims at the rice field CH in the prior art 4 The flux monitoring and estimating method has the problems of providing a daily paddy field CH 4 The remote sensing estimation method of flux combines the rice growth process with remote sensing inversion data to establish a daily paddy field CH 4 Flux remote sensing estimation model for realizing daily paddy field CH 4 Quantitative estimation of flux.
The invention aims at realizing the following aims, namely a daily paddy field CH 4 The remote sensing flux estimation method is characterized by comprising the following steps of:
CH measured daily by daily during rice growth period 4 Flux E is a dependent variable, in CH 4 The LST, SWI, LAI, GPP, ET five daily remote sensing indexes in the remote sensing data of the same time scale and the same longitude and latitude position are taken as independent variables for flux measurement, and the paddy field CH is constructed 4 Remote sensing estimation model one of flux:
wherein the unit of E is [ mu ] mol.m -2 ·s -1 The method comprises the steps of carrying out a first treatment on the surface of the LST is the surface temperature in the daytime, and the unit is K; SWI is surface water index, LAI is leaf area index, and unit is m 2 ·m -2 GPP is total primary productivity in g C m -2 The method comprises the steps of carrying out a first treatment on the surface of the ET is the surface evapotranspiration in mm.m -2 The method comprises the steps of carrying out a first treatment on the surface of the a. b, c, d, f is a coefficient to be determined by the method of the second step;
in the second step, the undetermined coefficients a, b, c, d, f are determined by the following method: in the rice field of the area to be estimated, by CH 4 Flux observer monitors paddy field CH of each day in rice growth period 4 Flux, and simultaneously obtain rice growth period and CH 4 Five daily remote sensing indexes of LST, SWI, LAI, GPP, ET of the flux observer and longitude and latitude positions are respectively substituted into two sides of an equation of a remote sensing estimation model in the first step, a concrete value of a, b, c, d, f is obtained by calculation, and therefore LST, SWI, LAI, GPP, ET is used as an independent variable and CH is used 4 The flux is a remote sensing estimation model II of the dependent variable; the remote sensing estimation model II is suitable for the paddy field CH day by day in the rice growth period of the area to be estimated 4 Remote sensing estimation of flux.
The daily paddy field CH of the invention 4 Remote sensing estimation method of flux considers external environment condition and rice growth process to paddy field CH 4 On the basis of flux influence, establishing a daily paddy field CH 4 Flux remote sensing estimation model, thereby realizing pair-by-pairPaddy field CH 4 Quantitative estimation and continuous monitoring of flux, thereby not only monitoring rice field CH 4 The dynamic change of flux can also improve the estimation precision; meanwhile, the estimation method of the invention is used for deeply understanding the CH of the rice field 4 The flux change rule has important guiding value for exploring the scientific problem of agriculture for climate change. Therefore, the invention can be used for paddy field CH day by day 4 The flux estimation and evaluation field has important technical innovation advantages.
To facilitate determination of the undetermined coefficients in the remote sensing estimation model, the second step of the present invention comprises the steps of:
2.1, obtaining a vector map of a paddy field in an area to be estimated, and determining the growth period of the paddy field;
2.2 selecting a field area of at least 200m×200m in the area to be estimated, and laying CH at the center of the field 4 Flux observation instrument, develop paddy field CH in paddy rice growth period 4 Daily monitoring of flux;
2.3 analysis of CH obtained in 2.2 steps 4 Daily monitoring data of flux;
2.4 according to the vector map of step 2.1, obtaining the original remote sensing data of the paddy field area covered by the vector map in the rice growth period from the satellite remote sensing database, and respectively analyzing and determining the original remote sensing data and CH (CH) 4 Each daily remote sensing index LST, SWI, LAI, GPP, ET value of the flux observer at the same longitude and latitude position;
2.5 the CH obtained in 2.3 steps 4 The flux data is used as the E value and the specific value of each corresponding daily remote sensing index LST, SWI, LAI, GPP, ET obtained in the step 2.4 to be respectively substituted into the remote sensing estimation model I in the step one, and the a, b, c, d, f coefficient value is obtained by calculation through a least square method.
Further, the time scale is each day in the rice growth period.
To facilitate accurate monitoring of CH 4 Flux, 2.2 steps, of the CH 4 The flux observer is mounted at a height of 2m from the ground.
Further, the rice growth period refers to the whole period from transplanting to maturation of rice.
Further, in step 2.4, the original remote sensing data is Moderate Resolution Imaging Spectroradiometer abbreviated as MODIS remote sensing data: including MOD09 surface reflectance data, MOD11 daytime surface temperature data, MOD15 leaf area index data, MOD16 surface evapotranspiration data, and MOD17 total primary productivity data.
In order to convert the original remote sensing data into daily remote sensing indexes, the method for determining each daily remote sensing index according to the original remote sensing data comprises the following steps:
determining LST according to MOD11 daytime surface temperature data, obtaining daily LST original value of paddy field region in rice growth period, multiplying conversion coefficient 0.02, extracting to obtain and obtain CH 4 Daily LST values of the flux observer at the same longitude and latitude positions;
obtaining earth surface reflectance values of paddy fields in the growing period of paddy rice in different wavebands day by day according to MOD09 earth surface reflectance data, calculating according to the following formula, and extracting to obtain a C & H & gt 4 Daily surface moisture index SWI values for flux observer and longitude and latitude positions:
wherein b2 and b6 are respectively the reflectivity of near infrared band with the wavelength of 841-876 nm and short wave infrared band with the wavelength of 1628-1652 nm;
obtaining LAI original value synthesized in the rice field for 8 days continuously in the rice growth period according to MOD15 leaf area index data, interpolating the LAI original value to daily scale by RStudio software and "phenofix" program package, multiplying the LAI original value by conversion coefficient 0.1, extracting to obtain the natural plant extract and CH 4 Daily LAI values of the flux observer at the same longitude and latitude positions;
according to MOD17 total primary productivity data, obtaining GPP original value synthesized in rice field region for 8 continuous days in rice growth period, interpolating GPP original value to daily scale by RStudio software and "phenofix" program package, dividing by conversion coefficient 80, extracting to obtain and obtaining CH 4 The daily GPP value of the flux observer and the longitude and latitude position;
according to MOD16 surface evapotranspiration dataObtaining an ET original value synthesized in the rice field region for 8 days in the rice growth period, interpolating the ET original value to a daily scale through RStudio software and a "phenofix" program package, dividing the ET original value by a conversion coefficient 80, and extracting to obtain a polypeptide-CH 4 The flux observer has the daily ET value of the longitude and latitude position.
Detailed Description
The daily paddy field CH of the present invention will be described in detail by taking a paddy field (a paddy field as an area to be evaluated) of a river basin in Jiangsu province as an example 4 A remote sensing estimation method of flux. The rice field has a certain representativeness in Jianghuai regions, and the rice has a growth period of about 6 months, 17 days, about 10 months and about 20 days for transplanting each year. And the field management measures such as fertilization, weeding, irrigation and the like are the same as those of local field production. Specific steps will be described in detail below.
a) Before the beginning of transplanting rice in 5-6 months, selecting a 500m multiplied by 500m paddy field of a river basin (to-be-estimated area) in Jiangsu province as a research sample area, acquiring a paddy field vector map of the area, and determining that the time scale of analysis is 1 day;
b) Setting one LI-7700 open-circuit CH set in the middle of rice field in the rice growing period 4 Flux observation instrument, record the geographical position (33 degree 05'N,119 degree 58' E) of the instrument, develop paddy field CH based on the related technique of vorticity 4 Flux monitoring to obtain paddy field CH in 2017-2019 paddy rice growth period 4 Monitoring results of the flux;
c) Paddy rice field CH from 2017 to 2019 obtained in step b) based on EddyPro software 4 Analyzing the flux monitoring result, and processing to obtain rice field CH on a daily scale in the rice growth period 4 The time series results of the flux averages are described in table 1.
Note that: the results are presented for the first 5 days only, at length.
d) Obtaining MODIS remote sensing data covering the paddy field range in the area to be estimated in the rice growth period, namely original remote sensing data, including MOD09 surface reflectivity data, MOD11 daytime surface temperature data, MOD15 leaf area index data, MOD16 surface evapotranspiration data and MOD17 total primary productivity data, from a downloading website (https:// MODIS. Gsfc. Nasa. Gov/data/dataprod /) of the MODIS remote sensing database by utilizing the paddy field vector map obtained in the step a).
e) The step mainly comprises analyzing the MODIS remote sensing data obtained in step d) to obtain the rice and the rice CH in step b) 4 5 daily remote sensing index time sequence results of the flux observer at the same longitude and latitude positions; the 5 daily remote sensing indexes are the daytime surface temperature LST, the surface water index SWI, the leaf area index LAI, the total primary productivity GPP and the surface evapotranspiration ET respectively;
for the daytime surface temperature LST, obtaining the daily LST original value of the paddy field area in the rice growth period through MOD11 daytime surface temperature data, multiplying the result by a conversion coefficient of 0.02, and extracting to obtain the data and step b) CH 4 The LST values of the flux observer at the same longitude and latitude positions day by day, and partial LST results are shown in table 2;
note that: the first 5 results are listed only for space.
For the surface water index SWI, obtaining the surface reflectivity values of different wave bands day by day in the paddy field area in the rice growth period through MOD09 surface reflectivity data. Based on SWI calculation formula, extracting CH in step b) 4 Daily SWI values of the flux observer at the same longitude and latitude positions;
calculation formula of SWI:。
wherein SWI is the surface water index;b2 andb6 are respectively the reflectivities of near infrared band with the wavelength of 841-876 nm and short wave infrared band with the wavelength of 1628-1652 nm, and partial SWI results are shown in Table 3;
note that: the first 5 results are listed only for space.
For leaf area index LAI, obtaining LAI original value synthesized in rice field region for 8 days in rice growth period by MOD15 leaf area index data, interpolating the result to daily scale by RStudio software and "phenofix" program package, multiplying by conversion coefficient 0.1, extracting to obtain CH of step b) 4 The daily LAI values of the flux observer at the same longitude and latitude positions, and partial LAI results are shown in table 4:
note that: at length, only the top 5 results are listed
For the total primary productivity GPP, obtaining a GPP original value synthesized in a paddy field area within a rice growth period for 8 days through 8 days MOD17 total primary productivity data, interpolating the result to a GPP value with a daily scale through RStudio software and a "phenophix" program package, dividing the GPP value by a conversion coefficient 80, and extracting to obtain a data of the total primary productivity GPP CH in the step b) 4 The daily GPP values of the flux observer and the longitude and latitude positions are shown in a table 5;
note that: the first 5 results are listed only for space.
For the ground surface evapotranspiration ET, obtaining an ET original value synthesized in a rice field area in a rice growth period for 8 days through 8-day MOD16 ground surface evapotranspiration data, interpolating the result through RStudio software and a "phenophix" program package to obtain an ET value in a daily time scale, dividing the ET value by a conversion coefficient 80, and extracting to obtain a CH (channel) of the step b) 4 The daily ET values of the flux observer at the same longitude and latitude positions are shown in Table 6, and partial ET results are shown in Table 6:
note that: the first 5 results are listed only for space.
f) Rice field CH day by day within the period of rice growth period from 2017 to 2019 obtained in the step c) and the step e) 4 The flux result and the daily remote sensing index result are divided into two groups, one group is used as a training group for establishing a paddy field CH suitable for the area 4 And the other group of the flux remote sensing estimation models is used as a test sample, and the applicability of the models is verified. Paddy field CH day by day in rice growth period in 2017 and 2018 4 The flux result and 5 contemporaneous remote sensing index results are substituted into a remote sensing estimation model one to establish a daily paddy field CH 4 Flux remote sensing estimation model two:
（R 2 = 0.82）
wherein E is i Is the day-by-day scale rice growth periodiPaddy field CH 4 Estimated value of flux ([ mu ] mol.m -2 ·s -1 ）；LST i Is the day-by-day scale rice growth periodiA heaven surface temperature (K); GPP (GPP) i Is the day-by-day scale rice growth periodiTotal primary productivity of heaven (g C.m) -2 ）；ET i Is the day-by-day scale rice growth periodiAmount of surface evapotranspiration (mm.m) -2 ）、LAI i Is the first rice in the growth periodiArea index of heaven leaf (m) 2 ·m -2 ）、SWI i Is the day-by-day scale rice growth periodiTable water index.
In order to verify the applicability of the model, the time sequence results of 5 remote sensing indexes day by day in the rice growth period in 2019 are substituted into the rice field CH 4 Obtaining the daily paddy field CH in the rice growth period in the flux remote sensing estimation model 4 Estimated value of flux, and rice field CH in the same period 4 Compared with the observation value of flux as shown in Table 7, the correlation coefficient of the flux and the observation value is 0.83%p<0.01 Root mean square error of 0.05 mu mo)l･m -2 ·s -1 This reflects that the model can better estimate the CH of the paddy field 4 Flux.
The paddy field CH determined in step f) according to the present embodiment 4 Flux remote sensing estimation model II realizes daily paddy field CH in different years of paddy rice growth period in river basin 4 Continuous remote sensing estimation of flux.
Claims (6)
1. Daily paddy field CH 4 The remote sensing flux estimation method is characterized by comprising the following steps of:
first, CH measured day by day in rice growth period 4 Flux E is a dependent variable, in CH 4 The LST, SWI, LAI, GPP, ET five daily remote sensing indexes in the remote sensing data of the same time scale and the same longitude and latitude position are taken as independent variables for flux measurement, and the paddy field CH is constructed 4 Remote sensing estimation model one of flux:
；
wherein the unit of E is [ mu ] mol.m -2 ·s -1 The method comprises the steps of carrying out a first treatment on the surface of the LST is the surface temperature in the daytime, and the unit is K; SWI is surface water index, LAI is leaf area index, and unit is m 2 ·m -2 GPP is total primary productivity in g C m -2 The method comprises the steps of carrying out a first treatment on the surface of the ET is the surface evapotranspiration in mm.m -2 The method comprises the steps of carrying out a first treatment on the surface of the a. b, c, d, f is a coefficient to be determined by the method of the second step;
in the second step, the undetermined coefficients a, b, c, d, f are determined by the following method: in the rice field of the area to be estimated, by CH 4 Flux observer monitors paddy field CH of each day in rice growth period 4 Flux, and simultaneously obtain rice growth period and CH 4 LST, SWI, LAI, GPP, ET daily remote sensing indexes of the flux observer and longitude and latitude positions are respectively substituted into the remote sensing estimation model of the first stepThe specific value of a, b, c, d, f is calculated on both sides of the equation to determine LST, SWI, LAI, GPP, ET as the argument and CH 4 The flux is a remote sensing estimation model II of the dependent variable; the remote sensing estimation model II is suitable for the paddy field CH day by day in the rice growth period of the area to be estimated 4 Remote sensing estimation of flux.
2. Daily paddy field CH according to claim 1 4 The remote sensing estimation method of flux is characterized in that the second step comprises the following steps:
2.1, obtaining a vector map of a paddy field in an area to be estimated, and determining the growth period of the paddy field;
2.2 selecting a field area of at least 200m×200m in the area to be estimated, and laying CH at the center of the field 4 Flux observation instrument, develop paddy field CH in paddy rice growth period 4 Daily monitoring of flux;
2.3 analysis of CH obtained in 2.2 steps 4 Daily monitoring data of flux;
2.4 according to the vector map of step 2.1, obtaining the original remote sensing data of the paddy field area covered by the vector map in the rice growth period from the satellite remote sensing database, and respectively analyzing and determining the original remote sensing data and CH (CH) 4 Each daily remote sensing index LST, SWI, LAI, GPP, ET value of the flux observer at the same longitude and latitude position;
2.5 the CH obtained in 2.3 steps 4 The flux data is used as the E value and the specific value of each corresponding daily remote sensing index LST, SWI, LAI, GPP, ET obtained in the step 2.4 to be respectively substituted into the remote sensing estimation model I in the step one, and the a, b, c, d, f coefficient value is obtained by calculation through a least square method.
3. Daily paddy field CH according to claim 2 4 A remote sensing estimation method of flux is characterized in that in 2.2 steps, the CH 4 The flux observer is mounted at a height of 2m from the ground.
4. Daily paddy field CH according to claim 1 or 2 4 A method for remote sensing estimation of flux,the method is characterized in that the rice growth period refers to the whole period from transplanting to maturing of rice.
5. Daily paddy field CH according to claim 2 4 The remote sensing estimation method of flux is characterized in that in the step 2.4, the original remote sensing data is Moderate Resolution Imaging Spectroradiometer, abbreviated as MODIS remote sensing data: including MOD09 surface reflectance data, MOD11 daytime surface temperature data, MOD15 leaf area index data, MOD16 surface evapotranspiration data, and MOD17 total primary productivity data.
6. Daily paddy field CH according to claim 5 4 The remote sensing estimation method of flux is characterized in that in the step 2.4, the method for determining each daily remote sensing index according to the original remote sensing data comprises the following steps:
determining LST according to MOD11 daytime surface temperature data, obtaining daily LST original value of paddy field region in rice growth period, multiplying conversion coefficient 0.02, extracting to obtain and obtain CH 4 Daily LST values of the flux observer at the same longitude and latitude positions;
obtaining earth surface reflectance values of paddy fields in the growing period of paddy rice in different wavebands day by day according to MOD09 earth surface reflectance data, calculating according to the following formula, and extracting to obtain a C & H & gt 4 Daily surface moisture index SWI values for flux observer and longitude and latitude positions:
；
wherein b2 and b6 are respectively the reflectivity of near infrared band with the wavelength of 841-876 nm and short wave infrared band with the wavelength of 1628-1652 nm;
obtaining LAI original value synthesized in the rice field for 8 days continuously in the rice growth period according to MOD15 leaf area index data, interpolating the LAI original value to daily scale by RStudio software and "phenofix" program package, multiplying the LAI original value by conversion coefficient 0.1, extracting to obtain the natural plant extract and CH 4 Daily LAI values of the flux observer at the same longitude and latitude positions;
according to MOD17 total primary generationGenerating force data, obtaining GPP original value synthesized in rice field region for 8 days continuously in rice growth period, interpolating GPP original value to daily scale by RStudio software and "phenophix" program package, dividing by conversion coefficient 80, extracting to obtain and obtaining CH 4 The daily GPP value of the flux observer and the longitude and latitude position;
according to MOD16 surface evapotranspiration data, obtaining an ET original value synthesized in the rice field region for 8 days in the rice growth period, interpolating the ET original value to a daily scale through RStudio software and a "phenofix" program package, dividing by a conversion coefficient 80, and extracting to obtain a polypeptide-N-CH 4 The flux observer has the daily ET value of the longitude and latitude position.
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| CN108121854A (en) * | 2017-12-04 | 2018-06-05 | 国网江苏省电力有限公司电力科学研究院 | A kind of region Carbon flux evaluation method based on remote sensing data |
| KR101948107B1 (en) * | 2018-11-13 | 2019-02-14 | 전남대학교산학협력단 | Estimation method for ripening ratio of rice |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103513290A (en) * | 2013-10-24 | 2014-01-15 | 环境保护部卫星环境应用中心 | Regional terrestrial ecosystem respiratory monitoring method based on remote sensing |
| CN108121854A (en) * | 2017-12-04 | 2018-06-05 | 国网江苏省电力有限公司电力科学研究院 | A kind of region Carbon flux evaluation method based on remote sensing data |
| KR101948107B1 (en) * | 2018-11-13 | 2019-02-14 | 전남대학교산학협력단 | Estimation method for ripening ratio of rice |
Non-Patent Citations (2)
| Title |
|---|
| 基于遥感和通量观测数据的生态***净碳交换估算;牟苏斌;;现代信息科技;20200810(15);全文 * |
| 滨海湿地碳源/汇模拟***的构建与应用;陆颖;仲启铖;王璐;曹流芳;刘倩;王开运;;计算机应用与软件;20150315(03);全文 * |
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