CN117609706A - Method for interpolating data of carbon water flux - Google Patents
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- 230000004907 flux Effects 0.000 title claims abstract description 196
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- VUZPPFZMUPKLLV-UHFFFAOYSA-N methane;hydrate Chemical compound C.O VUZPPFZMUPKLLV-UHFFFAOYSA-N 0.000 title claims description 18
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- 150000001720 carbohydrates Chemical class 0.000 claims abstract description 16
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- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims description 26
- 229910052799 carbon Inorganic materials 0.000 claims description 26
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 claims description 18
- 229910002092 carbon dioxide Inorganic materials 0.000 claims description 9
- 239000001569 carbon dioxide Substances 0.000 claims description 9
- 229920006395 saturated elastomer Polymers 0.000 claims description 9
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- 230000000243 photosynthetic effect Effects 0.000 claims description 3
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Abstract
The invention relates to a method for interpolating data of water flux, which comprises the following steps: s1, acquiring flux data and meteorological data in a specified time period; wherein the flux data in the designated time period comprises a plurality of pieces of water flux data which are sequentially arranged according to the sequence of the corresponding time stamps; each piece of carbohydrate flux data corresponds to a timestamp for collecting the carbohydrate flux data; s2, carrying out abnormal value elimination processing on the flux data to obtain flux data subjected to abnormal value elimination processing corresponding to a specified time period; s3, correcting the meteorological data to obtain corrected meteorological data; s4, extracting missing features of any section of missing data in the flux data after the abnormal value elimination processing corresponding to the designated time period; s5, interpolating the missing data of any segment based on the missing features of the missing data and the meteorological data after correction processing to obtain an interpolation result.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a method for interpolating data of water flux.
Background
In the prior art of interpolation of the carbon flux data, linear interpolation is often used to fill in missing data points. However, linear interpolation assumes that the data is continuously changing within the missing region, which may not be practical. The non-linear nature of the data may lead to inaccurate interpolation results or unreliable predictions. In addition, the conventional interpolation method often cannot directly cope with the situations of poor data quality, outliers, or noise. These problems may lead to distortion and misleading of the interpolation results.
Disclosure of Invention
In view of the above-mentioned drawbacks and shortcomings of the prior art, the present invention provides a method for interpolating data of a carbon water flux, which solves the technical problems of inaccurate interpolation results or distortion of interpolation results in the prior art of interpolating data of a carbon water flux.
In order to achieve the above purpose, the main technical scheme adopted by the invention comprises the following steps:
the embodiment of the invention provides a method for interpolating data of a water flux, which comprises the following steps:
s1, acquiring flux data and meteorological data in a specified time period;
wherein the flux data in the designated time period comprises a plurality of pieces of water flux data which are sequentially arranged according to the sequence of the corresponding time stamps; each piece of carbohydrate flux data corresponds to a timestamp for collecting the carbohydrate flux data;
the meteorological data includes: saturated vapor pressure difference VPD value and radiation data of each minute in a specified time period; the radiation data includes: total radiation TR, incident short wave radiation SRIN, photosynthetically active radiation PAR;
s2, carrying out abnormal value elimination processing on the flux data to obtain flux data subjected to abnormal value elimination processing corresponding to a specified time period;
s3, correcting the meteorological data to obtain corrected meteorological data;
s4, extracting missing features of any section of missing data in the flux data after the abnormal value elimination processing corresponding to the designated time period;
s5, interpolating the missing data of any segment based on the missing features of the missing data and the meteorological data after correction processing to obtain an interpolation result.
Preferably, the S2 specifically includes:
s21, eliminating the carbon water flux data meeting any abnormal value condition in flux data in a specified time period to obtain initial flux data;
the outlier condition includes:
carbon dioxide flux values in the carbon water flux data are greater than or equal to 100 μmol/m 2 /s；
Carbon dioxide flux values in the carbon water flux data are less than or equal to-50 μmol/m 2 /s；
S22, judging whether the quality grade in the initial flux data is greater than 2, if so, eliminating all the water flux data with the quality grade of 7, 8 or 9 in the initial flux data to obtain flux data subjected to abnormal value elimination processing;
if the abnormal value is not found, eliminating all the water flux data with the quality grade of 2 in the initial flux data to obtain flux data after abnormal value elimination treatment.
Preferably, in the step S3, the correction processing is performed on the meteorological data, and specifically includes:
s31, judging whether the saturated vapor pressure difference VPD value of each minute in the meteorological data in the specified time period meets a preset range, and if not, acquiring a new saturated vapor pressure difference VPD value corresponding to the minute by adopting a formula I;
the first formula is:
wherein,
wherein P is the air pressure value in the minute meteorological data;
T 0 air temperature values in the minute weather data;
RH is the relative humidity corresponding to the minute meteorological data;
s32, calculating sunrise and sunset time according to site position information, so as to extract night radiation data Rg in the radiation data, judging whether the night radiation data Rg meet preset conditions, and if the night radiation data Rg do not meet the preset conditions, updating the night radiation data Rg according to a preset updating mode to obtain updated night radiation data Rg;
the night radiation data Rg includes: total radiation TR at night, incident short wave radiation SRIN at night, photosynthetically active radiation PAR at night;
the preset condition is that the total night radiation TR, the incident short wave radiation SRIN at night and the photosynthetically active radiation PAR at night in the night radiation data Rg are all more than or equal to 0.
Preferably, the preset range is 0 to 50.
Preferably, if the nocturnal radiation data Rg is not satisfied, the nocturnal radiation data Rg is updated according to a preset updating manner, which specifically includes:
if the night radiation data Rg is not satisfied, setting the value of the total night radiation TR and/or the incident short wave radiation SRIN and/or the photosynthetic active radiation PAR to be 0, and obtaining updated night radiation data Rg.
Preferably, the S4 specifically includes:
s41, extracting first flux data, last flux data and time stamps corresponding to the first flux data in the first data set, and forming a first time stamp sequence;
the first flux data is the carbon water flux data with the carbon dioxide flux value being null;
the first data set is flux data after abnormal value elimination processing corresponding to a specified time period;
s42, based on the first time stamp sequence, obtaining the difference value of subtracting the previous time stamp from the next time stamp in any two adjacent time stamps in the first time stamp sequence;
s43, obtaining a first time difference sequence delta T based on the difference value of subtracting the previous time stamp from the next time stamp in any two adjacent time stamps in the first time stamp sequence stp ；
Wherein DeltaT stp c-(c-1) C-1 th element in the first time difference sequence;
wherein T is c C-th timestamp of n total timestamps in the first sequence of timestamps;
s44, acquiring missing features of any section of missing data based on the first time difference sequence.
Preferably, the step S44 specifically includes:
s441, adding lengths of elements which are continuously adjacent in the first time difference sequence and have the value equal to 1 to obtain the length of N segments of missing data;
wherein the length of each element in the first time difference sequence is 1;
s442, adding lengths of elements which are continuously adjacent in the first time difference sequence and have the numerical value not equal to 1 to obtain total continuous lengths of N+1 sections;
s443, regarding the i-th continuous length of the N+1 continuous lengths as the forward continuous length Ts of the length of the i-th missing data of the length of the N missing data fi ；
A backward continuous length Ts of the length of the ith section missing data in the length of the N section missing data, which is the (i+1) th section continuous length in the N+1 section continuous lengths bi ；
Wherein the missing features include a length of missing data, a forward continuous length, a backward continuous length.
Preferably, the step S5 specifically includes:
if Tm i Judging whether the first numerical value is smaller than the F value or not when the ratio of the/T is larger than 0.06, if the first numerical value is larger than or equal to the F value, interpolating the i-th missing data by adopting a large-scale interpolation mode according to the modified meteorological data to obtain a large-scale interpolation result, and taking the large-scale interpolation result as an interpolation result;
Tm i the length of the data missing in the ith section in the length of the data missing in the N sections;
T st to specify the start of the time period, T ed For the end of a specified time period;
wherein the first value is W;
W＝Tm i /(Ts fi +Ts bi )；
F＝4.17×(Ts fi +Ts bi )/0.06T。
preferably, the step S5 further specifically includes:
s51, if Tm i When the ratio of/T is less than or equal to 0.06, interpolating the i-th missing data by adopting a small-scale interpolation mode based on the meteorological data after correction processing to obtain a small-scale interpolation result; or, if Tm i When the ratio of/T is greater than or equal to 0.06 and the first value is smaller than the F value, correcting the processed meteorological data, and performing interpolation on the i-th missing data by adopting a small-scale interpolation mode to obtain a small-scale interpolation result;
s52, combining the small-scale interpolation result and the flux data subjected to outlier rejection processing corresponding to the specified time period to form a process interpolation flux data set, and extracting missing features of any section of missing data in the process interpolation flux data set;
s53, interpolating the missing data of any segment in the flux data set based on the missing feature of the missing data of the segment and the modified meteorological data to obtain an interpolation result.
Preferably, the step S53 specifically includes:
s531, judging whether the ratio of the length of any section of missing data in the process interpolation flux data set to T is greater than 0.06, if the length of the section of missing data in the process interpolation flux data set is greater than 0.06, judging whether the second value is smaller than the Q value, if the second value is greater than or equal to the Q value, interpolating the section of missing data in the process interpolation flux data set in a large-scale interpolation mode according to the modified meteorological data to obtain a large-scale interpolation result, and taking the large-scale interpolation result as the interpolation result;
wherein the second value is E;
e = length of any segment of missing data in the interpolated flux data/(forward continuous length of the segment of missing data in the interpolated flux data + backward continuous length of the segment of missing data in the interpolated flux data);
q=4.17× (forward continuous length of the segment of missing data in the interpolation flux data set+backward continuous length of the segment of missing data in the interpolation flux data set)/0.06T;
s532, if the ratio of the length of any section of missing data in the process interpolation flux data set to T is smaller than or equal to 0.06, based on the modified meteorological data, performing interpolation on the section of missing data in the process interpolation flux data set in a small-scale interpolation mode to obtain a small-scale interpolation result; or when the ratio of the length of any section of missing data in the process interpolation flux data set to T is more than or equal to 0.06 and the second value is less than the Q value, interpolating the section of missing data in the process interpolation flux data set by adopting a small-scale interpolation mode based on the modified meteorological data to obtain a small-scale interpolation result;
s533, combining the small-scale interpolation result with the process interpolation flux data set to form a new process interpolation flux data set;
s534, repeating the steps S531-S533 until the ratio of the length of any section of missing data in the new process interpolation flux data set to T is greater than 0.06, and the second value is greater than or equal to the Q value, and interpolating the section of missing data in the new process interpolation flux data set by adopting a large-scale interpolation mode according to the modified meteorological data to obtain a large-scale interpolation result, wherein the large-scale interpolation result is used as the interpolation result.
The beneficial effects of the invention are as follows: according to the method for interpolating the water flux data, disclosed by the invention, the missing characteristics of any section of missing data in flux data after the abnormal value elimination processing corresponding to the designated time period are extracted, and the missing data of any section of missing data is interpolated based on the missing characteristics of the missing data and the meteorological data after the correction processing, so that an interpolation result is obtained, and therefore, the accuracy of the interpolation result is improved by adopting the method for interpolating the water flux data.
Drawings
FIG. 1 is a flow chart of a method of interpolating data of a water flux according to the present invention;
fig. 2 is a schematic structural diagram of a device for interpolating data of water flux in an embodiment of the invention.
Detailed Description
The invention will be better explained for understanding by referring to the following detailed description of the embodiments in conjunction with the accompanying drawings.
In order to better understand the above technical solution, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
Referring to fig. 1, the present embodiment provides a method for interpolating data of a water flux, including:
s1, acquiring flux data and meteorological data in a specified time period.
Wherein the flux data in the designated time period comprises a plurality of pieces of water flux data which are sequentially arranged according to the sequence of the corresponding time stamps; each piece of carbohydrate flux data corresponds to a timestamp for collecting the carbohydrate flux data; the meteorological data includes: saturated vapor pressure difference VPD value and radiation data of each minute in a specified time period; the radiation data includes: total radiation TR, incident short wave radiation SRIN, photosynthetically active radiation PAR.
S2, carrying out abnormal value elimination processing on the flux data to obtain flux data subjected to abnormal value elimination processing corresponding to the designated time period.
In this embodiment, the S2 specifically includes:
s21, eliminating the carbon water flux data meeting any abnormal value condition in flux data in a specified time period to obtain initial flux data; the outlier condition includes: carbon dioxide flux values in the carbon water flux data are greater than or equal to 100 μmol/m 2 S; carbon dioxide flux values in the carbon water flux data are less than or equal to-50 μmol/m 2 /s。
S22, judging whether the quality grade in the initial flux data is greater than 2, if so, eliminating all the water flux data with the quality grade of 7, 8 or 9 in the initial flux data to obtain flux data after abnormal value elimination processing.
If the abnormal value is not greater than 2, eliminating all the carbon water flux data with the quality grade of 2 in the initial flux data to obtain flux data after abnormal value elimination processing.
In practical application, if the interpolation is participated in, the fluctuation of interpolation results is likely to be abnormal and errors are likely to be increased, and the interpolation results are eliminated from the flux data meeting any abnormal value condition and the flux data meeting any abnormal value condition (the condition that the flux data is more than 2 exists, all the flux data with the quality score of 7 or 8 or 9 in the initial flux data and the condition that the flux data is not 2 exists, and all the flux data with the quality grade of 2 in the initial flux data) in the flux data.
And S3, correcting the meteorological data to obtain corrected meteorological data.
In the step S3, the correction processing is performed on the meteorological data, and specifically includes:
s31, judging whether the saturated vapor pressure difference VPD value of each minute in the meteorological data in the specified time period meets a preset range, and if not, acquiring a new saturated vapor pressure difference VPD value corresponding to the minute by adopting a formula I. In this embodiment, the preset range is 0-50.
The first formula is:
wherein,
wherein P is the air pressure value in the minute meteorological data; t (T) 0 Air temperature values in the minute weather data; RH is the relative humidity corresponding to the minute weather data.
S32, calculating sunrise and sunset time according to site position information, extracting night radiation data Rg in the radiation data, judging whether the night radiation data Rg meet preset conditions, and if the night radiation data Rg do not meet the preset conditions, updating the night radiation data Rg according to a preset updating mode to obtain updated night radiation data Rg.
The night radiation data Rg includes: the total radiation TR at night, the incident short wave radiation SRIN at night, and the photosynthetically active radiation PAR at night.
The preset condition is that the total night radiation TR, the incident short wave radiation SRIN at night and the photosynthetically active radiation PAR at night in the night radiation data Rg are all more than or equal to 0.
If the radiation data Rg at night is not satisfied, updating the radiation data Rg at night according to a preset updating mode, specifically including:
if the night radiation data Rg is not satisfied, setting the value of the total night radiation TR and/or the incident short wave radiation SRIN and/or the photosynthetic active radiation PAR to be 0, and obtaining updated night radiation data Rg.
In practical application, if the meteorological data itself is abnormal, great uncertainty is introduced into the interpolation result, and correction processing is performed on the meteorological data, so that the accuracy of the interpolation result can be greatly improved, the interpolation difficulty is reduced, and the calculation efficiency is improved.
S4, extracting missing features of any section of missing data in the flux data after the abnormal value elimination processing corresponding to the designated time section.
Specifically, the S4 specifically includes:
s41, extracting first flux data, last flux data and time stamps corresponding to the first flux data in the first data set, and forming a first time stamp sequence.
The first flux data is carbon water flux data with a carbon dioxide flux value of null value.
The first data set is flux data after abnormal value elimination processing corresponding to a specified time period.
S42, based on the first time stamp sequence, the difference value obtained by subtracting the previous time stamp from the next time stamp in any two adjacent time stamps in the first time stamp sequence is obtained.
S43, based on any two adjacent time in the first time stamp sequenceSubtracting the difference value of the previous time stamp from the next time stamp in the stamps to obtain a first time difference sequence delta T stp ；
Wherein DeltaT stp c-(c-1) C-1 th element in the first time difference sequence;
wherein T is c C-th timestamp of n total timestamps in the first sequence of timestamps;
s44, acquiring missing features of any segment of missing data based on the first time difference sequence, wherein the S44 specifically comprises:
s441, adding lengths of elements which are continuously adjacent in the first time difference sequence and have the value equal to 1 to obtain the length of N segments of missing data; wherein the length of each element in the first time difference sequence is 1.
S442, adding lengths of elements which are continuously adjacent in the first time difference sequence and have the numerical value not equal to 1 to obtain total continuous lengths of N+1 sections.
S443, regarding the i-th continuous length of the N+1 continuous lengths as the forward continuous length Ts of the length of the i-th missing data of the length of the N missing data fi 。
A backward continuous length TS of the length of the ith missing data in the length of the N missing data, which is the (1+1) th continuous length in the N+1 continuous lengths bi 。
Wherein the missing features include a length of missing data, a forward continuous length, a backward continuous length.
S5, interpolating the missing data of any segment based on the missing features of the missing data and the meteorological data after correction processing to obtain an interpolation result. The step S5 specifically comprises the following steps:
if Tm i And if the ratio of the ratio to the T is greater than 0.06, judging whether the first numerical value is smaller than the F value, if the first numerical value is greater than or equal to the F value, interpolating the i-th missing data by adopting a large-scale interpolation mode according to the modified meteorological data to obtain a large-scale interpolation result, and taking the large-scale interpolation result as the interpolation result.
Tm i The length of the data missing in the ith section in the length of the data missing in the N sections;
T st to specify the start of the time period, T ed For the end of a specified time period;
wherein the first value is W.
W＝Tm i /(Ts fi +Ts bi )；
F＝4.17×(Ts fi +Ts bi )/0.06T。
In practical application of this embodiment, the step S5 further specifically includes:
s51, if Tm i When the ratio of/T is less than or equal to 0.06, interpolating the i-th missing data by adopting a small-scale interpolation mode based on the meteorological data after correction processing to obtain a small-scale interpolation result; or, if Tm i And when the ratio of/T is greater than or equal to 0.06 and the first value is smaller than the F value, correcting the processed meteorological data, and performing interpolation on the i-th missing data by adopting a small-scale interpolation mode to obtain a small-scale interpolation result.
S52, combining the small-scale interpolation result and the flux data subjected to outlier rejection processing corresponding to the specified time period to form a process interpolation flux data set, and extracting missing features of any section of missing data in the process interpolation flux data set.
S53, interpolating the missing data of any segment in the flux data set based on the missing feature of the missing data of the segment and the modified meteorological data to obtain an interpolation result.
In practical applications, the step S53 specifically includes:
s531, judging whether the ratio of the length of any section of missing data in the process interpolation flux data set to T is greater than 0.06, if the length of the section of missing data in the process interpolation flux data set is greater than 0.06, judging whether the second value is smaller than the Q value, if the second value is greater than or equal to the Q value, interpolating the section of missing data in the process interpolation flux data set by adopting a large-scale interpolation mode according to the modified meteorological data, obtaining a large-scale interpolation result, and taking the large-scale interpolation result as the interpolation result.
Wherein the second value is E;
e = length of any segment of missing data in the interpolated flux data/(forward continuous length of the segment of missing data in the interpolated flux data + backward continuous length of the segment of missing data in the interpolated flux data).
Q=4.17× (forward continuous length of the segment of missing data in the interpolation flux data set+backward continuous length of the segment of missing data in the interpolation flux data set)/0.06T.
The method of the data interpolation of the water flux can be divided into a large-scale interpolation method and a small-scale interpolation method, and particularly, the small-scale interpolation method is mainly an interpolation method for short-time missing data, and because the small-scale interpolation method occupies a small total length proportion, the interpolation result is more reliable, and the small-scale interpolation result can participate in small-scale interpolation performed in subsequent iteration and final large-scale interpolation. The large-scale interpolation mode is mainly aimed at the interpolation of missing data for a long time, and the interpolation reliability is lower than that of small-scale interpolation, so that the interpolation mode cannot be used as the basis of other data interpolation. By selectively using the large-scale interpolation mode and the small-scale interpolation mode, the overall accuracy and reliability of the interpolation result can be improved.
S532, if the ratio of the length of any section of missing data in the process interpolation flux data set to T is smaller than or equal to 0.06, based on the modified meteorological data, performing interpolation on the section of missing data in the process interpolation flux data set in a small-scale interpolation mode to obtain a small-scale interpolation result; or when the ratio of the length of any section of missing data in the process interpolation flux data set to T is greater than or equal to 0.06 and the second value is smaller than the Q value, interpolating the section of missing data in the process interpolation flux data set by adopting a small-scale interpolation mode based on the modified meteorological data to obtain a small-scale interpolation result.
And S533, combining the small-scale interpolation result and the process interpolation flux data set to form a new process interpolation flux data set.
S534, repeating the steps S531-S533 until the ratio of the length of any section of missing data in the new process interpolation flux data set to T is greater than 0.06, and the second value is greater than or equal to the Q value, and interpolating the section of missing data in the new process interpolation flux data set by adopting a large-scale interpolation mode according to the modified meteorological data to obtain a large-scale interpolation result, wherein the large-scale interpolation result is used as the interpolation result.
The interpolation result obtained in this embodiment is used to evaluate the carbon balance status of an ecosystem or an area, and specifically includes:
and calculating the carbon balance of the ecological system or the area by using the interpolation result. Carbon flux balance methods are commonly used to determine the carbon balance status of an ecosystem or region by calculating the difference between the amount of carbon sequestration and the amount of carbon sequestration per unit area. The carbon fixation amount and the carbon discharge amount per unit area can be calculated according to the measured flux data, and can be predicted by using a model and an estimation method.
The carbon sink strength of an ecosystem or region can be determined by comparing the amount of carbon sequestration per unit area and the amount of carbon removal and considering the time scale. If the carbon sequestration amount per unit area is larger than the carbon emission amount, the ecological system or the area is indicated to be a carbon sink, and the carbon sink is indicated to have the capability of absorbing and fixing carbon. If the carbon emission amount per unit area is larger than the carbon fixation amount, the ecological system or the area is indicated to be a carbon source, and the ecological system or the area has the capability of carbon emission and indicates a normal carbon source. The value of carbon sink strength may be used to measure the strength of the carbon absorbing or emitting capacity of an ecosystem or region.
According to the method for interpolating the water flux data, which is disclosed by the embodiment, due to the fact that the missing feature of any section of missing data in the flux data after the abnormal value elimination processing corresponding to the designated time section is extracted, and the missing feature of any section of missing data and the meteorological data after the correction processing are used for interpolating the section of missing data to obtain an interpolation result, the accuracy of the interpolation result is improved by adopting the method for interpolating the water flux data.
Referring to fig. 2, in this embodiment, there is also provided an apparatus for interpolating data of a water flux, including: at least one processor; and at least one memory communicatively coupled to the processor, wherein the memory stores program instructions executable by the processor, the processor invoking the program instructions to perform a method of carbon flux data interpolation as in the above-described embodiments.
In the description of the present invention, it should be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
In the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured," and the like are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium; may be a communication between two elements or an interaction between two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
In the present invention, unless expressly stated or limited otherwise, a first feature is "on" or "under" a second feature, which may be in direct contact with the first and second features, or in indirect contact with the first and second features via an intervening medium. Moreover, a first feature "above," "over" and "on" a second feature may be a first feature directly above or obliquely above the second feature, or simply indicate that the first feature is higher in level than the second feature. The first feature being "under", "below" and "beneath" the second feature may be the first feature being directly under or obliquely below the second feature, or simply indicating that the first feature is level lower than the second feature.
In the description of the present specification, the terms "one embodiment," "some embodiments," "examples," "particular examples," or "some examples," etc., refer to particular features, structures, materials, or characteristics described in connection with the embodiment or example as being included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that alterations, modifications, substitutions and variations may be made in the above embodiments by those skilled in the art within the scope of the invention.
Claims (10)
1. A method of interpolating a carbon flux data, comprising:
s1, acquiring flux data and meteorological data in a specified time period;
wherein the flux data in the designated time period comprises a plurality of pieces of water flux data which are sequentially arranged according to the sequence of the corresponding time stamps; each piece of carbohydrate flux data corresponds to a timestamp for collecting the carbohydrate flux data;
the meteorological data includes: saturated vapor pressure difference VPD value and radiation data of each minute in a specified time period; the radiation data includes: total radiation TR, incident short wave radiation SRIN, photosynthetically active radiation PAR;
s2, carrying out abnormal value elimination processing on the flux data to obtain flux data subjected to abnormal value elimination processing corresponding to a specified time period;
s3, correcting the meteorological data to obtain corrected meteorological data;
s4, extracting missing features of any section of missing data in the flux data after the abnormal value elimination processing corresponding to the designated time period;
s5, interpolating the missing data of any segment based on the missing features of the missing data and the meteorological data after correction processing to obtain an interpolation result.
2. The method of interpolating a carbohydrate flux data according to claim 1, wherein S2 specifically comprises:
s21, eliminating the carbon water flux data meeting any abnormal value condition in flux data in a specified time period to obtain initial flux data;
the outlier condition includes:
carbon dioxide flux values in the carbon water flux data are greater than or equal to 100 μmol/m 2 /s；
Carbon dioxide flux values in the carbon water flux data are less than or equal to-50 μmol/m 2 /s；
S22, judging whether the quality grade in the initial flux data is greater than 2, if so, eliminating all the water flux data with the quality grade of 7, 8 or 9 in the initial flux data to obtain flux data subjected to abnormal value elimination processing;
if the abnormal value is not found, eliminating all the water flux data with the quality score of 2 in the initial flux data to obtain flux data after abnormal value elimination treatment.
3. The method of interpolating carbohydrate flux data according to claim 2, wherein the correcting process for the meteorological data in S3 specifically includes:
s31, judging whether the saturated vapor pressure difference VPD value of each minute in the meteorological data in the specified time period meets a preset range, and if not, acquiring a new saturated vapor pressure difference VPD value corresponding to the minute by adopting a formula I;
the first formula is:
wherein,
wherein P is the air pressure value in the minute meteorological data;
T 0 air temperature values in the minute weather data;
RH is the relative humidity corresponding to the minute meteorological data;
s32, calculating sunrise and sunset time according to site position information, so as to extract night radiation data Rg in the radiation data, judging whether the night radiation data Rg meet preset conditions, and if the night radiation data Rg do not meet the preset conditions, updating the night radiation data Rg according to a preset updating mode to obtain updated night radiation data Rg;
the night radiation data Rg includes: total radiation TR at night, incident short wave radiation SRIN at night, photosynthetically active radiation PAR at night;
the preset condition is that the total night radiation TR, the incident short wave radiation SRIN at night and the photosynthetically active radiation PAR at night in the night radiation data Rg are all more than or equal to 0.
4. A method of interpolating a carbon water flux data as claimed in claim 3, wherein the predetermined range is 0-50.
5. The method for interpolating carbohydrate flux data according to claim 4, wherein if said nocturnal radiation data Rg is not satisfied, updating said nocturnal radiation data Rg according to a predetermined updating manner, comprising:
if the night radiation data Rg is not satisfied, setting the value of the total night radiation TR and/or the incident short wave radiation SRIN and/or the photosynthetic active radiation PAR to be 0, and obtaining updated night radiation data Rg.
6. The method of interpolating a carbohydrate flux data according to claim 5, wherein S4 specifically comprises:
s41, extracting first flux data, last flux data and time stamps corresponding to the first flux data in the first data set, and forming a first time stamp sequence;
the first flux data is the carbon water flux data with the carbon dioxide flux value being null;
the first data set is flux data after abnormal value elimination processing corresponding to a specified time period;
s42, based on the first time stamp sequence, obtaining the difference value of subtracting the previous time stamp from the next time stamp in any two adjacent time stamps in the first time stamp sequence;
s43, obtaining a first time difference sequence delta T based on the difference value of subtracting the previous time stamp from the next time stamp in any two adjacent time stamps in the first time stamp sequence stp ；
ΔT stp ＝{ΔT stp 2-1 ，ΔT stp 3-2 ，…，ΔT stp c-(c-1) ，...，ΔT stp n-(n-1) }；
Wherein DeltaT stp c-(c-1) C-1 th element in the first time difference sequence;
wherein T is c C-th timestamp of n total timestamps in the first sequence of timestamps;
s44, acquiring missing features of any section of missing data based on the first time difference sequence.
7. The method of interpolating a carbohydrate flux data according to claim 6, wherein S44 specifically includes:
s441, adding lengths of elements which are continuously adjacent in the first time difference sequence and have the value equal to 1 to obtain the length of N segments of missing data;
wherein the length of each element in the first time difference sequence is 1;
s442, adding lengths of elements which are continuously adjacent in the first time difference sequence and have the numerical value not equal to 1 to obtain total continuous lengths of N+1 sections;
s443, regarding the i-th continuous length of the N+1 continuous lengths as the forward continuous length Ts of the length of the i-th missing data of the length of the N missing data fi ；
A backward continuous length Ts of the length of the ith section missing data in the length of the N section missing data, which is the (i+1) th section continuous length in the N+1 section continuous lengths bi ；
Wherein the missing features include a length of missing data, a forward continuous length, a backward continuous length.
8. The method of interpolating a carbohydrate flux data according to claim 7, wherein S5 specifically comprises:
if Tm i When the ratio of the ratio to the T is greater than 0.06, judging whether the first value is smaller than the F value, if the first value is greater than or equal to the F valueAccording to the modified meteorological data, interpolation is carried out on the i-th missing data in a large-scale interpolation mode to obtain a large-scale interpolation result, and the large-scale interpolation result is used as an interpolation result;
Tm i the length of the data missing in the ith section in the length of the data missing in the N sections;
T st to specify the start of the time period, T ed For the end of a specified time period;
wherein the first value is W;
W＝Tm i /(Ts fi +Ts bi )；
F＝4.17×(Ts fi +Ts bi )/0.06T。
9. the method of interpolating a carbohydrate flux data according to claim 8, wherein S5 further specifically comprises:
s51, if Tm i When the ratio of/T is less than or equal to 0.06, interpolating the i-th missing data by adopting a small-scale interpolation mode based on the meteorological data after correction processing to obtain a small-scale interpolation result; or, if Tm i When the ratio of/T is greater than or equal to 0.06 and the first value is smaller than the F value, correcting the processed meteorological data, and performing interpolation on the i-th missing data by adopting a small-scale interpolation mode to obtain a small-scale interpolation result;
s52, combining the small-scale interpolation result and the flux data subjected to outlier rejection processing corresponding to the specified time period to form a process interpolation flux data set, and extracting missing features of any section of missing data in the process interpolation flux data set;
s53, interpolating the missing data of any segment in the flux data set based on the missing feature of the missing data of the segment and the modified meteorological data to obtain an interpolation result.
10. The method of interpolating a carbohydrate flux data according to claim 8, wherein S53 specifically includes:
s531, judging whether the ratio of the length of any section of missing data in the process interpolation flux data set to T is greater than 0.06, if the length of the section of missing data in the process interpolation flux data set is greater than 0.06, judging whether the second value is smaller than the Q value, if the second value is greater than or equal to the Q value, interpolating the section of missing data in the process interpolation flux data set in a large-scale interpolation mode according to the modified meteorological data to obtain a large-scale interpolation result, and taking the large-scale interpolation result as the interpolation result;
wherein the second value is E;
e = length of any segment of missing data in the interpolated flux data/(forward continuous length of the segment of missing data in the interpolated flux data + backward continuous length of the segment of missing data in the interpolated flux data);
q=4.17× (forward continuous length of the segment of missing data in the interpolation flux data set+backward continuous length of the segment of missing data in the interpolation flux data set)/0.06T;
s532, if the ratio of the length of any section of missing data in the process interpolation flux data set to T is smaller than or equal to 0.06, based on the modified meteorological data, performing interpolation on the section of missing data in the process interpolation flux data set in a small-scale interpolation mode to obtain a small-scale interpolation result; or when the ratio of the length of any section of missing data in the process interpolation flux data set to T is more than or equal to 0.06 and the second value is less than the Q value, interpolating the section of missing data in the process interpolation flux data set by adopting a small-scale interpolation mode based on the modified meteorological data to obtain a small-scale interpolation result;
s533, combining the small-scale interpolation result with the process interpolation flux data set to form a new process interpolation flux data set;
s534, repeating the steps S531-S533 until the ratio of the length of any section of missing data in the new process interpolation flux data set to T is greater than 0.06, and the second value is greater than or equal to the Q value, and interpolating the section of missing data in the new process interpolation flux data set by adopting a large-scale interpolation mode according to the modified meteorological data to obtain a large-scale interpolation result, wherein the large-scale interpolation result is used as the interpolation result.
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