CN114154892B - Agricultural drought grade division method based on information diffusion - Google Patents
Agricultural drought grade division method based on information diffusion Download PDFInfo
- Publication number
- CN114154892B CN114154892B CN202111494404.7A CN202111494404A CN114154892B CN 114154892 B CN114154892 B CN 114154892B CN 202111494404 A CN202111494404 A CN 202111494404A CN 114154892 B CN114154892 B CN 114154892B
- Authority
- CN
- China
- Prior art keywords
- drought
- index
- information
- grade
- fuzzy
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 33
- 238000009792 diffusion process Methods 0.000 title claims abstract description 31
- 238000012544 monitoring process Methods 0.000 claims abstract description 60
- 238000013507 mapping Methods 0.000 claims abstract description 11
- 239000011159 matrix material Substances 0.000 claims abstract description 11
- 238000012545 processing Methods 0.000 claims description 21
- 238000007781 pre-processing Methods 0.000 claims description 10
- 238000012937 correction Methods 0.000 claims description 9
- 230000005484 gravity Effects 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 4
- 239000013307 optical fiber Substances 0.000 claims description 3
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 claims description 2
- 238000005516 engineering process Methods 0.000 abstract description 4
- 239000002689 soil Substances 0.000 description 9
- 230000006870 function Effects 0.000 description 6
- 230000001149 cognitive effect Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 241000282414 Homo sapiens Species 0.000 description 3
- 230000019771 cognition Effects 0.000 description 3
- 238000010276 construction Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 241000282412 Homo Species 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 210000004556 brain Anatomy 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 238000003908 quality control method Methods 0.000 description 1
- 238000011002 quantification Methods 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Mining
Abstract
The application belongs to satellite remote sensing technology application, and particularly relates to an agricultural drought grade classification method based on information diffusion, which comprises the following steps: acquiring remote sensing data and drought form data of a monitoring target in a preset time period; calculating a drought index value; converting drought morphology data into a fuzzy subset; constructing a sample set; constructing a fuzzy information matrix by using an information diffusion method so as to obtain a mapping relation between drought index values and drought grades; and evaluating the drought grade of the monitoring target. According to the agricultural drought grade classification method based on information diffusion, for uncertainty existing in the drought grade dynamic determination process of the existing agricultural drought remote sensing index, qualitative information of drought grade judgment which is easy to obtain is converted into a fuzzy subset by utilizing a fuzzy set theory, and then a fuzzy relation matrix between the drought index and the drought grade is constructed by adopting an information diffusion method, so that regional agricultural drought monitoring is carried out.
Description
Technical Field
The application belongs to satellite remote sensing technology application, and particularly relates to an agricultural drought grade division method based on information diffusion.
Background
At present, drought indexes are commonly used for expressing drought phenomena when drought monitoring is carried out on farmlands and crops. Drought index itself is objective, and a mapping relationship between index and drought grade needs to be established for a specific index to characterize the severity of drought.
Specifically, after obtaining the corresponding drought index value, the drought degree can be characterized by two methods: the first method is to divide the threshold value of drought index, and characterize the drought degree by setting certain grading standard; the second method is to establish the fitting relation between the drought index and the soil moisture through the data on the points, calculate the soil relative humidity on the surface, and monitor and evaluate the drought by combining the grading standard of the quantification of the soil relative humidity.
However, both the above methods have drawbacks in practical application, specifically as follows:
for the first method, because index grading standards are greatly different in different areas and different times, even for the same research area, grading standards obtained by different researchers are inconsistent, and drought differences evaluated by different grading standards are relatively large, so that reasonable drought grades are difficult to determine. In addition, because of uncertainty of the relation between the drought index and the drought grade (severity), if a specific grading standard is adopted in each monitoring period, description of the drought grade monitoring result and the actual drought occurrence degree can be slightly deviated by using a remote sensing means.
Aiming at the second method, the fitting relation between the drought index and the soil moisture is required to be established, so that site soil moisture data are required to be acquired; on one hand, the soil water content needs to be measured by an instrument, so that the time and the labor are consumed, and a plurality of investment and uninterrupted updating equipment are needed, so that the good operation of the system is maintained; on the other hand, site soil relative humidity data of a study area are difficult to obtain in many cases, and great human randomness is often involved in drought grade assessment.
Disclosure of Invention
In order to solve at least one technical problem in the prior art, the application provides an agricultural drought grading method based on information diffusion.
The application discloses an agricultural drought grade classification method based on information diffusion, which comprises the following steps:
step 101, acquiring remote sensing data and drought form data of a monitoring target in a preset time period, wherein the drought form data comprises position information of the monitoring target and drought grade information judged manually;
102, selecting a remote sensing index of a preset type as the drought index of the monitoring target, and calculating a corresponding drought index value according to the remote sensing data;
step 103, converting the drought shape data into fuzzy subsets by using fuzzy set theory;
step 104, forming a sample set by the drought index value of the step 102 and the fuzzy subset of the step 103;
105, constructing a fuzzy information matrix by using the sample set as a parameter and using an information diffusion method, so as to obtain a mapping relation between the drought index value and the drought grade expressed by the drought form data;
and 106, evaluating the drought grade of the monitoring target based on the mapping relation obtained in the step 105.
According to at least one embodiment of the present application, the drought level in the drought level information includes no drought, light drought, medium drought, heavy drought, and extreme drought.
According to at least one embodiment of the present application, the step 103 specifically includes:
step 1031, setting a given argument U, U to the closed interval [0,1]]Is any one of the maps mu B The method comprises the following steps:
μ B :U→[0,1],
wherein U is the argument of fuzzy set B, then B is called a fuzzy subset on U, and the value mu B (x) For x belonging to the membership of B, function μ B Is a membership function of B;
step 1032, assuming that domain U is a finite set, express a fuzzy subset B of U with zade notation:
B＝μ B (x 1 )/x 1 +μ B (x 2 )/x 2 …+μ B (x n )/x n ；
wherein x corresponds to the drought grade, mu in the drought grade information B (x) Is the possibility corresponding to each drought grade.
According to at least one embodiment of the present application, in the step 102, the predetermined type of telemetry index is a temperature vegetation drought index.
According to at least one embodiment of the present application, the temperature vegetation drought index TVDI is obtained by the following formula:
wherein ,TS Representing the surface temperature of any pixel;
representing the lowest surface temperature corresponding to the same vegetation index of a certain land; />
Representing the highest surface temperature corresponding to the same vegetation index of a certain place;
in addition, in the case of the optical fiber,
the specific calculation formula is as follows:
in the formula ：a1 ，b 1 Fitting coefficients representing wet edge equations; a, a 2 ，b 2 Fitting coefficients representing the dry-edge equation; VI represents a vegetation index.
According to at least one embodiment of the present application, in step 102, before calculating the corresponding drought index value according to the remote sensing data, the method further includes the following steps:
and carrying out remote sensing image preprocessing, cloud removal processing and shadow removal processing on the remote sensing data in sequence.
According to at least one embodiment of the present application, the remote sensing image preprocessing includes at least one of the following:
radiometric calibration processing, atmospheric correction processing, orthographic correction processing, geometric correction processing and image mosaic processing.
According to at least one embodiment of the present application, in said step 103, before converting said drought morphology data into a fuzzy subset, further comprising the steps of:
and preprocessing the drought form data to remove invalid data and interference data.
In step 104, according to at least one embodiment of the present application, a sample set is constructed with geographic location as a tie, drought index values as inputs, and fuzzy subsets as outputs.
According to at least one embodiment of the present application, in step 106, the drought grade distribution map of the monitoring target is directly obtained by using the drought index value of the monitoring target based on the mapping relationship, so as to evaluate the drought grade of the monitoring target.
The application has at least the following beneficial technical effects:
aiming at the uncertainty existing in the current dynamic determination process of the drought grade of the agricultural drought remote sensing index, the method for dividing the agricultural drought grade based on information diffusion provides a dynamic drought grade determination method taking qualitative information as a reference, specifically, qualitative information of easy-to-acquire drought grade judgment is converted into a fuzzy subset by utilizing a fuzzy set theory, and then a fuzzy relation matrix between the remote sensing drought index and the agricultural drought grade is constructed by adopting an information diffusion method so as to describe the fuzzy relation between the drought index and the drought grade, thereby carrying out regional agricultural drought monitoring.
Drawings
FIG. 1 is a flow chart of an agricultural drought classification method based on information diffusion according to the present application;
FIG. 2 is a diagram of dry grade cognitive judgment in an embodiment of the present application;
FIG. 3 is a crop distribution diagram in three northeast provinces in an embodiment of the present application;
FIG. 4 is a spatial distribution diagram of TVDI in the first 8 months of 2009 in three northeast province in an embodiment of the application;
fig. 5 is an agricultural drought grade distribution diagram of the present application obtained by fuzzy reasoning with qualitative information as reference, in the 8 th month of 2009 in the three northeast province.
Detailed Description
In order to make the purposes, technical solutions and advantages of the implementation of the present application more clear, the technical solutions in the embodiments of the present application will be described in more detail below with reference to the accompanying drawings in the embodiments of the present application. The described embodiments are some, but not all, of the embodiments of the present application. The embodiments described below by referring to the drawings are exemplary and intended for the purpose of explaining the present application and are not to be construed as limiting the present application.
Example 1
Taking the example of monitoring agricultural drought in the third northeast province in 8 th month of 2009 (application area is shown in fig. 3).
Specifically, referring to fig. 1, the method for classifying agricultural drought grades based on information diffusion in this embodiment includes the following steps:
step 101, obtaining MODIS remote sensing data and drought state data of a monitoring target (namely crops in the region of three provinces in northeast) in a preset time period (namely 8 th and the last ten days in 2009), wherein the drought state data comprises position information of the monitoring target and drought grade information of human judgment (such as judgment of a planter and an agricultural field expert).
The type of the remote sensing data can be selected appropriately according to the needs, and in the embodiment, MODIS remote sensing data are adopted; likewise, a suitable drought index, in this embodiment a TVDI index, is selected according to the specific application scenario (which may be determined according to the monitoring objective), such as the monitored area, the crop growth period, the monitored spatio-temporal scale, the availability of data, etc. In addition, in the present embodiment, the drought level in the drought level information includes no drought, light drought, medium drought, heavy drought, and extreme drought.
Step 102, selecting a remote sensing index of a preset type as a drought index of a monitoring target, and calculating a corresponding drought index value according to MODIS remote sensing data.
It should be noted that, in order to obtain the agricultural drought condition of the area, the agricultural drought index may be calculated by using remote sensing data; the drought index may be, for example, ATI, PDI, MPDI, TVDI, TCI, VCI, etc., and may be specifically selected according to the monitoring target; in this embodiment, since the observation time is 8 late in 2009, the crop is in the middle and late stage of growth, the vegetation coverage is high, and the Temperature Vegetation Drought Index (TVDI) is preferentially selected as the drought monitoring index in the case where the vegetation index VI and the land surface temperature LST are available.
In addition, in this embodiment, before calculating the corresponding drought index value, preprocessing is further required to be performed on the MODIS remote sensing data, which specifically includes sequentially performing quality control processes such as remote sensing image preprocessing, cloud removal processing, shadow removal processing, and the like, where the remote sensing image preprocessing includes at least one of radiation calibration processing, atmospheric correction processing, orthographic correction processing, geometric correction processing, and image mosaic processing.
Further, in this embodiment, the temperature vegetation drought index TVDI is obtained by the following formula:
wherein ,TS Representing the surface temperature of any pixel;
representing the lowest surface temperature corresponding to the same vegetation index of a certain land; />
Representing the highest surface temperature corresponding to the same vegetation index of a certain place;
in addition, in the case of the optical fiber,
the specific calculation formula is as follows:
in the formula ：a1 ，b 1 Fitting coefficients representing wet edge equations; a, a 2 ，b 2 Fitting coefficients representing the dry-edge equation; VI represents a vegetation index; the vegetation index VI may be calculated using the reflectance data, where EVI may also be selected as the vegetation index. The larger the TVDI, the more arid.
It should be further noted that, because the grading standards of TVDI are many and are confusing, and the grading standards of TVDI in different areas and different times have great differences, the drought difference between the same research area and the evaluation of different TVDI grading standards is great, so it is difficult to determine a reasonable TVDI drought grade. For this purpose, the ground data should be used as a reference, and the drought level should be dynamically determined by specific analysis. In consideration of time and labor consumption and relative lack of data when measuring the relative humidity of soil by using an instrument, in the subsequent steps, the observed drought form data of farmlands and crops are used as ground reference data, are converted into quantitative information in a certain mode, and are used as output parameters of a model to establish a mapping relation between drought indexes and drought grades, so that establishment from the drought indexes to the drought grades is completed.
Step 103, converting drought shape data into fuzzy subsets by utilizing fuzzy set theory.
It should be noted that, drought state data is the drought degree of crops which can be judged by growers and agricultural field experts through experience, and meanwhile, the position information of the crops is recorded, and the GB_T 32136-2015 "agricultural drought grade" divides drought conditions into 4 grades: the drought shape index of farmland and crops with various drought grades is described in detail. However, some interference factors inevitably exist in the drought state information of the ground farmland and crops provided by the growers and the experts, and data preprocessing is needed before the information is utilized, mainly including that invalid values are removed through screening and interference data are removed by combining corresponding remote sensing index values, so that the reliability of samples is guaranteed to the greatest extent possible.
Further, the filtered drought morphology information of the farmland and crops is converted into a fuzzy subset.
According to the cognition law of human beings, the cognition and judgment of the brain on a certain object are fuzzy, for example, the 'withering in daytime due to drought leaves' is defined as medium drought, the 'dead seedlings, withering of leaves and falling off of fruits due to drought' corresponds to heavy drought, and the 'withering' or the 'withering' is difficult to absolutely judge aiming at a certain phenomenon in the actual judging process. And more climates are a comprehensive assessment of drought conditions in a certain crop population rather than in individual plants, which makes the situation more complex. Often humans use the word "more prone to … …" or the like to express views, more specifically, knowledge of drought patterns in farms and crops over a certain spatial range can be represented by figure 2. At this time, the possibility belonging to no drought, light drought, medium drought, heavy drought and extreme drought is considered to be 0, 0.5, 0.8, 0.7, 0.2, respectively, with the highest possibility of medium drought. Thus, the expression can acquire the cognitive judgment information of drought forms of farmlands and crops as much as possible.
Specifically, the steps of converting drought morphology information into fuzzy subsets using fuzzy set theory are as follows:
setting χ if x ε B B (x) =1, if
Then χ is B (x) =0, i.e.:
the absolute membership in the classical set theory is activated, i.e. the membership of an element to a set is no longer this is the case, but only 0 or 1, but any value between 0,1 can be taken.
Let U be the argument U, U to the closed interval [0,1]]Is any one of the maps mu B ：
A fuzzy subset B of U may be determined.
The fuzzy subset is generally abbreviated as fuzzy set, if U is the argument of fuzzy set B, then B is called as a fuzzy subset on U, B is abbreviated as fuzzy set of U, and the value mu B (x) For x belonging to the membership of B, function μ B Is a membership function of B.
If the universe U is a finite set, the zade notation can be used to express:
B＝μ B (x 1 )/x 1 +μ B (x 2 )/x 2 …+μ B (x n )/x n (5)；
the zade notation of the above formula is not a partial sum, but is a notation. The denominator is an element of the domain U (namely, the drought grade corresponding to the drought grade information), and the numerator is the membership degree corresponding to the element (the possibility corresponding to each drought grade). When the membership degree of an element is 0, the corresponding item may not be written.
For example, agricultural drought conditions are classified into five according to drought disaster observation record dataThe following grades: no drought, light drought, moderate drought, heavy drought and extreme drought, respectively designated as upsilon 1 ，υ 2 ，υ 3 ，υ 4 ，υ 5 (i.e., corresponding to x in the above formula (5)).
Step 104, the drought index value of step 102 and the fuzzy subset of step 103 are combined to form a sample set.
Specifically, the step takes the geographic position as a tie, takes the drought index value as input and takes the fuzzy subset as output to construct a sample set, and the obtained sample set is shown in table 1:
TABLE 1 parameters for constructing two-dimensional information diffusion model with qualitative information as reference
Taking sample number 36 in the table as an example, the TVDI value corresponding to the sample number is 0.90, and the fuzzy subset generated by the cognitive judgment of the drought state of the farmland and the crops can be expressed as follows:
J＝0.0/υ 1 +0.0/υ 2 +0.1/υ 3 +0.6/υ 4 +0.4/υ 5 。
and 105, constructing a fuzzy information matrix by using the sample set as a parameter and using an information diffusion method, so as to obtain a mapping relation between the drought index value and the drought grade expressed by the drought form data.
When the information diffusion technology is applied to perform actual calculation, the construction of the monitoring interval (also called a monitoring point sequence) Z is generally based on the original sample data X itself (i.e. TVDI value), and the construction process is as shown in formula (6):
in the formula ,vi Represents the ith monitoring point in the sequence Z, and k representsShowing the number of monitoring points to be constructed in Z, wherein Deltaz represents the step length of the monitoring point sequence.
The estimation function of normal information diffusion is as follows:
where n represents the sample volume and h represents the diffusion coefficient. The diffusion coefficient h is calculated by the formula (8),
wherein ,
when the sample data is estimated by using the information diffusion technique, the following steps are followed:
1) Constructing a monitoring point sequence Z by using sample data X according to a relation formula (6);
2) Calculating a diffusion coefficient h according to the relation (8);
3) According to the relation (7), the monitoring point sequence Z in Z i Substituting information diffusion function as value of variable x
Calculating to obtain information quantity p (z) of sample data diffused to each monitoring point i )；
4) Normalizing the information quantity sequences of all the monitoring points according to the following formula (9) to obtain a fuzzy relation matrix;
5) Substituting the following gravity center formula (10), calculating the gravity center value of the monitoring point as an estimated value z' of the sample data:
wherein z' represents the calculated gravity center value of the monitoring point, z i Represents the ith monitoring point, p (z i ) ' represents the information amount of the i-th monitoring point after normalization.
In this embodiment, an information diffusion method is used to construct an input-output relationship between the remote sensing drought index TVDI and the drought level, and a monitoring space is constructed as follows (where U corresponds to the monitoring point sequence Z, U in the above formula (6) 1 –u 73 I.e. to the monitoring point Z in the above mentioned Z 1 -z k )：
U＝{u 1 ,u 2 ,u 3 ,u 4 ,…,u 71 ,u 72 ,u 73 }＝{0.23,0.24,0.25,0.26,…,0.93,0.94,0.95}
Wherein, the drought class set is expressed as:
V＝{v 1 ,v 2 ,v 3 ,v 4 ,v 5 "Dry land
And then generating a fuzzy relation matrix based on an information diffusion method by utilizing a fuzzy subset generated by cognition judgment of farmland and crop drought forms in the sample:
and 106, directly obtaining a drought grade distribution map of the monitoring target by using the drought index value of the monitoring target based on the mapping relation obtained in the step 105, so as to evaluate the drought grade of the monitoring target.
Specifically, a fuzzy relation between TVDI and drought grade can be obtained according to the fuzzy relation matrix. And (3) performing approximate reasoning by using the fuzzy relation matrix, and inverting by using the agricultural drought remote sensing monitoring index to obtain a corresponding agricultural drought grade, wherein a measure space between the two can be established according to the corresponding agricultural drought grade. The TVDI of fig. 4 was approximated using the fuzzy relationship between TVDI and drought rating, and the results are shown in fig. 5.
As can be seen from fig. 4, the drought is mainly distributed in western areas of the Liaoning, jilin and Heilongjiang provinces, especially western areas of the Liaoning and Jilin provinces, and the drought grade distribution obtained from the data is relatively similar, which indicates that if the cognitive judgment on the drought forms of farmlands and crops can be acquired under the condition of lack of the site-based soil relative humidity data, the obtained drought grade distribution is relatively more in accordance with the actual drought grade distribution based on the information diffusion technology. It is worth noting that when drought level monitoring is performed on drought degree of a certain space-time scale based on qualitative information, cognitive judgment information on drought forms of farmlands and crops needs to be related to all levels appearing in the period as much as possible.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions easily conceivable by those skilled in the art within the technical scope of the present application should be covered in the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (6)
1. An agricultural drought grade dividing method based on information diffusion is characterized by comprising the following steps:
step 101, acquiring remote sensing data and drought form data of a monitoring target in a preset time period, wherein the drought form data comprises position information of the monitoring target and artificially judged drought form information of farmlands and crops; the drought state information of the farmland and the crops is based on the drought state index of the farmland and the crops described in GB_T 32136-2015 agricultural drought grade;
102, selecting a remote sensing index of a preset type as the drought index of the monitoring target, and calculating a corresponding drought index value according to the remote sensing data;
the predetermined type of remote sensing index is a temperature vegetation drought index TVDI, and is obtained through the following formula:
wherein ,TS Representing the surface temperature of any pixel;
representing the lowest surface temperature corresponding to the same vegetation index of a certain land; />
Representing the highest surface temperature corresponding to the same vegetation index of a certain place;
in addition, in the case of the optical fiber,
the specific calculation formula is as follows:
in the formula ：a1 ，b 1 Fitting coefficients representing wet edge equations; a, a 2 ，b 2 Fitting coefficients representing the dry-edge equation; VI represents a vegetation index;
step 103, converting the drought state data obtained in step 101 into a fuzzy subset by using a fuzzy set theory, which specifically comprises the following steps:
step 1031, setting a given argument U, U to the closed interval [0,1]]Is any one of the maps mu B The method comprises the following steps:
μ B ：U→[0，1]，
wherein U is the argument of fuzzy set B, then it is calledB is a fuzzy subset on U, the value μ B (x) For x belonging to the membership of B, function μ B Is a membership function of B;
step 1032, assuming that domain U is a finite set, express a fuzzy subset B of U with zade notation:
B＝μ B (x 1 )/x 1 +μ B (x 2 )/x 2 …μ B (x n )/x n ；
wherein ,x1 、x 2 …x n Respectively corresponding to the drought grade and mu in the drought grade information B (x) Is the corresponding possibility of each drought grade;
step 104, forming a sample set by the drought index value of the step 102 and the fuzzy subset of the step 103;
105, constructing a fuzzy information matrix by using the sample set as a parameter and using an information diffusion method, so as to obtain a mapping relation between the drought index value and the drought grade expressed by the drought form data;
the method specifically comprises the following steps:
1) Constructing a monitoring point sequence Z by using the original sample data X, namely TVDI values according to the relation (6);
in the formula ,zi The ith monitoring point in the sequence Z is represented, k represents the number of monitoring points to be constructed in Z, and Deltaz represents the step length of the monitoring point sequence;
2) Calculating a diffusion coefficient h according to the relation (8):
wherein ,
3) The monitoring point sequence Z in Z i Substituting the value of the variable x into the information diffusion function represented by the relational expression (7)
Calculating to obtain information quantity p (z) of sample data diffused to each monitoring point i )；
Where n represents the sample volume and h represents the diffusion coefficient;
4) Normalizing the information quantity sequences of all the monitoring points according to a formula (9) to obtain a fuzzy relation matrix;
5) Substituting the following gravity center formula (10), calculating the gravity center value of the monitoring point as an estimated value z' of the sample data:
wherein z' represents the calculated gravity center value of the monitoring point, z i Represents the ith monitoring point, p (z i ) ' represents the normalized information of the ith monitoring point;
and 106, evaluating the drought grade of the monitoring target based on the mapping relation obtained in the step 105.
2. The agricultural drought grading method according to claim 1, further comprising the steps of, before calculating the corresponding drought index value from the remote sensing data in step 102:
and carrying out remote sensing image preprocessing, cloud removal processing and shadow removal processing on the remote sensing data in sequence.
3. The agricultural drought grading method of claim 2, wherein the remote sensing image pre-processing includes at least one of:
radiometric calibration processing, atmospheric correction processing, orthographic correction processing, geometric correction processing and image mosaic processing.
4. The agricultural drought grading method according to claim 1, further comprising the steps of, before converting the drought morphology data into fuzzy subsets, in said step 103:
and preprocessing the drought form data to remove invalid data and interference data.
5. The agricultural drought grading method according to claim 1, wherein in said step 104, a sample set is constructed with geographic locations as ties, drought index values as inputs, fuzzy subsets as outputs.
6. The agricultural drought grading method according to claim 1, wherein in step 106, the drought grade profile of the monitoring target is directly obtained using the drought index value of the monitoring target based on the mapping relationship, thereby evaluating the drought grade of the monitoring target.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111494404.7A CN114154892B (en) | 2021-12-08 | 2021-12-08 | Agricultural drought grade division method based on information diffusion |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111494404.7A CN114154892B (en) | 2021-12-08 | 2021-12-08 | Agricultural drought grade division method based on information diffusion |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114154892A CN114154892A (en) | 2022-03-08 |
| CN114154892B true CN114154892B (en) | 2023-05-23 |
Family
ID=80453966
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111494404.7A Active CN114154892B (en) | 2021-12-08 | 2021-12-08 | Agricultural drought grade division method based on information diffusion |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114154892B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017127291A1 (en) * | 2016-01-22 | 2017-07-27 | The Climate Corporation | Forecasting national crop yield during the growing season using weather indices |
| CN107451522A (en) * | 2017-04-28 | 2017-12-08 | 山东省农业可持续发展研究所 | A kind of agricultural arid monitoring and early alarming and forecasting method |
| WO2019079793A2 (en) * | 2017-10-20 | 2019-04-25 | Brix Usa Llc | System for decreasing the drought impact in the performance of a culture, methods for preparing the component i of the system, method for decreasing the drought impact on the performance of a culture using such system and the agricultural tool being used therein |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108629494B (en) * | 2018-04-19 | 2020-11-06 | 三峡大学 | Drought level assessment method and system |
-
2021
- 2021-12-08 CN CN202111494404.7A patent/CN114154892B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017127291A1 (en) * | 2016-01-22 | 2017-07-27 | The Climate Corporation | Forecasting national crop yield during the growing season using weather indices |
| CN107451522A (en) * | 2017-04-28 | 2017-12-08 | 山东省农业可持续发展研究所 | A kind of agricultural arid monitoring and early alarming and forecasting method |
| WO2019079793A2 (en) * | 2017-10-20 | 2019-04-25 | Brix Usa Llc | System for decreasing the drought impact in the performance of a culture, methods for preparing the component i of the system, method for decreasing the drought impact on the performance of a culture using such system and the agricultural tool being used therein |
Non-Patent Citations (4)
| Title |
|---|
| Drought risk evaluation model with interval number ranking and its application;Xiao Liu et al.;Science of The Total Environment;1042-1057 * |
| 农业干旱灾害风险评价及预测预警研究进展;杨志勇;刘琳;曹永强;于赢东;严登华;;水利经济(02);16-21，79-21+79 * |
| 基于可变模糊算法的大连市农业干旱风险评价;曹永强;李香云;马静;伊吉美;;资源科学(05);191-196191-196 * |
| 遥感技术在农业旱情监测中的应用分析;孟成真;;农业与技术(11);178-179 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114154892A (en) | 2022-03-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Kumar et al. | Evaluating the utility of satellite soil moisture retrievals over irrigated areas and the ability of land data assimilation methods to correct for unmodeled processes | |
| CN110309985B (en) | Crop yield prediction method and system | |
| Aggarwal | Uncertainties in crop, soil and weather inputs used in growth models: Implications for simulated outputs and their applications | |
| Hubbard | Spatial variability of daily weather variables in the high plains of the USA | |
| Kucharik | Evaluation of a process-based agro-ecosystem model (Agro-IBIS) across the US Corn Belt: Simulations of the interannual variability in maize yield | |
| Hu et al. | Improvement of sugarcane crop simulation by SWAP-WOFOST model via data assimilation | |
| Zapata et al. | Estimation of the base temperature and growth phase duration in terms of thermal time for four grapevine cultivars | |
| CN106718363B (en) | Irrigation test method and test platform for fine agriculture | |
| JP2001523008A (en) | Environmental monitoring system | |
| CN111489092B (en) | Method and system for evaluating suitable growing area of plant cultivation and planting environment | |
| Acevedo-Opazo et al. | Assessment of an empirical spatial prediction model of vine water status for irrigation management in a grapevine field | |
| CN114120132A (en) | Crop yield estimation method and device combining meteorological remote sensing and red-edge wave band remote sensing | |
| Peng et al. | Relationships between remote-sensing-based agricultural drought indicators and root zone soil moisture: a comparative study of Iowa | |
| Verdugo-Vásquez et al. | Towards an empirical model to estimate the spatial variability of grapevine phenology at the within field scale | |
| Zhang et al. | Improvement in estimation of soil water deficit by integrating airborne imagery data into a soil water balance model | |
| Chen et al. | Improving the practicability of remote sensing data-assimilation-based crop yield estimations over a large area using a spatial assimilation algorithm and ensemble assimilation strategies | |
| Fortes Gallego et al. | Using NDVI and guided sampling to develop yield prediction maps of processing tomato crop | |
| Melkonyan | Environmental and socio-economic vulnerability of agricultural sector in Armenia | |
| US20220132724A1 (en) | Advanced crop manager for crops stress mitigation | |
| Yin et al. | A new integrated index for drought stress monitoring based on decomposed vegetation response factors | |
| CN112613648A (en) | Method for training drought monitoring model, drought monitoring method and equipment | |
| CN114154892B (en) | Agricultural drought grade division method based on information diffusion | |
| Moral et al. | Integration of climatic indices in an objective probabilistic model for establishing and mapping viticultural climatic zones in a region | |
| CN114611699A (en) | Soil moisture downscaling method and device, electronic equipment and storage medium | |
| CN113111799A (en) | Cultivated land soil fertility level monitoring method based on ensemble empirical mode decomposition |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20240322 Address after: 100125 Building 2, national agricultural exhibition hall, No. 16, North East Third Ring Road, Chaoyang District, Beijing Patentee after: Big data development center of the Ministry of agriculture and rural areas Country or region after: China Address before: 100125, No. 41, wheat Street, Chaoyang District, Beijing Patentee before: Ministry of Agriculture and Rural Planning and Design Institute Country or region before: China |