CN112148785A - Crop distribution drawing - Google Patents

Crop distribution drawing Download PDF

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CN112148785A
CN112148785A CN202011016570.1A CN202011016570A CN112148785A CN 112148785 A CN112148785 A CN 112148785A CN 202011016570 A CN202011016570 A CN 202011016570A CN 112148785 A CN112148785 A CN 112148785A
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左丽君
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Aerospace Information Research Institute of CAS
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Abstract

The invention discloses a crop distribution map, which is characterized in that the area of various crops on each grid is calculated according to grid scale crop planting mode information extracted from remote sensing data and the area proportion of various crops on the planting mode of a corresponding administrative unit, so that a crop space distribution map is manufactured. The invention combines the crop phenology characteristics identified by remote sensing with the actual phenology of the crops to determine the spatial distribution condition of the crops, and can realize large-range and multi-scale crop spatial distribution mapping with higher spatial heterogeneity through the characteristics of macro, quick acquisition, multi-scale and the like of remote sensing.

Description

Crop distribution drawing
Technical Field
The invention relates to the field of data analysis, in particular to a crop distribution chart.
Background
The crop space-time distribution reflects the situation that human beings use land to engage in agricultural production in space at different periods, and is important basic data for developing researches on the patterns and functions of farmland ecosystems, the circulation of the land ecosystems, global changes and the like. The understanding of the crop space-time distribution has important significance for guaranteeing national food safety and sustainable development of resource environment.
At present, the crop space-time distribution information acquisition approach mainly comprises administrative statistics and remote sensing monitoring. The statistical method can only reflect the quantity change of the crops in a certain administrative unit, and the spatial difference of the crops in the statistical unit is difficult to reflect; moreover, the statistical data acquisition consumes a large amount of manpower, material resources and financial resources, and is also interfered by human factors. The simple remote sensing technology has the influence of factors such as atmospheric interference, scale conversion, mixed pixels and the like, and is difficult to acquire the spatial distribution information of the large-area-scale full crop series. Therefore, the statistical data and the remote sensing data are deeply fused, and the development of the spatial distribution mapping of multiple crops and even the whole crop type is particularly necessary.
Disclosure of Invention
The invention aims to provide a crop distribution chart,
in order to achieve the purpose, the technical scheme adopted by the invention is as follows:
the invention comprises the following steps
A, acquiring remote sensing data information, constructing a vegetation index time sequence curve of a pixel scale, extracting multiple species information through the number of wave crests of the time sequence curve of the pixel scale, and extracting, classifying and refining planting season information reflected by the pixel scale according to the occurrence time of the wave crests to obtain planting mode information of each raster crop;
b, acquiring the seeding area data of the administrative unit crops, merging the crops in different seasons of the administrative unit, and obtaining the seeding area information of the crops in different seasons of various planting modes;
and C, calculating the areas of various crops on each grid according to the grid scale crop planting mode extracted by remote sensing, the corresponding cultivated area distribution of the grid scale crop planting mode and the area information of the seasonal crops on different planting modes, and manufacturing a crop space distribution map.
Further, the characteristic parameters comprise the number of time sequence curve peaks and time points of the time sequence curve peaks, and the grid planting mode information is obtained by presumption according to the characteristic parameters:
specifically, the preprocessing includes denoising the data by using a time series harmonic analysis method to smooth a time series curve.
Further, the specific flow of pixel scale extraction is as follows:
let each time phase pixel value of pixel i be a1，a2…a23，
Comparing EVI magnitude relation between two adjacent pixels, and using delta aiThe sequence data represents:
Figure BDA0002699267670000021
② calculating adjacent delta aiThe difference between:
ΔΔai＝Δai+1-Δai (3-8)
wherein, if Δ Δ ai<0, then i-1 is the time point of the local peak, ai-1Is the local peak EVI value; if Δ Δ ai>0, then i-1 is the time point of the local trough, ai-1Is the local trough EVI value.
And thirdly, because of the existence of the winter peak of the crop and the peak information of some interference, the real peak point of the growing period of the crop needs to be further extracted. If the peak point satisfies that the EVI value of two continuous time points increases before and the EVI value of two continuous time points decreases after, the point belongs to the EVI peak point of the growing period of the crops. The sum of the effective peak points is the multimaturity information of the pixel, namely the peak number is 1, and the pixel is mature for one year; the number of wave peaks is 2, and the product is twice cooked in one year; the number of wave peaks is more than 2, the temperature is more mature in one year, the temperature of the secondary maturation zone is required to be more than 10 ℃ and more than 3500 ℃, and the temperature of the secondary maturation zone is required to be more than 10 ℃ and more than 5000 ℃.
Specifically, the administrative unit divides crops into winter crops, spring crops and summer crops according to the lunar calendar information of the crop types, and the crops are corresponding to various planting mode refinements existing in the administrative unit, and the planting mode refinement method comprises the following steps
a, for the area with triple cropping potential, if the extracted multiple cropping result is triple cropping, dividing the plot into three-season crop multiple cropping in winter-spring-summer; if the extracted multiple cropping result is double cropping, extracting the time point of occurrence of the peak value of the extracted time series curve, determining whether the peak value of crop growth reflected by the curve occurs in spring, summer or autumn, and further dividing the plot into a winter-summer multiple cropping mode or a spring-summer multiple cropping mode; and if the extracted multiple cropping result is one-time cropping, extracting a peak occurrence point of the time series curve, and determining that the planting mode is a winter mode, a spring mode or a summer mode.
b, for the area with double cropping potential, if the double cropping result extracted in the step 2 is double cropping, dividing the plot into a winter-summer double cropping mode; and if the extracted multiple cropping result is one-time cropping, extracting a peak occurrence point of the time series curve, and determining that the planting mode is a winter mode, a spring mode or a summer mode.
And c, directly extracting peak occurrence points for the region which can only realize one-time harvest, and determining whether the peak occurrence points are in a spring mode or a summer mode.
Further, the crop space distribution map is based on the following formula, the grid scale crop area is calculated, and spatialization is carried out to form the crop space distribution map.
Ail＝CAjl×HIiik
Wherein A isijRepresents the area of crop i in grid l; CAikRepresenting the area of the planting pattern j in the grid l; HI (high-intensity)iikRepresents the area proportion of the plot of the crop i in the planting pattern j in the grid l, wherein the grid 1 is extracted by the administrative unit.
In particular, the area ratio of various crops in a unit area of cultivation on a plot with different planting patterns (HI)ijk) The formula is as follows:
HIijk＝HAikj/CAjk
wherein, HAilIndicating the area of sowing of the crop i in the administration unit k, CAklThe area of the manifold pattern j in the administration unit k is shown.
Further, in the step A, curve fitting is carried out according to different multiple cropping potential areas by adopting corresponding frequency numbers, a second-maturing area adopts 3, and a third-maturing area adopts 4.
Compared with the prior art, the invention has the following beneficial effects:
the invention combines the crop phenology characteristics identified by remote sensing with the actual phenology of the crops to determine the spatial distribution condition of the crops, realizes the crop spatial distribution mapping with large range, multiple scales and higher spatial heterogeneity by the characteristics of macro, quick acquisition, multiple scales and the like of remote sensing, and can acquire the spatial distribution information of the crops which have both spatial heterogeneity information and full crop information by the combination of deep mining of remote sensing data and statistical data.
Drawings
FIG. 1 is a schematic diagram of a crop distribution charting process provided by the present invention;
Detailed Description
The present invention will be further illustrated by the following examples and the accompanying drawings, which include, but are not limited to, the following examples.
Referring to FIG. 1, in the present embodiment, the winter wheat is used as an example to perform a crop distribution chart
Step 1: data are denoised by a time series harmonic analysis method (harmonic analysis of time series, HANTS) to obtain a smooth time series curve. And (3) performing curve fitting in different multi-cropping potential areas by adopting different frequencies (Numberoff sequences), wherein the second-maturing area adopts 3, and the third-maturing area adopts 4.
Step 2: after curve denoising, extracting multiple species information in pixel scale, wherein the specific flow is as follows:
let each time phase pixel value of pixel i be a1，a2…a23，
Comparing EVI magnitude relation between two adjacent pixels, and using delta aiThe sequence data represents:
Figure BDA0002699267670000051
② calculating adjacent delta aiThe difference between:
ΔΔai＝Δai+1-Δai (3-8)
wherein, if Δ Δ ai<0, then i-1 is the time point of the local peak, ai-1Is the local peak EVI value; if Δ Δ ai>0, then i-1 is the time point of the local trough, ai-1As local trough EVI values。
And thirdly, because the information of the winter peak of winter wheat and some interfering wave peaks exists, the real peak point of the crop in the growing period needs to be further extracted. If the peak point satisfies that the EVI value of two continuous time points increases before and the EVI value of two continuous time points decreases after, the point belongs to the EVI peak point of the growing period of the crops. The sum of the effective peak points is the multimaturity information of the pixel, namely the peak number is 1, and the pixel is mature for one year; the number of wave peaks is 2, and the product is twice cooked in one year; if the number of peaks is greater than 2, the product is more mature than one year.
The temperature of the secondary ripening area is required to be higher than 3500 ℃ when the temperature is more than or equal to 10 ℃, and the temperature of the secondary ripening area is required to be higher than 5000 ℃ when the temperature is more than or equal to 10 ℃.
And step 3: on the basis of extracting the multiple cropping information, the planting season information reflected by the pixel is extracted, and then the planting mode is extracted.
1 for the area with triple cropping potential, if the triple cropping result extracted in the step 2 is triple cropping, dividing the plot into winter-spring-summer triple crop multiple cropping; if the extracted multiple cropping result is double cropping, extracting the time point of occurrence of the peak value of the time series curve extracted in the step 2, determining whether the peak value of the crop growth reflected by the curve occurs in spring, summer or autumn, and further dividing the plot into a winter-summer multiple cropping mode or a spring-summer multiple cropping mode; and if the extracted multiple cropping result is one-time cropping, extracting a peak occurrence point of the time series curve, and determining that the planting mode is a winter mode, a spring mode or a summer mode.
2 for the area with double cropping potential, if the double cropping result extracted in the step 2 is double cropping, dividing the plot into a winter-summer double cropping mode; and if the extracted multiple cropping result is one-time cropping, extracting a peak occurrence point of the time series curve, and determining that the planting mode is a winter mode, a spring mode or a summer mode.
And 3, directly extracting peak occurrence points for the region which can only realize the first-maturing, and determining the mode to be a spring mode or a summer mode.
And 4, step 4: and calculating the cultivated land area with the winter crop planting mode in each administrative unit by taking the administrative unit with the obtained statistical data of the winter wheat seeding area as a boundary.
And 5: according to the farming season information of the crop types contained in each administrative unit, the crops are divided into winter crops, spring crops and summer crops, and the winter crops correspond to the planting modes of the winter crops existing in the administrative unit.
Step 6: and calculating the area proportion of the winter wheat in each administrative statistical unit in the land plots of various planting modes. Calculating the area ratio of winter wheat in unit cultivated area on the land parcels with different planting modes (HI) according to the planting mode corresponding to the winter crop in each administrative unit determined in the step 5 and by combining the area of the planting mode land parcel in the administrative unit obtained in the step 4ijk) The formula is as follows:
HIjk＝HAkj/CAjk (1)
wherein, HAilIndicates the sowing area of winter wheat in administrative Unit k, CAklRepresents the area of the planting pattern j containing winter crops in the administration k.
And 7: and calculating the areas of the winter wheat on the grids in different planting modes according to the grid planting mode information and the corresponding farmland distribution and combining the areas of the winter wheat crops on the planting modes of the administrative units, thereby manufacturing a winter wheat space distribution map. Specifically, the area of the winter wheat is calculated in a grid scale based on the following formula, and spatialization is performed to form a crop space distribution map.
Al＝CAjl×HIjl
Wherein A islRepresents the area of winter wheat in grid l; CAljRepresenting the area of the planting pattern j in the grid l; HI (high-intensity)ljAnd (3) representing the area proportion of the winter wheat in the plot of the planting mode j in the grid l, wherein the grid l belongs to the administrative unit k and is obtained in the step 6.
It will be clear to a person skilled in the art that the scope of the present invention is not limited to the examples discussed in the foregoing, but that several amendments and modifications thereof are possible without deviating from the scope of the present invention as defined in the attached claims. While the invention has been illustrated and described in detail in the drawings and the description, such illustration and description are to be considered illustrative or exemplary and not restrictive. The invention is not limited to the disclosed embodiments.
Variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure, and the appended claims. In the claims, the term "comprising" does not exclude other steps or elements. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage. Any reference signs in the claims shall not be construed as limiting the scope of the invention.
The above-mentioned embodiment is only one of the preferred embodiments of the present invention, and should not be used to limit the scope of the present invention, but all the insubstantial modifications or changes made within the spirit and scope of the main design of the present invention, which still solve the technical problems consistent with the present invention, should be included in the scope of the present invention.

Claims (8)

1. The crop distribution drawing method is characterized by comprising the following steps
A, acquiring remote sensing data information, constructing a vegetation index time sequence curve of a pixel scale, extracting multiple species information through the number of wave crests of the time sequence curve of the pixel scale, and extracting, classifying and refining planting season information reflected by the pixel scale according to the occurrence time of the wave crests to obtain planting mode information of each raster crop;
b, acquiring the seeding area data of the administrative unit crops, merging the crops in different seasons of the administrative unit, and obtaining the seeding area information of the crops in different seasons of various planting modes;
and C, calculating the areas of various crops on each grid according to the grid scale crop planting mode extracted by remote sensing, the corresponding cultivated area distribution of the grid scale crop planting mode and the area information of the seasonal crops on different planting modes, and manufacturing a crop space distribution map.
2. The crop distribution map of claim 1, wherein the characteristic parameters comprise a time series curve peak number and a time point of a time series curve peak, and the grid planting pattern information is derived from the characteristic parameters.
3. The crop distribution map of claim 1, wherein said preprocessing comprises denoising the data using time series harmonic analysis to smooth the time series curve.
4. The crop distribution mapping of claim 1, wherein the specific process of pixel scale extraction is as follows:
let each time phase pixel value of pixel i be a1，a2…a23，
Comparing EVI magnitude relation between two adjacent pixels, and using delta aiThe sequence data represents:
Figure FDA0002699267660000011
② calculating adjacent delta aiThe difference between:
ΔΔai＝Δai+1-Δai (3-8)
wherein, if Δ Δ ai<0, then i-1 is the time point of the local peak, ai-1Is the local peak EVI value; if Δ Δ ai>0, then i-1 is the time point of the local trough, ai-1Is the local trough EVI value.
And thirdly, because of the existence of the winter peak of the crop and the peak information of some interference, the real peak point of the growing period of the crop needs to be further extracted. If the peak point satisfies that the EVI value of two continuous time points increases before and the EVI value of two continuous time points decreases after, the point belongs to the EVI peak point of the growing period of the crops. The sum of the effective peak points is the multimaturity information of the pixel, namely the peak number is 1, and the pixel is mature for one year; the number of wave peaks is 2, and the product is twice cooked in one year; the number of wave peaks is more than 2, the temperature is more mature in one year, the temperature of the secondary maturation zone is required to be more than 10 ℃ and more than 3500 ℃, and the temperature of the secondary maturation zone is required to be more than 10 ℃ and more than 5000 ℃.
5. The crop distribution chart according to claim 1, wherein the administrative unit divides the crops into winter crops, spring crops and summer crops according to the information of the lunar calendar of the crop type, and corresponds the crops to various types of planting pattern refinements existing in the administrative unit, and the method for the planting pattern refinements comprises
a, for the area with triple cropping potential, if the extracted multiple cropping result is triple cropping, dividing the plot into three-season crop multiple cropping in winter-spring-summer; if the extracted multiple cropping result is double cropping, extracting the time point of occurrence of the peak value of the extracted time series curve, determining whether the peak value of crop growth reflected by the curve occurs in spring, summer or autumn, and further dividing the plot into a winter-summer multiple cropping mode or a spring-summer multiple cropping mode; and if the extracted multiple cropping result is one-time cropping, extracting a peak occurrence point of the time series curve, and determining that the planting mode is a winter mode, a spring mode or a summer mode.
b, for the area with double cropping potential, if the double cropping result extracted in the step 2 is double cropping, dividing the plot into a winter-summer double cropping mode; and if the extracted multiple cropping result is one-time cropping, extracting a peak occurrence point of the time series curve, and determining that the planting mode is a winter mode, a spring mode or a summer mode.
And c, directly extracting peak occurrence points for the region which can only realize one-time harvest, and determining whether the peak occurrence points are in a spring mode or a summer mode.
6. The crop distribution map of claim 1, wherein the crop space distribution map is formed by calculating a grid-scale crop area and spatializing the grid-scale crop area based on the following formula.
Ail＝CAjl×HIiik
Wherein A isijRepresents the area of crop i in grid l; CAikRepresenting the area of the planting pattern j in the grid l; HI (high-intensity)iikRepresents the area proportion of the plot of the crop i in the planting pattern j in the grid l, wherein the grid 1 is extracted by the administrative unit.
7. The crop distribution map of claim 1, wherein the ratio of area occupied by each type of crop per area of field on a plot for different planting patterns (HI)ijk) The formula is as follows:
HIijk＝HAikj/CAjk
wherein, HAilIndicating the area of sowing of the crop i in the administration unit k, CAklIndicates the area of planting pattern j in administration k.
8. The crop distribution chart according to claim 1, wherein in step a, curve fitting is performed according to different multiple cropping potential areas by using corresponding frequency numbers, the second cropping area is 3, and the third cropping area is 4.
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Families Citing this family (4)

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Publication number Priority date Publication date Assignee Title
CN114092804A (en) * 2021-11-06 2022-02-25 支付宝(杭州)信息技术有限公司 Method and device for identifying remote sensing image
CN114067158B (en) * 2021-11-17 2022-09-23 江苏天汇空间信息研究院有限公司 Farmland use state monitoring system and method applying multi-source remote sensing data
CN114926565B (en) * 2022-05-24 2024-04-12 中国科学院东北地理与农业生态研究所 Soybean drafting method based on green degree and water comprehensive index
CN114782838B (en) * 2022-06-17 2022-10-18 中化现代农业有限公司 Rice identification method and device, electronic equipment and storage medium

Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102592181A (en) * 2011-12-30 2012-07-18 中国农业科学院农业资源与农业区划研究所 Method for optimizing spatial distribution of statistical data about crop planting area
CN102855351A (en) * 2012-08-09 2013-01-02 中国科学院地理科学与资源研究所 Crop straw resource spatialization method based on statistical data and remotely-sensed data
CN103500421A (en) * 2013-10-09 2014-01-08 福州大学 Frequency characteristic-based farmland cropping index extraction method
CN108345992A (en) * 2018-01-31 2018-07-31 北京师范大学 A kind of multiple crop index extracting method and device
CN108764255A (en) * 2018-05-21 2018-11-06 二十世纪空间技术应用股份有限公司 A kind of extracting method of winter wheat planting information
CN109115770A (en) * 2018-06-14 2019-01-01 中科禾信遥感科技（苏州）有限公司 A kind of a wide range of crops remote-sensing monitoring method and device
CN109360117A (en) * 2018-10-08 2019-02-19 西充恒河农牧业开发有限公司 A kind of crop growing mode recognition methods
US10303677B2 (en) * 2015-10-14 2019-05-28 The Climate Corporation Computer-generated accurate yield map data using expert filters and spatial outlier detection
CN110175931A (en) * 2019-05-10 2019-08-27 北京师范大学 A kind of method of a wide range of rapidly extracting crop acreage and phenology information
CN110443420A (en) * 2019-08-05 2019-11-12 山东农业大学 A kind of crop production forecast method based on machine learning
CN111598019A (en) * 2020-05-19 2020-08-28 华中农业大学 Crop type and planting mode identification method based on multi-source remote sensing data

Patent Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102592181A (en) * 2011-12-30 2012-07-18 中国农业科学院农业资源与农业区划研究所 Method for optimizing spatial distribution of statistical data about crop planting area
CN102855351A (en) * 2012-08-09 2013-01-02 中国科学院地理科学与资源研究所 Crop straw resource spatialization method based on statistical data and remotely-sensed data
CN103500421A (en) * 2013-10-09 2014-01-08 福州大学 Frequency characteristic-based farmland cropping index extraction method
US10303677B2 (en) * 2015-10-14 2019-05-28 The Climate Corporation Computer-generated accurate yield map data using expert filters and spatial outlier detection
CN108345992A (en) * 2018-01-31 2018-07-31 北京师范大学 A kind of multiple crop index extracting method and device
CN108764255A (en) * 2018-05-21 2018-11-06 二十世纪空间技术应用股份有限公司 A kind of extracting method of winter wheat planting information
CN109115770A (en) * 2018-06-14 2019-01-01 中科禾信遥感科技（苏州）有限公司 A kind of a wide range of crops remote-sensing monitoring method and device
CN109360117A (en) * 2018-10-08 2019-02-19 西充恒河农牧业开发有限公司 A kind of crop growing mode recognition methods
CN110175931A (en) * 2019-05-10 2019-08-27 北京师范大学 A kind of method of a wide range of rapidly extracting crop acreage and phenology information
CN110443420A (en) * 2019-08-05 2019-11-12 山东农业大学 A kind of crop production forecast method based on machine learning
CN111598019A (en) * 2020-05-19 2020-08-28 华中农业大学 Crop type and planting mode identification method based on multi-source remote sensing data

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
宋茜等: "农作物空间分布遥感制图发展方向探讨", 《中国农业资源与区划》 *
左丽君等: "基于MODIS/EVI 的中国北方耕地复种指数提取", 《农业工程学报》 *

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