AU2020103047A4 - Crop Distribution Mapping - Google Patents
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- 238000009826 distribution Methods 0.000 title claims abstract description 39
- 238000013507 mapping Methods 0.000 title claims abstract description 18
- 238000009355 double cropping Methods 0.000 claims abstract description 17
- 238000009356 triple cropping Methods 0.000 claims abstract description 11
- 238000009336 multiple cropping Methods 0.000 claims description 48
- 230000005070 ripening Effects 0.000 claims description 9
- 238000000034 method Methods 0.000 claims description 6
- 230000001932 seasonal effect Effects 0.000 claims description 6
- 238000004458 analytical method Methods 0.000 claims description 4
- 238000000605 extraction Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 3
- 238000007781 pre-processing Methods 0.000 claims description 2
- 101100497384 Drosophila melanogaster CASK gene Proteins 0.000 claims 1
- 241000209140 Triticum Species 0.000 description 11
- 235000021307 Triticum Nutrition 0.000 description 11
- 230000002123 temporal effect Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012271 agricultural production Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000012656 cationic ring opening polymerization Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 235000021393 food security Nutrition 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/26—Visual data mining; Browsing structured data
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
- G06T11/20—Drawing from basic elements, e.g. lines or circles
- G06T11/203—Drawing of straight lines or curves
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
- G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
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- Databases & Information Systems (AREA)
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- Data Mining & Analysis (AREA)
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Abstract
The present invention discloses crop distribution mapping, including: calculating the areas of
various crops on each grid based on the grid-scale crop planting pattern information extracted by
remote sensing and the area ratio of various crops of the planting pattern in a corresponding
administrative unit, so as to create a crop spatial distribution map. The present invention combines
the phenological characteristics identified by remote sensing of the crops with the actual
phenological characteristics of the crops to determine the spatial distribution of the crops, and
large-scale and multi-scale crop spatial distribution mapping with high spatial heterogeneity is
achieved by macroscopic remote sensing, rapid acquisition, multi-scale and other features.
1/1
Landuse HANTS eighborhood Remotesensing
denoisin omparison metho time series
map data
Plot planting pattern
inglcropping Double cropping Triple cropping
potentialregion potential region potential region
Single cropping Duble ]Tripte Singte Double Triple o
plot cropping plot Croppingplo dropping plo cropping plot Cropping plot
Winter- Winter Winter- Winter
Spring Winter Sunmer mode spring spring
mode mode mode mode summermode
Summer Spring Spring
Winter
mode mode mode summer
mode mode C
Summer Summer Sprig
mode mode summer mode
Araratios of various crops in cultivated
landsof different-planting patters --------
Spatial distribution map of different
crop seeded areas
Figure 1
Description
1/1
Landuse HANTS eighborhood Remotesensing denoisin omparison metho time series map data
Plot planting pattern inglcropping Double cropping Triple cropping potentialregion potential region potential region
Single cropping Duble ]Tripte Singte Double Triple o plot cropping plot Croppingplo dropping plo cropping plot Cropping plot
Winter- Winter Winter- Winter Spring Winter Sunmer mode spring spring mode mode mode mode summermode Spring Winter Summer mode Spring mode C mode mode mode summer
Summer Summer Sprig mode summer mode mode
Araratios of various crops in cultivated landsof different-planting patters --------
Spatial distribution map of different crop seeded areas
Figure 1
Technical Field
The present invention relates to the field of data analysis, in particular to crop distribution
mapping.
Background Art
The spatial and temporal distribution of crops reflects the status of humans using lands for
agricultural production in different periods, and it is an important basic data for conducting
researches on the patterns and functions of farmland ecosystems, terrestrial ecosystem circulation,
global changes and the like. Understanding the temporal and spatial distribution of crops is of great
significance for ensuring national food security and sustainable development of resources and
environment.
At present, the ways to obtain information on the temporal and spatial distribution of crops
mainly include administrative statistics and remote sensing monitoring. Statistical methods can
only reflect changes in the number of crops in a certain administrative unit, and it is difficult to
reflect the spatial differences of crops in the statistical unit; and moreover, the acquisition of
statistical data consumes a lot of manpower, material resources and financial resources, and is also
disturbed by human factors. Simple remote sensing technology has the influence of atmospheric
interference, scale conversion, mixed pixels and other factors, and it is difficult to obtain the spatial
distribution information of the whole crop series in a large regional scale. Therefore, it particularly
necessary to perform in-depth integration on statistical data and remote sensing data and carry out
spatial distribution mapping of multiple crops and even all crop types.
Summary of the Invention
The purpose of the present invention is to provide crop distribution mapping.
In order to achieve the above purpose, the technical solutions adopted by the present invention
are as follows:
The present invention includes the following steps:
A. obtaining remote sensing data information, constructing a pixel-scale vegetation index
time series curve, extracting multiple cropping information through the number of peaks of the
pixel-scale time series curve, extracting, classifying and thinning planting season information reflected by the pixel scale according to the peak occurrence time to obtain planting pattern information of crops in each grid;
B. obtaining crop seeded area data of an administrative unit, merging different seasonal crops
of the administrative unit to obtain the seeded area information of different seasonal crops of
various planting patterns; and
C. calculating the areas of various crops on each grid based on the grid-scale crop planting
pattern extracted by remote sensing and corresponding agricultural acreage distribution and the
area information of the various seasonal crops in the different planting patterns, so as to create a
crop spatial distribution map.
Further, the characteristic parameters include the number of peaks of the time series curve
and time points of the peaks of the time series curve, and the grid planting pattern information is
inferred according to the characteristic parameters.
Specifically, the preprocessing includes: performing denoising processing on the data by
using a harmonic analysis method of time series to smooth the time series curve.
Further, the specific process of the pixel scale extraction is as follows:
supposing time phase pixel values of a pixel i as ai, a2... a23,
comparing the size relationship of EVI between two adjacent pixels, and expressing the
same by using Aa sequential data:
r 1a if ai 1>a, if -ai1 <ai(3-7)
Aa calculating the difference between adjacent
AAa,= Aa 1 - (3-8) Aa
wherein, if AAa<0, then, i-1 represents the time point where a local peak is located, and
ai-1 represents the EVI value of the local peak; and if AAai>0, i-1 represents the time point
where the local trough is located, and a i-1 represents the EVI value of the local trough.
Due to the presence of pre-winter crest of the crop and some disturbing peak information,
it is necessary to further extract the true peak point of the crop during the growth period. If the
peak point satisfies the increase in the EVI value at two consecutive time points before and the decrease in the EVI value at two consecutive time points afterwards, then the point belongs to the EVI peak point during the growth period of the crop. The sum of effective peak points is the multiple cropping information of the pixel, that is, if the number of peaks is 1, the crop is a crop ripening once within one year; if the number of peaks is 2, the crop is a crop ripening twice within one year; and if the number of peaks is greater than 2, the crop is a crop ripening multiple times within one year, furthermore, a double cropping region must meet the requirement of >10°C, and the accumulated temperature is higher than 3500°C, and a multiple cropping region meet the requirement of>10°C, and the accumulated temperature is higher than 5000°C. Specifically, the administrative unit divides crops into winter crops, spring crops and summer crops according to the agricultural calendar information of the types of the crops, and corresponds them to various planting patterns existing in the administrative unit in a thinning manner, and the thinning method of the planing modes includes: a. for a region where the multiple cropping potential is triple cropping, if the extracted multiple cropping result is triple cropping, classifying the plot as winter-spring-summer three season crop multiple cropping; if the extracted multiple cropping result is double cropping, extracting a peak occurrence time point of the extracted time series curve, determining whether the crop growth peak reflected by the curve occurs in spring, summer or autumn, and then classifying the plot as a winter-summer multiple cropping mode or a spring-summer multiple cropping mode; and if the extracted multiple cropping result is single cropping, extracting the peak occurrence point of the time series curve, and determining the planting pattern as a winter mode, a spring mode or a summer mode. b. for a region where the multiple cropping potential is double cropping, if the multiple cropping result extracted in step 2 is double cropping, then classifying the plot as a winter-summer multiple cropping mode; and if the extracted multiple cropping result is single cropping, extracting the peak occurrence point of the time series curve, and determining the planting pattern as the winter mode, the spring mode or the summer mode. c. for a region where only single cropping can be implemented, directly extracting the peak occurrence point, and determining the planting pattern as the spring mode or the summer mode. Further, the crop spatial distribution map is created by calculating a grid-scale crop area based on the following formula, and performing spatialization to form the crop spatial distribution map:
Aii= CAxHIik
wherein, Ai; represents the area of the crop i in the grid 1; CAtk represents the area of the
planting patterns in the grid 1; HIi/ represents the area ratio of the crop i in the grid I in the planting
patterns, wherein the grid I is extracted by the administrative unit.
Specifically, the area ratio (HIijk) of various crops in per unit agricultural acreage on plots of
different planting patterns is as follows:
HIik = HAikj/CAk
wherein, HA r1 represents the seeded area of the crop i in the administrative unit k, and CAi
represents the area of the multiple cropping modej in the administrative unit k.
Further, in step A, curve fitting is performed according to different multiple cropping potential
regions by using corresponding numbers of frequencies, 3 is adopted for a double cropping region,
and 4 is adopted for a triple cropping region.
Compared with the prior art, the present invention has the following beneficial effects:
The present invention combines the phenological characteristics identified by remote sensing
of the crops with the actual phenological characteristics of the crops to determine the spatial
distribution of the crops, and large-scale and multi-scale crop spatial distribution mapping with
high spatial heterogeneity is achieved by macroscopic remote sensing, rapid acquisition, multi
scale and other features, and through the in-depth mining of remote sensing data and the
combination of statistical data, crop spatial distribution information that has both spatial
heterogeneity information and full crop information can be obtained.
Brief Description of the Drawings
Fig. 1 is a schematic flow diagram of a crop distribution mapping method provided by the
present invention.
Detailed Description of the Embodiments
Hereinafter, the present invention will be further explained based on the embodiments and
the drawings. The modes of the present invention include, but are not limited to, the following
embodiments.
As shown in Fig. 1, in the present embodiment, winter wheat is taken as an example to
perform crop distribution mapping:
Step 1: denoising processing is performed on the data by using a harmonic analysis method of time Series (Harmonic Analysis of Time Series, HANTS) to obtain a smooth time series curve.
For different multiple cropping potential regions, curve fitting is performed by using different
numbers of frequencies (Number of frequencies), 3 is adopted for a double cropping region, and
4 is adopted for a triple cropping region.
Step 2: after the curve is denoised, multiple cropping information is extracted at the pixel
scale. The specific process is as follows:
supposing time phase pixel values of a pixel i as ai, a2... a23,
comparing the size relationship of EVI between two adjacent pixels, and expressing the
same by using Aa sequential data:
Aa = r1 if a>ai -f a < a (3-7)
calculating the difference between adjacent Aa,.
AAa,= Aa 1- (3-8) Aa
wherein, if AAa<0,then, i-1 represents the time point where a local peak is located, and
ai-1 represents the EVI value of the local peak; and if AAai>0, i-1 represents the time point
where the local trough is located, and a1 -1 represents the EVI value of the local trough.
Due to the presence of pre-winter crest of the winter wheat and some disturbing peak
information, it is necessary to further extract the true peak point of the crop during the growth
period. If the peak point satisfies the increase in the EVI value at two consecutive time points
before and the decrease in the EVI value at two consecutive time points afterwards, then the point
belongs to the EVI peak point during the growth period of the crop. The sum of effective peak
points is the multiple cropping information of the pixel, that is, if the number of peaks is 1, the
crop is a crop ripening once within one year; if the number of peaks is 2, the crop is a crop ripening
twice within one year; and if the number of peaks is greater than 2, the crop is a crop ripening
multiple times within one year.
®The double cropping region must meet the requirement of >10°C, and the accumulated
temperature is higher than 3500°C, and the multiple cropping region meet the requirement of
>10°C, and the accumulated temperature is higher than 5000°C.
Step 3: based on the extraction of multiple cropping information, the planting season information reflected by the pixels is extracted, and then the planting pattern is extracted. for a region where the multiple cropping potential is triple cropping, if the multiple cropping result extracted in step 2 is triple cropping, the plot is classified as winter-spring-summer three-season crop multiple cropping; if the extracted multiple cropping result is double cropping, a peak occurrence time point of the time series curve extracted in step 2 is extracted, whether the crop growth peak reflected by the curve occurs in spring, summer or autumn is determined, and then the plot is classified as a winter-summer multiple cropping mode or a spring-summer multiple cropping mode; and if the extracted multiple cropping result is single cropping, the peak occurrence point of the time series curve is extracted, and the planting pattern is determined as a winter mode, a spring mode or a summer mode. for a region where the multiple cropping potential is double cropping, if the multiple cropping result extracted in step 2 is double cropping, then the plot is classified as a winter-summer multiple cropping mode; and if the extracted multiple cropping result is single cropping, the peak occurrence point of the time series curve is extracted, and the planting pattern is determined as the winter mode, the spring mode or the summer mode. for a region where only single cropping can be implemented, the peak occurrence point is directly extracted, and the planting pattern is determined as the spring mode or the summer mode. Step 4: with the obtained administrative unit of the statistical data of winter wheat seeded area as boundary, agricultural acreages of the planting patterns of winter crops in the administrative units are calculated. Step 5: according to the agricultural calendar information of the crop types contained in the administrative units, the crops are divided into winter crops, spring crops and summer crops, and correspond to the planting patterns of the winter crops in the administrative units. Step 6: the area ratios of the winter wheat in plots of various planting patterns in the administrative units are calculated. According to the planting patterns corresponding to the winter crops in the administrative units determined in step 5, combined with the area of the plot of the planting pattern in the administrative unit obtained in step 4, the area ratio HIk of the winter wheat in per unit agricultural acreage on plots of different planting patterns is calculation, and the formula is as follows:
HIJk = HAkJ/CAk (1) wherein, HA r1 represents the seeded area of the crop i in the administrative unit k, and CAk
represents the area of the multiple cropping modej in the administrative unit k.
Step 7: according to grid planting pattern information and the corresponding cultivated land
distribution, combined with the area of the winter wheat crop on the planting pattern of the
administrative unit to which it belongs, the areas of the winter wheat on different planting pattern
grids are calculated, and then a spatial distribution map of winter wheat is made. Specifically,
based on the following formula, a grid-scale winter wheat area is calculated, and spatialization is
performed to form a crop spatial distribution map.
Ai,= CAxHIiik
wherein, Ai; represents the area of the winter wheat in the grid1; CAik represents the area of
the planting patterns in the grid 1; HIi/ represents the area ratio of the winter wheat in the grid I in
the plot of the planting pattern j, wherein the grid I belongs to the administrative unit k and is
obtained in step 6.
It is clear to those skilled in the art that the scope of the present invention is not limited to the
examples discussed above, and it is possible to make several changes and modifications to the
examples without departing from the scope of the present invention defined by the appended
claims. Although the present invention has been illustrated and described in detail in the drawings
and description, such description and description are only illustrative or schematic, rather than
restrictive. The present invention is not limited to the disclosed embodiments.
By studying the drawings, the description and the claims, those skilled in the art can
understand and realize the modifications of the disclosed embodiments when implementing the
present invention. In the claims, the term "including" does not exclude other steps or elements.
The fact that certain measures are cited in mutually different dependent claims does not mean that
a combination of these measures cannot be used advantageously. Any reference sign in the claims
do not constitute a limitation to the scope of the present invention.
The above-mentioned embodiment is only one of the preferred embodiments of the present
invention, and should not be used to limit the protection scope of the present invention. However,
for any insignificant change or polish made in the main design idea and spirit of the present
invention, the solved technical problem is still consistent with the present invention, and should be included in the protection scope of the present invention.
Claims (8)
- CLAIMS 1. Crop distribution mapping, characterized by comprising the following steps: A. obtaining remote sensing data information, constructing a pixel-scale vegetation index time series curve, extracting multiple cropping information through the number of peaks of the pixel-scale time series curve, extracting, classifying and thinning planting season information reflected by the pixel scale according to the peak occurrence time to obtain planting pattern information of crops in each grid; B. obtaining crop seeded area data of an administrative unit, merging different seasonal crops of the administrative unit to obtain the seeded area information of different seasonal crops of various planting patterns; and C. calculating the areas of various crops on each grid based on the grid-scale crop planting pattern extracted by remote sensing and corresponding agricultural acreage distribution and the area information of the various seasonal crops in the different planting patterns, so as to create a crop spatial distribution map.
- 2. The crop distribution mapping according to claim 1, wherein the characteristic parameters comprise the number of peaks of the time series curve and time points of the peaks of the time series curve, and the grid planting pattern information is inferred according to the characteristic parameters.
- 3. The crop distribution mapping according to claim 1, wherein the preprocessing comprises: performing denoising processing on the data by using a harmonic analysis method of time series to smooth the time series curve.
- 4. The crop distribution mapping according to claim 1, wherein the specific process of the pixel scale extraction is as follows: supposing time phase pixel values of a pixel i as ai, a2... a23,Comparing the size relationship of EVI between two adjacent pixels, and expressing the same by using Aa sequential data:1 if ai+ 1 > ai t-1 if ai+1 < adjain7)calculating the difference between adjacent Aa `AAa,= Aa 1- (3-8) Aawherein, if AAai<0, then, i- represents the time point where a local peak is located, andai-1 represents the EVI value of the local peak; and if AAai>0, i-1 represents the time pointwhere the local trough is located, and ai-1 represents the EVI value of the local trough; anddue to the presence of pre-winter crest of the crop and some disturbing peak information,it is necessary to further extract the true peak point of the crop during the growth period. If thepeak point satisfies the increase in the EVI value at two consecutive time points before and thedecrease in the EVI value at two consecutive time points afterwards, then the point belongs to theEVI peak point during the growth period of the crop. The sum of effective peak points is themultiple cropping information of the pixel, that is, if the number of peaks is 1, the crop is a cropripening once within one year; if the number of peaks is 2, the crop is a crop ripening twice withinone year; and if the number of peaks is greater than 2, the crop is a crop ripening multiple timeswithin one year, furthermore, a double cropping region must meet the requirement of >10°C, andthe accumulated temperature is higher than 3500°C, and a multiple cropping region meet therequirement of>10°C, and the accumulated temperature is higher than 5000°C.
- 5. The crop distribution mapping according to claim 1, wherein the administrative unit dividescrops into winter crops, spring crops and summer crops according to the agricultural calendarinformation of the types of the crops, and corresponds them to various planting patterns existingin the administrative unit in a thinning manner, and the thinning method of the planing modescomprises:a. for a region where the multiple cropping potential is triple cropping, if the extractedmultiple cropping result is triple cropping, classifying the plot as winter-spring-summer threeseason crop multiple cropping; if the extracted multiple cropping result is double cropping,extracting a peak occurrence time point of the extracted time series curve, determining whetherthe crop growth peak reflected by the curve occurs in spring, summer or autumn, and thenclassifying the plot as a winter-summer multiple cropping mode or a spring-summer multiplecropping mode; and if the extracted multiple cropping result is single cropping, extracting the peakoccurrence point of the time series curve, and determining the planting pattern as a winter mode,a spring mode or a summer mode; b. for a region where the multiple cropping potential is double cropping, if the multiple cropping result extracted in step 2 is double cropping, then classifying the plot as a winter-summer multiple cropping mode; and if the extracted multiple cropping result is single cropping, extracting the peak occurrence point of the time series curve, and determining the planting pattern as the winter mode, the spring mode or the summer mode; and c. for a region where only single cropping can be implemented, directly extracting the peak occurrence point, and determining the planting pattern as the spring mode or the summer mode.
- 6. The crop distribution mapping according to claim 1, wherein the crop spatial distributionmap is created by calculating a grid-scale crop area based on the following formula, andperforming spatialization to form the crop spatial distribution map:Aii= CAxHIikwherein, Ai; represents the area of the crop i in the grid 1; CAtk represents the area of theplanting patterns in the grid 1; HIIkrepresents the area ratio of the crop i in the grid I in the plantingpatterns, wherein the grid I is extracted by the administrative unit.
- 7. The crop distribution mapping according to claim 1, wherein the area ratio (HIijk) ofvarious crops in per unit agricultural acreage on plots of different planting patterns is as follows:HIik = HAikj/CAkwherein, HA r1 represents the seeded area of the crop i in the administrative unit k, and CAkirepresents the area of the planting patterns in the administrative unit k.
- 8. The crop distribution mapping according to claim 1, wherein in step A, curve fitting isperformed according to different multiple cropping potential regions by using correspondingnumbers of frequencies, 3 is adopted for a double cropping region, and 4 is adopted for a triplecropping region.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011016570.1 | 2020-09-24 | ||
| CN202011016570.1A CN112148785B (en) | 2020-09-24 | 2020-09-24 | Crop distribution charting method |
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| Publication Number | Publication Date |
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| AU2020103047A4 true AU2020103047A4 (en) | 2020-12-24 |
Family
ID=73838804
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| AU (1) | AU2020103047A4 (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114067158A (en) * | 2021-11-17 | 2022-02-18 | 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 |
| 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 |
| CN114782838A (en) * | 2022-06-17 | 2022-07-22 | 中化现代农业有限公司 | Rice identification method and device, electronic equipment and storage medium |
| CN114926565A (en) * | 2022-05-24 | 2022-08-19 | 中国科学院东北地理与农业生态研究所 | Soybean mapping method based on green degree and moisture comprehensive index |
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| 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 |
| CN102855351B (en) * | 2012-08-09 | 2015-07-08 | 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 |
| CN103500421B (en) * | 2013-10-09 | 2017-01-11 | 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 |
| CN108345992B (en) * | 2018-01-31 | 2021-07-09 | 北京师范大学 | Multiple cropping index extraction method and device |
| CN108764255B (en) * | 2018-05-21 | 2021-06-01 | 二十一世纪空间技术应用股份有限公司 | Method for extracting winter wheat planting information |
| CN109115770B (en) * | 2018-06-14 | 2019-05-24 | 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 |
| CN110175931B (en) * | 2019-05-10 | 2020-04-24 | 北京师范大学 | Method for rapidly extracting crop planting area and phenological information in large range |
| CN110443420B (en) * | 2019-08-05 | 2023-05-09 | 山东农业大学 | Crop yield prediction method based on machine learning |
| CN111598019B (en) * | 2020-05-19 | 2023-05-26 | 华中农业大学 | Crop type and planting mode identification method based on multi-source remote sensing data |
-
2020
- 2020-09-24 CN CN202011016570.1A patent/CN112148785B/en not_active Expired - Fee Related
- 2020-10-27 AU AU2020103047A patent/AU2020103047A4/en not_active Ceased
Cited By (5)
| 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 |
| CN114067158A (en) * | 2021-11-17 | 2022-02-18 | 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 |
| CN114926565A (en) * | 2022-05-24 | 2022-08-19 | 中国科学院东北地理与农业生态研究所 | Soybean mapping method based on green degree and moisture comprehensive index |
| 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 |
| CN114782838A (en) * | 2022-06-17 | 2022-07-22 | 中化现代农业有限公司 | Rice identification method and device, electronic equipment and storage medium |
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| Publication number | Publication date |
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| CN112148785B (en) | 2021-09-28 |
| CN112148785A (en) | 2020-12-29 |
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