CN106372592A - Winter wheat plantation area calculation method based on winter wheat area index - Google Patents
Winter wheat plantation area calculation method based on winter wheat area index Download PDFInfo
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Abstract
The present invention relates to a winter wheat plantation area calculation method based on a winter wheat area index. The comprises: performing preprocessing of multi-date remote sensing image, and obtaining the NDVI data in a winter wheat growth period; dividing the multi-date remote sensing image into regular nets, setting weight sample central points on the regular girds, and employing a visual interpretation mode to determine the types of the samples in the girds as winter wheat and non-winter wheat; obtaining the NDVI values corresponding to the weight sample central points in the multi-date gird data, respectively calculating the NDVI mean values of the winter wheat and non-winter wheat, comparing the size of the two and setting the weight; performing weight operation of the NDVI mean values according to the weight setting result to obtaining the winter wheat area index; and employing a self-adaptive mode to extract the winter wheat area index optimal threshold value, and obtaining the plantation area of the winter wheat. The winter wheat plantation area calculation method based on the winter wheat area index can realize the high-precision calculation of the winter wheat area of the area scale of the multi-date high resolution images, the degree automation of the method is high, and the consistency of the identification result is high.
Description
Technical field
The present invention relates to agricultural remote sensing technical field, national or provincial particularly to a kind of multidate based on area index
Yardstick remote sensing image winter wheat area extracting method.
Background technology
The accurate acquisition of the sown area spatial distribution data of large crops is country, provincial government's formulation grain political affairs
Plan, it is ensured that the important evidence of grain security, as the winter wheat of national large crops, occupies the larger ratio of national food total amount
Example.Because sown area is the committed step of agricultural output assessment, therefore, develop the winter wheat area extraction side of current regional scale
Method, has great importance to the monitoring of agriculture feelings.Developing rapidly with remote sensing technology, it is quick, accurately obtains crop-planting
Spatial distribution provides effective means, and carrying out winter wheat area extraction identification main method using remote sensing technology has single phase
Method, multidate method, Spatial sampling method etc..Single phase method is passed through to choose single scape remote sensing image in crucial phenological period, using supervision
The multiple technologies method such as classification, unsupervised classification, object oriented classification, the information such as spectrum based on atural object, unity and coherence in writing feature, to work
Thing is identified and area extraction；Multidate method is the phenology feature according to target crop to be identified, chooses suitable multidate
Image, by building vegetation index or spectral signature information collection, using with Mono temporal data class as method enter row crop and divide
Class and area extraction；Spatial sampling method is the crop area evaluation method that remote sensing is combined with traditional statistical investigation method, for
On regional scale, all standing remote sensing image obtains difficulty, sorting technique is difficult to standardization and proposes, can be in statistical sampling frame
Under frame, region crop area quantity is calculated by the crop area that remote sensing technology obtains sampling region.This several method above
It is based on middle low resolution remote sensing image, and expertise is more relied on more, lack based on multidate middle high-resolution remote sensing shadow
The research of regional scale large crop area automatic identifying method of picture.
Vegetation index (normalized difference vegetation index, ndvi) is current
For detecting vegetation growth state, vegetation coverage and the cardinal index eliminating partial radiation error, for reflection high-resolution
Crop in remotely-sensed data also has certain advantage, but carries out the differentiation needs of agrotype according to each phase ndvi threshold value
Rely on enough expertises, artificially subjective.
Content of the invention
Present invention aim at, for how using under multidate, Moderate-High Spatial Resolution Remote Sensing Image extraction regional scale
Winter wheat area makes the problem that winter wheat area remote sensing monitoring possesses higher automatic operating ability provide solution party simultaneously
Method.
For solving above-mentioned technical problem, the invention provides a kind of be based on winter wheat area index (winter wheat
Area index, wwai) winter wheat planting area extracting method, the method is by entering to the type of sample point in remote sensing image
Row visual interpretation, all types of ndvi value of statistical sample point, to arrange crop extraction threshold value, can achieve more quickly mesh
Mark type-collection.Therefore, on the basis of normalized differential vegetation index (ndvi) sequential image, the sample point of setting type identification,
Crop is arranged by sample point visual identification result and extracts threshold value, to realize in high-resolution remote sensing image regional scale crop more
Plus the rapid extraction of automatization.
The method that the present invention provides comprises the following steps:
S1: pretreatment is carried out to research area multi-temporal remote sensing image, obtains the ndvi time series data in During Growing Period of Winter Wheat；
S2: be divided into regular grid by studying area's multi-temporal remote sensing image by equidistant principle；
S3: setting weights sample and threshold value center of a sample point on regular grid, determine net by the way of visual interpretation
Sample type in lattice, and type is divided into winter wheat and other two kinds of types of ground objects；
S4: obtain multidate grid data in the corresponding ndvi value of weights center of a sample point, calculate respectively winter wheat with
Other ground class two kinds of ndvi meansigma methodss, and compare the setting that both sizes carry out weights；
S5: result is set according to weights and ndvi meansigma methodss is weighted operate, to build winter wheat area index
(wwai) value；
S6: winter wheat area index optimal threshold is extracted using adaptive mode, using described winter wheat area index
Excellent threshold value obtains the cultivated area of winter wheat in described region.
Specifically, the method comprising the steps of:
S1: in winter wheat planting area to be calculated, obtain the multi-temporal remote sensing shadow covering multiple During Growing Period of Winter Wheats
Picture；Wherein, each period of duration corresponds to a width image, and every width image derives from the same sensor of same satellite；
Described image is carried out with pretreatment, obtains and the one-to-one ndvi of each pixel in image；
S2: corresponding for each period of duration image is divided by equidistant principle respectively, obtains multiple regularly arranged
Shape, size identical benchmark grid；
S3: select a pixel on identical position in each described benchmark grid, as weights center of a sample point,
Judge each weights center of a sample point as winter wheat sample or as non-winter wheat sample using visual interpretation mode；
S4: count the corresponding winter wheat total sample number of weights center of a sample point and Fei Dong in the corresponding image of each period of duration
Semen Tritici aestivi total sample number, according to the corresponding ndvi of each weights center of a sample point, obtains winter wheat sample in this period of duration respectively
Ndvi meansigma methodss and the ndvi meansigma methodss of non-winter wheat sample；
If the ndvi meansigma methodss of winter wheat sample are more than the ndvi meansigma methodss of described non-winter wheat sample in described period of duration,
Then set in this period of duration winter wheat weights as 1, be otherwise set as -1；
S5: the ndvi meansigma methodss of winter wheat sample in each period of duration are multiplied by winter wheat weights in this period of duration, obtain
Winter wheat weighted value in each period of duration；Obtain the meansigma methodss of the winter wheat weighted value in whole the plurality of period of duration, that is,
Obtain winter wheat area index；
S6: using adaptive mode extract a certain period of duration image in winter wheat area index optimal threshold, one by one than
Compared with the corresponding vegetation index of each pixel in this period of duration image and described period of duration winter wheat area index
The size of optimal threshold, if the corresponding vegetation index of pixel is not less than described optimal threshold, judges this picture
Vegetarian refreshments is winter wheat, otherwise for non-winter wheat；Count the total number of winter wheat pixel in described image, with described winter wheat picture
The area that the total number of vegetarian refreshments × each pixel represents, obtains final product the cultivated area of winter wheat in described period of duration.
In said method:
The plurality of period of duration of step s1 is included from sowing to ripe multiple periods；Be preferably winter wheat sowing, emerge,
Tiller, survive the winter, turn green, jointing, whole period of duration of heading, milking maturity and maturation.
Wherein, in described period of duration, monthly at least one width image need to be less than 10% clear sky image for cloud amount, shoots in each
Any one time point in individual period of duration, preferably all shoots the center time point in each period of duration.
In order to improve the effect of the gathered image of step s1, so that it is guaranteed that the accuracy of area extraction result, the obtaining of image
Take the growth time according to winter wheat to determine, and ensure the monthly cloudless image of 1 scape.Shoot the remote sensing satellite of image described in step s1
The spectral band that camera is covered includes red spectral band and near infrared light wave band, the preferably HONGGUANG of 0.63 μm～0.69 μm of wavelength
Wave band and the near infrared light wave band that wavelength is 0.77 μm～0.89 μm.Shoot step s1 described in image for high and medium between resolution
Satellite-remote-sensing image, resolution is preferably 1～50m, more preferably 1～20m；Accordingly, each pixel described corresponds to
Planting area area be 1m2～2500m2, preferably 1m2～500m2.
Wherein, the computational methods of described acquisition ndvi are: ndvi=(nir-r)/(nir+r)；In formula, nir is near-infrared
Reflectance at wave band, r is the reflectance at red spectral band.Absolute value≤1 of described ndvi, i.e. -1≤ndvi≤1, if
Ndvi is negative value then it represents that covered ground is cloud, water, snow etc., to visible ray high reflection；If ndvi be 0 then it represents that have rock or
Exposed soil etc., nir and r approximately equal；If ndvi be on the occasion of, indicate vegetative coverage, and with coverage increase and increase.
Pretreatment described in step s1 includes one or more of radiation calibration, atmospheric correction, geometric correction.In order to improve
Pretreating effect, described Calibration Method is preferably: l (λ)=gain dn+bias；In formula, l (λ) is at sensor entrance pupil
Radiance value (can be calculated using equation below: w/ (m2Sr μm)), gain is gain coefficient, and dn is observation gray scale
Value, bias is biasing coefficient.Described atmospheric correction preferably uses 6s model to be carried out.Described geometric correction preferably uses regional network and puts down
Differential mode type is carried out.
In described step s2, image stress and strain model will be carried out according to necessarily equidistant principle.In order to ensure calculate
Accuracy, each corresponding cultivated area of benchmark grid is preferably 1km2～50km2, more preferably 1km2～20km2.Described base
Quasi- grid is preferably matrix, and that is, in image, the row, column number of benchmark grid is equal.
As a kind of preferred version, in described step s2, corresponding for each period of duration image is carried out 3 by equidistant principle
The result dividing carries out the quartering as benchmark grid, then by each benchmark grid, forms two grades of grids；In described step s3,
Using each benchmark grid middle position in upper left two grades of grids central point as weights center of a sample point.
Described step s4 be specifically defined with to build formula as follows:
In formula, p is weights, and i represents the i-th phase image, and subscript w represents winter wheat type, and subscript o represents non-winter wheat class
Type.
Extract the accuracy of winter wheat planting area in order to improve this methods described, the present invention preferably will arrange threshold value sample
This.
Specifically, described step s3 also includes: another same position in each described benchmark grid arranges one
Threshold value center of a sample point, judges each threshold value center of a sample point as winter wheat sample or as the non-winter using described visual interpretation mode
Semen Tritici aestivi sample.
As a kind of preferred version, in described step s2, corresponding for each period of duration image is carried out by equidistant principle
The result dividing carries out the quartering as benchmark grid, then by each benchmark grid, forms two grades of grids；By each benchmark grid
In be located at bottom-right two grades of grids central point as threshold value center of a sample point.
Adaptive mode described in step s6, particularly as follows: described winter wheat area exponential quantity is carried out decile, obtains multiple faces
Long-pending index threshold.In order to improve accuracy, preferably described winter wheat area index is divided into 101 areas from 0% to 100%
Index threshold, the difference between each adjacent area index threshold is 1%；For example, when winter wheat area index is 50, by
The 0% of described area index to the 100% of described area index, is divided into 101 area index threshold values, specially 0,0.5,1,
1.5th, 2,2.5 ... 49,49.5,50, the difference between each adjacent area index threshold is the 1% of described area index (i.e.
0.5).
For each area index threshold value: will be each in corresponding with period of duration described in step s6 for this area index threshold value image
The vegetation index of individual threshold value center of a sample point is compared, if the normalization difference vegetation of threshold value center of a sample point
When index is not less than described area index threshold value, judge that this threshold value center of a sample point is winter wheat, otherwise for non-winter wheat；With threshold
The visual interpretation mode result of determination of value center of a sample point is the above-mentioned judged result of standard test, calculates this area index threshold value
Judgment accuracy；Count the judgment accuracy of the plurality of area index threshold value, using accuracy highest area index threshold value as
The optimal threshold of described period of duration winter wheat area index.
The method that the present invention provides can realize the regional scale winter wheat area high accuracy of multidate high resolution image
Extraction, and the high degree of automation of method, recognition result concordance is stronger.
Brief description
The winter wheat area index method flow chart that Fig. 1 provides for the present invention；
Fig. 2 is on May 17th, 2014 Anping County and surrounding ground stress and strain model and sample point selection schematic diagram；
Fig. 3 is on May 17th, 2014 Anping County and periphery winter wheat area index image；
Fig. 4 is on May 17th, 2014 Anping County and periphery winter wheat spatial distribution map.
Specific embodiment
Following examples are used for the present invention is described, but are not limited to the scope of the present invention.
Embodiment 1
Taking extract Anping County and periphery winter wheat area as a example technical scheme is expanded on further.The present embodiment
Flow process is as shown in Figure 1, comprising:
Step s1, trial zone multi-temporal remote sensing data acquisition and pretreatment, and calculate the ndvi of each phase.
Trial zone is predominantly located at Anping County and surrounding area, and Anping County is subordinate to Hebei province's Hengshui City, and geographical coordinate is e115 °
19 ' to 115 ° 40 ', n38 ° 14 ' to 38 ° 21 ', height above sea level 26m.Belong to the continental monsoon climatic region of semi-dry and semi-humid, spring
Short of rain windy, summer high temperature is rainy, and autumn air temperature is moderate, winter cold few snow, makes a clear distinction between the four seasons, changes in temperature dry and wet differs greatly, year
Sunshine time 2400～3100h, average annual precipitation 300～800mm, 1 monthly mean temperature below 3 DEG C, 7 monthly mean temperatures 18～
27℃.
Winter wheat in Anping County and periphery trial zone typically before annual October 10 autumn after successively start sow, under
Completed before 20, one on June to harvest, nearly 8 months of period of duration.According to winter wheat growth feature in Growing season, choose and cover trial zone
On October in 2013 26, November 27, December 29, on January 3rd, 2014,2 months 9 days, March 29, April 14, May 17
Day, number satellite of high score 9 scape wfv image on June 27, cover winter wheat from planting to the whole growth and development stage harvesting.
Number satellite of high score has 4 16m resolution multi-spectral camera (wfv1～wfv4), and every camera bag contains blue (0.45～0.52 μ
M), green (0.52～0.59 μm), red (0.63～0.69 μm) and 4 wave bands of near-infrared (0.77～0.89 μm), 4 camera combination
Width is up to 800km, revisiting period 2d.On the basis of atmospheric correction and geometric correction, calculate the ndvi value of each phase image, meter
Calculating formula is:
In formula, ref4And ref3It is respectively the reflectance of wfv the 4th and the 3rd wave band.
The image covering trial zone is divided into regular grid according to equidistant principle by step s2.In view of trial zone
Size and crop-planting distribution situation, according to equidistant 10 × 10 grids of Rule, as a benchmark grid, test
The size of each grid of area is 4.1km × 4.1km；On the basis of each benchmark grid, then it is divided into 2 × 2 grid,
Obtain upper left, the center of the 2 sub- grid in bottom right, referred to as upper left center, bottom right center respectively；In the range of trial zone, total symbiosis
Become 100 upper left grids and 100 bottom right grids (see Fig. 2).
Step s3, on regular grid, setting weights sample and threshold value sample, sample type is divided into winter wheat and Fei Dong
Semen Tritici aestivi two types, know the type of judgment sample otherwise by visual observation.With the 100 upper lefts center in each benchmark grid
As weights sample, according to visually judging 47 winter wheat points, 53 non-winter wheat points, winter wheat ratio 47%；With benchmark
100 bottom rights center in grid as threshold value sample, in respect of 44 winter wheat, 56 non-winter wheat points, winter wheat ratio
44%.
Step s4, according to the ndvi value of weights center of a sample point, calculates winter wheat and non-winter wheat two types respectively
Ndvi meansigma methodss, and compare the setting that both sizes carry out weights.Calculate two kinds of types of ground objects of each issue ndvi image respectively
Weights sample mean, specific to Anping County and surrounding area it is simply that calculate 9 phase images in each issue 47 winter wheat types and
The meansigma methodss of 53 non-winter wheat weights samples, obtain each 9 meansigma methodss of two classes respectively；Relatively each issue two types ndvi are average
The size of value, if current winter wheat ndvi meansigma methodss are more than non-winter wheat ndvi meansigma methodss, weights are set to 1, if the current winter is little
Wheat ndvi meansigma methodss are less than non-winter wheat ndvi meansigma methodss, then weights are set to -1.
Step s5, the weights of setting are imparted in corresponding winter wheat ndvi meansigma methodss of each phase, build winter wheat area
Index (wwai).Weights set by each issue are separately to the image in corresponding period, will each phase winter wheat ndvi average
Value is multiplied with weights, and takes 9 phase products to add the meansigma methodss of sum, and the meansigma methodss obtaining are as winter wheat area index (wwai).
Step s6, adaptive should determine that of winter wheat area index (wwai) optimal threshold.Using self adaptation wwai extraction side
Formula, realizes automatically extracting of zones of different optimum wwai threshold value.By winter wheat area index with 1% ratio decile, from 0% ratio
Example starts to 100% end, can obtain 101 corresponding wwai threshold values, this threshold value is used for threshold value sample in heading stage image
The winter wheat identification of point, and by recognition result and visual identification Comparative result, count the corresponding accuracy rate of each threshold value, select
In 101 results, accuracy highest threshold value is as the optimal threshold of heading stage winter wheat area index；
During Growing Period of Winter Wheat (i.e. heading stage) image taking on May 17th, 2014 shooting, as sample, compares this shadow one by one
The size of the corresponding ndvi of each pixel and described optimal threshold in picture, if the corresponding ndvi of pixel is not less than described optimum
Threshold value, then judge this pixel for winter wheat, otherwise for non-winter wheat；Count the always individual of winter wheat pixel in described image
Number, the area (16m × 16m) being represented with total number 2491766 × each pixel of described winter wheat pixel, it is calculated
In heading stage, the cultivated area of winter wheat is about 637.89km2.
Fig. 3 is on May 17th, 2014 Anping County and periphery winter wheat area index image, it can be seen that the winter is little
Winter wheat area index (wwai) value of wheat and barley type will be apparently higher than non-winter wheat type.In 9 phase images, with raw video
Ndvi meansigma methodss are compared, and the fold differences of winter wheat and non-winter wheat are increased to 2.08 from 1.43 times by winter wheat area index
Times, illustrate that winter wheat area index (wwai) has distinctive amplification winter wheat ndvi value and raising winter wheat is little with the non-winter
The ability of wheat type difference.
Fig. 4 is the result using winter wheat area exponent extracting winter wheat area, and under optimal threshold, threshold value sample has
Highest overall recognition accuracy, has reached 94.0%, the accuracy of identification of winter wheat is 95.5%.As can be seen from the figure Anping County
And the plantation of periphery winter wheat is widely distributed and uniform, matches with actual plantation situation.
Although, above used general explanation, specific embodiment and test, the present invention made retouch in detail
State, but on the basis of the present invention, it can be made some modifications or improvements, this is apparent to those skilled in the art
's.Therefore, these modifications or improvements without departing from theon the basis of the spirit of the present invention, belong to claimed
Scope.
Claims (10)
1. a kind of winter wheat planting area computational methods based on winter wheat area index are it is characterised in that comprise the following steps:
S1: in winter wheat planting area to be calculated, obtain the multi-temporal remote sensing image covering multiple During Growing Period of Winter Wheats；Its
In, each period of duration corresponds to a width image, and every width image derives from the same sensor of same satellite；
Described image is carried out with pretreatment, obtains and the one-to-one vegetation index of each pixel in image；
S2: corresponding for each period of duration image is divided by equidistant principle respectively, obtain multiple regularly arranged shapes,
Size identical benchmark grid；
S3 a: pixel is selected on identical position in each described benchmark grid, as weights center of a sample point, adopts
Visual interpretation mode judges each weights center of a sample point as winter wheat sample or as non-winter wheat sample；
S4: count the corresponding winter wheat total sample number of weights center of a sample point and non-winter wheat in the corresponding image of each period of duration
Total sample number, according to each corresponding vegetation index of weights center of a sample point, obtains the winter in this period of duration respectively
The vegetation index meansigma methodss of Semen Tritici aestivi sample and the vegetation index meansigma methodss of non-winter wheat sample；
If the vegetation index meansigma methodss of winter wheat sample are more than described non-winter wheat sample in described period of duration
Vegetation index meansigma methodss, then set in this period of duration winter wheat weights as 1, be otherwise set as -1；
S5: the vegetation index meansigma methodss of winter wheat sample in each period of duration are multiplied by winter wheat in this period of duration
Weights, obtain the winter wheat weighted value in each period of duration；Obtain winter wheat weighted value in whole the plurality of period of duration
Meansigma methodss, obtain final product winter wheat area index；
S6: extract the optimal threshold of winter wheat area index in a certain period of duration image using adaptive mode, compare this one by one
In period of duration image, the corresponding vegetation index of each pixel is optimum with described period of duration winter wheat area index
The size of threshold value, if the corresponding vegetation index of pixel is not less than described optimal threshold, judges this pixel
For winter wheat, otherwise for non-winter wheat；Count the total number of winter wheat pixel in described image, with described winter wheat pixel
The area that represents of total number × each pixel, obtain final product the cultivated area of winter wheat in described period of duration.
2. method according to claim 1 is it is characterised in that described image is Resolution Satellite Image between high and medium, often
The corresponding area of individual pixel is 1m2～2500m2, preferably 1m2～500m2.
3. method according to claim 1 and 2 is it is characterised in that each corresponding area of benchmark grid described is 1km2~
50km2, preferably 1km2～20km2；
In preferably described image, the row, column number of benchmark grid is equal.
4. the method according to claims 1 to 3 any one is it is characterised in that the plurality of period of duration of step s1 covers
From sowing to ripe multiple periods；Be preferably cover winter wheat sowing, emerge, tiller, survive the winter, turn green, jointing, heading, breast
Ripe and ripe whole periods.
5. method according to claim 4 is it is characterised in that the corresponding width image of each period of duration described is that cloud amount is little
In 10% clear sky image, shoot any one time point in each period of duration, preferably all shoot in each period of duration
Center time point.
6. the method according to Claims 1 to 5 any one is it is characterised in that shoot the remote sensing of image described in step s1
The spectral band that Satellite Camera is covered includes red spectral band and near infrared light wave band, preferably 0.63 μm～0.69 μm of wavelength
Red spectral band and the near infrared light wave band that wavelength is 0.77 μm～0.89 μm；
The computational methods of described acquisition vegetation index ndvi are: ndvi=(nir-r)/(nir+r)；In formula, nir
For the reflectance near infrared band, r is the reflectance at red spectral band.
7. the method according to claim 1～6 any one is it is characterised in that pretreatment described in step s1 includes radiating
One or more of calibration, atmospheric correction, geometric correction；
Wherein, described Calibration Method is preferably: l (λ)=gain dn+bias；In formula, l (λ) is spoke at sensor entrance pupil
Penetrate brightness value, gain is gain coefficient, dn is observation gray value, bias is biasing coefficient；
And/or, described atmospheric correction preferably uses 6s model to be carried out；
And/or, described geometric correction preferably uses block adjustment model and carries out.
8. the method according to claim 1～7 any one is it is characterised in that described step s3 also includes: in each institute
State one pixel of selection on another identical position in benchmark grid, as threshold value center of a sample point, visually sentenced using described
Read mode judges each threshold value center of a sample point sample as winter wheat sample or non-winter wheat sample.
9. method according to claim 8 is it is characterised in that in described step s2, by corresponding for each period of duration image
Carry out the quartering by the result that equidistant principle is divided as benchmark grid, then by each benchmark grid, form two grades of nets
Lattice；
In described step s3, using each benchmark grid middle position in upper left two grades of grids central pixel point as weights sample
Central point, using each benchmark grid middle position in bottom-right two grades of grids central pixel point as threshold value center of a sample point.
10. method according to claim 8 or claim 9 is it is characterised in that described adaptive mode is particularly as follows: will be little for the described winter
Wheat flour amasss exponential quantity and carries out decile, obtains multiple area index threshold values；Preferably by described winter wheat area index from 0% to
100% is divided into 101 area index threshold values, and the difference between each adjacent area index threshold is 1%；
For each area index threshold value: by each threshold in corresponding with period of duration described in step s6 for this area index threshold value image
The vegetation index of value center of a sample point is compared, if the vegetation index of threshold value center of a sample point
During not less than described area index threshold value, judge that this threshold value center of a sample point is winter wheat, otherwise for non-winter wheat；With threshold value sample
The visual interpretation mode result of determination of this central point is the above-mentioned judged result of standard test, calculates the judgement of this area index threshold value
Accuracy；
Count the judgment accuracy of the plurality of area index threshold value, using accuracy highest area index threshold value as described fertility
The optimal threshold of phase winter wheat area index.
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| 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 |
| CN116485784A (en) * | 2023-06-12 | 2023-07-25 | 天地信息网络研究院(安徽)有限公司 | Winter idle field remote sensing extraction method based on time sequence NDVI |
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| CN117593542A (en) * | 2023-11-27 | 2024-02-23 | 首都师范大学 | Grain production area ground subsidence difference evolution characteristic calculation method, device and medium |
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