CN107340268A - A kind of dry crop recognition methods based on remote sensing time series data - Google Patents
A kind of dry crop recognition methods based on remote sensing time series data Download PDFInfo
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Abstract
The present invention relates to a kind of dry crop recognition methods based on remote sensing time series data.Time series data of this method based on research area's vegetation index and short infrared wave band, obtain the crop growth peak period in each growth cycle, and then infer crop growth early stage and Later growth, SWIR and EVI increments product index, SWIR the and EVI increment product indexs in crop growth later stage of crop growth early stage is established successively, the comprehensive SWIR and EVI increment product indexs for forming the whole crop growth phase, the finally SWIR and EVI increments product index development dry crop identification according to the whole crop growth phase.This method makes full use of dry crop within whole growth period, SWIR wave band amplitudes of variation are larger and changed in the opposite direction with EVI, and the SWIR wave band amplitudes of variation of water body crop it is smaller and with EVI change directions it is relatively uniform the characteristics of, design SWIR the and EVI increment product indexs of whole crop growth phase, identify there is that robustness is good, nicety of grading is high, automaticity and strong antijamming capability for dry crop.
Description
Technical field
The present invention relates to remote sensing information process field, and in particular to a kind of dry crop identification based on remote sensing time series data
Method.
Background technology
Accurate quick grasp crop acreage and its spatial distribution scope, for ensuring that grain security is most important.
The widely dispersed of large dry crop such as wheat, corn, potato, occupies critical role in China's agricultural production.Other
Dry crop such as peanut, cotton, rape, soybean, sesame etc., as oil plant, feed or textile raw material, lived with people close
Cut is closed.Therefore the spatial distribution of fast automatic monitoring dry crop, has unusual meaning.Traditional agriculture sampling adjusts
Checking method be difficult to break through manpower and financial cost is high, time-consuming and can not all standing limitation.Remote sensing image has ageing
By force, the characteristics of coverage is big, played an important role in crop area monitoring.It is distant that China has tentatively established agriculture feelings
Sense monitoring and information service system, realize the real-time monitoring of staple crops, meadow and agricultural disaster.However, in China's agriculture
In the crop remote sensing monitoring business of feelings information remote sensing monitoring system, there are two aspect problems to merit attention：(1) in staple crops
In identification, it usually needs arable land mask data is used, because each side such as the ageing and precision of land use/covering data are asked
Topic, inevitably brings error, so as to directly influence the precision of crop area remote sensing appraising；(2) because crops give birth to
Sexual intercourse weather is more frequent in long-term, and the remote sensing image in some periods is influenceed by cloud unavoidably, overture when causing Indices
Line is interfered, and is brought challenges for the Classification in Remote Sensing Image method based on more phases or time series.For problem 1, it is necessary to strengthen drought
The fast automatic monitoring of ground Crop spatial distribution, can disposably extract all dry crop spatial distribution scopes, to enter one
Step carries out various dry crop identifications and provides mask data respectively.For problem 2, it is necessary to strengthen sequential Classification in Remote Sensing Image technology and grind
Study carefully, the interference for avoiding sexual intercourse weather to bring as far as possible, establish automaticity height, the sequential sorting technique of strong robustness.
The present invention intends in view of the above-mentioned problems, establishing a kind of method of energy efficient identification dry crop.The present invention is by abundant
Wet crop and dry crop are excavated in the crop growth phase in vegetation index and short infrared wave band remote sensing temporal aspect, is big
The identification of scope dry crop provides a kind of new method and thinking.
The content of the invention
It is an object of the invention to provide a kind of dry crop recognition methods based on remote sensing time series data, this method is applicable
In the demand of a wide range of rapid remote sensing monitoring, have that automaticity is high, easy to use, robustness is good and a nicety of grading height etc.
Feature.
To achieve the above object, the technical scheme is that：A kind of dry crop identification based on remote sensing time series data
Method, comprise the following steps,
Step S01：The time series data of research area's vegetation index, short infrared wave band is established by pixel；
Step S02：The EVI maximums in each growth cycle are calculated by pixel, obtain the crop growth peak period time；
Step S03：According to the crop growth peak period time, crop growth early stage and Later growth are inferred；
Step S04：Establish SWIR the and EVI increment product indexs of crop growth early stage；
Step S05：Establish SWIR the and EVI increment product indexs in crop growth later stage；
Step S06：Establish SWIR the and EVI increment product indexs of whole crop growth phase；
Step S07：Dry farming growing area extraction is carried out according to SWIR and EVI increments product index.
In an embodiment of the present invention, the step S02, which is implemented, is：It is first depending on multiple crop index spatial distribution of ploughing
Figure infers crop growth number of cycles by pixel, then calculates the EVI maximums in each growth cycle by pixel, obtains every
The crop growth peak period time corresponding to individual growth cycle.
In an embodiment of the present invention, in the step S03, crop growth peak period time of occurrence is pushed away forward 70 days,
It is defined as the crop seeding phase, and crop growth early stage will be defined as this period crop seeding phase to peak of growing season；Together
When by crop growth peak period time of occurrence toward pusher 50 days, be defined as the crop harvesting phase, and then by crop growth peak period
It is defined as the crop growth later stage to this period harvest time.
In an embodiment of the present invention, in the step S04, the difference based on SWIR in crop seeding phase and peak of growing season
Value, the SWIR increments of crop growth early stage are calculated, are designated as DS1；Difference based on crop seeding phase Yu peak of growing season EVI,
The EVI increments of crop growth early stage are calculated, are designated as DE1；By by DS1And DE1It is multiplied, obtains crop growth early stage
SWIR and EVI increment product indexs T1：
T1=DS1×DE1=(SWIRheading-SWIRseedling)×(EVIheading-EVIseedling)
Wherein, EVIheading、EVIseedlingRespectively crop growth peak period, sowing time EVI numerical value, SWIRheading、
SWIRseedlingCrop growth peak period, sowing time SWIR numerical value respectively.
In an embodiment of the present invention, in the step S05, the difference based on crop growth peak period Yu harvest time SWIR
Value, the SWIR increments in crop growth later stage are calculated, are designated as DS2；Difference based on crop harvesting phase Yu peak of growing season EVI,
The EVI increments in crop growth later stage are calculated, are designated as DE2；By by DS2And DE2It is multiplied, obtains the crop growth later stage
SWIR and EVI increment product indexs T2：
T2=DS2×DE2=(SWIRharvesting-SWIRheading)×(EVIharvesting-EVIheading)
Wherein, EVIheading、EVIharvestingRespectively crop growth peak period, harvest time EVI numerical value,
SWIRheading、SWIRharvestingRespectively crop growth peak period, harvest time SWIR numerical value.
In an embodiment of the present invention, in the step S06, by calculating crop growth early stage and Later growth SWIR
With EVI increment product indexs, SWIR the and EVI increment product indexs T of crop growth phase is established：
T=T1+T2
Wherein, T1, T2The respectively SWIR and EVI increment product indexs of crop growth early stage and Later growth.
In an embodiment of the present invention, in the step S07, for dry land crop, in the whole crop growth phase
SWIR and EVI increments product index is negative, and paddy field crops are positive number, therefore can be according to the whole crop growth phase
SWIR and EVI increment product indexs T carries out dry crop identification；If T is less than threshold value ω, dry crop is identified as.
In an embodiment of the present invention, in the step S07, it is -0.03 to set threshold value ω values, and ω is in difference for the threshold value
Adjusted in the practical application of region in the range of 0.01.
In an embodiment of the present invention, this method is applied to dry crop or the remote sensing area of other land use patterns is estimated
In calculation field.
Compared to prior art, the invention has the advantages that：
(1) according to vegetation index time series data, the crops key thing for embodying crop growth changing rule is automatically determined
Hou Qi, so as to effectively eliminate the interference of other period signals；
(2) avoid being based only on the thinking that vegetation index time series data design objective carries out crops identification, ingenious ground heddle
The short infrared wave band time series data to earth's surface humidity sensitive is closed, fully excavates wet crop and dry crop in crop growth
Surface humidity changing rule in phase, effectively to carry out the crops based on sequential remotely-sensed data, quickly new think of has been opened up in identification automatically
Road；
(3) excessive manual intervention is not needed, method is clear, can not as a result stablize by other assistance datas
Reliably.
Brief description of the drawings
Fig. 1 is the implementation process figure of the embodiment of the present invention.
EVI, SWIR time-sequence curve chart of Fig. 2 corns and rice.
Fig. 3 crop growth SWIR early stage and EVI increments DS1、DE1Schematic diagram.
Fig. 4 crop growth later stages SWIR and EVI increments DS2、DE2Schematic diagram.
The spatial distribution map of the dry farming growing area in Fig. 5 researchs area.
Embodiment
Below in conjunction with the accompanying drawings, technical scheme is specifically described.
A kind of dry crop recognition methods based on remote sensing time series data of the present invention, comprises the following steps,
Step S01：The time series data of research area's vegetation index, short infrared wave band is established by pixel；
Step S02：The EVI maximums in each growth cycle are calculated by pixel, obtain the crop growth peak period time；
Step S03：According to the crop growth peak period time, crop growth early stage and Later growth are inferred；
Step S04：Establish SWIR the and EVI increment product indexs of crop growth early stage；
Step S05：Establish SWIR the and EVI increment product indexs in crop growth later stage；
Step S06：Establish SWIR the and EVI increment product indexs of whole crop growth phase；
Step S07：Dry farming growing area extraction is carried out according to SWIR and EVI increments product index.
In an embodiment of the present invention, the step S02, which is implemented, is：It is first depending on multiple crop index spatial distribution of ploughing
Figure infers crop growth number of cycles by pixel, then calculates the EVI maximums in each growth cycle by pixel, obtains every
The crop growth peak period time corresponding to individual growth cycle.
In the step S03, crop growth peak period time of occurrence is pushed away forward 70 days, is defined as the crop seeding phase,
And crop growth early stage will be defined as this period crop seeding phase to peak of growing season；Crop growth peak period is gone out simultaneously
Past pusher 50 days between current, it is defined as the crop harvesting phase, and then crop growth peak period to this period harvest time is determined
For the crop growth later stage.
In the step S04, the difference based on crop seeding phase with SWIR in peak of growing season, before calculating crop growth
The SWIR increments of phase, are designated as DS1；Difference based on crop seeding phase Yu peak of growing season EVI, calculate crop growth early stage
EVI increments, are designated as DE1；By by DS1And DE1It is multiplied, obtains SWIR the and EVI increment product indexs of crop growth early stage
T1：
T1=DS1×DE1=(SWIRheading-SWIRseedling)×(EVIheading-EVIseedling)
Wherein, EVIheading、EVIseedlingRespectively crop growth peak period, sowing time EVI numerical value, SWIRheading、
SWIRseedlingCrop growth peak period, sowing time SWIR numerical value respectively.
In the step S05, the difference based on crop growth peak period Yu harvest time SWIR, the crop growth later stage is calculated
SWIR increments, be designated as DS2；Difference based on crop harvesting phase Yu peak of growing season EVI, calculate the crop growth later stage
EVI increments, are designated as DE2；By by DS2And DE2It is multiplied, obtains SWIR the and EVI increment product indexs in crop growth later stage
T2：
T2=DS2×DE2=(SWIRharvesting-SWIRheading)×(EVIharvesting-EVIheading)
Wherein, EVIheading、EVIharvestingRespectively crop growth peak period, harvest time EVI numerical value,
SWIRheading、SWIRharvestingRespectively crop growth peak period, harvest time SWIR numerical value.
In the step S06, by calculating crop growth early stage and Later growth SWIR and EVI increment product index,
Establish SWIR the and EVI increment product indexs T of crop growth phase：
T=T1+T2
Wherein, T1, T2The respectively SWIR and EVI increment product indexs of crop growth early stage and Later growth.
In the step S07, for dry land crop, refer in whole crop growth phase SWIR and EVI increment product
Number is negative, and paddy field crops are positive number, therefore can be according to whole crop growth phase SWIR and EVI increment product index
T carries out dry crop identification；If T is less than threshold value ω, dry crop is identified as.In the step S07, threshold value ω is set to take
It is worth for -0.03, threshold value ω is adjusted in different zones practical application in the range of 0.01.
It is below the specific implementation process of the present invention.
Term is explained：
MODIS data：Moderate Imaging Spectroradiomete data, full name are Moderate Resolution Imaging
Spectroradiometer。
Vegetation index：Vegetation index is to characterize vegetation growth state and the factor of spacial distribution density.Common vegetation
Index has NDVI and EVI.NDVI is normalized differential vegetation index, and full name is Normalized Difference Vegetation
Index.EVI is enhancement mode meta file, and full name is EnhancedVegetation Index.
Short infrared wave band：ShortWave Infraredband, abbreviation SWIR.
Day by day EVI time series datas in year：Since New Year's Day, in chronological order, the data of the EVI in 1 year are recorded day by day
Row.
Day by day SWIR time series datas in year：Since New Year's Day, in chronological order, the data of the SWIR in 1 year are recorded day by day
Row.
A kind of dry crop recognition methods based on remote sensing time series data of the present invention, as shown in Figure 1：Initially set up research area
The time series data of vegetation index and short infrared wave band, the crop growth peak period in each growth cycle is then obtained, and then
Infer crop growth early stage and Later growth, establish SWIR and EVI increments product index, the agriculture of crop growth early stage successively
SWIR the and EVI increment product indexs in plant growth later stage, synthesis form SWIR the and EVI increments of whole crop growth phase and multiplied
Product index, the finally SWIR and EVI increments product index development dry crop identification according to the whole crop growth phase.
A kind of dry crop recognition methods based on remote sensing time series data, further comprises the steps：
Step S01：The time series data of research area's vegetation index, short infrared wave band is established by pixel：
EVI, SWIR time series data collection, wherein EVI calculation formula are day by day in foundation research area's year respectively：
In formula, ρRed, ρBlue, ρNIRThe respectively reflectivity of feux rouges, blue light and near infrared band.
Day by day the foundation of EVI, SWIR time series data, it can be calculated based on MODIS remote sensing images wave band data day by day, also may be used
To be obtained based on 8 days maximum MODIS remote sensing image wave band datas being combined to by linear interpolation.Utilize Whittaker
Smoother methods, time series data collection in original year is smoothed, so as to obtain in research area smooth year day by day
EVI, SWIR time series data collection, the basis as dry crop identification.Such as base EVI, SWIR time series data collection day by day within the year, build
Vertical research area corn and soybean, the single harvest rice and EVI, SWIR time-sequence curve chart of double cropping of rice is shown in Fig. 2.
Step S02：The EVI maximums in each growth cycle are calculated by pixel, obtain the crop growth peak period time：
It is first depending on studying area arable land multiple crop index spatial distribution map, infers the crop growth cycle in 1 year by pixel
Number.Multiple crop index of such as ploughing is 1, represents that the pixel had a crop growth cycle in 1 year；Arable land multiple crop index
For 2, represent that the pixel had two crop growth cycles in 1 year；The rest may be inferred.In research area arable land region, by picture
Member obtains the maximum of the EVI timing curves in each crop growth cycle, and the corresponding time is in the growth cycle
The crop growth peak period time.If the arable land multiple crop index of the pixel is n, by the crop growth peak period time obtained successively
Pn is recorded as, wherein n typically can be using value as 1,2,3.
Step S03：According to the crop growth peak period time, crop growth early stage and Later growth are inferred：
By pixel according to the crop growth peak period time in each growth cycle, sowing time and the harvest of crops are inferred
Phase, and then determine crop growth early stage and Later growth.In crop seeding, it usually needs turn over and remove weeds, now
Vegetative coverage is few, and EVI exponential numbers generally reach extremely low value.With crops emergence and be grown into, EVI timing curves
Also rapid increase therewith, until peak of growing season reach peak value.After crop growth peak period, into harvest time, crops harvesting
Usual earth's surface reappears naked state afterwards, and EVI timing curves decline rapidly.Determine that the sowing time of crops and the method for harvest time have
A lot, can such as infer according to valley that EVI timing curves occur before and after crop growth peak period crops sowing time and
Harvest time.But exist due to the interference of remote sensing image noise, at the valley appearance of EVI timing curves it is very big it is uncertain because
Element, directly influence the accuracy of acquired crop seeding phase and harvest time.For simplicity, in this patent, by crops
Peak of growing season time of occurrence pushes away forward 70 days, is defined as the crop seeding phase, and by this section of crop seeding phase to peak of growing season
Time is defined as crop growth early stage.Crop growth peak period time of occurrence was defined as crops toward pusher 50 days simultaneously
Harvest time, and then crop growth peak period to this period harvest time is defined as the crop growth later stage.
Step S04：Establish SWIR the and EVI increment product indexs of crop growth early stage：
SWIR the and EVI increment product indexs of crop growth early stage are established, its process is as follows：First, by calculating agriculture
Crop sowing time to peak of growing season SWIR difference, as the SWIR increments of crop growth early stage, it is designated as DS1；Then, pass through
The EVI increments of the difference of crop seeding phase to peak of growing season EVI, i.e. crop growth early stage are calculated, are designated as DE1.Passing through will
The SWIR of crop growth early stage increment is multiplied with the EVI increments of crop growth early stage, obtains crop growth early stage
SWIR and EVI increment product indexs, are designated as T1.Its formula is as follows：
T1=DS1×DE1=(SWIRheading-SWIRseedling)×(EVIheading-EVIseedling)
Wherein, EVIheading、EVIseedlingRespectively crop growth peak period, sowing time EVI numerical value, SWIRheading、
SWIRseedlingCrop growth peak period, sowing time SWIR numerical value respectively.
By taking corn, the single harvest rice as an example, the SWIR of crop growth early stage increment DS1, crop growth EVI early stage increments
DE1Schematic diagram see Fig. 3.
Step S05：Establish SWIR the and EVI increment product indexs in crop growth later stage：
SWIR the and EVI increment product indexs in crop growth later stage are established, its process is as follows：First, by calculating agriculture
Plant growth peak period and crop harvesting phase SWIR difference, as the SWIR increments in crop growth later stage, are designated as DS2；So
Afterwards, by calculating the EVI increments of the difference of crop harvesting phase and peak of growing season EVI, i.e. crop growth later stage, it is designated as DE2。
The SWIR in crop growth later stage increment is multiplied with the EVI increments in crop growth later stage, obtains the crop growth later stage
SWIR and EVI increment product indexs, be designated as T2.Its formula is as follows：
T2=DS2×DE2=(SWIRharvesting-SWIRheading)×(EVIharvesting-EVIheading)
Wherein, EVIheading、EVIharvestingRespectively crop growth peak period, harvest time EVI numerical value,
SWIRheading、SWIRharvestingRespectively crop growth peak period, harvest time SWIR numerical value.
By taking corn, the single harvest rice as an example, the SWIR in crop growth later stage increment DS2, crop growth later stage EVI increments
DE2Schematic diagram see Fig. 4.
Step S06：Establish SWIR the and EVI increment product indexs of whole crop growth phase
Based on crop growth early stage and Later growth SWIR and EVI increment product index, i.e. T1And T2, establish crops
SWIR the and EVI increment product indexs T in growth period.Its formula is as follows：
T=T1+T2
Wherein, T1, T2The respectively SWIR and EVI increment product indexs of crop growth early stage and Later growth.
Step S07：SWIR and EVI increments product index according to the whole crop growth phase carries out dry farming growing area and carried
Take.
For dry land crop, before crop seeding, the surface humidity in dry land crop field is generally than relatively low.
With crop seeding, emerge and be grown into developing, earth's surface is gradually covered by crops, and surface humidity gradually increases.Generally
In vegetation peak period this period, the water content of crop plant and blade reaches higher level, gradual with crop harvesting
Decline.Because surface water in SWIR wave bands can produce an absworption peak, thus it is corresponding, for dry land crop
Speech, in crop seeding, it is grown into until in the whole growth period of maturation, rising after falling before occurs in the numerical value of SWIR wave bands
The characteristics of, generally reach valley in crop growth peak period.
For the crops of paddy field, in crop seeding or early stage is transplanted, paddy field is generally covered by moisture, surface humidity
It is very high.In the sowing or transplanting of paddy field crops and in a period of time for being grown into, now paddy field significant portion is water body
Covered, surface humidity is generally constantly in the high level of comparison.With crops vegetation further growth develop, farmland by
Gradually by crops vegetative coverage.With sowing/transplanting of paddy field crops, it is grown into up to ripe this period, due to agriculture
The water content of crop vegetation is low compared with pure water body humidity, surface humidity would generally undergo one gradually slightly decline after gradually smoothly
Process.Within the growth period of whole paddy field crops, on the timing curve of SWIR wave bands, be usually expressed as slightly rise after by
Gradually stable process.
Therefore, dry land crop SWIR and EVI increment product indexs are usually the less negative of numerical value, and paddy field crops
Usually positive number.Can be according to crop growth phase SWIR and EVI increment product indexs, the dry farming growing area in the area that conducts a research carries
Take.If certain pixel meets T in some crop growth phase<ω, the then crops that this growth period of the pixel is planted are nonirrigated farmland
Crops, otherwise it is not dry land crop.ω is constant, and ω is set as -0.03 in the present embodiment.Carried in foundation the present embodiment
The method of confession, by taking Heilongjiang Province of China as an example, the research area dry land crop spatial distribution map obtained is shown in Fig. 5.
Above is presently preferred embodiments of the present invention, all changes made according to technical solution of the present invention, caused function are made
During with scope without departing from technical solution of the present invention, protection scope of the present invention is belonged to.
Claims (9)
- A kind of 1. dry crop recognition methods based on remote sensing time series data, it is characterised in that：Comprise the following steps,Step S01：The time series data of research area's vegetation index, short infrared wave band is established by pixel；Step S02：The EVI maximums in each growth cycle are calculated by pixel, obtain the crop growth peak period time；Step S03：According to the crop growth peak period time, crop growth early stage and Later growth are inferred；Step S04：Establish SWIR the and EVI increment product indexs of crop growth early stage；Step S05：Establish SWIR the and EVI increment product indexs in crop growth later stage；Step S06：Establish SWIR the and EVI increment product indexs of whole crop growth phase；Step S07：Dry farming growing area extraction is carried out according to SWIR and EVI increments product index.
- A kind of 2. dry crop recognition methods based on remote sensing time series data according to claim 1, it is characterised in that：Institute State step S02 specific implementations i.e.：It is first depending on ploughing multiple crop index spatial distribution map by the pixel deduction crop growth cycle Number, the EVI maximums in each growth cycle then are calculated by pixel, obtain the crop growth corresponding to each growth cycle The peak period time.
- A kind of 3. dry crop recognition methods based on remote sensing time series data according to claim 1, it is characterised in that：Institute State in step S03, crop growth peak period time of occurrence is pushed away forward 70 days, be defined as the crop seeding phase, and by crops It is defined as crop growth early stage this period sowing time to peak of growing season；Simultaneously time of occurrence by crop growth peak period backward Push away 50 days, be defined as the crop harvesting phase, and then crop growth peak period to this period harvest time is defined as crops life The long later stage.
- A kind of 4. dry crop recognition methods based on remote sensing time series data according to claim 1, it is characterised in that：Institute State in step S04, based on the difference of SWIR in crop seeding phase and peak of growing season, calculate the SWIR increasings of crop growth early stage Amount, is designated as DS1；Difference based on crop seeding phase Yu peak of growing season EVI, calculate the EVI increments of crop growth early stage, note For DE1；By by DS1And DE1It is multiplied, obtains SWIR the and EVI increment product indexs T of crop growth early stage1：T1=DS1×DE1=(SWIRheading-SWIRseedling)×(EVIheading-EVIseedling)Wherein, EVIheading、EVIseedlingRespectively crop growth peak period, sowing time EVI numerical value, SWIRheading、 SWIRseedlingCrop growth peak period, sowing time SWIR numerical value respectively.
- A kind of 5. dry crop recognition methods based on remote sensing time series data according to claim 1, it is characterised in that：Institute State in step S05, the difference based on crop growth peak period Yu harvest time SWIR, the SWIR for calculating the crop growth later stage increases Amount, is designated as DS2；Difference based on crop harvesting phase Yu peak of growing season EVI, calculate the EVI increments in crop growth later stage, note For DE2；By by DS2And DE2It is multiplied, obtains SWIR the and EVI increment product indexs T in crop growth later stage2：T2=DS2×DE2=(SWIRharvesting-SWIRheading)×(EVIharvesting-EVIheading)Wherein, EVIheading、EVIharvestingRespectively crop growth peak period, harvest time EVI numerical value, SWIRheading、 SWIRharvestingRespectively crop growth peak period, harvest time SWIR numerical value.
- A kind of 6. dry crop recognition methods based on remote sensing time series data according to claim 1, it is characterised in that：Institute State in step S06, by calculating crop growth early stage and Later growth SWIR and EVI increment product index, establish crops SWIR the and EVI increment product indexs T in growth period：T=T1+T2Wherein, T1, T2The respectively SWIR and EVI increment product indexs of crop growth early stage and Later growth.
- A kind of 7. dry crop recognition methods based on remote sensing time series data according to claim 1, it is characterised in that：Institute State in step S07, be negative in whole crop growth phase SWIR and EVI increment product index for dry land crop, and Paddy field crops are positive number, therefore can carry out nonirrigated farmland work according to whole crop growth phase SWIR and EVI increment product index T Thing identifies；If T is less than threshold value ω, dry crop is identified as.
- A kind of 8. dry crop recognition methods based on remote sensing time series data according to claim 7, it is characterised in that：Institute State in step S07, it is -0.03 to set threshold value ω values, and threshold value ω is adjusted in different zones practical application in the range of 0.01 It is whole.
- 9. a kind of dry crop recognition methods based on remote sensing time series data according to claim 1 to 8 any one, its It is characterised by：This method is suitable for the remote sensing area reckoning field of dry crop or other land use patterns.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109840516A (en) * | 2019-03-06 | 2019-06-04 | 福州大学 | A kind of water body variation automatic identifying method based on timing remote sensing image |
| CN111191963A (en) * | 2020-01-21 | 2020-05-22 | 福州大学 | Peanut space distribution mapping method based on pigment change characteristics in growing period |
| CN111398331A (en) * | 2019-12-19 | 2020-07-10 | 河海大学 | Method for evaluating frost resistance of concrete under freezing and thawing cycle action |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7260242B2 (en) * | 2003-08-15 | 2007-08-21 | United States Of America As Represented By The Secretary Of The Air Force | Spatial surface prior information reflectance estimation (SPIRE) algorithms |
| CN101858971A (en) * | 2010-06-02 | 2010-10-13 | 浙江大学 | Rice yield remote sensing estimation method based on MODIS data |
| CN105893977A (en) * | 2016-04-25 | 2016-08-24 | 福州大学 | Rice mapping method based on self-adaptive feature selection |
| US20160290918A1 (en) * | 2014-09-12 | 2016-10-06 | The Climate Corporation | Forecasting national crop yield during the growing season |
| CN106772429A (en) * | 2016-12-24 | 2017-05-31 | 福州大学 | Increase and decrease the corn autodraft method of Ratio index based on peak of growing season NMDI |
-
2017
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7260242B2 (en) * | 2003-08-15 | 2007-08-21 | United States Of America As Represented By The Secretary Of The Air Force | Spatial surface prior information reflectance estimation (SPIRE) algorithms |
| CN101858971A (en) * | 2010-06-02 | 2010-10-13 | 浙江大学 | Rice yield remote sensing estimation method based on MODIS data |
| US20160290918A1 (en) * | 2014-09-12 | 2016-10-06 | The Climate Corporation | Forecasting national crop yield during the growing season |
| CN105893977A (en) * | 2016-04-25 | 2016-08-24 | 福州大学 | Rice mapping method based on self-adaptive feature selection |
| CN106772429A (en) * | 2016-12-24 | 2017-05-31 | 福州大学 | Increase and decrease the corn autodraft method of Ratio index based on peak of growing season NMDI |
Non-Patent Citations (1)
| Title |
|---|
| CORNELIUS SENF ET.AL.: ""Mapping Rubber Plantations and Natural Forests in Xishuangbanna (Southwest China) Using Multi-Spectral Phenological Metrics from MODIS Time Series"", 《REMOTE SENS.》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109840516A (en) * | 2019-03-06 | 2019-06-04 | 福州大学 | A kind of water body variation automatic identifying method based on timing remote sensing image |
| CN109840516B (en) * | 2019-03-06 | 2022-10-14 | 福州大学 | Water body change automatic identification method based on time sequence remote sensing image |
| CN111398331A (en) * | 2019-12-19 | 2020-07-10 | 河海大学 | Method for evaluating frost resistance of concrete under freezing and thawing cycle action |
| CN111191963A (en) * | 2020-01-21 | 2020-05-22 | 福州大学 | Peanut space distribution mapping method based on pigment change characteristics in growing period |
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