CN110390287A - A kind of crop maturity phase prediction technique based on satellite remote sensing - Google Patents
A kind of crop maturity phase prediction technique based on satellite remote sensing Download PDFInfo
- Publication number
- CN110390287A CN110390287A CN201910645296.5A CN201910645296A CN110390287A CN 110390287 A CN110390287 A CN 110390287A CN 201910645296 A CN201910645296 A CN 201910645296A CN 110390287 A CN110390287 A CN 110390287A
- Authority
- CN
- China
- Prior art keywords
- modis
- time
- image data
- indicate
- crop
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 29
- 238000009395 breeding Methods 0.000 claims abstract description 13
- 230000001488 breeding effect Effects 0.000 claims abstract description 13
- 230000002123 temporal effect Effects 0.000 claims abstract description 9
- 239000011159 matrix material Substances 0.000 claims description 45
- 238000004364 calculation method Methods 0.000 claims description 15
- 230000004927 fusion Effects 0.000 claims description 14
- 238000001228 spectrum Methods 0.000 claims description 10
- 238000002310 reflectometry Methods 0.000 claims description 9
- 238000012952 Resampling Methods 0.000 claims description 4
- 230000003595 spectral effect Effects 0.000 claims description 4
- 230000017105 transposition Effects 0.000 claims description 4
- 238000003306 harvesting Methods 0.000 description 4
- 241000209140 Triticum Species 0.000 description 3
- 235000021307 Triticum Nutrition 0.000 description 3
- 238000012271 agricultural production Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 230000003044 adaptive effect Effects 0.000 description 2
- 241001269238 Data Species 0.000 description 1
- 230000002411 adverse Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000003066 decision tree Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000009472 formulation Methods 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 208000000509 infertility Diseases 0.000 description 1
- 230000036512 infertility Effects 0.000 description 1
- 231100000535 infertility Toxicity 0.000 description 1
- 230000035800 maturation Effects 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 238000007500 overflow downdraw method Methods 0.000 description 1
- 238000005086 pumping Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/24323—Tree-organised classifiers
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/25—Fusion techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Forestry; Mining
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
- G06V20/188—Vegetation
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A40/00—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production
- Y02A40/10—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production in agriculture
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Life Sciences & Earth Sciences (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Artificial Intelligence (AREA)
- Health & Medical Sciences (AREA)
- General Engineering & Computer Science (AREA)
- General Business, Economics & Management (AREA)
- Evolutionary Computation (AREA)
- Evolutionary Biology (AREA)
- Marketing (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- General Health & Medical Sciences (AREA)
- Tourism & Hospitality (AREA)
- Mining & Mineral Resources (AREA)
- Primary Health Care (AREA)
- Development Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Marine Sciences & Fisheries (AREA)
- Animal Husbandry (AREA)
- Agronomy & Crop Science (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Multimedia (AREA)
- Image Processing (AREA)
Abstract
The crop maturity phase prediction technique based on satellite remote sensing that the invention discloses a kind of, belongs to agricultural remote sensing technical field, first merges MODIS image data and sentry's image data, obtain the MODIS image data with high spatial resolution；Calculate the MODIS NDVI value of the MODIS image data with high spatial resolution；The MODIS NDVI value is reconstructed in temporal sequence, obtains the MODIS NDVI time series of high time resolution；Using the MODIS NDVI time series, the MODIS NDVI value of Crop growing stage and the MODIS NDVI value for being predicted time crop tasseling stage in N are extracted before being predicted the time；The crop maturity phase is calculated using the MODIS NDVI value of the breeding time and the MODIS NDVI value of tasseling stage；The present invention has obtained having concurrently the image data of high spatial, high time resolution, to meet requirement of the precision agriculture in room and time resolution ratio.
Description
Technical field
The present invention relates to agricultural remote sensing fields, and in particular to a kind of crop maturity phase prediction technique based on satellite remote sensing.
Background technique
Agricultural production is and guided based on satellite remote sensing prediction crop maturity phase, formulation harvesting sequence, is remote sensing in accurate agriculture
An important application in industry.It is all often to large space regional scale when predicting the crop maturity phase currently with satellite remote sensing
The crop maturity phase is monitored prediction, is not possible to meet the requirement of precision agriculture in spatial resolution and the timeliness of prediction.
Summary of the invention
It is an object of the invention to: the crop maturity phase prediction technique based on satellite remote sensing that the present invention provides a kind of, solution
It has determined when predicting the crop maturity phase currently with satellite remote sensing, the technical issues of spatial resolution is low and poor in timeliness.
The technical solution adopted by the invention is as follows:
A kind of crop maturity phase prediction technique based on satellite remote sensing, comprising the following steps:
Step 1: MODIS image data and sentry's image data being merged, obtain that there is high spatial resolution
MODIS image data；
Step 2: calculating the MODIS NDVI value of the MODIS image data with high spatial resolution；
Step 3: the MODIS NDVI value being reconstructed in temporal sequence, obtains the MODIS of high time resolution
NDVI time series；
Step 4: utilizing the MODIS NDVI time series, extract and be predicted before the time Crop growing stage in N
MODIS NDVI value and the MODIS NDVI value for being predicted time crop tasseling stage；
Step 5: calculating crop maturity using the MODIS NDVI value of the breeding time and the MODIS NDVI value of tasseling stage
Phase.
Further, the specific steps merged in the step 1 are as follows:
Step 11: calculate similar pixel between MODIS image data and sentry's image data to goal pels spectrum away from
From, time gap and space length, the fusion weight of each goal pels is calculated；
Step 12: there is the MODIS image data of high spatial resolution using STARFM model and fusion weight calculation.
Further, in the step 11, the calculation formula of the fusion weight is as follows:
Sij=| VL(xi,yj,ti)-VM(xi,yj,ti) | (1),
Tij=| VM(xi,yj,ti)-VM(xi,yj,tj) | (2),
Wherein, SijIndicate spectrum intervals, TijIndicate time gap, DijRepresentation space distance, WijIndicate weighting function,
(xi, yj) indicate pixel position；tiAnd tjIndicate the acquisition time of image, w indicates the size of window, VL(xi,yj,ti) indicate
tiMoment given position (xi, yj) 10m resolution ratio sentry's image data reflectivity, the VM(xi,yj,ti) indicate tiMoment
Given position (xi, yj) 250m resolution ratio MODIS image data resampling after reflectivity, (xw/2, yw/2) indicate sliding window
Center pel, A indicates constant of the representation space distance relative to the spectral differences opposite sex and time difference opposite sex importance.
Further, in the step 12, the formula that there is the MODIS image data of high spatial resolution to use is calculated
Are as follows:
Further, the step 3 specifically:
Step 31: MODIS NDVI value is fabricated to original matrix X0 m×n, wherein m indicates pixel points, and n indicates image number
Amount；
Step 32: by the original matrix X0 m×nIt is converted into the flat X of squarem×n, and utilize matrix Mm×1Store the original matrix X0 m×n
In every row average value；
Step 33: utilizing the flat X of the squarem×nThe transposition X flat with the squarem×n TIntersectionproduct is calculated, covariance matrix is obtained
Cm×n, calculation formula is as follows:
Step 33: utilizing the covariance matrix Cm×nCalculate the diagonal matrix A of non-zero characteristics rootm×m, calculation formula is such as
Under:
Cm×m×Vm×m=Vm×m×Am×m(7),
Wherein, λ indicates non-zero characteristics root；
Step 34: by the corresponding eigenvector projection of the non-zero characteristics root to the flat X of squarem×n, obtain described eigenvector pair
The freedom degree PC answeredm×n, formula is as follows:
PCm×n=Vm×m×Xm×n(9)；
Step 35: missing data being reconstructed using the freedom degree, obtains matrix Xω, the formula of use are as follows:
Wherein, k indicates optimal encumbrance, EOFm×nIndicate described eigenvector；
Step 36: by the matrix XωWith matrix Mm×1It is added, obtains reconstruction result matrix, and by the reconstruction result square
Battle array is converted into reconstruct MODIS NDVI value, obtains the MODIS NDVI time series of high time resolution.
Further, in the step 5, the crop maturity phase is calculated method particularly includes:
Step 51: calculating separately the integral area Y of breeding time in N before being predicted the time, the formula of use are as follows:
Wherein, SaFor the interim tasseling stage of crop growth, SbFor the crop growth interim maturity period；
Step 52: calculating the average value M of the integral area Y of breeding time in N before being predicted the time, utilize the average value
The maturity period for being predicted the time is calculated in M and the MODIS NDVI value for being predicted time tasseling stage.
In conclusion by adopting the above-described technical solution, the beneficial effects of the present invention are:
The present invention is based on satellite remote sensings to predict the crop maturity phase, can formulate harvesting sequence and and guide agricultural production, to agriculture
Industry machinery carries out reasonable schedule, significant to the mechanical harvesting in scale crop-planting region.And it receives in due course
It cuts, adverse weather is avoided to influence, be the key link in agricultural production, harvest is too early or can all influence yield too late, is unfavorable for
High yield increases income.
The present invention has obtained having concurrently the image data of high spatial, high time resolution, to meet precision agriculture in sky
Between and temporal resolution on requirement.
Detailed description of the invention
In order to illustrate the technical solution of the embodiments of the present invention more clearly, below will be to needed in the embodiment attached
Figure is briefly described, it should be understood that the following drawings illustrates only certain embodiments of the present invention, therefore is not construed as pair
The restriction of range for those of ordinary skill in the art without creative efforts, can also be according to this
A little attached drawings obtain other relevant attached drawings.
Fig. 1 is overall flow figure of the invention.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated.It should be appreciated that described herein, specific examples are only used to explain the present invention, not
For limiting the present invention, i.e., described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is logical
The component for the embodiment of the present invention being often described and illustrated herein in the accompanying drawings can be arranged and be designed with a variety of different configurations.
Therefore, the detailed description of the embodiment of the present invention provided in the accompanying drawings is not intended to limit below claimed
The scope of the present invention, but be merely representative of selected embodiment of the invention.Based on the embodiment of the present invention, those skilled in the art
Member's every other embodiment obtained without making creative work, shall fall within the protection scope of the present invention.
It should be noted that the relational terms of term " first " and " second " or the like be used merely to an entity or
Operation is distinguished with another entity or operation, and without necessarily requiring or implying between these entities or operation, there are any
This actual relationship or sequence.Moreover, the terms "include", "comprise" or its any other variant be intended to it is non-exclusive
Property include so that include a series of elements process, method, article or equipment not only include those elements, but also
Further include other elements that are not explicitly listed, or further include for this process, method, article or equipment it is intrinsic
Element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that including described
There is also other identical elements in the process, method, article or equipment of element.
Feature and performance of the invention are described in further detail with reference to embodiments.
A kind of crop maturity phase prediction technique based on satellite remote sensing, comprising the following steps:
Step 1: MODIS image data and sentry's image data being merged, obtain that there is high spatial resolution
MODIS image data；
Step 2: calculating the MODIS NDVI value of the MODIS image data with high spatial resolution；
Step 3: the MODIS NDVI value being reconstructed in temporal sequence, obtains the MODIS of high time resolution
NDVI time series；
Step 4: utilizing the MODIS NDVI time series, extract and be predicted before the time Crop growing stage in N
MODIS NDVI value and the MODIS NDVI value for being predicted time crop tasseling stage；
Step 5: calculating crop maturity using the MODIS NDVI value of the breeding time and the MODIS NDVI value of tasseling stage
Phase.
The specific steps merged in the step 1 are as follows:
Step 11: calculate similar pixel between MODIS image data and sentry's image data to goal pels spectrum away from
From, time gap and space length, the fusion weight of each goal pels is calculated；
Step 12: there is the MODIS image data of high spatial resolution using STARFM model and fusion weight calculation.
Further, in the step 11, the calculation formula of the fusion weight is as follows:
Sij=| VL(xi,yj,ti)-VM(xi,yj,ti) | (12),
Tij=| VM(xi,yj,ti)-VM(xi,yj,tj) | (13),
Wherein, SijIndicate spectrum intervals, TijIndicate time gap, DijRepresentation space distance, WijIndicate weighting function,
(xi, yj) indicate pixel position；tiAnd tjIndicate the acquisition time of image, w indicates the size of window, VL(xi,yj,ti) indicate
tiMoment given position (xi, yj) 10m resolution ratio sentry's image data reflectivity, the VM(xi,yj,ti) indicate tiMoment
Given position (xi, yj) 250m resolution ratio MODIS image data resampling after reflectivity, (xw/2, yw/2) indicate sliding window
Center pel, A indicates constant of the representation space distance relative to the spectral differences opposite sex and time difference opposite sex importance.
Further, in the step 12, the formula that there is the MODIS image data of high spatial resolution to use is calculated
Are as follows:
Further, the step 3 specifically:
Step 31: MODIS NDVI value is fabricated to original matrix X0 m×n, wherein m indicates pixel points, and n indicates image number
Amount；
Step 32: by the original matrix X0 m×nIt is converted into the flat X of squarem×n, and utilize matrix Mm×1Store the original matrix X0 m×n
In every row average value；
Step 33: utilizing the flat X of the squarem×nThe transposition X flat with the squarem×n TIntersectionproduct is calculated, covariance matrix is obtained
Cm×n, calculation formula is as follows:
Step 33: utilizing the covariance matrix Cm×nCalculate the diagonal matrix A of non-zero characteristics rootm×m, calculation formula is such as
Under:
Cm×m×Vm×m=Vm×m×Am×m(18),
Wherein, λ indicates non-zero characteristics root；
Step 34: by the corresponding eigenvector projection of the non-zero characteristics root to the flat X of squarem×n, obtain described eigenvector pair
The freedom degree PC answeredm×n, formula is as follows:
PCm×n=Vm×m×Xm×n(20)；
Step 35: missing data being reconstructed using the freedom degree, obtains matrix Xω, the formula of use are as follows:
Wherein, k indicates optimal encumbrance, EOFm×nIndicate described eigenvector；
Step 36: by the matrix XωWith matrix Mm×1It is added, obtains reconstruction result matrix, and by the reconstruction result square
Battle array is converted into reconstructed image data, obtains the MODIS NDVI time series of high time resolution.
Further, in the step 5, the crop maturity phase is calculated method particularly includes:
Step 51: calculating separately the integral area Y of breeding time in N before being predicted the time, the formula of use are as follows:
Wherein, SaFor the interim tasseling stage of crop growth, SbFor the crop growth interim maturity period；
Step 52: calculating the average value M of the integral area Y of breeding time in N before being predicted the time, utilize the average value
The maturity period for being predicted the time is calculated in M and the MODIS NDVI value for being predicted time tasseling stage.
Embodiment
The present embodiment is further described for the present invention.
A kind of crop maturity phase prediction technique based on satellite remote sensing, comprising the following steps:
Step 1: MOD09GQ data application MRT (the Modis Reprojection in the time of infertility of wheat in area will be studied
Tool) tool carries out batch and moves on to shadow, from MODIS (moderate-resolution imaging
Spectroradiometer it) is obtained in low spatial resolution data obtaining time information and sentry's image data (Sentinel-2)
High spatial resolution information is taken, using adaptive space-time fusion method is based on, merges MODIS image data and sentry's image number
According to obtaining the MODIS image data with high spatial resolution, the specific steps of fusion are as follows:
Step 11: calculate similar pixel between MODIS image data and sentry's image data to goal pels spectrum away from
From, time gap and space length, the fusion weight of each goal pels is calculated；
The calculation formula of the fusion weight is as follows:
Sij=| VL(xi,yj,ti)-VM(xi,yj,ti) | (23),
Tij=| VM(xi,yj,ti)-VM(xi,yj,tj) | (24),
Wherein, SijIndicate spectrum intervals, TijIndicate time gap, DijRepresentation space distance, WijIndicate weighting function,
(xi, yj) indicate pixel position；tiAnd tjIndicate the acquisition time of image, w indicates the size of window, VL(xi,yj,ti) indicate
tiMoment given position (xi, yj) 10m resolution ratio sentry's image data reflectivity, the VM(xi,yj,ti) indicate tiMoment
Given position (xi, yj) 250m resolution ratio MODIS image data resampling after reflectivity, (xw/2,yw/2) indicate sliding window
The center pel of mouth, A indicate constant of the representation space distance relative to the spectral differences opposite sex and time difference opposite sex importance.
Step 12: there is the MODIS image data of high spatial resolution, fortune using STARFM model and fusion weight calculation
With STARFM (Spatial and Temporal Adaptive ReflectanceFusion Model) model, that is, prediction time
High-resolution image Reflectivity for Growing Season acquisition can be merged by the high-resolution and low resolution image at other moment, it is contemplated that
The smooth information of pixel is closed in space, is carried out convolution algorithm by weight factor to each pixel and is merged, the formula of use
Are as follows:
The mode that STARFM is determined be by searched in fixed window similar pixel to goal pels spectrum intervals, when
Between distance and space length three's linear multiplication, thus fusion obtain having concurrently high spatial, high time resolution it is similar
The image data of Sentinel-2 spatial resolution.
Step 2: calculating MODIS the NDVI ((Normalized of the MODIS image data with high spatial resolution
Difference Vegetation Index) value；
VNDVI=(ρNIR-ρred)/(ρNIR+ρred) (28),
Wherein, ρNIRIndicate the reflectance value of near infrared band, ρredIndicate the reflectance value of red wave band.
Step 3: the MODIS NDVI value being reconstructed in temporal sequence, obtains the MODIS of high time resolution
NDVI time series；
Due to the high time resolution of MODIS data, it is caused to be influenced by cloud than more serious, therefore, is based on image data
Temporal correlation utilize DIEOF (Data Interpolating Empirical Orthogonal Functions) method
Its missing data is reconstructed, method particularly includes:
Step 31: MODIS NDVI value is fabricated to original matrix X0 m×n, wherein m indicates pixel points, and n indicates image number
Amount；
Step 32: by the original matrix X0 m×nIt is converted into the flat X of squarem×n, and utilize matrix Mm×1Store the original matrix X0 m×n
In every row average value；
Step 33: utilizing the flat X of the squarem×nThe transposition X flat with the squarem×n TIntersectionproduct is calculated, covariance matrix is obtained
Cm×n, calculation formula is as follows:
Step 33: utilizing the covariance matrix Cm×nCalculate the diagonal matrix A of non-zero characteristics rootm×m, calculation formula is such as
Under:
Cm×m×Vm×m=Vm×m×Am×m,
Wherein, λ indicates non-zero characteristics root；
Step 34: by the corresponding eigenvector projection of the non-zero characteristics root to the flat X of squarem×n, obtain described eigenvector pair
The freedom degree PC answeredm×n, formula is as follows:
PCm×n=Vm×m×Xm×n(31)；
Step 35: missing data being reconstructed using the freedom degree, obtains matrix Xω, the formula of use are as follows:
Wherein, k indicates optimal encumbrance, EOFm×nIndicate described eigenvector；
Step 36: by the matrix XωWith matrix Mm×1It is added, obtains reconstruction result matrix, and by the reconstruction result square
Battle array is converted into reconstructed image data, obtains the MODIS NDVI time series of high time resolution.
MODIS NDVI data are synthesized in temporal sequence, smothing filtering reduction is carried out using Savitzky-Golay method
The interference of noise reconstructs MODIS NDVI time series；Based on MODIS NDVI time series, to Crops Classification, in conjunction with crop
The features such as phenology, spectrum establish the classificating knowledge rule of wheat, using the classification method of decision tree, generate agrotype distribution
Figure, obtains wheat planting region figure；
Step 5: utilizing the MODIS NDVI time series, extract and be predicted before the time Crop growing stage in N
MODIS NDVI value and the MODIS NDVI value for being predicted time crop tasseling stage；The present embodiment was for nearest 3 years, i.e., 2016
Years -2018 years remotely-sensed datas extract in this 3 years the MODIS NDVI value of Crop growing stage and 2019 (being predicted the time)
The MODIS NDVI value of crop tasseling stage.
Step 6: calculating crop maturity using the MODIS NDVI value of the breeding time and the MODIS NDVI value of tasseling stage
Phase.
Calculate the crop maturity phase method particularly includes:
Step 61: calculating separately by the integral area Y of breeding time in 3 years, the formula of use are as follows:
Wherein, SaFor the interim tasseling stage of crop growth, SbFor the crop growth interim maturity period；
Step 62: the average value M of the integral area Y of breeding time in 3 is calculated, using the average value M as 2019 from pumping
The integral area in male phase to maturity period, and then maturation in 2019 is calculated using the MODIS NDVI value of tasseling stage in 2019
Phase.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all in essence of the invention
Made any modifications, equivalent replacements, and improvements etc., should all be included in the protection scope of the present invention within mind and principle.
Claims (6)
1. a kind of crop maturity phase prediction technique based on satellite remote sensing, it is characterised in that: the following steps are included:
Step 1: MODIS image data and sentry's image data being merged, the MODIS shadow with high spatial resolution is obtained
As data；
Step 2: calculating the MODIS NDVI value of the MODIS image data with high spatial resolution；
Step 3: the MODIS NDVI value being reconstructed in temporal sequence, when obtaining the MODIS NDVI of high time resolution
Between sequence；
Step 4: utilizing the MODIS NDVI time series, extract the MODIS of Crop growing stage in N before being predicted the time
NDVI value and the MODIS NDVI value for being predicted time crop tasseling stage；
Step 5: calculating the crop maturity phase using the MODIS NDVI value of the breeding time and the MODIS NDVI value of tasseling stage.
2. a kind of crop maturity phase prediction technique based on satellite remote sensing according to claim 1, it is characterised in that: described
The specific steps merged in step 1 are as follows:
Step 11: calculate similar pixel between MODIS image data and sentry's image data to goal pels spectrum intervals, when
Between distance and space length, calculate the fusion weight of each goal pels；
Step 12: there is the MODIS image data of high spatial resolution using STARFM model and fusion weight calculation.
3. according to right want 2 described in a kind of crop maturity phase prediction technique based on satellite remote sensing, it is characterised in that: the step
In rapid 11, the calculation formula of the fusion weight is as follows:
Sij=| VL(xi,yj,ti)-VM(xi,yj,ti) | (1),
Tij=| VM(xi,yj,ti)-VM(xi,yj,tj) | (2),
Wherein, SijIndicate spectrum intervals, TijIndicate time gap, DijRepresentation space distance, WijIndicate weighting function, (xi, yj)
Indicate the position of pixel；tiAnd tjIndicate the acquisition time of image, w indicates the size of window, VL(xi,yj,ti) indicate tiMoment
Given position (xi, yj) 10m resolution ratio sentry's image data reflectivity, the VM(xi,yj,ti) indicate tiMoment is to positioning
Set (xi, yj) 250m resolution ratio MODIS image data resampling after reflectivity, (xw/2,yw/2) indicate sliding window center
Pixel, A indicate constant of the representation space distance relative to the spectral differences opposite sex and time difference opposite sex importance.
4. a kind of crop maturity phase prediction technique based on satellite remote sensing according to claim 3, it is characterised in that: the step
In rapid 12, the formula that there is the MODIS image data of high spatial resolution to use is calculated are as follows:
5. according to right want 1 described in a kind of crop maturity phase prediction technique based on satellite remote sensing, it is characterised in that: the step
Rapid 3 specifically:
Step 31: MODIS NDVI value is fabricated to original matrix X0 m×n, wherein m indicates pixel points, and n indicates image quantity；
Step 32: by the original matrix X0 m×nIt is converted into the flat X of squarem×n, and utilize matrix Mm×1Store the original matrix X0 m×nIn it is every
Capable average value；
Step 33: utilizing the flat X of the squarem×nThe transposition X flat with the squarem×n TIntersectionproduct is calculated, covariance matrix C is obtainedm×n, meter
It is as follows to calculate formula:
Step 33: utilizing the covariance matrix Cm×nCalculate the diagonal matrix A of non-zero characteristics rootm×m, calculation formula is as follows:
Cm×m×Vm×m=Vm×m×Am×m(7),
Wherein, λ indicates non-zero characteristics root；
Step 34: by the corresponding eigenvector projection of the non-zero characteristics root to the flat X of squarem×n, it is corresponding to obtain described eigenvector
Freedom degree PCm×n, formula is as follows:
PCm×n=Vm×m×Xm×n(9)；
Step 35: missing data being reconstructed using the freedom degree, obtains matrix Xω, the formula of use are as follows:
Wherein, k indicates optimal encumbrance, EOFm×nIndicate described eigenvector；
Step 36: by the matrix XωWith matrix Mm×1It is added, obtains reconstruction result matrix, and the reconstruction result matrix is turned
Reconstruct MODIS NDVI value is turned to, the MODIS NDVI time series of high time resolution is obtained.
6. according to right want 1 described in a kind of crop maturity phase prediction technique based on satellite remote sensing, it is characterised in that: the step
In rapid 5, the crop maturity phase is calculated method particularly includes:
Step 51: calculating separately the integral area Y of breeding time in N before being predicted the time, the formula of use are as follows:
Wherein, SaFor the interim tasseling stage of crop growth, SbFor the crop growth interim maturity period；
Step 52: calculate the average value M for being predicted the integral area Y of breeding time in N before the time, using the average value M and
The maturity period for being predicted the time is calculated in the MODIS NDVI value for being predicted time tasseling stage.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910645296.5A CN110390287A (en) | 2019-07-17 | 2019-07-17 | A kind of crop maturity phase prediction technique based on satellite remote sensing |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910645296.5A CN110390287A (en) | 2019-07-17 | 2019-07-17 | A kind of crop maturity phase prediction technique based on satellite remote sensing |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110390287A true CN110390287A (en) | 2019-10-29 |
Family
ID=68285003
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910645296.5A Pending CN110390287A (en) | 2019-07-17 | 2019-07-17 | A kind of crop maturity phase prediction technique based on satellite remote sensing |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110390287A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110501302A (en) * | 2019-07-29 | 2019-11-26 | 武汉大学 | A kind of Enteromorpha distribution drawing generating method of multi-source evidence fusion data |
| CN111931988A (en) * | 2020-07-09 | 2020-11-13 | 黑龙江省农业科学院农业遥感与信息研究所 | Rice maturity period prediction method combining remote sensing image, crop model and weather forecast |
| CN115983503A (en) * | 2023-03-18 | 2023-04-18 | 杭州领见数字农业科技有限公司 | Crop maturity prediction method, equipment and storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103336995A (en) * | 2013-04-19 | 2013-10-02 | 国家电网公司 | Method for constructing real-time light metering network of million kilowatt level photovoltaic power generation base |
| CN107423850A (en) * | 2017-07-04 | 2017-12-01 | 中国农业大学 | Region corn maturity period Forecasting Methodology based on time series LAI curve integral areas |
| CN109961418A (en) * | 2019-03-19 | 2019-07-02 | 中国林业科学研究院资源信息研究所 | A kind of cloudless Image compounding algorithm based on multidate optical remote sensing data |
-
2019
- 2019-07-17 CN CN201910645296.5A patent/CN110390287A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103336995A (en) * | 2013-04-19 | 2013-10-02 | 国家电网公司 | Method for constructing real-time light metering network of million kilowatt level photovoltaic power generation base |
| CN107423850A (en) * | 2017-07-04 | 2017-12-01 | 中国农业大学 | Region corn maturity period Forecasting Methodology based on time series LAI curve integral areas |
| CN109961418A (en) * | 2019-03-19 | 2019-07-02 | 中国林业科学研究院资源信息研究所 | A kind of cloudless Image compounding algorithm based on multidate optical remote sensing data |
Non-Patent Citations (12)
| Title |
|---|
| 孙锐等: "MODIS和HJ-1CCD数据时空融合重构NDVI时间序列", 《遥感学报》 * |
| 张喜旺等: "基于时间序列MODIS NDVI的农作物物候信息提取", 《中国农学通报》 * |
| 李丹等: "基于HJ-1数据的冬小麦种植面积、长势与收割进度遥感监测", 《中国农学通报》 * |
| 李旭文等: "基于欧洲航天局"哨兵-2A"卫星的太湖蓝藻遥感监测", 《中国环境监测》 * |
| 杨浩等: "基于HJ-1A/1BCCD时间序列影像的水稻生育期监测", 《农业工程学报》 * |
| 杨阳等: "基于MODIS NDVI数据的郑州市植物物候监测研究", 《西南林业大学学报(自然科学)》 * |
| 辛景峰等: "利用NOAA NDVI数据集监测冬小麦生育期的研究", 《遥感学报》 * |
| 韩伟孝等: "基于长时序Landsat 5/8多波段遥感影像的青海湖面积变化研究", 《地球科学进展》 * |
| 顾晓鹤等: "基于MODIS与TM时序插补的省域尺度玉米遥感估产", 《农业工程学报》 * |
| 高书鹏: "基于时空数据融合技术的云南省橡胶林识别方法研究", 《中国硕士学位论文全文数据库》 * |
| 黄健熙等: "卫星遥感和积温-辐射模型预测区域冬小麦成熟期", 《农业工程学报》 * |
| 黄健熙等: "基于时间序列HJ-1 A/B卫星数据的冬小麦成熟期预测", 《农业机械学报》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110501302A (en) * | 2019-07-29 | 2019-11-26 | 武汉大学 | A kind of Enteromorpha distribution drawing generating method of multi-source evidence fusion data |
| CN110501302B (en) * | 2019-07-29 | 2020-06-09 | 武汉大学 | Enteromorpha distribution map generation method of multi-source evidence fusion data |
| CN111931988A (en) * | 2020-07-09 | 2020-11-13 | 黑龙江省农业科学院农业遥感与信息研究所 | Rice maturity period prediction method combining remote sensing image, crop model and weather forecast |
| CN115983503A (en) * | 2023-03-18 | 2023-04-18 | 杭州领见数字农业科技有限公司 | Crop maturity prediction method, equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Johansen et al. | Predicting biomass and yield in a tomato phenotyping experiment using UAV imagery and random forest | |
| Dobrowski et al. | Grapevine dormant pruning weight prediction using remotely sensed data | |
| Reis et al. | Automatic detection of bunches of grapes in natural environment from color images | |
| CN110390287A (en) | A kind of crop maturity phase prediction technique based on satellite remote sensing | |
| CN111598019A (en) | Crop type and planting mode identification method based on multi-source remote sensing data | |
| CN110414738A (en) | A kind of crop yield prediction technique and system | |
| CN113642464B (en) | Time sequence remote sensing image crop classification method combining TWDTW algorithm and fuzzy set | |
| CN114926748A (en) | Soybean remote sensing identification method combining Sentinel-1/2 microwave and optical multispectral images | |
| Li et al. | Precise identification of maize in the North China Plain based on Sentinel-1A SAR time series data | |
| CN116227758B (en) | Agricultural product maturity prediction method and system based on remote sensing technology and deep learning | |
| Olenskyj et al. | End-to-end deep learning for directly estimating grape yield from ground-based imagery | |
| CN108596029A (en) | Crop classification method, apparatus, computer equipment and storage medium | |
| CN108805079A (en) | The recognition methods of winter wheat and device | |
| Kukkonen et al. | How much can airborne laser scanning based forest inventory by tree species benefit from auxiliary optical data? | |
| Sun et al. | Monitoring rice lodging grade via Sentinel-2A images based on change vector analysis | |
| CN114663778A (en) | Method and device for improving crop yield | |
| Zheng et al. | Using high spatial and temporal resolution data blended from SPOT-5 and MODIS to map biomass of summer maize | |
| Mian et al. | Wheat biomass estimation in different growth stages based on color and texture features of UAV images | |
| Rajpoot et al. | JUTE CROP PRODUCTION ESTIMATION IN MAJOR STATES OF INDIA: A COMPARATIVE STUDY OF LAST 6 YEARS’FASAL AND DES ESTIMATES | |
| Ventura et al. | Remote sensing and UAV vegetation index comparison with on-site FAPAR measurement | |
| Araújo et al. | Satellite and UAV-based anomaly detection in vineyards | |
| Longo-Minnolo et al. | Appraising the stem water potential of citrus orchards from UAV-based multispectral imagery | |
| AU2021103519A4 (en) | A system for water stress identification in crops and method there of | |
| CN114782835B (en) | Crop lodging area proportion detection method and device | |
| Nagano et al. | Detecting fluctuation of rice cultivated areas in semi-arid regions by combined use of MODIS and Landsat imageries |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20191029 |