CN109509112A - Global soybean and main maize area yield assessment method and system based on MODIS NDVI - Google Patents
Global soybean and main maize area yield assessment method and system based on MODIS NDVI Download PDFInfo
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- CN109509112A CN109509112A CN201811287814.2A CN201811287814A CN109509112A CN 109509112 A CN109509112 A CN 109509112A CN 201811287814 A CN201811287814 A CN 201811287814A CN 109509112 A CN109509112 A CN 109509112A
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- 235000010469 Glycine max Nutrition 0.000 title claims abstract description 28
- 235000016383 Zea mays subsp huehuetenangensis Nutrition 0.000 title claims abstract description 18
- 235000009973 maize Nutrition 0.000 title claims abstract description 18
- 238000002310 reflectometry Methods 0.000 claims abstract description 26
- 238000004519 manufacturing process Methods 0.000 claims abstract description 17
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- 238000007781 pre-processing Methods 0.000 claims description 6
- 238000004088 simulation Methods 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 4
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- G—PHYSICS
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- 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
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Abstract
The present invention relates to a kind of global soybean based on MODIS NDVI and main maize area yield assessment method and system, the method passes through to global corn and soybean main production country Argentina, Brazil, China, Canada, India, Mexico and the U.S., various countries' corn for obtaining on the NDVI image that is calculated using MOD09A1 reflectivity data, crop distributed data and United States Department of Agriculture official website and soybean yields are as inputting, establish linear regression model (LRM), the optimal period of each main production country estimation corn and soybean is found, and estimates their yield.
Description
Technical field
The present invention relates to Mapping remote sensing technology technical fields, and in particular to a kind of based on the global soybean of MODIS NDVI and corn
Main producing region yield assessment method and system.
Background technique
MODIS is a middle low resolution satellite, is widely used in crop monitoring field, compared to high score in other
Resolution satellite has the characteristics that monitoring range is big, revisiting period is short and time continuity is good, particularly suitable for large scale large area
Crop remote sensing monitoring.There are mainly two types of the vegetation index products of MODIS data, is NDVI and EVI respectively, in crop growing state
Using relatively broad in monitoring, draught monitor and crop yield forecast.Carrying out agricultural output assessment using remote sensing satellite can be obvious
The workload for saving monitoring station and sampling survey, saves a large amount of human and material resources and financial resources.Meanwhile grain yield is related to state
Family's safety and everyone actual benefit obtain grain yield data to Chinese agricultural policy formulation, grain-production quota and grain
The regulation of price cheating lattice is of great significance.
Grinding for regional area such as province or state rank is tended in existing research and utilization MODIS vegetation index prediction crop yield
Study carefully, but rarely have research for the estimation of the crop yield of country scale, main reason is that Production Forecast Models are in smaller prison
Survey the precision that region is easier to ensure that prediction.If it is excessive to monitor region area, the society & culture between the different location in region
Situation, crop growth environment, kind and breeding time etc. all can be possible different, and crop feature space variability is larger, increases crop
It monitors difficulty and directly constrains the precision of crop yield inverting.In addition, due to the difference in crop phenological period, the work of different regions
The best period of object production forecast is not consistent, i.e., not consistent with the highest monitoring time point of crop yield correlation.
Therefore, it is remote sensing appraising of the scale to crop yield that the special heterogeneity of crop, which is affected with country,.In addition, MODIS satellite
Spatial resolution is lower, if monitoring region crop field block is smaller, such as the small peasant economy of China, will there is mixed pixel effect
It answers, mixed pixel mixes atural object comprising more, influences yield inversion accuracy.
Summary of the invention
In view of this, the present invention provide global soybean based on MODIS NDVI and main maize area yield assessment method and
System, to global corn and soybean main production country Argentina, Brazil, China, Canada, India, Mexico and the U.S., with
The various countries' corn obtained on NDVI image, crop distributed data and the United States Department of Agriculture official website that MOD09A1 reflectivity data calculates
With soybean yields as inputting, linear regression model (LRM) is established, finds the optimal period of each main production country estimation corn and soybean, and
Estimate their yield.
To achieve the above object, the present invention discloses a kind of global soybean based on MODIS NDVI and main maize area yield
Appraisal procedure the described method comprises the following steps:
S1, MOD09A1 reflectivity data, MOD09A1 reflectivity data every 8 days phases, by described each issue are obtained
MOD09A1 reflectivity data is pre-processed, and is read data and is calculated each issue of corresponding NDVI image；
S2, in Crop growing stage, according to the NDVI image of many years of generation more phases, using crop distribution image as covering
Film, non-crop pixel are not involved in statistical calculation, calculate the NDVI mean value of each time main producing region, according to NDVI mean value and corresponding year
The crop yield statistical data of part carries out linear modelling Y=a0+a1NDVI, wherein NDVI is independent variable, and crop yield is because becoming
Amount, is fitted using least square method, and the coefficient a that digital simulation goes out0、a1With the goodness of fit side R；
S3, using the goodness of fit side R as standard, choose optimal models, an optimal models corresponding optimal period, by optimal mould
The coefficient of type is applied on the NDVI image in corresponding period, and any country crop yield distribution map is calculated, wherein
The standard for choosing optimal models is R Fang Yuegao, and model is more excellent；
S4, the crop yield distribution map according to any country carry out statistics and calculate acquisition country work
Object total output.
In the above-mentioned technical solutions, the annual MOD09A1 reflectivity data of each main production country had 46 phases in the step S1.
In the above-mentioned technical solutions, which is characterized in that MOD09A1 reflectivity data carries out pretreatment step in the step S1
It suddenly include re-projection, splicing and cutting.
In the above-mentioned technical solutions, which is characterized in that the step S2 the following steps are included:
S21, using linear model, establish corresponding yield model respectively for several periods in every kind of Crop growing stage,
Using the side R as standard, yield model is ranked up；
S22, being increasing with the crop yield statistical data obtained on USDA, corresponding yield model can carry out
It updates.
In the above-mentioned technical solutions, in the step S3, the side's R maximum period is the corresponding crop yield estimation
Optimal period, optimal period corresponding coefficient a0、a1It is then optimal coefficient.
In the above-mentioned technical solutions, in the step S4, overall crop yield calculates step and includes:
S41, any national per unit area yield distribution map is transformed under Albert equivalent projection；
S42, each pixel that will belong to crop in image obtain the picture by the area of the pixel multiplied by pixel value
Crop yield at member；
S43, the crop yield at all pixels is added.
It is described invention additionally discloses a kind of global soybean based on MODIS NDVI and main maize area yield assessment system
System includes image capturing and preprocessing module, statistical modeling module, application model module, statistics computing module；
Image capturing and preprocessing module, for obtaining MOD09A1 reflectivity data, the MOD09A1 reflectivity data
Every 8 days phases, each issue of MOD09A1 reflectivity data is pre-processed, read data and calculates each issue of corresponding NDVI shadow
Picture；
Statistical modeling module, for several period NDVI images in the Crop growing stage according to many years more phases, in conjunction with work
Object is distributed image, calculates the NDVI mean value of each time crop, according to the crop yield number of crop NDVI mean value and corresponding time
According to progress linear modelling Y=a0+a1NDVI, wherein NDVI be independent variable, crop yield is dependent variable, using least square method into
Row fitting, the coefficient a that digital simulation goes out0、a1With the goodness of fit side R；
Application model module, for using the goodness of fit side R as standard, obtaining optimal models, and application optimal models is
Number, is calculated any country crop yield distribution map, wherein and the standard for choosing optimal models is R Fang Yuegao,
Model is more excellent；
Computing module is counted, for according to any country crop yield distribution map, carrying out statistics calculating acquisition should
Country overall crop yield.
The present invention is based on the global soybean of MODIS NDVI and main maize area yield assessment method and system, have following
The utility model has the advantages that being directed to country scale, in plant growth conditions, kind and breeding time etc. there are in the case where larger difference, find
The optimal time and optimal models of crop production forecast weaken as much as possible since crop above-mentioned condition Differences on Crop is anti-
The influence drilled.The method realizes have quickly and a keyization estimates global corn and soybean main production country using Python
The characteristics of yield, and precision is high, for world's corn and soybean when the quick predict of annual output and grain situation is with important
Practical significance.
Detailed description of the invention
Fig. 1 is that the present invention is based on the global soybean of MODIS NDVI and main maize area yield assessment method flow diagram；
Fig. 2 is that the present invention is based on the global soybean of MODIS NDVI and main maize area yield assessment system module figure；
Specific embodiment
Present invention is further described in detail with reference to the accompanying drawing, and it is big that the present invention provides the whole world based on MODIS NDVI
Beans and main maize area yield assessment method, as shown in Figure 1, the described method comprises the following steps:
S1, MOD09A1 reflectivity data, MOD09A1 reflectivity data every 8 days phases, by described each issue are obtained
MOD09A1 reflectivity data is pre-processed, and is read data and is calculated each issue of corresponding NDVI image；
Wherein, MOD09A1 data obtain (https: //e4ftl01.cr.usgs.gov), Mei Gezhu from the official website NASA
It produces state and covers several pieces of MOD09A1 block datas.
Wherein, the annual MOD09A1 reflectivity data of each main production country had 46 phases in the step S1.In the step S1
It includes re-projection, splicing and cutting that MOD09A1 reflectivity data, which carries out pre-treatment step,.
S2, in Crop growing stage, according to the NDVI image of many years of generation more phases, using crop distribution image as covering
Film, non-crop pixel are not involved in statistical calculation, calculate the NDVI mean value of each time main producing region, according to NDVI mean value and corresponding year
The crop yield statistical data of part carries out linear modelling Y=a0+a1NDVI, wherein NDVI is independent variable, and crop yield is because becoming
Amount, is fitted using least square method, and the coefficient a that digital simulation goes out0、a1With the goodness of fit side R；
Wherein, the step S2 the following steps are included:
S21, according to linear model, establish corresponding yield for several periods in the breeding time of every kind of crop and estimate mould
Type is ranked up production estimation model using the side R as standard；
S22, being increasing with the crop yield statistical data obtained on USDA, corresponding yield model will carry out
It is corresponding to update.
Wherein, the USDA refers to United States Department of Agriculture's Organic certification.
S3, using the goodness of fit side R as standard, choose optimal models, an optimal models corresponding optimal period, by optimal mould
The coefficient of type is applied on the NDVI image in corresponding period, and any country crop yield distribution map is calculated, wherein
The standard for choosing optimal models is R Fang Yuegao, and model is more excellent；
Wherein, in the step S3, the side's R maximum period is the optimal period of the corresponding crop yield estimation,
Optimal period corresponding coefficient a0、a1It is then optimal coefficient.
S4, according to any country crop yield distribution map, carry out statistics and calculate to obtain the country crop
Total output.
Wherein, in the step S4, overall crop yield calculates step and includes:
S41, any country per unit area yield distribution map is transformed under Albert equivalent projection；
S42, each pixel for belonging to crop in image is obtained by the area of each pixel multiplied by pixel value
Crop yield at the pixel；
S43, the crop yield at all pixels is added.
Wherein, the method has following characteristics:
It is a wide range of: using country as scale, cover main corn and soybean main production country, including Argentina, Brazil, China,
Canada, India, Mexico and the U.S..
Automation: it in the case where providing crop distribution, MOD09A1 data and historical production data, can be found with a key
And optimal models are applied, without the parameter that setting is complicated.
Precision is high: being tested, is modeled using the yield data of 2012-2015,2016 to the method for proposition
Year yield data as verifying, (output statistics data source is from United States Department of Agriculture official website http://statistics.amis-
Outlook.org/data/index.html#COMPARE), every country production forecast precision is generally 80% or more, part
The crop of country can reach 95% or more, and for precision statistics referring to table 1, table 1 is corns in 2016 and soybean main production country production forecast
The yield data comparison provided in value and United States Department of Agriculture official website.
Invention additionally discloses a kind of global soybean based on MODIS NDVI and main maize area yield assessment systems, special
Sign is that the system comprises image capturings and preprocessing module, statistical modeling module, application model module, statistics to calculate mould
Block；
Image capturing and preprocessing module, for obtaining MOD09A1 reflectivity data, the MOD09A1 reflectivity data
Every 8 days phases, each issue of MOD09A1 reflectivity data is pre-processed, read data and calculates each issue of corresponding NDVI shadow
Picture；
Statistical modeling module calculates each time for several NDVI images according to more phases for many years in Crop growing stage
NDVI mean value, linear modelling Y=a is carried out according to NDVI mean value and the crop yield data in corresponding time0+a1NDVI, wherein
NDVI is independent variable, and crop yield is dependent variable, is fitted using least square method, the coefficient a that digital simulation goes out0、a1With it is quasi-
Close the goodness side R；
Application model module, for using the goodness of fit side R as standard, obtaining optimal models, and application optimal models is
Number, is calculated any country crop yield distribution map, wherein and the standard for choosing optimal models is R Fang Yuegao,
Model is more excellent；
Computing module is counted, for according to any country crop yield distribution map, carrying out statistics calculating acquisition should
Country overall crop yield.
The part not illustrated in specification is the prior art or common knowledge.Present embodiment is merely to illustrate the hair
It is bright, rather than limit the scope of the invention, the modifications such as equivalent replacement that those skilled in the art make the present invention are recognized
To be fallen into invention claims institute protection scope.
Claims (7)
1. a kind of global soybean and main maize area yield assessment method based on MODIS NDVI, which is characterized in that the side
Method the following steps are included:
S1, MOD09A1 reflectivity data, MOD09A1 reflectivity data every 8 days phases, by each issue of MOD09A1 are obtained
Reflectivity data is pre-processed, and is read data and is calculated each issue of corresponding NDVI image；
S2, in Crop growing stage, according to the NDVI image of many years of generation more phases, using crop distribution image as exposure mask,
Non- crop pixel is not involved in statistical calculation, calculates the NDVI mean value of each time main producing region, according to NDVI mean value and corresponding time
Crop yield statistical data carry out linear modelling Y=a0+a1NDVI, wherein NDVI is independent variable, and crop yield is dependent variable,
It is fitted using least square method, and the coefficient a that digital simulation goes out0、a1With the goodness of fit side R；
S3, using the goodness of fit side R as standard, choose optimal models, an optimal models corresponding optimal period, by optimal models
Coefficient is applied on the NDVI image in corresponding period, any country crop yield distribution map is calculated, wherein described
The standard for choosing optimal models is R Fang Yuegao, and model is more excellent；
S4, the crop yield distribution map according to any country, carry out statistics calculate obtain the country crop it is total
Yield.
2. a kind of global soybean and main maize area yield assessment method based on MODIS NDVI according to claim 1,
It is characterized in that, the annual MOD09A1 reflectivity data of each main production country had 46 phases in the step S1.
3. a kind of global soybean and main maize area yield assessment method based on MODIS NDVI according to claim 1,
It is characterized in that, it includes re-projection, splicing and cutting that MOD09A1 reflectivity data, which carries out pre-treatment step, in the step S1.
4. a kind of global soybean and main maize area yield assessment method based on MODIS NDVI according to claim 1,
It is characterized in that, the step S2 the following steps are included:
S21, using linear model, corresponding yield model is established respectively for several periods in every kind of Crop growing stage, with the side R
For standard, yield model is ranked up；
S22, being increasing with the crop yield statistical data obtained on USDA, corresponding yield model can carry out more
Newly.
5. a kind of global soybean and main maize area yield assessment method based on MODIS NDVI according to claim 4,
It is characterized in that, the side's R maximum period is the optimal period of the corresponding crop yield estimation, institute in the step S3
State corresponding coefficient a of optimal period0、a1It is then optimal coefficient.
6. a kind of global soybean and main maize area yield assessment method based on MODIS NDVI according to claim 1,
It is characterized in that, overall crop yield calculates step and includes: in the step S4
S41, any national per unit area yield distribution map is transformed under Albert equivalent projection；
S42, each pixel that will belong to crop in image obtain at the pixel by the area of the pixel multiplied by pixel value
Crop yield；
S43, the crop yield at all pixels is added.
7. a kind of global soybean and main maize area yield assessment system based on MODIS NDVI, which is characterized in that the system
System includes image capturing and preprocessing module, statistical modeling module, application model module, statistics computing module；
Image capturing and preprocessing module, for obtaining MOD09A1 reflectivity data, every 8 days of the MOD09A1 reflectivity data
One phase pre-processed each issue of MOD09A1 reflectivity data, reads data and calculates each issue of corresponding NDVI image；
Statistical modeling module, for dividing in conjunction with crop according to several period NDVI images in the Crop growing stage of more phases for many years
Cloth image calculates the crop NDVI mean value in each time, according to crop NDVI mean value and the crop yield data in corresponding time into
Row linear modelling Y=a0+a1NDVI, wherein NDVI is independent variable, and crop yield is dependent variable, is intended using least square method
It closes, the coefficient a that digital simulation goes out0、a1With the goodness of fit side R；
Application model module, for using the goodness of fit side R as standard, obtaining optimal models, and the coefficient of application optimal models, meter
Calculation obtains any country crop yield distribution map, wherein the standard for choosing optimal models is R Fang Yuegao, model
It is more excellent；
Computing module is counted, for statistics being carried out and calculating the acquisition country according to any country crop yield distribution map
Or regional overall crop yield.
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| CN112749597A (en) * | 2019-10-31 | 2021-05-04 | 北京珈和遥感科技有限公司 | Crop area single-yield remote sensing estimation method based on GF1 high-resolution image |
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Application publication date: 20190322 |