CN107122739B - Crop yield estimation model for reconstructing VI time series curve based on Extreme mathematical model - Google Patents
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Abstract
A crop yield estimation model for reconstructing a VI time series curve based on an Extreme mathematical model belongs to the technical field of crop yield prediction. The method aims to solve the problem that the universality of the established crop estimation model is poor due to the low acquisition rate of the remote sensing image used in the existing crop estimation. Firstly, collecting a remote sensing image of the whole production period of a crop in a to-be-estimated area, and preprocessing the remote sensing image; then, cutting according to the vector diagram range of the land parcel to be estimated to obtain a vegetation index VI time sequence curve of each pixel in a cut image; sequencing the VI time sequence curve according to the behavior pixels to obtain the time sequence of cutting the image; fitting vegetation index VI time sequence curves of all pixels by adopting a mathematical model Extreme; determining optimal fitting VI, and extracting characteristic parameters of a vegetation index VI time sequence curve; and finally, obtaining an estimated yield model. The method is used for predicting the crop yield.
Description
Technical Field
The invention relates to a crop yield estimation model for reconstructing a VI time series curve based on an Extreme mathematical model, belonging to the technical field of crop yield prediction.
Background
In the process of obtaining the remote sensing images, because the remote sensing images are influenced by weather, soil, hydrology and the like, generally, all the remote sensing images in the whole year crop growth period range of a research area are difficult to obtain, and therefore, when the crop yield is predicted, an estimated yield model with good universality is difficult to establish.
Although the coverage range of MODIS data is large, data of a single time phase is easily affected by clouds and fog to cause abnormal conditions, a multi-day VI (NDVI and EVI) synthesis technology is required to be used, a VI maximum value method is generally adopted in the MODIS data synthesis method, but the synthesized image eliminates cloud pollution and possibly causes that the VI value of a local area is too small, in order to solve the problem that the VI synthesis has local data inaccuracy, most researches carry out smooth reconstruction processing on time sequence MODIS _ VI, domestic and foreign scholars carry out a large amount of researches on the NDVI time sequence data smooth reconstruction method, the main methods comprise a Savitsky-Golay filtering method, an asymmetric Gaussian fitting function fitting method and an L Gaussian fitting function fitting method.
Disclosure of Invention
The invention aims to solve the problem that the universality of the established crop estimation model is poor due to the low acquisition rate of remote sensing images used in the conventional crop estimation, and provides a crop estimation model for reconstructing a VI time series curve based on an Extreme mathematical model.
The invention relates to a crop estimation model for reconstructing a VI time series curve based on an Extreme mathematical model, which comprises the following steps:
the method comprises the following steps: collecting remote sensing images of the whole production period of crops in a to-be-estimated area, and sequentially carrying out radiometric calibration, atmospheric correction and forward correction pretreatment on all the remote sensing images to obtain processed remote sensing images;
step two: cutting the processed remote sensing image according to the range of a plot vector diagram of a to-be-estimated region, extracting a vegetation coverage index NDVI and an enhanced vegetation index EVI of the cut image, and further obtaining a vegetation index VI time sequence curve of each pixel in the cut image;
step three: sequencing vegetation index VI time sequence curves of all pixels according to behavior pixels to obtain the time sequence of cutting images;
step four: determining a mathematical model Extreme consistent with the vegetation index VI time sequence curve, and fitting the vegetation index VI time sequence curves of all the pixels by adopting the mathematical model Extreme:
Wherein y is a reconstructed fitting value of a vegetation index VI0For offset, A is amplitude, e is Exp function, z is intermediate variable, x is vegetation index VI, xcIs the maximum value of the model, and w is the full width at half maximum of the curve;
step five: determining optimal fitting VI according to the mathematical model Extreme and preset fitting precision, and extracting characteristic parameters of a vegetation index VI time sequence curve;
step six: performing correlation and correlation regression analysis of the enhanced vegetation index EVI and the crop yield per unit by taking day as a unit, selecting a period with the highest correlation coefficient or a characteristic parameter as an estimation factor, establishing an estimation model, and estimating the crop yield of the estimation area by adopting the estimation model; the estimated yield model is as follows:
C＝a*ek*y；
where C is the estimated yield, a is the first coefficient, and k is the second coefficient.
The method has the advantages that the method utilizes the Extreme mathematical model to carry out VI time sequence curve reconstruction fitting on the L andsat remote sensing images, thereby realizing the prediction of crop yield, can utilize images in a few periods to reconstruct VI time sequences in a growth period range, and provides convenience for the remote sensing estimation of crop yield.
The invention extracts the NDVI and EVI time sequence curves of the crops, can select a few periods of the growing period of the crops to carry out model fitting on the VI time sequence by utilizing originPro8 according to the time sequence curve of the growing period of the crops through an asymmetric mathematical model in the existing software, and reconstructs the VI time sequence by taking days as units. The method can determine the time phase of estimated production to the day, solves the problems of poor image quality and low acquisition rate in the past, and realizes the yield prediction of agricultural crops with large scale range and high precision based on the application of the remote sensing image with high spatial and temporal resolution.
Drawings
FIG. 1 is a VI time series plot for each pixel obtained in step two of the specific embodiment of the present invention; in the figure, P is an NDVI time series curve, and Q is an EVI time series curve;
FIG. 2 is a reconstructed fitted vegetation index plot of the present invention; in the figure, a triangle represents 8 time phases, a square represents 6 time phases, a prism represents 5 time phases, a chain line represents 8 time phase reconstruction curves, a dotted line represents 6 time phase reconstruction curves, and a two-dot chain line represents 5 time phase reconstruction curves;
FIG. 3 is a schematic diagram of reconstructing an EVI time series curve in units of days and extracting characteristic parameters of the EVI time series curve; VI in the figuremaxIs the maximum value of the vegetation index, VIkIs the vegetation index rate of change.
Detailed Description
The first embodiment is as follows: the following describes the present embodiment with reference to fig. 1 to 3, and the crop estimation model based on the Extreme mathematical model to reconstruct the VI time series curve in the present embodiment includes the following steps:
the method comprises the following steps: collecting remote sensing images of the whole production period of crops in a to-be-estimated area, and sequentially carrying out radiometric calibration, atmospheric correction and forward correction pretreatment on all the remote sensing images to obtain processed remote sensing images;
step two: cutting the processed remote sensing image according to the range of a plot vector diagram of a to-be-estimated region, extracting a vegetation coverage index NDVI and an enhanced vegetation index EVI of the cut image, and further obtaining a vegetation index VI time sequence curve of each pixel in the cut image;
step three: sequencing vegetation index VI time sequence curves of all pixels according to behavior pixels to obtain the time sequence of cutting images;
step four: determining a mathematical model Extreme consistent with the vegetation index VI time sequence curve, and fitting the vegetation index VI time sequence curves of all the pixels by adopting the mathematical model Extreme:
Wherein y is a reconstructed fitting value of a vegetation index VI0For offset, A is amplitude, e is Exp function, z is intermediate variable, x is vegetation index VI, xcIs the maximum value of the model, and w is the full width at half maximum of the curve;
step five: determining optimal fitting VI according to the mathematical model Extreme and preset fitting precision, and extracting characteristic parameters of a vegetation index VI time sequence curve;
step six: performing correlation and correlation regression analysis of the enhanced vegetation index EVI and the crop yield per unit by taking day as a unit, selecting a period with the highest correlation coefficient or a characteristic parameter as an estimation factor, establishing an estimation model, and estimating the crop yield of the estimation area by adopting the estimation model; the estimated yield model is as follows:
C＝a*ek*y；
where C is the estimated yield, a is the first coefficient, and k is the second coefficient.
The remote sensing images of the whole production period of the crops in the area to be estimated comprise L andsat _ TM _5 remote sensing images and L andsat _ ETM _7 remote sensing images.
The cutting image of the area to be estimated comprises a plurality of pixels.
Extracting vegetation coverage index NDVI and enhanced vegetation index EVI of the cut image in the second step of the embodiment by using a Band math module in ENVI5.3 software; the pixel sorting in the third step can be carried out in Excel; and selecting the pixel VI value of the remote sensing image period from the data in the Excel sequenced in the step three, importing the pixel VI value into originPro8, performing curve fitting, and respectively selecting 5/6/8 methods to perform curve fitting. The Fitting step was entered in OriginPro8 by the Analysis data Analysis module. And selecting a Non-linear Fitting function, Non-linear Curve Fitting, according to the NDVI time series curve. And selecting a Function in nonlinear curve fitting, determining a mathematical model Extreme consistent with the VI time sequence curve, and fitting all pixels of the land in batch. And determining the optimal fitting VI according to the fitting result and the fitting precision. Correlation and correlation regression analysis of day-by-day EVI with crop yield was performed in SPSS. And finally, the estimated yield model can be adopted to predict the yield of another plot of the farm in the same year, and the precision is evaluated through the prediction precision and the RMSE root mean square error.
Examples of specific validation are as follows:
the method comprises the steps of firstly, obtaining 2002 year-round images of L andsat _ TM _5 and L andsat _ ETM _7 remote sensing images of the whole growing period of cotton in a test area, and carrying out radiometric calibration, atmospheric correction and positive shooting correction pretreatment on the remote sensing images.
Step two: and (3) cutting the image processed in the step one according to the vector diagram range of the farm land parcel, and extracting NDVI and EVI in ENVI5.3 by using a Band math module, so as to obtain a VI time sequence curve of each pixel element, as shown in the attached figure 1.
Step three: all pixels of a plot are sorted according to row pixels and are sorted by periods of remote sensing images in Excel. The results of the ranking are shown in Table 1. The number of picture elements is determined by the actual image data.
TABLE 1 remote sensing image VI time sequence arrangement
N in table 1 indicates the number of picture elements.
Step four: and importing the pixel VI numerical value of the selected remote sensing image period in the Excel sequenced in the step three into originPro 8. The curve fitting step is performed, and 5/6/8 three selection methods are respectively selected for curve fitting during the selection period, as shown in fig. 2.
Analysis data Analysis module in OriginPro8, enter Fitting step. And selecting a Non-linear Fitting function, namely Non-linear customer Fitting, according to the NDVI time series Curve.
And selecting a Function in nonlinear curve fitting, determining a mathematical model Extreme consistent with the VI time sequence curve, and fitting all pixels of the land in batch.
Step five: and (3) deriving parameters of a fitting equation, reconstructing a curve equation of each pixel, determining an optimal fitting VI according to the result equation after the fourth step of fitting and the fitting precision, reconstructing an EVI time series curve by taking the day as a unit, and extracting characteristic parameters of the EVI time series curve, wherein the characteristic parameters are shown in the attached drawing 3.
Step six: correlation and correlation regression analysis of day-by-day EVI with crop yield was performed in SPSS. And selecting the period or characteristic parameter with the highest correlation coefficient as an estimation factor, wherein the estimation factor is shown in a table 2.
TABLE 2
In Table 2, EVImaxFor the maximum value of EVI reconstruction, EVI _198 is the EVI reconstruction value for 198 days, and 198-214_ AVG is the EVI reconstruction average value for 198-214 days.
And modeling is carried out between the yield and the estimated yield model to obtain an estimated yield model, the estimated yield model is used for carrying out yield prediction on another plot of the farm in the same year, and precision evaluation is carried out through prediction precision and root mean square error.
TABLE 3 yield prediction model and accuracy evaluation
The invention uses TM5 and ETM7, wherein TM/ETM is a remote sensor carried by a terrestrial resource satellite L ANDSAT, the spatial resolution is 30m, the time resolution is 16 days, and the 30m spatial resolution is very suitable for the research of crop estimation on city and county level and farm scale.
Claims (2)
1. A crop estimation model for reconstructing a VI time series curve based on an Extreme mathematical model is characterized by comprising the following steps:
the method comprises the following steps: collecting remote sensing images of the whole production period of crops in a to-be-estimated area, and sequentially carrying out radiometric calibration, atmospheric correction and forward correction pretreatment on all the remote sensing images to obtain processed remote sensing images;
step two: cutting the processed remote sensing image according to the range of a plot vector diagram of a to-be-estimated region, extracting a normalized vegetation index NDVI and an enhanced vegetation index EVI of the cut image, and further obtaining a vegetation index VI time sequence curve of each pixel in the cut image, wherein the vegetation index VI time sequence curve comprises an NDVI time sequence curve and an EVI time sequence curve;
step three: sequencing vegetation index VI time sequence curves of all pixels according to the time sequence of rows as pixels and columns to obtain the time sequence of cutting images;
step four: determining a mathematical model Extreme consistent with a vegetation index VI time sequence curve, and fitting the vegetation index VI after all pixels in the third step by adopting the mathematical model Extreme:
Wherein y is a reconstructed fitting value of a vegetation index VI0For offset, A is amplitude, e is Exp function, z is intermediate variable, x is vegetation index VI, xcIs the maximum value of the modelW is the curve full width at half maximum;
step five: and determining the optimal fitting VI in the fourth step according to the mathematical model Extreme and the preset fitting precision as follows: extracting characteristic parameters of the enhanced vegetation index EVI time series curve; the characteristic parameters are an EVI reconstruction maximum value, an EVI reconstruction value at 198 days and an EVI reconstruction average value at 198-214 days;
step six: performing correlation and correlation regression analysis of the enhanced vegetation index EVI and the crop yield per unit by taking day as a unit, selecting the characteristic parameter with the highest correlation coefficient as an estimation factor, establishing an estimation model, and estimating the crop yield of the estimation area by adopting the estimation model; the estimated yield model is as follows:
C＝a*ek*y1；
wherein C is the estimated yield, a is the first coefficient, k is the second coefficient, and y1 is the characteristic parameter.
2. The crop estimation model for reconstructing VI time series curve based on the Extreme mathematical model as claimed in claim 1, wherein the remote sensing images of the whole production period of the crop in the area to be estimated comprise L andsat _ TM _5 remote sensing image and L andsat _ ETM _7 remote sensing image.
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| CN109614763B (en) * | 2019-01-30 | 2019-09-06 | 北京师范大学 | A kind of area crops yield estimation method correcting crop modeling based on multi-source information substep |
| CN110245327B (en) * | 2019-03-28 | 2023-04-18 | 国智恒北斗好年景农业科技有限公司 | Wheat yield per unit remote sensing estimation method based on GF-1 data reconstruction |
| CN112304902B (en) * | 2020-11-02 | 2023-10-20 | 航天宏图信息技术股份有限公司 | Real-time monitoring method and device for crop weather |
| CN113223040B (en) * | 2021-05-17 | 2024-05-14 | 中国农业大学 | Banana estimated yield method and device based on remote sensing, electronic equipment and storage medium |
| CN115879836B (en) * | 2023-03-08 | 2023-05-12 | 吉林高分遥感应用研究院有限公司 | Soybean crop remote sensing large-area rapid yield estimation method based on coupling mechanism model |
| CN117689959B (en) * | 2024-01-30 | 2024-06-14 | 中交第二公路勘察设计研究院有限公司 | Remote sensing classification method for fusing vegetation life cycle features |
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