CN110909821B - Method for carrying out high-space-time resolution vegetation index data fusion based on crop reference curve - Google Patents
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Abstract
The invention discloses a high-space-time resolution vegetation index data set reconstruction method based on a crop reference curve, which comprises the steps of constructing a crop reference curve sample library, obtaining a plurality of MODIS reference curves of different crops based on pure pixels by utilizing crop category information and spatial positions of a crop classification map CD L to form an MODIS crop reference curve sample library, simultaneously combining MODIS local curves to be used as MODIS candidate curves to participate in curve matching, determining an initial fitting curve, determining a final fitting curve, and obtaining a L andsat final fitting curve by performing secondary fitting smoothing on local differences existing between the L andsat initial fitting curve and an original L andsat time sequence NDVI image, thereby simultaneously obtaining high-space-time resolution vegetation index data with MODIS time resolution and L andsat spatial resolution.
Description
Technical Field
The invention relates to the technical field of agricultural remote sensing, in particular to a method for reconstructing a vegetation index with high space-time resolution based on a crop reference curve, which is suitable for agricultural remote sensing monitoring research of different farmland systems.
Background
However, remote sensing data acquired by the earth observation satellite have the problem of mutual restriction on high time resolution and high spatial resolution, while remote sensing monitoring represented by field scale research areas has higher requirements on time resolution and spatial resolution, so that a specific method is required to generate space-time resolution remote sensing data, L and MODIS images are taken as examples, the L and MODIS images have medium and high spatial resolution of 30m, can better meet the requirements of field scale remote sensing monitoring, but the 16-day reentry period and the more serious cloud coverage problem greatly limit the capability of L and MODIS images for monitoring rapid earth surface changes, and on the contrary, the MODIS images have the capability of acquiring data every day, but the lower spatial resolution (250 m) is difficult to realize the monitoring of a complex farmland system.
In order to meet the requirement of the remote sensing monitoring on the high-spatial-temporal-resolution data, if the requirement is simply considered from the perspective of a data source, more earth observation satellites with different application characteristics can be emitted, so that enough high-spatial-temporal-resolution images can be obtained, but the method needs more advanced software and hardware technologies, huge research and development production cost and the like, and has no practical operability. Therefore, based on the existing multi-source remote sensing image, the method for obtaining the high-space-time resolution image by utilizing the space-time fusion technology is concerned from the beginning of the proposal, and has great development and application. The technology well combines the time, space and spectral characteristics of different sensors, simulates the corresponding relation between the high-time low-time resolution data and the high-time low-space resolution data acquired by different satellites in the same region through an algorithm, generates an image with high time-space resolution by fusion, and well solves the problem of limitation of inconsistent time-space resolution on the remote sensing monitoring efficiency and precision. In the development process of recent decades, a space-time fusion classical algorithm STARFM has a good application effect in a plurality of fields such as vegetation dynamic change monitoring, crop phenological information extraction, water resource monitoring, surface temperature monitoring and the like, and a series of improved algorithms such as STAARCH, ESTARFM, FSDAF and the like are generated at the same time. The theoretical basis of such a spatio-temporal fusion algorithm is that, for high and low spatial resolution images in the same time period in the same region, the image features of the high and low spatial resolution images in the same time phase are in one-to-one correspondence, so that continuous time phase change features on the low spatial resolution image can be mapped onto the high spatial resolution image, and the defect of lack of time phase change information caused by a long acquisition period of the high spatial resolution image is overcome, so that spatial structure information provided by the high spatial resolution image and time dimension information provided by the low spatial resolution image are well combined, and image data with high spatio-temporal resolution are generated. However, the space-time fusion method has some limitations, and a low-spatial-resolution image which provides time dimensional information at first has a more serious mixed pixel phenomenon, so that the obtained space-time fusion product has higher space-time resolution, but the accuracy is often not high when ground feature identification and classification are carried out, misjudgment is easy to occur, and the algorithm is particularly suitable for remote sensing monitoring and identification of a spatial homogeneous region, but is not suitable for a region with larger spatial heterogeneity, and the classification identification of linear ground features, small ground features or irregular ground features is greatly influenced. Secondly, the space-time fusion algorithm needs to match and fit a plurality of high-space low-time-resolution images and low-space high-time-resolution images acquired under a clear air condition, the low-space-resolution images have higher time resolution and can meet the requirement of the number of available images under the clear air condition, but the high-space-resolution images are easily influenced by factors such as cloud coverage due to longer acquisition period, so that the number of the images available for matching is limited. Especially in areas with cloudy, rainy weather, the available high spatial resolution images are often fewer, and the error of the fusion result is larger.
Through the existing research, 84 percent of the areas of the global agricultural production units are all less than 0.02km2(approximately 140 x 140m,) while different regions have certain regional differences, for example, the area of the American agricultural production unit is relatively large, the average value and the median value can reach 0.193km2 and 0.278km2, and the area of more than half of the field in part of African countries is less than 0.004km2(approximately 63 x 63m), while such smaller area fields typically have higher spatial heterogeneity.Therefore, when agricultural production conditions of field scales are monitored by using a remote sensing means, a common space-time fusion algorithm has great limitation and is not ideal in application effect. Meanwhile, considering that the agricultural production units of the field scale are widely distributed in the global scope, the remote sensing method for realizing the accurate monitoring of the agricultural production activities of the field scale has very important significance for reducing global poverty and guaranteeing the grain safety.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a method for carrying out high-space-time resolution vegetation index data fusion based on a crop reference curve aiming at the defects of the prior art, and carry out space-time fusion reconstruction based on a multi-source remote sensing image to obtain high-space-time resolution vegetation index data which are suitable for remote sensing monitoring of a complex farmland system with high spatial heterogeneity.
In order to solve the technical problem, the invention provides a method for carrying out high-space-time resolution vegetation index data fusion based on a crop reference curve.
A method for conducting high-spatial-temporal-resolution vegetation index data fusion based on crop reference curves comprises the steps of firstly, constructing a crop reference curve sample library, obtaining a plurality of MODIS reference curves of different crops based on pure pixels by utilizing crop category information and spatial positions of a crop classification map CD L to form an MODIS crop reference curve sample library, meanwhile, combining MODIS local curves to be used as MODIS candidate curves to participate in curve matching, secondly, determining an initial fitting curve, conducting fitting on pixel values of L andsat time sequence NDVI images and image pixel values of all curve corresponding positions in the MODIS candidate curves, finding a candidate curve with the highest fitting degree to serve as an optimal reference curve based on the MODIS crop reference curves, then conducting fitting on L andsat time sequence NDVI images by utilizing fitting parameters of the optimal reference curve to obtain L andsat initial curves, thirdly, determining a final fitting curve, obtaining MODdsat initial fitting curves by conducting fitting on L andsat initial fitting curves and original L dsat time sequence NDVI images, obtaining MODisat spatial-spatial resolution and high-spatial-resolution data by conducting fitting on secondary mean time sequence NDVI images and obtaining MODIS data with high spatial resolution.
The method comprises the following steps:
a, setting a moving window with the size of 25 × 25 pixels by utilizing crop category information in a crop classification map CD L, and through the moving pixel window, when more than 95% of pixels in the window belong to a single crop category in the crop classification map, considering the window pixels as pure pixels and acquiring the position information of the CD L pure pixel window;
b, resampling and reprojection preprocessing are carried out on the MODIS data with low spatial resolution, so that the MODIS data has the same spatial resolution and projection information with high spatial resolution L andsat data and crop classification map CD L data, geometric registration is carried out on the MODIS data and the L andsat data, and then MODIS time sequence vegetation index NDVI data are obtained through calculation;
and step C, selecting a position corresponding to a part of pixel area in the middle of the window on the MODIS time sequence vegetation index NDVI data by using the position information of the CD L pure pixel window obtained in the step A, extracting an NDVI average value of pixels contained in the position to obtain a plurality of MODIS time sequence NDVI curves of different types of crops, eliminating the abnormal values of the part of curves by adopting a linear interpolation method, and constructing a sample library by using the abnormal values as MODIS reference curves of different ground objects.
The method comprises the following steps:
step D: obtaining a time-series vegetation index NDVI curve of each pixel of the MODIS image by using the original value of the MODIS time-series vegetation index NDVI data obtained in the step B, referring to the MODIS local curve of the pixel, then comprehensively considering the MODIS local curve and the MODIS reference curve sample library obtained in the step C, and combining the MODIS local curve and the MODIS reference curve sample library together to be used as an MODIS candidate curve to participate in curve matching;
step E, utilizing L andsat time sequence NDVI data obtained by calculation in the step B to analyze the correlation indexes R of all image pixels of the data and all curves in the MODIS crop reference curve sample library obtained in the step C2The size of the data and the correlation index R of all image pixels of the L andsat time sequence NDVI data and the MODIS local curve obtained in the step D2Size, correlation index R2The largest curve is used as the MODIS optimal reference curve of the image pixels of L andsat time series NDVI data, and then L andsat time series NDVI data in the step B and the MODIS optimal reference curve are further fitted through a formula (1) to obtain a L andsat fitting curve.
L(x)＝a*M(x+x0)+b (1)
Wherein x represents a day of the year, L (x) is a L andasat time series NDVI curve function, M (x) is an MODIS optimal reference curve function, x0 represents the offset in time which may exist between two curves, the offset is within +/-30 days, and a and b are fitting parameters calculated by using a least square method;
step F, calculating a correlation index R between the L andsat time sequence NDVI image pixel value in the step B and all candidate curve pixel values of the corresponding position MODIS2Analysis of the correlation index R between the two2Selecting an optimal MODIS reference curve;
and G, confirming an optimal reference curve by using the steps E and F, wherein the optimal time offset x0 is 0, obtaining corresponding fitting parameters a and b simultaneously, and then obtaining a L andsat initial fitting curve by using the MODIS optimal reference curve and the corresponding fitting parameters.
Step H, calculating a difference value between an original L andsat time sequence NDVI pixel value (clear sky pixel) and a corresponding pixel NDVI fitting value on a L andast initial fitting curve, fitting a linear interpolation result to the initial fitting curve by adopting a linear interpolation method to reduce the difference between the two, and finally utilizing a Gaussian function in an ID L programming platform to further smooth the optimized fitting curve to enable the curve to contain all available original L andsat time sequence NDVI values as much as possible, so that the curve is called a final fitting curve.
The method, step F, has 60 correlation indexes R for each MODIS crop reference curve2The value range is concentrated between 0.11 and 0.92, then the candidate curve with the maximum correlation index is selected as the curve to be matched of the MODIS crop reference curve, the correlation index between the curve to be matched corresponding to all the MODIS crop reference curves and the L andsat time sequence NDVI data is compared, the value range of the correlation index between the MODIS local curve and the L andsat time sequence NDVI data is concentrated between 0.78 and 0.92, if the MODIS local curve correlation index R is2If the value is maximum, directly taking the MODIS local curve as the optimal reference curve; if the correlation index R2If the maximum value appears in the curve to be matched of the MODIS crop reference curve, calculating all the candidate curve correlation indexes R2Is selected to be higher than R2And averaging the candidate curves of the average value again, and taking the finally obtained average curve as an optimal reference curve.
In the method, MODIS reference curves of different ground objects are obtained by using a pure pixel region selected by a result with higher classification precision in known crop classification map CD L data, and the specific setting parameters in the step A are empirical parameters according to multiple experiments.
Has the advantages that:
the invention provides a data space-time fusion method based on a crop reference curve according to the characteristic that vegetation index data with different spatial resolutions of different sensor images have a linear relation, which is suitable for remote sensing monitoring of a complex farmland system with higher spatial heterogeneity.A clear pixel area of different types of crops is determined by setting a moving window by using known crop classification layer CD L data, an MODIS crop reference curve sample library is constructed based on the clear pixel area, an initial fitting curve is obtained by comparing L andsat image pixel time sequence NDVI data with an MODIS crop reference curve and the correlation between MODIS corresponding pixel original NDVI data local curves, the effect of L andsat original fitting curves in curve fitting is retained to the maximum extent by methods of linear interpolation, Gaussian function fitting and the like, the phenomenon of initial excessive fitting is further reduced, a final fitting curve is obtained, a high time and high space resolution combined vegetation index data set with high spatial resolution is generated, the problem that the vegetation index data with high spatial resolution and high spatial resolution combined vegetation index data with high spatial resolution are combined is generated by using a traditional vegetation index data set with high spatial resolution, the maximum limit of the high time resolution and high spatial resolution combined vegetation index set is retained, the problem that the optimal image data can be generated by using a high spatial resolution and the high spatial resolution combined image data, the high spatial resolution combined with the high spatial resolution and the high spatial heterogeneity is reduced.
Drawings
FIG. 1 is a MODIS reference graph of four typical features; the upper left is a forest, the upper right is a double-season crop, the lower left is corn, and the lower right is soybean;
FIG. 2 is a plot of MODIS reference curves, best reference curves, initial fit curves, final fit curves, and L andsat time series NDVI values for four typical features, top left for corn, top right for soybean, bottom left for a dual season crop, and bottom right for a forest, (1) MODIS reference curves, (2) best reference curves, (3) initial fit curves, (4) final fit curves, circles indicate L andsat time series NDVI values;
FIG. 3 is a comparison of NDVI fitting curves based on a crop reference curve and based on STARFM in three crop types in 2013, wherein a is a crop classification chart, graphs (b), (c) and (d) are respectively graphs of corn, soybean and double-season crops corresponding to the positions of dots in the graph (a), the dots in the graphs (b), (c) and (d) are time-series L andsat images, the large dots are STARFM paired images, (1) is a fitting curve based on a crop reference curve method, (2) is a fitting curve based on a STARFM method, and (3) is an MODIS reference curve;
Detailed Description
The present invention will be described in detail with reference to specific examples.
The invention provides a method for carrying out high-space-time resolution vegetation index data fusion based on a crop reference curve, which comprises the following steps:
s1, the research area of the embodiment is located in a Choptank river basin of the east coast of the United states, including partial areas of Maryland, Telawa and New Jersey, the land utilization mode of the area is mainly 58% of agricultural land, 33% of forest and only 9% of urban construction land, the most common crop planting system comprises corn-soybean rotation and double-season planting of soybean-winter wheat, the data of the crop classification maps CD L in 2013 and 2014 are taken as reliable crop distribution data, then a moving window with the size of 25 × 25 pixel elements is set, and when more than 95% of the pixel elements in the window belong to a single crop category in the crop classification map CD L through the moving pixel element window, the window is considered as a pure pixel element window, and the position information of the CD L pure pixel element window is obtained at the same time, and the research embodiment mainly selects the pure windows of four categories of land objects such as corn, soybean, double-season crop (winter wheat and soybean) and forest;
s2, using MRT (MODIS reproduction tool) to preprocess MODIS data (MCD43A4 collection 6 with resolution of 500m, time range from 2013, 1/1 to 2014, 12/31/10/7/image matrix h12v05) with low spatial resolution by resampling and Reprojection, so that the spatial resolution and projection information are the same as those of high spatial resolution L and crop classification CD L data, resampling method is bilinear interpolation, Reprojection is carried out by UNIVERSA L TRANSVERSE MERCARTOR GRIDSYSTEM (universal transverse axis ink card support grid system), MODIS data and L and data are automatically geometrically registered (automatic interpolation method), MODIS time series vegetation index NDVI data is obtained by calculation, meanwhile, high spatial resolution L and data used in the research comprise 2013 and annual polarization method, 20184, and 18-19-18-19-18-17-18-19-18-19-18-16-19-18-14-16-18-16-19-18-14-16-7-16-18-7-16-7-eight-7-16-eight-7-eight-three-four;
s3, selecting a position on the MODIS time sequence vegetation index NDVI data corresponding to a part of pixel area (16 x 16 pixels) in the middle of the CD L pure pixel window by using the position information of the CD L pure pixel window obtained in the step S1, and extracting to obtain an NDVI average value of pixels contained in the position, so that a plurality of MODIS time sequence NDVI curves of different types of crops can be obtained according to the number of the pure pixel windows of the different types of crops, in the research example, 23 corn curves, 1 soybean curve, 5 double-season crop (winter wheat and soybean) curves and 26 forest curves are obtained in 2013, 24 corn curves, 3 soybean curves, 10 double-season crop (winter wheat and soybean) curves and 23 forest curves are obtained in 2014, wherein the abnormal values still exist in the partial curves, the abnormal values are eliminated by adopting a linear interpolation method in the example, and the abnormal values are used as the MODIS crop reference curves of the different crops to construct a MODIS reference curve sample library, and a typical MODIS reference curve graph is shown in 2014 1;
s4, obtaining a time-series vegetation index NDVI curve of each pixel of the MODIS image by using the original value of the MODIS time-series vegetation index NDVI data obtained by calculation in the step S2, namely the time-series vegetation index NDVI curve of the pixel, namely the MODIS local curve of the pixel, wherein the L andsat image pixel and the corresponding MDOSI image pixel may both be pure pixels, and the NDVI curves of the two pixels have good correlation or both are mixed pixels but still have good correlation, so that the MODIS local curve and the MODIS crop reference curve sample library obtained in the step S3 are comprehensively considered in the research, and the MODIS local curve and the MODIS crop reference curve sample library are combined together to be used as a candidate curve to participate in curve matching;
s5, analyzing all image pixels of the data, all curves in the MODIS crop reference curve sample library obtained in the step S3 and all curves in the MODIS crop reference curve sample library obtained in the step S4 by using the L andsat time series NDVI data obtained in the step S2Correlation index R of MODIS local curve2Size, correlation index R2The maximum curve is used as the MODIS optimal reference curve of the image pixels of L andsat time-series NDVI data, and then L andsat time-series NDVI data and the MODIS optimal reference curve in step S2 are further fitted by formula (1), so as to obtain a L andsat fitting curve.
L(x)＝a*M(x+x0)+b (1)
Wherein x represents a day of the year, L (x) is a L andasat time series NDVI curve function, M (x) is an MODIS optimal reference curve function, x0 represents the offset in time which may exist between two curves, the offset is within +/-30 days, and a and b are fitting parameters calculated by using a least square method;
s6, calculating the correlation index R between the L andsat time sequence NDVI image pixel value and all candidate curve pixel values of the corresponding position MODIS in the step S22Analysis of the correlation index R between the two2And (5) sizing and selecting an optimal MODIS reference curve. For each MODIS crop reference curve, there are 60 correlation indexes R2The value range is concentrated between 0.11 and 0.92, then the candidate curve with the maximum correlation index is selected as the curve to be matched of the MODIS crop reference curve, the correlation index between the curve to be matched corresponding to all the MODIS crop reference curves and the L andsat time sequence NDVI data is compared, the value range of the correlation index between the MODIS local curve and the L andsat time sequence NDVI data is concentrated between 0.78 and 0.92, if the MODIS local curve correlation index R is2If the value is maximum, directly taking the MODIS local curve as the optimal reference curve; if the correlation index R2If the maximum value appears in the curve to be matched of the MODIS crop reference curve, calculating all the candidate curve correlation indexes R2Is selected to be higher than R2As shown in FIG. 2, the black dots are the time-series NDVI values of L andsat calculated in step S2, the curve (1) is the MODIS reference curve obtained in step S3, and the curve (2) is the MODIS best reference curve obtained by fitting the steps S5 and S6；
S7, confirming an optimal reference curve by utilizing the steps S5 and S6, wherein the optimal time offset x0 is 0, corresponding fitting parameters a and b are obtained simultaneously, and then obtaining L andsat initial fitting curve by utilizing the MODIS optimal reference curve and the corresponding fitting parameters, as shown in FIG. 2, the curve (3) is an initial fitting curve, and the initial fitting curve and the L andsat time sequence NDVI value of the target pixel area have better fitting relation, but still part of L andsat original NDVI value can not be well fitted on the initial fitting curve;
s8, obtaining a difference value between corresponding pixel NDVI fitting values on a L andast initial fitting curve by calculating an original L andsat time sequence NDVI pixel value (clear sky pixel) and the step S7, fitting a linear interpolation result to the initial fitting curve by using a linear interpolation method to reduce the difference between the two fitting values, and finally further smoothing the optimized fitting curve by using a Gaussian function in an ID L programming platform to enable the curve to contain all available original L andsat time sequence NDVI values as much as possible, so that the curve is called a final fitting curve, as shown in FIG. 2, a curve (4) is an L andsat final fitting curve, and at the moment, the L andsat time sequence NDVI value and the L andsat final fitting curve have a good fitting relationship.
In order to compare the effects of the data reconstruction method based on the crop reference curve in the present research, a comparison experiment is performed by using a space-time fusion classical algorithm STARFM at the same time, and the algorithm needs to fit a plurality of paired images obtained under a clear air condition, as shown in fig. 3, the model is a NDVI fitted curve of three different crops obtained in a research area 2013 based on the crop reference curve and based on the STARFM algorithm, wherein the curve (3) is a MODIS reference curve, the large black dots are cloud-free paired images available for the STARFM algorithm, the curve (2) is a fitted curve obtained based on the STARFM algorithm, the small black dots are all L andsat images available for the reconstruction method based on the crop reference curve adopted in the present research, and the curve (1) is a fitted curve obtained based on the reconstruction method of the crop reference curve.
In conclusion, the research sets a pure pixel area through known crop classification data, obtains a crop reference curve based on low spatial resolution by using a pure pixel concept, then performs fitting conversion and secondary optimization adjustment on the high spatial resolution image and the crop reference curve through fitting modes such as translation and stretching, well converts time dimension information of the low spatial resolution crop reference curve to the high spatial resolution image, realizes high spatial and temporal resolution vegetation index data fusion reconstruction based on the crop reference curve, can well solve the problems of interference and limitation of factors such as mixed pixels and cloud coverage on data fusion of areas with high spatial heterogeneity by an original data space-time fusion algorithm, and has important significance for remote sensing monitoring of farmland systems of fields with different complexity degrees.
It will be understood that modifications and variations can be made by persons skilled in the art in light of the above teachings and all such modifications and variations are intended to be included within the scope of the invention as defined in the appended claims.
Claims (4)
1. A method for carrying out high spatial-temporal resolution vegetation index data fusion based on a crop reference curve is characterized by comprising the first step of constructing a crop reference curve sample library, obtaining a plurality of MODIS reference curves of different crops based on pure pixels by utilizing crop category information and spatial positions of a crop classification diagram, forming an MODIS crop reference curve sample library, and simultaneously combining MODIS local curves to be used as MODIS candidate curves to participate in curve matching, the second step of determining an initial fitting curve, fitting and matching pixel values of L andsat time sequence NDVI images and image pixel values of all corresponding positions of all curves in the MODIS candidate curves, finding a candidate curve with the highest fitting degree as an optimal reference curve based on the MODIS crop reference curve, fitting L andsat time sequence NDVI images by utilizing fitting parameters of the optimal reference curve to obtain L andsat initial fitting curves, the third step of determining a final fitting curve, fitting and smoothing the final fitting curve to obtain MODisat time resolution and space resolution after fitting of a secondary vegetation index with high spatial resolution, so that the MODIS L and space resolution are obtained;
the first step comprises the following steps:
step A: setting a moving window with the size of 25 × 25 pixels by utilizing the crop category information in the crop classification map, and when more than 95% of pixels in the window belong to a single crop category in the crop classification map through the moving pixel window, considering the window pixel as a pure pixel and acquiring the position information of the pure pixel window of the crop classification map;
b, resampling and reprojection preprocessing are carried out on the MODIS data with low spatial resolution, so that the MODIS data has the same spatial resolution and projection information as the L andsat data with high spatial resolution and the crop classification map data, geometric registration is carried out on the MODIS data and the L andsat data, and then the data are calculated to obtain the NDVI data of the MODIS time sequence vegetation index;
and C: b, selecting positions corresponding to part of pixel areas in the middle of the window on the MODIS time sequence vegetation index NDVI data by using the position information of the pure pixel windows of the crop classification images obtained in the step A, and extracting to obtain NDVI average values of pixels contained in the positions to obtain a plurality of MODIS time sequence NDVI curves of different types of crops; for the abnormal values of part of curves, a linear interpolation method is adopted to eliminate the abnormal values, and the curves are used as MODIS reference curves of different ground objects to construct a sample library;
the second step comprises the following steps:
step D: obtaining a time-series vegetation index NDVI curve of each pixel of the MODIS image by using the original value of the MODIS time-series vegetation index NDVI data obtained in the step B, referring to the MODIS local curve of the pixel, then comprehensively considering the MODIS local curve and the MODIS reference curve sample library obtained in the step C, and combining the MODIS local curve and the MODIS reference curve sample library together to be used as an MODIS candidate curve to participate in curve matching;
step E, utilizing L andsat time sequence NDVI data obtained by calculation in the step B to analyze the correlation indexes R of all image pixels of the data and all curves in the MODIS crop reference curve sample library obtained in the step C2The size of the data and the correlation index R of all image pixels of the L andsat time sequence NDVI data and the MODIS local curve obtained in the step D2Size, correlation index R2The maximum curve is used as an MODIS optimal reference curve of an image pixel of L andsat time series NDVI data, and then L andsat time series NDVI data in the step B and the MODIS optimal reference curve are further fitted through a formula (1) to obtain a L andsat fitting curve;
L(x)＝a*M(x+x0)+b (1)
wherein x represents a day of the year, L (x) is a L andasat time series NDVI curve function, M (x) is an MODIS optimal reference curve function, x0 represents the offset in time which may exist between two curves, the offset is within +/-30 days, and a and b are fitting parameters calculated by using a least square method;
step F, calculating a correlation index R between the L andsat time sequence NDVI image pixel value in the step B and all candidate curve pixel values of the corresponding position MODIS2Analysis of the correlation index R between the two2Selecting an optimal MODIS reference curve;
and G, confirming an optimal reference curve by using the steps E and F, wherein the optimal time offset x0 is 0, obtaining corresponding fitting parameters a and b simultaneously, and then obtaining a L andsat initial fitting curve by using the MODIS optimal reference curve and the corresponding fitting parameters.
2. The method of claim 1, wherein step three comprises the steps of step H of calculating the difference between the original L andsat time series NDVI pixel values and the corresponding pixel NDVI fitted values on the L andast initial fitted curve obtained in step G, fitting the linear interpolation result to the initial fitted curve by linear interpolation to reduce the difference between the two, and finally smoothing the optimized fitted curve by using a Gaussian function in the ID L programming platform to make the curve contain all available original L andsat time series NDVI values as much as possible, and then calling the curve as a final fitted curve.
3. The method as claimed in claim 2, wherein in step F, there are 60 correlation indices R for each MODIS crop reference curve2The value range is concentrated between 0.11 and 0.92, then the candidate curve with the maximum correlation index is selected as the curve to be matched of the MODIS crop reference curve, the correlation index between the curve to be matched corresponding to all the MODIS crop reference curves and the L andsat time sequence NDVI data is compared, the value range of the correlation index between the MODIS local curve and the L andsat time sequence NDVI data is concentrated between 0.78 and 0.92, if the correlation index R of the MODIS local curve is concentrated between 0.11 and 0.922If the value is maximum, directly taking the MODIS local curve as the optimal reference curve; if the correlation index R2If the maximum value appears in the curve to be matched of the MODIS crop reference curve, calculating all the candidate curve correlation indexes R2Is selected to be higher than R2And averaging the candidate curves of the average value again, and taking the finally obtained average curve as an optimal reference curve.
4. The method according to claim 1, wherein the MODIS reference curves of different land features are obtained by using the selected pure pixel areas with higher classification precision results in the known crop classification map data, and the specific setting parameters in the step A are empirical parameters according to a plurality of experiments.
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