LU501209B1 - Method and apparatus for improving spatial resolution of data, computer-readable storage medium, and terminal device - Google Patents
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Abstract
The present disclosure provides a method and apparatus for improving a spatial resolution of data, a storage medium, and a terminal device. The method includes: preprocessing acquired AVHRR NDVI data and MODIS NDVI data within a preset time period to acquire monthly scale data, AVHRR referring to advanced very high resolution radiometer, NDVI referring to normalized difference vegetation index, and MODIS referring to moderate resolution imaging spectroradiometer; inputting the monthly scale data into a scale transformation model for downscaling; and subjecting the downscaled data to error analysis, and optimizing the scale transformation model according to an analysis result. The present disclosure can transform a low-resolution spatial scale of AVHRR NDVI data in a region into a high-resolution spatial scale of MODIS NDVI data, thereby improving data accuracy and availability.
Description
TECHNICAL FIELD The present disclosure relates to the technical field of remote sensing (RS) image recognition, and in particular to a method and apparatus for improving a spatial resolution of data, a computer-readable storage medium, and a terminal device.
BACKGROUND Vegetation is linked with natural elements such as climate, soil and landform. The vegetation index (VI) is a parameter indicating the growth and biomass of vegetation, which can help people better explain the evolution of vegetation. Currently, the VI models include ratio vegetation index (RVI), difference vegetation index (DVI), normalized difference vegetation index (NDVI), etc. Among them, NDVI is the most widely used.
The advanced very high resolution radiometer (AVHRR) NDVI data is currently the global consecutive data set with the longest coverage period, and it has received extensive attention at home and abroad. In particular, the global inventory modeling and mapping studies (GIMMS) NDVI data set has become the most widely used data set among the AVHRR NDVI data due to its long time series, wide coverage, time and space comparability, and strong dynamic vegetation change characterization capabilities. It is widely used in fields such as regional to global scale dynamic vegetation change detection and cause analysis, regional land degradation identification, vegetation productivity simulation and carbon balance research, deepening people's understanding of dynamic vegetation changes.
However, the AVHRR is not specially designed for vegetation research. For dynamic vegetation research, it has problems such as lack of airborne calibration, transit time drift, low spatial resolution, wide band settings, and susceptibility to water vapor interference.
SUMMARY A technical problem to be solved by embodiments of the present disclosure is to provide a method and apparatus for improving a spatial resolution of data, a computer-readable storage medium, and a terminal device. The present disclosure can transform a low-resolution spatial scale of AVHRR NDVI data in a region into a high-resolution spatial scale of moderate resolution imaging spectroradiometer (MODIS) NDVI data in a region, thereby improving data accuracy and availability.
In order to solve the above technical problem, an embodiment of the present disclosure provides a method for improving a spatial resolution of data. The method includes: preprocessing acquired AVHRR NDVI data and MODSI NDVI data within a preset time period to acquire monthly scale data; inputting the monthly scale data into a scale transformation model for downscaling; and subjecting the downscaled data to error analysis, and optimizing the scale transformation model according to an analysis result.
Further, the scale transformation model may be acquired as follows: acquiring coarse-resolution temporal projection information of the AVHRR NDVI data through a relative change rate of vegetation between each month and a base period; calculating coefficients of variation (CVs) of a high-resolution MODIS NDVI and a coarse-resolution AVHRR NDVI according to respective standard deviations and means, and characterizing high-resolution spatial projection information by a ratio of the two CVs; and acquiring the scale transformation model according to a random difference value, the coarse-resolution temporal projection information and the high-resolution spatial projection information.
Further, the scale transformation model may be: NDVI eye = NDVIyxypi X (14 Kaye X Revxy) + E xy. Revx,y = Modis_CV /avhrr_CV Kyye= (NDVI eye — NDVI xypi) / NDVI xy pi where, NDVI, is high-resolution NDVI of pixels x, y and time t after downscaling; NDVI, yp 1s a median of AVHRR NDVI of the pixels x and y in a baseline period; modis_CV,avhrr_CV are the CVs of the MODIS NDVI and the AVHRR NDVI; Ry, , is the ratio of the CVs; and Exp is the random difference value.
Further, the preprocessing acquired AVHRR NDVI data and MODSI NDVI data within a preset time period may include: acquiring the AVHRR NDVI data and the MODSI NDVI data within the preset time period;
and subjecting the AVHRR NDVI data and the MODSI NDVI data to cleaning and correction to acquire monthly scale data.
In order to solve the above technical problem, an embodiment of the present disclosure further provides an apparatus for improving a spatial resolution of data.
The apparatus includes: a data processing module, configured to preprocess acquired AVHRR NDVI data and MODSI NDVI data within a preset time period to acquire monthly scale data; a scale processing module, configured to input the monthly scale data into a scale transformation model for downscaling; and a model optimization module, configured to subject the downscaled data to error analysis, and optimize the scale transformation model according to an analysis result.
Further, the scale processing module may be configured to: acquire coarse-resolution temporal projection information of the AVHRR NDVI data through a relative change rate of vegetation between each month and a base period; calculate CVs of a high-resolution MODIS NDVI and a coarse-resolution AVHRR NDVI according to respective standard deviations and means, and characterize high-resolution spatial projection information by a ratio of the two CVs; and acquire the scale transformation model according to a random difference value, the coarse-resolution temporal projection information and the high-resolution spatial projection information.
Further, the scale transformation model may be: NDVIyxye = NDVIgyym X (1+ Kaye X Revxy) + Exyt Revx,y = Modis_CV /avhrr_CV Keye= (NDVI eye — NDVI yp) / NDVI sy pi where, NDVI, is high-resolution NDVI of pixels x, y and time t after downscaling; NDVI, yp 1s a median of AVHRR NDVI of the pixels x and y in a baseline period; modis_CV,avhrr_CV are the CVs of the MODIS NDVI and the AVHRR NDVI, Reyx,y is the ratio of the CVs; and Éx34 is the random difference value.
Further, the data processing module may be configured to:
acquire the AVHRR NDVI data and the MODSI NDVI data within the preset time period; and subject the AVHRR NDVI data and the MODSI NDVI data to cleaning and correction to acquire monthly scale data.
An embodiment of the present disclosure further provides a computer-readable storage medium. The computer-readable storage medium includes a computer program, and the computer program is run to control a device where the computer-readable storage medium is located to implement the above method.
An embodiment of the present disclosure further provides a terminal device. The terminal device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, where the processor executes the computer program to implement the above method.
The embodiments of the present disclosure provide a method and apparatus for improving a spatial resolution of data, a computer-readable storage medium, and a terminal device. The present disclosure preprocessed acquired AVHRR NDVI data and MODSI NDVI data within a preset time period to acquire monthly scale data, inputs the monthly scale data into a scale transformation model for downscaling, and subjects the downscaled data to error analysis, and optimizes the scale transformation model according to an analysis result. Compared with the prior art, the present disclosure can transform a low-resolution spatial scale of AVHRR NDVI data in a region into a high-resolution spatial scale of MODIS NDVI data, thereby improving data accuracy and availability.
BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a flowchart of a method for improving a spatial resolution of data according to the present disclosure; FIG. 2 shows downscaling of the method for improving a spatial resolution of data according to the present disclosure; FIG. 3 shows error analysis of the method for improving a spatial resolution of data according to the present disclosure; FIG. 4 is a block view of an apparatus for improving a spatial resolution of data according to an embodiment of the present disclosure; and FIG. 5 is a block view of a terminal device according to the present disclosure.
> LU501209
DETAILED DESCRIPTION The technical solutions in the embodiments of the present disclosure are described clearly and completely below with reference to the drawings in the embodiments of the present disclosure. Apparently, the described embodiments are merely a part rather than all of the embodiments of the present disclosure. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present disclosure without creative efforts should fall within the protection scope of the present disclosure.
It should be noted that step numbers in this specification are only intended to facilitate explanation of specific embodiments, and are not used to limit the sequence of steps. The method provided in the embodiment may be executed by a related server, and the following description is made by taking the server as an execution subject.
An embodiment of the present disclosure provides a method for improving a spatial resolution of data. As shown in FIGS. 1 to 3, the method includes steps S11 to S13: S11: Preprocess acquired AVHRR NDVI data and MODSI NDVI data within a preset time period to acquire monthly scale data.
Specifically, the AVHRR NDVI data and the MODSI NDVI data within the preset time period are acquired, and the AVHRR NDVI data and the MODSI NDVI data are subjected to cleaning and correction to acquire monthly scale data.
Further, the acquired AVHRR NDVI data is input into an AVHRR NDVI data cleaning model to clear empty data in the AVHRR NDVI data, and after the empty data is cleared, the data with a larger deviation is replaced. The replacement data is data predicted by an AVHRR NDVI data model or actual data, and the AVHRR NDVI data model is iteratively trained by a neural network.
Further, the acquired MODIS NDVI data is input into an MODIS NDVI data cleaning model to clear empty data in the MODIS NDVI data, and after the empty data is cleared, the data with a larger deviation is replaced. The replacement data is data predicted by MODIS NDVI data model or actual data, and the MODIS NDVI data model is iteratively trained by a neural network. S12: Input the monthly scale data into a scale transformation model for downscaling.
Specifically, coarse-resolution temporal projection information of AVHRR NDVI data is acquired through a relative change rate of vegetation between each month and a base period. Coefficients of variation (CVs) of a high-resolution MODIS NDVI and a coarse-resolution AVHRR NDVI are calculated according to respective standard deviations and means, and high-resolution spatial projection information is characterized by a ratio of the two CVs. The scale transformation model is acquired according to a random difference value, the coarse-resolution temporal projection information and the high-resolution spatial projection information.
The scale transformation model 1s: NDVIgyyr = NDVIg,yp X (14 Koyt X Revay) + E xy Revx,y = Modis_CV /avhrr_CV Keye= (NDVI xye — NDVILx,y,p1) / NDVI yp where, NDVI, is high-resolution NDVI of pixels x, y and time t after downscaling; NDVI, yp 1s a median of AVHRR NDVI of the pixels x and y in a baseline period; modis_CV,avhrr_CV are the CVs of the MODIS NDVI and the AVHRR NDVI, Reyx,y is the ratio of the CVs; and Exp is the random difference value.
Further, considering the time scale, the time change characteristics of the AVHRR NDVI data are expressed by K,y:- First, the median of AVHRR NDVI in a certain reference period of the entire time series is acquired, which represents a medium level of AVHRR NDVI in the reference period. A difference between the AVHRR NDVI of each month minus the median of the reference period is divided by the median of the reference period to obtain a dynamic NDVI change in each month during the entire data observation period, that is, a change rate Ky This value expresses the coarse-resolution AVHRR temporal projection information.
The information transformation is a very average time transformation, just a time change on the 1 km * 1 km spatial scale. In order to acquire more refined spatial information of different plant types, it is necessary to consider MODIS NDVI data. The MODIS spatial information is more refined, which can further distinguish the NDVI changes of different feature types in the pixel.
The MODIS NDVI data has a higher spatial resolution and a shorter time series. Assuming that the vegetation information is basically unchanged, that is, the spatial information does not change much, the spatial information contained in the MODIS NDVI data 1s projected onto the time period of the previous twenty years or so. That is, the spatial information of the MODIS NDVI data is assigned to the AVHRR NDVI data, thereby making it more refined on the spatial scale.
The CVs of MODIS NDVI and AVHRR NDVI are considered to realize the spatial projection information. They indicate the degree of dispersion of the data, which is equal to the ratio of a standard deviation to a mean. In order to express the difference of spatial information between AVHRR NDVI and MODIS NDVI, a parameter, namely Rcyyy, is set, that is, the CV of MODIS NDVI divided by that of AVHRR NDVI. Because the MODIS NDVI has a higher resolution, it has a larger CV, and a larger overall change. On the contrary, the AVHRR NDVI has a smaller CV, and a relatively average overall change. Therefore, through the ratio Rcvxy between the two CVs, the change is adjusted. For example, when there are more uniform water bodies and bare ground in some grids, the CV of the NDVI will be very small, and the change is reduced by this ratio. Conversely, for grids with obvious seasonal characteristics that contain more vegetation, the degree of change in the NDVI is correspondingly enlarged. Correspondingly, the part of special terrain contained in all grid images are zoomed in and zoomed out to acquire more accurate NDVI.
Finally, downscaling calculation is performed through time and space changes. That is, the median of MODIS NDVI in the reference period is multiplied by the time and space change information of AVHRR NDVI to acquire the change data of AVHRR NDVI. Then this change data is added to the median, plus the random difference value to characterize an error generated in the calculation process, thereby generating a downscaled result.
Further, taking the California region of the United States as an example, FIG. 2 shows the NDVI data images of the region in March from 2010 to 2012. The first row shows original AVHRR NDVI data images, the second row shows MODIS NDVI data images, and the third row shows downscaled AVHRR NDVI data images. S13: Subject the downscaled data to error analysis, and optimize the scale transformation model according to an analysis result.
Specifically, as shown in FIG. 3, error analysis is performed on the above results to analyze a root mean square error (RMSE) and a mean absolute error (MAE). These two indicators are used to describe the error between the predicted and true values. The figure shows that the error between the downscaled AVHRR NDVI data and the MODIS NDVI data is very small, indicating that the downscaling results are reliable. When the scale transformation model is optimized according to the analysis result, a constraint may be added or the value range of the adjustment variable may be adjusted. The embodiment of the present disclosure provides a method for improving a spatial resolution of data. The present disclosure preprocessed acquired AVHRR NDVI data and MODSI NDVI data within a preset time period to acquire monthly scale data, inputs the monthly scale data into a scale transformation model for downscaling, and subjects the downscaled data to error analysis, and optimizes the scale transformation model according to an analysis result. Compared with the prior art, the present disclosure can transform a low-resolution spatial scale of AVHRR NDVI data in a region into a high-resolution spatial scale of MODIS NDVI data, thereby improving data accuracy and availability. FIG. 4 is a block view of an apparatus for improving a spatial resolution of data according to the present disclosure. The apparatus includes: a data processing module 21, configured to preprocess acquired AVHRR NDVI data and MODSI NDVI data within a preset time period to acquire monthly scale data. Specifically, the AVHRR NDVI data and the MODSI NDVI data within the preset time period are acquired.
The AVHRR NDVI data and the MODSI NDVI data are subjected to cleaning and correction to acquire monthly scale data. Further, the acquired AVHRR NDVI data is input into an AVHRR NDVI data cleaning model to clear empty data in the AVHRR NDVI data, and after the empty data is cleared, the data with a larger deviation is replaced. The replacement data is data predicted by an AVHRR NDVI data model or actual data, and the AVHRR NDVI data model is iteratively trained by a neural network.
Further, the acquired MODIS NDVI data is input into a MODIS NDVI data cleaning model to clear empty data in the MODIS NDVI data, and after the empty data is cleared, the data with a larger deviation is replaced. The replacement data is data predicted by MODIS NDVI data model or actual data, and the MODIS NDVI data model is iteratively trained by a neural network.
The apparatus further includes: a scale processing module 22, configured to input the monthly scale data into a scale transformation model for downscaling.
Specifically, coarse-resolution temporal projection information of AVHRR NDVI data is acquired through a relative change rate of vegetation between each month and a base period. CVs of a high-resolution MODIS NDVI and a coarse-resolution AVHRR NDVI are calculated according to respective standard deviations and means, and high-resolution spatial projection information is characterized by a ratio of the two CVs.
The scale transformation model is acquired according to a random difference value, the coarse-resolution temporal projection information and the high-resolution spatial projection information.
The scale transformation model is: NDVIyxye = NDVIyxypi X (14 Keye X Revxy) + Exyt Revx,y = Modis_CV /avhrr_CV Keye= (NDVI eye — NDVI yp) / NDVI sy pi where, NDVI, is high-resolution NDVI of pixels x, y and time t after downscaling; NDVI, yp 1s a median of AVHRR NDVI of the pixels x and y in a baseline period, modis_CV,avhrr_CV are the CVs of the MODIS NDVI and the AVHRR NDVI; Reyx,y is the ratio of the CVs; and Éx34 is the random difference value.
Further, considering the time scale, the time change characteristics of the AVHRR NDVI data are expressed by K,y:- First, the median of AVHRR NDVI in a certain reference period of the entire time series is acquired, which represents a medium level of AVHRR NDVI in the reference period. A difference between the AVHRR NDVI of each month minus the median of the reference period is divided by the median of the reference period to obtain a dynamic NDVI change in each month during the entire data observation period, that is, a change rate Ky This value expresses the coarse-resolution AVHRR temporal projection information. The information transformation is a very average time transformation, just a time change on the 1 km * 1 km spatial scale. In order to acquire more refined spatial information of different plant types, it is necessary to consider MODIS NDVI data. The MODIS spatial information is more refined, which can further distinguish the NDVI changes of different feature types in the pixel. The MODIS NDVI data has a higher spatial resolution and a shorter time series. Assuming that the vegetation information is basically unchanged, that is, the spatial information does not change much, the spatial information contained in the MODIS NDVI data is projected onto the time period of the previous twenty years or so. That is, the spatial information of the MODIS NDVI data is assigned to the AVHRR NDVI data, thereby making it more refined on the spatial scale.
The CVs of MODIS NDVI and AVHRR NDVI are considered to realize the spatial projection information. They indicate the degree of dispersion of the data, which is equal to the ratio of a standard deviation to a mean. In order to express the difference of spatial information between AVHRR NDVI and MODIS NDVI, a parameter, namely Rcyyy, is set, that is, the CV of MODIS NDVI divided by that of AVHRR NDVI. Because the MODIS NDVI has a higher resolution, it has a larger CV, and a larger overall change. On the contrary, the AVHRR NDVI has a smaller CV, and a relatively average overall change. Therefore, through the ratio Rcvxy between the two CVs, the change is adjusted. For example, when there are more uniform water bodies and bare ground in some grids, the CV of the NDVI will be very small, and the change is reduced by this ratio. Conversely, for grids with obvious seasonal characteristics that contain more vegetation, the degree of change in the NDVI is correspondingly enlarged. Correspondingly, the part of special terrain contained in all grid images are zoomed in and zoomed out to acquire more accurate NDVI.
Il LU501209 Finally, downscaling calculation 1s performed through time and space changes. That 1s, the median of MODIS NDVI in the reference period is multiplied by the time and space change information of AVHRR NDVI to acquire the change data of AVHRR NDVI. Then this change data is added to the median, plus the random difference value to characterize an error generated in the calculation process, thereby generating a downscaled result. The apparatus further includes: a model optimization module 23, configured to subject the downscaled data to error analysis, and optimize the scale transformation model according to an analysis result. The embodiment of the present disclosure provides an apparatus for improving a spatial resolution of data. The present disclosure preprocessed acquired AVHRR NDVI data and MODSI NDVI data within a preset time period to acquire monthly scale data, inputs the monthly scale data into a scale transformation model for downscaling, and subjects the downscaled data to error analysis, and optimizes the scale transformation model according to an analysis result. Compared with the prior art, the present disclosure can transform a low-resolution spatial scale of AVHRR NDVI data in a region into a high-resolution spatial scale of MODIS NDVI data, thereby improving data accuracy and availability.
An embodiment of the present disclosure further provides a computer-readable storage medium. The computer-readable storage medium includes a computer program, and the computer program is run to control a device where the computer-readable storage medium is located to implement the above method.
An embodiment of the present disclosure further provides a terminal device. FIG. 5 is a block view of the terminal device according to a preferred embodiment of the present disclosure. The terminal device includes a processor 10, a memory 20, and a computer program stored in the memory 20 and configured to be executed by the processor 10, where the processor 10 executes the computer program to implement the above method. Preferably, the computer program may be divided into one or more than one module/unit, for example, a computer program 1, a computer program 2, etc. The one or more than one module/unit are stored in the memory 20 and executed by the processor 10 to achieve the present disclosure. The one or more than one module/unit may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used for describing an execution process of the computer program in the terminal device.
The processor 10 may be a central processing unit (CPU), and may also be a general-purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or a programmable logic device, a discrete gate, a transistor logic device, a discrete hardware component, etc. The general-purpose processor may be a microprocessor. Alternatively, the processor 10 may also be any conventional processor. The processor 10 may be a control center of the terminal device, which connects various parts of the terminal device by using various interfaces and wires. The memory 20 includes a program storage area and a data storage area. The program storage area may store an application program required for an operating device or at least one function, etc. The data storage area may store related data etc. In addition, the memory 20 may include a high-speed random access memory (RAM), and may further include a non-volatile memory, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card or a flash card. Alternatively, the memory 20 may also be other volatile solid-state storage device. It should be noted that the terminal device may include, but 1s not limited to, a processor and a memory. Those skilled in the art should understand that the block view of FIG. 5 is only an example of the terminal device, and does not constitute a limitation on the terminal device. The terminal device may include more or fewer components than shown, or a combination of certain components, or different components. The embodiments of the present disclosure provide a method and apparatus for improving a spatial resolution of data, a computer-readable storage medium, and a terminal device. The present disclosure preprocessed acquired AVHRR NDVI data and MODSI NDVI data within a preset time period to acquire monthly scale data, inputs the monthly scale data into a scale transformation model for downscaling, and subjects the downscaled data to error analysis, and optimizes the scale transformation model according to an analysis result. Compared with the prior art, the present disclosure can transform a low-resolution spatial scale of AVHRR NDVI data in a region into a high-resolution spatial scale of MODIS NDVI data, thereby improving data accuracy and availability.
The above described are preferred embodiments of the present disclosure, and it should be noted that for those of ordinary skill in the art, various improvements and modifications may be made without departing from the principles of the present disclosure.
These improvements and modifications should be regarded as falling within the protection scope of the present disclosure.
Claims (10)
1. A method for improving a spatial resolution of data, wherein the method comprises: preprocessing acquired AVHRR NDVI data and MODIS NDVI data within a preset time period to acquire monthly scale data, AVHRR referring to advanced very high resolution radiometer, NDVI referring to normalized difference vegetation index, and MODIS referring to moderate resolution imaging spectroradiometer; inputting the monthly scale data into a scale transformation model for downscaling; and subjecting the downscaled data to error analysis, and optimizing the scale transformation model according to an analysis result.
2. The method for improving a spatial resolution of data according to claim 1, wherein the scale transformation model 1s acquired as follows: acquiring coarse-resolution temporal projection information of the AVHRR NDVI data through a relative change rate of vegetation between each month and a base period; calculating coefficients of variation (CVs) of a high-resolution MODIS NDVI and a coarse-resolution AVHRR NDVI according to respective standard deviations and means, and characterizing high-resolution spatial projection information by a ratio of the two CVs; and acquiring the scale transformation model according to a random difference value, the coarse-resolution temporal projection information and the high-resolution spatial projection information.
3. The method for improving a spatial resolution of data according to claim 2, wherein the scale transformation model is: NDVInxye = NDVI yet X (1+ Kip X Reyxy) + Exyt Revx,y = Modis_CV /avhrr_CV Keye= (NDVI eye — NDVI yp) / NDVI sy pi wherein, NDVI, . is high-resolution NDVI of pixels x, y and time t after downscaling; NDVI, yp 1s a median of AVHRR NDVI of the pixels x and y in a baseline period; modis_CV,avhrr_CV are the CVs of the MODIS NDVI and the AVHRR NDVI, Reyx,y is the ratio of the two CVs; and Exp is the random difference value.
4. The method for improving a spatial resolution of data according to claim 1, wherein the preprocessing acquired AVHRR NDVI data and MODSI NDVI data within a preset time period comprises: acquiring the AVHRR NDVI data and the MODSI NDVI data within the preset time period; and subjecting the AVHRR NDVI data and the MODSI NDVI data to cleaning and correction to acquire monthly scale data.
5. An apparatus for improving a spatial resolution of data, wherein the apparatus comprises: a data processing module, configured to preprocess acquired AVHRR NDVI data and MODSI NDVI data within a preset time period to acquire monthly scale data; a scale processing module, configured to input the monthly scale data into a scale transformation model for downscaling; and a model optimization module, configured to subject the downscaled data to error analysis, and optimize the scale transformation model according to an analysis result.
6. The apparatus for improving a spatial resolution of data according to claim 5, wherein the scale processing module is configured to: acquire coarse-resolution temporal projection information of the AVHRR NDVI data through a relative change rate of vegetation between each month and a base period; calculate CVs of a high-resolution MODIS NDVI and a coarse-resolution AVHRR NDVI according to respective standard deviations and means, and characterize high-resolution spatial projection information by a ratio of the two CVs; and acquire the scale transformation model according to a random difference value, the coarse-resolution temporal projection information and the high-resolution spatial projection information.
7. The apparatus for improving a spatial resolution of data according to claim 5, wherein the scale transformation model is: NDVInxye = NDVI yet X (1+ Kip X Reyxy) + Exyt Revx,y = Modis_CV /avhrr_CV Keye = (NDVIpegt = NDVI yp) / NDVI xp wherein, NDVI, . is high-resolution NDVI of pixels x, y and time t after downscaling;
NDVI, yp 1s a median of AVHRR NDVI of the pixels x and y in a baseline period; modis_CV, avhrr_CV are the CVs of the MODIS NDVI and the AVHRR NDVI; Ry, , is the ratio of the CVs; and Exp is the random difference value.
8. The apparatus for improving a spatial resolution of data according to claim 5 , wherein the data processing module is configured to: acquire the AVHRR NDVI data and the MODSI NDVI data within the preset time period; and subject the AVHRR NDVI data and the MODSI NDVI data to cleaning and correction to acquire monthly scale data.
9. A computer-readable storage medium, wherein the computer-readable storage medium comprises a computer program, and the computer program is run to control a device where the computer-readable storage medium is located to implement the method for improving a spatial resolution of data according to any one of claims 1 to 4.
10. A terminal device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the method for improving a spatial resolution of data according to any one of claims 1 to 4.
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