A high-temporal-and-spatial-resolution mpdi dataset correction method and device

Abstract

The application discloses a high-temporal-and-spatial-resolution MPDI data set correction method and device, and the method comprises the following steps: fusing a base period low spatial resolution reflectivity data set, a prediction period low spatial resolution reflectivity data set and a base period high spatial resolution reflectivity data set to obtain a prediction period high-temporal-and-spatial-resolution reflectivity data set; calculating corresponding base period low spatial resolution MPDI values, prediction period low spatial resolution MPDI values, base period high spatial resolution MPDI values and prediction period high spatial resolution MPDI values based on the reflectivity data sets; obtaining a first difference value and a second difference value based on the MPDI values; obtaining an aggregated difference value based on the second difference value, determining a difference correction coefficient based on the aggregated difference value and the first difference value, correcting the second difference value according to the difference correction coefficient to obtain a corrected difference value; and correcting the prediction period high spatial resolution MPDI values according to the corrected difference value to obtain corrected prediction period high spatial resolution MPDI values. The precision of the MPDI data set can be improved, and thus the accuracy of drought monitoring can be improved.

Description

Technical Field

[0001] This invention belongs to the field of remote sensing data processing technology, specifically relating to a method and apparatus for correcting high spatiotemporal resolution MPDI datasets. Background Technology

[0002] The Modified Perpendicular Drought Index (MPDI) is a common drought monitoring indicator that effectively monitors soil moisture. At large regional scales, constructing low spatial resolution time-series MPDI datasets can effectively monitor the spatiotemporal dynamics of drought. However, at small and medium scales, the low spatial resolution makes it difficult to meet the needs. In existing technologies, an effective solution is to combine spatiotemporal data fusion algorithms to fuse remote sensing data with different spatiotemporal characteristics to obtain high spatiotemporal resolution reflectance data, and then construct a high spatiotemporal resolution MPDI dataset for drought monitoring at small and medium regional scales. However, the accuracy of MPDI datasets constructed by existing technologies is not high, and if they are directly used for drought monitoring, it will lead to errors in the monitoring results.

[0003] Therefore, how to improve the accuracy of MPDI datasets, thereby improving the accuracy of drought monitoring, is a technical problem that needs to be solved by those skilled in the art. Summary of the Invention

[0004] The purpose of this invention is to solve the technical problem that the accuracy of MPDI datasets is not high enough in the prior art.

[0005] To achieve the above technical objectives, in one aspect, the present invention provides a method for correcting high spatiotemporal resolution MPDI datasets, the method comprising:

[0006] The base period low spatial resolution reflectance dataset, the prediction period low spatial resolution reflectance dataset, and the base period high spatial resolution reflectance dataset are fused to obtain the prediction period high spatiotemporal resolution reflectance dataset.

[0007] Based on the base period low spatial resolution reflectance dataset, the prediction period low spatial resolution reflectance dataset, the base period high spatial resolution reflectance dataset, and the prediction period high spatiotemporal resolution reflectance dataset, the corresponding base period low spatial resolution MPDI value, the prediction period low spatial resolution MPDI value, the base period high spatial resolution MPDI value, and the prediction period high spatial resolution MPDI value are determined respectively.

[0008] A first difference is obtained based on the base period low spatial resolution MPDI value and the prediction period low spatial resolution MPDI value, and a second difference is obtained based on the base period high spatial resolution MPDI value and the prediction period high spatial resolution MPDI value.

[0009] The second difference is aggregated to the regional scale corresponding to the low spatial resolution reflectance data to obtain the aggregated difference. The difference correction coefficient is determined based on the aggregated difference and the first difference. The second difference is then corrected according to the difference correction coefficient to obtain the corrected difference.

[0010] The high spatial resolution MPDI value for the prediction period is corrected based on the correction difference to obtain the corrected high spatial resolution MPDI value for the prediction period.

[0011] Furthermore, the base period low spatial resolution MPDI value, the forecast period low spatial resolution MPDI value, the base period high spatial resolution MPDI value, and the forecast period high spatial resolution MPDI value are calculated using the following formulas:

[0012] ;

[0013] In the formula, M represents the vegetation cover, and M represents the slope of the soil line in the target area. The reflectance in the red band after atmospheric correction. The reflectance in the near-infrared band after atmospheric correction. For red band constants, This is a constant for the near-infrared band.

[0014] Furthermore, the vegetation coverage The specific calculation is performed using the following formula:

[0015] ;

[0016] In the formula, Normalized Difference Vegetation Index (NDVI) For pixels consisting entirely of bare soil, the NDVI value is... NDVI value for pixels completely covered by vegetation.

[0017] Furthermore, the aggregation difference is specifically determined by the following formula:

[0018] ;

[0019] In the formula, For aggregation difference, The second difference, High spatial resolution data contained within pixels in low spatial resolution reflectance data The number of pixels.

[0020] Furthermore, the difference correction coefficient is specifically determined by the following formula:

[0021] ;

[0022] In the formula, For correction factor, This is the first difference.

[0023] Furthermore, the corrected difference is obtained using the following formula:

[0024] ;

[0025] In the formula, To correct the difference, This is the difference correction factor. This represents the high spatial resolution MPDI value for the prediction period.

[0026] On the other hand, the present invention also provides a high spatiotemporal resolution MPDI dataset correction device, the device comprising:

[0027] The fusion module is used to fuse the base period low spatial resolution reflectance dataset, the prediction period low spatial resolution reflectance dataset, and the base period high spatial resolution reflectance dataset to obtain the prediction period high spatiotemporal resolution reflectance dataset.

[0028] The MPDI calculation module is used to calculate the corresponding base period low spatial resolution MPDI value, prediction period low spatial resolution MPDI value, base period high spatial resolution MPDI value and prediction period high spatiotemporal resolution MPDI value based on the base period low spatial resolution reflectance dataset, prediction period low spatial resolution reflectance dataset, base period high spatial resolution reflectance dataset and prediction period high spatiotemporal resolution reflectance dataset, respectively.

[0029] The MPDI difference calculation module is used to obtain a first difference based on the base period low spatial resolution MPDI value and the prediction period low spatial resolution MPDI value, and a second difference based on the base period high spatial resolution MPDI value and the prediction period high spatial resolution MPDI value.

[0030] The MPDI difference correction module is used to aggregate the second difference to the regional scale corresponding to the low spatial resolution reflectance data to obtain the aggregated difference, and determine the difference correction coefficient based on the aggregated difference and the first difference, and correct the second difference according to the difference correction coefficient to obtain the corrected difference.

[0031] The MPDI correction module is used to correct the high spatial resolution MPDI value of the prediction period according to the correction difference to obtain the corrected high spatial resolution MPDI value of the prediction period.

[0032] This invention provides a method and apparatus for correcting high spatiotemporal resolution MPDI datasets. Compared with existing technologies, this method includes fusing a base-period low spatial resolution reflectance dataset, a prediction-period low spatial resolution reflectance dataset, and a base-period high spatial resolution reflectance dataset to obtain a prediction-period high spatiotemporal resolution reflectance dataset; and determining corresponding base-period low spatial resolution MPDI values, prediction-period low spatial resolution MPDI values, and base-period high spatial resolution MPDI values ​​based on the base-period low spatial resolution reflectance dataset, the prediction-period low spatial resolution MPDI dataset, the base-period high spatial resolution reflectance dataset, and the prediction-period high spatiotemporal resolution MPDI dataset, respectively. The system calculates the MPDI (Multi-Level Difference) values ​​for the base period and the predicted period using high spatial resolution MPDI (Multi-Level Difference). A first difference is obtained based on the base period low spatial resolution MPDI value and the predicted period low spatial resolution MPDI value. A second difference is obtained based on the base period high spatial resolution MPDI value and the predicted period high spatial resolution MPDI value. The second difference is then aggregated to the regional scale corresponding to the low spatial resolution reflectance data to obtain an aggregated difference. A difference correction coefficient is determined based on the aggregated difference and the first difference. The second difference is then corrected according to the difference correction coefficient to obtain a corrected difference. Finally, the predicted period high spatial resolution MPDI value is corrected according to the corrected difference to obtain a corrected predicted period high spatial resolution MPDI value. This method improves the accuracy of the MPDI dataset, thereby enhancing the accuracy of drought monitoring. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments or prior art of this specification, the drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 The diagram shown is a flowchart illustrating the high spatiotemporal resolution MPDI dataset correction method provided in the embodiments of this specification.

[0035] Figure 2 The diagram shown is a structural schematic of the high spatiotemporal resolution MPDI dataset correction device provided in the embodiments of this specification.

[0036] Figure 3 The diagram shown is a schematic representation of the first difference in an embodiment of this specification.

[0037] Figure 4 The diagram shown is a schematic representation of the second difference in an embodiment of this specification.

[0038] Figure 5 The diagram shown is a schematic representation of the polymerization difference in an embodiment of this specification.

[0039] Figure 6 The diagram shown is a schematic diagram of the correction of the difference in the embodiments of this specification;

[0040] Figure 7 The diagram shown is a schematic of the corrected high spatial resolution MPDI value for the prediction period in an embodiment of this specification. Detailed Implementation

[0041] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0042] like Figure 1 The diagram illustrates a flowchart of a high spatiotemporal resolution MPDI dataset correction method provided in an embodiment of this specification. While this specification provides the method operation steps or apparatus structure shown in the following embodiments or figures, based on conventional methods or without creative effort, the method or apparatus may include more or fewer operation steps or module units after partial merging. In steps or structures where there is no logically necessary causal relationship, the execution order of these steps or the module structure of the apparatus is not limited to the execution order or module structure shown in the embodiments or figures of this specification. When the method or module structure is applied in actual devices, servers, or terminal products, it can be executed sequentially or in parallel according to the method or module structure shown in the embodiments or figures (e.g., in a parallel processor or multi-threaded processing environment, or even in a distributed processing or server cluster implementation environment).

[0043] The high spatiotemporal resolution MPDI dataset correction method provided in the embodiments of this specification can be applied to terminal devices such as clients and servers. Figure 1 As shown, the method specifically includes the following steps:

[0044] Step S101: Merge the base period low spatial resolution reflectance dataset, the prediction period low spatial resolution reflectance dataset, and the base period high spatial resolution reflectance dataset to obtain the prediction period high spatiotemporal resolution reflectance dataset.

[0045] Specifically, a low spatial resolution reflectance dataset can be an MOD09GA 500m image, while a high spatial resolution reflectance dataset can be a GF6 WFV 16m image. The method involves fusing reflectance datasets with different spatiotemporal characteristics to obtain data that combines the advantages of both types of data. Typically, reflectance data with high spatial resolution but low temporal resolution is fused with reflectance datasets with low spatial resolution but high temporal resolution to obtain a high spatiotemporal resolution reflectance dataset. The fusion algorithm is one that already exists in existing technologies.

[0046] Step S102: Determine the corresponding base period low spatial resolution MPDI value, prediction period low spatial resolution MPDI value, base period high spatial resolution MPDI value, and prediction period high spatiotemporal resolution MPDI value based on the base period low spatial resolution reflectance dataset, prediction period low spatial resolution reflectance dataset, base period high spatial resolution reflectance dataset, and prediction period high spatiotemporal resolution reflectance dataset, respectively.

[0047] It should be noted that the base period low spatial resolution MPDI value is in the base period The MPDI value corresponding to the low spatial resolution reflectance dataset, and the low spatial resolution MPDI value during the prediction period. The MPDI value corresponding to the low spatial resolution reflectance dataset, and the high spatial resolution MPDI value in the base period are in the base period. The MPDI value corresponding to the high spatial resolution reflectance dataset, and the high spatial resolution MPDI value during the prediction period are in the prediction period. MPDI values ​​corresponding to the temporal high spatial resolution reflectance dataset.

[0048] In this embodiment, the base period low spatial resolution MPDI value, the prediction period low spatial resolution MPDI value, the base period high spatial resolution MPDI value, and the prediction period high spatial resolution MPDI value are calculated using the following formulas:

[0049] ;

[0050] ;

[0051] In the formula, M represents the vegetation cover, and M represents the slope of the soil line in the target area. The reflectance in the red band after atmospheric correction. The reflectance in the near-infrared band after atmospheric correction. For red band constants, For the near-infrared band constant, Normalized Difference Vegetation Index (NDVI) For pixels consisting entirely of bare soil, the NDVI value is... NDVI value for pixels completely covered by vegetation.

[0052] Step S103: Obtain a first difference based on the base period low spatial resolution MPDI value and the prediction period low spatial resolution MPDI value, and obtain a second difference based on the base period high spatial resolution MPDI value and the prediction period high spatial resolution MPDI value.

[0053] Specifically, the first difference and the second difference are obtained using the following formula:

[0054] ;

[0055] ;

[0056] In the formula, The first difference, The second difference, For the low spatial resolution MPDI value of the prediction period, The base period low spatial resolution MPDI value, For the high spatial resolution MPDI value of the prediction period, For the base period high spatial resolution MPDI value, such as Figure 3 The diagram shown is a schematic representation of the first difference. Figure 4 The diagram shown is a schematic of the second difference.

[0057] Step S104: Aggregate the second difference to the region scale corresponding to the low spatial resolution reflectance data to obtain the aggregated difference, and determine the difference correction coefficient based on the aggregated difference and the first difference, and correct the second difference according to the difference correction coefficient to obtain the corrected difference.

[0058] Specifically, aggregated difference means aggregating the MPDI difference, or second difference, from the high spatial resolution data onto the regional scale of the low spatial resolution reflectance data.

[0059] In this embodiment of the application, the aggregation difference is specifically determined by the following formula:

[0060] ;

[0061] In the formula, For aggregation difference, The second difference, High spatial resolution data contained within pixels in low spatial resolution reflectance data The number of pixels, such as Figure 5 The diagram shown illustrates the aggregation difference.

[0062] The correction factor is determined using the following formula:

[0063] ;

[0064] In the formula, This is the difference correction factor. This is the first difference.

[0065] Specifically, the corrected difference is obtained using the following formula:

[0066] ;

[0067] In the formula, To correct the difference, This is the difference correction factor. For the high spatial resolution MPDI value of the prediction period, such as Figure 6 The diagram shown illustrates the correction of the difference.

[0068] Step S105: Correct the second difference according to the difference correction coefficient to obtain the corrected difference, and correct the prediction period high spatial resolution MPDI value according to the corrected difference to obtain the corrected prediction period high spatial resolution MPDI value.

[0069] Specifically, the corrected high spatial resolution MPDI value for the forecast period is obtained using the following formula:

[0070] ;

[0071] In the formula, To correct the high spatial resolution MPDI value in the post-prediction period, such as Figure 7 The image shows a schematic diagram of the corrected high spatial resolution MPDI values ​​for the prediction period.

[0072] Based on the above-described high spatiotemporal resolution MPDI dataset correction method, one or more embodiments of this specification also provide a platform or terminal for high spatiotemporal resolution MPDI dataset correction. This platform or terminal may include devices, software, modules, plug-ins, servers, clients, etc., using the methods described in the embodiments of this specification, combined with necessary hardware implementation devices. Based on the same innovative concept, the systems in one or more embodiments provided in this specification are as described in the following embodiments. Since the implementation schemes and methods for solving the system problem are similar, the specific system implementations in the embodiments of this specification can refer to the implementation of the aforementioned methods. Repeated descriptions will not be repeated. The terms "unit" or "module" used below can refer to a combination of software and / or hardware that achieves a predetermined function. Although the systems described in the following embodiments are preferably implemented in software, hardware implementations, and a combination of software and hardware, are also possible and contemplated.

[0073] Specifically, Figure 2 This is a schematic diagram of the module structure of one embodiment of the high spatiotemporal resolution MPDI dataset correction device provided in this specification, as shown below. Figure 2 As shown, the high spatiotemporal resolution MPDI dataset correction device provided in this specification includes:

[0074] The fusion module 201 is used to fuse the base period low spatial resolution reflectance dataset, the prediction period low spatial resolution reflectance dataset, and the base period high spatial resolution reflectance dataset to obtain the prediction period high spatiotemporal resolution reflectance dataset.

[0075] MPDI calculation module 202 is used to calculate the corresponding base period low spatial resolution MPDI value, prediction period low spatial resolution MPDI value, base period high spatial resolution MPDI value and prediction period high spatiotemporal resolution MPDI value based on the base period low spatial resolution reflectance dataset, prediction period low spatial resolution reflectance dataset, base period high spatial resolution reflectance dataset and prediction period high spatiotemporal resolution reflectance dataset, respectively.

[0076] MPDI difference calculation module 203 is used to obtain a first difference based on the base period low spatial resolution MPDI value and the prediction period low spatial resolution MPDI value, and to obtain a second difference based on the base period high spatial resolution MPDI value and the prediction period high spatial resolution MPDI value.

[0077] MPDI difference correction module 204 is used to aggregate the second difference to the regional scale corresponding to the low spatial resolution reflectance data to obtain an aggregated difference, and determine a difference correction coefficient based on the aggregated difference and the first difference, and correct the second difference according to the difference correction coefficient to obtain a corrected difference.

[0078] MPDI correction module 205 is used to correct the prediction period high spatial resolution MPDI value according to the correction difference to obtain the corrected prediction period high spatial resolution MPDI value.

[0079] It should be noted that the system described above may include other implementation methods based on the description of the corresponding method embodiments. The specific implementation methods can be referred to the description of the corresponding method embodiments above, and will not be elaborated here.

[0080] This application also provides an electronic device, including:

[0081] processor;

[0082] Memory used to store the processor's executable instructions;

[0083] The processor is configured to perform the methods provided in the embodiments described above.

[0084] The electronic device provided in this application stores executable instructions for a processor in a memory. When the processor executes the executable instructions, it can fuse a base-period low spatial resolution reflectance dataset, a prediction-period low spatial resolution reflectance dataset, and a base-period high spatial resolution reflectance dataset to obtain a prediction-period high spatiotemporal resolution reflectance dataset. Based on the base-period low spatial resolution reflectance dataset, the prediction-period low spatial resolution reflectance dataset, the base-period high spatial resolution reflectance dataset, and the prediction-period high spatiotemporal resolution reflectance dataset, the corresponding base-period low spatial resolution MPDI value, the prediction-period low spatial resolution MPDI value, and the base-period high spatial resolution MPDI value are determined respectively. The system uses PDI values ​​and forecast high spatial resolution MPDI values. Based on the base period low spatial resolution MPDI values ​​and forecast low spatial resolution MPDI values, a first difference is obtained. Based on the base period high spatial resolution MPDI values ​​and forecast high spatial resolution MPDI values, a second difference is obtained. The second difference is aggregated to the regional scale corresponding to the low spatial resolution reflectance data to obtain an aggregated difference. A difference correction coefficient is determined based on the aggregated difference and the first difference. The second difference is then corrected according to the difference correction coefficient to obtain a corrected difference. Finally, the forecast high spatial resolution MPDI value is corrected according to the corrected difference to obtain a corrected forecast high spatial resolution MPDI value. This method can improve the accuracy of the MPDI dataset, thereby improving the accuracy of drought monitoring.

[0085] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0086] The methods or apparatus described in the embodiments provided in this specification can implement business logic through a computer program and record it on a storage medium. The storage medium can be read and executed by a computer to achieve the effects of the solutions described in the embodiments of this specification, such as:

[0087] The base period low spatial resolution reflectance dataset, the prediction period low spatial resolution reflectance dataset, and the base period high spatial resolution reflectance dataset are fused to obtain the prediction period high spatiotemporal resolution reflectance dataset.

[0088] Based on the base period low spatial resolution reflectance dataset, the prediction period low spatial resolution reflectance dataset, the base period high spatial resolution reflectance dataset, and the prediction period high spatiotemporal resolution reflectance dataset, the corresponding base period low spatial resolution MPDI value, the prediction period low spatial resolution MPDI value, the base period high spatial resolution MPDI value, and the prediction period high spatial resolution MPDI value are determined respectively.

[0089] A first difference is obtained based on the base period low spatial resolution MPDI value and the prediction period low spatial resolution MPDI value, and a second difference is obtained based on the base period high spatial resolution MPDI value and the prediction period high spatial resolution MPDI value.

[0090] The second difference is aggregated to the regional scale corresponding to the low spatial resolution reflectance data to obtain the aggregated difference. The difference correction coefficient is determined based on the aggregated difference and the first difference. The second difference is then corrected according to the difference correction coefficient to obtain the corrected difference.

[0091] The high spatial resolution MPDI value for the prediction period is corrected based on the correction difference to obtain the corrected high spatial resolution MPDI value for the prediction period.

[0092] The storage medium can include physical devices for storing information, typically digitizing the information and then storing it using electrical, magnetic, or optical methods. The storage medium can include: devices that store information using electrical energy, such as various types of memory, like RAM and ROM; devices that store information using magnetic energy, such as hard disks, floppy disks, magnetic tapes, magnetic core memory, bubble memory, and USB flash drives; and devices that store information using optical methods, such as CDs or DVDs. Of course, there are other readable storage media, such as quantum memories and graphene memories.

[0093] The embodiments in this specification are not limited to conforming to industry communication standards, standard computer resource data update and data storage rules, or the situations described in one or more embodiments of this specification. Slightly modified implementations based on certain industry standards or custom methods or embodiments can also achieve the same, equivalent, or similar, or predictable, implementation effects as described above. Embodiments that utilize these modified or modified methods for data acquisition, storage, judgment, and processing still fall within the scope of optional implementations of the embodiments in this specification.

[0094] The controller can be implemented in any suitable manner. For example, it can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code form, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, ASICs, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included therein for implementing various functions can also be considered as structures within the hardware component. Alternatively, the means for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0095] The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or plug-ins may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection between devices or units, and may be electrical, mechanical, or other forms.

[0096] These computer program instructions can also be loaded onto a computer or other programmable resource data updating device, causing a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable device for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0097] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, system embodiments are basically similar to method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. In the description of this specification, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this specification. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described can be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0098] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of this invention.

Claims

1. A method for correcting a high spatiotemporal resolution MPDI dataset, characterized in that, The method includes: The base period low spatial resolution reflectance dataset, the prediction period low spatial resolution reflectance dataset, and the base period high spatial resolution reflectance dataset are fused to obtain the prediction period high spatiotemporal resolution reflectance dataset. Based on the base period low spatial resolution reflectance dataset, the prediction period low spatial resolution reflectance dataset, the base period high spatial resolution reflectance dataset, and the prediction period high spatiotemporal resolution reflectance dataset, the corresponding base period low spatial resolution MPDI value, the prediction period low spatial resolution MPDI value, the base period high spatial resolution MPDI value, and the prediction period high spatial resolution MPDI value are calculated respectively. A first difference is obtained based on the base period low spatial resolution MPDI value and the prediction period low spatial resolution MPDI value, and a second difference is obtained based on the base period high spatial resolution MPDI value and the prediction period high spatial resolution MPDI value. The second difference is aggregated to the regional scale corresponding to the low spatial resolution reflectance data to obtain the aggregated difference. The difference correction coefficient is determined based on the aggregated difference and the first difference. The second difference is then corrected according to the difference correction coefficient to obtain the corrected difference. The high spatial resolution MPDI value for the prediction period is corrected based on the correction difference to obtain the corrected high spatial resolution MPDI value for the prediction period.

2. The high spatiotemporal resolution MPDI dataset correction method as described in claim 1, characterized in that, The base period low spatial resolution MPDI value, the forecast period low spatial resolution MPDI value, the base period high spatial resolution MPDI value, and the forecast period high spatial resolution MPDI value are calculated using the following formulas: ； In the formula, M represents the vegetation cover, and M represents the slope of the soil line in the target area. The reflectance in the red band after atmospheric correction. The reflectance in the near-infrared band after atmospheric correction. For red band constants, This is a constant for the near-infrared band.

3. The high spatiotemporal resolution MPDI dataset correction method as described in claim 2, characterized in that, The vegetation coverage The specific calculation is performed using the following formula: ； In the formula, Normalized Difference Vegetation Index (NDVI) For pixels consisting entirely of bare soil, the NDVI value is... NDVI value for pixels completely covered by vegetation.

4. The high spatiotemporal resolution MPDI dataset correction method as described in claim 1, characterized in that, The aggregation difference is specifically determined by the following formula: ； In the formula, For aggregation difference, The second difference, High spatial resolution data contained within pixels in low spatial resolution reflectance data The number of pixels.

5. The high spatiotemporal resolution MPDI dataset correction method as described in claim 4, characterized in that, The difference correction coefficient is determined using the following formula: ； In the formula, This is the difference correction factor. This is the first difference.

6. The high spatiotemporal resolution MPDI dataset correction method as described in claim 1, characterized in that, The corrected difference is obtained using the following formula: ； In the formula, To correct the difference, This is the difference correction factor. This is the second difference.

7. A high spatiotemporal resolution MPDI dataset correction device, characterized in that, The device includes: The fusion module is used to fuse the base period low spatial resolution reflectance dataset, the prediction period low spatial resolution reflectance dataset, and the base period high spatial resolution reflectance dataset to obtain the prediction period high spatiotemporal resolution reflectance dataset. The MPDI calculation module is used to calculate the corresponding base period low spatial resolution MPDI value, prediction period low spatial resolution MPDI value, base period high spatial resolution MPDI value and prediction period high spatiotemporal resolution MPDI value based on the base period low spatial resolution reflectance dataset, prediction period low spatial resolution reflectance dataset, base period high spatial resolution reflectance dataset and prediction period high spatiotemporal resolution reflectance dataset, respectively. The MPDI difference calculation module is used to obtain a first difference based on the base period low spatial resolution MPDI value and the prediction period low spatial resolution MPDI value, and a second difference based on the base period high spatial resolution MPDI value and the prediction period high spatial resolution MPDI value. The MPDI difference correction module is used to aggregate the second difference to the regional scale corresponding to the low spatial resolution reflectance data to obtain the aggregated difference, and determine the difference correction coefficient based on the aggregated difference and the first difference, and correct the second difference according to the difference correction coefficient to obtain the corrected difference. The MPDI correction module is used to correct the high spatial resolution MPDI value of the prediction period according to the correction difference to obtain the corrected high spatial resolution MPDI value of the prediction period.

Images