Remote sensing soil moisture downscaling method, electronic equipment and medium based on high / low frequency band brightness temperature fusion

Abstract

This application discloses a remote sensing soil moisture downscaling method, electronic device, and medium that fuses high- and low-frequency band brightness temperature. The method may include: acquiring FY-3B W-band TB data and FY-3B VSM data; resampling the FY-3B W-band TB data; calculating the thermal inertia index (TI) for the corresponding pixel on a given day at a global 25-km resolution pixel scale and a global 12.5-km resolution pixel scale; constructing a VSM-TI response relationship model; constructing a VSM downscaling relationship for converting 25-km resolution pixels to 12.5-km resolution pixels based on the VSM-TI response relationship model; and calculating global 12.5-km resolution FY-3B VSM data based on the VSM downscaling relationship. The present invention produces a more accurate, high-resolution soil moisture image data product in special scenarios where local soil moisture changes are very drastic.

Description

Technical Field

[0001] The present invention relates to the field of soil moisture analysis, and more specifically to a remote sensing soil moisture downscaling method, electronic equipment, and medium for integrating high / low frequency band brightness temperature. Background Art

[0002] Soil water, the unsaturated water within the soil layer, is widely distributed across the land surface and is essential for plant growth. Therefore, soil water resources are crucial for the quality of agricultural production. Furthermore, soil water serves as the link between surface water and groundwater, entering the atmosphere as evapotranspiration and being replenished in the soil surface as precipitation. It is a core factor in surface energy exchange and a crucial component of the global surface energy cycle. Global-scale soil water distribution provides a crucial basis for evaluating and assessing surface evapotranspiration patterns (vegetation growth) and precipitation patterns (ecosystem health). It also serves as essential data for studying regional hydrological characteristics and climate change. Against the backdrop of global warming, global-scale soil water distribution data have become indispensable for scientists studying global climate and hydrological changes and developing sound global climate response models. This data provides a more scientific basis for the formulation of global sustainable development policies.

[0003] The Global Soil Moisture Dataset is the most extensive dataset currently available for soil moisture research. Traditional soil moisture monitoring primarily relies on obtaining real-time surface soil moisture values ​​through manual or automated observations at meteorological stations. However, these observation methods are not only time-consuming and labor-intensive, but the resulting soil moisture values ​​also suffer from insufficient spatial representation. Firstly, the measured data only represent the average soil moisture content within a horizontal range of less than 10 cm, measured at a single probe point. Secondly, due to the prohibitive cost of establishing observation stations and the complexities of the global geographical environment, it is almost impossible to establish a sufficient number of soil moisture observation stations with consistent observation specifications and uniform distribution across all seven continents to meet the needs of observational mapping. Advances in technology, including the acquisition of satellite remote sensing data and the advancement of research into satellite remote sensing soil moisture retrieval methods, have effectively overcome the spatial continuity and cost disadvantages of using station-based soil moisture data, making it possible to obtain near-real-time images of surface soil moisture distribution with high temporal and spatial resolution using satellite remote sensing data.

[0004] Microwave remote sensing data has excellent penetration into clouds and vegetation canopies, making it an ideal data source for monitoring soil moisture changes. Commonly used microwave remote sensing data include microwave radiometer datasets and microwave scatterometer (radar) datasets. The mechanism for inverting surface soil moisture from microwave scatterometer data is complex. The data's high resolution occupies a large space and is expensive. The processing steps are cumbersome, and the inversion process places strict and precise requirements on auxiliary surface parameters, resulting in an overall high cost for the application of this data. Microwave radiometers are a passive microwave technology with high global coverage and easy access, making them suitable for monitoring surface soil moisture over large areas. Microwave radiometers include datasets from both ascending and descending modes. Each mode has a revisit period of 1-3 days for mid- and low-latitude regions. Their high temporal resolution enables near-real-time monitoring of spatial changes in surface soil moisture, approaching one or two times daily.

[0005] The principle of using microwave remote sensing technology to invert surface soil moisture primarily relies on the strong penetration of microwave signals in lower frequency bands, such as the X-, C-, and L-bands, into the atmosphere and vegetation layers, and their ability to characterize the dielectric constant of the soil-water mixture in the soil. This is based on the passive microwave radiation transmission model as its primary mathematical and physical basis. However, the spatial resolution of Earth observation signals from current satellite-borne radiometers in the X-, C-, and L-bands is too low. The original resolution of a single Earth observation "footprint" is generally around 30-60 km, and the grid resolution of the resampled image is also between 25-36 km. This greatly limits the precision of satellite-borne soil moisture products in describing global surface soil moisture changes, as well as their value in related fields such as agricultural disaster monitoring, hydrological process simulation, and climate change research.

[0006] To further improve the Earth observation precision of soil moisture data products from spaceborne microwave radiometers, the classic "oversampling" algorithm was used to spatially downscale the brightness temperature observed in low-frequency microwave bands such as X / C / L. This enabled the acquisition of a global, refined soil moisture data product with a spatial resolution of 9-12.5 km on a global scale. However, subsequent studies have shown that the actual accuracy of soil moisture data products obtained based on the "oversampling" downscaling method is heavily dependent on the original microwave soil moisture inversion results. In particular, in areas where local soil moisture changes are very drastic, this algorithm cannot truly reflect the actual difference between the soil moisture content in the refined grid and the original coarse-resolution grid. Therefore, the relevant downscaling methods still need to be further improved.

[0007] Therefore, it is necessary to develop a remote sensing soil moisture downscaling method, electronic equipment and medium that integrates high / low frequency band brightness temperature.

[0008] The information disclosed in the background technology section of the present invention is only intended to deepen the understanding of the general background technology of the present invention, and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art already known to those skilled in the art. Summary of the Invention

[0009] The present invention proposes a remote sensing soil moisture downscaling method, electronic equipment, and medium that fuses high- and low-frequency band brightness temperatures. This method uses W-band brightness temperature, which actually meets the conditions for higher observation spatial resolution, as high-resolution auxiliary information input, and can produce higher-precision high-resolution soil moisture image data products in special scenarios where local soil moisture changes are very drastic.

[0010] In a first aspect, the present disclosure provides a remote sensing soil moisture downscaling method using high / low frequency band brightness temperature fusion, including:

[0011] Acquire FY-3B W-band TB data and global 25-km resolution FY-3B VSM data;

[0012] The FY-3B W-band TB data were resampled to global 25-km resolution pixels and global 12.5-km resolution pixels respectively;

[0013] Calculate the thermal inertia index TI of the corresponding pixel on the same day at the global 25-km resolution pixel scale and the global 12.5-km resolution pixel scale respectively;

[0014] Constructing a VSM-TI response relationship model based on the global 25-km resolution pixels;

[0015] Constructing a VSM downscaling equation for converting 25-km resolution pixels to 12.5-km resolution pixels based on the VSM-TI response relationship model;

[0016] The global 12.5-km resolution FY-3B VSM data were calculated according to the VSM downscaling relationship.

[0017] Preferably, calculating the thermal inertia index TI includes:

[0018] On the global 25-km pixel scale, the FY-3B W-band TB data at adjacent moments within the same day are differentiated, and the difference is used as the thermal inertia index TI of the pixel on that day.

[0019] Preferably, the adjacent moments within the same day are the orbit ascending moment and the orbit descending moment within the same day.

[0020] Preferably, the difference of the FY-3B W band TB data at adjacent moments in the same day is calculated, and the difference is used as the thermal inertia index TI of the pixel on that day, which includes:

[0021] Assume that on a certain day t0, the FY-3B satellite orbit-raising transit time t01 and orbit-descending transit time t02 correspond to each other, and the next adjacent day t1 corresponds to the FY-3B satellite orbit-raising transit time t11 and orbit-descending transit time t12;

[0022] The corresponding FY-3B W-band TB data at the above moments are TBW t01 TBW t02 TBW t11 TBW t12 ;

[0023] The daily average thermal inertia index TI corresponding to the orbit raising time on date t0 t01 for:

[0024] TI t01 =0.5×[(TBW t01 -TBW t02 )+(TBW t01 -TBW t12 )]

[0025] The daily average thermal inertia index TI corresponding to the orbit-dropping moment on date t1 t12 for:

[0026] TI t12 =0.5×[(TBW t01 -TBW t12 )+(TBW t11 -TBW t12 )].

[0027] Preferably, the VSM-TI response relationship model of the global 25-km pixel is:

[0028] VSM 25km =k×TI 25km +b

[0029] where k and b are model coefficient constants obtained by fitting the daily VSM and TI long time series at each 25-km pixel.

[0030] Preferably, the VSM downscaling relationship is:

[0031] VSM 12.5km =VSM 25km +k×(TI 12.5km -TI 25km )

[0032] Among them, VSM 12.5km The data are global 12.5-km resolution FY-3B VSM data.

[0033] In a second aspect, an embodiment of the present disclosure further provides an electronic device, the electronic device comprising:

[0034] a memory storing executable instructions;

[0035] A processor runs the executable instructions in the memory to implement the remote sensing soil moisture downscaling method based on the fusion of high / low frequency band brightness temperature.

[0036] In a third aspect, an embodiment of the present disclosure further provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the remote sensing soil moisture downscaling method of high / low frequency band brightness temperature fusion.

[0037] The methods and apparatus of the present invention have other features and advantages that will be apparent from or will be described in detail in the accompanying drawings and subsequent detailed descriptions incorporated herein, which together serve to explain the specific principles of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The above and other objects, features and advantages of the present invention will become more apparent through a more detailed description of exemplary embodiments of the present invention with reference to the accompanying drawings, wherein like reference numerals generally represent like components throughout the exemplary embodiments of the present invention.

[0039] Figure 1 A flowchart showing the steps of a remote sensing soil moisture downscaling method using high / low frequency band brightness temperature fusion according to an embodiment of the present invention is shown.

[0040] Figure 2a 、 Figure 2b 、 Figure 2c Schematic diagrams comparing original 25km resolution satellite data, 12.5km resolution satellite data obtained by the prior art, and 12.5km resolution satellite data obtained according to the present invention are shown respectively. DETAILED DESCRIPTION

[0041] The preferred embodiments of the present invention will be described in more detail below. Although the preferred embodiments of the present invention are described below, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0042] To facilitate understanding of the solutions and effects of the embodiments of the present invention, three specific application examples are given below. Those skilled in the art should understand that these examples are only for facilitating understanding of the present invention, and any specific details thereof are not intended to limit the present invention in any way.

[0043] Example 1

[0044] Figure 1 A flowchart showing the steps of a remote sensing soil moisture downscaling method using high / low frequency band brightness temperature fusion according to an embodiment of the present invention is shown.

[0045] like Figure 1 As shown in Figure 2, the remote sensing soil moisture downscaling method based on the fusion of high / low frequency band brightness temperature includes:

[0046] Step 101, obtaining FY-3B W-band TB data and global 25-km resolution FY-3B VSM data;

[0047] Step 102 , resampling the FY-3B W-band TB data to global 25-km resolution pixels and global 12.5-km resolution pixels respectively;

[0048] Step 103 , calculating the thermal inertia index TI of the corresponding pixel on the day at the global 25-km resolution pixel scale and the global 12.5-km resolution pixel scale respectively;

[0049] Step 104, constructing a VSM-TI response relationship model based on global 25-km resolution pixels;

[0050] Step 105, constructing a VSM downscaling relationship for converting 25-km resolution pixels to 12.5-km resolution pixels based on the VSM-TI response relationship model;

[0051] Step 106 , calculate the global 12.5-km resolution FY-3B VSM data according to the VSM downscaling equation.

[0052] In one example, calculating the thermal inertia index TI includes:

[0053] At the global 25-km pixel scale, the FY-3B W-band TB data at adjacent times within the same day are subtracted and the difference is used as the thermal inertia index (TI) of the pixel on that day.

[0054] In one example, adjacent time points within the same day are the ascending orbit time point and the descending orbit time point within the same day.

[0055] In one example, the difference between the FY-3B W band TB data at adjacent times on the same day is calculated, and the difference is used as the thermal inertia index TI of the pixel on that day, including:

[0056] Assume that on a certain day t0, the FY-3B satellite orbit-raising transit time t01 and orbit-descending transit time t02 correspond to each other, and the next adjacent day t1 corresponds to the FY-3B satellite orbit-raising transit time t11 and orbit-descending transit time t12;

[0057] The corresponding FY-3B W-band TB data at the above moments are TBW t01 TBW t02 TBW t11 TBW t12 ;

[0058] The daily average thermal inertia index TI corresponding to the orbit raising time on date t0 t01 for:

[0059] TI t01 =0.5×[(TBW t01 -TBW t02 )+(TBW t01 -TBW t12 )]

[0060] The daily average thermal inertia index TI corresponding to the orbit-dropping moment on date t1 t12 for:

[0061] TI t12 =0.5×[(TBW t01 -TBW t12 )+(TBW t11 -TBW t12 )]

[0062] In one example, the VSM-TI response relationship model for a global 25-km pixel is:

[0063] VSM 25km =k×TI 25km +b

[0064] where k and b are model coefficient constants obtained by fitting the daily VSM and TI long time series at each 25-km pixel.

[0065] In one example, the VSM downscaling relationship is:

[0066] VSM 12.5km =VSM 25km +k×(TI 12.5km -TI 25km )

[0067] Among them, VSM 12.5km The data are global 12.5-km resolution FY-3B VSM data.

[0068] Specifically, the FY-3B satellite-based daily 25-km resolution global passive microwave soil moisture data product (hereinafter referred to as FY-3B VSM data) and the FY-3B satellite-based W-band (89GHz) vertically polarized channel global brightness temperature (hereinafter referred to as FY-3B W-band TB) data are collected and organized. The FY-3B VSM data and W-band TB data required for this invention are both sourced from the Fengyun Satellite Remote Sensing Data Service Network. The VSM data is an L3 application product based on observations by the FY-3B Microwave Radiometer (MWRI), and this data product has been stored using the EASE GRID-1.0-25-km projection system. The W-band TB data is an L1 raw brightness temperature signal based on observations by the FY-3B MWRI. The data products are stored in a per-orbit swath format, with each orbital file containing 243 (rows) × 1797 (columns) observation signatures, with a sampling interval of 10 km between observation signatures. The above data are all stored in HDF format, divided into ascending transit data (transit at 1:40 noon local time) and descending transit data (transit at 1:40 midnight local time). The research period covers all dates between 2017 and 2018.

[0069] The FY-3B W-band TB data were resampled to the global 25-km resolution pixel (grid) corresponding to the FY-3B VSM data. They were also resampled to 12.5-km resolution (i.e., doubling the pixel density) for backup use. Resampling employed an average sampling method, resampling all observations from the same day and orbit mode (either ascending or descending) to the EASE GRID-1.0-25-km pixel within the corresponding longitude and latitude range. For the same pixel, the W-band vertically polarized brightness temperature values ​​of all observations from different orbit files whose geometric centers fell within the pixel's geographic range were averaged to form the resampled brightness temperature for that grid.

[0070] The W-band TB values ​​at adjacent moments within the same day (i.e., the ascending and descending moments within the same day) were subtracted at the 25-km pixel scale and the 12.5-km resolution pixel scale, and the difference was used as the thermal inertia index (TI) of the pixel on that day.

[0071] Here, it is assumed that on a certain day t0, the FY-3B satellite passes through the ascending orbit at time t01 and the descending orbit at time t02. The next adjacent day t1 also corresponds to the FY-3B satellite passing through the ascending orbit at time t11 and the descending orbit at time t12. The corresponding FY-3B W-band TB brightness temperatures at the above times are TBW t01 TBW t02 TBW t11 TBW t12, then the daily average thermal inertia index TI corresponding to the orbit raising time on date t0 is t01 Calculated as follows:

[0072] TI t01 =0.5×[(TBW t01 -TBW t02 )+(TBW t01 -TBW t12 )]

[0073] The daily average thermal inertia index TI corresponding to the orbit-dropping moment on date t1 t12 Calculated as follows:

[0074] TI t12 =0.5×[(TBW t01 -TBW t12 )+(TBW t11 -TBW t12 )]

[0075] Then, a VSM-TI response relationship model based on global 25-km pixel changes was constructed:

[0076] VSM 25km =k×TI 25km +b

[0077] Where k and b are the model coefficient constants obtained by fitting the daily VSM and TI long time series at each 25-km pixel. In this case, the training data period for fitting the long time series of the global model is the entire year of 2017.

[0078] The VSM downscaling relationship from 25-km resolution to 12.5-km resolution is further constructed based on the VSM-TI response relationship:

[0079] VSM 12.5km =VSM 25km +k×(TI 12.5km -TI 25km )

[0080] Among them, VSM 25km and VSM 12.5km Represents a 25-km resolution remote sensing pixel and any 12.5-km resolution sub-pixel in the pixel, TI 25km and TI 12.5km They represent the thermal inertia index of the corresponding spatiotemporal positions, and k is the VSM-TI response relationship model coefficient corresponding to the 25-km pixel, which is the same as k in the VSM-TI response relationship model.

[0081] The model was tested using data covering the entire year of 2018. The 25-km resolution VSM data and 12.5-km resolution W-band TB data available for the year were used as input parameters for the downscaling equation. Daily global 12.5-km resolution VSM data images for the entire year of 2018 (split into ascending and descending orbit data) were calculated.

[0082] Accuracy verification is performed. The standard reference data used to verify the examples of the present invention are measured data from ground soil moisture stations distributed in different regions of the world. This data is obtained through the website of the International Soil Moisture Observation Network. The data date range corresponds to the FY-3B satellite data, that is, it covers the whole year of 2018. At the same time, in order to better demonstrate the accuracy improvement of the 12.5-km soil moisture data product obtained after downscaling compared with the 25-km data product before downscaling and the products obtained by other existing downscaling methods, the specific scenarios to be verified are limited to those that meet the |VSM requirement within that year. 12.5km -VSM 25km |>0.04cm 3 / cm 3 The corresponding date and site of the scene show that the surface soil moisture has a strong spatial variability characteristic within the corresponding time and space range. Here, VSM 12.5km and VSM 25km They represent the soil moisture content values ​​in 12.5-km resolution and corresponding 25-km resolution pixels, respectively.

[0083] Figure 2a 、 Figure 2b 、 Figure 2c Schematic diagrams comparing original 25km resolution satellite data, 12.5km resolution satellite data obtained by the prior art, and 12.5km resolution satellite data obtained according to the present invention are shown respectively.

[0084] like Figure 2a-2c The verification results show that when the surface soil moisture has a strong spatial variability distribution characteristic, the average accuracy of the existing 12.5-km resolution soil moisture product obtained based on the oversampling downscaling method is slightly different from the accuracy of the original 25-km resolution product, and its RMSE is between 0.071-0.080 cm 3 / cm 3 The new downscaling method provided by the present invention can obtain a high spatial resolution surface soil moisture signal product with a higher matching degree with the ground station data (RMSE is about 0.042cm 3 / cm 3 ), that is, the present invention effectively improves the accuracy of 12.5-km resolution satellite soil moisture data products produced by existing downscaling methods when the surface soil moisture meets the distribution characteristics of strong spatial variability.

[0085] Example 2

[0086] The present disclosure provides an electronic device, which includes: a memory storing executable instructions; and a processor running the executable instructions in the memory to implement the above-mentioned remote sensing soil moisture downscaling method of high / low frequency band brightness temperature fusion.

[0087] An electronic device according to an embodiment of the present disclosure includes a memory and a processor.

[0088] The memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), a hard disk, flash memory, etc.

[0089] The processor may be a central processing unit (CPU) or other form of processing unit having data processing capability and / or instruction execution capability, and may control other components in the electronic device to perform desired functions. In one embodiment of the present disclosure, the processor is used to execute the computer-readable instructions stored in the memory.

[0090] Those skilled in the art should understand that in order to solve the technical problem of how to obtain a good user experience, this embodiment may also include well-known structures such as a communication bus and an interface, and these well-known structures should also be included in the scope of protection of this disclosure.

[0091] For detailed description of this embodiment, please refer to the corresponding description in the aforementioned embodiments, which will not be repeated here.

[0092] Example 3

[0093] An embodiment of the present disclosure provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the remote sensing soil moisture downscaling method of high / low frequency band brightness temperature fusion is implemented.

[0094] According to an embodiment of the present disclosure, a computer-readable storage medium stores non-transitory computer-readable instructions, which, when executed by a processor, execute all or part of the steps of the aforementioned methods of the embodiments of the present disclosure.

[0095] The above-mentioned computer-readable storage media include, but are not limited to, optical storage media (e.g., CD-ROMs and DVDs), magneto-optical storage media (e.g., MOs), magnetic storage media (e.g., magnetic tapes or mobile hard disks), media with built-in rewritable non-volatile memory (e.g., memory cards), and media with built-in ROM (e.g., ROM cartridges).

[0096] Those skilled in the art should understand that the above description of the embodiments of the present invention is only for the purpose of illustrative purposes only to illustrate the beneficial effects of the embodiments of the present invention, and is not intended to limit the embodiments of the present invention to any given examples.

[0097] While various embodiments of the present invention have been described above, the above description is intended to be illustrative, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A remote sensing soil moisture downscaling method based on high / low frequency band brightness temperature fusion, characterized in that: include: Acquire FY-3B W-band TB data and global 25-km resolution FY-3B VSM data; The FY-3B W-band TB data were resampled to global 25-km resolution pixels and global 12.5-km resolution pixels respectively; Calculate the thermal inertia index TI of the corresponding pixel on the same day at the global 25-km resolution pixel scale and the global 12.5-km resolution pixel scale respectively; Constructing a VSM-TI response relationship model based on the global 25-km resolution pixels; Constructing a VSM downscaling equation for converting 25-km resolution pixels to 12.5-km resolution pixels based on the VSM-TI response relationship model; The global 12.5-km resolution FY-3B VSM data were calculated according to the VSM downscaling relationship.

2. The remote sensing soil moisture downscaling method based on high / low frequency band brightness temperature fusion according to claim 1, wherein: Calculating the thermal inertia index TI includes: On the global 25-km pixel scale, the FY-3B W-band TB data at adjacent moments within the same day are differentiated, and the difference is used as the thermal inertia index TI of the pixel on that day.

3. The remote sensing soil moisture downscaling method based on high / low frequency band brightness temperature fusion according to claim 2, wherein: Adjacent times within the same day are the orbit raising time and orbit lowering time within that day.

4. The remote sensing soil moisture downscaling method based on high / low frequency band brightness temperature fusion according to claim 2, wherein: The difference between the FY-3B W band TB data at adjacent times within the same day is calculated, and the difference is used as the thermal inertia index TI of the pixel on that day, including: Assume that on a certain day t0, the FY-3B satellite orbit-raising transit time t01 and orbit-descending transit time t02 correspond to each other, and the next adjacent day t1 corresponds to the FY-3B satellite orbit-raising transit time t11 and orbit-descending transit time t12; The corresponding FY-3B W-band TB data at the above moments are TBW t01 TBW t02 TBW t11 TBW t12 ; The daily average thermal inertia index TI corresponding to the orbit raising time on date t0 t01 for: TI t01 =0.5×[(TBW t01 -TBW t02 )+(TBW t01 -TBW t12 )] The daily average thermal inertia index TI corresponding to the orbit-dropping moment on date t1 t12 for: TI t12 =0.5×[(TBW t01 -TBW t12 )+(TBW t11 -TBW t12 )]。 5. The remote sensing soil moisture downscaling method based on high / low frequency band brightness temperature fusion according to claim 1, wherein: The VSM-TI response relationship model for the global 25-km pixel is: VSM 25km =k×TI 25km +b where k and b are model coefficient constants obtained by fitting the daily VSM and TI long time series at each 25-km pixel.

6. The remote sensing soil moisture downscaling method based on high / low frequency band brightness temperature fusion according to claim 1, wherein: The VSM downscaling relationship is: VSM 12.5km =VSM 25km +k×(TI 12.5km -YOU 25km ) Among them, VSM 12.5km The data are global 12.5-km resolution FY-3B VSM data.

7. An electronic device, characterized in that: The electronic device comprises: a memory storing executable instructions; A processor, wherein the processor runs the executable instructions in the memory to implement the remote sensing soil moisture downscaling method of high / low frequency band brightness temperature fusion according to any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the remote sensing soil moisture downscaling method for high / low frequency band brightness temperature fusion according to any one of claims 1 to 6.

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