A method for downscaling soil water dynamics of SMAP based on TOPMODEL theory

Abstract

The application discloses a kind of SMAP soil hydrodynamic downscaling method based on TOPMODEL theory, by standardization processing, make original SMAP data and DEM data match in spatial attribute, obtain standardization SMAP data;Based on DEM data, calculate confluence area and slope, then calculate topographic index according to TOPMODEL model;Through standardization SMAP data construction time series and execute space-time stabilization processing, obtain basic soil moisture field;Based on topographic index and basic soil moisture field, calculate and obtain preliminary soil moisture data by downscaling model, obtain target soil water grid data with higher resolution by quality optimization to preliminary soil moisture data;The downscaling method of the application structure is simple and convenient for batch operation, can improve low-resolution SMAP soil water data to high-resolution soil water data with same resolution as DEM data in mountainous area, hilly and valley and other complex terrain area.

Description

Technical Field

[0001] This invention relates to the field of remote sensing geoscience application technology, specifically to a SMAP soil hydrodynamic downscaling method based on TOPMODEL theory. Background Technology

[0002] Soil moisture is a key parameter in hydrology, meteorology, and agriculture. Satellite remote sensing technology, especially the passive microwave soil moisture products provided by the SMAP (Soil Moisture Active Passive) satellite, can achieve large-scale observation, but its spatial resolution is usually above 10 kilometers, making it difficult to capture the spatial details of soil moisture at small and medium scales caused by topographic relief and underlying surface heterogeneity.

[0003] Currently, downscaling methods are a key technical approach to improve the spatial resolution of remote sensing soil water. Most mainstream methods are based on statistical downscaling, but these methods are largely based on empirical statistical relationships and lack clear hydrophysical mechanism support. Therefore, they are insufficient in representing hydrophysical processes and face challenges in spatial characterization accuracy under complex underlying surface conditions.

[0004] Currently, there is a lack of methods for downscaling SMAP data in complex terrain environments. Summary of the Invention

[0005] This invention provides a SMAP soil hydrodynamic downscaling method based on TOPMODEL theory to solve the problem that there is a lack of methods in the prior art for downscaling SMAP data in complex terrain environments.

[0006] This invention is achieved through the following technical solution:

[0007] A SMAP soil hydrodynamic downscaling method based on TOPMODEL theory includes the following steps:

[0008] S110. Obtain the original SMAP soil moisture data and DEM data of the study area;

[0009] S120. Based on the original SMAP soil moisture data, through standardization processing, the original SMAP soil moisture data and the DEM data are matched in spatial attributes to obtain standardized SMAP data.

[0010] S130. Based on the DEM data, preprocessing is performed to obtain topographic data suitable for hydrological analysis, the topographic data including catchment area. and slope Based on the terrain data, the terrain index is calculated according to the TOPMODEL model. The TOPMODEL model is as follows:

[0011] ;

[0012] S140. Based on the standardized SMAP data, a time series is constructed, and the time series is processed by spatiotemporal stabilization to obtain the basic soil moisture field.

[0013] S150. Based on the basic soil moisture field and the topographic index, preliminary soil moisture data are calculated using a downscaling model.

[0014] S160. Based on the preliminary soil moisture data, the target soil water raster data is obtained through quality optimization processing.

[0015] The principle of this invention: The original SMAP soil moisture data has a low resolution but contains soil moisture data, while the DEM data contains geographic information of the study area and has a high resolution. The original SMAP soil moisture data and DEM data differ in both spatial reference and resolution. First, the original SMAP soil moisture data and DEM data are matched in terms of spatial attributes to obtain standardized SMAP data. The DEM data is preprocessed to obtain topographic data, which is then substituted into the TOPMODEL model to calculate the topographic index. The topographic index characterizes the water storage and drainage capacity of the land surface; the higher the topographic index, the more prone the corresponding area is to water accumulation. The TOPMODEL model is a topographic-based semi-partition model. The Buhlin hydrological model, utilizing the TOPMODEL model, simplifies complex hydrological processes into topographically driven processes, resulting in a simpler model structure and fewer parameters. The TOPMODEL model enables this approach to downscale raw SMAP soil moisture data from complex terrain areas such as mountains, hills, and valleys. A time series is then constructed from standardized SMAP data, followed by spatiotemporal stabilization to obtain a basic soil moisture field. Using topographic indices and the basic soil moisture field, a downscaling model is employed to calculate high-resolution preliminary soil moisture data. Finally, the quality of the preliminary soil moisture data is optimized to remove outliers, thereby obtaining the target soil water grid data.

[0016] Preferably, the standardized SMAP data is obtained using a first optional procedure, which is as follows:

[0017] S120A. Based on the first projection system, the row and column indices of the original SMAP soil moisture data are mapped to geographic coordinates to establish a two-dimensional spatial coordinate field and obtain the first data.

[0018] S120B: By standardizing and integrating the first data, a second data that is consistent in both the time and space dimensions is obtained;

[0019] S120C. The second data is processed by spatialization standards to make the second data have the same spatial reference, spatial resolution and spatial coverage as the DEM data, thereby obtaining standardized SMAP data.

[0020] The first optional workflow is a forward processing workflow. It performs coordinate mapping on the raw SMAP data to obtain first data carrying geographic coordinates. Then, it performs normalization and integration processing to ensure that each geographic coordinate has data at different time points, forming second data that maintains consistency in both time and spatial dimensions. Through spatial normalization processing, the second data and the DEM data have consistent spatial reference, spatial resolution, and spatial coverage, thus obtaining standardized SMAP data. The first optional workflow is suitable for scenarios with moderate data volume or high transparency in the processing workflow.

[0021] Preferably, the standardized SMAP data is obtained using a second optional procedure, which includes:

[0022] S120D: Map the spatial range of the DEM data back to the original coordinate system of the original SMAP data to obtain the spatial range of the smallest data subset to be read in the original SMAP data, and generate the corresponding data index based on the spatial range.

[0023] S120E: Based on the data index, read the corresponding initial data subset from the original SMAP data in a directional manner;

[0024] S120F: Perform missing value repair and spatial integrity correction on the initial data subset to obtain the target data subset;

[0025] S120G: Reproject the target data subset onto the coordinate system used by the DEM data to obtain the standardized SMAP data.

[0026] The second optional process is a reverse processing process, which is suitable for scenarios involving large-area and long-term batch processing, and has high data reading and processing efficiency.

[0027] Preferably, in step S130, a lower limit threshold is introduced. For the slope tangent value To perform stability processing, the safe slope tangent is calculated using the following formula. :

[0028] .

[0029] When the slope is close to zero The value tends to 0, leading to The calculated value tends to infinity, so a lower threshold is set. This can prevent the terrain index from tending to infinity, thus solving the problem of terrain index calculation failure in flat areas.

[0030] Preferably, step S130 is implemented through the following steps:

[0031] S130E: Divide the DEM data into multiple spatial blocks with overlapping boundaries, and independently calculate hydro-topographic parameters within each spatial block to obtain a standard spatial block;

[0032] S130F: By splicing and merging all the standard spatial blocks, continuous and consistent flow direction and confluence area data of the entire region are obtained.

[0033] S130G. Based on the overall flow direction and catchment area data, the optimized TOPMODEL model is used to calculate the topographic index. The optimized TOPMODEL model is as follows:

[0034] .

[0035] Spatial partitioning of DEM data and parallel processing of all spatial blocks improves the processing efficiency of large-scale, high-resolution DEM data; the new terrain index calculation model improves computational stability.

[0036] The DEM data is divided into blocks, processed, and stitched together. Block processing is faster and can solve the problem that a single machine's memory cannot accommodate large-scale DEM data.

[0037] Preferably, in step S130, the slope angle is introduced. At least one of the following factors, surface roughness R and vegetation cover V, is used to expand the topographic index. Obtain extended terrain index Expanded Terrain Index The calculation formula is:

[0038] ,

[0039] in, , , , These are the weighting coefficients; This is the preset reference slope angle.

[0040] By introducing factors such as slope aspect, roughness, and vegetation to construct an extended topographic index, the topographic index can comprehensively consider the influence of more surface features on soil moisture distribution, thereby improving the adaptability of the downscaling model under complex terrain and underlying surface conditions.

[0041] Preferably, the spatiotemporal stabilization processing employs an adaptive time window mechanism to process the time series; the adaptive time window mechanism is as follows:

[0042] When the cumulative rainfall exceeds the first threshold, the cumulative snowmelt exceeds the second threshold, or the fluctuation range of evapotranspiration exceeds the preset safety range, the length W of the time window will be shortened to the preset minimum length.

[0043] During the stable period, based on the fluctuation of the soil moisture sequence The length of the time window is adjusted using an adaptive adjustment formula to keep it within a preset range. The adaptive adjustment formula is as follows:

[0044] ,

[0045] Where W is the length of the time window; This is the preset maximum time window length; This is the preset minimum time window length; This is the scaling factor; This is a reference noise scale.

[0046] The adaptive adjustment mechanism can dynamically adjust the window length according to the fluctuations in hydrological events such as rainfall and snowmelt or soil moisture sequences, so that the construction of the basic soil moisture field can better reflect the actual hydrological dynamics.

[0047] Preferably, the spatiotemporal stabilization process uses a time window of a preset length to process the time series, specifically including:

[0048] Within a preset time window, the maximum and minimum values ​​of soil moisture in the time series are extracted. Based on the maximum and minimum values, the basic soil moisture field is obtained through the spatiotemporal stabilization process.

[0049] By using a preset time window and extreme value constraints, effective suppression of time series fluctuations can be achieved without dynamic window adjustment. It features a simple algorithm structure, low computational complexity, and strong stability, making it suitable for application scenarios with relatively stable meteorological conditions or high computational efficiency requirements.

[0050] Preferably, the downscaling model is:

[0051] ;

[0052] Let be the downscaled soil moisture of the i-th pixel. This refers to the basic soil moisture field. Sensitivity coefficient For the basic soil moisture field Average terrain index within the corresponding coarse-resolution grid cell; Let be the terrain index of the i-th pixel.

[0053] Preferably, in the downscaling model, the sensitivity coefficient m is obtained by calibration using ground observation data, cross-validation, or the least squares method.

[0054] Ground observation data calibration is suitable for research areas with a relatively dense number of ground observation points, such as agricultural experimental sites; cross-validation calibration is suitable for scenarios with fewer ground observation points, but where it is necessary to prevent model overfitting; least squares calibration is a general calibration method.

[0055] Compared with the prior art, the present invention has at least the following advantages and beneficial effects:

[0056] The original SMAP soil moisture data and DEM data are spatially matched to obtain standardized SMAP data. DEM data is preprocessed to obtain topographic data, which is then substituted into the TOPMODEL model to calculate the topographic index. The topographic index characterizes the water storage and drainage capacity of the land surface; the larger the topographic index, the more prone the corresponding area is to water accumulation. The TOPMODEL model is a topographic-based semi-distributed hydrological model. Using the TOPMODEL model, complex hydrological processes can be simplified into topographically dominated hydrological processes, resulting in a simple model structure and fewer parameters. Utilizing the TOPMODEL model, this approach can be applied to mountainous, hilly, and riverine areas. The original SMAP soil moisture data for complex terrain areas such as valleys is downscaled; then, a time series is constructed using standardized SMAP data, followed by spatiotemporal stabilization processing to obtain the basic soil moisture field; at this point, using the topographic index and the basic soil moisture field, a downscaling model is used to calculate and obtain preliminary soil moisture data with higher resolution. The quality of the preliminary soil moisture data is optimized to remove outliers, thereby obtaining the target soil water grid data and improving the resolution of the soil moisture data. The time series constructed from standardized SMAP data undergoes spatiotemporal stabilization processing, which reduces interference in the original SMAP data and makes the data calculation more stable and accurate. Attached Figure Description

[0057] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:

[0058] Figure 1 This is a flowchart of the SMAP soil hydrodynamic downscaling method based on TOPMODEL theory, a specific embodiment of the present invention.

[0059] Figure 2This is an elevation map of the study area in a specific embodiment of the present invention;

[0060] Figure 3 This is a topographic index map of the study area in a specific embodiment of the present invention;

[0061] Figure 4 This represents the composite maximum value of the original SMAP soil moisture data for the study area from August 28, 2025 to September 1, 2025 in a specific embodiment of the present invention.

[0062] Figure 5 for Figure 4 Comparison of the synthetic maximum values ​​of the original SMAP soil moisture data after downscaling;

[0063] Figure 6 This is the minimum value of the composite original SMAP soil moisture data for the study area from August 28, 2025 to September 1, 2025 in a specific embodiment of the present invention.

[0064] Figure 7 for Figure 6 A comparison of the composite minimum values ​​of the original SMAP soil moisture data after downscaling. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments and accompanying drawings. The illustrative embodiments and descriptions of this invention are for explaining the invention only and are not intended to limit the invention. In the description of this application, it should be understood that terms such as "front," "rear," "left," "right," "upper," "lower," "vertical," "horizontal," "high," "low," "inner," and "outer," indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description. They do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as limiting the scope of protection of this application.

[0066] Example 1:

[0067] refer to Figure 1 , Figure 1 This is a flowchart illustrating a specific embodiment of the SMAP soil hydrodynamic downscaling method based on TOPMODEL theory, which includes the following steps:

[0068] S110. Obtain the original SMAP soil moisture data and DEM data of the study area.

[0069] SMAP data refers to global soil moisture data measured by NASA's SMAP satellites using radar and radiation.

[0070] DEM data is digital elevation model data, which can accurately describe the surface topography. (Reference) Figure 2 , Figure 2 This is an elevation map of the study area in a specific embodiment of the present invention. Figure 2 In the image, DEM Value represents the elevation of the study area. Darker colors indicate lower elevations. "High" represents the highest elevation at 7141m, and "Low" represents the lowest elevation at 363m. Figure 2 The kilometers in the lower right corner indicate the scale.

[0071] Raw SMAP soil moisture data was obtained from NASA's official platform. This raw SMAP data has a spatial resolution of 9 km, covers the entire study area over the specified time period, and includes the time stamp, projection parameters, data quality indicators, and variable structure information. DEM data for the corresponding area was obtained from the USGS EarthExplorer database. This DEM data has a resolution of 90 m to ensure accurate depiction of surface topography. The acquired DEM data underwent checks for projection information, spatial continuity, and missing data, and was initially cropped as needed to ensure complete coverage of the study area.

[0072] S120. Based on the original SMAP soil moisture data, through standardization processing, such as projection transformation, resampling and spatial clipping, the original SMAP soil moisture data and DEM data are matched in spatial attributes to obtain standardized SMAP data.

[0073] Spatial attribute matching means that the original SMAP soil moisture data and DEM data have consistent spatial reference, aligned cell grids, and overlapping spatial extents. Spatial reference consistency means that the two use the exact same geographic coordinate system or projected coordinate system; cell grid alignment means that the two have the same spatial resolution; overlapping spatial extents means that the two cover the same geographic area.

[0074] In step S120, the standardized SMAP data is obtained using a first optional procedure, which is as follows:

[0075] S120A. Based on the first projection system, the row and column indices of the original SMAP soil moisture data are mapped to geographic coordinates to establish a two-dimensional spatial coordinate field and obtain the first data.

[0076] Specifically, the first projection system can be the EASE-Grid 2.0 equal-area grid projection system. The first projection system includes, but is not limited to, the grid projection type, reference ellipsoid, central meridian, and grid resolution. The horizontal and vertical indices of the original SMAP soil moisture data are mapped to the projection plane of the first projection system, so that the horizontal and vertical indices are converted into corresponding latitude and longitude coordinates. The index is used to uniquely identify any grid cell in the original SMAP soil moisture data, forming a two-dimensional spatial coordinate field that completely covers the original SMAP soil moisture data, thereby obtaining the first data. At this time, the first data has complete projection attributes and accurate geographic coordinates, providing spatial positioning basis for subsequent reprojection processing.

[0077] S120B: By standardizing and integrating the first data, second data that remains consistent in both the time and spatial dimensions is obtained.

[0078] The first set of data is read sequentially in chronological order, and then normalized and integrated. The steps of the normalization and integration process include:

[0079] S121. Unify the variable names, structure format, and grid attributes of the first data so that the first data at different times remains consistent in format;

[0080] For example, rename the first data according to the format "region-date-data type-resolution"; convert data from different sources or from different periods into the same array structure and storage format.

[0081] S122. According to the quality label layer provided by the SMAP data, remove invalid observations or invalid fill values ​​in the first data and apply masking to abnormal pixels.

[0082] S123. Organize the first data of all time phases into a regular multi-temporal grid sequence according to the time identifier, so that the first data has a complete and continuous time dimension, thereby obtaining the second data.

[0083] S120C processes the second data through spatialization standards, ensuring that the second data and DEM data have consistent spatial reference, spatial resolution, and spatial coverage, thus obtaining standardized SMAP data.

[0084] The steps of spatial standardization include:

[0085] The second data is reprojected to a projection system consistent with the DEM data, such as the WGS_1984_Albers coordinate system, to ensure that the georeference of the second data is consistent.

[0086] Based on the cell size of the DEM data, the second data is resampled to ensure that the spatial resolution of the second data is consistent with that of the DEM data; and missing cells are processed by local interpolation or spatial filling.

[0087] Based on the study area boundaries, the second data is automatically cropped to ensure that the second data strictly matches the DEM data in terms of geographical scope, thereby obtaining standardized SMAP data.

[0088] S130. Based on DEM data, preprocessing is performed to obtain topographic data suitable for hydrological analysis. The topographic data includes catchment area. and slope Based on terrain data, the terrain index is calculated according to the TOPMODEL model. The TOPMODEL model is:

[0089] .

[0090] The TOPMODEL model is a terrain-based semi-distributed hydrological model. Using the TOPMODEL model, complex hydrological processes can be simplified into topographically dominated hydrological processes, resulting in a simple model structure and fewer parameters. The TOPMODEL model enables this approach to downscale the original SMAP soil moisture data in complex terrain areas such as mountains, hills, and valleys.

[0091] In this embodiment, the preprocessing steps include:

[0092] S130A: The DEM data is filled with depressions to create a continuous and logically clear flow path structure, thus obtaining initial DEM data. Specifically:

[0093] First, the DEM data undergoes a quality check to identify and address abnormal elevation values. Then, ArcPy is used to call ArcGIS's Fill tool to fill depressions in the DEM. This Fill tool employs a minimum priority queue-based surface filling algorithm, which progressively raises depressions without outlets in the DEM data, allowing water to flow continuously down the natural slope. During the Fill process, ArcPy automatically analyzes local low-lying areas and performs minor elevation corrections, ensuring the DEM data possesses a continuous and reasonable water flow path structure, thus obtaining the initial DEM data.

[0094] S130B: Based on the initial DEM data, the slope of each pixel is calculated. and slope tangent .

[0095] Based on the initial DEM data, ArcPy's Slope tool is used to calculate the slope of each cell. Internally, the Slope tool employs the Horn algorithm to perform local elevation difference on the 3×3 neighborhood, calculating a slope raster representing the degree of slope inclination, and outputting slope values ​​in angle format (in degrees). During execution, ArcPy automatically estimates the horizontal and vertical elevation gradients based on the DEM cell size, thus obtaining a slope that meets the requirements of hydrological analysis. The slope tangent can be obtained by further transformation of the slope aspect results. .

[0096] S130C, based on the initial DEM data, calculate the catchment area α of each cell.

[0097] Flow direction calculation was performed using ArcPy's FlowDirection tool, which is based on the D8 flow direction model. It determines the steepest descent direction of the water flow by comparing the elevation difference between the center pixel and its eight neighboring pixels, and outputs the flow direction raster in coded form. Then, the FlowAccumulation tool was used to perform laminar flow accumulation calculations on the flow direction raster to count the number of upstream catchment units for each pixel. ArcPy automatically accumulates the number of upstream pixels along the flow direction network in this step, outputting laminar flow accumulation values ​​expressed as pixel counts. Combining this with the resolution of the DEM data, the catchment area is obtained by multiplying the laminar flow accumulation value by the pixel area. .

[0098] S130D, based on slope tangent and catchment area The topography index λ is calculated using the TOPMODEL model; the TOPMODEL model is as follows:

[0099] .

[0100] In this embodiment, slope Too small a value may cause the denominator of the TOPMODEL formula to approach zero. ArcPy uses the Con function to... Set a minimum threshold so that The calculation is relatively stable, and can be specifically described as follows:

[0101] By introducing a lower limit threshold For the slope tangent value To perform stability processing, the safe slope tangent is calculated using the following formula. :

[0102] .

[0103] When the catchment area When the value is 0, which represents the ridgeline position, a topographic index can be assigned based on experiments. The specific value.

[0104] Preliminary topographic index was calculated Afterwards, necessary statistical analysis and visualization checks are performed to identify abnormal areas and make appropriate corrections, and then the terrain index is adjusted. Normalize to a reasonable range to facilitate subsequent applications.

[0105] refer to Figure 3 , Figure 3 This is a topographic index map of the study area in a specific embodiment of the present invention. The TI Value in the map represents the topographic index. The darkest areas in the image represent the terrain index. The larger the value, the more "High" indicates in the graph. The maximum value is 38.067; the lightest color represents the terrain index. The smaller the value, the more "Low" it represents in the graph. The minimum value is 7.28112.

[0106] S140. Based on standardized SMAP data, a time series is constructed, and the basic soil moisture field is obtained by spatiotemporal stabilization processing of the time series.

[0107] In this embodiment, standardized SMAP data are reordered according to time identifiers to construct a continuous SMAP time series with a unified coordinate system and spatial resolution, in which the data of each geographic coordinate location at different time points in the time series correspond to each other.

[0108] Spatiotemporal stabilization aims to generate a spatiotemporally continuous and stable baseline soil moisture field. The time series of raw SMAP data typically contains random noise, such as from sensor errors; the time series may also include transient and localized hydrometeorological events such as showers and evaporation variations caused by diurnal temperature variations. Directly using these time series for downscaling will incorrectly amplify and spatialize these high-frequency disturbances and transient fluctuations, resulting in unreasonable spatial noise in the high-resolution results.

[0109] In this embodiment, the spatiotemporal stabilization process uses a time window of preset length to process the time series, specifically including:

[0110] Within a preset time window, the maximum and minimum values ​​of soil moisture in the time series are extracted. Based on the maximum and minimum values, a basic soil moisture field is obtained through spatiotemporal stabilization processing.

[0111] The preset length can be 5 days. By statistically analyzing the time series and using the max or min function, the maximum and minimum soil moisture values ​​within the 5-day time window are obtained. These values ​​are used to characterize the range of soil moisture changes within that time period and to serve as constraints or fluctuation criteria for spatiotemporal stabilization. This allows for range limitation, anomaly suppression, or smoothing of the basic soil moisture field, thereby reducing non-physical abrupt changes in soil moisture over short timescales.

[0112] Spatial interpolation or resampling is performed on the standardized SMAP data after spatiotemporal stabilization, and the data is mapped to a high-resolution grid consistent with the DEM. The mapped moisture field is the basic soil moisture field. Mapping methods can employ nearest neighbor, bilinear, or area-weighted approaches to ensure that low-resolution SMAP information is appropriately propagated to each DEM cell.

[0113] S150, based on basic soil moisture field and terrain index Preliminary soil moisture data were obtained by using a downscaling model. .

[0114] In this embodiment, the downscaling model is:

[0115] ;

[0116] Let be the downscaled soil moisture of the i-th pixel. Based on the soil moisture field Sensitivity coefficient Basic soil moisture field Average terrain index within the corresponding coarse-resolution grid cell; Let be the terrain index of the i-th pixel.

[0117] Downscaled soil moisture data can be used as a reference. Figures 4-7 ,in, Figure 4 This represents the composite maximum value of the original SMAP soil moisture data for the study area from August 28, 2025 to September 1, 2025 in a specific embodiment of the present invention. Figure 5 for Figure 4 Comparison of the synthetic maximum values ​​of the original SMAP soil moisture data after downscaling; Figure 6 This is the minimum value of the composite original SMAP soil moisture data for the study area from August 28, 2025 to September 1, 2025 in a specific embodiment of the present invention. Figure 7 for Figure 6 A comparison of the composite minimum values ​​of the original SMAP soil moisture data after downscaling.

[0118] exist Figures 4-7 In this context, "Soil Moisture Value" represents the soil moisture level; the darker the color, the higher the soil moisture value. Figures 4-7 In this context, "High" represents the maximum soil moisture content, and "Low" represents the minimum soil moisture content.

[0119] S160, based on preliminary soil moisture data Through quality optimization processing, high-resolution soil water raster data of the target were obtained.

[0120] The steps of quality optimization include:

[0121] The SM obtained after downscaling i The data undergoes rigorous physical range testing, limiting soil water values ​​to between residual soil moisture content and saturated soil moisture content, or to the effective range of [0,1]. Abnormally high values, abnormally low values, or missing pixels are corrected through neighborhood interpolation, time series smoothing, or statistical thresholding to improve the spatial consistency and reliability of soil water products, thereby obtaining the target high-resolution soil water raster data.

[0122] In the downscaling model, the sensitivity coefficient m can be obtained through ground observation data, cross-validation, or least squares calibration.

[0123] In this embodiment, the sensitivity coefficient m can be obtained through least squares calibration, and the specific steps are as follows:

[0124] The study area was divided into several spatial regions, and a downscaling model was constructed for each region:

[0125]

[0126] in, Observed soil moisture values ​​for each region, The values ​​are the original SMAP soil moisture data. This represents the normalized deviation of the topographic index of the i-th pixel relative to the average topographic index of the SMAP pixels.

[0127] In areas with ground-based observation stations, the sensitivity coefficient m is estimated using the least squares method, and the calculation formula is as follows:

[0128]

[0129] in, Let be the soil moisture observation value within the area of ​​the i-th ground observation station. Depend on The results were obtained from the representative downscaling model.

[0130] Example 2

[0131] Example 2 is largely the same as Example 1, except that the method of converting the original SMAP data into standardized SMAP data has been adjusted.

[0132] In this embodiment, the standardized SMAP data is obtained using a second optional procedure, which includes:

[0133] S120D maps the spatial extent of the DEM data back to the original coordinate system of the original SMAP data to obtain the spatial extent of the smallest subset of data to be read in the original SMAP data, and generates the corresponding data index based on the spatial extent.

[0134] The boundary coordinates are back-projected from the DEM to the original coordinate space of the original SMAP data, i.e., the EASE-Grid coordinate system. This yields the minimum spatial range that the original SMAP data should theoretically cover. After mapping this range to the SMAP data grid index coordinates, the minimum row and column index interval can be automatically determined.

[0135] S120E, based on the data index, reads the corresponding initial data subset from the original SMAP data in a targeted manner, effectively reducing the workload of data I / O.

[0136] S120F: Perform missing value imputation and spatial integrity correction on the initial data subset to obtain the target data subset, specifically including:

[0137] If some SMAP files in the initial data subset are missing projection metadata or have local coordinate holes, the official default projection parameters of SMAP can be used as a substitute projection matrix, and spatial holes can be repaired by neighborhood bilinear, inverse distance weighted, or time proximity interpolation to obtain the target data subset.

[0138] S120G reprojects a subset of the target data onto the coordinate system used by the DEM data, ensuring that the subset of the target data is spatially aligned with the DEM grid, thereby obtaining standardized SMAP data.

[0139] The data transformation method in this embodiment can ensure that the SMAP data at all time steps can be effectively reprojected without being affected by local projection loss, while significantly reducing unnecessary data reading and improving the overall processing efficiency and robustness of step 120.

[0140] Example 3

[0141] Example 3 is largely the same as Example 1, except that:

[0142] Step S130 can be specifically implemented through the following steps:

[0143] S130E divides the DEM data into multiple spatial blocks with overlapping boundaries, and performs hydrogeophysical parameter calculations independently within each spatial block, such as depression filling, flow direction calculation, and fluid deposition calculation, to obtain a standard spatial block.

[0144] S130F, based on the principle of consistency in overlapping areas, by splicing and merging all standard spatial blocks, continuous and consistent flow direction and confluence area data for the entire region are obtained, specifically:

[0145] (1) For non-overlapping regions, the calculation results of each standard spatial block are directly used;

[0146] (2) For overlapping areas, compare the calculation results of adjacent standard spatial blocks, identify differences, and then use a weighted average method to fuse the differences. The weights are determined based on the distance from the pixel to the block boundary, with lower weights at the boundary and higher weights at the center.

[0147] (3) For discrete data such as flow direction, the voting method is used to determine the final value.

[0148] After stitching together all the standard spatial blocks, a global consistency check is performed, and local corrections are made to obtain the flow direction and catchment area data for the entire region.

[0149] S130G, based on the flow direction and catchment area data of the entire region, uses the optimized TOPMODEL model to calculate the topographic index. The optimized TOPMODEL model is as follows:

[0150]

[0151] In this embodiment, the terrain index The calculation model is different from the terrain index in Example 1. The calculation model uses the base-10 logarithm (log) instead of the natural logarithm (ln), resulting in a more moderate numerical range. In this embodiment, the argument in the calculation model formula is increased by 1 compared to the calculation model in Embodiment 1, which avoids the argument value from approaching 0.

[0152] In step S130, the slope angle is introduced. At least one of the following factors: surface roughness R, vegetation cover V, etc., to expand the topographic index. Obtain extended terrain index Expanded Terrain Index The calculation formula is:

[0153] ,

[0154] in, , , , These are the weighting coefficients; This is the preset reference slope angle.

[0155] It is usually set to 1; Reflecting the importance of slope aspect in influencing soil moisture, especially in humid regions. Smaller, arid regions Larger; Determined based on the region's surface characteristics, generally, greater roughness indicates stronger infiltration capacity and higher soil water retention capacity, especially in mountainous areas and fractured terrain regions. Larger values; function The design needs to consider the dual role of vegetation in soil moisture: on the one hand, vegetation transpiration consumes water, and on the other hand, the vegetation canopy intercepts water and the litter layer increases infiltration; function Piecewise functions are typically used: when vegetation cover is low, vegetation transpiration consumes water. It decreases as V increases; when vegetation cover is high, Increases with increasing V; weight It varies with vegetation type and climate zone.

[0156] In this embodiment, before step S130E, it can also be... Set a minimum threshold and introduce a lower limit threshold. For the slope tangent value To perform stability processing, the safe slope tangent is calculated using the following formula. :

[0157] .

[0158] Example 4

[0159] Example 4 is largely the same as Example 1, except that:

[0160] In this embodiment, the spatiotemporal stabilization process employs an adaptive time window mechanism to process the time series; the adaptive time window mechanism is as follows:

[0161] When the cumulative rainfall exceeds the first threshold, the cumulative snowmelt exceeds the second threshold, or the fluctuation range of evapotranspiration exceeds the preset safety range, the length W of the time window is shortened to the preset minimum length, specifically 3 days, to reduce the smoothing effect of time averaging, so that the basic soil moisture field can quickly respond to and capture the dynamic changes in soil moisture caused by real hydrological events, and avoid the real signal being over-smoothed and distorted.

[0162] During the stable period, based on the fluctuation of the soil moisture sequence The length of the time window is adjusted using an adaptive adjustment formula, for example, 7 to 10 days, to keep the time window length within a preset range. The adaptive adjustment formula is as follows:

[0163] ,

[0164] Where W is the length of the time window; This is the preset maximum time window length; This is the preset minimum time window length; This is the scaling factor; This is a reference noise scale.

[0165] During the stable period, by combining data over a longer period of time, random noise can be effectively suppressed, and a more stable background state of soil moisture can be extracted.

[0166] In this embodiment, by associating the window length with indicators such as the fluctuation range of cumulative rainfall, cumulative snowmelt, and evapotranspiration, the time window is shortened during periods of active hydrological events to quickly capture the true dynamics; and the time window is extended during periods of stability to maximize the suppression of random noise. This achieves adaptive stabilization of the time window and provides reliable data for subsequent downscaling calculations.

[0167] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0168] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Additionally, the term "connection" as used herein, unless otherwise specified, can refer to a direct connection or an indirect connection via other components.

Claims

1. A SMAP soil hydrodynamic downscaling method based on TOPMODEL theory, characterized in that, Includes the following steps: S110. Obtain the original SMAP soil moisture data and DEM data of the study area; S120. Based on the original SMAP soil moisture data, through standardization processing, the original SMAP soil moisture data and the DEM data are matched in spatial attributes to obtain standardized SMAP data. S130. Based on the DEM data, preprocessing is performed to obtain topographic data suitable for hydrological analysis, the topographic data including catchment area. and slope ; Based on the terrain data, the terrain index is calculated according to the TOPMODEL model. The TOPMODEL model is as follows: ； Introducing slope angle Surface roughness R, vegetation cover At least one factor in the topographic index, expanding the topographic index Obtain extended terrain index Expanded Terrain Index The calculation formula is: ； in, , , , These are the weighting coefficients; The preset reference slope angle, The tangent value represents the safe slope. S140. Based on the standardized SMAP data, a time series is constructed, and the time series is processed through spatiotemporal stabilization to obtain the basic soil moisture field; the spatiotemporal stabilization process employs an adaptive time window mechanism to process the time series; the adaptive time window mechanism is as follows: When the cumulative rainfall exceeds the first threshold, the cumulative snowmelt exceeds the second threshold, or the fluctuation range of evapotranspiration exceeds the preset safety range, the length W of the time window will be shortened to the preset minimum length. During the stable period, based on the fluctuation of the soil moisture sequence The length of the time window is adjusted using an adaptive adjustment formula to keep it within a preset range. The adaptive adjustment formula is as follows: ； Where W is the length of the time window; This is the preset maximum time window length; This is the preset minimum time window length; This is the scaling factor; For reference noise scale; S150. Based on the basic soil moisture field and the topographic index, preliminary soil moisture data are calculated using a downscaling model. S160. Based on the preliminary soil moisture data, the target soil water raster data is obtained through quality optimization processing.

2. The SMAP soil hydrodynamic downscaling method based on TOPMODEL theory according to claim 1, characterized in that, The standardized SMAP data is obtained using a first optional procedure, which is as follows: S120A. Based on the first projection system, the row and column indices of the original SMAP soil moisture data are mapped to geographic coordinates to establish a two-dimensional spatial coordinate field and obtain the first data. S120B: By standardizing and integrating the first data, a second data that is consistent in both the time and space dimensions is obtained; S120C. The second data is processed by spatialization standards to make the second data have the same spatial reference, spatial resolution and spatial coverage as the DEM data, thereby obtaining standardized SMAP data.

3. The SMAP soil hydrodynamic downscaling method based on TOPMODEL theory according to claim 1, characterized in that, The standardized SMAP data is obtained using a second optional procedure, which includes: S120D: Map the spatial range of the DEM data back to the original coordinate system of the original SMAP data to obtain the spatial range of the smallest data subset to be read in the original SMAP data, and generate the corresponding data index based on the spatial range. S120E: Based on the data index, read the corresponding initial data subset from the original SMAP data in a directional manner; S120F: Perform missing value repair and spatial integrity correction on the initial data subset to obtain the target data subset; S120G: Reproject the target data subset onto the coordinate system used by the DEM data to obtain the standardized SMAP data.

4. The SMAP soil hydrodynamic downscaling method based on TOPMODEL theory according to claim 1, characterized in that, In step S130, a lower limit threshold is introduced. For the slope tangent value To perform stability processing, the safe slope tangent is calculated using the following formula. : 。 5. The SMAP soil hydrodynamic downscaling method based on TOPMODEL theory according to claim 4, characterized in that, Step S130 is implemented through the following steps: S130E: Divide the DEM data into multiple spatial blocks with overlapping boundaries, and independently calculate hydro-topographic parameters within each spatial block to obtain a standard spatial block; S130F: By splicing and merging all the standard spatial blocks, continuous and consistent flow direction and confluence area data of the entire region are obtained. S130G. Based on the overall flow direction and catchment area data, the optimized TOPMODEL model is used to calculate the topographic index. The optimized TOPMODEL model is as follows: 。 6. The SMAP soil hydrodynamic downscaling method based on TOPMODEL theory according to claim 1, characterized in that, The spatiotemporal stabilization process employs a time window of preset length to process the time series, specifically including: Within a preset time window, the maximum and minimum values ​​of soil moisture in the time series are extracted. Based on the maximum and minimum values, the basic soil moisture field is obtained through the spatiotemporal stabilization process.

7. The SMAP soil hydrodynamic downscaling method based on TOPMODEL theory according to claim 1, characterized in that, The downscaling model is as follows: ； Let be the downscaled soil moisture of the i-th pixel. This refers to the basic soil moisture field. Sensitivity coefficient For the basic soil moisture field Average terrain index within the corresponding coarse-resolution grid cell; Let be the terrain index of the i-th pixel.

8. The SMAP soil hydrodynamic downscaling method based on TOPMODEL theory according to claim 7, characterized in that, In the downscaling model, the sensitivity coefficient m is obtained by calibration using ground observation data, cross-validation, or the least squares method.

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