GNSS-R soil humidity quality evaluation method, system, equipment and medium

Abstract

The invention discloses a GNSS-R soil humidity quality evaluation method, system and equipment and a medium. The method comprises the following steps: acquiring a GNSS-R inverted soil humidity data set; respectively acquiring corresponding reference soil humidity data sets from a plurality of independent data sources based on the date information; screening effective soil humidity data and corresponding position coordinates from the soil humidity data set according to the quality identification code and the soil humidity value; performing space-time matching on the effective soil humidity data and the corresponding position coordinates with each pre-processed reference soil humidity data set to obtain a plurality of groups of matching data in one-to-one correspondence with the plurality of independent data sources; and calculating a corresponding quantitative evaluation index based on the soil humidity value contained in each group of matching data. According to the method, the problem that the accuracy and reliability of soil humidity data are insufficient when the GNSS-R technology is independently used is solved, and the quality evaluation capability and data credibility of the GNSS-R soil humidity data are improved.

Description

Technical Field

[0001] This invention relates to the field of soil moisture remote sensing monitoring and quality assessment technology, and in particular to a GNSS-R soil moisture quality assessment method, system, equipment and medium. Background Technology

[0002] Soil moisture, as a key indicator reflecting soil water content, plays a crucial role in numerous fields such as meteorology, agriculture, hydrology, and ecology. In meteorology, accurate soil moisture data is fundamental to improving the accuracy of weather forecasts, especially for precipitation and temperature, and is critical for early warning and response decisions regarding extreme weather disasters such as droughts and floods. In agricultural production, soil moisture directly affects crop growth, yield, and quality. Rational irrigation decisions rely on precise monitoring of soil moisture to achieve efficient water resource utilization and avoid damage to crops caused by excessive or insufficient water. In hydrological research, soil moisture is a core parameter for understanding hydrological processes such as surface runoff and groundwater recharge, contributing to the rational planning and management of water resources. In ecosystems, soil moisture influences vegetation distribution and growth, as well as ecosystem stability, and is essential for maintaining ecological balance.

[0003] Traditional methods for monitoring soil moisture primarily involve in-situ ground measurements, such as the oven-drying weighing method and the time domain reflectometer (TDR) method. While the oven-drying weighing method is a classic and reliable standard for measuring soil moisture, its operation is extremely cumbersome, requiring significant time and manpower, resulting in low efficiency. Furthermore, it can only acquire data from discrete points, failing to comprehensively reflect the soil moisture distribution over a large area and thus making it unsuitable for large-area, real-time dynamic monitoring of soil moisture. Although the TDR method is relatively simple to operate and has a faster measurement speed, its monitoring range is limited by the length and distribution of the probe, and it is also susceptible to interference from factors such as soil texture and salinity, affecting measurement accuracy.

[0004] With the development of remote sensing technology, satellite-based remote sensing monitoring methods have provided new approaches for soil moisture monitoring. For example, optical remote sensing retrieves soil moisture by monitoring electromagnetic wave signals reflected or emitted from the soil surface; however, it is severely limited by conditions such as clouds and atmosphere, and cannot effectively acquire data in cloudy or rainy weather, resulting in poor data continuity and integrity. Active microwave remote sensing (such as synthetic aperture radar SAR) can achieve all-weather, all-day monitoring, but its spatial resolution is low, making it difficult to meet the requirements for high-precision monitoring of local areas, and its data processing is complex and costly. Although passive microwave remote sensing is relatively sensitive to soil moisture, it suffers from low spatial resolution and the observation data is easily affected by factors such as vegetation cover and topography.

[0005] Global Navigation Satellite System Reflectance (GNSS-R) technology, as an emerging remote sensing technology, utilizes signals emitted by navigation satellites that are reflected off the Earth's surface and received by a receiver. By analyzing the characteristics of the reflected signals, surface parameters, including soil moisture, can be retrieved. GNSS-R technology has unique advantages: abundant signal sources, wide coverage enabling global-scale monitoring; high spatiotemporal resolution allowing for frequent data acquisition to meet the needs of monitoring dynamic changes in soil moisture; and relatively low cost, requiring no dedicated signal transmitter. However, soil moisture data acquired solely through GNSS-R technology still needs further improvement in terms of accuracy and reliability, requiring comprehensive evaluation and verification in conjunction with other data sources.

[0006] Therefore, there is an urgent need to provide a technical solution to address the above problems. Summary of the Invention

[0007] To address the aforementioned technical problems, this invention provides a GNSS-R method, system, device, and medium for assessing soil moisture quality.

[0008] In a first aspect, the present invention provides a GNSS-R method for assessing soil moisture quality, the technical solution of which is as follows: Obtain the soil moisture dataset retrieved from GNSS-R, which includes soil moisture values, quality identification codes, location coordinates, and date information; Based on the date information, corresponding reference soil moisture datasets are obtained from multiple independent data sources; Based on the quality identification code and the soil moisture value, valid soil moisture data and corresponding location coordinates are filtered out from the soil moisture dataset; Each reference soil moisture dataset is preprocessed, and the effective soil moisture data and corresponding location coordinates are spatiotemporally matched with each preprocessed reference soil moisture dataset to obtain multiple sets of matching data that correspond one-to-one with the multiple independent data sources. Based on the soil moisture values ​​contained in each set of matched data, corresponding quantitative evaluation indicators are calculated; the quantitative evaluation indicators include root mean square error, mean absolute error, deviation, and correlation coefficient.

[0009] The beneficial effects of the GNSS-R soil moisture quality assessment method of the present invention are as follows: The method of this invention obtains soil moisture data retrieved from GNSS-R and filters valid data using quality identification codes. It acquires reference soil moisture data from multiple independent data sources for spatiotemporal matching and calculates quantitative evaluation indicators such as root mean square error, mean absolute error, bias, and correlation coefficient. This solves the problem of insufficient accuracy and reliability of soil moisture data when GNSS-R technology is used alone, and improves the quality assessment capability and data credibility of GNSS-R soil moisture data.

[0010] Based on the above scheme, the GNSS-R soil moisture quality assessment method of the present invention can be further improved as follows.

[0011] In one alternative approach, the step of obtaining the GNSS-R inverted soil moisture dataset includes: Obtain the soil moisture dataset, which includes soil moisture values, quality identification codes, location coordinates, and date information, from the data provider using GNSS-R technology inversion. The soil moisture value represents the volumetric water content of the shallow surface layer, and the location coordinates are based on the WGS84 coordinate system.

[0012] The advantages of adopting the above optional methods are: it further clarifies the acquisition method of GNSS-R soil moisture dataset, specifies that soil moisture value represents the shallow volumetric water content of the surface, and adopts WGS84 coordinate system-location coordinates to improve the data standardization degree and subsequent matching accuracy.

[0013] In one alternative approach, the multiple data sources include at least: a reanalysis dataset, an SMAP dataset, and an SMOS dataset; the step of obtaining corresponding reference soil moisture datasets from multiple independent data sources based on the date information includes: Based on the date information, determine the target date range; From the reanalysis dataset, the SMAP dataset, and the SMOS dataset, respectively obtain reference soil moisture datasets corresponding to the target date range.

[0014] The advantages of adopting the above optional approach are as follows: further limiting the reanalysis dataset, SMAP dataset and SMOS dataset as independent reference data sources, automatically determining the target date range and extracting the corresponding data based on date information, constructing a multi-source comparison and verification framework, enriching the spatiotemporal coverage capability of the evaluation benchmark, and enhancing the robustness and objectivity of the quality evaluation results.

[0015] In one optional approach, the step of filtering valid soil moisture data and corresponding location coordinates from the soil moisture dataset based on the quality identification code and the soil moisture value includes: The soil moisture dataset is filtered according to the quality identification code. Soil moisture data with a preset quality identification code are identified as valid soil moisture data, and the soil moisture value and location coordinates corresponding to the valid soil moisture data are determined.

[0016] The advantages of using the above-mentioned optional method are as follows: the soil moisture dataset is further filtered based on the quality identification code, and the data that meets the preset value conditions is determined as valid data and the corresponding location coordinates are extracted. Low confidence observations are eliminated from the source, unreliable data is reduced in the evaluation, and the overall reliability of the data used for subsequent matching and calculation is ensured.

[0017] In one optional approach, the step of preprocessing each reference soil moisture dataset separately, and then performing spatiotemporal matching of the valid soil moisture data and corresponding location coordinates with each preprocessed reference soil moisture dataset to obtain multiple sets of matching data corresponding one-to-one with the multiple independent data sources, includes: The reanalysis dataset is preprocessed with format normalization and missing value imputation; the SMAP dataset is preprocessed with multi-day data synthesis and outlier identification; and the SMOS dataset is preprocessed with invalid data removal and numerical range filtering. Each data point and its corresponding location coordinates in the effective soil moisture data are subjected to spatial coordinate transformation and time alignment operations with the preprocessed reanalysis dataset, the SMAP dataset, and the SMOS dataset, respectively, to generate a first set of matching data corresponding to the reanalysis dataset, a second set of matching data corresponding to the SMAP dataset, and a third set of matching data corresponding to the SMOS dataset.

[0018] The beneficial effects of adopting the above optional methods are as follows: performing format standardization and missing value imputation preprocessing on the reanalysis dataset, performing multi-day data synthesis and outlier identification preprocessing on the SMAP dataset, and performing invalid data removal and numerical range filtering preprocessing on the SMOS dataset. The availability of various reference data is improved and the spatiotemporal matching accuracy is optimized through differentiated preprocessing strategies.

[0019] In one alternative approach, the step of calculating the corresponding quantitative assessment index based on soil moisture values ​​contained in any set of matched data includes: Extract the soil moisture value from any set of matching data as the first soil moisture value, and extract the corresponding reference soil moisture value as the second soil moisture value; Based on the first soil moisture value and the second soil moisture value, the root mean square error, mean absolute error, deviation and correlation coefficient of any set of matching data are calculated to obtain the quantitative evaluation index of any set of matching data.

[0020] The beneficial effects of adopting the above optional method are as follows: further extract the GNSS-R soil moisture value and the reference soil moisture value from the matching data, and use them as the first soil moisture value and the second soil moisture value, respectively. On this basis, calculate the root mean square error, mean absolute error, deviation and correlation coefficient, quantify the magnitude of deviation and consistency level from multiple dimensions, and provide an objective evaluation basis.

[0021] In one alternative approach, it also includes: A scatter density plot is drawn based on the first and second soil moisture values ​​of any set of matched data, and the values ​​of the quantitative evaluation indicators of any set of matched data are marked on the scatter density plot.

[0022] The beneficial effects of adopting the above optional method are as follows: further, based on the first soil moisture value and the second soil moisture value of the matching data, a scatter density map is drawn, and the quantitative evaluation index values ​​are marked on the map. The numerical results and data distribution patterns are visualized and integrated, making the evaluation information more intuitive and facilitating the rapid identification of data aggregation characteristics, related trends and abnormal deviation patterns.

[0023] Secondly, this invention provides a GNSS-R soil moisture quality assessment system, the technical solution of which is as follows: The acquisition module is used to acquire the soil moisture dataset retrieved by GNSS-R. The soil moisture dataset includes soil moisture values, quality identification codes, location coordinates, and date information. The extraction module is used to obtain corresponding reference soil moisture datasets from multiple independent data sources based on the date information; The filtering module is used to filter out valid soil moisture data and corresponding location coordinates from the soil moisture dataset based on the quality identification code and the soil moisture value; The matching module is used to preprocess each reference soil moisture dataset and perform spatiotemporal matching between the effective soil moisture data and the corresponding location coordinates and each preprocessed reference soil moisture dataset to obtain multiple sets of matching data that correspond one-to-one with the multiple independent data sources. The evaluation module is used to calculate the corresponding quantitative evaluation index based on the soil moisture value contained in each set of matched data; the quantitative evaluation index includes root mean square error, mean absolute error, deviation, and correlation coefficient.

[0024] The beneficial effects of the GNSS-R soil moisture quality assessment system of the present invention are as follows: The system of this invention obtains soil moisture data retrieved from GNSS-R and filters valid data using quality identification codes. It acquires reference soil moisture data from multiple independent data sources for spatiotemporal matching and calculates quantitative evaluation indicators such as root mean square error, mean absolute error, bias, and correlation coefficient. This solves the problem of insufficient accuracy and reliability of soil moisture data when GNSS-R technology is used alone, and improves the quality assessment capability and data credibility of GNSS-R soil moisture data.

[0025] Thirdly, the technical solution of an electronic device according to the present invention is as follows: It includes a memory, a processor, and a program stored in the memory and running on the processor, wherein the processor executes the program to implement the steps of the GNSS-R soil moisture quality assessment method of the present invention.

[0026] Fourthly, the technical solution of a computer-readable storage medium provided by the present invention is as follows: The computer-readable storage medium stores instructions that, when read, cause the computer-readable storage medium to perform the steps of the GNSS-R soil moisture quality assessment method of the present invention.

[0027] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0028] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 This is a schematic flowchart of an embodiment of a GNSS-R soil moisture quality assessment method according to the present invention; Figure 2 A scatter density plot of ERA5-Land reference soil moisture values ​​and GNSS-R soil moisture values; Figure 3 A scatter density plot of SMAP reference soil moisture values ​​and GNSS-R soil moisture values; Figure 4 A scatter density plot of SMOS reference soil moisture values ​​and GNSS-R soil moisture values; Figure 5 This is a schematic diagram of an embodiment of the GNSS-R soil moisture quality assessment system of the present invention; Figure 6 This is a schematic diagram of an embodiment of an electronic device according to the present invention. Detailed Implementation

[0029] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0030] Figure 1 This diagram illustrates a flowchart of an embodiment of a GNSS-R soil moisture quality assessment method provided by the present invention. This GNSS-R soil moisture quality assessment method can be executed by electronic devices such as terminal devices or servers. The terminal device can be any fixed or mobile terminal, such as user equipment (UE), mobile device, user terminal, terminal, cellular phone, cordless phone, personal digital assistant (PDA), handheld device, computing device, vehicle-mounted device, or wearable device. The server can be a single server or a server cluster consisting of multiple servers. Any electronic device can implement the GNSS-R soil moisture quality assessment method by having its processor call computer-readable instructions stored in its memory. Figure 1 As shown, it includes the following steps: S1. Obtain the soil moisture dataset retrieved by GNSS-R. The soil moisture dataset includes soil moisture values, quality identification codes, location coordinates, and date information.

[0031] GNSS-R refers to Global Navigation Satellite System Reflectance Measurement, a remote sensing technology that utilizes electromagnetic wave signals emitted by navigation satellites, reflected from the Earth's surface, captured by specific receivers, and analyzed to obtain information on surface geophysical parameters. For example, a meteorological observation constellation receives and processes surface reflection signals from multiple navigation satellite systems to infer surface soil moisture conditions. Inversion refers to the process of using observed data or signals to derive target parameter values ​​that cannot be directly observed by constructing or applying physical models, empirical models, or statistical algorithms. For example, based on the power and phase variations of GNSS reflection signals received by satellites, a surface scattering model can be used to calculate the volumetric water content of shallow soil. A soil moisture dataset refers to a structured collection of data containing a series of soil moisture values ​​and related attribute information, derived from remote sensing observations, numerical simulations, or ground measurements. For example, a global soil moisture product file generated by a satellite mission includes the moisture value, geographical location, observation time, and data quality identifier for each data point. Soil moisture content refers to a specific numerical value used to quantify the water content in soil, usually expressed as a percentage or fraction of the water volume per unit volume of soil. For example, at a specific geographical location and time, the soil volumetric water content obtained through remote sensing is 0.25 m³. 3 / m 3 A quality identifier is a numerical code or label attached to data to indicate the quality level or reliability of that data point; for example, in satellite soil moisture products, each data point is accompanied by a quality indicator, with the number "0" representing the highest data quality, meeting high-standard application requirements. Location coordinates are numerical pairs used to accurately pinpoint the geographical location of a data point on the Earth's surface, typically consisting of longitude and latitude; for example, the geographical location associated with a soil moisture data point is 115.5 degrees east longitude and 38.2 degrees north latitude. Date information refers to the specific time or period during which the original observation, measurement, or model simulation corresponding to the data occurred; for example, the time marked on a soil moisture data record is "June 20, 2024, 12:00:00".

[0032] S2. Based on the date information, obtain the corresponding reference soil moisture datasets from multiple independent data sources.

[0033] Multiple independent data sources refer to several data sources that differ from each other in terms of data generation principles, observation platforms, processing algorithms, or publishing institutions, and are independent of each other. For example, the three soil moisture data used for comparison and verification come from a reanalysis system of a meteorological center, an active and passive microwave satellite of a space agency, and a microwave radiometer satellite of a space agency, respectively. A reference soil moisture dataset refers to a set of soil moisture data obtained from other independent data sources that has high recognized accuracy and stability and is used as an evaluation benchmark or comparison standard. For example, global surface soil moisture gridded data downloaded from a reanalysis system of an international meteorological data platform is used as a reference benchmark for evaluating contemporaneous satellite-retrieved soil moisture products.

[0034] S3. Based on the quality identification code and the soil moisture value, filter out the valid soil moisture data and the corresponding location coordinates from the soil moisture dataset.

[0035] Among them, effective soil moisture data refers to a subset of data that is considered reliable and can be used for subsequent scientific analysis or comparative evaluation, selected from the original soil moisture dataset according to preset quality control rules; for example, from global GNSS-R inverted soil moisture data, all data points with the best quality identification code are selected to form the core data for quality assessment.

[0036] S4. Preprocess each reference soil moisture dataset separately, and perform spatiotemporal matching between the effective soil moisture data and the corresponding location coordinates and each preprocessed reference soil moisture dataset to obtain multiple sets of matching data that correspond one-to-one with the multiple independent data sources.

[0037] Spatiotemporal matching refers to the technical process of processing data from different datasets onto a unified time reference and spatial framework to enable comparison under the same conditions. For example, discrete point observation data from spaceborne GNSS-R is transformed into a regular latitude and longitude grid of reanalysis data using spatial interpolation methods, ensuring consistency in time representation between the two. Matched data refers to data pairs or sequences from the target dataset and reference dataset that form a one-to-one correspondence in time and space after spatiotemporal matching processing. For example, a soil moisture value (0.28 m) retrieved from GNSS-R under a specific latitude and longitude grid and on a specific date. 3 / m 3 Soil moisture values ​​(0.26 m) provided by reanalysis data from the same grid and the same date. 3 / m 3 Together they form a matching data pair.

[0038] S5. Based on the soil moisture values ​​contained in each set of matched data, calculate the corresponding quantitative evaluation indicators; the quantitative evaluation indicators include root mean square error, mean absolute error, deviation, and correlation coefficient.

[0039] Among them, quantitative evaluation indicators refer to mathematical statistical parameters used to objectively measure and characterize the consistency, error magnitude, or correlation strength between two sets of data in numerical form. Root mean square error (RMSE) is a statistical measure of the overall difference between predicted or observed values ​​and actual or reference values. It is calculated as the square root of the average of the squared errors of all samples. For example, calculating the average of the squared differences between GNSS-R soil moisture values ​​and reference values ​​in a set of matched data, and then taking the square root of this average, yields the RMS error; the smaller the value, the smaller the overall error. Mean absolute error (MAE) is a statistical measure of the average level of absolute deviation between predicted or observed values ​​and actual or reference values. It is calculated as the arithmetic mean of the absolute errors of all samples. For example, calculating the absolute values ​​of the differences between GNSS-R soil moisture values ​​and reference values ​​in a set of matched data, and then taking the average of these absolute values, yields the MAE; the smaller the value, the smaller the average deviation. Bias refers to a statistic that measures whether a series of predicted or observed values ​​systematically overestimates or underestimates a reference series. It is calculated as the difference between the mean of the predicted series and the mean of the reference series. For example, the difference between the mean of the GNSS-R soil moisture values ​​in all matched data and the mean of the corresponding reference soil moisture values ​​is the bias. A positive bias indicates systematic overestimation, and a negative bias indicates systematic underestimation. Correlation coefficient refers to a statistic that measures the degree and direction of linear correlation between two variable series. Its value ranges from -1 to +1. For example, calculating the Pearson correlation coefficient between the GNSS-R soil moisture value series and the reference soil moisture value series; the closer the result is to +1 or -1, the stronger the linear correlation, and the closer it is to 0, the weaker the linear correlation.

[0040] The technical solution of this embodiment obtains soil moisture data retrieved from GNSS-R and filters valid data using quality identification codes. It acquires reference soil moisture data from multiple independent data sources for spatiotemporal matching and calculates quantitative evaluation indicators such as root mean square error, mean absolute error, bias, and correlation coefficient. This solves the problem of insufficient accuracy and reliability of soil moisture data when GNSS-R technology is used alone, and improves the quality assessment capability and data credibility of GNSS-R soil moisture data.

[0041] In one alternative approach, S1 specifically includes: The soil moisture dataset, which includes soil moisture values, quality identification codes, location coordinates, and date information, is obtained from the data provider using GNSS-R technology inversion.

[0042] The soil moisture value represents the volumetric water content of the shallow surface layer, and the location coordinates are based on the WGS84 coordinate system.

[0043] Volumetric water content refers to a physical quantity characterizing soil moisture, defined as the ratio of the volume of water in the soil to the total volume of the soil, including pores; for example, a soil volumetric water content of 0.30 m³ / s is... 3 / m 3 , indicating that at 1 m 3 Water occupies 0.30 m³ of the total soil volume. 3 The volume.

[0044] Among the above-mentioned optional methods, the acquisition method of GNSS-R soil moisture dataset is further clarified, the soil moisture value is specified to represent the shallow surface volumetric water content, and the WGS84 coordinate system is used to improve the data standardization and subsequent matching accuracy.

[0045] In one alternative approach, the multiple data sources include at least: the reanalysis dataset, the SMAP dataset, and the SMOS dataset; S2 specifically includes: Based on the date information, the target date range is determined.

[0046] The target date range refers to the specific date or time period determined based on the date information of the data to be evaluated, from which data needs to be synchronously obtained from the reference data source. For example, if the plan is to evaluate GNSS-R soil moisture data on June 20, 2024, then soil moisture data on June 20, 2024, or that can represent that day, will be extracted from each reference data source. This date is the target date range.

[0047] From the reanalysis dataset, the SMAP dataset, and the SMOS dataset, respectively obtain reference soil moisture datasets corresponding to the target date range.

[0048] The reanalysis dataset refers to a spatiotemporally continuous and physically consistent dataset of meteorological and surface state parameters generated by fusing historical multi-source observation data with numerical weather prediction models using data assimilation techniques. For example, near-surface soil moisture data included in a global atmospheric reanalysis product released by a meteorological center; in this embodiment, it is assumed to be the ERA5-Land dataset. The SMAP (Soil Moisture Active Passive) dataset refers to a global soil moisture product dataset generated by processing remote sensing observation data acquired by soil moisture active and passive detection satellites; for example, SMAP satellite level 3 daily soil moisture product data obtained from a scientific data distribution platform. The SMOS (Soil Moisture and Ocean Salinity) dataset refers to a global soil moisture product dataset generated by processing remote sensing observation data acquired by soil moisture and ocean salinity satellites; for example, SMOS satellite level 3 surface soil moisture data downloaded from a satellite data center.

[0049] Among the above-mentioned optional methods, the reanalysis dataset, SMAP dataset, and SMOS dataset are further defined as independent reference data sources. The target date range is automatically determined based on date information and the corresponding data is extracted. A multi-source comparison and verification framework is constructed to enrich the spatiotemporal coverage of the evaluation benchmark and enhance the robustness and objectivity of the quality evaluation results.

[0050] In one alternative approach, S3 specifically includes: The soil moisture dataset is filtered according to the quality identification code. Soil moisture data with a preset quality identification code are identified as valid soil moisture data, and the soil moisture value and location coordinates corresponding to the valid soil moisture data are determined.

[0051] The preset value refers to a specific numerical value that is predefined in the algorithm or process for conditional judgment or data classification; for example, in the data quality control step, setting the screening threshold of the quality identification code to 0 means that only data with a quality code equal to 0 is selected.

[0052] In the above-mentioned optional methods, the soil moisture dataset is further filtered based on the quality identification code. Data that meets the preset value conditions is determined to be valid data and the corresponding location coordinates are extracted. Low-confidence observations are eliminated from the source, unreliable data is reduced in the evaluation, and the overall reliability of the data used for subsequent matching and calculation is ensured.

[0053] In one alternative approach, S4 specifically includes: The reanalysis dataset is preprocessed with format normalization and missing value imputation; the SMAP dataset is preprocessed with multi-day data synthesis and outlier identification; and the SMOS dataset is preprocessed with invalid data removal and numerical range filtering.

[0054] Format standardization and missing value imputation refer to processing data to conform to a unified organizational format, variable naming, and storage standards, and estimating and supplementing missing or invalid parts of data records using appropriate methods. For example, renaming soil moisture variables with different names in a reanalysis dataset to "soil_moisture" and using linear interpolation to fill in missing grid values ​​in the time series using data from before and after the missing grid values. Multi-day data synthesis and outlier identification refer to the process of fusing observations from multiple consecutive days to generate a spatially complete daily product for data that cannot be fully covered by a single day, and detecting and correcting data points that significantly deviate from the normal range of variation. For example, for microwave satellite soil moisture data, averaging three consecutive days of observation data by grid to synthesize global coverage data representing the middle day, and identifying and correcting variations exceeding physically reasonable thresholds (e.g., 0.15 m). 3 / m 3 The grid values ​​are defined as follows. Invalid data removal and numerical range filtering refer to the operation of deleting data records marked as invalid, missing, or obviously erroneous, as well as those whose values ​​exceed the physically possible range; for example, when processing satellite soil moisture data, first remove records marked as missing values, and then only retain soil moisture values ​​between 0 and 0.5 m. 3 / m 3 Data within this reasonable range.

[0055] Each data point and its corresponding location coordinates in the effective soil moisture data are subjected to spatial coordinate transformation and time alignment operations with the preprocessed reanalysis dataset, the SMAP dataset, and the SMOS dataset, respectively, to generate a first set of matching data corresponding to the reanalysis dataset, a second set of matching data corresponding to the SMAP dataset, and a third set of matching data corresponding to the SMOS dataset.

[0056] Spatial coordinate transformation and time alignment refer to the processing steps of transforming data from one spatial reference system to another specified spatial reference system, ensuring that all data involved in the comparison correspond precisely in the time dimension. For example, converting the latitude and longitude coordinates of GNSS-R data from the WGS84 geographic coordinate system to the equal-area projected coordinate system used by a certain satellite data, while simultaneously adjusting the effective time of all data to the same date. The first set of matched data refers to the paired data set with corresponding relationships formed after spatiotemporally matching the effective soil moisture data of GNSS-R with the preprocessed reanalysis dataset. The second set of matched data refers to the paired data set with corresponding relationships formed after spatiotemporally matching the effective soil moisture data of GNSS-R with the preprocessed SMAP dataset. The third set of matched data refers to the paired data set with corresponding relationships formed after spatiotemporally matching the effective soil moisture data of GNSS-R with the preprocessed SMOS dataset.

[0057] Among the above optional methods, format standardization and missing value imputation preprocessing are further performed on the reanalysis dataset, multi-day data synthesis and outlier identification preprocessing are performed on the SMAP dataset, and invalid data removal and numerical range filtering preprocessing are performed on the SMOS dataset. The usability of various reference data is improved and the spatiotemporal matching accuracy is optimized through differentiated preprocessing strategies.

[0058] In one alternative approach, the step of calculating the corresponding quantitative assessment index based on soil moisture values ​​contained in any set of matched data includes: Extract the soil moisture value from any set of matching data as the first soil moisture value, and extract the corresponding reference soil moisture value as the second soil moisture value.

[0059] The first soil moisture value refers to the soil moisture value derived from the GNSS-R soil moisture dataset to be evaluated in a pair of matched data. The second soil moisture value refers to the soil moisture value derived from the reference soil moisture dataset used as a comparison benchmark in a pair of matched data.

[0060] Based on the first soil moisture value and the second soil moisture value, the root mean square error, mean absolute error, deviation and correlation coefficient of any set of matching data are calculated to obtain the quantitative evaluation index of any set of matching data.

[0061] In this embodiment, it should be noted that: 1) The root mean square error (RMSE) measures the overall error level of GNSS-R soil moisture data relative to reference soil moisture data. In the calculation, it is assumed that there are n valid data pairs in the matched data, and each data pair contains a first soil moisture value. and a corresponding second soil moisture value The formula for calculating the root mean square error is: The smaller the root mean square error, the lower the overall deviation between the GNSS-R data and the reference data, and the higher the overall accuracy of the data.

[0062] 2) Mean absolute error (MAE) reflects the average absolute deviation between GNSS-R soil moisture data and reference soil moisture data. The calculation formula is: The mean absolute error directly represents the average deviation between the soil moisture value and the reference value. The smaller the value, the higher the average accuracy of the data.

[0063] 3) Bias is used to reveal whether there is a systematic trend of deviation from the reference data in GNSS-R inverted soil moisture data. The calculation formula is: in, Represents all first soil moisture values The arithmetic mean, This represents all corresponding reference soil moisture values. The deviation is the arithmetic mean of the data. A positive deviation indicates that the GNSS-R soil moisture data is generally higher than the reference data; a negative deviation indicates that the GNSS-R soil moisture data is generally lower than the reference data. The absolute value of the deviation indicates the intensity of the deviation.

[0064] 4) The correlation coefficient is used to assess the strength of the linear correlation between GNSS-R soil moisture data and reference soil moisture data. The calculation formula is: The correlation coefficient R ranges from -1 to +1. A value closer to +1 indicates a strong positive linear correlation between the GNSS-R data and the reference data; a value closer to -1 indicates a strong negative linear correlation; and a value close to 0 indicates a weak linear relationship. A higher correlation coefficient indicates a stronger consistency in the patterns of change between the GNSS-R data and the reference data.

[0065] The above calculation process can provide a comprehensive and objective quantitative evaluation of the accuracy, consistency and reliability of GNSS-R inverted soil moisture data from multiple complementary dimensions such as overall error, mean absolute deviation, deviation direction and linear correlation mode, providing key quantitative basis for the scientific application of data products, algorithm optimization and quality verification.

[0066] In the above-mentioned optional methods, the GNSS-R soil moisture value and the reference soil moisture value are further extracted from the matching data and used as the first soil moisture value and the second soil moisture value, respectively. On this basis, the root mean square error, mean absolute error, deviation and correlation coefficient are calculated to quantify the magnitude of deviation and the level of consistency from multiple dimensions and provide an objective evaluation basis.

[0067] In one alternative approach, it also includes: A scatter density plot is drawn based on the first and second soil moisture values ​​of any set of matched data, and the values ​​of the quantitative evaluation indicators of any set of matched data are marked on the scatter density plot.

[0068] A scatter plot is a two-dimensional statistical graph that displays the relationship between two variables in a scatter pattern and uses color depth or point density to reflect areas of concentrated data point distribution. For example, using GNSS-R soil moisture value on the horizontal axis and SMAP soil moisture value on the vertical axis, all matching data points are plotted, and gradient colors are used to represent the degree of point clustering in different areas. The numerical values ​​of quantitative evaluation indicators refer to the specific results of various evaluation statistics calculated from the matched dataset. For example, after calculating based on the GNSS-R and SMAP matched dataset, the output might be a root mean square error of 0.0520 and a correlation coefficient of 0.8609.

[0069] In the above-mentioned optional methods, a scatter density map is further drawn based on the first soil moisture value and the second soil moisture value of the matching data, and the quantitative evaluation index values ​​are marked on the map. The numerical results and data distribution patterns are visualized and integrated, making the evaluation information more intuitive and facilitating the rapid identification of data clustering characteristics, related trends and abnormal deviation patterns.

[0070] In this embodiment, Figure 2 A scatter density plot showing the ERA5-Land reference soil moisture value and the GNSS-R soil moisture value is presented. (Example) Figure 2 As shown in the figure, each point represents a pair of spatiotemporally matched data. The density of the points reflects the degree of data aggregation. The root mean square error, mean absolute error, bias, and correlation coefficient calculated based on the set of matched data are also labeled. Figure 3 A scatter density plot showing SMAP reference soil moisture values ​​and GNSS-R soil moisture values ​​is presented. (Example) Figure 3 As shown in the figure, this diagram illustrates the comparison between GNSS-R data and SMAP data, along with their corresponding quantitative evaluation indicators, in the same manner. Figure 4 A scatter density plot showing SMOS reference soil moisture values ​​and GNSS-R soil moisture values ​​is presented. Figure 4As shown, this figure completes the visualization of the comparison with the third reference source, SMOS. Table 1 summarizes the quantitative index results of the comparison and evaluation of GNSS-R soil moisture data with ERA5-Land, SMAP, and SMOS reference data, respectively. As shown in Table 1, the root mean square error, correlation coefficient, bias, and mean absolute error values ​​for each comparison combination are listed, providing a multi-source and quantitative evaluation basis for GNSS-R data quality.

[0071] Table 1: To better illustrate the technical solution of this embodiment, the following example is used for complete explanation: 1) Obtain the soil moisture dataset retrieved from GNSS-R. This dataset is a globally covered Level 2 product, containing soil moisture values, quality identification codes, location coordinates based on the WGS84 coordinate system, date information accurate to the second, and navigation system identifiers. Simultaneously, obtain corresponding reference soil moisture datasets based on the date information from multiple independent data sources, including the ERA5-Land reanalysis dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF), Level 3 global daily soil moisture products from designated space data platforms for active and passive soil moisture observation missions, and Level 3 soil moisture products from designated satellite data centers for soil moisture and ocean salinity missions.

[0072] 2) Preprocess all acquired data. For GNSS-R soil moisture data, filtering is performed based on quality identification codes, retaining valid soil moisture data with quality identification codes of specific preset values ​​and their corresponding location coordinates. For ERA5-Land data, format standardization and missing value imputation are performed, soil moisture variables are uniformly named, and missing values ​​are imputed using time-linear interpolation. For satellite data from active and passive soil moisture observation missions, multi-day data synthesis and outlier identification are performed, averaging the grid data from three consecutive days to generate daily global coverage data, and correcting data exceeding reasonable thresholds. For satellite data from soil moisture and ocean salinity missions, invalid data removal and numerical range filtering are performed, removing missing values ​​and retaining only soil moisture values ​​between 0 and 0.5 m. 3 / m 3 Data within the range.

[0073] 3) After preprocessing, the valid GNSS-R soil moisture data and their location coordinates are spatiotemporally matched with the three types of preprocessed reference datasets. The matching process involves spatial coordinate transformation and precise temporal alignment. For ERA5-Land data, nearest neighbor interpolation is used to map GNSS-R scatter points to its regular grid. For active and passive soil moisture observation satellite data, GNSS-R coordinates are transformed to its dedicated Ease-Grid 2 projected coordinate system to achieve grid matching. For soil moisture and ocean salinity mission satellite data, bilinear interpolation is used to calculate the corresponding reference value for each GNSS-R data point. Time alignment preserves data from the same day, ultimately forming three sets of matched datasets corresponding to the three types of reference data sources.

[0074] 4) Based on the generated matching dataset, calculate the quantitative evaluation indicators. The formula for calculating the root mean square error is: , where GNSS(i) is the i-th GNSS-R soil moisture value (first soil moisture value), Ref(i) is the i-th reference soil moisture value (second soil moisture value), and N is the sample size. The formula for calculating the mean absolute error is: The formula for calculating the deviation is: The correlation coefficient is the difference between the means of two sequences. The formula for calculating the correlation coefficient is: After the evaluation is completed, a scatter density map can be drawn based on the GNSS-R values ​​and reference values ​​in any set of matched data, and the specific values ​​of each quantitative evaluation index calculated can be marked on the map.

[0075] Figure 5 A schematic diagram of an embodiment of a GNSS-R soil moisture quality assessment system 200 provided by the present invention is shown. Figure 5 As shown, the GNSS-R soil moisture quality assessment system 200 includes: The acquisition module 201 is used to acquire the soil moisture dataset retrieved by GNSS-R, which includes soil moisture values, quality identification codes, location coordinates and date information. Extraction module 202 is used to obtain corresponding reference soil moisture datasets from multiple independent data sources based on the date information; The filtering module 203 is used to filter out valid soil moisture data and corresponding location coordinates from the soil moisture dataset based on the quality identification code and the soil moisture value. Matching module 204 is used to preprocess each reference soil moisture dataset, and to perform spatiotemporal matching of the effective soil moisture data and the corresponding location coordinates with each preprocessed reference soil moisture dataset to obtain multiple sets of matching data that correspond one-to-one with the multiple independent data sources. The evaluation module 205 is used to calculate the corresponding quantitative evaluation index based on the soil moisture value contained in each set of matched data; the quantitative evaluation index includes root mean square error, mean absolute error, deviation and correlation coefficient.

[0076] In one alternative embodiment, the acquisition module 201 is specifically used for: Obtain the soil moisture dataset, which includes soil moisture values, quality identification codes, location coordinates, and date information, from the data provider using GNSS-R technology inversion. The soil moisture value represents the volumetric water content of the shallow surface layer, and the location coordinates are based on the WGS84 coordinate system.

[0077] In one alternative approach, the multiple data sources include at least: a reanalysis dataset, an SMAP dataset, and an SMOS dataset; the acquisition module 202 is specifically used for: Based on the date information, determine the target date range; From the reanalysis dataset, the SMAP dataset, and the SMOS dataset, respectively obtain reference soil moisture datasets corresponding to the target date range.

[0078] In an alternative embodiment, the filtering module 203 is specifically used for: The soil moisture dataset is filtered according to the quality identification code. Soil moisture data with a preset quality identification code are identified as valid soil moisture data, and the soil moisture value and location coordinates corresponding to the valid soil moisture data are determined.

[0079] In an alternative embodiment, the matching module 204 is specifically used for: The reanalysis dataset is preprocessed with format normalization and missing value imputation; the SMAP dataset is preprocessed with multi-day data synthesis and outlier identification; and the SMOS dataset is preprocessed with invalid data removal and numerical range filtering. Each data point and its corresponding location coordinates in the effective soil moisture data are subjected to spatial coordinate transformation and time alignment operations with the preprocessed reanalysis dataset, the SMAP dataset, and the SMOS dataset, respectively, to generate a first set of matching data corresponding to the reanalysis dataset, a second set of matching data corresponding to the SMAP dataset, and a third set of matching data corresponding to the SMOS dataset.

[0080] In an alternative embodiment, the evaluation module 205 is specifically used for: Extract the soil moisture value from any set of matching data as the first soil moisture value, and extract the corresponding reference soil moisture value as the second soil moisture value; Based on the first soil moisture value and the second soil moisture value, the root mean square error, mean absolute error, deviation and correlation coefficient of any set of matching data are calculated to obtain the quantitative evaluation index of any set of matching data.

[0081] In an alternative embodiment, it further includes: a visualization module; the visualization module is used for: A scatter density plot is drawn based on the first and second soil moisture values ​​of any set of matched data, and the values ​​of the quantitative evaluation indicators of any set of matched data are marked on the scatter density plot.

[0082] It should be noted that the beneficial effects of the GNSS-R soil moisture quality assessment system 200 provided in the above embodiments are the same as those of the GNSS-R soil moisture quality assessment method described above, and will not be repeated here. Furthermore, the system provided in the above embodiments is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the system can be divided into different functional modules according to the actual situation to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process is detailed in the method embodiments, and will not be repeated here.

[0083] The GNSS-R soil moisture quality assessment system 200 of the present invention can be a computer program (including program code) running on a computer device. For example, the GNSS-R soil moisture quality assessment system 200 of the present invention is an application software that can be used to execute the corresponding steps in the GNSS-R soil moisture quality assessment method of the present invention.

[0084] In some embodiments, the GNSS-R soil moisture quality assessment system 200 of the present invention can be implemented in a combination of hardware and software. As an example, the GNSS-R soil moisture quality assessment system 200 of the present invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the GNSS-R soil moisture quality assessment method of the present invention. For example, the processor in the form of a hardware decoding processor can be one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0085] The modules described in the embodiments of this invention can be implemented in software or hardware. The names of the modules are not, in some cases, limiting the scope of the module itself.

[0086] An electronic device according to an embodiment of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any of the above-mentioned GNSS-R soil moisture quality assessment methods. That is, an electronic device according to an embodiment of the present invention may include, but is not limited to: a processor and a memory; the memory is used to store the computer program; the processor is used to execute the GNSS-R soil moisture quality assessment method shown in any embodiment of the present invention by calling the computer program.

[0087] In one alternative embodiment, an electronic device is provided, such as Figure 6 As shown, Figure 6 The illustrated electronic device 4000 includes a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, for example, via a bus 4002. Optionally, the electronic device 4000 may further include a transceiver 4004, which can be used for data interaction between the electronic device and other electronic devices, such as sending and / or receiving data. It should be noted that in practical applications, the transceiver 4004 is not limited to one type, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present invention.

[0088] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this invention. Processor 4001 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0089] Bus 4002 may include a path for transmitting information between the aforementioned components. Bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 4002 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6 The bus 4002 is represented by only one thick line, but this does not mean that there is only one bus or one type of bus.

[0090] The memory 4003 may be ROM (Read Only Memory) or other types of static storage devices capable of storing static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices capable of storing information and instructions, or EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0091] The memory 4003 stores application code (computer program) for executing the present invention, and its execution is controlled by the processor 4001. The processor 4001 executes the application code stored in the memory 4003 to implement the content shown in the foregoing method embodiments.

[0092] Among them, electronic devices can also be terminal devices. A terminal device can be any terminal device that can install applications and access web pages through applications, including at least one of smartphones, tablets, laptops, desktop computers, smart speakers, smartwatches, smart TVs, and smart in-vehicle devices.

[0093] It should be noted that, Figure 6 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0094] An embodiment of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements any of the above-described GNSS-R soil moisture quality assessment methods.

[0095] Alternatively, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, a floppy disk, and an optical data storage device, etc.

[0096] In an exemplary embodiment, a computer program product or computer program is also provided, which includes computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform the GNSS-R soil moisture quality assessment method described above.

[0097] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0098] It should be understood that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0099] The computer-readable storage medium provided in this invention can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0100] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the method shown in the above embodiments.

[0101] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-disclosed concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this invention.

[0102] It should be noted that the terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and represent a limitation on a specific order or sequence. Where appropriate, the order of use for similar objects can be interchanged so that the embodiments of this application described herein can be implemented in an order other than that shown or described.

[0103] Those skilled in the art will recognize that this invention can be implemented as a system, method, or computer program product. Therefore, this invention can be specifically implemented in the following forms: it can be entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, this invention can also be implemented as a computer program product contained in one or more computer-readable media, which includes computer-readable program code.

[0104] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A GNSS-R method for assessing soil moisture quality, characterized in that, include: Obtain the soil moisture dataset retrieved from GNSS-R, which includes soil moisture values, quality identification codes, location coordinates, and date information; Based on the date information, corresponding reference soil moisture datasets are obtained from multiple independent data sources; Based on the quality identification code and the soil moisture value, valid soil moisture data and corresponding location coordinates are filtered out from the soil moisture dataset; Each reference soil moisture dataset is preprocessed, and the effective soil moisture data and corresponding location coordinates are spatiotemporally matched with each preprocessed reference soil moisture dataset to obtain multiple sets of matching data that correspond one-to-one with the multiple independent data sources. Based on the soil moisture values ​​contained in each set of matched data, corresponding quantitative evaluation indicators are calculated; the quantitative evaluation indicators include root mean square error, mean absolute error, deviation, and correlation coefficient.

2. The GNSS-R soil moisture quality assessment method according to claim 1, characterized in that, The steps for obtaining the soil moisture dataset retrieved by GNSS-R include: Obtain the soil moisture dataset, which includes soil moisture values, quality identification codes, location coordinates, and date information, from the data provider using GNSS-R technology inversion. The soil moisture value represents the volumetric water content of the shallow surface layer, and the location coordinates are based on the WGS84 coordinate system.

3. The GNSS-R soil moisture quality assessment method according to claim 1, characterized in that, The multiple data sources include at least: reanalysis datasets, SMAP datasets, and SMOS datasets; the step of obtaining corresponding reference soil moisture datasets from multiple independent data sources based on the date information includes: Based on the date information, determine the target date range; From the reanalysis dataset, the SMAP dataset, and the SMOS dataset, respectively obtain reference soil moisture datasets corresponding to the target date range.

4. The GNSS-R soil moisture quality assessment method according to claim 3, characterized in that, The step of filtering valid soil moisture data and corresponding location coordinates from the soil moisture dataset based on the quality identification code and the soil moisture value includes: The soil moisture dataset is filtered according to the quality identification code. Soil moisture data with a preset quality identification code are identified as valid soil moisture data, and the soil moisture value and location coordinates corresponding to the valid soil moisture data are determined.

5. The GNSS-R soil moisture quality assessment method according to claim 4, characterized in that, The steps of preprocessing each reference soil moisture dataset and then performing spatiotemporal matching between the effective soil moisture data and the corresponding location coordinates and each preprocessed reference soil moisture dataset to obtain multiple sets of matching data corresponding one-to-one with the multiple independent data sources include: The reanalysis dataset is preprocessed with format normalization and missing value imputation; the SMAP dataset is preprocessed with multi-day data synthesis and outlier identification; and the SMOS dataset is preprocessed with invalid data removal and numerical range filtering. Each data point and its corresponding location coordinates in the effective soil moisture data are subjected to spatial coordinate transformation and time alignment operations with the preprocessed reanalysis dataset, the SMAP dataset, and the SMOS dataset, respectively, to generate a first set of matching data corresponding to the reanalysis dataset, a second set of matching data corresponding to the SMAP dataset, and a third set of matching data corresponding to the SMOS dataset.

6. The GNSS-R soil moisture quality assessment method according to any one of claims 1 to 5, characterized in that, The steps for calculating the corresponding quantitative assessment index based on soil moisture values ​​contained in any set of matched data include: Extract the soil moisture value from any set of matching data as the first soil moisture value, and extract the corresponding reference soil moisture value as the second soil moisture value; Based on the first soil moisture value and the second soil moisture value, the root mean square error, mean absolute error, deviation and correlation coefficient of any set of matching data are calculated to obtain the quantitative evaluation index of any set of matching data.

7. The GNSS-R soil moisture quality assessment method according to claim 6, characterized in that, Also includes: A scatter density plot is drawn based on the first and second soil moisture values ​​of any set of matched data, and the values ​​of the quantitative evaluation indicators of any set of matched data are marked on the scatter density plot.

8. A GNSS-R soil moisture quality assessment system, characterized in that, include: The acquisition module is used to acquire the soil moisture dataset retrieved by GNSS-R. The soil moisture dataset includes soil moisture values, quality identification codes, location coordinates, and date information. The extraction module is used to obtain corresponding reference soil moisture datasets from multiple independent data sources based on the date information; The filtering module is used to filter out valid soil moisture data and corresponding location coordinates from the soil moisture dataset based on the quality identification code and the soil moisture value; The matching module is used to preprocess each reference soil moisture dataset and perform spatiotemporal matching between the effective soil moisture data and the corresponding location coordinates and each preprocessed reference soil moisture dataset to obtain multiple sets of matching data that correspond one-to-one with the multiple independent data sources. The evaluation module is used to calculate the corresponding quantitative evaluation index based on the soil moisture value contained in each set of matched data; the quantitative evaluation index includes root mean square error, mean absolute error, deviation, and correlation coefficient.

9. An electronic device, characterized in that, The electronic device includes a processor coupled to a memory storing at least one computer program, which is loaded and executed by the processor to enable the electronic device to implement the GNSS-R soil moisture quality assessment method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one computer program, which, when executed by a processor, implements the GNSS-R soil moisture quality assessment method as described in any one of claims 1 to 7.

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