A soil moisture inversion method and related device based on dynamic dual-channel model

Abstract

The present application discloses a soil moisture inversion method based on a dynamic dual-channel model and a related device. The method includes: resampling the vegetation index of the inversion time period obtained by the spatial averaging method, and then reprojecting it to an equal-area expandable earth grid, filtering and linearly interpolating the vegetation index time series of each pixel, and obtaining the daily dynamic vegetation index of the inversion time period; calculating the vegetation moisture content by the daily dynamic vegetation index, and then calculating the initial value of the vegetation optical thickness by the vegetation moisture content; calculating the surface roughness parameter based on the initial value of the vegetation optical thickness calculated by the climatological vegetation index or the daily dynamic vegetation index; inputting the surface roughness parameter and the initial value of the vegetation optical thickness into the zero-order radiation transfer model to invert soil moisture. The present application adopts the daily dynamic vegetation index to characterize the vegetation characteristics, which can more accurately describe the changing characteristics of the surface vegetation in terms of time and space scales, thereby improving the accuracy of soil moisture inversion.

Description

Technical Field

[0001] The present application relates to the technical field of soil moisture inversion, and in particular to a soil moisture inversion method and related devices based on a dynamic dual-channel model. Background Art

[0002] Soil moisture information plays a key role in predicting climate change, monitoring agricultural drought, and warning of geological disasters. Especially in areas where extreme rainfall is becoming more frequent, accurate and rapid monitoring of soil moisture is a key way to prevent geological disasters such as landslides and collapses. With the development of satellite remote sensing technology and the opening of various types of satellite-borne sensor data, methods for monitoring the spatial distribution and dynamic changes of soil moisture based on satellite remote sensing technology have developed rapidly, and their inversion accuracy and applicability have also been significantly improved. Due to its strong penetration ability into vegetation and the surface, all-weather monitoring capability, and sensitivity to soil moisture changes, passive microwaves have become the main means of soil moisture monitoring.

[0003] The existing technology uses a dual-channel model as a benchmark model to invert global soil moisture. This method can make full use of the dual-polarization observation data of the passive microwave radiometer and invert the soil moisture and vegetation optical thickness through the zero-order radiation transfer model (τ-ω model). The specific process of using the dual-channel model for soil moisture inversion in the existing technology is as follows: Figure 1 As shown. The soil moisture inversion process based on the dual-channel model mainly includes 6 parts: ① According to the definition of surface emissivity (the ratio of the radiation emitted by the surface to the radiation emitted by the black body at the same temperature), the brightness temperature of passive microwave observation is converted into surface emissivity in combination with the surface physical temperature; ② The influence of vegetation effect is removed by combining the surface vegetation parameters; ③ The influence of roughness effect is removed by combining the surface roughness parameters; ④ Based on the Fresnel equation, the reflectivity of the smooth surface of bare soil is converted into the dielectric constant of bare soil; ⑤ Based on the relationship between the dielectric constant of bare soil, other soil property parameters and soil moisture constructed by the dielectric model, the soil moisture is calculated; ⑥ Considering the response difference of different polarization brightness temperatures to surface characteristics, a cost function about the simulated value and the real observed value of the surface total radiation brightness temperature is constructed, and it is iteratively calculated to output the soil moisture and vegetation optical thickness values ​​corresponding to the minimum value of the cost function.

[0004] Steps ② and ③ are used to quantify the vegetation effect and roughness effect in the radiation transmission process, which is the key to accurately inverting soil moisture. The existing dual-channel model uses the multi-year average vegetation index (i.e., climatological vegetation index) synthesized from the MODIS NDVI (Normalized Difference Vegetation Index) data product from 2000 to 2010 to characterize vegetation characteristics, which has the following shortcomings:

[0005] 1) The climatological vegetation index cannot accurately represent the characteristics of surface vegetation: i) There are long-term numerical deviations. For example, global warming may promote vegetation growth in some mid- and high-latitude regions, advance the start of the growing season, and delay the end date, which may make the climatological vegetation index in mid- and high-latitude regions lower than the actual situation. ii) It cannot represent the interannual fluctuations in vegetation conditions caused by interannual fluctuations in moisture conditions (wet and dry years, etc.). For example, the vegetation growth state in drought years is lower than that in average years (negative anomaly phenomenon), while the vegetation growth state in wet years has positive anomalies. iii) It cannot monitor changes in vegetation growth caused by human-led farmland cultivation and land management activities, such as adjusting agricultural farming patterns, increasing field irrigation, and changing crop types in a certain region according to agricultural policies or climate change.

[0006] 2) Using the climatological vegetation index to calculate the static surface roughness parameter will transfer the error of the climatological vegetation index in characterizing the vegetation state to the static surface roughness parameter, resulting in an increase in the error from vegetation in the soil moisture inversion model, further affecting the inversion accuracy of soil moisture;

[0007] 3) The static surface roughness parameters do not conform to the changes in the real surface, ignore the temporal variation characteristics of the real surface, and do not take into account the changes in surface geometry caused by climate change, vegetation growth, and field management, such as the decrease in surface roughness caused by precipitation and the increase in surface roughness caused by rapid vegetation growth;

[0008] 4) The static surface roughness parameter ignores the vegetation change information contained in the roughness parameter when the vegetation index reaches saturation. In densely vegetated areas and during periods of vigorous vegetation growth, the vegetation index value is prone to saturation, which will make the vegetation index unable to continue to represent vegetation change information, and this information will be compensated to the surface roughness value through the algorithm structure. The static surface roughness scheme removes the vegetation change information when the vegetation index is saturated while using the vegetation optical thickness constraint to remove the influence of vegetation. Summary of the invention

[0009] The present application provides a soil moisture inversion method and related devices based on a dynamic dual-channel model, which are used to improve the technical problem that the existing technology uses climatological vegetation indices to characterize vegetation status, resulting in a significant error, thereby reducing the accuracy of soil moisture inversion.

[0010] In view of this, the first aspect of the present application provides a soil moisture inversion method based on a dynamic dual-channel model, comprising:

[0011] The obtained vegetation index of the inversion time period is resampled by using the spatial averaging method, and the resampled vegetation index is reprojected to an equal-area expandable earth grid. The vegetation index time series of each pixel grid is filtered and linearly interpolated to obtain the daily dynamic vegetation index of the inversion time period.

[0012] Calculate the vegetation moisture content through the daily dynamic vegetation index, and then calculate the initial value of vegetation optical thickness through the vegetation moisture content;

[0013] Calculating the surface roughness parameter based on the initial value of vegetation optical thickness calculated based on the climatological vegetation index or the daily dynamic vegetation index;

[0014] The surface roughness parameter and the initial value of the vegetation optical thickness are input into a zero-order radiation transfer model to invert soil moisture.

[0015] Optionally, the calculating of the surface roughness parameter based on the initial value of vegetation optical thickness calculated based on the climatological vegetation index or the daily dynamic vegetation index includes:

[0016] The static surface roughness parameters are calculated by the climatological vegetation index;

[0017] Or, inputting the initial value of vegetation optical thickness calculated based on the daily dynamic vegetation index into a zero-order radiation transfer model for calculation to obtain vertically polarized and horizontally polarized surface total radiation brightness temperature simulation values;

[0018] A first cost function with the daily-scale surface roughness parameter as the inversion target is constructed through the vertically polarized and horizontally polarized surface total radiation brightness temperature simulation values ​​and the vertically polarized and horizontally polarized surface total radiation brightness temperature observation values;

[0019] Minimize the first cost function by iterative calculation, and output the daily-scale surface roughness parameter corresponding to the minimum value of the first cost function;

[0020] Calculating the monthly scale surface roughness parameter by using the daily scale surface roughness parameter;

[0021] The annual scale surface roughness parameter is calculated by using the daily scale surface roughness parameter.

[0022] Optionally, the first cost function is:

[0023]

[0024] Where SM is soil moisture, DH is the daily-scale surface roughness parameter; , are the observed values ​​of total surface radiation brightness temperature for vertical polarization and horizontal polarization respectively; , They are the simulated values ​​of the total surface radiation brightness temperature for vertical polarization and horizontal polarization respectively.

[0025] Optionally, calculating the monthly scale surface roughness parameter by using the daily scale surface roughness parameter includes:

[0026] The monthly average value of the daily-scale surface roughness parameter is calculated to obtain the monthly-scale surface roughness parameter.

[0027] Optionally, calculating the annual scale surface roughness parameter by using the daily scale surface roughness parameter includes:

[0028] The multi-year average value of the daily-scale surface roughness parameter on the corresponding date when the initial value of the vegetation optical thickness meets the restriction conditions is calculated to obtain the annual-scale surface roughness parameter.

[0029] Optionally, the step of inputting the surface roughness parameter and the initial value of the vegetation optical thickness into a zero-order radiation transfer model to invert soil moisture includes:

[0030] Input the static surface roughness parameter and the initial value of the vegetation optical thickness into a zero-order radiation transfer model to obtain the vertically polarized and horizontally polarized surface total radiation brightness temperature simulation values ​​corresponding to the static surface roughness parameter; or, input the daily-scale surface roughness parameter, the monthly-scale surface roughness parameter, and the annual-scale surface roughness parameter and the initial value of the vegetation optical thickness into a zero-order radiation transfer model, respectively, to obtain the vertically polarized and horizontally polarized surface total radiation brightness temperature simulation values ​​corresponding to the surface roughness parameters at each time scale;

[0031] A second cost function with actual values ​​of soil moisture and vegetation optical thickness as inversion targets is constructed through the simulated values ​​of vertically polarized and horizontally polarized surface total radiation brightness temperature corresponding to the static surface roughness parameters or the surface roughness parameters at each time scale, the observed values ​​of surface total radiation brightness temperature, and the initial value of vegetation optical thickness;

[0032] By iteratively calculating and minimizing the second cost function, a soil moisture inversion result based on the daily dynamic vegetation index and the static surface roughness parameter is obtained, or a soil moisture inversion result based on the daily dynamic vegetation index and the surface roughness parameters of each time scale is obtained.

[0033] Optionally, the second cost function is:

[0034]

[0035] Where SM is soil moisture; , are the observed values ​​of total surface radiation brightness temperature for vertical polarization and horizontal polarization respectively; , are the simulated values ​​of the total surface radiation brightness temperature for vertical polarization and horizontal polarization respectively; is the actual value of vegetation optical thickness; is the initial value of vegetation optical thickness; is the regularization parameter.

[0036] The second aspect of the present application provides a soil moisture inversion device based on a dynamic dual-channel model, comprising:

[0037] A data processing unit is used to resample the vegetation index of the inversion time period obtained by using a spatial averaging method, and reproject the resampled vegetation index to an equal-area expandable earth grid, and filter and linearly interpolate the vegetation index time series of each pixel grid to obtain a daily dynamic vegetation index of the inversion time period;

[0038] A first calculation unit is used to calculate the moisture content of vegetation through the daily dynamic vegetation index, and then calculate the initial value of vegetation optical thickness through the vegetation moisture content;

[0039] A second calculation unit is used to calculate the surface roughness parameter based on the initial value of vegetation optical thickness calculated by the climatological vegetation index or the daily dynamic vegetation index;

[0040] The inversion unit is used to input the surface roughness parameter and the initial value of the vegetation optical thickness into a zero-order radiation transfer model to invert soil moisture.

[0041] A third aspect of the present application provides an electronic device, the device comprising a processor and a memory;

[0042] The memory is used to store program code and transmit the program code to the processor;

[0043] The processor is used to execute the soil moisture inversion method based on the dynamic dual-channel model described in any one of the first aspects according to the instructions in the program code.

[0044] In a fourth aspect, the present application provides a computer-readable storage medium, which is used to store program code. When the program code is executed by a processor, it implements the soil moisture inversion method based on the dynamic dual-channel model described in any one of the first aspects.

[0045] It can be seen from the above technical solutions that this application has the following advantages:

[0046] The soil moisture inversion method based on the dynamic dual-channel model provided in the present application generates a daily dynamic vegetation index within the inversion time period by acquiring the vegetation index within the inversion time period, which can more accurately describe the changing characteristics of surface vegetation in terms of time and space scales, improve the error problem existing in the climatological vegetation index, and thus improve the accuracy of soil moisture inversion; the daily dynamic vegetation index is used to calculate the surface roughness parameter, which can improve the data quality in the calculation of the surface roughness parameter, improve the vegetation error transmission and accumulation problems caused by the use of the climatological vegetation index to calculate the surface roughness parameter, and thus improve the inversion accuracy of soil moisture. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0048] Figure 1 A schematic flow chart of a soil moisture inversion method based on an existing dual channel model (DCA) provided in an embodiment of the present application;

[0049] Figure 2 A schematic flow chart of a soil moisture inversion method based on a dynamic dual channel model (DDCA) provided in an embodiment of the present application;

[0050] Figure 3 Another schematic flow chart of a soil moisture inversion method based on a dynamic dual channel model (DDCA) provided in an embodiment of the present application;

[0051] Figure 4 A time series comparison chart of the climatological vegetation index and the dynamic vegetation index provided in the embodiment of the present application;

[0052] Figure 5 A time series diagram of the dynamic vegetation index of randomly selected farmland pixels around the world before and after preprocessing provided in the embodiment of the present application;

[0053] Figure 6 A time series comparison diagram of surface roughness of different solutions provided in the embodiments of the present application;

[0054] Figure 7 A time series comparison diagram of DDCA soil moisture and DCA soil moisture provided in the embodiment of the present application;

[0055] Figure 8 The soil moisture time series diagram and verification scatter diagram of a certain farmland pixel in the United States provided in the embodiment of the present application;

[0056] Fig. 9 The soil moisture time series diagram and verification scatter plot of some farmland and grassland pixels in China provided in the embodiments of the present application. DETAILED DESCRIPTION

[0057] The present application provides a soil moisture inversion method and related devices based on a dynamic dual-channel model, which are used to improve the technical problem that the existing technology uses climatological vegetation indices to characterize vegetation status, which has non-negligible errors and thus leads to a decrease in soil moisture inversion accuracy.

[0058] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0059] For easier understanding, please refer to Figure 2 and 3 The present application embodiment provides a soil moisture inversion method based on a dynamic dual-channel model, including:

[0060] Step 110, resample the vegetation index of the inversion time period using a spatial averaging method, reproject the resampled vegetation index to an equal-area expandable earth grid, filter and linearly interpolate the vegetation index time series of each pixel grid, and obtain the daily dynamic vegetation index of the inversion time period.

[0061] You can refer to Figure 4 This application takes into account the existing dual-channel model using the climatological vegetation index (NDVI) based on 2000-2010. clim ) characterizes the state of surface vegetation after 2015. There are long-term numerical deviations, the inability to characterize the inter-annual fluctuations in vegetation conditions caused by inter-annual fluctuations in moisture conditions (flood-dry years, etc.), and the inability to monitor vegetation changes caused by human-led farmland cultivation and land management activities. Therefore, this application uses the dynamic vegetation index in the soil moisture inversion method to improve the errors and shortcomings caused by the above-mentioned climatological vegetation index. This application uses the NDVI data product observed in real time by MODIS (Moderate-resolution Imaging Spectroradiometer) to generate a dynamic vegetation index (NDVI) to replace the climatological vegetation index. dyn), describes the real vegetation growth changes through dynamic vegetation index, optimizes the calculation method of vegetation moisture content, updates parameter values ​​such as vegetation moisture content and initial value of vegetation optical thickness, and calculates surface roughness parameters.

[0062] Among them, MODIS is a remote sensing instrument developed by NASA (National Aeronautics and Space Administration, NASA) and carried on the Terra and Aqua satellites, providing ground observation data to the world since 2000. The MOD13C1 and MYD13C1 NDVI data products come from the Terra and Aqua satellites, respectively, with a temporal resolution of 16 days and a spatial resolution of 0.05°. The algorithms and formats of the two data products are the same, and they can complement each other in time series, providing global NDVI data products with a temporal resolution of 8 days and a spatial resolution of 0.05°.

[0063] In this application, the MOD13C1 / YD13C1 NDVI data products within the inversion time period are obtained, and the daily dynamic vegetation index consistent with the SMAP (Soil Moisture Active Passive) brightness temperature (BrightnessTemperature, Tb) temporal and spatial resolution is obtained through the following steps:

[0064] First, vegetation indices with poor quality can be eliminated based on the quality control marks of NDVI data products;

[0065] Secondly, to maintain consistency with the SMAP Tb geographic information format, the vegetation index can be resampled to 9 km using a spatial average method and reprojected to an equal-area expandable earth grid;

[0066] Then, in order to obtain a daily vegetation index series with a continuous time series, the Savitzky-Golay filter was applied to the vegetation index time series of each pixel for smoothing, and linear interpolation was applied to complete the time series;

[0067] Finally, in order to eliminate the impact of data discontinuity and insufficient data, pixels with more than 4 consecutive missing 8-day vegetation index series and less than 30 valid observations in the entire inversion time period were removed.

[0068] When performing soil moisture inversion, the data sets that need to be used include SMAP SPL3_P_E V5 Tb and surface parameter auxiliary data sets.

[0069] Among them, SMAP is an earth satellite launched by NASA in January 2015, mainly used to provide global soil moisture products. The SMAP microwave radiometer has two polarization modes: horizontal (Horizontal, H) and vertical (Vertical, V). It observes the earth's surface at an incidence angle of 40°, and the transit time is 6 am (descending orbit) and 6 pm (ascending orbit) local time. It can provide global surface total radiation brightness temperature observation data every 2 to 3 days. Since the temperature of vegetation and near-surface soil is roughly equal at 6 am in the morning of SMAP's earth observation products, the surface total radiation brightness temperature observation data at 6 am local time is selected to invert soil moisture.

[0070] Auxiliary datasets include land cover type, vegetation single scattering albedo (ω), surface effective temperature, and soil texture. The land cover type is classified according to the Interplanetary Geosphere Project and obtained from MCD12Q1 V5, with a total of 17 land cover types. Vegetation single scattering albedo is obtained from a lookup table based on land cover type. Soil texture data (including soil bulk density and clay percentage) can be obtained from the Soil-Grids 250m data product. Surface effective temperature data comes from the Earth System Model output data provided by NASA's Goddard Modeling and Assimilation Office. Auxiliary datasets are all processed to the same spatial resolution and projection system as the surface global radiance temperature.

[0071] Step 120: Calculate the vegetation moisture content through the daily dynamic vegetation index, and then calculate the initial value of the vegetation optical thickness through the vegetation moisture content.

[0072] This application calculates the daily vegetation water content (VWC) through the daily dynamic vegetation index, and then calculates the initial value of the daily vegetation optical thickness. Vegetation water content (unit kg / m 2 ) is used to describe the total water content of the aboveground part of vegetation, including the leaf canopy and stem water content. Related research shows that VWC can be calculated using an empirical model:

[0073]

[0074] In the formula, the value range of dynamic NDVI is [-1,1]; NDVI max is the multi-year maximum value of dynamic NDVI during the inversion period; NDVI min It is the multi-year minimum value of the dynamic NDVI within the inversion time period. If the inversion time period is less than or equal to 1 year, the maximum and minimum values ​​of the year are taken. sf is the vegetation stem factor parameter, which is used to estimate the moisture residing in the stem. The value is obtained by fitting the ground test and is set according to the surface cover type.

[0075] The interaction between surface vegetation and soil moisture directly affects the surface total radiation brightness temperature observed by the microwave radiometer. The initial value of vegetation optical thickness can be calculated by the following formula:

[0076]

[0077] In the formula, is the initial value of vegetation optical thickness; b is a coefficient obtained by fitting the ground test and is related to factors such as vegetation structure and surface cover type.

[0078] Step 130: Calculate the surface roughness parameter based on the initial value of vegetation optical thickness calculated based on the climatological vegetation index or the daily dynamic vegetation index.

[0079] In one embodiment, the static surface roughness parameter (CH) can be calculated by the climatological vegetation index, and soil moisture inversion can be performed by the static surface roughness parameter. The acquisition process of the climatological vegetation index belongs to the prior art, and its specific acquisition process will not be described here. In the scheme of using the static surface roughness parameter to invert soil moisture, it is assumed that the surface roughness value is constant and does not change with time.

[0080] In another embodiment, considering the scheme of using static surface roughness parameters for soil moisture inversion, the static surface roughness parameters calculated by the climatological vegetation index may contain errors from the climatological vegetation index in characterizing the vegetation state, which will increase the error from vegetation in the algorithm and further increase the uncertainty of soil moisture inversion. In addition, the scheme using static surface roughness parameters assumes that the surface roughness does not change with time and the value is constant, which does not conform to the changes in the real surface, ignores the temporal change characteristics of the real surface, and does not take into account the changes in surface geometry caused by climate change, vegetation growth, and field management, such as the increase in surface roughness caused by rapid vegetation growth. In addition, the existing dual-channel model uses a static surface roughness parameter scheme, which ignores the vegetation change information contained in the roughness parameter when the vegetation index reaches saturation. When the vegetation index is used to calculate the surface roughness parameter, the vegetation index value is prone to saturation in densely vegetated areas and during periods of vigorous vegetation growth, which will make the vegetation index unable to continue to represent vegetation change information, and this information will be compensated into the surface roughness value. The static surface roughness scheme uses the vegetation optical thickness constraint to remove the influence of vegetation on the surface roughness, and also removes the vegetation change information when the vegetation index is saturated, thereby affecting the inversion accuracy of soil moisture.

[0081] In order to improve the above-mentioned problems existing in soil moisture inversion using static surface roughness parameters, the present application calculates the daily-scale surface roughness parameter (DH) based on the dynamic vegetation index; then, based on the daily-scale surface roughness parameter, the monthly-scale surface roughness parameter (MH) is calculated by considering the time lag characteristics of climate and vegetation changes in the surface representation; finally, based on the daily-scale surface roughness parameter, the vegetation optical thickness constraint condition is used to minimize the impact of vegetation on roughness, and the annual-scale surface roughness parameter (YH) is calculated. Among them, the calculation method of the annual-scale surface roughness parameter is the same as that of the static surface roughness parameter, the difference is that the static surface roughness parameter is calculated using the climatic vegetation index, while the annual-scale surface roughness parameter in this embodiment is calculated using the dynamic vegetation index. The new daily-scale surface roughness parameter and monthly-scale surface roughness parameter constructed by the present application can, to a certain extent, make up for the lack of vegetation information in the algorithm due to the saturation of the vegetation index, and can obtain more comprehensive surface information, thereby improving the accuracy of soil moisture inversion.

[0082] Specifically, the specific calculation process of the surface roughness parameters of the three time scales constructed in this application can be:

[0083] S131, inputting the initial value of vegetation optical thickness calculated based on the daily dynamic vegetation index into the zero-order radiation transfer model for calculation, and obtaining the simulated values ​​of the total radiation brightness temperature of the vertical polarization and the horizontal polarization;

[0084] The zero-order radiative transfer model considers the influence of surface vegetation on the total surface radiation brightness temperature, regards the vegetation canopy as a uniform medium, and ignores the multiple scattering effect of the vegetation canopy. The specific form of the zero-order radiative transfer model is as follows:

[0085]

[0086] Where Tb is the total surface radiation brightness temperature, in K; p is the vertical or horizontal polarization mode; T s and T c are the physical temperatures of the soil and vegetation canopy, respectively, in K. Usually, T s and T c are considered the same (especially in the morning), and are replaced by the effective surface temperature; is the p-polarized emissivity of the rough surface of bare soil; is the p-polarization reflectivity of the rough surface of bare soil, and The relationship is: =1- ; is the optical thickness of the vegetation canopy; is the single-shot albedo of the vegetation canopy; is the incident angle of the radiometer, in units of (The incident angle of the SMAP L-band radiometer is ).

[0087] The rough surface emissivity is described by the following formula:

[0088]

[0089] In the formula, is the reflectivity of the smooth surface of p-polarized bare soil, which is calculated by the dielectric constant of bare soil through the Fresnel equation. The relationship between the dielectric constant and soil moisture is constructed by the Mironov dielectric mixing model. h is the surface roughness parameter, which is related to the microwave polarization mode, frequency and surface geometric characteristics. Q is the polarization mixing coefficient, which can be calculated through h, Q = 0.1771h.

[0090] The initial value of vegetation optical thickness calculated by the dynamic vegetation index and various surface parameters in the data set obtained in step 110 (including vegetation single scattering rate, surface effective temperature, land cover type, soil clay content, soil bulk density, passive microwave frequency, passive microwave radiometer incident angle, etc.) are input into the above-mentioned zero-order radiation transmission model for calculation to obtain the vertically polarized and horizontally polarized surface total radiation brightness temperature simulation values.

[0091] S132, constructing a first cost function with a daily-scale surface roughness parameter as an inversion target through vertically polarized and horizontally polarized surface total radiation brightness temperature simulation values ​​and vertically polarized and horizontally polarized surface total radiation brightness temperature observation values;

[0092] Among them, the first cost function is:

[0093]

[0094] Where SM is soil moisture, DH is the daily-scale surface roughness parameter; , are the observed values ​​of total surface radiation brightness temperature for vertical polarization and horizontal polarization respectively; , are the simulated values ​​of the total surface radiation brightness temperature for vertical polarization and horizontal polarization respectively;

[0095] The inversion target of the first cost function is the daily-scale surface roughness parameter, not the soil moisture. The role of soil moisture here is to make the model structure complete and ensure the smooth operation of the first cost function.

[0096] S133, minimizing the first cost function through iterative calculation, and outputting a daily-scale surface roughness parameter corresponding to the minimum value of the first cost function;

[0097] The first cost function is minimized by iterative calculation, and the daily-scale surface roughness parameter corresponding to the minimum value of the first cost function is output, so as to generate a time series of daily-scale surface roughness parameter DH(i, j, k) for each pixel grid (i, j), where k is the daily-scale time series index.

[0098] S134, calculating the monthly scale surface roughness parameter by using the daily scale surface roughness parameter;

[0099] Since the change of surface roughness may have a time lag relative to vegetation and climate change, this application further considers calculating the monthly scale surface roughness parameter. The monthly average of the daily scale surface roughness parameter can be calculated to obtain the monthly scale surface roughness parameter, that is:

[0100]

[0101] Where MH(i, j, m) is the monthly surface roughness parameter of the pixel grid (i, j) in the mth month; m represents the month index in a year, with a value between 1 and 12; is the averaging function.

[0102] S135, calculating the annual scale surface roughness parameter by using the daily scale surface roughness parameter;

[0103] The annual-scale surface roughness parameter is obtained by calculating the multi-year average of the daily-scale surface roughness parameter on the corresponding date when the initial value of the vegetation optical thickness meets the restriction requirements. In order to minimize the impact of surface vegetation on roughness, the initial value of the vegetation optical thickness calculated by the dynamic vegetation index is used to numerically constrain the daily-scale surface roughness parameter time series, and the annual-scale surface roughness parameter of each pixel grid is calculated using the following formula:

[0104]

[0105] Where YH(i, j) is the annual-scale surface roughness parameter of pixel grid (i, j); They are the multi-year maximum and multi-year minimum values ​​of the time series of the initial value of vegetation optical thickness of the pixel grid (i, j). If the inversion time period is less than or equal to 1 year, the maximum and minimum values ​​of the current year are taken.

[0106] Step 140: Input the surface roughness parameter and the initial value of vegetation optical thickness into the zero-order radiation transfer model to invert soil moisture.

[0107] The initial value of vegetation optical thickness, surface roughness parameters (static surface roughness parameters or surface roughness parameters at multiple time scales), and surface parameters in the auxiliary data set are input into the zero-order radiation transfer model for soil moisture inversion to obtain daily soil moisture inversion results.

[0108] In one embodiment, soil moisture inversion is performed using a static surface roughness parameter calculated using a climatological vegetation index. At this time, the static surface roughness parameter, the initial value of vegetation optical thickness calculated in step 120, and the surface parameters and initial soil moisture values ​​in the auxiliary data set are input into a zero-order radiation transfer model to obtain vertically polarized and horizontally polarized surface total radiation brightness temperature simulation values ​​corresponding to the static surface roughness parameter; a second cost function with soil moisture and vegetation optical thickness actual values ​​as inversion targets is constructed using vertically polarized and horizontally polarized surface total radiation brightness temperature simulation values ​​corresponding to the static surface roughness parameter, the surface total radiation brightness temperature observation values, and the initial value of vegetation optical thickness; and the second cost function is minimized by iterative calculation to obtain the soil moisture inversion result corresponding to the static surface roughness parameter.

[0109] In another embodiment, the surface roughness parameters include three time-scale surface roughness parameters (DH, MH, YH), and the daily-scale surface roughness parameters, monthly-scale surface roughness parameters, and annual-scale surface roughness parameters are respectively input into the zero-order radiation transfer model together with the initial value of vegetation optical thickness, the surface parameters in the auxiliary data set, and the initial value of soil moisture, to obtain the vertical polarization and horizontal polarization surface total radiation brightness temperature simulation values ​​corresponding to the surface roughness parameters of each time scale; the second cost function with the actual values ​​of soil moisture and vegetation optical thickness as the inversion target is constructed through the vertical polarization and horizontal polarization surface total radiation brightness temperature simulation values, the surface total radiation brightness temperature observation values, and the initial value of vegetation optical thickness; the second cost function is minimized by iterative calculation to obtain the soil moisture inversion results corresponding to the surface roughness parameters of different time scales. Among them, the second cost function is:

[0110]

[0111] In the formula, is the actual value of vegetation optical thickness, is the initial value of vegetation optical thickness, It is a regularization parameter used to reduce the spatiotemporal noise in the second cost function. It can be set to 20 or a specific value can be set according to actual conditions. It is not specifically limited here.

[0112] The soil moisture inversion method based on the dynamic dual-channel model provided in the present application generates a daily dynamic vegetation index within the inversion time period by acquiring the vegetation index within the inversion time period, which can more accurately describe the changing characteristics of surface vegetation in terms of time and space scales, improve the errors existing in the climatological vegetation index when characterizing the real growth state of surface vegetation, and thus improve the accuracy of soil moisture inversion; further, the present application adopts the dynamic vegetation index to calculate the surface roughness parameter, improves the data quality in the calculation of the surface roughness parameter, and can improve the vegetation error transmission and accumulation problems caused by the use of the climatological vegetation index to calculate the surface roughness parameter; and, the present application constructs new daily-scale surface roughness parameters and monthly-scale surface roughness parameters, which can obtain more comprehensive surface information, thereby improving the accuracy of soil moisture inversion.

[0113] The above is an embodiment of a soil moisture inversion method based on a dynamic dual-channel model provided by the present application. The following is a specific application example of a soil moisture inversion method based on a dynamic dual-channel model provided by the present application.

[0114] Related research shows that the SMAP passive microwave soil moisture product based on the existing dual channel algorithm (DCA) has not yet reached its target accuracy (0.04m 3 / m 3 ), and there are obvious numerical differences with other soil moisture data products, especially in vegetation-covered areas, which greatly limits its application in high-precision research such as agricultural production, weather forecasting, and hydrological models. These problems may come from the following aspects: 1) The climatological vegetation index used in DCA is not suitable for describing the actual situation of surface vegetation at this stage, and cannot accurately describe the vegetation status under the background of climate change, farming management, etc.; 2) DCA uses the climatological vegetation index to calculate the static surface roughness parameter, which will transfer the error of the climatological vegetation index in representing the surface vegetation status to the surface roughness parameter, resulting in an increase in the error from vegetation in the soil moisture inversion model, further affecting the inversion accuracy of soil moisture; 3) The static surface roughness parameter does not conform to the actual surface changes and ignores the temporal change characteristics of the actual surface, which may be caused by vegetation growth and weather conditions; 4) The static surface roughness scheme ignores the vegetation change information contained in the roughness parameter when the vegetation index reaches saturation. In densely vegetated areas and during periods of vigorous vegetation growth, the vegetation index value is prone to saturation, which makes it impossible for the vegetation index to continue to represent vegetation change information, and this information will be compensated to the surface roughness value through the algorithm structure. The static surface roughness scheme removes the vegetation change information when the vegetation index is saturated while using the vegetation optical thickness constraint to remove the vegetation influence.

[0115] In order to improve the above problems, this application adopts dynamic vegetation index and matches surface roughness schemes of different time scales to perform soil moisture inversion, and carries out soil moisture inversion on a global scale to verify the effectiveness of the soil moisture inversion method based on the dynamic dual-channel model proposed in this application.

[0116] The embodiment of the present application randomly selects farmland pixels (such as Europe, 43.79°N, 4.09°E) around the world, and uses the soil moisture inversion method based on the dynamic vegetation index (DDCA) proposed in the present application to invert the daily soil moisture of the pixel from April 2015 to December 2021. The specific process is as follows:

[0117] S1. Calculate the daily dynamic vegetation index ( );

[0118] Based on the vegetation index with a time resolution of 8 days, the daily dynamic vegetation index of the farmland pixel during the study period was obtained through quality control, filtering and interpolation. ),like Figure 5 As shown in the figure, the original data of 8 days are marked with circles, and the data after interpolation and filtering are marked with lines. Figure 5 It can be seen that the preprocessing method can better smooth the original Time series, and retain its vegetation change information during peak and trough periods, ensuring daily accuracy and availability.

[0119] Usually, the vegetation index value range of vegetation coverage area is 0-1. The closer the value is to 1, the denser the vegetation. Usually, the vegetation index value of forest area is 0.6 or above, and the vegetation index value of desert area is less than 0.1. The range of farmland pixels in the embodiment of the present application is 0.4-0.7, indicating that vegetation grows in this area all year round. There are obvious periodic fluctuations in the time series, which are usually related to the growth cycle of field crops and field management measures.

[0120] S2. Calculate the initial values ​​of vegetation moisture content and optical thickness ( );

[0121] The daily dynamic vegetation index generated in step S1 is used to calculate the vegetation moisture content, and then the initial value of the vegetation optical thickness is calculated based on the vegetation moisture content. These two vegetation parameters have different values ​​and units from the daily dynamic vegetation index, but since they are both calculated using empirical models, the two are similar. Figure 5 The vegetation index time series change trend is consistent with that in the .

[0122] S3, calculating the surface roughness parameter (h);

[0123] In one embodiment, the climatological vegetation index may be used to calculate the static surface roughness parameter.

[0124] In another embodiment, after using the daily dynamic vegetation index to calculate the initial value of vegetation optical thickness, the present application optimizes the calculation method of the surface roughness parameters and develops three new surface roughness schemes with different time scales (DH, MH, YH). Specifically, using the surface vegetation parameters based on the daily dynamic vegetation index, the daily-scale surface roughness parameters are obtained through the zero-order radiation transfer model combined with the iterative calculation of the first cost function, and then the monthly-scale surface roughness parameters are calculated by multi-year monthly average; based on the daily-scale surface roughness parameters, the annual-scale surface roughness parameters are obtained through vegetation optical thickness condition constraints and multi-year average calculations. These three surface roughness schemes describe the surface roughness state and temporal changes of the farmland pixels, such as Figure 6 As shown in Figure 2, the static surface roughness solution calculated by the climatological vegetation index is also Figure 6 Displayed in.

[0125] like Figure 6 As shown in the figure, it can be found that both the daily-scale surface roughness parameter and the monthly-scale surface roughness parameter show obvious periodic changes in surface roughness, which are manifested as first rising and then falling within a year, reaching a peak around August / September. This change is closely related to the law of vegetation growth. In the early stage of vegetation growth, the surface becomes rough due to tillage or initial vegetation growth; in the peak season of vegetation growth, the vegetation biomass increases, accompanied by subtle changes in vegetation structure, and the surface roughness is relatively stable; in the late stage of vegetation growth, harvesting, tillage and vegetation death will increase the surface roughness. In addition, the updated annual-scale surface roughness scheme value is about 0.4 higher than the static surface roughness parameter, indicating that the surface during the study period is rougher than that described by the static surface roughness parameter, which further illustrates the surface roughness changes brought about by the optimization of the surface vegetation scheme.

[0126] S4, inputting the surface roughness parameter and the initial value of vegetation optical thickness into the zero-order radiation transfer model to invert soil moisture;

[0127] The optimized surface vegetation parameters and surface roughness parameters are used to invert the surface soil moisture of the farmland pixel during the study period through the soil moisture inversion method based on the dynamic dual channel model (DDCA) in this application, and its time series is plotted, as shown in Figure 2. Figure 7As shown. From April 2015 to December 2021, the soil moisture of the farmland pixel showed obvious seasonal changes in the time series, with low values ​​in summer and autumn and high values ​​in winter and spring. This change pattern is closely related to the crops planted. Several DDCA soil moisture values ​​are similar, which are generally lower than the existing DCA inverted soil moisture and closer to the actual soil moisture value. Obviously, the DDCA inversion method provided by this application has higher inversion accuracy.

[0128] In order to verify the inversion effect of DDCA provided in this application, the DDCA soil moisture data products were verified using the soil moisture measured data from the International Soil Moisture Network (ISMN) and the Chinese Automatic Soil Moisture Observation Stations (CASMOS).

[0129] ISMN is a global soil moisture database that is jointly maintained by international cooperation. It collects and coordinates global soil moisture measured data and is widely used in satellite inversion and land surface model simulation soil moisture product quality assessment research. This application embodiment selects ISMN site data from April 2015 to December 2021, and uses the following steps to screen and process it: 1) To keep the time consistent with the SMAP Tb observation data, select the top layer (5cm) soil moisture data at the SMAP descending transit time (6 am); 2) Eliminate measured values ​​marked as abnormal according to the quality mark; 3) Eliminate sites with less than 30 valid observation data per year; 4) Only select observation sites that have been used in related studies.

[0130] The measured soil moisture data in CASMOS is provided by the China Meteorological Administration, and includes hourly and layer-by-layer soil moisture data of China's automatic observation stations from 2017 to 2018. The present application embodiment selects CASMOS station data from January 2017 to December 2018, and uses the following steps to screen and process it: 1) To keep the time consistent with the SMAP Tb observation data, select the top layer (10cm) soil moisture data at the SMAP orbit descending time (6 am); 2) Eliminate the measured values ​​marked as missing and poor quality according to the quality mark; 3) Eliminate stations with less than 30 valid observation data per year.

[0131] The effect comparison between DDCA and existing DCA soil moisture products provided in this application:

[0132] (1) Differences in spatial distribution between the dynamic vegetation index used by DDCA and the climatological vegetation index used by DCA

[0133] The vegetation index product with a time resolution of 8 days from April 2015 to December 2021 is used and processed using the method in step 110 to obtain the global daily dynamic vegetation index ( ) and compared with the climatological vegetation index ( ) for comparison, using the vegetation index change rate Indicates the spatial difference between the two. Among them, the vegetation index change rate The calculation formula is:

[0134]

[0135] Where k is the daily time index; n is the total number of days; is the kth day ; is the kth day .

[0136] On about 65% of the world's land, Value higher than The relative change in the value is about 10%, especially in the arid areas of Central Asia, Australia, North America and South America. The phenomenon of large-scale greening of vegetation around the world is mainly caused by global warming and afforestation activities in various countries in recent years. In countries such as Canada, Brazil and Kazakhstan, there has been a decline in the vegetation index, which may be caused by meteorological and agricultural droughts and forest fires caused by climate change. In summary, It is impossible to describe the true spatial distribution and temporal variation of vegetation growth, especially in the mid- and high-latitude regions of the Northern Hemisphere where agricultural production is dominant.

[0137] (2) Differences in spatial distribution between the surface roughness used by DDCA and the static surface roughness used by DCA

[0138] The climatological vegetation index cannot accurately describe the growth state of surface vegetation, and the static surface roughness parameters calculated using the climatological vegetation index will also be inconsistent with the actual surface conditions. Therefore, this application updates the static surface roughness parameters used in DCA and proposes three surface roughness schemes at different time scales for inverting soil moisture.

[0139] The three surface roughness schemes calculated based on the daily dynamic vegetation index have similar numerical spatial distributions, but they differ significantly in vegetation growth and change areas. In vegetation growth areas where the annual average vegetation index is greater than 0.3, such as northern Canada, eastern United States, central and western Russia, and the Amazon, the new surface roughness scheme has a significant increase in value compared with the static surface roughness parameter. The increase in surface roughness in these areas may be caused by changes in vegetation types in forest ecosystems and an increase in the number of forest fires under climate change. These two situations will lead to the death of adult trees, the increase of young trees, and the patchiness of vegetation growth in the region, making the surface observed by microwaves rougher. Another reason for the increase in surface roughness may be the subsequent effect of vegetation index saturation. In densely vegetated areas or periods of vigorous vegetation growth, the vegetation index cannot further characterize vegetation changes after reaching saturation (about 0.8), resulting in the loss of vegetation change information in the initial value of vegetation optical depth, but this information can be shown in the surface roughness parameter. Specifically, when the vegetation index reaches saturation, vegetation will still cause changes in the Tb observations of the microwave radiometer. This part of the radiation cannot be reflected by the vegetation parameters, but is allocated to the surface radiation. Based on this principle, when the soil moisture is constant, the radiation caused by this part of vegetation changes is allocated to the surface roughness, thereby obtaining a higher surface roughness inversion value. In major agricultural regions around the world, such as the United States, Canada, southern China, and India, the new surface roughness schemes show numerical differences from static surface roughness parameters, and most areas show an increase in surface roughness. In farmland areas, the surface is greatly affected by human activities. Crop rotation and frequent land management activities can cause crop-related seasonal changes in the geometric properties of the cultivated land surface structure within a year, which may point to surface parameters with obvious time series fluctuations and rougher surface parameters.

[0140] In summary, compared with the static surface roughness parameter scheme, the surface roughness scheme at different time scales calculated using the daily dynamic vegetation index can better describe the impact of vegetation and agricultural management on surface changes, while retaining the terrain undulation information reflected in the CH scheme to the greatest extent.

[0141] (3) Spatial distribution of soil moisture

[0142] The experiments were conducted to obtain the spatial distribution of the average soil moisture values ​​corresponding to the four DDCA and DCA during the study period on a global scale. Overall, the spatial distribution patterns of several soil moisture values ​​are basically the same: in arid climate areas (such as Australia, western United States, central Asia, and the Sahara Desert in Africa), the soil moisture values ​​are low, with an average value of less than 0.15m 3 / m 3In forest areas with relatively dense vegetation (such as the Amazon rainforest, eastern North America, and southeastern Asia), the soil is relatively moist, with the average soil moisture content in most areas exceeding 0.3m 3 / m 3 .

[0143] Comparing the average values ​​of DDCA soil moisture and DCA soil moisture, it is found that in the DDCA soil moisture dataset, the DDCA-CH soil moisture values ​​are lower in most parts of the world, especially in areas with high vegetation coverage, with negative deviations up to -0.08 m 3 / m 3 , which is about 13% of the average soil moisture in the region. In areas with dense vegetation coverage, this negative deviation of DDCA-CH soil moisture is of reference value for correcting the overestimation of DCA soil moisture. The other three DDCA soil moistures show a wetter state than DCA soil moisture in areas with moderate vegetation coverage at high latitudes in the Northern Hemisphere, and the increase in soil moisture is about 0.03m 3 / m 3 , the relative change is less than 10%. This phenomenon of overestimation of soil moisture is related to the use of a new surface roughness scheme in the inversion method. The new surface roughness scheme contains relatively rich and real surface roughness information, making the description of the radiation transfer process more detailed and accurate, thereby obtaining a more realistic soil moisture inversion value, which corresponds to the greening trend of the surface in this area.

[0144] (4) Soil moisture time series characteristics and comparative verification results

[0145] In order to more directly reflect the superiority of DDCA over DCA in inverting soil moisture, some stations were randomly selected around the world and the surface soil moisture of the corresponding pixels during the study period were inverted. The time series characteristics and comparative verification results were plotted on Figure 8 middle. Figure 8 The paper shows the variation patterns of four DDCA and DCA soil moisture data products in time series at a farmland site in the United States and the verification scatter plot of the site measured data. Fig. 9 The time series characteristics and verification scatter plots of the best-performing DDCA data products compared with DCA data products at some actual measurement sites in China are shown.

[0146] like Figure 8 As shown in the figure, DDCA has better effect and higher accuracy in soil moisture inversion than DCA, corresponding to a higher correlation coefficient R and a lower ubRMSE, indicating that DDCA can effectively correct the overestimation of soil moisture by DCA, especially in areas with high soil moisture values ​​(>0.3m 3 / m 3). Throughout the entire time series, DDCA can accurately capture the changing characteristics of surface soil moisture.

[0147] like Fig. 9 As shown in the figure, DDCA also shows better soil moisture inversion performance at grassland and farmland sites selected in China, especially in areas with high soil moisture values, where DDCA can effectively correct the overestimation of DCA. When the soil moisture is low, the soil moisture values ​​inverted by DDCA and DCA are similar. This is because the vegetation in low soil moisture areas is sparse, and the advantage of DDCA using the dynamic vegetation index cannot be highlighted. In areas with high soil moisture values, the vegetation is relatively dense, and the superiority of updating and optimizing surface vegetation and roughness parameters can be reflected, thereby obtaining more accurate soil moisture inversion values.

[0148] The above is a specific application example of a soil moisture inversion method based on a dynamic dual-channel model provided by the present application. The following is an embodiment of a soil moisture inversion device based on a dynamic dual-channel model provided by the present application.

[0149] The present application provides a soil moisture inversion device based on a dynamic dual-channel model, comprising:

[0150] A data processing unit is used to resample the vegetation index of the inversion time period by using a spatial averaging method, and reproject the resampled vegetation index to an equal-area expandable earth grid, and filter and linearly interpolate the vegetation index time series of each pixel grid to obtain a daily dynamic vegetation index of the inversion time period;

[0151] The first calculation unit is used to calculate the vegetation moisture content through the daily dynamic vegetation index, and then calculate the initial value of vegetation optical thickness through the vegetation moisture content;

[0152] A second calculation unit is used to calculate the surface roughness parameter based on the initial value of vegetation optical thickness calculated by the climatological vegetation index or the daily dynamic vegetation index;

[0153] The inversion unit is used to input the surface roughness parameters and the initial value of vegetation optical thickness into the zero-order radiation transfer model to invert soil moisture.

[0154] As a further improvement, the second computing unit is specifically configured to:

[0155] The static surface roughness parameters are calculated by the climatological vegetation index;

[0156] Alternatively, the initial value of vegetation optical thickness calculated based on the daily dynamic vegetation index is input into the zero-order radiation transfer model for calculation to obtain the simulated values ​​of the total surface radiation brightness temperature of vertical polarization and horizontal polarization;

[0157] The first cost function with daily-scale surface roughness parameters as the inversion target is constructed through the simulated values ​​of vertically polarized and horizontally polarized surface total radiation brightness temperature and the observed values ​​of vertically polarized and horizontally polarized surface total radiation brightness temperature.

[0158] Minimize the first cost function by iterative calculation, and output the daily-scale surface roughness parameter corresponding to the minimum value of the first cost function;

[0159] The monthly scale surface roughness parameters are calculated through the daily scale surface roughness parameters;

[0160] The annual-scale surface roughness parameters are calculated from the daily-scale surface roughness parameters.

[0161] Among them, the first cost function is:

[0162]

[0163] Where SM is soil moisture, DH is the daily-scale surface roughness parameter; , are the observed values ​​of total surface radiation brightness temperature for vertical polarization and horizontal polarization respectively; , They are the simulated values ​​of the total surface radiation brightness temperature for vertical polarization and horizontal polarization respectively.

[0164] As a further improvement, the inversion unit is specifically used for:

[0165] The static surface roughness parameter and the initial value of the vegetation optical thickness are input into the zero-order radiation transfer model to obtain the vertically polarized and horizontally polarized surface total radiation brightness temperature simulation values ​​corresponding to the static surface roughness parameter, or the daily-scale surface roughness parameter, the monthly-scale surface roughness parameter, the annual-scale surface roughness parameter and the initial value of the vegetation optical thickness are input into the zero-order radiation transfer model to obtain the vertically polarized and horizontally polarized surface total radiation brightness temperature simulation values ​​corresponding to the surface roughness parameter at each time scale;

[0166] A second cost function with actual values ​​of soil moisture and vegetation optical thickness as inversion targets is constructed through the simulated values ​​of vertically polarized and horizontally polarized surface total radiation brightness temperature corresponding to static surface roughness parameters or surface roughness parameters at various time scales, the observed values ​​of surface total radiation brightness temperature, and the initial value of vegetation optical thickness;

[0167] By iteratively minimizing the second cost function, the soil moisture inversion results based on the daily dynamic vegetation index and the static surface roughness parameters, or the soil moisture inversion results based on the daily dynamic vegetation index and the surface roughness parameters of each time scale are obtained.

[0168] Among them, the second cost function is:

[0169]

[0170] Where SM is soil moisture; , are the observed values ​​of total surface radiation brightness temperature for vertical polarization and horizontal polarization respectively; , are the simulated values ​​of the total surface radiation brightness temperature for vertical polarization and horizontal polarization respectively; is the actual value of vegetation optical thickness; is the initial value of vegetation optical thickness; is the regularization parameter.

[0171] The soil moisture inversion device based on the dynamic dual-channel model provided in the present application generates a daily dynamic vegetation index for the inversion time period by acquiring the vegetation index for the inversion time period, which can more accurately describe the changing characteristics of surface vegetation in terms of time and space scales, improve the long-term numerical deviation and other problems existing in the climatological vegetation index, and thus improve the accuracy of soil moisture inversion; the daily dynamic vegetation index is used to calculate the surface roughness parameter, which improves the data quality in the calculation of the surface roughness parameter and improves the vegetation error transmission and accumulation problems caused by the use of the climatological vegetation index to calculate the surface roughness parameter; and the present application constructs new daily-scale surface roughness parameters and monthly-scale surface roughness parameters, which can obtain more comprehensive surface information and further improve the accuracy of soil moisture inversion.

[0172] The embodiment of the present application also provides an electronic device, the device comprising a processor and a memory;

[0173] The memory is used to store the program code and transmit the program code to the processor;

[0174] The processor is used to execute the soil moisture inversion method based on the dynamic dual-channel model in the aforementioned method embodiment according to the instructions in the program code.

[0175] An embodiment of the present application also provides a computer-readable storage medium, which is used to store program code. When the program code is executed by a processor, the soil moisture inversion method based on the dynamic dual-channel model in the aforementioned method embodiment is implemented.

[0176] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0177] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0178] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0179] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0180] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0181] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0182] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions for executing all or part of the steps of the method described in each embodiment of the present application through a computer device (which can be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (full name in English: Read-Only Memory, English abbreviation: ROM), random access memory (full name in English: Random Access Memory, English abbreviation: RAM), disk or optical disk and other media that can store program codes.

[0183] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A soil moisture inversion method based on a dynamic dual-channel model, It is characterized in that include: The obtained vegetation index of the inversion time period is resampled by using the spatial averaging method, and the resampled vegetation index is reprojected to an equal-area expandable earth grid. The vegetation index time series of each pixel grid is filtered and linearly interpolated to obtain the daily dynamic vegetation index of the inversion time period. Calculate the vegetation moisture content through the daily dynamic vegetation index, and then calculate the initial value of vegetation optical thickness through the vegetation moisture content; Calculating the surface roughness parameter based on the initial value of vegetation optical thickness calculated based on the climatological vegetation index or the daily dynamic vegetation index includes: The static surface roughness parameters are calculated by the climatological vegetation index; Or, inputting the initial value of vegetation optical thickness calculated based on the daily dynamic vegetation index into a zero-order radiation transfer model for calculation to obtain vertically polarized and horizontally polarized surface total radiation brightness temperature simulation values; A first cost function with the daily-scale surface roughness parameter as the inversion target is constructed through the vertically polarized and horizontally polarized surface total radiation brightness temperature simulation values ​​and the vertically polarized and horizontally polarized surface total radiation brightness temperature observation values; Minimize the first cost function by iterative calculation, and output the daily-scale surface roughness parameter corresponding to the minimum value of the first cost function; Calculating the monthly scale surface roughness parameter by using the daily scale surface roughness parameter; Calculating the annual scale surface roughness parameter by using the daily scale surface roughness parameter; Wherein, the first cost function is: ； Where SM is soil moisture, DH is the daily-scale surface roughness parameter; , are the observed values ​​of total surface radiation brightness temperature for vertical polarization and horizontal polarization respectively; , are the simulated values ​​of the total surface radiation brightness temperature for vertical polarization and horizontal polarization respectively; The surface roughness parameter and the initial value of the vegetation optical thickness are input into a zero-order radiation transfer model to invert soil moisture.

2. The soil moisture inversion method based on the dynamic dual-channel model according to claim 1, It is characterized in that The calculating of the monthly scale surface roughness parameter by using the daily scale surface roughness parameter comprises: The monthly average value of the daily-scale surface roughness parameter is calculated to obtain the monthly-scale surface roughness parameter.

3. The soil moisture inversion method based on the dynamic dual-channel model according to claim 1, It is characterized in that The calculating of the annual scale surface roughness parameter by using the daily scale surface roughness parameter comprises: The multi-year average value of the daily-scale surface roughness parameter on the corresponding date when the initial value of the vegetation optical thickness meets the restriction conditions is calculated to obtain the annual-scale surface roughness parameter.

4. The soil moisture inversion method based on the dynamic dual-channel model according to claim 1, It is characterized in that The step of inputting the surface roughness parameter and the initial value of the vegetation optical thickness into a zero-order radiation transfer model to invert soil moisture comprises: Input the static surface roughness parameter and the initial value of the vegetation optical thickness into a zero-order radiation transfer model to obtain the vertically polarized and horizontally polarized surface total radiation brightness temperature simulation values ​​corresponding to the static surface roughness parameter; or, input the daily-scale surface roughness parameter, the monthly-scale surface roughness parameter, and the annual-scale surface roughness parameter and the initial value of the vegetation optical thickness into a zero-order radiation transfer model, respectively, to obtain the vertically polarized and horizontally polarized surface total radiation brightness temperature simulation values ​​corresponding to the surface roughness parameters at each time scale; A second cost function with actual values ​​of soil moisture and vegetation optical thickness as inversion targets is constructed through the simulated values ​​of vertically polarized and horizontally polarized surface total radiation brightness temperature corresponding to the static surface roughness parameters or the surface roughness parameters at each time scale, the observed values ​​of surface total radiation brightness temperature, and the initial value of vegetation optical thickness; By iteratively calculating and minimizing the second cost function, a soil moisture inversion result based on the daily dynamic vegetation index and the static surface roughness parameter is obtained, or a soil moisture inversion result based on the daily dynamic vegetation index and the surface roughness parameters of each time scale is obtained.

5. The soil moisture inversion method based on the dynamic dual-channel model according to claim 4, It is characterized in that The second cost function is: ； Where SM is soil moisture; , are the observed values ​​of total surface radiation brightness temperature for vertical polarization and horizontal polarization respectively; , are the simulated values ​​of the total surface radiation brightness temperature for vertical polarization and horizontal polarization respectively; is the actual value of vegetation optical thickness; is the initial value of vegetation optical thickness; is the regularization parameter.

6. A soil moisture inversion device based on a dynamic dual-channel model, It is characterized in that include: A data processing unit is used to resample the vegetation index of the inversion time period obtained by using a spatial averaging method, and reproject the resampled vegetation index to an equal-area expandable earth grid, and filter and linearly interpolate the vegetation index time series of each pixel grid to obtain a daily dynamic vegetation index of the inversion time period; A first calculation unit is used to calculate the moisture content of vegetation through the daily dynamic vegetation index, and then calculate the initial value of vegetation optical thickness through the vegetation moisture content; A second calculation unit is used to calculate the surface roughness parameter based on the initial value of vegetation optical thickness calculated by the climatological vegetation index or the daily dynamic vegetation index; The second computing unit is specifically configured to: The static surface roughness parameters are calculated by the climatological vegetation index; Or, inputting the initial value of vegetation optical thickness calculated based on the daily dynamic vegetation index into a zero-order radiation transfer model for calculation to obtain vertically polarized and horizontally polarized surface total radiation brightness temperature simulation values; A first cost function with the daily-scale surface roughness parameter as the inversion target is constructed through the vertically polarized and horizontally polarized surface total radiation brightness temperature simulation values ​​and the vertically polarized and horizontally polarized surface total radiation brightness temperature observation values; Minimize the first cost function by iterative calculation, and output the daily-scale surface roughness parameter corresponding to the minimum value of the first cost function; Calculating the monthly scale surface roughness parameter by using the daily scale surface roughness parameter; Calculating the annual scale surface roughness parameter by using the daily scale surface roughness parameter; Wherein, the first cost function is: ； Where SM is soil moisture, DH is the daily-scale surface roughness parameter; , are the observed values ​​of total surface radiation brightness temperature for vertical polarization and horizontal polarization respectively; , are the simulated values ​​of the total surface radiation brightness temperature for vertical polarization and horizontal polarization respectively; The inversion unit is used to input the surface roughness parameter and the initial value of the vegetation optical thickness into a zero-order radiation transfer model to invert soil moisture.

7. An electronic device, It is characterized in that The device comprises a processor and a memory; The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the soil moisture inversion method based on the dynamic dual-channel model according to any one of claims 1-5 according to the instructions in the program code.

8. A computer-readable storage medium, It is characterized in that The computer-readable storage medium is used to store program codes, and when the program codes are executed by a processor, the soil moisture inversion method based on the dynamic dual-channel model described in any one of claims 1 to 5 is implemented.

Images