Microwave scattering land surface soil moisture detection method and device

Abstract

The invention provides a microwave scattering land surface soil moisture detection method, which comprises the following steps of: for a target pixel, observing a backscattering coefficient change and a corresponding vegetation index NDVI value under the condition that vegetation covers a ground surface on a target date; and according to the empirical relationship of the NDVI on the backscattering influence, excluding the vegetation influence from the backscattering coefficient variation under the vegetation coverage earth surface condition, and obtaining the backscattering coefficient variation of the target pixel under the bare soil condition. And obtaining the variation of the soil moisture according to the variation of the back scattering of the target pixel under the bare soil condition. The invention further comprises a device for implementing the method. The problems that in the prior art, model calculation is complex, and input data precision is required to be high are solved.

Description

Technical Field

[0001] The present application relates to the field of earth remote sensing technology, and in particular to a method and device for detecting soil moisture on land surface using a microwave scatterometer. Background Art

[0002] Soil moisture is a basic parameter reflecting soil status, which has a direct impact on the growth of plants and animals and the physical and chemical properties of soil, and plays an important role in water cycle and energy cycle such as evaporation and infiltration, groundwater recharge and surface runoff. Accurate monitoring of soil moisture has important application value in crop growth monitoring, flood and drought disaster prediction and weather forecast. The global land area is 29.2%, and the vegetation coverage area accounts for approximately 30% to 40% of the land. Among them, soil moisture has an important impact on the change of vegetation coverage area. At the same time, soil moisture is also a basic parameter reflecting soil status, which also has a direct impact on the growth of plants and animals and the physical and chemical properties of soil, and plays an important role in water cycle and energy cycle such as evaporation and infiltration, groundwater recharge and surface runoff.

[0003] In microwave remote sensing technology, passive microwave remote sensing is an effective way to detect surface soil moisture, but it also has problems such as low resolution and serious data missing. With the launch of fan-beam rotating scanning system scatterometers on FY3E and CFOSAT satellites, it is very necessary to develop soil moisture data based on a more effective multi-angle measurement and high spatial resolution active microwave satellite observation data, which is an effective supplement to global soil moisture data. The existing schemes include theoretical models (geometric optical model GOM, physical optical model POM, small perturbation model SPM and integral equation model IEM / AIEM) and empirical and semi-empirical models (Oh model, Dubios model, and Shi model) in the bare soil backscattering model. The vegetation model only has the water cloud model (Water-Cloud Model) and the microwave radiation transfer equation model (MIMICS).

[0004] For relatively rough surfaces, that is, when the surface normalized root mean square height kδ is large, GOM uses the resident phase approximation, that is, it is assumed that electromagnetic waves can only be scattered along the direction where there are mirror points on the surface. This approximation obtains a geometric optical model.

[0005] When the normalized RMS height of the surface is small, the POM adopts the scalar approximation method, that is, the surface autocorrelation function is expanded at a RMS slope of 0, and then the low-order terms are retained to obtain the physical optics model.

[0006] The classic model of SPM small-scale roughness is the small perturbation model, which requires that the surface standard deviation is less than about 5% of the wavelength of the electromagnetic wave. Roughness application range: Geometric optics model GOM> physical optics model POM> small perturbation model SPM.

[0007] The IEM / AIEM integral equation model is a surface scattering model based on the electromagnetic wave radiation transmission equation and has a wide range of applications. AIEM is an improved model and the simulation results and accuracy have been further improved.

[0008] The surface roughness range applied by Oh is wide, but it relies too much on empirical parameters observed on the ground, and its data are obtained from a vehicle-mounted scatterometer system. Therefore, the universal applicability of this model needs to be further verified.

[0009] Dubois used the ground scatterometer measurement data of the RASAM system of Oh and Berne University to invert the empirical model of bare earth surface root mean square height and soil moisture.

[0010] Shi et al.

[14] examined different polarization combinations of L-band AIRSAR and SIR-C data to estimate their effects in retrieving soil moisture and surface roughness.

[0011] Correction for the effects of surface vegetation is very important because the structural characteristics of vegetation are very complex, thus forming different microwave scattering mechanisms.

[0012] MIMICS (based on microwave radiation transmission equation) is a vegetation scattering model based on the first-order solution of the microwave radiation transmission equation. It is also the most widely used theoretical model for studying microwave vegetation scattering characteristics. It describes the vegetation structure in detail, but it is mainly established for surfaces covered by tall vegetation such as forests. The input parameters are complex and numerous, making it difficult to apply.

[0013] Water-Cloud Model EPWAttema proposed a water-cloud model for estimating soil moisture on the surface covered by crops. It treats the entire crop cover as a consistent scatterer without considering the effect of multiple scattering. However, under some crop cover (such as corn, sorghum, etc.) and at specific wavelengths, double scattering between vegetation and soil accounts for a certain proportion of the total backscattering, and simply ignoring it will cause large errors.

[0014] These models are limited in practical application due to their narrow applicable roughness range.

[0015] Although the existing model describes the scattering characteristics of the forest canopy more completely, the model is more complex in depicting the vegetation canopy and has too many model parameters to be applied in actual inversion.

[0016] Existing technical models require accurate surface parameters as input data, but the spatial heterogeneity of surface parameters and the inaccuracy of the model make it still difficult to obtain large-scale, highly robust inversion results. Summary of the invention

[0017] The present application proposes a method and device for detecting soil moisture on land surface by microwave scattering, which solves the problem of complex model calculation and high-precision input data requirement in the prior art.

[0018] In a first aspect, an embodiment of the present application provides a method for detecting soil moisture on a land surface by microwave scattering, comprising the following steps:

[0019] For the target pixel (i, j), observe the change in backscatter coefficient under the vegetation cover surface conditions on the target date t And the corresponding vegetation index NDVI value V 1 ;

[0020] According to the empirical relationship of the influence of NDVI on backscattering, the influence of vegetation is excluded from the change of backscattering coefficient under the condition of vegetation-covered surface, and the change of backscattering coefficient under the condition of bare soil of the target pixel is obtained;

[0021] The change in soil moisture is obtained according to the change in backscattering of the target pixel under bare soil conditions.

[0022] In one embodiment of the present application, determining an empirical relationship of the effect of NDVI on backscattering comprises the following steps:

[0023] Generate a scatter plot between the change in backscatter coefficient under the vegetation cover surface conditions during the observation period and the NDVI of the corresponding date;

[0024] A plurality of sampling points of the highest value segment of the variation are obtained within each NDVI discrete value range, and a relationship curve between the variation and NDVI is generated by fitting.

[0025] In one embodiment of the present application, the change in soil moisture is obtained based on the change in backscattering, as shown in formula (2) of the embodiment of the present application.

[0026] In one embodiment of the present application, the influence of vegetation is excluded from the change in the backscattering coefficient under the condition of vegetation-covered surface, and the change in the backscattering coefficient of the target pixel under bare soil conditions is obtained, as shown in formulas (1.1) to (1.2) of the embodiment of the present application.

[0027] In one embodiment of the present application, the NDVI influences the backscattering, expressed as f veg (V 1 )=a*V 1, which represents the empirical relationship of the effect of NDVI on backscatter. For FY3E satellite remote sensing data, a = -4.06, or, for CFOSAT satellite remote sensing data, a = -9.70.

[0028] In a second aspect, the embodiment of the present application further provides a microwave scattering land surface soil moisture detection device, which is used to implement the method described in any embodiment of the first aspect of the present application, including:

[0029] The acquisition module is used to observe the change of the backscatter coefficient under the condition of vegetation coverage on the target pixel (i, j). And the corresponding vegetation index NDVI value V 1 ;

[0030] A generation module is used to exclude the influence of vegetation from the variation of backscatter coefficient under the condition of vegetation-covered surface according to the empirical relationship of the influence of NDVI on backscatter, so as to obtain the variation of backscatter coefficient under the condition of bare soil of the target pixel;

[0031] The determination module is used to obtain the change in soil moisture according to the change in backscattering of the target pixel under bare soil conditions.

[0032] In a third aspect, an embodiment of the present application further proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in any embodiment of the first aspect of the present application.

[0033] In a fourth aspect, an embodiment of the present application further proposes an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method described in any embodiment of the first aspect of the present application is implemented.

[0034] At least one of the above technical solutions adopted in the embodiments of the present application can achieve the following beneficial effects:

[0035] The present application proposes a method for detecting changes in soil moisture by inverting it using a microwave scatterometer. The present application first uses a logarithmic function to describe the response relationship between bare soil and soil moisture, then combines the NDVI hierarchical fitting empirical function equation to eliminate the vegetation effect, and finally uses the corrected backscatter coefficient change and soil moisture extreme value to realize the soil moisture inversion of the microwave scatterometer. By optimizing the soil moisture inversion process, the present application has the beneficial effects of simplifying calculations and reducing parameter dependence, effectively improving the applicability and inversion accuracy of the method. At the same time, the present application can weaken the interference of vegetation effects, improve the stability of results, and is suitable for data fusion of multi-source sensors, which significantly expands the application scope and practical value of the technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0037] Figure 1 This is a flow chart of a microwave scattering land surface soil moisture detection method according to an embodiment of the present application;

[0038] Figure 2 This is a flow chart of a method for detecting soil moisture on a land surface using microwave scattering according to another embodiment of the present application;

[0039] Figure 3 is the influence of NDVI on the change of backscatter coefficient;

[0040] Figure 4 is the fitting curve f(NDVI) between the maximum backscatter difference of FY3E and NDVI;

[0041] Figure 5 f(NDVI) is the fitting curve of the maximum backscatter difference of CFOSAT and NDVI;

[0042] Figure 6 The spatial distribution of global soil moisture retrieval for FY3E (local);

[0043] Figure 7 The spatial distribution of CFOSAT global soil moisture retrieval (local);

[0044] Figure 8 (a)-(b) shows the accuracy verification results of the soil moisture observation network;

[0045] Fig. 9 A microwave scattering land surface soil moisture detection device according to an embodiment of the present application;

[0046] Fig.10 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. 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 this field without making creative work are within the scope of protection of the present application.

[0048] This application proposes a method for detecting soil moisture changes by inverting the soil moisture using a microwave scatterometer. This application first uses a logarithmic function to describe the response relationship between bare soil and soil moisture instead of the traditional method that requires a large number of parameters to simulate, then combines the NDVI hierarchical fitting empirical function equation to eliminate the vegetation effect and simplify the description of the vegetation canopy, and finally uses the corrected backscatter coefficient change and soil moisture extreme value to realize the soil moisture inversion across the microwave scatterometer sensor.

[0049] The technical solutions provided by various embodiments of the present application are described in detail below in conjunction with the accompanying drawings.

[0050] Figure 1 The method flow chart of the embodiment of the present application is as follows. The embodiment of the present application proposes a method for detecting soil moisture on a land surface by microwave scattering, including the following steps 110 to 130:

[0051] Step 110: for the target pixel (i, j), observe the change in backscatter coefficient and the corresponding vegetation index NDVI value under the vegetation coverage surface condition on the target date t.

[0052] Where i and j represent the position of the pixel. In the present application, the backscatter coefficient change refers to the change of the scatter coefficient value relative to its minimum value during the observation period. For each pixel point, first determine the date of the minimum backscatter value of the pixel point in a year, and then determine the backscatter coefficient change of the pixel point on other dates, and the backscatter coefficient change is the difference between the backscatter value of the other dates and the backscatter value of the date.

[0053] Accordingly, on the target date t, the corresponding NDVI value is V 1 Under vegetation coverage conditions, the change in backscatter coefficient is expressed as

[0054] Step 120: According to the empirical relationship of the influence of NDVI on backscattering, the influence of vegetation is excluded from the variation of backscattering coefficient under the condition of vegetation-covered surface, so as to obtain the variation of backscattering coefficient under the condition of bare soil of the target pixel.

[0055] Furthermore, step 120 includes steps 121 to 122.

[0056] Step 121, in one embodiment of the present application, determining the empirical relationship of the effect of NDVI on backscattering comprises the following steps:

[0057] Step 121A, generating a scatter plot between the backscatter coefficient change under the vegetation coverage surface condition during the observation period and the NDVI of the corresponding date;

[0058] Step 121B, obtaining a number of sampling points of the highest value segment of the variation in each NDVI discrete value range, and fitting to generate a relationship curve between the variation and NDVI.

[0059] In the embodiment of the present application, the empirical relationship of the effect of NDVI on backscattering is expressed as f veg (V 1 )=a*V 1 .

[0060] Step 122, in one embodiment of the present application, excluding the influence of vegetation from the backscatter coefficient variation under the condition of vegetation-covered surface to obtain the backscatter coefficient variation under the condition of bare soil of the target pixel, includes:

[0061]

[0062] or

[0063]

[0064] Among them, in pixel (i,j), is the change in backscatter coefficient on target date t, is the maximum change of backscattering during the observation period, V 1 is the NDVI on the target date t; The actual measured value at the target date t in pixel (i, j), During the observation period, the pixel (i, j) is in the range of NDVI = V 1 The maximum actual measured value at veg (V 1 ) is when NDVI=V 1 The influence of NDVI on backscattering.

[0065] Step 130: Obtain a change in soil moisture according to a change in backscattering of the target pixel under bare soil conditions.

[0066] In one embodiment of the present application, the backscattering variation includes the backscattering coefficient variation on the target date and the maximum backscattering coefficient variation within the observation period.

[0067] To determine the soil moisture change, the relationship between the moisture change and the backscatter change should be considered. Here, the moisture change refers to the change of the moisture value of the target pixel on the target date relative to its minimum value during the observation period. For each pixel, first determine the date of the minimum moisture value of the pixel in a year, and then determine the moisture change of the pixel on other dates, which is the difference between the moisture value on other dates and the moisture value on this date.

[0068] According to the change detection method, the maximum backscatter coefficient and the maximum moisture value occur at the same time, and the minimum backscatter coefficient and the minimum moisture value occur at the same time, so:

[0069]

[0070] in, is the change in backscatter coefficient on the target date t, is the maximum change of backscattering during the observation period, SM max is the maximum soil moisture, SM min is the minimum soil moisture, SM t is the soil moisture on the target date t within the observation period, and S represents the sensitivity of the microwave signal to soil moisture.

[0071] It should be noted that this application uses data from optical remote sensing observations to calculate NDVI, and then uses empirical functions to remove vegetation effects. It does not exclude the use of other optical observations (parameters) to replace NDVI. This application protects the means of removing vegetation effects using optical observations. This application does not exclude the use of other soil moisture products to replace MCCA SMAP, and this application requests protection for the means of using extreme values ​​of soil moisture for change detection. This application does not exclude the use of other scatterometers or radar backscatter coefficient data to replace FY3E or CFOSAT, and protects the means of using the difference in backscatter coefficient changes for change detection.

[0072] It should also be noted that the execution subject of each step of the method provided in the embodiment of the present application can be the same device, or the method can be executed by different devices. For example, the execution subject of step 110 and step 120 can be device 1, and the execution subject of step 130 can be device 2; for another example, the execution subject of step 110 can be device 1, and the execution subject of steps 120 and 130 can be device 2; and so on.

[0073] like Figure 2 As shown, the embodiment of the present application further includes a scatterometer data incident angle correction algorithm before step 110 .

[0074] The single-day data preprocessing and global coverage display of the C / Ku-band backscatter data of FY-3E and CFOSAT are normalized to 40° to remove the influence of angle on soil moisture inversion. The main calculation methods are as follows:

[0075]

[0076] σ′ is the fitted slope of n observations at the same observation position, and σ″ is the fitted curvature of n observations at the same observation position.

[0077] like Figure 2 As shown, the embodiment of the present application includes a method for detecting soil moisture changes under bare soil conditions. The backscattering of bare soil is mainly affected by soil moisture and soil surface roughness. When the bare soil roughness changes slightly, it can be considered that its contribution to backscattering remains stable. Therefore, by continuously observing the same area using microwaves, the effect of roughness on backscattering changes can be ignored. At this time, it is considered that the observed surface backscattering changes are mainly caused by soil moisture changes. Therefore, establishing a response relationship between backscattering changes and soil moisture changes can achieve the inversion of soil moisture.

[0078] Assuming that there is a nonlinear response relationship between microwave backscattering and soil moisture under bare soil conditions, in the best embodiment of the present application, a logarithmic function is used in the change detection method to describe the response relationship between the two. There is the following formula:

[0079] σ soil =log n (SM+S)+Q (4)

[0080] where σ soil is the microwave backscattering coefficient, SM is the volumetric soil moisture content (m 3 / m 3 ), n represents the base of the logarithmic function, S represents the sensitivity of microwave signals to soil moisture, and Q is mainly affected by soil roughness, microwave wavelength, incident angle and other parameters. For time series microwave data, since the same sensor is used for observation within the observation period, Q is regarded as a constant in formula (4).

[0081] During the repeated observation period, when the soil moisture reaches the maximum value SM max When , the backscattering reaches its maximum value At this time there are:

[0082]

[0083] Likewise, when the soil moisture reaches the minimum value SM min When , the backscattering reaches the minimum value have:

[0084]

[0085] The maximum change of soil moisture content during the observation period ΔSM max =SM max -SM min According to formulas (5) to (6), the maximum change of the backscatter coefficient during the observation period can be expressed as:

[0086]

[0087] in is the maximum change of backscattering during the observation period.

[0088] For the soil moisture SM on the tth day during the observation period t And the corresponding backscattering coefficient The change of soil moisture relative to the minimum soil moisture on day t, ΔSM t =SM t -SM min , at this time:

[0089]

[0090] in is the backscatter coefficient of the tth day relative to the minimum backscatter coefficient during the observation period Therefore, according to formula (7) and (8), we can get formula (2), and the change of soil moisture can be obtained according to the change of backscattering.

[0091] like Figure 2 As shown, the embodiment of the present application includes a method for detecting soil moisture changes under vegetation conditions.

[0092] For vegetation-covered surfaces, the change in backscattering is affected not only by the change in soil moisture but also by the vegetation coverage on the surface. Assume that the total backscattering change is expressed as the sum of the change in backscattering under bare soil and the effect of vegetation:

[0093]

[0094] Δσ t represents the change in backscatter coefficient relative to the driest moment on the tth day during the observation period, f veg (v) represents the effect of vegetation on the backscatter variation. is the change in backscattering relative to the bare soil condition. veg (v) There are empirical expressions.

[0095] Figure 3 is the influence of NDVI on the change of backscatter coefficient. Figure 3 , combined with the observed change in the surface backscatter coefficient under vegetation cover conditions And the corresponding vegetation index NDVI = V 1 , the change of the surface backscatter coefficient under bare soil conditions can be obtained. The change of the surface backscatter coefficient under vegetation cover conditions can be calculated from the scatterometer observation data, and NDVI is calculated from the MERSI optical observation data.

[0096] Therefore, for the backscattering change under a given NDVI, it is converted into the backscattering change under bare soil conditions (i.e., NDVI = 0) through formulas (1.1) to (1.2). Based on the empirical relationship of the influence of NDVI on backscattering, the backscattering coefficient change under bare soil conditions for each pixel is calculated, and the relative soil moisture content of each observation can be calculated according to formula (2).

[0097] In one embodiment of the present application, the change in backscatter coefficient under the condition of observing the surface covered with vegetation is It is expressed as:

[0098]

[0099] in is the change in backscatter coefficient on target date t, The actual measured value at the pixel (i, j) on the target date t, f veg (v 1 ) is when NDVI=V 1 The NDVI influences the backscattering value. For NDVI = V 1 The minimum backscatter coefficient of the target pixel under the condition of vegetation coverage.

[0100] When the backscattering change is at its maximum value during the observation period, the influence of vegetation on the maximum change in backscattering can be expressed by the following formula:

[0101]

[0102] Δσ max is the maximum change in backscattering during the observation period, Δσ soil is the change in backscattering of the corresponding surface soil, For NDVI = V 1 The maximum backscatter coefficient of the target pixel under the condition of vegetation coverage on the surface.

[0103] Therefore, according to formulas (10.1) to (10.2), we have the following expression:

[0104]

[0105] V 1 is the NDVI at time t; is the actual measured value of pixel (i, j) at time t, is the value of pixel (i, j) in the range of NDVI = V 1 The maximum actual measured value at .

[0106] Figure 3 The fitting empirical formula is f veg (V 1 )=a*V1. Where a is a negative value. Figure 3 The two straight lines shown have the same slope. This is based on the assumption that the maximum value distribution with NDVI reflects the contribution of NDVI to the change in backscatter coefficient and has nothing to do with soil moisture. In other words, the relationship between NDVI and f under different soil moisture contents is veg (V 1 ) have the same relationship.

[0107] It should be noted that this application optimizes the vegetation effect by establishing an empirical function, and proposes to use the maximum change after removing the vegetation effect for soil moisture change detection, where f veg (V 1 ) can include primary, secondary and other forms.

[0108] Figure 4 The maximum backscatter difference of FY3E and the fitting curve of NDVI f(NDVI). The observation period is one year, and the daily collection values ​​are distributed in the figure. The data used for fitting are extracted, assuming that NDVI and f under different soil moisture contents veg (V 1 ). Therefore, in the sampling value distribution map, some sampling points close to the upper boundary of the sampling point distribution are selected according to the set ratio or quantity. For example, the value segment of the backscatter coefficient change corresponding to each NDVI discrete value is located at 30 sampling values ​​close to the maximum value. For the FY3E satellite remote sensing data, a=-4.06 is obtained by linear fitting of the selected sampling points. It should be noted that since the selected sampling point set is not a strict maximum value, the linear formula constant 5.95 obtained by fitting is not the maximum value of the backscatter coefficient change. Regarding the discrete resolution of NDVI, this application grades the global NDVI, and solves the backscatter coefficient difference in turn at intervals of 0.01, and uses the backscatter coefficient difference and each level of NDVI to fit the empirical function equation to remove the impact of vegetation.

[0109] Figure 5 is the fitting curve f(NDVI) of the CFOSAT maximum backscatter difference and NDVI. The sampling point selection rule used is the same as Figure 4 In the embodiment, for CFOSAT satellite remote sensing data, a = -9.70. The difference in coefficients is due to the different bands of the two satellites, and NDVI is defined at different remote sensing frequencies.

[0110] Figure 6 is the spatial distribution of FY3E global soil moisture retrieval (local), Figure 7This is the spatial distribution of CFOSAT global soil moisture inversion (partial). The soil moisture inversion of the domestic Sino-French ocean satellite CFOSAT and Fengyun-3E FY3E was carried out using the self-developed change detection algorithm. The results are shown in the figure. The soil moisture inversion effect in Africa is significant. The soil moisture is relatively dry in desert areas and relatively wet in forest and grassland areas. The overall dry-wet distribution is reasonable.

[0111] Figure 8 (a) ~ (b) are the accuracy verification results of the soil moisture observation network. (a) FY3E inversion soil moisture verification result, (b) CFOSAT inversion soil moisture verification result. This patent uses measured data to verify the change detection algorithm to invert soil moisture. The soil moisture results inverted by CFOSAT and FY3E both achieve excellent accuracy, which fully demonstrates the reliability and universality of this algorithm.

[0112] Fig. 9 The present invention also provides a microwave scattering land surface soil moisture detection device, which is used to implement the method described in any embodiment of the first aspect of the present invention, including an acquisition module 21, a generation module 22 and a determination module 23.

[0113] The acquisition module is used to observe the change in backscatter coefficient under the condition of vegetation coverage on the target pixel (i, j). And the corresponding vegetation index NDVI value V 1 .

[0114] The generation module is used to exclude the influence of vegetation from the variation of the backscatter coefficient under the condition of vegetation-covered surface according to the empirical relationship of the influence of NDVI on backscatter, so as to obtain the variation of the backscatter coefficient under the condition of bare soil of the target pixel.

[0115] The determination module is used to obtain the change in soil moisture according to the change in backscattering of the target pixel under bare soil conditions.

[0116] In one embodiment of the present application, the generation module includes a first generation unit, and the first generation unit is used to determine the empirical relationship of the influence of NDVI on backscatter, including the following steps:

[0117] Generate a scatter plot between the change in backscatter coefficient under the vegetation cover surface conditions during the observation period and the NDVI of the corresponding date;

[0118] A plurality of sampling points of the highest value segment of the variation are obtained within each NDVI discrete value range, and a relationship curve between the variation and NDVI is generated by fitting.

[0119] In one embodiment of the present application, the generation module includes a second generation unit, and the first generation unit is used to exclude the influence of vegetation from the change in the backscattering coefficient under the condition of vegetation-covered surface, and obtain the change in the backscattering coefficient of the target pixel under the condition of bare soil.

[0120] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0121] Therefore, the present application also proposes a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method described in any embodiment of the present application is implemented.

[0122] Furthermore, the present application also proposes an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in any embodiment of the present application when executing the computer program.

[0123] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0124] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0125] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0126] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory. The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0127] Fig.10 60 is a structural diagram of an electronic device provided in an embodiment of the present application. The electronic device 60 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiment of the present application. It includes: one or more processors 62; a storage device 61, which is used to store one or more programs. When the one or more programs are run by the one or more processors 62, the one or more processors 62 implement the microwave scattering land surface soil moisture detection method.

[0128] The electronic device 60 further includes an input device 63 and an output device 64; the processor 62, storage device 61, input device 63 and output device 64 in the electronic device can be connected via a bus or other means, and the figure takes the connection via a bus 65 as an example.

[0129] The storage device 61, as a computer-readable storage medium, can be used to store software programs, computer executable programs and module units, such as program instructions corresponding to the microwave scattering land surface soil moisture detection method in the embodiment of the present application. The storage device 61 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function; the data storage area may store data created according to the use of the terminal, etc. In addition, the storage device 61 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the storage device 61 may further include a memory remotely arranged relative to the processor 62, and these remote memories may be connected via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0130] The input device 63 may be used to receive input numbers, character information or voice information, and to generate key signal inputs related to user settings and function control of the electronic device. The output device 64 may include electronic devices such as a display screen and a speaker.

[0131] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0132] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A microwave scattering land surface soil moisture detection method, characterized in that: The following steps are involved: For the target pixel (i, j), observe the change in backscatter coefficient under the vegetation cover surface conditions on the target date t And the corresponding vegetation index NDVI value V1; According to the empirical relationship of the influence of NDVI on backscattering, the influence of vegetation is excluded from the change of backscattering coefficient under the condition of vegetation-covered surface, and the change of backscattering coefficient under the condition of bare soil of the target pixel is obtained; The change in soil moisture is obtained according to the change in backscattering of the target pixel under bare soil conditions.

2. A method for detecting soil moisture on land surface by microwave scattering as claimed in claim 1, characterized in that: The empirical relationship for determining the effect of NDVI on backscatter consists of the following steps: Generate a scatter plot between the change in backscatter coefficient under the vegetation cover surface conditions during the observation period and the NDVI of the corresponding date; A plurality of sampling points of the highest value segment of the variation are obtained within each NDVI discrete value range, and a relationship curve between the variation and NDVI is generated by fitting.

3. The microwave scattering land surface soil moisture detection method according to claim 1, characterized in that: The step of obtaining the change in soil moisture according to the change in backscattering further comprises: in, is the change in backscatter coefficient on the target date t, is the maximum change of backscattering during the observation period, SM max is the maximum soil moisture, SM min is the minimum soil moisture, SM t is the soil moisture on the target date t within the observation period, and S represents the sensitivity of the microwave signal to soil moisture.

4. The microwave scattering land surface soil moisture detection method according to claim 1, characterized in that: The step of excluding the influence of vegetation from the backscatter coefficient variation under the condition of vegetation-covered surface to obtain the backscatter coefficient variation under the condition of bare soil of the target pixel includes: Among them, in pixel (i,j), is the change in backscatter coefficient on target date t, is the maximum change of backscatter during the observation period, V1 is the NDVI of the target date t; The actual measured value at the target date t in pixel (i, j), During the observation period, the maximum actual measured value of pixel (i, j) when NDVI = V1, f veg (V1) is the impact value of NDVI on backscattering when NDVI=V1.

5. The microwave scattering land surface soil moisture detection method according to claim 1, characterized in that: The effect of NDVI on backscatter is expressed as f veg (V1) = a*V1.

6. The microwave scattering land surface soil moisture detection method according to claim 1, characterized in that: The change of backscatter coefficient under the condition of observing vegetation coverage It is expressed as: is the change in backscatter coefficient on target date t, The actual measured value at the pixel (i, j) on the target date t, f veg (V1) is the influence value of NDVI on backscattering when NDVI=V1.

7. The microwave scattering land surface soil moisture detection method according to claim 1, characterized in that: The empirical relationship for the effect of NDVI on backscatter is expressed as f veg (V1) = a*V1; For the FY3E satellite remote sensing data, a = -4.06, or, for the CFOSAT satellite remote sensing data, a = -9.

70.

8. A microwave scattering land surface soil moisture detection device, used to implement the method according to any one of claims 1 to 7, characterized in that: include: The acquisition module is used to observe the change of the backscatter coefficient under the condition of vegetation coverage on the target pixel (i, j). And the corresponding vegetation index NDVI value V1; A generation module is used to exclude the influence of vegetation from the variation of backscatter coefficient under the condition of vegetation-covered surface according to the empirical relationship of the influence of NDVI on backscatter, so as to obtain the variation of backscatter coefficient under the condition of bare soil of the target pixel; The determination module is used to obtain the change in soil moisture according to the change in backscattering of the target pixel under bare soil conditions.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

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