CN115840224A - Multi-target function change detection method for soil moisture inversion - Google Patents
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Abstract
The invention discloses a multi-target function change detection method for soil moisture inversion, belongs to the field of remote sensing image processing and synthetic aperture radar soil moisture inversion, and solves the problem that spatial heterogeneity of local scale surface parameters is neglected in a point-by-surface mode to cause poor universality of a model when site data is used as priori knowledge in the existing region. The method is characterized in that a multi-target function model for acquiring 'pure' backscattering is constructed by combining a water cloud model based on multi-temporal and long-time sequence SAR observation information and multi-spectrum optical remote sensing data; a multi-objective function model is introduced into a traditional change detection algorithm, and a soil moisture inversion method which is simple in parameter and more universal is developed. The invention reduces the dependence on prior knowledge and a research area, realizes the extraction of soil moisture information with wide area, high precision and high space-time resolution, and provides reliable theoretical basis and feasible practical method for scientifically monitoring local environment and climate.
Description
Technical Field
The invention belongs to the technical field of remote sensing image processing and synthetic aperture radar inversion of soil moisture, and particularly relates to a multi-target function change detection method for soil moisture inversion.
Background
Soil moisture is an important component of a surface ecosystem, and on one hand, water is provided for the growth and development of plants; on the other hand, the change of soil moisture can cause the change of ground surface photo-thermal properties such as soil heat capacity, ground surface albedo, ground surface evaporation and the like, and the sensible heat, latent heat and long-wave radiation flux transmitted from the ground surface to the atmosphere are changed to directly or indirectly influence other circle layers such as an atmosphere circle and the like. Therefore, the method for acquiring the soil moisture information with wide area, high precision and high space-time resolution has important scientific value and practical significance in multiple aspects of agricultural production, seepage and runoff, weather forecast, flood early warning, evapotranspiration and the like.
At present, the soil moisture parameters are mainly obtained by two methods: ground equipment measurement and remote sensing technology observation. The ground equipment measurement mainly comprises a gravity method, a time domain reflectometer, a capacitance sensor, a heat pulse sensor and the like. The method can accurately acquire the soil humidity information of different earth surface depths of point scale, is flexible and easy to operate, is influenced by the heterogeneity of earth surface space, and the measurement result cannot represent the soil humidity information in the neighborhood of the observation point, and particularly needs to be provided with a large amount of manpower and material resources in the soil humidity monitoring of large-scale areas. Remote Sensing (RS) technology is considered to be an effective way to obtain surface soil moisture at different spatio-temporal scales. Among other things, optical remote sensing can provide multiple satellite datasets with high spatial resolution, yet it is susceptible to cloud cover and cannot be operated at night. The microwave remote sensing has all-weather and all-day working capability and is highly sensitive to the dielectric constant of the soil, so that a physical foundation is provided for soil moisture retrieval. According to the working principle, microwave remote sensing can be divided into active microwave remote sensing and passive microwave remote sensing. Passive microwave telemetry generally has higher temporal resolution, but the spatial resolution is coarser (e.g., SMOS and SMAP sensors). In contrast, active microwave remote sensing (e.g., synthetic aperture radar) has finer spatial resolution, which is greatly beneficial to agricultural management and field water resource utilization.
In view of the penetrability of Synthetic Aperture Radar (SAR) signals and the sensitivity to the surface dielectric constant, the dielectric constant is generally solved by constructing a function model of radar echo signals (backscattering coefficients), and then the surface water content of the soil is calculated according to a dielectric mixed model. In order to describe the relation between the surface parameters and radar backscattering, researchers construct theoretical models (SPM, I EM, AI EM and the like) of surface scattering based on a radiation transmission theory; establishing an empirical model (Duboi s, oh) and a semi-empirical model (Sh i) for describing bare ground scattering based on multiband, multi-polarization, multi-incidence angle and ground observation data; a plurality of soil moisture inversion algorithms are provided based on a neural network, and the development of the models and the algorithms provides important basis for SAR surface scattering imaging simulation.
However, the above model has strong dependence on the study area and the prior knowledge, the parameters and unknowns of the theoretical model are excessive, and the modulation process is complex. Although site data can be used as a priori knowledge in some regions, the spatial heterogeneity of local scale surface parameters is ignored in the point-by-point mode, and the universality of the model is poor. A soil moisture inversion method with higher universality is provided by relying on less prior knowledge, and important theoretical support is provided for scientifically monitoring the local ecological environment and climate change.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a multi-target function change detection method for inverting soil moisture, which aims to: and a more universal soil moisture inversion method is provided by means of the least prior knowledge, so that the soil moisture inversion accuracy is further improved.
The technical scheme adopted by the invention is as follows:
a multi-objective function change detection method for inversion of soil moisture comprises the following steps:
step 1: normalizing the total backscattering coefficients of the multi-scene SAR images with different incidence angles to a same reference incidence angle based on the pixel scale;
step 2: establishing a multi-target function model to model the total backscattering coefficient of each SAR image based on each SAR image subjected to the normalized correction of the incidence angle, wherein the total backscattering coefficient of each SAR image obtained by modeling is the sum of the building backscattering coefficient, the water backscattering coefficient, the bare soil backscattering coefficient and the vegetation area backscattering coefficient; wherein the vegetation region backscattering coefficient comprises a vegetation backscattering coefficient and a soil backscattering coefficient under vegetation;
and step 3: extracting backscattering coefficients of buildings, water bodies and vegetation of each scene of SAR image based on the optical image, and removing the backscattering coefficients of the buildings, the water bodies and the vegetation of each scene of SAR image from the total backscattering coefficients of each scene of SAR image obtained in the step 1 by combining the model established in the step 2 to obtain the surface soil backscattering coefficients of each scene of SAR image;
and 4, step 4: and calculating upper and lower limits of the soil backscattering coefficient as dry and wet threshold values based on the surface soil backscattering coefficient of each SAR image, and completing soil humidity inversion by combining the dry and wet threshold values and based on a multi-target change detection method.
Preferably, the specific steps of step 1 are:
step 1.1: each SAR image is subjected to down-sampling, the spatial resolution of each SAR image is reduced, and the total backscattering coefficient of each SAR image with a coarser resolution is obtained, so that unnecessary influence from heterogeneous variables is reduced;
step 1.2: carrying out linear regression analysis on the total backscattering coefficient of each scene SAR image with the relatively coarse resolution to obtain an angle correction coefficient beta [ dB/° ];
step 1.3: based on the pixel scale, each SAR image is subjected to incident angle normalization correction, and the correction formula is expressed as follows:
σ 0 (θ,t)＝σ 0 (Θ,t)-β(Θ-θ)[dB]
wherein σ 0 (Θ, t) represents a backscattering coefficient obtained when the incident angle is Θ and t time; sigma 0 (θ, t) refers to the backscattering coefficient normalized at the angle of incidence θ; angle correction coefficient beta [ dB/° ]]Is based on step 1.2 for all observations at Θ and σ at one image pixel 0 (Θ, t) by linear regression.
Preferably, the specific steps of step 2 are:
let total S SAR images, the total backscattering coefficient of each image is expressed as sigma s (i, j) wherein S ∈ [1.. S]I and j are the pixel row number of the image respectively; the earth surface and ground object types are divided into four types of buildings, water bodies, bare soil and vegetation areas, and the sigma is used for the four types s (i, j) can be modeled as:
wherein λ is 2 Is the attenuation factor of radar waves penetrating the vegetation layer,
and &>
Backscatter signals for bare soil and vegetation, respectively>
And &>
Respectively building and water body pixelsThe backscatter value of (a).
Preferably, the specific steps of step 3 are:
step 3.1: calculating N pieces of binary image data of the monthly normalized building index (NDBI) and the N pieces of monthly normalized water body index (NDWI) which are the same as the coverage area and the coverage time period of the SAR image by combining optical remote sensing data;
step 3.2: taking the N monthly normalized building indexes and the N monthly normalized water body indexes obtained in the step 3.1 as constraint conditions, removing the building backscattering coefficient and the water body backscattering coefficient of each SAR image based on the model built in the step 2, and finishing the extraction of the bare soil backscattering coefficient and the vegetation area backscattering coefficient of each SAR image;
step 3.3: based on the step 3.2, removing the vegetation backscattering coefficient of each SAR image by using the water cloud model, and finally obtaining the surface soil backscattering coefficient of each SAR image.
Preferably, the specific steps of step 4 are as follows:
step 4.1: calculating upper and lower limits of the backscattering coefficient of the soil SAR data as dry and wet threshold values based on the surface soil backscattering coefficient of each scene SAR image acquired in the step 3.3; the method specifically comprises the following steps:
based on the surface soil backscattering coefficient of each scene SAR image, calculating the upper and lower limits of the backscattering coefficient of the soil SAR data as dry and wet threshold values (respectively
And &>
) (ii) a Wherein it is present>
Taking the average value of the surface soil backscattering coefficient lower than A% in all SAR images, and then taking the average value of the backscattering coefficients of the surface soil lower than A% in all SAR images>
Get the highest among all SAR imagesThe average of the surface soil backscattering coefficient at B% can be expressed as:
wherein N is the number of all observed values; n is a radical of dry And N wet The number of backscattering coefficients lower than A% and higher than B% respectively in the time series, wherein A% is less than B%, A% is equal to 5%, and B% is equal to 95%.
Step 4.2: setting the difference value between the dry threshold value and the wet threshold value determined in the step 4.1 as sensitivity, and completing soil humidity inversion based on a multi-target change detection method; the method specifically comprises the following steps:
setting the difference between the dry and wet thresholds determined in step 4.1 as sensitivity S, can be expressed as:
and completing soil humidity inversion based on a multi-target change detection method, as shown in the following formula:
in summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
1. according to the scheme, the model is built according to the backscattering coefficient of the SAR data according to the ground feature type, the multi-target function model is built, the influence of other ground features except surface soil on backscattering is effectively reduced, and the problem of insufficient ground feature scattering decoupling in the current soil moisture inversion process is solved.
2. The multi-target change detection method for soil moisture inversion provided by the scheme reduces the dependency on the prior knowledge and a research area, realizes the extraction of soil moisture information with wide area, high precision and high space-time resolution, and provides reliable theoretical basis and feasible practical method for scientifically monitoring the local ecological environment and climate change.
Drawings
The invention will now be described by way of example and with reference to the accompanying drawings in which:
FIG. 1 is an overall layout of the present invention;
FIG. 2 is a schematic diagram of a multi-objective function model according to an embodiment of the present invention;
FIG. 3 is a diagram of a study area according to an embodiment of the present invention;
FIG. 4 is (a) NDVI, (b) vegetation water content, (c) "clean" backscattering coefficient for an embodiment of the present invention;
FIG. 5 is (a) a dry reference value, (b) a wet reference value, (c) a sensitivity value for an embodiment of the present invention;
fig. 6 shows soil moisture inversion values of the example of the present invention in 2018, month 1 and day 10.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
Example (b):
in this example, the 86 scene Sentinel-1A SAR image data from 1 month 1 in 2018 to 31 months 2020 and 12 months 31 in 2020 is used to select the khan khar salt lake in the china Qinghai as the research area.
The invention provides a multi-target change detection method for soil moisture inversion, which comprises the following steps of:
step 1: normalizing the total backscattering coefficients of the multi-scene SAR images with different incidence angles to a same reference incidence angle based on the pixel scale;
step 2: establishing a multi-target function model to model the total backscattering coefficient of each SAR image based on each SAR image subjected to the normalized correction of the incidence angle, wherein the total backscattering coefficient of each SAR image obtained by modeling is the sum of the building backscattering coefficient, the water backscattering coefficient, the bare soil backscattering coefficient and the vegetation area backscattering coefficient; wherein the vegetation region backscattering coefficients comprise vegetation backscattering coefficients and soil backscattering coefficients under vegetation (see fig. 2);
and step 3: extracting backscattering coefficients of buildings, water bodies and vegetation of each SAR image based on the optical image, and removing the backscattering coefficients of the buildings, the water bodies and the vegetation of each SAR image based on the total backscattering coefficients of each SAR image obtained in the step 1 by combining the model established in the step 2 to obtain surface soil backscattering coefficients of each SAR image (see figure 2);
and 4, step 4: and calculating upper and lower limits of the soil backscattering coefficient as dry and wet threshold values based on the surface soil backscattering coefficient of each SAR image, and completing soil humidity inversion by combining the dry and wet threshold values and based on a multi-target change detection method.
The step 1 specifically comprises the following steps:
the backscattering coefficient values of SAR images are observed in a series of local incidence angles of different orbits, and research has proved that radar backscattering has strong dependence on the local incidence angles. On bare or sparsely planted land, the backscattering decreases rapidly with increasing angle of incidence. In order to ensure the accuracy of the final result, the total backscattering coefficients of the multi-scene SAR images with different incident angles are normalized to be the same reference incident angle.
1.1: in IW mode, the single view spatial resolution of Sentinel-1 is 5m × 20m; since the backscatter signal is affected by features such as different features, different heights, etc., it is not preferable to directly use this resolution for SSM estimation, while the coarse resolution SSM data has proven to have a high correlation with the field measurement data; therefore, the step performs down-sampling processing on each SAR image, reduces the spatial resolution of each SAR image, obtains the total backward scattering coefficient of each SAR image with coarser resolution, and reduces unnecessary influence from heterogeneous variables; the spatial resolution used in this embodiment is 500m.
1.2: linear regression analysis was performed based on the total backscattering coefficient (during the period from 2018 to 2020 and 12 months) of each set of Sentinel-1A SAR image sampled at 500m, and since the center position within the incident angle range of the Sentinel-1 data in the experimental area was about 40 °, the reference incident angle θ =40 ° was set in this step, thereby reducing the total error of extrapolation to the maximum extent and finally obtaining the angle correction coefficient β [ dB/° ].
1.3: the normalized correction of the incidence angle of each scene of the SAR image based on the pixel scale can be expressed as:
σ 0 (θ,t)＝σ 0 (Θ,t)-β(Θ-θ)[dB]
wherein σ 0 (Θ, t) represents a backscattering coefficient obtained when the incident angle is Θ and t time; sigma 0 (θ, t) refers to the backscattering coefficient normalized at the angle of incidence θ; angle correction coefficient beta [ dB/° ]]Is based on step 1.2 for all observations at Θ and σ at one image pixel 0 (Θ, t) by linear regression.
The step 2 specifically comprises the following steps:
2.1: let total S SAR images, the total backscattering coefficient of each image is expressed as sigma s (i, j) where S e [1.. S]I and j are pixel row and column numbers of the image respectively; the earth surface and ground object types are divided into four types of buildings, water bodies, bare soil and vegetation areas, and the sigma is used for the four types s (i, j) can be modeled as:
wherein λ is 2 Is the attenuation factor of radar waves penetrating the vegetation layer,
and &>
Backscatter signals for bare soil and vegetation, respectively>
And &>
The backscattering values for the building and water pixels, respectively.
The step 3 specifically comprises the following steps:
3.1, objective Function 1, extracting SAR image backscattering values of bare soil and vegetation covered soil areas. Calculating N pieces of monthly normalized building index (NDBI) and N pieces of monthly normalized water body index (NDWI) binary image data which are the same as the coverage area and the coverage time period of the SAR image as constraint conditions, and respectively expressing the data as follows:
f is an integer, 0 represents TRUE, 1 represents FALSE, and N belongs to [1.. N ]]. At this time, in order to reduce data redundancy and unify the spatial resolution of the image, 2N NDBI and NDWI data are integrated to obtain a final F Mask (i, j) the image, as shown in the following equation:
F Mask (i,j)＝F NDBI (i,j)∪F NDWI (i,j)∈[0,1]
from the above analysis, the Objective Function 1 and two constraints can be expressed as:
F σ1 (i,j)＝σ s (i,j)-F Mask (i,j)
F NDBI (i,j)＝TRUE
F NDWI (i,j)＝TRUE
3.2, objective Function 2, extracting the backscattering value of the SAR image of the soil area without vegetation influence. The backscattered signals of the water body and the buildings can be removed by using the Objective Function 1, however, the inversion result of the soil moisture can still be affected by the existence of the vegetation, as shown in the formula (10). In the step, radar backscattering signals on the vegetation field are simulated by using a water cloud model (CWM) so as to further eliminate the influence of vegetation. The only relevant parameters of the water cloud model are vegetation parameters and soil moisture, as shown in the following equations (11) and (12):
λ 2 ＝exp(-2BV 2 /cosΘ) (12)
wherein V 1 And V 2 Is a vegetation related parameter, usually the water content of vegetation (M) veg ) A and B are parameters depending on the vegetation type, and the result of the existing research shows that A is 0.0012, B is 0.091, and theta is an incidence angle.
The study area of the examples is shown in FIG. 3. Since the effect of the water body on the backscattering coefficient has been removed by using NDWI in the Objective Function 1, in order to reduce the error of the water body on calculating the vegetation water content, the present embodiment uses NDVI to perform vegetation water content estimation (see fig. 4 (a)), and the calculation formula is as follows:
at this time, the vegetation water content of the embodiment is as shown in fig. 4 (b), and the Objective Function 2 can be expressed as:
finally, the surface soil backscattering coefficient of each SAR image is obtained, and a reference example thereof is shown in fig. 4 (c).
The step 4 specifically comprises the following steps:
4.1: with reference to fig. 5, based on the surface soil backscattering coefficient of each SAR image, the upper and lower limits of the backscattering coefficient of the soil SAR data are calculated as dry and wet threshold values (respectively, dry and wet threshold values)
And &>
) Wherein is present>
Taking the average of the surface soil backscattering coefficients lower than 5% in all SAR images (see FIG. 5 (a)), (see FIG. 5 (a)), (see FIG. 5 for B)>
The surface soil backscattering coefficient of more than 95% of all SAR images is averaged (see fig. 5 (b)), and can be expressed as:
where N is the number of all observations. N is a radical of dry And N wet The number of backscattering coefficients below 5% and above 95% in the time series, respectively.
4.2:
And &>
The difference between them is defined as sensitivity S (see fig. 5 (c)), and can be expressed as:
with reference to fig. 6, the soil moisture inversion of the embodiment is performed by using the multi-target change detection method provided by the present invention, as shown in the following formula:
wherein M is s (t) surface soil moisture at time t, θ is a reference angle of incidence,
and &>
Representing the radar backscattering coefficients during drought and humid periods, respectively.
The above-mentioned embodiments only express the specific embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for those skilled in the art, without departing from the technical idea of the present application, several changes and modifications can be made, which all belong to the protection scope of the present application.
Claims (8)
1. A multi-objective function change detection method for inverting soil moisture is characterized by comprising the following steps:
step 1: normalizing the total backscattering coefficients of the multi-scene SAR images with different incidence angles to a same reference incidence angle based on the pixel scale;
step 2: establishing a multi-target function model to model the total backscattering coefficient of each SAR image based on each SAR image subjected to the normalized correction of the incidence angle, wherein the total backscattering coefficient of each SAR image obtained by modeling is the sum of the building backscattering coefficient, the water backscattering coefficient, the bare soil backscattering coefficient and the vegetation area backscattering coefficient; wherein the vegetation region backscattering coefficient comprises a vegetation backscattering coefficient and a soil backscattering coefficient under vegetation;
and step 3: extracting backscattering coefficients of buildings, water bodies and vegetation of each scene SAR image based on the optical image, and removing the backscattering coefficients of the buildings, the water bodies and the vegetation of each scene SAR image from the total backscattering coefficients of each scene SAR image obtained in the step 1 by combining the model established in the step 2 to obtain the surface soil backscattering coefficient of each scene SAR image;
and 4, step 4: and calculating upper and lower limits of the soil backscattering coefficient as dry and wet threshold values based on the surface soil backscattering coefficient of each SAR image, and completing soil humidity inversion by combining the dry and wet threshold values and based on a multi-target change detection method.
2. The method for detecting the change of the multi-objective function for inverting the soil moisture according to claim 1, wherein the step 1 specifically comprises the following steps:
step 1.1: each SAR image is subjected to down-sampling, the spatial resolution of each SAR image is reduced, and the total backward scattering coefficient of each SAR image with a coarser resolution is obtained, so that unnecessary influence from heterogeneous variables is reduced;
step 1.2: carrying out linear regression analysis on the total backscattering coefficient of each scene SAR image with the relatively coarse resolution to obtain an angle correction coefficient beta [ dB/° ];
step 1.3: based on the pixel scale, each SAR image is subjected to incident angle normalization correction, and the correction formula is expressed as follows:
σ 0 (θ,t)＝σ 0 (Θ,t)-β(Θ-θ)[dB]
wherein σ 0 (Θ, t) represents a backscattering coefficient obtained when the incident angle is Θ and t time; sigma 0 (θ, t) refers to the backscattering coefficient normalized at the angle of incidence θ; angle correction coefficient beta [ dB/° ]]Is based on step 1.2 for all observations on one image pixel at Θ and σ 0 (Θ, t) by linear regression.
3. The method for detecting the multi-objective function change of the inversion soil moisture according to claim 2, wherein the step 2 specifically comprises the following steps:
let total S SAR images, the total backscattering coefficient of each image is expressed as sigma s (i, j) where S ∈ [1S ]]I and j are the pixel row number of the image respectively; the earth surface and ground object types are divided into four types of buildings, water bodies, bare soil and vegetation areas, and the sigma is used for the four types s (i, j) can be modeled as:
wherein λ is 2 Is the attenuation factor of radar waves penetrating the vegetation layer,
and
respectively are bare earth andthe back-scattered signal of the vegetation is,
and
the backscattering values for the building and water pixels, respectively.
4. The method for detecting the multi-objective function change of the inversion soil moisture according to claim 3, wherein the step 3 specifically comprises the following steps:
step 3.1: calculating N pieces of binary image data of the monthly normalized building index (NDBI) and the N pieces of monthly normalized water body index (NDWI) which are the same as the coverage area and the coverage time period of the SAR image by combining optical remote sensing data;
step 3.2: taking the N monthly normalized building indexes and the N monthly normalized water body indexes obtained in the step 3.1 as constraint conditions, removing the building backscattering coefficient and the water body backscattering coefficient of each SAR image based on the model built in the step 2, and finishing the extraction of the bare soil backscattering coefficient and the vegetation area backscattering coefficient of each SAR image;
step 3.3: based on the step 3.2, removing the vegetation backscattering coefficient of each SAR image by using the water cloud model, and finally obtaining the surface soil backscattering coefficient of each SAR image.
5. The method for detecting the multi-objective function change of the inversion soil moisture according to claim 4, wherein the step 4 specifically comprises the following steps:
step 4.1: calculating upper and lower limits of the backscattering coefficient of the soil SAR data as dry and wet threshold values based on the surface soil backscattering coefficient of each scene SAR image acquired in the step 3.3;
step 4.2: and (4) setting the difference value between the dry threshold value and the wet threshold value determined in the step (4.1) as sensitivity, and completing soil humidity inversion based on a multi-target change detection method.
6. The method for detecting the multi-objective function change of the inversion soil moisture according to claim 5, wherein the step 4.1 is specifically as follows:
based on the surface soil backscattering coefficient of each SAR image, calculating the upper and lower limits of the backscattering coefficient of the soil SAR data as dry and wet threshold values (respectively
And
wherein the content of the first and second substances,
taking the average value of the surface soil backscattering coefficients lower than A% in all SAR images,
taking the average value of the surface soil backscattering coefficients higher than B% in all SAR images, the average value can be expressed as:
wherein N is the number of all observed values; n is a radical of dry And N wet The number of backscattering coefficients below A% and above B% in the time series, respectively, with A% being less than B%.
7. The method for detecting the multi-objective function change of the inversion soil moisture according to claim 6, wherein the step 4.2 is specifically as follows:
setting the difference between the dry and wet thresholds determined in step 4.1 as sensitivity S, can be expressed as:
and completing soil humidity inversion based on a multi-target change detection method, as shown in the following formula:
wherein M is s (t) surface soil moisture at time t, θ is a reference angle of incidence,
and
representing the radar backscattering coefficients during drought and humid periods, respectively.
8. The method for detecting the variation of the multi-objective function for inverting the soil moisture according to claim 6, wherein A% is equal to 5% and B% is equal to 95%.
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| 张友静;王军战;鲍艳松;: "多源遥感数据反演土壤水分方法", 水科学进展, no. 02, pages 222 - 227 * |
| 李奎;张瑞;段金亮;吕继超;: "利用SAR影像与多光谱数据反演广域土壤湿度", 农业工程学报, no. 07, pages 134 - 140 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117574161A (en) * | 2024-01-17 | 2024-02-20 | 航天宏图信息技术股份有限公司 | Surface parameter estimation method, device and equipment based on generation of countermeasure network |
| CN117574161B (en) * | 2024-01-17 | 2024-04-16 | 航天宏图信息技术股份有限公司 | Surface parameter estimation method, device and equipment based on generation of countermeasure network |
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