CN114880883B - Mountain land surface soil moisture remote sensing estimation method and device and electronic equipment - Google Patents

Mountain land surface soil moisture remote sensing estimation method and device and electronic equipment Download PDF

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CN114880883B
CN114880883B CN202210791660.0A CN202210791660A CN114880883B CN 114880883 B CN114880883 B CN 114880883B CN 202210791660 A CN202210791660 A CN 202210791660A CN 114880883 B CN114880883 B CN 114880883B
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赵伟
蔡俊飞
周蕊
欧毅
骆剑承
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Institute of Mountain Hazards and Environment IMHE of CAS
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Abstract

The invention belongs to the technical field of remote sensing, and particularly relates to a remote sensing estimation method, a device and electronic equipment for soil moisture on the surface of a mountain land, wherein the method comprises the following steps: acquiring spatio-temporal seamless earth surface temperature data and normalized vegetation index data; utilizing the elevation data of the mountainous region to correct the terrain effect; calculating a temperature vegetation drought index; constructing a first nonlinear relation model; determining the moisture data of the soil on the surface of the first mountain land; reconstructing a second nonlinear relation model between the mountain land surface temperature data and the normalized vegetation index data after the terrain effect correction and the first mountain land surface soil moisture data; and obtaining second mountain land surface soil moisture data by using iterative solution until no plaque effect exists. The method realizes the high-resolution and high-accuracy inversion of the soil moisture of the mountain land surface; the mountain elevation data is used for correcting the surface parameter data, so that the influence of mountain terrain effect on the surface parameter data, particularly the surface temperature data, is reduced.

Description

Mountain land surface soil moisture remote sensing estimation method and device and electronic equipment
Technical Field
The invention belongs to the technical field of remote sensing, and particularly relates to a time-space seamless high-resolution mountain land surface soil moisture remote sensing estimation method, a device and electronic equipment.
Background
Soil moisture is an important land surface system element and an important index in research in the fields of ecology, environment, agriculture and the like. At present, methods for acquiring soil moisture mainly comprise ground station observation and satellite remote sensing inversion.
The ground station observation method is difficult to capture the time-space heterogeneity of soil moisture, and is difficult to acquire large-range soil moisture data in a short time, so that the ground station observation method cannot meet the practical application.
The satellite remote sensing inversion can be divided into an optical remote sensing technology and a microwave remote sensing technology according to different working wave bands, and the two remote sensing technologies can achieve the purpose of monitoring soil moisture. Although the soil moisture inverted based on the optical remote sensing technology has high spatial resolution, the soil moisture is easily affected by cloud coverage and atmospheric conditions, and is difficult to acquire spatially continuous images, the optical data and the soil moisture signal are in an indirect relation, and the inversion process is relatively complex, so that the practicability is poor when the soil moisture data are acquired, and high-precision data cannot be acquired.
The microwave remote sensing technology is divided into active microwave remote sensing and passive microwave remote sensing according to the working mode of a sensor. The active microwave remote sensing inversion of the soil moisture has the advantage of higher spatial resolution, but because the inversion is easily influenced by factors such as terrain, vegetation and the like and the satellite revisiting period is longer, the continuous soil moisture data in time is difficult to obtain; passive microwave remote sensing is to invert soil moisture by detecting the brightness and temperature of ground features in a microwave band through a microwave radiometer, the spatial resolution of soil moisture data obtained by using a passive microwave remote sensing technology is very low, mostly in the order of dozens of kilometers, and cannot meet the requirements of current practical application, and the spatial resolution needs to be reduced to 1 kilometer or less in hydrological and agricultural research in many areas.
The existing series of downscaling applications are focused on areas with low altitude, relatively smooth relief, and relatively weak spatial heterogeneity, such as agricultural areas or river watersheds. However, the topography is a major source of dynamic heterogeneity of soil moisture, which flows and accumulates with the gradient of gravitational potential energy; due to large mountainous terrain fluctuation, complex earth surface and extremely strong spatial heterogeneity, the influence of the terrain effect on remote sensing data, particularly visible light/near infrared remote sensing data, is extremely large; in addition, the mountainous, cloudy and foggy weather easily causes the vacancy of optical remote sensing data.
High-resolution optical data such as surface temperature data is used as the most widely used scale reduction factor, which is very important for the scale reduction process of soil moisture, and the application of the soil moisture space scale reduction method is severely limited by the mountain land topography effect and the vacancy of the data. In the downscaling model construction process, influence of mountain land topography on downscaling factors is not considered on an ideal horizontal plane, high-precision mountain land surface soil moisture downscaling results cannot be obtained due to the mountain land topography effect, and meanwhile, the downscaling results are discontinuous in space due to the fact that the downscaling factors such as surface temperature are influenced by cloud coverage.
Disclosure of Invention
The invention provides a remote sensing estimation method and device for soil moisture on a mountain land surface and electronic equipment, and aims to solve the problem that the soil moisture with seamless space-time and high spatial resolution cannot be accurately estimated.
In a first aspect, the invention provides a remote sensing estimation method for soil moisture on the surface of a mountain land, which comprises the following steps:
acquiring space-time seamless earth surface parameter data, wherein the space-time seamless earth surface parameter data comprises earth surface temperature data and normalized vegetation index data;
establishing a periodic change characteristic model of the earth surface temperature data, and determining the earth surface temperature data with time and space being seamless;
carrying out terrain effect correction on the time-space seamless ground surface temperature data by using the mountain elevation data to obtain mountain ground surface temperature data after the terrain effect correction;
calculating a temperature vegetation drought index by using the mountain land surface temperature data corrected by the terrain effect and the normalized vegetation index data;
constructing a first nonlinear relation model between the mountain land surface temperature data after the terrain effect correction and the normalized vegetation index data and the first mountain land surface soil moisture by using the temperature vegetation drought index;
determining the first mountain land surface soil moisture data according to the first nonlinear relation model by using the mountain land surface temperature data after the terrain effect correction and the normalized vegetation index data;
reconstructing a second nonlinear relation model between the mountain land surface temperature data after the terrain effect correction and the normalized vegetation index data and the first mountain land surface soil moisture data by using an iterative solution method based on an adaptive window on a set scale and by using the temperature vegetation drought index;
importing the mountain land surface temperature data after the terrain effect correction and the normalized vegetation index data into the second nonlinear relation model to obtain second mountain land surface soil moisture data;
judging whether the second mountain land surface soil moisture data has a plaque effect or not; if the second mountain land surface soil moisture data do not have the plaque effect, the second mountain land surface soil moisture data are target mountain land surface soil moisture data; and if the plaque effect exists, obtaining the second mountain land surface soil moisture data by reusing the iterative solution method until the second mountain land surface soil moisture data does not have the plaque effect.
In a second aspect, the invention provides a mountain land surface soil moisture remote sensing estimation device, which comprises an acquisition unit, a model establishment unit, a correction unit, a processing unit, a model establishment unit, a first output unit, a model reconstruction unit, a second output unit and an iteration solving unit;
the acquisition unit is used for acquiring space-time seamless earth surface parameter data, and the space-time seamless earth surface parameter data comprises earth surface temperature data and normalized vegetation index data;
the model establishing unit is used for establishing a periodic change characteristic model of the earth surface temperature data and determining the earth surface temperature data with space-time seamless;
the correction unit is used for carrying out terrain effect correction on the time-space seamless ground surface temperature data by utilizing the mountain elevation data to obtain mountain ground surface temperature data after the terrain effect correction;
the processing unit is used for calculating a temperature vegetation drought index by utilizing the mountain land surface temperature data after the terrain effect correction and the normalized vegetation index data;
the model construction unit is used for constructing a first nonlinear relation model between the mountain land surface temperature data after the terrain effect correction and the normalized vegetation index data and the soil moisture of the first mountain land surface by utilizing the temperature vegetation drought index;
the first output unit is used for determining the first mountain land surface soil moisture data according to the first nonlinear relation model by using the mountain land surface temperature data after the terrain effect correction and the normalized vegetation index data;
the model reconstruction unit is used for reconstructing a second nonlinear relation model between the mountain land surface temperature data after the terrain effect correction and the normalized vegetation index data and the first mountain land surface soil moisture data on the basis of an adaptive window on a set scale by using the temperature vegetation drought index through an iterative solution method;
the second output unit is configured to import the mountain land surface temperature data after the terrain effect correction and the normalized vegetation index data into the second nonlinear relation model to obtain second mountain land surface soil moisture data;
the iteration solving unit is used for judging whether the second mountain land surface soil moisture data has the plaque effect; if the second mountain land surface soil moisture data do not have the plaque effect, the second mountain land surface soil moisture data are target mountain land surface soil moisture data; and if the plaque effect exists, obtaining the second mountain land surface soil moisture data by reusing the iterative solution method until the second mountain land surface soil moisture data does not have the plaque effect.
In a third aspect, the present invention provides an electronic device, comprising:
a processor and a memory;
the memory is used for storing computer operation instructions;
and the processor is used for executing the remote sensing estimation method of the soil moisture on the mountain land surface by calling the computer operation instruction.
The invention has the beneficial effects that:
(1) The problems of low spatial resolution of passive microwave remote sensing inversion soil moisture and low accuracy of optical remote sensing inversion soil moisture influenced by cloud coverage are solved, and high-resolution and high-accuracy inversion of the soil moisture of the earth surface of the mountainous region is realized;
(2) On the basis of the self-adaptive window, the mountain elevation data is used for correcting the earth surface parameter data, so that the influence of mountain terrain effect on the earth surface parameter data, particularly earth surface temperature data is reduced, the earth surface parameter data participating in the construction of the soil moisture estimation model is closer to a true value, and the accuracy of the high-resolution mountain earth surface soil moisture estimation result is improved;
(3) And a soil moisture relation model is constructed on a high-resolution scale by an iterative solution method, and the soil moisture of the mountain land surface is calculated, so that the improvement of the resolution and the precision of the estimation result of the soil moisture of the mountain land surface is facilitated.
On the basis of the technical scheme, the invention can be further improved as follows.
Further, establishing a surface temperature data periodic variation characteristic model, and determining the space-time seamless surface temperature data, wherein the method comprises the following steps:
according to the annual periodic variation characteristics of the earth surface temperature data, the annual periodic variation characteristics of the earth surface temperature are set to be a cosine combined periodic model, and the periodic model is used for fitting based on annual effective observation data to obtain day-scale space-time seamless earth surface temperature data.
The method has the advantages that due to the fact that the earth surface temperature data have the characteristic of high space-time heterogeneity, the annual periodical data of the earth surface temperature data are converted into the cosine combined periodical model to obtain the space-time seamless day-scale earth surface temperature data, average value or multi-day synthetic data are avoided, and the method is favorable for accurately representing the actual situation of the earth surface temperature under the cloud and mist coverage condition.
Further, utilizing the mountain elevation data to correct the terrain effect of the time-space seamless earth surface temperature data to obtain mountain earth surface temperature data after the terrain effect correction, and the method comprises the following steps:
upscaling the mountain elevation data to the space scale of the time-space seamless earth surface temperature data in a space aggregation mode, performing linear fitting on all the time-space seamless earth surface temperature data and the mountain elevation data in a window based on a self-adaptive window, and determining the change rate of the time-space seamless earth surface temperature data along with the mountain elevation data in the daily scale in each window;
and obtaining the mountain land surface temperature data after the terrain effect correction through elevation normalization according to the mountain land elevation data, the change rate and the time-space seamless land surface temperature data of the daily scale in each window.
The method has the advantages that the space-time seamless earth surface parameter data are obtained through the optical remote sensing data space filling method, and the resolution ratio of the mountain land surface soil moisture estimation is improved; the method has the advantages that the terrain effect correction is performed on the earth surface temperature data by utilizing the mountain land elevation data, the accurate time-space seamless earth surface temperature data can be obtained, the influence of the mountain land effect on the earth surface parameter data, particularly the earth surface temperature data, is reduced, and the earth surface parameter data are closer to the true value.
Further, constructing a first nonlinear relationship model between the terrain effect corrected mountain land surface temperature data and the normalized vegetation index data and the first mountain land surface soil moisture by using the temperature vegetation drought index, including:
upscaling the mountain land surface temperature data and the normalized vegetation index data after the terrain effect correction to the space scale of a passive microwave soil moisture product in a space aggregation mode;
and selecting a window with the same size as that in the terrain effect correction process, and constructing a first nonlinear relation model between the mountain land surface temperature data after the terrain effect correction, the normalized vegetation index data and the first mountain land surface soil moisture.
The method has the advantages that the temperature vegetation drought index is higher than the correlation between other vegetation indexes and the soil moisture of the earth surface, and an accurate relation model can be established.
Further, the space-time seamless earth surface parameter data is obtained by adopting a multi-source remote sensing data acquisition mode of passive microwave remote sensing, visible light near infrared remote sensing and thermal infrared remote sensing.
The technical scheme has the advantages that the space-time seamless earth surface parameter data are obtained by adopting a multi-source remote sensing data mode, the problem that data are inaccurate in the traditional single remote sensing observation mode is solved, the problems that the space resolution is low in passive microwave remote sensing inversion of soil moisture data and soil is easily affected by cloud and fog coverage in the process of optical remote sensing inversion of soil moisture data are solved, and the inversion of the high-resolution mountain earth surface soil moisture is realized;
further, the manner of judging whether the second mountain land surface soil moisture data has the plaque effect is as follows:
judging whether the second mountain land surface soil moisture data has a plaque effect or not according to whether the second mountain land surface soil moisture data has a pixel window boundary or not; if the pixel window boundary exists in the second mountain land surface soil moisture data, the plaque effect exists; otherwise, no plaque effect is present. The high-resolution mountain land soil moisture with continuous space and smooth space is obtained.
Drawings
FIG. 1 is a flowchart of a method for estimating soil moisture on the surface of a mountain land according to example 1 of the present invention;
FIG. 2 is a schematic diagram of a triangular feature spatial relationship between a surface temperature and a normalized vegetation index in example 1 of the present invention;
FIG. 3 is an elevation diagram of a research area of a mountain land surface soil moisture downscaling test;
FIG. 4 is a simulation diagram comparing results before and after the scale reduction of soil moisture on the surface of the mountain land;
FIG. 5 is a schematic view of a soil moisture estimation device of a mountain land surface according to embodiment 2 of the present invention;
fig. 6 is a schematic diagram of an electronic device in embodiment 3 of the present invention.
Icon: 60-an electronic device; 610-a processor; 620-bus; 630-a memory; 640-a transceiver.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Example 1
As an embodiment, as shown in fig. 1, to solve the above technical problem, the present embodiment provides a remote sensing estimation method for soil moisture on a mountain land surface, including the steps of:
acquiring space-time seamless earth surface parameter data, wherein the space-time seamless earth surface parameter data comprises earth surface temperature data and normalized vegetation index data;
establishing a periodic change characteristic model of the earth surface temperature data, and determining the earth surface temperature data with time and space being seamless;
carrying out terrain effect correction on the time-space seamless ground surface temperature data by using the mountain elevation data to obtain mountain ground surface temperature data after the terrain effect correction;
calculating a temperature vegetation drought index by using the mountain land surface temperature data and the normalized vegetation index data after the terrain effect correction;
constructing a first nonlinear relation model between mountain land surface temperature data after terrain effect correction and normalized vegetation index data and first mountain land surface soil moisture by using the temperature vegetation drought index;
determining first mountain land surface soil moisture data according to a first nonlinear relation model by using mountain land surface temperature data and normalized vegetation index data after terrain effect correction;
reconstructing a second nonlinear relation model between the mountain land surface temperature data and the normalized vegetation index data after the terrain effect correction and the first mountain land surface soil moisture data on the basis of a self-adaptive window on a set scale by using a temperature vegetation drought index by using an iterative solution method;
importing the mountain land surface temperature data and the normalized vegetation index data after the terrain effect correction into a second nonlinear relation model to obtain second mountain land surface soil moisture data;
judging whether the second mountain land surface soil moisture data has a plaque effect or not; if the second mountain land surface soil moisture data do not have the plaque effect, the second mountain land surface soil moisture data are target mountain land surface soil moisture data; and if the plaque effect exists, obtaining the second mountain land surface soil moisture data by re-using the iterative solution method until the second mountain land surface soil moisture data does not have the plaque effect.
The invention has the beneficial effects that:
(1) The problems of low spatial resolution of passive microwave remote sensing inversion soil moisture and low accuracy of optical remote sensing inversion soil moisture influenced by cloud coverage are solved, and the inversion of the soil moisture of the mountain land surface with high resolution (more than or equal to 1 km) and high accuracy is realized;
(2) On the basis of the self-adaptive window, the mountain elevation data is used for correcting the earth surface parameter data, so that the influence of mountain terrain effect on the earth surface parameter data, particularly earth surface temperature data is reduced, the earth surface parameter data participating in the construction of the soil moisture estimation model is closer to a true value, and the accuracy of the high-resolution mountain earth surface soil moisture estimation result is improved;
(3) And a soil moisture relation model is constructed on a high-resolution scale by an iterative optimal solution solving method, and the soil moisture of the mountain land surface is calculated, so that the improvement of the resolution and the precision of the estimation result of the soil moisture of the mountain land surface is facilitated.
Optionally, when the spatio-temporal seamless earth surface temperature data and the normalized vegetation index data are obtained, data of a 1km spatial resolution product day by a medium-resolution Imaging spectrometer (MODIS), and data of a 1km grade product synthesized by the MODIS for the normalized vegetation index for 16 days are selected.
Optionally, establishing a surface temperature data periodic variation characteristic model, and determining time-space seamless surface temperature data, including:
according to the annual periodic variation characteristics of the earth surface temperature data, converting the annual periodic data of the earth surface temperature data into a cosine combined periodic model, and fitting by using the periodic model based on annual effective observation data to obtain daily-scale space-time seamless earth surface temperature data.
In the practical application process, the earth surface temperature data and the normalized vegetation index data are obtained by optical remote sensing observation and inversion, and are easily influenced by cloud and fog shielding, and the mountainous regions have more cloudy and foggy weather, so that a large number of null pixels exist in the mountainous region of the observation result. The normalized vegetation index data can reflect the daily vegetation coverage condition on the premise that the vegetation coverage does not change much within 16 days; because the earth surface temperature data has high time-space heterogeneity, optionally, the time-space seamless day-scale earth surface temperature data is obtained by converting the annual periodical data of the earth surface temperature data into a cosine combined periodical model, so that the adoption of average value or multi-day synthetic data is avoided, and the true situation of the earth surface temperature under the cloud and mist coverage condition can be accurately represented.
Is provided with
Figure DEST_PATH_IMAGE001
The daily surface temperature after day d filling,
Figure 983587DEST_PATH_IMAGE002
by converting the annual periodic data of the surface temperature data into a cosine combined periodic model, expressed by the formula:
Figure DEST_PATH_IMAGE003
；
the earth surface temperature data of the cloud and fog covered day can be obtained by adopting the annual effective observation data and utilizing the periodic model for fitting, so that the earth surface temperature data with daily scale and time and space being seamless is obtained.
Optionally, the step of performing terrain effect correction on the time-space seamless earth surface temperature data by using the mountain elevation data to obtain mountain earth surface temperature data after the terrain effect correction includes:
upscaling mountain elevation data to a space scale of spatio-temporal seamless earth surface temperature data in a space aggregation mode, performing linear fitting on the spatio-temporal seamless earth surface temperature data and the mountain elevation data of all daily scales in a window based on a self-adaptive window, and determining the change rate of the spatio-temporal seamless earth surface temperature data of the daily scales in each window along with the mountain elevation data;
and obtaining the mountain land surface temperature data after the terrain effect correction through elevation normalization according to the mountain land elevation data, the change rate and the time-space seamless land surface temperature data of the daily scale in each window.
In the practical application process, the terrain changes to the earth surface temperature data with extremely strong space-time heterogeneity, so that the terrain effect of the earth surface temperature is corrected by using the mountain land elevation data based on the negative correlation relationship between the altitude and the earth surface temperature. Optionally, the mountain elevation data selects 30m spatial resolution data of a spacecraft Radar terrain mapping Mission (SRTM).
In order to correct mountain land topography effect and construct a high-quality relation model, the invention combines a self-adaptive window, corrects the daily surface temperature after filling by using mountain land elevation data, obtains the surface temperature data after terrain effect correction at a local window, firstly scales up mountain land elevation data (such as 30 m) to the product scale of the surface temperature data (such as 1 km) in a space aggregation mode, and sets the product scale
Figure 582058DEST_PATH_IMAGE004
Showing the corrected terrain surface temperature of the jth pixel element in the ith window,
Figure DEST_PATH_IMAGE005
represents the surface temperature of the jth pixel in the ith window before correction,
Figure 98228DEST_PATH_IMAGE006
representing the elevation of the jth pixel element in the ith window,
Figure DEST_PATH_IMAGE007
representing the elevation of the pixel at the center of the ith window;
Figure 123953DEST_PATH_IMAGE008
is the change rate of the surface temperature along with the elevation in the ith window, the change rate is obtained by linear fitting of all the surface temperature values and the elevation values in each window, and the unit is (K/m), then:
Figure DEST_PATH_IMAGE009
；
the method has the advantages that the spatial-temporal seamless earth surface parameter data are obtained through the optical remote sensing data space filling method, and the resolution of the mountain earth surface soil moisture estimation is improved; the method has the advantages that the terrain effect correction is performed on the earth surface temperature data by utilizing the mountain land elevation data, the accurate time-space seamless earth surface temperature data can be obtained, the influence of the mountain land effect on the earth surface parameter data, particularly the earth surface temperature data, is reduced, and the earth surface parameter data are closer to the true value.
Optionally, a first nonlinear relationship model between the mountain land surface temperature data and the normalized vegetation index data after the terrain effect correction and the first mountain land surface soil moisture is constructed by using the temperature vegetation drought index, and the method includes:
upscaling the mountain land surface temperature data and the normalized vegetation index data after the terrain effect correction to the scale of a Passive microwave Soil Moisture product (such as 25-km ESA (European Space Agency) CCI (marginal Change Initiative) remote sensing Soil Moisture data, 36-km SMAP (Soil Moisture Active) Passive microwave Soil Moisture product and the like) in a Space aggregation mode;
and selecting a window with the same size as that in the terrain effect correction process, and constructing a first nonlinear relation model between the mountain land surface temperature data and the normalized vegetation index data after the terrain effect correction and the soil moisture of the first mountain land surface.
The drought index of the temperature vegetation is the water stress of a triangular feature space of the ground surface temperature-normalized vegetation index after polymerization and is set
Figure 859828DEST_PATH_IMAGE010
The drought index of the vegetation at the temperature is shown,
Figure DEST_PATH_IMAGE011
which is indicative of the temperature of the earth's surface,
Figure 261990DEST_PATH_IMAGE012
and
Figure DEST_PATH_IMAGE013
respectively representing wet edges (surface evapotranspiration is equal to potential evapotranspiration and is wet edges, as shown in the schematic diagram of the triangular characteristic space relationship of the surface temperature-normalized vegetation index shown in figure 2, the horizontal axis is wet edges) and dry edges (soil water effectiveness is low, surface evapotranspiration is small and is dry edges, as shown in figure 2, the vertical axis is dry edges) in the triangular characteristic space of the surface temperature-normalized vegetation index, and using an equation
Figure 868552DEST_PATH_IMAGE014
，
Figure DEST_PATH_IMAGE015
Calculated, wherein the regression coefficients A, B, C and D can be fitted by the earth surface temperature-normalized vegetation index feature space,
Figure 563713DEST_PATH_IMAGE016
is a normalized vegetation index. The expression of the temperature vegetation drought index is as follows:
Figure DEST_PATH_IMAGE017
。
in the practical application process, the temperature vegetation drought index is higher than the correlation between other vegetation indexes and the surface soil moisture, an accurate nonlinear relation model can be established, and the temperature vegetation drought index and the surface soil moisture show a significant linear negative correlation.
And (3) establishing an expression of a nonlinear relation model between the temperature vegetation drought index and the first mountain land surface soil moisture by setting lambda as a negative correlation coefficient and delta as a relation model constant:
Figure 786884DEST_PATH_IMAGE018
；
substituting the expression of the temperature vegetation drought index into the formula to obtain the expression of a first nonlinear relation model between the mountain land surface temperature data and the normalized vegetation index data after the terrain effect correction and the first mountain land surface soil moisture:
Figure DEST_PATH_IMAGE019
；
in the formula (I), the compound is shown in the specification,
Figure 727158DEST_PATH_IMAGE020
showing the soil moisture of the passive microwave soil moisture product,b 1 、b 2 、b 3 、b 4 、b 5 are all nonlinear regression model coefficients, delta is a relation model constant,
Figure DEST_PATH_IMAGE021
representing the time-space seamless earth surface temperature data after the terrain correction of the passive microwave soil moisture product,
Figure 453806DEST_PATH_IMAGE022
normalized vegetation index data representing passive microwave soil moisture products.
Optionally, the manner of obtaining the spatiotemporal seamless earth surface parameter data is to obtain the spatiotemporal seamless earth surface parameter data by using a manner of obtaining multisource remote sensing data of passive microwave remote sensing, visible light near infrared remote sensing and thermal infrared remote sensing.
In the practical application process, the spatial-temporal seamless earth surface parameter data are obtained by adopting a multi-source remote sensing data mode, the problem that the traditional data are inaccurate based on a single remote sensing observation mode is solved, the problems that the space resolution is low in passive microwave remote sensing inversion soil moisture data and the soil is easily affected by cloud and fog coverage in the process of optical remote sensing inversion soil moisture data are solved, and the inversion of the high-resolution mountain land surface soil moisture is realized.
Optionally, the manner of determining whether the second mountain land surface soil moisture data has the plaque effect is as follows:
judging whether the second mountain land surface soil moisture data has a plaque effect or not according to whether the second mountain land surface soil moisture data has a pixel window boundary or not; if the pixel window boundary exists in the soil moisture data of the second mountain land surface, the plaque effect exists; otherwise, the patch effect is not existed, and the soil moisture shows a smooth distribution effect on the spatial distribution.
In the practical application process, after the moisture data of the first mountain land surface soil is determined, the moisture of the mountain land surface soil is determined
Figure DEST_PATH_IMAGE023
The estimation result has a plaque effect, and in order to ensure that the high-precision high-resolution mountain land surface soil moisture is finally obtained, an iterative optimal solution solving method is used, the space-time seamless land surface temperature and the normalized vegetation index after terrain correction are reconstructed on the basis of a self-adaptive window on the scale of 1-km, and the high-resolution mountain land surface soil moisture is preliminarily estimated
Figure 86913DEST_PATH_IMAGE023
BetweenThe second nonlinear relation model of (2), the second nonlinear relation model being expressed as a function
Figure 62959DEST_PATH_IMAGE023
：
Figure 806924DEST_PATH_IMAGE024
；
In the formula (I), the compound is shown in the specification,b h1 、b h2 、b h3 、b h4 、b h5 are all the coefficients of a non-linear regression model,
Figure DEST_PATH_IMAGE025
is a constant of the relationship model and is,
Figure 152192DEST_PATH_IMAGE026
representing high resolution (1 km) terrain corrected spatio-temporal seamless surface temperature data,
Figure DEST_PATH_IMAGE027
normalized vegetation index data representing high resolution (1 km).
Leading the space-time seamless earth surface temperature and the vegetation index of 1-km scale into the reconstructed second nonlinear relation model to obtain the mountain land surface soil moisture data SM with higher precision and high resolution (1 km) and space-time seamless h 。
Data SM for judging soil moisture of mountain land surface h If the patch effect exists, combining the size of an adaptive window determined by the passive microwave mountain land surface soil moisture product scale on the scale of 1-km to construct a space-time seamless land surface temperature and normalized vegetation index after terrain effect correction and estimated mountain land surface soil moisture data SM h Introducing the earth surface parameters of 1-km scale space-time seamless earth surface temperature, vegetation index and the like into a new second nonlinear relation model, and estimating new mountain land earth surface soil moisture data SM h Repeating the process until no plaque effect is presentFinally, outputting high-resolution (more than 1 km) time-space seamless mountain land surface soil moisture data SM with target precision h 。
The method is applied to a mountain land research area, such as an elevation map of a mountain land surface soil moisture downscaling test research area shown in figure 3, wherein the left side of the map is a high-altitude mountain land, the right side of the map is a plateau, the horizontal axis is a latitude, and the vertical axis is a longitude, and the method is suitable for being used as test area and mountain land elevation data of the method. The results obtained by the soil moisture estimation application of the invention are shown in the figure 4, which shows the comparison simulation diagram of the results before and after the scale reduction of the mountain land surface soil moisture, the first action is passive microwave mountain land surface soil moisture products, and the second action is mountain land surface soil moisture data estimated by the invention. As can be seen from FIG. 4, the data of the moisture content of the soil on the mountain land estimated by the invention has higher spatial resolution.
Example 2
Based on the same principle as the method shown in embodiment 1 of the present invention, as shown in fig. 5, the embodiment of the present invention further provides a remote sensing estimation device for soil moisture of a mountain land, which includes an obtaining unit, a model establishing unit, a correcting unit, a processing unit, a model establishing unit, a first output unit, a model reconstructing unit, a second output unit and an iterative solution unit;
the system comprises an acquisition unit, a data processing unit and a data processing unit, wherein the acquisition unit is used for acquiring space-time seamless ground surface parameter data which comprise ground surface temperature data and normalized vegetation index data;
the model establishing unit is used for establishing a surface temperature data periodic variation characteristic model and determining space-time seamless surface temperature data;
the correction unit is used for carrying out terrain effect correction on the time-space seamless ground surface temperature data by utilizing the mountain elevation data to obtain mountain ground surface temperature data after the terrain effect correction;
the processing unit is used for calculating a temperature vegetation drought index by utilizing the mountain land surface temperature data and the normalized vegetation index data after the terrain effect correction;
the model construction unit is used for constructing a first nonlinear relation model between the mountain land surface temperature data and the normalized vegetation index data after the terrain effect correction and the first mountain land surface soil moisture by utilizing the temperature vegetation drought index;
the first output unit is used for determining first mountain land surface soil moisture data according to a first nonlinear relation model by using the mountain land surface temperature data and the normalized vegetation index data after the terrain effect correction;
the model reconstruction unit is used for reconstructing a second nonlinear relation model between the mountain land surface temperature data after the terrain effect correction and the normalized vegetation index data and the first mountain land surface soil moisture data on the basis of a self-adaptive window on a set scale by using a temperature vegetation drought index through an iterative solution method;
the second output unit is used for importing the mountain land surface temperature data and the normalized vegetation index data after the terrain effect correction into a second nonlinear relation model to obtain second mountain land surface soil moisture data;
the iteration solving unit is used for judging whether the second mountain land surface soil moisture data has the plaque effect; if the second mountain land surface soil moisture data do not have the plaque effect, the second mountain land surface soil moisture data are target mountain land surface soil moisture data; and if the plaque effect exists, obtaining the second mountain land surface soil moisture data by re-using the iterative solution method until the second mountain land surface soil moisture data does not have the plaque effect.
Optionally, the model building unit includes a first conversion unit and a fitting unit:
the first conversion unit is used for converting the annual periodic data of the earth surface temperature data into a cosine combined periodic model according to the annual periodic change characteristics of the earth surface temperature data to obtain space-time seamless earth surface temperature data with a set period scale;
and the fitting unit is used for fitting by utilizing a periodic model based on the annual effective observation data to obtain day-scale space-time seamless earth surface temperature data.
Optionally, the correction unit includes a filling unit, a linear fitting unit, and a syndrome unit:
the filling unit is used for filling the daily-scale space-time seamless earth surface temperature data based on the self-adaptive window;
the linear fitting unit is used for upscaling the mountain elevation data to the space scale of the time-space seamless earth surface temperature data in a space aggregation mode, performing linear fitting on all the earth surface temperature data in the self-adaptive window and the mountain elevation data, and determining the change rate of the time-space seamless earth surface temperature data of the daily scale in each window along with the mountain elevation data;
and the corrector subunit is used for obtaining the mountain land surface temperature data after the terrain effect correction according to the mountain land elevation data, the change rate and the time-space seamless land surface temperature data of the daily scale in each window.
Optionally, the model building unit includes a second conversion unit and a model building subunit:
the second conversion unit is used for upscaling the mountain land surface temperature data and the normalized vegetation index data after the terrain effect correction to the space scale of the passive microwave soil moisture product in a space aggregation mode;
and the model construction subunit is used for selecting a window with the same size as that in the terrain effect correction process, and constructing a first nonlinear relation model between the mountain land surface temperature data and the normalized vegetation index data after the terrain effect correction and the soil moisture of the first mountain land surface.
Optionally, the manner of obtaining the spatiotemporal seamless earth surface parameter data is to obtain the spatiotemporal seamless earth surface parameter data by using a manner of obtaining multisource remote sensing data of passive microwave remote sensing, visible light near infrared remote sensing and thermal infrared remote sensing.
Optionally, the iterative solution unit includes: the judging unit is used for judging whether the second mountain land surface soil moisture data has the plaque effect or not; the mode for judging whether the second mountain land surface soil moisture data has the plaque effect is as follows: determining whether the second mountain land surface soil moisture data has a plaque effect or not according to whether the second mountain land surface soil moisture data has a pixel window boundary or not; if the pixel window boundary exists in the soil moisture data of the second mountain land surface, the plaque effect exists; otherwise no plaque effect is present.
Example 3
Based on the same principle as the method shown in the embodiment of the present invention, an embodiment of the present invention further provides an electronic device, as shown in fig. 6, which may include but is not limited to: a processor and a memory; a memory for storing a computer program; a processor for executing the method according to any of the embodiments of the present invention by calling a computer program.
In an alternative embodiment, an electronic device is provided, the electronic device 60 shown in fig. 6 comprising: a processor 610 and a memory 630. Wherein the processor 610 is coupled to the memory 630, such as via the bus 620.
Optionally, the electronic device 60 may further include a transceiver 640, and the transceiver 640 may be used for data interaction between the electronic device and other electronic devices, such as transmission of data and/or reception of data. It should be noted that the transceiver 640 is not limited to one in practical application, and the structure of the electronic device 60 is not limited to the embodiment of the present invention.
The processor 610 may be a CPU central processing unit, general processor, DSP data signal processor, ASIC application specific integrated circuit, FPGA field programmable gate array or other programmable logic device, hardware component, or any combination thereof. The processor 610 may also be a combination of computing functions, e.g., comprising one or more microprocessors, DSPs, and microprocessors, among others.
Bus 620 may include a path that transfers information between the above components. Bus 620 may be a PCI peripheral component interconnect standard bus or an EISA extended industry standard architecture bus or the like. The bus 620 may be divided into a control bus, a data bus, an address bus, and the like. For ease of illustration, only one thick line is shown in FIG. 6, but this is not intended to represent only one bus or type of bus.
Memory 630 may be, but is not limited to, a ROM read-only memory or other type of static storage device that may store static information and instructions, a RAM random-access memory or other type of dynamic storage device that may store information and instructions, an EEPROM electrically erasable programmable read-only memory, a CD-ROM or other optical disk storage, optical disk storage (including optical disks, laser disks, compact disks, digital versatile disks, etc.), magnetic disk storage media, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.
The memory 630 is used for storing application program codes (computer programs) for performing aspects of the present invention and is controlled to be executed by the processor 610. The processor 610 is configured to execute application program code stored in the memory 630 to implement the aspects illustrated in the foregoing method embodiments.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (7)

1. The remote sensing estimation method for the soil moisture on the surface of the mountain land is characterized by comprising the following steps:
acquiring space-time seamless ground surface parameter data, wherein the space-time seamless ground surface parameter data comprises ground surface temperature data and normalized vegetation index data;
establishing a periodic change characteristic model of the earth surface temperature data, and determining the earth surface temperature data with time and space being seamless;
utilize mountain region elevation data to carry out the correction of topography effect to seamless earth's surface temperature data space-time, obtain the mountain region earth's surface temperature data after the correction of topography effect, include: upscaling the mountain elevation data to the space scale of the time-space seamless earth surface temperature data in a space aggregation mode, performing linear fitting on all the earth surface temperature data and the mountain elevation data in a window based on a self-adaptive window, and determining the change rate of the time-space seamless earth surface temperature data along with the mountain elevation data in the daily scale in each window; obtaining mountain land surface temperature data after the terrain effect correction through elevation normalization according to the mountain land elevation data, the change rate and the space-time seamless land surface temperature data of the daily scale in each window;
calculating a temperature vegetation drought index by using the mountain land surface temperature data corrected by the terrain effect and the normalized vegetation index data;
constructing a first nonlinear relation model between the mountain land surface temperature data after the terrain effect correction and the normalized vegetation index data and the first mountain land surface soil moisture by using the temperature vegetation drought index;
determining first mountain land surface soil moisture data according to the first nonlinear relation model by using the mountain land surface temperature data after the terrain effect correction and the normalized vegetation index data;
reconstructing a second nonlinear relationship model between the mountain land surface temperature data after the terrain effect correction and the normalized vegetation index data and the first mountain land surface soil moisture data by using an iterative solution method based on an adaptive window on a set scale and utilizing the temperature vegetation drought index;
importing the mountain land surface temperature data after the terrain effect correction and the normalized vegetation index data into the second nonlinear relation model to obtain second mountain land surface soil moisture data;
judging whether the second mountain land surface soil moisture data has a plaque effect or not; if the second mountain land surface soil moisture data do not have the plaque effect, the second mountain land surface soil moisture data are target mountain land surface soil moisture data; and if the plaque effect exists, obtaining the second mountain land surface soil moisture data by reusing the iterative solution method until the second mountain land surface soil moisture data does not have the plaque effect.
2. The remote sensing estimation method for the moisture of the soil on the earth surface of the mountainous region according to claim 1, wherein a characteristic model of periodic change of the soil surface temperature data is established, and the time-space seamless soil surface temperature data is determined, and the method comprises the following steps:
according to the annual periodic variation characteristics of the earth surface temperature data, the annual periodic variation characteristics of the earth surface temperature are set to be a cosine combined periodic model, and the periodic model is used for fitting based on annual effective observation data to obtain day-scale space-time seamless earth surface temperature data.
3. The remote sensing estimation method for mountain land surface soil moisture according to claim 1, wherein the constructing a first non-linear relationship model between the mountain land surface temperature data after the terrain effect correction and the normalized vegetation index data and the first mountain land surface soil moisture by using the temperature vegetation drought index comprises:
upscaling the mountain land surface temperature data and the normalized vegetation index data after the terrain effect correction to the space scale of a passive microwave soil moisture product in a space aggregation mode;
and selecting a window with the same size as that in the terrain effect correction process, and constructing a first nonlinear relation model between the mountain land surface temperature data after the terrain effect correction, the normalized vegetation index data and the first mountain land surface soil moisture.
4. The remote sensing estimation method for the soil moisture on the earth surface of the mountainous region according to claim 1, wherein the space-time seamless data of the earth surface parameters is acquired by adopting a multi-source remote sensing data acquisition mode of passive microwave remote sensing, visible light near infrared remote sensing and thermal infrared remote sensing.
5. The remote sensing estimation method for the moisture of the soil on the mountain land according to claim 1, wherein the mode for judging whether the second mountain land soil moisture data has the plaque effect is as follows: judging whether the second mountain land surface soil moisture data has a plaque effect or not according to whether the second mountain land surface soil moisture data has a pixel window boundary or not; if the pixel window boundary exists in the second mountain land surface soil moisture data, the plaque effect exists; otherwise, no plaque effect is present.
6. The remote sensing estimation device for the soil moisture on the earth surface of the mountain land is characterized by comprising an acquisition unit, a model building unit, a correction unit, a processing unit, a model building unit, a first output unit, a model reconstruction unit, a second output unit and an iteration solving unit;
the acquisition unit is used for acquiring space-time seamless earth surface parameter data, and the space-time seamless earth surface parameter data comprises earth surface temperature data and normalized vegetation index data;
the model establishing unit is used for establishing a periodic change characteristic model of the earth surface temperature data and determining the earth surface temperature data with space-time seamless;
the correction unit is used for utilizing the mountain elevation data to correct the terrain effect of the time-space seamless earth surface temperature data to obtain mountain earth surface temperature data after the terrain effect correction, and comprises: upscaling the mountain elevation data to the space scale of the time-space seamless earth surface temperature data in a space aggregation mode, performing linear fitting on all the earth surface temperature data and the mountain elevation data in a window based on a self-adaptive window, and determining the change rate of the time-space seamless earth surface temperature data along with the mountain elevation data in the daily scale in each window; obtaining mountain land surface temperature data after the terrain effect is corrected through elevation normalization according to the mountain land elevation data, the change rate and the time-space seamless surface temperature data of the daily scale in each window;
the processing unit is used for calculating a temperature vegetation drought index by using the mountain land surface temperature data after the terrain effect correction and the normalized vegetation index data;
the model construction unit is used for constructing a first nonlinear relation model between the mountain land surface temperature data after the terrain effect correction and the normalized vegetation index data and the soil moisture of the first mountain land surface by utilizing the temperature vegetation drought index;
the first output unit is used for determining first mountain land surface soil moisture data according to the first nonlinear relation model by using the mountain land surface temperature data after the terrain effect correction and the normalized vegetation index data;
the model reconstruction unit is used for reconstructing a second nonlinear relation model between the mountain land surface temperature data after the terrain effect correction and the normalized vegetation index data and the first mountain land surface soil moisture data on the basis of an adaptive window on a set scale by using an iterative solution method;
the second output unit is used for importing the mountain land surface temperature data after the terrain effect correction and the normalized vegetation index data into the second nonlinear relation model to obtain second mountain land surface soil moisture data;
the iteration solving unit is used for judging whether the second mountain land surface soil moisture data has the plaque effect; if the second mountain land surface soil moisture data do not have the plaque effect, the second mountain land surface soil moisture data are target mountain land surface soil moisture data; and if the plaque effect exists, obtaining the second mountain land surface soil moisture data by reusing the iterative solution method until the second mountain land surface soil moisture data does not have the plaque effect.
7. An electronic device, comprising:
a processor and a memory;
the memory is used for storing computer operation instructions;
the processor is used for executing the remote sensing estimation method of the soil moisture on the mountain land surface according to any one of claims 1 to 5 by calling the computer operation instruction.
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