CN112014542B - Vegetation coverage area soil moisture map manufacturing method, device, storage medium and equipment - Google Patents
Vegetation coverage area soil moisture map manufacturing method, device, storage medium and equipment Download PDFInfo
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Abstract
The invention discloses a method for manufacturing a soil moisture map of a vegetation coverage, which comprises the following steps: establishing a canopy water content empirical model and a local parameterized water cloud model of the area to be surveyed by using satellite data and actually measured vegetation canopy water content data, and calculating to obtain a bare soil backscattering coefficient of the area to be surveyed according to the canopy water content empirical model and the local parameterized water cloud model; coupling an AIEM model, a LUT method model, an Oh model, a Dubois model and a bare soil backscattering coefficient to obtain a soil water content graph group; classifying and performing algorithm evaluation on the soil water content map group of the area to be surveyed based on the land utilization type map, the clay content percentage map and the gradient map to obtain three groups of evaluation results; and screening out the soil moisture content map with highest precision in each group from the three groups of evaluation results, and splicing the soil moisture content maps with highest precision in each group of maps to obtain the soil moisture map of the complete area to be surveyed. The method is coupled by multiple models, and a high-precision soil moisture map of a to-be-surveyed area covered by vegetation is obtained.
Description
Technical Field
The invention relates to the technical field of geology and hydrology surveys, in particular to a method, a device, a storage medium and equipment for manufacturing a soil moisture map of a vegetation coverage.
Background
The soil moisture with the surface layer of 5cm is taken as an important participant of water circulation, is a key factor and an important medium for land and atmospheric substance energy exchange, is an important parameter of a climate prediction model, a drought early warning model, a hydrologic model, a crop estimation model and the like, and has important scientific significance and practical value in high-precision soil moisture drawing.
Traditional single point measurement can acquire high-precision data, but is difficult to meet the requirements of 'surface' scale and continuous soil moisture monitoring.
Disclosure of Invention
Object of the invention
The invention aims to provide a method, a device, a storage medium and equipment for manufacturing a soil moisture map of a vegetation coverage area so as to solve the problem that the prior art is difficult to meet the requirements of 'surface' scale and continuous soil moisture monitoring.
(II) technical scheme
To solve the above problems, a first aspect of the present invention provides a method for making a soil moisture map of a vegetation coverage, including:
establishing a canopy water content empirical model and a local parameterized water cloud model of a region to be surveyed by using satellite data and actual measured vegetation canopy water content data, and calculating to obtain a bare soil backscattering coefficient of the region to be surveyed according to the canopy water content empirical model and the local parameterized water cloud model;
coupling an AIEM model, a LUT method model, an Oh model and a Dubois model with the bare soil backscattering coefficient to obtain a soil water content graph group;
classifying and performing algorithm evaluation on the soil water content map group in the area to be surveyed based on a land utilization type map, a clay content percentage map and a gradient map to obtain three groups of evaluation results;
and screening out the soil moisture content map with highest precision in each group from the three groups of evaluation results, and splicing the soil moisture content maps with highest precision in each group of the maps to obtain the soil moisture map of the complete area to be surveyed.
Further, the establishing a canopy water content empirical model and a local parameterized water cloud model of the area to be surveyed by using satellite data and actually measured vegetation canopy water content data, and calculating the bare soil backscattering coefficient of the area to be surveyed according to the canopy water content empirical model and the local parameterized water cloud model specifically includes:
extracting a vegetation index from satellite data, and establishing a canopy water content empirical model coupled with the vegetation index by utilizing the vegetation index and actually measured vegetation canopy water content data to obtain canopy water content;
extracting a HH polarization backscattering coefficient in the satellite data and a VV polarization backscattering coefficient in the actually measured vegetation canopy water content data, establishing a water cloud model with localized parameters according to the HH polarization backscattering coefficient and the VV polarization backscattering coefficient, and calculating to obtain a bare soil backscattering coefficient of a region to be surveyed according to the water cloud model with localized parameters.
Further, the vegetation index comprises: NDVI, EVI, DVI and RVI.
Further, the coupling of the AIEM model, LUT method model, oh model, dubois model and the bare soil backscattering coefficient to obtain a soil moisture content map set specifically includes:
generating an AIEM simulation database by using an AIEM model based on satellite data and measured vegetation canopy water content data;
based on the AIEM simulation database, analyzing the response form of the bare soil backscattering coefficient and the input parameter, determining the relation configuration of the soil moisture and the input parameter, establishing an empirical model by utilizing the actually measured vegetation canopy water content data and the bare soil backscattering coefficient, and using the model as a drawing algorithm of a first soil water content map;
based on an AIEM simulation database, establishing an LUT method by using a cost function, and generating a soil moisture inversion result and a root mean square height inversion result, wherein the soil moisture inversion result is a second soil moisture content map, a third soil moisture content map and a fourth soil moisture content map respectively, and the root mean square height inversion result is a first root mean square height, a second root mean square height and a third root mean square height respectively;
based on the canopy water content empirical model and the bare soil backscattering coefficient, calculating a fifth soil water content map and a fourth root mean square height by using an Oh model;
and taking the first root-mean-square height, the second root-mean-square height, the third root-mean-square height and the fourth root-mean-square height as input data of a Dubois model, and obtaining four soil moisture inversion results corresponding to four root-mean-square height results according to the bare soil backscattering coefficient and the canopy moisture empirical model, wherein the inversion results are a sixth soil moisture content map, a seventh soil moisture content map, an eighth soil moisture content map and a ninth soil moisture content map respectively.
Further, the classification and algorithm evaluation are performed on the areas to be surveyed based on the land utilization type graph, the clay content percentage graph and the gradient graph, and the three groups of evaluation results are obtained specifically including:
classifying pixels in the area to be surveyed according to the land utilization type graph, the clay content percentage graph and the gradient graph, selecting an algorithm with highest precision according to the evaluation index, and respectively calculating algorithm results corresponding to each category to obtain three groups of evaluation results.
Further, the step of screening out the soil moisture content map with highest precision in each group from the three groups of evaluation results, and splicing the soil moisture content maps with highest precision in each group of maps to obtain a complete soil moisture map of a region to be surveyed specifically comprises the following steps:
screening out the soil water content map with highest precision in each group from the three groups of evaluation results;
and splicing the soil water content graphs with highest precision in each group to obtain a complete soil moisture graph of the area to be surveyed.
According to another aspect of the present invention, there is provided a plant covered area soil moisture map making apparatus comprising:
the bare soil backscattering coefficient calculation module is used for establishing a canopy water content empirical model and a local parameterized water cloud model of the area to be surveyed by utilizing satellite data and actual vegetation canopy water content data, and calculating the bare soil backscattering coefficient of the area to be surveyed according to the canopy water content empirical model and the local parameterized water cloud model;
the coupling module is used for coupling the AIEM model, the LUT method model, the Oh model and the Dubois model with the bare soil backscattering coefficient to obtain a soil water content graph group;
the classification module is used for classifying and performing algorithm evaluation on the soil water content map group in the area to be surveyed based on the land utilization type map, the clay content percentage map and the gradient map to obtain three groups of evaluation results;
and the splicing module is used for screening out the soil moisture content map with highest precision in each group from the three groups of evaluation results, and splicing the soil moisture content maps with highest precision in each group of maps to obtain the soil moisture map of the complete area to be surveyed.
Further, the bare soil backscattering coefficient calculation module specifically includes:
the canopy water content calculation unit is used for extracting a vegetation index from satellite data, and establishing a canopy water content empirical model of vegetation index coupling by utilizing the vegetation index and actually measured vegetation canopy water content data to obtain canopy water content;
the bare soil backscattering coefficient calculation unit is used for extracting an HH polarization backscattering coefficient in the satellite data and a VV polarization backscattering coefficient in the actually measured vegetation canopy water content data, establishing a water cloud model with localized parameters according to the HH polarization backscattering coefficient and the VV polarization backscattering coefficient, and calculating to obtain the bare soil backscattering coefficient of the area to be surveyed according to the water cloud model with localized parameters.
Further, the vegetation index comprises: NDVI, EVI, DVI and RVI.
Further, the coupling module specifically includes:
the AIEM simulation unit is used for generating an AIEM simulation database by using an AIEM model based on satellite data and actually measured vegetation canopy water content data;
the empirical model generating unit is used for analyzing the response form of the bare soil backscattering coefficient and the input parameter based on the AIEM simulation database, determining the relation configuration of the soil moisture and the input parameter, establishing an empirical model by utilizing the actually measured vegetation canopy moisture content data and the bare soil backscattering coefficient, and taking the model as a drawing algorithm of a first soil moisture content map;
the LUT method establishing unit is used for establishing an LUT method based on an AIEM simulation database by using a cost function to generate a soil moisture inversion result and a root mean square height inversion result, wherein the soil moisture inversion result is a second soil moisture content map, a third soil moisture content map and a fourth soil moisture content map respectively, and the root mean square height inversion result is a first root mean square height, a second root mean square height and a third root mean square height respectively;
the Oh model unit is used for calculating a fifth soil water content graph and a fourth root mean square height by using the Oh model based on the canopy water content empirical model and the bare soil backscattering coefficient;
the Dubois model unit is used for taking the first root mean square height, the second root mean square height, the third root mean square height and the fourth root mean square height as input data of the Dubois model, and obtaining four soil moisture inversion results corresponding to four root mean square height results according to the bare soil backscattering coefficient and the canopy moisture empirical model, wherein the inversion results are a sixth soil moisture content map, a seventh soil moisture content map, an eighth soil moisture content map and a ninth soil moisture content map respectively.
Further, the classification module is specifically configured to classify the pixels in the to-be-surveyed area according to the land utilization type graph, the clay content percentage graph and the slope graph, and calculate algorithm results corresponding to each category according to an algorithm with highest evaluation index selection precision, so as to obtain three groups of evaluation results.
Further, the splicing module specifically includes:
the screening unit is used for screening out the soil water content map with highest precision in each group from the three groups of evaluation results;
and the splicing unit is used for splicing the soil moisture content graphs with the highest precision in each group to obtain a complete soil moisture graph of the area to be surveyed.
According to a further aspect of the present invention there is provided a computer storage medium having stored thereon a computer program which when executed by a processor performs the steps of any of the methods of the above claims.
According to a further aspect of the present invention there is provided an electronic device comprising a memory, a display, a processor and a computer program stored on said memory and executable on said processor, said processor implementing the steps of any one of the methods described in the previous claims when said program is executed by said processor.
(III) beneficial effects
The technical scheme of the invention has the following beneficial technical effects:
compared with the prior art, the method provided by the invention realizes 'surface' scale and continuous soil moisture monitoring, and the soil moisture map manufactured by the method provided by the invention has high precision.
Drawings
FIG. 1 is a flow chart of a method for making a vegetation cover soil moisture map according to a first embodiment of the invention;
FIG. 2 is a flow chart of a method for high-precision soil moisture mapping of a vegetation cover based on multi-model coupling and background knowledge in accordance with an embodiment of the present invention;
FIG. 3 is a schematic diagram of an optimal solution method based on specific background knowledge according to an embodiment of the invention;
FIG. 4 is a plot of empirical vegetation water content model results based on 19 ground synchronization verification sample data sets in accordance with an embodiment of the present invention;
FIG. 5 is a plot of soil moisture results for an optimal solution method based on 60 ground synchronous validation sample data sets according to one embodiment of the present invention, (a) classification strategy based on land utilization, (b) classification strategy based on clay content percentage, (c) classification strategy based on grade;
FIG. 6 is a graph of soil moisture results based on an optimal solution method according to an embodiment of the present invention.
Detailed Description
The objects, technical solutions and advantages of the present invention will become more apparent by the following detailed description of the present invention with reference to the accompanying drawings. It should be understood that the description is only illustrative and is not intended to limit the scope of the invention. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the present invention.
It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In addition, the technical features of the different embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
The lower surface is the interface between the atmosphere and the solid ground or liquid water surface at the lower boundary, and is the main heat source and water vapor source of the atmosphere and the boundary surface of the movement of the lower atmosphere. The properties of the underlying surface have a great influence on the physical state and chemical composition of the atmosphere. The underlying surface can also be said to be a characteristic of the earth's surface, such as sea-land distribution, topography and surface roughness, vegetation, soil moisture, snow cover area, etc., which has a significant effect on the climate.
The canopy refers to the dense top layer of the tree branches and leaves. Because of the high rate of photosynthesis in the canopy, the plants have high yields of fruits, seeds, flowers and leaves, support biodiversity, and are capable of attracting a large number of wild animals and plants. The canopy is a water, gas and heat exchange place and plays an important role in the regulation area and the global climate. Besides absorbing solar energy and regulating climate, the canopy can intercept rainfall, weaken kinetic energy of raindrops, protect the under-forest leaf layer from strong light, dry wind and heavy rain, and keep the moist climate of the forest.
The continuous development of remote sensing technology has brought about a monitoring mode based on visible light/near infrared, thermal infrared and microwave as data bases, and brings new ideas for regional scale soil moisture mapping.
HH and VV are polarization modes representing electromagnetic waves, and refer to horizontal transmission of horizontally received signals, and vertical transmission of vertically received signals.
As shown in fig. 1, in a first aspect of the embodiment of the present invention, there is provided a method for making a soil moisture map of a vegetation coverage, including:
s1: establishing a canopy water content empirical model and a local parameterized water cloud model of the area to be surveyed by using satellite data and actually measured vegetation canopy water content data, and calculating to obtain a bare soil backscattering coefficient of the area to be surveyed according to the canopy water content empirical model and the local parameterized water cloud model;
s2: coupling an AIEM model, a LUT method model, an Oh model, a Dubois model and a bare soil backscattering coefficient to obtain a soil water content graph group;
s3: classifying and performing algorithm evaluation on the soil water content map group of the area to be surveyed based on the land utilization type map, the clay content percentage map and the gradient map to obtain three groups of evaluation results;
s4: and screening out the soil moisture content map with highest precision in each group from the three groups of evaluation results, and splicing the soil moisture content maps with highest precision in each group of maps to obtain the soil moisture map of the complete area to be surveyed.
The massive remote sensing data provides a convenient condition for regional scale soil moisture mapping, however, the current optical remote sensing data is greatly influenced by weather factors; most thermal infrared remote sensing algorithms are only suitable for soil moisture monitoring in bare soil and sparse vegetation areas and in the cloud condition.
In the vegetation canopy effect removal stage, the vegetation index coupled canopy water content empirical model aiming at satellite spectrum characteristics is innovatively introduced, so that a vegetation water content result which is more suitable for a complex underlying surface than a traditional single-parameter secondary empirical model is obtained, surface scale and continuous soil moisture monitoring are realized, and the soil moisture map manufactured by the method is high in precision.
Specifically, four vegetation indexes extracted from GF-1 satellite data and actually measured vegetation canopy water content data are utilized to establish a canopy water content empirical model of four vegetation indexes coupled, HH and VV polarization backscattering coefficients extracted from GF-3 satellite data and field experimental data are utilized to establish a water cloud model with localized parameters, and bare soil backscattering coefficients of a research area are calculated. Four of these were NDVI, EVI, DVI and RVI respectively.
Through specific implementation, the vegetation canopy effect removal stage is provided with four vegetation index coupled canopy water content empirical models aiming at GF-1 satellite spectrum characteristics, and vegetation water content results which are more suitable for complex underlying surfaces than the traditional single-parameter secondary empirical models are obtained.
Optionally, establishing a canopy water content empirical model and a local parameterized water cloud model of the area to be surveyed by using satellite data and actually measured vegetation canopy water content data, and calculating a bare soil back scattering coefficient of the area to be surveyed according to the canopy water content empirical model and the local parameterized water cloud model specifically includes:
extracting a vegetation index from satellite data, and establishing a canopy water content empirical model coupled with the vegetation index by using the vegetation index and actually measured vegetation canopy water content data to obtain canopy water content;
extracting HH polarization backscattering coefficient in satellite data and VV polarization backscattering coefficient in measured vegetation canopy water content data, establishing a water cloud model with localized parameters according to the HH polarization backscattering coefficient and the VV polarization backscattering coefficient, and calculating to obtain bare soil backscattering coefficient of the area to be surveyed according to the water cloud model with localized parameters.
Optionally, the satellite data includes: GF-1 satellite data and GF-3 satellite data.
Optionally, coupling the AIEM model, the LUT method model, the Oh model, the Dubois model and the bare soil backscattering coefficient to obtain the soil moisture content map set specifically includes:
generating an AIEM simulation database by using an AIEM model based on satellite data and measured vegetation canopy water content data;
based on an AIEM simulation database, analyzing response forms of bare soil backscattering coefficients and input parameters, determining a relation configuration of soil moisture and the input parameters, establishing an empirical model by using actual measurement vegetation canopy moisture content data and the bare soil backscattering coefficients, and using the model as a drawing algorithm of a first soil moisture content map;
based on an AIEM simulation database, establishing an LUT method by using a cost function, and generating a soil moisture inversion result and a root mean square height inversion result, wherein the soil moisture inversion result is a second soil moisture content map, a third soil moisture content map and a fourth soil moisture content map respectively, and the root mean square height inversion result is a first root mean square height, a second root mean square height and a third root mean square height respectively;
based on the canopy water content empirical model and the bare soil backscattering coefficient, calculating a fifth soil water content map and a fourth root mean square height by using an Oh model;
and taking the first root-mean-square height, the second root-mean-square height, the third root-mean-square height and the fourth root-mean-square height as input data of a Dubois model, and obtaining four soil moisture inversion results corresponding to the four root-mean-square height results according to bare soil backscattering coefficients and a canopy moisture content empirical model, wherein the inversion results are a sixth soil moisture content map, a seventh soil moisture content map, an eighth soil moisture content map and a ninth soil moisture content map respectively.
The embodiment realizes multi-model coupling, and plays the advantages of each model to adapt to the ground surface condition of a complex underlying surface research area; in the LUT method, the soil moisture of the pixels and the root mean square height are innovatively obtained simultaneously while the soil moisture is inverted.
Optionally, classifying and algorithmically evaluating the to-be-surveyed area based on the land utilization type graph, the clay content percentage graph and the gradient graph, and obtaining three groups of evaluation results specifically comprises:
and classifying pixels in the region to be surveyed according to the land utilization type graph, the clay content percentage graph and the gradient graph, selecting an algorithm with highest precision according to the evaluation index, and respectively calculating algorithm results corresponding to each category to obtain three groups of evaluation results.
Optionally, the soil moisture content map with highest precision in each group is screened from the three groups of evaluation results, and the soil moisture content map with highest precision in each group of maps is spliced, so that the soil moisture map of the complete area to be surveyed is obtained specifically comprises:
screening out the soil water content map with highest precision in each group from the three groups of evaluation results;
and splicing the soil water content graphs with highest precision in each group to obtain a complete soil moisture graph of the area to be surveyed.
In the embodiment, priori knowledge is newly introduced into the optimal solution method, and the optimal solution with higher precision than that of a single model soil moisture mapping algorithm is obtained.
According to another aspect of the present invention, there is provided a plant covered area soil moisture map making apparatus comprising:
the bare soil backscattering coefficient calculation module is used for establishing a canopy water content empirical model and a local parameterized water cloud model of the area to be surveyed by utilizing satellite data and actual vegetation canopy water content data, and calculating to obtain the bare soil backscattering coefficient of the area to be surveyed according to the canopy water content empirical model and the local parameterized water cloud model;
the coupling module is used for coupling the AIEM model, the LUT method model, the Oh model, the Dubois model and the bare soil backscattering coefficient to obtain a soil water content graph group;
the classification module is used for classifying and performing algorithm evaluation on the area to be surveyed based on the land utilization type graph, the clay content percentage graph and the gradient graph to obtain three groups of evaluation results;
and the splicing module is used for screening out the soil moisture content map with the highest precision in each group from the three groups of evaluation results, and splicing the soil moisture content maps with the highest precision in each group of the maps to obtain the soil moisture map of the complete area to be surveyed.
Optionally, the bare soil backscattering coefficient calculation module specifically includes:
the canopy water content calculation unit is used for extracting a vegetation index from satellite data, and establishing a canopy water content empirical model of vegetation index coupling by using the vegetation index and actually measured vegetation canopy water content data to obtain canopy water content;
the bare soil backscattering coefficient calculation unit is used for extracting an HH polarization backscattering coefficient in satellite data and a VV polarization backscattering coefficient in actual measurement vegetation canopy water content data, establishing a water cloud model with localized parameters according to the HH polarization backscattering coefficient and the VV polarization backscattering coefficient, and calculating to obtain the bare soil backscattering coefficient of the area to be surveyed according to the water cloud model with localized parameters.
Optionally, the vegetation index comprises: NDVI, EVI, DVI and RVI.
Optionally, the coupling module specifically includes:
the AIEM simulation unit is used for generating an AIEM simulation database by using an AIEM model based on satellite data and actually measured vegetation canopy water content data;
the experimental model generating unit is used for analyzing the response form of the bare soil backscattering coefficient and the input parameter based on the AIEM simulation database, determining the relation configuration of the soil moisture and the input parameter, establishing an experimental model by utilizing the actually measured vegetation canopy moisture content data and the bare soil backscattering coefficient, and using the model as a drawing algorithm of a first soil moisture content map;
the LUT method establishing unit is used for establishing an LUT method based on the AIEM simulation database by using a cost function to generate a soil moisture inversion result and a root mean square height inversion result, wherein the soil moisture inversion result is a second soil moisture content map, a third soil moisture content map and a fourth soil moisture content map respectively, and the root mean square height inversion result is a first root mean square height, a second root mean square height and a third root mean square height respectively;
the Oh model unit is used for calculating a fifth soil water content map and a fourth root mean square height by using the Oh model based on the canopy water content empirical model and the bare soil backscattering coefficient;
the Dubois model unit is used for taking the first root mean square height, the second root mean square height, the third root mean square height and the fourth root mean square height as input data of the Dubois model, and obtaining four soil moisture inversion results corresponding to the four root mean square height results according to bare soil backscattering coefficients and a canopy moisture content empirical model, wherein the inversion results are a sixth soil moisture content map, a seventh soil moisture content map, an eighth soil moisture content map and a ninth soil moisture content map respectively.
Optionally, the classification module is specifically configured to classify pixels in the to-be-surveyed area according to the land utilization type graph, the clay content percentage graph and the slope graph, and select an algorithm with highest precision according to the evaluation index, and calculate algorithm results corresponding to each category respectively to obtain three groups of evaluation results.
Optionally, the splicing module specifically includes:
the screening unit is used for screening out the soil water content map with highest precision in each group from the three groups of evaluation results;
and the splicing unit is used for splicing the soil moisture content graphs with the highest precision in each group to obtain a complete soil moisture graph of the area to be surveyed.
As shown in FIG. 2, in one embodiment of the present invention, a method for high-precision soil moisture mapping of vegetation coverage based on multi-model coupling and background knowledge is established in combination with domestic GF-1 and GF-3 images.
The method is realized by the following technical steps:
step 1), based on GF-1 and GF-3 satellite data and synchronous experimental vegetation water content data, establishing a canopy water content empirical model and a local parameterized water cloud model of 4 vegetation index coupling, and obtaining a bare soil backscattering coefficient for removing vegetation canopy water content effect;
step 2) coupling four common models (AIEM model, LUT method, oh model and Dubois model) to obtain 9 soil moisture inversion algorithms;
and 3) screening and combining nine algorithms according to three background knowledge design research area classification strategies, and finally obtaining a high-precision soil moisture optimal solution diagram of the research area, as shown in fig. 3.
The technical flow of the vegetation canopy effect removing technology based on GF-1 and GF-3 satellite data, the technical flow of the soil moisture inversion algorithm based on AIEM model, LUT method, oh model and Dubois model multi-model coupling, and the technical flow of the high-precision soil moisture optimal decomposition method based on background knowledge are important method innovations of the soil moisture. The technical process realizes removal of canopy effect, and lays a good foundation for realizing research of bare soil moisture inversion algorithm; the method comprises the steps of obtaining 5 soil moisture inversion results and 4 root mean square height inversion results by using an AIEM (automatic index) model, an LUT (look-up table) method, obtaining 4 soil moisture inversion results corresponding to the 4 root mean square height inversion results as input data of the Dubois model, wherein the technical process is coupled with 4 common soil moisture inversion algorithms, and accumulating the algorithms and the results for obtaining a high-precision soil moisture map by using an optimal solution method; the technical process of the high-precision soil moisture optimal decomposition method based on background knowledge adopts a strategy of pixel classification and algorithm screening on the basis of the technical process result, and a high-precision soil moisture map of a research area is obtained based on the background knowledge, as shown in figures 4-6.
(one) a technical process for removing vegetation canopy effect based on GF-1 satellite data and GF-3 satellite data
4 vegetation indexes extracted from GF-1 satellite data and actually measured vegetation canopy water content data are utilized to establish a canopy water content empirical model of 4 vegetation indexes coupling, HH and VV polarization backscattering coefficients extracted from GF-3 satellite data and field experiment data are utilized to establish a water cloud model with localized parameters, and bare soil backscattering coefficients of a research area are calculated and obtained.
(II) soil moisture inversion algorithm technical flow based on AIEM, LUT method, oh model and Dubois model multimode coupling
Four common soil moisture inversion models are coupled to obtain 9 soil moisture diagrams. The specific operation is divided into five steps:
(1) Generating an AIEM simulation database by using an AIEM model based on the ground satellite synchronization experiment data set and GF-3 satellite parameters;
(2) Based on the AIEM simulation database obtained in the first step, the response form of the bare soil backscattering coefficient and the input parameters is analyzed, and the relation configuration of the soil moisture and the input parameters is determined. Based on the above, an empirical model is established by using the measured soil moisture content and the bare soil backscattering coefficient, and the model is used as a drawing algorithm of a soil moisture content (mv 1) graph.
(3) Similarly, based on the AIEM simulation database, a LUT method was established using 3 cost functions, yielding three soil moisture inversion results (mv 2, mv3, and mv 4) and three root mean square height inversion results (s 1, s2, and s 3).
(4) At the same time as the above three steps, the soil moisture (mv 5) and root mean square height result (s 4) were calculated using the Oh model based on the ground satellite synchronization experiment and the bare soil backscattering coefficient.
(5) Through the four steps, four root mean square height results are obtained in total and are used as input data of the Dubois model. According to the backscattering coefficient and the ground satellite synchronization experiment, the soil moisture is actually measured, and four soil moisture inversion results (mv 6, mv7, mv8 and mv 9) corresponding to four root mean square height results are obtained.
Third, technical process of high-precision soil moisture optimal solution method based on background knowledge
On the basis of the 9 soil moisture algorithms, pixel classification and algorithm selection strategies are adopted, and the optimal solution of the soil moisture map is drawn based on background knowledge. The specific operation process is as follows: first, for specific background knowledge, a classification strategy constructed based on the knowledge is adopted to classify pixels in a research area, and an algorithm with highest precision is selected according to an evaluation index. And respectively calculating algorithm results corresponding to the categories. Secondly, splicing all kinds of results to form a soil moisture map of the whole research area; and finally, performing precision comparison on three soil moisture maps based on three sets of background knowledge, and screening the soil moisture optimal solution map with highest precision.
According to a further aspect of the present invention, there is provided a computer storage medium having stored thereon a computer program which when executed by a processor performs the steps of any of the methods of the above-mentioned aspects.
According to a further aspect of the present invention there is provided an electronic device comprising a memory, a display, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of any one of the methods of the above technical solutions when the program is executed by the processor.
The invention aims to protect a method for manufacturing a soil moisture map of a vegetation coverage, which comprises the following steps: establishing a canopy water content empirical model and a local parameterized water cloud model of the area to be surveyed by using satellite data and actually measured vegetation canopy water content data, and calculating to obtain a bare soil backscattering coefficient of the area to be surveyed according to the canopy water content empirical model and the local parameterized water cloud model; coupling an AIEM model, a LUT method model, an Oh model, a Dubois model and a bare soil backscattering coefficient to obtain a soil water content graph group; classifying and performing algorithm evaluation on the area to be surveyed based on the land utilization type graph, the clay content percentage graph and the gradient graph to obtain three groups of evaluation results; and screening out the soil moisture content map with highest precision in each group from the three groups of evaluation results, and splicing the soil moisture content maps with highest precision in each group of maps to obtain the soil moisture map of the complete area to be surveyed. The method integrates multi-source data of optical satellite data, radar satellite data and ground synchronization experimental data, achieves multi-model coupling including a water cloud model, an AIEM model, a LUT method, an Oh model and a Dubois model, introduces priori knowledge into an optimal solution method, and obtains a high-precision soil moisture map of a to-be-surveyed area covered by vegetation. The method realizes the monitoring of the 'surface' scale and continuous soil moisture, and the soil moisture map manufactured by the method has high precision.
It is to be understood that the above-described embodiments of the present invention are merely illustrative of or explanation of the principles of the present invention and are in no way limiting of the invention. Accordingly, any modification, equivalent replacement, improvement, etc. made without departing from the spirit and scope of the present invention should be included in the scope of the present invention. Furthermore, the appended claims are intended to cover all such changes and modifications that fall within the scope and boundary of the appended claims, or equivalents of such scope and boundary.
Claims (14)
1. The method for manufacturing the soil moisture map of the vegetation coverage is characterized by comprising the following steps of:
establishing a canopy water content empirical model and a local parameterized water cloud model of a region to be surveyed by using satellite data and actual measured vegetation canopy water content data, and calculating to obtain a bare soil backscattering coefficient of the region to be surveyed according to the canopy water content empirical model and the local parameterized water cloud model;
coupling an AIEM model, a LUT method model, an Oh model and a Dubois model with the bare soil backscattering coefficient to obtain a soil water content graph group;
classifying and performing algorithm evaluation on the soil water content map group in the area to be surveyed based on a land utilization type map, a clay content percentage map and a gradient map to obtain three groups of evaluation results;
and screening out the soil moisture content map with highest precision in each group from the three groups of evaluation results, and splicing the soil moisture content maps with highest precision in each group of the maps to obtain the soil moisture map of the complete area to be surveyed.
2. The method according to claim 1, wherein the establishing a canopy water content empirical model and a locally parameterized water cloud model of the area to be surveyed using satellite data and measured vegetation canopy water content data, and calculating the bare soil backscattering coefficient of the area to be surveyed according to the canopy water content empirical model and the locally parameterized water cloud model specifically comprises:
extracting a vegetation index from satellite data, and establishing a canopy water content empirical model coupled with the vegetation index by utilizing the vegetation index and actually measured vegetation canopy water content data to obtain canopy water content;
extracting a HH polarization backscattering coefficient in the satellite data and a VV polarization backscattering coefficient in the actually measured vegetation canopy water content data, establishing a water cloud model with localized parameters according to the HH polarization backscattering coefficient and the VV polarization backscattering coefficient, and calculating to obtain a bare soil backscattering coefficient of a region to be surveyed according to the water cloud model with localized parameters.
3. The method of claim 2, wherein the vegetation index comprises: NDVI, EVI, DVI and RVI.
4. The method of claim 1, wherein the coupling the AIEM model, LUT method model, oh model, dubois model, and the bare soil backscattering coefficient to obtain the soil moisture map set specifically comprises:
generating an AIEM simulation database by using an AIEM model based on satellite data and measured vegetation canopy water content data;
based on the AIEM simulation database, analyzing the response form of the bare soil backscattering coefficient and the input parameter, determining the relation configuration of the soil moisture and the input parameter, establishing an empirical model by utilizing the actually measured vegetation canopy water content data and the bare soil backscattering coefficient, and using the model as a drawing algorithm of a first soil water content map;
based on an AIEM simulation database, establishing an LUT method by using a cost function, and generating a soil moisture inversion result and a root mean square height inversion result, wherein the soil moisture inversion result is a second soil moisture content map, a third soil moisture content map and a fourth soil moisture content map respectively, and the root mean square height inversion result is a first root mean square height, a second root mean square height and a third root mean square height respectively;
based on the canopy water content empirical model and the bare soil backscattering coefficient, calculating a fifth soil water content map and a fourth root mean square height by using an Oh model;
and taking the first root-mean-square height, the second root-mean-square height, the third root-mean-square height and the fourth root-mean-square height as input data of a Dubois model, and obtaining four soil moisture inversion results corresponding to four root-mean-square height results according to the bare soil backscattering coefficient and the canopy moisture empirical model, wherein the inversion results are a sixth soil moisture content map, a seventh soil moisture content map, an eighth soil moisture content map and a ninth soil moisture content map respectively.
5. The method of claim 1, wherein classifying and algorithmically evaluating the survey area based on the land utilization pattern, the clay content percentage map, and the slope map, and obtaining three sets of evaluation results comprises:
classifying pixels in the area to be surveyed according to the land utilization type graph, the clay content percentage graph and the gradient graph, selecting an algorithm with highest precision according to the evaluation index, and respectively calculating algorithm results corresponding to each category to obtain three groups of evaluation results.
6. The method according to claim 5, wherein the steps of screening out the soil moisture content map with the highest precision in each group from the three groups of evaluation results, and splicing the soil moisture content maps with the highest precision in each group of maps to obtain the soil moisture map of the complete survey area comprise:
screening out the soil water content map with highest precision in each group from the three groups of evaluation results;
and splicing the soil water content graphs with highest precision in each group to obtain a complete soil moisture graph of the area to be surveyed.
7. The utility model provides a vegetation coverage soil moisture map making devices which characterized in that includes:
the bare soil backscattering coefficient calculation module is used for establishing a canopy water content empirical model and a local parameterized water cloud model of the area to be surveyed by utilizing satellite data and actual vegetation canopy water content data, and calculating the bare soil backscattering coefficient of the area to be surveyed according to the canopy water content empirical model and the local parameterized water cloud model;
the coupling module is used for coupling the AIEM model, the LUT method model, the Oh model and the Dubois model with the bare soil backscattering coefficient to obtain a soil water content graph group;
the classification module is used for classifying and performing algorithm evaluation on the soil water content map group in the area to be surveyed based on the land utilization type map, the clay content percentage map and the gradient map to obtain three groups of evaluation results;
and the splicing module is used for screening out the soil moisture content map with highest precision in each group from the three groups of evaluation results, and splicing the soil moisture content maps with highest precision in each group of maps to obtain the soil moisture map of the complete area to be surveyed.
8. The apparatus of claim 7, wherein the bare soil backscattering coefficient calculation module specifically comprises:
the canopy water content calculation unit is used for extracting a vegetation index from satellite data, and establishing a canopy water content empirical model of vegetation index coupling by utilizing the vegetation index and actually measured vegetation canopy water content data to obtain canopy water content;
the bare soil backscattering coefficient calculation unit is used for extracting an HH polarization backscattering coefficient in the satellite data and a VV polarization backscattering coefficient in the actually measured vegetation canopy water content data, establishing a water cloud model with localized parameters according to the HH polarization backscattering coefficient and the VV polarization backscattering coefficient, and calculating to obtain the bare soil backscattering coefficient of the area to be surveyed according to the water cloud model with localized parameters.
9. The apparatus of claim 8, wherein the vegetation index comprises: NDVI, EVI, DVI and RVI.
10. The apparatus of claim 7, wherein the coupling module specifically comprises:
the AIEM simulation unit is used for generating an AIEM simulation database by using an AIEM model based on satellite data and actually measured vegetation canopy water content data;
the empirical model generating unit is used for analyzing the response form of the bare soil backscattering coefficient and the input parameter based on the AIEM simulation database, determining the relation configuration of the soil moisture and the input parameter, establishing an empirical model by utilizing the actually measured vegetation canopy moisture content data and the bare soil backscattering coefficient, and taking the model as a drawing algorithm of a first soil moisture content map;
the LUT method establishing unit is used for establishing an LUT method based on an AIEM simulation database by using a cost function to generate a soil moisture inversion result and a root mean square height inversion result, wherein the soil moisture inversion result is a second soil moisture content map, a third soil moisture content map and a fourth soil moisture content map respectively, and the root mean square height inversion result is a first root mean square height, a second root mean square height and a third root mean square height respectively;
the Oh model unit is used for calculating a fifth soil water content graph and a fourth root mean square height by using the Oh model based on the canopy water content empirical model and the bare soil backscattering coefficient;
the Dubois model unit is used for taking the first root mean square height, the second root mean square height, the third root mean square height and the fourth root mean square height as input data of the Dubois model, and obtaining four soil moisture inversion results corresponding to four root mean square height results according to the bare soil backscattering coefficient and the canopy moisture empirical model, wherein the inversion results are a sixth soil moisture content map, a seventh soil moisture content map, an eighth soil moisture content map and a ninth soil moisture content map respectively.
11. The apparatus of claim 10, wherein the classification module is specifically configured to classify the pixels in the to-be-surveyed area according to a land utilization type map, a clay content percentage map, and a slope map, and select an algorithm with highest accuracy according to an evaluation index, and calculate algorithm results corresponding to each class respectively to obtain three sets of evaluation results.
12. The apparatus of claim 11, wherein the splicing module specifically comprises:
the screening unit is used for screening out the soil water content map with highest precision in each group from the three groups of evaluation results;
and the splicing unit is used for splicing the soil moisture content graphs with the highest precision in each group to obtain a complete soil moisture graph of the area to be surveyed.
13. A computer storage medium, characterized in that the storage medium has stored thereon a computer program which, when executed by a processor, implements the steps of the method according to any of claims 1-6.
14. An electronic device comprising a memory, a display, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method of any one of claims 1-6 when the program is executed.
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