CN116029162B - Flood disaster inundation range monitoring method and system by using satellite-borne GNSS-R data - Google Patents
Flood disaster inundation range monitoring method and system by using satellite-borne GNSS-R data Download PDFInfo
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Abstract
本申请涉及数据处理技术领域，提供一种利用星载GNSS‑R数据的洪涝灾害淹没范围监测方法和系统。该方法包括：通过对DEM处理得到地表粗糙度，然后利用该地表粗糙度和植被光学厚度数据，对星载GNSS‑R数据中提取得到的NBRCS进行校正，并根据校正后的NBRCS计算地表反射率；基于地表反射率和土壤水分数据构建土壤水分拟合模型；根据星载GNSS‑R土壤水分数据和田间持水量数据，计算淹没监测指数；对淹没监测指数与洪水数据进行匹配以确定淹没监测指数的洪水阈值，最后根据洪水阈值确定洪涝灾害淹没的范围。由于DEM的时空分辨率与GNSS‑R数据高度适配，结合非相干假设，提升了地表反射率的校正精度，同时考虑田间持水量的影响，提高了洪涝灾害淹没范围处理结果的精度。
This application relates to the technical field of data processing, and provides a method and system for monitoring the submerged range of flood disasters using satellite-borne GNSS‑R data. The method includes: obtaining the surface roughness by DEM processing, and then using the surface roughness and vegetation optical thickness data to correct the NBRCS extracted from the spaceborne GNSS-R data, and calculating the surface reflectance according to the corrected NBRCS ; Construct a soil moisture fitting model based on surface albedo and soil moisture data; calculate the inundation monitoring index based on the spaceborne GNSS‑R soil moisture data and field water capacity data; match the inundation monitoring index with the flood data to determine the inundation monitoring index Finally, according to the flood threshold, determine the submerged range of flood disasters. Since the spatio-temporal resolution of DEM is highly compatible with GNSS-R data, combined with the non-coherent assumption, the correction accuracy of the surface albedo is improved. At the same time, the influence of field water holding capacity is considered, and the accuracy of the processing results of the flood disaster submerged area is improved.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a flood disaster inundation range monitoring method and system utilizing satellite-borne GNSS-R data.
Background
Flood is one of the most damaging and non-preventable natural disasters in the world, and is characterized by high frequency, wide range and strong suddenly. The occurrence of floods has a serious impact on human life and economy. In recent years, the ecosystem has changed considerably due to the frequent occurrence of flood inundations. Timely and accurately searching flood inundation conditions and quantifying the time-varying range of the flood inundation conditions, and can provide scientific basis for the prediction of environmental influence.
The traditional remote sensing processing and searching method for the flood disaster inundation range mainly relies on an optical sensor or a microwave sensor to acquire related data, wherein the optical remote sensing data has the characteristic of high spatial resolution (can reach tens of meters or hundreds of meters), but the optical sensor is easily affected by cloud and fog, continuous and long-time serial remote sensing image data are difficult to acquire, and the flood disaster inundation range cannot be effectively processed and monitored in time; the microwave sensor can be divided into an active microwave sensor and a passive microwave sensor, the active microwave sensor, such as a Synthetic Aperture Radar (SAR), can produce data with high spatial resolution (up to 100 meters), but the time resolution is usually 7-14 days, the lower time resolution leads to the flooding range that flood disasters cannot be obtained in real time through the active microwave sensor, the passive microwave sensor has higher time resolution, can provide all-weather ground monitoring capability, but has very low spatial resolution (usually tens of kilometers), and cannot provide details of flood flooding and the flood flooding range with high resolution.
The global satellite navigation positioning system (Global Navigation Satellite System, GNSS) is not only capable of providing accurate navigation positioning functions, but its reflected signals (GNSS Reflectometry, GNSS-R) can also be used for surface parameter monitoring. The satellite-borne GNSS-R has the characteristics of high spatial resolution and low revisit time when measuring global ocean/terrestrial geophysical parameters, so that the satellite-borne GNSS-R can provide more frequent sampling compared with a passive microwave satellite, for example, a representative satellite CYGNSS of the satellite-borne GNSS-R has the revisit time of about 2.8-7 hours per day, and the spatial resolution is about 3 multiplied by 0.5 km.
In the existing technical scheme for monitoring the flood disaster inundation range based on the satellite-borne GNSS-R data, in order to improve the data precision, the earth surface reflectivity is usually firstly directly extracted from the satellite-borne GNSS-R data based on a reflection equation and then corrected by using the earth surface roughness parameter of the SAMP satellite, however, on one hand, the existing earth surface reflectivity extraction method from the satellite-borne GNSS-R data is usually based on the assumption that the earth surface reflectivity is coherent reflection, but in practice the earth surface is incoherent reflection in most cases, and the unreasonable assumption leads to certain error in the extracted earth surface reflectivity; on the other hand, since the GNSS-R and SMAP belong to different satellite platforms respectively, the spatial resolution and the temporal resolution of the surface roughness provided by the SMAP satellite cannot be well matched with the satellite-borne GNSS-R data, resulting in insufficient accuracy of the corrected surface reflectivity, and further affecting the accuracy of the processing result of the flood disaster flooding range.
Accordingly, there is a need to provide an improved solution to the above-mentioned deficiencies of the prior art.
Disclosure of Invention
The present application aims to provide a flood disaster inundation range monitoring method and system using on-board GNSS-R data, which can process and retrieve daily flood inundation dynamic range using on-board GNSS-R measurement data to solve or alleviate the problems in the prior art.
In order to achieve the above object, the present application provides the following technical solutions:
the application provides a flood disaster inundation range monitoring method utilizing satellite-borne GNSS-R data, which comprises the following steps:
calculating the surface roughness by using the surface elevation data;
correcting the cross section parameters of the bistatic radar based on the Fresnel reflection equation according to the surface roughness and the vegetation optical thickness data so as to calculate the surface reflectivity according to the corrected bistatic radar cross section parameters; wherein the bistatic radar section parameters are extracted from satellite-borne GNSS-R data;
constructing a soil moisture fitting model based on the earth surface reflectivity and SMAP soil moisture data to calculate and obtain satellite-borne GNSS-R soil moisture data;
calculating a flooding monitoring index according to the satellite-borne GNSS-R soil moisture data and the field water holding capacity data;
and matching the flooding monitoring index with the pre-acquired flood data to obtain a flood threshold of the flooding monitoring index, so as to determine the flooding range of the flooding disaster according to the flood threshold.
Preferably, the formula for calculating the flooding monitoring index is as follows:
in the method, in the process of the invention,GSSIIfor the flooding monitoring index,SM t is thattThe value of the satellite-borne GNSS-R soil moisture data at the moment,SM min is the minimum value of the satellite-borne GNSS-R soil moisture data in a preset time range,SM FC and (5) obtaining field water holding capacity data.
Preferably, the calculation formula of the field water holding capacity data is as follows:
in the method, in the process of the invention,
represents the soil moisture at a pressure of 1500kpa,Sis the proportion of the sand stone to be occupied,Cthe proportion of the clay is given by weight,OMthe ratio of the organic matters is calculated.
Preferably, the calculation formula for correcting the cross-section parameter of the bistatic radar is as follows:
in the method, in the process of the invention,SRrepresenting the reflectivity of the earth's surface,σrepresenting the cross-sectional parameters of the bistatic radar,τrepresenting the optical thickness data of the vegetation,MSSrepresenting the roughness of the surface of the earth,θrepresenting the angle of incidence in the on-board GNSS-R data.
Preferably, the soil moisture fitting model is constructed by a linear regression method based on the correlation between the surface reflectivity and the SMAP soil moisture data,
the soil moisture fitting model is as follows:
in the method, in the process of the invention,SM GNSS-R representing the calculated on-board GNSS-R soil moisture data,SRand a and b are empirical parameters obtained by fitting the earth surface reflectivity in the historical satellite-borne GNSS-R data and the SMAP soil moisture data.
Preferably, the matching the flooding monitoring index with pre-acquired flood data to obtain a flood threshold of the flooding monitoring index specifically includes:
determining a search area of the flood data, and taking all pixel points in the image corresponding to the inundation monitoring index as reference pixel points for determining the flood threshold value;
calculating the distance between all pixel points in the flood data in the search area and a target pixel point, and taking a pixel point corresponding to the minimum distance in all pixel points in the flood data as a matching point of the target pixel point; wherein the target pixel point is any one of the reference pixel points;
determining a flood level corresponding to the flooding monitoring index on the target pixel point according to a threshold value corresponding to the flood data on the matching point;
and counting flood grades corresponding to the inundation monitoring indexes on all the reference pixel points to determine a flood threshold of the inundation monitoring indexes.
Preferably, the method further comprises:
if no matching point corresponding to the target pixel point is found in the search area, discarding the target pixel point, and not serving as a reference pixel point for determining the flood threshold value of the flood monitoring index.
The embodiment of the application provides a flood disaster inundation range monitoring system using satellite-borne GNSS-R data, which comprises the following steps:
a first calculation unit configured to calculate a surface roughness using the surface elevation data;
the correcting unit is configured to correct the cross section parameters of the bistatic radar based on the Fresnel reflection equation according to the surface roughness and the vegetation optical thickness data so as to calculate the surface reflectivity according to the corrected bistatic radar cross section parameters; wherein the bistatic radar section parameters are extracted from satellite-borne GNSS-R data;
the fitting unit is configured to construct a soil moisture fitting model based on the earth surface reflectivity and the SMAP soil moisture data so as to calculate and obtain satellite-borne GNSS-R soil moisture data;
the second calculating unit is configured to calculate a flooding monitoring index according to the satellite-borne GNSS-R soil moisture data and the field water holding capacity data;
the matching unit is configured to match the pre-acquired flood data with the flooding monitoring index to obtain a flood threshold of the flooding monitoring index, so as to determine the flooding range of the flooding disaster according to the flood threshold.
Preferably, the formula for calculating the flooding monitoring index is as follows:
in the method, in the process of the invention,GSSIIfor the flooding monitoring index,SM t is thattThe value of the satellite-borne GNSS-R soil moisture data at the moment,SM min is the minimum value of the satellite-borne GNSS-R soil moisture data in a preset time range,SM FC and (5) obtaining field water holding capacity data.
Preferably, the calculation formula for correcting the cross-section parameter of the bistatic radar is as follows:
in the method, in the process of the invention,SRrepresenting the reflectivity of the earth's surface,σrepresenting the parameters of the cross section of the bistatic radar,τrepresenting the optical thickness data of the vegetation,MSSrepresenting the roughness of the surface of the earth,θrepresenting the angle of incidence in the on-board GNSS-R data.
The beneficial effects are that:
according to the technical scheme, the surface roughness is calculated through surface elevation Data (DEM), then the surface roughness and vegetation optical thickness data are utilized to correct bistatic radar section parameters (NBRCS) extracted from satellite-borne GNSS-R data, and the surface reflectivity (Surface reflectance, SR) is calculated according to the corrected NBRCS; then constructing a soil moisture fitting model based on the earth surface reflectivity and SMAP soil moisture data to calculate and obtain satellite-borne GNSS-R soil moisture data; according to the satellite-borne GNSS-R soil moisture data and the field water holding capacity data, calculating a flooding monitoring index; and matching the flooding monitoring index with the pre-acquired flood data to determine a flood threshold of the flooding monitoring index, and finally determining the flooding range of the flooding disaster according to the flood threshold. In the process, as the surface roughness is calculated by using DEM data, the spatial resolution and the time resolution of the surface roughness can be well matched with satellite-borne GNSS-R data, the surface roughness is used for correcting NBRCS, the calculated surface reflectivity is obtained on the basis of incoherent assumption, and the precision of the surface reflectivity is improved; by introducing field water-holding capacity data and combining satellite-borne GNSS-R soil moisture data, the problem that the conventional flooding monitoring index calculation ignores field moisture change to cause inconsistency with actual conditions is avoided, the rigor and scientificity of the flooding monitoring index are improved, and meanwhile, the precision of monitoring the flooding range of the flooding disaster is improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. Wherein:
FIG. 1 is a flow chart of a flood disaster inundation range monitoring method utilizing on-board GNSS-R data according to some embodiments of the present application;
FIG. 2 is a schematic diagram of matching a inundation monitoring index with pre-acquired flood data provided in accordance with some embodiments of the present application;
FIG. 3 is a graph comparing flood inundation ranges determined using satellite-borne GNSS-R data with SMAP bright temperature data provided in accordance with some embodiments of the present application;
fig. 4 is a schematic structural diagram of a flood disaster inundation range monitoring system utilizing satellite-borne GNSS-R data according to some embodiments of the present application.
Detailed Description
The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments. Various examples are provided by way of explanation of the present application and not limitation of the present application. Indeed, it will be apparent to those skilled in the art that modifications and variations can be made in the present application without departing from the scope or spirit of the application. For example, features illustrated or described as part of one embodiment can be used on another embodiment to yield still a further embodiment. Accordingly, it is intended that the present application include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Exemplary method
The embodiment of the application provides a flood disaster inundation range monitoring method using satellite-borne GNSS-R data, as shown in fig. 1-3, the method comprises the following steps:
step S101, calculating the surface roughness by using the surface elevation data.
In the embodiment of the application, the surface roughness MSS is used for performing systematic correction on the surface reflectivity in the satellite-borne GNSS-R data so as to improve the accuracy of the surface reflectivity obtained by extracting the satellite-borne GNSS-R data.
In the conventional correction method, the surface roughness provided by the pre-acquired SMAP data is directly used to substitute the formula to calculate the surface reflectivity, however, the SMAP and the GNSS-R belong to different satellite platforms, and the acquired data are not matched in spatial resolution and time resolution, so that before the surface roughness provided by the SMAP data is used to correct the surface reflectivity of the satellite-borne GNSS-R data, the surface roughness provided by the SMAP data needs to be subjected to scale alignment, which results in insufficient precision of the surface reflectivity of the corrected satellite-borne GNSS-R data.
The method provided by the embodiment of the application breaks through the limitation of the prior art, and is based on the fact that in GNSS-R observation, the earth surface reflectivity is regarded as a mean square slope, so that the earth surface roughness MSS is obtained by calculating the mean square slope by using earth surface elevation data, namely DEM data. Because the DEM data has the characteristics of high precision, high resolution and easy acquisition, the calculation result can be better matched with the space-borne GNSS-R data in spatial resolution and time resolution, so that the processing precision of the subsequent steps is improved.
Step S102, correcting the cross section parameters of the bistatic radar based on a Fresnel reflection equation according to the surface roughness and vegetation optical thickness data so as to calculate the surface reflectivity according to the corrected bistatic radar cross section parameters; the bistatic radar section parameters are extracted from the satellite-borne GNSS-R data.
In the embodiment of the application, NBRCS parameters are firstly extracted from satellite-borne GNSS-R data, then the NBRCS parameters are corrected by using the surface roughness calculated in the previous steps, and the surface reflectivity is calculated according to the corrected NBRCS parameters. After the earth surface reflectivity is corrected by the earth surface attribute, the accuracy can meet the requirement of monitoring the flooding range of the flood disaster.
It should be noted that, in the conventional earth surface reflectivity is calculated based on the assumption that earth surface reflection is coherent reflection, however, in practical application, earth surface is not coherent reflection but incoherent reflection in most cases, for this purpose, the embodiment uses the NBRCS parameter extracted from the satellite-borne GNSS-R data to characterize incoherent reflection characteristics of the satellite-borne GNSS-R satellite signal on the earth surface, and corrects the NBRCS parameter based on the fresnel reflection equation and the transmission process of the satellite-borne GNSS-R data on the earth surface according to earth surface roughness and vegetation optical thickness data to calculate earth surface reflectivity SR, thereby further improving accuracy of the calculated earth surface reflectivity SR.
In some embodiments, the calculation formula for correcting the bistatic radar cross-section parameters is as follows:
in the method, in the process of the invention,SRrepresenting the reflectivity of the earth's surface,σrepresenting the cross-sectional parameters of the bistatic radar,τrepresents the optical thickness data of vegetation,MSSrepresents the roughness of the earth's surface,θrepresenting the angle of incidence in the on-board GNSS-R data.
And step S103, constructing a soil moisture fitting model based on the earth surface reflectivity and the SMAP soil moisture data so as to calculate and obtain satellite-borne GNSS-R soil moisture data.
In some embodiments, the soil moisture fitting model is constructed by using a linear regression method based on the correlation between the surface reflectivity and the SMAP soil moisture data, and specifically, the soil moisture fitting model is:
in the method, in the process of the invention,SM GNSS-R representing the calculated on-board GNSS-R soil moisture data,SRand a and b are empirical parameters obtained by fitting the earth surface reflectivity of the historical satellite-borne GNSS-R with SMAP soil moisture data.
In the embodiment of the application, the correlation degree between the corrected satellite-borne GNSS-R earth surface reflectivity and SMAP soil moisture data is analyzed, and a linear regression method is adopted to establish soil moisture fitting models of different pixels for each pixel so as to solve the satellite-borne GNSS-R soil moisture data. The a and the b are experience parameters, which can be obtained by fitting according to historical data, for example, the satellite-borne GNSS-R earth surface reflectivity in 2018 and SMAP soil moisture data can be used for fitting the experience parameters.
And step S104, calculating a flooding monitoring index according to the satellite-borne GNSS-R soil moisture data and the field water holding capacity data.
Based on analysis of the time sequence change of the satellite-borne GNSS-R and the relation between the GNSS-R and the submerged state, the embodiment of the application provides a new submerged monitoring index, namely GSSII, which is calculated according to the soil moisture data of the satellite-borne GNSS-R in a long time sequence and by combining the field water-holding capacity data, so that the flood submerged condition of each pixel in a monitoring range can be timely and accurately reflected, the time-varying range of the flood submerged condition can be quantized, and scientific basis is provided for the prediction of environmental influence.
In some embodiments, for at timetThe calculated formula of the inundation monitoring index (namely GSFII index retrieval model) of the obtained satellite-borne GNSS-R soil moisture data is as follows:
in the method, in the process of the invention,GSSIIin order to flood the monitoring index,SM t is thattThe value of the soil moisture data of the time satellite-borne GNSS-R,SM min for the minimum value of the satellite-borne GNSS-R soil moisture data within the preset time range,SM FC is the field water holding capacity data. The preset time range may be any time period, and in order to improve the real-time performance of prediction, a time range with a duration less than 1 year, such as a half month, a quarter, etc. is preferable.
Formula (3) adopts satellite-borne GNSS-R soil moisture data to calculate a flooding monitoring index, and because the satellite-borne GNSS-R soil moisture data is obtained by performing linear fitting on SMAP soil moisture data, the maximum value of the satellite-borne GNSS-R soil moisture data can be influenced by the SMAP soil moisture data.
Specifically, in some embodiments, the method for calculating the field water holding capacity data is as follows:
in the method, in the process of the invention,
represents the soil moisture at a pressure of 1500kpa,Sis the proportion of the sand stone to be occupied,Cthe proportion of the clay is given by weight,OMthe ratio of the organic matters is calculated.
Step 105, matching the flooding monitoring index with the pre-acquired flood data to obtain a flood threshold of the flooding monitoring index, so as to determine the flooding range of the flooding disaster according to the flood threshold.
It should be appreciated that the inundation monitoring index is calculated based on the on-board GNSS-R soil moisture data, and therefore inherits the characteristics of the on-board GNSS-R data in terms of spatial distribution, while the on-board GNSS-R data is represented as discrete punctual data, and therefore, the inundation monitoring index is also discrete points in space, which may be pixel points in grid format or punctual data in vector format. For convenience of subsequent operations, taking the inundation monitoring index as raster data for example, it will be understood that the raster data of the inundation monitoring index is essentially an image with the same spatial resolution as that of the satellite-borne GNSS-R data, and the value of each pixel is the inundation monitoring index value of the pixel.
In order to obtain the flooding degree (also referred to as a flooding level) corresponding to the flooding monitoring index on each discrete pixel point, the flooding monitoring index needs to be associated with the severity of the flooding disaster. The flooding degree represents the percentage of flooding in the pixel point, and can be specifically divided into: non-flooding, mild flooding, moderate flooding, and severe flooding. And finally, determining the flooding range of the flooding disaster according to the flooding grade of the flooding monitoring index on each pixel point.
In order to increase accuracy of flood threshold determination, flood data obtained in advance in the embodiment of the present application is VIIRS daily synthesized flood percentage data with a spatial resolution of 375 meters, which is abbreviated as FF (Floodwater Fraction). And the VIIRS daily synthesized flood percentage data is used as a reference of a flood threshold, and a large number of high-quality reference pixel points can be obtained by utilizing the time resolution ratio of the flood percentage data, so that the flood threshold of the inundation monitoring index is more accurate.
In order to match the flooding monitoring index obtained by the satellite-borne GNSS-R calculation with the VIIRS flood data, further, in some embodiments, the flooding monitoring index is matched with the flood data acquired in advance to obtain a flood threshold of the flooding monitoring index, which specifically includes: determining a search area of flood data, and taking all pixel points in an image corresponding to the inundation monitoring index as reference pixel points for determining a flood threshold value; calculating the distance between all pixel points in the flood data in the search area and the target pixel point, and taking the pixel point corresponding to the minimum distance in all pixel points in the flood data as a matching point of the target pixel point; wherein the target pixel point is any reference pixel point; determining a flood level corresponding to the flooding monitoring index on the target pixel point according to a threshold value corresponding to the flood data on the matching point; and (3) calculating flood grades corresponding to the inundation monitoring indexes on all the reference pixel points to determine a flood threshold value of the inundation monitoring indexes.
Based on the foregoing, it can be seen that, at each time point, the flooding monitoring index corresponds to one image, images at different time points can be selected from the time series data, and all pixels in the images are used as reference pixels for determining the flooding threshold.
Then, all the reference pixel points are traversed to determine the pixel points with which each reference pixel point matches in the flood data. Specifically, for any reference pixel point (also referred to as a target pixel point), a search area of the target pixel point in flood data is determined, for example, an area with the target pixel point as a circle center and a radius of 500 meters is determined, then distances between all pixel points of the flood data in the search area and the target pixel point are calculated, and a flood data pixel point corresponding to the minimum distance is used as a matching point of the target pixel point. It will be appreciated that there are two corresponding values at the matching point location, namely the inundation monitoring index and the daily composite flood percentage of VIIRS, FF. With the matching points, the flooding percentage corresponding to the flooding monitoring index on the target pixel point can be determined according to the FFs on the matching points, so that the flooding grade of the target pixel point is determined. And finally, calculating flood grades corresponding to the flood monitoring indexes on all the reference pixel points, and determining the threshold value of the flood monitoring index corresponding to the flood grade.
Illustratively, fig. 2 shows a process of flood data to inundation monitoring index matching. As shown in fig. 2, A, B, C, D, E represents reference pixel points in discrete distribution, namely, discrete points of the satellite-borne GNSS-R data, the pixel values of which are inundation monitoring index values, and the pixel points of the VIIRS flood data are represented by grids, so that in order to determine the flood level corresponding to the inundation monitoring index at each A, B, C, D, E point, each point A, B, C, D, E needs to be traversed, and the matching points of each point and each pixel in the VIIRS flood data need to be searched. Taking the point B as an example, the corresponding search area is a circular area with the point B as a circle center and the radius of 500 meters, then calculating the distance between the center point of the pixel point of flood data in the search area and the point B one by one, and taking the pixel point with the smallest distance as a matching point of the point B, thereby determining the flood grade to which the point B belongs.
It should be noted that, the range of the value change of the daily synthesized flood percentage of the VIIRS is 0-100%, and the four flood grades, namely, the flood percentage value ranges corresponding to non-flooding, light flooding, moderate flooding and serious flooding are as follows: and if the sum of the flooding monitoring indexes is 0-30%, 30-50%, 50-90% and more than 90%, determining the flood grade of each pixel by the value of the flooding monitoring index through the matching process.
In practice, the VIIRS flood data coverage is not comprehensive, that is, global coverage is not formed, so that some reference pixel points may not find matching points in the search area, and therefore, in some embodiments, the method further includes: if no matching point corresponding to the target pixel point is found in the search area, discarding the target pixel point, and not taking the target pixel point as a reference pixel point for determining a flood threshold for submerging the monitoring index.
That is, if the reference pixel point is in the search area, that is, the radius is 500 meters, the matching point with the VIIRS flood data cannot be found, the reference pixel point is not used as the reference pixel point for determining the flood threshold value, so that the calculation resource is saved, and the stability of the calculation result is improved.
According to the method provided by the embodiment of the application, the inundation monitoring index obtained by calculation of the satellite-borne GNSS-R data can be used for obtaining the inundation range of the flood disasters in time and with high resolution. In order to verify the accuracy and precision of the method, the flood disaster range obtained by the satellite-borne GNSS-R data is compared with the SMAP bright temperature data, and the bright temperature data and the rainfall have negative correlation, so that the consistency degree of the processing result and the real situation of the method can be verified by comparing the bright temperature data with the rainfall. The specific comparison result is shown in fig. 3, and as can be seen from fig. 3, the flooding range of the flood disaster obtained by the method is highly consistent with the SMAP bright temperature data, so that the method can intuitively obtain the high-resolution and accurate flooding range of the flood disaster.
In summary, in the technical solution provided in the embodiments of the present application, the surface roughness is calculated by the surface elevation Data (DEM), and then the surface roughness and the vegetation optical thickness data are utilized to correct the bistatic radar section parameters extracted from the satellite-borne GNSS-R data, and calculate the surface reflectivity according to the corrected NBRCS; then constructing a soil moisture fitting model based on the earth surface reflectivity and SMAP soil moisture data to calculate and obtain satellite-borne GNSS-R soil moisture data; according to the satellite-borne GNSS-R soil moisture data and the field water holding capacity data, calculating a flooding monitoring index; and matching the flooding monitoring index with the pre-acquired flood data to determine a flood threshold of the flooding monitoring index, and finally determining the flooding range of the flooding disaster according to the flood threshold. In the process, as the surface roughness is calculated by using DEM data, the spatial resolution and the time resolution of the surface roughness can be well matched with satellite-borne GNSS-R data, the surface roughness is used for correcting NBRCS, the calculated surface reflectivity is obtained on the basis of incoherent assumption, and the precision of the surface reflectivity is improved; by introducing field water-holding capacity data and combining satellite-borne GNSS-R soil moisture data, the problem that the actual situation is not consistent due to the fact that the field moisture change is ignored in the past of the calculation of the flooding monitoring index is avoided, and the precision of monitoring the flooding range of the flooding disaster is further improved.
In the present application, the retrieval of daily flood inundation dynamic range is achieved with measurement data of the on-board GNSS-R. Using the flooding state of the GNSS-R reflection signal data in the time periods of month, quarter and the like, constructing a monitoring index (Season flood inundation index, GSFII) of the flood flooding scope on the space and time scale, and applying the index to large-scale flood disaster flooding scope distribution monitoring.
According to the method, the satellite signals are subjected to system correction by constructing a flood disaster inundation range monitoring method of the satellite-borne GNSS-R data, so that the extraction accuracy of the satellite-borne GNSS-R reflectivity is optimized; and then, establishing a GSFII index retrieval model according to the relation between the time sequence of the satellite-borne GNSS-R reflectivity and the state of the quarter flood inundation range, and obtaining the distribution condition of the whole flood disaster inundation range of the research area.
Exemplary System
The embodiment of the application provides a flood disaster inundation range monitoring system utilizing satellite-borne GNSS-R data, as shown in fig. 4, the system comprises:
the first calculation unit 401 is configured to calculate the surface roughness using the surface elevation data.
A correction unit 402 configured to correct the bistatic radar section parameters based on fresnel reflection equations according to the surface roughness and vegetation optical thickness data, to calculate a surface reflectivity according to the corrected bistatic radar section parameters; the bistatic radar section parameters are extracted from the satellite-borne GNSS-R data.
And a fitting unit 403, configured to construct a soil moisture fitting model based on the surface reflectivity and the SMAP soil moisture data, so as to calculate and obtain satellite-borne GNSS-R soil moisture data.
A second calculation unit 404 is configured to calculate a inundation monitoring index from the on-board GNSS-R soil moisture data and the field water holding capacity data.
And the matching unit 405 is configured to match the flooding monitoring index with pre-acquired flood data to obtain a flood threshold of the flooding monitoring index, so as to determine a flooding range of the flooding disaster according to the flood threshold.
In some embodiments, the inundation monitoring index is calculated as follows:
in the method, in the process of the invention,GSSIIin order to flood the monitoring index,SM t is thattThe value of the soil moisture data of the time satellite-borne GNSS-R,SM min for the minimum value of the satellite-borne GNSS-R soil moisture data within the preset time range,SM FC is the field water holding capacity data.
In some embodiments, the calculation formula for correcting the bistatic radar cross-section parameters is as follows:
in the method, in the process of the invention,SRrepresenting the reflectivity of the earth's surface,σrepresenting the parameters of the cross section of the bistatic radar,τrepresents the optical thickness data of vegetation,MSSrepresents the roughness of the earth's surface,θrepresenting the angle of incidence in the on-board GNSS-R data.
The flood disaster inundation range monitoring system using the satellite-borne GNSS-R data provided by the embodiment of the application can realize the flow and the steps of the flood disaster inundation range monitoring method using the satellite-borne GNSS-R data provided by any embodiment, and achieve the same technical effects, and are not described in detail herein.
The foregoing description is only of the preferred embodiments of the present application and is not intended to limit the same, but rather, various modifications and variations may be made by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.
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