CN112800379B - MODIS remote sensing snow information processing method and device - Google Patents
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Abstract
The invention discloses a processing method and a device for MODIS remote sensing accumulated snow information, wherein a forecast watershed is divided into a plurality of natural watershed units, the height Cheng Dai is divided, and the natural watershed units and Gao Chengdai are overlapped to obtain a calculation unit of the forecast watershed; overlapping the MODIS inversion snow depth information with the computing units in space to obtain snow depth information of grids corresponding to each computing unit in the forecast flow; performing interpolation processing on the snow depth information in the calculation units with the grid proportion of invalid snow depth information exceeding a set threshold value, and calculating the average snow depth of snow accumulation areas in all the calculation units after the processing; and carrying out time scale interpolation calculation on the average snow depth of the snow area in the calculation unit to obtain daily snow depth information of the snow area of the calculation unit. The invention solves the defects that the existing MODIS snow accumulation product has partial invalid information and is excessively large in time scale and difficult to meet the hydrologic forecasting requirement, and is beneficial to improving the forecasting precision of the snow-melting runoff.
Description
Technical Field
The invention relates to the technical field of hydrologic forecasting, in particular to a method and a device for processing MODIS remote sensing snow information.
Background
Most of river water sources in northwest areas of China are snow-melting water or mixed water of snow-melting water and precipitation, wherein snow melting is a main source of river water, so that hydrologic forecasting in northwest areas is carried out, and the space-time distribution situation of snow in the forecasting flow area must be mastered. The main way to obtain the snow information is manual/automatic observation and satellite remote sensing information inversion: because of the limitation of observation, snow observation information is point information, and the spatial distribution of snow in the whole river basin is difficult to obtain; with further improvement of snow inversion algorithm and application of satellite remote sensing products with higher resolution, inversion based on satellite remote sensing has become an important acquisition means of snow information.
Satellite remote sensing information for snow inversion is mainly divided into two types, namely passive microwave remote sensing and visible light-infrared remote sensing: the passive microwave sensor is mainly SMMR of Nimbus-7, SMM/I of DMSP series, SSMI/S of AMSR-E, DMSP series on Aqua, the spatial resolution is usually 20-70km, and the time resolution is 1 day; visible light-infrared remote sensing is represented by a medium resolution imaging spectrometer MODIS carried on two satellites, namely Terra and Aqua, the spatial resolution is 250m-500m, and the time resolution is 1 day or 8/10 day for synthesis. Because the spatial resolution of passive microwave remote sensing is thicker (more than 25 km), the spatial distribution of snow in a flow area is difficult to be described, and the MODIS remote sensing inversion snow accumulation information has fine spatial resolution (250 m or 500 m), the snow melting runoff prediction is used as a snow accumulation information input source by using the MODIS remote sensing inversion information. The MODIS remote sensing inversion snow information used in hydrologic forecasting is snow coverage rate, snow depth information is not contained, invalid information exists, and although the snow coverage rate is higher, the visible light-infrared remote sensing is affected by cloud layers, the average snow recognition rate is only 17.8% in cloudy or cloudy days, and the available snow information is thicker in time resolution and is usually synthesized in 8/10 days. However, the time scale of hydrologic forecast mainly using snow-melting runoff is usually 1 day, so when using MODIS remote sensing snow information, the time scale needs to be processed. At present, a method for processing snow cover information on a time scale mainly comprises linear interpolation, but in the seasons of unstable snow cover in spring and autumn, the snow cover condition changes frequently along with time, and the daily snow cover information obtained by calculating the snow cover product synthesized on 8/10 days in a linear interpolation mode has a larger difference from the actual snow cover information, so that the accuracy of prediction of snow melting runoff is affected.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides a method and a device for processing MODIS remote sensing snow information, which solve the problems that the existing MODIS remote sensing snow information has no snow depth data and invalid information, and the time scale is large, so that the requirement of hydrologic forecasting is difficult to meet.
In order to achieve the above object, the present invention adopts the following technical scheme: a processing method of MODIS remote sensing snow information comprises the following steps:
dividing the forecast basin into a plurality of natural basin units based on the digital elevation model data, dividing the height Cheng Dai, and superposing the natural basin units and Gao Chengdai to obtain a calculation unit of the forecast basin;
obtaining MODIS inversion snow depth information, and overlapping the MODIS inversion snow depth information with the computing units in space to obtain snow depth information of grids corresponding to each computing unit in a forecast flow field;
judging the effectiveness of the snow depth information, performing interpolation processing on the snow depth information in the computing units with the grid proportion of invalid snow depth information exceeding a set threshold value, and calculating the average snow depth of snow accumulation areas in all the computing units after the processing;
and carrying out time scale interpolation calculation on the average snow depth of the snow area in the calculation unit to obtain daily snow depth information of the snow area of the calculation unit.
Further, the determining the validity of the snow depth information, and performing interpolation processing on the snow depth information in the computing unit having the mesh proportion of the invalid snow depth information exceeding the set threshold value, includes:
traversing snow depth information in each computing unit in the form of grid data one by one, judging and marking the effectiveness of the snow depth data of each grid in the computing unit, counting the proportion of the grid number of the ineffective snow depth information in the computing unit, and defining the computing unit as a to-be-processed computing unit if the grid number of the ineffective snow depth information is greater than a threshold value set by the total grid, otherwise, as a normal computing unit;
and interpolating invalid snow data in the unit to be processed by combining the terrain and the historical contemporaneous snow coverage information.
Furthermore, the interpolation of invalid snow data in the unit to be processed is performed by combining the terrain and the historical contemporaneous snow coverage information, and the method comprises the following steps:
according to the topography of each grid of the unit to be processed and historical snow information, establishing a functional relation between the elevation, gradient and snow distribution in the unit: h=a 1 ×(Z-Z b )+a 2 ×(S-S b ) +b, wherein H is the grid snow depth; z is the elevation of the grid, Z b Is a reference elevation; s is the gradient of the grid, S b As a reference gradient, a 1 ，a 2 B is a constant in the function, respectively,
substituting the gradient and the elevation of the grids with invalid snow depth information in the unit to be processed into a functional relation, and calculating to obtain the snow depth of each grid of the unit to be processed.
Further, the calculating the average snow depth of the snow accumulation area in the processed calculating unit includes:
and calculating the total snow depth of grids with snow depth larger than 0 in the calculating unit, and dividing the total snow depth by the total number of grids with snow depth larger than 0 to obtain the average snow depth in the snow accumulation area in the calculating unit.
Further, the snow depth information of the snow area day by day in the calculating unit includes:
the nth snow depth H of a certain grid in the calculating unit n The calculation formula is as follows: h n ＝H n-1 -H m，n +H s，n Wherein H is n-1 For the grid snow depth of the n-1 th day of snow information day, H m，n Depth of snow melt on nth day, H s，n When the temperature on the nth day exceeds the critical temperature, H is the snowfall depth on the nth day s，n 0, if the temperature on the nth day is lower than the critical temperature, H m，n Is 0;
wherein C is s Is the snow-melting runoff coefficient; alpha is a holiday factor; delta T is an air temperature adjustment value generated by the computing unit and the reference weather station due to different elevations; t (T) n The measured air temperature is the nth day; s is S n For snow coverage rate ρ w 、ρ s The densities of water and snow are respectively;
and calculating the snow depth of the nth day of all grids in the calculating unit, further calculating to obtain the snow depth information of the snow area of the nth day in the calculating unit, and further obtaining the snow depth information of the snow area of the calculating unit day by day.
Further, if the snow depth H of the previous day n-1 Snow melt depth H less than day n m，n The grid snow depth H n Is 0.
A processing device for MODIS remote sensing snow information, comprising:
the computing unit acquisition module is used for dividing the forecast watershed into a plurality of natural watershed units based on the digital elevation model data, dividing the height Cheng Dai, and superposing the natural watershed units and Gao Chengdai to obtain a computing unit of the forecast watershed;
the snow depth information acquisition and interpolation module is used for acquiring MODIS inversion snow depth information, and overlapping the MODIS inversion snow depth information with the computing units in space to obtain snow depth information of grids corresponding to each computing unit in the forecast flow; judging the effectiveness of the snow depth information, performing interpolation processing on the snow depth information in the computing units with the grid proportion of invalid snow depth information exceeding a set threshold value, and calculating the average snow depth of snow accumulation areas in all the computing units after the processing;
and the daily snow depth information acquisition module is used for carrying out time scale interpolation calculation on the average snow depth of the snow accumulation area in the calculation unit to obtain daily snow depth information of the snow accumulation area of the calculation unit.
Further, the determining the validity of the snow depth information, and performing interpolation processing on the snow depth information in the computing unit having the mesh proportion of the invalid snow depth information exceeding the set threshold value, includes:
traversing snow depth information in each computing unit in the form of grid data one by one, judging and marking the effectiveness of the snow depth data of each grid in the computing unit, counting the proportion of the grid number of the ineffective snow depth information in the computing unit, and defining the computing unit as a to-be-processed computing unit if the grid number of the ineffective snow depth information is greater than a threshold value set by the total grid, otherwise, as a normal computing unit;
and interpolating invalid snow data in the unit to be processed by combining the terrain and the historical contemporaneous snow coverage information.
Furthermore, the interpolation of invalid snow data in the unit to be processed is performed by combining the terrain and the historical contemporaneous snow coverage information, and the method comprises the following steps:
according to the topography of each grid of the unit to be processed and historical snow information, establishing a functional relation between the elevation, gradient and snow distribution in the unit: h=a 1 ×(Z-Z b )+a 2 ×(S-S b )+b，Wherein H is the depth of grid snow; z is the elevation of the grid, Z b Is a reference elevation; s is the gradient of the grid, S b As a reference gradient, a 1 ，a 2 B is a constant in the function, respectively,
substituting the gradient and the elevation of the grids with invalid snow depth information in the unit to be processed into a functional relation, and calculating to obtain the snow depth of each grid of the unit to be processed;
the average snow depth of the snow accumulation area in the calculation unit after calculation processing comprises the following steps:
and calculating the total snow depth of grids with snow depth larger than 0 in the calculating unit, and dividing the total snow depth by the total number of grids with snow depth larger than 0 to obtain the average snow depth in the snow accumulation area in the calculating unit.
Further, the snow depth information of the snow area day by day in the calculating unit includes:
the nth snow depth H of a certain grid in the calculating unit n The calculation formula is as follows: h n ＝H n-1 -H m，n +H s，n Wherein H is n-1 For the grid snow depth of the n-1 th day of snow information day, H m，n Depth of snow melt on nth day, H s，n When the temperature on the nth day exceeds the critical temperature, H is the snowfall depth on the nth day s，n 0, if the temperature on the nth day is lower than the critical temperature, H m，n Is 0;
wherein C is s Is the snow-melting runoff coefficient; alpha is a holiday factor; delta T is an air temperature adjustment value generated by the computing unit and the reference weather station due to different elevations; t (T) n The measured air temperature is the nth day; s is S n For snow coverage rate ρ w 、ρ s The densities of water and snow are respectively;
and calculating the snow depth of the nth day of all grids in the calculating unit, further calculating to obtain the snow depth information of the snow area of the nth day in the calculating unit, and further obtaining the snow depth information of the snow area of the calculating unit day by day.
The invention has the beneficial effects that: according to the invention, the river basin to be forecasted is divided into a plurality of natural river basin units through DEM data, and a proper hydrologic forecast calculation unit is constructed to be matched with the spatial scale of MODIS remote sensing snow information; performing validity judgment on the remote sensing snow information, and interpolating aiming at invalid information to obtain final calculation unit snow information; based on actually measured snow information and snow melting calculation, remote sensing snow information is processed into time scale data meeting hydrologic forecasting requirements, and a reliable data source is provided for accurate snow melting runoff forecasting.
Drawings
Fig. 1 is a flowchart of a method for processing MODIS remote sensing snow information in an embodiment of the invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present invention, and are not intended to limit the scope of the present invention.
Example 1:
as shown in fig. 1, a method for processing MODIS remote sensing snow information includes the steps of:
step 1, dividing a forecast watershed into a plurality of natural watershed units based on Digital Elevation Model (DEM) data, and carrying out high Cheng Dai division, and overlapping the natural watershed units and Gao Chengdai to obtain a calculation unit of the forecast watershed;
based on Digital Elevation Model (DEM) data, digital terrain information is extracted in a forecast watershed range, and the forecast watershed is divided into a plurality of natural watershed units. Meanwhile, based on DEM data, spatial analysis tool bars of GIS software are adopted to conduct high Cheng Dai division, and finally natural drainage basin units are overlapped with Gao Chengdai to form a calculation unit for forecasting the drainage basin, and the calculation unit is used as a basic unit for hydrologic forecasting.
Based on Digital Elevation Model (DEM) data, a hydrologic tool bag of GIS software is adopted to carry out depression filling processing, calculate water flow direction and converging grids, define a river channel and the like, on the basis, a forecast drainage basin range converging into the water outlet is obtained through defining the water outlet position of the forecast drainage basin, digital terrain information is extracted in the forecast drainage basin range, and the forecast drainage basin is divided into a plurality of natural drainage basin units.
Because the natural river basin unit is divided based on the DEM, river channels and watersheds of the river basin are combined, factors of snow-melting runoffs such as the negative/positive slope direction, the terrain and the like are distinguished, and the elevation is not combined for distinguishing.
In the hydrologic forecast combined with snow melting, the temperature influences the temperature to a great extent, so that the natural river basin units directly divided by GIS software cannot meet the hydrologic forecast snow melting calculation requirement without considering elevation information. Therefore, the invention adopts the spatial analysis tool bar of the GIS to carry out the high Cheng Dai division based on the DEM data, constructs the equal-altitude zone of the forecast river basin, the natural river basin is generally divided into more than 4 altitude zones, and the altitude range of each altitude zone is not more than 500m. When the height Cheng Chaxiao of DEM data in the natural flow field is 2000m, the DEM data are equally divided into 4 heights Cheng Dai according to the height difference; if greater than 2000m, every 500m is divided into one height Cheng Dai.
Based on the forecast watershed construction of the natural watershed unit and the elevation zone, the natural watershed unit and the elevation zone are overlapped by adopting the overlapping analysis function of GIS software, so that a plurality of intersections of the natural watershed unit and the elevation zone are formed, and the intersections are the calculation units of the forecast watershed. The calculation unit integrates the topographic features such as elevation, slope direction, watershed and the like, effectively considers the feature information influencing snow melting, and lays a data foundation for hydrologic forecast calculation.
Step 2, obtaining MODIS inversion snow depth information; and overlapping the MODIS inversion snow depth information with the computing units in space to obtain the snow depth information of all grids corresponding to each computing unit in the forecast flow domain.
The MODIS snow depth remote sensing monitoring system developed by Fu Hua and the like is adopted, spectral characteristics of different covers on the ground surface are collected, influence factors of snow depth of seasons, terrains, underlying surfaces and the like are combined, the snow depth is observed by utilizing MODIS hyperspectrum, multiband reflectivity and a measuring station, a mathematical statistics method is adopted, and a MODIS snow depth regression model is established, so that MODIS inversion snow depth information of spatial distribution is obtained on the basis of calculating snow area and coverage rate.
The MODIS inversion snow depth information is grid data with 500m resolution, and is synthesized on sunny days and 8/10 days due to the influence of cloud layers, and is grid information with geographic coordinates, the grid snow depth data inverted by the MODIS are overlapped with the computing units, so that the corresponding relation between each computing unit and the snow depth grid is obtained, and the snow depth spatial distribution of each computing unit is obtained.
Step 3, judging the effectiveness of the snow depth information of the corresponding grids of each computing unit in the forecast flow, performing interpolation processing on the snow depth information in the computing units with the grid proportion exceeding the set threshold value of the ineffective snow depth information, and calculating the average snow depth of the snow accumulation areas in all the computing units after the processing;
traversing snow depth information in each computing unit in the form of grid data one by one, judging and marking the effectiveness of the snow depth data of each grid in the computing unit, and counting the duty ratio of the grid number of invalid snow depth information in the computing unit; if the grid number of the invalid snow depth information is greater than 1/5 of the total grid, the calculation unit is defined as a to-be-processed calculation unit, otherwise, the calculation unit is a normal calculation unit.
Each calculation unit in the flow domain comprises a plurality of grid snow depth information, the snow depth data are expressed in cm, when the MODIS inversion snow depth information is obtained, a certain range of snow depth is set as a normal value during the inversion of the MODIS snow depth information, and the conventional definition is that: 0-49 represents the actual snow depth, 250-252 represents the body of water, 253, 254 represent the cloud cover, and the rest are all ineffective. In the invention, cloud layers and undefined data are marked as invalid data, namely 0-49 and 250-252 are valid numbers, and the snow depth of 250-252 is 0; the remaining data are invalid numbers.
For a unit to be processed, the snow data of invalid grids in the unit are interpolated by combining the terrain and historical contemporaneous snow coverage information:
and establishing a functional relation between the elevation, gradient and snow distribution in the unit according to the topography of each grid of the unit to be processed and the historical snow information. The relation is as follows: h=a 1 ×(Z-Z b )+a 2 ×(S-S b ) +b, wherein H is the grid snow depth; z is the elevation of the grid, Z b Is a reference elevation; s is the gradient of the grid，S b The reference elevation and the reference gradient are the minimum elevation and the minimum gradient of the unit, a 1 ，a 2 B, respectively constants in functions, and obtaining the snow information in advance through the terrain of the effective grid in the unit to be processed; .
Substituting the gradient and the elevation of the invalid grids in the unit to be processed into a functional relation, and calculating to obtain the snow depth of each grid of the unit to be processed. And after the calculation one by one, the snow depth of all grids in the unit to be processed is effective information. For all calculation units in the forecasting river basin, calculating the average snow depth of the snow areas in the units one by one according to grid snow depth information: and calculating the total snow depth of all grids with snow depths greater than 0, and dividing the total snow depth by the total number of grids with snow depths greater than 0 to obtain the average snow depth in the snow area.
And 4, carrying out time scale interpolation calculation on the average snow depth of the snow area of the calculation unit based on the actual precipitation, the air temperature monitoring information and the snow melting prediction model, and obtaining the daily snow depth information of the snow area of the calculation unit.
The snow depth information inverted by MODIS needs to be combined with rainfall (rainfall comprises rainfall and snowfall), air temperature monitoring information and a snow melting prediction model to carry out time scale interpolation calculation, and average snow depth of a snow area of each calculation unit is processed into daily snow depth information.
The inverted snow depth is 1 data every 10 days except for a clear sky day, so that the time scale of the average snow depth of the snow area in the calculation unit is the same, the time scale of the snow depth data of the calculation unit cannot meet the requirement of hydrologic forecasting, and the data needs to be processed into daily snow depth information for meeting hydrologic forecasting of a snow melting area.
The snow depth information of the snow area day by day is calculated by a calculating unit, and the calculating process comprises the following steps:
1) Defining the day with snow information as the n-1 day, and calculating the snow depth of a certain grid in the unit as H n-1 Snow depth H on the nth day of the grid n The calculation formula is as follows: h n ＝H n-1 -H m，n +H s，n Wherein H is m，n Depth of snow melt on nth day, H s，n Is the snowfall depth on the nth day. Based on the common natural phenomenon, the snowfall and the snow melt are not usually present on the same day, thus H m，n 、H s，n 1 of them has a value of 0, and if the temperature on the nth day exceeds the critical temperature (generally 0 DEG), H s，n 0, snow is melted, and the snow depth is reduced; if the temperature on the nth day is lower than the critical temperature, H m，n And 0, the precipitation is snowfall, and is converted into snow depth through snow water equivalent, and the snow depth is increased without melting snow.
Depth of snow melt H on nth day m，n And calculating the snow melting amount through snow and water density conversion by using a snow melting runoff amount calculation formula in the snow melting prediction model:wherein C is s Is the snow-melting runoff coefficient; alpha is a degree day factor and represents the snow melting radial flow depth of unit time and temperature and unit cm/DEG C.d; delta T is an air temperature adjustment value generated by a calculation unit and a reference weather station due to different elevations, and is in units of DEG C d; t (T) n The measured air temperature is the nth day, and the unit is the temperature d; s is S n The snow coverage rate is the grid duty ratio of inversion snow depth information, wherein the snow depth is greater than 0; ρ w 、ρ s The density of water and snow are respectively in g/cm 3 。C s Alpha is calculated by adopting the prior art according to the information of the temperature, snow and runoff which are historically measured in the river basin. The calculated snow depth information can be checked through the actual measurement information of the manual/automatic snow observation station.
According to the calculated snow melting depth, the snow depth H of the day before each grid in the calculating unit is calculated n-1 Depth of snow melt H in the same day m，n Comparing; depth of snow accumulated in the previous day H n-1 Less than the depth H of snow melt m，n The grid snow depth H n At 0, then the average snow depth of the snow accumulation area in the calculation unit is recalculated.
And calculating the snow depth of the nth day of all grids in the calculating unit, and further calculating the snow depth information of the nth day snow area in the calculating unit.
2) And calculating the snow depth of the current unit n, n+1, n+ … … n+k daily day by day according to the formula until the next remote sensing snow information day, so as to obtain the snow depth information day by day in the complete calculation unit.
And calculating snow depth information of each calculation unit one by one to obtain daily snow depth information of each calculation unit in a forecast basin, and taking the snow information with the time scale of day and the space scale of the calculation unit as one of input information of hydrologic forecast for the forecast call of the snow-melting runoff to participate in hydrologic forecast calculation.
Example 2:
a processing device for MODIS remote sensing snow information, comprising:
the computing unit acquisition module is used for dividing the forecast watershed into a plurality of natural watershed units based on the digital elevation model data, dividing the height Cheng Dai, and superposing the natural watershed units and Gao Chengdai to obtain a computing unit of the forecast watershed;
the snow depth information acquisition and interpolation module is used for acquiring MODIS inversion snow depth information, and overlapping the MODIS inversion snow depth information with the computing units in space to obtain snow depth information of grids corresponding to each computing unit in the forecast flow; judging the effectiveness of the snow depth information, performing interpolation processing on the snow depth information in the computing units with the grid proportion of invalid snow depth information exceeding a set threshold value, and calculating the average snow depth of snow accumulation areas in all the computing units after the processing;
and the daily snow depth information acquisition module is used for carrying out time scale interpolation calculation on the average snow depth of the snow accumulation area in the calculation unit to obtain daily snow depth information of the snow accumulation area of the calculation unit.
Further, the determining the validity of the snow depth information, and performing interpolation processing on the snow depth information in the computing unit having the mesh proportion of the invalid snow depth information exceeding the set threshold value, includes:
traversing snow depth information in each computing unit in the form of grid data one by one, judging and marking the effectiveness of the snow depth data of each grid in the computing unit, counting the proportion of the grid number of the ineffective snow depth information in the computing unit, and defining the computing unit as a to-be-processed computing unit if the grid number of the ineffective snow depth information is greater than a threshold value set by the total grid, otherwise, as a normal computing unit;
and interpolating invalid snow data in the unit to be processed by combining the terrain and the historical contemporaneous snow coverage information.
Furthermore, the interpolation of invalid snow data in the unit to be processed is performed by combining the terrain and the historical contemporaneous snow coverage information, and the method comprises the following steps:
according to the topography of each grid of the unit to be processed and historical snow information, establishing a functional relation between the elevation, gradient and snow distribution in the unit: h=a 1 ×(Z-Z b )+a 2 ×(S-S b ) +b, wherein H is the grid snow depth; z is the elevation of the grid, Z b Is a reference elevation; s is the gradient of the grid, S b As a reference gradient, a 1 ，a 2 B is a constant in the function, respectively,
substituting the gradient and the elevation of the grids with invalid snow depth information in the unit to be processed into a functional relation, and calculating to obtain the snow depth of each grid of the unit to be processed;
the average snow depth of the snow accumulation area in the calculation unit after calculation processing comprises the following steps:
and calculating the total snow depth of grids with snow depth larger than 0 in the calculating unit, and dividing the total snow depth by the total number of grids with snow depth larger than 0 to obtain the average snow depth in the snow accumulation area in the calculating unit.
Further, the snow depth information of the snow area day by day in the calculating unit includes:
the nth snow depth H of a certain grid in the calculating unit n The calculation formula is as follows: h n ＝H n-1 -H m，n +H s，n Wherein H is n-1 For the grid snow depth of the n-1 th day of snow information day, H m，n Depth of snow melt on nth day, H s，n When the temperature on the nth day exceeds the critical temperature, H is the snowfall depth on the nth day s，n 0, if the temperature on the nth day is lower than the critical temperature, H m，n Is 0;
wherein C is s Is the snow-melting runoff coefficient; alpha is a holiday factor; delta T is an air temperature adjustment value generated by the computing unit and the reference weather station due to different elevations; t (T) n The measured air temperature is the nth day; s is S n For snow coverage rate ρ w 、ρ s The densities of water and snow are respectively;
and calculating the snow depth of the nth day of all grids in the calculating unit, further calculating to obtain the snow depth information of the snow area of the nth day in the calculating unit, and further obtaining the snow depth information of the snow area of the calculating unit day by day.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that modifications and variations could be made by those skilled in the art without departing from the technical principles of the present invention, and such modifications and variations should also be regarded as being within the scope of the invention.
Claims (8)
1. The method for processing the MODIS remote sensing snow information is characterized by comprising the following steps of:
dividing the forecast basin into a plurality of natural basin units based on the digital elevation model data, dividing the height Cheng Dai, and superposing the natural basin units and Gao Chengdai to obtain a calculation unit of the forecast basin;
obtaining MODIS inversion snow depth information, and overlapping the MODIS inversion snow depth information with the computing units in space to obtain snow depth information of grids corresponding to each computing unit in a forecast flow field;
judging the effectiveness of the snow depth information, performing interpolation processing on the snow depth information in the computing units with the grid proportion of invalid snow depth information exceeding a set threshold value, and calculating the average snow depth of snow accumulation areas in all the computing units after the processing;
performing time scale interpolation calculation on the average snow depth of the snow area in the calculation unit to obtain daily snow depth information of the snow area of the calculation unit;
the snow depth information of the snow area day by day in the calculation unit comprises:
the nth snow depth H of a certain grid in the calculating unit n The calculation formula is as follows: h n ＝H n-1 -H m,n +H s,n Wherein H is n-1 For the grid snow depth of the n-1 th day of snow information day, H m,n Depth of snow melt on nth day, H s,n When the temperature on the nth day exceeds the critical temperature, H is the snowfall depth on the nth day s,n 0, if the temperature on the nth day is lower than the critical temperature, H m,n Is 0;
wherein C is s Is the snow-melting runoff coefficient; alpha is a holiday factor; delta T is an air temperature adjustment value generated by the computing unit and the reference weather station due to different elevations; t (T) n The measured air temperature is the nth day; s is S n For snow coverage rate ρ w 、ρ s The densities of water and snow are respectively;
and calculating the snow depth of the nth day of all grids in the calculating unit, further calculating to obtain the snow depth information of the snow area of the nth day in the calculating unit, and further obtaining the snow depth information of the snow area of the calculating unit day by day.
2. The method for processing the remote sensing snow information of the MODIS according to claim 1, wherein the method comprises the following steps: the determining the validity of the snow depth information, and performing interpolation processing on the snow depth information in a computing unit with the grid proportion of invalid snow depth information exceeding a set threshold value, includes:
traversing snow depth information in each computing unit in the form of grid data one by one, judging and marking the effectiveness of the snow depth data of each grid in the computing unit, counting the proportion of the grid number of the ineffective snow depth information in the computing unit, and defining the computing unit as a to-be-processed computing unit if the grid number of the ineffective snow depth information is greater than a threshold value set by the total grid, otherwise, as a normal computing unit;
and interpolating invalid snow data in the unit to be processed by combining the terrain and the historical contemporaneous snow coverage information.
3. The method for processing the remote sensing snow information of the MODIS according to claim 2, wherein the method is characterized by comprising the following steps of: the method for interpolating invalid snow data in a unit to be processed by combining terrain and historical contemporaneous snow coverage information comprises the following steps:
according to the topography of each grid of the unit to be processed and historical snow information, establishing a functional relation between the elevation, gradient and snow distribution in the unit: h=a 1 ×(Z-Z b )+a 2 ×(S-S b ) +b, wherein H is the grid snow depth; z is the elevation of the grid, Z b Is a reference elevation; s is the gradient of the grid, S b As a reference gradient, a 1 ，a 2 B is a constant in the function, respectively,
substituting the gradient and the elevation of the grids with invalid snow depth information in the unit to be processed into a functional relation, and calculating to obtain the snow depth of each grid of the unit to be processed.
4. The method for processing the remote sensing snow information of the MODIS according to claim 1, wherein the method comprises the following steps: the average snow depth of the snow accumulation area in the calculation unit after calculation processing comprises the following steps:
and calculating the total snow depth of grids with snow depth larger than 0 in the calculating unit, and dividing the total snow depth by the total number of grids with snow depth larger than 0 to obtain the average snow depth in the snow accumulation area in the calculating unit.
5. The method for processing the remote sensing snow information of the MODIS according to claim 1, wherein the method comprises the following steps: depth of snow accumulated in the previous day H n-1 Snow melt depth H less than day n m,n The grid snow depth H n Is 0.
6. An apparatus for processing MODIS remote sensing snow information, which is characterized in that: comprising the following steps:
the computing unit acquisition module is used for dividing the forecast watershed into a plurality of natural watershed units based on the digital elevation model data, dividing the height Cheng Dai, and superposing the natural watershed units and Gao Chengdai to obtain a computing unit of the forecast watershed;
the snow depth information acquisition and interpolation module is used for acquiring MODIS inversion snow depth information, and overlapping the MODIS inversion snow depth information with the computing units in space to obtain snow depth information of grids corresponding to each computing unit in the forecast flow; judging the effectiveness of the snow depth information, performing interpolation processing on the snow depth information in the computing units with the grid proportion of invalid snow depth information exceeding a set threshold value, and calculating the average snow depth of snow accumulation areas in all the computing units after the processing;
the daily snow depth information acquisition module is used for carrying out time scale interpolation calculation on the average snow depth of the snow accumulation area in the calculation unit to obtain daily snow depth information of the snow accumulation area of the calculation unit;
the snow depth information of the snow area day by day in the calculation unit comprises:
the nth snow depth H of a certain grid in the calculating unit n The calculation formula is as follows: h n ＝H n-1 -H m,n +H s,n Wherein H is n-1 For the grid snow depth of the n-1 th day of snow information day, H m,n Depth of snow melt on nth day, H s,n When the temperature on the nth day exceeds the critical temperature, H is the snowfall depth on the nth day s,n 0, if the temperature on the nth day is lower than the critical temperature, H m,n Is 0;
wherein C is s Is the snow-melting runoff coefficient; alpha is a holiday factor; delta T is an air temperature adjustment value generated by the computing unit and the reference weather station due to different elevations; t (T) n The measured air temperature is the nth day; s is S n For snow coverage rate ρ w 、ρ s The densities of water and snow are respectively;
and calculating the snow depth of the nth day of all grids in the calculating unit, further calculating to obtain the snow depth information of the snow area of the nth day in the calculating unit, and further obtaining the snow depth information of the snow area of the calculating unit day by day.
7. The device for processing information of remote sensing snow on a vehicle according to claim 6, wherein: the determining the validity of the snow depth information, and performing interpolation processing on the snow depth information in a computing unit with the grid proportion of invalid snow depth information exceeding a set threshold value, includes:
traversing snow depth information in each computing unit in the form of grid data one by one, judging and marking the effectiveness of the snow depth data of each grid in the computing unit, counting the proportion of the grid number of the ineffective snow depth information in the computing unit, and defining the computing unit as a to-be-processed computing unit if the grid number of the ineffective snow depth information is greater than a threshold value set by the total grid, otherwise, as a normal computing unit;
and interpolating invalid snow data in the unit to be processed by combining the terrain and the historical contemporaneous snow coverage information.
8. The device for processing information of remote sensing snow on a vehicle according to claim 7, wherein: the method for interpolating invalid snow data in a unit to be processed by combining terrain and historical contemporaneous snow coverage information comprises the following steps:
according to the topography of each grid of the unit to be processed and historical snow information, establishing a functional relation between the elevation, gradient and snow distribution in the unit: h=a 1 ×(Z-Z b )+a 2 ×(S-S b ) +b, wherein H is the grid snow depth; z is the elevation of the grid, Z b Is a reference elevation; s is the gradient of the grid, S b As a reference gradient, a 1 ，a 2 B is a constant in the function, respectively,
substituting the gradient and the elevation of the grids with invalid snow depth information in the unit to be processed into a functional relation, and calculating to obtain the snow depth of each grid of the unit to be processed;
the average snow depth of the snow accumulation area in the calculation unit after calculation processing comprises the following steps:
and calculating the total snow depth of grids with snow depth larger than 0 in the calculating unit, and dividing the total snow depth by the total number of grids with snow depth larger than 0 to obtain the average snow depth in the snow accumulation area in the calculating unit.
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