Precipitation measuring method based on Luoga No.2 01 star

Abstract

The embodiment of the invention discloses a precipitation measurement method based on Luoma No.2 01 star, and relates to the technical field of precipitation measurement, and the method comprises the steps: obtaining earth observation data, collected by the Luoma No.2 01 star, of a to-be-observed region and actually measured precipitation data of a ground precipitation station, obtaining DN value distribution in a preset region with the ground precipitation station as the circle center, and fitting the actually measured rainfall data with the DN value distribution in the corresponding preset area to obtain a precipitation measurement model, and obtaining precipitation distribution of the to-be-observed area based on the precipitation measurement model. According to the method provided by the invention, the DN value of the to-be-observed area is selected for calculation, so that interference generated by echoes of a ground object during measurement through radar echoes in related technologies is avoided, and the precipitation measurement precision can be improved; the defect of lack of observation data in complex terrain areas and areas with sparse distribution of ground rainfall stations can be made up, and the efficiency and precision of precipitation measurement are improved.

Description

Technical Field

[0001] The present application relates to the technical field of precipitation measurement, and in particular to a precipitation measurement method based on the Luojia-2 01 satellite. Background Art

[0002] Precipitation is an extremely important physical quantity in the study of atmospheric, oceanic, hydrological and environmental sciences. Efficient and accurate acquisition of large-scale precipitation distribution is of great significance to weather forecasting, disaster prevention and mitigation, and industrial and agricultural production.

[0003] At present, the measurement of precipitation is divided into two types according to the location of the monitoring platform: land-based and space-based. Among them, the land-based monitoring method has defects such as low density of station network, uneven distribution of stations and limited coverage. For the uneven temporal and spatial distribution of precipitation in complex terrain areas, the representativeness of the monitoring data is poor, and it is impossible to accurately measure the distribution of precipitation in a large range. Satellite remote sensing, as a unique earth observation technology, is widely used in large-scale monitoring of precipitation, obtaining high-resolution, large spatial coverage, continuous observation, and near-real-time precipitation information, which can be used to make up for the shortcomings of land-based monitoring methods.

[0004] In the early days, satellite-borne remote sensing methods for precipitation measurement were mainly passive remote sensing, including visible light and infrared remote sensors of geostationary satellites and low-Earth orbit satellites, and a series of inversion algorithms were produced. However, these passive remote sensing technologies only indirectly invert near-ground precipitation from observed precipitation or upper information of clouds, and cannot penetrate thick clouds, cannot monitor all day and all weather, and cannot meet the needs of refined forecasts. The microwave band has good penetration and can supplement the information of visible light and infrared bands. Therefore, microwave radars carried on satellite constellations can greatly reduce the inversion error of near-ground precipitation and provide vertical distribution data of precipitation.

[0005] Although there are satellites dedicated to monitoring precipitation both at home and abroad, the number of rain-measuring satellites is still very limited due to the high cost of millimeter-wave precipitation radars, the difficulty in purchasing components, and the difficulty in developing precipitation measurement radars. In addition, the spatial resolution of current precipitation satellites is relatively low, making it difficult to meet the observation needs of both temporal resolution and spatial resolution.

[0006] Therefore, there is currently a lack of a method that can achieve efficient and accurate precipitation measurement over a large area. Summary of the invention

[0007] The embodiment of the present application provides a precipitation measurement method based on the Luojia-2 01 satellite to solve the defects of the above-mentioned related technologies. The technical solution is as follows:

[0008] In a first aspect, an embodiment of the present application provides a precipitation measurement method based on the Luojia-2 01 satellite, comprising:

[0009] Obtaining the earth observation data of the area to be observed collected by the Luojia-2 01 satellite, and obtaining the measured precipitation data of the area to be observed by multiple ground rain gauges;

[0010] Based on the earth observation data, respectively obtain the DN value distribution in a preset area with each of the ground rainfall stations as the center;

[0011] Fitting the measured precipitation data measured by each of the ground rain gauges with the DN value distribution in the corresponding preset area, determining the corresponding relationship between the DN value and the measured precipitation data, and obtaining a precipitation measurement model;

[0012] The DN value distribution in the area to be observed is input into the precipitation measurement model to obtain the precipitation distribution in the area to be observed.

[0013] In an optional solution of the first aspect, the earth observation data includes remote sensing image data in the area to be observed;

[0014] The obtaining of the earth observation data of the area to be observed collected by the Luojia-2 01 satellite, and the obtaining of the measured precipitation data of the area to be observed by multiple ground rain gauges, include:

[0015] Determining the geographical scope covered by the remote sensing image data in the earth observation data;

[0016] The measured precipitation data that falls within the geographical range and is in the same time period as the remote sensing image data is intercepted from the station data measured by each ground rain gauge station.

[0017] In an optional solution of the first aspect, obtaining measured precipitation data of multiple ground rain gauges for the area to be observed includes:

[0018] Selecting a plurality of data observation points in the remote sensing image data, and calculating the distance between each measured point of the plurality of ground rainfall stations and each of the data observation points;

[0019] Based on the distance, the influence weight of each measured point on the corresponding data observation point is calculated respectively;

[0020] Obtain estimated precipitation data of the corresponding data observation point by weighted calculation based on the impact weight;

[0021] The distribution of measured precipitation data of the area to be observed is obtained based on the estimated precipitation data of the multiple data observation points and the corresponding position coordinates.

[0022] In an optional solution of the first aspect, fitting the measured precipitation data measured by each of the ground rain gauges with the DN value distribution in the corresponding preset area, determining the corresponding relationship between the DN value and the measured precipitation data, and obtaining the precipitation measurement model includes:

[0023] The exponential formula for determining DN value and precipitation includes:

[0024]

[0025] Determine the coefficient term and the exponential term of the exponential form formula by fitting the measured precipitation data measured by each of the ground rain gauges with the DN value distribution in the corresponding preset area;

[0026] Obtaining the precipitation measurement model based on the determined coefficient term and the exponential term;

[0027] Wherein, P is the DN value; R is the precipitation, c1 is the coefficient term, and c2 is the exponential term.

[0028] In an optional solution of the first aspect, the obtaining, based on the earth observation data, the distribution of DN values ​​in a preset area with each of the ground rainfall stations as the center includes:

[0029] Respectively obtain the DN value of each pixel unit on the remote sensing image within a preset area with each of the ground rainfall stations as the center;

[0030] Calculate the average DN value of the DN value in each pixel unit in the preset area;

[0031] The step of fitting the measured precipitation data measured by each of the ground rain gauges with the DN value distribution in the corresponding preset area includes:

[0032] The measured precipitation data measured by each of the ground rain gauges are respectively fitted with the average DN value in the corresponding preset area.

[0033] In an optional solution of the first aspect, after fitting the measured precipitation data measured by each of the ground rain gauges with the DN value distribution in the corresponding preset area, determining the corresponding relationship between the DN value and the measured precipitation data, and obtaining the precipitation measurement model, the method further includes:

[0034] Obtain the estimated precipitation of each pixel unit on the remote sensing image data output by the precipitation measurement model, obtain the average of the estimated precipitation in the area to be observed, obtain the measured precipitation of each pixel unit on the remote sensing image data measured by a ground rain gauge station used to verify the precipitation measurement model, and obtain the average of the measured precipitation in the area to be observed;

[0035] Calculate the coefficient of determination and the root mean square error of the estimated precipitation mean relative to the measured precipitation mean, using the formula:

[0036]

[0037] If the determination coefficients obtained by multiple calculations of the precipitation measurement model by the ground rain gauge station used to verify the precipitation measurement model are all greater than the preset determination coefficient threshold, and the root mean square errors are all less than the preset root mean square error threshold, then the corresponding precipitation measurement model is output;

[0038] Among them, M i is the measured precipitation of each pixel unit, S i is the estimated precipitation for each pixel unit, is the mean of the measured precipitation, is the estimated precipitation mean, RMSE is the root mean square error, R 2 is the determination coefficient.

[0039] In an optional solution of the first aspect, inputting the DN value distribution in the area to be observed into the precipitation measurement model to obtain the precipitation distribution in the area to be observed includes:

[0040] Obtaining an estimated precipitation amount for each pixel unit on the remote sensing image, and obtaining a precipitation distribution image of the area to be observed based on the estimated precipitation amount for each pixel unit;

[0041] Determine each precipitation center and the precipitation area corresponding to each precipitation center in combination with the precipitation distribution image;

[0042] Each of the precipitation centers and each of the precipitation areas are marked in the precipitation distribution image.

[0043] In a second aspect, the embodiment of the present application further provides a precipitation measurement device based on the Luojia-2 01 satellite, comprising:

[0044] The data acquisition module is used to obtain the earth observation data of the area to be observed collected by the Luojia-2 01 satellite, and obtain the measured precipitation data of the area to be observed by multiple ground rain gauges;

[0045] A data analysis module, used for respectively obtaining the DN value distribution in a preset area with each of the ground rainfall stations as the center according to the ground observation data;

[0046] The data analysis module is also used to fit the measured precipitation data measured by each ground rain gauge station with the DN value distribution in the corresponding preset area, determine the corresponding relationship between the DN value and the measured precipitation data, and obtain a precipitation measurement model;

[0047] The measurement module is used to input the DN value distribution in the area to be observed into the precipitation measurement model to obtain the precipitation distribution in the area to be observed.

[0048] In a third aspect, an embodiment of the present application further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method provided in the first aspect of the embodiment of the present application or any one of the implementation methods of the first aspect is implemented.

[0049] In a fourth aspect, the present application also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method provided by the first aspect of the embodiment of the present application or any one of the implementations of the first aspect.

[0050] The beneficial effects brought about by the technical solutions provided by some embodiments of the present application include at least:

[0051] The embodiment of the present application provides a precipitation measurement method based on the Luojia-2 01 satellite. By selecting the DN value of the area to be observed for calculation, it avoids the interference of the echo of ground objects on precipitation estimation when the related technology only measures by radar echo, and can effectively improve the precipitation measurement accuracy.

[0052] This application obtains an accurate precipitation measurement model by determining the correspondence between the DN value and the measured precipitation data. It can be applied to large-scale precipitation monitoring and analysis, which to a certain extent makes up for the deficiency of observing precipitation only through ground stations, and can provide effective data support for heavy rain forecasting, flood forecasting, disaster prevention and mitigation, etc.

[0053] Furthermore, since there are relatively few precipitation data actually measured by ground rain gauges, especially in areas with complex terrain, there is often a problem of missing measured data. The precipitation measurement method based on the Luojia-2 01 satellite provided in this application is a space-based monitoring method that can make up for the lack of observation data in some areas, provide data support for complex terrain areas and areas with sparse distribution of ground rain gauges, and improve the efficiency and accuracy of precipitation measurement. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the present application or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0055] Figure 1 It is a flow chart of a precipitation measurement method based on Luojia-2-01 satellite according to an embodiment of the present application;

[0056] Figure 2 It is a schematic diagram of a fitting curve of a precipitation measurement method based on the Luojia-2 01 satellite in an embodiment of the present application;

[0057] Figure 3 It is a schematic diagram of precipitation distribution of a precipitation measurement method based on Luojia-2-01 satellite in an embodiment of the present application;

[0058] Figure 4 It is a schematic diagram of precipitation distribution of a precipitation measurement method based on Luojia-2-01 satellite in an embodiment of the present application;

[0059] Figure 5 It is a structural schematic diagram of a precipitation measurement device based on Luojia-2-01 satellite provided in an embodiment of the present application;

[0060] Figure 6 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in this application. Obviously, the described embodiments are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0062] The terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device including a series of steps or modules is not limited to the listed steps or modules, but may optionally include steps or modules that are not listed, or may optionally include other steps or modules that are inherent to these processes, methods, products or devices.

[0063] It should be noted that the terms "first\second" involved in the present application are only used to distinguish similar objects, and do not represent a specific order for the objects. It is understandable that "first\second" can be interchanged with a specific order or sequence where permitted. It should be understood that the objects distinguished by "first\second" can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those described or illustrated herein.

[0064] It should be noted that the single-band precipitation radar or dual-band precipitation radar carried by the precipitation measurement satellite in the related technology can scan and obtain information on precipitation in different forms at different heights, and can detect the three-dimensional structural information of the precipitation system. Different band precipitation radars have different distributions of radar reflectivity changes with altitude, and their feedback on meteorological particles such as ice, snow, and rain is also different. When a precipitation event occurs, meteorological particles will cause the echo signal to change, and thus the radar reflectivity factor will change. The specific precipitation information can be identified based on the change in the strength of the reflectivity factor. However, the radar echo is easily affected by ground vegetation, buildings, water bodies, etc., which can easily lead to a reduction in measurement accuracy.

[0065] The land-based measurement method relies on the layout of ground rain gauges. Some areas may have defects such as low station network density, uneven station distribution, and limited coverage. In addition, the measurement values ​​obtained by land-based ground rain gauges can generally only be regarded as point data and cannot accurately reflect the precipitation within the entire observation range.

[0066] The present application is described in detail below with reference to specific embodiments.

[0067] Next, combine Figure 1 , introduces the precipitation measurement method based on Luojia-2-01 satellite provided in the embodiment of this application. For details, please refer to Figure 1 , Figure 1 The figure shows a flow chart of a precipitation measurement method based on Luojia-2-01 satellite provided in an embodiment of the present application. Figure 1 As shown, the precipitation measurement based on Luojia-2 01 satellite includes the following steps:

[0068] S101, obtaining the earth observation data of the area to be observed collected by the Luojia-2 01 satellite, and obtaining the measured precipitation data of the area to be observed by multiple ground rain gauges.

[0069] S102: Based on the earth observation data, respectively obtain the DN value distribution in a preset area with each of the ground rainfall stations as the center.

[0070] S103, fitting the measured precipitation data measured by each of the ground rain gauges with the DN value distribution in the corresponding preset area, determining the corresponding relationship between the DN value and the measured precipitation data, and obtaining a precipitation measurement model.

[0071] S104, inputting the DN value distribution in the area to be observed into the precipitation measurement model to obtain the precipitation distribution in the area to be observed.

[0072] It can be understood that the DN value (Digital Number) is the brightness value of the remote sensing image pixel, specifically the grayscale value of each pixel unit in the image. The value is related to the sensor's radiation resolution, ground object emissivity, atmospheric transmittance and scattering rate, etc.

[0073] In some embodiments, in S101, the earth observation data of the area to be observed collected by the Luojia-2 01 satellite and the measured precipitation data of the area to be observed obtained by multiple ground rain gauges should correspond to the same geographical range and the same time period. It can be understood that the measured precipitation data measured by the ground rain gauges should at least cover the geographical range of each remote sensing image in the earth observation data, and the time when the measured precipitation data is measured should fall within the time period in which each remote sensing image in the earth observation data is taken.

[0074] For example, the measured precipitation data within 30 minutes before and 30 minutes after the moment each remote sensing image in the earth observation data is captured is collected.

[0075] It can be understood that the measured precipitation data can be the directly output precipitation amount, can be the hourly precipitation intensity, or can be other parameters, which is not limited in the embodiments of the present application.

[0076] It should be noted that, since the measured precipitation data collected in the ground rain gauge station is generally point data, and the ground observation data of the area to be observed collected by the Luojia-2 01 satellite is generally surface data, before executing the step S103 to fit the DN value with the measured precipitation data, it is necessary to perform interpolation calculation on the measured precipitation data to obtain the measured surface data of the area to be observed, so as to fit a more reasonable precipitation distribution result, specifically including:

[0077] Select multiple data observation points (x, y) in the remote sensing image data, and calculate each measured point (x i ,y i ) and each of the data observation points (x, y) i , apply the formula:

[0078]

[0079] Based on the distance di Calculate each measured point (x i ,y i ) has an influence weight w on the corresponding data observation point (x, y) i , apply the formula:

[0080]

[0081] Based on the influence weight w i The estimated precipitation data Z0 of the corresponding data observation point (x, y) is obtained by weighted calculation, and the formula is applied:

[0082]

[0083] Obtaining the distribution of measured precipitation data of the area to be observed based on the estimated precipitation data of the multiple data observation points (x, y) and the corresponding position coordinates;

[0084] In this way, the spatial distribution of measured precipitation data covering the area to be observed can be obtained.

[0085] Among them, (x, y) represents the coordinate value of the data observation point; (x i ,y i ) represents the coordinate value of the i-th measured point; d i represents the distance from the ith measured point to the data observation point; w i represents the weight of the ith measured point to the data observation point; Z0 represents the estimated precipitation data of the data observation point, which is obtained by weighted summation of n measured points; Z(x i ,y i ) represents the measured precipitation data of the i-th measured point.

[0086] In some embodiments, in order to avoid sudden changes in the DN value at the location of the ground rainfall station, which would affect the fitting effect, the average DN value in a certain area may be selected for the fitting curve to execute step S103.

[0087] The circumscribed rectangular area of ​​a circle with a preset radius and each ground rainfall station as the center point can be determined as the preset area, and the DN value distribution within the circumscribed rectangular area can be obtained. The DN value distribution can be easily obtained from remote sensing image data in earth observation data.

[0088] In some embodiments, the DN value corresponding to each pixel unit on the remote sensing image in the preset area corresponding to each ground rainfall station can be obtained respectively; and the average DN value of the DN value of each pixel unit in the preset area is calculated. Wherein, each pixel unit may include at least one pixel point, which is not limited in the embodiment of the present application.

[0089] For example, the average DN value of a circumscribed rectangular area with a radius of 1 km and a ground rain gauge as the center can be selected as the data for fitting the precipitation measurement model.

[0090] In some embodiments, an exponential relationship between the satellite-borne radar echo reflectivity and precipitation can be derived based on the corresponding relationship between the two in the related art, including:

[0091] Z=aR b ;

[0092] Among them, Z is the radar echo reflectivity, the unit is dB; R is the precipitation, the unit is mm / h; a and b are empirical parameters.

[0093] The above ZR formula is transformed to obtain:

[0094]

[0095] It should be noted that the radar echo reflectivity and the DN value of the Luojia-2 image both reflect the strength of the ground object echo signal, and both show a positive correlation. Therefore, the stronger the ground object echo signal, the higher the radar echo reflectivity and the higher the imaging DN value.

[0096] It is easy to get the corresponding relationship between DN value and precipitation, and the exponential formula is as follows:

[0097]

[0098] Among them, P is the DN value; R is the precipitation, mm / h, c1 is the coefficient term, and c2 is the exponential term.

[0099] Specifically, the coefficient terms and exponent terms of the above exponential formula can be determined by fitting the actual precipitation data measured by each ground rain gauge with the DN value distribution in the corresponding preset area, so as to obtain the precipitation measurement model by the determined coefficient terms and exponent terms.

[0100] In some embodiments, some ground rain gauges may be selected to verify the precipitation measurement model to determine whether the correlation between the remote sensing DN value and the precipitation amount is accurate.

[0101] The leave-one-out cross-validation method can be used to verify and calibrate the precipitation measurement model. Specifically, multiple groups of measured precipitation data from ground rain gauges can be set to verify the precipitation measurement model, including:

[0102] Obtain the estimated precipitation of each pixel unit on the remote sensing image data output by the precipitation measurement model, obtain the average of the estimated precipitation in the area to be observed, obtain the measured precipitation of each pixel unit on the remote sensing image data measured by a ground rain gauge station used to verify the precipitation measurement model, and obtain the average of the measured precipitation in the area to be observed;

[0103] Calculate the coefficient of determination and the root mean square error of the estimated precipitation mean relative to the measured precipitation mean, using the formula:

[0104]

[0105] If the determination coefficients obtained by multiple calculations of the precipitation measurement model by the ground rain gauge station used to verify the precipitation measurement model are all greater than the preset determination coefficient threshold, and the root mean square errors are all less than the preset root mean square error threshold, then the corresponding precipitation measurement model is output;

[0106] Among them, M i is the measured precipitation of each pixel unit, S i is the estimated precipitation for each pixel unit, is the mean of the measured precipitation, is the estimated mean precipitation.

[0107] In some embodiments, there may be a small number of ground rain gauges in some areas, resulting in a limited amount of measured sample data for measured precipitation data. While it is impossible to absolutely increase the amount of measured sample data, the observation sample space for parameter calibration can be increased by repeatedly utilizing limited data.

[0108] In some embodiments, measured precipitation data from nine ground rain gauges may be used as validation sets to obtain nine sets of model parameters. At the same time, when evaluating the cross-validation results, the root mean square error (RMSE_cal) of the calibration period and the root mean square error (RMSE_val) of the validation period in the nine cross-validations may be calculated. When the nine sets of models obtained by cross-validation all have good fitting effects, the precipitation measurement model fitted with the full data set (measured data No. 1-9) is compared to select the precipitation measurement model.

[0109] Exemplarily, the precipitation measurement model is fitted based on the parameters of the precipitation and its average DN value corresponding to Table 1.

[0110] Table 1 Rainfall intensity and average DN value at each station

[0111]

[0112] Combined with the data in Table 1, the precipitation measurement model was verified and calibrated using the leave-one-out cross-validation method. The specific calculation results are shown in Table 2.

[0113] By comparing the root mean square error of the calibration period and the validation period during cross-validation, it was found that in 66.7% of cases, the root mean square error of the calibration period (RMSE_val) was lower than the root mean square error of the validation period (RMSE_cal), and the determination coefficient R of the 9-fold cross-validation was 2 All of them are ≥0.75, indicating that the cross-validation results have a good fit, and the calibrated parameters (coefficient terms and exponential terms) are better. The corresponding precipitation measurement model can be used for precipitation measurement.

[0114] At the same time, the precipitation measurement model obtained by fitting the full data set can be compared with the 9 precipitation measurement models obtained by cross-validation, and the determination coefficient R of the precipitation measurement model obtained based on the full data set can be easily determined. 2 The root mean square error (RMSE) is higher and is similar to that of other precipitation measurement models, which indicates that the obtained fitting effect is better.

[0115] Therefore, the model parameters can be calibrated through the full data set of measured precipitation data from as many ground rain gauges as possible. Based on the data in Tables 1 and 2, the precipitation measurement model parameters can be calculated as coefficient term C1 = 1.2E+08 and exponent term C2 = -1.948.

[0116] Table 2 Cross validation results and full data set fitting results

[0117]

[0118] Specifically, based on the data in Table 1 and Table 2, the RP relationship curve can be fitted as follows: Figure 2 As shown in the figure, the precipitation measurement model is established as y = 1.2 × 10 8 *x -1.948 , coefficient of determination R 2 =0.848, the vertical axis of the image is precipitation, that is, precipitation intensity, and the horizontal axis is DN value, that is, pixel value. Based on the RP relationship of the above fitted precipitation measurement model, the precipitation near these 9 stations is measured, and the average absolute error of rainfall intensity measurement is 2.3mm / h, and the minimum absolute error reaches 0.29mm / h, which has high accuracy and can be used for regional precipitation measurement.

[0119] In some embodiments, based on the precipitation measurement model determined in S103, the step S104 is further performed to easily obtain the precipitation distribution in the area to be observed.

[0120] Specifically, the estimated precipitation of each pixel unit on the remote sensing image can be obtained, and the precipitation distribution image of the area to be observed can be obtained based on the estimated precipitation of each pixel unit;

[0121] Determine each precipitation center and the precipitation area corresponding to each precipitation center in combination with the precipitation distribution image;

[0122] Each of the precipitation centers and each of the precipitation areas are marked in the precipitation distribution image.

[0123] Through the above steps, a distribution map of precipitation can be obtained, so that the area with concentrated precipitation can be determined by cluster analysis, image analysis, etc., for example, the center of the rainstorm and the coverage of the rainstorm can be determined.

[0124] In a specific embodiment, the entire area to be observed can be calculated based on the RP relationship of the determined precipitation measurement model to obtain a precipitation distribution image of the area to be observed, for example Figure 3 As shown, most areas in the observation area have light to moderate rain, with an average rainfall intensity of 15.5 mm / h. There are two obvious heavy precipitation areas, which are further intercepted and analyzed. Figure 4 As shown in the figure, the average precipitation intensity in area ① is 22.5 mm / h, reaching the level of heavy rain; the average precipitation intensity in area ② is 29.0 mm / h, reaching the level of heavy rain. This shows that the calculated precipitation distribution map can accurately identify the heavy rain area, further identify the regional heavy rain center, and estimate the precipitation intensity at the moment, thus providing data support for early warning and forecasting work.

[0125] The following are device embodiments of the present application, which can be used to execute the method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.

[0126] See next Figure 5 , is a schematic diagram of the structure of a precipitation measurement device based on the Luojia-2 01 satellite provided by an exemplary embodiment of the present application. The device can be implemented as all or part of a terminal through software, hardware, or a combination of both, and can also be integrated on a server as an independent module. The precipitation measurement device based on the Luojia-2 01 satellite in the embodiment of the present application can be applied to a terminal or a cloud. The device 50 includes a data acquisition module 501, a data analysis module 502, and a measurement module 503, wherein:

[0127] The data acquisition module 501 is used to obtain the earth observation data of the area to be observed collected by the Luojia-2 01 satellite, and obtain the measured precipitation data of the area to be observed by multiple ground rain gauges;

[0128] The data analysis module 502 is used to obtain the DN value distribution in a preset area with each of the ground rainfall stations as the center according to the ground observation data;

[0129] The data analysis module 502 is also used to fit the measured precipitation data measured by each of the ground rain gauges with the DN value distribution in the corresponding preset area, determine the corresponding relationship between the DN value and the measured precipitation data, and obtain a precipitation measurement model;

[0130] The measurement module 503 is used to input the DN value distribution in the area to be observed into the precipitation measurement model to obtain the precipitation distribution in the area to be observed.

[0131] It should be noted that the device 50 provided in the above embodiment only uses the division of the above functional modules as an example when executing the precipitation measurement method based on the Luojia-2 01 satellite. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device provided in the above embodiment and the embodiment of the precipitation measurement method based on the Luojia-2 01 satellite belong to the same concept. The implementation process is detailed in the method embodiment, which will not be repeated here.

[0132] An embodiment of the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any of the above-mentioned method embodiments when executing the program.

[0133] See also Figure 6 , which is a structural block diagram of an electronic device provided in an embodiment of the present application.

[0134] like Figure 6 As shown, the electronic device 600 includes: a processor 601 and a memory 602 .

[0135] In the embodiment of the present application, the processor 601 is the control center of the computer system, which can be a processor of a physical machine or a processor of a virtual machine. The processor 601 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 601 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array).

[0136] The processor 601 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also called a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state.

[0137] The memory 602 may include one or more computer-readable storage media, which may be non-transitory. The memory 602 may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In some embodiments of the present application, the non-transitory computer-readable storage medium in the memory 602 is used to store at least one instruction, which is used to be executed by the processor 601 to implement the method in the embodiment of the present application.

[0138] In some embodiments, the electronic device 600 further includes: a peripheral device interface 603 and at least one peripheral device 604. The processor 601, the memory 602 and the peripheral device interface 603 can be connected via a bus or a signal line. Each peripheral device 604 can be connected to the peripheral device interface 603 via a bus, a signal line or a circuit board. Specifically, the peripheral device 604 includes: a display screen, a camera and an audio circuit. The peripheral device interface 603 can be used to connect at least one peripheral device related to I / O (Input / Output) to the processor 601 and the memory 602.

[0139] In some embodiments of the present application, the processor 601, the memory 602, and the peripheral device interface 603 are integrated on the same chip or circuit board; in some other embodiments of the present application, any one or two of the processor 601, the memory 602, and the peripheral device interface 603 can be implemented on a separate chip or circuit board. This embodiment of the present application does not specifically limit this.

[0140] The electronic device structure block diagram shown in the embodiment of the present application does not constitute a limitation on the electronic device 600. The electronic device 600 may include more or fewer components than shown in the figure, or combine certain components, or adopt a different component arrangement.

[0141] The embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of the method of any of the above embodiments are implemented. The computer-readable storage medium may include, but is not limited to, any type of disk, including a floppy disk, an optical disk, a DVD, a CD-ROM, a micro drive, and a magneto-optical disk, a ROM, a RAM, an EPROM, an EEPROM, a DRAM, a VRAM, a flash memory device, a magnetic card or an optical card, a nanosystem (including a molecular memory IC), or any type of medium or device suitable for storing instructions and / or data.

[0142] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, by hardware. Based on this understanding, the above technical solution can be essentially or in other words, the part that contributes to the relevant technology can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiment.

[0143] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A precipitation measurement method based on Luojia-2 01 satellite, characterized in that: include: Obtaining the earth observation data of the area to be observed collected by the Luojia-2 01 satellite, and obtaining the measured precipitation data of the area to be observed by multiple ground rain gauges; Based on the earth observation data, respectively obtain the DN value distribution in a preset area with each of the ground rainfall stations as the center; Fitting the measured precipitation data measured by each of the ground rain gauges with the DN value distribution in the corresponding preset area, determining the corresponding relationship between the DN value and the measured precipitation data, and obtaining a precipitation measurement model; The DN value distribution in the area to be observed is input into the precipitation measurement model to obtain the precipitation distribution in the area to be observed.

2. A precipitation measurement method based on Luojia-2 01 satellite according to claim 1, characterized in that: The earth observation data includes remote sensing image data in the area to be observed; The obtaining of the earth observation data of the area to be observed collected by the Luojia-2 01 satellite, and the obtaining of the measured precipitation data of the area to be observed by multiple ground rain gauges, include: Determining the geographical scope covered by the remote sensing image data in the earth observation data; The measured precipitation data that falls within the geographical range and is in the same time period as the remote sensing image data is intercepted from the station data measured by each ground rain gauge station.

3. A precipitation measurement method based on Luojia-2-01 satellite according to claim 2, characterized in that: The step of obtaining measured precipitation data of the area to be observed from multiple ground rain gauges includes: Selecting a plurality of data observation points in the remote sensing image data, and calculating the distance between each measured point of the plurality of ground rainfall stations and each of the data observation points; Based on the distance, the influence weight of each measured point on the corresponding data observation point is calculated respectively; Obtain estimated precipitation data of the corresponding data observation point by weighted calculation based on the impact weight; The distribution of measured precipitation data of the area to be observed is obtained based on the estimated precipitation data of the multiple data observation points and the corresponding position coordinates.

4. The precipitation measurement method based on Luojia-2-01 satellite according to claim 2 is characterized in that: The measured precipitation data measured by each ground rain gauge station are respectively fitted with the DN value distribution in the corresponding preset area, and the corresponding relationship between the DN value and the measured precipitation data is determined to obtain a precipitation measurement model, including: The exponential formula for determining DN value and precipitation includes: Determine the coefficient term and the exponential term of the exponential form formula by fitting the measured precipitation data measured by each of the ground rain gauges with the DN value distribution in the corresponding preset area; Obtaining the precipitation measurement model based on the determined coefficient term and the exponential term; Wherein, P is the DN value; R is the precipitation, c1 is the coefficient term, and c2 is the exponential term.

5. A precipitation measurement method based on Luojia-2-01 satellite according to claim 4, characterized in that: The obtaining, based on the earth observation data, the distribution of DN values ​​in a preset area with each of the ground rainfall stations as the center, comprises: Respectively obtain the DN value of each pixel unit on the remote sensing image within a preset area with each of the ground rainfall stations as the center; Calculate the average DN value of the DN value in each pixel unit in the preset area; The step of fitting the measured precipitation data measured by each of the ground rain gauges with the DN value distribution in the corresponding preset area includes: The measured precipitation data measured by each of the ground rain gauges are respectively fitted with the average DN value in the corresponding preset area.

6. A precipitation measurement method based on Luojia-2-01 satellite according to claim 2 or 4, characterized in that: After fitting the measured precipitation data measured by each of the ground rain gauges with the DN value distribution in the corresponding preset area, determining the corresponding relationship between the DN value and the measured precipitation data, and obtaining the precipitation measurement model, the method further includes: Obtain the estimated precipitation of each pixel unit on the remote sensing image data output by the precipitation measurement model, obtain the average of the estimated precipitation in the area to be observed, obtain the measured precipitation of each pixel unit on the remote sensing image data measured by a ground rain gauge station used to verify the precipitation measurement model, and obtain the average of the measured precipitation in the area to be observed; Calculate the coefficient of determination and the root mean square error of the estimated precipitation mean relative to the measured precipitation mean, using the formula: If the determination coefficients obtained by multiple calculations of the precipitation measurement model by the ground rain gauge station used to verify the precipitation measurement model are all greater than the preset determination coefficient threshold, and the root mean square errors are all less than the preset root mean square error threshold, then the corresponding precipitation measurement model is output; Among them, M i is the measured precipitation of each pixel unit, S i is the estimated precipitation amount for each pixel unit, is the mean of the measured precipitation, is the estimated precipitation mean, RMSE is the root mean square error, R 2 is the determination coefficient.

7. The precipitation measurement method based on Luojia-2-01 satellite according to claim 2 is characterized in that: Inputting the DN value distribution in the area to be observed into the precipitation measurement model to obtain the precipitation distribution in the area to be observed, including: Obtaining an estimated precipitation amount for each pixel unit on the remote sensing image, and obtaining a precipitation distribution image of the area to be observed based on the estimated precipitation amount for each pixel unit; Determine each precipitation center and the precipitation area corresponding to each precipitation center in combination with the precipitation distribution image; Each of the precipitation centers and each of the precipitation areas are marked in the precipitation distribution image.

8. A precipitation measurement device based on Luojia-2 01 satellite, characterized in that: include: The data acquisition module is used to obtain the earth observation data of the area to be observed collected by the Luojia-2 01 satellite, and obtain the measured precipitation data of the area to be observed by multiple ground rain gauges; A data analysis module, used for respectively obtaining the DN value distribution in a preset area with each of the ground rainfall stations as the center according to the ground observation data; The data analysis module is also used to fit the measured precipitation data measured by each ground rain gauge station with the DN value distribution in the corresponding preset area, determine the corresponding relationship between the DN value and the measured precipitation data, and obtain a precipitation measurement model; The measurement module is used to input the DN value distribution in the area to be observed into the precipitation measurement model to obtain the precipitation distribution in the area to be observed.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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