A precipitation measurement method based on Luojia-2 01 satellite

Abstract

The embodiment of the present application discloses a precipitation measurement method based on the Luojia-2 01 satellite, which relates to the technical field of precipitation measurement. The method includes: obtaining the earth observation data of the area to be observed collected by the Luojia-2 01 satellite and the measured precipitation data of the ground rain gauge, obtaining the DN value distribution within a preset area centered on the ground rain gauge, fitting the measured precipitation data with the DN value distribution within the corresponding preset area respectively to obtain a precipitation measurement model, and obtaining the precipitation distribution of the area to be observed based on the precipitation measurement model. By using the method provided in the present application and calculating with the DN values of the area to be observed, the interference caused by the echoes of ground objects during the measurement by radar echoes in the related art is avoided, the precipitation measurement accuracy can be improved, and the defects of the lack of observation data in complex terrain areas and areas with sparse distribution of ground rain gauges can also be made up, improving the efficiency and accuracy of precipitation measurement.

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 research of atmosphere, ocean, hydrology and environmental science. Efficient and accurate acquisition of large-scale precipitation distribution is of great significance for meteorological forecasting, disaster prevention and mitigation, and industrial and agricultural production.

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

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

[0005] Although there are already satellites dedicated to precipitation monitoring at home and abroad, due to the high cost of millimeter-wave precipitation radar, difficult procurement of components, and great difficulty in developing precipitation measurement radar, the number of rain-measuring satellites is still very limited, and the current spatial resolution of precipitation satellites is relatively low, making it difficult to meet the observation requirements with both time resolution and spatial resolution.

[0006] Therefore, there is currently a lack of a method that can efficiently and accurately measure precipitation over a large area. Summary of the Invention

[0007] The embodiments of the present application provide a precipitation measurement method based on the Luojia-2 01 satellite to solve the defects of the above-mentioned related technologies. The technical solutions are as follows:

[0008] In a first aspect, the embodiments of the present application provide a precipitation measurement method based on the Luojia-2 01 satellite, including:

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

[0010] Based on the earth observation data, obtain the DN value distribution within a preset area centered on each ground rain gauge respectively;

[0011] Fit the measured precipitation data measured by each ground rain gauge with the DN value distribution within the corresponding preset area respectively, determine the corresponding relationship between the DN value and the measured precipitation data, and obtain a precipitation measurement model;

[0012] Input the DN value distribution within the area to be observed into the precipitation measurement model to obtain the precipitation distribution of the area to be observed.

[0013] In an alternative scheme of the first aspect, the earth observation data includes remote sensing image data within the area to be observed;

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

[0015] Determine the geographical range covered by the remote sensing image data in the earth observation data;

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

[0017] In an alternative scheme of the first aspect, the step of obtaining the measured precipitation data of the area to be observed by multiple ground rain gauges includes:

[0018] Select multiple data observation points in the remote sensing image data, and calculate the distance between each measured point of the multiple ground rain gauges and each data observation point;

[0019] Based on the distances, calculate the influence weights of each measured point on the corresponding data observation point respectively;

[0020] Based on the influence weights, calculate the estimated precipitation data of the corresponding data observation point by weighted calculation;

[0021] Based on the estimated precipitation data of the multiple data observation points and the corresponding position coordinates, obtain the measured precipitation data distribution of the area to be observed.

[0022] In an alternative embodiment of the first aspect, the step of fitting the measured precipitation data measured by each of the ground rain gauges to the DN value distribution within the corresponding preset area respectively to determine the corresponding relationship between the DN value and the measured precipitation data, and obtaining the precipitation measurement model includes:

[0023] Determining an exponential formula for the DN value and precipitation, including:

[0024]

[0025] Determining the coefficient term and the exponential term of the exponential formula through the fitting results of the measured precipitation data measured by each of the ground rain gauges to the DN value distribution within 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 alternative embodiment of the first aspect, the step of respectively obtaining the DN value distribution within the preset area centered on each of the ground rain gauges based on the earth observation data includes:

[0029] Respectively obtaining the DN value within each pixel unit on the remote sensing image within the preset area centered on each of the ground rain gauges;

[0030] Calculating the average DN value of the DN values within each pixel unit within the preset area;

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

[0032] Fitting the measured precipitation data measured by each of the ground rain gauges to the average DN value within the corresponding preset area respectively.

[0033] In an alternative embodiment of the first aspect, after the step of fitting the measured precipitation data measured by each of the ground rain gauges to the DN value distribution within the corresponding preset area respectively to determine the corresponding relationship between the DN value and the measured precipitation data, and obtaining the precipitation measurement model, the method further includes:

[0034] Obtaining the estimated precipitation of each pixel unit on the remote sensing image data output by the precipitation measurement model to obtain the average estimated precipitation within the area to be observed, and obtaining the measured precipitation of each pixel unit on the remote sensing image data measured by the ground rain gauge for verifying the precipitation measurement model to obtain the average measured precipitation within the area to be observed;

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

[0036]

[0037] If the coefficient of determination obtained by calculating the precipitation measurement model multiple times through the ground rain gauge stations used to verify the precipitation measurement model is greater than the preset coefficient of determination threshold, and the root mean square error is less than the preset root mean square error threshold, then output the corresponding precipitation measurement model;

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

[0039] In an optional solution of the first aspect, 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, including:

[0040] Obtain the estimated precipitation of each pixel unit on the remote sensing image, and obtain the precipitation distribution image of the area to be observed based on the estimated precipitation of each pixel unit;

[0041] Combine the precipitation distribution image to determine each precipitation center and the precipitation area corresponding to each precipitation center;

[0042] Mark each precipitation center and each precipitation area on the precipitation distribution image.

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

[0044] A data acquisition module, configured to acquire the earth observation data of the area to be observed collected by the Luojia-2 01 satellite, and acquire the measured precipitation data of the area to be observed by multiple ground rain gauge stations;

[0045] A data analysis module, configured to respectively obtain the DN value distribution within a preset area centered on each ground rain gauge station according to the earth observation data;

[0046] The data analysis module is further configured to fit the measured precipitation data measured by each of the ground rain gauges with the DN value distribution in the corresponding preset area respectively, determine the corresponding relationship between the DN value and the measured precipitation data, and obtain a precipitation measurement model;

[0047] A measurement module, configured 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, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the method provided in the first aspect or any implementation manner of the first aspect of the embodiment of the present application is implemented.

[0049] In a fourth aspect, the present application further provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method provided in the first aspect or any implementation manner of the first aspect of the embodiment of the present application is implemented.

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

[0051] A precipitation measurement method based on the Luojia-2 01 satellite provided in the embodiment of the present application calculates by selecting the DN value of the area to be observed, avoiding the interference of the echoes of ground objects on precipitation estimation when only measuring through radar echoes in the related art, and can effectively improve the precipitation measurement accuracy.

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

[0053] Furthermore, since the precipitation data actually measured by the ground rain gauges is relatively small, especially in complex terrain areas, there is often a problem of missing measured data. The precipitation measurement method based on the Luojia-2 01 satellite provided in the present application, as a space-based monitoring means, can make up for the defect of 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] To more clearly illustrate the technical solutions in the present application or related technologies, the following will briefly introduce the drawings required for use in the embodiments or related technology descriptions. Obviously, the drawings in the following descriptions are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0055] Figure 1 is a schematic flowchart of a precipitation measurement method based on the Luojia-2 01 satellite in an embodiment of the present application;

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

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

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

[0060] Figure 6 is a schematic structural diagram of an electronic device provided in an embodiment of the present application. Detailed implementation manners

[0061] To make the objectives, technical solutions, and advantages of the present application clearer, the following will clearly and completely describe the technical solutions in the present application with reference to the drawings in the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present application.

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

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

[0064] It should be noted that the single-band precipitation radar or dual-band precipitation radar carried by precipitation measurement satellites in the related art can scan and obtain information on precipitation of different heights and forms, and can detect the three-dimensional structure information of the precipitation system. For precipitation radars with different bands, the distribution of their radar reflectivity changes with height is different, and the feedback on meteorological particles such as ice, snow, and rain is also different. When a precipitation event occurs, the meteorological particles will cause the echo signal to change, and thus the radar reflectivity factor will change. The specific precipitation information can be identified according to the strength change of the reflectivity factor. However, the radar echo is easily affected by ground vegetation, buildings, water bodies, etc., which easily leads to a decrease in measurement accuracy.

[0065] The land-based measurement method depends on the layout of ground rain gauges. There may be defects in some areas such as low density of the station network layout, uneven distribution of stations, and limited coverage. Moreover, the measurement values obtained by land-based ground rain gauges can generally only be regarded as point data and cannot accurately reflect the precipitation in the entire observation range.

[0066] The following describes the present application in detail with specific embodiments.

[0067] Next, in combination with Figure 1 , the precipitation measurement method provided by the embodiment of the present application based on Luojia-2 01 satellite is introduced. For details, please refer to Figure 1 , Figure 1 shows a schematic flowchart of a precipitation measurement method provided by an embodiment of the present application based on Luojia-2 01 satellite. As Figure 1 shown, the precipitation measurement based on Luojia-2 01 satellite includes the following steps:

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

[0069] S102, based on the earth observation data, obtain the DN value distribution within a preset area centered on each ground rain gauge respectively.

[0070] S103, fitting the measured precipitation data measured by each ground rain gauge 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: 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.

[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 obtained 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, 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 is understandable that the measured precipitation data can be directly output as precipitation amount, can be 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 by the ground rain gauge station is generally point data, and the earth observation data of the observation area collected by the Luojia-2 01 satellite is generally surface data, before executing 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 observation area, so as to obtain a more reasonable precipitation distribution result by fitting, which specifically includes:

[0077] Select multiple data observation points (x, y) in the remote sensing image data, calculate the average value of 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 the influence weight w of each of the measured points (x i , y i ) on the corresponding data observation point (x, y) respectively, and apply the formula: i ,

[0080]

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

[0082]

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

[0084] In this way, the spatial distribution of the 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 i-th measured point to the data observation point; w i represents the weight of the i-th measured point on 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 the sudden change of the DN value at the location of the ground rain gauge, which affects the fitting effect, the average DN value within a certain area can be selected for fitting the curve to perform the steps of S103.

[0087] It is possible to determine the circumscribed rectangular area of the circle with each ground rain gauge as the center point and a preset radius as the preset area, and obtain the DN value distribution within the circumscribed rectangular area. The DN value distribution can be easily obtained from the remote sensing image data in the earth observation data.

[0088] In some embodiments, the DN value corresponding to each pixel unit on the remote sensing image within the preset area corresponding to each ground rain gauge can be obtained respectively; calculate the average DN value of the DN values within each pixel unit within the preset area. Each pixel unit may include at least one pixel point, and the embodiments of the present application do not limit this.

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

[0090] In some embodiments, an exponential relationship formula between the spaceborne radar echo reflectivity and precipitation in the related art can be derived, including:

[0091] Z = aR b ;

[0092] where Z represents the radar echo reflectivity in dB; R represents the precipitation in mm / h; a and b are empirical parameters.

[0093] By transforming the above Z-R formula, we get:

[0094]

[0095] It should be noted that both the radar echo reflectivity and the DN value of the LUOJIA-2 image reflect the intensity 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 obtain the correspondence between the DN value and the precipitation, and the exponential formula is as follows:

[0097]

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

[0099] Specifically, the coefficient term and the exponential term of the above exponential formula can be determined by the fitting results of the measured precipitation data measured by each ground rain gauge and the DN value distribution in the corresponding preset area, and the precipitation measurement model can be obtained with the determined coefficient term and exponential term.

[0100] In some embodiments, some ground rain gauges can 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 of ground rain gauges can be set to verify the precipitation measurement model, including:

[0102] Obtain the estimated precipitation of each pixel unit on the remotely sensed image data output by the precipitation measurement model, and obtain the mean value of the estimated precipitation in the area to be observed. Obtain the measured precipitation of each pixel unit on the remotely sensed image data measured by the ground rain gauge used to verify the precipitation measurement model, and obtain the mean value of the measured precipitation in the area to be observed;

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

[0104]

[0105] If the coefficient of determination calculated multiple times for the precipitation measurement model by the ground rain gauge used to verify the precipitation measurement model is greater than the preset coefficient of determination threshold, and the root mean square error is less than the preset root mean square error threshold, then output the corresponding precipitation measurement model;

[0106] where, M i is the measured precipitation of each pixel unit, S i is the estimated precipitation of each pixel unit, is the mean value of the measured precipitation, is the mean value of the estimated precipitation.

[0107] In some embodiments, there may be a situation where the distribution of ground rain gauges in some areas is relatively sparse, resulting in a limited amount of measured sample data for the measured precipitation data and it is impossible to absolutely increase the amount of measured sample data. The observation sample space for parameter calibration can be increased by reusing the limited data.

[0108] In some embodiments, the measured precipitation data of 9 ground rain gauges can be successively used as the validation set to obtain 9 sets of model parameters. At the same time, when evaluating the cross-validation results, the root mean square error during the calibration period (RMSE_cal) and the root mean square error during the validation period (RMSE_val) in 9 cross-validations can be calculated. In the case where the 9 sets of models obtained by cross-validation all have good fitting effects, compare the precipitation measurement models fitted with the full data set (measured data from No. 1 to No. 9), so as to optimize 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 Site rainfall intensity and its average DN value

[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] Comparing the root mean square errors (RMSE) during the calibration period and the validation period in cross-validation, it was found that in 66.7% of the cases, the RMSE during the calibration period (RMSE_val) was lower than the RMSE during the validation period (RMSE_cal), and the determination coefficient R of the 9-fold cross-validation 2 was ≥ 0.75 in all cases, indicating that the cross-validation results had a good fit, and the calibrated parameters (coefficient term and exponent term) were relatively optimal. The corresponding precipitation measurement model could be used for precipitation measurement.

[0114] At the same time, the precipitation measurement model obtained by fitting the full dataset could be compared with the 9 precipitation measurement models obtained by cross-validation. It was easy to determine that the determination coefficient R of the precipitation measurement model obtained based on the full dataset 2 was higher, and the root mean square error (RMSE) was similar to the root mean square errors of other precipitation measurement models, indicating that the obtained fitting effect was better.

[0115] Therefore, the calibration of model parameters could be carried out using the full dataset of measured precipitation data from as many ground rain gauges as possible. Based on the data in Table 1 and Table 2, the parameters of the precipitation measurement model were calculated as the coefficient term C1 = 1.2E+08 and the exponent term C2 = -1.948.

[0116] Table 2 Cross-validation results and fitting results of the full dataset

[0117]

[0118] Specifically, based on the data in Table 1 and Table 2, the R-P relationship curve could be fitted as Figure 2 shown, and the precipitation measurement model was established as y = 1.2×10 8 *x -1.948 , with the determination coefficient R 2 = 0.848. The vertical axis of the graph was the precipitation amount or precipitation intensity, and the horizontal axis was the DN value or pixel value. Based on the R-P relationship formula of the above-fitted precipitation measurement model, the precipitation amounts near these 9 stations were measured, and the average absolute error of rain intensity measurement was 2.3 mm / h, and the minimum absolute error reached 0.29 mm / h, indicating high precision and could be used for regional precipitation measurement.

[0119] In some embodiments, based on the precipitation measurement model determined in S103, the steps of S104 were further executed, and it was easy to 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 a precipitation distribution image of the area to be observed can be obtained based on the estimated precipitation of each pixel unit;

[0121] Each precipitation center and the precipitation area corresponding to each precipitation center are determined 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 precipitation distribution map can be obtained, and the areas where precipitation is concentrated can be determined by means of cluster analysis, image analysis, etc. For example, the heavy rain center and the coverage area of the heavy rain can be determined.

[0124] In a specific embodiment, the R - P relationship formula of the determined precipitation measurement model can be used to calculate the entire area to be observed, and a precipitation distribution image of the area to be observed can be obtained. For example Figure 3 as shown, there is light to moderate rain in most areas of the area to be observed, and the average rainfall intensity is 15.5 mm / h. There are two obvious heavy precipitation areas. Further intercept and analyze them. Intercept and calculate and analyze the two obvious heavy precipitation areas. As Figure 4 shown, it is statistically obtained that the average precipitation intensity in area ① is 22.5 mm / h, reaching the heavy rain level; the average precipitation intensity in area ② is 29.0 mm / h, reaching the heavy rain level. It shows that the calculated precipitation distribution map can accurately identify the heavy rain area, and further can identify the regional heavy rain center and estimate the precipitation intensity at a certain moment, so as to provide data support for early warning and forecasting work.

[0125] The following is an apparatus embodiment of the present application, which can be used to execute the method embodiment of the present application. For the details not disclosed in the apparatus embodiment of the present application, please refer to the method embodiment of the present application.

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

[0127] The data acquisition module 501 is used to obtain the earth observation data of the area to be observed collected by 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 respectively obtain the DN value distribution within a preset area centered on each of the ground rain gauges according to the earth observation data;

[0129] The data analysis module 502 is further used to respectively fit the measured precipitation data measured by each of the ground rain gauges with the DN value distribution within 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 within the area to be observed into the precipitation measurement model to obtain the precipitation distribution within the area to be observed.

[0131] It should be noted that when the device 50 provided in the above embodiment executes the precipitation measurement method based on the Luojia-2 01 satellite, only the above division of each functional module is used for illustration. In practical applications, the above functions can be allocated to different functional modules according to needs, 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, and 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 on the memory and executable on the processor. When the processor executes the program, it implements the steps of the method in any of the above embodiments.

[0133] Please refer to Figure 6 , which is a structural block diagram of an electronic device provided by an embodiment of the present application.

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

[0135] In an embodiment of the present application, the processor 601 is the control center of the computer system, which can be the processor of a physical machine or the processor of a virtual machine. The processor 601 can include one or more processing cores, such as a 4-core processor or an 8-core processor. The processor 601 can be implemented in at least one of the following hardware forms: 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 wake state, also known as the 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, and the computer-readable storage media may be non-transitory. The memory 602 may further include high-speed random access memory and non-volatile memory, such as one or more disk storage devices and flash 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, and the at least one instruction is used to be executed by the processor 601 to implement the method in the embodiments 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 may be connected through a bus or signal lines. Each peripheral device 604 may be connected to the peripheral device interface 603 through a bus, signal lines, or a circuit board. Specifically, the peripheral device 604 includes: a display screen, a camera, and an audio circuit. The peripheral device interface 603 may 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 may be implemented on a separate chip or circuit board. The embodiments of the present application do not make specific limitations in this regard.

[0140] The block diagram of the electronic device structure shown in the embodiments 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 different component arrangements.

[0141] The embodiments of the present application also provide a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the steps of the method in any of the foregoing embodiments are implemented. Among them, the computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, microdrives, and magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic cards or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0142] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the related technology, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disks, optical disks, etc., and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[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 them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate 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, Including: Obtaining the earth observation data of the area to be observed collected by 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, obtaining the DN value distribution within a preset area centered on each of the ground rain gauges; Fitting the measured precipitation data measured by each of the ground rain gauges with the DN value distribution within the corresponding preset area respectively, determining the corresponding relationship between the DN value and the measured precipitation data, and obtaining a precipitation measurement model; Inputting the DN value distribution within the area to be observed into the precipitation measurement model to obtain the precipitation distribution of the area to be observed; The step of fitting the measured precipitation data measured by each of the ground rain gauges with the DN value distribution within the corresponding preset area respectively, determining the corresponding relationship between the DN value and the measured precipitation data, and obtaining a precipitation measurement model includes: Deriving an exponential relationship formula based on the corresponding relationship between the spaceborne radar echo reflectivity and the precipitation, including: Z = aR b ; Transforming to obtain: Where, Z represents the radar echo reflectivity, with the unit of dB; R represents the precipitation, with the unit of mm / h; a and b are empirical parameters; Determining the exponential form formula between the DN value and the precipitation, including: Determining the coefficient term and the exponential term of the exponential form formula through the fitting results of the measured precipitation data measured by each of the ground rain gauges with the DN value distribution within the corresponding preset area; Obtaining the precipitation measurement model based on the determined coefficient term and the exponential term; Where, P is the DN value; R is the precipitation, c1 is the coefficient term, and c2 is the exponential term; After obtaining the precipitation measurement model, it further includes: Obtaining the estimated precipitation of each pixel unit on the remote sensing image data output by the precipitation measurement model, obtaining the average value of the estimated precipitation within the area to be observed, obtaining the measured precipitation of each pixel unit on the remote sensing image data measured by the ground rain gauges used to verify the precipitation measurement model, and obtaining the average value of the measured precipitation within the area to be observed; Calculating the determination coefficient and the root mean square error of the average value of the estimated precipitation relative to the average value of the measured precipitation, using the formula: If the determination coefficients calculated multiple times for the precipitation measurement model by the ground rain gauges 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 output the corresponding precipitation measurement model; Among them, M i is the measured precipitation of each pixel unit, S i is the estimated precipitation of each pixel unit, is the mean value of the measured precipitation, is the mean value of the estimated precipitation, RMSE is the root mean square error, R 2 is the coefficient of determination.

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

3. The precipitation measurement method based on Luojia-2 01 satellite according to claim 2, wherein, The step of obtaining the measured precipitation data of the area to be observed by multiple ground rain gauges includes: Select multiple data observation points from the remote sensing image data, and calculate the distances between each measured point of the multiple ground rain gauges and each of the data observation points; Based on the distances, calculate the influence weights of each measured point on the corresponding data observation point respectively; Based on the influence weights, calculate the estimated precipitation data of the corresponding data observation point by weighted calculation; Based on the estimated precipitation data of the multiple data observation points and the corresponding position coordinates, obtain the distribution of measured precipitation data in the area to be observed.

4. A precipitation measurement method based on Luojia-2 01 satellite according to claim 1, wherein The obtaining the DN value distribution within a preset area centered on each ground rain gauge based on the earth observation data respectively includes: Obtain the DN values within each pixel unit on the remote sensing image within the preset area centered on each ground rain gauge respectively; Calculate the average DN value of the DN values within each pixel unit within the preset area; The fitting the measured precipitation data measured by each ground rain gauge with the DN value distribution within the corresponding preset area respectively includes: Fit the measured precipitation data measured by each ground rain gauge with the average DN value within the corresponding preset area respectively.

5. The precipitation measurement method based on the Luojia-2 01 satellite according to claim 2, wherein, 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, including: Obtain the estimated precipitation of each pixel unit on the remote sensing image, and obtain the precipitation distribution image of the area to be observed based on the estimated precipitation of each pixel unit; Combine the precipitation distribution image to determine each precipitation center and the precipitation area corresponding to each precipitation center; Mark each precipitation center and each precipitation area in the precipitation distribution image.

6. A precipitation measurement device based on Luojia-2 01 satellite, characterized in that, It includes: A data acquisition module, configured to obtain the earth observation data of the area to be observed collected by 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, configured to obtain the DN value distribution within a preset area centered on each ground rain gauge respectively according to the earth observation data; The data analysis module is further configured to fit the measured precipitation data measured by each ground rain gauge with the DN value distribution within the corresponding preset area respectively, determine the corresponding relationship between the DN value and the measured precipitation data, and obtain the precipitation measurement model; A measurement module, configured 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; The fitting the measured precipitation data measured by each ground rain gauge with the DN value distribution within the corresponding preset area respectively, determining the corresponding relationship between the DN value and the measured precipitation data, and obtaining the precipitation measurement model includes: Derive an exponential relationship formula based on the corresponding relationship between the spaceborne radar echo reflectivity and the precipitation, including: Z = aR b ; It is deformed to obtain: Where, Z represents the radar echo reflectivity, with the unit of dB; R represents the precipitation, with the unit of mm / h; a and b are empirical parameters; Determine the exponential form formula of the DN value and the precipitation, including: The coefficient term and the exponential term of the exponential form formula are determined respectively by the fitting results of the measured precipitation data measured by each of the ground rain gauges and the DN value distribution within the corresponding preset area; Based on the determined coefficient term and exponential term, the precipitation measurement model is obtained; wherein, P is the DN value; R is the precipitation, c1 is the coefficient term, and c2 is the exponential term; After obtaining the precipitation measurement model, it further includes: Obtaining the estimated precipitation of each pixel unit on the remote sensing image data output by the precipitation measurement model to obtain the average value of the estimated precipitation in the area to be observed, and obtaining the measured precipitation of each pixel unit on the remote sensing image data measured by the ground rain gauge for verifying the precipitation measurement model to obtain the average value of the measured precipitation in the area to be observed; Calculating the determination coefficient and the root mean square error of the average value of the estimated precipitation relative to the average value of the measured precipitation, and applying the formula: If the determination coefficients obtained by calculating the precipitation measurement model multiple times through the ground rain gauge for verifying 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 of each pixel unit, is the mean value of the measured precipitation, is the mean value of the estimated precipitation, RMSE is the root mean square error, R 2 is the coefficient of determination.

7. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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