A remote sensing monitoring method for urban night light environment based on combined UAV and SDGSAT-1 satellite data

Abstract

The present invention proposes a remote sensing monitoring method for urban nighttime light environment by combining unmanned aerial vehicles and SDGSAT-1 satellite data. First, the high-resolution nighttime surface incident illumination of a typical small area is estimated based on the nighttime multispectral image of the unmanned aerial vehicle. Then, the high-resolution surface illumination estimated by the unmanned aerial vehicle is scaled to the resolution of the SDGSAT-1 satellite image, and the surface illumination model is trained in conjunction with the SDGSAT-1 satellite data to estimate the nighttime surface illumination of the entire city, so as to monitor and evaluate the urban nighttime light environment. The present invention can effectively monitor the urban light environment and provide a scientific basis for urban planning and light environment management.

Description

Technical Field

[0001] The present invention relates to the technical field of night light environment, and particularly relates to a remote sensing monitoring method for urban night light environment combining unmanned aerial vehicle and SDGSAT-1 satellite data. Background Art

[0002] Artificial light sources are widely used in modern society, bringing many conveniences to human life. Night lighting can improve the visibility of roads and facilitate people's night travel. Merchants create a good consumption environment with night lights, which not only enriches the nightlife of residents but also further stimulates consumption and promotes urban economic development. In some areas, insufficient lighting will affect the night life of residents. On the other hand, excessive artificial lighting will also cause light pollution. In order to create a scientific and reasonable urban night light environment, it is necessary to monitor and control the urban night lighting.

[0003] Traditional methods for monitoring night light environment usually rely on equipment such as illuminometers for on-site fixed-point observations or mobile observations. Such ground monitoring methods can only obtain limited point-scale observation data. However, the distribution of light environment has significant spatial heterogeneity, and limited measurement samples cannot effectively reflect the spatial distribution characteristics of the night light environment at the urban scale. Satellite remote sensing can provide large-scale and long-term spatially continuous observations. Currently, night light remote sensing satellites such as DMSP / OLS, NPP / VIIRS, Luojia 1-01, and JL1-3B have been put into use. However, the resolution of most night light remote sensing data is relatively low. For example, the spatial resolution of DMSP / OLS is 2.8 km, NPP / VIIRS is 750 m, and Luojia 1-01 is 130 m, which cannot effectively reflect the spatial details of the light environment. Although JL1-3B has a resolution as high as 0.92 m, its swath width is only 11 km, which cannot meet the observation requirements at the urban scale. Most night light remote sensing sensors also have only one channel and cannot fully capture the spectral characteristics of different types of light sources. The new SDGSAT-1 satellite's low-light band can provide a panchromatic band with a resolution of 10 m and three multispectral images with a resolution of 40 m, and its imaging swath width is up to 300 km, having the potential for refined monitoring of the urban-scale night light environment. In addition, due to the spatial heterogeneity of the night light environment, the spatial scale difference between satellite remote sensing image pixels and single-point observations will bring great uncertainty to the estimation. The emergence of unmanned aerial vehicles provides a new technical means for monitoring the night light environment. Unmanned aerial vehicles can not only provide high-resolution night light images but also have relatively low costs and are flexible to use. However, the flight range of unmanned aerial vehicles is limited, and they can only carry out observations in a small area and are not sufficient to cover the entire city. Summary of the Invention

[0004] The object of the present invention is to provide a method for remotely sensing and monitoring the urban night light environment by combining unmanned aerial vehicle (UAV) and SDGSAT-1 satellite data, which can effectively monitor the urban night light environment and provide a scientific basis for urban planning and light environment governance.

[0005] To achieve the above technical object, the technical solution adopted by the present invention is as follows:

[0006] A method for remotely sensing and monitoring the urban night light environment by combining UAV and SDGSAT-1 satellite data, the method comprising the following steps:

[0007] Select an observation area, conduct UAV aerial photography observations and on-site observations, and respectively obtain multi-spectral remote sensing images at night and during the day, as well as the observed illuminance values of the observation points; calibrate the multi-spectral remote sensing images during the day to obtain the surface reflectance information of the observation area;

[0008] Combined with the surface reflectance information of the observation area, convert the night-time surface reflected radiance received by the UAV into surface incident irradiance;

[0009] Taking the surface incident irradiance of the observation point as the independent variable and the observed illuminance value of the observation point as the dependent variable, use the random forest algorithm to train the first surface incident illuminance estimation model based on the UAV night-time image; apply the trained first surface incident illuminance estimation model to the surface incident irradiance corresponding to the UAV to obtain the UAV surface incident illuminance of the observation area, and upscale it to the same resolution as the SDGSAT-1 satellite remote sensing image;

[0010] According to the radiometrically calibrated SDGSAT-1 satellite remote sensing image, surface reflectance data, building coverage ratio and tree coverage ratio, calculate the SDGSAT-1 satellite surface incident irradiance of the observation area;

[0011] Taking the SDGSAT-1 satellite surface incident irradiance of the observation area as the independent variable and the corresponding upscaled UAV surface incident illuminance as the dependent variable, use the random forest algorithm to train the second surface incident illuminance estimation model based on the SDGSAT-1 satellite; apply the trained second surface incident illuminance estimation model to the surface incident irradiance of the SDGSAT-1 satellite of the entire city to obtain the night-time incident surface illuminance distribution map of the entire city.

[0012] Furthermore, the process of conducting UAV aerial photography observations and on-site observations includes the following steps:

[0013] While the SDGSAT-1 satellite passes over at night for imaging, select an observation area, and use the UAV equipped with a multi-spectral imager to conduct flight observations to obtain night-time multi-spectral remote sensing images.

[0014] During the drone flight observation, use a handheld illuminometer to measure and record the downward illuminance value of the observation point horizontally upward, and use a handheld RTK device to record the latitude and longitude position data of the observation point;

[0015] During the day at similar times, use a drone to carry a multispectral imager to conduct aerial photography observations in the same observation area, obtain the daytime multispectral remote sensing images within the observation area, and take calibration whiteboard images before and after the flight observation;

[0016] Use drone orthophoto processing software to stitch the multispectral remote sensing images at night and during the day respectively.

[0017] Furthermore, in the process of converting the surface reflected radiance received by the drone into the surface incident irradiance in combination with the surface reflectance information of the observation area, the following steps are included:

[0018] Retain the data of the three visible light bands of RGB in the multispectral remote sensing images of the drone at night to correct the multispectral remote sensing images of the drone at night;

[0019] Convert the surface reflected radiance received by the drone into the surface incident irradiance:

[0020] ;

[0021] In the formula, is the surface incident irradiance of the i-th band of the drone; is the radiance at the entrance pupil of the i-th band of the drone; is the surface reflectance of the i-th band of the drone.

[0022] Furthermore, the process of training the first surface incident illuminance estimation model based on the drone night images using the random forest algorithm includes:

[0023] According to the latitude and longitude information of the measured points, extract the surface incident irradiance values of the three visible light bands of RGB in the corresponding multispectral remote sensing images of the drone at night;

[0024] Using the surface incident irradiance of the observation point as the independent variable and the observed illuminance value of the observation point as the dependent variable, train the first surface incident illuminance estimation model based on the drone night images using the random forest algorithm; during the training process, optimize the model parameters using grid search and verify the model accuracy using ten-fold cross-validation.

[0025] Furthermore, the process of upscaling the surface incident illuminance of the drone in the observation area to the same resolution as the SDGSAT-1 satellite remote sensing image includes the following steps:

[0026] Loop through each 10 - meter grid, calculate the average surface incident illuminance of the pixels that are neither buildings nor trees within each 10 - meter grid, and obtain a surface incident illuminance map of the observation area with a resolution of 10 meters.

[0027] Furthermore, use remote sensing processing software to perform mosaicking and cropping pre - processing on the SDGSAT – 1 satellite images, and perform radiometric calibration on them to convert the gray - scale values into radiance values:

[0028] ;

[0029] In the formula, is the radiance at the entrance pupil of the satellite's i - th band; is the gray - scale value of the satellite's i - th band; and are the gain and offset of the satellite's i - th band respectively.

[0030] Furthermore, the process of calculating the surface incident radiance of the SDGSAT - 1 satellite in the observation area includes the following steps:

[0031] Perform Gram - Schmidt orthogonal transformation fusion on the three visible - light bands (RGB) with a resolution of 40m and the panchromatic band with a resolution of 10m in the SDGSAT - 1 satellite to obtain three visible - light band images with a resolution of 10m;

[0032] Extract the surface reflectance data of the three visible - light bands (RGB) from the satellite's daytime surface reflectance products of adjacent time - phases; overlay the building vector data and tree - cover data on the SDGSAT - 1 pixels, and calculate the building coverage and tree coverage within each pixel with a resolution of 10m;

[0033] Use the following formula to calculate the surface incident radiance of the SDGSAT - 1 satellite in the observation area:

[0034] ;

[0035] In the formula, is the surface incident radiance of the satellite's i - th band; represents the radiance at the entrance pupil of the satellite's i - th band; is the surface reflectance of the satellite's i - th band; is the building coverage; is the tree coverage.

[0036] Furthermore, the method further includes:

[0037] Characterize the urban light environment based on the night - time surface incident illuminance distribution chart obtained by remote sensing, and combine the surface incident illuminance distribution map, urban map, and functional zoning to identify the areas with insufficient and excessive night - time lighting in the city.

[0038] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0039] The urban night light environment remote sensing monitoring method combining unmanned aerial vehicle (UAV) and SDGSAT-1 satellite data of the present invention uses UAV images as a medium, effectively reducing the uncertainty caused by the spatial scale difference between ground observations and satellite image pixels; and corrects the fused SDGSAT-1 multispectral channels using building coverage, tree coverage, and surface reflectivity, thereby accurately estimating the incident irradiance on the surface and effectively monitoring the urban night light environment. The method provided by the present invention can accurately reflect the spatial distribution of the urban night light environment and provide technical support for the reasonable control of urban night lighting. Description of the Drawings

[0040] Figure 1 It is a flow chart of the urban night light environment remote sensing monitoring method combining unmanned aerial vehicle and SDGSAT-1 satellite data of the present invention;

[0041] Figure 2 It is a scatter plot between the estimated value and the observed value of the incident illuminance on the surface based on UAV data;

[0042] Figure 3 It is a spatial distribution map of the night surface illuminance in two observation areas, where a) corresponds to typical area 1 and b) corresponds to typical area 2;

[0043] Figure 4 It is a scatter plot between the estimated value and the measured value of the incident illuminance on the surface based on SDGSAT-1 satellite data;

[0044] Figure 5 It is a spatial distribution map of the night surface illuminance in Nanjing City;

[0045] Figure 6 It is a radiation calibration coefficient map of the low-light band of the SDGSAT-1 satellite. Detailed Embodiment

[0046] The following further describes the embodiments of the present invention in detail with reference to the drawings.

[0047] The present invention discloses an urban night light environment remote sensing monitoring method combining unmanned aerial vehicle and SDGSAT-1 satellite data, and the method includes the following steps:

[0048] Select an observation area, conduct UAV aerial photography observations and on-site observations, and respectively obtain multi-spectral remote sensing images at night and during the day and the observed illuminance values at the observation points; calibrate the multi-spectral remote sensing images during the day to obtain the surface reflectivity information of the observation area;

[0049] Combined with the surface reflectance information of the observation area, convert the surface reflected radiance received by the UAV into the surface incident irradiance;

[0050] Taking the surface incident irradiance of the observation point as the independent variable and the observed illuminance value of the observation point as the dependent variable, use the random forest algorithm to train the first surface incident illuminance estimation model based on the UAV night image; apply the trained first surface incident illuminance estimation model to the surface incident radiance corresponding to the UAV to obtain the UAV surface incident illuminance of the observation area, and upscale it to the same resolution as the SDGSAT-1 satellite remote sensing image;

[0051] According to the radiometrically calibrated SDGSAT-1 satellite remote sensing image, surface reflectance data, building coverage ratio and tree coverage ratio, calculate the SDGSAT-1 satellite surface incident radiance of the observation area;

[0052] Taking the SDGSAT-1 satellite surface incident radiance of the observation area as the independent variable and the corresponding upscaled UAV surface incident illuminance as the dependent variable, use the random forest algorithm to train the second surface incident illuminance estimation model based on the SDGSAT-1 satellite; apply the trained second surface incident illuminance estimation model to the surface incident radiance of the SDGSAT-1 satellite of the whole city to obtain the night-time incident surface illuminance distribution map of the whole city.

[0053] The present invention first estimates the high-resolution night-time surface illuminance of a typical small area based on the UAV night-time multispectral image, and then upscales it to the resolution of the SDGSAT-1 satellite data to train the night-time surface illuminance estimation model based on the SDGSAT-1 satellite data, and estimates the night-time surface incident illuminance of the whole city, so as to monitor and evaluate the urban night-time light environment status.

[0054] 1) UAV and ground observation

[0055] While the SDGSAT-1 satellite passes over at night for imaging, select a typical small area to carry out UAV aerial photography observation. Use the UAV to carry a multispectral imager for flight observation, and set parameters such as the flight area, flight path, altitude, heading and lateral overlap, and time interval through ground station software such as DJI GS Pro to obtain night-time multispectral remote sensing images.

[0056] During the UAV flight observation, a ground observation experiment is carried out synchronously. Use a hand-held illuminometer to measure and record the downward illuminance value of the observation point horizontally upward, and use a hand-held RTK device to record the longitude and latitude position data of the observation point.

[0057] Use the UAV to carry a multispectrometer to carry out aerial photography observation in the same observation area during the day at similar times to obtain the day-time multispectral remote sensing images of the typical area. And take calibration whiteboard images before and after the flight observation.

[0058] 2) Data processing

[0059] Use the orthophoto image processing software of the unmanned aerial vehicle (UAV) to splice the multi-spectral remote sensing images at night and during the day respectively. Calibrate the multi-spectral remote sensing images during the day based on the whiteboard information to obtain the surface reflectance information of the observation area. Since the night lighting environment only considers the visible light range, only the data of the 3 visible light bands of the multi-spectral image are retained.

[0060] Use the remote sensing processing software to perform preprocessing such as mosaicking and cutting on the SDGSAT-1 satellite images, and perform radiometric calibration on them to convert the gray value into radiance value:

[0061] (1);

[0062] In the formula, is the radiance at the entrance pupil of the i-th band of the satellite; is the gray value of the i-th band of the satellite; and are the gain and offset of the i-th band of the satellite respectively. The radiometric calibration coefficients of the low-light band of the SDGSAT-1 satellite are as Figure 6 shown.

[0063] 3) Estimation of surface incident illuminance in typical areas based on UAV

[0064] The radiation signal received by the UAV is the light reflected from the ground, and the night light environment mainly depends on the incident light rather than the reflected light. Considering the high resolution of the UAV images and the absence of the mixed pixel effect, convert the surface reflected radiance received by the UAV into surface incident radiance.

[0065] (2);

[0066] In the formula, is the surface incident radiance of the i-th band of the UAV; is the radiation brightness at the entrance pupil of the i-th band of the UAV; is the surface reflectance of the i-th band of the UAV.

[0067] According to the longitude and latitude information of the measured points, extract the corrected surface incident radiance values of the 3 visible light bands of the UAV night images corresponding to them. Using the surface incident radiance as the independent variable and the field-measured illuminance value as the dependent variable, use the random forest algorithm to train the surface incident illuminance estimation model based on the UAV night images. During the training process, use grid search to optimize the model parameters and use ten-fold cross-validation to verify the model accuracy. Determine the optimal estimation model and apply it to the UAV surface incident radiance data to obtain the surface incident illuminance map of the typical small area.

[0068] 4) Estimation of Surface Illuminance in the Study Area Based on the SDGSAT-1 Satellite

[0069] There is a large spatial scale difference between the 40m-resolution multispectral pixels of the SDGSAT-1 satellite and the ground observation points. Direct estimation will bring great uncertainty to the results. Therefore, first, perform Gram-Schmidt orthogonal transformation fusion on the 3 multispectral bands with a resolution of 40m and the panchromatic band with a resolution of 10m of the SDGSAT-1 satellite to obtain 3 multispectral band images with a resolution of 10m. Second, upscale the high-resolution surface incident illuminance estimated by the UAV to a resolution of 10m, and then combine it with the SDGSAT-1 satellite data in the corresponding area to train an illuminance estimation model based on the SDGSAT-1 satellite image, so as to better eliminate the deviation of the results.

[0070] Considering that the 10m-resolution pixels of the SDGSAT-1 satellite may contain various land cover types such as ground, buildings, and trees, and the signal received by the satellite sensor is the combined signal of the light signals of various land covers within the 10m pixel. However, the satellite receives the reflected light from the ground, there is almost no light on the top of the building, and the tree crown will block the reflected light. These factors will cause a deviation between the satellite-received signal and the actual light environment, and it is necessary to remove the influence of buildings and tree canopies.

[0071] The remote sensing sensor receives the incident light brightness reflected from the surface, and it can be considered that there is no light in the building and tree-covered areas within the pixel. Therefore, the apparent radiance can be expressed as:

[0072] (3);

[0073] In the formula, is the radiance at the entrance pupil of the i-th band of the satellite; is the surface incident radiance of the i-th band of the satellite; is the surface reflectivity of the i-th band of the satellite; is the building coverage; is the tree coverage.

[0074] Transform formula (3) to obtain the corrected formula for surface incident radiance:

[0075] (4);

[0076] Surface reflectance data for three visible light bands are extracted from satellite daytime surface reflectance products of adjacent time phases; building vector data and tree cover data are overlaid on SDGSAT-1 pixels, and the building coverage and tree coverage within each 10-meter resolution pixel are calculated. The corrected SDGSAT-1 surface incident irradiance is calculated using formula (4).

[0077] The high-resolution surface incident illuminance of a typical small area estimated by UAV remote sensing is upscaled to 10-meter resolution, which is consistent with the SDGSAT-1 data. During the upscaling process, a 10-meter grid loop is performed, and the mean surface incident illuminance of the pixels within each 10-meter grid that are not buildings and trees is statistically calculated to obtain the surface incident illuminance map of the typical small area at 10-meter resolution.

[0078] The corrected SDGSAT-1 satellite surface incident irradiance for three bands of the typical small area is extracted. Taking this as the independent variable and the corresponding 10-meter resolution UAV surface incident illuminance as the dependent variable, a random forest algorithm is used to train a surface incident illuminance estimation model. During the training process, grid search is used to optimize the model parameters, and ten-fold cross-validation is used to verify the model accuracy. The optimal estimation model is determined and applied to the corrected SDGSAT-1 satellite surface incident irradiance of the entire city to obtain the distribution map of the nocturnal incident surface illuminance of the entire city.

[0079] 5) Nocturnal light environment analysis

[0080] The urban light environment is characterized based on the nocturnal surface incident illuminance distribution map obtained from remote sensing. By combining the surface incident illuminance distribution map with the urban map, functional zoning, etc., areas with insufficient and excessive nocturnal lighting in the city are identified, thereby effectively evaluating the urban light environment level and providing an important decision-making basis for the subsequent scientific planning and management of nocturnal lighting.

[0081] Example

[0082] The present invention combines UAV observations and SDGSAT-1 satellite remote sensing data to estimate the surface incident illuminance and monitor the urban nocturnal light environment. The following are the specific implementation steps of the example. The technical flow chart is shown in Figure 1 .

[0083] 1) Select two typical small areas (Typical Area 1 and Typical Area 2), and conduct drone observations and field observations simultaneously during the night-time transit imaging of the SDGSAT-1 satellite. Use a DJI Phantom 4 multispectral drone to obtain night-time multispectral image data of the study area. The flight altitude of the drone is set at 400 m, and the forward overlap rate and side overlap rate are set at 75%. During the drone aerial imaging process, conduct ground observations simultaneously. Use a TES-1399R handheld illuminometer to measure the incident illuminance values of each observation point upwards, and use a handheld RTK device to record the longitude and latitude information of the measured points. In addition, conduct aerial observations using a DJI Phantom 4 multispectral drone in the morning of the next day to obtain daytime multispectral images, and take images of the calibration whiteboard before the flight observations.

[0084] 2) Use Pix4D Mapper software to stitch the drone multispectral images of the night-time and daytime respectively to obtain the daytime and night-time multispectral orthoimages of the two typical areas, with a spatial resolution of 0.25 m. Calibrate the daytime multispectral images based on the whiteboard to obtain the surface reflectance of the R, G, and B bands of these two areas. In addition, use the calibration coefficients and formula (1) in Figure 6 to perform radiometric calibration on the multispectral and panchromatic bands of the SDGSAT-1 satellite remote sensing images, and convert the gray values into radiance values.

[0085] 3) Based on the drone night-time multispectral images and daytime surface reflectance images of the two typical small areas, correct them through formula 2 to obtain the surface incident radiance.

[0086] 4) Use the random forest algorithm to train the surface illuminance estimation model with the ground measured illuminance as the dependent variable and the drone surface incident radiance at the corresponding position as the independent variable. During the training process, use the grid search algorithm to debug the three parameters of the number of decision trees, the depth of the decision trees, and the feature sampling ratio, and conduct accuracy tests through ten-fold cross-validation. The scatter plot of the optimal model verification is shown in Figure 2 , apply this model to the drone surface incident radiance, and obtain the surface incident illuminance maps with a resolution of 0.25 m for the two typical small areas (as shown in Figure 3 ).

[0087] 5) Use the Gram-schmidt orthogonal transformation fusion method to fuse the 3 multispectral bands with a resolution of 40 m and 1 panchromatic band with a resolution of 10 m of the SDGSAT-1 satellite to obtain 3 multispectral band images with a resolution of 10 m.

[0088] 6) Select the Sentinel-2 reflectance products of Nanjing City with imaging dates close to those of SDGSAT-1, and extract the surface reflectance of three visible light bands. Overlay the 1m resolution land cover classification map of Nanjing City on the SDGSAT-1 remote sensing image, and calculate the tree cover ratio and building cover ratio within each 10m resolution pixel. According to the calibrated SDGSAT-1 remote sensing image of Nanjing City, surface reflectance data, building cover ratio, and tree cover ratio, correct them through formula (4) to obtain the remote sensing data of surface incident irradiance in Nanjing City.

[0089] 7) Upscale the 0.25m surface incident illuminance data of two typical small areas obtained by the unmanned aerial vehicle. During the upscaling process, circularly calculate the average illuminance of non-building and non-vegetation pixels among 1600 0.25m resolution pixels within each 10m resolution grid to generate the 10m resolution surface incident illuminance of the two typical small areas. Using the 10m resolution surface incident illuminance as the dependent variable and the 10m resolution SDGSAT-1 surface radiance remote sensing data obtained through fusion and correction as the independent variable, train the surface illuminance estimation model using the random forest algorithm. During the training process, debug the three parameters of the number of decision trees, the depth of decision trees, and the feature sampling ratio using the grid search algorithm, and conduct accuracy tests through ten-fold cross-validation. The scatter plot of the optimal model verification is shown in Figure 4 Apply this model to the SDGSAT-1 surface incident radiance to obtain the distribution map of the incident surface illuminance at night in Nanjing City ( Figure 5 ).

[0090] 8) As can be seen from the figure, there are obvious brightness differences in the light environment of Nanjing City. The brightness values in urban areas such as the main urban area, Jiangbei New Area, and Jiangning District are relatively high, especially in commercial centers and densely populated areas such as Xinjiekou and Confucius Temple. While the brightness in ecological protection areas such as the southern part of Jiangning District, the northern part of Liuhe District, and Qixia Mountain is relatively low. However, the brightness in some peripheral residential areas, such as some newly built communities in Jiangning and Liuhe, is insufficient, which may affect night safety and living convenience. And the brightness values in some residential areas in the urban area are on the high side, with obvious light pollution problems.

[0091] 9) The results of the present invention show that based on ground measured data, unmanned aerial vehicle data, and SDGSAT-1 remote sensing image data, the urban light environment can be effectively monitored, providing technical support for the reasonable control of night urban lighting.

[0092] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0093] Obviously, those skilled in the art can make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalent technologies, this application is also intended to include these changes and modifications.

Claims

1. A method for remote sensing monitoring of urban night light environment using combined unmanned aerial vehicle and SDGSAT-1 satellite data, characterized in that: The method comprises the following steps: Select an observation area, conduct drone aerial photography and field observation, obtain multispectral remote sensing images at night and during the day, and the observed illumination values ​​of the observation points; calibrate the multispectral remote sensing images during the day to obtain the surface reflectance information of the observation area; Combined with the surface reflectivity information of the observation area, the nighttime surface reflected radiance received by the drone is converted into surface incident radiance; The surface incident radiance of the observation point is taken as the independent variable, and the observed illuminance value of the observation point is taken as the dependent variable. The random forest algorithm is used to train the first surface incident illuminance estimation model based on UAV night images. The trained first surface incident illuminance estimation model is applied to the surface incident radiance corresponding to the UAV to obtain the UAV surface incident illuminance of the observation area, and it is upscaled to the same resolution as the SDGSAT-1 satellite remote sensing image. The SDGSAT-1 satellite surface incident radiance of the observation area is calculated based on the SDGSAT-1 satellite remote sensing images after radiation calibration, surface reflectance data, building coverage ratio and tree coverage ratio; The SDGSAT-1 satellite surface incident radiance in the observation area is taken as the independent variable, and the corresponding upscaled UAV surface incident illuminance is taken as the dependent variable. The random forest algorithm is used to train the second surface incident illuminance estimation model based on the SDGSAT-1 satellite. The trained second surface incident illuminance estimation model is applied to the surface incident radiance of the SDGSAT-1 satellite in the entire city, and the night-time incident surface illuminance distribution map of the entire city is obtained.

2. The urban night light environment remote sensing monitoring method of the combined unmanned aerial vehicle and SDGSAT-1 satellite data according to claim 1 is characterized in that, The process of conducting drone aerial observations and field observations includes the following steps: While the SDGSAT-1 satellite is passing by at night, we select observation areas and use drones equipped with multispectral imagers to conduct flight observations to obtain multispectral remote sensing images at night. During the UAV flight observation, a handheld illuminance meter was used to measure and record the downlink illuminance value of the observation point horizontally upward, and a handheld RTK device was used to record the latitude and longitude position data of the observation point; Use a multispectral imager mounted on a drone to conduct aerial observations in the same observation area during the daytime at a similar time phase, obtain daytime multispectral remote sensing images in the observation area, and take calibration whiteboard images before and after the flight observation; The multispectral remote sensing images at night and during the day are stitched together using UAV orthophoto processing software.

3. The urban night light environment remote sensing monitoring method of the combined unmanned aerial vehicle and SDGSAT-1 satellite data according to claim 1 is characterized in that, Combined with the surface reflectivity information of the observation area, the process of converting the surface reflected radiance received by the drone into the surface incident radiance includes the following steps: The data of the three visible light bands of RGB in the multispectral remote sensing images of the drone at night are retained to correct the multispectral remote sensing images of the drone at night; Convert the surface reflected radiance received by the drone into the surface incident radiance: ； In the formula, is the surface incident radiance of the UAV in the i-th band; is the radiation brightness at the entrance pupil of the drone in the i-th band; is the surface reflectivity of the UAV in the i-th band.

4. The urban night light environment remote sensing monitoring method of the combined unmanned aerial vehicle and SDGSAT-1 satellite data according to claim 1 is characterized in that, The process of using the random forest algorithm to train the first surface incident illumination estimation model based on UAV night images includes: According to the latitude and longitude information of the measured points, the surface incident radiance values ​​of the three visible light bands of RGB in the corresponding multispectral remote sensing images of the UAV at night are extracted; Taking the surface incident radiance of the observation point as the independent variable and the observed illuminance value of the observation point as the dependent variable, the random forest algorithm is used to train the first surface incident illuminance estimation model based on UAV night images. During the training process, grid search is used to optimize the model parameters, and ten-fold cross validation is used to verify the model accuracy.

5. The urban night light environment remote sensing monitoring method of the combined unmanned aerial vehicle and SDGSAT-1 satellite data according to claim 1 is characterized in that, The process of upscaling the UAV surface incident illumination in the observation area to the same resolution as the SDGSAT-1 satellite remote sensing image includes the following steps: The 10-meter grids are cycled one by one, and the average surface incident illumination of non-building and tree pixels in each 10-meter grid is counted to obtain the surface incident illumination map of the observation area with a 10-meter resolution.

6. The urban night light environment remote sensing monitoring method of the combined unmanned aerial vehicle and SDGSAT-1 satellite data according to claim 1 is characterized in that, The SDGSAT-1 satellite images were pre-processed by mosaicking and cropping using remote sensing processing software, and radiometric calibration was performed to convert the grayscale values ​​into radiance values: ； In the formula, is the radiance of the satellite at the entrance pupil in the i-th band; is the gray value of the satellite's i-th band; and are the satellite i-th band gain and offset respectively.

7. The urban night light environment remote sensing monitoring method of the combined unmanned aerial vehicle and SDGSAT-1 satellite data according to claim 1 is characterized in that, The process of calculating the SDGSAT-1 satellite surface incident radiance of the observation area includes the following steps: The three visible light bands of RGB with a resolution of 40m and the panchromatic band with a resolution of 10m in the SDGSAT-1 satellite were fused by Gram-Schmidt orthogonal transformation to obtain the three visible light band images with a resolution of 10m. The surface reflectance data of the three visible light bands of RGB are extracted from the satellite daytime surface reflectance products of the adjacent phase; the building vector data and tree cover data are superimposed on the SDGSAT-1 pixels, and the building coverage and tree cover in each 10-meter resolution pixel are calculated; The SDGSAT-1 satellite surface incident radiance in the observation area is calculated using the following formula: ； In the formula, is the surface incident radiation brightness of the satellite in the i-th band; represents the radiance of the satellite at the entrance pupil in the i-th band; is the surface reflectivity of the satellite’s i-th band; is the building coverage; is the tree cover.

8. The urban night light environment remote sensing monitoring method of the combined unmanned aerial vehicle and SDGSAT-1 satellite data according to claim 1 is characterized in that, The method further comprises: The nighttime surface incident illumination distribution map obtained based on remote sensing represents the urban light environment. The nighttime insufficiently and excessively illuminated areas in the city are identified by combining the surface incident illumination distribution map with the city map and functional zoning.

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