A near-infrared waveband stable image element analog reference source monitoring method and device

Abstract

This application discloses a method and device for monitoring a near-infrared band stable pixel simulated reference source. The method includes the following steps: performing a two-layer screening of primary data from a medium-resolution spectral imager, involving day / night determination and stable pixel site matching, to obtain target observation data; selecting atmospheric reanalysis data as the background field, completing spatiotemporal matching with the target observation data, and performing interpolation preprocessing; inputting the preprocessed atmospheric reanalysis data into a radiative transfer model, and performing synchronous reflectance simulation based on the screened stable pixel sites to obtain simulated reflectance data; calculating the average relative deviation between the observed reflectance and the simulated reflectance, and performing multi-dimensional statistical monitoring and accuracy evaluation on the average relative deviation.

Description

Technical Field

[0001] This invention relates to the field of satellite remote sensing observation technology, and in particular to a method and device for monitoring a near-infrared band stable pixel analog reference source. Background Technology

[0002] The Medium Resolution Spectral Imager (MERSI) is the core payload of the FY-3 series of polar-orbiting meteorological satellites. Its visible and near-infrared band observation data are widely used in land cover monitoring, atmospheric composition inversion, and meteorological element forecasting. The accuracy and reliability of the data directly determine the precision of remote sensing applications and meteorological operations.

[0003] Currently, conventional techniques for verifying the accuracy of MERSI observation data rely heavily on synchronous field observations and cross-validation of data across satellites. These methods suffer from high observation costs, limited spatiotemporal coverage, and significant susceptibility to weather conditions. They cannot achieve high-frequency, full-coverage accuracy monitoring and are insufficient to meet the operational and automated real-time quality assessment requirements.

[0004] Constructing simulated reference sources based on atmospheric radiative transfer models is an important technical direction for satellite remote sensing data accuracy assessment. Its core is to simulate satellite payload observations using atmospheric background field data, and then use these simulated values ​​as a reference to perform bias analysis and bias stability monitoring of the observation data. Currently, the operational application of this method in the visible and near-infrared bands is mainly as a stable target site calibration technique. This technique has the following drawbacks: First, it lacks standardized and diverse rules for selecting stable target reference areas. Existing operational applications mostly rely on manual experience to select fixed reference sites, limiting the available areas. Second, atmospheric background field data largely depends on radiosonde observation data, which not only requires long-term, regular release of radiosonde balloons, resulting in high operational costs, but also suffers from high-altitude drift issues. The spatiotemporal matching between the atmospheric background field and satellite observation data is poor, easily introducing simulation errors, further limiting the automated and operational application of this method.

[0005] The desert region has stable underlying surface properties and high surface homogeneity, making it an ideal reference surface for satellite remote sensing observations in the visible and near-infrared spectral bands. The European Centre for Medium-Range Weather Forecasts (ECMWF) Generation 5 Atmospheric Reanalysis dataset (ERA5) offers advantages such as high spatiotemporal resolution, coverage of multiple meteorological elements, and stable and continuous acquisition. Combined with radiative transfer models, it can achieve high-precision satellite observation simulations. Currently, there is no technical solution in this field that combines a standardized stable target reference area, high-precision atmospheric reanalysis data, and radiative transfer simulation technology to construct a standardized simulation reference source adapted to the MERSI payload and establish a multi-dimensional deviation statistical monitoring method. Therefore, how to overcome the aforementioned shortcomings of existing MERSI data accuracy verification methods and achieve automated, operational quality assessment of observational data has become an urgent technical problem to be solved in this field. Summary of the Invention

[0006] In view of the above-mentioned defects or improvement needs of the existing technology, the present invention provides a near-infrared band stable pixel simulation reference source monitoring method and device. Its purpose is to construct a standardized, low-cost automated simulation reference source to solve the technical problems in the existing verification methods, such as poor spatiotemporal matching due to inconsistent reference sources and reliance on high-cost radiosonde data, and difficulty in achieving operational real-time monitoring.

[0007] To achieve the above objectives, the present invention provides a method and device for monitoring a near-infrared band stable pixel analog reference source, comprising the following steps:

[0008] The target observation data is obtained by performing a two-layer screening process, which involves day / night determination and stable pixel site matching, on the primary data of the medium resolution spectral imager.

[0009] Atmospheric reanalysis data is selected as the background field to complete the spatiotemporal matching with the target observation data, and interpolation preprocessing is performed.

[0010] The preprocessed atmospheric reanalysis data is input into the radiative transfer model, and the reflectance is synchronously simulated based on the screened stable pixel sites to obtain simulated reflectance data.

[0011] The average relative deviation between the observed reflectance and the simulated reflectance is calculated, and the average relative deviation is subjected to multi-dimensional statistical monitoring and accuracy evaluation.

[0012] In one embodiment of the present invention, the day / night determination is as follows:

[0013] Read the solar zenith angle dataset corresponding to the first-level data, extract the maximum effective value, and if the maximum effective value is less than 90 degrees, it is determined to be daytime observation data;

[0014] The stable pixel site matching is as follows: radiometric calibration, atmospheric correction and cloud detection are performed on the primary data, candidate pixels for bare soil desert are initially screened by normalized differential vegetation index, spatial homogeneity, temporal stability and atmospheric cleanliness index of candidate pixels are calculated band by band, pixels that meet all the indexes are retained, and continuous pixel areas are selected as stable pixel sites.

[0015] In one embodiment of the present invention, the spatiotemporal matching method includes:

[0016] Extract the starting observation time of the target observation data, match the atmospheric reanalysis data of the two closest time intervals, and read the required meteorological elements. Convert the units and formats of all meteorological elements into the standard form required by the radiative transfer model.

[0017] In one embodiment of the present invention, the interpolation preprocessing includes horizontal spatial interpolation, bilinear interpolation for regular grid data, and nearest neighbor matching for cloud and precipitation area profiles.

[0018] In one embodiment of the present invention, the interpolation preprocessing includes vertical spatial interpolation, using logarithmic pressure linear interpolation for temperature and humidity profiles and gas composition.

[0019] In one embodiment of the present invention, the interpolation preprocessing includes time interpolation, which uses linear interpolation of data from two time points before and after the observation time after horizontal interpolation.

[0020] In one embodiment of the present invention, the method for synchronous simulation of reflectivity includes:

[0021] The parameters of the radiative transfer model are set according to the channel frequency and observation zenith angle of the near-infrared band of the medium resolution spectral imager, and the surface type is set to desert underlying surface, and the corresponding surface emissivity is assigned to the desert underlying surface.

[0022] The simulated reflectance data were obtained by weighting the clear sky radiation and the full cloud radiation based on cloud coverage rate for all-cloud, clear sky, and cloudy weather conditions respectively.

[0023] In one embodiment of the present invention, the multi-dimensional statistical monitoring includes:

[0024] The average relative deviation of each near-infrared band of the medium-resolution spectral imager is calculated according to a set period, and the mean, standard deviation and root mean square error of the average relative deviation are statistically analyzed. At the same time, the distribution characteristics of the deviation with the observation angle and time series are analyzed.

[0025] In one embodiment of the present invention, the accuracy assessment includes:

[0026] Establish an accuracy evaluation standard and set a preset reflectance deviation threshold. If the average relative deviation is within the reflectance deviation threshold range, the accuracy of the observation data is deemed to be qualified.

[0027] If the reflectivity deviation threshold is exceeded, the source of the error is located and calibration recommendations are given.

[0028] The present invention also provides a near-infrared band stable pixel analog reference source monitoring device, the device comprising:

[0029] One or more processors;

[0030] A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the near-infrared band stable pixel analog reference source monitoring method as described in the first aspect.

[0031] In summary, the technical solutions conceived by this invention have the following beneficial effects compared with the prior art:

[0032] This invention combines a stable underlying surface, high-precision atmospheric data, and a physical model to construct a standardized simulation reference source, solving the problems of inconsistent reference benchmarks and high costs. Simultaneously, through data filtering and multi-dimensional interpolation, it achieves high-precision spatiotemporal matching between atmospheric and observational data, reducing simulation errors. Furthermore, based on multiple statistical indicators and multi-dimensional analysis, this invention enables refined deviation monitoring and effectively locates errors. The fully automated algorithm of this invention can be directly integrated into operational systems, supporting real-time monitoring and widespread application. Attached Figure Description

[0033] Figure 1 This is a schematic diagram of a near-infrared band stable pixel simulation reference source monitoring method provided in Embodiment 1;

[0034] Figure 2 This is a schematic diagram of a near-infrared band stable pixel analog reference source monitoring device provided in Embodiment 2. Detailed Implementation

[0035] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings, but it should be understood that the scope of protection of the present invention is not limited to the specific embodiments.

[0036] Unless otherwise expressly stated, throughout the specification and claims, the term "comprising" or its variations such as "including" or "comprises" shall be understood to include the stated elements or components without excluding other elements or other components.

[0037] Example 1:

[0038] The purpose of this embodiment is to overcome the technical problems of existing MERSI observation data lacking standardized and low-cost simulated reference sources for calibration and accuracy verification, and having a relatively limited monitoring area, and to provide a MERSI visible and near-infrared band stable pixel simulated reference source monitoring method.

[0039] This embodiment automatically selects stable pixel sites in desert areas. Combining ERA5 high-resolution atmospheric reanalysis data with the RTTOV radiative transfer model, it constructs a standardized MERSI simulation reference source through steps such as two-layer data screening, refined spatiotemporal preprocessing, synchrotron radiative transfer simulation, and multi-dimensional deviation statistics. This enables automated, multi-dimensional monitoring and evaluation of the accuracy of MERSI visible and near-infrared band observation data, meeting the needs of operational quality control of satellite remote sensing data.

[0040] like Figure 1 As shown, this embodiment provides a near-infrared band stable pixel analog reference source monitoring method, such as... Figure 1 As shown, it includes the following steps:

[0041] S101: The target observation data is obtained by performing a two-layer screening process, which involves day / night determination and stable pixel site matching, on the primary data of the medium resolution spectral imager.

[0042] This first embodiment uses L1-level data in the visible and near-infrared bands from the FY-3 satellite MERSI payload. A two-layer screening process, involving day / night cycle determination and stable pixel site matching, is performed to obtain target observation data that meets the monitoring conditions.

[0043] First, determine day / night cycle: Read the L1 data of MERSI-III, that is, the solar zenith angle dataset corresponding to the geolocation auxiliary file (GEO file) of the L1 data generated by the medium resolution spectral imager of MERSI-III on the Fengyun-3 satellite. Extract the maximum effective value Max(Sun_zenith, angle) from the maximum effective values ​​in the dataset. If the following conditions are met:

[0044]

[0045] If the image is determined to be daytime observation data, it will proceed to the stable pixel site matching stage; otherwise, the data will be discarded.

[0046] Next, stable pixel site matching is performed. Based on MERSI visible and near-infrared band data, stable pixel site selection is completed through a full process of preprocessing, index screening, and aggregation and grading.

[0047] Radiometric calibration, atmospheric correction, and cloud detection were performed on the first-level data generated by the medium-resolution spectral imager. Candidate pixels for bare soil desert were initially screened using the normalized differential vegetation index (NDVI). The atmospheric cleanliness indexes of 5×5 neighborhood spatial uniformity, multi-period dry season temporal stability, and aerosol optical thickness (AOD) were calculated band by band. Pixels that met all the criteria were retained, and continuous pixel areas were selected as dedicated stable pixel sites for MERSI.

[0048] In this first embodiment, radiometric calibration is first performed using the calibration coefficients inherent in the payload, converting the original observations into physically meaningful apparent reflectance. Secondly, an atmospheric correction algorithm suitable for the visible-near-infrared band is used to perform atmospheric correction on the data to eliminate the effects of atmospheric scattering and absorption, thus obtaining preliminary surface reflectance data. Simultaneously, cloud detection is performed using the cloud-inherent cloud detection markers or a general cloud detection algorithm, and pixels covered by clouds are removed to ensure that subsequent analysis is based on observations under clear or mostly clear sky conditions.

[0049] Based on atmospherically corrected surface reflectance data, the Normalized Difference Vegetation Index (NDVI) is calculated for each pixel. A low NDVI threshold is set (e.g., NDVI < 0.1) to filter out pixels whose corresponding land cover types are highly likely to be sparsely vegetated bare soil or desert areas, thus initially identifying a large number of candidate pixels.

[0050] For each candidate pixel in the bare soil desert selected in the initial screening, three stability quantification indicators are calculated band by band (i.e., each visible and near-infrared channel of MERSI):

[0051] Centered on the pixel, calculate the standard deviation of the reflectance of this band within a 5×5 pixel neighborhood window. The smaller the standard deviation, the more spatially homogeneous the pixel and its surrounding area are.

[0052] Historical images of the region taken during multiple dry seasons (periods of low precipitation and minimal changes in surface humidity) are selected from the same period. The coefficient of variation or time series standard deviation of the reflectance of the pixel in the same band is calculated over many years. The smaller the value, the more stable the pixel reflectance becomes over time.

[0053] Based on L1 data, the aerosol optical thickness at the pixel location is calculated using an atmospheric correction inversion process or a specialized lookup table algorithm. A lower AOD value indicates a lower atmospheric aerosol content and cleaner atmospheric conditions during observation, resulting in less interference with the observation signal.

[0054] Reasonable thresholds are set for the three indicators mentioned above. For example, the standard deviation of spatial homogeneity must be below a certain value, the coefficient of variation of temporal stability must be below a certain value, and AOD must be below a certain value. Only pixels that simultaneously meet the thresholds of all three indicators are retained, i.e., pixels that meet all three indicators. Finally, among these compliant discrete pixels, spatially contiguous pixel regions with sufficiently large areas are identified and selected, and formally defined as MERSI dedicated stable pixel sites. This site will serve as a fixed geographic reference target for subsequent construction of simulated reference sources and on-orbit radiometric accuracy monitoring.

[0055] S102: Select atmospheric reanalysis data as the background field, complete the spatiotemporal matching with the target observation data, and perform interpolation preprocessing.

[0056] In this example, the European Centre for Medium-Range Weather Forecasts (ECMWF) Generation 5 Atmospheric Reanalysis 37-layer dataset (ERA5) is selected as the atmospheric background field data. This data has a spatial resolution of 0.25° × 0.25° and a temporal resolution of 1 hour. Spatiotemporal matching and multidimensional interpolation preprocessing are performed on it with the target observation data.

[0057] Extract the starting observation time of the target observation data in S101, match the ERA5 data of the two closest time points, and read the meteorological elements such as latitude and longitude, pressure layer, temperature, water vapor, ozone, cloud water fraction, cloud ice fraction, cloud fraction, surface air pressure, surface temperature, 2-meter dew point temperature, 2-meter air temperature, and 10-meter wind speed (U / V component) from the ERA5 data. Convert the units and formats of all elements into the standard form required by the RTTOV radiative transfer model.

[0058] The spatiotemporally matched ERA5 profile data were then subjected to horizontal spatial interpolation, vertical spatial interpolation, and temporal interpolation in sequence:

[0059] For data on regular grids, horizontal interpolation uses bilinear interpolation. In simulations with cloud and precipitation areas, cloud and precipitation profiles are matched using the nearest neighbor method. In standard bilinear interpolation, the interpolation value is calculated from the values ​​of the four nearest neighbors of a closed rectangular grid. , , , The interpolation points are obtained through combined calculations. , The calculation formula is:

[0060]

[0061] Where a1, a2, a3, and a4 are the variable values ​​corresponding to the four nearest neighbors, and b is the variable value corresponding to the interpolation point. The weights are calculated as follows:

[0062]

[0063]

[0064] In the vertical interpolation process, the temperature and humidity profiles and gas composition are interpolated using a logarithmic pressure linear interpolation method. The vertical layer interpolation method uses data before and after the isobaric surface layer for interpolation. For the horizontally interpolated data m1 and m2, the corresponding pressure layers are p1 and p2, respectively. If the vertically interpolated data for the interpolated layer pressure p (p1 <= p <= p2) is m, the calculation formula is:

[0065]

[0066] After horizontal interpolation of the background field data, temporal interpolation is performed. The temporal interpolation method involves simple linear interpolation using data before and after the observation time. For the horizontally interpolated data b1 and b2, the predicted times are t1 and t2, respectively. If the temporally interpolated data for the observation time tp (t1 <= tp <= t2) is c, the calculation formula is:

[0067]

[0068] weight value w t for:

[0069]

[0070] S103: Input the preprocessed atmospheric reanalysis data into the radiative transfer model, and perform synchronous reflectance simulation based on the screened stable pixel sites to obtain simulated reflectance data.

[0071] The preprocessed ERA5 atmospheric profile data from S102 was input into the RTTOV radiative transfer model. Based on the atmospheric radiative transfer equation and under the assumption of a plane-parallel atmosphere in local dynamic and thermal equilibrium, a synchronous simulation of reflectance in the MERSI visible and near-infrared bands was completed, yielding simulated reflectance data that spatiotemporally matches the observed data.

[0072] Based on the MERSI visible and near-infrared band channel frequencies, observation zenith angles, and other payload parameters, the channel configuration and observation geometry parameters of the RTTOV model were set, and the surface type was set to desert underlying surface with corresponding surface emissivity assigned.

[0073] For different meteorological conditions—partly cloudy, clear sky, and cloudy—the radiative transfer values ​​are calculated separately. The total radiation is the weighted sum of clear sky radiation and partly cloudy radiation.

[0074]

[0075]

[0076]

[0077] in:

[0078] v: Channel frequency; θ: Zenith angle; Total radiation; Clear-sky radiation; Full cloud radiation; Transmittance from the Earth's surface to the top of the atmosphere; : Transmittance from cloud top to atmospheric top; N: Cloud cover (between 0 and 1); : Surface emissivity; B(v, T): Planck function; μ: cosine of zenith angle; Ts: Surface temperature; Cloud top temperature.

[0079] S104: Calculate the average relative deviation between the observed reflectance and the simulated reflectance, and perform multi-dimensional statistical monitoring and accuracy evaluation on the average relative deviation.

[0080] In this first embodiment, the relative deviation between observed reflectance and simulated reflectance is calculated on a daily basis, and multi-dimensional statistical monitoring and accuracy evaluation are completed in conjunction with quality control conditions.

[0081] For each MERSI visible and near-infrared band channel, the average relative deviation between observed and simulated reflectance is calculated.

[0082] Multi-dimensional statistical analysis is performed on the relative deviation data, with statistical indicators including mean, standard deviation, and root mean square error. At the same time, the distribution characteristics of deviation with observation angle and long time series are analyzed to achieve refined monitoring of the accuracy of MERSI observation data.

[0083] Based on the statistical results, an accuracy evaluation standard for MERSI visible and near-infrared band observation data is established. If the average relative deviation is within the preset threshold range, the accuracy of the observation data is deemed acceptable; if it exceeds the threshold, the source of error is located and targeted calibration suggestions are given.

[0084] Example 2:

[0085] This second embodiment provides a near-infrared band stable pixel analog reference source monitoring device, used to execute the near-infrared band stable pixel analog reference source monitoring method provided in the first embodiment, such as... Figure 2 As shown, the device includes:

[0086] One or more processors;

[0087] A storage device for storing one or more programs that, when executed by one or more processors, enable the one or more processors to implement the near-infrared band stable pixel analog reference source monitoring method as described in Embodiment 1.

[0088] Figure 2 This is a schematic diagram of the near-infrared band stable pixel analog reference source monitoring device provided in Embodiment 2 of the present invention. Figure 2 The near-infrared band stable pixel analog reference source monitoring device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0089] like Figure 2 As shown, the near-infrared band stable pixel analog reference source monitoring device is presented in the form of a general-purpose device. The components of the near-infrared band stable pixel analog reference source monitoring device may include, but are not limited to: one or more processors or processing units, memory, and buses connecting different system components (including memory and processing units).

[0090] A bus refers to one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. Examples of these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0091] Near-infrared band stable pixel analog reference source monitoring equipment typically includes a variety of computer-readable media. These media can be any available media that can be accessed by the near-infrared band stable pixel analog reference source monitoring equipment, including volatile and non-volatile media, and portable and non-portable media.

[0092] The memory may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory. The near-infrared band stable pixel analog reference source monitoring device may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system may be used to read and write non-removable, non-volatile magnetic media (…). Figure 2 Not shown; usually referred to as a "hard drive"). Although Figure 2 As not shown, disk drives for reading and writing to removable non-volatile disks (e.g., "floppy disks") and optical disc drives for reading and writing to removable non-volatile optical discs (e.g., CD-ROMs, DVD-ROMs, or other optical media) may be provided. In these cases, each drive may be connected to a bus via one or more data media interfaces. The memory may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0093] A program / utility having a set (at least one) of program modules can be stored, for example, in memory. Such program modules include, but are not limited to, an operating system, one or more applications, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. The program modules typically perform the functions and / or methods described in the embodiments of this invention.

[0094] The near-infrared band stabilized pixel analog reference source monitoring device can also communicate with one or more external devices (e.g., keyboards, pointing devices, displays, etc.), and with one or more devices that enable users to interact with the near-infrared band stabilized pixel analog reference source monitoring device, and / or with any device that enables the near-infrared band stabilized pixel analog reference source monitoring device to communicate with one or more other devices (e.g., network cards, modems, etc.). This communication can be performed through an input / output (I / O) interface. Furthermore, the near-infrared band stabilized pixel analog reference source monitoring device can also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter. Figure 2 As shown, the network adapter communicates with other modules of the near-infrared band stable pixel analog reference source monitoring device via a bus. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the near-infrared band stable pixel analog reference source monitoring device, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0095] The processing unit executes various functional applications and data processing by running programs stored in the memory, such as implementing the near-infrared band stable pixel analog reference source monitoring method provided in any embodiment of the present invention.

[0096] It is worth noting that the information interaction and execution process between the modules and units in the above-mentioned device and system are based on the same concept as the processing method embodiment of the present invention. For details, please refer to the description in the method embodiment of the present invention, and will not be repeated here.

[0097] Those skilled in the art will understand that all or part of the steps in the various methods of the embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc.

[0098] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for monitoring a near-infrared band stable pixel simulated reference source, characterized in that, Includes the following steps: The target observation data is obtained by performing a two-layer screening process, which involves day / night determination and stable pixel site matching, on the primary data of the medium resolution spectral imager. Atmospheric reanalysis data is selected as the background field to complete the spatiotemporal matching with the target observation data, and interpolation preprocessing is performed. The preprocessed atmospheric reanalysis data is input into the radiative transfer model, and the reflectance is synchronously simulated based on the screened stable pixel sites to obtain simulated reflectance data. The average relative deviation between the observed reflectance and the simulated reflectance is calculated, and the average relative deviation is subjected to multi-dimensional statistical monitoring and accuracy evaluation.

2. The near-infrared band stable pixel analog reference source monitoring method according to claim 1, characterized in that, The day / night cycle determination is as follows: Read the solar zenith angle dataset corresponding to the first-level data, extract the maximum effective value, and if the maximum effective value is less than 90 degrees, it is determined to be daytime observation data; The stable pixel site matching is as follows: radiometric calibration, atmospheric correction and cloud detection are performed on the primary data, candidate pixels for bare soil desert are initially screened by normalized differential vegetation index, spatial homogeneity, temporal stability and atmospheric cleanliness index of candidate pixels are calculated band by band, pixels that meet all the indexes are retained, and continuous pixel areas are selected as stable pixel sites.

3. The near-infrared band stable pixel analog reference source monitoring method according to claim 1, characterized in that, The spatiotemporal matching method includes: Extract the starting observation time of the target observation data, match the atmospheric reanalysis data of the two closest time intervals, and read the required meteorological elements. Convert the units and formats of all meteorological elements into the standard form required by the radiative transfer model.

4. The near-infrared band stable pixel analog reference source monitoring method according to claim 3, characterized in that, The interpolation preprocessing includes horizontal spatial interpolation, bilinear interpolation for regular grid data, and nearest neighbor matching for cloud and precipitation area profiles.

5. The near-infrared band stable pixel analog reference source monitoring method according to claim 3, characterized in that, The interpolation preprocessing includes vertical spatial interpolation, and logarithmic pressure linear interpolation is used for temperature and humidity profiles and gas composition.

6. The near-infrared band stable pixel analog reference source monitoring method according to claim 3, characterized in that, The interpolation preprocessing includes time interpolation, which uses linear interpolation of data from two time points before and after the observation time after horizontal interpolation.

7. The near-infrared band stable pixel analog reference source monitoring method according to claim 1, characterized in that, The method for synchronous simulation of reflectivity includes: The parameters of the radiative transfer model are set according to the channel frequency and observation zenith angle of the near-infrared band of the medium resolution spectral imager, and the surface type is set to desert underlying surface, and the corresponding surface emissivity is assigned to the desert underlying surface. The simulated reflectance data were obtained by weighting the clear sky radiation and the full cloud radiation based on cloud coverage rate for all-cloud, clear sky, and cloudy weather conditions respectively.

8. The near-infrared band stable pixel analog reference source monitoring method according to claim 1, characterized in that, The multi-dimensional statistical monitoring includes: The average relative deviation of each near-infrared band of the medium-resolution spectral imager is calculated according to a set period, and the mean, standard deviation and root mean square error of the average relative deviation are statistically analyzed. At the same time, the distribution characteristics of the deviation with the observation angle and time series are analyzed.

9. The near-infrared band stable pixel analog reference source monitoring method according to claim 1 or 8, characterized in that, The accuracy assessment includes: Establish an accuracy evaluation standard and set a preset reflectance deviation threshold. If the average relative deviation is within the reflectance deviation threshold range, the accuracy of the observation data is deemed to be qualified. If the reflectivity deviation threshold is exceeded, the source of the error is located and calibration recommendations are given.

10. A near-infrared band stable pixel analog reference source monitoring device, characterized in that, The device includes: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the near-infrared band stable pixel analog reference source monitoring method as described in any one of claims 1 to 9.

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