On-orbit automatic updating method for calibration parameters of reflection band of remote sensing instrument

Abstract

The invention relates to the technical field of communication, and provides a remote sensing instrument reflection wave band calibration parameter on-orbit automatic updating method, which adopts simulation reflectivity of multiple stable targets to calculate the deviation of observation reflectivity, can improve the reliability of a processing result through a two-layer data quality control strategy, and aims at the annual periodic change of the relative deviation of the observation reflectivity. A corresponding year day-by-day correction model is established, and accurate observation reflectivity deviation can be obtained; and aiming at the annual periodic change of the calibration slope, a corresponding annual day-by-day correction model is established, so that the relatively accurate calibration slope and attenuation rate can be obtained. According to the method, time-continuous and stable on-orbit observation deviation calculation and the calibration parameter on-orbit automatic updating method and device based on observation deviation and attenuation are provided, more stable and accurate instrument observation deviation change and updating suggestions can be obtained, and the requirement for timely finding and correcting on-orbit change is met.

Description

Technical Field

[0001] The invention relates to the field of communication technology, and in particular to an on-orbit automatic updating method for remote sensing instrument reflection band calibration parameters. Background Art

[0002] Remote sensing instruments are affected by the space working environment and the changes in device characteristics, and their on-orbit performance changes. In the reflection band, especially the short-wave part, there is a significant decay problem in the radiation response time. It is necessary to detect its changes in time and provide corresponding calibration correction parameters to compensate for the impact of instrument response decay on radiation data and maintain the quality and stability of radiation data. For instruments with reliable on-board calibration devices, such as MODIS and VIIRS, regular solar observations can be used to timely process the relative change information of the instrument radiation response, and calibration correction is implemented accordingly. For instruments without reliable on-board calibration devices, calibration parameter information at a specific time is usually obtained by alternative calibration methods such as simultaneous sub-satellite point observation comparison (SNO) with high-performance reference instruments and comparison with top of atmosphere observations based on ground synchronous measurements (field calibration).

[0003] The technical solution of simultaneous sub-satellite observation comparison (SNO) based on high-performance reference instruments is limited by the orbits of the reference instruments and the target instruments, and has the problem of limited comparison opportunities. For example, the cross-comparison samples of two sun-synchronous near-polar orbit satellites are distributed in the polar region. The technical solution of atmospheric top observation comparison (field calibration) based on ground-based synchronous measurement is limited by the implementation of ground-based synchronous measurement and the clear sky conditions of the site, and has problems with manpower and material costs, as well as limited effective comparison opportunities. At the same time, due to the influence of the potential nonlinear response characteristics of the instrument, the spatial and radiation distribution differences of the sample targets, and the observation geometry conditions, the specific technical solution leads to the fluctuation of the obtained radiation deviation or calibration parameters, which affects the reliability of the monitoring results. Since it cannot provide time-continuous and stable monitoring results, it cannot meet the needs of timely detection and reliable correction of on-orbit changes in instrument response. Therefore, a method for automatic on-orbit update of remote sensing instrument reflection band calibration parameters is needed. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides an on-orbit automatic updating method for the reflection band calibration parameters of a remote sensing instrument, which solves the problem that it cannot provide time-continuous and stable monitoring results and cannot meet the needs of timely detection and reliable correction of on-orbit changes in instrument response.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0006] A method for automatically updating the calibration parameters of a reflection band of a remote sensing instrument on-orbit comprises the following steps:

[0007] Step 1: Select stable target sites distributed around the world, extract target instrument observation data, and calculate the instantaneous simulated reflectivity of the top of the atmosphere of the stable target site based on the set plan, environmental field data and radiation transfer model. After correction of the sun-earth distance and solar zenith angle, the simulated reflectivity factor RefF is obtained. sim , forming the instrument observation reflectivity factor RefF mea and the simulated reflectivity factor RefF sim The matching sample data pairs include the earth observation count value EV, the cold sky observation count value SV, the satellite zenith angle (SenZ), the solar zenith angle (SolZ), the daily count Julday, etc.

[0008] Step 2: Perform the first quality control screening on the matched sample data of observed and simulated reflectance according to the set plan to remove unreliable factors such as large simulation error, saturated observation data, cloud / dust and other non-clear sky effects, and form the matched sample data set after the first quality control;

[0009] Step 3: Based on the matched sample data set after the first quality control, for the i-th day, with a set time window of [i-N1, i+N2] days, perform (EV-SV) and RefF sim Data fitting is performed, and abnormal data is removed for the time window to obtain the fitting slope Slope of the i-th day. The fitting slope forms the first Slope time series;

[0010] Step 4: Based on the Slope time series obtained in step 3, a selected date i0 such as the launch date or the start-up date is used as the reference time, and the time variation model Func_Slope of Slope is obtained according to the set scheme. The daily SlopeM time series data estimated based on the time variation model Func_Slope is averaged to obtain the average value SlopeA.

[0011] Step 5: Calculate the new observed reflectivity RefF based on SlopeM mea2 (RefF mea2 = SlopeM*(EV-SV)), repeat steps 2 to 4 to form the matching sample data set after the second quality control, the second Slope time series data set, and the second daily SlopeM time series data set;

[0012] Step 6: Calculate PDif (PDif = RefF based on the matched sample data set after the second quality control mea / RefF sim -1) and PDif2 (PDif2 = RefF mea2 / RefF sim -1), forming the PDif and PDif2 time series data sets;

[0013] Step 7: Based on the PDif2 time series data and according to the set plan, the PDif daily correction model Func_PDif is established through the calculation and processing of the multi-year average daily data, which is used to correct the annual changes of the PDif data, and the PDifCor after the annual change correction is obtained;

[0014] Step 8: Based on the second Slope time series data set, according to the set plan, the daily annual Slope correction model Func_Slope is established by calculating and processing the detrended and multi-year averaged daily data. It is used to correct the annual changes of the Slope data and obtain the SlopeCor after the annual change correction.

[0015] Step 9: Based on the PDifCor and SlopeCor results, determine whether the calibration coefficient needs to be updated according to the set plan;

[0016] Step 10: When the determination in step 9 requires updating of the calibration coefficients, the calibration coefficients to be updated are calculated for the first and non-first updates respectively according to the set scheme.

[0017] Preferably, the observation reflectivity calculation step in step 1 is as follows: input the target instrument L1 data, find the observation pixel point closest to it according to the latitude and longitude position of the stable target site, extract the data of the n*n window with it as the center, and calculate the window mean (Mean), standard deviation (Std), coefficient of variation CV (CV = Std / Mean), and obtain the observation reflectivity factor RefF according to the radiation calibration conversion relationship mea , forming stable target observation data.

[0018] Preferably, the simulated reflectivity calculation step in step 1 is as follows: input stable target observation data, and perform spatiotemporal matching with pre-constructed atmospheric aerosol / water vapor / ozone, surface BRDF climate state data, and atmospheric temperature, humidity, pressure, wind and other environmental field data according to latitude, longitude and time information, and use any radiation transmission mode such as MODTRAN and 6SV to calculate the instantaneous reflectivity Ref of the top of the atmosphere for the target observation angle. sim Calculate and correct the solar zenith angle and the distance between the sun and the earth to obtain the simulated reflectivity factor RefF when the sun is at the zenith and the average distance between the sun and the earth sim .

[0019] Preferably, the specific steps of quality control screening in step 2 are:

[0020] S1. According to the set angle quality control threshold, the solar zenith angle M° and the satellite zenith angle N° are used to identify SolZ and SenZ, and the data exceeding the threshold are eliminated;

[0021] S2. According to the set observation saturation threshold, the EV of each channel is judged and the data exceeding the threshold is eliminated;

[0022] S3, according to the set spatial uniformity quality control reference channel F CV and threshold, perform CV EV Identification: remove data exceeding the threshold;

[0023] S4, according to the set cloud / dust detection channels F1 and F2 and thresholds, perform channel observation reflectivity feature identification and remove data exceeding the threshold;

[0024] S5. Quality control reference channel F according to the set observation deviation RE and threshold, and the relative deviation between observed and simulated reflectivity (RefF mea / RefF sim -1) Identification, remove data that exceeds the threshold.

[0025] Preferably, the specific steps for each time window in step three are:

[0026] S1, Reject (EV-SV) or RefF sim Data less than 0;

[0027] S2, with (EV-SV) as x, RefF sim is y, and is fitted according to the set model;

[0028] S3, calculating the standard deviation of the deviation between the simulated reflectivity and the model-estimated reflectivity, taking 2 times the standard deviation as the threshold, and eliminating the data exceeding the threshold;

[0029] S4. Repeat S2, S3 and S2 to obtain the fitting slope Slope of the i-th day.

[0030] Preferably, the specific steps of processing the Slope time series of step 3 in step 4 are:

[0031] S1, taking the selected date i0 as the reference time, based on the Slope time series, according to the set model, such as linear, quadratic polynomial, etc., perform time change trend fitting;

[0032] S2, calculate the standard deviation of the deviation between the actual Slope value and the model estimated value, take 2 times the standard deviation as the threshold, and remove the data exceeding the threshold;

[0033] S3, repeat steps S1, S2 and S1 to obtain the time variation model of Slope Func_Slope (DSL), where DSL is the number of days from the reference time;

[0034] S4. Based on the Func_Slope (DSL) model estimation, obtain the daily SlopeM time series;

[0035] S5. Average the daily SlopeM time series data to obtain SlopeA.

[0036] Preferably, the specific steps of step 7 to establish the PDif annual correction model and perform change correction are:

[0037] S1. Based on the PDif2 time series data, the PDif2 data of the daily count of July in the same year are averaged to obtain the multi-year average daily PDif2 of the year. ave-daily data;

[0038] S2. Calculate PDif2 ave-daily The mean of the data set, PDif2 ave ;

[0039] S3. According to the set model, such as Sin function, PDif2 is performed ave-daily -PDif2 ave The time variation fitting of PDif is used to obtain the daily correction model Func_PDif (Julday), Julday = 1, 2...366;

[0040] S4. Obtain the corrected observation deviation PDifCor = PDif - Func_PDif (Julday).

[0041] Preferably, the specific steps of establishing the Slope annual correction model and performing change correction in step eight are:

[0042] S1, based on the second Slope and daily SlopeM time series data, remove the trend of the data set over time, Slope2 = Slope / SlopeM;

[0043] S2. Average the Slope2 data of the July day count in the same year to obtain the average daily Slope2 of multiple years. ave-daily data;

[0044] S3. Calculate Slope2 ave-daily Mean of the data set, Slope2 ave ;

[0045] S4. According to the set model, such as Sin function, Slope2 ave / Slope2 ave-dailyFit the time variation of the slope to obtain the daily correction model Func_Slope (Julday), Julday = 1, 2…366;

[0046] S5. Obtain the corrected calibration slope SlopeCor = Slope*Func_Slope(Julday).

[0047] Preferably, the specific steps of determining in step nine are:

[0048] S1. For the i-th day, the sliding average processing of PDifCor time series data is performed with a set time window to obtain PDif i ;

[0049] S2, in chronological order, PDif i When the set threshold is exceeded, it is determined that the calibration coefficient needs to be updated;

[0050] S3. For non-first updates, use the decay rate scheme for judgment;

[0051] S4. For the i-th day, calculate the decay rate Decay relative to the last update

[0052] Take the selected date i0 (e.g., the day of the launch, the day of the power on) as the reference time, and calculate the normalized response NRes: extract the SlopeCor results from the reference time to the i-th day, and perform time variation trend fitting according to the set model, such as linear, quadratic polynomial, etc., to obtain the time variation model of SlopeCor Func_SlopeCor (DSL); NRes (i) = Func_SlopeCor (i0) / Func_SlopeCor (i);

[0053] Based on the NRes time series, the time variation trend is fitted according to the set model, such as linear, quadratic polynomial, etc., to obtain the NRes time variation model Func_NRes (DSL);

[0054] Calculate the decay rate Decay: Decay = 1-Func_NRes(i) / Func_NRes(T1), where T1 is the number of logs updated for the first time:

[0055] S5. When Decay exceeds the set threshold, it is determined that the calibration coefficient needs to be updated.

[0056] Preferably, in step ten, for the first update, Func_SlopeCor(T1) is used as the calibration coefficient to be updated; for a non-first update, Func_SlopeCor(T1)*(1+Decay) is used as the calibration coefficient to be updated.

[0057] The present invention provides an on-orbit automatic updating method for the calibration parameters of the reflection band of a remote sensing instrument. It has the following beneficial effects:

[0058] 1. The present invention adopts the simulated reflectivity of multiple stable targets to calculate the deviation of observed reflectivity. The two-layer data quality control strategy can improve the reliability of the processing results, and can obtain more accurate changes in observed reflectivity deviations. Through normalized response calculation, the changes in instrument performance can be more accurately reflected. In addition, both the instrument observation deviation and the normalized response take into account the annual periodic changes, and establish a corresponding daily correction model for the year, which can remove the impact of annual periodic changes and obtain more stable and accurate long-term change results.

[0059] 2. The present invention adopts two schemes, namely, reflectivity relative deviation and attenuation rate, to determine whether calibration update is needed. The relative deviation scheme is used for judgment first, and the update judgment based on relative deviation adopts the time window sliding average scheme, which can remove the influence of data randomness. After implementing a calibration update, the attenuation rate scheme is used for judgment, which can further avoid the influence of data randomness of the relative deviation scheme and make the judgment result more stable.

[0060] 3. Based on this method, the present invention can realize daily real-time monitoring of instrument observation deviations and performance changes, provide timely business calibration update judgments, and support calibration correction compensation to avoid deterioration of data quality and keep data accuracy stable within a predetermined range. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a process diagram of the present invention;

[0062] Figure 2 This is a diagram of the PDif annual correction model result using the band 1 of FY-3D / MERSI as an experimental example of the present invention;

[0063] Figure 3 This is a PDif result diagram of the calibration coefficient update process of the present invention using band 1 of FY-3D / MERSI as an experimental example. DETAILED DESCRIPTION

[0064] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0065] Example:

[0066] Please refer to the attached Figure 1 The embodiment of the present invention provides an on-orbit automatic updating method for the calibration parameters of a remote sensing instrument reflection band, comprising the following steps:

[0067] Step 1: Select stable target sites distributed around the world, extract target instrument observation data, and calculate the instantaneous simulated reflectivity of the top of the atmosphere of the stable target site based on the set plan, environmental field data and radiation transfer model to form a matching sample data pair of instrument observation and simulated reflectivity, which includes the ground observation count value EV, cold sky observation count value SV, satellite zenith angle (SenZ), solar zenith angle (SolZ), daily count Julday, etc.

[0068] The calculation steps of the observed reflectivity are as follows: input the L1 data of the target instrument, find the observation pixel point closest to it according to the latitude and longitude position of the stable target site, extract the data of the n*n window with it as the center, and calculate the window mean (Mean), standard deviation (Std), and coefficient of variation CV (CV = Std / Mean) to form the stable target observation data, including the ground observation count value (EV), the cold sky observation count value (SV), the ground observation count value after dark background subtraction (DN, DN = EV-SV), and the observation reflectivity of the sun at the zenith average sun-earth distance (RefF mea ), satellite zenith angle (SenZ), solar zenith angle (SolZ); n is 3, (k0, k1, k2) are calibration coefficients.

[0069] The formula is: RefF mea =k0+DN*k1+DN*DN*k2

[0070] The simulation reflectivity calculation steps are as follows: input stable target observation data, match it with the pre-built atmospheric aerosol / water vapor / ozone, surface BRDF climate data, and atmospheric temperature, humidity, pressure, wind and other environmental field data in time and space according to the latitude, longitude and time information, and use any radiation transfer model such as MODTRAN and 6SV to calculate the instantaneous reflectivity Ref of the top of the atmosphere according to the target observation angle. sim Calculate and correct the solar zenith angle and the distance between the sun and the earth to obtain the simulated reflectivity factor (RefF sim =Ref sim *cos(SolZ)*dcorF);

[0071] The formula for the correction factor of the distance between the sun and the earth is: dcorF = 1. / (1-0.01673*cos(0.9856*(Julday-4)*3.1415926 / 180)) 2

[0072] Julday is the day of the year relative to January 1

[0073] Step 2: Perform the first quality control screening on the matching sample data of observed and simulated reflectance according to the set plan to remove unreliable factors such as large simulation errors, saturated observation data, and non-clear sky effects such as clouds / dust, to form a matching sample data set after the first quality control.

[0074] S1. According to the set angle quality control threshold, such as the solar zenith angle of 60° and the satellite zenith angle of 50°, SolZ and SenZ are identified to remove the data exceeding the threshold;

[0075] S2. According to the set observation saturation threshold, such as 4000, EV identification of each channel is performed, and data exceeding the threshold is eliminated;

[0076] S3, according to the set spatial uniformity quality control reference channel and threshold, such as the second channel (550nm), 0.1, CV EV Identification: remove data exceeding the threshold;

[0077] S4. According to the set cloud / dust detection reference channel and threshold, such as the first channel (470nm) and the second channel (550nm), 0.65, the sum of the observed reflectivity factors of the two channels is judged, and the data exceeding the threshold is eliminated;

[0078] S5. According to the set observation deviation quality control reference channel and threshold, such as the fourth channel (865nm), 30%, the relative deviation between the observed and simulated reflectance (RefF mea / RefF sim -1) Identification, eliminating data exceeding the threshold;

[0079] Step 3: Based on the matched sample data set after the first quality control, for the i-th day, with a set time window, such as [i-4, i+5] days, perform (EV-SV) and RefF sim Data fitting, the fitting slope forms the first Slope time series, where, for each time window:

[0080] S1, Reject (EV-SV) or RefF sim Data less than 0;

[0081] S2, with (EV-SV) as x, RefF sim For y, fit according to the set model, such as linear;

[0082] S3, calculating the standard deviation of the deviation between the simulated reflectivity and the model-estimated reflectivity, taking 2 times the standard deviation as the threshold, and eliminating the data exceeding the threshold;

[0083] S4. Repeat S2, S3 and S2 to obtain the fitting slope Slope of the i-th day.

[0084] Step 4: Based on the Slope time series in step 3, obtain the daily SlopeM time series and the average SlopeA. The specific steps are as follows:

[0085] S1, taking the selected date i0 such as the day of the launch of the star or the day of the start-up as the reference time, based on the Slope time series, according to the set model, such as linear, quadratic polynomial, etc., the time change trend is fitted;

[0086] S2, calculate the standard deviation of the deviation between the actual Slope value and the model estimated value, take 2 times the standard deviation as the threshold, and remove the data exceeding the threshold;

[0087] S3, repeat steps S1, S2 and S1 to obtain the time variation model of Slope Func_Slope(DSL), where DSL is the number of days from the reference time;

[0088] S4. Based on the Func_Slope (DSL) model estimation, obtain the daily SlopeM time series;

[0089] S5. Average the daily SlopeM time series data to obtain SlopeA.

[0090] Step 5: Calculate the new observed reflectivity RefF based on SlopeM mea2 (RefF mea2 =SlopeM*(EV-SV)), repeat steps 2 to 4 to form the matching sample data set after the second quality control, the second Slope time series data set, and the second daily SlopeM time series data set.

[0091] Step 6: Calculate PDif (PDif = RefF based on the matched sample data set after the second quality control mea / RefF sim -1) and PDif2 (PDif2 = RefF mea2 / RefF sim -1), forming the PDif and PDif2 time series datasets.

[0092] Step 7: Establish a PDif intra-year correction model based on the PDif2 time series data to correct the intra-year changes of the PDif data. The detailed steps are as follows:

[0093] S1. Based on the PDif2 time series data, the PDif2 data of the daily count of July in the same year are averaged to obtain the daily PDif2 averaged over multiple years.ave-daily data;

[0094] S2. Calculate PDif2 ave-daily The mean of the data set, PDif2 ave ;

[0095] S3. According to the set model, such as Sin function, PDif2 is performed ave-daily -PDif2 ave The time variation fitting of PDif is used to obtain the daily correction model Func_PDif (Julday), Julday = 1, 2...366;

[0096] S4. Obtain the corrected observation deviation PDifCor = PDif - Func_PDif (Julday).

[0097] Step 8: Based on the second Slope time series data set, establish a Slope annual correction model to correct the annual changes of Slope data. The detailed steps are as follows:

[0098] S1, based on the second Slope and daily SlopeM time series data, remove the trend of the data set over time, Slope2 = Slope / SlopeM;

[0099] S2. Average the Slope2 data of the July day count in the same year to obtain the average daily Slope2 of multiple years. ave-daily data;

[0100] S3. Calculate Slope2 ave-daily Mean of the data set, Slope2 ave ;

[0101] S4. According to the set model, such as Sin function, Slope2 ave / Slope2 ave-daily Fit the time variation of the slope to obtain the daily correction model Func_Slope (Julday), Julday = 1, 2…366;

[0102] S5. Obtain the corrected calibration slope SlopeCor = Slope*Func_Slope(Julday).

[0103] Step 9: Based on the PDifCor and SlopeCor results, determine whether the calibration coefficient needs to be updated according to the set plan. The detailed steps are as follows:

[0104] S1. For the i-th day, a sliding average of the PDifCor time series data is performed with a set time window (e.g., [i-5, i] days) to obtain PDif i ;

[0105] S2, in chronological order, PDif i When it exceeds the set threshold (e.g., 0.02), it is determined that the calibration coefficient needs to be updated;

[0106] S3. For non-first updates, use the decay rate scheme for judgment;

[0107] S4. For the i-th day, calculate the decay rate Decay relative to the last update:

[0108] Take the selected date i0 (e.g., the day of the launch, the day of the power on) as the reference time, and calculate the normalized response NRes: extract the SlopeCor results from the reference time to the i-th day, and perform time variation trend fitting according to the set model, such as linear, quadratic polynomial, etc., to obtain the time variation model of SlopeCor Func_SlopeCor (DSL); NRes (i) = Func_SlopeCor (i0) / Func_SlopeCor (i);

[0109] Based on the NRes time series, the time variation trend is fitted according to the set model, such as linear, quadratic polynomial, etc., to obtain the NRes time variation model Func_NRes (DSL);

[0110] Calculate the decay rate Decay: Decay = 1-Func_NRes(i) / Func_NRes(T1), where T1 is the number of the first updated log.

[0111] S5. When Decay exceeds the set threshold (eg, -0.02), it is determined that the calibration coefficient needs to be updated.

[0112] Step 10: Determine whether the calibration coefficient needs to be updated. For the first update, Func_SlopeCor(T1) is used as the calibration coefficient to be updated; for a non-first update, Func_SlopeCor(T1)*(1+Decay) is used as the calibration coefficient to be updated.

[0113] The present invention is based on the simulated reflectivity of multiple stable targets, utilizes the deviation between the simulated reflectivity and the observed reflectivity, and corrects the deviation for the annual periodic (daily) change, so as to continuously and accurately reflect the change of instrument observation deviation; through the normalized response and attenuation rate of the instrument based on the calibration slope, and corrects the annual periodic (daily) change, a more stable and accurate instrument change result is obtained. When judging whether calibration update is needed, the present invention adopts two schemes of reflectivity relative deviation and attenuation rate. First, the time window sliding average method is used to reduce the influence of data randomness, and then the attenuation rate scheme is used to further improve the stability of judgment. This method realizes daily real-time monitoring of instrument observation deviation and performance change, provides timely judgment of calibration update, supports calibration correction compensation, ensures that data quality does not deteriorate, and maintains data accuracy within a predetermined range.

[0114] Experimental example:

[0115] Please refer to the attached Figure 2 After the Func_PDif correction model processing, the data volatility is significantly reduced, please refer to the attached Figure 3 During the whole process, there were three calibration coefficient updates, namely July 18, 2019, November 18, 2020, and April 25, 2022.

[0116] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for automatically updating the calibration parameters of a remote sensing instrument reflection band on-orbit, characterized in that: The following steps are involved: Step 1: Select stable target sites distributed around the world, extract target instrument observation data, and calculate the instantaneous simulated reflectivity of the top of the atmosphere of the stable target site based on the set plan, environmental field data and radiation transfer model. After correction of the sun-earth distance and solar zenith angle, the simulated reflectivity factor RefF is obtained. sim , forming the instrument observation reflectivity factor RefF mea and the simulated reflectivity factor RefF sim The matching sample data pairs include the earth observation count value EV, the cold sky observation count value SV, the satellite zenith angle (SenZ), the solar zenith angle (SolZ), the daily count Julday, etc. Step 2: Perform the first quality control screening on the matched sample data of observed and simulated reflectance according to the set plan to remove unreliable factors such as large simulation error, saturated observation data, cloud / dust and other non-clear sky effects, and form the matched sample data set after the first quality control; Step 3: Based on the matched sample data set after the first quality control, for the i-th day, with a set time window of [i-N1, i+N2] days, perform (EV-SV) and RefF sim Data fitting is performed, and abnormal data is removed for the time window to obtain the fitting slope Slope of the i-th day. The fitting slope forms the first Slope time series; Step 4: Based on the Slope time series obtained in step 3, a selected date i0 such as the launch date or the start-up date is used as the reference time, and the time variation model Func_Slope of the Slope is obtained according to the set scheme. The daily SlopeM time series data estimated based on the time variation model Func_Slope is averaged to obtain the average value SlopeA. Step 5: Calculate the new observed reflectivity RefF based on SlopeM mea2 (RefF mea2 = SlopeM*(EV-SV)), repeat steps 2 to 4 to form the matching sample data set after the second quality control, the second Slope time series data set, and the second daily SlopeM time series data set; Step 6: Calculate PDif (PDif = RefF based on the matched sample data set after the second quality control mea / RefF sim -1) and PDif2 (PDif2 = RefF mea2 / RefF sim -1), forming the PDif and PDif2 time series data sets; Step 7: Based on the PDif2 time series data and according to the set plan, the PDif daily correction model Func_PDif is established through the calculation and processing of the multi-year average daily data, which is used to correct the annual changes of the PDif data, and the PDifCor after the annual change correction is obtained; Step 8: Based on the second Slope time series data, according to the set plan, by calculating and processing the daily data of the year with detrending and multi-year average, a daily correction model Func_Slope is established for the correction of the annual change of Slope data, and the SlopeCor after the annual change correction is obtained; Step 9: Based on the PDifCor and SlopeCor results, determine whether the calibration coefficient needs to be updated according to the set plan; Step 10: When the determination in step 9 requires updating of the calibration coefficients, the calibration coefficients to be updated are calculated for the first update and the non-first update respectively according to the set scheme.

2. The on-orbit automatic updating method of the remote sensing instrument reflection band calibration parameters according to claim 1, characterized in that: The observation reflectivity calculation step in step 1 is as follows: input the target instrument L1 data, find the observation pixel point closest to it according to the latitude and longitude position of the stable target site, extract the data of the n*n window with it as the center, and calculate the window mean (Mean), standard deviation (Std), coefficient of variation CV (CV = Std / Mean), and obtain the observation reflectivity factor RefF according to the radiation calibration conversion relationship mea , forming stable target observation data.

3. The on-orbit automatic updating method of the remote sensing instrument reflection band calibration parameters according to claim 1 is characterized in that: The calculation steps of the simulated reflectivity in step 1 are as follows: input the stable target observation data, match it with the pre-built atmospheric aerosol / water vapor / ozone, surface BRDF climate data, and atmospheric temperature, humidity, pressure, wind and other environmental field data in time and space according to the latitude, longitude and time information, and use any radiation transfer model such as MODTRAN and 6SV to calculate the instantaneous reflectivity Ref of the top of the atmosphere according to the target observation angle. sim Calculate and correct the solar zenith angle and the distance between the sun and the earth to obtain the simulated reflectivity factor RefF when the sun is at the zenith and the average distance between the sun and the earth sim .

4. The on-orbit automatic updating method of the remote sensing instrument reflection band calibration parameters according to claim 1, characterized in that: The specific steps of quality control screening in step 2 are: S1. According to the set angle quality control threshold, the solar zenith angle M° and the satellite zenith angle N° are used to identify SolZ and SenZ, and the data exceeding the threshold are eliminated; S2. According to the set observation saturation threshold, EV identification of each channel is performed, and data exceeding the threshold is eliminated; S3, according to the set spatial uniformity quality control reference channel F CV and threshold, perform CV EV Identification: remove data exceeding the threshold; S4, according to the set cloud / dust detection channels F1 and F2 and thresholds, perform channel observation reflectivity feature identification and remove data exceeding the threshold; S5. Quality control reference channel F according to the set observation deviation RE and threshold, and the relative deviation between observed and simulated reflectivity (RefF mea / RefF sim -1) Identification, remove data that exceeds the threshold.

5. The on-orbit automatic updating method of the remote sensing instrument reflection band calibration parameters according to claim 1, characterized in that: The specific steps for each time window in step 3 are: S1, Reject (EV-SV) or RefF sim Data less than 0; S2, with (EV-SV) as x, RefF sim is y, and is fitted according to the set model; S3, calculating the standard deviation of the deviation between the simulated reflectivity and the model-estimated reflectivity, taking 2 times the standard deviation as the threshold, and eliminating the data exceeding the threshold; S4. Repeat S2, S3 and S2 to obtain the fitting slope Slope of the i-th day.

6. The on-orbit automatic updating method of the remote sensing instrument reflection band calibration parameters according to claim 1, characterized in that: The specific steps of processing the Slope time series of step 3 in step 4 are: S1, taking the selected date i0 as the reference time, based on the Slope time series, according to the set model, such as linear, quadratic polynomial, etc., to perform time change trend fitting; S2, calculate the standard deviation of the deviation between the actual Slope value and the model estimated value, take 2 times the standard deviation as the threshold, and remove the data exceeding the threshold; S3, repeat steps S1, S2 and S1 to obtain the time variation model of Slope Func_Slope (DSL), where DSL is the number of days from the reference time; S4. Based on the Func_Slope (DSL) model estimation, obtain the daily SlopeM time series; S5. Average the daily SlopeM time series data to obtain SlopeA.

7. The method for automatically updating the calibration parameters of the reflection band of a remote sensing instrument on-orbit according to claim 1, characterized in that: The specific steps of step 7 to establish the PDif intra-year correction model and make change corrections are: S1. Based on the PDif2 time series data, the PDif2 data of the daily count of July in the same year are averaged to obtain the multi-year average daily PDif2 of the year. ave-daily data; S2. Calculate PDif2 ave-daily The mean of the data set, PDif2 ave ; S3. According to the set model, such as Sin function, PDif2 is performed ave-daily -PDif2 ave The time variation fitting of PDif is used to obtain the daily correction model Func_PDif (Julday), Julday = 1, 2...366; S4. Obtain the corrected observation deviation PDifCor = PDif - Func_PDif (Julday).

8. The on-orbit automatic updating method of the remote sensing instrument reflection band calibration parameters according to claim 1 is characterized in that: The specific steps of establishing the Slope intra-year correction model and performing change correction in step eight are as follows: S1, based on the second Slope and daily SlopeM time series data, remove the trend of the data set over time, Slope2 = Slope / SlopeM; S2. Average the Slope2 data of the July day count in the same year to obtain the average daily Slope2 of multiple years. ave-daily data; S3. Calculate Slope2 ave-daily Mean of the data set, Slope2 ave ; S4. According to the set model, such as Sin function, Slope2 ave / Slope2 ave-daily Fit the time variation of the slope to obtain the daily correction model Func_Slope (Julday), Julday = 1, 2…366; S5. Obtain the corrected calibration slope SlopeCor = Slope*Func_Slope(Julday).

9. The on-orbit automatic updating method of the remote sensing instrument reflection band calibration parameters according to claim 1, characterized in that: The specific steps of determining in step nine are: S1. For the i-th day, the sliding average processing of PDifCor time series data is performed with a set time window to obtain PDif i ; S2, in chronological order, PDif i When the set threshold is exceeded, it is determined that the calibration coefficient needs to be updated; S3. For non-first updates, use the decay rate scheme for judgment; S4. For the i-th day, calculate the decay rate Decay relative to the last update Take the selected date i 0 (e.g., the day of the launch, the day of the power on) as the reference time, and calculate the normalized response NRes: extract the SlopeCor results from the reference time to the i-th day, and perform time variation trend fitting according to the set model, such as linear, quadratic polynomial, etc., to obtain the time variation model of SlopeCor Func_SlopeCor (DSL); NRes (i) = Func_SlopeCor (i0) / Func_SlopeCor (i); Based on the NRes time series, the time variation trend is fitted according to the set model, such as linear, quadratic polynomial, etc., to obtain the NRes time variation model Func_NRes (DSL); Calculate the decay rate Decay: Decay = 1-Func_NRes(i) / Func_NRes(T1), where T1 is the number of logs updated for the first time: S5. When Decay exceeds the set threshold, it is determined that the calibration coefficient needs to be updated.

10. The on-orbit automatic updating method of the remote sensing instrument reflection band calibration parameters according to claim 9, characterized in that: In the step 10, for the first update, Func_SlopeCor(T1) is used as the calibration coefficient to be updated; for a non-first update, Func_SlopeCor(T1)*(1+Decay) is used as the calibration coefficient to be updated.

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