Multi-source data fusion accumulated snow coverage acquisition method based on wind cloud satellite

Abstract

The invention belongs to the technical field of satellite accumulated snow remote sensing product processing, and particularly provides a wind cloud satellite-based multi-source data fusion accumulated snow coverage acquisition method, which comprises a wind cloud satellite data accumulated snow coverage identification product SFY; the method comprises the following steps of: acquiring an accumulated snow covering synthetic product SMODIS of the morning star and the afternoon star of the MODIS; obtaining SIMS, SRE and SMW of the pre-processed accumulated snow product; generating a preliminary accumulated snow covering fusion product; generating an accumulated snow coverage time filtering product; generating an accumulated snow coverage space filtering product; an accumulated snow covering product based on land surface observation correction is generated; generating a snow line correction product of the accumulated snow coverage area; and a completely cloudless day-by-day accumulated snow covering product is obtained. According to the method, more accumulated snow information is provided by fusing multi-source data, and cloud pollution in an accumulated snow observation image is removed, so that high-precision day-by-day cloudless dynamic accumulated snow coverage monitoring is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of satellite snow remote sensing product processing, and in particular, relates to a method for acquiring snow cover by fusing multi-source data using Fengyun satellites as the main source. Background Art

[0002] Snow is a major component of the cryosphere, affecting processes such as global climate change, regional hydrological cycles, and surface radiation balance. Snow can have a long-term impact on atmospheric circulation and weather and climate systems by changing heat flux, thermal processes, and vertical atmospheric motion. It can also modulate the spatial and temporal distribution of precipitation by affecting the strength of the monsoon, thereby affecting the distribution of water resources. In addition, snowmelt, as an important form of river runoff replenishment, can provide water to more than one-sixth of the world's population. Against the backdrop of global warming leading to accelerated melting and retreat of mountain glaciers and melting of snow, dynamic monitoring of snow distribution is becoming increasingly important for water resources planning and management, people's livelihood and production, and socio-economic development.

[0003] At present, monitoring of snow distribution mainly relies on meteorological observation stations, microwave remote sensing and optical satellite remote sensing. Ground meteorological stations can usually obtain relatively accurate snow depth data through manual monitoring of snow, but the data is too sparse and difficult to observe in areas with complex terrain and few people. Passive microwave remote sensing has the potential to penetrate clouds to observe snow, but the spatial resolution is very coarse and it is difficult to achieve refined observation. In contrast, optical remote sensing satellites with high temporal and spatial resolution are the main means of snow cover monitoring at present, and can accurately identify snow-covered areas in clear weather. However, optical satellite remote sensing observations cannot penetrate clouds, and snow information will be lost due to problems such as cloud cover or detector saturation. At present, the optical remote sensing satellite snow cover monitoring method is mainly based on the characteristics of optical images such as MODIS sensors. It uses time filtering, spatial filtering, and elevation information-based filtering to remove cloud pollution in snow observation images, improve snow monitoring accuracy, and obtain cloud-free snow cover products every day.

[0004] However, the current snow cover monitoring methods still have some problems. The data source used is relatively single, usually based on optical images from MODIS sensors. Some methods add IMS snow and ice data as a supplement. However, when the observed images have large areas of cloud pollution, there are still a lot of missing data and decreased spatial resolution after cloud removal. The snow monitoring effect is still not ideal. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a method for obtaining snow cover by fusing multi-source data based on Fengyun satellites, which fuses multi-source data to provide more snow information and removes cloud pollution in snow observation images to achieve high-precision, cloudless dynamic snow cover monitoring day by day.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is: a method for obtaining snow cover by fusing multi-source data mainly based on Fengyun satellites, comprising the following steps: Step 1: Obtain the snow cover identification product S from Fengyun satellite data FY ; Step 2: Select the meteorological observation stations in the region according to the observation data quality and spatial representativeness, perform quality control on the snow observation data of the meteorological observation stations according to the meteorological data business quality control requirements, and obtain the snow data set S of the meteorological observation stations. station ; Step 3: Obtain the MODIS morning and afternoon snow cover composite product S MODIS ; Step 4: Obtain IMS snow and ice data, reanalysis datasets, and microwave data to obtain the preprocessed snow product S IMS , S RE , S MW ; Step 5: Identify product S based on snow cover data from Fengyun satellites FY Fusion of the snow cover composite product S of the morning and afternoon stars of the Moderate Resolution Imaging Spectrometer MODIS Generate preliminary snow cover fusion product S1; Step 6: Given a time window range Δt, synthesize the preliminary snow cover fusion product S1 within the time window to generate the snow cover time filtered product S2; Step 7: Given the spatial window ranges Δs1 and Δs2, the snow cover temporal filter product S2 is converted into the snow cover spatial filter product S3 according to the spatial window synthesis rule; Step 8: Given a time series range Δtm, according to the meteorological observation station snow data set S station Estimate the conditional probability of snow cover in pixels in the snow cover spatial filtering product S3, as well as the total probability of pixels being covered by snow / non-snow, and generate the snow cover product S4 based on land surface observation correction; Step 9: Generate the snow cover regional snow line correction product S5 according to the snow cover product S4 corrected based on land surface observations and the regional snow line synthesis rule; Step 10: Compare the snow line correction product S5 and the snow product S IMS , S RE , S MW Fusion obtains a completely cloudless daily snow cover product S6.

[0007] In the preferred solution, in the step 1, the method for obtaining the snow cover identification product of the Fengyun satellite data is as follows: firstly, the spectral observation data of the Fengyun satellite medium-resolution spectral imager is obtained, and the Fengyun satellite snow cover identification algorithm is constructed to preliminarily identify the image pixels as cloud, land, snow, water, and sea / lake ice categories; The identification rules of the Fengyun satellite snow identification algorithm are: 1) Cloud discrimination: Cloud pixel categories include cloud, cloud shadow, missing value, uncertain value, detector saturation, and fill value. The discrimination method is: Cloud identification method: ; Cloud shadow identification method: ; Missing values, uncertain values, detector saturation, filling values, and other pixels that do not meet the following 2), 3), 4), and 5) identification categories are classified as cloud pixels; 2) Land identification method: ; 3) Snow accumulation determination method: ; 4) Water body identification method: ; ; 5) Sea / lake ice identification method: ; ; ; in, They are the image channel data of the near-infrared band, red light band, short-wave infrared band and far-infrared band of the Fengyun satellite; The vegetation index NDVI and snow cover index NDSI calculated from the image data respectively; , , , , , , , , , , are the empirical threshold parameters of the Fengyun satellite snow identification algorithm. The 0.85, 1.15, 0.3, 0.205, 0.05, -0.05, 0.2, 0.25, 0.1, 244K, and 0.01 in brackets are the commonly used empirical thresholds in the corresponding cloud, land, snow, water, and sea / lake ice identification methods, which are adjusted according to the actual conditions of the satellite images and the study area.

[0008] In the preferred solution, in step 2, a snow accumulation data set S of a meteorological observation station is obtained. station The following steps are involved: Step S2.1, select meteorological observation stations in the region according to the quality and spatial representativeness of observation data, extract the multi-year snow cover, surface cover, terrain characteristic values, snow environment characteristic values ​​within the meteorological observation station and its surrounding window range, and the spatial representativeness evaluation level of snow observation at the meteorological station, select meteorological observation stations with high spatial representativeness in the region, and obtain snow observation data; Step S2.2, perform quality control on the snow observation data of the meteorological observation station in accordance with the quality control requirements of meteorological data business, including: checking the limit value of snow depth and checking the internal consistency of snow depth, snow pressure and minimum temperature; Step S2.3: define the snow depth observation record of the meteorological observation station as 999999 as missing data, 0 as no snow, 999990 or snow depth less than 0.5 cm as partial snow, and snow depth greater than or equal to 0.5 cm as snow, and obtain the snow cover dataset S of the meteorological observation station. station .

[0009] In the preferred solution, in step 3, the MODIS morning star and afternoon star snow cover synthesis product S is obtained. MODIS , including the following steps: Step S3.1, obtain the MODIS snow cover product afternoon star MYD10A1 and morning star MOD10A1, and convert The pixels are identified as snow. The pixels of inland water and ocean are reclassified as water; the pixels of cloud, missing data, no accuracy, undetermined, night and detector saturation are reclassified as cloud; Step S3.2, the snow cover products of the afternoon star MYD10A1 and the morning star MOD10A1 are used to generate the morning star and afternoon star composite images according to the morning and afternoon star synthesis rule. The morning star and afternoon star synthesis rule is: if there is snow in a certain pixel of any image of MYD10A1 and MOD10A1, the pixel is identified as snow; if a certain pixel is a cloud in one image and not a snow pixel in the other image, the pixel is identified as a non-snow pixel; if one image product is missing on the day, the other product is used alone; Step S3.3: Use the majority algorithm to resample the composite images of the morning and afternoon stars to the spatial resolution of the Fengyun satellites to obtain the MODIS morning and afternoon star snow cover composite product S MODIS .

[0010] In a preferred embodiment, the step 4 comprises the following steps: S4.1. Obtain IMS snow and ice data, reanalysis data sets and microwave data, and use the majority algorithm to resample the snow cover product of the IMS snow and ice data to match the spatial resolution of the Fengyun satellites to generate a new snow cover product S IMS ; S4.2. Use interpolation algorithms to resample the snow depth and snow water equivalent of the reanalysis dataset or microwave data to make them consistent with the spatial resolution of the Fengyun satellite. If the pixel snow depth or snow water equivalent meets , then the pixel is reclassified as snow; if , the pixel is reclassified as land, where Represent the pixel snow depth and snow water equivalent, are the corresponding empirical threshold parameters respectively. The 0 in the bracket is the commonly used empirical threshold, which needs to be adjusted according to the actual situation. The snow product S is generated from the reanalysis dataset and microwave data respectively. RE and S MW .

[0011] In the preferred solution, in step 5, the regional snow environment characteristics are analyzed, and the snow cover identification product S is used based on the Fengyun satellite data. FY Based on the data, according to the multi-source satellite synthesis rules, the MODIS morning satellite and afternoon satellite snow cover synthesis product S MODIS , remove S FY The cloud pixels in the image are used to generate the preliminary fusion product S1; The rules for multi-source satellite synthesis are as follows: select a certain satellite image as the base data; if a certain pixel in any multi-source satellite image has snow, then the pixel is identified as snow; if a certain pixel is a cloud in one image and a non-snow pixel in another image, then the pixel is identified as a non-snow pixel; other pixels are consistent with the base data; if the base data is missing a product on that day, then only other satellite images are used. The expression formula is: ; ; ; The priority of pixel identification in the multi-source satellite synthesis rule follows: ; in, It's time Date, Location The pixel at It is the snow pixel; is the non-snow pixel value or the snow pixel value; is the cloud pixel value.

[0012] In the preferred solution, in step 6, the synthesis rule of the preliminary snow cover fusion product S1 within the time window is: if the current day is t, then select the snow image S1 of t-1 and t+1, if a certain pixel on day t is a cloud, and both t-1 and t+1 are non-snow pixels or snow pixels, then the pixel on day t is identified as a non-snow pixel or a snow pixel; and so on, select t-1 and t+2, t-2 and t+1... until t-Δt1 and t+Δt2, where the time window size Δt=Δt1+Δt2, reclassify the cloud pixels of t according to the above rules, and generate the snow cover time filtering product S2, which is expressed as: ; in, , , The location The pixels at time t, t-Δt1, and t+Δt2.

[0013] In the preferred solution, in step seven, the spatial window synthesis rule is: take the cloud pixel of the image as the central pixel, if the snow pixels in the spatial window Δs1 near it account for more than half, then the central pixel is judged as a snow pixel, if the non-snow pixels account for more than half, then the central pixel is judged as a non-snow pixel; take the remaining cloud pixel as the central pixel, if there is a snow pixel in the spatial window Δs2 near it with an altitude lower than the central pixel, then the central pixel is judged as a snow pixel, otherwise the central pixel is still a cloud pixel, and the expression is: ; ; in, , For location , The altitude of It's location The pixel at time t.

[0014] In the preferred solution, in step eight, within the time Δtm, the conditional probability and total probability of the pixel are calculated as follows: ; ; in, Indicates time ,Location , and satisfy the time Within the selected time range Δtm; Indicates time , No. Meteorological station observations, and meet the time Within the selected time range Δtm; is the non-snow pixel value or the snow pixel value; It's location The total number of valid pixels at is the conditional probability between pixels and sites; is the total probability of the pixel; According to the conditional probability and total probability at the cloud pixel, the pixel is judged to be snow or non-snow. The judgment rule expression is: .

[0015] In the preferred scheme, in the step nine, the operation method for generating the snow line correction product S5 for the snow-covered area is: divide the space into multiple sub-areas according to the watershed and terrain factors, calculate the average elevation demS of all snow pixels and the average elevation demL of all land pixels in each sub-area in the snow cover product S4 corrected based on land surface observations on a daily basis, if the cloud cover in the sub-area is greater than Cp or the snow is less than Sp, where Cp and Sp are the area percentages of clouds and snow in the watershed, respectively, then demS and deML are not retained; otherwise, demS and deML are retained; respectively count the demS and deML of each sub-area each year, perform time series interpolation on them, and synthesize the snow line correction product S5 for the snow-covered area according to the regional snow line synthesis rule; The regional snowline synthesis rule is: if the elevation of the cloud pixel in the sub-region is greater than demS and the slope If the elevation of the cloud pixel in the sub-region is less than 60°, the cloud pixel is identified as snow. If the elevation of the cloud pixel in the sub-region is less than deML, the cloud pixel is identified as land. The remaining pixels remain unchanged, and the expression is: ; .

[0016] In the preferred solution, in step 10, the specific operation method is: if at the cloud pixel of S5, S IMS , S RE , S MW If there is snow in any product, the cloud pixel is identified as snow, otherwise it is identified as non-snow pixel. The expression is: .

[0017] The present invention provides a method for obtaining snow cover by fusing multi-source data based on Fengyun satellites, which has the following beneficial effects: 1. The present invention is based on the Fengyun meteorological satellite, and integrates meteorological observation data, reanalysis data, MODIS, microwave data sets and other multi-source data to extract snow cover. It fully taps the potential of Fengyun satellites in snow cover monitoring, integrates rich multi-source data to provide more snow information, removes cloud pollution in snow observation images, and realizes high-precision, cloudless and dynamic snow cover monitoring day by day.

[0018] 2. The present invention fully exploits the information of snow cover products from multi-source remote sensing data such as domestic Fengyun satellites, measured snow depth data from meteorological stations, and reanalysis snow data sets, and constructs a snow cover acquisition method based on Fengyun satellite multi-source data fusion according to the spatiotemporal characteristics of snow. By calculating the snow / non-snow condition probability from pixels to meteorological stations, estimating the regional snow line elevation on the time series, etc., the cloud or missing data in the snow cover product can be reclassified, the cloud effect can be removed, the missing remote sensing information can be filled, and high-quality cloud-free snow cover products can be generated, realizing high-precision dynamic snow monitoring.

[0019] 3. The daily cloudless snow cover dataset based on domestic Fengyun satellite data is of great reference value in the development and application of independent equipment. It can provide snow data support for scientific research such as climate analysis and runoff prediction, and provide a scientific basis for government work such as comprehensive utilization of water resources and disaster prevention and mitigation. It has significant scientific value and economic benefits.

[0020] 4. The present invention establishes a land observation correction method based on snow accumulation data of meteorological observation stations to improve snow cover products. Based on the environmental characteristic values ​​of meteorological observation stations in the region and their surrounding window areas, meteorological observation stations with high data quality and spatial representativeness are screened. By calculating the snow / non-snow conditional probability of pixels to meteorological stations, the snow characteristics of pixels and stations are associated, and the actual snow accumulation data of the stations are used in combination with the total snow accumulation probability of pixels to achieve reclassification of image cloud pixels, reduce cloud coverage, and improve data quality.

[0021] 5. The present invention has constructed a snow cover product fusion technology based on multi-source data, mainly Fengyun satellite data. Relying on multi-source remote sensing data, it integrates technical methods such as spatiotemporal filtering, land surface observation correction, and regional snow line elevation, to further improve the quality of domestic Fengyun satellite data and the ability of remote sensing monitoring applications of water resources such as snow. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The present invention will be further described below in conjunction with the accompanying drawings and embodiments: Figure 1 It is a flow chart of the present invention; Figure 2 This is a schematic diagram of the judgment logic of step 1 of the present invention; Figure 3 It is a logic block diagram of the present invention; Figure 4 This is a multi-source data fusion snow cover product map on October 13, 2019 in an embodiment of the present invention; Figure 5 This is the MODIS snow product image of October 13, 2019 of the embodiment of the present invention; Figure 6 This is the IMS snow and ice data diagram of October 13, 2019 in an embodiment of the present invention; Figure 7 This is the Landsat8, multi-source fusion snow cover product, MODIS snow accumulation product and IMS snow and ice data map on October 13, 2019 in the embodiment of the present invention; Figure 8 This is a multi-source data fusion snow cover product image on January 20, 2019 in an embodiment of the present invention; Fig. 9 This is the MODIS snow product image on January 20, 2019 of the embodiment of the present invention; Fig.10 This is the IMS snow and ice data diagram on January 20, 2019 of the embodiment of the present invention; Fig.11 This is a graph of Landsat8, multi-source fusion snow cover product, MODIS snow accumulation product and IMS snow and ice data on January 20, 2019, according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0024] refer to Figure 1~Figure 3 As shown in the figure, a snow cover acquisition method based on multi-source data fusion of Fengyun satellites is used to remove the cloud from the snow cover product in the upper reaches of the Jinsha River, and finally a daily cloud-free product is obtained, which includes: Step 1: Obtain the MERSI spectral observation data of the upper reaches of the Jinsha River from FY-3D. According to the Fengyun satellite snow cover identification algorithm and combined with the land cover classification data, the image pixels are identified as land (coded 25), snow (coded 200), water (coded 37), ocean (coded 39), sea / lake ice (coded 100), and cloud (coded 50). Pixels with missing values ​​(coded 0), uncertain values ​​(coded 1), detector saturation (coded 254), and fill values ​​(coded 255) are classified as clouds to obtain the Fengyun satellite data snow cover identification product S. FY .

[0025] Step 2: Select meteorological observation stations in the study area based on the quality and spatial representativeness of the observation data. A total of 11 meteorological observation stations in the upper reaches of the Jinsha River were selected. The snow observation data of the meteorological observation stations were quality controlled according to the meteorological data business quality control requirements, including snow depth limit value check and internal consistency check of snow depth, snow pressure and minimum temperature. The snow depth observation record of the meteorological observation station was defined as 999999 as missing data, 0 as no snow (land), 99990 or snow depth less than 0.5 cm as partial snow, and snow depth greater than 0.5 cm as snow. The snow data set S of the meteorological observation station was obtained. station .

[0026] Step 3: Obtain the MODIS (Moderate-resolution Imaging Spectroradiometer) snow cover products MYD10A1 and MOD10A1, identify the pixels as snow (coded 0), land (coded 25), water (coded 37), ocean (coded 39), sea / lake ice (coded 100), and cloud (coded 50), and then generate the MODIS morning and afternoon star composite image according to the morning and afternoon star composite rules.

[0027] Step 4: Use the majority algorithm to resample the snow cover product of the IMS (interactive multisensors snow and ice mapping system) snow and ice data to match the spatial resolution of the Fengyun satellite and generate a new IMS snow cover product.

[0028] The mode algorithm is a simple algorithm that selects the value with the most occurrences. The mode refers to the value that appears most frequently in a set of data. In different types of statistical analysis, the mode may be used to describe the central tendency of the data or the asymmetry of the distribution.

[0029] Step 5: Identify product S based on snow cover data from Fengyun satellites FY , according to the multi-source satellite synthesis rules, MODIS morning and afternoon star snow cover products are fused to remove cloud pixels in the wind and cloud snow products. If there is snow in a pixel in any image, the pixel is identified as snow; if a pixel is a cloud in the wind and cloud satellite image, but a non-snow pixel in the MODIS morning and afternoon star snow cover product, and the non-snow pixel is ground, water, sea / lake ice, the pixel is identified as a non-snow pixel; if the base data is missing on the day, only the MODIS morning and afternoon star snow cover product is used; generate the preliminary snow cover fusion product S1.

[0030] Step 6: Synthesize the preliminary snow cover fusion product S1 within 3 days to generate the snow cover time filtered product S2, and the rules are as follows.

[0031] If the current day is t, the snow images of t-1 and t+1 are selected. If a certain pixel on t is a cloud, and both t-1 and t+1 are non-snow pixels or snow pixels, the pixel on t is identified as a non-snow pixel or snow pixel. Then select the products of t-1 and t+2, t-2 and t+1 in turn, and continue to reclassify the cloud pixels of t according to the above rules to generate the snow cover time filtering product S2.

[0032] Step 7: Generate snow cover spatial filtering product S3 by combining the snow cover temporal filtering product S2 according to the following spatial synthesis rules.

[0033] Taking the cloud pixel of the image as the central pixel, if any category of pixels (snow, land, water, sea / lake ice) accounts for more than half of the pixels in the 4 neighborhoods around it, the central pixel is identified as a pixel of that category; taking the remaining cloud pixel as the central pixel, if there is a snow pixel in the 8 neighborhoods around it with an altitude lower than the central pixel, the central pixel is identified as a snow pixel.

[0034] Step 8: Generate snow cover product S4 based on land surface observation correction.

[0035] Every month, the snow cover product S4 based on land surface observation correction is generated according to the conditional probability of snow cover in the pixel in the snow cover time filtering product S3 of the meteorological observation station and the total probability of the pixel being covered by snow / non-snow.

[0036] Step 9: Generate snow line correction product S5 for snow-covered areas.

[0037] The upper reaches of the Jinsha River are divided into four sub-regions according to factors such as watersheds and topography. The average elevation demS of all snow pixels and the average elevation demL of all land pixels in each sub-region in the snow cover product S4 corrected based on land surface observations are calculated daily. If the cloud cover in the sub-region is greater than Cp=70% or the snow cover is less than Sp=5%, demS and deML are not retained; otherwise, demS and deML are retained. The demS and deML of each sub-region are counted each year, and the time series are interpolated. The snow line correction product S5 of the snow cover area is synthesized according to the regional snow line synthesis rules.

[0038] Step 10: Generate daily cloud-free snow cover product S6.

[0039] The snow line correction product S5 of the snow-covered area is fused with the IMS snow product. If there is snow in the IMS snow product at the cloud pixel of S5, the cloud pixel is identified as snow, otherwise it is identified as a non-snow pixel, and finally a completely cloudless daily snow cover product S6 is obtained.

[0040] In order to evaluate the accuracy of the snow cover acquisition method based on multi-source data fusion of Fengyun satellites proposed in the present invention, and to test the accuracy of the snow cover product fused with multi-source data, the snow area accuracy index was adopted, and the snow image of Landsat8 satellite with a resolution of 30m was used as the reference data, and the fusion product, MODIS snow product and IMS product were compared respectively.

[0041] Accuracy indicators include overall classification accuracy (Accuracy), recall rate (Recall), and snow classification accuracy (Precision).

[0042]

[0043] Total classification accuracy (Accuracy) = (TP+TN) / (TP+FN+FP+TN); Recall = TP / (TP+FN); Snow classification accuracy = TP / (TP+FP); MODIS snow products are processed according to the morning and afternoon star synthesis, time filter synthesis (time window is 3 days), spatial filter synthesis (spatial windows are 4 neighborhoods and 8 neighborhoods respectively) and regional snow line synthesis rules (no time series interpolation is performed, only single-day images are processed).

[0044] The verification data used include 79 Landsat8 images of the upper reaches of the Jinsha River in 2019. The final verification results are shown in the following table, where the average cloud cover and day percentage are calculated according to the product image results for the whole year of 2019.

[0045]

[0046] In general, a snow cover acquisition method based on multi-source data fusion of Fengyun satellites can completely remove cloud contamination in snow cover products while maintaining good snow area accuracy. From the initial fusion product S1 to the final cloud-free snow cover product S6, the snow area accuracy after each processing step remains basically stable, with the total classification accuracy, snow classification accuracy, and recall rate being around 0.91, 0.84, and 0.91, respectively; the cloud cover of the product gradually decreases, among which the cloud removal in the time filtering and regional snow line correction steps is the largest, with an average cloud cover reduction of more than 16%, and the number of days with cloud cover greater than 20% reduced by more than 30%.

[0047] Compared with the MODIS snow product with clouds, the overall classification accuracy, snow classification accuracy and recall rate of the multi-source data fusion snow cover product S6 are basically the same; compared with the cloudless IMS snow and ice product, the overall classification accuracy, recall rate and snow classification accuracy of the multi-source data fusion snow cover product are improved by 0.10, 0.16 and 0.04 respectively. This shows that the multi-source data fusion snow cover product has greater advantages than the MODIS snow product and the IMS snow and ice product, and can provide high-precision cloudless snow cover products for dynamic monitoring of snow distribution, snowmelt runoff forecast and other research.

[0048] like Figures 4 to 6 As shown in the figure, the spatial distribution of the multi-source data fusion snow cover product and the MODIS snow cover product is relatively consistent, but the MODIS snow cover product is obscured by clouds by about 13.3%, and the snow area identified by the IMS snow and ice data is significantly smaller. Figure 7 As shown, it is a comparison between the Landsat8 image at the source of the Jinsha River and the multi-source fusion snow cover product, MODIS snow accumulation product and IMS snow and ice data. It can be found that the IMS snow and ice data has poor details in the spatial distribution of snow cover, a high local missed judgment rate and poor accuracy.

[0049] like Figures 8-10 The snow product shown is from January 20, 2019. Due to good observation conditions, the cloud cover of MODIS snow product is less, and the spatial distribution of snow is not much different from the multi-source data fusion snow cover product. However, the IMS snow and ice product has significantly more snow in the middle and lower reaches of the implementation area. Fig.11 In the local detail map, the multi-source data fusion snow cover product, MODIS snow cover product and Landsat8 NDSI image are relatively consistent, while the IMS product is very different from Landsat8. This shows that the snow cover identification of the IMS product is relatively unstable, while the snow cover identification of the multi-source data fusion snow cover product is more stable and reliable.

[0050] In summary, the present invention proposes a snow cover acquisition method based on multi-source data fusion of Fengyun satellites, which comprehensively considers the spatiotemporal distribution characteristics of snow such as ground meteorological observation stations and reanalysis data sets. While maintaining the temporal resolution of the snow product at 1 day and the spatial resolution of 1 km, it achieves complete removal of cloud contamination in the snow product and ensures high product accuracy, effectively making up for the shortcomings of binary snow products, ensuring product reliability and availability, and improving the accuracy of snow monitoring.

[0051] The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in this field.

Claims

1. A method for obtaining snow cover by fusion of multi-source data mainly based on Fengyun satellites, characterized in that: The following steps are involved: Step 1: Obtain the snow cover identification product S from Fengyun satellite data FY ; Step 2: Select the meteorological observation stations in the region according to the observation data quality and spatial representativeness, perform quality control on the snow observation data of the meteorological observation stations according to the meteorological data business quality control requirements, and obtain the snow data set S of the meteorological observation stations. station ; Step 3: Obtain the MODIS morning and afternoon snow cover composite product S MODIS ; Step 4: Obtain IMS snow and ice data, reanalysis datasets, and microwave data to obtain the preprocessed snow product S IMS , S RE , S MW ; Step 5: Identify product S based on snow cover data from Fengyun satellites FY Fusion of the snow cover composite product S of the morning and afternoon stars of the Moderate Resolution Imaging Spectrometer MODIS Generate preliminary snow cover fusion product S1; Step 6: Given a time window range Δt, synthesize the preliminary snow cover fusion product S1 within the time window to generate the snow cover time filtered product S2; Step 7: Given the spatial window ranges Δs1 and Δs2, the snow cover temporal filter product S2 is converted into the snow cover spatial filter product S3 according to the spatial window synthesis rule; Step 8: Given a time series range Δtm, according to the meteorological observation station snow data set S station Estimate the conditional probability of snow cover in pixels in the snow cover spatial filtering product S3, as well as the total probability of pixels being covered by snow / non-snow, and generate the snow cover product S4 based on land surface observation correction; Step 9: Generate the snow cover regional snow line correction product S5 according to the snow cover product S4 corrected based on land surface observations and the regional snow line synthesis rule; Step 10: Compare the snow line correction product S5 and the snow product S IMS , S RE , S MW Fusion obtains a completely cloudless daily snow cover product S6.

2. The method for obtaining snow cover by fusion of multi-source data based on Fengyun satellites according to claim 1 is characterized in that: In the step 1, the method for obtaining the snow cover identification product of the Fengyun satellite data is as follows: firstly, the spectral observation data of the Fengyun satellite medium-resolution spectral imager is obtained, and the Fengyun satellite snow cover identification algorithm is constructed to preliminarily identify the image pixels as cloud, land, snow, water, and sea / lake ice; The identification rules of the Fengyun satellite snow identification algorithm are: 1) Cloud discrimination: Cloud pixel categories include cloud, cloud shadow, missing value, uncertain value, detector saturation, and fill value. The discrimination method is: Cloud identification method: ； Cloud shadow identification method: ； Missing values, uncertain values, detector saturation, filling values, and other pixels that do not meet the following 2), 3), 4), and 5) identification categories are classified as cloud pixels; 2) Land identification method: ； 3) Snow accumulation determination method: ； 4) Water body identification method: ； ； 5) Sea / lake ice identification method: ； ； ； in, They are the image channel data of the near-infrared band, red light band, short-wave infrared band and far-infrared band of the Fengyun satellite; The vegetation index NDVI and snow cover index NDSI calculated from the image data respectively; , , , , , , , , , , are the empirical threshold parameters of the Fengyun satellite snow identification algorithm. The 0.85, 1.15, 0.3, 0.205, 0.05, -0.05, 0.2, 0.25, 0.1, 244K, and 0.01 in brackets are the commonly used empirical thresholds in the corresponding cloud, land, snow, water, and sea / lake ice identification methods, which are adjusted according to the actual conditions of the satellite images and the study area.

3. The method for obtaining snow cover by fusion of multi-source data based on Fengyun satellites according to claim 1 is characterized in that: In step 2, obtain the snow accumulation data set S of the meteorological observation station station The following steps are involved: Step S2.1, select meteorological observation stations in the region according to the quality and spatial representativeness of observation data, extract the multi-year snow cover, surface cover, terrain characteristic values, snow environment characteristic values ​​within the meteorological observation station and its surrounding window range, and the spatial representativeness evaluation level of snow observation at the meteorological station, select meteorological observation stations with high spatial representativeness in the region, and obtain snow observation data; Step S2.2, perform quality control on the snow observation data of the meteorological observation station in accordance with the quality control requirements of meteorological data business, including: checking the limit value of snow depth and checking the internal consistency of snow depth, snow pressure and minimum temperature; Step S2.3: define the snow depth observation record of the meteorological observation station as 999999 as missing data, 0 as no snow, 999990 or snow depth less than 0.5 cm as partial snow, and snow depth greater than or equal to 0.5 cm as snow, and obtain the snow cover dataset S of the meteorological observation station. station .

4. The method for obtaining snow cover by fusion of multi-source data based on Fengyun satellites according to claim 1 is characterized in that: In step 3, the MODIS morning and afternoon snow cover composite product S is obtained. MODIS , including the following steps: Step S3.1, obtain the MODIS snow cover product afternoon star MYD10A1 and morning star MOD10A1, and convert The pixels are identified as snow. The pixels of inland water and ocean are reclassified as water; the pixels of cloud, missing data, no accuracy, undetermined, night and detector saturation are reclassified as cloud; Step S3.2, the snow cover products of the afternoon star MYD10A1 and the morning star MOD10A1 are used to generate the morning star and afternoon star composite images according to the morning and afternoon star synthesis rule. The morning star and afternoon star synthesis rule is: if there is snow in a certain pixel of any image of MYD10A1 and MOD10A1, the pixel is identified as snow; if a certain pixel is a cloud in one image and not a snow pixel in the other image, the pixel is identified as a non-snow pixel; if one image product is missing on the day, the other product is used alone; Step S3.3: Use the majority algorithm to resample the composite images of the morning and afternoon stars to the spatial resolution of the Fengyun satellites to obtain the MODIS morning and afternoon star snow cover composite product S MODIS .

5. The method for obtaining snow cover by fusion of multi-source data based on Fengyun satellites according to claim 1 is characterized in that: The step 4 comprises the following steps: S4.

1. Obtain IMS snow and ice data, reanalysis data sets and microwave data, and use the majority algorithm to resample the snow cover product of the IMS snow and ice data to match the spatial resolution of the Fengyun satellites to generate a new snow cover product S IMS ; S4.

2. Use interpolation algorithms to resample the snow depth and snow water equivalent of the reanalysis dataset or microwave data to make them consistent with the spatial resolution of the Fengyun satellite. If the pixel snow depth or snow water equivalent meets , then the pixel is reclassified as snow; if , the pixel is reclassified as land, where Represent the pixel snow depth and snow water equivalent, are the corresponding empirical threshold parameters respectively. The 0 in the bracket is the commonly used empirical threshold, which needs to be adjusted according to the actual situation. The snow product S is generated from the reanalysis dataset and microwave data respectively. RE and S MW .

6. The method for obtaining snow cover by fusion of multi-source data based on Fengyun satellites according to claim 1, characterized in that: In step 5, the regional snow cover environment characteristics are analyzed, and the snow cover identification product S is obtained by using the Fengyun satellite data. FY Based on the data, according to the multi-source satellite synthesis rules, the MODIS morning satellite and afternoon satellite snow cover synthesis product S MODIS , remove S FY The cloud pixels in the image are used to generate the preliminary fusion product S1; The rules for multi-source satellite synthesis are as follows: select a certain satellite image as the base data; if a certain pixel in any multi-source satellite image has snow, then the pixel is identified as snow; if a certain pixel is a cloud in one image and a non-snow pixel in another image, then the pixel is identified as a non-snow pixel; other pixels are consistent with the base data; if the base data is missing a product on that day, then only other satellite images are used. The expression formula is: ； ； ； The priority of pixel identification in the multi-source satellite synthesis rule follows: ； in, It's time Date, Location The pixel at It is the snow pixel; is the non-snow pixel value or the snow pixel value; is the cloud pixel value.

7. The method for obtaining snow cover by fusion of multi-source data based on Fengyun satellites according to claim 6 is characterized in that: In step 6, the synthesis rule of the preliminary snow cover fusion product S1 within the time window is: if the current day is t, then select the snow image S1 of t-1 and t+1, if a certain pixel on t is a cloud, and both t-1 and t+1 are non-snow pixels or snow pixels, then the pixel on t is identified as a non-snow pixel or snow pixel; and so on, select t-1 and t+2, t-2 and t+1... until t-Δt1 and t+Δt2, where the time window size Δt=Δt1+Δt2, reclassify the cloud pixels of t according to the above rules, and generate the snow cover time filtering product S2, which is expressed as: ； in, , , The location Place, time t day, t-Δt1 day, t+Δt2 Pixel of the day.

8. The method for obtaining snow cover by fusion of multi-source data based on Fengyun satellites according to claim 7 is characterized in that: In step 7, the spatial window synthesis rule is: take the cloud pixel of the image as the central pixel, if the snow pixels in the spatial window Δs1 near it account for more than half, then the central pixel is judged as a snow pixel, if the non-snow pixels account for more than half, then the central pixel is judged as a non-snow pixel; take the remaining cloud pixel as the central pixel, if there is a snow pixel in the spatial window Δs2 near it with an altitude lower than the central pixel, then the central pixel is judged as a snow pixel, otherwise the central pixel is still a cloud pixel, and the expression is: ； ； in, , For location , The altitude of It's location The pixel at time t.

9. The method for obtaining snow cover by fusion of multi-source data based on Fengyun satellites according to claim 8, characterized in that: In step eight, within the time Δtm, the conditional probability and total probability of the pixel are calculated as follows: ； ； in, Indicates time ,Location , and satisfy the time Within the selected time range Δtm; Indicates time , No. Meteorological station observations, and meet the time Within the selected time range Δtm, is the non-snow pixel value or the snow pixel value; It's location The total number of valid pixels at is the conditional probability between pixels and sites; is the total probability of the pixel; According to the conditional probability and total probability at the cloud pixel, the pixel is judged to be snow or non-snow. The judgment rule expression is: ; in, It's time ,Location The pixel at For location Pixel at Relative to the Observations at weather stations The conditional probability of i Indicates snow or non-snow; Indicates the time t Time, Observations from weather stations; Indicates location The total probability that a pixel is snowy or non-snowy within the time range Δtm.

10. The method for obtaining snow cover by fusion of multi-source data based on Fengyun satellites according to claim 9, characterized in that: In the step nine, the operation method for generating the snow line correction product S5 for the snow-covered area is as follows: the space is divided into multiple sub-areas according to the watershed and terrain factors, and the average elevation demS of all snow pixels and the average elevation demL of all land pixels in each sub-area in the snow cover product S4 corrected based on land surface observation are calculated daily; if the cloud cover in the sub-area is greater than Cp or the snow cover is less than Sp, where Cp and Sp are the area percentages of clouds and snow in the watershed, respectively, demS and deML are not retained; otherwise, demS and deML are retained; demS and deML of each sub-area are counted respectively each year, and time series interpolation is performed on them, and the snow line correction product S5 for the snow-covered area is synthesized according to the regional snow line synthesis rule; The regional snowline synthesis rule is: if the elevation of the cloud pixel in the sub-region is greater than demS and the slope If the elevation of the cloud pixel in the sub-region is less than 60°, the cloud pixel is identified as snow. If the elevation of the cloud pixel in the sub-region is less than deML, the cloud pixel is identified as land. The remaining pixels remain unchanged, and the expression is: ； 。 11. The method for obtaining snow cover by fusion of multi-source data based on Fengyun satellites according to claim 10, characterized in that: In the step 10, the specific operation method is: if at the cloud pixel of S5, S IMS , S RE , S MW If there is snow in any product, the cloud pixel is identified as snow, otherwise it is identified as non-snow pixel. The expression is: 。

Images