Common product generation method and device based on remote sensing satellite, equipment and medium

Abstract

The invention provides a general product generation method and device based on a remote sensing satellite, equipment and a medium, and relates to the technical field of remote sensing data, and the method comprises the steps: obtaining remote sensing data of a target area through a geological resource environment hyperspectral microsatellite, the remote sensing data comprises blue wave band data, green wave band data, yellow wave band data, red light wave band data, red edge wave band data, near infrared wave band data and short wave infrared wave band data; determining a vegetation detection product according to the blue wave band data, the green wave band data, the red light wave band data, the red edge wave band data, the near infrared wave band data and the short wave infrared wave band data; according to the blue wave band data, the green wave band data, the red light wave band data, the near-infrared wave band data and the short-wave infrared wave band data, determining a water body and ice and snow detection product; and determining a geological exploration product according to the green band data, the yellow band data, the red band data, the near-infrared band data and the short-wave infrared band data. The product precision is the same.

Description

Technical Field

[0001] The present invention relates to the technical field of remote sensing data, and more particularly, to a method, device, equipment and medium for generating common products based on remote sensing satellites. Background Art

[0002] Remote sensing technology is a technology for remotely detecting the information of the Earth's surface or atmosphere through sensors, and it has been widely used in the fields of ecological environment monitoring and geological exploration. Currently, commonly used remote sensing satellites include Landsat 8 / 9, MODIS, Sentinel-2, and Hyperion, etc. These satellites provide multi-spectral or hyperspectral data, and generate a series of remote sensing common products based on different band combinations, such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Land Surface Water Index (LSWI), Normalized Difference Snow Index (NDSI), and MinI (Mineral Identification Index), etc. These ecological and geological remote sensing monitoring common products are widely used in application fields such as vegetation cover monitoring, water body and snow and ice monitoring, soil moisture analysis, and geological mineral exploration.

[0003] In the related art, the generation of ecological and geological remote sensing monitoring common products usually depends on different data sources, and different data sources will lead to differences in the accuracy between ecological and geological remote sensing monitoring common products in different application fields, making it difficult to achieve cross-domain collaborative processing of ecological and geological information. Summary of the Invention

[0004] The problem solved by the present invention is how to ensure the same accuracy between ecological and geological remote sensing monitoring products to achieve cross-domain collaborative processing of ecological and geological information.

[0005] To solve the above problems, the present invention provides a method, device, equipment and medium for generating common products based on remote sensing satellites.

[0006] In the first aspect, the present invention provides a method for generating common products based on remote sensing satellites, including:

[0007] Obtaining remote sensing data of a target area through a geological resource and environment hyperspectral microsatellite, wherein the remote sensing data includes blue band data, green band data, yellow band data, red light band data, red edge band data, near infrared band data, and shortwave infrared band data;

[0008] Determining a vegetation detection product according to the blue band data, the green band data, the red light band data, the red edge band data, the near infrared band data, and the shortwave infrared band data;

[0009] Determine water body and ice and snow detection products based on the blue band data, the green band data, the red band data, the near-infrared band data, and the short-wave infrared band data;

[0010] Determine geological detection products based on the green band data, the yellow band data, the red band data, the near-infrared band data, and the short-wave infrared band data.

[0011] Optionally, the vegetation detection products include the normalized difference vegetation index, the red-edge chlorophyll vegetation index, the modified soil-adjusted vegetation index, the soil-adjusted vegetation index, the optimized soil-adjusted vegetation index, the enhanced vegetation index, the difference vegetation index, and the ratio vegetation index;

[0012] The determination of vegetation detection products based on the blue band data, the green band data, the red band data, the red-edge band data, the near-infrared band data, and the short-wave infrared band data includes:

[0013] Determine the normalized difference vegetation index, the red-edge chlorophyll vegetation index, the modified soil-adjusted vegetation index, the soil-adjusted vegetation index, the optimized soil-adjusted vegetation index, the enhanced vegetation index, the difference vegetation index, and the ratio vegetation index based on the near-infrared band data and the red band data.

[0014] Optionally, the vegetation detection products include the normalized difference red-edge vegetation index, the green normalized difference vegetation index, the green chlorophyll vegetation index, the atmosphere-resistant vegetation index, the structure-insensitive pigment vegetation index, the visible atmospheric resistance index, and the standardized burn ratio;

[0015] The determination of vegetation detection products based on the blue band data, the green band data, the red band data, the red-edge band data, the near-infrared band data, and the short-wave infrared band data includes:

[0016] Determine the normalized difference red-edge vegetation index based on the near-infrared band data and the red-edge band data;

[0017] Determine the green normalized difference vegetation index and the green chlorophyll vegetation index based on the near-infrared band data and the green band data;

[0018] Determine the atmosphere-resistant vegetation index and the structure-insensitive pigment vegetation index based on the near-infrared band data, the red band data, and the blue band data;

[0019] Determine the visible atmospheric resistance index based on the red band data, the green band data, and the blue band data;

[0020] Determine the standardized combustion rate based on the near-infrared band data and the short-wave infrared band data.

[0021] Optionally, the water body and ice and snow detection products include the normalized difference water index, the turbid water index, the cyanobacteria and macrophyte index, the phytoplankton index, and the normalized difference snow index;

[0022] Determining the water body and ice and snow detection products according to the blue band data, the green band data, the red band data, the near-infrared band data, and the short-wave infrared band data includes:

[0023] Determine the normalized difference water index based on the near-infrared band data and the green band data;

[0024] Determine the turbid water index based on the red band data and the short-wave infrared band data;

[0025] Determine the cyanobacteria and macrophyte index based on the blue band data, the green band data, and the short-wave infrared band data;

[0026] Determine the phytoplankton index based on the near-infrared band data, the red band data, and the short-wave infrared band data;

[0027] Determine the normalized difference snow index based on the green band data and the short-wave infrared band data.

[0028] Optionally, the geological exploration products include ferrous-ferric iron distribution data, iron oxide index, red soil zone data, ferrous silicate index, silicate mineral data, and intermediate argillaceous zone mineral data;

[0029] Determining the geological exploration products according to the green band data, the yellow band data, the red band data, the near-infrared band data, and the short-wave infrared band data includes:

[0030] Determine the ferrous-ferric iron distribution data based on the near-infrared band data and the short-wave infrared band data;

[0031] Determine the iron oxide index based on the near-infrared band data and the short-wave infrared band data;

[0032] Determine the red soil zone data, the ferrous silicate index, the silicate mineral data, and the intermediate argillaceous zone mineral data based on the short-wave infrared band data.

[0033] Optionally, the step of determining a geological exploration product based on the green band data, the yellow band data, the red light band data, the near-infrared band data, and the short-wave infrared band data further includes:

[0034] Determining the ferrous and ferric iron distribution data based on the green band data, the red light band data, and the yellow band.

[0035] Optionally, after determining the geological exploration product based on the green band data, the yellow band data, the red light band data, the near-infrared band data, and the short-wave infrared band data, it further includes:

[0036] Inputting the vegetation detection product and the geological exploration product into the pre-constructed ecosystem stability scoring model of the target area to generate an ecosystem stability score, where the ecosystem stability scoring model is constructed by using a machine learning algorithm based on the historical vegetation detection products and historical geological exploration products of the target area;

[0037] Generating a water ecosystem report for the target area in coordination with the water body and ice and snow detection products according to the ecosystem stability score.

[0038] In a second aspect, the present invention provides a common product generation device based on a remote sensing satellite, including:

[0039] An acquisition module, configured to acquire remote sensing data of a target area through a hyperspectral microsatellite for geological resources and environment, where the remote sensing data includes blue band data, green band data, yellow band data, red light band data, red edge band data, near-infrared band data, and short-wave infrared band data;

[0040] A vegetation determination module, configured to determine a vegetation detection product based on the blue band data, the green band data, the red light band data, the red edge band data, the near-infrared band data, and the short-wave infrared band data;

[0041] A water body and ice and snow determination module, configured to determine a water body and ice and snow detection product based on the blue band data, the green band data, the red light band data, the near-infrared band data, and the short-wave infrared band data;

[0042] A geological determination module, configured to determine a geological exploration product based on the green band data, the yellow band data, the red light band data, the near-infrared band data, and the short-wave infrared band data.

[0043] In a third aspect, the present invention provides an electronic device, including a memory and a processor;

[0044] The memory is used to store a computer program;

[0045] The processor is used to implement the method for generating common products based on remote sensing satellites as described in the first aspect when executing the computer program.

[0046] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method for generating common products based on remote sensing satellites as described in the first aspect is implemented.

[0047] The beneficial effects of the method, device, electronic device and storage medium for generating common products based on remote sensing satellites of the present invention are as follows:

[0048] Through remote sensing satellites, that is, remote sensing data obtained by the hyperspectral microsatellite for geological resources and environment, the reflection and absorption characteristics of different substances on the surface of the target area can be comprehensively covered, so as to accurately analyze vegetation, water bodies, ice and snow, and geological conditions. According to the blue band data, green band data, yellow band data, red light band data, red edge band data, near-infrared band data and short-wave infrared band data in the remote sensing data, high-precision vegetation detection products, water body and ice and snow detection products, and geological detection products can be determined respectively. Since all remote sensing data are obtained by the hyperspectral microsatellite for geological resources and environment, and each remote sensing data obtained by the hyperspectral microsatellite for geological resources and environment is under the same precision standard, the vegetation detection products, water body and ice and snow detection products, and geological detection products further obtained from the remote sensing data obtained by the hyperspectral microsatellite for geological resources and environment have the same precision, and cross-domain collaborative processing of ecological and geological information can be realized. Description of the Drawings

[0049] Figure 1 It is a schematic flowchart of the method for generating common products based on remote sensing satellites provided by an embodiment of the present invention;

[0050] Figure 2 It is a schematic structural diagram of the device for generating common products based on remote sensing satellites provided by an embodiment of the present invention;

[0051] Figure 3 It is a schematic structural diagram of the electronic device provided by an embodiment of the present invention. Detailed Embodiments

[0052] To make the above objects, features, and advantages of the present invention more apparent and understandable, the following provides a detailed description of specific embodiments of the present invention with reference to the accompanying drawings. Although some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments described herein. Instead, these embodiments are provided to more thoroughly and completely understand the present invention. It should be understood that the drawings and embodiments of the present invention are only for exemplary purposes and are not used to limit the protection scope of the present invention.

[0053] It should be understood that the various steps described in the method embodiments of the present invention can be executed in different orders and / or executed in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this regard.

[0054] The term "including" and its variants used herein are open-ended, that is, "including but not limited to"; the term "based on" is "at least partially based on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments"; the term "optionally" means "optional embodiments". The relevant definitions of other terms will be given in the following description. It should be noted that the concepts such as "first" and "second" mentioned in the present invention are only used to distinguish different devices, modules, or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules, or units.

[0055] It should be noted that the modification of "one" and "multiple" mentioned in the present invention is illustrative rather than restrictive. Those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0056] The names of the messages or information exchanged between multiple devices in the embodiments of the present invention are only for illustrative purposes and are not used to limit the scope of these messages or information.

[0057] In the related art, the generation of ecological and geological remote sensing monitoring products usually depends on different data sources, and different data sources will result in differences in the accuracy between ecological and geological remote sensing monitoring products in different application fields, making it difficult to achieve cross-domain collaborative processing of ecological and geological information. In addition, due to the limitations of the existing satellites in the spectral coverage range, some ecological and geological parameters cannot be accurately extracted, affecting the accuracy and applicability of the data products.

[0058] In view of the problems existing in the above related art, this embodiment provides a method, device, equipment, and medium for generating common products based on remote sensing satellites.

[0059] As Figure 1 shown, a method for generating common products based on remote sensing satellites provided by an embodiment of the present invention includes:

[0060] Obtain remote sensing data of a target area through a remote sensing satellite, namely a hyperspectral microsatellite for geological resources and environment. The remote sensing data includes blue band data, green band data, yellow band data, red light band data, red edge band data, near-infrared band data, and short-wave infrared band data.

[0061] Specifically, the hyperspectral microsatellite for geological resources and environment is a high-resolution earth observation satellite, mainly used in fields such as geological survey, resource exploration, environmental monitoring, and disaster warning. Through the hyperspectral microsatellite for geological resources and environment, remote sensing data of the target area can be obtained. The remote sensing data is arranged in ascending order of wavelength range, including blue band data, green band data, yellow band data, red light band data, red edge band data, near-infrared band data, and short-wave infrared band data.

[0062] Determine a vegetation detection product according to the blue band data, the green band data, the red light band data, the red edge band data, the near-infrared band data, and the short-wave infrared band data.

[0063] Specifically, determine a vegetation detection product according to the blue band data, the green band data, the red light band data, the red edge band data, the near-infrared band data, and the short-wave infrared band data. The vegetation detection product can reflect various conditions of vegetation, such as health conditions and vegetation growth trends.

[0064] Determine a water body and ice and snow detection product according to the blue band data, the green band data, the red light band data, the near-infrared band data, and the short-wave infrared band data.

[0065] Specifically, determine a water body and ice and snow detection product according to the blue band data, the green band data, the red light band data, the near-infrared band data, and the short-wave infrared band data. The water body and ice and snow detection product can reflect various conditions of the water body and ice and snow, such as water quality conditions and phytoplankton conditions.

[0066] Determine a geological detection product according to the green band data, the yellow band data, the red light band data, the near-infrared band data, and the short-wave infrared band data.

[0067] Specifically, determine a geological detection product according to the green band data, the yellow band data, the red light band data, the near-infrared band data, and the short-wave infrared band data. The geological detection product can reflect geological mineral values, such as the distribution of ferrous and ferric ions, laterite zones, and salt minerals.

[0068] In this embodiment, the remote sensing data obtained by the hyperspectral microsatellite for geological resources and environment can comprehensively cover the reflection and absorption characteristics of different substances on the surface of the target area, so as to accurately analyze vegetation, water bodies, ice and snow, and geological conditions. According to the blue band data, green band data, yellow band data, red light band data, red edge band data, near-infrared band data and short-wave infrared band data in the remote sensing data, high-precision vegetation detection products, water body and ice and snow detection products, and geological detection products are respectively determined. Since all the remote sensing data are obtained by the hyperspectral microsatellite for geological resources and environment, and each remote sensing data obtained by the hyperspectral microsatellite for geological resources and environment is under the same precision standard, the vegetation detection products, water body and ice and snow detection products, and geological detection products further obtained from the remote sensing data obtained by the hyperspectral microsatellite for geological resources and environment have the same precision, and cross-domain collaborative processing of ecological and geological information can be realized.

[0069] Optionally, the vegetation detection products include normalized difference vegetation index, red edge chlorophyll vegetation index, modified soil adjusted vegetation index, soil adjusted vegetation index, optimized soil adjusted vegetation index, enhanced vegetation index, difference vegetation index and ratio vegetation index;

[0070] Determining the vegetation detection products according to the blue band data, the green band data, the red light band data, the red edge band data, the near-infrared band data and the short-wave infrared band data includes:

[0071] Determining the normalized difference vegetation index, the red edge chlorophyll vegetation index, the modified soil adjusted vegetation index, the soil adjusted vegetation index, the optimized soil adjusted vegetation index, the enhanced vegetation index, the difference vegetation index and the ratio vegetation index according to the near-infrared band data and the red light band data.

[0072] Specifically, the vegetation detection products include the Normalized Difference Vegetation Index (NDVI), the Red Edge Chlorophyll Vegetation Index (ReCI), the Modified Soil Adjusted Vegetation Index (MSAVI), the Soil Adjusted Vegetation Index (SAVI), the Optimized Soil Adjusted Vegetation Index (OSAVI), the Enhanced Vegetation Index (EVI), the Difference Vegetation Index (DVI), and the Ratio Vegetation Index (RVI). According to the blue band data, the green band data, the red band data, the red edge band data, the near-infrared band data, and the short-wave infrared band data, the Normalized Difference Vegetation Index formula, the Red Edge Chlorophyll Vegetation Index formula, the Modified Soil Adjusted Vegetation Index formula, the Soil Adjusted Vegetation Index formula, the Optimized Soil Adjusted Vegetation Index formula, the Enhanced Vegetation Index formula, the Difference Vegetation Index, and the Ratio Vegetation Index formula are used to determine the Normalized Difference Vegetation Index, the Red Edge Chlorophyll Vegetation Index, the Modified Soil Adjusted Vegetation Index, the Soil Adjusted Vegetation Index, the Optimized Soil Adjusted Vegetation Index, the Enhanced Vegetation Index, the Difference Vegetation Index, and the Ratio Vegetation Index.

[0073] Among them, the Normalized Difference Vegetation Index can reflect the health status and growth trend of vegetation; the Red Edge Chlorophyll Vegetation Index responds to the chlorophyll content in nitrogen-nourished leaves, shows the photosynthetic activity of the canopy, and has the best display effect during the active development stage of vegetation; the Modified Soil Adjusted Vegetation Index aims to reduce the impact of soil on crop monitoring results, is applicable to situations where the Normalized Difference Vegetation Index cannot provide accurate values, especially in cases with a high proportion of bare soil, sparse vegetation, or low chlorophyll content in plants. The Modified Soil Adjusted Vegetation Index has the best response effect at the beginning of the crop production season, that is, when seedlings start to grow, and is used to analyze young crops; the Soil Adjusted Vegetation Index is applicable to arid areas with sparse vegetation (less than 15% of the total area of the region) and bare soil surfaces.

[0074] The Normalized Difference Vegetation Index formula includes:

[0075] NDVI = (NIR - RED1) / (NIR + RED1);

[0076] Where NDVI is the Normalized Difference Vegetation Index, NIR is the near-infrared band data with a wavelength range of 840 - 860 nm, and RED1 is the red band data with a wavelength range of 670 - 690 nm.

[0077] The Red Edge Chlorophyll Vegetation Index formula includes:

[0078] ReCI = (NIR / RED1) - 1;

[0079] Where ReCI is the Red Edge Chlorophyll Vegetation Index, NIR is the near-infrared band data with a wavelength range of 840 - 860 nm, and RED1 is the red band data with a wavelength range of 670 - 690 nm.

[0080] The improved soil-adjusted vegetation index formula includes:

[0081] MSAVI = (2 * NIR + 1 - sqrt((2 * NIR + 1)^2 - 8 * (NIR - RED1))) / 2;

[0082] Wherein, MSAVI is the improved soil-adjusted vegetation index, NIR is the near-infrared band data in the band range of 840 - 860 nm, and RED1 is the red band data in the band range of 670 - 690 nm.

[0083] The soil-adjusted vegetation index formula includes:

[0084] SAVI = ((NIR - RED1) / (NIR + RED1 + L)) * (1 + L);

[0085] Wherein, SAVI is the soil-adjusted vegetation index, NIR is the near-infrared band data in the band range of 840 - 860 nm, RED1 is the red band data in the band range of 670 - 690 nm, L is the soil adjustment factor, and L ranges from -1 to +1, depending specifically on the green vegetation density in the target area. In areas with high green vegetation, L = 0, and in this case, SAVI is the same as NDVI. On the contrary, for areas with low green vegetation, L = 1. Exemplarily, L is set to 0.5 to adapt to most land covers.

[0086] The optimized soil-adjusted vegetation index formula includes:

[0087] OSAVI = (NIR - RED1) / (NIR + RED1 + 0.16);

[0088] Wherein, OSAVI is the optimized soil-adjusted vegetation index, NIR is the near-infrared band data in the band range of 840 - 860 nm, and RED1 is the red band data in the band range of 670 - 690 nm. The optimized soil-adjusted vegetation index is the modified soil-adjusted vegetation index, which takes into account the standard value of the canopy background adjustment factor, that is, 0.16. When the canopy coverage is low, compared with the soil-adjusted vegetation index, this adjustment allows for greater soil variation in the optimized soil-adjusted vegetation index, and the optimized soil-adjusted vegetation index has better sensitivity to canopy coverage exceeding 50%.

[0089] The enhanced vegetation index formula includes:

[0090] EVI = 2.5 * ((NIR - RED1) / ((NIR) + (C1 * RED1) - (C2 * BLUE) + M));

[0091] Among them, EVI is the enhanced vegetation index formula, NIR is the near-infrared band data with a band range of 840 - 860 nm, RED1 is the red band data with a band range of 670 - 690 nm, C1 and C2 are coefficients, BLUE is the blue band data with a band range of 480 - 500 nm, and M is the adjustment factor for soil and canopy background. The enhanced vegetation index has stronger anti-atmospheric interference ability and anti-noise ability compared to the normalized difference vegetation index, and is more suitable for weather conditions with higher aerosol content and lush vegetation areas.

[0092] The difference vegetation index includes:

[0093] DVI = NIR - RED1;

[0094] Among them, DVI is the difference vegetation index, NIR is the near-infrared band data with a band range of 840 - 860 nm, and RED1 is the red band data with a band range of 670 - 690 nm.

[0095] The ratio vegetation index formula includes:

[0096] RVI = NIR / RED1;

[0097] Among them, RVI is the ratio vegetation index, NIR is the near-infrared band data with a band range of 840 - 860 nm, and RED1 is the red band data with a band range of 670 - 690 nm.

[0098] Optionally, the vegetation detection product includes the normalized difference red-edge vegetation index, the green normalized difference vegetation index, the green chlorophyll vegetation index, the atmosphere-resistant vegetation index, the structure-insensitive pigment vegetation index, the visible atmosphere resistance index, and the standardized burn ratio;

[0099] Determining the vegetation detection product according to the blue band data, the green band data, the red band data, the red-edge band data, the near-infrared band data, and the short-wave infrared band data includes:

[0100] Determining the normalized difference red-edge vegetation index according to the near-infrared band data and the red-edge band data;

[0101] Determining the green normalized difference vegetation index and the green chlorophyll vegetation index according to the near-infrared band data and the green band data;

[0102] Determining the atmosphere-resistant vegetation index and the structure-insensitive pigment vegetation index according to the near-infrared band data, the red band data, and the blue band data;

[0103] Determine the visible atmospheric resistance index based on the red light band data, the green light band data, and the blue light band data;

[0104] Determine the normalized burn rate based on the near-infrared light band data and the short-wave infrared light band data.

[0105] Specifically, the vegetation detection products include the normalized difference red-edge vegetation index, the green normalized difference vegetation index, the green chlorophyll vegetation index, the atmospheric resistant vegetation index, the structure-insensitive pigment vegetation index, the visible atmospheric resistance index, and the normalized burn rate. Based on the near-infrared light band data and the red-edge light band data, the normalized difference red-edge vegetation index is determined using the normalized difference red-edge vegetation index. The formula for the normalized difference red-edge vegetation index is as follows:

[0106] NDRE = (NIR – REDEDGE) / (NIR + REDEDGE);

[0107] where NDRE is the normalized difference red-edge vegetation index, NIR is the near-infrared light band data with a wavelength range of 840 - 860 nm, and REDEDGE is the red-edge light band data with a wavelength range of 740 - 750 nm. The normalized difference red-edge vegetation index is applicable to high-density tree canopy coverage. For the best data accuracy, NDRE can be used in combination with NDVI.

[0108] Based on the near-infrared light band data and the green light band data, the green normalized difference vegetation index formula and the green chlorophyll vegetation index formula are used to determine the green normalized difference vegetation index and the green chlorophyll vegetation index respectively. The formula for the green normalized difference vegetation index is as follows:

[0109] GNDVI = (NIR – GREEN) / (NIR + GREEN);

[0110] where GNDVI is the green normalized difference vegetation index, NIR is the near-infrared light band data with a wavelength range of 840 - 860 nm, and GREEN is the green light band data with a wavelength range of 540 - 560 nm. The green normalized difference vegetation index is applicable to detecting wilted or aging crops and measuring the nitrogen content in leaves when there is no red light band, and monitoring vegetation in dense tree canopies or at the mature stage.

[0111] The formula for the green chlorophyll vegetation index is as follows:

[0112] GCI = NIR / GREEN – 1;

[0113] Among them, GCI is the green chlorophyll vegetation index, NIR is the near-infrared band data with a band range of 840 - 860 nm, and GREEN is the green band data with a band range of 540 - 560 nm. The green chlorophyll vegetation index is used to estimate the chlorophyll content in various plants. The chlorophyll content reflects the physiological state of the vegetation, which decreases in stressed plants, so it can be used as a measure of vegetation health.

[0114] According to the near-infrared band data, the red band data, and the blue band data, the atmospheric resistant vegetation index formula and the structure-insensitive pigment vegetation index formula are respectively used to determine the atmospheric resistant vegetation index and the structure-insensitive pigment vegetation index. The atmospheric resistant vegetation index formula includes:

[0115] ARVI = (NIR – (2 * RED1) + BLUE) / (NIR + (2 * RED1) + BLUE);

[0116] Among them, ARVI is the atmospheric resistant vegetation index, NIR is the near-infrared band data with a band range of 840 - 860 nm, RED1 is the red band data with a band range of 670 - 690 nm, and BLUE is the blue band data with a band range of 480 - 500 nm. The atmospheric resistant vegetation index is the first vegetation index that is relatively insensitive to atmospheric factors (such as aerosols). By doubling the near-infrared band data and increasing the blue band, the NDVI is corrected to reduce the atmospheric scattering effect. Compared with other indicators, ARVI is insensitive to aerosols and is particularly suitable for monitoring farmland where straw is burned and tropical mountainous areas often covered by smoke and dust.

[0117] The structure-insensitive pigment vegetation index formula includes:

[0118] SIPI = (NIR – BLUE) / (NIR – RED1);

[0119] Among them, SIPI is the structure-insensitive pigment vegetation index, NIR is the near-infrared band data with a band range of 840 - 860 nm, RED1 is the red band data with a band range of 670 - 690 nm, and BLUE is the blue band data with a band range of 480 - 500 nm. The structure-insensitive pigment vegetation index is beneficial for analyzing vegetation with a variable canopy structure. It estimates the ratio of carotenoids to chlorophyll. An increasing value indicates vegetation stress. If the structure-insensitive pigment vegetation index increases, it may mean crop diseases, which usually lead to chlorophyll loss in the vegetation.

[0120] Based on the red-band data, the green-band data, and the blue-band data, the visible atmospheric resistance index is determined using the visible atmospheric resistance index formula, which includes:

[0121] VARI = (GREEN – RED2) / (GREEN + RED 2 – BLUE);

[0122] where VARI is the visible atmospheric resistance index, GREEN is the green-band data in the wavelength range of 540 - 560 nm, RED 2 is the red-band data in the wavelength range of 620 - 650 nm, and BLUE is the blue-band data in the wavelength range of 480 - 500 nm. The visible atmospheric resistance index is suitable for RGB or color images because it applies to the entire visible part of the electromagnetic spectrum (including the red band, the green band, and the blue band). Its specific task is to enhance and highlight vegetation under strong atmospheric effects while smoothing out illumination variations. Due to its lower sensitivity to atmospheric effects, the error of VARI in vegetation monitoring under different atmospheric thickness conditions is less than 10%.

[0123] Based on the near-infrared band data and the short-wave infrared band data, the normalized burn ratio is determined using the normalized burn ratio formula, which includes:

[0124] NBR = (NIR – SWIR) / (NIR + SWIR);

[0125] where NBR is the normalized burn ratio, NIR is the near-infrared band data in the wavelength range of 840 - 860 nm, and SWIR is the short-wave infrared band data in the wavelength range of 1580 - 1620 nm. The normalized burn ratio is used to highlight the burned areas after a fire.

[0126] Optionally, the water body and ice and snow detection products include the normalized difference water index, the turbid water body index, the cyanobacteria and large aquatic plant index, the phytoplankton index, and the normalized difference snow index;

[0127] Determining the water body and ice and snow detection products based on the blue-band data, the green-band data, the red-band data, the near-infrared band data, and the short-wave infrared band data includes:

[0128] Determining the normalized difference water index based on the near-infrared band data and the green-band data;

[0129] Determining the turbid water body index based on the red-band data and the short-wave infrared band data;

[0130] Determine the cyanobacteria and large aquatic plants index based on the blue band data, the green band data, and the shortwave infrared band data;

[0131] Determine the phytoplankton index based on the near-infrared band data, the red band data, and the shortwave infrared band data;

[0132] Determine the normalized difference snow index based on the green band data and the shortwave infrared band data.

[0133] Specifically, the water body and ice and snow detection products include the normalized difference water index, the turbid water body index, the cyanobacteria and large aquatic plants index, the phytoplankton index, and the normalized difference snow index. According to the near-infrared band data and the green band data, use the normalized difference water index formula to determine the normalized difference water index. The normalized difference water index formula includes:

[0134] NDWI = (GREEN - NIR) / (GREEN + NIR);

[0135] Wherein, NDWI is the normalized difference water index, NIR is the near-infrared band data with a band range of 840 - 860 nm, and GREEN is the green band data with a band range of 540 - 560 nm. The normalized difference water index has great advantages in pure water body extraction and is applicable to detecting flooded farmland, on-site flood distribution, detecting irrigated farmland, wetland distribution, etc.

[0136] According to the red band data and the shortwave infrared band data, use the turbid water body index formula to determine the turbid water body index. The turbid water body index formula includes:

[0137] TWI = RED1 - SWIR;

[0138] Wherein, TWI is the turbid water body index, RED1 is the red band data with a band range of 670 - 690 nm, and SWIR is the shortwave infrared band data with a band range of 1580 - 1620 nm. The turbid water body index is used to judge the turbidity of the water body and is also used in the judgment of algal blooms.

[0139] According to the blue band data, the green band data, and the shortwave infrared band data, use the cyanobacteria and large aquatic plants index formula to determine the cyanobacteria and large aquatic plants index. The cyanobacteria and large aquatic plants index formula includes:

[0140] CMI = GREEN - BLUE - (SWIR - BLUE) * ((λ GREEN - λ BLUE ) / (λ SWIR - λBLUE ));

[0141] Among them, CMI is the cyanobacteria and macrophytes index, GREEN is the green band data in the band range of 540 - 560 nm, BLUE is the blue band data in the band range of 480 - 500 nm, SWIR is the short-wave infrared band data in the band range of 1580 - 1620 nm, λ GREEN is the central wavelength of GREEN, λ BLUE is the central wavelength of BLUE, λ SWIR is the central wavelength of SWIR. The cyanobacteria and macrophytes index is used to extract cyanobacteria and aquatic plants in water by detecting the chlorophyll content in the water body.

[0142] According to the near-infrared band data, the red light band data, and the short-wave infrared band data, the phytoplankton index formula is used to determine the phytoplankton index. The phytoplankton index formula includes:

[0143] FAI = NIR - RED1 - (SWIR - RED1) * ((λ NIR - λ RED1 ) / (λ SWIR - λ RED1 ));

[0144] Among them, FAI is the phytoplankton index, NIR is the near-infrared band data in the band range of 840 - 860 nm, RED1 is the red light band data in the band range of 670 - 690 nm, SWIR is the short-wave infrared band data in the band range of 1580 - 1620 nm, λ NIR is the central wavelength of NIR, λ RED1 is the central wavelength of RED1, λ SWIR is the central wavelength of SWIR. The phytoplankton index is used for the extraction of floating algae blooms and can also solve the problem that CMI cannot distinguish cyanobacteria and aquatic plants.

[0145] According to the green band data and the short-wave infrared band data, the normalized difference snow index formula is used to determine the normalized difference snow index. The normalized difference snow index formula includes:

[0146] NDSI = (GREEN – SWIR) / (GREEN + SWIR);

[0147] Among them, NDSI is the normalized difference snow index, GREEN is the green band data in the band range of 540 - 560 nm, and SWIR is the short-wave infrared band data in the band range of 1580 - 1620 nm. The normalized difference snow index can effectively reduce the influence of noise such as vegetation and building shadows on lake ice monitoring.

[0148] Optionally, the geological exploration product includes ferrous-ferric iron distribution data, iron oxide index, laterite zone data, ferrous silicate index, silicate mineral data, and intermediate argillaceous zone mineral data;

[0149] Determining the geological exploration product according to the green band data, the yellow band data, the red light band data, the near-infrared band data, and the short-wave infrared band data includes:

[0150] Determining the ferrous-ferric iron distribution data according to the near-infrared band data and the short-wave infrared band data;

[0151] Determining the iron oxide index according to the near-infrared band data and the short-wave infrared band data;

[0152] Determining the laterite zone data, the ferrous silicate index, the silicate mineral data, and the intermediate argillaceous zone mineral data according to the short-wave infrared band data.

[0153] Specifically, the geological exploration product includes ferrous-ferric iron distribution data, iron oxide index, laterite zone data, ferrous silicate index, silicate mineral data, and intermediate argillaceous zone mineral data. According to the near-infrared band data and the short-wave infrared band data, using the first ferrous-ferric iron distribution data formula, the ferrous-ferric iron distribution data is determined, and the first ferrous-ferric iron distribution data formula includes;

[0154] Index1 = B22 / (B12 + B13 + B14)

[0155] Wherein, Index1 is the ferrous-ferric iron distribution data formula, B12 is the near-infrared band data with a band range of 760 - 780 nm, B13 is the near-infrared band data with a band range of 800 - 830 nm, B14 is the near-infrared band data with a band range of 840 - 860 nm, and B22 is the short-wave infrared band data with a band range of 800 - 830 nm.

[0156] According to the near-infrared band data and the short-wave infrared band data, using the iron oxide index formula, the iron oxide index is determined, and the iron oxide index formula includes;

[0157] Index 2 = (B18 + B19) / (B12 + B13 + B14)

[0158] Among them, B12 is the near-infrared band data with a band range of 760 - 780 nm, B13 is the near-infrared band data with a band range of 800 - 830 nm, B14 is the near-infrared band data with a band range of 840 - 860 nm, B18 is the short-wave infrared band data with a band range of 1580 - 1620 nm, and B19 is the short-wave infrared band data with a band range of 1630 - 1670 nm.

[0159] According to the short-wave infrared band data, the red soil zone data, the ferrous silicate index, the silicate mineral data, and the intermediate argillaceous zone mineral data are respectively determined by using the red soil zone data formula, the ferrous silicate index formula, the silicate mineral data formula, and the intermediate argillaceous zone mineral data formula. The red soil zone data formula includes:

[0160] Index3 = (B18 + B19) / B22;

[0161] Among them, Index3 is the red soil zone data, B18 is the short-wave infrared band data with a band range of 1580 - 1620 nm, B19 is the short-wave infrared band data with a band range of 1630 - 1670 nm, and B22 is the short-wave infrared band data with a band range of 800 - 830 nm.

[0162] The ferrous silicate index formula includes:

[0163] Index4 = B22 / (B18 + B19)

[0164] Among them, Index4 is the ferrous silicate index, B18 is the short-wave infrared band data with a band range of 1580 - 1620 nm, B19 is the short-wave infrared band data with a band range of 1630 - 1670 nm, and B {22} is the short-wave infrared band data with a band range of 800 - 830 nm.

[0165] The silicate mineral data formula includes:

[0166] Index5 = (B23 + B26) / (B24 + B25)

[0167] Among them, B23 is the short-wave infrared band data with a band range of 2190 - 2230 nm, B24 is the short-wave infrared band data with a band range of 2250 - 2290 nm, B25 is the short-wave infrared band data with a band range of 2320 - 2360 nm, and B26 is the short-wave infrared band data with a band range of 2400 - 2480 nm.

[0168] The intermediate argillaceous zone mineral data formula includes:

[0169] Index6 = (B22 + B24) / B23;

[0170] Wherein, Index6 is the mineral data of the middle shale zone, B22 is the short-wave infrared band data with a band range of 800 - 830 nm, B23 is the short-wave infrared band data with a band range of 2190 - 2230 nm, and B24 is the short-wave infrared band data with a band range of 2250 - 2290 nm.

[0171] Optionally, determining the geological exploration product according to the green band data, the yellow band data, the red light band data, the near-infrared band data, and the short-wave infrared band data further includes:

[0172] Determining the ferrous and ferric iron distribution data according to the green band data, the red light band data, and the yellow band.

[0173] Specifically, according to the green band data, the red light band data, and the yellow band, the second ferrous and ferric iron distribution data formula is used to determine the ferrous and ferric iron distribution data. The second ferrous and ferric iron distribution data formula includes:

[0174] Index1 = (B7 + B8) / (B4 + B5 + B6);

[0175] Wherein, Index1 is the ferrous and ferric iron distribution data, B4 is the green band data with a band range of 520 - 540 nm, B5 is the green band data with a band range of 540 - 560 nm, B6 is the yellow band data with a band range of 560 - 590 nm, B7 is the red light band data with a band range of 620 - 650 nm, and B8 is the red light band data with a band range of 670 - 690 nm.

[0176] Optionally, after determining the geological exploration product according to the green band data, the yellow band data, the red light band data, the near-infrared band data, and the short-wave infrared band data, it further includes:

[0177] Inputting the vegetation detection product and the geological exploration product into the ecosystem stability scoring model of the target area constructed in advance to generate an ecosystem stability score, wherein the ecosystem stability scoring model is constructed by using a machine learning algorithm according to the historical vegetation detection products and historical geological exploration products of the target area;

[0178] According to the ecosystem stability score, collaborating with the water body and ice and snow detection products to generate a water body ecological report of the target area.

[0179] Specifically, first perform spatial registration and resolution unification on vegetation detection products and geological detection products, such as the green chlorophyll vegetation index and the ferrous silicate index, to eliminate scale differences. Then, input the processed green chlorophyll vegetation index and ferrous silicate index into a pre-constructed ecosystem stability scoring model to obtain the ecosystem stability score, which can be used to determine whether the target area is stable. At the same time, based on Stability_Score, collaborate with the turbidity water index (TWI) in the water body and ice and snow detection products to set a preset threshold for the ecosystem stability score. If the ecosystem stability score is lower than the preset threshold, it indicates that the ecosystem is stable. If it is higher than or equal to the preset threshold, it indicates that the ecosystem is unstable. The formula for the ecosystem stability score is expressed as:

[0180] Stability_Score = f(GCI, Index4);

[0181] Among them, Stability_Score is the ecosystem stability score, that is, the water body ecosystem score, which reflects the balance state between vegetation health and the mineral weathering rate, and f is the mapping function.

[0182] If Stability_Score is lower than the preset threshold, it means that the model determines that the plants are healthy through GCI and the geology is stable through Index4, that is, the ecosystem is stable. However, if it is determined that the water body is turbid through TWI, it is considered that no geological change has occurred and the pollution may be caused by humans. It is determined that there is a risk of human pollution in the area, and the corresponding water body ecosystem report is human impact, which requires extra attention.

[0183] If Stability_Score is higher than the preset threshold, it means that the model determines that the plants are unhealthy through GCI and the geology is unstable through Index4, that is, the ecosystem is unstable. And if it is determined that the water body is also turbid through TWI, it is determined that the turbidity is mainly caused by natural mineral suspensions, and it is determined that there may be natural impacts in the area, and the corresponding water body ecosystem report is natural impact, which does not require much attention.

[0184] If Stability_Score is lower than the preset threshold, it means that the model determines that the plants are healthy through GCI and the geology is stable through Index4, that is, the ecosystem is stable. And if it is determined that the water body is not turbid through TWI, it is considered that no geological change has occurred and there is no pollution, and it is determined that there is no risk in the area, and the corresponding water body ecosystem report is no impact.

[0185] Exemplarily, when constructing an ecosystem stability scoring model, a non-linear mapping relationship between the historical vegetation detection products and historical geological detection products of the target area and the ecosystem stability training scores can be established through machine learning algorithms such as random forests or support vector machines. For example, a non-linear mapping relationship between the historical green chlorophyll vegetation index and the historical ferrous silicate index and the ecosystem stability training scores, and the model is trained with the training objective of minimizing the error between the predicted scores and the actual ecological survey results to obtain an ecosystem stability scoring model, where the ecosystem stability training scores are obtained based on the historical green chlorophyll vegetation index and the ferrous silicate index and the actually detected ecological conditions.

[0186] As Figure 2 shown, an apparatus for generating common products based on remote sensing satellites provided by an embodiment of the present invention includes:

[0187] An acquisition module, configured to acquire remote sensing data of a target area through a hyperspectral microsatellite for geological resources and environment, where the remote sensing data includes blue band data, green band data, yellow band data, red light band data, red edge band data, near-infrared band data, and short-wave infrared band data;

[0188] A vegetation determination module, configured to determine a vegetation detection product according to the blue band data, the green band data, the red light band data, the red edge band data, the near-infrared band data, and the short-wave infrared band data;

[0189] A water body and ice and snow determination module, configured to determine a water body and ice and snow detection product according to the blue band data, the green band data, the red light band data, the near-infrared band data, and the short-wave infrared band data;

[0190] A geology determination module, configured to determine a geological detection product according to the green band data, the yellow band data, the red light band data, the near-infrared band data, and the short-wave infrared band data.

[0191] As Figure 3 shown, an electronic device provided by an embodiment of the present invention includes a memory and a processor; the memory is used to store a computer program; the processor is used to implement the method for generating common products based on remote sensing satellites as described above when executing the computer program.

[0192] Or, an electronic device includes a memory and a processor coupled to the memory; the memory is configured to store a computer program; the processor is configured to perform the following operations when executing the computer program:

[0193] Obtain remote sensing data of the target area through a hyperspectral microsatellite for geological resources and environment, wherein the remote sensing data includes blue band data, green band data, yellow band data, red light band data, red edge band data, near-infrared band data, and short-wave infrared band data;

[0194] Determine vegetation detection products based on the blue band data, the green band data, the red light band data, the red edge band data, the near-infrared band data, and the short-wave infrared band data;

[0195] Determine water body and ice and snow detection products based on the blue band data, the green band data, the red light band data, the near-infrared band data, and the short-wave infrared band data;

[0196] Determine geological detection products based on the green band data, the yellow band data, the red light band data, the near-infrared band data, and the short-wave infrared band data.

[0197] A computer-readable storage medium provided by an embodiment of the present invention, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned common product generation method based on a remote sensing satellite is implemented.

[0198] Or, a non-volatile computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the processor performs the following operations:

[0199] Obtain remote sensing data of the target area through a hyperspectral microsatellite for geological resources and environment, wherein the remote sensing data includes blue band data, green band data, yellow band data, red light band data, red edge band data, near-infrared band data, and short-wave infrared band data;

[0200] Determine vegetation detection products based on the blue band data, the green band data, the red light band data, the red edge band data, the near-infrared band data, and the short-wave infrared band data;

[0201] Determine water body and ice and snow detection products based on the blue band data, the green band data, the red light band data, the near-infrared band data, and the short-wave infrared band data;

[0202] Determine geological detection products based on the green band data, the yellow band data, the red light band data, the near-infrared band data, and the short-wave infrared band data.

[0203] An electronic device that can be used as a server or a client of the present invention will now be described. It is an example of a hardware device that can be applied to various aspects of the present invention. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0204] The electronic device includes a computing unit that can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) or a computer program loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for device operation can also be stored. The computing unit, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0205] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc. In this application, the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiments of the present invention. In addition, the functional units in various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0206] Although the present invention is disclosed as above, the protection scope of the present invention is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, and these changes and modifications will all fall within the protection scope of the present invention.

Claims

1. A method for generating common products based on remote sensing satellites, characterized in that, Including: Obtaining remote sensing data of a target area through a hyperspectral microsatellite for geological resources and environment, wherein the remote sensing data includes blue band data, green band data, yellow band data, red light band data, red edge band data, near-infrared band data, and short-wave infrared band data; Determining a vegetation detection product based on the blue band data, the green band data, the red light band data, the red edge band data, the near-infrared band data, and the short-wave infrared band data; Determining water body and ice and snow detection products based on the blue band data, the green band data, the red light band data, the near-infrared band data, and the short-wave infrared band data; Determining a geological detection product based on the green band data, the yellow band data, the red light band data, the near-infrared band data, and the short-wave infrared band data.

2. The method for generating common products based on remote sensing satellites according to claim 1, wherein The vegetation detection product includes a normalized difference vegetation index, a red edge chlorophyll vegetation index, a modified soil adjusted vegetation index, a soil adjusted vegetation index, an optimized soil adjusted vegetation index, an enhanced vegetation index, a difference vegetation index, and a ratio vegetation index; The determining the vegetation detection product according to the blue band data, the green band data, the red light band data, the red edge band data, the near-infrared band data, and the short-wave infrared band data includes: Determining the normalized difference vegetation index, the red edge chlorophyll vegetation index, the modified soil adjusted vegetation index, the soil adjusted vegetation index, the optimized soil adjusted vegetation index, the enhanced vegetation index, the difference vegetation index, and the ratio vegetation index according to the near-infrared band data and the red light band data.

3. The method for generating common products based on remote sensing satellites according to claim 1, wherein The vegetation detection product includes a normalized difference red edge vegetation index, a green normalized difference vegetation index, a green chlorophyll vegetation index, an atmosphere resistant vegetation index, a structure insensitive pigment vegetation index, a visible atmosphere resistance index, and a standardized burn ratio; The determining the vegetation detection product according to the blue band data, the green band data, the red light band data, the red edge band data, the near-infrared band data, and the short-wave infrared band data includes: Determining the normalized difference red edge vegetation index according to the near-infrared band data and the red edge band data; Determining the green normalized difference vegetation index and the green chlorophyll vegetation index according to the near-infrared band data and the green band data; Determining the atmosphere resistant vegetation index and the structure insensitive pigment vegetation index according to the near-infrared band data, the red light band data, and the blue band data; Determining the visible atmosphere resistance index according to the red light band data, the green band data, and the blue band data; Determining the standardized burn ratio according to the near-infrared band data and the short-wave infrared band data.

4. The method for generating common products based on remote sensing satellites according to claim 1, wherein The water body and ice and snow detection products include a normalized difference water index, a turbid water body index, a cyanobacteria and macrophyte index, a phytoplankton index, and a normalized difference snow index; Determining water body and ice and snow detection products based on the blue band data, the green band data, the red light band data, the near-infrared band data, and the short-wave infrared band data includes: Determining the normalized difference water index according to the near-infrared band data and the green band data; Determining the turbid water body index according to the red light band data and the short-wave infrared band data; Determining the cyanobacteria and large aquatic plant index according to the blue band data, the green band data, and the short-wave infrared band data; Determining the phytoplankton index according to the near-infrared band data, the red light band data, and the short-wave infrared band data; Determining the normalized difference snow index according to the green band data and the short-wave infrared band data.

5. The common product generation method based on remote sensing satellites according to claim 1, characterized in that The geological exploration product includes ferrous-ferric iron distribution data, iron oxide index, red soil zone data, ferrous silicate index, silicate mineral data, and intermediate argillaceous zone mineral data; Determining geological exploration products according to the green band data, the yellow band data, the red light band data, the near-infrared band data, and the short-wave infrared band data includes: Determining the ferrous-ferric iron distribution data according to the near-infrared band data and the short-wave infrared band data; Determining the iron oxide index according to the near-infrared band data and the short-wave infrared band data; Determining the red soil zone data, the ferrous silicate index, the silicate mineral data, and the intermediate argillaceous zone mineral data according to the short-wave infrared band data.

6. The method for generating common products based on remote sensing satellites according to claim 5, characterized in that, Determining geological exploration products according to the green band data, the yellow band data, the red light band data, the near-infrared band data, and the short-wave infrared band data further includes: Determining the ferrous-ferric iron distribution data according to the green band data, the red light band data, and the yellow band.

7. The method for generating common products based on remote sensing satellites according to claim 1, wherein After determining the geological exploration products according to the green band data, the yellow band data, the red light band data, the near-infrared band data, and the short-wave infrared band data, it further includes: Inputting the vegetation detection product and the geological detection product into the pre-constructed ecosystem stability scoring model of the target area to generate an ecosystem stability score, where the ecosystem stability scoring model is constructed by using a machine learning algorithm based on the historical vegetation detection product and historical geological detection product of the target area; Generating a water body ecological report of the target area in coordination with the water body and ice and snow detection products according to the ecosystem stability score.

8. A common product generation device based on a remote sensing satellite, characterized in that, Including: An acquisition module for acquiring remote sensing data of a target area through a hyperspectral microsatellite for geological resources and environment, where the remote sensing data includes blue band data, green band data, yellow band data, red light band data, red edge band data, near-infrared band data, and short-wave infrared band data; A vegetation determination module for determining a vegetation detection product according to the blue band data, the green band data, the red light band data, the red edge band data, the near-infrared band data, and the short-wave infrared band data; A water body and ice and snow determination module, configured to determine water body and ice and snow detection products according to the blue band data, the green band data, the red band data, the near-infrared band data, and the short-wave infrared band data; A geology determination module, configured to determine geology detection products according to the green band data, the yellow band data, the red band data, the near-infrared band data, and the short-wave infrared band data.

9. An electronic device, characterized in that, It includes a memory and a processor; The memory is used for storing a computer program; The processor is configured to, when executing the computer program, implement the method for generating common products based on a remote sensing satellite according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, A computer program is stored on the storage medium, and when the computer program is executed by the processor, the method for generating common products based on a remote sensing satellite according to any one of claims 1 to 7 is implemented.

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