Reservoir carbon sequestration accounting methods, related devices and media based on multi-source remote sensing data

Abstract

This application discloses a method, related apparatus, and medium for reservoir carbon sequestration based on multi-source remote sensing data. The method includes: acquiring multi-source remote sensing data of a target reservoir within a target time period; based on the multi-source remote sensing data, retrieving a dynamic map of the water surface area, a spatiotemporal distribution map of chlorophyll a concentration, a spatiotemporal distribution map of CDOM (chlorophyll-matrix dominance), and a spatiotemporal distribution map of water surface temperature for the target reservoir within the target time period; inputting the above four maps into a preset carbon sequestration model for carbon sequestration processing to obtain a spatiotemporal distribution map of greenhouse gas flux per unit area corresponding to the target reservoir, where each pixel in the spatiotemporal distribution map of greenhouse gas flux per unit area carries a corresponding greenhouse gas flux per unit area; performing spatiotemporal integration processing on the spatiotemporal distribution map of greenhouse gas flux per unit area and the dynamic map of the water surface area to obtain the target carbon sequestration amount of the target reservoir within the target time period. Implementing the method of this application embodiment can improve the accuracy of reservoir carbon sequestration.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method, related apparatus and medium for calculating reservoir carbon sequestration based on multi-source remote sensing data. Background Technology

[0002] Carbon sinks refer to the processes, activities, and mechanisms for removing carbon dioxide from the atmosphere. In the context of addressing global climate change, carbon sink research is receiving increasing attention. While there has been considerable research on carbon sink accounting methods in fields such as forestry, a comprehensive method for carbon sink accounting in water conservancy projects is still lacking.

[0003] Traditional ground-based monitoring methods (such as flux towers and static box methods) are time-consuming, labor-intensive, and costly. They can usually only obtain data from discrete points and specific time periods, making it difficult to achieve large-scale monitoring of the entire reservoir body, especially remote areas, which affects the accuracy of reservoir carbon sequestration accounting. Summary of the Invention

[0004] This application provides a method, related apparatus, and medium for calculating reservoir carbon sequestration based on multi-source remote sensing data, which can improve the accuracy of reservoir carbon sequestration calculation.

[0005] In a first aspect, embodiments of this application provide a reservoir carbon sequestration accounting method based on multi-source remote sensing data, which includes:

[0006] Acquire multi-source remote sensing data of the target reservoir within the target time period;

[0007] Based on the multi-source remote sensing data, the following maps were obtained: dynamic map of water surface area, spatiotemporal distribution map of chlorophyll a concentration, spatiotemporal distribution map of CDOM concentration, and spatiotemporal distribution map of water surface temperature of the target reservoir during the target time period.

[0008] The dynamic map of water surface area, the spatiotemporal distribution map of chlorophyll a concentration, the spatiotemporal distribution map of CDOM, and the spatiotemporal distribution map of water surface temperature are input into a preset carbon sink accounting model for carbon sink accounting processing to obtain a spatiotemporal distribution map of greenhouse gas flux per unit area corresponding to the target reservoir. Each pixel in the spatiotemporal distribution map of greenhouse gas flux per unit area carries the corresponding greenhouse gas flux per unit area.

[0009] Spatiotemporal integration is performed on the spatiotemporal distribution map of the greenhouse gas flux per unit area and the dynamic map of the water surface area to obtain the target total carbon amount of the target reservoir during the target time period.

[0010] In some embodiments, the multi-source remote sensing data includes multispectral optical remote sensing data, synthetic aperture radar data, and thermal infrared remote sensing data.

[0011] In some embodiments, the dynamic water surface area diagram is obtained based on the following steps:

[0012] Based on the synthetic aperture radar data, the water body boundary is initially extracted to obtain an initial dynamic map of the water surface area.

[0013] The initial dynamic map of the water surface area is modified by boundary correction based on the multispectral optical remote sensing data to obtain the dynamic map of the water surface area.

[0014] In some embodiments, the spatiotemporal distribution map of chlorophyll a concentration is obtained based on the following steps:

[0015] Near-infrared reflectance and red reflectance are extracted from the multispectral optical remote sensing data;

[0016] Based on the near-infrared light reflectance and the red light reflectance, as well as the preset correspondence between the near-infrared light and red light reflectance ratio and chlorophyll a concentration, a spatiotemporal distribution map of chlorophyll a concentration is generated.

[0017] In some embodiments, the CDOM spatiotemporal distribution map is obtained based on the following steps:

[0018] Extract the blue light band reflectance and the green light band reflectance from the multispectral optical remote sensing data;

[0019] Based on the blue light band reflectance and the green light band reflectance, as well as the preset blue-green band ratio and the correspondence between CDOM concentration, the spatiotemporal distribution map of CDOM is generated.

[0020] In some embodiments, the spatiotemporal distribution map of water surface temperature is obtained based on the following steps:

[0021] The thermal infrared remote sensing data is radiometrically calibrated to obtain the radiance data of the top atmospheric layer.

[0022] Based on the split-window algorithm, atmospheric correction is performed on the top atmospheric radiance data to obtain the surface radiance data;

[0023] Based on the emissivity of the water body, the surface radiance data is inverted using the Planck function to obtain the spatiotemporal distribution map of the water surface temperature.

[0024] In some embodiments, the spatiotemporal distribution map of greenhouse gas flux per unit area includes multiple sub-spatiotemporal distribution maps of greenhouse gas flux corresponding to different times, and the dynamic map of water surface area includes sub-water surface distribution maps corresponding to different times; the spatiotemporal integration processing of the spatiotemporal distribution map of greenhouse gas flux per unit area and the dynamic map of water surface area to obtain the target carbon aggregate of the target reservoir during the target time period includes:

[0025] The spatiotemporal distribution maps of the sub-greenhouse gas fluxes and the sub-water surface distribution maps at corresponding times are determined as a group to obtain multiple sets of distribution maps;

[0026] For each set of distribution maps, the actual water surface area corresponding to each pixel in the spatiotemporal distribution map of the sub-greenhouse gas flux is determined in the sub-water surface distribution map. The sub-carbon sink corresponding to the current set of distribution maps is determined based on the greenhouse gas flux value per unit area corresponding to each pixel and the actual water surface area, so as to obtain the sub-carbon sink corresponding to each set of distribution maps.

[0027] The sum of the sub-carbon sinks in each distribution map is determined as the target total carbon sink.

[0028] Secondly, embodiments of this application also provide a reservoir carbon sequestration accounting device based on multi-source remote sensing data, which includes:

[0029] The transceiver unit is used to acquire multi-source remote sensing data of the target reservoir within the target time period.

[0030] The processing unit is used to invert the dynamic map of water surface area, spatiotemporal distribution map of chlorophyll a concentration, spatiotemporal distribution map of CDOM, and spatiotemporal distribution map of water surface temperature of the target reservoir during the target time period based on the multi-source remote sensing data; input the dynamic map of water surface area, spatiotemporal distribution map of chlorophyll a concentration, spatiotemporal distribution map of CDOM, and spatiotemporal distribution map of water surface temperature into a preset carbon sink accounting model for carbon sink accounting processing to obtain the spatiotemporal distribution map of greenhouse gas flux per unit area corresponding to the target reservoir, where each pixel in the spatiotemporal distribution map of greenhouse gas flux per unit area carries the corresponding greenhouse gas flux per unit area; and perform spatiotemporal integration processing on the spatiotemporal distribution map of greenhouse gas flux per unit area and the dynamic map of water surface area to obtain the target carbon total amount of the target reservoir during the target time period.

[0031] Thirdly, embodiments of this application also provide a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described method.

[0032] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program, the computer program including program instructions that, when executed by a processor, can implement the above-described method.

[0033] This application provides a method, related apparatus, and medium for calculating reservoir carbon sequestration based on multi-source remote sensing data. The method includes: acquiring multi-source remote sensing data of a target reservoir within a target time period; based on the multi-source remote sensing data, retrieving a dynamic map of the water surface area, a spatiotemporal distribution map of chlorophyll a concentration, a spatiotemporal distribution map of CDOM (chlorophyll-matrix dome), and a spatiotemporal distribution map of water surface temperature for the target reservoir within the target time period; inputting the dynamic map of the water surface area, the spatiotemporal distribution map of chlorophyll a concentration, the spatiotemporal distribution map of CDOM, and the spatiotemporal distribution map of water surface temperature into a preset carbon sequestration model for carbon sequestration processing to obtain a spatiotemporal distribution map of greenhouse gas flux per unit area corresponding to the target reservoir, where each pixel in the spatiotemporal distribution map of greenhouse gas flux per unit area carries a corresponding greenhouse gas flux per unit area; and performing spatiotemporal integration processing on the spatiotemporal distribution map of greenhouse gas flux per unit area and the dynamic map of the water surface area to obtain the target carbon sequestration amount of the target reservoir within the target time period. The embodiments of this application can perform carbon sequestration calculations on the entire reservoir using multi-source remote sensing data, thereby improving the accuracy of reservoir carbon sequestration calculations. Attached Figure Description

[0034] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 A flowchart illustrating the reservoir carbon sequestration accounting method based on multi-source remote sensing data provided in this application embodiment;

[0036] Figure 2 A schematic diagram of a sub-process of the reservoir carbon sequestration accounting method based on multi-source remote sensing data provided in the embodiments of this application;

[0037] Figure 3 A schematic block diagram of a reservoir carbon sequestration accounting device based on multi-source remote sensing data provided in the embodiments of this application;

[0038] Figure 4 A schematic block diagram of a computer device provided in an embodiment of this application. Detailed Implementation

[0039] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0040] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0041] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0042] It should also be further understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0043] This application provides a method, related apparatus, and medium for calculating reservoir carbon sequestration based on multi-source remote sensing data.

[0044] The entity executing the reservoir carbon sequestration calculation method based on multi-source remote sensing data can be the reservoir carbon sequestration calculation device based on multi-source remote sensing data provided in the embodiments of this application, or a computer device that integrates the reservoir carbon sequestration calculation device based on multi-source remote sensing data. The reservoir carbon sequestration calculation device based on multi-source remote sensing data can be implemented in hardware or software, and the computer device can be a terminal or a server.

[0045] Figure 1 This is a flowchart illustrating the reservoir carbon sequestration accounting method based on multi-source remote sensing data provided in this application embodiment. Figure 1 As shown, the method includes the following steps S110-S140.

[0046] S110. Obtain multi-source remote sensing data of the target reservoir within the target time period.

[0047] In this embodiment, the multi-source remote sensing data includes multispectral optical remote sensing data, synthetic aperture radar data, and thermal infrared remote sensing data.

[0048] The thermal infrared remote sensing data may come from Landsat series satellites, the multispectral optical remote sensing data may come from Sentinel-2 and / or Landsat series satellites, and the synthetic aperture radar data may come from Sentinel-1 satellites.

[0049] It should be noted that all remote sensing data obtained in this application have undergone standardized preprocessing and spatiotemporal fusion. The multi-source remote sensing data is a spatiotemporally continuous dataset, as detailed below:

[0050] The acquired multi-source remote sensing data were sequentially subjected to radiometric calibration, atmospheric correction, and geometric fine correction.

[0051] The corrected multi-source remote sensing data are image registered and unified to the same geographic coordinate system;

[0052] The registered image is cropped based on the boundary range of the target reservoir;

[0053] The temporal-spatial fusion algorithm is used to fuse remote sensing data with different spatial and temporal resolutions to generate a standardized dataset with complete and consistent spatiotemporal sequences.

[0054] S120. Based on the multi-source remote sensing data, the following are obtained by inversion: dynamic map of water surface area, spatiotemporal distribution map of chlorophyll a concentration, spatiotemporal distribution map of CDOM, and spatiotemporal distribution map of water surface temperature of the target reservoir during the target time period.

[0055] In some embodiments, the dynamic water surface area diagram is obtained based on the following steps:

[0056] The water body boundary is initially extracted based on the synthetic aperture radar data to obtain an initial dynamic map of the water surface area; the boundary of the initial dynamic map of the water surface area is then corrected based on the multispectral optical remote sensing data to obtain the final dynamic map of the water surface area.

[0057] Specifically, in synthetic aperture radar (SAR) data, the water surface acts like a mirror, reflecting radar signals away from the sensor, resulting in very weak echo signals that appear black in the image. By setting a low threshold, water bodies (black) can be easily separated from non-water bodies (bright) to obtain the initial water body boundary. However, when the wind speed is too high, it will ripple the water surface and generate a large number of tiny waves. These waves will increase backscattering, making the water surface brighter in the SAR image and easily misjudged as non-water bodies. This will result in the extracted water body area being smaller than the actual area, meaning that the obtained initial water body boundary may be inaccurate. To solve this problem, this embodiment combines multispectral optical remote sensing data to correct the water body boundary. Multispectral optical remote sensing data (optical images), under cloudless conditions, can extract water body boundaries from optical images based on water body indices such as the Normalized Difference Water Index (NDWI). This is more accurate and has less noise than the boundary extracted by SAR, and can better capture complex terrain details. Therefore, the initial water body boundary can be corrected based on multispectral optical remote sensing data from cloudless areas. However, in cloudy areas, the cloud cover will obscure the surface information, resulting in data loss.

[0058] As can be seen, this embodiment combines synthetic aperture radar data and multispectral optical remote sensing data to invert the dynamic water surface area map, which can pursue the ultimate accuracy while ensuring data continuity.

[0059] The dynamic water surface area map can provide the carbon sequestration range in step S130 to reduce the amount of carbon sequestration, and provide the actual water surface area corresponding to different pixels in step S140 to calculate the total amount of carbon sequestration.

[0060] In addition, the dynamically changing water surface area ensures the accuracy of total calculations in different seasons (high water season / low water season).

[0061] In some embodiments, the spatiotemporal distribution map of chlorophyll a concentration is obtained based on the following steps:

[0062] Near-infrared reflectance and red reflectance are extracted from the multispectral optical remote sensing data; based on the near-infrared reflectance and red reflectance, and the preset correspondence between the near-infrared and red reflectance ratios and chlorophyll a concentration, a spatiotemporal distribution map of chlorophyll a concentration is generated.

[0063] Specifically, chlorophyll a has an absorption peak in the red light band, while its reflectance increases sharply in the near-infrared band due to the "red edge effect" of vegetation. By analyzing the reflectance ratios of these bands, an empirical relationship with chlorophyll a concentration can be established, that is, a pre-constructed correspondence between the reflectance ratio of near-infrared light and red light and the chlorophyll a concentration can be established.

[0064] Chlorophyll a concentration reflects the abundance of phytoplankton (algae) in a reservoir. Algae absorb dissolved carbon dioxide from the water through photosynthesis and convert it into organic carbon. This is the most important carbon sequestration process in a reservoir. Therefore, a higher chlorophyll a concentration usually means that the water body in that area has a stronger carbon sequestration capacity. Chlorophyll a concentration is an important parameter for calculating carbon sequestration.

[0065] In some embodiments, the spatiotemporal distribution map of chromophoric dissolved organic matter (CDOM) is obtained based on the following steps:

[0066] The blue band reflectance and green band reflectance are extracted from the multispectral optical remote sensing data; based on the blue band reflectance and green band reflectance, and the preset correspondence between the blue-green band ratio and CDOM concentration, the spatiotemporal distribution map of CDOM is generated.

[0067] Specifically, CDOM has a strong absorption effect in the blue light band, causing water bodies rich in CDOM to appear yellow or brown. The concentration of CDOM can be inverted by establishing the ratio of blue light band reflectance to green light band reflectance.

[0068] CDOM (Dissolved Organic Carbon) is an important component of dissolved organic carbon, primarily originating from terrestrial inputs (such as humic acid in soil) or the decomposition of biological residues within water bodies. High CDOM concentrations indicate the presence of abundant "fuel" available for microbial decomposition in the water. Microorganisms release CO2 during the decomposition of these organic materials, and may even produce the more potent greenhouse gas CH4 under anaerobic conditions. Therefore, CDOM is a potential carbon source indicator. Spatiotemporal distribution maps of CDOM can identify areas within reservoirs that are more likely to become greenhouse gas "hotspots," and CDOM concentration is also a crucial parameter for assessing carbon sinks.

[0069] In some embodiments, the spatiotemporal distribution map of water surface temperature is obtained based on the following steps:

[0070] The thermal infrared remote sensing data is radiometrically calibrated to obtain the top atmospheric radiance data; atmospheric correction is then performed on the top atmospheric radiance data based on the split-window algorithm to obtain the surface radiance data.

[0071] Based on the emissivity of the water body, the surface radiance data is inverted using the Planck function to obtain the spatiotemporal distribution map of the water surface temperature.

[0072] Temperature directly affects the rate of all biochemical reactions in water. For algae, elevated temperatures promote photosynthesis (enhancing carbon sequestration) within a certain range, but excessively high temperatures inhibit it. For microorganisms, elevated temperatures significantly accelerate their respiration rate as they decompose organic matter (increasing carbon sources), especially methanogenic activity in methanogens, which is extremely sensitive to temperature.

[0073] It is evident that water temperature is a key physical parameter driving the diffusion rate of greenhouse gases (CO2, CH4), and it is also an important parameter for calculating carbon sinks.

[0074] S130. Input the dynamic map of water surface area, the spatiotemporal distribution map of chlorophyll a concentration, the spatiotemporal distribution map of CDOM, and the spatiotemporal distribution map of water surface temperature into a preset carbon sink accounting model for carbon sink accounting processing to obtain the spatiotemporal distribution map of greenhouse gas flux per unit area corresponding to the target reservoir. Each pixel in the spatiotemporal distribution map of greenhouse gas flux per unit area carries the corresponding greenhouse gas flux per unit area.

[0075] In some embodiments, before inputting the dynamic map of water surface area, the spatiotemporal distribution map of chlorophyll a concentration, the spatiotemporal distribution map of CDOM, and the spatiotemporal distribution map of water surface temperature into a preset carbon sink accounting model, it is necessary to unify the four input distribution maps to the same spatial resolution and the same geographic coordinate system, and ensure that all data are aligned on the timestamp.

[0076] In some embodiments, the carbon sequestration model provided in this application employs a physical model path (path one) and an AI prediction path (path two) for parallel computation, and then fuses the calculation results of the two to obtain the final result (a spatiotemporal distribution map of greenhouse gas flux per unit area corresponding to the target reservoir). First, the calculation range is delineated based on the dynamic map of the water surface area, and then the following steps are performed within the delineated calculation range:

[0077] Path 1: Calculation based on physical models, which include a primary productivity estimation sub-model (specifically, a vertical generalized production model (VGPM)) and a respiration estimation sub-model (specifically, a Q10 temperature coefficient model).

[0078] The primary productivity estimation module uses the spatiotemporal distribution map of chlorophyll a concentration and the spatiotemporal distribution map of water surface temperature as the main inputs. The chlorophyll a concentration is combined with the water surface temperature data of the corresponding pixel to calculate the daily net primary productivity of each pixel.

[0079] The respiration estimation sub-model uses the spatiotemporal distribution maps of CDOM and water surface temperature as the main inputs. It uses CDOM concentration as a proxy variable for dissolved organic carbon and combines water surface temperature (which regulates the rate of microbial metabolism) to estimate the CO2 emission flux generated by the respiration of aquatic microbial communities.

[0080] Then, the first carbon sink accounting data for each pixel is determined based on the daily net primary productivity and CO2 emission flux for each pixel.

[0081] Path 2: AI prediction path, including the carbon sink accounting sub-model in the carbon sink accounting model.

[0082] The carbon sink accounting sub-model (specifically a multi-task learning convolutional neural network) takes stacked data from four input distribution maps as direct input. Through end-to-end learning, the carbon sink accounting sub-model network automatically extracts complex patterns and nonlinear relationships related to greenhouse gas fluxes from the original features, outputting second carbon sink accounting data.

[0083] Finally, by weighted fusion of the first carbon sink accounting data and the second carbon sink accounting data, the greenhouse gas flux value per unit area of ​​each pixel is obtained, and a spatiotemporal distribution map of the greenhouse gas flux value per unit area is generated.

[0084] In addition, the carbon sequestration model was trained and optimized simultaneously. Specifically, the flux data measured synchronously on the ground at the corresponding time was used as a supervision signal to train the carbon sequestration model.

[0085] The greenhouse gas flux per unit area carried by each pixel represents the net carbon exchange intensity at that location at a specific time.

[0086] S140. Perform spatiotemporal integration processing on the spatiotemporal distribution map of the greenhouse gas flux per unit area and the dynamic map of the water surface area to obtain the target carbon aggregate of the target reservoir during the target time period.

[0087] Specifically, in some embodiments, please refer to Figure 2 Step S140 includes:

[0088] S1401. Determine the spatiotemporal distribution map of the sub-greenhouse gas flux and the sub-water surface distribution map at the corresponding time as a group to obtain multiple sets of distribution maps;

[0089] S1402. For each distribution map, determine the actual water surface area corresponding to each pixel in the spatiotemporal distribution map of the sub-greenhouse gas flux in the sub-water surface distribution map, and determine the sub-carbon sink corresponding to the current distribution map based on the greenhouse gas flux value per unit area corresponding to each pixel and the actual water surface area, so as to obtain the sub-carbon sink corresponding to each distribution map.

[0090] S1403. The sum of the sub-carbon sinks of each distribution map is determined as the target total carbon sink.

[0091] Specifically, although each pixel represents a unit of the same size in the image matrix, when it is mapped to the real Earth surface, the actual geographical area represented by each pixel will be different due to map projection distortion and sensor perspective. Therefore, different pixels correspond to different actual water surface areas, and each sub-water surface distribution map of the dynamic water surface area map carries the actual water surface area of ​​the corresponding pixel.

[0092] In this embodiment, the carbon sink of a pixel is obtained by multiplying the greenhouse gas flux per unit area of ​​each pixel by the corresponding actual water surface area. Then, after determining the carbon sink of each pixel in the spatiotemporal distribution map of the sub-greenhouse gas flux, the sum of the carbon sink of each pixel in the spatiotemporal distribution map of the sub-greenhouse gas flux is the sub-carbon sink of the corresponding group distribution map (or the corresponding sub-greenhouse gas flux spatiotemporal distribution map). Then, the sum of the sub-carbon sink of each group distribution map is determined as the target carbon sink, thereby realizing the carbon sink accounting of the reservoir in the target period. In this embodiment, the target period can be one year, one quarter, or one month.

[0093] In summary, this application acquires multi-source remote sensing data of the target reservoir within a target time period; based on the multi-source remote sensing data, it inverts to obtain the dynamic map of the water surface area, the spatiotemporal distribution map of chlorophyll a concentration, the spatiotemporal distribution map of CDOM, and the spatiotemporal distribution map of water surface temperature of the target reservoir within the target time period; it inputs the dynamic map of water surface area, the spatiotemporal distribution map of chlorophyll a concentration, the spatiotemporal distribution map of CDOM, and the spatiotemporal distribution map of water surface temperature into a preset carbon sink accounting model for carbon sink accounting processing, obtaining the spatiotemporal distribution map of greenhouse gas flux per unit area corresponding to the target reservoir, where each pixel in the spatiotemporal distribution map of greenhouse gas flux per unit area carries the corresponding greenhouse gas flux per unit area; it performs spatiotemporal integration processing on the spatiotemporal distribution map of greenhouse gas flux per unit area and the dynamic map of water surface area to obtain the target carbon sink of the target reservoir within the target time period. This application embodiment can perform overall carbon sink accounting for the reservoir using multi-source remote sensing data, thereby improving the accuracy of reservoir carbon sink accounting.

[0094] Figure 3 This is a schematic block diagram of a reservoir carbon sequestration accounting device based on multi-source remote sensing data, provided in an embodiment of this application. Figure 3 As shown, corresponding to the above-described reservoir carbon sequestration method based on multi-source remote sensing data, this application also provides a reservoir carbon sequestration calculation device 300 based on multi-source remote sensing data. This reservoir carbon sequestration calculation device 300 includes units for executing the above-described reservoir carbon sequestration calculation method based on multi-source remote sensing data. This device 300 can be configured in a terminal or server. Specifically, please refer to... Figure 3The reservoir carbon sequestration accounting device 300 based on multi-source remote sensing data includes a transceiver unit 301 and a processing unit 302, wherein:

[0095] Transceiver unit 301 is used to acquire multi-source remote sensing data of the target reservoir within the target time period;

[0096] Processing unit 302 is used to invert the dynamic map of water surface area, spatiotemporal distribution map of chlorophyll a concentration, spatiotemporal distribution map of CDOM, and spatiotemporal distribution map of water surface temperature of the target reservoir during the target time period based on the multi-source remote sensing data; input the dynamic map of water surface area, spatiotemporal distribution map of chlorophyll a concentration, spatiotemporal distribution map of CDOM, and spatiotemporal distribution map of water surface temperature into a preset carbon sink accounting model for carbon sink accounting processing to obtain the spatiotemporal distribution map of greenhouse gas flux per unit area corresponding to the target reservoir, wherein each pixel in the spatiotemporal distribution map of greenhouse gas flux per unit area carries the corresponding greenhouse gas flux per unit area; and perform spatiotemporal integration processing on the spatiotemporal distribution map of greenhouse gas flux per unit area and the dynamic map of water surface area to obtain the target carbon total amount of the target reservoir during the target time period.

[0097] In some embodiments, the multi-source remote sensing data includes multispectral optical remote sensing data, synthetic aperture radar data, and thermal infrared remote sensing data.

[0098] In some embodiments, the processing unit 302 obtains the dynamic water surface area graph by performing the following steps:

[0099] Based on the synthetic aperture radar data, the water body boundary is initially extracted to obtain an initial dynamic map of the water surface area.

[0100] The initial dynamic map of the water surface area is modified by boundary correction based on the multispectral optical remote sensing data to obtain the dynamic map of the water surface area.

[0101] In some embodiments, the spatiotemporal distribution map of chlorophyll a concentration is obtained based on the following steps:

[0102] Near-infrared reflectance and red reflectance are extracted from the multispectral optical remote sensing data;

[0103] Based on the near-infrared light reflectance and the red light reflectance, as well as the preset correspondence between the near-infrared light and red light reflectance ratio and chlorophyll a concentration, a spatiotemporal distribution map of chlorophyll a concentration is generated.

[0104] In some embodiments, the processing unit 302 obtains the CDOM spatiotemporal distribution map by performing the following steps:

[0105] Extract the blue light band reflectance and the green light band reflectance from the multispectral optical remote sensing data;

[0106] Based on the blue light band reflectance and the green light band reflectance, as well as the preset blue-green band ratio and the correspondence between CDOM concentration, the spatiotemporal distribution map of CDOM is generated.

[0107] In some embodiments, the processing unit 302 obtains the spatiotemporal distribution map of the water surface temperature by performing the following steps:

[0108] The thermal infrared remote sensing data is radiometrically calibrated to obtain the radiance data of the top atmospheric layer.

[0109] Based on the split-window algorithm, atmospheric correction is performed on the top atmospheric radiance data to obtain the surface radiance data;

[0110] Based on the emissivity of the water body, the surface radiance data is inverted using the Planck function to obtain the spatiotemporal distribution map of the water surface temperature.

[0111] In some embodiments, the spatiotemporal distribution map of greenhouse gas flux per unit area includes multiple sub-spatiotemporal distribution maps of greenhouse gas flux corresponding to different times, and the dynamic map of water surface area includes sub-water surface distribution maps corresponding to different times; when the processing unit 302 performs the step of performing spatiotemporal integration processing on the spatiotemporal distribution map of greenhouse gas flux per unit area and the dynamic map of water surface area to obtain the target carbon aggregate of the target reservoir in the target time period, it is specifically used for:

[0112] The spatiotemporal distribution maps of the sub-greenhouse gas fluxes and the sub-water surface distribution maps at corresponding times are determined as a group to obtain multiple sets of distribution maps;

[0113] For each set of distribution maps, the actual water surface area corresponding to each pixel in the spatiotemporal distribution map of the sub-greenhouse gas flux is determined in the sub-water surface distribution map. The sub-carbon sink corresponding to the current set of distribution maps is determined based on the greenhouse gas flux value per unit area corresponding to each pixel and the actual water surface area, so as to obtain the sub-carbon sink corresponding to each set of distribution maps.

[0114] The sum of the sub-carbon sinks in each distribution map is determined as the target total carbon sink.

[0115] In summary, the embodiments of this application can perform carbon sequestration calculations on the entire reservoir using multi-source remote sensing data, thereby improving the accuracy of reservoir carbon sequestration calculations.

[0116] It should be noted that those skilled in the art can clearly understand that the specific implementation process of the reservoir carbon sequestration accounting device and its various units based on multi-source remote sensing data can be referred to the corresponding descriptions in the foregoing method embodiments. For the sake of convenience and brevity, these details will not be repeated here.

[0117] The aforementioned reservoir carbon sequestration accounting device based on multi-source remote sensing data can be implemented as a computer program, which can be used in various ways, such as... Figure 4 It runs on the computer device shown.

[0118] Please see Figure 4 , Figure 4 This is a schematic block diagram of a computer device provided in an embodiment of this application. The computer device 400 can be a terminal or a server. The terminal can be an electronic device with communication functions, such as a smartphone, tablet, laptop, desktop computer, personal digital assistant, or wearable device. The server can be a standalone server or a server cluster composed of multiple servers.

[0119] See Figure 4 The computer device 400 includes a processor 402, a memory, and a network interface 405 connected via a system bus 401. The memory may include a non-volatile storage medium 403 and internal memory 404.

[0120] The non-volatile storage medium 403 may store an operating system 4031 and a computer program 4032. The computer program 4032 includes program instructions that, when executed, cause the processor 402 to perform a reservoir carbon sequestration accounting method based on multi-source remote sensing data.

[0121] The processor 402 provides computing and control capabilities to support the operation of the entire computer device 400.

[0122] The internal memory 404 provides an environment for the operation of the computer program 4032 in the non-volatile storage medium 403. When the computer program 4032 is executed by the processor 402, the processor 402 can execute a reservoir carbon sequestration accounting method based on multi-source remote sensing data.

[0123] This network interface 405 is used for network communication with other devices. Those skilled in the art will understand that... Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device 400 to which the present application is applied. The specific computer device 400 may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0124] The processor 402 is used to run a computer program 4032 stored in the memory to perform the following steps:

[0125] Acquire multi-source remote sensing data of the target reservoir within the target time period;

[0126] Based on the multi-source remote sensing data, the following maps were obtained: dynamic map of water surface area, spatiotemporal distribution map of chlorophyll a concentration, spatiotemporal distribution map of CDOM concentration, and spatiotemporal distribution map of water surface temperature of the target reservoir during the target time period.

[0127] The dynamic map of water surface area, the spatiotemporal distribution map of chlorophyll a concentration, the spatiotemporal distribution map of CDOM, and the spatiotemporal distribution map of water surface temperature are input into a preset carbon sink accounting model for carbon sink accounting processing to obtain a spatiotemporal distribution map of greenhouse gas flux per unit area corresponding to the target reservoir. Each pixel in the spatiotemporal distribution map of greenhouse gas flux per unit area carries the corresponding greenhouse gas flux per unit area.

[0128] Spatiotemporal integration is performed on the spatiotemporal distribution map of the greenhouse gas flux per unit area and the dynamic map of the water surface area to obtain the target total carbon amount of the target reservoir during the target time period.

[0129] It should be understood that in the embodiments of this application, the processor 402 may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0130] It will be understood by those skilled in the art that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program includes program instructions and can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the process steps of the embodiments of the above methods.

[0131] Therefore, this application also provides a storage medium. This storage medium can be a computer-readable storage medium. The storage medium stores a computer program, wherein the computer program includes program instructions. When executed by a processor, the program instructions cause the processor to perform the following steps:

[0132] Acquire multi-source remote sensing data of the target reservoir within the target time period;

[0133] Based on the multi-source remote sensing data, the following maps were obtained: dynamic map of water surface area, spatiotemporal distribution map of chlorophyll a concentration, spatiotemporal distribution map of CDOM concentration, and spatiotemporal distribution map of water surface temperature of the target reservoir during the target time period.

[0134] The dynamic map of water surface area, the spatiotemporal distribution map of chlorophyll a concentration, the spatiotemporal distribution map of CDOM, and the spatiotemporal distribution map of water surface temperature are input into a preset carbon sink accounting model for carbon sink accounting processing to obtain a spatiotemporal distribution map of greenhouse gas flux per unit area corresponding to the target reservoir. Each pixel in the spatiotemporal distribution map of greenhouse gas flux per unit area carries the corresponding greenhouse gas flux per unit area.

[0135] Spatiotemporal integration is performed on the spatiotemporal distribution map of the greenhouse gas flux per unit area and the dynamic map of the water surface area to obtain the target total carbon amount of the target reservoir during the target time period.

[0136] The storage medium can be any computer-readable storage medium capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory (ROM), magnetic disk, or optical disk.

[0137] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0138] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For example, the division of each unit is merely a logical functional division, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0139] The steps in the methods of this application embodiment can be adjusted, merged, or deleted according to actual needs. The units in the apparatus of this application embodiment can be merged, divided, or deleted according to actual needs. Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0140] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a terminal, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.

[0141] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A reservoir carbon sink accounting method based on multi-source remote sensing data, characterized in that, include: Acquire multi-source remote sensing data of the target reservoir within the target time period, including multispectral optical remote sensing data, synthetic aperture radar data, and thermal infrared remote sensing data; Based on the multi-source remote sensing data, the following maps were obtained: dynamic map of water surface area, spatiotemporal distribution map of chlorophyll a concentration, spatiotemporal distribution map of CDOM concentration, and spatiotemporal distribution map of water surface temperature of the target reservoir during the target time period. The dynamic map of water surface area, the spatiotemporal distribution map of chlorophyll a concentration, the spatiotemporal distribution map of CDOM, and the spatiotemporal distribution map of water surface temperature are input into a preset carbon sink accounting model for carbon sink accounting processing to obtain a spatiotemporal distribution map of greenhouse gas flux per unit area corresponding to the target reservoir. Each pixel in the spatiotemporal distribution map of greenhouse gas flux per unit area carries a corresponding greenhouse gas flux per unit area. The dynamic map of water surface area is used to limit the spatial range of carbon sink accounting in the carbon sink accounting model. Spatiotemporal integration processing is performed on the spatiotemporal distribution map of greenhouse gas flux per unit area and the dynamic map of water surface area to obtain the target total carbon amount of the target reservoir during the target time period. The dynamic map of water surface area provides the actual water surface area corresponding to each pixel in the spatiotemporal distribution map of greenhouse gas flux per unit area. The carbon sink accounting model obtains the spatiotemporal distribution map of the greenhouse gas flux per unit area using the following method: Based on the physical model path, the daily net primary productivity of each pixel is calculated using the spatiotemporal distribution map of chlorophyll a concentration and the spatiotemporal distribution map of water surface temperature, and the CO2 emission flux generated by the respiration of aquatic microbial communities is calculated using the spatiotemporal distribution map of CDOM and the spatiotemporal distribution map of water surface temperature. The first carbon sink accounting data is obtained based on the daily net primary productivity and the CO2 emission flux of each pixel. Based on the AI ​​prediction path, the stacked data of the dynamic map of water surface area, the spatiotemporal distribution map of chlorophyll a concentration, the spatiotemporal distribution map of CDOM, and the spatiotemporal distribution map of water surface temperature are input into the carbon sink accounting sub-model in the carbon sink accounting model to obtain the second carbon sink accounting data. The first carbon sink accounting data and the second carbon sink accounting data are weighted and fused to generate the spatiotemporal distribution map of the greenhouse gas flux per unit area.

2. The method of claim 1, wherein, The dynamic water surface area diagram is obtained based on the following steps: Based on the synthetic aperture radar data, the water body boundary is initially extracted to obtain an initial dynamic map of the water surface area. The initial dynamic map of the water surface area is modified by boundary correction based on the multispectral optical remote sensing data to obtain the dynamic map of the water surface area.

3. The method of claim 1, wherein, The spatiotemporal distribution map of chlorophyll a concentration was obtained based on the following steps: Near-infrared reflectance and red reflectance are extracted from the multispectral optical remote sensing data; Based on the near-infrared light reflectance and the red light reflectance, as well as the preset correspondence between the near-infrared light and red light reflectance ratio and chlorophyll a concentration, a spatiotemporal distribution map of chlorophyll a concentration is generated.

4. The method of claim 1, wherein, The CDOM spatiotemporal distribution map is obtained based on the following steps: Extract the blue light band reflectance and the green light band reflectance from the multispectral optical remote sensing data; Based on the blue light band reflectance and the green light band reflectance, as well as the preset blue-green band ratio and the correspondence between CDOM concentration, the spatiotemporal distribution map of CDOM is generated.

5. The method of claim 1, wherein, The spatiotemporal distribution map of water surface temperature was obtained based on the following steps: The thermal infrared remote sensing data is radiometrically calibrated to obtain the radiance data of the top atmospheric layer. Based on the split-window algorithm, atmospheric correction is performed on the top atmospheric radiance data to obtain the surface radiance data; Based on the emissivity of the water body, the surface radiance data is inverted using the Planck function to obtain the spatiotemporal distribution map of the water surface temperature.

6. The method according to any one of claims 1 to 5, characterized in that, The spatiotemporal distribution map of greenhouse gas flux per unit area includes multiple sub-spatiotemporal distribution maps of greenhouse gas flux at different times, and the dynamic map of water surface area includes sub-water surface distribution maps at different times; the spatiotemporal integration processing of the spatiotemporal distribution map of greenhouse gas flux per unit area and the dynamic map of water surface area to obtain the target carbon aggregate of the target reservoir during the target time period includes: The spatiotemporal distribution maps of the sub-greenhouse gas fluxes and the sub-water surface distribution maps at corresponding times are determined as a group to obtain multiple sets of distribution maps; For each set of distribution maps, the actual water surface area corresponding to each pixel in the spatiotemporal distribution map of the sub-greenhouse gas flux is determined in the sub-water surface distribution map. The sub-carbon sink corresponding to the current set of distribution maps is determined based on the greenhouse gas flux value per unit area corresponding to each pixel and the actual water surface area, so as to obtain the sub-carbon sink corresponding to each set of distribution maps. The sum of the sub-carbon sinks in each distribution map is determined as the target total carbon sink.

7. A reservoir carbon sink accounting device based on multi-source remote sensing data, characterized in that, include: The transceiver unit is used to acquire multi-source remote sensing data of the target reservoir within the target time period. The multi-source remote sensing data includes multispectral optical remote sensing data, synthetic aperture radar data, and thermal infrared remote sensing data. The processing unit is configured to, based on the multi-source remote sensing data, invert and obtain the dynamic map of the water surface area, the spatiotemporal distribution map of chlorophyll a concentration, the spatiotemporal distribution map of CDOM, and the spatiotemporal distribution map of water surface temperature of the target reservoir during the target time period; input the dynamic map of water surface area, the spatiotemporal distribution map of chlorophyll a concentration, the spatiotemporal distribution map of CDOM, and the spatiotemporal distribution map of water surface temperature into a preset carbon sink accounting model for carbon sink accounting processing, thereby obtaining the spatiotemporal distribution map of greenhouse gas flux per unit area corresponding to the target reservoir, wherein each pixel in the spatiotemporal distribution map of greenhouse gas flux per unit area carries the corresponding greenhouse gas flux per unit area, wherein the dynamic map of water surface area is used to define the spatial range of carbon sink accounting in the carbon sink accounting model; perform spatiotemporal integration processing on the spatiotemporal distribution map of greenhouse gas flux per unit area and the dynamic map of water surface area to obtain the target carbon total amount of the target reservoir during the target time period, wherein the dynamic map of water surface area provides the actual water surface area corresponding to each pixel in the spatiotemporal distribution map of greenhouse gas flux per unit area; The carbon sink accounting model obtains the spatiotemporal distribution map of the greenhouse gas flux per unit area using the following method: Based on the physical model path, the daily net primary productivity of each pixel is calculated using the spatiotemporal distribution map of chlorophyll a concentration and the spatiotemporal distribution map of water surface temperature, and the CO2 emission flux generated by the respiration of aquatic microbial communities is calculated using the spatiotemporal distribution map of CDOM and the spatiotemporal distribution map of water surface temperature. The first carbon sink accounting data is obtained based on the daily net primary productivity and the CO2 emission flux of each pixel. Based on the AI ​​prediction path, the stacked data of the dynamic map of water surface area, the spatiotemporal distribution map of chlorophyll a concentration, the spatiotemporal distribution map of CDOM, and the spatiotemporal distribution map of water surface temperature are input into the carbon sink accounting sub-model in the carbon sink accounting model to obtain the second carbon sink accounting data. The first carbon sink accounting data and the second carbon sink accounting data are weighted and fused to generate the spatiotemporal distribution map of the greenhouse gas flux per unit area.

8. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the reservoir carbon sequestration accounting method based on multi-source remote sensing data as described in any one of claims 1-6.

9. A storage medium, characterized in that, The storage medium stores a computer program, which includes program instructions. When executed by a processor, the program instructions cause the processor to perform the reservoir carbon sequestration accounting method based on multi-source remote sensing data as described in any one of claims 1-6.

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