119 results about "Arbitrary Fluorescence Unit" patented technology

The invention relates to the technical field of environment assessment, in particular to a coastal zone culture area multi-dimensional environment assessment method and a cloud platform. The method comprises the following steps: continuously recording dissolved oxygen concentration, pH value, centigrade temperature, salinity unit, turbidity scattering unit, chlorophyll fluorescence intensity, ammonia nitrogen milligram per liter and nitrite milligram per liter, synchronously calling multi-source remote sensing images covering a culture area and an adjacent water area, extracting remote sensing reflectivity data through image correction and atmospheric correction, and calculating to obtain water color parameters. According to the method, the water quality sensor array is deployed at the key point of the culture area, continuous multi-parameter environmental data acquisition is carried out, multi-source remote sensing images are synchronously integrated, water color information is extracted in real time, and the timeliness and data accuracy of culture water area environmental monitoring are improved; trend decomposition and dynamic baseline construction are carried out based on the data sequence, so that the environmental fluctuation evaluation of the breeding area is more objective and accurate.

The invention discloses a regional GPP assimilation inversion system fusing foundation-multi-source satellite chlorophyll fluorescence, and belongs to the technical field of terrestrial ecosystem carbon cycle monitoring. The signal unmixing and simulation generation module is used for unmixing the total fluorescence of the mixed pixels into pure chlorophyll fluorescence (SIF) and driving an ecological model to generate SIF and GPP simulation values based on environmental stress data; the state component decoupling module is used for decoupling the total GPP analog value into a light response fast component, a light response slow component and a light response memory component; the state evolution prediction module is used for predicting dynamic evolution of the slow component and the memory component; and the self-adaptive assimilation inversion module is used for performing inversion on the region GPP by taking the slow component and the memory component as state variables, taking the SIF observation value as observation data and combining a prediction result of the state evolution prediction module. According to the method, systematic deviation caused by model simplification or data singleness in a traditional method is overcome.

The invention discloses a sky-ground integrated sunlight-induced vegetation chlorophyll fluorescence monitoring system, and belongs to the technical field of remote sensing monitoring. The system provides functions, parameter constraints and cooperation relations of space-based, empty-based and foundation chlorophyll fluorescence information acquisition and data analysis processing and application modules. Spatial scale matching, spectral scale matching, observation angle matching and atmospheric element matching are performed on the acquired sky-ground multi-element data with different scales, so that reflection characteristics, heat dissipation, photochemical indexes, fluorescence emission and atmospheric transmission characteristics during vegetation photosynthesis can be completely acquired; the invention provides a parameter relation and a method for extracting a fluorescence full-spectrum radiation information curve based on oxygen absorption and Fraunhofer dark line. On the basis of sky and landmark calibration and model verification, chlorophyll fluorescence detection of different fineness degrees of global large-range, key areas and ground crown planting scales can be carried out, various stress effects of vegetation can be monitored in real time, and the primary total productivity of the vegetation can be accurately estimated.

The invention relates to the technical field of remote sensing monitoring, and discloses a lake cyanobacterial bloom detection method and system fused with a remote sensing image. The method comprises the following steps: acquiring a multispectral remote sensing image and a synthetic aperture radar image of a target lake; calculating a phycocyanobilin characteristic index and a chlorophyll fluorescence peak index according to the characteristic wave band reflectivity, and screening pixels meeting discrimination conditions to obtain an optical water bloom distribution mask; calculating a radar inversion phycocyanobilin index through a nonlinear regression model according to the dual-polarization backscattering coefficient to obtain a radar water bloom distribution mask; and fusing the double masks to obtain a cyanobacterial bloom monitoring result. The method solves the problems that an existing cyanobacterial bloom detection method cannot realize all-weather monitoring, lacks cyanobacterial specific recognition capability and is insufficient in reliability of a single data source, and improves the timeliness, accuracy and reliability of cyanobacterial bloom detection.

The invention provides a sunlight-induced chlorophyll fluorescence reconstruction method and system based on satellite observation data. Comprising the following steps: collecting and processing ground site observation data, air temperature data, leaf area index data, MODIS bidirectional reflectance distribution function nadir correction reflectivity data, enhanced vegetation index data, normalized difference moisture index data, global land cover classification data, and OCO-2 and TROPOMI satellite observation SIF data; performing data reconstruction by using the OCO-2 SIF and the TROPOMI SIF as original data respectively by using a machine learning method; then, an empirical formula between the two kinds of reconstructed SIF data and the site observation SIF is constructed, and estimation parameters on the site are obtained through optimization; then, calculating estimation parameters of the empirical formula on each land coverage type; and finally, substituting the two types of reconstructed SIF data into an empirical formula to obtain a reconstructed SIF with site SIF information.

The invention relates to the technical field of vegetation remote sensing observation, in particular to a chlorophyll fluorescence observation system based on laser radar, which comprises a target evaluation module used for responding to an observation evaluation condition to determine an observation analysis strategy of each observation target; the observation analysis module is used for determining the dominant direction of the mirror surface of the observation target and determining an inversion execution strategy of the observation target in response to the inversion execution condition; the dominant inversion module is used for determining a pre-estimation execution wave band set and determining observation pre-estimation data based on the polarization reflection parameters of the pre-estimation execution wave bands; the multi-parameter inversion module is used for determining an inversion weight coefficient of each observation wave band and determining whether to adjust the inversion weight index of each observation wave band in response to a weight analysis condition; the auxiliary analysis module is used for responding to the auxiliary analysis condition to determine the auxiliary analysis mode of the observation target, and the reliability of the fluorescence observation result of the observation target is improved.

The invention provides an automatic illumination adjusting method and system based on seed and seedling cultivation, and relates to the technical field of illumination control and intelligent agriculture, the method comprises the following steps: firstly, collecting seedling chlorophyll fluorescence parameters to construct a light stress index, and accurately identifying a light inhibition risk; then calculating a physiologically permissible light intensity threshold adaptive to the dark response capability of the plant by combining the environment temperature and the CO2 concentration; positioning a shielded weak light area by using three-dimensional point cloud voxelization and a ray tracing technology, and calculating a shadow compensation gain coefficient; and finally, generating a control matrix in combination with a dynamic left-rotation light strategy, and driving an independent addressing LED array and a variable-angle lens to execute local precise light supplement and light vector adjustment. Through physiological feedback and spatial light field reconstruction, the problem that traditional illumination control neglects individual tolerance difference and canopy shielding is solved, photooxidation damage is effectively prevented, and the growth uniformity and the light energy utilization rate of seedling groups are remarkably improved.

The invention provides a method for estimating the photosynthetic nitrogen content of rice leaves based on sunlight-induced chlorophyll fluorescence. The method comprises the following steps: acquiring an observed value SIF of canopy sunlight-induced chlorophyll fluorescence based on induction equipment, and converting the observed value SIF into a total fluorescence excitation value SIFPSII of a light system II; on the basis of the rice photosynthetic physiological process, the incident photosynthetically active radiation I and the total fluorescence excitation value SIFPSII of a light system II, the electron transmission rate J is obtained through calculation, and the maximum electron transmission rate Jmax is obtained through calculation; then, the maximum carboxylation rate Vcmax is calculated based on the electron transmission rate J, so that the photosynthetic nitrogen content Ncb distributed to a carboxylation system and the photosynthetic nitrogen content Net distributed to an electron transmission system are calculated respectively, and the photosynthetic nitrogen content Ncb and the photosynthetic nitrogen content Net are superposed to obtain an estimated value PN of the photosynthetic nitrogen content of the leaves. According to the method, accurate estimation of the photosynthetic nitrogen content of the rice leaves is realized based on the mechanism relation between sunlight-induced chlorophyll fluorescence and the photosynthesis process of the rice, and accurate estimation of the photosynthetic nitrogen content of the rice in the field and regional scale can be realized.

The invention discloses a satellite-borne sunlight-induced chlorophyll fluorescence spectrum detection imaging system, and belongs to the technical field of spectral imaging. The system includes a telescope optical system, a field-of-view separator assembly, a hyperspectral spectrometer system, and a hyperspectral spectrometer system. A vegetation chlorophyll signal from the earth surface is condensed by the telescope optical system and then is separated into a first view field object signal and a second view field object signal by the view field separator assembly, and chlorophyll fluorescence information and vegetation photochemical index information are synchronously acquired by the hyper-spectral spectrograph system and the hyperspectral spectrograph system respectively. According to the invention, synchronous high-precision detection of chlorophyll fluorescence and vegetation physiological parameters is realized, dynamic spectrum sampling is supported to match fluorescence peak valley characteristics, and an advanced remote sensing technical means is provided for global vegetation photosynthesis and carbon cycle research.

The embodiment of the invention discloses a time-space prediction method and device for sun-induced chlorophyll fluorescence. The method comprises the following steps: acquiring a time-space data set; based on the spatio-temporal data set, taking a county administrative unit as a reference unit to perform area aggregation, dividing a large-scale area into a plurality of homogenized sub-areas, and selecting a representative spatial point from each sub-area; extracting the SIF of the corresponding representative spatial point and variable feature information of the key covariable, and constructing a four-dimensional space-time cube based on the variable feature information; sTL decomposition is carried out on the four-dimensional space-time cube to obtain a trend term, a seasonal term and a residual term, and flattening processing is carried out on the trend term, the seasonal term and the residual term to obtain a time sequence feature vector; the time sequence feature vector is input into a trained LSTM-Transform model for prediction, and a trend-season point value prediction result is obtained; and jointly interpolating the point value prediction result and a preset covariable to a preset target prediction area by using an ANUSPLIN thin plate smooth spline interpolation method, and generating an SIF large-scale space-time prediction map.

The invention provides a winter wheat drought remote sensing monitoring method fused with downscaling sunlight-induced chlorophyll fluorescence (SIF) data. A winter wheat drought remote sensing monitoring method fused with downscaling SIF data comprises the steps that S1, input data of an SIF data reconstruction machine learning model are selected, and the input data comprise MODIS reflectivity, fPAR, LAI and ERA5 reanalysis data of temperature, saturation vapor pressure difference, photosynthetically active radiation and soil humidity data; s2, training and constructing a machine learning model; s3, construction and effect inspection of a long-time-sequence high-resolution downscaling SIF product data set are carried out; and S4, constructing a novel drought fluorescence monitoring index. The winter wheat drought remote sensing monitoring method provided by the invention has the advantages that the problem of how to fuse SIF information can be solved, the timeliness and accuracy of agricultural drought monitoring can be improved in mechanism, and the agricultural meteorological disaster prevention and reduction capability can be effectively improved.

The invention provides an open environment crop chlorophyll fluorescence parameter imaging detection method and device, and the method comprises the steps: carrying out the crop canopy segmentation of a crop canopy hyperspectral image based on a crop canopy mask image, and obtaining a crop canopy image; performing hierarchical segmentation based on the crop canopy image according to a clustering algorithm to obtain an upper-layer leaf image and a lower-layer leaf image; determining upper-layer chlorophyll fluorescence intensity of the upper-layer leaf image and lower-layer chlorophyll fluorescence intensity of the lower-layer leaf image; respectively compensating the upper chlorophyll fluorescence intensity and the lower chlorophyll fluorescence intensity based on the balanced brightness and crop absorption photosynthetically active radiation to obtain an upper chlorophyll fluorescence signal correction value and a lower chlorophyll fluorescence signal correction value; and constructing a visual chlorophyll fluorescence parameter spatial distribution image based on the upper-layer chlorophyll fluorescence signal correction value and the lower-layer chlorophyll fluorescence signal correction value. The method can realize rapid and accurate imaging detection of the chlorophyll fluorescence parameters of the crop canopies in the open environment.

The invention provides a tea seedling drought detection method and system combined with chlorophyll fluorescence imaging in the technical field of tea seedling planting management, and the method comprises the steps: S1, obtaining chlorophyll fluorescence historical images of a large number of tea seedlings under different drought grades, performing preprocessing including data annotation and data expansion on each historical image and then constructing a data set; s2, creating a tea seedling drought detection model based on a YOLOv8n network, and setting a loss function of the tea seedling drought detection model as an MPD IoU function; s3, dividing the data set into a training set, a verification set and a test set based on a preset proportion, and respectively training, verifying and testing the tea seedling drought detection model through the training set, the verification set and the test set; and S4, deploying the tea seedling drought detection model passing the test, and performing tea seedling drought detection by using the deployed tea seedling drought detection model. The method has the advantage that the precision of tea seedling drought detection is greatly improved.

The invention provides a crop functional chlorophyll fluorescence inversion method, system and device and a readable medium, and relates to the technical field of ecological environment monitoring, and the method comprises the steps: obtaining a multi-source data cube; acquiring a crop type distribution diagram based on the multi-source data cube; constructing a sun-induced chlorophyll fluorescence SIF inversion basic model according to the crop type distribution diagram; and outputting a crop solar-induced chlorophyll fluorescence time sequence product according to the solar-induced chlorophyll fluorescence SIF inversion basic model. According to the crop functional chlorophyll fluorescence inversion method provided by the invention, the multi-source data cube is used as input, and two special processing links which are completely independent in physical mechanism and dynamic correction are constructed; high-precision and high-physical-reliability SIF time sequence product production for crops can be realized, and a reliable data basis is provided for precise monitoring of photosynthesis of an agricultural ecosystem.

The invention discloses a chlorophyll fluorescence continuous monitoring system and method based on a miniature laser radar. According to the method, non-invasive detection and analysis are carried out on plants by utilizing a sensing technology, a spectrum technology and an imaging technology through a non-destructive detection technology under the condition that the tissue structure and chemical components of the plants are not damaged. The system comprises a transmitting circuit, a detection circuit, a double-layer optical filter and a core processor. According to the method, photosynthetically active radiation can be calculated by acquiring leaf fluorescence constant signals, ambient light information and ambient temperature information; and the chlorophyll fluorescence quantum productivity is calculated by combining a chlorophyll fluorescence measurement method, so that the detection method for quantifying chlorophyll fluorescence on the leaf canopy level is realized. The modulated fluorescence technology disclosed by the invention has the characteristics of rapid, simple, sensitive and reliable measurement, no interference on a sample, capability of measuring under the condition of ambient light and the like, and plays an effective role in promoting solving of a plurality of ecological key problems.

The application discloses a water-carbon flux optimization method based on carbonyl sulfide (COS) and sun / solar-induced chlorophyll fluorescence (SIF), and belongs to the technical field of land ecosystem model optimization and application. The application combines COS and SIF observation information, adopts a four-dimensional variation algorithm to perform data assimilation on observation data and simulation data, simultaneously restrains plant photosynthesis processes and gas transmission processes, and then realizes joint optimization of carbon flux and water flux. Meanwhile, the application compares and verifies simulation data before and after optimization and observation data, and proves effectiveness and reliability of the method.

The application relates to the technical field of intelligent agriculture and plant protection, and particularly discloses a broccoli black spot early diagnosis system and method based on multi-modal machine learning fusion, which comprises a portable collection platform, a mobile experiment terminal and a server. The portable collection platform is integrated with a hyperspectral imaging camera, a chlorophyll fluorescence imager, a dark-adapted leaf clamp and a camera for sample barcode recognition, and is provided with an adjustable height support and a controllable light source to ensure the consistency of data collection conditions. The mobile experiment terminal is internally provided with a physicochemical index rapid detection module, including a portable PCR instrument for on-site nucleic acid rapid extraction and amplification and a miniature ultraviolet spectrophotometer for rapid determination of pigment content. The broccoli black spot early diagnosis system and method based on multi-modal machine learning fusion are highly integrated, portable and automated, are suitable for a field environment and provide an efficient disease diagnosis tool for non-expert users.

The invention relates to the technical field of crop spectrum, and discloses a crop spectrum and photosynthesis monitoring device, which comprises a main control unit, a sensing unit and a spectrograph unit, and the main control unit comprises a microcontroller and is used for realizing acquisition, calculation, storage and transmission of spectrum and temperature data of the sensing unit and further controlling the temperature control unit to adjust the temperature of the equipment, and a framework of an embedded core board and a bottom board is adopted as a whole. By integrating a single wide-spectrum and high-resolution optical fiber spectrometer, crop spectrum information of 400-900 nm can be obtained, high-resolution detection smaller than or equal to 1 nm is achieved, wavelength, irradiance, radiance, reflectivity and light intensity information within the wide-spectrum range of crops are output, and the spectrum information of the crops can be obtained through an algorithm library integrated in the main control unit. And inversion output of indication parameters such as sunlight-induced chlorophyll fluorescence, a novel leaf chlorophyll index, a normalized vegetation index, an enhanced vegetation index, a normalized red edge index and a photochemical vegetation index of crops is completed.

The present invention relates to the technical field of drought remote sensing monitoring, and specifically discloses a drought remote sensing monitoring method and system integrating multi-source remote sensing data, the method comprising: dividing the area to be measured into a plurality of position units; obtaining the temperature, altitude and air pressure data of each position unit in the area to be measured, and calculating the saturated water vapor pressure difference of each position unit; simultaneously obtaining the soil moisture and sunlight-induced chlorophyll fluorescence data of each position unit in the area to be measured; performing data normalization; selecting the driest position unit as the reference position unit, comprehensively calculating the saturated water vapor pressure difference, soil moisture and sunlight-induced chlorophyll fluorescence dimensions, calculating the distance between other position units and the reference position unit, and obtaining the drought index of each position unit; and using the drought index to realize drought monitoring of different positions in the area to be measured. The present invention can more accurately quantify the degree of drought, comprehensively extract the comprehensive information of drought events, and has good reliability and accuracy.

The utility model discloses a field automatic photosynthetic instrument, which comprises a base, a light source module, a light source module and a light source module, the base is provided with a fixing clamp and a sample opening, and a sealing element is arranged at the sample opening; the chlorophyll fluorescence detection assembly is arranged on the base; the gas exchange detection assembly is arranged on the base; the box body is of a transparent structure, and an opening is formed in one side of the box body; the moving mechanism is connected with the base and the box body, and the moving mechanism can drive the box body to move in the direction close to or away from the base; the base is adaptively connected with the opening, so that the box body forms a closed structure; and the base is separated from the opening, so that the box body forms an open structure. According to the utility model, not only can long-term field unattended long-term monitoring be met, but also gas exchange and chlorophyll fluorescence can be measured, and the two kinds of measurement can be carried out independently or simultaneously, so that comparison and analysis among different measurement results are facilitated.

The invention relates to a multi-environment regulation and control in-situ real-time photosynthetic phenotype imaging system applied to the field of photosynthetic phenotype imaging of crop plants, which comprises an environment control system, a photoelectric control system, an image acquisition system, a plant light system, a computer, a touch screen and a plant sample cultivated in a detection box, the box type multi-environment regulation and control in-situ real-time photosynthetic phenotype imaging system realizes automatic regulation and control of test environment factors, realizes in-situ real-time monitoring and imaging of chlorophyll fluorescence parameters of plants in a box body in an experiment process, improves the accuracy of chlorophyll fluorescence parameter detection, improves the efficiency of research work, and has a wide application prospect. The detection angle of the fluorescence imaging CCD camera is parallel to the to-be-detected blade by matching with the adjusting belt, the consistency of the detection distance is ensured, the warping part is smoothed by matching with the smoothing unit, and the accuracy of the detection result is ensured.

Implementations of a data acquisition unit for a ground vehicle may include an enclosure; a single board computer included in the enclosure; and a base board; operatively coupled with the single board computer. The data acquisition unit may include an optocoupler operatively coupled with the single board computer; at least one chlorophyll fluorescence sensor coupled with the optocoupler; at least one camera coupled to the base board; and a global positioning sensor coupled with the base board. The data acquisition unit may include a flow meter coupled with the single board computer; and a power source operatively coupled with the single board computer and with the base board.

The invention discloses a plant photosynthetic physiology three-dimensional imaging device and method, and belongs to the field of plant chlorophyll fluorescence three-dimensional detection and analysis. The plant photosynthetic physiology three-dimensional imaging device and method aim at solving the problems that in the prior art, plant chlorophyll fluorescence three-dimensional imaging is complex, single-view reconstruction of a complex geometric structure and a shielding part is limited, and the photosynthetic efficiency heterogeneity analysis capacity is poor. According to the method, the uniformity of an exciting light source is improved by adopting a free-form surface reflection cup and a staggered light source array; two groups of precise three-dimensional point cloud information of the plant are obtained by adopting double-path structured light, and matching, segmentation and complementation are executed to realize high-quality reconstruction of the three-dimensional form of the plant; and finally fusing the plant color image and the chlorophyll fluorescence image to realize three-dimensional visual display of plant photosynthetic physiology. The plant photosynthetic physiology three-dimensional imaging precision and speed can be improved, and technical support is provided for crop photosynthetic physiology heterogeneity analysis.

The present invention relates to the field of pattern recognition technology, and in particular to a plant stress identification system and method that integrates canopy fluorescence and reflection spectrum features, a dynamic excitation light source detects whether the historical stress identification position of the area to be identified is greater than the first risk distance from the position to be identified, a decision is made on whether the dynamic excitation light source triggers a strong risk identification state or a low risk identification state to control the triggering of a fluorescence detector, polls the first chlorophyll fluorescence information in the area to be identified, and obtains the identification stress type in the area to be identified; based on the identification stress type, randomly obtains the plant POI in the first chlorophyll fluorescence information, obtains the stress fluorescence record to determine whether it has been subjected to stress, collects the reflection spectrum, queries multiple stress identification data tables based on deviations from normal values, and adjusts the luminous intensity and angle of the dynamic excitation light source for stress identification. The present invention obtains the physiological state information of the plant by analyzing the plant reflection spectrum, and accurately determines whether the plant is in a stress state and the type of stress.

The application discloses a water body chlorophyll concentration profile inversion method and a laser radar detection method, wherein the inversion method comprises the following steps: obtaining laser radar detection echo signals at a specific depth in a target area, which comprises a fluorescence channel and a Raman channel; expressing a backscattering signal profile of the fluorescence channel as a function comprising a fluorescence backscattering coefficient; expressing a backscattering signal profile of the Raman channel as a function comprising a Raman backscattering coefficient; normalizing the backscattering signal profile function of the fluorescence channel with the backscattering signal profile function of the Raman channel to obtain a normalized signal; inverting the fluorescence backscattering coefficient based on a perturbation method; inverting a chlorophyll fluorescence absorption coefficient at a specific laser wavelength based on the inverted fluorescence backscattering coefficient; and inverting a chlorophyll concentration profile in the water body based on the inverted chlorophyll fluorescence absorption coefficient. The inversion method can invert the water body chlorophyll concentration profile from one measurement.

1. Name of the design product: Crop Spectrometer. 2. Use of the design product: It is used to automatically measure parameters such as crop vegetation index and sun-induced chlorophyll fluorescence, and to understand the photosynthesis and growth conditions of field crops in real time. 3. Design key points of the design product: Lies in the shape. 4. Picture or photograph that best shows the design key points: Perspective view.

The present invention provides a method and system for reconstructing sunlight-induced chlorophyll fluorescence based on satellite observation data, comprising: collecting and processing ground site observation data, temperature data, leaf area index data, MODIS bidirectional reflectance distribution function nadir-corrected reflectance data, enhanced vegetation index data, normalized difference moisture index data, global land cover classification data, OCO-2 and TROPOMI satellite observation SIF data; then using a machine learning method to reconstruct data using OCO-2 SIF and TROPOMI SIF as original data respectively; then, constructing an empirical formula between the two reconstructed SIF data and the site observation SIF, and optimizing to obtain estimated parameters at the site; then, calculating the estimated parameters of the empirical formula for each land cover type; finally, substituting the two reconstructed SIF data into the empirical formula to obtain a reconstructed SIF with site SIF information.

The application discloses a kind of plant chlorophyll fluorescence three-dimensional imaging method, belong to plant chlorophyll fluorescence three-dimensional field.The plant chlorophyll fluorescence three-dimensional imaging method of present application is complicated, needs multi-angle shooting, synthesis image needs a lot of data and so on for the problems existing in prior art, the present application provides a kind of plant chlorophyll fluorescence three-dimensional imaging device and method, and plant single-view image is obtained under different position light source, and plant is three-dimensionally reconstructed by plant neural radiation field, generates plant fine three-dimensional model, simultaneously, plant fine three-dimensional model is optimized in combination with rendering, obtains plant three-dimensional image and plant chlorophyll fluorescence three-dimensional image, by which the precision and speed of plant chlorophyll fluorescence three-dimensional imaging under single view angle can be improved, data processing time is reduced, more accurate, comprehensive information is provided for plant growth, disease and adversity response and the like research.

The invention discloses a method, system and medium for machine learning inversion of sunlight-induced chlorophyll fluorescence, and relates to the technical field of vegetation sunlight-induced chlorophyll fluorescence remote sensing inversion, and the method comprises the steps: obtaining an actual measurement data set, and generating a synthetic data set; introducing principal component analysis to carry out dimension reduction preprocessing on features in the synthetic data set to obtain a preprocessed synthetic data set, and taking the preprocessed synthetic data set as a training set; introducing noise of different levels into the training set to train each machine learning model, and obtaining each trained machine learning model; pre-processing the incident irradiance and the reflected radiance in the measured data set, taking the pre-processed incident irradiance and reflected radiance as input of each trained machine learning model, and comparing performance indexes of output results of each model to obtain an optimal machine learning model; executing an SIF inversion task based on the optimal machine learning model; the method is used for high-resolution and rapid inversion of the SIF.