108 results about "Chlorophyll fluorescence" 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 corn yield prediction method and system based on chlorophyll fluorescence and deep learning, and the method comprises the steps: obtaining the yield prediction related data of a target region for many years in the past, and carrying out the preprocessing of the yield prediction related data; screening yield prediction related data of the hot and dry years according to a preset threshold value, and constructing a training data set under a stress condition based on the yield prediction related data of the hot and dry years; constructing a deep neural network model, and training, verifying and testing the deep neural network model by adopting the training data set to obtain a corn yield prediction model based on DNN; and inputting to-be-predicted data into the trained corn yield prediction model, outputting a corn yield prediction result, and generating a spatial distribution diagram. According to the method, the spatial distribution goodness of fit between a prediction result and official statistical data is remarkably superior to that of a traditional method, and a quantifiable and low-cost solution is provided for precise agricultural decision making under the extreme climate condition.

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 discloses a vegetable disease incubation period detection method and system based on bimodal time sequence collaborative fusion, and the method comprises the steps: obtaining a leaf image through building an acquisition environment, constructing a training sample data set, and constructing a downy mildew incubation period spectral feature adaptive enhancement (AW-FPF) module; the method comprises the following steps: decomposing a hyperspectral signal into low-frequency and high-frequency components through spectrum time sequence adaptive wavelet decoupling, obtaining an enhanced feature tensor through spectrum multi-scale pathological feature frequency-time dual-path aggregation fusion frequency domain and time domain paths, and obtaining a first feature sequence through weighted screening by using a multi-head attention mechanism; a downy mildew incubation period prediction (HyChl-TFNet) model containing a hyperspectral branch, a chlorophyll fluorescence parameter branch, a feature fusion branch and a classifier is constructed, bimodal features are processed and fused to output a day number prediction result, accurate recognition of the downy mildew incubation period is achieved, the detection precision can be controlled to the day number level, and the detection accuracy is improved. And an accurate time basis is provided for early prevention and control of diseases.

The invention discloses a potato cold resistance identification method and system, and relates to the technical field of cold resistance identification. The method comprises the following steps: cultivating potato tissue culture seedlings in a standardized manner, and randomly dividing the seedlings into a low-temperature stress group and a normal-temperature control group; performing standardized low-temperature treatment including slow descent and constant-temperature stages on the stress group; the method comprises the following steps: acquiring multi-source data such as chlorophyll fluorescence parameters, canopy thermal imaging, digital image phenotypes and biochemical indexes, and forming a unified data set after derivative index calculation and standardized fusion; inputting the fusion data into an improved neural network model, training the model through a particle swarm optimization algorithm, and outputting a quantitative potato cold resistance index; automatically evaluating the cold resistance grade according to the potato cold resistance index and generating an identification report; standardization of the identification process, fusion analysis of multi-dimensional indexes and intelligent result analysis are achieved, and the accuracy, efficiency and practicability of potato cold resistance identification are remarkably improved.

The invention discloses a differential chlorophyll fluorescence inversion method, system and device and a storage medium, and is applied to the field of vegetation remote sensing and physiological ecological monitoring, and the method comprises the steps: obtaining vegetation state monitoring data of a target region; on the basis of vegetation state monitoring data, asynchronous leaf-changing type evergreen arbors and synchronous deciduous type arbors are identified; carrying out SIF simulation on the asynchronous leaf-changing type evergreen arbors and the synchronous deciduous type arbors by adopting corresponding models respectively; according to the phenological phase function and the stress response function, generating correction factors corresponding to the asynchronous leaf-changing evergreen arbors and the synchronous deciduous arbors; the obtained initial SIF value is optimized through a corresponding correction factor; fusing the optimized SIF values to generate an SIF time sequence product; according to the method, differential model configuration and physical mechanism driven online correction are coupled, so that the problems of model mismatching and physiological mechanism deficiency in heterogeneous vegetation SIF inversion are solved, and an SIF time sequence product which is high in precision and clear in physiological and ecological significance is generated.

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 relates to the technical field of submerged plant planting, and discloses an intelligent submerged plant planting method and system based on bionic light compensation and a storage medium. The system comprises the steps that a bionic sea anemone tentacle flexible base disc is constructed, and a shape memory polyurethane fixing arm and a silica gel suction cup are arranged; preparing a three-layer gradient nutrient sustained-release microcapsule and loading the microcapsule into a nutrient cavity of the basal disc; a water depth spectrum attenuation coefficient is calculated through a chlorophyll fluorescence attenuation inversion algorithm, and a depth adaptive light compensation parameter is generated; degradable spiral barb ground anchors are adopted for fixing, and intelligent planting of submerged plants is achieved. The technical problems that anchoring is unstable, nutrition supply is not matched and light compensation is not accurate in the deep water area submerged plant planting process are solved, and the success rate and the ecological restoration effect of plant planting in the deep water area 3 m or above are improved.

The invention relates to the technical field of agricultural drought monitoring and precise irrigation, in particular to a crop water stress diagnosis method and system based on sunlight-induced chlorophyll fluorescence, crop canopy oxygen absorption band spectral data is collected through a hyperspectral sensor, and a sunlight-induced chlorophyll fluorescence intensity SIF value is solved by adopting a fluorescence extraction algorithm. The method comprises the following steps: taking an SIF value of a crop canopy without water stress as a reference canopy SIF value, determining a difference between the SIF value of a to-be-detected crop canopy and the reference canopy SIF value, then generating a relative SIF water deficit index SIFWDI by combining a preset formula, establishing a mapping relation between the SIFWDI and a soil water deficit index by utilizing a regression analysis model, and outputting a diagnosis report. Variable irrigation decision making and early warning are triggered according to the stress level, and model parameter self-adaptive optimization is achieved through a feedback mechanism. The system realizes lossless, real-time and high-precision water stress monitoring and irrigation management.

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 discloses a wetland carbon sink monitoring system and a carbon sink evaluation method, and the wetland carbon sink monitoring system is designed based on open circuit / closed circuit vorticity. Detection equipment of the wetland carbon sink monitoring system comprises a carbon dioxide analyzer, a three-dimensional ultrasonic anemorumbometer, a methane analyzer, sunlight-induced chlorophyll fluorescence observation equipment and micrometeorological monitoring equipment, and a carbon sink model is constructed based on the wetland carbon sink monitoring system. Main greenhouse gases are monitored in real time, high-frequency continuous atmosphere-vegetation interface and atmosphere-water body interface carbon income and expenditure data are obtained, wetland and water body ecological system carbon sink time change characteristics are revealed, a carbon sink model is constructed in combination with micrometeorological monitoring and environmental data, the Hongze lake wetland carbon sink pattern and influence factors are clarified, and the wetland carbon sink time change characteristics are analyzed. And estimating the wetland carbon sink potential of the Hongze lake.

The application provides a tobacco virus classification model construction method based on tobacco leaf hyperspectrum, and belongs to the technical field of tobacco virus classification models. Fractional differential spectrum enhancement processing is adopted, persistent homology eigenvalues and Betti number sequence values are calculated through spectrum topological manifold embedding of enhanced spectrum data, dimension reduction is performed through kernel principal component analysis to obtain topological embedding feature vectors, fusion feature vectors are output through a multimodal attention fusion module in combination with thermal infrared temperature field data and chlorophyll fluorescence kinetic curve data, data enhancement is performed on minority class samples by using a spectral generative adversarial network, the fusion feature vectors are input into a virus classification and recognition model adopting a focal loss function and a dynamic network adjustment mechanism for training, virus type classification and disease severity grade recognition of tobacco leaves to be tested are realized, and the problem that different types of tobacco viruses cannot be accurately recognized and distinguished in the early stage of disease is solved.

The present application belongs to the technical field of pattern recognition and intelligent agriculture, and particularly relates to a crop disease and pest intelligent identification and crop condition perception method. The method first collects crop environment and image data through a multi-source perception network, extracts features using a physical information neural network, and introduces a physiological constraint operator containing a transpiration model and a chlorophyll fluorescence decay parameter therein. At the same time, micro-meteorological parameters are injected into the network as weight correction factors to establish the coupling relationship between environmental factors and physiological characteristics, so as to realize decoupling identification of physiological adversity and pathological infection. Finally, state evaluation is carried out in combination with a physiological adversity formula, and the identification result and crop condition report are output. The present application uses physical laws to decouple the identification logic, accurately distinguishes adversity and infection with similar visual forms, reduces the dependence on sample size, and improves the accuracy and robustness of crop condition monitoring in complex environments.

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 present application relates to the technical field of agricultural drought monitoring and precision irrigation, and particularly relates to a crop water stress diagnosis method and system based on sunlight-induced chlorophyll fluorescence, which collects crop canopy oxygen absorption band spectral data through a hyperspectral sensor, and calculates sunlight-induced chlorophyll fluorescence intensity SIF values by using a fluorescence extraction algorithm. The SIF value of a non-water-stressed crop canopy is taken as a reference canopy SIF value, the difference between the SIF value of a to-be-measured crop canopy and the reference canopy SIF value is determined, then a relative SIF water deficit index SIFWDI is generated by combining a preset formula, a regression analysis model is used to establish a mapping relationship between the SIFWDI and a soil water deficit index to output a diagnosis report, a variable irrigation decision and early warning are triggered according to a stress level, and model parameter adaptive optimization is realized through a feedback mechanism. The system realizes non-destructive, real-time and high-precision water stress monitoring and irrigation management.

The invention discloses a method for extracting spring phenology of an evergreen broad-leaved forest, and relates to the technical field of ecological observation, and the method comprises the following steps: carrying out negative pixel correction processing and time interpolation processing on collected sunlight-induced chlorophyll fluorescence data; fusing the processed sunlight-induced chlorophyll fluorescence data with the temperature variable data, constructing a time sequence, and performing fitting processing to obtain a target time sequence; extracting spring phenology of the evergreen broad-leaved forest by adopting a plurality of preset vegetation phenology extraction methods based on the target time sequence; performing precision evaluation on the extracted spring phenology of the evergreen broad-leaved forest by using ground observation data; and based on a precision evaluation result, determining the optimal spring phenology of the evergreen broad-leaved forest from the extracted spring phenology of the evergreen broad-leaved forest. According to the method, through negative value pixel correction and time interpolation processing, sunlight-induced chlorophyll fluorescence data and temperature variable data are fused, and the combination of a multi-fitting method and a multi-extraction method is adopted, so that the accuracy of a result is improved.

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 application discloses a kind of evergreen broad-leaved forest spring phenology extraction method, it is related to ecological observation technical field, the method comprises: to the collected sunlight-induced chlorophyll fluorescence data is handled with negative value pixel correction and time interpolation processing;The sunlight-induced chlorophyll fluorescence data after processing is fused with temperature variable data, constructs time series and is handled to obtain target time series;Based on target time series, a variety of preset vegetation phenology extraction methods are used to extract evergreen broad-leaved forest spring phenology respectively;The accuracy of the extracted evergreen broad-leaved forest spring phenology is evaluated using ground observation data;Based on the accuracy evaluation result, the optimal evergreen broad-leaved forest spring phenology is determined from the extracted evergreen broad-leaved forest spring phenology.The application improves the accuracy of the results by negative value pixel correction and time interpolation processing, the fusion of sunlight-induced chlorophyll fluorescence data and temperature variable data, the combination of multiple fitting methods and multiple extraction methods.

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 rape proper fertilization amount test screening method, which comprises the following steps: acquiring chlorophyll fluorescence parameters of rape canopies of all test areas, screening the test areas with abnormal chlorophyll fluorescence parameters according to the chlorophyll fluorescence parameters, and generating a rape canopy physiological status early warning list; based on the rape canopy physiological status early warning list, positioning sonic sensor nodes, generating a sonic sensor node group, and scanning rape root zone soil according to the sonic sensor node group to obtain a soil medium acoustic characteristic data set; and converting the soil medium acoustic characteristic data set into an acoustic wave velocity distribution diagram and a signal absorption coefficient diagram through an acoustic imaging algorithm, and carrying out space registration and data fusion to generate a rape root zone soil acoustic characteristic profile map. According to the method, lossless visual representation of the root zone soil structure and the root morphology is realized, so that accurate root spatial distribution and physical property insight are provided.

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.

This invention discloses an early detection and evaluation method and system for the abiotic stress tolerance of new forest tree germplasm. The method includes: collecting chlorophyll fluorescence indicators, phenotypic indicators, and physiological and biochemical indicators of forest trees under abiotic stress conditions, and performing data cleaning and standardization; performing principal component analysis on the multi-source standardized dataset to screen candidate indicators; performing grey relational analysis using the comprehensive score of forest tree stress tolerance as a reference sequence and the candidate indicators as a comparison sequence; determining the core indicators and their weights based on the variance contribution rate of the principal component analysis and the correlation degree of the grey relational analysis; obtaining the abiotic stress assessment index from the core indicators using a membership function; and classifying the tolerance level of forest tree germplasm using a clustering algorithm based on this index. This invention achieves early, rapid, and accurate evaluation of the abiotic stress tolerance of forest tree germplasm, and can effectively guide forest tree stress resistance breeding work.

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.

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 wheat leaf net photosynthetic rate estimation method based on sunlight-induced chlorophyll fluorescence, which comprises the following steps: step 1, data acquisition: acquiring radiance spectral data of wheat leaves in different growth periods, extracting SIF spectrums, synchronously acquiring net photosynthetic rates of different wheat leaves, and constructing a wheat fluorescence photosynthetic data set; step 2, screening wavebands with good correlation with the net photosynthetic rate of the leaves; 3, analyzing the influence of the physiological factors and the environmental factors on the relationship between the SIF and the net photosynthetic rate; and step 4, selecting the index of the optimal wheat net photosynthetic rate SIF, and constructing a wheat leaf net photosynthetic rate estimation model based on the SIF index. According to the method, the SIF index sensitive to the net photosynthetic rate of the wheat leaf is constructed by analyzing and comparing the influence of different physiological parameters, structural parameters and environmental factors on different wavebands of the wheat SIF and the net photosynthetic rate of the leaf, so that the net photosynthetic rate of the wheat leaf is estimated, and a technical support is provided for quickly acquiring the photosynthetic physiological characteristics of the wheat.

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