201 results about "Chlorophyll concentration" patented technology

The invention provides a river water quality monitoring method based on multi-source remote sensing data, and belongs to the technical field of river water quality monitoring. The method comprises the following steps: establishing an inherent optical characteristic model of a river water body based on a Hybrid water body radiation transmission model, optimizing a calculation path by adopting a shortest path algorithm, and carrying out water body optical component inversion by adopting a quasi-analysis algorithm and a generalized inherent optical characteristic algorithm in combination with a multispectral characteristic enhancement model to obtain absorption coefficients of all components; and constructing a multi-band combination index to realize optical coupling effect decoupling, and applying the fine-tuned water quality parameter inversion basic model to a target river area to output a suspended matter concentration distribution diagram, a chlorophyll concentration distribution diagram and a transparency distribution diagram. The technical problem of low precision of remote sensing inversion of water quality parameters caused by mutual coupling of multiple optical active components in a river water body is solved.

The invention discloses a marine environment dynamic monitoring system based on cooperation of artificial intelligence and multi-source remote sensing, relates to the technical field of marine environment monitoring, and realizes multi-dimensional monitoring of a sea surface state by receiving multi-source data of a plurality of monitoring ends. Chlorophyll concentration mutation is detected in real time by setting sliding window difference, a subsequent processing flow is dynamically triggered, and the response speed of sudden events (such as red tide) is increased; aI is utilized to predict clock offset, interpolation reconstruction, weighted fusion and ocean current compensation technologies are combined, and space-time asynchronous errors of multi-source data are remarkably reduced; and generating a data cube fusing the backscattering coefficient, the temperature field and the chlorophyll gradient, and calculating a diffusion velocity field, thereby providing high-precision decision support for marine disaster early warning. The limitation of a traditional single data source is broken through, efficient cooperation of multi-modal data is driven through artificial intelligence, and the real-time performance, the accuracy and the dynamic analysis capability of marine environment monitoring are improved.

The invention provides a multi-fusion seaweed field ecosystem observation method, system, device and medium, and belongs to the technical field of ecological monitoring, the method comprises the following steps: obtaining chlorophyll a concentration, seaweed canopy spectrum and three-dimensional biomass point cloud; aligning the chlorophyll a concentration of the target sea area with the seaweed canopy spectrum, and fusing the three-dimensional biomass point cloud to generate a three-dimensional biomass model; constructing an in-situ sampling network to monitor water quality parameters, benthic organism video streams and eDNA metagenome sequencing data, calibrating a three-dimensional biomass model, executing anomaly detection through a lightweight LSTM model, and identifying benthic organism species in real time through an improved YOLOv5 model; constructing a graph neural network, outputting a carbon sink prediction value, generating a brown tide early warning signal when the carbon sink prediction value is lower than a dynamic threshold value, optimizing a patrol path of the unmanned aerial vehicle based on reinforcement learning, and improving the sampling frequency of the water quality sensor. According to the invention, multi-fusion monitoring of the seaweed field is realized, the ecological condition is accurately evaluated, and abnormity is warned in advance.

The invention relates to the technical field of water environment risk monitoring, in particular to a method for evaluating the algal bloom risk of a water body. The method comprises the following steps: collecting historical monitoring data of a to-be-evaluated water body, wherein the historical monitoring data comprises blue-green algae abundance data, water quality data and hydrological data; analyzing the correlation between the cyanobacteria abundance or chlorophyll a concentration and the water quality and hydrological data of the to-be-evaluated water body; hydrological and water quality parameters with the highest correlation with the cyanobacteria abundance or chlorophyll a concentration are screened out; hydrology and water quality parameters of a water body to be evaluated are taken as predictive variables, and cyanobacteria abundance or chlorophyll a concentration is taken as a response variable to construct a Bayesian network model; the weight of each parameter in the Bayesian network model is calculated, and the algal bloom risk probability that the cyanobacteria abundance exceeds a specific threshold value under the given parameter condition is calculated according to the weights. According to the invention, the scene-based probability deduction of the stable period and the dynamic period is realized through the double-branch Bayesian network model, so that the accuracy and timeliness of algal bloom risk assessment are improved.

The invention discloses a lake algae vertical distribution daily change monitoring method based on a satellite remote sensing image. The invention relates to the technical field of environmental engineering and image processing, and solves the problem that vertical distribution daily variation of algae is difficult to obtain through single observation. The method comprises the following steps: collecting data of algae samples at different depths of a lake, establishing satellite-ground synchronous data, establishing a vertically distributed mathematical model, and parameterizing distribution characteristics of algae in a water depth direction; the remote sensing image is preprocessed, and an inversion model is established based on the relation between the surface chlorophyll concentration in the in-situ data and the remote sensing reflectivity. Utilizing machine learning to optimize model parameters, and combining with a vertical distribution parameterization result to construct an algae vertical distribution remote sensing monitoring model; the method integrates remote sensing data to invert lake algae vertical distribution, performs time sequence analysis on data obtained by inversion, estimates algae vertical distribution and spatial change conditions of a target water area, and provides important scientific basis and technical support for algae bloom prevention and control and environmental protection.

The invention provides a cyanobacterial bloom monitoring method and system. The cyanobacterial bloom monitoring method comprises the steps that a target water body remote sensing image of a target water body area of a monitoring area is acquired; screening a chlorophyll a sensitive wave band of the target water body remote sensing image, and obtaining chlorophyll a inversion concentration based on a preset chlorophyll a concentration inversion model; calculating a normalized vegetation index according to the multi-band remote sensing reflectivity value of the target water body remote sensing image; according to the chlorophyll a inversion concentration and the normalized vegetation index, constructing a cyanobacterial bloom grading threshold dynamic calibration model, and performing cyanobacterial bloom risk grading on the target water body area; and according to the cyanobacterial bloom risk grading result of the target water body area, generating an early warning instruction corresponding to the risk grade. The cyanobacterial bloom grading threshold value can be dynamically adjusted, so that the subjective deviation of manual weighting can be eliminated, and the real-time performance, accuracy and efficiency of cyanobacterial bloom monitoring and early warning are effectively improved.

The invention discloses a fishery sea area monitoring system based on satellite remote sensing, and belongs to the technical field of fishery monitoring, and the system specifically comprises a remote sensing data acquisition module which obtains and preprocesses sea surface temperature, chlorophyll concentration and sea surface height abnormal data; the buoy data acquisition module synchronously acquires temperature-salinity profile data of the Argo buoy array, and the layout density of the Argo buoy array is dynamically adjusted along with historical fish catch and ocean frontal surface intensity; the space-time alignment module divides a time window through an orbital period, corrects space offset in combination with a buoy drift trajectory, sets a time buffer area based on a georotation flow rate and a satellite image resolution, and generates a space-time unified three-dimensional marine environment data set; the fishery suitability calculation module constructs a biophysical coupling model, correlates surface chlorophyll and subsurface thermohaline structures, and calculates the suitability index of each water layer; the fishery division module intelligently defines a core area and an edge area according to the index three-dimensional distribution characteristics; according to the invention, accurate fishery identification based on multi-source data fusion is realized.

The invention relates to the field of river pollution treatment, and discloses a water quality treatment method, system, equipment and medium based on a remote sensing inversion technology, and the method comprises the following steps: S1, collecting a hyperspectral remote sensing image of a to-be-monitored water body, the hyperspectral remote sensing image comprising chlorophyll concentration, suspended matter concentration and dissolved oxygen; s2, based on the hyperspectral remote sensing image, preprocessing the hyperspectral remote sensing image, including geometric correction, atmospheric correction, noise removal and waveband fusion, to obtain a preprocessed image meeting an inversion precision requirement; and S3, inputting the preprocessed image into a water quality inversion model, and obtaining pixel-level chlorophyll, suspended solids and dissolved oxygen concentration data based on physical radiation transmission. By combining the hyperspectral remote sensing image and the water quality inversion model, large-range, real-time and high-precision water quality monitoring and pollution zoning are realized, model parameters are dynamically updated, and the timeliness and precision of water quality treatment are improved.

The invention discloses a sea surface chlorophyll concentration prediction method and system based on remote sensing and deep learning, and relates to the technical field of marine ecological environment, and the method comprises the steps: inputting the marine chlorophyll a data of a target region into a trained marine chlorophyll concentration prediction model, and obtaining the marine chlorophyll concentration information of the target region; wherein the prediction model is an improved marine chlorophyll a concentration prediction model based on a convolutional long short-term memory network; the periodic feature extraction module is used for extracting historical synchronous related periodic features based on historical synchronous chlorophyll concentration data, and the noise removal and fusion module is used for eliminating noise output by the recent spatial-temporal feature extraction module and the periodic feature extraction module and fusing output results to obtain a marine chlorophyll a concentration data prediction result. According to the invention, the sea surface chlorophyll concentration can be predicted more accurately.

The invention discloses a chlorophyll calculation method based on a double-branch multi-scale fusion network model, which belongs to the technical field of chlorophyll calculation, is used for calculating a chlorophyll a concentration value, and comprises the following steps: taking the chlorophyll a concentration value of a measurement site as a label of a water body characteristic sequence data set, dividing the data set into a training set and a verification set, inputting the training set into the double-branch multi-scale fusion network model to obtain a trained double-branch multi-scale fusion network model; inputting the verification set into the double-branch multi-scale fusion network model to obtain a verified double-branch multi-scale fusion network model; and taking the neighborhood spectral feature tensor and the neighborhood inherent optical feature tensor as the input of the verified double-branch multi-scale fusion network model to obtain the chlorophyll a concentration value of the pixel at the corresponding position. According to the method, the calculation result of the chlorophyll a with relatively high precision is obtained through neural network processing, and the complex relationship among the concentration change of the chlorophyll a in the water body, the spectrum and other optical parameters can be captured.

The invention discloses a water source chlorophyll a concentration prediction model design method based on machine learning. The method comprises the following steps: acquiring chlorophyll a concentration data in a to-be-predicted region for a continuous period of time; carrying out data preprocessing on the chlorophyll a concentration data, and filtering high-frequency noise by adopting wavelet transform preprocessing; constructing a concentration prediction model, carrying out data preprocessing on chlorophyll a concentration data, and filtering high-frequency noise by adopting wavelet transform preprocessing; constructing different concentration prediction models, and inputting the processed chlorophyll a concentration data and physicochemical parameters into the prediction models to obtain a chlorophyll a concentration data prediction result; and comparing prediction results of different prediction models, and determining the prediction model. According to the prediction model design method, the WT-GRU model is adopted to preprocess the data through wavelet transform, the wavelet transform effectively extracts key time scale characteristics through signal decomposition, and the accuracy of chlorophyll a concentration prediction is remarkably improved.

The invention provides a regional ecological environment quality evaluation method and system based on remote sensing data, and relates to the technical field of environment remote sensing and ecological monitoring, and the method comprises the steps: extracting red light and near-infrared reflectivity at the peak of a growing season by using Landsat8 satellite data, and calculating a mixed vegetation index; synchronously measuring vegetation indexes of pure vegetation and bare soil on site to obtain a vegetation coverage rate, and obtaining water transparency, suspended solid concentration and chlorophyll a concentration through on-site measurement on the basis of green light, red light, blue light and near-infrared reflectivity in a heavy rainfall period and a dry season; the method comprises the following steps: constructing a linear regression model of reflectivity and water quality parameters through a least square method, calculating a water quality index, constructing an extreme weather influence factor based on ten-year extreme weather data, and finally fusing a vegetation coverage rate, water quality, the extreme weather influence factor, annual precipitation, annual average temperature, optimal regional vegetation temperature and historical rainfall extremum. And constructing an ecological quality index, and dividing ecological environment quality grades.

The invention relates to an optical satellite absolute radiometric calibration method, device and equipment under ocean low brightness based on an offshore pseudo-invariant field and a medium, and belongs to the technical field of in-orbit absolute radiometric calibration. According to the method, a marine pseudo-invariant field with stable water and atmospheric optical characteristics is determined according to marine observation data of a calibrated optical satellite, the off-water radiance of the marine pseudo-invariant field is calculated according to the chlorophyll a concentration of the marine pseudo-invariant field, the zenith radiance is theoretically calculated, a data source is provided for absolute radiometric calibration of a satellite to be calibrated, and calibration is completed. The method explores the potential of the marine pseudo-invariant field in supplementing the traditional radiometric calibration method, gets rid of the limitation of a fixed and single marine optical buoy MOBY site data source, improves the data volume, the coverage area and the acquisition frequency of effective calibration data, provides guarantee for the continuity of an optical satellite observation task, and has a wide application prospect. And a plurality of calibration sources can be utilized to realize rapid and accurate calibration of the optical satellite, so that the calibration efficiency of the offshore optical satellite is remarkably improved.

The invention relates to the technical field of lake chlorophyll a inversion, and provides an inland lake chlorophyll a inversion method and system based on hyperspectral data, and the content of chlorophyll a in a lake range is inverted through a multiband index model and a regression method. Firstly, hyperspectral image data of a lake is obtained and preprocessed to remove noise and improve data quality. Then, extracting a water body range by adopting an NDWI index, and extracting wave band information of different sampling points; based on optical characteristics of lake water quality, a multiband index model considering pigment absorption and remote sensing reflectivity is constructed, and an inversion model is optimized in combination with regression analysis. According to the method, the hyperspectral remote sensing image data are collected and fused only through actual measurement of a small number of points, the constructed model is applied to the lake chlorophyll a distribution characteristics on the scale of a monitoring area, accurate inversion of the concentration of the chlorophyll a in the whole area of the lake can be achieved, and the problem that the accuracy is low when remote sensing data is singly adopted for inversion is solved.

The embodiment of the invention discloses a reservoir carbon sink accounting method based on multi-source remote sensing data, a related device and a medium. The method comprises the following steps: acquiring multi-source remote sensing data of a target reservoir in a target time period; according to the multi-source remote sensing data, performing inversion to obtain a water surface area dynamic diagram, a chlorophyll a concentration space-time distribution diagram, a CDOM space-time distribution diagram and a water surface temperature space-time distribution diagram of the target reservoir in the target time period; the four diagrams are input into a preset carbon sequestration model for carbon sequestration processing, a unit area greenhouse gas flux value space-time distribution diagram corresponding to the target reservoir is obtained, and each pixel in the unit area greenhouse gas flux value space-time distribution diagram carries a corresponding unit area greenhouse gas flux value; and performing space-time integral processing on the space-time distribution diagram of the greenhouse gas flux value per unit area and the dynamic diagram of the water surface area to obtain a target total carbon sink amount of the target reservoir in the target time period. By implementing the method provided by the embodiment of the invention, the accuracy of reservoir carbon sink accounting can be improved.

The invention belongs to the technical field of remote sensing information processing, and particularly relates to a water chlorophyll a concentration inversion-oriented remote sensing time-space-spectrum fusion method, which realizes improvement of spatial resolution of a chlorophyll a concentration inversion sensitive wave band by fusing complementary information of different sensors on time-space-spectrum resolution. The method comprises the following steps of: establishing a water body chlorophyll a concentration inversion-oriented MSI and OLCI space-time spectrum fusion deep learning network, embedding a time sequence dynamic adjustment module, and establishing a water body chlorophyll a concentration inversion-oriented MSI and OLCI space-time spectrum fusion deep learning network for water body chlorophyll a concentration inversion. According to the method, the network can intelligently combine time and space features to generate a more accurate prediction image, an Adaboost machine learning joint inversion model is constructed based on time-space-spectrum fusion data and corresponding limited ground station data, and high-precision remote sensing inversion of the concentration of chlorophyll a is realized.

The invention discloses a carbon sink calculation method based on a space-time fusion technology and flux data, and the method comprises the steps: carrying out the space-time fusion synthesis of a remote sensing image, calculating a leaf area index and chlorophyll concentration data with high space-time resolution, and improving the time and space resolution of carbon sink data; calculating daily net primary productivity data by using a light energy utilization rate model based on the leaf area index data and the meteorological data; correcting parameters of the light energy utilization rate model based on the daily net primary productivity data and the daily flux data; migrating the corrected parameters of the light energy utilization rate model to a flux-free site area based on the ratio of the chlorophyll concentrations to obtain the migrated parameters of the light energy utilization rate model; the migrated parameters of the light energy utilization rate model are transmitted back to the light energy utilization rate model, the net primary productivity is calculated, and the carbon sink data are calculated based on the net primary productivity, so that the carbon sink calculation method is higher in applicability, and reliable technical support is provided for carbon cycle research of an ecological system.

The invention provides a water chlorophyll a concentration inversion method and system based on multi-modal data and a lightweight model, and relates to the technical field of water environment remote sensing evaluation. The method comprises the following steps: firstly, acquiring a Gaofeng No.5 satellite remote sensing image, a sentinel No.3 satellite image and ground actual measurement data, and completing image preprocessing and water body pixel extraction; constructing a hyperspectral index and an aquatic vegetation index, and fusing the hyperspectral index and the aquatic vegetation index with the water body temperature, the pH environmental factors and the spectral reflectivity to form a multi-dimensional feature sample set; a core feature subset is obtained through random forest and XGBoost coupling feature selection, and a lightweight student model is trained based on knowledge distillation; and constructing a to-be-predicted feature sample for the to-be-predicted time phase image and the environment factor, inputting the to-be-predicted feature sample into the lightweight student model to obtain a chlorophyll a concentration predicted value, and generating a spatial distribution map and a quality control map layer. According to the invention, high-precision, low-redundancy and efficient deployment chlorophyll a concentration inversion is realized.

The invention discloses a phytoplankton primary productivity in-situ fluorescence detection device and method, and belongs to the technical field of aquatic ecology monitoring. The device adopts a modular integrated design and comprises a multi-band LED excitation light source, a laser induction module, a photomultiplier receiving system and an anti-vibration circuit structure, and synchronous detection of chlorophyll concentration and photosynthetic activity is realized through a short-optical-path optical design. The method is based on spectrum inversion and fluorescence kinetic analysis, a standard spectrum library is constructed, chlorophyll concentration distribution and photosynthetic parameters are calculated through least square fitting, and primary productivity data are output in combination with a photoresponse model. The device has the diameter of 67 mm, supports deployment of Argo and other mobile platforms, solves the problems that a traditional method is poor in timeliness and equipment is discrete, and has the advantages of being high in detection efficiency, high in anti-interference performance and wide in applicable water depth range.

The invention provides a water chlorophyll a concentration inversion method based on a hyperspectral remote sensing image, which relates to the technical field of remote sensing inversion and comprises the steps of image preprocessing, water pixel extraction, actually measured sample space registration, spectral feature construction, model score optimization, concentration prediction and the like. According to the method, an optimal scheme is screened in a feature-model combination through cross validation and a unified scoring function, the optimal scheme is applied to whole image calculation, and a chlorophyll a concentration spatial distribution map and a matched quality control map layer are output. The method has the characteristics of high precision, self-adaption and high engineering practicability, and is suitable for a water quality remote sensing inversion scene driven by multi-source hyperspectral data.

The invention discloses a deep sea color inversion method fusing satellite radiation simulation under a multi-level sea color background, and the method comprises the steps: carrying out the simulation through employing a simulation algorithm based on an OSOAA radiation transmission model, obtaining simulation data including simulation wave band apparent reflectivity, and constructing a multi-level sea color data set; secondly, constructing a deep sea color inversion network with the orientation and wave band feature joint characterization capability, including a residual network and a binary attention mechanism, and performing sea color inversion based on multi-level sea color data; and finally, evaluating by using a test part of the data set, collecting real satellite remote sensing and sea color data, and testing. According to the method, the problems of low robustness and reliability of deep learning in a complex scene and the like caused by scarcity of current sea color marking data are solved, the problem of end-to-end inversion of sea color parameters such as chlorophyll concentration is solved, and the inversion efficiency is improved.

The invention provides a method for cooperatively regulating and controlling chlorophyll-a concentration variability through vortex-upwelling, and relates to the technical field of ocean remote sensing and environmental monitoring. A multi-source satellite and reanalysis data set is obtained, and a stationary time sequence is constructed; identifying a dominant variation period in chlorophyll-a concentration, sea surface temperature and sea surface height anomalies, and extracting a monthly abnormal value sequence; performing complex empirical orthogonal function analysis on the sea surface height anomaly, extracting a dominant spatial variation mode of the sea surface height anomaly, and identifying a vortex distribution position and form; classifying and screening target vortexes according to vortex types and seasons, carrying out normalization processing on the chlorophyll-a concentration, aligning to vortex centers, and analyzing a spatial response relationship between the vortexes and the chlorophyll-a concentration; and drawing a density time-depth profile map and a CHL-SLA composite map by combining surface chlorophyll-a concentration, sea surface height anomaly and vertical Argo buoy data, and analyzing an influence mechanism of vortex-upwelling on chlorophyll-a horizontal distribution.

The invention provides a lake and reservoir algal bloom prediction and early warning system and method based on a PCA-RBF neural network and time-space fusion. Multi-dimensional water quality parameters are monitored and obtained in real time through a water quality sensor array device carried on an unmanned ship; key water quality parameters are dynamically screened by calculating Pearson's correlation coefficients of the water quality parameters, and PCC gt is reserved; a time sequence data set is generated according to the index of 0.22; inputting the time sequence data set into a PCA-RBF neural network prediction model for dynamic prediction, outputting a chlorophyll a concentration prediction value, and generating a prediction sequence; calculating the water bloom outbreak probability of the prediction sequence of the chlorophyll a concentration by adopting a time sequence decomposition and ARIMA combined algorithm; and carrying out risk grade division on the algal bloom outbreak probability according to a grading early warning rule. According to the method, dimension reduction is carried out on multi-dimensional water quality parameters through the PCA-RBF neural network, the concentration of chlorophyll a is predicted, a concentration time sequence rule is analyzed by adopting an STL-ARIMA combined algorithm, and graded early warning signals are output by fusing spatio-temporal characteristics, so that accurate prevention and control of water bloom risks are realized.

The invention provides a seawater chlorophyll a concentration three-dimensional remote sensing detection method, medium, equipment and product, and relates to the technical field of ocean remote sensing. The method comprises the following steps: acquiring satellite remote sensing data and photon counting laser radar data of a to-be-detected area; obtaining a seawater chlorophyll a concentration profile based on the diffusion attenuation coefficient; the method comprises the following steps: constructing an improved VGGNet model which comprises a multi-branch VGGNet, replacing standard convolution in the VGGNet with depth separable convolution, and introducing an SE attention mechanism into a backbone network; the time and position variables, the satellite remote sensing data, the photon density, the backscattering coefficient, the diffusion attenuation coefficient and the water depth serve as input variables of the model, the seawater chlorophyll a concentration profile serves as a target variable, and the model is trained; and inputting new satellite remote sensing data into the trained model to generate three-dimensional distribution prediction of the seawater chlorophyll a concentration. The method can better extract the features in the data, improves the prediction precision of the model, and is suitable for a complex water body environment.

The invention discloses a method for determining a chlorophyll concentration value of phytoplankton based on ocean heat waves. The method comprises the following steps: acquiring ocean green ecological remote sensing image data and ocean heat wave data of a target sea area; and inputting the ocean heat wave data and the ocean green ecological remote sensing image data of the target sea area into a chlorophyll concentration determination model, so that the chlorophyll concentration determination model determines the ocean heat wave grade of the target sea area according to the ocean heat wave data, inputting the ocean green ecological remote sensing image data into a chlorophyll concentration determination sub-model according to the ocean heat wave grade, enabling the chlorophyll concentration determination sub-model to extract corresponding multispectral remote sensing image features and ocean microwave remote sensing image features, and outputting a phytoplankton chlorophyll concentration value of the target sea area; and transmitting the phytoplankton chlorophyll concentration value of the target sea area to a marine ecological monitoring system to assist the marine ecological monitoring system in sea area monitoring. The method can improve the accuracy of determining the chlorophyll concentration value of the phytoplankton and the application reliability.

The invention belongs to the technical field of ocean laser radar remote sensing detection, and particularly relates to an ocean subsurface water body element vertical structure inversion method and system and a computer program. The method comprises the following steps: carrying out preprocessing, distance correction and logarithm conversion on a laser radar echo signal; constructing an iterative hybrid inversion model to invert a water body attenuation coefficient and a particulate matter backscattering coefficient; performing scattering correction for multiple times; matching multi-source data to construct a deep learning model; and finally inverting the chlorophyll concentration and the vertical section of the granular organic carbon. According to the method, the multiple scattering effect of the water body and the influence of system parameters are comprehensively considered, data driving and a physical model are combined, high-precision and automatic marine water body element inversion can be achieved under the complex water quality condition, and the method is suitable for ecological environment monitoring and research of large-range and multi-type sea areas.

The invention discloses a Chla monitoring method based on visible-near infrared spectrum and machine learning, which is a modeling method for performing chlorophyll a concentration parameter inversion by using visible-near infrared hyperspectral data, and combines primary screening of spectral characteristic wave bands, training sample expansion based on GAN, spectral characteristic wave band fine screening based on CARS and a regression modeling technology. The problems of high dimension of hyperspectral data and insufficient samples are solved, the overall Chl-a modeling precision is improved, and the method is suitable for water eutrophication monitoring, marine ecological assessment and environment remote sensing application.

The invention relates to a method and device for deducing the total amount of algae in a water body based on remote sensing and environmental parameters, computer equipment and a storage medium. The method comprises the following steps: acquiring a remote sensing image of a to-be-deduced area, and a water surface chlorophyll concentration and a water body profile parameter set of each preset sampling pixel position in the remote sensing image, and acquiring remote sensing image features of a water body surface according to the remote sensing image of the to-be-deduced area; determining a water surface algae density prediction model by adopting at least one machine learning algorithm, and obtaining the total amount of water surface algae through the water surface algae density prediction model; determining a water algae density prediction model by adopting at least one regression model, and obtaining the total amount of water algae through the water algae density prediction model; and obtaining the total algae amount of the to-be-deduced area based on the total water surface algae amount and the total water algae amount. By adopting the method, the accuracy and reliability of total algae amount estimation can be remarkably improved.

The invention belongs to the technical field of remote sensing image data processing, and relates to a multi-feature fusion coastal zone culture pond extraction method, which comprises the following steps: obtaining spectral features and polarization features based on an obtained Sentinel-1 image and an obtained Sentinel-2 image; calculating and evaluating an NDWI time sequence based on the NDWI to generate a time sequence synthesis NDWI image; a water body main body is obtained through the hierarchical feature fusion decision tree; obtaining morphological characteristics based on the water body object; obtaining the chlorophyll a concentration and the dynamic characteristic factor of the chlorophyll a concentration based on the Sentinel-2 image; and extracting a culture pond through a random forest classifier, and generating a culture pond spatial distribution diagram. According to the method, the spectral features, the polarization features, the morphological features, the chlorophyll a concentration and the chlorophyll a concentration dynamic feature factors are fused, and the decision tree and the random forest classifier are fused through the hierarchical features, so that the problems of low accuracy and poor stability of existing culture pond extraction are solved.

The invention belongs to the technical field of water ecology risk early warning, and provides an algal bloom risk remote sensing intelligent identification method and system, and the method comprises the steps: obtaining remote sensing image data, meteorological data and water quality data of a target region, and carrying out the preprocessing; performing frequency domain feature extraction to generate a three-dimensional frequency domain feature vector; fusing the global feature representation obtained by modeling and the generated three-dimensional frequency domain feature vector by using a multi-task deep learning network to obtain a feature map; obtaining an algae bloom binary segmentation probability graph and a continuous value distribution graph of chlorophyll a concentration based on the characteristic graph; generating an image semantic embedding vector by utilizing a semantic embedding head, and performing semantic alignment on the image semantic embedding vector by adopting a pre-trained knowledge graph to generate a research and judgment information text; and performing cross validation on the study and judgment information text, the algae bloom binary segmentation probability graph and the continuous value distribution graph of the chlorophyll a concentration to obtain a final algae bloom risk judgment result. According to the invention, the algal bloom risk remote sensing intelligent identification is realized.