107 results about "Chlorophyll concentration" patented technology

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 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 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 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 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.

The invention belongs to the technical field of satellite remote sensing application, and discloses an active and passive satellite remote sensing fused ocean three-dimensional chlorophyll field reconstruction method and system. According to the method, satellite-borne laser radar data is subjected to correction, signal processing and biological optical model inversion, and a chlorophyll a concentration vertical section along an orbit is obtained; forming a training set by the passive satellite observation optical variable and the marine environment variable of which the profile is matched with the space-time, so as to train a long short-term memory (LSTM) neural network model; and utilizing the trained model to reconstruct a three-dimensional chlorophyll a concentration field of a target area according to passive observation and environment variables of the target area. By fusing the advantages of a satellite-borne laser radar ICESat-2 satellite and a passive optical remote sensing satellite, three-dimensional chlorophyll a concentration field detection based on active and passive fusion remote sensing is developed, the structure and function of a marine ecosystem can be deeply known, and three-dimensional dynamic observation of the ocean is realized.

The invention discloses a time sequence reconstruction and machine learning coupled chlorophyll monitoring data breakpoint restoration method, and belongs to the technical field of water quality monitoring. The invention discloses a chlorophyll monitoring data breakpoint restoration method based on coupling of time sequence reconstruction and machine learning, and the method comprises the following steps: S1, collecting water quality monitoring data, and cleaning the monitoring data to obtain preprocessed data; s2, performing time sequence reconstruction on the preprocessed data to obtain a weekly average 1 data set; s3, respectively constructing a radial basis function neural network model and a back propagation neural network model by taking the chlorophyll concentration as a response variable and the conventional water quality parameter as a predictive variable; s4, performing performance evaluation on each model by taking a root mean square error, an average absolute percentage error, goodness of fit and relative error distribution statistics as evaluation indexes, and screening out an optimal model; and S5, applying the conventional water quality parameters in the breakpoint interval of the chlorophyll monitoring data in the water body to the optimal model, and outputting the restored chlorophyll concentration value to complete the dynamic restoration of the breakpoint.

The invention relates to the technical field of forest vegetation monitoring, in particular to a forest vegetation parameter real-time monitoring method based on unmanned aerial vehicle images, which comprises the following steps: starting an unmanned aerial vehicle, carrying out real-time image acquisition on a predetermined forest area through a camera, carrying out continuous image capture, synchronously calibrating the camera and setting matched differentiated illumination and depth-of-field conditions; and generating forest image acquisition data. According to the method, the chlorophyll concentration and the leaf area index can be accurately measured through analysis of different color wavelength reflectance, the understanding and tracking precision of the vegetation physiological state is improved, long-term vegetation changes can be carefully monitored through time sequence analysis, the prediction capacity of forest ecological behaviors is enhanced, and the method is suitable for popularization and application. Drought response and pest and disease damage signs are monitored in real time, the timeliness and accuracy of health state evaluation are enhanced, the real-time performance and continuity of data are ensured by dynamically updating a forest vegetation database, and the dynamic monitoring and decision support capacity of forest management is remarkably improved.

The invention relates to the technical field of ocean detection equipment, and particularly provides an ocean laser radar system and a seawater multi-parameter detection method. The system comprises a laser emission subsystem, an optical receiving subsystem and a signal acquisition and control subsystem. The laser emission subsystem can emit multi-wavelength pulse laser to seawater; the optical receiving subsystem adopts a double-receiving telescope structure, respectively receives scattering and fluorescence signals with different properties, and realizes multi-channel signal separation through light splitting, light filtering and polarization light splitting elements; and the signal acquisition and control subsystem realizes synchronous acquisition and processing of multi-channel data. The system can synchronously invert water optical parameters, water particulate matter depolarization ratio and color ratio, chlorophyll concentration, suspended load concentration and other multi-parameter profiles, and is suitable for marine environment unattended monitoring of a fixed platform and a mobile platform.

The invention discloses a water conservancy project river water quality real-time monitoring system based on multispectral remote sensing. The problems that traditional monitoring space coverage is limited, data feedback lags behind, precision is insufficient and early warning is low in efficiency can be solved. The system comprises a multispectral remote sensing data acquisition module, a data transmission module, a data processing and analysis module, an early warning and display module and a power management module. The acquisition module takes an unmanned aerial vehicle or a satellite as a carrying platform, and acquires river water quality characteristic spectrum and illumination data through a cuboid sensor with a multispectral detection unit and an illumination intensity sensor; the processing module accurately calculates parameters such as chlorophyll a concentration and the like by combining a high-performance computer with preprocessing software and an updatable regression type water quality inversion model; the early warning display module displays a result through a touch screen and realizes early warning through sound-light alarm and information pushing; all the modules are cooperatively used for monitoring according to preset intervals, and real-time, comprehensive and accurate monitoring of the river water quality is achieved.

The invention discloses an XGBoost chlorophyll a concentration aerial remote sensing inversion method based on characteristic wave band selection, and relates to the technical field of environmental monitoring. By constructing an integrated input characteristic vector, an optimal characteristic wave band most relevant to the chlorophyll a concentration, an enhanced chlorophyll a index and specially designed correction characteristics are fused; and more targeted information is provided for the model. Moreover, correction features obtained through calculation of spectral difference values of adjacent shadow regions and non-shadow regions are introduced, and training data containing samples of the two regions are utilized to train an XGBoost model, so that the XGBoost model can autonomously learn and quantify a composite interference effect brought by illumination and suspended matter concentration change. Therefore, according to the technology, inversion noise caused by complex environmental factors can be effectively inhibited, high-precision and full-coverage inversion of the chlorophyll a concentration under different illumination and turbidity conditions is realized, and the robustness and the practical application value of the method are remarkably improved.

The invention belongs to the technical field of water environment monitoring and early warning, and discloses a lake and reservoir chlorophyll a concentration prediction method based on an SO-KNN model. According to the invention, multi-time scale meteorological cumulative effect features are introduced to enrich information representation, and an SO-KNN intelligent prediction model is constructed. According to the method, under the conditions of data scarcity and non-equilibrium, the chlorophyll a concentration, especially the high-precision and strong-generalization prediction capability of the water bloom risk critical point, is remarkably improved. The model is simple in structure and efficient in calculation, the common defects of overfitting, insufficient generalization ability and the like of a complex machine learning model in the scene are effectively overcome, and a reliable and practical innovative technical solution is provided for early water bloom warning of northern reservoirs and water areas with similar data conditions.

The invention discloses a space-time fusion method for lake chlorophyll a concentration remote sensing inversion, and relates to the technical field of environment monitoring. Wherein a plurality of chlorophyll a concentration inversion graphs are generated through multi-source satellite data, space, time and attribute three-dimensional features are extracted based on the plurality of chlorophyll a concentration inversion graphs, and finally collaborative fusion of multi-dimensional features is realized through a tensor fusion mechanism. Thus, through integration of multi-source remote sensing images, high temporal-spatial resolution extraction of the lake surface chlorophyll a is realized, cooperative utilization of multi-dimensional features is completed in a decision-making layer, fundamental bottlenecks of a traditional fusion method in the aspects of physical significance transparency and data universality are overcome, the temporal-spatial change trend of lake water quality parameters can be further mastered, and the method is suitable for being applied to lake water quality monitoring. And the development of lake water environment research is further promoted.

The invention discloses an intelligent water bloom prediction and early warning system and method based on an AI large model. The system is mainly composed of a data acquisition module, a data preprocessing module, an AI large model module, an early warning module and a visualization module. The data acquisition module is responsible for widely collecting multi-source data of a target water area. A water quality sensor, an underwater robot and other equipment go deep into a water body to accurately collect physical and chemical parameters of the water body, a meteorological station is utilized to obtain comprehensive meteorological data in real time, hydrological data is obtained from a water conservancy department database or on-site hydrological monitoring equipment, and satellite remote sensing data is obtained by means of a satellite receiving station or a third-party remote sensing data platform. Macroscopic information such as water color, chlorophyll a concentration distribution and water area change is covered. The invention belongs to the field of water environment monitoring and early warning, and particularly relates to an intelligent water bloom predicting and early warning system and method based on an AI large model.

The invention discloses a method for evaluating the ecological risk of a perfluorinated compound based on the chlorophyll concentration of microalgae, which comprises the following steps: collecting a water body of a region to be evaluated, detecting the concentration of the perfluorinated compound in the water body, and obtaining a localized microalgae culture system at the same time; adding perfluorinated compound solutions with different concentrations into the localized microalgae culture system to form an experimental group, and setting a blank group without perfluorinated compounds; sampling and detecting the chlorophyll concentration of the microalgae in the experimental group and the blank group, and calculating the chlorophyll inhibition ratio of the microalgae; calculating the half effect concentration according to the function relationship between the concentration of the perfluorinated compound and the chlorophyll inhibition ratio of the microalgae; according to the concentration and the half effect concentration of the perfluorinated compounds in the water body, calculating to obtain a risk quotient for judging and evaluating the ecological risk of the perfluorinated compounds in the water body. According to the method, the half effect concentration, the mixed exposure effect and the risk quotient value are integrated to serve as ecological risk composite criteria, and the method has the advantages of being high in environmental correlation, high in sensitivity and high in indication performance.

The present application belongs to the technical field of water ecological risk early warning, and provides an algal bloom risk remote sensing intelligent identification method and system, remote sensing image data, meteorological data and water quality data of a target area are acquired and preprocessed; frequency domain feature extraction is performed to generate a three-dimensional frequency domain feature vector; a multi-task deep learning network is used to fuse the global feature representation obtained by modeling and the generated three-dimensional frequency domain feature vector to obtain a feature map; based on the feature map, an algal bloom binary segmentation probability map and a continuous value distribution map of chlorophyll a concentration are obtained; a semantic embedding head is used to generate an image semantic embedding vector, a pre-trained knowledge graph is used to perform semantic alignment on the image semantic embedding vector to generate research and judgment information text; the research and judgment information text, the algal bloom binary segmentation probability map and the continuous value distribution map of chlorophyll a concentration are cross-validated to obtain a final algal bloom risk judgment result. The present application realizes algal bloom risk remote sensing intelligent identification.

The present application relates to a kind of lake reservoir type chlorophyll concentration prediction method, device and medium, method includes the following steps: obtaining the water quality, water dynamics and weather of the online automatic monitoring data of lake reservoir including forecast point and upstream point;The online automatic monitoring data is preprocessed, and the data after processing is obtained;The data after processing is carried out feature extraction to obtain the water quality feature and water dynamics index feature of forecast point and upstream point, and construct cumulative illumination feature;Upstream water quality feature, water dynamics index feature and illumination feature are input into the fusion prediction model pre-trained, to obtain upstream transport chlorophyll prediction result and time series influence chlorophyll prediction result, using error reciprocal method upstream transport chlorophyll prediction result and time series influence chlorophyll prediction result are fused;The chlorophyll prediction result obtained by fusion is output.Compared with prior art, the present application has the advantages of high accuracy, strong stability and the like.

The invention provides a method for distinguishing chlorophyll a concentration of an upwelling region influenced by different types of cyclones, and relates to the technical field of ocean remote sensing and environment monitoring. Preliminarily judging the influence of vortex on chlorophyll a concentration distribution; dividing the research area into a near-shore area and a far-shore area according to the water depth, respectively drawing vortex center-chlorophyll a concentration fusion maps for the two areas, and comparing the influence of cyclone and anti-cyclone of the two areas on chlorophyll a concentration distribution; dividing the vortex into a near-shore vortex and a far-shore vortex according to the ratio of the distance from the vortex center to the coastline to the vortex radius, respectively drawing vortex center-chlorophyll a concentration fusion graphs, and comparing the influence of cyclone and anti-cyclone on the chlorophyll a concentration distribution; and matching the temperature and salt data of the Argo buoy with the vortex position, analyzing the correlation between the potential density abnormity and the chlorophyll a concentration, and distinguishing the influence mechanism of vertical nutrition input and horizontal advection on the chlorophyll a concentration.

The application provides a method for vortex-uplift flow coordinated regulation chlorophyll-a concentration variability, relates to the field of ocean remote sensing and environmental monitoring technology, obtains multi-source satellite and reanalysis data set, constructs smooth time series; identifies dominant variability period in chlorophyll-a concentration, sea surface temperature and sea surface height anomaly, extracts monthly anomaly value sequence; applies complex empirical orthogonal function analysis to sea surface height anomaly, extracts its dominant spatial variability mode, identifies vortex distribution position and shape; according to vortex type and season, classifies and filters target vortex, carries out normalization processing to chlorophyll-a concentration and aligns to vortex center, analyzes spatial response relationship between vortex and chlorophyll-a concentration; in combination with surface chlorophyll-a concentration, sea surface height anomaly and vertical Argo buoy data, density time-depth profile graph and CHL-SLA composite graph are drawn, and the influence mechanism of vortex-uplift flow on chlorophyll-a horizontal distribution is analyzed.

This invention discloses a chlorophyll concentration profile inversion method based on a physical information neural network, belonging to the field of physical information machine learning technology. It is used for chlorophyll concentration profile inversion, including acquiring marine optical profile observation data to form observation sample data, and performing fractional standardization on the observation sample data; constructing and training a physical information neural network model, destandardizing the predicted data, and constructing a physical constraint loss function; inputting the data to be inverted into the trained physical information neural network model, and calculating the chlorophyll concentration profile using the model's predicted values. This invention constructs a physical information neural network model and introduces a physical constraint loss function to ensure that the inverted absorption coefficient, backscattering coefficient, and irradiance profile conform to the light transmission law in water, avoiding physical anomalies generated by purely data-driven methods, and achieving cross-domain inversion from apparent optical quantities to intrinsic optical quantities without the need for step-by-step calculations or intermediate parameter estimation.

A detection apparatus 1 for detecting an upwelling region includes: a simulation unit 11 for simulating sea surface temperature data, chlorophyll-a concentration data, and ocean current velocity data of regions in the ocean; a preprocessing unit 12 for creating a ground truth label of an upwelling region on the basis of a vertical velocity included in the ocean current velocity data; a classification unit 14 for training a learning model for outputting a classification result of an upwelling region in response to the input of sea surface temperature data and chlorophyll-a concentration data, wherein the training is performed by inputting the simulated sea surface temperature data and chlorophyll-a concentration data to the learning model, and further inputting the ground truth label of the upwelling region to the learning model; and a detection unit 17 for acquiring a classification result of an upwelling region in the ocean from the learning model by inputting sea surface temperature data and chlorophyll-a concentration data of the ocean acquired from a satellite to the learning model.

The present application belongs to the field of analytical detection technology, and particularly relates to a water quality detection circuit and a water quality analyzer. The water quality detection circuit is used for detecting chlorophyll in water quality, and comprises: a single-chip microcomputer; a light source control circuit unit, comprising a light source constant current driving circuit module and a light source, and the light source constant current driving circuit module is used for driving the light source; a light source feedback circuit unit, used for feeding back intensity variation of the light source to the single-chip microcomputer; the single-chip microcomputer adjusts a driving voltage of the light source according to the feedback of the intensity variation of the light source, so that the light intensity of the light source is in a target interval; and a fluorescence detection circuit unit, used for detecting fluorescence generated after the light source irradiates a sample to be detected and performing ambient light compensation, so as to obtain a chlorophyll concentration. The present application detects and compensates the light source, maintains the light intensity of the light source unchanged, makes the fluorescence excited by the chlorophyll more stable, additionally, compensates ambient light such as sunlight and lamp light, avoids interference of external light sources, and effectively improves the precision of chlorophyll detection.

The application discloses a method for rapidly deducing the denitrification rate of lake sediments by using water chlorophyll, which comprises the following steps: (1) determining the water chlorophyll concentration of the corresponding point of the sediments; (2) selecting a segmented numerical model: according to the measured water chlorophyll concentration and the corresponding sediment denitrification rate, the parameters in the segmented numerical model are fitted to obtain the segmented numerical model; (3) deducing the denitrification rate of the sediments: the chlorophyll concentration is substituted into the selected numerical model to deduce the denitrification rate of the sediments. The application is aimed at the problems that the existing determination operation of the nitrification-denitrification coupled denitrification rate of the sediments is complicated, time-consuming and not suitable for large-scale sample determination, and provides a method for rapidly deducing the denitrification rate of the sediments by using water chlorophyll. The method for directly deducing the denitrification rate by using the easily measured index is simple, feasible and beneficial to high-frequency multi-point data acquisition.

The invention discloses an ocean sub-mesoscale process instance segmentation method based on frame supervision, and relates to the field of ocean remote sensing information processing. The invention aims to solve the problem of low segmentation precision of the existing frame supervision instance segmentation method. The method comprises the following steps: forming a first training set by using a chlorophyll concentration remote sensing image and a frame label of an ocean sub-mesoscale process; training a teacher model by using the first training set, and outputting an initial prediction mask set by the trained teacher model; performing mask correction on the initial prediction mask set to obtain a pseudo mask; forming a second training set by using the chlorophyll concentration remote sensing image, the pseudo mask and the frame label of the ocean sub-mesoscale process, and training and optimizing the student model by using the second training set to obtain a trained student model; and inputting a chlorophyll concentration remote sensing image to be tested into the trained student model to obtain an ocean sub-mesoscale process instance segmentation result. The method is used for obtaining the ocean sub-mesoscale process region.

The invention relates to a river reservoir chlorophyll concentration prediction method based on deep learning and satellite remote sensing. The method comprises the following steps: firstly, obtaining research area satellite multispectral image data based on research area water chlorophyll concentration measured data and a Google Earth Engine (GEE) platform; thirdly, a data interpolation method based on a random forest (RF) model is provided, meteorological, hydrological and water quality monitoring data and water body chlorophyll concentration inversion data are combined, and river reservoir chlorophyll concentration time sequence data with the stable step length are established; and finally, by introducing an LSTM neural network model, constructing a river reservoir chlorophyll concentration prediction model suitable for specific weather, hydrology and water quality conditions based on the river reservoir chlorophyll concentration time sequence data with the stable step length. The invention aims to provide a novel method for predicting the chlorophyll concentration of the water body from the meteorological, hydrological and water quality conditions of the river reservoir through combination of satellite remote sensing inversion and a deep learning algorithm, and technical support is provided for ecological environment management of the river reservoir.

The invention relates to a lake and reservoir chlorophyll concentration temporal and spatial change analysis method based on multi-source remote sensing. The method comprises the following steps: cooperatively collecting multispectral images and real-time water sample data through remote sensing and ground equipment, and obtaining a chlorophyll concentration measured value with geographic coordinates and timestamps; training a convolutional neural network inversion model by taking the measured value as a label, and processing the image to obtain a chlorophyll concentration distribution matrix; decomposing dynamic components through spectral analysis, extracting hydrological related subset data and optimizing to obtain a heterogeneity feature vector, and filtering interference through machine learning to obtain a pure signal sequence if sediment suspension disturbance exceeds the standard; calculating environment and climate influence weights based on the sequence, correcting the concentration, and generating optimized space-time distribution representation after validity score verification; finally, key monitoring points are extracted through differential monitoring grid division, and a chlorophyll concentration monitoring parameter set is formed. According to the method, the time-space continuity and accuracy of the monitored chlorophyll concentration data are guaranteed, and the monitoring efficiency is improved.

The invention belongs to the technical field of water quality detection, and discloses a chlorophyll a concentration inversion model construction method and system based on regional adaptation. A quality control method; a Chl-a estimation model is established; and an error analysis method. According to the method, two satellite systems of Sentine-3 OLCI and Sentine-2 MSI of the European Spatial Administration are adopted to carry out modeling and verification of Chl-a remote sensing estimation on two lakes, namely the nebula lake and the Fuxian lake which are located in the same region and have great difference in nutrition level, and research of business remote sensing inversion.

The application provides a method and device for constructing and classifying an island and reef bottom classification model, and relates to the technical field of model classification. The method for constructing the island and reef bottom classification model comprises: obtaining a data set of an island and reef area, wherein the data set comprises remote sensing data, water depth data and chlorophyll concentration data which are inversely calculated based on the remote sensing data; based on a preset neural network model, performing feature extraction on the data set respectively, constructing a positive sample pair according to the obtained spectral features and water depth-chlorophyll features of the same preset position in the island and reef area, constructing a whole sample pair according to the obtained spectral features of the selected preset position and the water depth-chlorophyll features of all preset positions in the island and reef area, and constructing a sample feature pair according to the positive sample pair and the whole sample pair. The application can improve the accuracy of island and reef bottom classification under a small amount of label data.

The chlorophyll a prediction method based on spatial heterogeneity perception graph timing confrontation network relates to the technical field of chlorophyll a prediction, and is used for solving the problems that the subjectivity is strong in response to spatial heterogeneity by artificial partition, the partition boundary is not fine enough, and the statistical characteristic difference in the region is large, etc.The reconstructed daily scale chlorophyll a concentration remote sensing data and numerical simulation sea surface temperature data SST are taken as inputs, through spatial heterogeneity partition based on the time evolution behavior of chlorophyll a, graph convolution network GCN spatial coding, time convolution network TCN time coding and regional discriminator constraint, short-term prediction of the spatial distribution of chlorophyll a concentration in the future several days is realized, so that the representation ability of the model to the inhomogeneous change process of chlorophyll a in the complex offshore sea area is improved.