254 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.

A method for inversing a depth of a subsurface chlorophyll-a maxima (SCM) of an oceanic water body based on remote sensing reflectance is provided. The method includes: band selection, R value calculation, data collection, model construction, and ZSCM calculation. This method can not only reduce the dependence of machine learning on sea surface chlorophyll-a concentrations, sea surface temperature data and sea surface height data, and reduce the calculation resource consumption of running machine learning, but also effectively improve the inversion accuracy, and effectively reduce the calculation resource consumption and inversion difficulty of inversing the SCM.

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 relates to a river water ecological space global monitoring method and system, and the method comprises the steps: recognizing and segmenting a water body image from an RGB image photographed by an unmanned plane based on a U-Net deep learning model, and forming a water body recognition image; converting and reconstructing the RGB image aerially photographed by the unmanned aerial vehicle into a water body hyperspectral image based on a hyperspectral information continuation reconstruction model; the U-Net deep learning model extracts a multispectral segment sequence of pixel points of a water body in the water body hyperspectral image and sends the multispectral segment sequence to the classification regression model; and calculating the multispectral segment sequence based on a classification regression model so as to label a concentration classification result of chlorophyll a in a water body hyperspectral image, thereby realizing visualization of chlorophyll concentration. The method gets rid of limitation of technical means and time acquisition of hyperspectral data acquisition, and realizes the effect of acquiring river global images at any time and at any moment.

The invention relates to the technical field of water ecological environment remote sensing monitoring, and discloses a vertical pre-classification and deep learning-based total algae amount remote sensing estimation method and system, and the method comprises the steps: obtaining a chlorophyll a concentration inversion result of a water surface layer; obtaining a chlorophyll a profile distribution type based on the chlorophyll a concentration inversion result of the water body surface layer and the water environment information; and based on the chlorophyll a profile distribution type and the surface chlorophyll a concentration inversion result, performing vertical multi-layer continuous chlorophyll a concentration and total algae amount inversion to obtain an estimation result of the total algae amount. According to the method, the advantages of floating algae profile monitoring and a deep learning algorithm are fused, and stable and high-precision total algae amount estimation can be carried out in a water body which is greatly influenced by a terrestrial source and has complex vertical distribution and optical characteristics of floating algae. According to the method, more effective technical support is provided for remote sensing estimation research of the total amount of algae, occurrence of harmful algae blooms is found and warned in time, and the influence of algae bloom disasters is reduced.

The invention provides a machine learning-based marine chlorophyll concentration inversion method, which comprises the following steps of: clustering remote sensing reflectivity data to obtain clusters corresponding to different water body types, and applying training sets to various machine learning models for training according to the water body types to obtain a plurality of machine learning models; and performing performance verification on different optimization machine models under different water body types by using the verification set to obtain optimal machine learning models under different water body types, and finally forming a chlorophyll concentration dynamic inversion model. And carrying out chlorophyll concentration inversion by adopting the optimal machine learning model of the corresponding water body type, so that the model has a wide application scene. The chlorophyll concentration dynamic inversion model provided by the invention has very strong universality, and the global sea area water color information monitoring capability is effectively improved.

The invention relates to the field of chlorophyll a concentration prediction, and discloses a 1D-DSCAM-based subsurface chlorophyll a concentration inversion method, which comprises the following steps of: obtaining sea surface temperature and sea surface chlorophyll a concentration data of satellite remote sensing and subsurface chlorophyll a concentration, subsurface temperature and salinity profile data of a biogeochemical buoy; carrying out preprocessing, constructing a data set, and inputting a training set into a 1D-DSCAM-GRU deep learning model to carry out model training; the model comprises an Embedding module, a 1D-DSCAM module and a multi-layer GRU module; and preprocessing sea surface temperature and sea surface chlorophyll a concentration data obtained by satellite remote sensing of a sea area to be predicted and subsurface layer temperature and salinity profile data obtained by a biogeochemical buoy, and inputting the data into the qualified model for subsurface layer chlorophyll a concentration prediction. According to the method disclosed by the invention, the prediction precision can be 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.

A photon counting laser radar-based open water chlorophyll a concentration profile detection method is used for accurately acquiring profile concentration distribution of chlorophyll a in a water body. According to the method, ICESat-2 photon counting laser radar data and MODIS satellite remote sensing images are combined, a photon propagation process is modeled by using a Monte Carlo simulation technology, energy attenuation and scattering characteristics of photons in a water body are calculated, an extinction coefficient and a backscattering coefficient are further derived, and chlorophyll a concentration distribution is inversed according to the parameters. High-precision water chlorophyll a concentration profile data can be provided by accurately simulating the propagation process of photons in a water body and considering the optical characteristics and environmental factors of the water body. MODIS remote sensing data is used for assistance, the accuracy and reliability of an inversion result are further improved, and accurate chlorophyll concentration detection can be achieved in different open water body areas. The method is suitable for monitoring the chlorophyll concentration of water bodies such as oceans, lakes and rivers, and provides a scientific basis for water body ecological environment evaluation.

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 invention discloses a river and lake chlorophyll a concentration remote sensing monitoring and early warning method, which comprises the following steps: carrying out time sequence remote sensing data radiation normalization correction processing by utilizing multi-source sentinel No.2 and Landsat series remote sensing data; constructing a large-range and long-time-sequence river and lake chlorophyll a remote sensing sample library by combining water quality monitoring data continuously observed on the ground and a chlorophyll a characteristic wave band; further combining with a random forest algorithm to construct a river and lake chlorophyll a intelligent inversion model fused with convolutional features, and combining with a GEE satellite remote sensing data platform to realize large-range chlorophyll a remote sensing product generation; a time sequence chlorophyll a remote sensing data product is further combined, a water quality monitoring station is used as a truth value constraint, a long short-term memory network model with an improved step length is adopted, a river and lake chlorophyll a prediction product generation model is constructed, and river and lake water quality risk dynamic early warning is achieved.

The invention relates to a water bloom early warning method based on water body chlorophyll a concentration and algae activity, which comprises the following steps: collecting and processing algae growth data of a target area water body to obtain the chlorophyll a concentration and algae photosynthetic activity index data of the target area water body; performing algal bloom risk grading according to the chlorophyll a concentration of the water body in the target area and the algae photosynthetic activity index data; analyzing each water bloom risk level weight based on a historical water bloom occurrence condition in a target time period corresponding to the target area; constructing a data set based on historical water quality data, historical meteorological data and each water bloom risk grade weight in a target time period corresponding to the target area; training an artificial neural network by using the data set to obtain a water bloom early warning data model; and performing target area water bloom early warning by using the water bloom early warning data model. According to the invention, the water bloom early warning accuracy can be improved, and the water bloom early warning in a real sense is realized.

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.