150 results about "Chlorophyll a" patented technology

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 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 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 discloses an apple anthracnose leaf blight recognition five-dimensional data fusion method based on a patrol robot, relates to the technical field of anthracnose leaf blight recognition, and solves the problems of limited sensing dimension, large illumination interference and inaccurate spatial positioning in the prior art. Comprising the following steps: S1, acquiring surface curvature characteristics of apple leaves, collecting RGB-D images of the leaves, constructing a three-dimensional topological structure of the leaves, and positioning scab space distribution; s2, acquiring spectral reflection information of apple leaves, quantifying biochemical components of chlorophyll a and carotenoid, and constructing a vegetation index feature set sensitive to diseases; s3, apple tree canopy temperature information is collected, a transpiration anomaly detection model is constructed, and multi-modal fusion of thermal infrared and point cloud data is realized; and S4, integrating spatial distribution, spectral reflection and thermal radiation information, constructing a five-dimensional phenotypic characteristic matrix model, and comprehensively analyzing apple phenotypic characteristics. According to the method, the recognition accuracy under the conditions of blade shielding, uneven illumination, blade posture change and environment interference can be remarkably improved.

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 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 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.

A method for early warning of algal bloom levels based on an Ordinal Forests model includes the following steps: S1, preprocessing water quality data from a system for online monitoring of water quality and water ecology; S2, determining an algal bloom level according to a chlorophyll a value of the pre-processed water quality data; S3, using a resampling method to solve the problem of imbalanced algal bloom level data, and synthesizing a dataset of balanced algal bloom levels; and S4, taking the newly synthesized dataset in the S3 as an input variable, constructing a model for early warning of algal bloom levels based on the Ordinal Forests model, and performing early warning of algal bloom levels through the trained model for early warning of algal bloom levels.

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 discloses an intelligent algae control method and device based on target recognition, and relates to the technical field of wastewater biological treatment. According to the method, algal bloom severity identification is carried out based on the average concentration of chlorophyll a, algae identification is carried out according to images at different positions, algae identification results are obtained, the algae identification results of the images at different positions are counted, the number of different types of algae in a target algae control water area is obtained, and the algal bloom severity is determined. According to the method, the hydration severity, the number of the different types of algae and the growth rate can be automatically monitored, different algae control schemes are set according to the different hydration severity, the number of the different types of algae and the growth rate, and the algae control efficiency is improved. The functions of all-weather fixed-point water quality monitoring, water quality data analysis and early warning, algae outbreak early growth inhibition and the like of an unmanned ship algae control system are achieved, and efficient targeted algae control is completed.

The invention relates to the technical field of ocean observation, and provides a multi-element sea surface chlorophyll a concentration prediction method based on feature processing. The method comprises the following steps: acquiring historical sea surface chlorophyll a concentration data, historical sea surface temperature data, historical sea surface salinity data and historical ocean current velocity data in a to-be-predicted region for a continuous period of time, inputting the data into different channels of a convolutional network, extracting five channel feature maps, and performing down-sampling processing; inputting the four channel feature maps subjected to down-sampling processing into a first SELayer layer, calculating the weight of each channel feature map to obtain four weighted channel feature maps, obtaining four spatial feature maps by adopting an attention mixing module, obtaining four weighted spatial feature maps by adopting a second SELayer layer, and fusing the feature maps by adopting a convolutional network and up-sampling processing to obtain a new channel feature map; and outputting the sea surface chlorophyll a concentration data in the to-be-predicted region in a future period of time through the full connection layer.

The invention relates to the technical field of lake and reservoir water bloom prediction, in particular to an intelligent lake and reservoir water bloom prediction system and method based on a DTW algorithm. Comprising a data acquisition module, a matrix construction module, a weight distribution module and a display alarm module. Concentration values of chlorophyll a at different positions of a reservoir are collected through a data collection module, the concentration values are arranged according to a time sequence and divided into two parts, a matrix building module is used for calculating the similarity between a comparison sequence Xc and a training data set Xt, a prediction matrix is built with a time step length n as a row vector and a sub-sequence with the similarity m as a column vector, and a prediction result is obtained. According to the model, only chlorophyll a is adopted as monitoring data, the requirement for original data is greatly reduced, the calculation amount of the model is reduced through the DTW algorithm, and the accuracy and robustness of a prediction result are guaranteed by dynamically adjusting the weight between different similarities.

The invention relates to the technical field of marine ecological environment regulation and control, in particular to an offshore wind power driven ecological floating island blue carbon capture and regulation self-adaption method, which comprises the following steps: S1, acquiring the flow velocity gradient, chlorophyll a concentration and dissolved inorganic carbon content of a target sea area, and acquiring torque fluctuation data of a main shaft of a wind power device; s2, calculating a unit area carbon flux threshold value of the floating island; s3, generating a floating island matrix permeability adjusting instruction according to the carbon flux threshold value; s4, triggering a salinity gradient power generation module to generate compensation electric energy; s5, activating a micro-fluidic chip by using the compensation electric energy, and controlling the opening and closing frequency of pores of the mangrove forest; and S6, dynamically correcting the distribution of balancing weights of the floating island, so that the deposition rate of blue carbon is stabilized in a target interval. According to the method, wind power energy prediction, carbon flux calculation and floating island self-adaptive structure regulation and control are integrated in real time, efficient and stable operation of the blue carbon capture process of the floating island in the complex marine environment is achieved, and therefore the carbon fixing efficiency is remarkably improved.

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 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 present application relates to the technical field of water environment treatment and ecological restoration, and discloses a water transparency improving device and method based on an intelligent linkage dosing system, the water transparency improving device based on the intelligent linkage dosing system comprising a water transparency sensor, a suspended substance concentration sensor, a chlorophyll a sensor, a data acquisition and discrimination module, a medicament dosing control module, a medicament storage and dosing module and a medicament spraying module; the water transparency sensor, the suspended substance concentration sensor and the chlorophyll a sensor are arranged in a target water body; the data acquisition and discrimination module is connected with the water transparency sensor, the suspended substance concentration sensor and the chlorophyll a sensor respectively; the medicament dosing control module is connected with the data acquisition and discrimination module and the medicament storage and dosing module respectively; and the medicament storage and dosing module is connected with the medicament spraying module. The present application realizes intelligent and automatic improvement of water transparency.

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 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 present invention relates to the field of microbial technology, and particularly to a Cladophialophora sp. CNOCC PP35 and its application in enhancing the photosynthesis of Paphiopedilum purpuratum. The Cladophialophora sp. CNOCC PP35 provided by the present invention is a strain of dark septate endophytic fungus isolated from the mycorrhizae of adult plants of Paphiopedilum purpuratum, with the preservation number of GDMCC NO: 65862. When co-cultured with the axenic seedlings of Paphiopedilum purpuratum, this strain can effectively promote the formation of chlorophyll a and total chlorophyll in the axenic seedlings of Paphiopedilum purpuratum, increasing by 122.35% and 98.42% respectively compared with the axenic control group, and can enhance the photosynthesis of the axenic seedlings of Paphiopedilum purpuratum; this strain can effectively promote the growth of the seedlings of Paphiopedilum purpuratum, with the fresh weight increasing by 127.78% compared with the control group, can realize the mycorrhization of the axenic seedlings of Paphiopedilum purpuratum, and shorten the artificial seedling raising time.

The invention provides a coral reef area surface layer seawater chlorophyll a concentration inversion method based on a Landsat image, and belongs to the technical field of coral reefs, and the method comprises the following steps: (1) remote sensing image preprocessing; (2) constructing a chlorophyll a inversion empirical model; and (3) evaluating the precision of the inversion model. According to the chlorophyll a concentration inversion method, the monitoring precision and efficiency of the chlorophyll a concentration in the coral reef area are remarkably improved by optimizing the remote sensing image preprocessing process, screening the differentiated wave band combination of the bank reef and the reef and establishing the high-precision inversion model.

This invention presents a method for retrieving chlorophyll a concentration in surface water based on satellite remote sensing. The method acquires remote sensing images from a satellite that match the acquisition time of the water quality data. Based on the spectral information extracted from these images, a new grayscale image is generated. A grayscale co-occurrence matrix (HCEM) is calculated from this new image. The HCEM is then used to mine the texture features contained in the remote sensing images, and each texture feature is quantified. This results in a significantly improved model performance compared to models trained solely using spectral information. Furthermore, with the same amount of raw data, this invention achieves higher accuracy, which is beneficial for the precise measurement of surface water eutrophication.

The present invention belongs to the technical field of water quality remote sensing inversion, and specifically relates to a remote sensing inversion method for chlorophyll-a concentration targeting sparse site data, effectively solving the limitation of insufficient matching quantity between site data and remote sensing data in the remote sensing inversion of chlorophyll-a concentration; increasing the utilization rate of sparse site data and improving the inversion accuracy of chlorophyll-a concentration at the same time. The present invention obtains MSI-MODIS data pairs on the same date, and obtains a sample data set through preprocessing; constructs a spatio-temporal fusion deep residual learning network of MSI and MODIS for chlorophyll-a concentration inversion, so as to obtain time series data with the spatial resolution of the MSI sensor at the required time; on this basis, combined with the corresponding time series site chlorophyll-a concentration observation data, an inversion model is constructed through the machine learning algorithm of Extreme Gradient Boosting (XGBoost), realizing the remote sensing inversion of chlorophyll-a concentration with limited site data.

The application provides a backwater area water bloom prediction method and device, and relates to the technical field of environmental science. The method comprises the following steps: acquiring geographical hydrological data and environmental monitoring data of a target hydrological environment, and constructing a backwater area three-dimensional hydrodynamic water temperature model according to the geographical hydrological data and the environmental monitoring data; constructing a backwater area three-dimensional eutrophication model based on water temperature, water depth, horizontal direction flow velocity and wind speed obtained from the backwater area three-dimensional hydrodynamic water temperature model; constructing a BP neural network model based on the backwater area three-dimensional eutrophication model and training the BP neural network model; and obtaining a water bloom risk prediction result of the backwater area of the target hydrological environment by using the neural network model. The application provides data support for chlorophyll a calculation of the backwater area eutrophication model by constructing the backwater area three-dimensional hydrodynamic water temperature model, without the need to add a large number of high-frequency detection equipment, thereby greatly reducing the model operation time and improving the model prediction efficiency.