137 results about "Remote sensing reflectance" patented technology

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 discloses an uncontrolled multispectral remote sensing water depth inversion method and system, and belongs to the field of shallow sea water depth inversion methods. The method comprises the following steps: firstly, by calculating a depth invariant index of a target area, dividing the target area into a high substrate reflectivity type, a medium substrate reflectivity type and a low substrate reflectivity type; establishing corresponding high, medium and low substrate reflection spectrum sets according to the typical substrate type of the target area; and finally, based on a shallow water radiation transmission model, combining remote sensing reflectivity data of an adjacent optical deep water region, and establishing optimal matching between the simulated remote sensing reflectivity and the actual remote sensing reflectivity by matching a substrate reflection spectrum in a regional pixel-by-pixel manner, so as to obtain a continuous large-area bathymetric map of the target region. The method is suitable for water depth measurement without prior actual measurement of water depth in a multispectral remote sensing scene, solves the problem that a traditional water depth inversion algorithm does not fully consider complex substrate types and spectral variation, and provides a higher-precision method for shallow sea multispectral remote sensing uncontrolled water depth inversion.

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 technical field of water ecological environment remote sensing monitoring, and provides a water body turbidity remote sensing inversion method and system, a medium, equipment and a program. The water body turbidity remote sensing inversion method comprises the following steps: converting actually measured hyperspectral remote sensing reflectivity into satellite equivalent remote sensing reflectivity, and combining each wave band of the satellite equivalent remote sensing reflectivity to obtain a wave band combination; calculating a Pearson correlation coefficient between each single wave band and each wave band combination and the turbidity data, and screening the wave band combination meeting a set threshold value; encoding the wave band combination and the water environment data meeting a set threshold value, respectively obtaining wave band features, time features and water body type features, and constructing feature vectors; and by taking the feature vector as input and the turbidity data as output, constructing an objective function, and training a turbidity remote sensing inversion model, so as to minimize the objective function and select an optimal hyper-parameter combination in each round of iteration. The method can improve the prediction precision and reliability.

The invention relates to the technical field of environment assessment, in particular to a coastal zone culture area multi-dimensional environment assessment method and a cloud platform. The method comprises the following steps: continuously recording dissolved oxygen concentration, pH value, centigrade temperature, salinity unit, turbidity scattering unit, chlorophyll fluorescence intensity, ammonia nitrogen milligram per liter and nitrite milligram per liter, synchronously calling multi-source remote sensing images covering a culture area and an adjacent water area, extracting remote sensing reflectivity data through image correction and atmospheric correction, and calculating to obtain water color parameters. According to the method, the water quality sensor array is deployed at the key point of the culture area, continuous multi-parameter environmental data acquisition is carried out, multi-source remote sensing images are synchronously integrated, water color information is extracted in real time, and the timeliness and data accuracy of culture water area environmental monitoring are improved; trend decomposition and dynamic baseline construction are carried out based on the data sequence, so that the environmental fluctuation evaluation of the breeding area is more objective and accurate.

The invention discloses a 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 an inland water body turbidity satellite remote sensing method based on optical classification and spectral simulation, which comprises the following steps: S1, acquiring water body spectral data, water body turbidity, satellite remote sensing images and meteorological data of a to-be-detected area, and calculating water body remote sensing reflectivity; s2, preprocessing the satellite remote sensing image to obtain a water body area; performing equivalent calculation on the remote sensing reflectivity data of the water body actual measurement spectrum to obtain the remote sensing reflectivity of each channel of the satellite sensor; performing water body optical classification by combining the actually measured water body spectral shape and turbidity distribution characteristics of the research area; s3, performing correlation analysis on different optical types of the actually measured spectrum according to the actually measured turbidity and the remote sensing reflectivity of multiple channels of the satellite, selecting channel remote sensing reflectivity data with high turbidity correlation as a sample, and constructing a turbidity remote sensing model; the turbidity remote sensing model is trained and tested, and the optimal turbidity remote sensing model is screened out. The method can improve the precision of remote sensing inversion of the turbidity of the inland water body.

The invention discloses an inland water body water quality multi-index remote sensing monitoring method based on a knowledge graph. The method comprises the following steps: collecting multi-source remote sensing image data, water body appearance and inherent optical characteristic data and historical water quality monitoring data of a target water body area; analyzing a response relationship between the optical characteristics of the water body and water quality components by using the obtained remote sensing reflectivity-optical characteristic-water quality parameter combined data set, and determining key information elements of remote sensing inversion of each water quality parameter of the water body; and constructing an inland water quality multi-index remote sensing inversion knowledge graph, and realizing time-space continuous monitoring of multiple water quality indexes of the water body by utilizing remote sensing images. According to the method, remote sensing monitoring results are complemented by means of a map reasoning technology, so that the completeness of monitoring data is remarkably improved; the defects of the prior art in the aspects of multi-index comprehensive monitoring and knowledge association expression are overcome, and novel technical means and decision support are provided for lake and river ecological environment management.

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 coastal water quality classification inversion method and system based on multi-source remote sensing data, and the method comprises the steps: a data collection and preprocessing step: carrying out the consistency analysis of remote sensing reflectivity of all wavebands of two satellites, and obtaining similar wavebands and corresponding remote sensing reflectivity; a training sample data set construction step: distributing the matched data points according to water quality classification types to obtain a training set and a test set of each water quality category; a water quality classification model training step: selecting input features of the water quality classification model, and training the water quality classification model; and a water quality classification inversion step: obtaining a to-be-inverted satellite remote sensing image of the target sea area, and carrying out water quality classification inversion on the to-be-inverted satellite remote sensing image by using the trained water quality classification model to obtain a water quality level spatial distribution condition of the target sea area. According to the method, the precision and efficiency of coastal water quality classification remote sensing inversion are improved by balancing the training samples of the water quality classification model and selecting and optimizing the input features.

The invention discloses a vegetation coverage and management measure remote sensing inversion method combining multi-angle remote sensing data and layered vertical vegetation coverage, and the method comprises the following steps: (1), obtaining a multi-angle remote sensing data set, and carrying out the preprocessing of the multi-angle remote sensing data set; (2) selecting an optimal wave band through Borson correlation analysis and XGBoost feature screening; (3) estimating a C factor and a structured vegetation index S-Cs; (4) simulation of leaf reflectivity and transmissivity is carried out, and then vegetation canopy reflectivity is simulated; (5) converting the high spectral reflectivity of the vegetation canopy into equivalent remote sensing reflectivity of a satellite wave band, calculating vegetation indexes, and selecting the vegetation index with the highest correlation to participate in inversion and modeling of a leaf area index LAI; (6) determining an optimal angle combination and evaluating an inversion result of LAI; (7) establishing a novel quantitative coupling relation model between the multi-angle LAI and the SCs and between the SCs and the C factor, and then inverting the C factor; the invention provides a technical means with high practicability for monitoring the dynamic change and expansion process of soil erosion.

The invention provides a consistency correction method and device based on a multi-source satellite inversion water color index, and the method comprises the steps: converting the surface reflectance of a multi-source satellite sensor of a target water area into remote sensing reflectance, and carrying out the water body range extraction of the multi-source satellite sensor, so as to determine the water body range, carrying out multi-class interference pixel removal on the image of the surface reflectance in the water body range to obtain effective remote sensing reflectance; calculating a chromaticity angle inverted by the multi-source satellite sensor based on the effective remote sensing reflectivity and a CIE chromaticity system; performing spectral response correction on the chromaticity angle inverted by the multi-source satellite sensor based on a polynomial spectral correction model constructed by the water optical simulation data set; performing system deviation correction on the chromaticity angle of the multi-source satellite sensor after spectral response correction by a cross calibration correction model established based on the synchronous observation data of the multi-source satellite sensor; and determining the water color index of the target water area based on the chromaticity angle after system deviation correction.

The invention relates to a water color image simulation and evaluation method based on an atmosphere-ocean coupling vector radiation transmission model, and the method comprises the following steps: inputting atmospheric optics and ocean environment parameters obtained based on a target water area into an OSOAA radiation transmission model, and calculating a remote sensing reflectivity spectrum of the target water area; the remote sensing reflectivity spectrum obtained through calculation of the OSOAA model serves as input, and the remote sensing reflectivity spectrum is converted into an RGB value through a CIE color matching function; meanwhile, a remote sensing reflectivity spectrum obtained by the OSOAA model is converted into a Forel-Ule index through an FUME algorithm, and the Forel-Ule index is used for comparing RGB images and evaluating water color characteristics of a research area; all the obtained RGB values are converted into single-point colors, and a visual spatial dimension water color simulation image of the target water area is generated through the geographic position information. According to the method, natural water area water body image simulation is established based on the radiation transfer principle, the water color is evaluated, a scientific basis is provided for prevention and control of eutrophication of the water body, and the method has popularization and application prospects.

The invention discloses a cultivated land surface soil pH value estimation method based on MSI, and relates to the field of satellite remote sensing technology and application thereof, in particular to a cultivated land surface soil pH value estimation method. The invention aims to solve the problems that the existing soil pH value measuring method is limited by the sample size and cost, the timeliness is poor, and the soil pH value distribution is difficult to analyze on the regional or national scale. The method comprises the following steps: (1) acquiring remote sensing reflectivity of different wavebands; (2) taking reflectivity of different wave bands of MSI remote sensing reflectivity, 13 spectral indexes and environment variables as input variables of a random forest regression model; and (3) the output of the random forest regression model is a cultivated land surface soil pH predicted value, and rapid mapping of the cultivated land surface soil pH value is realized. By means of the advantages of low cost, high timeliness, spatial display and non-contact, the spatial distribution diagram of the pH content of the surface soil of the cultivated land in the target area can be obtained only by processing satellite image spectrum data.

The invention provides an atmospheric correction method and device for a solar zenith angle lower coastal water area radiation signal. The method provided by the invention comprises the following steps: generating noon remote sensing reflectivity satellite data based on observation data of a coastal water area by utilizing an ultraviolet-near infrared atmospheric correction combination model; utilizing a maximum cross correlation method to obtain adjusted noon remote sensing reflectivity satellite data based on the spatial resolution and the time resolution of the data; calculating a Rayleigh correction radiance based on a Rayleigh scattering lookup table corresponding to the observation data, and matching the Rayleigh correction radiance with the adjusted noon remote sensing reflectivity satellite data based on time information and position information to obtain a training set; and inputting the to-be-corrected coastal water area radiation signal into the model trained based on the training set, and determining the off-water radiance of the radiation signal. According to the method and the device provided by the invention, atmospheric correction of a coastal water area radiation signal at a high solar zenith angle is realized for a turbid coastal water area.

The invention provides a random forest shallow sea sediment classification method based on multi-temporal remote sensing image fusion, and relates to the technical field of sediment information extraction. Comprising the following steps: 1, collecting and preprocessing multi-temporal image data to obtain remote sensing reflectivity; 2, the water depth of each single-time-phase image is inverted, and the optimal water depth is obtained; 3, calculating bottom reflectivity characteristics of blue and green wave bands based on the optimal remote sensing image; 4, respectively calculating topographic features and spectral features based on the optimal water depth and the optimal remote sensing image; and 5, in combination with the bottom reflectivity features, the topographic features and the spectral features, carrying out random forest feature optimization and classification model training, and generating a substrate classification result. On the basis, the method solves the problems that an existing remote sensing image substrate classification method is insufficient in feature consideration, noise in a single-time-phase image can cause low classification precision, and therefore negative effects can be generated on accurate acquisition of substrate information.

The invention relates to the technical field of water environment remote sensing monitoring, solves the technical problem of systematic overestimation or underestimation under the condition of algae bloom outbreak or strong stratification due to the fact that a water body is regarded as an optical uniform monolayer parameter in a traditional method, and particularly relates to a stratified light field correction chlorophyll-a remote sensing inversion method and system for an eutrophic lake. Performing vertical type identification and three-layer layering by using multispectral / hyperspectral remote sensing reflectivity, a synchronous chlorophyll-a vertical profile and a diffusion attenuation coefficient, calculating light field weight and light path weighted concentration of each layer, constructing a layered light field correction coefficient, performing layered light field correction on the remote sensing reflectivity, and establishing an empirical chlorophyll-a inversion relationship; and generating a chlorophyll-a spatial distribution map and an algae bloom risk map. According to the method, the layered light field correction coefficient with clear physical significance is constructed to correct the water surface remote sensing reflectivity, so that the inversion precision and robustness under strong layering and complex optical conditions are improved.

A method for automatically monitoring dynamic changes of water blooms based on remote sensing images comprises the following steps: step 1, acquiring remote sensing reflectivity data of a multi-source remote sensing satellite in a screened time sequence range, calculating a spectral index, automatically determining an extraction threshold through pixel gradient statistics and an Otsu algorithm, and accurately identifying a water bloom area; and 2, calculating the water bloom area based on the multi-temporal data, researching the seasonal change, the long-term trend and the spatial distribution rule of the water bloom area in combination with a time sequence analysis method, and evaluating the accuracy of water bloom inversion by comparing the actually measured chlorophyll-a concentration with a water bloom inversion result. The application can monitor the change of the algal bloom area in real time.

The invention provides a shallow sea multiband substrate reflectivity remote sensing detection method and a shallow sea multiband substrate reflectivity remote sensing detection system. According to the method, four-band multispectral data and ICESat-2 laser sounding data are combined, a strategy of selecting feature points and iteratively searching optical parameters of a water body is adopted, and an index relationship between semi-analysis substrate reflectivity and spectral parameters is utilized to globally constrain a radiation transmission model; a quadratic polynomial model between the chlorophyll concentration of the feature points and the remote sensing reflectivity of the blue and green wave bands is constructed, and large-range rapid inversion of the optimal water attenuation coefficient is achieved; and further combining with a surface remote sensing reflectivity product to construct a climate state monthly average deepwater reflectivity data set, thereby realizing large-range efficient detection of optical shallow sea multiband substrate reflectivity without actually measured data support. According to the method, the application efficiency of the four-waveband multispectral data with the most abundant historical data is improved, the method is high in practicability, and the method has great significance in protection and management of shallow sea coral reef benthic ecology.

The invention discloses a multi-source remote sensing reflectivity fusion method based on orthogonal matching pursuit and physical constraint, and the method comprises the steps: taking a multi-source remote sensing image as a data basis, and constructing a multiband sparse dictionary through time sequence alignment and overlapping sampling; then, an orthogonal matching pursuit algorithm introducing physical constraints is utilized to gradually select an image atom which is most matched with a target date in a sparse reconstruction process, and physical consistency of a reconstruction result in the aspects of hydrological conditions, phenological characteristics, spectral reflectivity and the like is ensured; and finally, through residual error return correction, day-by-day seamless 30-meter six-waveband reflectivity reconstruction is realized. According to the method, the stability and the physical interpretability of a reconstruction result are remarkably improved while efficient calculation is kept, continuous, multi-band and high-resolution time sequence data generation can be achieved in complex ecological environments such as lake wetlands, and high-precision remote sensing data support is provided for hydrological process simulation, ecological environment evaluation and carbon cycle research.

The invention belongs to the technical field of water quality parameter inversion, and particularly relates to a machine learning-based suspended matter concentration remote sensing inversion method and system, and the method comprises the steps: collecting a satellite remote sensing image and synchronous actual measurement suspended matter concentration data, and obtaining a space gridding water body remote sensing reflectivity matrix through radiometric calibration, atmospheric correction and water body mask. Reconstructing and denoising through wavelet decomposition, and normalizing and standardizing the spectrum to obtain a spectrum numerical sequence; multi-scale waveband combination and differential features are constructed based on the sequence, and sensitive features are screened out by using XGBoost. A physical constraint term is constructed in combination with sensitive characteristics and a water body radiation transmission rule, and an intermediate inversion result is obtained through numerical iteration. And performing deviation compensation on an intermediate result by using a deep learning residual error, and finally performing spatial smoothing, consistency verification and high-concentration saturation optimization to obtain a high-precision and spatially continuous suspended matter concentration spatial distribution result. According to the method, efficient feature mining and physical mechanism deep fusion are realized, and the explanatory and generalization ability of the model is greatly improved.

The invention relates to the technical field of water quality detection, and further relates to a remote sensing water quality pollution detection method based on a super-resolution hybrid GAN, and the method comprises the steps: 1, inputting a remote sensing reflectivity image into a super-resolution hybrid generative adversarial network, and carrying out the collaborative reconstruction through a generator and a discriminator, so as to obtain a high-resolution remote sensing reflectivity image; step 2, carrying out weighted summation on the high-resolution remote sensing reflectivity image according to wave bands by utilizing the wave band pollutant absorption sensitivity coefficient calibrated by an experiment to obtain an original concentration estimation value of each pollutant; 3, obtaining the final concentration of each pollutant based on the confrontation consistency loss of the super-resolution hybrid generative adversarial network in combination with the maximum absorption peak wavelength of the pollutant; and 4, generating a comprehensive water quality pollution index. According to the method, the targets of high precision, high reliability and high adaptability of pollution detection under the driving of the remote sensing image are effectively realized.

The invention relates to the technical field of intelligent data identification, in particular to a wetland ecological drought condition identification method based on multi-source data. According to the technical scheme, the method comprises the following steps: data acquisition: acquiring a multi-source data set of a target wetland region in a preset time sequence; the multi-source data set at least comprises a remote sensing reflectivity data set, a surface temperature data set, a meteorological reanalysis data set and an on-site monitoring hydrological data set; and data processing: sequentially carrying out space-time registration, denoising and missing value interpolation processing on the multi-source data set to generate a standardized space-time data cube. According to the method, multi-source data can be integrated to generate a standardized spatio-temporal data cube, each ecological drought index threshold value is dynamically determined based on the background of the historical wet season of the wetland, the comprehensive drought intensity is calculated in a weighted mode according to the wetland type, and accurate wetland ecological drought spatio-temporal distribution, grade sequence and early warning information are output after multi-scale verification. And a basis is provided for wetland ecological protection and water resource regulation and control.

The invention belongs to the field of satellite remote sensing application, and particularly relates to a rapid mapping method for soil organic matter distribution, in particular to a rapid mapping method for 16m-resolution soil organic matter distribution of a Gaofen-1 satellite. The invention aims to solve the problems that the soil fertility condition of farmland cultivated land is reduced and the cultivated land quality is degraded. The method comprises the following steps: acquiring different wave band remote sensing reflectivity of a Gaofen-1 satellite WFV sensor in a target research area; remote sensing reflectivity and remote sensing reflectivity wave band combinations of different wave bands of WFV sensor images are used as input variables of a random forest algorithm, output variables are cultivated land surface soil organic matters (mg / g), and cultivated land surface soil pH mapping is achieved. Based on a domestic Gaofen-1 satellite WFV sensor, the method provided by the invention can realize dynamic monitoring of the cultivated land surface soil organic matters, and actual measurement sample point verification finds that the method provided by the invention can accurately estimate the cultivated land surface soil organic matters, and is beneficial to management in aspects of soil fertility condition monitoring, precision agriculture and land sustainable utilization.

The invention relates to the technical field of ocean remote sensing, in particular to an offshore water quality remote sensing inversion and classification method based on small sample deep learning, which comprises the following steps: acquiring offshore in-situ observation, satellite remote sensing and ocean reanalysis data; carrying out atmospheric correction, carrying out space-time reconstruction on the missing remote sensing reflectivity by adopting a data interpolation empirical orthogonal function, carrying out mathematical transformation on input features, and screening out an optimal feature subset by adopting a strategy based on an average absolute percentage error; constructing a deep learning network based on a generative small sample to invert the offshore soluble inorganic nitrogen and inorganic phosphorus concentration, and determining the water quality grade; and analyzing an inversion mechanism by using an SHAP method. According to the method, the problem of data space-time discontinuity is solved through the data interpolation empirical orthogonal function, the capture capability and inversion precision of the nonlinear relation under the small sample condition are remarkably improved by utilizing the generative network, the model interpretability is realized in combination with SHAP analysis, and scientific support is provided for offshore water quality fine management.

The invention relates to a tea tree chlorophyll content inversion method based on hyperspectral remote sensing. The method comprises the following steps: S1, acquiring remote sensing reflectivity data of tea tree leaves in a hyperspectral band; s2, performing spectral derivative processing on the hyperspectral reflectivity data obtained in the S1, and extracting enhanced spectral feature information; s3, selecting at least one characteristic wave band or wave band combination related to the chlorophyll content of the tea tree from the spectral derivative data; s4, establishing an inversion model between the chlorophyll content and the characteristic wave band reflectivity value; and S5, calculating the chlorophyll content of the target tea tree based on the inversion model and the hyperspectral remote sensing data. According to the method, rapid, lossless and high-precision monitoring of the chlorophyll content of the tea tree is realized, and the chlorophyll inversion precision and reliability are improved. The method has the advantages of high monitoring precision, abundant information amount, high model adaptability and the like, and can be widely applied to the fields of tea garden growth monitoring, nutrient diagnosis, precise fertilization, tea tree physiological research and the like.

The invention discloses a global chlorophyll a remote sensing inversion optimization method and device covering a high latitude area, and the method comprises the steps: carrying out the space-time matching of sea surface temperature based on an actual measurement data set of the actual measurement remote sensing reflectivity and the actual measurement chlorophyll a concentration, and carrying out the integrated construction of a training data set, the measured remote sensing reflectivity (412 nm, 443 nm, 490 nm, 555 nm and 670 nm), the sea surface temperature, the longitude, the latitude and the measured chlorophyll a concentration of five wave bands are included; a BP neural network is applied, a global chlorophyll a remote sensing inversion model covering a high latitude area is constructed, the training data set is adopted for training, an initial weight and a threshold value of the BP neural network are obtained through iterative solution optimization of a genetic algorithm, and a trained global chlorophyll a remote sensing inversion model is obtained; and inputting the satellite remote sensing reflectivity to be inverted and the matched sea surface temperature, longitude and latitude into the trained global chlorophyll a remote sensing inversion model, and performing inversion to obtain the chlorophyll a concentration. The method can effectively improve the global remote sensing inversion precision of chlorophyll a.

The invention relates to a water quality remote sensing inversion method based on an interpretable hybrid physical guidance neural network (HPGNN) model. The method comprises the following steps: collecting geotagged in-situ water quality parameter concentration with global representativeness and 1nm hyperspectral remote sensing reflectivity subjected to quality assurance and radiation correction from global lakes; converting the reflectance into equivalent reflectance of a sensor wave band by utilizing a spectral response function of a remote sensor; extracting space-time matched water surface remote sensing reflectivity by utilizing GEE; defining a mixed self-defined loss function fusing data driving, regularization and physical loss; and aiming at each water quality index, developing and training an interpretable high-precision spectrogram neural network model HPGNN using the converted reflectivity and the mixed self-defined loss function. Based on global representative data, the accurate water quality evaluation method is developed by mixing the self-defined loss function, short-term and long-term space-time dynamic analysis is achieved, an effective technical means is provided for water pollution prevention and control, and the method has wide application and popularization value.

The invention relates to the technical field of water quality parameter remote sensing inversion, and discloses a simple method suitable for high-turbidity estuary particulate organic carbon concentration remote sensing inversion, which comprises the following steps: collecting and preprocessing suspended particulate matter (SPM) concentration, particulate organic carbon (POC) concentration data and corresponding remote sensing reflectivity data of a water body; according to a wave spectrum response function of each wave band of a common optical satellite sensor, the equivalent remote sensing reflectivity Rrs (lambda i) of each wave band is obtained through simulation, and by comparing the correlation between the remote sensing reflectivity Rrs (lambda i) of the visible light-near infrared wave band and the actually measured suspended particulate matter (SPM) concentration, the POC inversion model constructed by the method adopts a subsection modeling strategy, so that the concentration of the SPM can be calculated. The concentration of the suspended particulate matters with strong correlation is preferably selected as a correlation parameter, so that a relatively large error generated by an existing multi-parameter model is effectively avoided, and the precision of the inversion of the concentration of the particulate organic carbon in the high-turbidity estuary region is remarkably improved.