82 results about "Remote sensing reflectance" patented technology

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 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 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 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 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.

The present invention discloses a remote sensing water quality inversion method combining a differential learning rate with spectral geometric characteristics, comprising: collecting satellite images of various sites and obtaining remote sensing reflectance of each site; deriving surface water monitoring site information and water quality index information data from a surface water database; eliminating remote sensing reflectance of sites with obvious abnormalities and constructing a remote sensing reflectance curve set; eliminating abnormal values ​​of water quality indicators; calculating spectral geometric feature data of the remote sensing reflectance curves of each site, merging the feature data into a feature matrix, and dividing the feature matrix into a training set and a test set; merging the water quality indicators after eliminating abnormal water quality index values ​​as a data set to be fitted into an output set, and dividing the output set into a training output set and a test output set; constructing a machine learning model, inputting the training set into the model for training to obtain a trained model; putting the test set into the trained model for testing, and after evaluation, deploying the optimal model online.

The application discloses a kind of based on satellite fusion's near-shore sea area nutrient salt concentration remote sensing inversion method, comprising the following steps: S1, the original L1C data of the optical image of high spatial resolution satellite and low spatial resolution satellite are respectively carried out Rayleigh correction, obtain after Rayleigh correction remote sensing reflectance;S2, cloud and land pixel in after Rayleigh correction remote sensing reflectance data are carried out mask processing;S3, based on low space respectively satellite data constructs nutrient salt training dataset;S4, constructs cross-satellite fusion training dataset;S5, establish AutoGluon-DIN machine learning model and AutoGluon-DIP machine learning model based on low resolution satellite, and model training is carried out;S6, establish AutoGluon-transfer machine learning model of fusion high resolution and low resolution satellite, and model training is carried out;S7, sequentially apply AutoGluon-transfer machine learning, AutoGluon-DIN machine learning model, AutoGluon-DIP machine learning model after training to high resolution satellite, obtain high spatial resolution's near-shore sea area nutrient salt concentration remote sensing inversion product.

This invention discloses a method, system, and equipment for monitoring the water environment of drinking water sources, belonging to the field of environmental monitoring technology. The method includes acquiring remote sensing image data, water quality monitoring data, and water spectral data; preprocessing the remote sensing image data to obtain the water surface remote sensing reflectance data at each sampling point in the target water area; preprocessing the water quality monitoring data and water spectral data to obtain sensitive bands; constructing a water quality parameter inversion model, which includes an XGBoost learner, an SVM learner, a RF learner, and a BPNN learner; training the water quality parameter inversion model using a training dataset and validating it using a test dataset; and inputting the water surface remote sensing reflectance data and the sensitive bands into the water quality parameter inversion model to obtain water environment monitoring data. This invention solves the technical problem of integrated air-space-ground drinking water source water environment monitoring.

This invention relates to the field of forestry resource monitoring technology, and discloses a dynamic monitoring system and method for forestry resources based on remote sensing imagery. The system includes: generating temporal remote sensing reflectance data within the same grid; generating a continuous canopy occupancy map; generating an infill set; determining the intrinsic neck scale; calculating the topological spectrum intensity of the continuous canopy infill; calculating the interlayer decoupling topological index; generating monitoring judgment results; and outputting change patches. This invention constructs an adaptive multi-scale erosion sequence based on the intrinsic neck scale, combines Eulerian features to quantify the infill topological fragmentation process, and integrates the degree of infill fragmentation with the stability state of the outer envelope through the interlayer decoupling topological index. This achieves accurate determination of change units and precise spatial patch location, avoiding the omissions or misjudgments of deep forest understory structure changes by traditional techniques, ensuring that the monitoring results accurately reflect the actual mechanisms of dynamic changes in forest resources.

This invention discloses a method for retrieving global ocean optical attenuation coefficients based on remote sensing reflectance. It obtains the remote sensing reflectance at any wavelength below the sea surface and the ratios related to inherent optical quantities using satellite-provided remote sensing data. For clear seawater and relatively clear coastal seawater, the QAA_v5 algorithm is used to obtain the absorption and backscattering coefficients at any wavelength; for turbid coastal seawater, the QAA-RGR algorithm is used. Different models for solving the scattering coefficients are used for different sea areas. The global ocean optical attenuation coefficient is obtained by adding the absorption and scattering coefficients. This invention divides the global ocean into regions and provides models for solving the absorption and scattering coefficients for different regions. It can directly calculate the optical attenuation characteristics of different sea areas using satellite-provided data, improving the reliability of solving the optical attenuation coefficient and enabling dynamic monitoring of the variation of the global ocean optical attenuation coefficient.

The invention discloses a water body classification and chlorophyll a concentration segmented inversion method based on a dynamic threshold value, which comprises the following steps of: constructing a water body nutrition state classification system by using a ratio index and an improved floating algae index (AFAI) according to the difference of remote sensing reflectivity of spectra of different types of water bodies in different wavebands, and constructing a regression model based on measured data, and the original static classification threshold is dynamic, so that self-adaptive adjustment of water body classification is realized. And finally, respectively constructing Chla inversion algorithms on the oligotrophic water body and the moderate algal bloom water body. Compared with a traditional fixed threshold value classification method, the dynamic threshold value can automatically adjust the classification boundary according to the environmental change, and the stability of the model under the seasonal change is enhanced. Meanwhile, compared with a single inversion algorithm, the segmented inversion method can significantly improve the estimation accuracy of Chla, and effectively improves the adaptability of an inversion model to water bodies in different nutritional states.

The invention relates to the technical field of forestry resource monitoring, and discloses a forestry resource dynamic monitoring system and method based on remote sensing images, and the method comprises the steps: generating same-grid time phase remote sensing reflectivity data; generating a continuous canopy occupancy map; generating an inner hole set; measuring the scale of the intrinsic neck; the continuous canopy inner hole topology spectrum intensity is calculated; calculating an inter-layer decoupling topology index; generating a monitoring judgment result; and outputting the change pattern spots. According to the method, a self-adaptive multi-scale erosion sequence is constructed on the basis of the intrinsic neck scale, the Euler feature is combined to quantify the inner hole topology fragmentation process, the inner hole fragmentation degree and the outer envelope stable state are integrated through the interlayer decoupling topology index, change unit judgment and space pattern spot accurate positioning are achieved, and the method has the advantages of being simple in structure and convenient to operate. And missing detection or misjudgment of the change of the deep structure under the forest in the traditional technology is avoided, so that the monitoring result conforms to the actual mechanism of the dynamic change of forest resources.

The application discloses a global chlorophyll-a remote sensing inversion optimization method and device covering high latitude areas, and comprises the following steps: performing spatio-temporal matching on sea surface temperature based on a measured data set of measured remote sensing reflectivity and measured chlorophyll-a concentration, and integrating and constructing a training data set, which contains five bands of measured remote sensing reflectivity (412, 443, 490, 555 and 670 nm), sea surface temperature, longitude, latitude and measured chlorophyll-a concentration; applying a BP neural network to construct a global chlorophyll-a remote sensing inversion model covering high latitude areas and training the model by using the training data set, the initial weight and threshold of the BP neural network are obtained by iterative optimization of a genetic algorithm, and a trained global chlorophyll-a remote sensing inversion model is obtained; and inputting satellite remote sensing reflectivity to be inverted, matched sea surface temperature, longitude and latitude into the trained global chlorophyll-a remote sensing inversion model, and inverting to obtain chlorophyll-a concentration. The application can effectively improve the global remote sensing inversion precision of chlorophyll-a.

The present application relates to the field of water total phosphorus integrated intelligent inversion, in particular to a water total phosphorus integrated intelligent inversion method based on feature layer screening.The present application performs principal component analysis on Sentinel 2B satellite image data, filters out five feature layers through the thought of dimension reduction, extracts five groups of principal component data at the positions of the sampling points, performs correlation analysis on the single principal component and the multiple principal component combination and the measured total phosphorus concentration, and performs correlation analysis on the measured total phosphorus concentration and the remote sensing reflectivity of each band of the image.The principal component analysis method reduces and deletes the original redundant data, so that the selected principal components are not correlated with each other, can greatly represent the original variable data, and improves the efficiency of model establishment.

A kind of coral reef bottom reflectivity multispectral satellite remote sensing retrieval method belongs to satellite ocean remote sensing application technical field.Based on shallow sea single scattering radiation transfer model, through the collection of adjacent pixel pairs of different depth and different bottom type and the pixel of same bottom type (such as sandy seabed) of different depth, the optimal band rotation coefficient and the ratio of blue-green band diffuse attenuation coefficient are obtained respectively, combined with the optical deep water remote sensing reflectance data of the adjacent shallow sea area, the depth-independent reflectance equation and the bottom type-independent reflectance equation are constructed respectively, and based on the two equations, the coral reef bottom reflectivity image is obtained by joint calculation.It is suitable for high-resolution multispectral satellite remote sensing image, and auxiliary data such as water depth and water property are not required, and the blue-green band reflectivity data of coral reef bottom can be directly calculated and obtained.

This invention belongs to the field of environmental monitoring technology and discloses a remote sensing inversion method for dissolved inorganic nitrogen and silicate in estuaries based on salinity synergy. The method includes the following steps: S1. During a field survey in the land-sea interaction zone of the estuary, remote sensing reflectance, salinity, and nutrient data are collected simultaneously. The measured hyperspectral reflectance is simulated as the equivalent reflectance of the satellite band using the spectral response function of the target satellite, which is used to construct the training and validation datasets for the model. S2. A nonlinear inversion model of remote sensing reflectance and salinity is established, and a nutrient mixture model of salinity and nutrients is constructed. The training dataset is used for model training. S3. The satellite remote sensing reflectance data of the estuarine area to be predicted is input into the trained nonlinear inversion model and combined with the nutrient mixture model to generate the spatial distribution of DIN concentration and DSi concentration in the estuarine area. This method achieves high-precision remote sensing inversion prediction of DIN concentration and dissolved silicate DSi concentration in the estuarine area.

The invention provides a water depth inversion method based on spectrum and geospatial information, which relates to the technical field of water depth inversion and comprises the following steps: acquiring a satellite high-resolution spectral image of a target position, and preprocessing the satellite high-resolution spectral image; extracting remote sensing reflectivity data from the preprocessed satellite high-resolution spectral image; constructing a prediction data set based on the remote sensing reflectivity data and the geographic space information of the target position; and inputting the prediction data set into the random forest regression model for processing to obtain water depth data of the target position. According to the method, the geospatial information and the spectral data are combined, so that the model can capture the spatial mode of the water depth instead of only depending on the color or transparency of water, and therefore, the method provided by the invention can obtain better water depth surveying and mapping accuracy in a turbid and complex watershed environment compared with the prior art; and reliable data support is provided for hydrology, landform and river management research.

The present application relates to the technical field of water quality detection, and more particularly to a remote sensing water quality pollution detection method based on a super-resolution hybrid GAN, which comprises the following steps: step 1: inputting a remote sensing reflectivity image into a super-resolution hybrid generative adversarial network, and obtaining a high-resolution remote sensing reflectivity image through the cooperation of a generator and a discriminator; step 2: using an experimentally calibrated band pollutant absorption sensitivity coefficient to perform weighted summation on the high-resolution remote sensing reflectivity image according to bands, and obtaining original estimated values of the concentrations of various pollutants; step 3: obtaining the final concentrations of various pollutants based on the adversarial consistency loss of the super-resolution hybrid generative adversarial network and in combination with the maximum absorption peak wavelength of the pollutants; and step 4: generating a comprehensive water quality pollution index. The present application effectively realizes the high-precision, high-credibility and high-adaptability target of pollution detection driven by remote sensing images.