52 results about "Remote sensing reflectance" patented technology

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.

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 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 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 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 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 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.

This invention discloses a method for assessing the impact of reservoir construction on river deoxygenation based on satellite remote sensing. The method includes the following steps: constructing a synchronous observation database of river dissolved oxygen and satellite remote sensing reflectance; training a random forest model and an extreme gradient boosting tree model based on input and output data indicators, and fusing them to construct an integrated inversion model for dissolved oxygen concentration; obtaining publicly available river widths and extracting long-term remote sensing reflectance spectral data located along the river's median line; obtaining engineering characteristic parameters of the target reservoir, driving the integrated inversion model for dissolved oxygen concentration, and reconstructing a long-term dataset of river dissolved oxygen concentration; and calculating the trend of dissolved oxygen concentration changes in two time periods, using the construction time of the target reservoir as a dividing point. This invention uses satellite remote sensing to assess the intensity of the impact of reservoir construction on river deoxygenation processes, providing a reliable and scalable assessment method for quantifying the disturbance and alteration of river ecosystems by human activities.

The invention relates to the technical field of lake eutrophication monitoring, in particular to a lake pigment concentration remote sensing estimation method, which comprises the following steps: firstly, acquiring remote sensing reflectivity data of a target water body, extracting reflectivity values of at least four characteristic wavebands 665nm, 710nm, 620nm and 750nm related to pigment absorption and fluorescence characteristics, and estimating water body absorption coefficients of at least two pigment sensitive wavebands; on the basis, a four-waveband combination index UMEP is constructed and is transformed according to a water body radiation transmission biological optical model, and the influence of environmental parameters is eliminated. By introducing a dynamic decomposition factor beta and an absorption coefficient difference epsilon, a mixed pigment absorption signal is decomposed into independent contributions of chlorophyll a and phycocyanin, and finally the concentrations of chlorophyll a and phycocyanin are calculated by using respective specific absorption coefficients. The method is clear in physical mechanism, can adapt to changes of optical characteristics of the water body, can be directly applied to multi-source satellite remote sensing images, and achieves synchronous and high-precision business inversion of the concentrations of the two key pigments in the lake water body in a large range.

The application discloses a neural network-based atmospheric correction algorithm optimization method, which comprises the following steps: obtaining m measured spectrum data of a specific sea area and obtaining matched j effective satellite spectrum data, scoring the j effective satellite spectrum data and scoring the m measured spectrum data to obtain m+j effective training sample numbers; training a neural network; applying selected n atmospheric correction algorithms to perform pixel-by-pixel preprocessing on satellite data of the specific sea area, obtaining n spectrum data for each pixel point, inputting the spectrum data into the trained neural network to obtain corresponding scores, averaging scores of all pixel points, and the algorithm with the maximum score is the optimal algorithm. The method disclosed by the application can quickly judge the adaptability of various atmospheric correction algorithms in a specific sea area, accurately select the most suitable algorithm, and thus improve the precision of remote sensing reflectance R rs data and indirectly improve the precision of water color information inversion results.

The present application is to solve the technical problem that most of the existing total suspended particulate matter remote sensing inversion algorithm is constructed on the basis of 550 nm remote sensing reflectance, and the remote sensing reflectance appears double peak characteristics with the increase of total suspended particulate matter concentration, which makes the expression of total suspended particulate matter concentration change of high turbidity water body gradually ineffective, and provides a turbid water total suspended particulate matter inversion method suitable for stationary orbit satellite GOCI. The inversion method takes the in-situ observed remote sensing reflectance and the equivalent remote sensing reflectance calculated by the satellite spectral response function as the input, determines the absorption peak and the two shoulder band positions of the non-absorption baseline of the spectral absorption characteristic curve through the correlation coefficient between the equivalent remote sensing reflectance, the first derivative, the second derivative and the total suspended particulate matter concentration, fits the linear relationship between the spectral absorption index and the total suspended particulate matter based on the spectral absorption index, and finally establishes the remote sensing inversion algorithm of the total suspended particulate matter suitable for the stationary orbit water color satellite of the high turbidity water body.

This invention discloses a method for acquiring remote sensing reflectance data based on anisotropic correction. The method includes acquiring the remote sensing reflectance of a water body and its corresponding relative azimuth, observation angle, and solar zenith angle; determining the type of water body based on its remote sensing reflectance; considering water body classification and establishing correction coefficient lookup tables for the spectral characteristics and optical properties of different water bodies; matching the correction coefficient at the current angle from the corresponding self-established lookup table based on the relative azimuth, observation angle, solar zenith angle, and water body type; calculating the remote sensing reflectance at the target angle based on the correction coefficient at the target angle in the lookup table and the inherent optical characteristic parameters based on the QAA algorithm. This method has good applicability to various types of water bodies and can overcome the limitation of commonly used fixed observation angles when acquiring remote sensing reflectance using an angle-adjustable floating optical device.

The invention provides a plateau lake phycocyanin concentration remote sensing inversion method, which comprises the following steps: respectively obtaining plateau lake phycocyanin concentration and plateau lake actually measured remote sensing reflectivity data: carrying out atmospheric correction on plateau lake satellite reflectivity data to obtain remote sensing reflectivity data; the precision of atmospheric correction is verified by using the actually measured remote sensing reflectivity; according to the inherent optical characteristics of phycocyanin, an enhanced three-band algorithm of phycocyanin concentration inversion is constructed through a bio-optical model and an empirical formula; substituting the actually measured phycocyanin concentration and the satellite remote sensing reflectivity into an enhanced three-band algorithm for linear regression, and establishing a phycocyanin concentration inversion model; the model is applied to satellite data, and remote sensing inversion of the phycocyanin concentration of the plateau lake is achieved. According to the method, interference of non-phycocyanin pigments on phycocyanin pigment signals can be reduced, and the phycocyanin inversion precision is further improved.

This invention belongs to the field of water quality parameter inversion technology, specifically a machine learning-based remote sensing inversion method and system for suspended particulate matter (SPM) concentration. The method includes: acquiring satellite remote sensing imagery and synchronously measured SPM concentration data; obtaining a spatially gridded water body remote sensing reflectance matrix through radiometric calibration, atmospheric correction, and water body masking; denoising through wavelet decomposition reconstruction; and obtaining a spectral numerical sequence through spectral normalization and standardization. Based on this sequence, a multi-scale band combination and differential features are constructed, and sensitive features are selected using XGBoost. Physical constraints are constructed by combining sensitive features with water body radiative transfer laws, and intermediate inversion results are obtained through numerical iteration. Deep learning residuals are used to compensate for biases in the intermediate results, and finally, through spatial smoothing, consistency verification, and high-concentration saturation optimization, a high-precision, spatially continuous spatial distribution result of SPM concentration is obtained. This invention achieves efficient feature mining and deep integration of physical mechanisms, significantly improving the model's interpretability and generalization ability.

The present application relates to a kind of remote sensing methods of large area lake particle organic carbon, comprising: obtaining satellite remote sensing data and calculating its equivalent reflectivity;Based on the equivalent reflectivity, the reflection peak height PH1 of green light band of multiple lakes is calculated;Several lake samples with the highest and lowest PH1 value are taken respectively as turbid water sample and clear water sample;And the correlation model of remote sensing reflectivity and POC concentration is established for turbid water sample and clear water sample;With preset step, the PH1 value threshold range of the multiple lakes is traversed, the multiple segmentation thresholds are used to redivide lake and estimate POC concentration, calculate remote sensing accuracy, with the PH1 value corresponding to the highest remote sensing accuracy as the final water classification threshold;Using the water classification threshold, multiple to-be-measured lakes are divided into turbid water sample and clear water sample, and the parameters of the correlation model are recalibrated, to obtain the final POC concentration estimation model.The method of the present application can realize the synchronous remote sensing of POC of different types of lakes in large area.

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 application provides a water body sediment concentration inversion method and device based on satellite remote sensing images, and the method comprises the following steps: pre-processing satellite remote sensing images of a measured water body to generate a remote sensing reflectance above water surface; performing optical classification on the measured water body based on the remote sensing reflectance above water surface to generate a first type of water body, a second type of water body, a third type of water body and a fourth type of water body; performing backscattering coefficient inversion on the remote sensing reflectance above water surface to determine the backscattering coefficient of the first type of water body, the backscattering coefficient of the second type of water body, the backscattering coefficient of the third type of water body and the backscattering coefficient of the fourth type of water body; and performing sediment concentration remote sensing inversion on the backscattering coefficient of the first type of water body, the backscattering coefficient of the second type of water body, the backscattering coefficient of the third type of water body and the backscattering coefficient of the fourth type of water body to generate water body sediment concentration. The method effectively realizes high-precision backscattering coefficient inversion at different reference wave bands, and improves the inversion precision.

The present application relates to the technical field of submerged plant monitoring, and discloses a submerged plant reflectance inversion method, device, equipment and storage medium. In the method, multispectral satellite images of a reference water area are first acquired, and the spatial assignment of the inherent optical properties of the target water area is realized by using a water body optical characteristic processing model and a spatial assignment method, thereby providing stable and reliable optical input for the subsequent physical inversion model, and reducing the difficulty of acquiring in-situ depth measurement data or bottom material spectrum data of the target water area. Then, the water depth distribution data of the target water area is acquired by using laser altimetry satellite data and photon processing and spatial interpolation, thereby providing strong geometric constraints for the model. Finally, the two types of data and remote sensing reflectance images are input into the physical inversion model, the water body influence is accurately stripped by using the physical inversion model, and the final submerged plant reflectance data is obtained, thereby significantly improving the inversion accuracy and stability of the finally acquired submerged plant reflectance.

A water body algae toxin concentration estimation method and system based on remote sensing image belong to the technical field of inland water body eutrophication lake water environment evaluation, solve the technical problems that the existing technology has high precision requirement of detection instrument, expensive equipment material, complex and low efficiency of water sample pretreatment process, and the remote sensing indirect inversion method has poor accuracy and is difficult to realize accurate quantification of microcystin. Step 1, obtain the remote sensing image of the water body, and pretreat to obtain the remote sensing reflectivity data; step 2, calculate the spectral index based on the remote sensing reflectivity data; step 3, construct a water body microcystin concentration remote sensing estimation model, take the spectral index as the input of the water body microcystin concentration remote sensing estimation model, and obtain the water body microcystin concentration. The present application is used for realizing efficient and accurate water body algae toxin concentration estimation.

The invention discloses a method and a system for calculating the water demand in the ten days of a crop growth period in different drought scenes, and the method comprises the steps: firstly collecting remote sensing crop classification data and vector data, and obtaining a water demand calculation region, a crop planting type and the longitude and latitude of a sample point; calculating NDVI time sequence data by using long-time sequence satellite remote sensing reflectivity data based on the longitude and latitude of the sample point, and performing filtering processing to obtain an NDVI crop growth process curve; after the key growth period of the crop is extracted, the lower the irrigation guarantee rate is, the more drought is, and the water demand of the crop in different growth periods under different drought situations is determined by combining the crop water demand quota data and the key growth period water demand ratio under different irrigation guarantee rates. According to the method, the water demand in different growth stages can be accurately calculated by associating the crop planting structure, the growth dynamic state and the water demand characteristic, and a support is provided for scientifically formulating an irrigation plan, reasonably distributing water resources, reducing waste and improving the water utilization efficiency.