906 results about "Vegetation Index" patented technology

A rapid monitoring and identification method for winter wheat drought levels includes: 1) obtaining multispectral images and ground-measured LAI through UAV-based multi-payload low-altitude remote sensing technology, and calculating vegetation indices such as Normalized Difference Vegetation Index (NDVI), Difference Vegetation Index (DVI), Ratio Vegetation Index (RVI), Enhanced Vegetation Index (EVI), Optimized soil adjusted vegetation index (OSAVI) and Transformed Chlorophyll Absorption Reflectance Index; 2) establishing regression equations between the calculated vegetation indices and measured LAI for different growth stages, and selecting the optimal model equation for each growth stage; 3) using the optimal model equation to invert the LAI of winter wheat at various growth stages, and calibrating the LAI thresholds for different drought stress levels; 4) acquiring the multispectral images of target plot through real-time monitoring, calculating the required vegetation indices, inverting to obtain the LAI value, and comparing it with the threshold to determine the current drought stress level.

The invention provides an urban green land coverage rate dynamic monitoring method and device. The method comprises the following steps: acquiring multispectral satellite remote sensing data, and preprocessing the multispectral satellite remote sensing data; carrying out cloud detection and shadow detection on the preprocessed remote sensing image, and calculating an effective pixel proportion index, a spectrum quality index, a time phase matching degree index and a spatial integrity index; based on the four indexes and the corresponding weights, constructing a fuzzy comprehensive evaluation matrix, and selecting an optimal data combination; calculating a normalized difference vegetation index (NDVI) based on the near infrared band and the red light band in the optimal data combination, and performing illumination angle correction and time sequence smoothing processing on the NDVI to obtain an optimized NDVI; performing green space identification on the optimized NDVI by adopting a first deep learning model, and outputting a green space distribution map; and calculating a green land area change rate, an NDVI difference value and connectivity index change based on the green land distribution map. According to the invention, high-precision dynamic monitoring of the green land coverage rate is realized, and the monitoring efficiency and accuracy are improved.

The invention provides a plateau lake agricultural non-point source pollution treatment method which comprises the following steps: acquiring vegetation indexes and surface temperature field data by using a remote sensing satellite, and generating a pollution source space thermodynamic diagram in combination with water quality and soil parameters of ground sampling points; performing space-time alignment and data fusion on the thermodynamic diagram and real-time runoff and soil permeability data acquired by the hydrological sensor network, and constructing a structured pollution migration database; on the basis of the database, a hybrid neural network model embedded with physical constraints is utilized to predict pollutant concentration distribution within 72 hours in the future; inputting the predicted value into a multi-stage optimization controller, and generating a control parameter set comprising treatment intensity, engineering parameters and a fertilization ratio; generating a treatment strategy map covering the drainage basin through a GIS; and deploying an Internet of Things monitoring node to collect the treated water quality data to form a closed-loop control link. The treatment efficiency and effect can be improved, the treatment cost is reduced, and the negative influence on the ecological environment is reduced.

A rapid monitoring and discrimination method for drought conditions in summer maize based on chlorophyll content includes: 1) Obtaining multi-spectral imagery through UAV multi-payload low-altitude remote sensing technology and measuring chlorophyll content on the ground. Additionally, calculating vegetation indices including Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and Renormalized Difference Vegetation Index (RENDVI). 2) Selecting vegetation indices and constructing regression equations with measured chlorophyll content during different growth stages. The regression equation with the highest correlation for each growth stage is chosen as the optimal model equation for that particular stage. 3) Using the optimal model equations to retrieve chlorophyll content for each period and determining thresholds for chlorophyll content across different drought levels through calibration. 4) Calculating the required vegetation indices from real-time multi-spectral imagery of the field under test, retrieving chlorophyll content, and comparing it with the established thresholds to assess the real-time drought level.

A remote sensing sub-pixel runoff inversion method based on an improved vegetation index method includes: extracting an image of a target water from remote sensing satellite data; calculating a width of the target water using remote sensing data, inversing a width of the target water using remote sensing subpixels, and using one of the widths of the target water with a higher accuracy as a target width; remote sensing and estimating a flow velocity and a depth of the target water, and calculating a runoff of a small river. The improved vegetation index method can decompose pixels to extract a river width with a higher accuracy, which can meet accuracy requirements of some small rivers in mountainous areas, and then effectively applied in flow inversion of the small rivers with high accuracy requirements to improve runoff inversion effect, which has good economy, practicability, flexibility, and a certain physical mechanism.

The invention relates to the technical field of wetland damage identification, in particular to a coastal wetland damage dynamic identification method and system based on a remote sensing technology, and provides the following scheme: calling multispectral remote sensing data through a cloud computing platform, performing cloud masking, atmospheric correction and resolution resampling, and generating a high-quality remote sensing image data set; collecting ground feature samples, and constructing a random forest classification model based on spectrums, vegetation indexes, water body indexes and texture features; applying the classification model to a remote sensing image, generating a coastal wetland classification chart, and distinguishing a natural wetland from a damaged area; through time sequence analysis, ground feature changes of the same spatial position are identified, and dynamic evolution characteristics, including change amplitude, rate and conversion relation, of the damaged area of the wetland are extracted; and evaluating the precision of the classification model by using the confusion matrix, and optimizing the model according to a verification result. According to the invention, automation and precision of dynamic monitoring of wetland damage are improved, and scientific support is provided for wetland protection and management.

The invention relates to the technical field of image processing, in particular to an irrigation prescription map inversion method based on unmanned aerial vehicle spectrum data. The method comprises the following steps: carrying out unmanned aerial vehicle data acquisition on a farmland area to obtain a multispectral image, DEM data and meteorological interpolation data; performing micro-landform parameter calculation according to the multispectral image, DEM data and unmanned aerial vehicle GPS data, and constructing a terrain-spectrum coupling model to obtain a terrain relief degree map and the terrain-spectrum coupling model; performing vegetation multi-dimensional state analysis by using a terrain-spectrum coupling model to obtain a vegetation index map, a canopy temperature map and a water stress preliminary judgment map; performing net radiation calculation by using the meteorological interpolation data to obtain a net radiation diagram; according to the net radiation diagram, the vegetation index diagram and the topographic relief degree diagram, soil heat flux estimation is conducted, and a soil heat flux diagram is obtained. According to the method, collaborative inversion is carried out by eliminating the terrain effect, and the precision and reliability of irrigation prescription map inversion are improved.

The invention relates to the technical field of remote sensing recognition, in particular to a remote sensing recognition method and system applied to ecological system investigation. The method comprises the following steps: acquiring multi-temporal remote sensing image data of a target area; extracting a land cover type of the multi-temporal remote sensing image data, and performing dominant human activity area identification on the multi-temporal remote sensing image data according to the land cover type to generate dominant human activity area data; acquiring night light data of the target area; performing space-time registration on the night light data of the target area and the multi-temporal remote sensing image data to generate fused night light remote sensing data; performing boundary region extraction on the multi-temporal remote sensing image data through the land cover type to obtain edge region data; and calculating a vegetation index and a noctilucence index of the marginal region data based on the fused noctilucence remote sensing data. According to the method, through multi-temporal and multi-source data fusion and multi-index time sequence analysis, the accuracy and comprehensiveness of ecological system investigation remote sensing recognition are improved.

The invention relates to the technical field of data processing, and discloses a seawall feature information extraction method and system based on multi-source remote sensing data. The method comprises the following steps: carrying out wave band combination enhancement, geometric correction and radiation correction on a multi-source remote sensing image to obtain standardized data; extracting a seawall target by using multi-scale segmentation; recognizing structural features through profile analysis and spectrum matching; calculating vegetation indexes and texture features to obtain ecological information; establishing a precision evaluation system; and finally constructing a feature information database. Through the technical means of multi-source data fusion processing, multi-scale feature extraction, ecological feature evaluation and the like, the precision and efficiency of seawall feature information extraction are improved, and the problems existing in the aspects of data acquisition, processing and evaluation in a traditional method are solved.

The invention discloses a sky-ground integrated rice growth vigor monitoring method, and relates to the technical field of remote sensing, and the method comprises the steps: carrying out the modeling of each growth stage of rice, and forming a rice growth model diagram; collecting data to form a rice remote sensing big data image library; training a deep learning feature extractor, and extracting the features of the rice image; estimating rice growth based on remote sensing image big data; estimating rice growth based on the enhanced vegetation index; and carrying out multi-scale cross validation on the accurate identification degree of the rice growth vigor, and finally forming a set of sky-ground integrated rice growth vigor monitoring method. According to the method, a deep learning algorithm and a vegetation enhancement index method are adopted, the growth vigor estimation is performed on remote sensing data of different sources, and the rice growth vigor estimation result is determined through cross validation, so that high-precision rice growth state monitoring is realized; the three remote sensing technologies complement one another to form a comprehensive and multi-angle rice growth monitoring method, and real-time and efficient farmland monitoring management can be achieved.

The invention provides a desertification monitoring grading and vegetation extraction method and system, and relates to the field of desertification remote sensing monitoring and ecological assessment, and the method comprises the steps: selecting a multi-temporal satellite image and an unmanned aerial vehicle image which cover a full research region according to a preset condition, and carrying out the data preprocessing of the multi-temporal satellite image and the unmanned aerial vehicle image; the method comprises the following steps: constructing a desertification difference index by fitting a feature space of a vegetation index and a surface albedo by using a multi-temporal satellite image, grading the desertification degree of a research area to obtain a grading result, and locking a key monitoring area in the research area according to the grading result; a vegetation sample image of a key monitoring area is obtained from an unmanned aerial vehicle image, HSL color space conversion and hue optimization processing are carried out on the vegetation sample image, and a normalized vegetation index based on HSL is constructed, so that vegetation information of the key monitoring area is finely extracted, a key desertification disaster area is effectively positioned, and the accuracy of the desertification disaster area is improved. And vegetation information in the region is finely extracted.

The invention provides a crop planting area intelligent extraction method and system based on multispectral remote sensing, and relates to the field of remote sensing image processing, and the method comprises the steps: obtaining and correcting a multiband remote sensing image; calculating a vegetation index and constructing a crop growth characterization index to extract canopy features; obtaining a multi-period feature map, calculating a spatial distribution entropy, and establishing an evaluation function to determine a time sequence fusion weight; a spatial constraint function is established based on the spectral distance, and a segmentation criterion is constructed by combining the spatial constraint function with time sequence features for classification iteration. According to the method, the accuracy and the discrimination degree of crop planting area extraction are improved, and crop identification requirements in a complex agricultural environment are met.

The invention discloses a neural network for enhancing resolution generalization in kilometer-level rice yield prediction. The neural network comprises a multi-scale feature extraction module, a space-time attention fusion module and a yield regression module. The multi-scale feature extraction module is used for extracting multi-granularity spatial features of input data through a convolution kernel, and reserving shallow details by adopting jump connection; the space-time attention fusion module dynamically weights time sequence correlation characteristics of meteorological data and vegetation indexes through a gating mechanism, and focuses on a main rice producing area by using space attention; a yield regression module integrates the multi-scale spatial features and the time sequence attention features to output a yield prediction map; according to the method, remote sensing input of different resolutions is dynamically adapted through a multi-scale convolution block MSC, the attention weight of a key space region is enhanced by using a multi-dimensional feature integrator MDFI, and dynamic association between weather and vegetation growth is captured in combination with a time sequence attention mechanism; therefore, the prediction precision and stability of the model in a cross-resolution and cross-region scene are remarkably improved.

The invention provides a sea ice area extraction method and device based on remote sensing, which are applied to the field of seawater detection, and the method comprises the steps: obtaining a remote sensing image of a target area; pixels of the remote sensing image are partitioned based on the normalized vegetation index, the pixels with the normalized vegetation index smaller than a preset vegetation index threshold value are divided into first type regions, and the first type regions comprise at least one of a water body region and a sea ice region; determining a normalized deicing index of the first type region; performing normalized deicing index threshold segmentation on the first type region through a maximum between-class variance method to obtain a sea ice classification threshold; and filtering and post-processing the remote sensing image based on the sea ice classification threshold, and taking the first type region of which the normalized deicing index is greater than the sea ice classification threshold as a sea ice region. The method is suitable for sea ice monitoring in a large area, and sea ice information can be efficiently and accurately extracted.

The invention relates to the technical field of image recognition, and discloses a multispectral image and deep learning-based forest disease and insect pest automatic recognition method, which comprises the following steps of 1, carrying a multispectral camera containing a red edge wave band through an unmanned aerial vehicle to obtain a forest region image; 2, calculating a red edge normalized vegetation index of the image; 3, performing time sequence modeling on the red edge normalized vegetation index data of more than five consecutive periods, and inputting a time sequence convolutional network to generate an early lesion probability graph; 4, detecting a pest and disease damage target by adopting a multi-scale adaptive feature pyramid network; 5, outputting a disease and pest distribution thermodynamic diagram; and 6, driving the unmanned aerial vehicle cluster to execute precise pesticide spraying. According to the method, through the high sensitivity of the red-edge wave band to chlorophyll degradation and in combination with sequential convolutional network dynamic modeling, an initial lesion area can be recognized 7-10 days before disease development, the early disease recognition capability is remarkably improved, the disease discovery period is shortened, and large-scale disease diffusion is effectively avoided.

The invention relates to the technical field of agriculture, and discloses a soil fertility remote sensing inversion method based on machine learning. Acquiring and preprocessing a historical soil database, historical remote sensing image data and target year remote sensing image data, including radiometric calibration, atmospheric correction and geometric correction; calculating a vegetation index and a leaf area index based on the preprocessed historical remote sensing image data, and determining a bare soil window period; creating a fishing net grid covering the research area based on the spatial distribution of the bare soil window period; historical soil parameters and remote sensing image wave band reflectivity values of corresponding window periods are extracted from the fishing net grids, and training samples are constructed; the training samples are divided into a training set and a verification set, and feature selection is carried out through a correlation coefficient method; establishing a soil parameter inversion model based on the training set by adopting a multi-model cooperative training mode; and performing soil fertility inversion on the remote sensing image data of the target year by using the trained model. In conclusion, the prediction precision of the soil fertility can be improved.

The invention discloses a phenological self-adaptive corn automatic mapping method, which comprises the following steps of: 1, screening to obtain Sentinel-2 time sequence satellite image data of a target area, preprocessing, and synthesizing a time sequence clear sky image data set with one scene in a half month; 2, filtering the time sequence clear sky image data set to obtain a denoised and smoothed time sequence satellite image data set; step 3, based on peak and valley values of the NDVI index of the filtered image, adaptively acquiring a corn identification key phenological period of the target area; 4, analyzing typical spectrum differences of the corn and other main background ground objects, providing a corrected red edge normalized vegetation index, and forming a typical index data set of the corn identification key phenological period with an NDVI index and an LSWI index; and step 5, automatically realizing rapid mapping of the corn in the target area by utilizing the division rule of the typical index data of the key phenological period of the corn and the main background ground feature, and realizing a corn planting distribution monitoring effect with higher precision and efficiency.

The invention relates to the technical field of side slope displacement monitoring, and discloses a side slope displacement monitoring method based on computer vision, which comprises the following steps: step 1, acquiring multi-temporal remote sensing image data of a monitoring area, constructing a time sequence based on a vegetation index, analyzing a disturbance trend of a sub-area, and generating a disturbance risk level map; 2, initial image feature points are extracted based on the slope monitoring image, the feature points are constructed into a graph structure according to the spatial proximity relation, and a graph data model with nodes connected with edges is formed; and step 3, based on the disturbance risk level graph and the graph structure, inputting the graph neural network model to carry out feature point stability modeling. The technical scheme of disturbance risk level map guidance, map neural network stability modeling and feature point stability score screening is adopted, and the technical effect that stable and traceable key feature points can be effectively recognized and screened out under the dynamic disturbance conditions of vegetation disturbance, climate change and the like is achieved.

The invention relates to the technical field of earthquakes, in particular to an optical remote sensing image feature landslide information extraction method. A double-time-phase NDVI time sequence analysis strategy is adopted, and landslide area recognition is achieved by constructing a vegetation index difference chart before and after an earthquake. A landslide mass area before an earthquake presents a high NDVI value due to complete vegetation coverage, and a vegetation index of the area in an image after a disaster is significantly attenuated due to surface disturbance, so that an obvious change response is formed. If the normalized vegetation index does not change, a non-landslide area can be determined, and the greater the change of the normalized vegetation index is, the greater the possibility of landslide occurrence is, and a landslide preselection area is determined; then Otsu threshold segmentation is carried out by combining cloud layer features and water body features, cloud layer and water body change parts are effectively eliminated, object-oriented geometric shape rule fine recognition is carried out on a preselected area, a DEM is adopted to calculate a slope value to constrain low-lying or flat earth surface changes, and efficient and accurate landslide information extraction is achieved.

The invention relates to the technical field of garden monitoring, in particular to a landscaping monitoring system and method, and the system comprises a collection module which is used for obtaining multi-angle images of a garden in stages, and selectively collecting the images according to a correlation judgment result; the analysis module is used for extracting vegetation indexes, disease targets, leaf health features and falling object state features; the identification selection module is used for generating a dynamic acquisition strategy according to the relevance abnormal index and triggering the acquisition module to directionally acquire the second-level image; the weighted fusion module is used for performing adaptive weighted fusion on the multi-source features and calculating a comprehensive wilt risk level; the early-warning module is used for generating early-warning signals and early-warning grades, the view limitation of traditional single-view-angle monitoring is broken through, large-range vegetation vitality abnormal areas can be rapidly screened, detail features of trunk diseases, low leaf withering and microscopic disease spots can be accurately captured, and the comprehensiveness and reliability of early withering recognition are remarkably improved.

The invention provides a cyanobacterial bloom monitoring method and system. The cyanobacterial bloom monitoring method comprises the steps that a target water body remote sensing image of a target water body area of a monitoring area is acquired; screening a chlorophyll a sensitive wave band of the target water body remote sensing image, and obtaining chlorophyll a inversion concentration based on a preset chlorophyll a concentration inversion model; calculating a normalized vegetation index according to the multi-band remote sensing reflectivity value of the target water body remote sensing image; according to the chlorophyll a inversion concentration and the normalized vegetation index, constructing a cyanobacterial bloom grading threshold dynamic calibration model, and performing cyanobacterial bloom risk grading on the target water body area; and according to the cyanobacterial bloom risk grading result of the target water body area, generating an early warning instruction corresponding to the risk grade. The cyanobacterial bloom grading threshold value can be dynamically adjusted, so that the subjective deviation of manual weighting can be eliminated, and the real-time performance, accuracy and efficiency of cyanobacterial bloom monitoring and early warning are effectively improved.

The invention relates to the technical field of image discrimination, and further relates to a restoration supervision evaluation method based on mine ecological image discrimination, and the method comprises the steps: 1, carrying out the preprocessing of an obtained remote sensing multispectral image of a target mine region, and obtaining a preprocessed image; fusing the standard vegetation index, the bare soil area and the reflectivity ratio to obtain a vegetation recovery index; 2, dividing the preprocessed image into a plurality of ecological patches by using an image segmentation algorithm; calculating to obtain a slope stability index in combination with the crushing degree; 3, evaluating a hydrological recovery index corresponding to the water body area in the preprocessed image through the combination of short-wave infrared and near-infrared bands; and calculating a comprehensive restoration index of the target mine area in combination with the vegetation restoration index, the slope stability index and the hydrological restoration index. The method has the advantages of being high in adaptability, wide in monitoring range and the like, and the supervision efficiency and scientificity of mine repair engineering can be remarkably improved.

The invention belongs to the field of remote sensing monitoring, and particularly relates to a land ecological condition monitoring system and method based on remote sensing data, and the method comprises the steps: obtaining a remote sensing image sequence through a preprocessing module, and obtaining initial cultivated land segmentation sub-regions and edge features; a feature extraction module extracts spatial features and fluctuation values such as normalized vegetation indexes and enhanced vegetation indexes based on the feature extraction module, and constructs a space-time coordinate system; the region determination module determines a real cultivated land area and a cultivated land area fluctuation value; the inversion module inverts the vegetation coverage, the moisture content and the soil fertility, and obtains an ecological degradation fluctuation value by combining with phenological period anomaly recognition; the early warning module performs real-time early warning based on the cultivated land area fluctuation value and the ecological degradation fluctuation value, and visually marks the position, the type and the severity of a change region through a GIS platform; according to the invention, precise monitoring and dynamic early warning of the ecological condition of the land are realized.

The invention discloses a soil water content inversion method and system based on L-band dual-polarization data and a medium, and belongs to the field of remote sensing technology and environment monitoring. The system comprises an SAR data acquisition module, an optical data processing module, a vegetation scattering separation module, an inversion model construction module, a spatial distribution calculation module and a risk assessment module, wherein the SAR data acquisition module is used for regularly acquiring L-band dual-polarized SAR data of a target area; the optical data processing module assists in identifying and separating vegetation scattering components by calculating vegetation indexes; the vegetation scattering separation module is used for extracting pure soil back scattering signals; the inversion model building module is used for training and optimizing a soil water content inversion model based on the pure soil back scattering signals and field sampling data; the spatial distribution calculation module is used for calculating a spatial distribution diagram of the surface soil water content of the target area; and the risk assessment module is used for carrying out landslide risk grade division and early warning management.

The invention discloses an inland river basin intelligent ecological scheduling rehearsal method fusing a scheduling process and an ecological process. The inland river basin intelligent ecological scheduling rehearsal method comprises the steps of multi-source data acquisition and digital twinborn construction; carrying out reservoir intelligent scheduling reinforcement learning modeling; ecological process lag response modeling is carried out; spatial diffusion modeling of ecological influence; performing cross attention guided ecological response interpolation; and carrying out rehearsal and visual display on the ecological scheduling scheme. According to the method, a hydrological-ecological response modeling mechanism is introduced, and a time-space response relationship between scheduling behaviors such as water level and water volume and ecological indexes such as vegetation indexes and habitat indexes is combined, so that lagging response characteristics of an ecological process to the scheduling behaviors can be quantitatively described, and the defect that a traditional scheduling model is insufficient in ecological expression capability is overcome. A reinforcement learning algorithm is utilized to fuse multi-source data for state perception and strategy iteration, and the scheduling strategy can be dynamically adjusted according to the current hydrological situation and ecological feedback result of the watershed. Compared with static rule type scheduling, the regulation and control efficiency and ecological adaptability are remarkably improved.

The invention relates to the technical field of ecological environment monitoring, and discloses a regional ecological environment monitoring method and system based on remote sensing image processing, and the method comprises the following steps: collecting remote sensing image data and climate factor data of a target region; performing spatial gridding and standardization processing on the data; establishing a nonlinear regression model to analyze a relation between climate factors and vegetation indexes; extracting spatio-temporal topological characteristics of the climate factor data and identifying a change mode; long-term prediction of regional vegetation productivity is carried out through time sequence model training; the regional ecological environment is analyzed and evaluated based on the prediction result, and a scientific basis is provided for ecological environment monitoring; the system comprises a data acquisition module, a data processing module, a nonlinear regression modeling module, a spatio-temporal topology analysis module, a time sequence prediction module and an ecological evaluation module. According to the method, the problems of small data coverage, low prediction precision and insufficient capture of nonlinear influence of climate change in the existing method are solved.

The invention provides a crop yield prediction method and system based on image analysis, relates to the field of yield prediction, and obtains a farmland multi-view remote sensing image through an unmanned aerial vehicle multispectral sensor, and executes aerial triangulation and ortho-rectification to obtain accurate exterior orientation elements and ortho-images. Then, vegetation indexes are extracted and combined with field yield measurement data, a yield regression model is constructed, and rapid prediction and grading of the crop yield are achieved. In order to reduce multi-view brightness inconsistency and give consideration to crop growth characteristics, prior information such as canopy row direction, leaf angle distribution and sealing degree is introduced, iterative correction is performed on the image by adopting a BRDF model, and radiation difference is corrected while geometric accuracy is guaranteed. Particularly, through staged trend analysis and multiple rounds of detection, priori conflicts can be found in time, incremental images can be flexibly collected, and resolving robustness and yield prediction precision are improved. The method can be widely applied to unmanned aerial vehicle remote sensing agricultural monitoring scenes.

The invention relates to the technical field of remote sensing monitoring, solves the technical problems that an existing soil salinity inversion method is insufficient in precision and poor in generalization ability, and particularly relates to a bare soil salinity inversion method fusing partial least squares and a random forest. Comprising the following steps: acquiring spectral reflectivity data and salinity spectral index of a multiband range and spatial resolution, and preprocessing to establish a digital orthoimage; removing a water body and a vegetation coverage area in the digital orthoimage by using a normalized vegetation index to obtain bare soil data containing 24-dimensional feature variables; establishing a fusion model and carrying out training evaluation; and performing prediction by taking Sentinel-2 satellite remote sensing data as input of the fusion model to obtain the salt content of the bare soil. According to the method, the precision, reliability and generalization ability of soil salinity inversion can be effectively improved, compared with a traditional single model, the nonlinear relation and noise in remote sensing data can be better processed, and the generalization ability of salinity inversion is remarkably improved.

The invention relates to the technical field of satellite remote sensing application, and discloses a crop classification method based on a multi-source satellite image, and the method comprises the steps: firstly obtaining multi-temporal satellite image data, extracting spectral reflectivity characteristics, generating a matrix, and constructing a classification model set; setting a feature parameter category set, and establishing a feature fusion correlation model based on the obtained texture, vegetation index parameters and spatial resolution data of the historical image; extracting spectrum and texture feature parameters of a crop area in the current image, and generating an optimized feature set in combination with model optimization; updating classification logic based on the optimized feature set and the classification model, generating a target classification scheme and calibrating a spatial relationship; and finally, acquiring crop growth cycle data, and establishing a time sequence feature association rule to optimize a crop type spatial distribution map. According to the method, through multi-source feature fusion and dynamic optimization, the crop classification precision and reliability are improved, and the method is suitable for a precision agricultural management scene.