220 results about "Leaf area index" patented technology

The invention relates to the technical field of automatic irrigation control, in particular to a multi-sensor fusion agricultural precise irrigation system which comprises a leaf area analysis module, an environment evaluation module, a task arrangement module, an execution regulation and control module and a feedback regulation module. According to the method, the crop leaf area index is introduced to be compared with the growth stage target value, the dynamic expression of the group moisture demand is realized, the environmental evapotranspiration influence factor is generated in combination with illumination and temperature disturbance parameters, the timeliness of water demand driving force identification is enhanced, and the task execution sequence is judged by adopting regional water pressure disturbance data; a multi-region irrigation resource allocation process is optimized, a water quantity and pressure difference double-factor adjustment mechanism is utilized, the rationality and response accuracy of time length allocation are improved, water supply state analysis and task target deviation comparison are performed, dynamic supplementary irrigation regulation and control are realized, the closed-loop capability and supplementary irrigation accuracy of irrigation period control are improved, and the irrigation period control efficiency is improved. And the stable allocation capability and the irrigation response efficiency of the irrigation system under the influence of multiple variables are enhanced.

The invention relates to the technical field of crop yield analysis, in particular to a crop yield prediction method based on a time sequence remote sensing image, and the method comprises the steps: obtaining an optical remote sensing image and a synthetic aperture radar image in a crop growth period, and constructing a time sequence image through complementary information fusion and under-cloud information recovery; the problems of cloud interference and data missing are solved, a multi-scale time regulation and control convolution module is used for processing a time sequence image, multi-scale time sequence features are extracted, feature fusion is carried out in combination with attention difference jump connection, and a rice distribution map is obtained; determining a rice planting area based on the rice distribution diagram, selecting an optimal assimilation time period in the rice planting area, and assimilating the leaf area index subjected to remote sensing inversion and a crop growth model simulation result to generate a simulated yield after assimilation; based on the multi-scale time sequence characteristics, a parameter dynamic response matrix related to crop growth model parameters is established, and residual error correction is performed on assimilated simulation yield, so that the rice yield prediction precision is improved.

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 provides a rice growth model construction method and system based on salt stress, and relates to the technical field of growth model.The rice growth model construction method comprises the steps that firstly, monitoring points are arranged in a planting sample area, and soil conductivity data of different depths are obtained through a multi-depth soil conductivity sensor and a Kriging interpolation method; collecting root system density, a multispectral remote sensing image and a leaf area index, and calculating a canopy salt stress index; measuring the concentration of related ions to obtain a sodium adsorption ratio, and establishing a salt stress coefficient by combining the soil conductivity and the irrigation volume; fusing canopy and soil stress indexes to generate a stress comprehensive index, and introducing a salinity feedback item to represent the crop compensation capability; and finally, constructing a hybrid machine learning model by adopting a convolutional neural network and a long-short-term memory network to realize rice growth dynamic prediction. According to the method, multi-dimensional salinity stress quantification of a soil-crop system is realized, and decision support is provided for salinization treatment in precision agriculture.

According to the unmanned aerial vehicle multi-source quantitative remote sensing rape growth state evaluation method, rape is divided into a seedling stage, a flowering stage and a legume stage according to growth nodes, ground hyperspectral and multi-spectral data are synchronously obtained, resolution is increased and decreased, the data are converted into all-dimensional, multi-scale and multi-temporal data, a growth state factor inversion model of the rape is established, and the rape growth state is evaluated. Evaluating the contribution of different growth state factors to the growth vigor of the rape, and evaluating the growth state of the rape based on comprehensive evaluation indexes of leaf area index, overground biomass and chlorophyll content. The method comprises the following steps: establishing a model based on multi-layer linear regression and partial least square optimization models of three types of growth factors LAI, AGB and CC, multi-mode prediction based on entropy aggregation and hierarchical analysis of entropy loading expert knowledge in combination with expert knowledge and agronomic knowledge, and setting a model with an optimal verification index in different growth periods as a growth state evaluation model in the period. The rape growth state evaluation period is short, the pertinence is strong, and the accuracy is good.

The invention discloses an urban green land carbon sink metering method and system based on multi-source data fusion, and relates to the technical field of environmental monitoring, and the method comprises the steps: collecting urban green land multi-source data, and carrying out the preprocessing; performing parameter completion on vegetation in the building shielding blind area by adopting a dual-channel generative adversarial network to obtain complete green land leaf area index distribution data; simulating a reflected light path of a building glass curtain wall by using a ray tracing algorithm, obtaining a photosynthetically active radiation correction coefficient received by a vegetation canopy, and identifying urban green land carbon sink distribution data through multi-scale data fusion; and obtaining a carbon sink amount error based on the urban green land carbon sink amount distribution data, performing dynamic correction through a Bayesian optimization algorithm, and generating an urban green land carbon sink amount measurement report. According to the method, the adversarial network is generated through two channels, and the cooperative training mechanism of the U-Net generator and the PatchGAN discriminator is utilized, so that the high-precision spatial continuity reconstruction of the leaf area index of the building shielding blind area is realized.

The invention relates to the technical field of unmanned aerial vehicle remote sensing, in particular to a crop leaf area index estimation method based on unmanned aerial vehicle image processing. The method comprises the following steps: acquiring a remote sensing image of an unmanned aerial vehicle monitoring area; identifying a topographic relief area according to the unmanned aerial vehicle monitoring area remote sensing image, and performing orthoimage reconstruction on the topographic relief area to generate a topographic relief area orthoimage; identifying a terrain shielding area based on an unmanned aerial vehicle monitoring area remote sensing image; image stitching distortion detection is carried out on the terrain occlusion area to obtain image stitching distortion data; performing spectral interpolation on the gap region based on the image splicing distortion data, and determining a plant growth stage of the gap region after the spectral interpolation; and performing lens distortion detection based on the image splicing distortion data to obtain lens distortion data. The precision and accuracy of crop leaf area index (LAI) estimation are improved based on the unmanned aerial vehicle remote sensing technology.

The invention discloses a salinization farmland crop yield prediction method, which is applied to the technical field of crop yield monitoring and prediction, and comprises the following steps: based on a vegetation index and a salinity index, inverting salinity by using a partial least square regression method; inverting the soil moisture based on an empirical relation model of the conditional vegetation temperature index and the actually measured soil moisture; inverting the leaf area index based on an empirical relation model of the normalized vegetation index and the actually measured leaf area index; the method comprises the following steps: constructing an HYDRUS-1D-EPIC coupling model considering salinity, carrying out sensitivity analysis by using an extended Fourier amplitude sensitivity test method, carrying out parameter calibration and posterior distribution inference on the coupling model by using an MCMC method, and sampling posterior distribution by using a DREAM method; and assimilating the inverted salinity, soil moisture and leaf area index to the coupling model by using an ensemble Kalman filtering method, and predicting the crop yield of the salinized farmland. According to the method, the yield prediction precision of the salinized crops is effectively improved.

The invention provides a single tree segmentation-biological parameter estimation method based on a vehicle-mounted LiDAR point cloud, and belongs to the technical field of urban landscaping intelligent monitoring. The point cloud individual tree segmentation algorithm based on tree geometric feature constraint is designed for solving the problem that individual tree segmentation is difficult due to crown overlapping in an urban scene, and the method takes a tree geometric structure as a constraint, combines point cloud reflection intensity information, a clustering algorithm, a main direction index and other means, and obtains the individual tree segmentation algorithm based on the tree geometric feature constraint. And accurate extraction of trunks and crowns in the scene point cloud is realized. Aiming at the problems of high feature redundancy, poor model interpretability and the like in an existing estimation method, a random forest model is taken as a basis, an adaptive feature selection algorithm is introduced to improve variable screening efficiency, hyper-parameters are dynamically adjusted by utilizing pigeon inspired optimization, and generalization and stability of the model are enhanced; an estimation model used for estimating biological parameters such as leaf area index, biomass and carbon reserve is designed.

The invention provides a small-region forest carbon sink accurate estimation method and system based on multiple constraints, and the method comprises the steps: respectively generating a canopy height model, a leaf area index and a chlorophyll content through obtaining unmanned plane laser radar data and multispectral data; generating a digital elevation model by adopting a lowest point filtering technology, realizing individual tree segmentation in combination with a gradient map and a watershed algorithm, and extracting individual tree geometric parameters; and constructing a carbon sink estimation model by using the geometric parameters and the physical and chemical parameters, and comprehensively calculating the carbon sink amount of the small region. The problems that a traditional carbon sink estimation method is insufficient in precision and low in automation level are solved, the single-tree segmentation precision and the carbon sink amount calculation precision are improved through deep fusion of geometric information and spectral information, full-process automation of data acquisition, data processing and carbon sink estimation is achieved, and the method is suitable for large-scale popularization and application. The method is suitable for carbon sink monitoring requirements of various forest types and complex terrain scenes.

The invention discloses a method for predicting the irrigation amount of crops in an arid saline-alkali soil area and related equipment. The method comprises the steps that the potential evapotranspiration of a target crop is calculated through a double-source evapotranspiration mechanism model based on key meteorological parameters, obtained through prediction, of a target area, and the leaf area index and the plant height are dynamically updated according to the accumulated temperature process; respectively constructing different stress inhibition functions aiming at the attention salt ions and the pH value in the saline-alkali soil solution, and applying composite inhibition at the stomatal conductance and / or the apparent evapotranspiration resistance site to correct the potential evapotranspiration so as to obtain the ideal crop evapotranspiration corrected by the salinity and the pH value; and based on the corrected ideal crop evapotranspiration, predicting day-by-day irrigation amount and water distribution schedule suggestions in a future preset period. The method can solve the problem that current crop irrigation of saline-alkali soil in an arid inland region depends on experience, influences on different salt types and contents are caused, and quantitative analysis is lacked.

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

The invention discloses an urban environment monitoring regulation and control system and method based on global greening data. The system comprises an image acquisition module, a monitoring module, an image analysis module, a sampling screening module, a global speculation module and a strategy generation module. The image acquisition module periodically acquires greening image data; the monitoring module collects vegetation monitoring data through a sensor group; the image analysis module extracts vegetation coverage areas and classifies the areas according to texture feature parameters (leaf area index, canopy height and texture uniformity); the sampling and screening module calculates a standard value of each unit area and screens and optimizes sampling areas; the global speculation module is combined with the sampling data to calculate monitoring data of an uncovered area; and the strategy generation module generates a targeted microclimate regulation and control strategy according to the monitoring data and the classification result. According to the invention, image analysis and sensor data are combined, precise monitoring and intelligent regulation and control of vegetation distribution are realized, and the ecological function and microclimate regulation and control effect of an urban greening area are improved.

The invention discloses an image restoration method for soybean leaf phenotype analysis. The method comprises the following steps: firstly, complementing missing edge information of soybean leaves through an edge generation network; then, extracting a coarse-to-fine layered feature map from the complemented edge information by using a structural auto-encoder, and inputting the layered feature map as a guide feature into an image restoration network to assist a restoration process; the soybean leaf structure information is accurately repaired based on the guiding characteristics; and finally, according to the completely repaired leaf image, obtaining phenotypic parameters such as a leaf area index, a structure texture and a color of the soybean plant. By means of the mode, gradient structured edge information and hierarchical feature guidance are introduced, the repairing effect of the soybean leaf picture can be improved, and therefore support is better provided for accurate and complete analysis of soybean leaf phenotype data.

The invention discloses an alpine meadow aboveground biomass monitoring method based on PROSAIL-BP, and belongs to the field of ecological remote sensing information processing. In order to overcome the defect that samples in the alpine region are insufficient and have high precision and high reliability, the method comprises the steps that an alpine meadow mask, remote sensing images and field biomass data in a target region are obtained, the remote sensing images are spliced, the wave band reflectivity is normalized, and the grassland region reflectivity is reserved in combination with mask cutting; predefining a PROSAIL model matched with the region features; performing Sobol global sensitivity analysis to obtain a first-order sensitivity index and a total-order sensitivity index of the parameter; screening a key wave band and four high-sensitivity parameters; uniformly sampling to generate a parameter group, simulating a hyperspectrum through PROSAIL, extracting the reflectivity of a key wave band, and constructing a data set by multiplying a leaf area index by a dry matter content as a target variable; and training a three-layer BP neural network, and processing the reflectivity output pixel biomass of the remote sensing key wave band. The method is applied to a remote sensing information processing system and has high precision and high reliability.

The invention relates to the technical field of information management, in particular to a smart agriculture precision big data information management system, which comprises a growth index extraction module, a stage data clustering module, a nutrient fluctuation identification module, an agricultural material strategy generation module and a scheduling information scheduling module. According to the method, the acceleration criterion is constructed based on the continuous period leaf area index change sequence, transition node identification is completed in combination with the photosynthetically active radiation utilization rate and the nitrogen absorption rate change trend, accurate positioning of crop growth stage change time points is achieved, the stability of stage classification in a space region is enhanced, and the accuracy of crop growth stage classification is improved. The agricultural material putting grade is judged and the fertilization frequency and dosage are blended by analyzing a root zone soil nutrient concentration difference value and a rate fluctuation frequency detection mode in combination with moisture content and root activity conditions, and operation period adjustment is guided in a task schedule in cooperation with a meteorological element fluctuation rate. The precision of farmland crop stage identification and the timeliness of nutrient fluctuation response are integrally improved.

The invention discloses a ground vegetation leaf area index remote sensing inversion method based on a remote sensing image. The method comprises the following steps: data acquisition and preprocessing; performing multi-scale space-time non-local filtering fusion; dynamic feature extraction; carrying out transfer learning fine tuning; time sequence deep learning integration; and model output and post-processing. According to the invention, through multi-source space-time fusion and dynamic feature distribution, vegetation LAI inversion with high resolution and high continuity is realized; the transfer learning and the time sequence deep network enhance the adaptability of the model to a new region and time sequence change; the stability and reliability of large-scale application are guaranteed through full-process automation and uncertainty evaluation, and the model is obviously superior to an existing single-source or static model.

The invention discloses a refined grain crop yield prediction method and system, belongs to the technical field of agricultural yield prediction, and solves the problems that the prediction precision is low and day-by-day prediction and overfitting cannot be realized when the crop yield of a small plot scale is predicted. The method comprises the following steps: obtaining food crop data, wherein the food crop data comprises meteorological data and crop emergence period data; dividing the prediction area into a plurality of parameter adjustment areas, and establishing a simulation algorithm based on a WOFOST crop mechanism model for each parameter adjustment area; performing day-by-day simulation by using a WOFOST-based crop mechanism model simulation algorithm to obtain a crop development process, a leaf weight, a stem weight, an ear weight, an overground part dry matter weight and a leaf area index; and taking the output data based on the WOFOST crop mechanism model simulation algorithm, the meteorological elements, the statistical yield data, the field test data and the agrometeorological observation data as the input of an LSTM algorithm, and obtaining a prediction result of the yield of the food crops. The method is suitable for real-time and refined prediction of the crop yield.

The invention discloses a wheat yield remote sensing prediction method combining phenological parameters, which comprises the following steps: S1, performing field observation in a key growth period of winter wheat, synchronously collecting canopy hyperspectral reflectivity data, leaf area index (LAI) and SPAD value of each observation sample point, and recording wheat grain yield of the corresponding sample point; s2, performing noise reduction preprocessing on the acquired canopy hyperspectral data, and extracting sensitive spectral parameters by combining principal component analysis (PCA) and correlation analysis methods; s3, taking the sensitive spectrum parameters, LAI and SPAD values as independent variables, taking the wheat grain yield as a dependent variable, and adopting partial least squares regression (PLSR) to construct a multivariable yield prediction model; and S4, performing wheat yield prediction on an independent test sample or regional scale remote sensing data by using the trained model. The method overcomes the defect that only yield sensitive spectrum parameters are used for predicting the effect, and accurate estimation of the model is achieved.

The invention relates to the technical field of smart agriculture, and specifically provides a crop growth situation prediction method based on YOLO and Transform space-time collaboration. The method aims at solving the problem that in the prior art, prediction precision is insufficient due to splitting of spatial features and time sequence information, and crop phenotypic features (plant height, leaf area index, canopy coverage and the like) in a farmland video stream are extracted in real time through an improved YOLO model; constructing a spatial-temporal feature encoder, and modeling a coupling relationship between historical time sequence environmental data (temperature, illumination and soil moisture content) and phenotypic features through a Transform network; designing a feature collaborative fusion module, and integrating spatial visual features and time sequence environment features by adopting an adaptive weight distribution mechanism; and establishing a growth situation prediction model based on space-time cooperation characteristics, and outputting key parameter probability distribution of a future growth stage.

The invention relates to the technical field of image processing, in particular to a GEE-based leaf area index remote sensing product spatial downscaling method, which comprises the following steps: acquiring a single image of a remote sensing product, screening out a surface reflectance image according to a space-time range of the single image of the remote sensing product, and preprocessing the surface reflectance image and the single image of the remote sensing product; establishing a screening mechanism to remove heterogeneous pixels in the preprocessed surface reflectance image to obtain a spatial homogeneous pixel set; aggregating the preprocessed surface reflectance image to a resolution which is the same as that of a single image of the remote sensing product, and constructing a space-time matching pixel set; randomly extracting pixels from the space-time matching pixel set to construct a training sample library, and optimizing data in the training sample library to obtain an optimized training sample library; and applying the machine learning model to the preprocessed surface reflectance image to generate a leaf area index remote sensing product in continuous spatial distribution. According to the invention, the precision and resolution of the LAI product are improved, and the applicability is enhanced.

The invention discloses a forest leaf area index estimation method based on a three-dimensional radiation transmission model and deep learning, and belongs to the technical field of ecological remote sensing. The method comprises the steps of 1, obtaining a modeling data set based on a DART model, and preprocessing the modeling data set to obtain a modeling training set; 2, constructing a pre-trained 1D-CNN forest LAI inversion model, and carrying out the training optimization of the pre-trained 1D-CNN forest LAI inversion model based on the modeling training set, and obtaining an optimization model; 3, constructing a migration data set based on Landsat satellite image data and actually measured LAI data, and performing parameter optimization on the optimization model based on the migration data set to obtain a migration learning forest LAI inversion model; and step 4, processing the Landsat image data needing to be predicted based on the transfer learning forest LAI inversion model to obtain an estimation result. According to the invention, a forest LAI estimation result with higher precision can be provided.

The invention provides a mangrove forest growth monitoring method and system, and the method comprises the steps: obtaining a mangrove forest multi-temporal remote sensing image of a target monitoring region, extracting a spectral feature index sum of the mangrove forest multi-temporal remote sensing image, and obtaining a leaf area index based on a leaf area index model; obtaining a water body suspended sediment concentration inversion result based on a sediment content inversion model; performing spatial statistical analysis on the leaf area index and the inversion result of the suspended sediment concentration in the water body to obtain a spatial statistical index; according to the leaf area index and the water body suspended sediment concentration inversion result, the stress degree of water body suspended sediment on mangrove forest growth is analyzed, and mangrove forest growth condition change parameters are obtained; and according to the spatial statistical index and the mangrove forest growth condition change parameter, performing stress degree grading on the target monitoring area, and generating a corresponding early warning instruction. The problem that the influence of suspended sediment in the water body on mangrove forest growth cannot be recognized in time can be solved, and the mangrove forest growth monitoring accuracy is improved.

The invention relates to the technical field of state recognition, in particular to an apple tree growth state intelligent recognition system which comprises a branch structure recognition module, a direction sequence calculation module, a growth stability judgment module, a fruit trend steering module and a vector trajectory offset module. According to the method, the continuous change characteristics of the branch direction in the time sequence dimension can be identified by constructing the inter-node main axis direction vector sequence and quantifying the change trend of the included angle of the inter-node main axis direction vector sequence, synchronous detection of the fruit diameter growth rate and the color difference change trend is combined, and the reflectivity and morphological symmetry linkage change is introduced as the judgment condition of the trend turning point; according to the method, fruit growth mutation nodes are effectively positioned, a growth trajectory sequence is constructed through multi-period branch angle amplification, diameter growth rate and leaf area index three-dimensional vectors, an individual growth direction offset region is accurately identified, and the precision and timeliness of growth anomaly identification are enhanced. And intelligent fruit tree management implementation based on the linkage relationship between the structure and the fruit parameters is effectively supported.

The invention relates to the technical field of intelligent agriculture and crop cultivation, in particular to a soybean planting chemical control self-adaptive regulation and control method and system based on canopy risk identification, and the method comprises the steps: constructing a digital twinborn model for dynamically simulating the growth process of a soybean population; monitoring a canopy structure, a leaf area index LAI and a light interception rate of the soybean in the key growth period by using an unmanned aerial vehicle or a sensor, and calibrating the digital twinborn model; future weather forecast data are input into the calibrated digital twinborn model for growth simulation, and light distribution is simulated by means of a pre-established AI model to identify soybean population closing and illumination competition risks; and according to the risk identification result, a chemical control decision is generated autonomously, and the variable pesticide spraying machine is controlled to perform precise variable operation of soybean planting based on the chemical control decision. According to the method, group development can be predicted in a prospective manner, and risks can be accurately quantified, so that an intelligent regulation and control mechanism with prospective and active properties is actively triggered, and active intervention of planting is realized.

The invention relates to the field of agricultural yield prediction, and particularly discloses a crop yield prediction method and system based on artificial intelligence, and the method comprises the steps: S1, recognizing crop types through the spectral features of a remote sensing image and field sampling data, and dividing corresponding key growth stages; s2, collecting multi-source feature data for each key growth stage, wherein the multi-source feature data comprises remote sensing image data, an extracted normalized difference vegetation index NDVI, an enhanced vegetation index EVI and a topographic factor; acquiring hourly temperature, humidity, precipitation and illumination intensity of each sub-region through a distributed sensor according to micro-meteorological data; obtaining plant height, leaf area index and fruit development parameters through unmanned aerial vehicle three-dimensional imaging according to crop phenotype data; by adopting the technical scheme of the invention, the terrain, microclimate and crop dynamic growth characteristics can be accurately fused, and the problem of non-uniform sample distribution is solved, so that the accuracy and regional adaptability of yield prediction are improved.

The invention provides a crop growth state intelligent analysis method based on a multi-source remote sensing image, and belongs to the technical field of crop growth analysis based on computer data processing. The method comprises the following steps: firstly, collecting a multi-source remote sensing time sequence image covering the whole growth cycle, and constructing a multi-modal growth characteristic data set with a uniform structure; secondly, designing a multi-channel time sequence modeling structure, and cooperatively extracting dynamic evolution trajectories of core characteristics such as canopy height, leaf area index, SPAD value and the like based on space-based spectrum and foundation form information; then, a current state deviation type is identified through a multi-type track distribution learning model, and problem tracing and explanation analysis are carried out in combination with a diffusion matching mechanism; finally, a variable adjustment strategy is generated based on a trajectory deviation result and a management target, and optimization recommendation of management measures such as irrigation frequency and fertilization intensity is realized. The method can be widely applied to the scenes of agricultural remote sensing monitoring, intelligent agricultural machinery scheduling, precise planting guidance and the like, and has high interpretability and practical value.

Provided is a method for quantitatively estimating potassium demand of regional corn plants during key growth stages. The method includes: S1: calculating critical potassium concentration values Kc for each growth stage of corn; S2: calculating potassium nutrition indexes (KNIs) for each growth stage of the corn; S3: constructing KNI inversion models for each growth stage of the corn; S4: obtaining leaf area index (LAI) data of the corn and calculating above-ground biomass W for each growth stage of the corn; S5: calculating potassium fertilizer utilization rates KAE for each growth stage of the corn based on above-ground potassium accumulations in each growth stage of the corn; S6: calculating relative dry biomass RDW for each growth stage of the corn, and optimal KNI values, denoted as KNItarget, for each growth stage of the corn; and S7: obtaining a plant potassium content absorption model Kabs for each growth stage of the corn.

The invention provides a remote sensing estimation method for high spatial-temporal resolution crop evapotranspiration in an arid and semi-arid region, which comprises the following steps of: acquiring cloudless or less-cloudless high spatial resolution optical remote sensing data of a crop growing season in a research region, and calculating or inverting remote sensing earth surface parameters required by high spatial resolution evapotranspiration; comprising a vegetation index NDVI, a leaf area index LAI, a vegetation coverage FVC, a surface water index LSWI and a surface albedo; the method comprises the following steps: acquiring meteorological reanalysis data of daily scales ERA5-Land and GLADAS of a research area, wherein the meteorological reanalysis data comprises relative humidity RH, average temperature Ta, air pressure Pa, solar short-wave radiation # imgabs0 #, downward long-wave radiation # imgabs1 # and upward long-wave radiation # imgabs2 #; according to the technical scheme, multi-source remote sensing data, an evapotranspiration physical model and a space-time fusion technology based on evapotranspiration characteristics are utilized, and estimation of high space-time resolution evapotranspiration of the arid and semi-arid agricultural areas is achieved.

The invention discloses a production method of a high-spatial-resolution seamless leaf area index product. Comprising the following steps: S1, producing a high-spatial-resolution seamless reflectivity image: reconstructing missing data of a high-spatial-resolution and low-spatial-resolution HLS image by using a high-temporal-resolution and low-spatial-resolution MODIS image so as to produce 30m spatial-resolution seamless reflectivity data per 12 days; and step S2, constructing a leaf area index inversion model based on a Transform model. The invention provides a Transform-based leaf area index inversion model, overcomes the defect that the existing leaf area index inversion model based on a long-short term memory neural network and a bidirectional long-short term memory neural network is difficult to fully utilize time sequence image information, and improves the inversion precision of the leaf area index. The technical process for producing the high-spatial-resolution seamless leaf area index product is provided, and the defect that a large amount of data is lost in an existing high-spatial-resolution leaf area index product is overcome.