105 results about "Leaf area index" patented technology

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 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 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 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 multi-scheme collaborative yield forecasting method based on data assimilation and model parameter optimization, and belongs to the technical field of agricultural information, and the method comprises the steps: obtaining historical and real-time data of a target region and a target crop growth season; constructing a plurality of combined simulation schemes of the WOFOST model; carrying out data assimilation on the leaf area index and the soil humidity by utilizing an ensemble Kalman filter (EnKF) method and combining a Gaussian disturbance strategy; performing sensitivity analysis and optimization on photosynthetic parameters of the WOFOST model, determining an optimal photosynthetic parameter combination and operating the model; improving a water stress function; constructing a rolling updating yield prediction framework, and dynamically optimizing a yield prediction result; and dynamically selecting an optimal simulation strategy to simulate and forecast the yield. According to the method, the yield simulation precision and forecasting stability of the crop model under different moisture years are remarkably improved, and a reference is provided for developing a new meteorological year adaptive dynamic simulation framework of the crop model.

The invention provides a rice leaf area index inversion method based on an unmanned aerial vehicle, and relates to the technical field of smart agriculture, and the method comprises the steps: S1, obtaining an RGB image of a to-be-detected rice canopy, a training feature set, and a corresponding rice leaf area index measured value; s2, processing the RGB image of the rice canopy to be measured to generate a digital orthoimage and a digital earth surface model; s3, generating spectral features, height features and texture features based on at least one of the RGB image, the digital orthoimage and the digital earth surface model of the rice canopy to be measured; s4, training a machine learning model by using the training feature set and the corresponding rice leaf area index measured value to obtain a rice leaf area index inversion model; and S5, inputting the spectral features, the height features and the texture features into a rice leaf area index inversion model to obtain a leaf area index prediction value of the rice canopy to be detected. And rice leaf area index inversion is carried out only by using the RGB sensor to collect images.

The invention discloses a self-adaptive crop growth prediction and farming decision-making system based on multi-source environmental data. The system comprises a multi-source data acquisition module, a data preprocessing module, an adaptive parameter adjustment module, an intelligent prediction engine and a user interaction module. According to the system, soil physical and chemical parameters, meteorological elements and crop variety characteristics can be fused, and a dynamic environment-crop feature vector is constructed; driving a parameter adaptive optimization mechanism through a multi-source data acquisition and feature analysis module, and dynamically adjusting key parameters of a prediction and decision model by adopting a Bayesian optimization framework; the whole-growth-period growth prediction of the leaf area index, biomass accumulation and the mature period is realized in combination with an improved LSTM neural network, and farming suggestions such as sowing, fertilization and irrigation are generated; and outputting a result through a visual platform, and supporting user feedback closed-loop optimization. According to the method, a'region-variety 'two-dimensional response mechanism is designed, collaborative self-adaption of growth prediction and decision parameters is realized, and the generalization and precision of the model are improved.

The invention discloses an unmanned aerial vehicle remote sensing monitoring method and system for a wheat leaf area index, and relates to the field of smart agriculture, and the method comprises the steps: collecting the spectral reflection and texture structure characteristics of wheat for the elongation stage, heading stage and flowering stage of wheat under different planting densities and fertilization conditions through employing a multispectral unmanned aerial vehicle remote sensing technology, taking as a data set; and on the basis of the data set, aiming at a single growth period and multiple growth periods, monitoring the wheat leaf area index by constructing different monitoring models. The invention provides an effective method for multispectral unmanned aerial vehicle remote sensing monitoring of wheat LAI, and has important guiding significance for realizing precise agricultural management and improving wheat yield.

The invention discloses an irrigation decision determination method considering a multi-source water conversion process, and relates to the technical field of agricultural water conservancy irrigation, and the method comprises the steps: obtaining a multi-spectral image and a thermal infrared image of crops in a target irrigation region, calculating a vegetation index and a leaf area index, and combining with the actually measured growth vigor data of the crops, a growth vigor model of crops is constructed by establishing a mapping relation, the rainfall, the irrigation volume, the canal system infiltration replenishment volume and the groundwater capillary rise volume of a target irrigation area are obtained, and a soil moisture income and expenditure model is constructed based on a water circulation process. According to the method, a plurality of paths such as rainfall, irrigation, canal system leakage and groundwater capillary rise are considered through the constructed soil water volume income and expenditure model, crop physiological moisture inflection points are accurately recognized by simulating the root zone volumetric moisture content and growth vigor response curve, threshold control irrigation based on crop requirements is achieved, and the crop yield is improved. And the multi-source input and conversion process of the farmland hydrological system is comprehensively reflected.

The invention relates to the technical field of intelligent water conservancy, in particular to an irrigation area water demand prediction and scheduling system based on intelligent water conservancy, which comprises a disturbance identification module, a humidity sensing module, a deviation judgment module, an irrigation sequencing module and an instruction generation module. According to the method, the change degree of future weather forecast and weather data on that day is quantified to pre-judge the disturbance of the future environment, the soil moisture sensitive areas which are violently responded to the disturbance are dynamically screened out from the global irrigation area on the basis of the pre-judge, and the soil moisture sensitive areas which are violently responded to the disturbance are selected for the screened areas. Further combining with the ideal moisture demand of the crops at the current growth stage, analyzing the dynamic deviation trend between the actual soil humidity and the ideal value in the continuous time, thereby accurately identifying the land parcels really in the moisture supply deviation state, abandoning a fixed irrigation plan, and improving the irrigation efficiency. And acquiring physiological indexes such as evapotranspiration rate and leaf area index of the crops in the deviated area in real time, thereby judging the real urgent degree of the crops in each area to moisture and carrying out irrigation sequencing.

The invention provides a winter wheat leaf area index estimation method and system based on unmanned aerial vehicle remote sensing, and the method comprises the following steps: S1, data acquisition: obtaining unmanned aerial vehicle multispectral remote sensing image data of a research region, and collecting corresponding field actual measurement data in different growth periods of winter wheat; according to the winter wheat leaf area index estimation method and system based on unmanned aerial vehicle remote sensing provided by the invention, through the step design of S1 to S7, especially the collaborative cooperation of data preprocessing, data fusion, model construction and optimization, during use, on one hand, the leaf area index of the winter wheat can be estimated; in S2, splicing and geographic coordinate system embedding processing are carried out on unmanned aerial vehicle multispectral remote sensing image data, spatial positions of different wavebands can be automatically aligned, the problem that the number of rows and columns of multiband data is inconsistent is solved fundamentally, and a spatially unified data source is provided for subsequent vegetation index calculation and feature extraction.

The invention provides a garden carbon sink function zoning method fusing multi-source remote sensing data, and relates to the technical field of garden carbon sink evaluation and remote sensing monitoring. The method comprises the following steps: acquiring multi-source remote sensing data such as optical remote sensing, radar remote sensing and laser radar; performing radiometric calibration, atmospheric correction and geometric correction preprocessing on the data; vegetation characteristic parameters such as a normalized vegetation index, a leaf area index and surface temperature are extracted; calculating garden carbon sink efficiency parameters based on the vegetation characteristic parameters; calculating garden carbon sink function zoning parameters in combination with topographic data; performing carbon sink function zoning on the garden area by adopting a clustering algorithm, and dividing high, medium and low carbon sink function level areas; and outputting a carbon sink function division spatial distribution diagram and a data report. According to the method, evaluation comprehensiveness is improved through multi-source data fusion, accurate quantification is realized through innovative parameter calculation, scientificity and repeatability are improved through an objective partitioning method, and garden carbon sink fine management is effectively supported.

The present application relates to the technical field of climate change warning, and discloses a mangrove blue carbon ecological risk identification and warning method and system for climate change, comprising the following steps: acquiring time series of global land surface characteristic satellite leaf area index data and environmental factors, performing detrending, deseasonalization and standardization processing to extract abnormal sequences; determining climate oscillation windows and neutral periods according to multivariate El Nino-Southern Oscillation index and dipole mode index, and calculating the mean value of the environmental factors in the neutral period as a reference; inputting the environmental factors into a long short-term memory network to extract attention weights, calculating the factor difference between the mean value of the climate oscillation window and the reference, mapping the factor difference by using a nonlinear saturation kernel function, combining the weights to obtain contribution amplitude, and selecting a dominant risk factor by comparing the direction; processing the absolute value of the leaf area index anomaly by using an S function to output risk intensity, and predicting future leaf area index anomaly based on the dominant risk factor to generate a warning evaluation.

The present application relates to the technical field of agricultural remote sensing and machine learning, and particularly relates to a leaf area index estimation method based on improved XGBoost. The method comprises the following steps: acquiring unmanned aerial vehicle multi-spectral images and sample leaf area index measured values, and constructing a vegetation index map sample data set after preprocessing; performing feature extraction and fusion on the vegetation index map by using a deep learning network; constructing an improved beaver optimization algorithm, generating an initial population by using a two-stage initialization strategy, updating the position of the architect subpopulation by using an elite directional felling strategy, recombining individuals and the global optimal solution by using a vertical and horizontal cross strategy; optimizing the XGBoost hyperparameters by using the improved beaver optimization algorithm, and establishing a leaf area index estimation model. The leaf area index estimation method based on improved XGBoost combines deep learning feature extraction, improved swarm intelligence optimization algorithm and integrated learning regression modeling, and is helpful to improve the prediction accuracy and stability of the leaf area index estimation model.

The invention relates to a leaf area index time sequence processing method, which comprises the following steps of: inputting a time sequence remote sensing image and suburb forest and economic forest classification data of the same region; calculating a normalized differential vegetation index of the research area and carrying out time sequence sorting; calculating vegetation coverage data of the research area and carrying out time sequence sorting; synthesizing to obtain monthly FVC time sequence data of the suburb forest region and the economic forest region; calculating a suburb forest leaf area index LAI in the research area and performing time sequence sorting; calculating the economic forest leaf area index LAI of the research area and performing time sequence sorting; performing seasonal decomposition on the suburb forest time sequence data after adaptive filtering; performing seasonal decomposition on the economic forest time series data after adaptive filtering; merging the suburb forest time sequence data and the economic forest time sequence data; and outputting to obtain final optimized data. The invention further relates to a leaf area index time sequence processing system. According to the invention, a purification LAI time sequence product which clearly represents long-term trend, mutation and gradual change signals of respective vegetation canopy structures can be output.

The invention discloses a leaf area index inversion calculation method, and aims to solve the technical problem that the existing leaf area index estimation accuracy is limited. According to the method, collected crop canopy data is analyzed by adopting internal feature importance, a feature importance ranking is output by adopting a random forest algorithm, the most valuable feature is effectively screened out to participate in inversion of the leaf area index, and the inversion complexity can be greatly reduced. Bayesian optimization parameters are adopted, and historical parameter adjustment information is fully utilized through Bayesian optimization iteration, so that deep search of the parameters is fully realized, unnecessary objective function evaluation is reduced, and parameter adjustment efficiency is improved. Wherein the Bayesian optimization adopts a Gaussian process, previous parameter information is further considered, prior information is continuously updated, and the prediction precision is improved.

The invention belongs to the technical field of ecological hydrology, and particularly discloses a forest underlying surface earth surface evapotranspiration estimation and component segmentation method based on kNDVI. According to the method, based on a maximum entropy increase model, under the constraint of a surface energy balance equation, the surface state parameters of a forest vegetation-free area are used for resolving to obtain the soil evaporation capacity of the bare soil; on the basis of a maximum entropy increase model, under the constraint of a canopy energy balance equation, the vegetation transpiration amount is obtained through calculation according to the canopy state parameters of the forest vegetation coverage area; calculating the canopy interception evaporation capacity according to the leaf area index and the rainfall capacity of the forest vegetation coverage area; and finally, based on the kNDVI index, carrying out weighted summation on the bare soil evaporation amount, the vegetation evaporation amount and the canopy interception evaporation amount in the forest to obtain the total evapotranspiration of the underlying surface of the forest. Compared with an existing evapotranspiration estimation model, the forest underlying surface earth surface evapotranspiration estimation method is lower in complexity and higher in estimation precision.

The application provides a growth promoting microbial preparation for relieving saline-alkali stress and application thereof, and belongs to the technical field of agricultural microorganisms. The microbial preparation provided by the application can significantly relieve the adverse effect of saline-alkali stress on the growth and development of corn, and can stably improve the growth indexes of corn such as plant height, stem diameter, fresh weight, dry weight, leaf area index and chlorophyll content in different saline-alkali degree plots, and continuously promotes the growth in the whole growth period of corn, especially promotes the grain fresh weight in the late growth period, which is beneficial to yield formation. Meanwhile, the different strains in the compound microbial preparation have good interaction ability, can play the synergistic effect of multiple bacteria and functional complementary effect, and further improve the adaptability and growth promoting effect of the microbial preparation in the complex soil environment. The application has the advantages of environmental friendliness, low cost, difficulty in producing drug resistance and the like, and has a good application prospect in the fields of agricultural yield increase in saline-alkali land and development of microbial fertilizers.

The application provides a multi-scheme cooperative yield prediction method based on data assimilation and model parameter optimization, and belongs to the field of agricultural information technology.The method comprises the following steps: obtaining historical and real-time data of a target region and a target crop growing season; constructing multiple combination simulation schemes of a WOFOST model; using an ensemble Kalman filter (EnKF) method combined with a Gaussian disturbance strategy to perform data assimilation on a leaf area index and soil humidity; performing sensitivity analysis and optimization on photosynthetic parameters of the WOFOST model, determining an optimal photosynthetic parameter combination, and running the model; improving a water stress function; constructing a rolling update yield prediction framework, dynamically optimizing yield prediction results; and dynamically selecting an optimal simulation strategy to perform yield simulation and prediction.The application significantly improves the yield simulation accuracy and prediction stability of the crop model under different water year types, and provides a reference for developing a new framework of crop model meteorological year type self-adaptive dynamic simulation.

The invention provides a method, a device and equipment for predicting influence of marsh vegetation change on surface temperature, and belongs to the technical field of ecological remote sensing and climate effect evaluation. The method comprises the following steps: firstly, determining vegetation pixel distribution as a research area range in an unchanged marsh wetland distribution range; a year-by-year and multi-year average growth season data set is constructed through a maximum value synthesis and arithmetic mean method; in the grid units, the pixels are divided into a high value group and a low value group according to the multi-year average growth season leaf area indexes, the difference value between the multi-year average growth season leaf area indexes of all the pixels in the high value group and the low value group and the average surface temperature value is calculated, and then the surface temperature change degree caused by the marsh wetland vegetation coverage change is quantified; and finally, realizing accurate prediction of future surface temperature influence in combination with a vegetation change trend on a pixel scale.

The invention relates to a broccoli early rice rotation analysis method and system based on multi-source data fusion, electronic equipment and a computer readable storage medium, and the method comprises the steps: obtaining multi-source data of a target field area, and carrying out the preprocessing, positioning and calibration of a field multi-view image and soil PH value detection data; and generating a soil PH value thermodynamic diagram through spatial superposition fusion and an interpolation algorithm. On the basis, a PH stress area is identified, and spatial consistency analysis is carried out on the PH stress area and a chlorophyll content diagram obtained through multispectral image inversion, so that the health degree of the crop root system is evaluated. Then, a growth model with the soil PH value, the root system health degree and the multispectral and thermal infrared image features as input is established, and key parameters such as the plant nitrogen content, the leaf area index and the water stress index are quantitatively inverted by means of the predefined PH-root system-canopy physiological response relation. And finally, quantitative analysis of economic, ecological and agronomic benefits is carried out based on inversion parameters, and a comprehensive crop rotation benefit report is generated.

The application discloses a vegetation restoration influence on evapotranspiration quantitative evaluation method and device and a storage medium, relates to the technical field of ecological environment, and comprises the following steps: collecting remote sensing leaf area index data in a target area, and preprocessing the remote sensing leaf area index data to obtain a vegetation parameter time sequence; based on the vegetation parameter time sequence, separating a trend component dominated by vegetation restoration and a fluctuation component dominated by climate fluctuation; inputting the trend component and the fluctuation component into an evapotranspiration model trained, to obtain first evapotranspiration data corresponding to the trend component and second evapotranspiration data corresponding to the fluctuation component; and calculating a difference value between the first evapotranspiration data and the second evapotranspiration data as an independent contribution value of vegetation restoration on evapotranspiration. The application can realize accurate quantification of the independent influence of vegetation restoration on evapotranspiration.

The invention discloses a litter leaf area index determination method, the proposed litter leaf area index has the same meaning as a vegetation leaf area index, can participate in related operation as the vegetation leaf area index, and can bring litters into a vegetation investigation system; the index is the number of covering layers of leaves on the earth surface, and the measuring and calculating method comprises the steps that the total spreading area of litter leaves on the unit area of the earth surface is established, and a litter leaf area index estimation model of forest lands such as needle-leaved, broad-leaved and needle-broad mixed forests is established by establishing the power exponent relation between the total spreading area and the dry weight of the litters in the unit area. Therefore, litter unit area dry weight index meaning conversion is achieved, and the method has better adaptive capacity in forest land vegetation investigation.

This application provides a method and apparatus for inverting the leaf area index (LAI) of a forest. The method first acquires information about the tree height and the total attenuation coefficient of each component in the forest to electromagnetic waves. The total attenuation coefficient includes the attenuation coefficients of leaves and branches. Then, based on the tree height and the total attenuation coefficients of each component, the product of the total attenuation coefficients is calculated. Finally, the LAI is inverted using the relationship between the product of the total attenuation coefficients and the LAI. This method utilizes the attenuation characteristics of the forest to electromagnetic waves to invert the LAI. Since the attenuation of electromagnetic waves is not affected by tree species, region, complex meteorological conditions, or the chosen mathematical model, it can more accurately reflect the attenuation of electromagnetic waves by the leaf area. Therefore, the accuracy of the LAI inversion using this method is higher.

The invention discloses a medium and late maturing corn precise cultivation method and system based on density regulation and control, and particularly relates to the field of agricultural plant cultivation environment control. A difference value is generated by collecting plant spacing information, a leaf area index is compared with a photosynthetic parameter, the growth state of crops in a region is judged, and water supply is adjusted according to the growth state; the cultivation density is optimized in combination with real-time environmental parameters, finally, the corresponding relation between the water demand and the cultivation density is converted into irrigation regulation and control, and accurate management of the corn in the dynamic environment is achieved. According to the method, through step-by-step difference calculation, parameter comparison and matching adjustment, the cultivation space distribution is balanced, and the problem caused by local over-density or over-sparse is avoided; through crossover operation of the leaf area index, the photosynthetic rate, the transpiration rate and the temperature, dual matching of the health index and the density is formed; through difference comparison of moisture and density correlation, dynamic adjustment of the irrigation amount is achieved, moisture distribution among plants is more reasonable, and the accuracy of cultivation management is improved.

The present application provides a kind of leaf area index determination method, device, equipment, medium and product, it is related to agricultural remote sensing and near-ground optical measurement technical field, to solve the defects of lower accuracy and poor efficiency in determining the leaf area index of crop in the prior art, realize the accuracy and efficiency of improving the leaf area index of crop is determined.Imcluding: the target hemispherical image collected is carried out image correction processing, obtain the target hemispherical image after correction, target hemispherical image is obtained by vertical upward shooting for shooting device in plant row center position;Determine the normalized difference vegetation index NDVI histogram corresponding to plant in the target hemispherical image after correction;Based on NDVI histogram and preset zenith angle weight, determine the void fraction of plant;Based on void fraction, the leaf area index LAI of plant is inversed.

The present application relates to a kind of improved radiation transmission model mangrove chlorophyll estimation method, comprising: the measured data of the tree height, crown width and leaf area index of mangrove are collected;Mathematical relationship between leaf area index and tree height, crown width is constructed;Simulated spectral data is generated as training set using improved radiation transmission model;The sensitive feature screening of mangrove functional traits is carried out;Random forest inversion model based on physical constraint is constructed;The present application estimates the chlorophyll content of mangrove using the random forest inversion model based on physical constraint.The beneficial effects of the present application are: the present application can effectively improve the precision of chlorophyll inversion, provide scientific basis for the fine inversion of mangrove chlorophyll, provide data support and decision-making reference for the protection and scientific management of mangrove.

The invention provides a method for regulating and controlling fertilization based on soil carbon sequestration rate and grassland manure nutrient demand, and relates to the technical field of agricultural regulation and control fertilization, the method comprises the following steps: carrying out grid division on a target field, collecting soil related data, determining the organic carbon content through a carbon dioxide flux method, and calculating the net carbon sequestration rate of each grid unit; based on the photosynthetic production principle and the leaf area index, in combination with the biomass loss rate and the growth stage, a nutrient concentration dynamic model is established, the nutrient absorption amount of each grid is accurately evaluated, a nutrient demand net function is established through the existing nutrient and manure release amount of soil, and the comprehensive nutrient demand index is further calculated. Differentiated fertilization of field grid units is realized by adopting a clustering algorithm. By integrating soil carbon flux monitoring, a vegetation growth model and a clustering analysis technology, fine identification and dynamic regulation and control of grassland nutrient requirements are realized, and the fertilization precision and the soil carbon sequestration efficiency are improved.

The present application relates to the technical field of remote sensing, in particular to a method for estimating total primary productivity of pantropical forest vegetation depending on leaf age, which comprises obtaining a weather basic data set and a leaf age-based vegetation leaf area index data set, grouping current leaves according to leaf age, including new leaves, mature leaves and old leaves, obtaining the maximum light energy utilization rate of all leaves, and outputting the final productivity through a remote sensing estimation model of total primary productivity. The present application considers the regulation of leaf phenology on canopy photosynthesis, distinguishes the photosynthetic capacity difference between leaf age components, and constructs a remote sensing estimation method suitable for total primary productivity of pantropical forest vegetation, so as to improve the simulation accuracy of total primary productivity of pantropical forest vegetation, and help to accurately monitor the evolution trend of photosynthesis of pantropical forest under climate change.