96 results about "Aboveground biomass" patented technology

The invention belongs to the technical field of agriculture, and particularly provides a compound microbial agent and application thereof. The compound microbial agent is prepared from bacillus velezensis RKN1111, bacillus subtilis N-3 and paenibacillus mucilaginosus HX-02, and the compound microbial agent is prepared from bacillus velezensis RKN1111, bacillus subtilis N-3 and paenibacillus mucilaginosus HX-02, and the compound microbial agent is prepared from bacillus velezensis RKN1111, bacillus subtilis N-3 and paenibacillus mucilaginosus HX-02. The compound microbial agent can effectively improve the pH and EC of saline alkali soil, increase the content of alkali-hydrolyzable nitrogen, available phosphorus and quick-acting potassium in the soil, and play a significant role in increasing the plant height, stem diameter, chlorophyll content and aboveground biomass of corn plants in coastal saline-alkali soil.

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 discloses a forest degeneration degree, degeneration type and degeneration process identification method, and relates to the field of forest resource monitoring and ecological environment evaluation.The method comprises the steps that multi-source data obtained in a research area is preprocessed, and the preprocessed multi-source data is determined; selecting multi-dimensional indexes from three aspects of forest degradation structure, composition and function, and constructing a multi-dimensional index system according to the preprocessed multi-source data; the multi-dimensional indexes comprise forest coverage rate, crushing degree, tree variety diversity, aboveground biomass and net primary productivity; generating a forest degradation index according to each index in the multi-dimensional index system; determining a forest degeneration degree according to the forest degeneration index, generating a forest degeneration type graph in combination with land coverage data to reveal degeneration differences of different forest types, analyzing a dynamic change track of a forest degeneration region in combination with a normalized combustion index time sequence, and identifying a forest degeneration process; according to the method, the multi-dimensional characteristics of degradation can be comprehensively revealed.

The invention discloses an emergent vegetation management method for increasing a biological carbon sequestration function, and belongs to the technical field of ecological system management and restoration, and the method comprises the following steps: in the stable growth period of emergent vegetation, discharging part of wetland moisture, and carrying out first mowing on overground parts of plants above the water surface; after the emergent vegetation further grows, the water level is restored to the end of the growth season, finally water is drained, secondary mowing is carried out, and all the emergent vegetation on the ground is harvested. According to the method, the overground biomass is increased through the plant photosynthetic carbon sequestration function by adjusting the appropriate harvesting time and mode of the wetland emergent vegetation, and therefore the biological carbon sequestration amount is increased. Compared with a traditional method for harvesting wetland vegetation at a time, the method has the advantages that the plant growth rate is remarkably increased, the overground biomass of the plants and the accumulation of organic carbon in surface soil are promoted, and the improvement of the wetland carbon sequestration function is facilitated.

The invention belongs to the field of degraded grassland restoration evaluation research, and discloses a degraded grassland restoration evaluation research method based on multivariate remote sensing data, and the method comprises the steps: carrying out the observation through employing a FragMap unmanned aerial vehicle aerial photography analysis system, and carrying out the preprocessing through employing Pixel Base Management Classifier software; collecting grassland above-ground biomass data; acquiring and preprocessing remote sensing data; constructing a grassland coverage and aboveground biomass remote sensing estimation model, and performing precision verification; analyzing grassland coverage and biomass space difference before and after restoration of the degraded grassland; and analyzing space-time dynamic changes of grassland coverage and biomass. According to the method, a high-temporal-spatial-resolution NDVI data set of a research area is constructed by combining a high-temporal-spatial-resolution data fusion algorithm, a grassland coverage and aboveground biomass remote sensing estimation model is constructed by combining ground survey and unmanned aerial vehicle aerial photography, and the grassland coverage and aboveground biomass change conditions before and after a degraded grassland restoration measure is implemented are systematically evaluated. And the restoration effects of different restoration measures on the degraded grassland are evaluated.

The invention provides a soil mixing and sowing method of typical herbaceous plants and a method for forming a biological ridge of a newly-built terrace edge ridge, which comprises the following steps of: 1) selecting qualified trifolium repens, ryegrass, alfalfa and vicia villosa seeds according to process requirements; 2) seed pretreatment: soaking trifolium repens and alfalfa seeds with warm water at 30-40 DEG C for 2-4 hours; the method comprises the following steps: soaking ryegrass and vetch seeds in cold water for 1-2 hours; (3) respectively and uniformly mixing the four seeds with soil according to the proportion required by the process; (4) uniformly sowing the seed-soil mixture on a target land parcel; and 5) management after sowing. According to the herbaceous vegetation soil mixing and sowing method, high germination and survival rate of the herbaceous vegetation can be guaranteed, the plant height, the above-ground biomass and the vegetation coverage of all the herbaceous vegetation are continuously increased in the growing season, and the root system biomass is in the trend of firstly decreasing and then increasing; and all herbaceous vegetation improves the water retention performance of the soil.

The invention discloses an automatic crop nitrogen fertilizer accurate management method based on a remote sensing image, and relates to the technical field of accurate fertilization, the method comprises the following steps: obtaining ground measured data containing overground biomass and plant nitrogen concentration in a key growth period, and establishing a nitrogen dilution curve model through nonlinear least square fitting; uAV multispectral images are collected synchronously, a vegetation index matrix is calculated, a machine learning regression model is trained in combination with ground data, and an inversion model of overground biomass and plant nitrogen accumulation is obtained; acquiring a current-season image before topdressing, and performing pixel-by-pixel inversion to generate a biomass and nitrogen accumulation spatial distribution diagram; and calculating a critical nitrogen accumulation amount according to the nitrogen dilution curve so as to obtain a nitrogen nutrition index, calculating a recommended topdressing amount in combination with a preset basic topdressing amount and a nitrogen fertilizer utilization rate, and outputting a prescription map which can be directly imported into a variable fertilization machine. The method belongs to localized modeling and machine prescription map generation, the technical threshold is lowered, the nitrogen fertilizer utilization rate is increased, and environmental pollution is reduced.

The invention relates to the technical field of forest fire prevention, in particular to a large-scale forest ground surface litter load calculation method. The method comprises the steps of obtaining a forest resource partition to which a target area belongs; determining the forest type of each forest class in the target area based on the national forest resource checking data; according to the forest resource subarea to which the target area belongs and the forest type of each forest class in the target area, establishing an above-ground biomass and stock regression model of each forest class in the target area; acquiring the aboveground biomass of each forest class in the target area according to the aboveground biomass and accumulation regression model; obtaining the land surface litter carrying capacity of each forest class in the target area by inquiring the proportion of the land surface litter carrying capacity in the aboveground biomass in the forest class of each forest type; and obtaining spatial distribution of the surface litter load of the target area according to the surface litter load of all forest classes in the target area. According to the method, the efficiency and accuracy of large-scale forest surface litter load calculation can be effectively improved.

The invention discloses a haloxylon ammodendron forest biomass estimation method based on unmanned aerial vehicle multi-source data collaborative inversion, and relates to the technical field of environment remote sensing and information, and the method comprises the steps: obtaining a training sample set; obtaining unmanned aerial vehicle multispectral image data and unmanned aerial vehicle laser radar point cloud data of each training sample plot; extracting a first type of feature set comprising vegetation index features and texture features; extracting a second type of feature set comprising a height feature and a density feature; fusing the two types of feature sets to form a joint feature set; screening the joint feature set to construct a target feature subset; and carrying out model training by utilizing a machine learning algorithm to obtain an above-ground biomass estimation model. According to the method, the technical problem that the existing single remote sensing data source is low in precision in estimating the biomass on the desert sparse haloxylon ammodendron forest land is solved, high-precision and automatic estimation from the sample land to the regional scale is realized, and technical support is provided for accurate accounting of the desert carbon reserve in the arid region.

The invention discloses a crop remote sensing yield estimation method and device fusing meteorological data and planting information, a medium and a product, and relates to the technical field of agricultural informationization, and the method comprises the steps: obtaining basic data of a to-be-estimated area; determining daily total primary productivity of the crops according to the meteorological data and the remote sensing image data; determining overground biomass of the crops according to the daily total primary productivity and the key growth period of the crops; according to the variety information and the remote sensing image data, constructing a filling period vegetation index attenuation model; the filling stage vegetation index attenuation model is used for dynamically determining a harvest index; according to the above-ground biomass and the harvest index of the crop, determining a per unit yield result of the crop; the accuracy of crop yield estimation can be improved.

The soybean aboveground biomass dynamic across growth period estimation method, system and program product relate to the crop yield technical field, fill the existing technology in the problem of combining research soybean AGB based on UAV remote sensing and machine learning relative blank. Through the unmanned aerial vehicle to soybean multiple growth stage data collection, and based on soybean growth stage data, respectively generate multispectral orthophoto and digital surface model; based on multispectral orthophoto and digital surface model, respectively extract the crown structure features, image texture features and multiple vegetation indexes related to aboveground biomass of crops; after improving the stacking model, using multiple data of soybean, crown structure features, image texture features and multiple vegetation indexes related to aboveground biomass of crops to train the improved stacking model, obtain the trained stacking model; based on the trained stacking model, complete the soybean aboveground biomass growth period estimation.

The invention relates to a remote sensing estimation method and system for grassland above-ground biomass and fixed carbon content thereof. The method comprises the following steps: acquiring a hyperspectral remote sensing image, and carrying out atmospheric correction to obtain a surface reflectance image; identifying grassland functional end members to obtain a localized grassland end member spectrum library; performing linear spectrum mixed decomposition in combination with the surface reflectance image and the localized grassland end member spectrum library to obtain a grassland plant functional abundance set; calculating a pixel-level dynamic specific carbon content in combination with time sequence multispectral image data to obtain a dynamic specific carbon content field; performing regression prediction calculation by combining the surface reflectance image, the radar data and the grassland plant functional abundance set to obtain a grassland above-ground biomass distribution map; and carrying out pixel-by-pixel multiplication calculation to obtain spatial distribution data of the overground carbon sequestration amount. By adopting the method, the problem of estimation deviation caused by using a fixed carbon content coefficient in the prior art can be solved, and the estimation precision is improved.

The present application relates to the field of agricultural ecological environment management and carbon sink monitoring technology, and more particularly to a tea tree lossless tea garden carbon fixation and sink increase method, comprising: pyrolyzing agricultural waste to obtain biochar and making the biochar into a specific standard biochar-based fertilizer; determining tea garden soil organic matter, cation exchange capacity and soil bulk density, calculating soil quality comprehensive coefficient by using improved Nemerow comprehensive index method and dividing soil quality grades according to the soil quality comprehensive coefficient; applying the biochar-based fertilizer in a differentiated application amount according to the soil quality grades; determining tea tree aboveground biomass after fertilization and calculating tea tree carbon storage according to a corresponding carbon storage model selected according to the age of the tea tree; simultaneously determining soil parameters to calculate tea garden soil carbon storage; and finally taking the tea tree carbon storage and the soil carbon storage as the total carbon sink of the tea garden. The present application realizes systematic, accurate and lossless monitoring of tea garden carbon fixation and sink increase technology.

The application discloses a temperate meadow steppe pasture grassland soil and vegetation synergic improvement method and application, and belongs to the technical field of grassland ecological restoration and degraded grassland improvement, and the method comprises the following steps: S1, pre-cutting roots to form narrow cracks; S2, forming an ice wedge, using winter natural cooling to make deep unfrozen water migrate to the cracks and expand by freezing, and the crack width is expanded; S3, hot melting and seeding, sowing the pasture grass seeds in the ice wedge cracks and applying a water-retaining agent when the surface soil is thawed in the next year; S4, spring root cutting, performing the root cutting operation when the soil layer thawing depth reaches 10-15 cm; and S5, applying a composite mixed fertilizer. Experiments show that the method can significantly improve the pasture grass community coverage, height and aboveground biomass, and simultaneously improve the contents of soil nutrient components, organic carbon and the like, and is suitable for the pasture grassland with the pasture grass as a dominant species, and has good ecological and economic benefits.

The present application provides a kind of grassland aboveground biomass physical inversion method and system, belong to ecological monitoring technical field, comprising: according to look-up table and the correction remote sensing image of measurement area, determine the actual vegetation parameter of each measurement point in measurement area;Look-up table includes multiple preset vegetation parameters and the preset vegetation spectral characteristics corresponding thereto;According to each actual vegetation parameter and the aboveground biomass measured value of corresponding measurement point, construct cost function, under the preset iteration termination condition, iteration solving grassland type corresponding optimization stem leaf ratio;According to optimization stem leaf ratio and each actual vegetation parameter, obtain the aboveground biomass optimization value of each measurement point;According to the aboveground biomass optimization value of all measurement points and the grassland type distribution diagram of all measurement area, generate the aboveground biomass distribution diagram of overall area.The present application is through look-up table and iteratively optimizes grassland type stem leaf ratio to realize the accuracy of improving the spatial distribution of overall area aboveground biomass by inverting measurement point vegetation parameter.

The invention provides a corn biomass hyperspectral estimation method and system based on fractional order differential optimization. The method comprises the following steps: collecting corn planting data of a target area; processing the original hyperspectral gray value image to obtain a multiband surface reflectance image, and obtaining original hyperspectral reflectance through ROI (Region of Interest) extraction and SG filtering; performing FOD fractional order differential transformation on the original hyperspectral reflectivity before and after the NDVI mask processing to obtain FOD spectral reflectivity characteristics under different orders; constructing a plurality of dual-band spectrum indexes and a plurality of three-band spectrum indexes, and performing high-dimensional feature screening on the plurality of dual-band spectrum indexes and the plurality of three-band spectrum indexes to form a candidate feature set; and by taking the candidate feature set as an input variable and the aboveground biomass actually measured on the ground as an output variable, constructing an aboveground biomass estimation model, carrying out training optimization on the aboveground biomass estimation model, and outputting an optimal estimation strategy of the aboveground biomass of the corn.

The application provides a method and device for evaluating aboveground biomass of Spartina alterniflora based on coverage and height. The method provided by the application comprises the following steps: setting a first quadrat in a Spartina alterniflora growth area, collecting all plants in the first quadrat, measuring the height and dry weight of each plant, establishing a first allometric growth relationship model between the height and dry weight of each plant based on the height and dry weight of each plant, setting a second quadrat in the Spartina alterniflora growth area, recording the quadrat coverage, average height and plant number in the second quadrat, calculating the total dry weight of the quadrat and converting the total dry weight into aboveground biomass, establishing a second allometric growth relationship model between the coverage, height and aboveground biomass based on the quadrat coverage, average height and aboveground biomass, matching a corresponding target allometric growth relationship model based on the scale and data conditions of a region to be evaluated, inputting corresponding data of the region to be evaluated into the target allometric growth relationship model, and calculating the aboveground biomass of the region to be evaluated.

The application belongs to the technical field of plant molecular breeding, and provides a corn ZmAAP37 Gene in regulating corn agronomic traits and grain nitrogen accumulation, the application includes at least one of the following: (1) increase the nitrogen content of corn kernel; (2) increase the free amino acid content of corn kernel; (3) improve the aboveground biomass of corn plant; (4) increase the plant height of corn plant; (5) delay the leaf senescence of corn plant; (6) improve the nitrogen utilization rate of corn. The application shows that, ZmAAP37 By increasing the transport of amino acids in leaves to phloem, the nitrogen remobilization is promoted, and finally the grain nitrogen concentration is significantly increased without affecting the grain yield. The application provides ZmAAP37 The gene can provide new gene resources for nitrogen utilization of crops, especially corn, and has great application value in regulating nitrogen utilization of crops.

The present application relates to forest aboveground biomass measurement, specifically relates to a kind of forest aboveground biomass remote sensing measurement method based on improved Boruta algorithm, improved Boruta algorithm's forest aboveground biomass remote sensing measurement method, it is characterized in that, the method includes the following steps: (1) selecting the remote sensing image data of the region to be measured and data pretreatment;(2) design improved Boruta algorithm;(3) SVR model is established using improved Boruta algorithm;(4) the forest aboveground biomass of the region to be measured is measured using model, the precision of the present application is higher, the AGB distribution condition measured and the actual surveying research ground topography and forest vegetation distribution condition basically coincide, provide a new method for forest measurement.

The invention discloses a soybean key growth period aboveground biomass estimation method based on a hyperspectral image and machine learning. Belongs to the technical field of electric digital data processing. According to the method, the technical problems of low information utilization rate, poor stability in the trans-growth period, insufficient model interpretation and the like in the overground biomass prediction of the key growth period of the soybeans in the existing method are solved. A feature fusion method of hyperspectral derivative transformation and wavelet decomposition is provided, noise is effectively suppressed, and the spectral difference in the growth period is highlighted; a multi-source feature coupling system is constructed, traditional spectral indexes are utilized, waveband combination and wavelet features are introduced, and feature diversity and robustness are improved; the stability of AGB estimation in the cross-growth period is improved through the Stacking integrated model, and the Stacking integrated model is superior to a single model.

The application discloses a temperate meadow steppe pasture grassland soil and vegetation synergic improvement method and application, and belongs to the technical field of grassland ecological restoration and degraded grassland improvement, and the method comprises the following steps: S1, pre-cutting roots to form narrow cracks; S2, forming an ice wedge, using winter natural cooling to make deep unfrozen water migrate to the cracks and expand by freezing, and the crack width is expanded; S3, hot melting and seeding, sowing the pasture grass seeds in the ice wedge cracks and applying a water-retaining agent when the surface soil is thawed in the next year; S4, spring root cutting, performing the root cutting operation when the soil layer thawing depth reaches 10-15 cm; and S5, applying a composite mixed fertilizer. Experiments show that the method can significantly improve the pasture grass community coverage, height and aboveground biomass, and simultaneously improve the contents of soil nutrient components, organic carbon and the like, and is suitable for the pasture grassland with the pasture grass as a dominant species, and has good ecological and economic benefits.

The invention relates to a method for constructing a model for estimating the aboveground biomass of non-uniformly planted rice by using multi-temporal unmanned aerial vehicle image data and an estimation method. The method comprises the following steps: dividing a full life cycle of rice into a vegetative period and a reproductive period by combining a high-resolution unmanned aerial vehicle multispectral image, obtaining a multispectral image of a target area by using an unmanned aerial vehicle in a tillering period, processing the image, extracting a rice pixel proportion, and correcting overground biomass and vegetation indexes by using a rice pixel proportion coefficient to obtain a corrected overground biomass and vegetation index; and then a ternary linear regression model of the vegetation index and the aboveground biomass is established through a multivariate stepwise regression method (SPSS), so that the aboveground biomass estimation precision of the non-uniformly planted rice is remarkably improved.

The invention discloses a multi-source remote sensing forest biomass estimation method and system for high-precision inversion, and relates to the technical field of forest biomass estimation, and the method comprises the steps: obtaining multi-source remote sensing data of a target artificial forest region; performing pretreatment; constructing a multi-dimensional feature vector based on the preprocessed multi-source remote sensing data, wherein the multi-dimensional feature vector comprises a spectral feature, a texture feature, a radar scattering feature and a forest three-dimensional structure feature; inputting the multi-dimensional feature vectors into an auto-encoder model, and performing nonlinear dimension reduction on high-dimensional features by an encoding layer to obtain low-dimensional deep feature representation; constructing an optimization model taking the above-ground biomass inversion error as a target function, and performing collaborative global optimization by adopting a simulated annealing algorithm to obtain an optimal feature combination and an optimal model parameter; an XGBoost above-ground biomass inversion model is constructed, and the inversion model is trained by using actually measured sample above-ground biomass data; and spatial continuous estimation of the above-ground biomass is realized. According to the method, the biomass inversion precision under the complex artificial forest condition is remarkably improved.

The invention provides a crop nitrogen application decision-making method and system, and the method comprises the steps: obtaining the nitrogen utilization efficiency of crops according to the plant nitrogen absorption amount, the nitrogen application amount and the soil available nitrogen content of the crops; calculating the critical nitrogen concentration according to the aboveground biomass of the crops; obtaining the nitrogen nutrition index of the crop according to the ratio of the actual nitrogen concentration to the critical nitrogen concentration of the plant; inputting the vegetation index, the growth period parameter, the meteorological data, the soil data and the photosynthetic physiological parameter of the plot where the crop is located and the nitrogen nutrition index of the crop into a Voting integrated machine learning model to obtain an inversion NNI spatial distribution diagram; according to the nitrogen nutrition index in the inversion NNI spatial distribution diagram and a preset nitrogen nutrition index threshold value, the required additional fertilization amount of each subarea in the land parcel is obtained. According to the method, multi-source data such as meteorology, soil and vegetation indexes are integrated, and the critical nitrogen concentration model is combined, so that the inversion precision of the nitrogen nutrition index of the crop is remarkably improved.

This invention relates to the field of plant disease control technology, and in particular to the application of dihydroxyacetone (DHA) in the control of root rot and / or the promotion of plant growth. This invention found that DHA can effectively antagonize the growth of the pathogen *Fusarium graminearum*, with an inhibition rate of 36.14%–48.65%. In soil culture experiments, at 7 days, both high concentrations (1M) and low concentrations (5mM) of DHA showed good control effects on maize root rot, with disease control efficacy reaching 14.17% and 27.08%, respectively. Simultaneously, both showed good growth-promoting effects; low concentrations of DHA increased the aboveground biomass of maize by 65.30% compared to diseased plants. At 14 days, the control efficacy of high concentrations (1M) and low concentrations (5mM) of DHA against maize root rot was 15.24% and 33.05%, respectively, and low concentrations of DHA increased the aboveground biomass of maize by 135.32% compared to diseased plants.

The invention discloses a portable foraging forage grass biomass intelligent inversion method and system, and the method comprises the steps: synchronously collecting a multispectral image and laser radar point cloud data of a target region through a handheld device, and carrying out the preprocessing of radiation correction, geometric registration and the like, inputting a spectral space double-flow Transform deep learning network to construct a grass seed semantic segmentation model, extracting spectral and spatial structure features, obtaining quantitative indexes such as the area and coverage of a target grass seed region, fusing to construct a biomass inversion model, determining and outputting estimated values of the total biomass of the grassland per unit area and the biomass of the foraging forage grass, and visualizing the estimated values of the total biomass of the grassland per unit area and the biomass of the foraging forage grass. According to the method, technologies of multi-source data acquisition, deep learning semantic segmentation, feature fusion, inversion modeling and the like are integrated, specific grass seed recognition, high-precision estimation of overground biomass and accurate classification of foraging forage grass and non-foraging forage grass are realized on the field site, high-quality forage grass and toxic and harmful plants are effectively distinguished, and the method is suitable for large-scale popularization and application. And reliable quality distribution information is provided for grassland resource evaluation.

The invention relates to the technical field of biology, in particular to application of ZmMAPKKK70 protein and a coding gene thereof in regulation and control of corn phosphorus utilization, the ZmMAPKKK70 gene is screened out through whole genome correlation analysis, the ZmMAPKKK70 gene is knocked out from corn through a gene editing technology, and an obtained corn mutant material has the advantages that the corn mutant material can be used for regulating and controlling the phosphorus utilization of the corn in a field with phosphate fertilizer or without phosphate fertilizer; the Pseudomonas aeruginosa has the advantages in a plurality of growth indexes, including stability of leaf development, increase of plant height and stem diameter and improvement of final biomass and yield, and especially shows relatively strong adaptive capacity and growth potential under different phosphate fertilizer supply conditions. The corn ZmMAPKKK70 is verified to negatively regulate plant phosphorus utilization and overground biomass, plants which are more tolerant to a low-phosphorus environment and larger in overground biomass are obtained, gene resources are provided for new variety cultivation, and mutant materials have wide agricultural application potential, are particularly suitable for farmland environments with limited resources and insufficient phosphate fertilizer supply and have broad application prospects. Meanwhile, the obvious high-yield potential is also shown under the condition that the phosphate fertilizer is sufficient.

The invention discloses a wetland ecosystem NPP estimation method, device and system. The method comprises the following steps: firstly, determining a to-be-estimated wetland ecosystem and collecting image data of the to-be-estimated wetland ecosystem; measuring the aboveground biomass AGB and the underground biomass BGB of the plant, and obtaining measured biomass data; collecting and preprocessing continuous reflectivity data to determine pixel spectral data; three biomass inversion models of a random forest model, an XGBoost model and a PLSR are constructed respectively, an AGB optimal inversion model and a BGB optimal inversion model are obtained through weighted fusion, and a wetland special NPP estimation model is determined; and finally, performing pixel-by-pixel calculation on image data of a wetland ecosystem to be estimated by utilizing the trained NPP estimation model special for the wetland to generate an aboveground biomass spatial distribution diagram, an underground biomass spatial distribution diagram and a net primary productivity spatial distribution diagram. According to the method, high-precision and unified estimation of the wetland ecosystem NPP can be realized.

The invention discloses a grassland accurate mowing stubble height determination method based on species component analysis, and the method comprises the steps: setting a quadrat in each experiment cell of a target grass mowing field, and carrying out the species investigation of plants in the quadrat; collecting the plant height in each quadrat; the plants with the natural heights measured are cut flush with the ground; the cut fresh grass is dried to constant weight and weighed, and the overground biomass of all species in each quadrat and the total biomass of the quadrat are obtained; for each quadrat, calculating the reference utilization rate of all species at a given height by using an iterative dichotomy and combining the average height, the above-ground biomass and the quadrat total biomass, and taking the given height corresponding to the reference utilization rate meeting a tolerance condition as a target stubble height; and determining the arithmetic average value of the target stubble height corresponding to each quadrat as the recommended stubble height. According to the method, the cutting utilization rate can be accurately and scientifically predicted and controlled, and the dual purposes of sustainable utilization of grassland resources and ecological protection are achieved.