47 results about "Primary productivity" patented technology

The invention discloses a method for inverting marine primary productivity based on airborne dual-wavelength laser radar remote sensing data, and the method comprises the steps: building a dual-wavelength high-precision water optical characteristic inversion model based on the airborne dual-wavelength laser radar remote sensing data, and obtaining a high-precision marine phytoplankton absorption coefficient vertical section; and inverting the marine primary productivity vertical section distribution coupled with the high-precision phytoplankton absorption coefficient. According to the invention, one laser wavelength is suitable for detecting an offshore sea area, the other laser wavelength is suitable for detecting an open sea area, and the dual-wavelength ocean laser radar improves the maximum detection depth of near and open seas and the inversion precision of the primary productivity vertical section distribution of a typical sea area. And the ocean carbon cycle space-time evolution precision and the observation capability in the aspect of ocean upper-layer energy conversion are further improved.

The invention provides a field-level rice yield remote sensing estimation method based on high-resolution total primary productivity and a dynamic harvest index, and the method comprises the following steps: firstly, obtaining a rice growth season time sequence Sentinel-2 image based on a remote sensing cloud platform, calculating a leaf area index, and obtaining meteorological data EAR5 products and CO2 data; then, a self-service sampling method and a multi-model process sensitivity analysis method are used for analyzing the influence rule of the rice growth season environment factors on the GPP, an improved double-leaf light energy utilization rate model is constructed, and high-resolution rice GPP high-precision estimation is achieved. And finally, forming an HIDYM method in combination with the dynamic harvest index to realize the yield estimation of the field-level rice in Jiangsu province for many years. The method can be used for quickly and accurately estimating the yield of the large-range field-level rice.

Through sourcing net-primary productivity additive algae-based biomass feedstock, the exclusive use of renewable energy in processing, and the appropriate formulation and processing, a novel algae-derived bio-based plastic is both carbon-negative and provides some performance advantages over existing algae-based film plastics especially with regard to optical clarity. A system may be provided that produces a carbon-negative bioplastic. The production of the bioplastic in a process chamber may be controlled by an electronic controller. The electronic controller may be controlled by a host system, such a server. The electronic controller may be configured to direct production of the bioplastic in the process chamber using hydrocolloid, which is derived from algae.

The invention provides a rice yield growth season high-precision prediction method based on a total primary productivity remote sensing product. The method comprises the following steps: firstly, calculating rice total primary productivity GPP and a normalized difference red-edge vegetation index NDRE in a rice growth season on a remote sensing cloud platform based on a time sequence Sentinel-2 image; then, respectively carrying out correlation analysis on the yield by utilizing GPP and NDRE of monthly-scale rice; performing harmonic fitting on the time sequence GPP based on a harmonic analysis technology, and determining an optimal time window in a growth season for rice yield prediction at an interval of 10 days; the rice yield is estimated by utilizing transfer learning and GPP and NDRE of the optimal time window, so that the advantages of GPP in yield estimation are clear; and an inter-annual GPP correction method suitable for different ecological points is constructed, so that the yield estimation precision of independent years is improved. According to the method, the yield of the rice in the field can be accurately estimated in seasons in a regional range, and accurate management of the rice can be promoted by farmers and agricultural technology promoters.

The invention provides a method and system for predicting the total primary productivity of an ecological system under drought stress, and the method comprises the steps: coupling a nonlinear response and a hydrothermal carbon physical mechanism under the drought stress; and an important reference basis with high operability is provided for globally and regionally predicting and evaluating a response mechanism of future drought evolution to an ecological system carbon sink function under a climate change scene.

The invention discloses a vegetation net primary productivity space-time trend analysis and climate and human contribution analysis method. The method comprises the following steps: (1) carrying out space-time trend analysis of different space unit vegetation NPP; (2) dividing the vegetation net primary productivity NPP into three types, namely actual vegetation net primary productivity ANPP, potential vegetation net primary productivity PNPP and artificial vegetation net primary productivity HNPP, and calculating to obtain three types of NPP data; and (3) according to the three types of NPP data obtained in the step (2), climate and human contribution analysis of different space unit vegetation NPP is carried out. The method has the advantages that time-space trend analysis of the vegetation net primary productivity is carried out, contribution characteristics of climate changes and human activities are quantified, and a solid foundation is laid for deeply understanding change characteristics and contribution rules of ecological system productivity and promoting implementation of a dual-carbon strategy and sustainable development.

Described herein are methods and systems useful for real-time high-resolution measurements of gross primary production (GPP).

The present invention discloses a device and method for lifting seabed nutrients in a shellfish raft culture area. The shellfish raft includes a raft and a limit cable connected between the seabed and the raft. The device for lifting seabed nutrients in a shellfish raft culture area includes a raft arranged on the seabed, a plurality of groups of jet pipes are fixedly arranged on the raft, and a plurality of jet nozzles are opened on each group of jet pipes. The jet pipes are connected with an air duct, and an upper pressurizing mechanism and a lower pressurizing mechanism are connected to the air duct. The present invention uses the upper pressurizing mechanism and the lower pressurizing mechanism to provide power for the air duct to introduce gas above the sea surface into the seabed, forming an air curtain, so that an upwelling is generated in the raft culture area, thereby lifting the bottom water with a high nutrient salt content to the surface layer, providing bait microalgae for growth, improving the abundance of bait microalgae in the culture area, improving the primary productivity of the raft area, and thus improving the survival rate and yield of raft cultured shellfish. At the same time, it can increase the dissolved oxygen in the surface water, thereby playing a role in optimizing the seawater environment in the culture area.

The invention relates to a farmland total primary productivity optimization simulation method based on particle filtering. The method comprises the following steps: constructing a biosphere-atmosphere exchange process simulator model and acquiring a random parameter set based on preset empirical distribution; driving the biosphere-atmosphere exchange process simulator model based on a random parameter group in the random parameter set and obtaining a corresponding simulation variable; obtaining an observation variable corresponding to the analog variable, and carrying out iterative operation on the model parameters based on the observation variable; and repeating the iterative operation until the assimilation of the observation data is completed. The method does not involve complex matrix transposition and inversion operation, is simpler and more efficient, and is more suitable for optimization of a complex ecological system process model; the condition limitation that a Kalman filtering algorithm requires that a processing system must be a linear system is overcome; the method breaks through the condition requirement that a variational method requires a system state variable to be based on Gaussian distribution, and the nonlinear and non-Gaussian random mode feature enables the method to have a wide application prospect.

The application provides a land ecosystem gross primary productivity determination method and system, and the method comprises the following steps: collecting historical eco-hydro-meteorological data and measured values of land ecosystem gross primary productivity in a research area; constructing a generalized water-energy complementary gross primary productivity model, wherein the generalized water-energy complementary gross primary productivity model comprises a comprehensive coordination index, an energy limitation correction parameter and an available water amount correction parameter, the water amount limitation reference productivity and the energy limitation reference productivity are nonlinearly coupled by the comprehensive coordination index, the energy limitation correction parameter and the available water amount correction parameter; calibrating the comprehensive coordination index, the energy limitation correction parameter and the available water amount correction parameter; inputting the to-be-calculated eco-hydro-meteorological data into the generalized water-energy complementary gross primary productivity model after parameter calibration, and obtaining the target land ecosystem gross primary productivity. The application improves the estimation accuracy of the gross primary productivity.

The invention discloses a remote sensing estimation method and a remote sensing estimation device for the primary productivity of marine phytoplankton in size fraction, and the method comprises the steps: building an estimation function of the photosynthetic quantum yield of the phytoplankton in the corresponding size fraction based on the relation between the light intensity and the photosynthetic quantum yield of the phytoplankton in the single size fraction; calculating the net primary productivity of the phytoplankton of the corresponding size fraction based on the photosynthetic quantum yield and the average light absorption coefficient of the phytoplankton of the single size fraction under the scale of the light intensity duration and the ocean depth measurement range; and adding the net primary productivity of the phytoplankton in the plurality of size fractions, and calculating to obtain the total primary productivity of the phytoplankton. By adopting the technical scheme, in a framework of light absorption and conversion of the phytoplankton, the light absorption coefficient of the phytoplankton with different size fractions and the size fraction difference of the photosynthetic quantum yield are considered at the same time, an effective scheme for quantitative expression estimation of the phytoplankton is established, the estimation error of the primary productivity caused by neglecting the size fraction difference can be effectively reduced, and the estimation accuracy of the primary productivity is improved. And the estimation accuracy is improved.

The invention discloses a mangrove plant community net primary productivity monitoring method based on trunk flow, and relates to ecology and botany. General regulations of mangrove plant community net primary productivity monitoring based on trunk stem flow, mangrove plant stem flow monitoring content and method, plant leaf and crown atmospheric stability isotope monitoring, mangrove plant community net primary productivity calculation method and other basic contents and requirements are given. The method comprises the following steps: monitoring the stem flow density of a sample tree by adopting a plant stem liquid flow monitoring system, obtaining the composition condition of plant leaf juice and 13C in the canopy atmosphere by adopting an isotope analyzer, and obtaining the canopy leaf area index by adopting a fisheye lens and related software; and calculating the real-time net primary productivity of the mangrove plant community according to the monitored data. The method can be applied to monitoring of net primary productivity of various mangrove forests or land forests. The method is accurate in monitoring, high in popularization degree and good in real-time performance, and solves the problems of poor continuity, high operation difficulty and the like in current mangrove plant community net primary productivity measurement and calculation.

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.

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 invention provides a cultivated land production function simulation evaluation and limiting factor identification method and system, and aims to weaken cultivated land production function spatial heterogeneity caused by research area hydrothermal condition difference and crop type difference by dividing agricultural climate resource homogeneous areas and distinguishing cultivated land crop types. On the basis, accumulated total primary productivity in a growth period corresponding to a grid unit scale crop is used as a cultivated land production function indication index, and dominant limiting factors influencing the cultivated land production function and triggering conditions of the dominant limiting factors are detected through a geographic regression model and a machine learning interpretability method; the cultivated land production function level of the whole research area is evaluated based on a model obtained through statistical regression fitting, main limiting factors and improvement targets of cultivated land production function improvement under different climate conditions and different crop type scenes are quantitatively calculated, and a construction scheme is provided for medium and low yield field transformation. The problem that medium and low-yield fields are difficult to improve due to the fact that existing cultivated land production capacity limiting factors are difficult to quantitatively calculate is solved.

This invention discloses a method for assessing ecosystem service losses due to regional climate change. The region includes counties, cities, or important ecological function zones. The steps are as follows: S1. Collect remote sensing data and preprocess the data through band combination, geometric correction, and projection transformation. Select a standard color composite scheme, establish interpretation markers, and perform visual interpretation. S2. Calculate and statistically analyze the unit area yield value and equivalent factor of grain crops. The equivalent factor is 1 / 7 of the average market value of grain in the current year. S3. Statistically analyze land use types, calculate the magnitude of land use change, reflecting the changes in the total amount of different land use types and the overall trend of land use type changes. S4. Select vegetation cover as an indicator, and adjust the ecosystem service value based on two parameters: net primary productivity and vegetation cover. This invention is of great significance for formulating ecosystem management strategies under the background of future climate change.

The invention discloses a novel in-situ primary productivity testing system, and relates to the technical field of aquatic ecology monitoring. Comprising an excitation light source module, an in-situ culture module, a fluorescence detection module, a control module and a data processing module, the excitation light source module comprises at least two excitation light sources with different wavelengths, at least one of the excitation light sources comprises a quantum dot material, and the excitation light sources are used for performing spectrum regulation and control on light emitted by the light sources so as to output specific excitation light; the in-situ culture module is used for performing in-situ culture on target algae and maintaining an in-situ environment for algae growth; the control module is respectively connected with the excitation light source module and the in-situ culture module, and is used for controlling the running state of the in-situ culture module and the alternate lightening time sequence of the at least two excitation light sources; and the fluorescence detection module is used for detecting characteristic fluorescence emitted by target algae after being excited by the excitation light source, and acquiring an intensity parameter of the characteristic fluorescence.

The invention discloses a method and device for determining the primary productivity of an ancient lake, and the method comprises the steps: sampling a black shale section in a drilling core, and determining the total organic carbon content, the element lanthanum La abundance value and the element cobalt Co abundance value in a core sample; according to the element La abundance value and the element Co abundance value, calculating the water depth of the ancient lake and the time of organic matters in the shallow burying stage of the ancient lake bottom when the rock core sample is deposited; determining the organic carbon flux of the ancient lake bottom according to the determined time of the organic matters in the shallow burying stage of the ancient lake bottom and the density of the rock core sample; and determining the primary productivity of the ancient lake by using a primary productivity formula according to the water depth of the ancient lake and the organic carbon flux at the bottom of the ancient lake.

The application discloses a CASA model net primary productivity simulation method and system, and the method comprises the following steps: collecting real-time position data of a side mold, pressure data of a hydraulic system and temperature data of a mold contact surface through a multi-sensor fusion module; based on the real-time position data, the pressure data and the temperature data, a dynamic friction and thermal deformation coupling model in a side mold movement process is constructed, and a position deviation trend in a current stroke stage is predicted; according to the position deviation trend and a preset mold closing accuracy requirement, a dynamic compensation strategy of the side mold stroke based on a fuzzy adaptive PID is generated; the dynamic compensation strategy is executed, the hydraulic cylinder flow and pressure are adjusted in real time through a high-frequency servo valve group, and position closed-loop correction is completed within one stroke cycle. By using the embodiment of the application, intelligent prediction and adaptive configuration of CASA model static parameters in a large range and a multi-vegetation type region can be realized, and the precision, the automation degree and the cross-region generalization capability of net primary productivity simulation are improved.

The present invention discloses a method for inverting ocean primary productivity based on airborne dual-wavelength lidar remote sensing data. This method establishes a high-precision dual-wavelength water optical property inversion model based on the airborne dual-wavelength lidar remote sensing data, obtains a high-precision vertical profile of the ocean phytoplankton absorption coefficient, and inverts the vertical profile distribution of ocean primary productivity coupled with the high-precision phytoplankton absorption coefficient. The dual-wavelength ocean lidar, which uses one laser wavelength suitable for detecting nearshore waters and another for detecting offshore waters, improves the inversion accuracy of the maximum detection depth in near and offshore waters and the vertical profile distribution of primary productivity in typical waters, further enhancing the accuracy of the spatiotemporal evolution of the ocean carbon cycle and the observation capability of energy conversion in the upper ocean.

To provide a coast environment monitoring system capable of evaluating the environmental status of a coast in a sea area taking into account a factor other than plants and algae.SOLUTION: A coast environment monitoring system according to the present invention comprises a measurement unit 1 equipped with multiple measuring instruments that measures dissolved oxygen concentration in seawater at multiple measurement points within a sea area, a primary production calculation unit 2 that calculates primary production generated by photosynthesis within the sea area based on the dissolved oxygen concentration measured by the measurement unit 1, a primary production estimation unit 3 that estimates the primary production at the current time under a condition relevant to primary production using previously acquired data or theoretical equations, an environmental condition determination unit 4 that determines the current environmental condition of the sea area based on the primary production calculated by the primary production calculation unit 2 and the primary production estimated by the primary production estimation unit 3 using a preset sea area environmental condition pattern, and a display 5 that outputs the determination result from the environmental condition determination unit 4.SELECTED DRAWING: Figure 1

The application discloses a farmland corn yield estimation method based on high-resolution remote sensing data, comprising the following steps: determining the net primary productivity (NPP) of a corn farmland at a pixel level; establishing an NPP estimation model, calculating the cumulative NPP in a growing season according to phenological data, and collecting provincial NPP data; calculating the proportion of each pixel in the provincial NPP, obtaining the pixel-level corn yield according to the provincial statistical corn yield data, and performing yield monitoring and spatial variation analysis. The application predicts the farmland corn yield based on high-resolution satellite remote sensing data, evaluates the variation of regional corn yield from the perspective of yield refinement, and realizes accurate guidance of agricultural production.

The invention provides a crop yield remote sensing inversion method based on a chlorophyll fluorescence mechanism, and belongs to the technical field of agricultural crop growth monitoring, and the method comprises the steps: obtaining sunlight-induced chlorophyll fluorescence remote sensing data, photosynthetically active radiation data and crop type distribution data in the whole growth period of crops; and distinguishing the photosynthetic path according to the crop type distribution data, calculating by adopting a corresponding photoreaction mechanism model based on the obtained data to obtain the total primary productivity of the crop, obtaining the net primary productivity by combining the historical net primary productivity and the total primary productivity ratio, and obtaining the crop yield based on carbon content inversion. By utilizing coupling association between sunlight-induced chlorophyll fluorescence and a photosynthesis process, the problem that photosynthetic physiology in vegetation is difficult to effectively capture due to dependence on a static vegetation index in an existing crop technology is solved; the problem of how to accurately associate instantaneous sunlight-induced chlorophyll fluorescence remote sensing data with photosynthesis of crops in the whole growth period is solved.

The present invention provides a method and device for measuring phytoplankton primary productivity, comprising the following steps: obtaining spectra of calibration samples at different temperatures; establishing a calibration plane based on the spectra, using water environment temperature and spectral baseline intensity as independent variables and fluorescence peak intensity as a dependent variable; obtaining the corrected fluorescence peak intensity of the calibration sample based on the calibration plane; establishing a calibration curve based on the corrected fluorescence peak intensity of the calibration sample and primary productivity; obtaining a fluorescence spectrum of a sample to be measured; obtaining the corrected fluorescence peak intensity of the sample to be measured based on the calibration plane; and obtaining the phytoplankton primary productivity of the sample to be measured from the corrected fluorescence peak intensity of the sample to be measured based on the calibration curve. The present invention can improve the accuracy of chlorophyll fluorescence measurements and further improve the measurement accuracy of phytoplankton primary productivity observed using laser-induced chlorophyll fluorescence.

The application discloses an intelligent prediction method for corn yield, a multilayer feedforward neural network model is obtained through training, the multilayer feedforward neural network model is trained through a genetic method, and the obtained prediction model has higher prediction accuracy when predicting the corn yield; the method can obtain more accurate prediction data of the corn growth state by assimilating vegetation net primary productivity data which has a greater influence on the corn yield, the network model is updated based on the prediction data, the model is better adapted to the current corn growth data, and the accuracy of the corn yield prediction is improved; the method can more scientifically and reasonably predict the corn yield by improving the prediction operation speed of the multilayer feedforward neural network model, so that the corn production and management measures can be accurately adjusted in time.

The invention discloses a total primary productivity inversion method and related equipment, and relates to the technical field of remote sensing data processing, and the method comprises the steps: obtaining land type purity data, first spectral data, second spectral data and first leaf area index data in a target region; screening the first spectral data and the first leaf area index data according to the land type purity data to obtain third spectral data and second leaf area index data; establishing a leaf area index inversion model according to the third spectral data and the second leaf area index data; substituting the second spectral data into a leaf area index inversion model to obtain target leaf area index data; and determining total primary productivity data of the target region according to the target leaf area index data. According to the method, the low-resolution data is screened through the land class purity data, and the inversion model is constructed to be applied to the high-resolution data, so that effective transition of cross-resolution data is realized, and the accuracy of total primary productivity inversion is improved.

The invention discloses a method and a system for estimating the gross primary productivity of vegetation, relates to the field of ecological remote sensing and carbon cycle research, and is used for estimating the gross primary productivity of grassland vegetation by using an IPCM model provided by the invention, so that the precision can be greatly improved. And the problems of large systematic deviation and insufficient trend consistency of the original PCM model in the GPP estimation of the climate fluctuation region are effectively solved. Meanwhile, the calculation of the VPD and SWC joint stress factor is completely based on remote sensing data, the calculation process is simple, and the practicability and the operation efficiency of the new model are greatly improved. In addition, the combined stress factor also shows good environmental adaptability and stable estimation effect in an ecological region with complex and diverse climate conditions and remarkable annual fluctuation.

The application provides a kind of cultivated land production function simulation evaluation and limiting factor identification method and system, by dividing agricultural climate resource homogeneous region and distinguishing cultivated land crop type, weaken the water and heat condition difference of research area and the spatial heterogeneity of cultivated land production function caused by crop type difference.On this basis, with the accumulated total primary productivity of corresponding growth period stage of grid unit scale crop as the indicator of cultivated land production function, through geographic regression model and machine learning interpretability method, the dominant limiting factor and its trigger condition affecting the cultivated land production function are detected, and the model obtained based on statistical regression fitting is used to evaluate the cultivated land production function level of the whole research area, and the main limiting factors and improvement targets of cultivated land production function improvement under different climate conditions and different crop type scenarios are quantitatively calculated, to provide construction scheme for low-yield field improvement.The application solves the problem that the existing cultivated land production capacity limiting factor is difficult to quantitatively calculate, so that the low-yield field is difficult to improve.

The invention discloses a farmland corn yield estimation method based on high-resolution remote sensing data. The method comprises the following steps: determining net primary productivity (NPP) of a pixel-level corn farmland; establishing an NPP estimation model, calculating accumulated NPP in the growing season according to the phenological data, and summarizing to obtain provincial NPP data; and calculating the proportion of each pixel in the NPP of the whole province, obtaining the pixel-level corn yield according to the province-level statistical corn yield data, and carrying out yield monitoring and spatial change analysis. According to the method, the farmland corn yield is predicted on the basis of the high-resolution satellite remote sensing data, the change of the regional corn yield is evaluated from the perspective of yield refinement, and accurate guidance of agricultural production is realized.

This invention provides a spatial simulation method for regional crop yield based on remote sensing water-carbon coupling processes. Driven by remotely sensed soil moisture data and multi-source meteorological data, this method constructs a coupled iterative framework of crop growth, remote sensing water-carbon coupling, and water balance modules. It achieves regional-scale spatial simulation of crop leaf area index, evapotranspiration, total primary productivity, and crop yield at a fixed time scale. The spatial distribution of crop yield is obtained by cumulatively calculating total primary productivity over the crop's growth period. This invention does not rely on field sample data, can reflect spatial differences in crop yield within a region, and is suitable for regional agricultural productivity assessment and agricultural drought impact analysis.