A method for calculating a carbon and water flux of an ecosystem based on a multiple weather stations, comprising:
- acquiring a first observation data set of each of multiple eddy covariance flux stations in a target area, wherein the first observation data set comprises carbon and water flux observation data and weather observation data of each eddy covariance flux station and vegetation, soil and topography data of a location of each eddy covariance flux station;
- or the first observation data set comprises carbon and water flux observation data and the weather observation data of each eddy covariance flux station and vegetation, soil and topography data and remote sensing data of a location of each eddy covariance flux station;
- selectively combining selected ones of the first observation data sets of the multiple eddy covariance flux stations according to at least one of a plant function type (PFT) and a climate type to obtain a plurality of combinations and a plurality of carbon and water flux data sets respectively corresponding to the plurality of combinations, wherein the plurality of combinations each include a plurality of training sets and a plurality of test sets and characterize a scenario of a landscape or an ecosystem;
- and the carbon and water flux data sets each comprise the first observation data sets of all eddy covariance flux stations within a corresponding combination;
- dividing a carbon and water flux data set corresponding to each of the plurality of combinations by a plurality of division modes to obtain a training set and a test set corresponding to each of the plurality of division modes;
- training by a first machine learning algorithm with the training set as an input for each of the plurality of division modes to obtain a carbon and water flux model for predicting a station carbon and water flux of the combination under the division mode;
- calculating a determination coefficient of the combination under the division mode with the test set as an input of the carbon and water flux model;
- calculating an Euclidean distance of each carbon and water flux impact factor between the training set and the test set to obtain a distance set of the combination under the plurality of division modes;
- and constructing an evaluation data set according to determination coefficients and Euclidean distance sets of the plurality of combinations under the plurality of division modes, wherein the carbon and water flux impact factor refers to data in the first observation data set other than the carbon and water flux observation data;
- training by a second machine learning algorithm with the evaluation data set as an input to obtain an evaluation model for predicting a determination coefficient;
- determining, for the multiple weather stations in the target area, a carbon and water flux model suitable for each of the multiple weather stations by using the evaluation model;
- acquiring actual observation data of each weather station;
- determining a carbon and water flux for the weather station using the carbon and water flux model suitable for the weather station based on the actual observation data of the weather station;
- monitoring, based on the determined carbon and water flux, a carbon flux rate and a temporal and spatial change trend of an ecosystem in the target area to evaluate a carbon sequestration capacity of the ecosystem in the target area;
- and
- designing a carbon sequestration for the ecosystem in the target area based on the estimated carbon sequestration capacity of the ecosystem in the target area to optimize the ecosystem in the target area.