Vegetation Photosynthesis Evaluation With Lag and Accumulation Effects
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Solution Overview
Problem
Existing methods fail to accurately consider the time effect of multi-climatic factors on vegetation photosynthesis, leading to underestimated impacts on vegetation carbon sink potential.
Innovation Solution
A method involving solar-induced chlorophyll fluorescence (SIF) data and multiple climatic factors, preprocessing, partial correlation coefficient analysis, and significance testing to evaluate the time effect on vegetation photosynthesis, including lag and accumulation effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to evaluate climatic factors impact, then the evaluation process is simple, but the accuracy is insufficient due to ignoring time effects
Solution Approach 1:
The patent applies preliminary action by calculating climate variables at different time scales (lag and accumulation effects) before performing correlation analysis. This involves computing VPDt(m,n) using historical climate data for m lagging months and n accumulation months, thereby preparing the necessary temporal transformed data in advance to capture the time-dependent relationships between climatic factors and vegetation photosynthesis.
Solution Approach 2:
The patent implements dynamics by transforming static climate data into dynamic variables that account for time lags and accumulation effects. The climate variable VPDt(m,n) dynamically adjusts based on different time scales, allowing the model to adapt to varying temporal relationships between climatic factors and vegetation responses, thereby improving measurement precision while managing complexity through systematic temporal transformation.
2Measurement precision
If time effect is considered in the evaluation, then the accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the time effect into distinct components: lag effect (m months) and accumulation effect (n months). This segmentation allows the complex temporal relationship to be broken down into manageable parts, where VPDt(m,n) is calculated as a function of historical climate data at specific time lags and accumulation periods, making the computational process more systematic and efficient.
Solution Approach 2:
The patent utilizes parameter changes by transforming the climate variable parameters to different time scales. The VPDt(m,n) parameter changes based on the selected lag (m) and accumulation (n) values, allowing the model to adjust temporal parameters to optimize the balance between capturing time effects and maintaining computational efficiency. This parameter transformation enables accurate evaluation while managing computational requirements through controlled temporal scaling.
Data Source
AI summary
A method for evaluating climate influencing factors of vegetation photosynthesis considering a time effect includes obtaining solar-induced chlorophyll fluorescence (SIF) data and data of climatic factors and preprocessing the data. A climate variable is obtained according to a time effect of the preprocessed data. A partial correlation coefficient and a significance test value are obtained according to the climate variable. An optimal time effect obtained according to the partial correlation coefficient, and an optimal partial correlation coefficient is extracted according to the optimal time effect. Percentages of significant positive correlation and significant negative correlation in a total area under different time effects according to the partial correlation coefficient and the significance test value are obtained, and the percentages are compared to obtain a comparison result. An evaluation is then made according to the comparison result, an impact of climatic factors on vegetation photosynthesis after the time effect is considered.