Remote sensing estimation method for net primary productivity of vegetation

Abstract

The invention discloses a remote sensing estimation method for net primary productivity of vegetation. The method comprises the steps of determining a photosynthetically active radiation ratio absorbed by vegetation according to a vegetation coverage index in remote sensing data; according to temperature data, rainfall and other data in the remote sensing data, determining the potential photosynthetic utilization rate of vegetation, the influence coefficient of air temperature on vegetation growth, the influence coefficient of atmospheric water vapor on plant growth and the influence coefficient of soil moisture loss on plant growth, and then determining the actual light energy utilization rate of the vegetation; then, after the maintenance respiration of the plant and the growth respiration of the plant are obtained, determining the autotrophic respiration of the plant; then, after photosynthetically active radiation in the remote sensing data is obtained, the net primary productivityof the vegetation can be determined and obtained in combination with the photosynthetically active radiation ratio absorbed by the vegetation, the actual light energy utilization rate of the vegetation and autotrophic respiration. The remote sensing estimation method for net primary productivity of vegetation has the characteristics of high estimation precision and high estimation efficiency.

Description

technical field [0001] The invention relates to the technical field of remote sensing data analysis, in particular to a remote sensing estimation method of vegetation net primary productivity. Background technique [0002] The traditional way of estimating vegetation net primary productivity is mainly to set quadrats in the field for field sampling, which is time-consuming and labor-intensive, and the efficiency is low. Moreover, in the prior art, there are mainly two models for estimating ecosystem productivity: statistical models and light energy utilization rate models. [0003] Among them, the statistical model has simple parameters and is easy to operate, but the simulation accuracy is not high. The light utilization efficiency model can fully consider the spatial heterogeneity of geographical elements when using remote sensing data as input. Usually, the light utilization efficiency model uses NDVI to estimate the photosynthetically active radiation APAR absorbed by t...

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Problems solved by technology

[0003] Among them, the statistical model parameters are simple and easy to operate, but the simulation accuracy is not high

The light utilization efficiency model can fully consider the spatial heterogeneity of geographical elements when using remote sensing data as input. Usually, the light utilization efficiency model uses NDVI to estimate the photosynthetically active radiation APAR absorbed by the canopy, calculates the light utilization efficiency LUE, and thus calculates The total primary productivity GPP is obtained, but after the primary productivity is calculated by the light energy utilization rate model, it is necessary to use other environmental factors that limit photosynthesis to further correct the potential productivity, resulting in large errors in the obtained productivity data

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