Method and system for estimating annual GPP based on EVI2 season change curve

Abstract

The invention provides a method and a system for estimating annual GPP based on an EVI2 season change curve. The method comprises the following steps: acquiring an annual complete time sequence remotesensing image, quantitatively processing the remote sensing image to obtain time sequence ground surface reflectivity data, cutting the time sequence ground surface reflectivity data, extracting ground surface reflectivity data, performing waveband operating on near-infrared wavebands and red wavebands in the ground surface reflectivity data, calculating the EVI2 time sequence data of remote sensing images in every studying area pixel by pixel, filtering the EVI2 time sequence data, calculating a curve integral pixel by pixel according to the filtered EVI2 time sequence data in order to obtain sigma EVI2, extracting a pre-established estimation model between the sigma EVI2 and a GPP sample based on vegetation types, and estimating the annual GPP in the studying area based on the sigma EVI2 and the estimation model. The method and the system can achieve the effective estimation of the regional total GPP for different vegetation types based on the vegetation EVI2 annual time sequence curve integral.

Description

technical field [0001] The invention belongs to the technical field of calculation of gross primary productivity, in particular to a method and system for estimating annual GPP (Gross Primary Productivity, total primary productivity) based on EVI2 (Enhanced Vegetation Index 2, two-band enhanced vegetation index) seasonal variation curve. Background technique [0002] The vegetation in the terrestrial ecosystem fixes and stores 25%-30% of the CO2 emitted by human activities through photosynthesis, and the amount of carbon that is fixed by photosynthesis on a unit area per unit time and converted into biomass is called vegetation Gross Primary Production (GPP). GPP is often used to estimate crop yields, and it is also an important parameter for calculating the carbon budget of terrestrial ecosystems, and plays an important role in the study of the global carbon cycle. Regional and global scale GPP estimates are helpful for monitoring and predicting vegetation growth, understa...

AI Technical Summary

Problems solved by technology

The most commonly used vegetation index is the normalized differential vegetation index (Normalized Differential Vegetation Index, NDVI), but as the vegetation grows, the chlorophyll in the vegetation canopy absorbs red light and the red light band of the vegetation spectrum tends to be saturated, and its sensitivity It will also decrease in areas with high vegetation coverage. These factors cause NDVI to be unable to effectively distinguish vegetation in areas with high vegetation coverage, and lead to increased errors when using NDVI to estimate vegetation parameters in areas with high vegetation coverage.

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