High-resolution vegetation productivity remote sensing estimation method based on downscaling

A high-resolution, downscaling technology, applied in computing, computer components, instruments, etc., can solve the problems of low accuracy and lack of universality of the vegetation productivity downscaling scheme, and achieve high precision and strong universality Effect

Inactive Publication Date: 2019-09-24
BEIJING NORMAL UNIVERSITY
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Problems solved by technology

[0008] The invention provides a remote sensing estimation method of high-resolution vegetation productivity based on downscaling, fully utilizes the advantages of different resolution remote sensing data, integrates high and low resolution remote sensing data, and const

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  • High-resolution vegetation productivity remote sensing estimation method based on downscaling
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  • High-resolution vegetation productivity remote sensing estimation method based on downscaling

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[0042] The specific technical solutions of the present invention are described in conjunction with the examples.

[0043] In this embodiment, the verification area of ​​the Heihe River Basin in China (38°10'~39°35'N, 99°57'~101°46'E) is selected.

[0044] The process flow of the high-resolution vegetation productivity remote sensing estimation method based on downscaling is as follows: figure 1 . Leaf Area Index (LAI) and FPAR are two key inputs of the vegetation productivity model MuSyQ-NPP. Therefore, in the vegetation productivity downscaling technical scheme, the factor LAI / FPAR for vegetation productivity estimation is firstly downscaled to obtain High-resolution LAI / FPAR of time series; spatial interpolation and terrain correction for temperature to obtain high-resolution temperature factors; high-resolution solar short-wave radiation simulation using mountain microclimate model (MT-CLIM); downscaled The high-resolution LAI / FPAR, the high-resolution temperature factor ...

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Abstract

The invention provides a high-resolution vegetation productivity remote sensing estimation method based on downscaling. The high-resolution vegetation productivity remote sensing estimation method based on the downscaling comprises: carrying out downscaling on factors LAI/FPAR estimated by vegetation productivity, and obtaining high-resolution LAI/FPAR of a time sequence; performing spatial interpolation and terrain correction on the temperature to obtain a high-resolution temperature factor; simulating high-resolution solar short-wave radiation by using a mountainous microclimate model; inputting the downscaled high-resolution LAI/FPAR, the terrain-corrected high-resolution temperature factor and the solar short wave radiation data into a vegetation productivity model MuSyQ-NPP to obtain the high-resolution GPP/NPP of the continuous time sequence. The advantages of remote sensing data with different resolutions are fully exerted, the remote sensing data with high resolution and low resolution are fused, a technical scheme of a high-resolution vegetation productivity product with higher precision and stronger universality is constructed, and the problems that an existing vegetation productivity downscaling scheme is low in precision and insufficient in universality are solved.

Description

technical field [0001] The invention belongs to the technical field of ecological biological productivity, and in particular relates to a remote sensing estimation method of high-resolution vegetation productivity based on downscaling. Background technique [0002] Vegetation is the main body of terrestrial ecosystems, and vegetation productivity is an important part of the study of carbon cycle and carbon budget, reflecting the impact of vegetation on atmospheric CO 2 the ability to fix. Gross Primary Productivity (GPP) refers to the assimilation of CO by the green plants absorbing solar energy through photosynthesis in the ecosystem. 2 Manufactured organic matter, Net Primary Production (Net Primary Production, NPP) refers to GPP minus the photosynthetic products consumed by heterotrophic respiration. GPP and NPP can not only characterize the growth status and growth process of vegetation, but also intuitively reflect the response of different ecosystems to global change...

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Application Information

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IPC IPC(8): G06K9/00G06Q10/06G06Q50/02
CPCG06Q10/067G06Q50/02G06V20/188
Inventor 孙睿余涛刘沁茹王梦佳
Owner BEIJING NORMAL UNIVERSITY
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