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Combined MODIS land surface temperature and greenness data for modeling vegetation phenology, physiology, and gross primary production in terrestrial ecosystems by Xiaojun Xu; Guomo Zhou; Huaqiang Du; Fangjie Mao; Lin Xu; Xuejian Li; Lijuan Liu is a Environmental Science article available to read on EtoBox.

What is Combined MODIS land surface temperature and greenness data for modeling vegetation phenology, physiology, and gross primary production in terrestrial ecosystems about?

Vegetation phenology such as the start (SOS) and end (EOS) of the growing season, physiology (represented by seasonal maximum capacity of carbon uptake, GPPmax), and gross primary production (GPP) are sensitive indicators for monitoring ecosystem response to environmental change. However, uncertainty and disagreement between models limit the use phenology metrics and GPP derived from remote sensing data. Statistical models for estimating phenology and physiology were constructed based on key predictor variables derived from enhanced vegetation index (EVI) and land surface temperature (LST) data. Then, a statistical model that integrated remote sensing-based phenology and physiology (RS-SMIPP) data was constructed to estimate seasonal and annual GPP. These models were calibrated and validated with GPP observations from 512 site-years of FLUXNET data covering four plant functional types (PFTs) in the northern hemisphere: deciduous broadleaf forest, evergreen needle-leaf forest, mixed forest, and grassland. Our results showed that phenology and physiology were accurately estimated with relative root mean squared error (RMSEr) <20%, and the errors varied among the PFTs. Spring EVI was

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Author

Xiaojun Xu; Guomo Zhou; Huaqiang Du; Fangjie Mao; Lin Xu; Xuejian Li; Lijuan Liu

Publisher

Elsevier Science; Elsevier ; Elsevier BV; Publons (ISSN 0048-9697)

Published

2020

Language

EN

Field

Environmental Science (Physical Sciences)