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

• Published in: The Science of the total environment 2020-07, Vol.726, p.137948-137948, Article 137948
• Main Authors: Xu, Xiaojun, Zhou, Guomo, Du, Huaqiang, Mao, Fangjie, Xu, Lin, Li, Xuejian, Liu, Lijuan
• Format: Article
• Language: English
• Subjects: Growing season; Maximum carbon uptake; Remote sensing; Vegetation indices
• ISSN: 0048-9697; 1879-1026
• DOI: 10.1016/j.scitotenv.2020.137948

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)