Generation of High Resolution Vegetation Productivity from a Downscaling Method

• Main Authors: Tao Yu, Rui Sun, Zhiqiang Xiao, Qiang Zhang, Juanmin Wang, Gang Liu
• Format: Article
• Language: English
• Published: MDPI AG 2018-11-01
• Series: Remote Sensing
• Subjects: GPP; NPP; downscaling; STARFM
• Online Access: https://www.mdpi.com/2072-4292/10/11/1748

Accurately estimating vegetation productivity is important in the research of terrestrial ecosystems, carbon cycles and climate change. Although several gross primary production (GPP) and net primary production (NPP) products have been generated and many algorithms developed, advances are still needed to exploit multi-scale data streams for producing GPP and NPP with higher spatial and temporal resolution. In this paper, a method to generate high spatial resolution (30 m) GPP and NPP products was developed based on multi-scale remote sensing data and a downscaling method. First, high resolution fraction photosynthetically active radiation (FPAR) and leaf area index (LAI) were obtained by using a regression tree approach and the spatial and temporal adaptive reflectance fusion model (STARFM). Second, the GPP and NPP were estimated from a multi-source data synergized quantitative algorithm. Finally, the vegetation productivity estimates were validated with the ground-based field data, and were compared with MODerate Resolution Imaging Spectroradiometer (MODIS) and estimated Global LAnd Surface Satellite (GLASS) products. Results of this paper indicated that downscaling methods have great potential in generating high resolution GPP and NPP.