https://dx.doi.org/10.1016/j.rse.2017.12.024">
 

Title

Satellite-derived LAI products exhibit large discrepancies and can lead to substantial uncertainty in simulated carbon and water fluxes

Abstract

Understanding the terrestrial carbon and water cycles is crucial for mitigation and adaptation for climate change. Leaf area index (LAI) is a key biophysical parameter in process-based ecosystem models for simulating gross primary productivity (GPP) and evapotranspiration (ET). The uncertainty in satellite-derived LAI products and their effects on the simulation of carbon and water fluxes at regional scales remain unclear. We evaluated three satellite-derived LAI products - MODIS (MCD15), GLASS, and Four-Scale Geometric Optical Model based LAI (FSGOM) over the period 2003–2012 using fine-resolution (30 m) LAI data and field LAI measurements. GLASS had higher accuracy than FSGOM and MCD15 for forests, while FSGOM had higher accuracy than MCD15 and GLASS for grasslands. The three LAI products differed in magnitude, spatial patterns, and trends in LAI. We then examined the resulting discrepancies in simulated annual GPP and ET over China using a process-based, diagnostic terrestrial biosphere model. Mean annual total GPP for China's terrestrial ecosystems based on GLASS (6.32 Pg C yr− 1) and FSGOM (6.15 Pg C yr− 1) was 22.5% and 19.2% higher than that based on MCD15 (5.16 Pg C yr− 1), respectively. Annual GPP based on GLASS and MCD15 increased over larger fractions of China's vegetated area (15.9% and 17.3%, respectively) than that based on FSGOM (12.6%). National annual ET based on GLASS (379.9 mm yr− 1) and FSGOM (374.4 mm yr− 1) was 7.9% and 6.3% higher than that based on MCD15 (352.1 mm yr− 1), respectively. Simulated ET increased in larger fractions of the vegetated area for GLASS (5.7%) and MCD15 (5.8%) than for FSGOM (3.9%). Our study shows that there were large discrepancies in LAI among satellite-derived LAI products and the biases of the LAI products could lead to substantial uncertainties in simulated carbon and water fluxes.

Publication Date

3-1-2018

Journal Title

Remote Sensing of Environment

Publisher

Elsevier

Digital Object Identifier (DOI)

https://dx.doi.org/10.1016/j.rse.2017.12.024

Document Type

Article

Rights

© 2017 Elsevier Inc. All rights reserved.

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