Integrating proximal broad-band vegetation indices and carbon fluxes to model gross primary productivity in a tropical dry forest

Abstract

The measurement of carbon exchange between vegetation and the atmosphere is vital to quantify the impact of environmental variables on the carbon sequestration capacity of forests, and to predict how they will respond to future climate. In this study we use proximal remote sensing, defined as observations made from non-contact radiometric or imaging sensors in close proximity to the forest canopy (10–20 m), as an intermediate upscaling tool between direct measurements of carbon fluxes and satellite-derived estimations of primary productivity in a tropical dry forest (TDF) in Jalisco, Mexico. Two broad-band vegetation indices (VIs), the normalized difference VI and the enhanced vegetation index 2 (EVI2), were calculated from proximally sensed canopy properties, validated with field estimates of the fraction of absorbed photosynthetically active radiation by photosynthetic tissue (fAPARgreen), and compared to estimates of gross primary productivity (GPP) and net ecosystem exchange of CO2, measured from a flux tower. The VIs captured the phenology of the TDF, both under typical summer rainfall and during an atypically-dry wet season in El Niño of 2009. The VIs also tracked a secondary leaf-flushing in the dry season of 2010. Our study suggests that (1) VIs are the best predictors of gross carbon uptake, able to explain up to 86% of variations in GPP; (2) VIs are accurate predictors of the photosynthetic capacity of green tissue, able to explain up to 99% of fAPARgreen variation; and (3) VIs and soil water content can be used to develop an empirical model that captures the seasonal trajectory of GPP from high respiration after the rain pulses, to rapid leaf development, and finally to slow senescence as the soil dries out. Proximal remote sensing constitutes a useful tool to link field-base measurements of carbon fluxes to satellite- or airborne-derived estimates of carbon exchange.