Bradley, AV; Gerard, FF; Barbier, N; Weedon, GP; Anderson, LO; Huntingford, C; Aragao, LEOC; Zelazowski, P; Arai, E (2011). Relationships between phenology, radiation and precipitation in the Amazon region. GLOBAL CHANGE BIOLOGY, 17(6), 2245-2260.
In tropical areas, Dynamic Global Vegetation Models (DGVMs) still have deficiencies in simulating the timing of vegetation phenology. To start addressing this problem, standard Fourier-based methods are applied to aerosol screened monthly remotely sensed phenology time series (Enhanced Vegetation Index, EVI) and two major driving factors of phenology: solar radiation and precipitation (for March 2000 through December 2006 over northern South America). At 1 x 1 km scale using, power (or variance) spectra on good quality aerosol screened time series, annual cycles in EVI are detected across 58.24% of the study area, the strongest (largest amplitude) occurring in the savanna. Terra Firme forest have weak but significant annual cycles in comparison with savannas because of the heterogeneity of vegetation and nonsynchronous phenological events within 1 x 1 km scale pixels. Significant annual cycles for radiation and precipitation account for 86% and 90% of the region, respectively, with different spatial patterns to phenology. Cross-spectral analysis was used to compare separately radiation with phenology/EVI, precipitation with phenology/EVI and radiation with precipitation. Overall the majority of the Terra Firme forest appears to have radiation as the driver of phenology (either radiation is in phase or leading phenology/EVI at the annual scale). These results are in agreement with previous research, although in Acre, central and eastern Peru and northern Bolivia there is a coexistence of 'in phase' precipitation over Terra Firme forest. In contrast in most areas of savanna precipitation appears to be a driver and savanna areas experiencing an inverse (antiphase) relationship between radiation and phenology is consistent with inhibited grassland growth due to soil moisture limitation. The resulting maps provide a better spatial understanding of phenology-driver relationships offering a bench mark to parameterize ecological models.