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Despite early speculation to the contrary, all tropical forests studied to date display seasonal variations in the presence of new leaves, flowers, and fruits. Past studies were focused on the timing of phenological events and their cues but not on the accompanying changes in leaf area that regulate vegetation-atmosphere exchanges of energy, momentum, and mass. Here we report, from analysis of 5 years of recent satellite data, seasonal swings in green leaf area of Ϸ25% in a majority of the Amazon rainforests. This seasonal cycle is timed to the seasonality of solar radiation in a manner that is suggestive of anticipatory and opportunistic patterns of net leaf flushing during the early to mid part of the light-rich dry season and net leaf abscission during the cloudy wet season. These seasonal swings in leaf area may be critical to initiation of the transition from dry to wet season, seasonal carbon balance between photosynthetic gains and respiratory losses, and litterfall nutrient cycling in moist tropical forests.remote sensing ͉ tropical forests phenology ͉ vegetation climate interaction T he trees of tropical rainforests are known to exhibit a range of phenological behavior, from episodes of ephemeral leaf bursts followed by long quiescent periods to continuous leafing, and from complete intraspecific synchrony to complete asynchrony (1). Several agents (e.g., herbivory, water stress, day length, light intensity, mineral nutrition, and flood pulse) have been identified as proximate cues for leafing and abscission in these communities (1-8). These studies were focused on the timing of phenological events but not on the accompanying changes in leaf area. Leaves selectively absorb solar radiation, emit longwave radiation and volatile organic compounds, and facilitate growth by regulating carbon dioxide influx and water vapor efflux from stomates. Therefore, leaf area dynamics are relevant to studies of climatic, hydrological, and biogeochemical cycles.The sheer size and diversity of rainforests preclude a synoptic view of leaf area changes from ground sampling. We therefore used data on green leaf area of the Amazon basin (Ϸ7.2 ϫ 10 6 km 2 ) derived from measurements made by the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Na- Results Seasonality in LAI Time Series.Leaf area data for the Amazon rainforests exhibit notable seasonality, with an amplitude (peakto-trough difference) that is 25% of the average annual LAI of 4.7 (Fig. 1A). This average amplitude of 1.2 LAI is about twice the error of a single estimate of MODIS LAI, and thus is not an artifact of remote observation or data processing (see SI Materials and Methods). The aggregate phenological cycle appears timed to the seasonality of solar radiation in a manner that is suggestive of anticipatory and opportunistic patterns of leaf flushing and abscission. These patterns result in leaf area leading solar radiation during the entire seasonal cycle, with higher leaf area during the shorter dry season when solar radiation loads are hig...

[1] This study investigates the performances of four major global Leaf Area Index (LAI) products at 1/11.2°spatial sampling and a monthly time step: ECOCLIMAP climatology, GLOBCARBON (from SPOT/VEGETATION and ATSR/AATSR), CYCLOPES (from SPOT/VEGETATION) and MODIS Collection 4 (main algorithm, from MODIS/TERRA). These products were intercompared during the 2001-2003 period over the BELMANIP network of sites. Their uncertainty was assessed by comparison with 56 LAI reference maps derived from ground measurements. CYCLOPES and MODIS depict realistic spatial variations at continental scale, while ECOCLIMAP poorly captures surface spatial heterogeneity, and GLOBCARBON tends to display erratic variations. ECOCLIMAP and GLOBCARBON show the highest frequency of successful retrievals while MODIS and CYCLOPES retrievals are frequently missing in winter over northern latitudes and over the equatorial belt. CYCLOPES and MODIS describe consistent temporal profiles over most vegetation types, while ECOCLIMAP does not show any interannual variations, and GLOBCARBON can exhibit temporal instability during the growing season over forests. The CYCLOPES, MODIS, and GLOBCARBON LAI values agree better over croplands and grasslands than over forests, where differences in vegetation structure representation between algorithms and surface reflectance uncertainties lead to substantial discrepancies between products. CYCLOPES does not reach high enough LAI values to properly characterize forests. In contrast, the other products have sufficient dynamic range of LAI to describe the global variability of LAI. Overall, CYCLOPES is the most similar product to the LAI reference maps. However, more accurate ground measurements and better representation of the global and seasonal variability of vegetation are required to refine this result.

Phenology is the study of recurring life‐cycle events, classic examples being the flowering of plants and animal migration. Phenological responses are increasingly relevant for addressing applied environmental issues. Yet, challenges remain with respect to spanning scales of observation, integrating observations across taxa, and modeling phenological sequences to enable ecological forecasts in light of future climate change. Recent advances that are helping to address these questions include refined landscape‐scale phenology estimates from satellite data, advanced, instrument‐based approaches for field measurements, and new cyberinfrastructure for archiving and distribution of products. These breakthroughs are improving our understanding in diverse areas, including modeling land‐surface exchange, evaluating climate–phenology relationships, and making land‐management decisions.

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