Long-term global data sets of vegetation Leaf Area Index (LAI) [2000][2001][2002][2003][2004][2005][2006][2007][2008][2009]. The trained neural network algorithm was then used to generate corresponding LAI3g and FPAR3g data sets with the following attributes: 15-day temporal frequency, 1/12 degree spatial resolution and temporal span of July 1981 to December 2011. The quality of these data sets for scientific research in other disciplines was assessed through (a) comparisons with field measurements scaled to the spatial resolution of the data products, (b) comparisons with broadly-used existing alternate satellite data-based products, (c) comparisons to plant growth limiting climatic variables in the northern latitudes and tropical regions, and (d) correlations of dominant modes of interannual variability with large-scale circulation anomalies such as the EI Niño-Southern Oscillation and Arctic Oscillation. These assessment efforts yielded results that attested to the suitability of these data sets for research use in other disciplines. The utility of these data sets is documented by comparing the seasonal profiles of LAI3g with profiles from 18 state-of-the-art Earth System Models: the models consistently overestimated the satellite-based estimates of leaf area and simulated delayed peak seasonal values in the northern latitudes, a result that is consistent with previous evaluations of similar models with ground-based data. The LAI3g and FPAR3g data sets can be obtained freely from the NASA Earth Exchange (NEX) website.

[1] The sensitivity of Amazon rainforests to dry-season droughts is still poorly understood, with reports of enhanced tree mortality and forest fires on one hand, and excessive forest greening on the other. Here, we report that the previous results of large-scale greening of the Amazon, obtained from an earlier version of satellitederived vegetation greenness data -Collection 4 (C4) Enhanced Vegetation Index (EVI), are irreproducible, with both this earlier version as well as the improved, current version (C5), owing to inclusion of atmosphere-corrupted data in those results. We find no evidence of large-scale greening of intact Amazon forests during the 2005 drought -approximately 11%-12% of these droughtstricken forests display greening, while, 28%-29% show browning or no-change, and for the rest, the data are not of sufficient quality to characterize any changes. These changes are also not unique -approximately similar changes are observed in non-drought years as well. Changes in surface solar irradiance are contrary to the speculation in the previously published report of enhanced sunlight availability during the 2005 drought. There was no co-relation between drought severity and greenness changes, which is contrary to the idea of drought-induced greening. Thus, we conclude that Amazon forests did not green-up during the 2005 drought. Citation: Samanta, A.,

During this decade, the Amazon region has suffered two severe droughts in the short span of five years – 2005 and 2010. Studies on the 2005 drought present a complex, and sometimes contradictory, picture of how these forests have responded to the drought. Now, on the heels of the 2005 drought, comes an even stronger drought in 2010, as indicated by record low river levels in the 109 years of bookkeeping. How has the vegetation in this region responded to this record‐breaking drought? Here we report widespread, severe and persistent declines in vegetation greenness, a proxy for photosynthetic carbon fixation, in the Amazon region during the 2010 drought based on analysis of satellite measurements. The 2010 drought, as measured by rainfall deficit, affected an area 1.65 times larger than the 2005 drought – nearly 5 million km2 of vegetated area in Amazonia. The decline in greenness during the 2010 drought spanned an area that was four times greater (2.4 million km2) and more severe than in 2005. Notably, 51% of all drought‐stricken forests showed greenness declines in 2010 (1.68 million km2) compared to only 14% in 2005 (0.32 million km2). These declines in 2010 persisted following the end of the dry season drought and return of rainfall to normal levels, unlike in 2005. Overall, the widespread loss of photosynthetic capacity of Amazonian vegetation due to the 2010 drought may represent a significant perturbation to the global carbon cycle.

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.[1] A large increase in near-infrared (NIR) reflectance of Amazon forests during the light-rich dry season and a corresponding decrease during the light-poor wet season has been observed in satellite measurements. This increase has been variously interpreted as seasonal change in leaf area resulting from net leaf flushing in the dry season or net leaf abscission in the wet season, enhanced photosynthetic activity during the dry season from flushing new leaves and as change in leaf scattering and absorption properties between younger and older leaves covered with epiphylls. Reconciling these divergent views using theory and observations is the goal of this article. The observed changes in NIR reflectance of Amazon forests could be due to similar, but small, changes in NIR leaf albedo (reflectance plus transmittance) resulting from the exchange of older leaves for newer ones, but with the total leaf area unchanged. However, this argument ignores accumulating evidence from ground-based reports of higher leaf area in the dry season than the wet season, seasonal changes in litterfall and does not satisfactorily explain why NIR reflectance of these forests decreases in the wet season. More plausibly, the increase in NIR reflectance during the dry season and the decrease during the wet season would result from changes in both leaf area and leaf optical properties. Such change would be consistent with known phenological behavior of tropical forests, ground-based reports of seasonal changes in leaf area, litterfall, leaf optical properties and fluxes of evapotranspiration, and thus, would reconcile the various seemingly divergent views.

This paper summarizes the implementation of a physically based algorithm for the retrieval of vegetation green Leaf Area Index (LAI) from Landsat surface reflectance data. The algorithm is based on the canopy spectral invariants theory and provides a computationally efficient way of parameterizing the Bidirectional Reflectance Factor (BRF) as a function of spatial resolution and wavelength. LAI retrievals from the application of this algorithm to aggregated Landsat surface reflectances are consistent with those of MODIS for homogeneous sites represented by different herbaceous and forest cover types. Example results illustrating the physics and performance of the algorithm suggest three key factors that influence the LAI retrieval process: 1) the atmospheric correction procedures to estimate surface reflectances; 2) the proximity of Landsatobserved surface reflectance and corresponding reflectances as characterized by the model simulation; and 3) the quality of the input land cover type in accurately delineating pure vegetated components as opposed to mixed pixels. Accounting for these factors, a pilot implementation of the LAI retrieval algorithm was demonstrated for the state of California utilizing the Global Land Survey (GLS) 2005 Landsat data archive. In a separate exercise, the performance of the LAI algorithm over California was evaluated by using the short-wave infrared band in addition to the red and near-infrared bands. Results show that the algorithm, while ingesting the short-wave infrared band, has the ability to delineate open canopies with understory effects and may provide useful information compared to a more traditional two-band retrieval. Future research will involve implementation of this algorithm at continental scales and a validation exercise will be performed in evaluating the accuracy of the 30-m LAI products at several field sites.

Zhao and Running (Reports, 20 August 2010, p. 940) reported a reduction in global terrestrial net primary production (NPP) from 2000 through 2009. We argue that the small trends, regional patterns, and interannual variations that they describe are artifacts of their NPP model. Satellite observations of vegetation activity show no statistically significant changes in more than 85% of the vegetated lands south of 70°N during the same 2000 to 2009 period.

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