Inconsistencies of interannual variability and trends in long-term satellite leaf area index products

Understanding the long-term performance of global satellite leaf area index (LAI) products is important for global change research. However, few effort has been devoted to evaluating the long-term time-series consistencies of LAI products. This study compared four long-term LAI products (GLASS, GLOBMAP, LAI3g, and TCDR) in terms of trends, interannual variabilities, and uncertainty variations from 1982 through 2011. This study also used four ancillary LAI products (GEOV1, MERIS, MODIS C5, and MODIS C6) from 2003 through 2011 to help clarify the performances of the four long-term LAI products. In general, there were marked discrepancies between the four long-term LAI products. During the pre-MODIS period (1982-1999), both linear trends and interannual variabilities of global mean LAI followed the order GLASS>LAI3g>TCDR>GLOBMAP. The GLASS linear trend and interannual variability were almost 4.5 times those of GLOBMAP. During the overlap period (2003-2011), GLASS and GLOBMAP exhibited a decreasing trend, TCDR no trend, and LAI3g an increasing trend. GEOV1, MERIS, and MODIS C6 also exhibited an increasing trend, but to a much smaller extent than that from LAI3g. During both periods, the R2 of detrended anomalies between the four long-term LAI products was smaller than 0.4 for most regions. Interannual variabilities of the four long-term LAI products were considerably different over the two periods, and the differences followed the order GLASS>LAI3g>TCDR>GLOBMAP. Uncertainty variations quantified by a collocation error model followed the same order. Our results indicate that the four long-term LAI products were neither intraconsistent over time nor interconsistent with each other. These inconsistencies may be due to NOAA satellite orbit changes and MODIS sensor degradation. Caution should be used in the interpretation of global changes derived from the four long-term LAI products

An Improved Atmospheric Correction Algorithm for Hyperspectral Remotely Sensed Imagery

There is an increased trend toward quantitative estimation of land surface variables from hyperspectral remote sensing. One challenging issue is retrieving surface reflectance spectra from observed radiance through atmospheric correction, most methods for which are intended to correct water vapor and other absorbing gases. In this letter, methods for correcting both aerosols and water vapor are explored. We first apply the cluster matching technique developed earlier for Landsat-7 ETM+ imagery to Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data, then improve its aerosol estimation and incorporate a new method for estimating column water vapor content using the neural network technique. The improved algorithm is then used to correct Hyperion imagery. Case studies using AVIRIS and Hyperion images demonstrate that both the original and improved methods are very effective to remove heterogeneous atmospheric effects and recover surface reflectance spectra.This work was supported in part by the National Aeronautics and Space Administration under EO1 Grant NCC5462

Retrieving Leaf Area Index With a Neural Network Method: Simulation and Validation

Leaf area index () is a crucial biophysical parameter that is indispensable for many biophysical and climatic models. A neural network algorithm in conjunction with extensive canopy and atmospheric radiative transfer simulations is presented in this paper to estimateLAIfromLandsat-7 Enhanced ThematicMapper Plus data. Two schemes were explored; the first was based on surface reflectance, and the second on top-of-atmosphere (TOA) radiance. The implication of the second scheme is that atmospheric corrections are not needed for estimating the surface LAI. A soil reflectance index (SRI) was proposed to account for variable soil background reflectances. Ground-measured LAI data acquired at Beltsville, MD were used to validate both schemes. The results indicate that both methods can be used to estimate LAI accurately. The experiments also showed that the use of SRI is very critical.This work was supported in part by the U.S. National Aeronautics and Space Administration (NASA) under Grant NAG5-6459 and Grant NCC5462

Prospects for Bioethanol Production from Macroalgae

Macroalgae (mainly marine macroalgae, i.e. seaweeds) are considered as a very promising source for bioethanol production, because they have high carbohydrate contents, superior productivity, and wide adaptability. Macroalgae are generally grouped into three major categories: red, green, and brown algae. Each category has thousands of species, and each species possesses its unique cellular structure, biochemistry, and constitutes. Converting macroalgae to bioethanol involves pretreatment, saccharification, fermentation, and distillation; and the establishment of economic pretreatment methods is always the first key step for bioethanol production. In present, dilute-acid or alkali hydrolysis is typically used to treat macroalgal biomass. Macroalgae can be depolymerized under mild conditions as they have low lignin content. The resulting polysaccharides can be converted to ethanol through enzymatic hydrolysis, followed by adding bacteria, such as Saccharomyces cerevisiae and recombinant Escherichia coli KO11. Compared with the separate hydrolysis and fermentation process, the simultaneous saccharification and fermentation process often provided higher ethanol titer and conversion efficiency. However, the research on bioethanol production from macroalgae is still in its early stage due to both technical and economic barriers, significant amount of research and development work is needed prior to the commercialization of bioethanol manufacture from macroalgae.Citation: Chen, J., Bai, J., Li, H., Chang, C., and Fang, S. (2015). Prospects for Bioethanol Production from Macroalgae. Trends in Renewable Energy, 1(3), 185-197. DOI: 10.17737/tre.2015.1.3.001

Customer-oriented Data Formats and Services for Global Land Data Assimilation System (GLDAS) Products at the NASA GES DISC

The Global Land Data Assimilation System (GLDAS) is generating a series of land surface state (e.g., soil moisture and surface temperature) and flux (e.g., evaporation and sensible heat flux) products simulated by four land surface Models (CLM, Mosaic, Noah and VIC). These products are now accessible at the Hydrology Data and Information Services Center (HDISC), a component of NASA Goddard Earth Sciences Data and Information Services Center (GESDISC)

Semantic Web Data Discovery of Earth Science Data at NASA Goddard Earth Sciences Data and Information Services Center (GES DISC)

Mirador is a web interface for searching Earth Science data archived at the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Mirador provides keyword-based search and guided navigation for providing efficient search and access to Earth Science data. Mirador employs the power of Google's universal search technology for fast metadata keyword searches, augmented by additional capabilities such as event searches (e.g., hurricanes), searches based on location gazetteer, and data services like format converters and data sub-setters. The objective of guided data navigation is to present users with multiple guided navigation in Mirador is an ontology based on the Global Change Master directory (GCMD) Directory Interchange Format (DIF). Current implementation includes the project ontology covering various instruments and model data. Additional capabilities in the pipeline include Earth Science parameter and applications ontologies

21-(3-Carboxy­propano­yl)-11β,17-dihydroxy­pregn-4-ene-3,20-dione monohydrate

In the title compound, C25H34O8·H2O, the two crylohexane rings adopt chair conformations. In the crystal, the organic mol­ecule and the water mol­ecule are linked by O—H⋯O hydrogen bonds, generating a three-dimensional network

Atmospheric Correction of Landsat ETM+ Land Surface Imagery—Part I: Methods

To extract quantitative information from the Enhanced Thematic Mapper-Plus (ETM+) imagery accurately, atmospheric correction is a necessary step. After reviewing historical development of atmospheric correction of Landsat thematic mapper (TM) imagery, we present a new algorithm that can effectively estimate the spatial distribution of atmospheric aerosols and retrieve surface reflectance from ETM+ imagery under general atmospheric and surface conditions. This algorithm is therefore suitable for operational applications. A new formula that accounts for adjacency effects is also presented. Several examples are given to demonstrate that this new algorithm works very well under a variety of atmospheric and surface conditions. The companion paper will validate this method using ground measurements, and illustrate the improvements of several applications due to atmospheric correction.This work was supported in part by the U.S. National Aeronautics and Space Administration (NASA), Washington, DC, under Grant NAG5-6459