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NASA NTRS2014

Ultra-Wideband, Dual-Polarized, Beam-Steering P-Band Array Antenna

A dual-polarized, wide-bandwidth (200 MHz for one polarization, 100 MHz for the orthogonal polarization) antenna array at P-band was designed to be driven by NASA's EcoSAR digital beam former. EcoSAR requires two wide P-band antenna arrays mounted on the wings of an aircraft, each capable of steering its main beam up to 35deg off-boresight, allowing the twin radar beams to be steered at angles to the flight path. The science requirements are mainly for dual-polarization capability and a wide bandwidth of operation of up to 200 MHz if possible, but at least 100 MHz with high polarization port isolation and low cross-polarization. The novel design geometry can be scaled with minor modifications up to about four times higher or down to about half the current design frequencies for any application requiring a dual-polarized, wide-bandwidth steerable antenna array. EcoSAR is an airborne interferometric P-band synthetic aperture radar (SAR) research application for studying two- and three-dimensional fine-scale measurements of terrestrial ecosystem structure and biomass, which will ultimately aid in the broader study of the carbon cycle and climate change. The two 2×8 element Pband antenna arrays required by the system will be separated by a baseline of about 25 m, allowing for interferometry measurements. The wide 100-to- 200-MHz bandwidth dual-polarized beams employed will allow the determination of the amount of biomass and even tree height on the ground. To reduce the size of the patches along the boresight dimension in order to fit them into the available space, two techniques were employed. One technique is to add slots along the edges of each patch where the main electric currents are expected to flow, and the other technique is to bend the central part of the patch away from the ground plane. The latter also facilitates higher mechanical rigidity. The high port isolation of more than 40 dB was achieved by employing a highly symmetrical feed mechanism for each pair of elements: three apertures coupling to the patch elements were placed along the two symmetry lines of the antenna element pair. Two apertures were used in tandem to excite two of the stacked patch elements for one polarization; the other was used to excite one element from one side and the other element from the other side, opposite in phase, taking care of the remaining polarization. The apertures narrow down to a small gap where they are excited by a crossing microstrip line to prevent any asymmetrical excitation of the two sides of the aperture gap, minimizing port-to-port coupling. Using patches that are non-planar leads to higher mechanical rigidity and smaller patch sizes to fit into the available space. Aperture coupling minimizes direct metal-to-metal connections. Using an aperture coupling feed mechanism results in a feed network for two antenna elements with a total of three feed points, plus one simple in-phase combiner to reduce it to two ports. It greatly reduces the complexity of the alternative, but more conventional, way of feeding a pair of two dual-polarized elements with high port isolation.

duToit, Cornelis↗
NASA NTRS2013

Development of a Pulsed 2-Micron Integrated Path Differential Absorption Lidar for CO2 Measurement

Atmospheric carbon dioxide (CO2) is an important greenhouse gas that significantly contributes to the carbon cycle and global radiation budget on Earth. Active remote sensing of CO2 is important to address several limitations that contend with passive sensors. A 2-micron double-pulsed, Integrated Path Differential Absorption (IPDA) lidar instrument for ground and airborne atmospheric CO2 concentration measurements via direct detection method is being developed at NASA Langley Research Center. This active remote sensing instrument will provide an alternate approach of measuring atmospheric CO2 concentrations with significant advantages. A high energy pulsed approach provides high-precision measurement capability by having high signal-to-noise ratio level and unambiguously eliminates the contamination from aerosols and clouds that can bias the IPDA measurement. Commercial, on the shelf, components are implemented for the detection system. Instrument integration will be presented in this paper as well as a background for CO2 measurement at NASA Langley research Center

Singh, Upendra N.↗
NASA NTRS2013

Mapping Impervious Surfaces Globally at 30m Resolution Using Global Land Survey Data

Impervious surfaces, mainly artificial structures and roads, cover less than 1% of the world's land surface (1.3% over USA). Regardless of the relatively small coverage, impervious surfaces have a significant impact on the environment. They are the main source of the urban heat island effect, and affect not only the energy balance, but also hydrology and carbon cycling, and both land and aquatic ecosystem services. In the last several decades, the pace of converting natural land surface to impervious surfaces has increased. Quantitatively monitoring the growth of impervious surface expansion and associated urbanization has become a priority topic across both the physical and social sciences. The recent availability of consistent, global scale data sets at 30m resolution such as the Global Land Survey from the Landsat satellites provides an unprecedented opportunity to map global impervious cover and urbanization at this resolution for the first time, with unprecedented detail and accuracy. Moreover, the spatial resolution of Landsat is absolutely essential to accurately resolve urban targets such a buildings, roads and parking lots. With long term GLS data now available for the 1975, 1990, 2000, 2005 and 2010 time periods, the land cover/use changes due to urbanization can now be quantified at this spatial scale as well. In the Global Land Survey - Imperviousness Mapping Project (GLS-IMP), we are producing the first global 30 m spatial resolution impervious cover data set. We have processed the GLS 2010 data set to surface reflectance (8500+ TM and ETM+ scenes) and are using a supervised classification method using a regression tree to produce continental scale impervious cover data sets. A very large set of accurate training samples is the key to the supervised classifications and is being derived through the interpretation of high spatial resolution (approx. 2 m or less) commercial satellite data (Quickbird and Worldview2) available to us through the unclassified archive of the National Geospatial Intelligence Agency (NGA). For each continental area several million training pixels are derived by analysts using image segmentation algorithms and tools and then aggregated to the 30m resolution of Landsat. Here we will discuss the production/testing of this massive data set for Europe, North and South America and Africa, including assessments of the 2010 surface reflectance data. This type of analysis is only possible because of the availability of long term 30m data sets from GLS and shows much promise for integration of Landsat 8 data in the future.

global land survey↗
NASA NTRS2014

Estimation of Crop Gross Primary Production (GPP): I. Impact of MODIS Observation Footprint and Impact of Vegetation BRDF Characteristics

Accurate estimation of gross primary production (GPP) is essential for carbon cycle and climate change studies. Three AmeriFlux crop sites of maize and soybean were selected for this study. Two of the sites were irrigated and the other one was rainfed. The normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), the green band chlorophyll index (CIgreen), and the green band wide dynamic range vegetation index (WDRVIgreen) were computed from the moderate resolution imaging spectroradiometer (MODIS) surface reflectance data. We examined the impacts of the MODIS observation footprint and the vegetation bidirectional reflectance distribution function (BRDF) on crop daily GPP estimation with the four spectral vegetation indices (VIs - NDVI, EVI, WDRVIgreen and CIgreen) where GPP was predicted with two linear models, with and without offset: GPP = a × VI × PAR and GPP = a × VI × PAR + b. Model performance was evaluated with coefficient of determination (R2), root mean square error (RMSE), and coefficient of variation (CV). The MODIS data were filtered into four categories and four experiments were conducted to assess the impacts. The first experiment included all observations. The second experiment only included observations with view zenith angle (VZA) ≤ 35◦ to constrain growth of the footprint size,which achieved a better grid cell match with the agricultural fields. The third experiment included only forward scatter observations with VZA ≤ 35◦. The fourth experiment included only backscatter observations with VZA ≤ 35◦. Overall, the EVI yielded the most consistently strong relationships to daily GPP under all examined conditions. The model GPP = a × VI × PAR + b had better performance than the model GPP = a × VI × PAR, and the offset was significant for most cases. Better performance was obtained for the irrigated field than its counterpart rainfed field. Comparison of experiment 2 vs. experiment 1 was used to examine the observation footprint impact whereas comparison of experiment 4 vs. experiment 3 was used to examine the BRDF impact. Changes in R2, RMSE,CV and changes in model coefficients "a" and "b" (experiment 2 vs. experiment 1; and experiment 4 vs. experiment 3) were indicators of the impacts. The second experiment produced better performance than the first experiment, increasing R2 (↑0.13) and reducing RMSE (↓0.68 g C m−2 d−1) and CV (↓9%). For each VI, the slope of GPP = a × VI × PAR in the second experiment for each crop type changed little while the slope and intercept of GPP = a × VI × PAR + b varied field by field. The CIgreen was least affected by the MODIS observation footprint in estimating crop daily GPP (R2, ↑0.08; RMSE, ↓0.42 g C m−2 d−1; and CV, ↓7%). Footprint most affected the NDVI (R2, ↑0.15; CV, ↓10%) and the EVI (RMSE, ↓0.84 g C m−2 d−1). The vegetation BRDF impact also caused variation of model performance and change of model coefficients. Significantly different slopes were obtained for forward vs. backscatter observations, especially for the CIgreen and the NDVI. Both the footprint impact and the BRDF impact varied with crop types, irrigation options, model options and VI options.

Chlorophyll↗
NASA NTRS2014

Digital Beamforming Synthetic Aperture Radar (DBSAR): Performance Analysis During the Eco-3D 2011 and Summer 2012 Flight Campaigns

The Digital Beamforming Synthetic Aperture radar (DBSAR) is a state-of-the-art airborne radar developed at NASA/Goddard for the implementation, and testing of digital beamforming techniques applicable to Earth and planetary sciences. The DBSAR measurements have been employed to study: The estimation of vegetation biomass and structure - critical parameters in the study of the carbon cycle; The measurement of geological features - to explore its applicability to planetary science by measuring planetary analogue targets. The instrument flew two test campaigns over the East coast of the United States in 2011, and 2012. During the campaigns the instrument operated in full polarimetric mode collecting data from vegetation and topography features.

SAR↗
NASA NTRS2013

Global Ocean Phytoplankton

Phytoplankton are free-floating algae that grow in the euphotic zone of the upper ocean, converting carbon dioxide, sunlight, and available nutrients into organic carbon through photosynthesis. Despite their microscopic size, these photoautotrophs are responsible for roughly half the net primary production on Earth (NPP; gross primary production minus respiration), fixing atmospheric CO2 into food that fuels our global ocean ecosystems. Phytoplankton thus play a critical role in the global carbon cycle, and their growth patterns are highly sensitive to environmental changes such as increased ocean temperatures that stratify the water column and prohibit the transfer of cold, nutrient richwaters to the upper ocean euphotic zone.

Franz, B. A.↗
NASA NTRS2012

Filling-in of Near-infrared Solar Lines by Terrestrial Fluorescence and Other Geophysical Effects: Simulations and Space-based Observations from SCIAMACHY and GOSAT

Global mapping of terrestrial vegetation fluorescence from space has recently been accomplished with high spectral resolution (nu/nu greater than 35 000) measurements from the Japanese Greenhouse gases Observing SAellite (GOSAT). These data are of interest because they can potentially provide global information on the functional status of vegetation including light-use efficiency and global primary productivity that can be used for global carbon cycle modeling. Quantifying the impact of fluorescence on the O2-A band is important as this band is used for photon pathlength characterization in cloud- and aerosol-contaminated pixels for trace-gas retrievals including CO2. Here, we examine whether fluorescence information can be derived from space using potentially lower-cost hyperspectral instrumentation, i.e., more than an order of magnitude less spectral resolution (nu/nu approximately 1600) than GOSAT, with a relatively simple algorithm. We discuss laboratory measurements of fluorescence near one of the few wide and deep solar Fraunhofer lines in the long-wave tail of the fluorescence emission region, the calcium (Ca) II line at 866 nm that is observable with a spectral resolution of approximately 0.5 nm. The filling-in of the Ca II line due to additive signals from various atmospheric and terrestrial effects, including fluorescence, is simulated. We then examine filling-in of this line using the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) satellite instrument. In order to interpret the satellite measurements, we developed a general approach to correct for various instrumental artifacts that produce false filling-in of solar lines in satellite measurements. The approach is applied to SCIAMACHY at the 866 nm Ca II line and to GOSAT at 758 and 770 nm on the shoulders of the O2-A feature where there are several strong solar Fraunhofer lines that are filled in primarily by vegetation fluorescence. Finally, we compare temporal and spatial variations of SCIAMACHY additive signals with those of GOSAT and the Enhanced Vegetation Index (EVI) from the MODerate-resolution Imaging Spectroradiometer (MODIS). Although the derived additive signals from SCIAMACHY are extremely weak at 866 nm, their spatial and temporal variations are consistent with chlorophyll a fluorescence or another vegetation-related source. We also show that fillingin occurs at 866 nm over some barren areas, possibly originating from luminescent minerals in rock and soil.

vegetation↗
NASA NTRS2013

Global Monitoring of Terrestrial Chlorophyll Fluorescence from Moderate-Spectral-Resolution Near-Infrared Satellite Measurements: Methodology, Simulations, and Application to GOME-2

Globally mapped terrestrial chlorophyll fluorescence retrievals are of high interest because they can provide information on the functional status of vegetation including light-use efficiency and global primary productivity that can be used for global carbon cycle modeling and agricultural applications. Previous satellite retrievals of fluorescence have relied solely upon the filling-in of solar Fraunhofer lines that are not significantly affected by atmospheric absorption. Although these measurements provide near-global coverage on a monthly basis, they suffer from relatively low precision and sparse spatial sampling. Here, we describe a new methodology to retrieve global far-red fluorescence information; we use hyperspectral data with a simplified radiative transfer model to disentangle the spectral signatures of three basic components: atmospheric absorption, surface reflectance, and fluorescence radiance. An empirically based principal component analysis approach is employed, primarily using cloudy data over ocean, to model and solve for the atmospheric absorption. Through detailed simulations, we demonstrate the feasibility of the approach and show that moderate-spectral-resolution measurements with a relatively high signal-to-noise ratio can be used to retrieve far-red fluorescence information with good precision and accuracy. The method is then applied to data from the Global Ozone Monitoring Instrument 2 (GOME-2). The GOME-2 fluorescence retrievals display similar spatial structure as compared with those from a simpler technique applied to the Greenhouse gases Observing SATellite (GOSAT). GOME-2 enables global mapping of far-red fluorescence with higher precision over smaller spatial and temporal scales than is possible with GOSAT. Near-global coverage is provided within a few days. We are able to show clearly for the first time physically plausible variations in fluorescence over the course of a single month at a spatial resolution of 0.5 0.5. We also show some significant differences between fluorescence and coincident normalized difference vegetation indices (NDVI) retrievals.

Fluorescence↗
NASA NTRS2012

Using EO-1 Hyperion to Simulate HyspIRI Products for a Coniferous Forest: The Fraction of PAR Absorbed by Chlorophyll (fAPAR(sub chl)) and Leaf Water Content(LWC)

This paper presents development of prototype products for terrestrial ecosystems in preparation for the future imaging spectrometer planned for the Hyperspectral Infrared Imager (HyspIRI) mission. We present a successful demonstration example in a coniferous forest of two product prototypes: fraction of photosynthetically active radiation (PAR) absorbed by chlorophyll of a canopy (fAPARchl) and leaf water content (LWC), for future HyspIRI implementation at 60-m spatial resolution. For this, we used existing 30-m resolution imaging spectrometer data available from the Earth Observing One (EO-1) Hyperion satellite to simulate and prototype the level one radiometrically corrected radiance (L1R) images expected from the HyspIRI visible through shortwave infrared spectrometer. The HyspIRIlike images were atmospherically corrected to obtain surface reflectance and spectrally resampled to produce 60-m reflectance images for wavelength regions that were comparable to all seven of the MODerate resolution Imaging Spectroradiometer (MODIS) land bands. Thus, we developed MODIS-like surface reflectance in seven spectral bands at the HyspIRI-like spatial scale, which was utilized to derive fAPARchl and LWC with a coupled canopy-leaf radiative transfer model (PROSAIL2) for the coniferous forest. With this paper, we provide additional evidence that the fAPARchl product is more realistic in describing the physiologically active canopy than the traditional fAPAR parameter for the whole canopy (fAPARcanopy), and thus, it should replace it in ecosystem process models to reduce uncertainties in terrestrial carbon cycle and ecosystem studies.

Earth Observing One↗
NASA NTRS2014

Assimilation of Freeze - Thaw Observations into the NASA Catchment Land Surface Model

The land surface freeze-thaw (F-T) state plays a key role in the hydrological and carbon cycles and thus affects water and energy exchanges and vegetation productivity at the land surface. In this study, we developed an F-T assimilation algorithm for the NASA Goddard Earth Observing System, version 5 (GEOS-5) modeling and assimilation framework. The algorithm includes a newly developed observation operator that diagnoses the landscape F-T state in the GEOS-5 Catchment land surface model. The F-T analysis is a rule-based approach that adjusts Catchment model state variables in response to binary F-T observations, while also considering forecast and observation errors. A regional observing system simulation experiment was conducted using synthetically generated F-T observations. The assimilation of perfect (error-free) F-T observations reduced the root-mean-square errors (RMSE) of surface temperature and soil temperature by 0.206 C and 0.061 C, respectively, when compared to model estimates (equivalent to a relative RMSE reduction of 6.7 percent and 3.1 percent, respectively). For a maximum classification error (CEmax) of 10 percent in the synthetic F-T observations, the F-T assimilation reduced the RMSE of surface temperature and soil temperature by 0.178 C and 0.036 C, respectively. For CEmax=20 percent, the F-T assimilation still reduces the RMSE of model surface temperature estimates by 0.149 C but yields no improvement over the model soil temperature estimates. The F-T assimilation scheme is being developed to exploit planned operational F-T products from the NASA Soil Moisture Active Passive (SMAP) mission.

GEOS5↗
NASA NTRS2013

Global Monitoring of Terrestrial Chlorophyll Fluorescence from Moderate-spectral-resolution Near-infrared Satellite Measurements: Methodology, Simulations, and Application to GOME-2

Globally mapped terrestrial chlorophyll fluorescence retrievals are of high interest because they can provide information on the functional status of vegetation including light-use efficiency and global primary productivity that can be used for global carbon cycle modeling and agricultural applications. Previous satellite retrievals of fluorescence have relied solely upon the filling-in of solar Fraunhofer lines that are not significantly affected by atmospheric absorption. Although these measurements provide near-global coverage on a monthly basis, they suffer from relatively low precision and sparse spatial sampling. Here, we describe a new methodology to retrieve global far-red fluorescence information; we use hyperspectral data with a simplified radiative transfer model to disentangle the spectral signatures of three basic components: atmospheric absorption, surface reflectance, and fluorescence radiance. An empirically based principal component analysis approach is employed, primarily using cloudy data over ocean, to model and solve for the atmospheric absorption. Through detailed simulations, we demonstrate the feasibility of the approach and show that moderate-spectral-resolution measurements with a relatively high signal-to-noise ratio can be used to retrieve far-red fluorescence information with good precision and accuracy. The method is then applied to data from the Global Ozone Monitoring Instrument 2 (GOME-2). The GOME-2 fluorescence retrievals display similar spatial structure as compared with those from a simpler technique applied to the Greenhouse gases Observing SATellite (GOSAT). GOME-2 enables global mapping of far-red fluorescence with higher precision over smaller spatial and temporal scales than is possible with GOSAT. Near-global coverage is provided within a few days. We are able to show clearly for the first time physically plausible variations in fluorescence over the course of a single month at a spatial resolution of 0.5 deg × 0.5 deg. We also show some significant differences between fluorescence and coincident normalized difference vegetation indices (NDVI) retrievals.

remote sensing↗
NASA NTRS2014

Reconciling the Differences between the Measurements of CO2 Isotopes by the Phoenix and MSL Landers

Precise stable isotope measurements of the CO2 in the martian atmosphere have the potential to provide important constraints for our understanding of the history of volatiles, the carbon cycle, current atmospheric processes, and the degree of water/rock interaction on Mars. There have been several different measurements by landers and Earth based systems performed in recent years that have not been in agreement. In particular, measurements of the isotopic composition of martian atmospheric CO2 by the Thermal and Evolved Gas Analyzer (TEGA) instrument on the Mars Phoenix Lander and the Sample Analysis at Mars (SAM) instrument on the Mars Science Laboratory (MSL) are in stark disagreement. This work attempts to use measurements of mass 45 and mass 46 of martian atmospheric CO2 by the SAM and TEGA instruments to search for agreement as a first step towards reaching a consensus measurement that might be supported by data from both instruments.

Niles, P. B.↗
NASA NTRS2014

A Regional CO2 Observing System Simulation Experiment for the ASCENDS Satellite Mission

Top-down estimates of the spatiotemporal variations in emissions and uptake of CO2 will benefit from the increasing measurement density brought by recent and future additions to the suite of in situ and remote CO2 measurement platforms. In particular, the planned NASA Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) satellite mission will provide greater coverage in cloudy regions, at high latitudes, and at night than passive satellite systems, as well as high precision and accuracy. In a novel approach to quantifying the ability of satellite column measurements to constrain CO2 fluxes, we use a portable library of footprints (surface influence functions) generated by the WRF-STILT Lagrangian transport model in a regional Bayesian synthesis inversion. The regional Lagrangian framework is well suited to make use of ASCENDS observations to constrain fluxes at high resolution, in this case at 1 degree latitude x 1 degree longitude and weekly for North America. We consider random measurement errors only, modeled as a function of mission and instrument design specifications along with realistic atmospheric and surface conditions. We find that the ASCENDS observations could potentially reduce flux uncertainties substantially at biome and finer scales. At the 1 degree x 1 degree, weekly scale, the largest uncertainty reductions, on the order of 50 percent, occur where and when there is good coverage by observations with low measurement errors and the a priori uncertainties are large. Uncertainty reductions are smaller for a 1.57 micron candidate wavelength than for a 2.05 micron wavelength, and are smaller for the higher of the two measurement error levels that we consider (1.0 ppm vs. 0.5 ppm clear-sky error at Railroad Valley, Nevada). Uncertainty reductions at the annual, biome scale range from 40 percent to 75 percent across our four instrument design cases, and from 65 percent to 85 percent for the continent as a whole. Our uncertainty reductions at various scales are substantially smaller than those from a global ASCENDS inversion on a coarser grid, demonstrating how quantitative results can depend on inversion methodology. The a posteriori flux uncertainties we obtain, ranging from 0.01 to 0.06 Pg C yr-1 across the biomes, would meet requirements for improved understanding of long-term carbon sinks suggested by a previous study.

remote sensing↗
NASA NTRS2014

Interannual Variation in Phytoplankton Class-Specific Primary Production at a Global Scale

We used the NASA Ocean Biogeochemical Model (NOBM) combined with remote sensing data via assimilation to evaluate the contribution of 4 phytoplankton groups to the total primary production. First we assessed the contribution of each phytoplankton groups to the total primary production at a global scale for the period 1998-2011. Globally, diatoms were the group that contributed the most to the total phytoplankton production (50, the equivalent of 20 PgC y-1. Coccolithophores and chlorophytes each contributed to 20 (7 PgC y-1 of the total primary production and cyanobacteria represented about 10 (4 PgC y(sub-1) of the total primary production. Primary production by diatoms was highest in high latitude (45) and in major upwelling systems (Equatorial Pacific and Benguela system). We then assessed interannual variability of this group-specific primary production over the period 1998-2011. Globally the annual relative contribution of each phytoplankton groups to the total primary production varied by maximum 4 (1-2 PgC y-1. We assessed the effects of climate variability on the class-specific primary production using global (i.e. Multivariate El Nio Index, MEI) and regional climate indices (e.g. Southern Annular Mode (SAM), Pacific Decadal Oscillation (PDO) and North Atlantic Oscillation (NAO)). Most interannual variability occurred in the Equatorial Pacific and was associated with climate variability as indicated by significant correlation (p 0.05) between the MEI and the class-specific primary production from all groups except coccolithophores. In the Atlantic, climate variability as indicated by NAO was significantly correlated to the primary production of 2 out of the 4 groups in the North Central Atlantic (diatomscyanobacteria) and in the North Atlantic (chlorophytes and coccolithophores). We found that climate variability as indicated by SAM had only a limited effect on the class-specific primary production in the Southern Ocean. These results provide a modeling and data assimilation perspective to phytoplankton partitioning of primary production and contribute to our understanding of the dynamics of the carbon cycle in the oceans at a global scale.

Chl-a↗
NASA NTRS2014

Interannual Variation in Phytoplankton Class-specific Primary Production at a Global Scale

Phytoplankton is responsible for over half of the net primary production on earth. The knowledge on the contribution of various phytoplankton groups to the total primary production is still poorly understood. Data from satellite observations suggest that for upwelling regions, photosynthetic rates by microplankton is higher than that of nanoplankton but that when the spatial extent is considered, the production by nanoplankton is comparable or even larger than microplankton. Here, we used the NASA Ocean Biogeochemical Model (NOBM) combined with remote sensing data via assimilation to evaluate the contribution of 4 phytoplankton groups to the total primary production. Globally, diatoms were the group that contributed the most to the total phytoplankton production (approx. 50%) followed by coccolithophores and chlorophytes. Primary production by diatoms was highest in high latitude (>45 deg) and in major upwelling systems (Equatorial Pacific and Benguela system). We assessed the effects of climate variability on the class-specific primary production using global (i.e. Multivariate El Nino Index, MEI) and 'regional' climate indices (e.g. Southern Annular Mode (SAM), Pacific Decadal Oscillation (PDO) and North Atlantic Oscillation (NAO)). Most interannual variability occurred in the Equatorial Pacific and was associated with climate variability. These results provide a modeling and data assimilation perspective to phytoplankton partitioning of primary production and contribute to our understanding of the dynamics of the carbon cycle in the oceans at a global scale.

OBM↗
NASA NTRS2014

Algorithm Development and Validation of CDOM Properties for Estuarine and Continental Shelf Waters Along the Northeastern U.S. Coast

An extensive set of field measurements have been collected throughout the continental margin of the northeastern U.S. from 2004 to 2011 to develop and validate ocean color satellite algorithms for the retrieval of the absorption coefficient of chromophoric dissolved organic matter (aCDOM) and CDOM spectral slopes for the 275:295 nm and 300:600 nm spectral range (S275:295 and S300:600). Remote sensing reflectance (Rrs) measurements computed from in-water radiometry profiles along with aCDOM() data are applied to develop several types of algorithms for the SeaWiFS and MODIS-Aqua ocean color satellite sensors, which involve least squares linear regression of aCDOM() with (1) Rrs band ratios, (2) quasi-analytical algorithm-based (QAA based) products of total absorption coefficients, (3) multiple Rrs bands within a multiple linear regression (MLR) analysis, and (4) diffuse attenuation coefficient (Kd). The relative error (mean absolute percent difference; MAPD) for the MLR retrievals of aCDOM(275), aCDOM(355), aCDOM(380), aCDOM(412) and aCDOM(443) for our study region range from 20.4-23.9 for MODIS-Aqua and 27.3-30 for SeaWiFS. Because of the narrower range of CDOM spectral slope values, the MAPD for the MLR S275:295 and QAA-based S300:600 algorithms are much lower ranging from 9.9 and 8.3 for SeaWiFS, respectively, and 8.7 and 6.3 for MODIS, respectively. Seasonal and spatial MODIS-Aqua and SeaWiFS distributions of aCDOM, S275:295 and S300:600 processed with these algorithms are consistent with field measurements and the processes that impact CDOM levels along the continental shelf of the northeastern U.S. Several satellite data processing factors correlate with higher uncertainty in satellite retrievals of aCDOM, S275:295 and S300:600 within the coastal ocean, including solar zenith angle, sensor viewing angle, and atmospheric products applied for atmospheric corrections. Algorithms that include ultraviolet Rrs bands provide a better fit to field measurements than algorithms without the ultraviolet Rrs bands. This suggests that satellite sensors with ultraviolet capability could provide better retrievals of CDOM. Because of the strong correlations between CDOM parameters and DOM constituents in the coastal ocean, satellite observations of CDOM parameters can be applied to study the distributions, sources and sinks of DOM, which are relevant for understanding the carbon cycle, modeling the Earth system, and to discern how the Earth is changing.

CDOM Properties↗
NASA NTRS2013

Interannual Variation in Phytoplankton Primary Production at a Global Scale

We used the NASA Ocean Biogeochemical Model (NOBM) combined with remote sensing data via assimilation to evaluate the contribution of four phytoplankton groups to the total primary production. First, we assessed the contribution of each phytoplankton groups to the total primary production at a global scale for the period 1998-2011. Globally, diatoms contributed the most to the total phytoplankton production ((is)approximately 50%, the equivalent of 20 PgC·y1). Coccolithophores and chlorophytes each contributed approximately 20% ((is) approximately 7 PgC·y1) of the total primary production and cyanobacteria represented about 10% ((is) approximately 4 PgC·y1) of the total primary production. Primary production by diatoms was highest in the high latitudes ((is) greater than 40 deg) and in major upwelling systems (Equatorial Pacific and Benguela system). We then assessed interannual variability of this group-specific primary production over the period 1998-2011. Globally the annual relative contribution of each phytoplankton groups to the total primary production varied by maximum 4% (1-2 PgC·y1). We assessed the effects of climate variability on group-specific primary production using global (i.e., Multivariate El Niño Index, MEI) and "regional" climate indices (e.g., Southern Annular Mode (SAM), Pacific Decadal Oscillation (PDO) and North Atlantic Oscillation (NAO)). Most interannual variability occurred in the Equatorial Pacific and was associated with climate variability as indicated by significant correlation (p (is) less than 0.05) between the MEI and the group-specific primary production from all groups except coccolithophores. In the Atlantic, climate variability as indicated by NAO was significantly correlated to the primary production of 2 out of the 4 groups in the North Central Atlantic (diatoms/cyanobacteria) and in the North Atlantic (chlorophytes and coccolithophores). We found that climate variability as indicated by SAM had only a limited effect on group-specific primary production in the Southern Ocean. These results provide a modeling and data assimilation perspective to phytoplankton partitioning of primary production and contribute to our understanding of the dynamics of the carbon cycle in the oceans at a global scale.

Coccolithophores↗
NASA NTRS2013

Influence of Leaf Area Index Prescriptions on Simulations of Heat, Moisture, and Carbon Fluxes

Leaf-area index (LAI), the total one-sided surface area of leaf per ground surface area, is a key component of land surface models. We investigate the influence of differing, plausible LAI prescriptions on heat, moisture, and carbon fluxes simulated by the Community Atmosphere Biosphere Land Exchange (CABLEv1.4b) model over the Australian continent. A 15-member ensemble monthly LAI data-set is generated using the MODIS LAI product and gridded observations of temperature and precipitation. Offline simulations lasting 29 years (1980-2008) are carried out at 25 km resolution with the composite monthly means from the MODIS LAI product (control simulation) and compared with simulations using each of the 15-member ensemble monthly-varying LAI data-sets generated. The imposed changes in LAI did not strongly influence the sensible and latent fluxes but the carbon fluxes were more strongly affected. Croplands showed the largest sensitivity in gross primary production with differences ranging from -90 to 60 %. PFTs with high absolute LAI and low inter-annual variability, such as evergreen broadleaf trees, showed the least response to the different LAI prescriptions, whilst those with lower absolute LAI and higher inter-annual variability, such as croplands, were more sensitive. We show that reliance on a single LAI prescription may not accurately reflect the uncertainty in the simulation of the terrestrial carbon fluxes, especially for PFTs with high inter-annual variability. Our study highlights that the accurate representation of LAI in land surface models is key to the simulation of the terrestrial carbon cycle. Hence this will become critical in quantifying the uncertainty in future changes in primary production.

land-surface modeling↗