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At least 811 records · Page 45

Integrating Solar Induced Fluorescence and the Photochemical Reflectance Index for Estimating Gross Primary Production in a Cornfield

The utilization of remotely sensed observations for light use efficiency (LUE) and tower-based gross primary production (GPP) estimates was studied in a USDA cornfield. Nadir hyperspectral reflectance measurements were acquired at canopy level during a collaborative field campaign conducted in four growing seasons. The Photochemical Reflectance Index (PRI) and solar induced chlorophyll fluorescence (SIF), were derived. SIF retrievals were accomplished in the two telluric atmospheric oxygen absorption features centered at 688 nm (O2-B) and 760 nm (O2-A). The PRI and SIF were examined in conjunction with GPP and LUE determined by flux tower-based measurements. All of these fluxes, environmental variables, and the PRI and SIF exhibited diurnal as well as day-to-day dynamics across the four growing seasons. Consistent with previous studies, the PRI was shown to be related to LUE (r2 = 0.54 with a logarithm fit), but the relationship varied each year. By combining the PRI and SIF in a linear regression model, stronger performances for GPP estimation were obtained. The strongest relationship (r2 = 0.80, RMSE = 0.186 mg CO2/m2/s) was achieved when using the PRI and SIF retrievals at 688 nm. Cross-validation approaches were utilized to demonstrate the robustness and consistency of the performance. This study highlights a GPP retrieval method based entirely on hyperspectral remote sensing observations.

cornfield↗

Generating Global Leaf Area Index from Landsat: Algorithm Formulation and Demonstration

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. ©

Landsat↗

Investigation of Sideband Index Response to Prototype Gear Tooth Damage

The objective of this analysis was to evaluate the ability of gear condition indicators (CI) to detect contact fatigue damage on spiral bevel gear teeth. Tests were performed in the NASA Glenn Spiral Bevel Gear Fatigue Rig on eight prototype gear sets (pinion/gear). Damage was initiated and progressed on the gear and pinion teeth. Vibration data was measured during damage progression at varying torque values while varying damage modes to the gear teeth were observed and documented with inspection photos. Sideband indexes (SI) and root mean square (RMS) CIs were calculated from the time synchronous averaged vibration data. Results found that both CIs respond differently to varying torque levels, damage levels and damage modes

health monitoring↗

Interannual Variations and Trends in Global Land Surface Phenology Derived from Enhanced Vegetation Index During 1982-2010

Land swiace phenology is widely retrieved from satellite observations at regional and global scales, and its long-term record has been demonstmted to be a valuable tool for reconstructing past climate variations, monitoring the dynamics of terrestrial ecosystems in response to climate impacts, and predicting biological responses to future climate scenarios. This srudy detected global land surface phenology from the advanced very high resolution radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) data from 1982 to 2010. Based on daily enhanced vegetation index at a spatial resolution of 0.05 degrees, we simulated the seasonal vegetative trajectory for each individual pixel using piecewise logistic models, which was then used to detect the onset of greenness increase (OGI) and the length of vegetation growing season (GSL). Further, both overall interannual variations and pixel-based trends were examIned across Koeppen's climate regions for the periods of 1982-1999 and 2000-2010, respectively. The results show that OGI and OSL varied considerably during 1982-2010 across the globe. Generally, the interarmual variation could be more than a month in precipitation-controlled tropical and dry climates while it was mainly less than 15 days in temperature-controlled temperate, cold, and polar climates. OGI, overall, shifted early, and GSL was prolonged from 1982 to 2010 in most climate regions in North America and Asia while the consistently significant trends only occurred in cold climate and polar climate in North America. The overall trends in Europe were generally insignificant. Over South America, late OGI was consistent (particularly from 1982 to 1999) while either positive or negative OSL trends in a climate region were mostly reversed between the periods of 1982-1999 and 2000-2010. In the Northern Hemisphere of Africa, OGI trends were mostly insignificant, but prolonged GSL was evident over individual climate regions during the last 3 decades. OGI mainly showed late trends in the Southern Hemisphere of Africa while GSL was reversed from reduced GSL trends (1982-1999) to prolonged trends (2000-2010). In Australia, GSL exhibited considerable interannual variation, but the consistent trend lacked presence in most regions. Finally, the proportion of pixels with significant trends was less than I% in most of climate regions although it could be as large as 10%.

remote sensing↗

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↗

Normal Incidence for Graded Index Surfaces

A plane wave is incident normally from vacuum (eta(sub 0) = 1) onto a smooth surface. The substrate has three layers; the top most layer has thickness d(sub 1) and permittivity epsilon(sub 1). The corresponding numbers for the next layer are d(sub 2); epsilon(sub 2), while the third layer which is semi-in nite has index eta(sub 3). The Hallikainen model [1] is used to relate volumetric soil moisture to the permittivity. Here, we consider the relation for the real part of the permittivity for a typical loam soil: acute epsilon(mv) = 2.8571 + 3.9678 x mv + 118:85 x mv(sup 2).

plane wave↗

Remotely Sensing the Photochemical Reflectance Index (PRI)

In remote sensing, the Photochemical Reflectance Index (PRI) provides insight into physiological processes occurring inside the leaves in a stand of plants. Developed by Gamon et al., (1990 and 1992), PRI evolved from laboratory measurements of the reflectance of individual leaves (Bilger et al.,1989). Yet in a remotely sensed image, a pixel measurement may include light from both reflecting and transmitting leaves. We conducted laboratory experiments comparing values of PRI based upon polarized reflectance and transmittance measurements of water and nutrient stressed leaves. We illuminated single detached leaves using a current controlled light source (Oriel model 66881) and measured the leaf weight using an analytical balance (Mettler model AE 260) and the light reflected and transmitted by the leaf during dry down using two Analytical Spectral Devices spectroradiometers. Polarizers on the incident and reflected light beams allowed us to divide the leaf reflectance into two parts: a polarized surface reflectance and a non-polarized 'leaf interior' reflectance. Our results underscore the importance when calculating PRI of removing the leaf surface reflection, which contains no information about physiological processes ongoing in the leaf interior. The results show that the leaf physiology information is in the leaf interior reflectance, not the leaf transmittance. Applied to a plant stand, these results suggest use of polarization measurements in sun-view directions that minimize the number of sunlit transmitting leaves in the sensor field of view.

Leaves↗

Temperature-Dependent Refractive Index of Cleartran® ZnS to Cryogenic Temperatures

First, let's talk about the CHARMS facility at NASA's Goddard Space Flight Center: Cryogenic, High-Accuracy Refraction Measuring System (CHARMS); design features for highest accuracy and precision; technologies we rely on; data products and examples; optical materials for which we've measured cryogenic refractive index.

Leviton, Doug↗

Interpreting the Ultraviolet Aerosol Index Observed with the OMI Satellite Instrument to Understand Absorption by Organic Aerosols: Implications for Atmospheric Oxidation and Direct Radiative Effects

Satellite observations of the ultraviolet aerosol index (UVAI) are sensitive to absorption of solar radiation by aerosols; this absorption affects photolysis frequencies and radiative forcing. We develop a global simulation of the UVAI using the 3-D chemical transport model GEOSChem coupled with the Vector Linearized Discrete Ordinate Radiative Transfer model (VLIDORT). The simulation is applied to interpret UVAI observations from the Ozone Monitoring Instrument (OMI) for the year 2007. Simulated and observed values are highly consistent in regions where mineral dust dominates the UVAI, but a large negative bias (-0.32 to -0.97) exists between simulated and observed values in biomass burning regions. We determine effective optical properties for absorbing organic aerosol, known as brown carbon (BrC), and implement them into GEOS-Chem to better represent observed UVAI values over biomass burning regions. The inclusion of absorbing BrC decreases the mean bias between simulated and OMI UVAI values from -0.57 to -0.09 over West Africa in January, from -0.32 to +0.0002 over South Asia in April, from -0.97 to -0.22 over southern Africa in July, and from -0.50 to +0.33 over South America in September. The spectral dependence of absorption after including BrC in the model is broadly consistent with reported observations for biomass burning aerosol, with absorbing Angstrom exponent (AAE) values ranging from 2.9 in the ultraviolet (UV) to 1.3 across the UV-Near IR spectrum. We assess the effect of the additional UV absorption by BrC on atmospheric photochemistry by examining tropospheric hydroxyl radical (OH) concentrations in GEOS-Chem. The inclusion of BrC decreases OH by up to 30% over South America in September, up to 20% over southern Africa in July, and up to 15% over other biomass burning regions. Global annual mean OH concentrations in GEOS-Chem decrease due to the presence of absorbing BrC, increasing the methyl chloroform lifetime from 5.62 to 5.68 years, thus reducing the bias against observed values. We calculate the direct radiative effect (DRE) of BrC using GEOS-Chem coupled with the radiative transfer model RRTMG (GC-RT). Treating organic aerosol as containing more strongly absorbing BrC changes the global annual mean all-sky top of atmosphere (TOA) DRE by +0.03Wm(exp -2) and all-sky surface DRE by -0.08Wm(exp -2). Regional changes of up to +0.3Wm(exp -2) at TOA and down to -1.5Wm(exp -2) at the surface are found over major biomass burning regions.

Air Pollution↗

Integrating Chlorophyll fapar and Nadir Photochemical Reflectance Index from EO-1/Hyperion to Predict Cornfield Daily Gross Primary Production

The concept of light use efficiency (Epsilon) and the concept of fraction of photosynthetically active ration (PAR) absorbed for vegetation photosynthesis (PSN), i.e., fAPAR (sub PSN), have been widely utilized to estimate vegetation gross primary productivity (GPP). It has been demonstrated that the photochemical reflectance index (PRI) is empirically related to ε. An experimental US Department of Agriculture (USDA) cornfield in Maryland was selected as our study field. We explored the potential of integrating fAPAR(sub chl) (defined as the fraction of PAR absorbed by chlorophyll) and nadir PRI (PRI(sub nadir)) to predict cornfield daily GPP. We acquired nadir or near-nadir EO-1/Hyperion satellite images that covered the cornfield and took nadir in-situ field spectral measurements. Those data were used to derive the PRI(sub nadir) and fAPAR (sub chl). The fAPAR (sub chl) is retrieved with the advanced radiative transfer model PROSAIL2 and the Metropolis approach, a type of Markov Chain Monte Carlo (MCMC) estimation procedure. We define chlorophyll light use efficiency Epsilon (sub chl) as the ratio of vegetation GPP as measured by eddy covariance techniques to PAR absorbed by chlorophyll (Epsilon(sub chl) = GPP/APAR (sub chl). Daily Epsilon (sub chl) retrieved with the EO-1 Hyperion images was regressed with a linear equation of PRI (sub nadir) Epsilon (sub chl) = Alpha × PRI (sub nadir) + Beta). The satellite Epsilon(sub chl- PRI (sub nadir) linear relationship for the cornfield was implemented to develop an integrated daily GPP model [GPP = (Alpha × PRI(sub nadir) + Beta) × fAPAR (sub chl) × PAR], which was evaluated with fAPAR (sub chl) and PRI (sub nadir) retrieved from field measurements. Daily GPP estimated with this fAPAR (sub chl-) PRI (nadir) integration model was strongly correlated with the observed tower in-situ daily GPP (R(sup 2) = 0.93); with a root mean square error (RMSE) of 1.71 g C mol−(sup -1) PPFD and coefficient of variation (CV) of 16.57%. Both seasonal Epsilon (sub chl) and PRI (sub nadir) were strongly correlated with fAPAR (sub chl ) retrieved from field measurements, which indicates that chlorophyll content strongly affects seasonal epsilon (sub chl) and PRI (sub nadir). We demonstrate the potential capacity to monitor GPP with space-based visible through shortwave infrared (VSWIR) imaging spectrometers such as NASA's soon to be decommissioned EO- 1/Hyperion and the future Hyperspectral Infrared Imager (HyspIRI).

HyspIRI↗

Simulation of the Ozone Monitoring Instrument Aerosol Index Using the NASA Goddard Earth Observing System Aerosol Reanalysis Products

We provide an analysis of the commonly used Ozone Monitoring Instrument (OMI) aerosol index (AI) product for qualitative detection of the presence and loading of absorbing aerosols. In our analysis, simulated top-of-atmosphere (TOA) radiances are produced at the OMI footprints from a model atmosphere and aerosol profile provided by the NASA Goddard Earth Observing System (GEOS-5) Modern-Era Retrospective Analysis for Research and Applications aerosol reanalysis (MERRAero). Having established the credibility of the MERRAero simulation of the OMI AI in a previous paper we describe updates in the approach and aerosol optical property assumptions. The OMI TOA radiances are computed in cloud-free conditions from the MERRAero atmospheric state, and the AI is calculated. The simulated TOA radiances are fed to the OMI aerosol retrieval algorithms, and its retrieved AI (OMAERUV AI) is compared to the MERRAero calculated AI. Two main sources of discrepancy are discussed: one pertaining the OMI algorithm assumptions of the surface pressure, which are generally different from what the actual surface pressure of an observation is, and the other related to simplifying assumptions in the molecular atmosphere radiative transfer used in the OMI algorithms. Surface pressure assumptions lead to systematic biases in the OMAERUV AI, particularly over the oceans. Simplifications in the molecular radiative transfer lead to biases particularly in regions of topography intermediate to surface pressures of 600hPa and 1013.25hPa. Generally, the errors in the OMI AI due to these considerations are less than 0.2 in magnitude, though larger errors are possible, particularly over land. We recommend that future versions of the OMI algorithms use surface pressures from readily available atmospheric analyses combined with high-spatial resolution topographic maps and include more surface pressure nodal points in their radiative transfer lookup tables.

remote sensing↗

Detection of Chlorophyll and Leaf Area Index Dynamics from Sub-weekly Hyperspectral Imagery

Temporally rich hyperspectral time-series can provide unique time critical information on within-field variations in vegetation health and distribution needed by farmers to effectively optimize crop production. In this study, a dense time series of images were acquired from the Earth Observing-1 (EO-1) Hyperion sensor over an intensive farming area in the center of Saudi Arabia. After correction for atmospheric effects, optimal links between carefully selected explanatory hyperspectral vegetation indices and target vegetation characteristics were established using a machine learning approach. A dataset of in-situ measured leaf chlorophyll (Chll) and leaf area index (LAI), collected during five intensive field campaigns over a variety of crop types, were used to train the rule-based predictive models. The ability of the narrow-band hyperspectral reflectance information to robustly assess and discriminate dynamics in foliar biochemistry and biomass through empirical relationships were investigated. This also involved evaluations of the generalization and reproducibility of the predictions beyond the conditions of the training dataset. The very high temporal resolution of the satellite retrievals constituted a specifically intriguing feature that facilitated detection of total canopy Chl and LAI dynamics down to sub-weekly intervals. The study advocates the benefits associated with the availability of optimum spectral and temporal resolution spaceborne observations for agricultural management purposes.

Houborg, Rasmus↗

Integrating the Social, Hydrological and Ecological Dimensions of Freshwater Health: The Freshwater Health Index

Degradation of freshwater ecosystems and the services they provide is a primary cause of increasing water insecurity, raising the need for integrated solutions to freshwater management. While methods for characterizing the multi-faceted challenges of managing freshwater ecosystems abound, they tend to emphasize either social or ecological dimensions and fall short of being truly integrative. This paper suggests that management for sustainability of freshwater systems needs to consider the linkages between human water uses, freshwater ecosystems and governance. We present a conceptualization of freshwater resources as part of an integrated social-ecological system and propose a set of corresponding indicators to monitor freshwater ecosystem health and to highlight priorities for management. We demonstrate an application of this new framework -the Freshwater Health Index (FHI) - in the Dongjiang River Basin in southern China, where stakeholders are addressing multiple and conflicting freshwater demands. By combining empirical and modeled datasets with surveys to gauge stakeholders' preferences and elicit expert information about governance mechanisms, the FHI helps stakeholders understand the status of freshwater ecosystems in their basin, how ecosystems are being manipulated to enhance or decrease water-related services, and how well the existing water re-source management regime is equipped to govern these dynamics over time. This framework helps to operationalize a truly integrated approach to water resource management by recognizing the interplay between governance, stakeholders, freshwater ecosystems and the services they provide

ecosystem services↗

Assimilation of Remotely Sensed Leaf Area Index into the Noah-MP Land Surface Model: Impacts on Water and Carbon Fluxes and States over the Continental U.S.

Accurate representation of vegetation states is required for the modeling of terrestrial water-energy-carbon exchanges and the characterization of the impacts of natural and anthropogenic vegetation changes on the land surface. This study presents a comprehensive evaluation of the impact of assimilating remote sensing-based Leaf Area Index (LAI) retrievals over the Continental U.S. in the Noah-MP land surface model, during a time period of 2000 to 2017. The results demonstrate that the assimilation has a beneficial impact on the simulation of key water budget terms such as soil moisture, evapotranspiration, snow depth, terrestrial water storage and streamflow, when compared with a large suite of reference datasets. In addition, the assimilation of LAI is also found to improve the carbon fluxes of Gross Primary Production (GPP) and Net Ecosystem Exchange (NEE). Most prominent improvements in the water and carbon variables are observed over the agricultural areas of the U.S., where assimilation improves the representation of vegetation seasonality impacted by cropping schedules. The systematic, added improvements from assimilation in a configuration that employs high quality boundary conditions highlight the significant utility of LAI data assimilation in capturing the impacts of vegetation changes.

anthropogenic vegetation↗

Developing an Air Quality Index for Space Vehicles and Habitats

The development of an adequate tool to help the layperson understand pollution levels in their environment is of high importance. This tool must be able to inform about the levels of pollution in a simple and understandable way but also can be used for decision-making and mitigation activities to protect the health of the exposed population. One of the most useful and up-to-date approaches for characterizing air pollution is the Air Quality Index (AQI). It is an easily-calculated, powerful, data-driven tool that summarizes a complex phenomenon, such as air pollution, in straightforward indicators. The AQI system has been developed in different countries around the world, mainly for outdoor environments, based on the results of risk assessments, epidemiological studies, and current local air pollution regulations and standards. There is a need for such a system in low gravity indoor environments where air quality is of fundamental importance to astronaut health, with concerns encompassing both gaseous contaminants and particulate matter. Earth-based AQIs cannot be extrapolated to microgravity indoor environments due to different aerosol transport characteristics and altered lung deposition in low and partial gravity. The objectives of this work are to explore what areas of expertise, types of research, and data will be required to formulate a spacecraft-specific AQI. An initial dataset is available for this effort, combined from two aerosol sampling experiments, which have characterized airborne particulate matter on the International Space Station (ISS). We outline future research needs for formulating a narrowly focused version of a widely-used metric, namely, an indoor AQI for future space missions.

Sorek-Hamer, Meytar↗

Completeness Index for Earthquake-Induced Landslide Inventories

Understanding the global relation between Earthquake-Induced Landslides (EQILs) and the factors contributing to their initiation is still an open topic within the geomorphological community. Accessing EQIL inventories and analyzing them concerning their potential causes is the key to explore such relation. However, each of the existing EQIL inventories has its level of completeness and associated uncertainty which makes any unified relationship which is challenging to obtain. So far, the completeness of EQIL inventories has never been clearly defined. As a result, it has never been accounted for in global EQIL predictive models. In this technical note, we propose a simple definition for the completeness of EQIL inventories. We analyze 30 digital EQIL for 21 earthquakes and develop a semi-quantitative method to estimate the completeness level, which we refer to as Completeness Index (CI). The CI results from a combination of topographic factors, ground shaking parameters, and a measure derived from landslide size statistics. The proposed CI consists of Low, Moderate, and High completeness classes which can be used to evaluate any landslide inventory. We made the whole procedure to compute the CI accessible in an ArcGIS toolbox together with a test dataset.

Hakan Tanyas↗

Developing a Customized Composite Drought Index for Pakistan

Pakistan experiences frequent and intense agricultural drought, varying spatially and temporally. Prolonged dry conditions often result in failed crop production. Using multiple variables, different components of drought can be captured across a multitude of climatic zones and throughout different seasons. Developing a composite drought index (CDI), specific for each district, will provide a more complete view of agricultural drought and enhance early warning systems.

Schwartz, Caily↗