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51 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Diurnal and Seasonal Variations in Chlorophyll Fluorescence Associated with Photosynthesis at Leaf and Canopy Scales

There is a critical need for sensitive remote sensing approaches to monitor the parameters governing photosynthesis, at the temporal scales relevant to their natural dynamics. The photochemical reflectance index (PRI) and chlorophyll fluorescence (F) offer a strong potential for monitoring photosynthesis at local, regional, and global scales, however the relationships between photosynthesis and solar induced F (SIF) on diurnal and seasonal scales are not fully understood. This study examines how the fine spatial and temporal scale SIF observations relate to leaf level chlorophyll fluorescence metrics (i.e., PSII yield, YII and electron transport rate, ETR), canopy gross primary productivity (GPP), and PRI. The results contribute to enhancing the understanding of how SIF can be used to monitor canopy photosynthesis. This effort captured the seasonal and diurnal variation in GPP, reflectance, F, and SIF in the O2A (SIFA) and O2B (SIFB) atmospheric bands for corn (Zea mays L.) at a study site in Greenbelt, MD. Positive linear relationships of SIF to canopy GPP and to leaf ETR were documented, corroborating published reports. Our findings demonstrate that canopy SIF metrics are able to capture the dynamics in photosynthesis at both leaf and canopy levels, and show that the relationship between GPP and SIF metrics differs depending on the light conditions (i.e., above or below saturation level for photosynthesis). The sum of SIFA and SIFB (SIFA+B), as well as the SIFA+B yield, captured the dynamics in GPP and light use efficiency, suggesting the importance of including SIFB in monitoring photosynthetic function. Further efforts are required to determine if these findings will scale successfully to airborne and satellite levels, and to document the effects of data uncertainties on the scaling.

Campbell, Petya K.↗
DOE OSTI2025

Combining geometric-optical and spectral invariants theories for modeling canopy fluorescence anisotropy

The spectral invariants theory ( p -theory) has received much attention in the field of quantitative remote sensing over the past few decades and has been adopted for modeling of canopy solar-induced chlorophyll fluorescence (SIF). However, the spectral invariant properties (SIP) in simple analytical formulae have not been applied for modeling canopy fluorescence anisotropy primarily because they are parameterized in terms of leaf total scattering, which precludes the differentiation between forward and backward leaf SIF emissions. In this study, we have developed the canopy-SIP SIF model by combining geometric-optical (GO) theory to account for asymmetric leaf SIF forward and backward emissions at the first-order scattering and by modeling multiple scattering based on the p-theory, thus avoiding the dependence on radiative transfer models. The applicability of the model simulations especially over 3D heterogeneous canopies was improved by incorporating canopy structure through multi-angular clumping index, and by modeling single scattering from the four components of the scene in view according to the GO approach. The results show good consistency with both the state-of-the-art SIF models and multi-angular field SIF observations over grass and chickpea canopies. Further, the coefficient of determination (R²) between the simulated SIF and field measurements was 0.75 (red) and 0.74 (far-red) for chickpea, and 0.65 (both red and far-red) for grass. The average relative error was approximately 3% for 1D homogeneous scenes when comparing the canopy-SIP SIF model simulations to the SCOPE model simulations, and around 4% for the 3D heterogeneous scene when comparing to the LESS model simulations. The results indicate that the proposed approach for separating asymmetric leaf SIF emissions is a robust way to keep a balance between satisfactory simulation accuracy and efficiency. Model simulations suggest that neglecting the leaf SIF asymmetry can lead to an underestimation of canopy red SIF by 6.3% to 42.6% for various leaf biochemical and canopy structural parameters. This study presents a simple but efficient analytical approach for canopy fluorescence modeling, with potential for large-scale canopy fluorescence simulations.

3D heterogeneous structure↗
DOE OSTI2020

Moisture availability mediates the relationship between terrestrial gross primary production and solar-induced chlorophyll fluorescence: Insights from global-scale variations

Effective use of solar-induced chlorophyll fluorescence (SIF) to estimate and monitor gross primary production (GPP) in terrestrial ecosystems requires a comprehensive understanding and quantification of the relationship between SIF and GPP. To date, this understanding is incomplete and somewhat controversial in the literature. Here we derived the GPP/SIF ratio from multiple data sources as a diagnostic metric to explore its global-scale patterns of spatial variation and potential climatic dependence. We found that the growing season GPP/SIF ratio varied substantially across global land surfaces, with the highest ratios consistently found in boreal regions. Spatial variation in GPP/SIF was strongly modulated by climate variables. The most striking pattern was a consistent decrease in GPP/SIF from cold-and-wet climates to hot-and-dry climates. We propose that the reduction in GPP/SIF with decreasing moisture availability may be related to stomatal responses to aridity. Furthermore, we show that GPP/SIF can be empirically modeled from climate variables using a machine learning (random forest) framework, which can improve the modeling of ecosystem production and quantify its uncertainty in global terrestrial biosphere models. Finally, our results point to the need for targeted field and experimental studies to better understand the patterns observed and to improve the modeling of the relationship between SIF and GPP over broad scales.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1999

The Effect of Surface Induced Flows on Bubble and Particle Aggregation

Almost 20 years have elapsed since a phenomenon called "radial specific coalescence" was identified. During studies of electrolytic oxygen evolution from the back side of a vertically oriented, transparent tin oxide electrode in alkaline electrolyte, one of the authors (Sides) observed that large "collector" bubbles appeared to attract smaller bubbles. The bubbles moved parallel to the surface of the electrode, while the electric field was normal to the electrode surface. The phenomenon was reported but not explained. More recently self ordering of latex particles was observed during electrophoretic deposition at low DC voltages likewise on a transparent tin oxide electrode. As in the bubble work, the field was normal to the electrode while the particles moved parallel to it. Fluid convection caused by surface induced flows (SIF) can explain these two apparently different experimental observations: the aggregation of particles on an electrode during electrophoretic deposition, and a radial bubble coalescence pattern on an electrode during electrolytic gas evolution. An externally imposed driving force (the gradient of electrical potential or temperature), interacting with the surface of particles or bubbles very near a planar conducting surface, drives the convection of fluid that causes particles and bubbles to approach each other on the electrode.

Guelcher, Scott A.↗
NASA NTRS2014

Prospects for Chlorophyll Fluorescence Remote Sensing from the Orbiting Carbon Observatory-2

The Orbiting Carbon Observatory-2 (OCO-2), scheduled to launch in July 2014, is a NASA mission designed to measure atmospheric CO2. Its main purpose is to allow inversions of net flux estimates of CO2 on regional to continental scales using the total column CO2 retrieved using high-resolution spectra in the 0.76, 1.6, and 2.0 nm ranges. Recently, it was shown that solar-induced chlorophyll fluorescence (SIF), a proxy for gross primary production (GPP, carbon uptake through photosynthesis), can be accurately retrieved from space using high spectral resolution radiances in the 750 nm range from the Japanese GOSAT and European GOME-2 instruments. Here, we use real OCO-2 thermal vacuum test data as well as a full repeat cycle (16 days) of simulated OCO-2 spectra under realistic conditions to evaluate the potential of OCO-2 for retrievals of chlorophyll fluorescence and also its dependence on clouds and aerosols. We find that the single-measurement precision is 0.3-0.5 Wm(exp -2)sr(exp -1) nm(exp -1) (15-25% of typical peak values), better than current measurements from space but still difficult to interpret on a single-sounding basis. The most significant advancement will come from smaller ground-pixel sizes and increased measurement frequency, with a 100-fold increase compared to GOSAT (and about 8 times higher than GOME-2). This will largely decrease the need for coarse spatial and temporal averaging in data analysis and pave the way to accurate local studies.We also find that the lack of full global mapping from the OCO-2 only incurs small representativeness errors on regional averages. Eventually, the combination of net ecosystem exchange (NEE) derived from CO2 source/sink inversions and SIF as proxy for GPP from the same satellite will provide a more process-based understanding of the global carbon cycle.

vegetation agriculture satellite↗
DOE OSTI2024

Wildfire‐induced increases in photosynthesis in boreal forest ecosystems of North America

Abstract Observations of the annual cycle of atmospheric CO 2 in high northern latitudes provide evidence for an increase in terrestrial metabolism in Arctic tundra and boreal forest ecosystems. However, the mechanisms driving these changes are not yet fully understood. One proposed hypothesis is that ecological change from disturbance, such as wildfire, could increase the magnitude and change the phase of net ecosystem exchange through shifts in plant community composition. Yet, little quantitative work has evaluated this potential mechanism at a regional scale. Here we investigate how fire disturbance influences landscape‐level patterns of photosynthesis across western boreal North America. We use Alaska and Canadian large fire databases to identify the perimeters of wildfires, a Landsat‐derived land cover time series to characterize plant functional types (PFTs), and solar‐induced fluorescence (SIF) from the Orbiting Carbon Observatory‐2 (OCO‐2) as a proxy for photosynthesis. We analyze these datasets to characterize post‐fire changes in plant succession and photosynthetic activity using a space‐for‐time approach. We find that increases in herbaceous and sparse vegetation, shrub, and deciduous broadleaf forest PFTs during mid‐succession yield enhancements in SIF by 8–40% during June and July for 2‐ to 59‐year stands relative to pre‐fire controls. From the analysis of post‐fire land cover changes within individual ecoregions and modeling, we identify two mechanisms by which fires contribute to long‐term trends in SIF. First, increases in annual burning are shifting the stand age distribution, leading to increases in the abundance of shrubs and deciduous broadleaf forests that have considerably higher SIF during early‐ and mid‐summer. Second, fire appears to facilitate a long‐term shift from evergreen conifer to broadleaf deciduous forest in the Boreal Plain ecoregion. These findings suggest that increasing fire can contribute substantially to positive trends in seasonal CO 2 exchange without a close coupling to long‐term increases in carbon storage.

Biodiversity & Conservation↗
NASA NTRS2017

Temporal Consistency Between Gross Primary Production and Solar-Induced Chlorophyll Fluorescence in the Ten Most Populous Megacity Areas over Years

The gross primary production (GPP) of vegetation in urban areas plays an important role in the study of urban ecology. It is difficult however, to accurately estimate GPP in urban areas, mostly due to the complexity of impervious land surfaces, buildings, vegetation, and management. Recently, we used the Vegetation Photosynthesis Model (VPM), climate data, and satellite images to estimate the GPP of terrestrial ecosystems including urban areas. Here, we report VPM-based GPP (GPPvpm) estimates for the world's ten most populous megacities during 2000-2014. The seasonal dynamics of GPPvpm during 2007-2014 in the ten megacities track well that of the solar-induced chlorophyll fluorescence (SIF) data from GOME-2 at 0.5deg x 0.5deg resolution. Annual GPPvpm during 2000-2014 also shows substantial variation among the ten megacities, and year-to-year trends show increases, no change, and decreases. Urban expansion and vegetation collectively impact GPP variations in these megacities. The results of this study demonstrate the potential of a satellite-based vegetation photosynthesis model for diagnostic studies of GPP and the terrestrial carbon cycle in urban areas.

urban↗
NASA NTRS2012

Canopy Level Chlorophyll Fluorescence and the PRI in a Cornfield

Two bio-indicators, the Photochemical Reflectance Index (PRI) and solar-induced red and far-red Chlorophyll Fluorescence (SIF), were derived from directional hyperspectral observations and studied in a cornfield on two contrasting days in the growing season. Both red and far-red SIF exhibited higher values on the day when the canopy in the early senescent stage, but only the far-red SIF showed sensitivity to viewing geometry. Consequently, the red/far-red SIF ratio varied greatly among azimuth positions while the largest values were obtained for the "hotspot" at both growth stages. This ratio was lower (approx.0.88 +/- 0.4) in early July than in August when the ratio approached equivalence (near approx.1). In concert, the PRI exhibited stronger responses to both zenith and azimuth angles and different values on the two growth stages. The potential of using these indices to monitor photosynthetic activities needs further investigation

Middleton, Elizabeth M.↗
DOE OSTI2024

Escape Ratio Contributes More Than Fluorescence Yield to SIF-GPP Relationship Over Crops and Rainforest

Solar-induced chlorophyll fluorescence (SIF) is an effective indicator to track the gross primary productivity (GPP). However, there is still a lack of a clear understanding for the contribution of physiological and structural factors to the SIF–GPP relationship at the canopy scale. To quantify the influence of different SIF components, particularly the photon escape ratio ($f$ esc ) and fluorescence yield (Φ F ), on the SIF–GPP relationship, this study evaluated the performance of various vegetation indices (VIs), SIF components, light use efficiency (LUE), and GPP over two typical biomes (crops and rainforest), using a range of satellite remote sensing products. In August of each year from 2018 to 2020, both SIF and GPP over United States (U.S.) Corn Belt are higher than those over the Amazon rainforest, attributed to the consistent pattern of higher $f$ esc and LUE over crops than over rainforest, as well as Φ F . Furthermore, the structural signals represented by $f$ esc (R = 0.41–0.64) can better capture the LUE variations than Φ F (R = 0.10–0.30) for each biome. In conclusion, this study highlights that $f$ esc , determined by canopy structure, has great potential to capture LUE and GPP changes within and across biomes.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2018

Estimation of Terrestrial Global Gross Primary Production (GPP) with Satellite Data-Driven Models and Eddy Covariance Flux Data

We estimate global terrestrial gross primary production (GPP) based on models that use satellite data within a simplified light-use efficiency framework that does not rely upon other meteorological inputs. Satellite-based geometry-adjusted reflectances are from the MODerate-resolution Imaging Spectroradiometer (MODIS) and provide information about vegetation structure and chlorophyll content at both high temporal (daily to monthly) and spatial (1 km) resolution. We use satellite-derived solar-induced fluorescence (SIF) to identify regions of high productivity crops and also evaluate the use of downscaled SIF to estimate GPP. We calibrate a set of our satellite-based models with GPP estimates from a subset of distributed eddy covariance flux towers (FLUXNET 2015). The results of the trained models are evaluated using an independent subset of FLUXNET 2015 GPP data. We show that variations in light-use efficiency (LUE) with incident PAR are important and can be easily incorporated into the models. Unlike many LUE-based models, our satellite-based GPP estimates do not use an explicit parameterization of LUE that reduces its value from the potential maximum under limiting conditions such as temperature and water stress. Even without the parameterized downward regulation, our simplified models are shown to perform as well as or better than state-of-the-art satellite data-driven products that incorporate such parameterizations. A significant fraction of both spatial and temporal variability in GPP across plant functional types can be accounted for using our satellite-based models. Our results provide an annual GPP value of 140 Pg C year 1 for 2007 that is within the range of a compilation of observation-based, model, and hybrid results, but is higher than some previous satellite observation-based estimates

CO2↗
NASA NTRS2019

Revision of Paschen's Law Relating to the ESD of Aerospace Vehicle Surfaces

The purpose of this work is to develop a form of Paschen's law that takes into account the flow of gas past electrode surfaces. This work was performed under a NASA Science Innovation Fund (SIF) project at the Kennedy Space Center in collaboration with the University of Central Florida. In 2010 the Electrostatics and Surface Physics Laboratory (ESPL) at the Kennedy Space Center performed an electrostatic safety analysis on the flight termination system (FTS) antenna for the Ares I rocket. Paschen's law, derived by Friedrich Paschen in 1889 to relate sparking voltage to gas pressure and electrode separation, does not take into account the effect of flowing gas between the electrodes. The safety of the FTS housing to triboelectric charging was shown only after extensive laboratory testing. Potential benefits are of a form of Paschen's law that considers gas velocity. This work is applicable to current and planned rockets and aerospace vehicles and could lead to possible relaxation of electrostatic launch criteria. Launch aborts can cost up to about a million US dollars depending on the vehicle. [ElectroStatic Discharge (ESD)]

Paschen↗
DOE OSTI2024

Phosphor Ceramic Composite for Tunable Warm White Light

Composite phosphor ceramics for warm white LED lighting were fabricated with K 2 SiF 6 :Mn 4+ (KSF) as both a narrowband red phosphor and a translucent matrix in which yellow-emitting Y 3 Al 5 O 12 :Ce 3+ (YAG) particles were dispersed. The emission spectra of these composites under blue LED excitation were studied as a function of YAG loading and thickness. Warm white light with a color temperature of 2716 K, a high CRI of 92.6, and an R9 of 77.6 was achieved. A modest improvement in the thermal conductivity of the KSF ceramic of up to 9% was observed with the addition of YAG particles. In addition, a simple model was developed for predicting the emission spectra based on several parameters of the composite ceramics and validated with the experimental results. The emission spectrum can be tuned by varying the dopant concentrations, thickness, YAG loading, and YAG particle size. This work demonstrates the utility of KSF/YAG composite phosphor ceramics as a means of producing warm white light, which are potentially suitable for higher-drive applications due to their increased thermal conductivity and reduced droop compared with silicone-dispersed phosphor powders.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

How Can We Harness the Power of Machine Learning With TEMPO Data?

There are many potential applications of machine learning for TEMPO data that include - Improve retrievals by reducing the effect of random instrument noise - Expand coverage by producing data in moderately cloudy conditions (see also Fasnacht et al. poster) - Help diagnose impacts of instrumental artifacts - Produce value-added products quickly by training on existing products from other sensors (land and ocean) - Speed up processing by training on products produced with full-physics algorithms (e.g., NO 2 slant column fitting may take ~1 hour/orbit but with a neural net it may take only minutes)

NO2↗
DOE OSTI2025

Estimating soybean yields from high-temporal-resolution multi-source data using deep learning

Accurate and timely crop yield prediction is crucial for ensuring food security and maintaining stable agricultural markets. In recent years, there has been a surge in interest in leveraging high-temporal-resolution, multi-source data for effective crop growth monitoring and yield estimation. A notable challenge arises from the difficulty in capturing the intricate interactions between variables across different time steps within these high-temporal-resolution time series datasets. This complexity hinders the reliable extraction of yield information from voluminous and often noisy datasets, especially during periods of extreme weather events. Here, in this study, we propose an Attention and Graph Isomorphism Network-enhanced Bi-directional Long Short-Term Memory network (AGB-LSTM) for estimating county-level soybean yield in the United States. This model integrates a diverse set of remote sensing data, including Near-Infrared Reflectance of Vegetation (NIRv), Sun-Induced chlorophyll Fluorescence (SIF), and Gross Primary Productivity (GPP), along with environmental covariates. The AGB-LSTM effectively leverages information related to crop yield from high-temporal-resolution time series data (5-days), achieving an accuracy of R²= 0.67 and rRMSE = 14.46%. This approach significantly outperforms traditional machine learning methods such as Random Forest (RF) (R²= 0.52, rRMSE = 17.36%) and Bi-LSTM (R²= 0.58, rRMSE = 16.17%). Sensitivity experiments with different time steps and ranges demonstrated that our model could accurately and stably predict yields 1 to 2 months before harvest. Moreover, data with a finer temporal resolution consistently improved prediction performance, resulting in an approximately 20% increase in and an approximately 20% decrease in rRMSE compared to using monthly composites. We also evaluated the robustness of the model under extreme climate events and observed strong performance (R²= 0.50, rRMSE = 21.32%). Finally, yield mapping for major soybean-producing regions in North America in 2023 revealed spatial patterns that closely matched USDA yield reports. Our findings suggest that the AGB-LSTM model is a promising and effective method for estimating yield and has notable potential for global crop yield forecasting.

Deep learning↗
DOE OSTI2020

New red phosphor ceramic K 2 SiF 6 :Mn 4+

We report a new transparent ceramic phosphor for use in LED lighting has been fabricated. The previously reported and optimized narrow-emitting red phosphor, K 2 SiF 6 :Mn 4+ (KSF), has been consolidated into a transparent ceramic phosphor for the first time, accomplished via hot-pressing the feedstock phosphor powder in a die under vacuum. KSF ceramics were fabricated with varying doping concentrations of Mn 4+ and their properties studied. The absorption and emission spectra of the ceramics were identical to the feedstock phosphor powders and are ideal for LED lighting with strong absorption at 450 nm and narrow emission around 630 nm. The absorbance of the ceramics was directly proportional to the doping concentration. The ceramics were excited at various blue light fluxes and their emission intensities measured to study the effect of Mn 4+ concentration on intensity-driven “droop” in the emission output. The ceramics with a lower Mn 4+ doping were more efficient under higher light fluxes due to a decrease in Auger upconversion losses. KSF ceramics can allow a much longer path length of the diode light through the phosphor, as compared to phosphor-in-silicone, enabling the use of low optical absorption and the associated reduced activator concentration. The ceramics are measured to have a thermal conductivity of ~1.0 W/m-K, higher than that of phosphor-in-silicone or phosphor-in-glass. Several of these properties make KSF ceramics potentially desirable for use in white light LEDs. Greater thermal conductivity helps with heat dissipation, the lower surface area of the ceramic compared to the powder minimizes the environmental vulnerability of KSF, and the ability to lower the Mn 4+ concentration reduces Auger recombination losses and mitigates the temperature rise, particularly at higher light flux.

36 MATERIALS SCIENCE↗