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NASA NTRS1981

Technology transfer program: Perspective

Most of NASA's technology transfer activities are in the area of land use (development, suitability, and planning); forestry (including wildlife and range and vegetation inventories) agriculture related activities; and water resources. The technology dissemination function is exercised through three regional applications centers which are involved in 91 applications projects within 22 states. In addition there are approximately eight application system verification transfer (ASVT) projects, 21 university applications branches, institutionalized liason activities with public interest groups, and user requirements activities. As the result of budget cuts, the ASVT and user requirements and awareness programs are to be phased out at the end of FY81. The university applications programs are to be phased down and terminated by 1985. NASA will continue to work with the user more in an R & D and an applications development capacity, and not in a national scale or administrative way.

Toyshov, A. J.↗

DOE OSTI2025

PAVC Gridded 20m Alaska NGEE Tier3 PFTs v1.0

These 20-meter spatial resolution gridded products provide per-pixel fractional cover (%) of Next Generation Ecosystem Experiments (NGEE) Arctic Plant Functional Types (PFTs) Tier 3 across Alaska, north of the boreal treeline. The products were developed for the NGEE Arctic project, which is improving Arctic vegetation representation and parameterization of the E3SM Land Model. This dataset includes 8 files containing fractional cover for NGEE Tier 3 PFTs (https://data.ess-dive.lbl.gov/view/doi:10.15485/2529470): (1) bryophytes; (2) lichens; (3) non-vascular plants, i.e., the sum of lichens and bryophytes; (4) deciduous shrubs, (5) evergreen shrubs, (6) forbs, (7) graminoids, and a non-PFT class, (8) litter. Each pixel contains the percent cover (expressed as a fraction of total ground cover) that was predicted by random-forest regression models. The random-forest models were trained on cover data collected at 978 plots from 2010 to 2021, of which are archived in the Pan-Arctic Vegetation Cover (PAVC) database (https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2483557). The plot cover was linked to 20-meter spatial resolution, satellite-derived predictor variables: Sentinel-2 spectra and Sentinel-1 polarizations averaged over the 2019 growing season, as well as topographical features derived from ArcticDEM. Then, spatio-temporally anomalous plot data that introduced large variability to the regression outcomes were dropped using the Cook’s distance outlier detection method, and the models were re-created using high-quality plots and their associated satellite derived explanatory variables per each PFT. The correlations between plot-observed and satellite-derived fractional cover for all PFTs were well correlated (R2 = 0.69–0.95 and 0.5 for litter) and had low RMSE bias (0.02–0.11). This research was performed as a part of the NGEE Arctic project. The NGEE Arctic project was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1998

Laser Induced Fluorescence (LIF) as a Remote Sensing Tool: A Review

Vegetational changes are primary indicators of the present and future ecological status of the globe. These are changes which not only impact upon the primary productivity, but the total of the biogeochemical processes occurring on the planet. The impacts of global climatic and other environmental changes on vegetation must be monitored by some means in order to develop models which will allow us to predict long term effects. Large scale monitoring is now possible only with remote sensing systems, primarily passive reflectance, obtained by the use of satellite and aircraft platforms. However, passive reflectance techniques at this time are limited in their ability to detect subtle changes in the concentration and oxidation states of the many compounds involved in the light reactions of photosynthesis. Knowledge of these changes we consider to be fundamental in the remote assessment of both the rate and efficiency of photosynthesis and also the early detection of stress damage. The above factors pointed to the desirability of a sensing technique with the sensitivity and specificity necessary for detecting and quantifying those biological entities involved in photosynthesis. Another optical technique for vegetation monitoring is fluorescence. Previously, the lack of adequate excitation light sources and detector technologies have limited the use of fluorescence on intact plant leaves in the field. It is only recently with the advent of lasers with short pulse duration and advanced detector technologies that fluorescence measurements in the remote mode have become possible in the presence of ambient light.

Chappelle, E. W.↗

NASA NTRS1983

Vegetation monitoring and classification using NOAA/AVHRR satellite data

A vegetation gradient model, based on a new surface hydrologic index and NOAA/AVHRR meteorological satellite data, has been analyzed along a 1300 km east-west transect across the state of Texas. The model was developed to test the potential usefulness of such low-resolution data for vegetation stratification and monitoring. Normalized Difference values (ratio of AVHRR bands 1 and 2, considered to be an index of greenness) were determined and evaluated against climatological and vegetation characteristics at 50 sample locations (regular intervals of 0.25 deg longitude) along the transect on five days in 1980. Statistical treatment of the data indicate that a multivariate model incorporating satellite-measured spectral greenness values and a surface hydrologic factor offer promise as a new technique for regional-scale vegetation stratification and monitoring.

Greegor, D. H., Jr.↗

NASA NTRS2013

Enhanced Deep Blue Aerosol Retrieval Algorithm: The Second Generation

The aerosol products retrieved using the MODIS collection 5.1 Deep Blue algorithm have provided useful information about aerosol properties over bright-reflecting land surfaces, such as desert, semi-arid, and urban regions. However, many components of the C5.1 retrieval algorithm needed to be improved; for example, the use of a static surface database to estimate surface reflectances. This is particularly important over regions of mixed vegetated and non- vegetated surfaces, which may undergo strong seasonal changes in land cover. In order to address this issue, we develop a hybrid approach, which takes advantage of the combination of pre-calculated surface reflectance database and normalized difference vegetation index in determining the surface reflectance for aerosol retrievals. As a result, the spatial coverage of aerosol data generated by the enhanced Deep Blue algorithm has been extended from the arid and semi-arid regions to the entire land areas.

Deep Blue↗

DOE OSTI2026

Data for Multisite Field Evaluation of Oil Accumulation and Agronomic Performance in Grain and Sweet Sorghums Engineered for Lipid Hyperaccumulation

Oil sorghum (OS) has been developed by engineering grain (TX430) and sweet (Ramada) genetic backgrounds to accumulate triacylglycerols (TAG) in vegetative tissues as an energy-dense feedstock for sustainable aviation fuel (SAF) and other biofuels. This study evaluated two TX430 OS lines (TxHO-2, TxHO-3) and two Ramada OS lines (RmHO-1, RmHO-2) alongside wild-type (WT) lines in NE and IL over 2 years (2023–2024) to quantify genotype × environment effects on agronomic performance and TAG accumulation. Across four environments, TX430 OS lines showed average TAG concentrations of 15.0 g kg−1 in leaves and 12.8 g kg−1 in stems, approximately 19-fold higher than WT. Ramada OS lines accumulated 26.1 g kg−1 in leaves and 12.3 g kg−1 in stems, approximately 25-fold and 13-fold increases over WT, respectively. OS lines in TX430 exhibited an 18% reduction in biomass (8.4 vs. 9.9 Mg ha−1 for WT), while Ramada OS lines had similar WT biomass (18.3 vs. 19.9 Mg ha−1 for WT). Among TX430 OS lines, TxHO-2 achieved the highest TAG yield (190 kg ha−1), while RmHO-1 led the Ramada lines (335 kg ha−1) due to higher biomass and similar TAG concentration. Enhanced TAG accumulation increased N, P, and K removal in TX430 lines but not in Ramada lines. Structural carbohydrate and ash concentration were unaffected. Overall, results confirm vegetative lipid accumulation as a viable strategy for high-biomass sorghum, supporting its potential as a dual-purpose feedstock for SAF. Future work should focus on minimizing biomass yield penalties and improving nutrient use efficiency in oil sorghum systems.

Agronomy↗

NASA NTRS1993

Deep cytoplasmic rearrangements in ventralized Xenopus embryos

Following fertilization in Xenopus, dramatic rearrangements of the egg cytoplasm relocalize maternally synthesized egg components. During the first cell cycle the vegetal yolk mass rotates relative to the egg surface, toward the sperm entry point (SEP) (J. P. Vincent, G. F. Oster, and J. C. Gerhart, 1986, Dev. Biol. 113, 484-500), while concomitant deep cytoplasmic rearrangements occur in the animal hemisphere (M. V. Danilchik and J. M. Denegre, 1991, Development 111, 845-856). In this paper we examine the role of vegetal yolk mass rotation in producing the animal cytoplasmic rearrangements. We inhibited rotation by uv-irradiating embryos during the first cell cycle, a treatment that yields an extremely ventralized phenotype. Both uv-irradiated embryos and unirradiated control embryos show cytoplasmic rearrangements in the animal hemisphere during the first cell cycle. Cytoplasmic rearrangements on the SEP side of the embryo associated with the path of the sperm pronucleus, plus a swirl on the anti-SEP (dorsal) side, are seen, whether or not yolk mass rotation has occurred. This result suggests a role for the expanding sperm aster in directing animal hemisphere cytoplasmic movements. In unirradiated control embryos the anti-SEP (dorsal) swirl is larger than that in uv-irradiated embryos and often extends into the vegetal hemisphere, consistent with the animal cytoplasm having been pulled dorsally and vegetally by the sliding vegetal yolk mass. Thus the yolk mass rotation may normally enhance the dorsalward cytoplasmic movement, begun by the sperm aster, enough to induce normal axis formation. We extended our observations of unirradiated control and uv-irradiated embryos through early cleavages. The vegetal extent of the anti-SEP (dorsal) swirl pattern seen in control embryos persists through the early cleavage period, such that labeled animal cytoplasm extends deep into dorsal third-tier blastomeres at the 32-cell stage. Significantly, in uv-irradiated embryos, which have not undergone vegetal rotation, most of this labeled material remains more equatorial.

Non-NASA Center↗

NASA NTRS1983

Automated vegetation classification using Thematic Mapper Simulation data

The present investigation is concerned with the results of a study of Thematic Mapper Simulation (TMS) data. One of the objectives of the study was related to an evaluation of the usefulness of the Thematic Mapper's (TM) improved spatial resolution and spectral coverage. The study was undertaken as part of a preparation for the efficient incorporation of Landsat 4 data into ongoing technology development in remote sensing. The study included an application of automated Landsat vegetation classification technology to TMS data. Results of comparing TMS data to Multispectral Scanner (MSS) data were found to indicate that all field definition, crop type discrimination, and subsequent proportion estimation may be greatly increased with the availability of TM data.

Nedelman, K. S.↗

NASA NTRS1986

Operational development of small plant growth systems

The results of a study undertaken on the first phase of an empricial effort in the development of small plant growth chambers for production of salad type vegetables on space shuttle or space station are discussed. The overall effort is visualized as providing the underpinning of practical experience in handling of plant systems in space which will provide major support for future efforts in planning, design, and construction of plant-based (phytomechanical) systems for support of human habitation in space. The assumptions underlying the effort hold that large scale phytomechanical habitability support systems for future space stations must evolve from the simple to the complex. The highly complex final systems will be developed from the accumulated experience and data gathered from repetitive tests and trials of fragments or subsystems of the whole in an operational mode. These developing system components will, meanwhile, serve a useful operational function in providing psychological support and diversion for the crews.

Scheld, H. W.↗

NASA NTRSDate not supplied

The Added Value of SMAP Soil Moisture in Crop Yield Forecasting Over Argentina

Argentina is one of the major producers and exporter of soybeans, corn, and wheat to the world market; therefore, the accurate and timely forecasting of those crops yield is crucial to national crop management and global food security. Previous studies have mainly focused on developing forecasting models for a specific crop type and location using a single source of data (e.g., vegetation indices), thus providing little insight into the forecasting models' performance on different crop types and regions. Besides, these models are based on traditional statistical regression algorithms, while more advanced machine learning approaches have not been explored. This study investigated the estimation of crop yields of three major crops (corn, soybean, and winter wheat) using Multiple Linear Regression (MLR) and Support Vector Machine (SVM), over major growing provinces in Argentina. Our models were trained and evaluated on data from 2015 to 2020, where three remote sensing products (Normalized difference vegetation index (NDVI), SMAP soil moisture, and MODIS evapotranspiration) were used as predictors. Our results indicated that accurate crop yield forecasts using the developed regression models could be made one to two months before harvest. The MLR and SVM model performance varied among different crop types, where soybean and corn exhibited better predictability compare to the wheat. In most cases, the SVM outperformed the multiple linear regression model due to its ability to capture the nonlinear and complex features of the crop-production process. The forecasted model that combines data from multiple sources outperformed single-source satellite data. The highest accuracy was obtained when the three data sources were all considered in the model development. Results also indicated that the inclusion of SMAP soil moisture improved crop yield forecasting in most provinces, and the most significant improvements occurred in the drier region.

Nazmus Shams Sazib↗

NASA NTRS1976

Soil, water, and vegetation conditions in south Texas

The author has identified the following significant results. Software development for a computer-aided crop and soil survey system is nearing completion. Computer-aided variety classification accuracies using LANDSAT-1 MSS data for a 600 hectare citrus farm were 83% for Redblush grapefruit and 91% for oranges. These accuracies indicate that there is good potential for computer-aided inventories of grapefruit and orange citrus orchards with LANDSAT-type MSS data. Mean digital values of clouds differed statistically from those for crop, soil, and water entities, and those for cloud shadows were enough lower than sunlit crop and soil to be distinguishable. The standard errors of estimate for the calibration of computer compatible tape coordinate system (pixel and record) to earth coordinate system (longitude and latitude) for 6 LANDSAT scenes ranged from 0.72 to 1.50 pixels and from 0.58 to 1.75 records.

Wiegand, C. L.↗

DOE OSTI2025

The Influence of Land‐Surface Conditions on the 2020–2021 Western US Drought

Abstract In summer 2021, 90% of the western United States (WUS) experienced drought, with over half of the region facing extreme or exceptional conditions, leading to water scarcity, crop loss, ecological degradation, and significant socio‐economic consequences. Beyond the established influence of oceanic forcing and internal atmospheric variability, this study highlights the importance of land‐surface conditions in the development of the 2020–2021 WUS drought, using observational data analysis and novel numerical simulations. Our results demonstrate that the soil moisture state preceding a meteorological drought, due to its intrinsic memory, is a critical factor in the development of soil droughts. Specifically, wet soil conditions can delay the transition from meteorological to soil droughts by several months or even nullify the effects of La Niña‐driven meteorological droughts, while drier conditions can exacerbate these impacts, leading to more severe soil droughts. For the same reason, soil droughts can persist well beyond the end of meteorological droughts. Our numerical experiments suggest a relatively weak soil moisture‐precipitation coupling during this drought period, corroborating the primary contributions of the ocean and atmosphere to this meteorological drought. Additionally, drought‐induced vegetation losses can mitigate soil droughts by reducing evapotranspiration and slowing the depletion of soil moisture. This study highlights the importance of soil moisture and vegetation conditions in seasonal‐to‐interannual drought predictions. Findings from this study have implications for regions like the WUS, which are experiencing anthropogenically‐driven soil aridification and vegetation greening, suggesting that future soil droughts in these areas may develop more rapidly, become more severe, and persist longer.

Jiang, Yelin [Lamont‐Doherty Earth Observatory Col↗

NASA NTRS1994

VEG: An intelligent workbench for analysing spectral reflectance data

An Intelligent Workbench (VEG) was developed for the systematic study of remotely sensed optical data from vegetation. A goal of the remote sensing community is to infer the physical and biological properties of vegetation cover (e.g. cover type, hemispherical reflectance, ground cover, leaf area index, biomass, and photosynthetic capacity) using directional spectral data. VEG collects together, in a common format, techniques previously available from many different sources in a variety of formats. The decision as to when a particular technique should be applied is nonalgorithmic and requires expert knowledge. VEG has codified this expert knowledge into a rule-based decision component for determining which technique to use. VEG provides a comprehensive interface that makes applying the techniques simple and aids a researcher in developing and testing new techniques. VEG also provides a classification algorithm that can learn new classes of surface features. The learning system uses the database of historical cover types to learn class descriptions of one or more classes of cover types.

Harrison, P. Ann↗

NASA NTRS2017

Discussion of Yellow Starthistle Response to Photosynthetic Irradiance, Photoperiod, and CO2

Yellow Starthistle (Centaurea solstitialis) is a native annual weed of Eurasia and since introduction into the United STates has become an invasive and noxious weed. It grows in a rosette habit during the vegetative state and usually bolts in summer to produce a large and branched flowering stem. Time to flowering in Yellow Starthistle has been attributed to photoperiod, nitrogen nutrition, temperature, and water stress. We executed a series of studies to investigate the role of light, both photoperiod and photosynthetic photon flux, on flowering and development in Yellow Starthistle. Treatments were presented in 4 ways: 1) varying day length with constant photosynthetic photon flus (PPF) providing increasing daily integrated Photosynthetic Photon (PP) exposure with longer day lengths 2) varying day length while adjusting PPF to maintain daily PP exposure for all treatments 3) extending photoperiod treatments beyond common 12-h photosynthetic period with low light levels to maintain both PPF and daily PP across all treatments4)reciprocal exchange of plant among photoperiod treatments Yellow Starthistle appears to be a long-day plant with a critical day length requirement between 14-h and 16-h to induce transition from vegetative to floral stages in development. PPF and daily absorbed photons did not affect time to vegetative floral stage transition, but did affect factors such as biomass accumulation and canopy parameters such as specific leaf mass. Reciprocal exchange of plants between floral inducing and inhibiting photoperiod treatments, starting at 2-weeks post germination, had no effect on to flower. Flowering was determined by photoperiod experienced during the first 2-weeks (or less) post germination.Yellow Starthistle net photosynthetic response to elevated atmospheric CO2 concentrations over a range of photosynthetically active radiation flux rates and temperatures will also be presented and discussed.

Photosynthetic↗

NASA NTRS1980

Using LANDSAT digital data for estimating green biomass

The author has identified the following significant results. Relationships between the quantity of mixed prairie rangeland vegetation and LANDSAT MSS response were studied during four growing seasons at test sites throughout the United States Great Plans region. A LANDSAT derived parameter, the normalized difference was developed from theoretical considerations fro statistical estimation of the amount and seasonal condition of rangeland vegetation. This parameter was tested for application to local assessment of green forage biomass and regional monitoring of range feed conditions and drought. Results show that for grasslands in the Great Plains with near continuous vegetative cover and free of heavy brush and forbs, the LANDSAT digital data can provide a useful estimate of the quantity of green forage biomass (within 250 kg/ha), and at least five levels of pasture and range feed conditions can be adequately mapped for extended regions.

Deering, D. W.↗

NASA NTRS2016

Discussion of Yellow Starthistle Response to Irradiance, Photoperiod, and CO2

Yellow Starthistle (Centaurea solstitialis) is a native annual weed of Eurasia and since introduction into the US has become an invasive and noxious weed. It grows in a rosette habit during the vegetative state and usually bolts in summer to produce a large and branched flowering stem. Time to flowering in Yellow Starthistle has been attributed to photoperiod, nitrogen nutrition, temperature, and water stress. We executed a series of studies to investigate the role of light, both photoperiod and photosynthetic photon flux, on flowering and development in Yellow Starthistle. Treatments were presented in 4 ways: (1) Varying day length with constant photosynthetic photon flus (PPF) - providing increasing daily integrated Photosynthetic Photon (PP) exposure with longer day lengths, (2) Varying day length while adjusting PPF to maintain daily PP exposure for all treatments, (3) Extending photoperiod treatments beyond common 12-h photosynthetic period with low light levels to maintain both PPF and daily PP across all treatments; and (4) Reciprocal exchange of plant among photoperiod treatments. Yellow Starthistle appears to be a long-day plant with a critical day length requirement between 14-h and 16-h to induce transition from vegetative to floral stages in development. PPF and daily absorbed photons did not affect time to vegetative / floral stage transition, but did affect factors such as biomass accumulation and canopy parameters such as specific leaf mass. Reciprocal exchange of plants between floral inducing and inhibiting photoperiod treatments, starting at 2-weeks post germination, had no effect on to flower. Flowering was determined by photoperiod experienced during the first 2-weeks (or less) post germination. Yellow Starthistle net photosynthetic response to elevated atmospheric CO2 concentrations over a range of photosynthetically active radiation flux rates and temperatures will also be presented and discussed.

Photons↗

NASA NTRS1998

Parameterization of Vegetation Aerodynamic Roughness of Natural Regions Satellite Imagery

Parameterizations of the frontal area index and canopy area index of natural or randomly distributed plants are developed, and applied to the estimation of local aerodynamic roughness using satellite imagery. The formulas are expressed in terms of the subpixel fractional vegetation cover and one non-dimensional geometric parameter that characterizes the plant's shape. Geometrically similar plants and Poisson distributed plant centers are assumed. An appropriate averaging technique to extend satellite pixel-scale estimates to larger scales is provided. The parameterization is applied to the estimation of aerodynamic roughness using satellite imagery for a 2.3 sq km coniferous portion of the Landes Forest near Lubbon, France, during the 1986 HAPEX-Mobilhy Experiment. The canopy area index is estimated first for each pixel in the scene based on previous estimates of fractional cover obtained using Landsat Thematic Mapper imagery. Next, the results are incorporated into Raupach's (1992, 1994) analytical formulas for momentum roughness and zero-plane displacement height. The estimates compare reasonably well to reference values determined from measurements taken during the experiment and to published literature values. The approach offers the potential for estimating regionally variable, vegetation aerodynamic roughness lengths over natural regions using satellite imagery when there exists only limited knowledge of the vegetated surface.

Jasinski, Michael F.↗

NASA NTRSDate not supplied

Development of Carbon Flux Model Using ABI Data Over the Conterminous US

The satellite-driven carbon flux estimation has been playing important role to estimate continental-scale carbon budget. One of the biggest recent advances in the satellite-driven carbon flux modeling is utilization of high-frequent geostationary satellites to estimate diurnal cycle in carbon fluxes. The satellite based carbon flux estimation used the polar orbiting satellite sensors (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS)), which allow us to observe target regions only once during the day. The new generation of geostationary satellite sensors provide frequent observations, often less than every 10 minutes. Here, we use GOES Advanced Baseline Imager (ABI) data to estimate hourly NEE over the conterminous US. We used the Terrestrial Observation Prediction System (TOPS) model for estimating hourly NEE. TOPS is a diagnostic ecosystem process model that simulates the fluxes of carbon and water through vegetation in response to climate variability. For the climate input, we developed hourly climate data using the same algorithm with NASA Earth Exchange Gridded Daily Meteorology (NEX-GDM) datasets based on machine learning techniques. The hourly climate data includes precipitation, maximum temperature, minimum temperature, dew point temperature, and solar radiation were derived from the Geostationary observations. The spatial patterns of ecosystem parameters used in TOPS are optimized using satellite Solar Induced Fluorescence (SIF) data. The high frequency GPP estimations from geostationary satellite sensors make it comparable to the instantaneous SIF data than daily GPP. We also used Ameriflux data for optimization of model parameters and the validation of the output. The derived data addresses the diurnal dynamics of carbon cycling at large scales and should help in reducing the uncertainties in carbon budget studies.

geostationary satellite↗