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All sourcesNASA NTRSDOE OSTI
NASA NTRS1986

Evaluation of a surface/vegetation parameterization using satellite measurements of surface temperature

Ground measurements of surface-sensible heat flux and soil moisture for a wheat-growing area of Beauce in France were compared with the values derived by inverting two boundary layer models with a surface/vegetation formulation using surface temperature measurements made from NOAA-AVHRR. The results indicated that the trends in the surface heat fluxes and soil moisture observed during the 5 days of the field experiment were effectively captured by the inversion method using the remotely measured radiative temperatures and either of the two boundary layer methods, both of which contain nearly identical vegetation parameterizations described by Taconet et al. (1986). The sensitivity of the results to errors in the initial sounding values or measured surface temperature was tested by varying the initial sounding temperature, dewpoint, and wind speed and the measured surface temperature by amounts corresponding to typical measurement error. In general, the vegetation component was more sensitive to error than the bare soil model.

Taconet, O.↗
NASA NTRS2002

Using Support Vector Machines to Automatically Extract Open Water Signatures from POLDER Multi-Angle Data Over Boreal Regions

This study used Support Vector Machines to classify multiangle POLDER data. Boreal wetland ecosystems cover an estimated 90 x 10(exp 6) ha, about 36% of global wetlands, and are a major source of trace gases emissions to the atmosphere. Four to 20 percent of the global emission of methane to the atmosphere comes from wetlands north of 4 degrees N latitude. Large uncertainties in emissions exist because of large spatial and temporal variation in the production and consumption of methane. Accurate knowledge of the areal extent of open water and inundated vegetation is critical to estimating magnitudes of trace gas emissions. Improvements in land cover mapping have been sought using physical-modeling approaches, neural networks, and active microwave, examples that demonstrate the difficulties of separating open water, inundated vegetation and dry upland vegetation. Here we examine the feasibility of using a support vector machine to classify POLDER data representing open water, inundated vegetation and dry upland vegetation.

Pierce, J.↗
DOE OSTI2022

Data used in “Enguehard et al. 2022, Machine-Learning Functional Zonation Approach for Characterizing Terrestrial–Aquatic Interfaces: Application to Lake Erie”

The package contains the data layers used in “Enguehard et al. 2022, Machine-Learning Functional Zonation Approach for Characterizing Terrestrial–Aquatic Interfaces: Application to Lake Erie”. Spatial data layers include: topography, wetland vegetation cover, time series of Landsat’s enhanced vegetation index (EVI) between 1990 and 2020. The study aims to characterize coastal wetlands with particular focus on the co-variability between plant dynamics, topography, soil, and other environmental factors. We proposed a functional zonation approach based on machine learning clustering to identify the spatial regions, i.e., zones that capture these co-varied properties. This approach was applied to publicly available datasets along Lake Erie, in the Great Lakes Region

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Predictive Risk Mapping of Schistosomiasis in Madagascar Using Ecological Niche Modeling and Precision Mapping

Schistosomiasis is a neglected tropical disease (NTD) found throughout tropical and subtropical Africa. In Madagascar, the condition is widespread and endemic in 74% of all administrative districts in the country. Despite the significant burden of the disease, high-resolution risk maps have yet to be produced to guide national control programs. This study used an ecological niche modeling (ENM) and precision mapping approach to estimate environmental suitability and disease transmission risk. The results show that suitability for schistosomiasis is widespread and covers 264,781 km 2 (102,232 sq miles). Covariates of significance to the model were the accessibility to cities, distance to water, enhanced vegetation index (EVI), annual mean temperature, land surface temperature (LST), clay content, and annual precipitation. Disease transmission risk is greatest in the central highlands, tropical east coast, arid-southwest, and northwest. An estimated 14.9 million people could be at risk of schistosomiasis; 11.4 million reside in rural areas, while 3.5 million are in urban areas. This study provides valuable insight into the geography of schistosomiasis in Madagascar and its potential risk to human populations. Because of the focal nature of the disease, these maps can inform national surveillance programs while improving understanding of areas in need of medical interventions.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1988

The structure of red-infrared scattergrams of semivegetated landscapes

A physically based linear stochastic geometric canopy soil reflectance model is presented for characterizing spatial variability of semivegetated landscapes at subpixel and regional scales. Landscapes are conceptualized as stochastic geometric surfaces, incorporating not only the variability in geometric elements, but also the variability in vegetation and soil background reflectance which can be important in some scenes. The model is used to investigate several possible mechanisms which contribute to the often observed characteristic triangular shape of red-infrared scattergrams of semivegetated landscapes. Scattergrams of simulated and semivegetated scenes are analyzed with respect to the scales of the satellite pixel and subpixel components. Analysis of actual aerial radiometric data of a pecan orchard is presented in comparison with ground observations as preliminary confirmation of the theoretical results.

Jasinski, Michael F.↗
NASA NTRS1989

The structure of red-infrared scattergrams of semivegetated landscapes

A physically based linear stochastic geometric canopy soil reflectance model is presented for characterizing spatial variability of semivegetated landscapes at subpixel and regional scales. Landscapes are conceptualized as stochastic geometric surfaces, incorporating not only the variability in geometric elements, but also the variability in vegetation and soil background reflectance which can be important in some scenes. The model is used to investigate several possible mechanisms which contribute to the often observed characteristic triangular shape of red-infrared scattergrams of semivegetated landscapes. Scattergrams of simulated semivegetated scenes are analyzed with respect to the scales of the satellite pixel and subpixel components. Analysis of actual aerial radiometric data of a pecan orchard is presented in comparison with ground observations as preliminary confirmation of the theoretical results.

Jasinski, Michael F.↗
NASA NTRS1988

Evapotranspiration from nonuniform surfaces - A first approach for short-term numerical weather prediction

Observations of surface heterogeneity of soil moisture from scales of meters to hundreds of kilometers are discussed, and a relationship between grid element size and soil moisture variability is presented. An evapotranspiration model is presented which accounts for the variability of soil moisture, standing surface water, and vegetation internal and stomatal resistance to moisture flow from the soil. The mean values and standard deviations of these parameters are required as input to the model. Tests of this model against field observations are reported, and extensive sensitivity tests are presented which explore the importance of including subgrid-scale variability in an evapotranspiration model.

Wetzel, Peter J.↗
NASA NTRS2012

Improved Prediction of Quasi-Global Vegetation Conditions Using Remotely-Sensed Surface Soil Moisture

The added value of satellite-based surface soil moisture retrievals for agricultural drought monitoring is assessed by calculating the lagged rank correlation between remotely-sensed vegetation indices (VI) and soil moisture estimates obtained both before and after the assimilation of surface soil moisture retrievals derived from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) into a soil water balance model. Higher soil moisture/VI lag correlations imply an enhanced ability to predict future vegetation conditions using estimates of current soil moisture. Results demonstrate that the assimilation of AMSR-E surface soil moisture retrievals substantially improve the performance of a global drought monitoring system - particularly in sparsely-instrumented areas of the world where high-quality rainfall observations are unavailable.

Bolten, John↗
NASA NTRS2018

Recent Updates in the SMAP Level-4 Soil Moisture Algorithm

The NASA Soil Moisture Active Passive (SMAP) mission generates, among other data sets, the Level-4 Soil Moisture (L4_SM) product. The L4_SM data are published with a mean latency of ~2.5 days from the time of observation and provide global, three-hourly, 9-km resolution estimates of surface and root-zone soil moisture and related land surface states and fluxes. The L4_SM algorithm is based on the assimilation of SMAP radiometer brightness temperature observations into the NASA Catchment land surface model using a spatially distributed ensemble Kalman filter (EnKF). In 2018, the L4_SM algorithm was upgraded from Version 3 to Version 4. Underlying the new version is a revised modeling system that includes improved input parameter datasets for land cover, topography, and vegetation height that are based on recent, high-quality, space-borne remote sensing observations. Additionally, SMAP Level-2 soil moisture retrievals and in situ soil moisture measurements were used to calibrate a particular Catchment model parameter that governs the recharge of surface soil moisture from below under non-equilibrium conditions, which brings the model's surface soil moisture more in line with the SMAP Level-2 and in situ soil moisture. Moreover, the calibration of the assimilated SMAP brightness temperatures changed substantially from Version 3 to Version 4, and the "catchment deficit" model variable was removed from the EnKF state vector to avoid degrading the model's groundwater estimates.Considerable effort went into the version upgrade, creating an expectation that the new version is improved over the old version. Indeed, some aspects of the new version are clearly better. However, other aspects are not. In this presentation we summarize the skill of the new and old versions vs. independent in situ measurements and in terms of data assimilation diagnostics, including, for example, the statistics of the (soil moisture) analysis increments and the observation-minus-forecast (brightness temperatures) residuals. We share our experience with trying to improve to the L4_SM product and the lessons learned from the effort.

Reichle, Rolf H.↗
DOE OSTI2023

UAS remote sensing (3DR SOLO platform): multispectral reflectance and normalized difference vegetation index, Seward Peninsula, Alaska, 2022

Airborne remote sensing data collected using a Parrot Sequoia+ multispectral sensor installed on a 3DR SOLO unoccupied aerial system (UAS) – operated by the Terrestrial Ecosystem Science & Technology group https://www.bnl.gov/envsci/testgroup/ at Brookhaven National Laboratory. This package includes data from 19 flights flown over the NGEE-Arctic, Kougarok Mile Marker (MM) 80, Kougarok Fire Complex (KFC) and Teller MM 27 sites in July 2022. Derived image products include point cloud, ortho-mosaiced multispectral image, a digital surface model (DSM) using the structure from motion (SfM) technique, and a normalized difference vegetation index (NDVI) map. Unprocessed and processed data products are included in this package (processing levels 0-2). Data and metadata are provided as text (*.txt, *.json, *hdr,), tabular (*.dat, *.csv), point cloud (*.laz), Cloud Optimized GeoTIFF (COG, *.tif), and image (*.jpg, *.tif, *png) formats.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), 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. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Deforestation reshapes land-surface energy-flux partitioning

Land-use and land-cover change significantly modify local land-surface characteristics and water/energy exchanges, which can lead to atmospheric circulation and regional climate changes. In particular, deforestation accounts for a large portion of global land-use changes, which transforms forests into other land cover types, such as croplands and grazing lands. Many previous efforts have focused on observing and modeling land–atmosphere–water/energy fluxes to investigate land–atmosphere coupling induced by deforestation. However, interpreting land–atmosphere–water/energy-flux responses to deforestation is often complicated by the concurrent impacts from shifts in land-surface properties versus background atmospheric forcings. In this study, we used 29 paired FLUXNET sites, to improve understanding of how deforested land surfaces drive changes in surface-energy-flux partitioning. Each paired sites included an intact forested and non-forested site that had similar background climate. We employed transfer entropy, a method based on information theory, to diagnose directional controls between coupling variables, and identify nonlinear cause–effect relationships. Transfer entropy is a powerful tool to detective causal relationships in nonlinear and asynchronous systems. The paired eddy covariance flux measurements showed consistent and strong information flows from vegetation activity (gross primary productivity (GPP)) and physical climate (e.g. shortwave radiation, air temperature) to evaporative fraction (EF) over both non-forested and forested land surfaces. More importantly, the information transfers from radiation, precipitation, and GPP to EF were significantly reduced at non-forested sites, compared to forested sites. We then applied these observationally constrained metrics as benchmarks to evaluate the Energy Exascale Earth System Model (E3SM) land model (ELM). ELM predicted vegetation controls on EF relatively well, but underpredicted climate factors on EF, indicating model deficiencies in describing the relationships between atmospheric state and surface fluxes. Moreover, changes in controls on surface energy flux partitioning due to deforestation were not detected in the model. We highlight the need for benchmarking model simulated surface-energy fluxes and the corresponding causal relationships against those of observations, to improve our understanding of model predictability on how deforestation reshapes land surface energy fluxes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The Colorado East River Community Observatory Data Collection

Abstract The U.S. Department of Energy's (DOE) Colorado East River Community Observatory (ER) in the Upper Colorado River Basin was established in 2015 as a representative mountainous, snow‐dominated watershed to study hydrobiogeochemical responses to hydrological perturbations in headwater systems. The ER is characterized by steep elevation, geologic, hydrologic and vegetation gradients along floodplain, montane, subalpine, and alpine life zones, which makes it an ideal location for researchers to understand how different mountain subsystems contribute to overall watershed behaviour. The ER has both long‐term and spatially‐extensive observations and experimental campaigns carried out by the Watershed Function Scientific Focus Area (SFA), led by Lawrence Berkeley National Laboratory, and researchers from over 30 organizations who conduct cross‐disciplinary process‐based investigations and modelling of watershed behaviour. The heterogeneous data generated at the ER include hydrological, genomic, biogeochemical, climate, vegetation, geological, and remote sensing data, which combined with model inputs and outputs comprise a collection of datasets and value‐added products within a mountainous watershed that span multiple spatiotemporal scales, compartments, and life zones. Within 5 years of collection, these datasets have revealed insights into numerous aspects of watershed function such as factors influencing snow accumulation and melt timing, water balance partitioning, and impacts of floodplain biogeochemistry and hillslope ecohydrology on riverine geochemical exports. Data generated by the SFA are managed and curated through its Data Management Framework. The SFA has an open data policy, and over 70 ER datasets are publicly available through relevant data repositories. A public interactive map of data collection sites run by the SFA is available to inform the broader community about SFA field activities. Here, we describe the ER and the SFA measurement network, present the public data collection generated by the SFA and partner institutions, and highlight the value of collecting multidisciplinary multiscale measurements in representative catchment observatories.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2012

Modeling the Ecosystem Services Provided by Trees in Urban Ecosystems: Using Biome-BGC to Improve i-Tree Eco

As the world becomes increasingly urban, the need to quantify the effect of trees in urban environments on energy usage, air pollution, local climate and nutrient run-off has increased. By identifying, quantifying and valuing the ecological activity that provides services in urban areas, stronger policies and improved quality of life for urban residents can be obtained. Here we focus on two radically different models that can be used to characterize urban forests. The i-Tree Eco model (formerly UFORE model) quantifies ecosystem services (e.g., air pollution removal, carbon storage) and values derived from urban trees based on field measurements of trees and local ancillary data sets. Biome-BGC (Biome BioGeoChemistry) is used to simulate the fluxes and storage of carbon, water, and nitrogen in natural environments. This paper compares i-Tree Eco's methods to those of Biome-BGC, which estimates the fluxes and storage of energy, carbon, water and nitrogen for vegetation and soil components of the ecosystem. We describe the two models and their differences in the way they calculate similar properties, with a focus on carbon and nitrogen. Finally, we discuss the implications of further integration of these two communities for land managers such as those in Maryland.

Brown, Molly E.↗
NASA NTRS2013

Selection of Hyperspectral Narrowbands (HNBs) and Composition of Hyperspectral Twoband Vegetation Indices (HVIs) for Biophysical Characterization and Discrimination of Crop Types Using Field Reflectance and Hyperion-EO-1 Data

The overarching goal of this study was to establish optimal hyperspectral vegetation indices (HVIs) and hyperspectral narrowbands (HNBs) that best characterize, classify, model, and map the world's main agricultural crops. The primary objectives were: (1) crop biophysical modeling through HNBs and HVIs, (2) accuracy assessment of crop type discrimination using Wilks' Lambda through a discriminant model, and (3) meta-analysis to select optimal HNBs and HVIs for applications related to agriculture. The study was conducted using two Earth Observing One (EO-1) Hyperion scenes and other surface hyperspectral data for the eight leading worldwide crops (wheat, corn, rice, barley, soybeans, pulses, cotton, and alfalfa) that occupy approx. 70% of all cropland areas globally. This study integrated data collected from multiple study areas in various agroecosystems of Africa, the Middle East, Central Asia, and India. Data were collected for the eight crop types in six distinct growth stages. These included (a) field spectroradiometer measurements (350-2500 nm) sampled at 1-nm discrete bandwidths, and (b) field biophysical variables (e.g., biomass, leaf area index) acquired to correspond with spectroradiometer measurements. The eight crops were described and classified using approx. 20 HNBs. The accuracy of classifying these 8 crops using HNBs was around 95%, which was approx. 25% better than the multi-spectral results possible from Landsat-7's Enhanced Thematic Mapper+ or EO-1's Advanced Land Imager. Further, based on this research and meta-analysis involving over 100 papers, the study established 33 optimal HNBs and an equal number of specific two-band normalized difference HVIs to best model and study specific biophysical and biochemical quantities of major agricultural crops of the world. Redundant bands identified in this study will help overcome the Hughes Phenomenon (or "the curse of high dimensionality") in hyperspectral data for a particular application (e.g., biophysical characterization of crops). The findings of this study will make a significant contribution to future hyperspectral missions such as NASA's HyspIRI. Index Terms-Hyperion, field reflectance, imaging spectroscopy, HyspIRI, biophysical parameters, hyperspectral vegetation indices, hyperspectral narrowbands, broadbands.

Vegetation↗
DOE OSTI2021

3,3’-Diindolylmethane Exhibits Significant Metabolism After Oral Dosing in Humans

3,3’-Diindolylmethane (DIM), a major phytochemical derived from ingestion of cruciferous vegetables such as Brussels sprouts, is also a dietary supplement. In preclinical models, DIM is an effective cancer chemopreventive agent and has been studied in a number of clinical trials. Previous pharmacokinetic studies in preclinical and clinical models have not reported DIM metabolites in plasma or urine following oral dosing and the pharmacological actions of DIM on target tissues is assumed to be solely via the parent compound. Seven subjects (6 males and 1 female) ranging from 26-65 years of age, on a cruciferous vegetable-restricted diet prior to and during the study, took 2 BioResponse-DIM® 150 mg capsules (45.3 mg DIM/capsule) in the evening for one week with a final dose the morning of the first blood draw. A complete time course was performed with plasma and urine collected over 48 hours and analyzed by UPLC-MS/MS. In addition to parent DIM, two mono-hydroxylated (2-ox- and 3-methylenehydroxy-DIM) and 1 dihydroxylated (3-methylenehydroxy-2-ox-DIM) metabolites, along with their sulfate and glucuronide conjugates, were present in both plasma and urine. The results reported here are indicative of significant phase 1 and phase 2 metabolism and differ from previous pharmacokinetic studies in rodents and humans which reported only parent DIM present following oral administration.

Vermillion-Maier, Monica↗
DOE OSTI2026

Effects of slope steepness and cross-slope wind speed on fire spreading behavior for various vegetation

Wildland fire behavior is significantly influenced by environmental factors such as slope steepness, wind speed, and fuel type. Understanding these interactions is critical for improving predictive models and fire management. This study explores how slope steepness and cross-slope wind speed influence fire spread dynamics in various fuel bed types. Simulations are conducted using a physics-based wildland fire model, HIGRAD/FIRETEC, across six slope angles (0–50 %), four cross-slope wind speeds (4–10 m s –1 ), and three fuel bed types (grass, shrubland, and forest). Representative cases are additionally compared with FARSITE fireline evolution. Fire behavior is categorized into distinct propagation types based on spread characteristics and analyzed. The fire propagation angle, which indicates deviation from the wind direction, generally increases with steeper slopes and decreases with stronger cross-slope winds. Secondary upslope propagation is observed in shrubland under moderate slopes, while secondary downwind propagation occurs in all fuel beds at higher wind speeds. These findings highlight fire spread characteristics that differ from predictions by traditional models like Rothermel’s. By capturing complex propagation patterns and dynamics, this study demonstrates the value of a physics-based, atmosphere-fire coupled model for accurate wildland fire prediction. Incorporating secondary propagations and the influence of fuel bed complexities into predictive models can improve the accuracy of fire spread forecasts, enabling more effective fire management and risk mitigation efforts.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2019

Remote Sensing of Solar-Induced Chlorophyll Fluorescence (SIF) in Vegetation: 50 Years of Progress

Remote sensing of solar-induced chlorophyll fluorescence (SIF) is a rapidly advancing front in terrestrial vegetation science, with emerging capability in space-based methodologies and diverse application prospects. Although remote sensing of SIF – especially from space – is seen as a contemporary new specialty for terrestrial plants, it is founded upon a multi-decadal history of research, applications, and sensor developments in active and passive sensing of chlorophyll fluorescence. Current technical capabilities allow SIF to be measured across a range of biological, spatial, and temporal scales. As an optical signal, SIF may be assessed remotely using highly-resolved spectral sensors and state-of-the-art algorithms to distinguish the emission from reflected and/or scattered ambient light. Because the red to far-red SIF emission is detectable non-invasively, it may be sampled repeatedly to acquire spatio-temporally explicit information about photosynthetic light responses and steady-state behaviour in vegetation. Progress in this field is accelerating with innovative sensor developments, retrieval methods, and modelling advances. This review distills the historical and current developments spanning the last several decades. It highlights SIF heritage and complementarity within the broader field of fluorescence science, the maturation of physiological and radiative transfer modelling, SIF signal retrieval strategies, techniques for field and airborne sensing, advances in satellite-based systems, and applications of these capabilities in evaluation of photosynthesis and stress effects. Progress, challenges, and future directions are considered for this unique avenue of remote sensing.

Review↗
NASA NTRS1991

Effects of vegetation on soil moisture distribution and flux with implications for the global hydrologic cycle

Recent climate modeling experiments have identified the critical need for a better understanding of land surface - atmosphere interactions. An important issue in global climate modeling is to be able to relate land surface and atmospheric processes. In the past this link has been inadequately represented due to the lack of understanding of the interaction between the processes and also due to the large spatial variability of the hydrological and soil properties. A project was initiated at the Marshall Space Flight Center (MSFC) in FY-90 under the Center's Directorate Discretionary Fund (CDDF) to study small-scale effects of vegetation on the distribution and fluxes of soil moisture. Installation of a large array of instruments was accomplished during that first year (FY-90). During this second year of the project, the instrumentation and data collection systems were improved and data has begun to be taken. Preliminary analysis of the data show that the equipment has been functioning properly. Some of the preliminary results that have recently been analyzed are given.

Macari, Emir Jose↗