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At least 1,279 records · Page 71
All sourcesNASA NTRSDOE OSTI
NASA NTRS1982

Extension of laboratory-measured soil spectra to field conditions

Spectral responses of two glaciated soils, Chalmers silty clay loam and Fincastle silt loam, formed under prairie grass and forest vegetation, respectively, were measured in the laboratory under controlled moisture equilibria using an Exotech Model 20C spectroradiometer to obtain spectral data in the laboratory under artificial illumination. The same spectroradiometer was used outdoors under solar illumination to obtain spectral response from dry and moistened field plots with and without corn residue cover, representing the two different soils. Results indicate that laboratory-measured spectra of moist soil are directly proportional to the spectral response of that same field-measured moist bare soil over the 0.52 micrometer to 1.75 micrometer wavelength range. The magnitudes of difference in spectral response between identically treated Chalmers and Fincastle soils are greatest in the 0.6 micrometers to 0.8 micrometer transition region between the visible and near infrared, regardless of field condition or laboratory preparation studied.

Stoner, E. R.↗
NASA NTRS1992

Radiometric calibration of a polarization-sensitive sensor

The radiometric accuracy of a sensor is adversely affected by scene polarization if its optical system is sensitive to polarization. Tests performed on the reflective bands of the NS001 Thematic Mapper simulator, an aircraft multispectral scanner, show that it is very sensitive to the polarization state of the incoming radiations. For 100 percent linearly polarized light, errors in the measured intensity vary from -40 to +40 percent, depending on the scan angle and spectral band. To estimate polarization-induced errors in the intensity measured at aircraft level, the intensity and polarization of the atmospheric radiances were simulated using a realistic earth-atmosphere radiative transfer model. For the polarization of atmospheric radiances in the solar meridian plane over a vegetated target, intensity errors may range from -10 to + 10 percent. The polarization-induced errors are highest in the shortest NS001 spectral band (0.450-0.525 microns) because of large atmospheric polarizations contributed by Rayleigh particles and small diluting effects caused by the small contributions of weakly polarized radiations coming from aerosols and the surface. Depending on the illumination and view angles, the errors in derived surface reflectance due to the radiance errors can be very large. In particular, for large off-nadir view angles in the forward scattered direction when the sun is low, the relative errors in the derived surface reflectance can be as large as 4 to 5 times the relative error in the radiances. Polarization sensitivity errors cannot be neglected for the shorter wavelengths when the surface reflectance contribution to atmospheric radiances is very small.

Ahmad, Suraiya P.↗
NASA NTRS1994

On the use of radiative surface temperature to estimate sensible heat flux over sparse shrubs in Nevada

The accurate partitioning of available energy into sensible and latent heat flux is crucial to the understanding of surface atmosphere interactions. This issue is more complicated in arid and semi arid regions where the relative contribution to surface fluxes from the soil and vegetation may vary significantly throughout the day and throughout the season. A three component model to estimate sensible heat flux over heterogeneous surfaces is presented. The surface was represented with two adjacent compartments. The first compartment is made up of two components, shrubs and shaded soil, the second of open 'illuminated' soil. Data collected at two different sites in Nevada (U.S.) during the Summers of 1991 and 1992 were used to evaluate model performance. The results show that the present model is sufficiently general to yield satisfactory results for both sites.

Chehbouni, A.↗
NASA NTRS1996

Surface and Atmospheric Parameter Retrieval From AVIRIS Data: The Importance of Non-Linear Effects

AVIRIS data represent a new and important approach for the retrieval of atmospheric and surface parameters from optical remote sensing data. Not only as a test for future space systems, but also as an operational airborne remote sensing system, the development of algorithms to retrieve information from AVIRIS data is an important step to these new approaches and capabilities. Many things have been learned since AVIRIS became operational, and the successive technical improvements in the hardware and the more sophisticated calibration techniques employed have increased the quality of the data to the point of almost meeting optimum user requirements. However, the potential capabilities of imaging spectrometry over the standard multispectral techniques have still not been fully demonstrated. Reasons for this are the technical difficulties in handling the data, the critical aspect of calibration for advanced retrieval methods, and the lack of proper models with which to invert the measured AVIRIS radiances in all the spectral channels. To achieve the potential of imaging spectrometry, these issues must be addressed. In this paper, an algorithm to retrieve information about both atmospheric and surface parameters from AVIRIS data, by using model inversion techniques, is described. Emphasis is put on the derivation of the model itself as well as proper inversion techniques, robust to noise in the data and an inadequate ability of the model to describe natural variability in the data. The problem of non-linear effects is addressed, as it has been demonstrated to be a major source of error in the numerical values retrieved by more simple, linear-based approaches. Non-linear effects are especially critical for the retrieval of surface parameters where both scattering and absorption effects are coupled, as well as in the cases of significant multiple-scattering contributions. However, sophisticated modeling approaches can handle such non-linear effects, which are especially important over vegetated surfaces. All the data used in this study were acquired during the 1991 Multisensor Airborne Campaign (MAC-Europe), as part of the European Field Experiment on a Desertification-threatened Area (EFEDA), carried out in Spain in June-July 1991.

Green Robert O.↗
NASA NTRS2015

Technical Report Series on Global Modeling and Data Assimilation: Soil Moisture Active Passive (SMAP) Project Calibration and Validation for the L4_C Beta-Release Data Product - Volume 42

During the post-launch Cal/Val Phase of SMAP there are two objectives for each science product team: 1) calibrate, verify, and improve the performance of the science algorithms, and 2) validate accuracies of the science data products as specified in the L1 science requirements according to the Cal/Val timeline. This report provides analysis and assessment of the SMAP Level 4 Carbon (L4_C) product specifically for the beta release. The beta-release version of the SMAP L4_C algorithms utilizes a terrestrial carbon flux model informed by SMAP soil moisture inputs along with optical remote sensing (e.g. MODIS) vegetation indices and other ancillary biophysical data to estimate global daily NEE and component carbon fluxes, particularly vegetation gross primary production (GPP) and ecosystem respiration (Reco). Other L4_C product elements include surface (<10 cm depth) soil organic carbon (SOC) stocks and associated environmental constraints to these processes, including soil moisture and landscape FT controls on GPP and Reco (Kimball et al. 2012). The L4_C product encapsulates SMAP carbon cycle science objectives by: 1) providing a direct link between terrestrial carbon fluxes and underlying freeze/thaw and soil moisture constraints to these processes, 2) documenting primary connections between terrestrial water, energy and carbon cycles, and 3) improving understanding of terrestrial carbon sink activity in northern ecosystems.

Cal/Val↗
NASA NTRS2016

Soil Moisture Active Passive Mission L4_C Data Product Assessment (Version 2 Validated Release)

The SMAP satellite was successfully launched January 31st 2015, and began acquiring Earth observation data following in-orbit sensor calibration. Global data products derived from the SMAP L-band microwave measurements include Level 1 calibrated and geolocated radiometric brightness temperatures, Level 23 surface soil moisture and freezethaw geophysical retrievals mapped to a fixed Earth grid, and model enhanced Level 4 data products for surface to root zone soil moisture and terrestrial carbon (CO2) fluxes. The post-launch SMAP mission CalVal Phase had two primary objectives for each science product team: 1) calibrate, verify, and improve the performance of the science algorithms, and 2) validate accuracies of the science data products as specified in the L1 science requirements. This report provides analysis and assessment of the SMAP Level 4 Carbon (L4_C) product pertaining to the validated release. The L4_C validated product release effectively replaces an earlier L4_C beta-product release (Kimball et al. 2015). The validated release described in this report incorporates a longer data record and benefits from algorithm and CalVal refinements acquired during the SMAP post-launch CalVal intensive period. The SMAP L4_C algorithms utilize a terrestrial carbon flux model informed by SMAP soil moisture inputs along with optical remote sensing (e.g. MODIS) vegetation indices and other ancillary biophysical data to estimate global daily net ecosystem CO2 exchange (NEE) and component carbon fluxes for vegetation gross primary production (GPP) and ecosystem respiration (Reco). Other L4_C product elements include surface (10 cm depth) soil organic carbon (SOC) stocks and associated environmental constraints to these processes, including soil moisture and landscape freeze/thaw (FT) controls on GPP and respiration (Kimball et al. 2012). The L4_C product encapsulates SMAP carbon cycle science objectives by: 1) providing a direct link between terrestrial carbon fluxes and underlying FT and soil moisture constraints to these processes, 2) documenting primary connections between terrestrial water, energy and carbon cycles, and 3) improving understanding of terrestrial carbon sink activity in northern ecosystems. There are no L1 science requirements for the L4_C product; however self-imposed requirements have been established focusing on NEE as the primary product field for validation, and on demonstrating L4_C accuracy and success in meeting product science requirements (Jackson et al. 2012). The other L4_C product fields also have strong utility for carbon science applications; however, analysis of these other fields is considered secondary relative to primary validation activities focusing on NEE. The L4_C targeted accuracy requirements are to meet or exceed a mean unbiased accuracy (ubRMSE) for NEE of 1.6 g C/sq m/d or 30 g C/sq m/yr, emphasizing northern (45N) boreal and arctic ecosystems; this is similar to the estimated accuracy level of in situ tower eddy covariance measurement-based observations (Baldocchi 2008).

Cal/Val↗
NASA NTRSDate not supplied

Building Capacity to Use Earth Observations for Land Monitoring: A Synoptic Review of NASA DEVELOP’s Terrestrial Projects

The NASA DEVELOP Program addresses environmental and public policy issues through interdisciplinary research studies that apply the lens of Earth observations to decision making. The program builds capacity in both programmatic participants (e.g., students and recent graduates) and partner organizations (e.g., federal agencies and non-governmental organizations) to use and integrate Earth science information into environmental decision making. DEVELOP conducts 50-60 projects each year, with approximately three quarters of projects focusing on terrestrial-related topics such as monitoring and assessing land use/land change, vegetation health, agriculture, water resources, and urban development. This presentation will share the DEVELOP model for building capacity, highlight example project case studies, and share lessons learned working with a wide variety of partner organizations.

Capacity Building↗
NASA NTRS2012

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↗
NASA NTRS1997

Internal Physical Features of a Land Surface Model Employing a Tangent Linear Model

The Earth's land surface, including its biomass, is an integral part of the Earth's weather and climate system. Land surface heterogeneity, such as the type and amount of vegetative covering., has a profound effect on local weather variability and therefore on regional variations of the global climate. Surface conditions affect local weather and climate through a number of mechanisms. First, they determine the re-distribution of the net radiative energy received at the surface, through the atmosphere, from the sun. A certain fraction of this energy increases the surface ground temperature, another warms the near-surface atmosphere, and the rest evaporates surface water, which in turn creates clouds and causes precipitation. Second, they determine how much rainfall and snowmelt can be stored in the soil and how much instead runs off into waterways. Finally, surface conditions influence the near-surface concentration and distribution of greenhouse gases such as carbon dioxide. The processes through which these mechanisms interact with the atmosphere can be modeled mathematically, to within some degree of uncertainty, on the basis of underlying physical principles. Such a land surface model provides predictive capability for surface variables including ground temperature, surface humidity, and soil moisture and temperature. This information is important for agriculture and industry, as well as for addressing fundamental scientific questions concerning global and local climate change. In this study we apply a methodology known as tangent linear modeling to help us understand more deeply, the behavior of the Mosaic land surface model, a model that has been developed over the past several years at NASA/GSFC. This methodology allows us to examine, directly and quantitatively, the dependence of prediction errors in land surface variables upon different vegetation conditions. The work also highlights the importance of accurate soil moisture information. Although surface variables are predicted imperfectly due to inherent uncertainties in the modeling process, our study suggests how satellite observations can be combined with the model, through land surface data assimilation, to improve their prediction.

Yang, Runhua↗
DOE OSTI2025

Extreme hydroclimates amplify the biophysical effects of advanced green-up in temperate China

Vegetation phenology modulates climate by altering energy and water exchange between the land and atmosphere. However, how extreme hydroclimatic conditions modify these phenology-climate feedbacks is still poorly understood. Here, in this study, we used a land–atmosphere-coupled Weather Research and Forecasting model to explore the impacts of advanced green-up on air temperature under different hydroclimate conditions across temperate China and to Mechanistic analysis elucidate the underlying biophysical mechanisms. By imposing a 14-day earlier green-up in line with recent satellite observations, we found that under mean climate conditions, an earlier leaf-out induces immediate surface cooling of 0.14 °C during green-up and a lagging 0.02 °C warming during senescence averaged for temperate China. Extremely humid conditions amplify the cooling effects to 0.18 °C during green-up, extending this cooling into the senescence period. Conversely, under extremely arid conditions, earlier green-up cools air temperature by 0.09 °C, and amplified senescence warming to 0.16 °C. Mechanism analysis revealed that evapotranspiration-dominated non-radiative processes drive immediate cooling during green-up while radiation and circulation process dominates the delayed but opposite warming/cooling effects during senescence in extremely arid/humid hydroclimates. Given the projected continuation of warming trends and increased frequency of extreme climatic events, it is imperative to incorporate the biophysical effects of vegetation phenology into local climate adaptation strategies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Wetter California Projected by CMIP6 Models With Observational Constraints Under a High GHG Emission Scenario

As the world's fifth-largest economy entity, California (CA) is vulnerable to climate changes especially the magnitude of winter (wet season) precipitation that is closely linked to regional drought severity, vegetation growth, and wildfire activities. However, the fate of CA precipitation in the future remains highly uncertain, for example, the state-of-the-art Earth system models (ESMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) projected –3% to +42% and –27% to +63% precipitation changes in northern and central-southern CA, respectively, under the high greenhouse gas (GHG) emission scenario (SSP585). In this work, we applied the Pareto optimality concept and used observed teleconnection patterns (causal relationships between ocean regions and CA precipitation) to mechanistically constrain CMIP6 projected CA precipitations. We estimated that precipitation will robustly increase by 0.4–1.3 mm d –1 (10–34%) over northern CA and increase by 0.1–0.5 mm d –1 (7–32%) over central-southern CA by the end of the 21st century compared with present-day. Up to 71% of ESM projection uncertainties were reduced mainly due to the strong and consistent causal relationship between North American west coast sea level pressure and CA precipitation in both observations and CMIP6 models. Our results suggest that teleconnection patterns are powerful mechanistic constraints that can help explain and reduce uncertainties in ESM projections.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2020

Delineation of vulnerable areas to water erosion in a mountain region using SDR-InVEST model: A case study of the Ourika watershed, Morocco

The Ourika watershed is characterized by higher than average flood flows, a rugged topography, a sparse vegetation cover and a friable substrate that makes it vulnerable to soil erosion. Soil is an important non-renewable asset capital, providing several ecosystem services that sustain life through physical and biological regulation processes. The loss, or otherwise, redistribution of biota by soil erosion in the Ourika watershed has received little attention and thus constitutes a key knowledge gap that needs to be addressed. This study quantifies the level of erosion and maps its spatial distribution in the watershed using the Sediment Delivery Ratio (SDR) of the Integrated Valuation of Ecosystem Services and Tradeoffs model (InVEST3.6.0). The SDR maps the overland sediment generation and delivery to the stream at the watershed level using rainfall, soil properties, topography, cover management and cropping practices as predefined set of input variables. Recommended internal model calibration was used and results were compared to similar studies carried out in different parts of Morocco for validation. The results reveal that 93% of the watershed is subject to a strong rainfall aggressiveness greater than 50 MJ mm ha–1 h–1 yr1 under an average rainfall intensity of 541 mm/year, rainfall capacity to initiate the detachment and transport of soil particles by runoff, and only 11.5% of the watershed area is protected with vegetative cover. We find that overall, the Ourika watershed is losing its soil to erosion at an alarming average rate of 258.48 ton.ha–1.yr–1, a rate well above the tolerable threshold of 7ton.ha–1.yr–1. The spatial distribution of this loss shows areas that need to be urgently remedied. The primary factors for soil erosion in the Ourika watershed appear to be related to the aggressive nature of local rainfall and the inherent susceptibility of soils, which can be costly to this cash-crop region. Because rainfall intensity, soil properties, and topography are intrinsic characteristics of the watershed, we suggest that seasonally adapted cropping rotations that maintains conservative practices with a high cover management may reduce soil erosion in the Ourika watershed.

Erosion↗
NASA NTRS1980

Microwave remote sensing of soil moisture content over bare and vegetated fields

Remote measurements of soil moisture contents over bare fields and fields covered with orchard grass, corn, and soybean were made during October 1979 with 1.4 GHz and 5 GHz microwave radiometers mounted on a truck. Ground truth of soil moisture content, ambient air, and soil temperatures was acquired concurrently with the radiometric measurements. The biomass of the vegetation was sampled about once a week. The measured brightness temperatures over bare fields were compared with those of radiative transfer model calculations using as inputs the acquired soil moisture and temperature data with appropriate values of dielectric constants for soil-water mixtures. Good agreement was found between the calculated and the measured results over 10-70 deg incident angles. The presence of vegetation was found to reduce the sensitivity of soil moisture sensing. At 1.4 GHz the sensitivity reduction ranged from approximately 20% for 10-cm tall grassland to over 60% for the dense soybean field. At 5 GHz the corresponding reduction in sensitivity ranged from approximately 70 to approximately 90%.

Wang, J. R.↗
DOE OSTI2021

Ensemble modeling of watershed-scale hydrologic effects of short-rotation woody crop production

Short-rotation woody crop (SRWC) production involves a set of silvicultural practices that aim to produce large volumes of biomass over relatively short time frames. The area over which these practices are employed is likely to increase in the coming decades as the demand for bioenergy increases, but the potential effects of this change in land management, including the hydrologic effects, are largely unknown. Here we outline the results from an ensemble modeling study that was developed to forecast the range of potential hydrological responses to the implementation of SRWC production over areas that are large (>1000 ha) relative to the size of individual clearcuts. The three models, SWAT, MIKE-SHE, and Envision-SRS, a physically based model designed to represent watersheds with dynamic land cover, represent a range of simulation tools that include hydrological response to landcover change. Results suggest that SRWC production will affect the hydrologic balance, primarily through changes in the volume of transpired water associated with the rapidly growing young stands. In particular, average annual actual evapotranspiration (ET) rates tend to decline under SRWC production in response to the less mature vegetation. These reductions in ET are balanced in the hydrological cycle through elevated groundwater recharge, expressed in the model results as elevated annual stream discharge.

09 BIOMASS FUELS↗
DOE OSTI2024

Geophysical and Environmental Monitoring Data, and Subsurface Flow Modelling Results for Chicken Bone Meadow, Mt. Snodgrass, Crested Butte, CO

This dataset includes geoelectrical monitoring data acquired between October 2021 and November 2022, soil moisture and temperature data, groundwater data obtained from borehole SNIB covering the period from June 2021 to September 2022, and hydrological modelling results. The data were acquired to investigate how variations in bedrock type and topography, and vegetation cover control subsurface flow dynamics. To provide insights into the subsurface flow dynamics and their controls, a monitoring transect was installed at the Chicken Bone Meadow, Mt. Snodgrass, Crested Butte, CO, measuring the spatio-temporal variations of soil moisture, soil and snow temperature, subsurface electrical resistivity variations, and groundwater dynamics. Field data are organized in a folder structure, with Electrical Resistivity Tomography (ERT) data being provided as one file per measurement, and data of the soil moisture and temperature sensors being provided as text files covering the entire monitoring period. The ‘Locations.csv’ file contains the location of all sensors, given in NAD83 – UTM Zone 13N. ERT monitoring data has been processed to filter data based on reciprocal errors (data with errors > 30% were removed), a linear error model was fitted to each survey, and to ensure a constant set of measurements for time-lapse inversion, filtered data were interpolated and assigned a 100% measurement error. Soil moisture and temperature data were acquired at 15 min intervals, and averaged to provide 1h data. Weather data and borehole data (groundwater depth, conductivity and temperature) were acquired at 30 min intervals, and are provided as daily measurements; all measurements are averaged, except of precipitation values, which are given as daily accumulation. The hydrological model was set up along the ERT monitoring transect, and net infiltration was used as surface boundary condition and derived from the weather data. Four different results are provided, (1) results for a parameterization using hydraulic permeability and porosity as derived from the ERT data through petrophysical relationships, and (2) three simplified model results, using 1 to 3 geological layers above the bedrock. Modelling was performed using PFLOTRAN, and for each model the PFLOTRAN input files are provided. The result files include weekly hydrological modelling results (e.g., saturation, velocities, pressures), as well as the model parameterization. The dataset additionally includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2022

Evaluating Approaches Relating Ecosystem Productivity with DESIS Spectral Information

Data from the DLR Earth Sensing Imaging Spectrometer (DESIS), mounted on the International Space Station (ISS), were used to develop and test algorithms for remotely retrieving ecosystem productivity. Twenty DESIS images were used from three widely separated forested study sites representing deciduous and conifer forests. Gross primary production (GPP) values from eddy covariance flux towers at the sites were matched with DESIS spectral reflectances collected on the same days. Multiple algorithms were successful relating spectral reflectance with GPP, including: spectral vegetation indices (SVI) sensitive to chlorophyll content, SVI used in a photosynthetic light-use efficiency model framework, spectral shape characteristics through spectral derivatives and absorption feature analysis, and statistical models leading to multiband hyperspectral indices from partial least squares regression. Successful algorithms were able to achieve R2 better than 0.7 using a diverse set of observations combining data from different sites from multiple years and at multiple times during the year. The demonstrated robustness of the algorithms provides some confidence in using DESIS imagery to map spatial patterns of GPP.

Gross Primary Productivity (GPP)↗
NASA NTRS2019

Mid-Latitude Freshwater Availability Reduced by Projected Vegetation Responses to Climate Change

Plants are expected to generate more global-scale runoff under increasing atmospheric carbon dioxide concentrations through their influence on surface resistance to evapotranspiration. Recent studies using Earth System Models from phase 5 of the Coupled Model Intercomparison Project ostensibly reaffirm this result, further suggesting that plants will ameliorate the dire reductions in water availability projected by other studies that use aridity metrics. Here we complicate this narrative by analysing the change in precipitation partitioning to plants, runoff and storage in multiple Earth system models under both high carbon dioxide concentrations and warming. We show that projected plant responses directly reduce future runoff across vast swaths of North America, Europe and Asia because bulk canopy water demands increase with additional vegetation growth and longer and warmer growing seasons. These runoff declines occur despite increased surface resistance to evapotranspiration and vegetation total water use efficiency, even in regions with increasing or unchanging precipitation. We demonstrate that constraining the large uncertainty in the multimodel ensemble with regional-scale observations of evapotranspiration partitioning strengthens these results. We conclude that terrestrial vegetation plays a large and unresolved role in shaping future regional freshwater availability, one that will not ubiquitously ameliorate future warming-driven surface drying.

Mankin, Justin S.↗