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NASA NTRS1995

The importance of mesoscale circulations generated by subgrid-scale landscape heterogeneities in general circulation models

A mesoscale atmospheric model was used to evaluate the impact of subgrid-scale landscape discontinuities on the vertical profiles of resolved temperature, moisture, and moist static energy in the planetary boundary layer (PBL) of general circulation models (GCMs). These profiles were produced with a 3D version of the model (using a horizontal grid resolution of 7.5 km and 13 vertical layers in the PBL) by averaging horizontally the various atmospheric variables over a 180 x 180 sq km domain-about the size of the horizontal domain represented by a single grid element in a GCM. They were compared to corresponding vertical profiles produced with a 1D version of the model, which simulates the PBL, as in a GCM, over a single horizontal grid element. Differences obtained between the horizontally averaged atmospheric variables produced with the 3D simulations and the 1D simulations emphasize the impact of subgrid-scale landscape discontinuities on GCM-resolved variables. Various types of landscape discontinuities, characterized by horizontal contrasts of surface wetness and size of land patches, were simulated under various background-wind conditions. Differences of temperature, specific humidity, and moist static energy as large as 4 K, 6 g/kg, and 10 kJ/kg were obtained in some cases. These differences were not affected significantly by moderate winds but were sensitive to the spatial distribution of surface wetness. These results emphasize the need to parameterize mesoscale processes induced by landscape discontinuities in GCMss.

Lynn, Barry H.↗

NASA NTRS1999

Lidar Altimeter Measurements of Canopy Structure: Methods and Validation for Closed Canopy, Broadleaf Forests

Lidar altimeter observations of vegetated landscapes provide a time-resolved measure of laser pulse backscatter energy from canopy surfaces and the underlying ground. Airborne lidar altimeter data was acquired using the Scanning Lidar Imager of Canopies by Echo Recovery (SLICER) for a successional sequence of four, closed-canopy, deciduous forest stands in eastern Maryland. The four stands were selected so as to include a range of canopy structures of importance to forest ecosystem function, including variation in the height and roughness of the outer-most canopy surface and the vertical organization of canopy stories and gaps. The character of the SLICER backscatter signal is described and a method is developed that accounts for occlusion of the laser energy by canopy surfaces, transforming the backscatter signal to a canopy height profile (CHP) that quantitatively represents the relative vertical distribution of canopy surface area. The transformation applies an increased weighting to the backscatter amplitude as a function of closure through the canopy and assumes a horizontally random distribution of the canopy components. SLICER CHPs, averaged over areas of overlap where lidar ground tracks intersect, are shown to be highly reproducible. CHP transects across the four stands reveal spatial variations in vegetation, at the scale of the individual 10 m diameter laser footprints, within and between stands. Averaged SLICER CHPs are compared to analogous height profile results derived from ground-based sightings to plant intercepts measured on plots within the four stands. Tbe plots were located on the segments of the lidar ground tracks from which averaged SLICER CHPs were derived, and the ground observations were acquired within two weeks of the SLICER data acquisition to minimize temporal change. The differences in canopy structure between the four stands is similarly described by the SLICER and ground-based CHP results, however a Chi-square test of similarity documents differences that are statistically significant. The differences are discussed in terms of measurement properties that define the smoothness of the resulting CHPs and Lidar Altimeter Measurements of Canopy Structure - Harding et al. canopy properties that may vertically bias the CHP representations of canopy structure. The statistical differences are most likely due to the more noisy character of the ground-based CHPs, especially high in the canopy where ground-based sightings are rare resulting in an underestimate of canopy surface area and height, and to departures from the assumption of horizontal randomness which bias the CHPs toward the observer (upward for SLICER and downward for ground-based CHPs). The results demonstrate that the SLICER observations reliably provide a measure of canopy structure that reveals ecologically interesting structural variations such as those characterizing a successional sequence of closed-canopy, broadleaf forest stands.

Harding, D. J.↗

NASA NTRS2021

NASA's Surface Biology and Geology Designated Observable: A Perspective on Surface Imaging Algorithms

The 2017–2027 National Academies’ Decadal Survey, Thriving on Our Changing Planet, recommended Surface Biology and Geology (SBG) as a “Designated Targeted Observable” (DO). The SBG DO is based on the need for capabilities to acquire global, high spatial resolution, visible to shortwave infrared (VSWIR; 380–2500 nm; ~30 m pixel resolution) hyperspectral (imaging spectroscopy) and multispectral midwave and thermal infrared (MWIR: 3–5 μm; TIR: 8–12 μm; ~60 m pixel resolution) measurements with sub-monthly temporal revisits over terrestrial, freshwater, and coastal marine habitats. To address the various mission design needs, an SBG Algo-rithms Working Group of multidisciplinary researchers has been formed to review and evaluate the algorithms applicable to the SBG DO across a wide range of Earth science disciplines, including terrestrial and aquatic ecology, atmospheric science, geology, and hydrology. Here, we summarize current state-of-the-practice VSWIR and TIR algorithms that use airborne or orbital spectral imaging observations to address the SBG DO priorities identified by the Decadal Survey: (i) terrestrial vegetation physiology, functional traits, and health; (ii) inland and coastal aquatic ecosystems physiology, functional traits, and health; (iii) snow and ice accumulation, melting, and albedo; (iv) active surface composition (eruptions, landslides, evolving landscapes, hazard risks); (v) effects of changing land use on surface energy, water, momentum, and carbon fluxes; and (vi) managing agriculture, natural habitats, water use/quality, and urban development. We review existing algorithms in the following categories: snow/ice, aquatic environments, geology, and terrestrial vegetation, and summarize the community-state-of-practice in each category. This effort synthesizes the findings of more than 130 scientists.

Aquatic↗

NASA NTRS1998

Analysis of Vegetation Within A Semi-Arid Urban Environment Using High Spatial Resolution Airborne Thermal Infrared Remote Sensing Data

High spatial resolution (5 m) remote sensing data obtained using the airborne Thermal Infrared Multispectral Scanner (TIMS) sensor for daytime and nighttime have been used to measure thermal energy responses for 2 broad classes and 10 subclasses of vegetation typical of the Salt Lake City, Utah urban landscape. Polygons representing discrete areas corresponding to the 10 subclasses of vegetation types have been delineated from the remote sensing data and are used for analysis of upwelling thermal energy for day, night, and the change in response between day and night or flux, as measured by the TIMS. These data have been used to produce three-dimensional graphs of energy responses in W/ sq m for day, night, and flux, for each urban vegetation land cover as measured by each of the six channels of the TIMS sensor. Analysis of these graphs provides a unique perspective for both viewing and understanding thermal responses, as recorded by the TIMS, for selected vegetation types common to Salt Lake City. A descriptive interpretation is given for each of the day, night, and flux graphs along with an analysis of what the patterns mean in reference to the thermal properties of the vegetation types surveyed in this study. From analyses of these graphs, it is apparent that thermal responses for vegetation can be highly varied as a function of the biophysical properties of the vegetation itself, as well as other factors. Moreover, it is also seen where vegetation, particularly trees, has a significant influence on damping or mitigating the amount of thermal radiation upwelling into the atmosphere across the Salt Lake City urban landscape. Published by Elsevier Science Ltd.

Quattrochi, Dale A.↗

NASA NTRS2002

A Remote Sensing Approach for Urban Environmental Decision-Making: An Atlanta, Georgia Case Study

Unquestionably, urbanization causes tremendous changes in land cover and land use, as well as impacting a host of environmental characteristics. For example, unlike natural surfaces, urban surfaces have very different thermal energy properties whereby they store solar energy throughout the day and continue to release it as heat well after sunset. This effect, known as the 'Urban Heat Island', serves as a catalyst for chemical reactions from vehicular exhaust and industrial activities leading to the deterioration in air quality, especially exacerbating the production of ground level ozone. 'Cool Community' strategies that utilize remote sensing data, are now being implemented as a way to reduce the impacts of the urban heat island and its subsequent environmental impacts. This presentation focuses on how remote sensing data have been used to provide descriptive and quantitative data for characterizing the Atlanta, Georgia metropolitan area - particularly for measuring surface energy fluxes, such as the thermal or "heat" energy that emanates from different land cover types across the Atlanta urban landscape. In turn, this information is useful for developing a better understanding of how the thermal characteristics of the city surface affect the urban heat island phenomena and, ultimately, air quality and other environmental parameters over the Atlanta metropolitan region. Additionally, this paper also provides insight on how remote sensing, with its synoptic approach, can be used to provide urban planners, local, state, and federal government officials, and other decision-makers, as well as the general public, with information to better manage urban areas as sustainable environments.

Quattrochi, Dale A.↗

NASA NTRS2009

Landform Erosion and Volatile Redistribution on Ganymede and Callisto

We have been modeling landscape evolution on the Galilean satellites driven by volatile transport. Our work directly addresses some of the most fundamental issues pertinent to deciphering icy Galilean satellite geologic histories by employing techniques currently at the forefront of terrestrial, martian, and icy satellite landscape evolution studies [e.g., 1-6], including modeling of surface and subsurface energy and volatile exchanges, and computer simulation of long-term landform evolution by a variety of processes. A quantitative understanding of the expression and rates of landform erosion, and of volatile redistribution on landforms, is especially essential in interpreting endogenic landforms that have, in many cases, been significantly modified by erosion [e.g., 7-9].

Moore, Jeffrey Morgan↗

NASA NTRSDate not supplied

Physics-Guided Deep Learning for Complex System Health Management and Decision Making

The landscape of complex engineered systems is rapidly evolving, from smart manufacturing facilities to next-generation transportation vehicles. As these systems become increasingly sophisticated and interconnected, the need for advanced health management systems grows ever more critical. These systems must go beyond simple monitoring, actively predicting potential failures before they occur. This paradigm shift from fixed maintenance schedules to condition-based predictions is key to optimizing system performance, enhancing safety, and paving the way for autonomous decision-making across various industries. Whether in industrial processes, energy systems, or advanced transportation, the ability to anticipate and prevent failures is becoming a cornerstone of operational excellence. To accurately predict the future health of any complex system, knowledge of its current health state and future operational conditions is essential. Recent advancements in data-driven algorithms have generated growing interest in artificial intelligence for industrial applications. However, the limitations of pure data-driven methods, particularly in industries where data acquisition is costly and limited, have become apparent. This has led to a focus on blending physics with data-driven algorithms, mitigating the drawbacks of both approaches while emphasizing their respective advantages. This research proposes a novel framework for integrating physics-based performance models with deep learning algorithms for the prognostics of complex safety-critical systems. In this approach, physics-based models serve as a blueprint, capturing fundamental system behaviors, while deep learning algorithms, leveraging real-world sensor data, fill in gaps and identify subtle patterns indicative of potential problems. This hybrid methodology, utilizing techniques such as Physics-Informed Neural Networks (PINNs), offers a powerful solution for predicting system health. By fusing domain knowledge with data-driven insights, this approach promises more accurate, adaptable, and reliable models for health prediction. The resulting framework is versatile, applicable across various sectors including aerospace, manufacturing, and energy systems, ultimately contributing to safer, more efficient operations in our increasingly complex technological landscape.

Diagnostics↗

NASA NTRS1999

Measuring Thermal Characteristics of Urban Landscapes

The additional heating of the air over the city is the result of the replacement of naturally vegetated surfaces with those composed of asphalt, concrete, rooftops and other man-made materials. The temperatures of these artificial surfaces can be 20 to 40 C higher than vegetated surfaces. Materials such as asphalt store much of the sun's energy and remains hot long after sunset. This produces a dome of elevated air temperatures 5 to 8 C greater over the city, compared to the air temperatures over adjacent rural areas. This effect is called the "urban heat island". Urban landscapes are a complex mixture of vegetated and nonvegetated surfaces. It is difficult to take enough temperature measurements over a large city area to characterize the complexity of urban radiant surface temperature variability. However, the use of remotely sensed thermal data from airborne scanners are ideal for the task. In a study funded by NASA, a series of flights over Huntsville, Alabama were performed in September 1994 and over Atlanta, Georgia in May 1997. Analysis of thermal energy responses for specific or discrete surfaces typical of the urban landscape (e.g., asphalt, building rooftops, vegetation) requires measurements at a very fine spatial scale (i.e., <15 m) to adequately resolve these surfaces and their attendant thermal energy regimes. Additionally, very fine scale spatial resolution thermal infrared data, such as that obtained from aircraft, are very useful for demonstrating to planning officials, policy makers, and the general populace, what the benefits are of the urban forest in both mitigating the urban heat island effect, in making cities more aesthetically pleasing and more habitable environments, and in overall cooling of the community. In this presentation we will examine the techniques of analyzing remotely sensed data for measuring the effect of various urban surfaces on their contribution to the urban heat island effect.

Luvall, Jeffrey C.↗

NASA NTRS1992

Quantitative modeling of soil genesis processes

For fine spatial scale simulation, a model is being developed to predict changes in properties over short-, meso-, and long-term time scales within horizons of a given soil profile. Processes that control these changes can be grouped into five major process clusters: (1) abiotic chemical reactions; (2) activities of organisms; (3) energy balance and water phase transitions; (4) hydrologic flows; and (5) particle redistribution. Landscape modeling of soil development is possible using digitized soil maps associated with quantitative soil attribute data in a geographic information system (GIS) framework to which simulation models are applied.

Levine, E. R.↗

NASA NTRS2013

SMAP Radar Processing and Calibration

The Soil Moisture Active Passive (SMAP) mission is part of the NASA space-based Earth observation program, and consists of an L-band radar and radiometer scheduled for launch into sun synchronous orbit in late 2014. A joint effort of the Jet Propulsion Laboratory (JPL) and the Goddard Space Flight Center (GSFC), the SMAP mission draws heavily on the design and risk reduction heritage of the Hydrosphere State (Hydros) mission [1], [2]. The SMAP science and applications objectives are to: 1) understand processes that link the terrestrial water, energy and carbon cycles, 2) estimate global water and energy fluxes at the land surface, 3) quantify net carbon flux in boreal landscapes, 4) enhance weather and climate forecast skill, and 5) develop improved flood prediction and drought monitoring capability. To meet these science objectives, SMAP ground processing will combine the attributes of the radar and radiometer observations (in terms of their spatial resolution and sensitivity to soil moisture, surface roughness, and vegetation) to estimate soil moisture with 4% volumetric accuracy at a resolution of 10 km, and freeze-thaw state at a resolution of 1-3 km. Model sensitivities translate the soil moisture accuracy to a radar backscatter accuracy of 1 dB (1 sigma) at 3 km resolution and a brightness temperature accuracy of 1.3 K at 40 km resolution. This paper will describe the level 1 radar processing and calibration challenges and the choices made so far for the algorithms and software implementation.

Synthetic Aperture Radar (SAR),↗

NASA NTRS1974

Technology Utilization House Study Report

The objectives of Project TECH are: (1) to construct a single family detached dwelling for demonstrating the application of advanced technology and minimizing the requirement for energy and utility services, and (2) to help influence future development in home construction by defining the interaction of integrated energy and water management systems with building configuration and construction materials. Components and methods expected to be cost effective over a 20 year span were studied. Emphasis was placed on the utilization of natural heating and cooling characteristics. Orientation and location of windows, landscaping, natural ventilation, and characteristics of the local climate and microclimate were intended to be used to best advantage. Energy conserving homes are most efficient when design for specific sites, therefore project TECH should not be considered a prototype design suitable for all locations. However, it does provide ideas and analytical methods which can be applied to some degree in all housing.

Source record↗

NASA NTRS2004

Estimating Spatially-distributed Surface Fluxes in a Semi-arid Great Basin Desert Using Landsat TM Thermal Data

The research described in this chapter demonstrates a method for using Landsat Thematic Mapper data to estimate instantaneous regional-scale energy fluxes over an arid valley in eastern Nevada, U.S.A. Here point-based models of surface energy and water balance fluxes were applied to individual pixels of a Landsat Thematic Mapper scene over the study area. Although the method used to estimate these instantaneous fluxes requires certain assumptions be made about the spatial distribution of several physical parameters, the results from this analysis and modeling suggest that it is possible to scale from point measurements of environmental state variables (i.e., net radiation flux, surface heat flux, sensible heat flux, and latent heat flux) to regional estimates of energy exchange to obtain an understanding of the spatial relationship between these fluxes and landscape variables.

Quattrochi, Dale A.↗

NASA NTRS2015

Soil Moisture Active Passive (SMAP) Mission Level 4 Surface and Root Zone Soil Moisture (L4_SM) Product Specification Document

This is the Product Specification Document (PSD) for Level 4 Surface and Root Zone Soil Moisture (L4_SM) data for the Science Data System (SDS) of the Soil Moisture Active Passive (SMAP) project. The L4_SM data product provides estimates of land surface conditions based on the assimilation of SMAP observations into a customized version of the NASA Goddard Earth Observing System, Version 5 (GEOS-5) land data assimilation system (LDAS). This document applies to any standard L4_SM data product generated by the SMAP Project. The Soil Moisture Active Passive (SMAP) mission will enhance the accuracy and the resolution of space-based measurements of terrestrial soil moisture and freeze-thaw state. SMAP data products will have a noteworthy impact on multiple relevant and current Earth Science endeavors. These include: Understanding of the processes that link the terrestrial water, the energy and the carbon cycles, Estimations of global water and energy fluxes over the land surfaces, Quantification of the net carbon flux in boreal landscapes Forecast skill of both weather and climate, Predictions and monitoring of natural disasters including floods, landslides and droughts, and Predictions of agricultural productivity. To provide these data, the SMAP mission will deploy a satellite observatory in a near polar, sun synchronous orbit. The observatory will house an L-band radiometer that operates at 1.40 GHz and an L-band radar that operates at 1.26 GHz. The instruments will share a rotating reflector antenna with a 6 meter aperture that scans over a 1000 km swath.

L4-SM↗

NASA NTRS1998

Project ATLANTA (ATlanta Land-use ANalysis: Temperature and Air quality): A Study of how the Urban Landscape Affects Meteorology and Air Quality Through Time

It is our intent through this investigation to help facilitate measures that can be Project ATLANTA (ATlanta Land-use ANalysis: applied to mitigate climatological or air quality Temperature and Air-quality) is a NASA Earth degradation, or to design alternate measures to sustain Observing System (EOS) Interdisciplinary Science or improve the overall urban environment in the future. investigation that seeks to observe, measure, model, and analyze how the rapid growth of the Atlanta. The primary objectives for this research effort are: 1) To In the last half of the 20th century, Atlanta, investigate and model the relationship between Atlanta Georgia has risen as the premier commercial, urban growth, land cover change, and the development industrial, and transportation urban area of the of the urban heat island phenomenon through time at southeastern United States. The rapid growth of the nested spatial scales from local to regional; 2) To Atlanta area, particularly within the last 25 years, has investigate and model the relationship between Atlanta made Atlanta one of the fastest growing metropolitan urban growth and land cover change on air quality areas in the United States. The population of the through time at nested spatial scales from local to Atlanta metropolitan area increased 27% between 1970 regional; and 3) To model the overall effects of urban and 1980, and 33% between 1980-1990 (Research development on surface energy budget characteristics Atlanta, Inc., 1993). Concomitant with this high rate of across the Atlanta urban landscape through time at population growth, has been an explosive growth in nested spatial scales from local to regional. Our key retail, industrial, commercial, and transportation goal is to derive a better scientific understanding of how services within the Atlanta region. This has resulted in land cover changes associated with urbanization in the tremendous land cover change dynamics within the Atlanta area, principally in transforming forest lands to metropolitan region, wherein urbanization has urban land covers through time, has, and will, effect consumed vast acreas of land adjacent to the city local and regional climate, surface energy flux, and air proper and has pushed the rural/urban fringe farther quality characteristics. Allied with this goal is the and farther away from the original Atlanta urban core. prospect that the results from this research can be An enormous transition of land from forest and applied by urban planners, environmental managers agriculture to urban land uses has occurred in the and other decision-makers, for determining how Atlanta area in the last 25 years, along with subsequent urbanization has impacted the climate and overall

Quattrochi, Dale A.↗

NASA NTRS2000

Thermal Remote Sensing and the Thermodynamics of Ecosystem Development

Luvall, Jeffrey C.↗

NASA NTRS2000

The Use of Thermal Remote Sensing to Study Thermodynamics of Ecosystem Development

Thermal remote sensing can provide environmental measuring tools with capabilities for measuring ecosystem development and integrity. Recent advances in applying principles of nonequilibrium thermodynamics to ecology provide fundamental insights into energy partitioning in ecosystems. Ecosystems are nonequilibrium systems, open to material and energy flows, which grow and develop structures and processes to increase energy degradation. More developed terrestrial ecosystems will be more effective at dissipating the solar gradient (degrading its energy content). This can be measured by the effective surface temperature of the ecosystem on a landscape scale. A series of airborne thermal infrared multispectral scanner data were collected from several forested ecosystems ranging from a western US douglas-fir forest to a tropical rain forest in Costa Rica. These data were used to develop measures of ecosystem development and integrity based on surface temperature.

Luvall, Jeffrey C.↗

NASA NTRS2001

Thermal Remote Sensing and the Thermodynamics of Ecosystems Development

Thermal remote sensing can provide environmental measuring tools with capabilities for measuring ecosystem development and integrity. Recent advances in applying principles of nonequilibrium thermodynamics to ecology provide fundamental insights into energy partitioning in ecosystems. Ecosystems are nonequilibrium systems, open to material and energy flows, which grow and develop structures and processes to increase energy degradation. More developed terrestrial ecosystems will be more effective at dissipating the solar gradient (degrading its energy content). This can be measured by the effective surface temperature of the ecosystem on a landscape scale. A series of airborne thermal infrared multispectral scanner data were collected from several forested ecosystems ranging from a western US douglas-fir forest to a tropical rain forest in Costa Rica. Also measured were agriculture systems. These data were used to develop measures of ecosystem development and integrity based on surface temperature.

Luvall, Jeffrey C.↗

NASA NTRS2002

Controls on Variations of Surface Energy, Water, and Carbon Budgets within Large-Scale Amazon Basin

A key research focus of the LBA Research Program is understanding the space-time variations in interlinked surface energy, water, and carbon budgets, the controls on these variations, and the implications of these controls on the carbon sequestering capacity of the large scale forest-pasture system that dominates the Amaz6nia landscape. Quantification of these variations and controls are investigated by a combination of in situ measurements, remotely sensed measurements from space, and a realistically forced hydrometeorological model coupled to a carbon assimilation model, capable of simulating details within the surface energy and water budgets along with the principle processes of photosynthesis and respiration. Herein we describe the results of an investigation concerning the space-time controls of carbon sources and sinks distributed over the large scale Amazon basin. The results are derived from a carbon-water-energy budget retrieval system for the large scale Amazon basin, which uses a coupled carbon assimilation-hydrometeorological model as an integrating system, forced by both in situ meteorological measurements and remotely sensed radiation and precipitation fluxes obtained from a combination of GOES, SSM/I, TOMS, and TRh4M satellite measurements. Results include validation of (a) retrieved surface radiation and precipitation fluxes based on 30-min averaged surface measurements taken at Ji-Parani in Rondania and Manaus in Amazonas, and (b) modeled sensible, latent, and C02 fluxes based on tower measurements taken at Reserva Jaru, Manaus and Fazenda Nossa Senhora. The space-time controls on carbon sequestration are partitioned into sets of factors classified by: (1) above canopy meteorology, (2) incoming surface radiation, (3) precipitation interception, and (4) indigenous stomatal processes varied over the different land covers of pristine rainforest, partially, and fully logged rainforests, and pasture lands. These are the principle meteorological, thermodynamical, hydrological, and biophysical control paths which perturb net carbon fluxes and sequestration, produce time-space switching of carbon sources and sinks, undergo modulation through atmospheric boundary layer feedbacks, and respond to any discontinuous intervention on the landscape itself such as produced by human intervention in converting rainforest to pasture or conducting selective/clearcut logging operations. The results demonstrate how relative carbon sequestration capacity of the Amazonian ecosystem responds to these controls, and how interpretation of space-time heterogeneities in carbon sequestration depends on a fairly exact quantification of the interacting non-linear properties of photosynthesis in response to incoming solar flux, air-canopy temperatures, and leaf water interception -- and soil respiration in response to upper layer soil temperature and water content. The results also show how the interpretation of the control processes is highly sensitive to the scales at which the surface fluxes are analyzed.

Smith, Eric A.↗