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NASA NTRS2012

Roles of Urban Tree Canopy and Buildings in Urban Heat Island Effects: Parameterization and Preliminary Results

Urban heat island (UHI) effects can strengthen heat waves and air pollution episodes. In this study, the dampening impact of urban trees on the UHI during an extreme heat wave in the Washington, D.C., and Baltimore, Maryland, metropolitan area is examined by incorporating trees, soil, and grass into the coupled Weather Research and Forecasting model and an urban canopy model (WRF-UCM). By parameterizing the effects of these natural surfaces alongside roadways and buildings, the modified WRF-UCM is used to investigate how urban trees, soil, and grass dampen the UHI. The modified model was run with 50% tree cover over urban roads and a 10% decrease in the width of urban streets to make space for soil and grass alongside the roads and buildings. Results show that, averaged over all urban areas, the added vegetation decreases surface air temperature in urban street canyons by 4.1 K and road-surface and building-wall temperatures by 15.4 and 8.9 K, respectively, as a result of tree shading and evapotranspiration. These temperature changes propagate downwind and alter the temperature gradient associated with the Chesapeake Bay breeze and, therefore, alter the strength of the bay breeze. The impact of building height on the UHI shows that decreasing commercial building heights by 8 m and residential building heights by 2.5 m results in up to 0.4-K higher daytime surface and near-surface air temperatures because of less building shading and up to 1.2-K lower nighttime temperatures because of less longwave radiative trapping in urban street canyons.

urban vegetation↗

NASA NTRS2010

Evaluating the Impact of Vegetation Cover and Atmospheric Characteristics on the Estimation of Snow Water Equivalent from Spaceborne Microwave Radiometry

A radiative transfer model for estimating snow water equivalent (SWE, mm) from satellite-observed brightness temperature (K) at 19 and 37 GHz (respectively, T(sub B(sub, sat,19)) and T(sub B(sub, sat,37)) over partially forested area is presented, as an extension of a previously published model, by considering scattering of radiation within the canopy. For the specific case of dense vegetation covering fractional area f, the model can be written as, SWE = alpha{ A. delta (T(sub B(sub, sat)) + B - C. f}/(l f), where delta T(sub B(sub, sat)), is the difference of T(sub B(sub, sat,19)) and T(sub B(sub, sat,37)), alpha(mm/K) is the slope of SWE vs. brightness temperature difference at 19 and 37 GHz that would be obtained by ignoring the presence of atmosphere, delta(T(sub B)sub g)), for a homogeneous snow cover (which varies with grain size). The parameters A, B, and C, are determined primarily by atmospheric characteristics, and for a likely range of atmospheric conditions appear to be in the range of, respectively, 1.15-1.63, 0.69-2.84 K and 0.59-2.39 K. Ignoring atmospheric correction would introduce bias towards underestimation of SWE (and also, snow cover area and snow depth). Increasing cloud liquid water path (L) has the effect of increasing A, and ignoring this variation of A with L would have the impact of biasing the estimate of SWE (and snow extent). Such biasing is further exacerbated with increasing f, because of the appearance of term (l-f) in the denominator. The impact of ignoring the intercept parameters (B and C) would be noticeable at low values of SWE (appearing as a bias towards underestimation of SWE), which has been determined to be about 6 mm for average environmental conditions. The uncertainty in estimating SWE due to variations in the atmospheric characteristics is likely to be less than 15%, but could be up to 25% for non-vegetated snow-covered areas. Better estimates of SWE (and snow extent) would be obtained by adjusting the parameters of the above model to regional differences in the atmospheric characteristics. The biases in determining SWE arising due to variations in atmospheric conditions and due to changes in fractional forest cover are not independent, since they interact as {A/(l-f)}. The present calculations also show that improvement in determining snow cover area from the microwave data is likely to occur when these data are corrected for atmospheric effects, as demonstrated by a specific case study.

Choudhury, Bhaskar J.↗

NASA NTRS1987

Determination of vegetated fraction of surface from satellite measurements

One input to the ground hydrology component of general circulation models is the fraction of gridbox covered (i.e., shaded) by vegetation. The FV is needed in order to specify the partitioning of evaporation between vegetated and nonvegetated surfaces. Satellite data could provide global and seasonally varying specification of FV. In this work, FV is derived from Landsat data for a site in western Kenya; the accuracy of the estimate is evaluated and then compared to the accuracy requirements of a ground hydrology model. Results show that the accuracy of Landsat estimation of FV is + or - 5 percent and that transpiration, evaporation from bare soil and the seasonality of evapotranspiration are strongly dependent on FV.

Cary, Ernestine↗

NASA NTRS1987

Heat transfer to and from vegetated surfaces - An analytical method for the bulk exchange coefficients

The semianalytical model outlined in a previous study (Massman, 1987) to describe momentum exchange between the atmosphere and vegetated surfaces is extended to include the exchange of heat. The methods employed are based on one-dimensional turbulent diffusivities, and use analytical solutions to the steady-state diffusion equation. The model is used to assess the influence that the canopy foliage structure and density, the wind profile structure within the canopy, and the shelter factor can have upon the inverse surface Stanton number (kB exp -1), as well as to explore the consequences of introducing a scalar displacement height which can be different from the momentum displacement height. In general, the triangular foliage area density function gives results which agree more closely with observations than that for constant foliage area density. The intended application of this work is for parameterizing the bulk aerodynamic resistances for heat and momentum exchange for use within large-scale models of plant-atmosphere exchanges.

Massman, William J.↗

NASA NTRS2020

Acoustic space occupancy: Combining ecoacoustics and lidar to model biodiversity variation and detection bias across heterogeneous landscapes

There is global interest in quantifying changing biodiversity in human-modified landscapes. Ecoacoustics may offer a promising pathway for supporting multi-taxa monitoring, but its scalability has been hampered by the sonic complexity of biodiverse ecosystems and the imperfect detectability of animal-generated sounds. The acoustic signature of a habitat, or soundscape, contains information about multiple taxa and may circumvent species identification, but robust statistical technology for characterizing community-level attributes is lacking. Here, we present the Acoustic Space Occupancy Model, a flexible hierarchical framework designed to account for detection artifacts from acoustic surveys in order to model biologically relevant variation in acoustic space use among community assemblages. We illustrate its utility in a biologically and structurally diverse Amazon frontier forest landscape, a valuable test case for modeling biodiversity variation and acoustic attenuation from vegetation density. We use complementary airborne lidar data to capture aspects of 3D forest structure hypothesized to influence community composition and acoustic signal detection. Our novel analytic framework permitted us to model both the assembly and detectability of soundscapes using lidar-derived estimates of forest structure. Our empirical predictions were consistent with physical models of frequency-dependent attenuation, and we estimated that the probability of observing animal activity in the frequency channel most vulnerable to acoustic attenuation varied by over 60%, depending on vegetation density. There were also large differences in the biotic use of acoustic space predicted for intact and degraded forest habitats, with notable differences in the soundscape channels predominantly occupied by insects. This study advances the utility of ecoacoustics by providing a robust modeling framework for addressing detection bias from remote audio surveys while preserving the rich dimensionality of soundscape data, which may be critical for inferring biological patterns pertinent to multiple taxonomic groups in the tropics. Our methodology paves the way for greater integration of remotely sensed observations with high-throughput biodiversity data to help bring routine, multi-taxa monitoring to scale in dynamic and diverse landscapes.

Airborne lidar↗

NASA NTRS1991

Comparison of ground and satellite based measurements of the fraction of photosynthetically active radiation intercepted by tall-grass prairie

The fraction, of photosynthetically active radiation intercepted by vegetation, F(sub ipar) is an important parameter for modeling the interactions between the land-surface and atmosphere and for estimating vegetation biomass productivity. This study was; therefore, an integral part of FIFE. The specific purpose of this experiment was to find out how well definitive measurements of F(sub ipar) on the ground relate to near-ground and satellite based spectral reflectance measurements. Concurrent measurements of F(sub ipar) and ground, helicopter, and satellite based reflectance measurements were taken at thirteen tall-grass prairie sites within the FIFE experimental area. The sites were subjected to various combinations of burning and grazing managements. The ground and helicopter based reflectance measurements were taken on the same day or few days from the time of the overpass of LANDSAT and SPOT satellites. Ground-based reflectance measurements and sun photometer readings taken at the times of the satellite overpasses were used to correct for atmospheric attenuation. Hand-held radiometer spectral indices were strongly correlated with helicopter and satellite based values (r=0.94 for helicopter, 0.93 for LANDSAT Thematic Mapper, and 0.86 for SPOT). However, the ground, helicopter, and satellite based normalized difference spectral vegetation indices showed low sensitivity to changes in F(sub ipar). Reflectance measurements were only moderately well correlated with measurements of F(sub ipar) (r=0.82 for hand-held radiometer, 0.84 for helicopter measurements, and 0.75 for the LANDSAT Thematic Mapper and SPOT). Improved spectral indices which can compensate for site differences are needed in order to monitor F(sub ipar) more reliably.

Demetriades-Shah, T. H.↗

NASA NTRS1992

Comparison of ground and satellite based measurements of the fraction of photosynthetically active radiation intercepted by tall-grass prairie

The fraction of photosynthetically active radiation intercepted by vegetation, F(sub ipar) is an important parameter for modeling the interactions between the land-surface and atmosphere and for estimating vegetation biomass productivity. This study was, therefore, an integral part of FIFE. The specific purpose of this experiment was to find out how well definitive measurements of F(sub ipar) on the ground relate to near-ground and satellite based spectral reflectance measurements. Concurrent measurements of F(sub ipar) and ground, helicopter, and satellite based reflectance measurements were taken at thirteen tall-grass prairie sites within the FIFE experimental area. The sites were subjected to various combinations of burning and grazing managements. The ground and helicopter based reflectance measurements were taken on the same day or few days from the time of the overpass of LANDSAT and SPOT satellites. Ground-based reflectance measurements and sun photometer readings taken at the times of the satellite overpasses were used to correct for atmospheric attenuation. Hand-held radiometer spectral indices were strongly correlated with helicopter and satellite based values (r = 0.94 for helicopter, 0.93 for LANDSAT Thematic Mapper, and 0.86 for SPOT). However, the ground, helicopter, and satellite based normalized difference spectral vegetation indices showed low sensitivity to changes in F(sub ipar). Reflectance measurements were only moderately well correlated with measurements of F(sub ipar) (r = 0.82 for hand-held radiometer, 0.84 for helicopter measurements, and 0.75 for the LANDSAT Thematic Mapper and SPOT). Improved spectral indices which can compensate for site differences are needed in order to monitor F(sub ipar) more reliably.

Demetriades-Shah, T. H.↗

NASA NTRS1985

Understanding the Radiant Scattering Behavior of Vegetated Scenes

Knowledge of the physics of the scattering behavior of vegetation will ultimately serve the remote sensing and earth science community in many ways. For example, it will provide: (1) insight and guidance in developing new extraction techniques of canopy characteristics, (2) a basis for better interpretation of off-nadir satellite and aircraft data, (3) a basis for defining specifications of future earth observing sensor systems, and (4) a basis for defining important aspects of physical and biological processes of the plant system. The overall objective of the three-year study is to improve our fundamental understanding of the dynamics of directional scattering properties of vegetation canopies through analysis of field data and model simulation data. The specific objectives are to: (1) collect directional reflectance data covering the entire exitance hemisphere for several common vegetation canopies with various geometric structure (both homogeneous and row crop structures), (2) develop a scene radiation model with a general mathematical framework which will treat 3-D variability in heterogeneous scenes and account for 3-D radiant interactions within the scene, (3) conduct validations of the model on collected data sets, and (4) test and expand proposed physical scattering mechanisms involved in reflectance distribution dynamics by analyzing both field and modeling data.

Kimes, D. S.↗

NASA NTRS2002

Investigation of Aerodynamic and Radiometric Land Surface Temperatures

The surface temperature, T(sub s), of a land surface measured by a radiometer, T(sub s,r), and the temperature "felt" by the air, T(sub aero), often differ significantly and are difficult if not impossible to define rigorously. However, recent studies conducted by the principal investigators with several land surface models suggest that this problem can be largely resolved. The main goal of this project was to use model-based and empirical studies to improve understanding and reconcile the difference between T(sub s,r) and T(sub aero), while maintaining consistency within the models and with theory and data. The results from this effort have contributed progress towards the effective use of remotely sensed surface temperature measurements taken from an arbitrary view angle over a partial canopy cover for producing high quality sensible and latent heat flux estimates. In addition, we have developed parameterizations that are designed to improve the representation of the roughness length for heat in climate and mesoscale models. This not only provides improved representation of surface energy balance in such models, but should also facilitate the use of surface temperature measurements for validating or updating the surface temperature produced by SVATs (soil-vegetation-atmosphere schemes) in climate or mesoscale models.

Friedl, Mark↗

NASA NTRS1998

Estimating Soil Moisture from Satellite Microwave Observations

Cooperative research in microwave remote sensing between the Hydrological Sciences Branch of the NASA Goddard Space Flight Center and the Earth Sciences Faculty of the Vrije Universiteit Amsterdam began with the Botswana Water and Energy Balance Experiment and has continued through a series of highly successful International Research Programs. The collaboration between these two research institutions has resulted in significant scientific achievements, most notably in the area of satellite-based microwave remote sensing of soil moisture. The Botswana Program was the first joint research initiative between these two institutions, and provided a unique data base which included historical data sets of Scanning Multifrequency Microwave Radiometer (SN4NM) data, climate information, and extensive soil moisture measurements over several large experimental sites in southeast Botswana. These data were the basis for the development of new approaches in physically-based inverse modelling of soil moisture from satellite microwave observations. Among the results from this study were quantitative estimates of vegetation transmission properties at microwave frequencies. A single polarization modelling approach which used horizontally polarized microwave observations combined with monthly composites of Normalized Difference Vegetation Index was developed, and yielded good results. After more precise field experimentation with a ground-based radiometer system, a dual-polarization approach was subsequently developed. This new approach realized significant improvements in soil moisture estimation by satellite. Results from the Botswana study were subsequently applied to a desertification monitoring study for the country of Spain within the framework of the European Community science research programs EFEDA and RESMEDES. A dual frequency approach with only microwave data was used for this application. The Microwave Polarization Difference Index (MPDI) was calculated from 37 GHz data and used to derive the one-way canopy transmissivity. Using a simple radiative transfer model, this information was combined with horizontally polarized 6.6 GHz SMMR observations to derive a 9-year time series of soil moisture for all of Spain at a one quarter degree spatial scale. Both day and night SMMR observations were used independently, in order to check the consistency of the results. A first order Fourier Transform was performed on the mean monthly soil moisture values to identify major characteristics of time series such as trend, amplitude, and phase shift.

Owe, M.↗

NASA NTRS2021

Classifying Forest Type in the National Forest Inventory Context with Airborne Hyperspectral and Lidar Data

Forest structure and composition regulate a range of ecosystem services, including biodiversity, water and nutrient cycling, and wood volume for resource extraction. Forest type is an important metric measured in the US Forest Service Forest Inventory and Analysis (FIA) program, the national forest inventory of the USA. Forest type information can be used to quantify carbon and other forest resources within specific domains to support ecological analysis and forest management decisions, such as managing for disease and pests. In this study, we developed a methodology that uses a combination of airborne hyperspectral and lidar data to map FIA-defined forest type between sparsely sampled FIA plot data collected in interior Alaska. To determine the best classification algorithm and remote sensing data for this task, five classification algorithms were tested with six different combinations of raw hyperspectral data, hyperspectral vegetation indices, and lidar-derived canopy and topography metrics. Models were trained using forest type information from 632 FIA subplots collected in interior Alaska. Of the thirty model and input combinations tested, the random forest classification algorithm with hyperspectral vegetation indices and lidar-derived topography and canopy height metrics had the highest accuracy (78% overall accuracy). This study supports random forest as a powerful classifier for natural resource data. It also demonstrates the benefits from combining both structural (lidar) and spectral (imagery) data for forest type classification.

random forest↗

NASA NTRS1984

Estimation of agronomic variables using spectral signatures

Techniques for the determination of leaf area index or leaf angle distribution from remote-sensing canopy-reflectance (CR) measurements are developed on the basis of empirical models relating CR to parameters such as soil and vegetation spectral properties, solar flux, and viewing angle. A general procedure for inverting CR models is presented and applied to the models of Suits (1972), Verhoef and Bunnik (1981), and Norman (1979) in the IR range. Numerical results for a soybean canopy are compared in a table, and the error sensitivity of the inverted models is shown to be relatively high, requiring the use of ancillary data such as soil reflectance, leaf reflectance, and leaf transmittance.

Goel, N. S.↗

NASA NTRS1991

Utilization of surface cover composition to improve the microwave determination of snow water equivalent in a mountain basin

Satellite microwave data have been used to derive areal snow water equivalent in flat homogeneous areas. Over heterogeneous mountainous areas different algorithms are needed to retrieve the water equivalent of the snow cover. A mixed pixel model based on the percentage of vegetation cover within a pixel has been developed to simulate the microwave brightness temperatures for the Rio Grande basin in southwestern Colorado. A relationship between the difference in microwave-brightness temperature at two different frequencies (37- and 18-GHz horizontal polarization), and the basin-wide average snow water equivalent was obtained. The areal snow-water equivalent values derived from the model were consistent with values generated by a reliable snowmelt run-off model using snow-cover extent data.

Chang, A. T. C.↗

NASA NTRS2015

Modelling Freshwater Resources at the Global Scale: Challenges and Prospects

Quantification of spatially and temporally resolved water flows and water storage variations for all land areas of the globe is required to assess water resources, water scarcity and flood hazards, and to understand the Earth system. This quantification is done with the help of global hydrological models (GHMs). What are the challenges and prospects in the development and application of GHMs? Seven important challenges are presented. (1) Data scarcity makes quantification of human water use difficult even though significant progress has been achieved in the last decade. (2) Uncertainty of meteorological input data strongly affects model outputs. (3) The reaction of vegetation to changing climate and CO2 concentrations is uncertain and not taken into account in most GHMs that serve to estimate climate change impacts. (4) Reasons for discrepant responses of GHMs to changing climate have yet to be identified. (5) More accurate estimates of monthly time series of water availability and use are needed to provide good indicators of water scarcity. (6) Integration of gradient-based groundwater modelling into GHMs is necessary for a better simulation of groundwater-surface water interactions and capillary rise. (7) Detection and attribution of human interference with freshwater systems by using GHMs are constrained by data of insufficient quality but also GHM uncertainty itself. Regarding prospects for progress, we propose to decrease the uncertainty of GHM output by making better use of in situ and remotely sensed observations of output variables such as river discharge or total water storage variations by multi-criteria validation, calibration or data assimilation. Finally, we present an initiative that works towards the vision of hyper resolution global hydrological modelling where GHM outputs would be provided at a 1-km resolution with reasonable accuracy.

Global hydrological model↗

NASA NTRS2021

Midwest Food Security & Agriculture II: Leveraging NASA Earth Observations to Analyze and Display Crop Phenology Data and Weather Conditions to Support Expansion of Small Grain Crops in the Midwest

Agriculture in the Midwest is dominated by monoculture systems that strip the soil of nutrients, decrease yields, and worsen water quality. Crop diversification and cultivating small grains is economically and ecologically advantageous, but limited in practice due to a lack of data about small-grain crop performance as well as monoculture-favoring insurance coverage, economic incentives, and sociocultural traditions. Practical Farmers of Iowa (PFI) strives to create more sustainable agriculture and supports Midwestern farmers in adding small grains into their crop rotations. The DEVELOP team partnered with the USDA Agricultural Research Service and the USA National Phenology Network to assist PFI in applying NASA Earth observation data to track small-grain crop performance. This project developed a Google Earth Engine user interface that enables PFI farmers to compare satellite-assessed vegetation production and climatic conditions of their farm with other PFI farmers. The team assessed the crop performance through Normalized Difference Vegetation Index (NDVI) values derived from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI). Additionally, the team created a regression model to analyze the predictive properties of vegetation indices and crop yield, ultimately establishing a relationship with mean NDVI from the early growing season. PFI farmers can utilize these products to make informed decisions about their crop production as well as advocate for more expansive insurance policies to protect small-grain crops.

Sophie Barrowman↗

NASA NTRS2020

Evaluation of GEOS-Simulated L-Band Microwave Brightness Temperature using Aquarius Observations over Non-Frozen Land across North America

L-band brightness temperature (T(sub b)) is one of the key remotely-sensed variables that provides information regarding surface soil moisture conditions. In order to harness the information in T(sub b) observations, a radiative transfer model (RTM) is investigated for eventual inclusion into a data assimilation framework. In this study, T(sub b) estimates from the RTM implemented in the NASA Goddard Earth Observing System (GEOS) were evaluated against the nearly four-year record of daily T(sub b) observations collected by L-band radiometers onboard the Aquarius satellite. Statistics between the modeled and observed T(sub b) were computed over North America as a function of soil hydraulic properties and vegetation types. Overall, statistics showed good agreement between the modeled and observed T(sub b) with a relatively low, domain-average bias (0.79 K (ascending) and -2.79 K (descending)), root mean squared error (11.0 K (ascending) and 11.7 K (descending)), and unbiased root mean squared error (8.14 K (ascending) and 8.28 K (descending)). In terms of soil hydraulic parameters, large porosity and large wilting point both lead to high uncertainty in modeled T(sub b) due to the large variability in dielectric constant and surface roughness used by the RTM. The performance of the RTM as a function of vegetation type suggests better agreement in regions with broadleaf deciduous and needleleaf forests while grassland regions exhibited the worst accuracy amongst the five different vegetation types.

L-band radiometry↗

NASA NTRS1984

Effects of Vegetation Canopy Structure on Microwave Scattering

The role of canopy structure on microwave backscattering is studied. Structure refers to the size, orientation, and vertical placement of scatterers in the canopy. Models to predict the backscattering coefficient, SIGMAO, of vegetation with explicit biophysical and explicit polarization-dependent parameters were developed. Preparation for field measurements with radar scatterometers was completed. Technical progress included: (1) the modification of the Attema and Ulaby (1977) model and its multilayer variation by Hoekman et al. (1983) to include polarization explicitly (i.e., to allow for separate backscattering cross sections, SIGMA, and extinction cross sections, Q, for each polarization), (2) the investigation of the modified model to isolate canopy element orientation parameters by the rationing of SIGMAO measurements for different polarization combinations, (3) the development of expressions for bistatic scattering and canopy-substrate scattering to supplement the models, (4) the performance of sensitivity analyses on these models, (5) the modification of the Attema and Ulaby model to alloy for changes, (6) the use of the modified model with a seasonal corn data set from Kansas (Eger et al., 1983), and (7) the initiation of preparations for empirical measurements with radar spectrometer and the Mobile Radar Scatterometer in irrigated cropland to test the models.

Paris, J. F.↗

NASA NTRS1984

Prescription of land-surface boundary conditions in GISS GCM 2: A simple method based on high-resolution vegetation data bases

A simple method was developed for improved prescription of seasonal surface characteristics and parameterization of land-surface processes in climate models. This method, developed for the Goddard Institute for Space Studies General Circulation Model II (GISS GCM II), maintains the spatial variability of fine-resolution land-cover data while restricting to 8 the number of vegetation types handled in the model. This was achieved by: redefining the large number of vegetation classes in the 1 deg x 1 deg resolution Matthews (1983) vegetation data base as percentages of 8 simple types; deriving roughness length, field capacity, masking depth and seasonal, spectral reflectivity for the 8 types; and aggregating these surface features from the 1 deg x 1 deg resolution to coarser model resolutions, e.g., 8 deg latitude x 10 deg longitude or 4 deg latitude x 5 deg longitude.

Matthews, E.↗