Remote sensing of solar radiation absorbed and reflected by vegetated land surfaces

NASA Technical Reports Server (NTRS)

Myneni, Ranga B.; Asrar, Ghassem; Tanre, Didier; Choudhury, Bhaskar J.

1992-01-01

1D and 3D radiative-transfer models have been used to investigate the problem of remotely sensed determination of vegetated land surface-absorbed and reflected solar radiation. Calculations were conducted for various illumination conditions to determine surface albedo, soil- and canopy-absorbed photosynthetically active and nonactive radiation, and normalized difference vegetation index. Simple predictive models are developed on the basis of the relationships among these parameters.

Monitoring Rangeland Health by Remote Sensing

USDA-ARS?s Scientific Manuscript database

Based on a land-cover classification from NASA’s MODerate resolution Imaging Spectroradiometer (MODIS), rangelands cover 48% of the Earth’s land surface, not including Antarctica. Nearly all analyses imply the most economical means of monitoring large areas of rangelands worldwide is with remote s...

Large-scale experimental technology with remote sensing in land surface hydrology and meteorology

NASA Technical Reports Server (NTRS)

Brutsaert, Wilfried; Schmugge, Thomas J.; Sellers, Piers J.; Hall, Forrest G.

1988-01-01

Two field experiments to study atmospheric and land surface processes and their interactions are summarized. The Hydrologic-Atmospheric Pilot Experiment, which tested techniques for measuring evaporation, soil moisture storage, and runoff at scales of about 100 km, was conducted over a 100 X 100 km area in France from mid-1985 to early 1987. The first International Satellite Land Surface Climatology Program field experiment was conducted in 1987 to develop and use relationships between current satellite measurements and hydrologic, climatic, and biophysical variables at the earth's surface and to validate these relationships with ground truth. This experiment also validated surface parameterization methods for simulation models that describe surface processes from the scale of vegetation leaves up to scales appropriate to satellite remote sensing.

Multiple Scale Remote Sensing for Monitoring Rangelands

USDA-ARS?s Scientific Manuscript database

Based on a land-cover classification from NASA’s MODerate resolution Imaging Spectroradiometer (MODIS), rangelands cover 48% of the Earth’s land surface, not including Antarctica. Nearly all analyses imply the most economical means of monitoring large areas of rangelands worldwide is with remote se...

Environmental application of remote sensing methods to coastal zone land use and marine resource management, Appendices A to E. [in southeastern Virginia

NASA Technical Reports Server (NTRS)

1972-01-01

Important data were compiled for use with the Richmond-Cape Henry Environmental Laboratory (RICHEL) remote sensing project in coastal zone land use and marine resources management, and include RICHEL climatological data and sources, a land use inventory, topographic and soil maps, and gaging records for RICHEL surface waters.

Magnitude and variability of land evaporation and its components at the global scale

USDA-ARS?s Scientific Manuscript database

A physics-based methodology is applied to estimate global land-surface evaporation from multi-satellite observations. GLEAM (Global Land-surface Evaporation: the Amsterdam Methodology) combines a wide range of remotely sensed observations within a Priestley and Taylor-based framework. Daily actual e...

Assessment of Mars Exploration Rover Landing Site Predictions

NASA Astrophysics Data System (ADS)

Golombek, M. P.

2005-05-01

Comprehensive analyses of remote sensing data during the 3-year effort to select the Mars Exploration Rover landing sites at Gusev crater and Meridiani Planum correctly predicted the safe and trafficable surfaces explored by the two rovers. Gusev crater was predicted to be a relatively low relief surface that was comparably dusty, but less rocky than the Viking landing sites. Available data for Meridiani Planum indicated a very flat plain composed of basaltic sand to granules and hematite that would look completely unlike any of the existing landing sites with a dark, low albedo surface, little dust and very few rocks. Orbital thermal inertia measurements of 315 J m-2 s-0.5 K-1 at Gusev suggested surfaces dominated by duricrust to cemented soil-like materials or cohesionless sand or granules, which is consistent with observed soil characteristics and measured thermal inertias from the surface. THEMIS thermal inertias along the traverse at Gusev vary from 285 at the landing site to 330 around Bonneville rim and show systematic variations that can be related to the observed increase in rock abundance (5-30%). Meridiani has an orbital bulk inertia of ~200, similar to measured surface inertias that correspond to observed surfaces dominated by 0.2 mm sand size particles. Rock abundance derived from orbital thermal differencing techniques suggested that Meridiani Planum would have very low rock abundance, consistent with the rock free plain traversed by Opportunity. Spirit landed in an 8% orbital rock abundance pixel, consistent with the measured 7% of the surface covered by rocks >0.04 m diameter at the landing site, which is representative of the plains away from craters. The orbital albedo of the Spirit traverse varies from 0.19 to 0.30, consistent with surface measurements in and out of dust devil tracks. Opportunity is the first landing in a low albedo portion of Mars as seen from orbit, which is consistent with the dark, dust-free surface and measured albedos. The close correspondence between surface characteristics inferred from orbital remote sensing data and that found at the landing sites argues that future efforts to select safe landing sites will be successful. Linking the five landing sites to their remote sensing signatures suggests that they span most of the important, likely safe surfaces available for landing on Mars.

Estimation of Multiple Parameters over Vegetated Surfaces by Integrating Optical-Thermal Remote Sensing Observations

NASA Astrophysics Data System (ADS)

Ma, H.

2016-12-01

Land surface parameters from remote sensing observations are critical in monitoring and modeling of global climate change and biogeochemical cycles. Current methods for estimating land surface parameters are generally parameter-specific algorithms and are based on instantaneous physical models, which result in spatial, temporal and physical inconsistencies in current global products. Besides, optical and Thermal Infrared (TIR) remote sensing observations are usually separated to use based on different models , and the Middle InfraRed (MIR) observations have received little attention due to the complexity of the radiometric signal that mixes both reflected and emitted fluxes. In this paper, we proposed a unified algorithm for simultaneously retrieving a total of seven land surface parameters, including Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), land surface albedo, Land Surface Temperature (LST), surface emissivity, downward and upward longwave radiation, by exploiting remote sensing observations from visible to TIR domain based on a common physical Radiative Transfer (RT) model and a data assimilation framework. The coupled PROSPECT-VISIR and 4SAIL RT model were used for canopy reflectance modeling. At first, LAI was estimated using a data assimilation method that combines MODIS daily reflectance observation and a phenology model. The estimated LAI values were then input into the RT model to simulate surface spectral emissivity and surface albedo. Besides, the background albedo and the transmittance of solar radiation, and the canopy albedo were also calculated to produce FAPAR. Once the spectral emissivity of seven MODIS MIR to TIR bands were retrieved, LST can be estimated from the atmospheric corrected surface radiance by exploiting an optimization method. At last, the upward longwave radiation were estimated using the retrieved LST, broadband emissivity (converted from spectral emissivity) and the downward longwave radiation (modeled by MODTRAN). These seven parameters were validated over several representative sites with different biome type, and compared with MODIS and GLASS product. Results showed that this unified inversion algorithm can retrieve temporally complete and physical consistent land surface parameters with high accuracy.

Separating vegetation and soil temperature using airborne multiangular remote sensing image data

NASA Astrophysics Data System (ADS)

Liu, Qiang; Yan, Chunyan; Xiao, Qing; Yan, Guangjian; Fang, Li

2012-07-01

Land surface temperature (LST) is a key parameter in land process research. Many research efforts have been devoted to increase the accuracy of LST retrieval from remote sensing. However, because natural land surface is non-isothermal, component temperature is also required in applications such as evapo-transpiration (ET) modeling. This paper proposes a new algorithm to separately retrieve vegetation temperature and soil background temperature from multiangular thermal infrared (TIR) remote sensing data. The algorithm is based on the localized correlation between the visible/near-infrared (VNIR) bands and the TIR band. This method was tested on the airborne image data acquired during the Watershed Allied Telemetry Experimental Research (WATER) campaign. Preliminary validation indicates that the remote sensing-retrieved results can reflect the spatial and temporal trend of component temperatures. The accuracy is within three degrees while the difference between vegetation and soil temperature can be as large as twenty degrees.

Exploring Remote Sensing Products Online with Giovanni for Studying Urbanization

NASA Technical Reports Server (NTRS)

Shen, Suhung; Leptoukh, Gregory G.; Gerasimov, Irina; Kempler, Steve

2012-01-01

Recently, a Large amount of MODIS land products at multi-spatial resolutions have been integrated into the online system, Giovanni, to support studies on land cover and land use changes focused on Northern Eurasia and Monsoon Asia regions. Giovanni (Goddard Interactive Online Visualization ANd aNalysis Infrastructure) is a Web-based application developed by the NASA Goddard Earth Sciences Data and Information Services Center (GES-DISC) providing a simple and intuitive way to visualize, analyze, and access Earth science remotely-sensed and modeled data. The customized Giovanni Web portals (Giovanni-NEESPI and Giovanni-MAIRS) are created to integrate land, atmospheric, cryospheric, and social products, that enable researchers to do quick exploration and basic analyses of land surface changes and their relationships to climate at global and regional scales. This presentation documents MODIS land surface products in Giovanni system. As examples, images and statistical analysis results on land surface and local climate changes associated with urbanization over Yangtze River Delta region, China, using data in Giovanni are shown.

DISAGGREGATION OF GOES LAND SURFACE TEMPERATURES USING SURFACE EMISSIVITY

USDA-ARS?s Scientific Manuscript database

Accurate temporal and spatial estimation of land surface temperatures (LST) is important for modeling the hydrological cycle at field to global scales because LSTs can improve estimates of soil moisture and evapotranspiration. Using remote sensing satellites, accurate LSTs could be routine, but unfo...

Traverse Planning Experiments for Future Planetary Surface Exploration

NASA Technical Reports Server (NTRS)

Hoffman, Stephen J.; Voels, Stephen A.; Mueller, Robert P.; Lee, Pascal C.

2012-01-01

The purpose of the investigation is to evaluate methodology and data requirements for remotely-assisted robotic traverse of extraterrestrial planetary surface to support human exploration program, assess opportunities for in-transit science operations, and validate landing site survey and selection techniques during planetary surface exploration mission analog demonstration at Haughton Crater on Devon Island, Nunavut, Canada. Additionally, 1) identify quality of remote observation data sets (i.e., surface imagery from orbit) required for effective pre-traverse route planning and determine if surface level data (i.e., onboard robotic imagery or other sensor data) is required for a successful traverse, and if additional surface level data can improve traverse efficiency or probability of success (TRPF Experiment). 2) Evaluate feasibility and techniques for conducting opportunistic science investigations during this type of traverse. (OSP Experiment). 3) Assess utility of remotely-assisted robotic vehicle for landing site validation survey. (LSV Experiment).

Data needs and data bases for climate studies

NASA Technical Reports Server (NTRS)

Matthews, Elaine

1986-01-01

Two complementary global digital data bases of vegetation and land use, compiled at 1 deg resolution from published sources for use in climate studies, are discussed. The data bases were implemented, in several individually tailored formulations, in a series of climate related applications including: land-surface prescriptions in three-dimensional general circulation models, global biogeochemical cycles (CO2, methane), critical-area mapping for satellite monitoring of land-cover change, and large-scale remote sensing of surface reflectance. The climate applications are discussed with reference to data needs, and data availability from traditional and remote sensing sources.

Hyperspectral Thermal Infrared Remote Sensing of the Land Surface and Target Identification using Airborne Interferometry

DTIC Science & Technology

2009-10-01

variational data assimilation technique are profiles of temperature, water vapour and ozone , surface temperature and spectrally varying emissivity. HOW TO...that are insensitive to the land surface because of the complexity of the land surface emissivity. We have utilised the techniques described here for...state as well as surface properties. Furthermore with by utilising a variational assimilation technique and a state of the art Numerical Weather

Remote Sensing of the Environmental Impacts of Utility-Scale Solar Energy Plants

NASA Astrophysics Data System (ADS)

Edalat, Mohammad Masih

Solar energy has many environmental benefits compared with fossil fuels but solar farming can have environmental impacts especially during construction and development. Thus, in order to enhance environmental sustainability, it is imperative to understand the environmental impacts of utility-scale solar energy (USSE) plants. During recent decades, remote sensing techniques and geographic information systems have become standard techniques in environmental applications. In this study, the environmental impacts of USSE plants are investigated by analyzing changes to land surface characteristics using remote sensing. The surface characteristics studied include land cover, land surface temperature, and hydrological response whereas changes are mapped by comparing pre-, syn-, and post- construction conditions. In order to study the effects of USSE facilities on land cover, the changes in the land cover are measured and analyzed inside and around two USSE facilities. The principal component analysis (PCA), minimum noise fraction (MNF), and spectral mixture analysis (SMA) of remote sensing images are used to estimate the subpixel fraction of four land surface endmembers: high-albedo, low-albedo, shadow, and vegetation. The results revealed that USSE plants do not significantly impact land cover outside the plant boundary. However, land-cover radiative characteristics within the plant area are significantly affected after construction. During the construction phase, site preparation practices including shrub removal and land grading increase high-albedo and decrease low-albedo fractions. The thermal effects of USSE facilities are studied by the time series analysis of remote sensing land surface temperature (LST). A statistical trend analysis of LST, with seasonal trends removed is performed with a particular consideration of panel shadowing by analyzing sun angles for different times of year. The results revealed that the LST outside the boundary of the solar plant does not change, whereas it significantly decreases inside the plant at 10 AM after the construction. The decrease in LST mainly occurred in winters due to lower sun's altitude, which casts longer shadows on the ground. In order to study the hydrological impacts of PV plants, pre- and post-installation hydrological response over single-axis technology is compared. A theoretical reasoning is developed to explain flows under the influence of PV panels. Moreover, a distributed parametric hydrologic model is used to estimate runoff before and after the construction of PV plants. The results revealed that peak flow, peak flow time, and runoff volume alter after panel installation. After panel installation, peak flow decreases and is observed to shift in time, which depends on orientation. Likewise, runoff volume increases irrespective of panel orientation. The increase in the tilt angle of panel results in decrease in the peak flow, peak flow time, and runoff. This study provides an insight into the environmental impacts of USSE development using remote sensing. The research demonstrates that USSE plants are environmentally sustainable due to minimal impact on land cover and surface temperature in their vicinity. In addition, this research explains the role of rainfall shadowing on hydrological behavior at USSE plants.

Physical properties (particle size, rock abundance) from thermal infrared remote observations: Implications for Mars landing sites

NASA Technical Reports Server (NTRS)

Christensen, P. R.; Edgett, Kenneth S.

1994-01-01

Critical to the assessment of potential sites for the 1997 Pathfinder landing is estimation of general physical properties of the martian surface. Surface properties have been studied using a variety of spacecraft and earth-based remote sensing observations, plus in situ studies at the Viking lander sites. Because of their value in identifying landing hazards and defining scientific objectives, we focus this discussion on thermal inertia and rock abundance derived from middle-infrared (6 to 30 microns) observations. Used in conjunction with other datasets, particularly albedo and Viking orbiter images, thermal inertia and rock abundance provide clues about the properties of potential Mars landing sites.

Cooling effect of rivers on metropolitan Taipei using remote sensing.

PubMed

Chen, Yen-Chang; Tan, Chih-Hung; Wei, Chiang; Su, Zi-Wen

2014-01-23

This study applied remote sensing technology to analyze how rivers in the urban environment affect the surface temperature of their ambient areas. While surface meteorological stations can supply accurate data points in the city, remote sensing can provide such data in a two-dimensional (2-D) manner. The goal of this paper is to apply the remote sensing technique to further our understanding of the relationship between the surface temperature and rivers in urban areas. The 2-D surface temperature data was retrieved from Landsat-7 thermal infrared images, while data collected by Formosat-2 was used to categorize the land uses in the urban area. The land surface temperature distribution is simulated by a sigmoid function with nonlinear regression analysis. Combining the aforementioned data, the range of effect on the surface temperature from rivers can be derived. With the remote sensing data collected for the Taipei Metropolitan area, factors affecting the surface temperature were explored. It indicated that the effect on the developed area was less significant than on the ambient nature zone; moreover, the size of the buffer zone between the river and city, such as the wetlands or flood plain, was found to correlate with the affected distance of the river surface temperature.

Cooling Effect of Rivers on Metropolitan Taipei Using Remote Sensing

PubMed Central

Chen, Yen-Chang; Tan, Chih-Hung; Wei, Chiang; Su, Zi-Wen

2014-01-01

This study applied remote sensing technology to analyze how rivers in the urban environment affect the surface temperature of their ambient areas. While surface meteorological stations can supply accurate data points in the city, remote sensing can provide such data in a two-dimensional (2-D) manner. The goal of this paper is to apply the remote sensing technique to further our understanding of the relationship between the surface temperature and rivers in urban areas. The 2-D surface temperature data was retrieved from Landsat-7 thermal infrared images, while data collected by Formosat-2 was used to categorize the land uses in the urban area. The land surface temperature distribution is simulated by a sigmoid function with nonlinear regression analysis. Combining the aforementioned data, the range of effect on the surface temperature from rivers can be derived. With the remote sensing data collected for the Taipei Metropolitan area, factors affecting the surface temperature were explored. It indicated that the effect on the developed area was less significant than on the ambient nature zone; moreover, the size of the buffer zone between the river and city, such as the wetlands or flood plain, was found to correlate with the affected distance of the river surface temperature. PMID:24464232

Surface Properties and Characteristics of Mars Landing Sites from Remote Sensing Data and Ground Truth

NASA Astrophysics Data System (ADS)

Golombek, M. P.; Haldemann, A. F.; Simpson, R. A.; Furgason, R. L.; Putzig, N. E.; Huertas, A.; Arvidson, R. E.; Heet, T.; Bell, J. F.; Mellon, M. T.; McEwen, A. S.

2008-12-01

Surface characteristics at the six sites where spacecraft have successfully landed on Mars can be related favorably to their signatures in remotely sensed data from orbit and from the Earth. Comparisons of the rock abundance, types and coverage of soils (and their physical properties), thermal inertia, albedo, and topographic slope all agree with orbital remote sensing estimates and show that the materials at the landing sites can be used as ground truth for the materials that make up most of the equatorial and mid- to moderately high-latitude regions of Mars. The six landing sites sample two of the three dominant global thermal inertia and albedo units that cover ~80% of the surface of Mars. The Viking, Spirit, Mars Pathfinder, and Phoenix landing sites are representative of the moderate to high thermal inertia and intermediate to high albedo unit that is dominated by crusty, cloddy, blocky or frozen soils (duricrust that may be layered) with various abundances of rocks and bright dust. The Opportunity landing site is representative of the moderate to high thermal inertia and low albedo surface unit that is relatively dust free and composed of dark eolian sand and/or increased abundance of rocks. Rock abundance derived from orbital thermal differencing techniques in the equatorial regions agrees with that determined from rock counts at the surface and varies from ~3-20% at the landing sites. The size-frequency distributions of rocks >1.5 m diameter fully resolvable in HiRISE images of the landing sites follow exponential models developed from lander measurements of smaller rocks and are continuous with these rock distributions indicating both are part of the same population. Interpretation of radar data confirms the presence of load bearing, relatively dense surfaces controlled by the soil type at the landing sites, regional rock populations from diffuse scattering similar to those observed directly at the sites, and root-mean-squared slopes that compare favorably with 100 m scale topographic slopes extrapolated from altimetry profiles and meter scale slopes from high-resolution stereo images. The third global unit has very low thermal inertia and very high albedo, indicating it is dominated by deposits of bright red atmospheric dust that may be neither load bearing nor trafficable. The landers have thus sampled the majority of likely safe and trafficable surfaces that cover most of Mars and show that remote sensing data can be used to infer the surface characteristics, slopes, and surface materials present at other locations.

A Physically-Based Drought Product Using Thermal Remote Sensing of Evapotranspiration

USDA-ARS?s Scientific Manuscript database

Thermal infrared (TIR) remote sensing of land-surface temperature (LST) provides valuable information about the sub-surface moisture status. While empirical indices measuring anomalies in LST and vegetation amount (e.g., as quantified by the Normalized Difference Vegetation Index; NDVI) have demonst...

L-band Microwave Remote Sensing and Land Data Assimilation Improve the Representation of Prestorm Soil Moisture Conditions for Hydrologic Forecasting

NASA Technical Reports Server (NTRS)

Crow, W. T.; Chen, F.; Reichle, R. H.; Liu, Q.

2017-01-01

Recent advances in remote sensing and land data assimilation purport to improve the quality of antecedent soil moisture information available for operational hydrologic forecasting. We objectively validate this claim by calculating the strength of the relationship between storm-scale runoff ratio (i.e., total stream flow divided by total rainfall accumulation in depth units) and pre-storm surface soil moisture estimates from a range of surface soil moisture data products. Results demonstrate that both satellite-based, L-band microwave radiometry and the application of land data assimilation techniques have significantly improved the utility of surface soil moisture data sets for forecasting stream flow response to future rainfall events.

L-band microwave remote sensing and land data assimilation improve the representation of pre-storm soil moisture conditions for hydrologic forecasting.

PubMed

Crow, W T; Chen, F; Reichle, R H; Liu, Q

2017-06-16

Recent advances in remote sensing and land data assimilation purport to improve the quality of antecedent soil moisture information available for operational hydrologic forecasting. We objectively validate this claim by calculating the strength of the relationship between storm-scale runoff ratio (i.e., total stream flow divided by total rainfall accumulation in depth units) and pre-storm surface soil moisture estimates from a range of surface soil moisture data products. Results demonstrate that both satellite-based, L-band microwave radiometry and the application of land data assimilation techniques have significantly improved the utility of surface soil moisture data sets for forecasting stream flow response to future rainfall events.

L-band microwave remote sensing and land data assimilation improve the representation of pre-storm soil moisture conditions for hydrologic forecasting

PubMed Central

Crow, W.T.; Chen, F.; Reichle, R.H.; Liu, Q.

2018-01-01

Recent advances in remote sensing and land data assimilation purport to improve the quality of antecedent soil moisture information available for operational hydrologic forecasting. We objectively validate this claim by calculating the strength of the relationship between storm-scale runoff ratio (i.e., total stream flow divided by total rainfall accumulation in depth units) and pre-storm surface soil moisture estimates from a range of surface soil moisture data products. Results demonstrate that both satellite-based, L-band microwave radiometry and the application of land data assimilation techniques have significantly improved the utility of surface soil moisture data sets for forecasting stream flow response to future rainfall events. PMID:29657342

City landscape changes effects on land surface temperature in Bucharest metropolitan area

NASA Astrophysics Data System (ADS)

Savastru, Dan M.; Zoran, Maria A.; Savastru, Roxana S.; Dida, Adrian I.

2017-10-01

This study investigated the influences of city land cover changes and extreme climate events on land surface temperature in relationship with several biophysical variables in Bucharest metropolitan area of Romania through satellite and in-situ monitoring data. Remote sensing data from IKONOS, Landsat TM/ETM+ and time series MODIS Terra/Aqua and NOAA AVHRR sensors have been used to assess urban land cover- temperature interactions over 2000 - 2016 period. Time series Thermal InfraRed (TIR) satellite remote sensing data in synergy with meteorological data (air temperatureAT, precipitations, wind, solar radiation, etc.) were applied mainly for analyzing land surface temperature (LST) pattern and its relationship with surface landscape characteristics, assessing urban heat island (UHI), and relating urban land cover temperatures (LST). The land surface temperature, a key parameter for urban thermal characteristics analysis, was also analyzed in relation with the Normalized Difference Vegetation Index (NDVI) at city level. Results show that in the metropolitan area ratio of impervious surface in Bucharest increased significantly during investigated period, the intensity of urban heat island and heat wave events being most significant. The correlation analyses revealed that, at the pixel-scale, LST and AT possessed a strong positive correlation with percent impervious surfaces and negative correlation with vegetation abundances at metropolitan scale respectively. The NDVI was significantly correlated with precipitation. The spatial average air temperatures in urban test areas rise with the expansion of the urban size.

Monitoring Land Surface Soil Moisture from Space with in-Situ Sensors Validation: The Huntsville Example

NASA Technical Reports Server (NTRS)

Wu, Steve Shih-Tseng

1997-01-01

Based on recent advances in microwave remote sensing of soil moisture and in pursuit of research interests in areas of hydrology, soil climatology, and remote sensing, the Center for Hydrology, Soil Climatology, and Remote Sensing (HSCARS) conducted the Huntsville '96 field experiment in Huntsville, Alabama from July 1-14, 1996. We, researchers at the Global Hydrology and Climate Center's MSFC/ES41, are interested in using ground-based microwave sensors, to simulate land surface brightness signatures of those spaceborne sensors that were in operation or to be launched in the near future. The analyses of data collected by the Advanced Microwave Precipitation Radiometer (AMPR) and the C-band radiometer, which together contained five frequencies (6.925,10.7,19.35, 37.1, and 85.5 GHz), and with concurrent in-situ collection of surface cover conditions (surface temperature, surface roughness, vegetation, and surface topology) and soil moisture content, would result in a better understanding of the data acquired over land surfaces by the Special Sensor Microwave Imager (SSM/I), the Tropical Rainfall Measuring Mission Microwave Imager (TMI), and the Advanced Microwave Scanning Radiometer (AMSR), because these spaceborne sensors contained these five frequencies. This paper described the approach taken and the specific objective to be accomplished in the Huntsville '97 field experiment.

Applications of Land Surface Temperature from Microwave Observations

USDA-ARS?s Scientific Manuscript database

Land surface temperature (LST) is a key input for physically-based retrieval algorithms of hydrological states and fluxes. Yet, it remains a poorly constrained parameter for global scale studies. The main two observational methods to remotely measure T are based on thermal infrared (TIR) observation...

Planning applications of remote sensing in Arizona

NASA Technical Reports Server (NTRS)

Clark, R. B.; Mouat, D. A.

1976-01-01

Planners in Arizona have been experiencing the inevitable problems which occur when large areas of rural and remote lands are converted to urban-recreational uses over a relatively short period of time. Among the planning problems in the state are unplanned and illegal subdivisions, surburban sprawl, surface hydrologic problems related to ephemeral stream overflow, rapidly changing land use patterns, large size of administrative units, and lack of land use inventory data upon which to base planning decisions.

Using microwave observations to estimate land surface temperature during cloudy conditions

USDA-ARS?s Scientific Manuscript database

Land surface temperature (LST), a key ingredient for physically-based retrieval algorithms of hydrological states and fluxes, remains a poorly constrained parameter for global scale studies. The main two observational methods to remotely measure T are based on thermal infrared (TIR) observations and...

Land surface and climate parameters and malaria features in Vietnam

NASA Astrophysics Data System (ADS)

Liou, Y. A.; Anh, N. K.

2017-12-01

Land surface parameters may affect local microclimate, which in turn alters the development of mosquito habitats and transmission risks (soil-vegetation-atmosphere-vector borne diseases). Forest malaria is a chromic issue in Southeast Asian countries, in particular, such as Vietnam (in 1991, approximate 2 million cases and 4,646 deaths were reported (https://sites.path.org)). Vietnam has lowlands, sub-tropical high humidity, and dense forests, resulting in wide-scale distribution and high biting rate of mosquitos in Vietnam, becoming a challenging and out of control scenario, especially in Vietnamese Central Highland region. It is known that Vietnam's economy mainly relies on agriculture and malaria is commonly associated with poverty. There is a strong demand to investigate the relationship between land surface parameters (land cover, soil moisture, land surface temperature, etc.) and climatic variables (precipitation, humidity, evapotranspiration, etc.) in association with malaria distribution. GIS and remote sensing have been proven their powerful potentials in supporting environmental and health studies. The objective of this study aims to analyze physical attributes of land surface and climate parameters and their links with malaria features. The outcomes are expected to illustrate how remotely sensed data has been utilized in geohealth applications, surveillance, and health risk mapping. In addition, a platform with promising possibilities of allowing disease early-warning systems with citizen participation will be proposed.

Research on lunar and planet development and utilization

NASA Astrophysics Data System (ADS)

Iwata, Tsutomu; Etou, Takao; Imai, Ryouichi; Oota, Kazuo; Kaneko, Yutaka; Maeda, Toshihide; Takano, Yutaka

1992-08-01

Status of the study on unmanned and manned lunar missions, unmanned Mars missions, lunar resource development and utilization missions, remote sensing exploration missions, survey and review to elucidate the problems of research and development for lunar resource development and utilization, and the techniques and equipment for lunar and planet exploration are presented. Following items were studied respectively: (1) spacecraft systems for unmanned lunar missions, such as lunar observation satellites, lunar landing vehicles, lunar surface rovers, lunar surface hoppers, and lunar sample retrieval; (2) spacecraft systems for manned lunar missions, such as manned lunar bases, lunar surface operation robots, lunar surface experiment systems, manned lunar take-off and landing vehicles, and lunar freight transportation ships; (3) spacecraft systems for Mars missions, such as Mars satellites, Phobos and Deimos sample retrieval vehicles, Mars landing explorers, Mars rovers, Mars sample retrieval; (4) lunar resource development and utilization; and (5) remote sensing exploration technologies.

Land Surface Microwave Emissivity Dynamics: Observations, Analysis and Modeling

NASA Technical Reports Server (NTRS)

Tian, Yudong; Peters-Lidard, Christa D.; Harrison, Kenneth W.; Kumar, Sujay; Ringerud, Sarah

2014-01-01

Land surface microwave emissivity affects remote sensing of both the atmosphere and the land surface. The dynamical behavior of microwave emissivity over a very diverse sample of land surface types is studied. With seven years of satellite measurements from AMSR-E, we identified various dynamical regimes of the land surface emission. In addition, we used two radiative transfer models (RTMs), the Community Radiative Transfer Model (CRTM) and the Community Microwave Emission Modeling Platform (CMEM), to simulate land surface emissivity dynamics. With both CRTM and CMEM coupled to NASA's Land Information System, global-scale land surface microwave emissivities were simulated for five years, and evaluated against AMSR-E observations. It is found that both models have successes and failures over various types of land surfaces. Among them, the desert shows the most consistent underestimates (by approx. 70-80%), due to limitations of the physical models used, and requires a revision in both systems. Other snow-free surface types exhibit various degrees of success and it is expected that parameter tuning can improve their performances.

Land Use and Land Cover (LULC) Change Detection in Islamabad and its Comparison with Capital Development Authority (CDA) 2006 Master Plan

NASA Astrophysics Data System (ADS)

Hasaan, Zahra

2016-07-01

Remote sensing is very useful for the production of land use and land cover statistics which can be beneficial to determine the distribution of land uses. Using remote sensing techniques to develop land use classification mapping is a convenient and detailed way to improve the selection of areas designed to agricultural, urban and/or industrial areas of a region. In Islamabad city and surrounding the land use has been changing, every day new developments (urban, industrial, commercial and agricultural) are emerging leading to decrease in vegetation cover. The purpose of this work was to develop the land use of Islamabad and its surrounding area that is an important natural resource. For this work the eCognition Developer 64 computer software was used to develop a land use classification using SPOT 5 image of year 2012. For image processing object-based classification technique was used and important land use features i.e. Vegetation cover, barren land, impervious surface, built up area and water bodies were extracted on the basis of object variation and compared the results with the CDA Master Plan. The great increase was found in built-up area and impervious surface area. On the other hand vegetation cover and barren area followed a declining trend. Accuracy assessment of classification yielded 92% accuracies of the final land cover land use maps. In addition these improved land cover/land use maps which are produced by remote sensing technique of class definition, meet the growing need of legend standardization.

Application of SAR Remote Sensing in Land Surface Processes Over Tropical region

NASA Technical Reports Server (NTRS)

Saatchi, Sasan S.

1996-01-01

This paper outlines the potential applications of polarimetric SAR systems over tropical regions such as mapping land use and deforestation, forest regeneration, wetland and inundation studies, and mapping land cover types for biodiversity and habitat conservation studies.

Monitoring land at regional and national scales and the role of remote sensing

NASA Astrophysics Data System (ADS)

Dymond, John R.; Bégue, Agnes; Loseen, Danny

There is a need world wide for monitoring land and its ecosystems to ensure their sustainable use. Despite the laudable intentions of Agenda 21 at the Rio Earth Summit, 1992, in which many countries agreed to monitor and report on the status of their land, systematic monitoring of land has yet to begin. The problem is truly difficult, as the earth's surface is vast and the funds available for monitoring are relatively small. This paper describes several methods for cost-effective monitoring of large land areas, including: strategic monitoring; statistical sampling; risk-based approaches; integration of land and water monitoring; and remote sensing. The role of remote sensing is given special attention, as it is the only method that can monitor land exhaustively and directly, at regional and national scales. It is concluded that strategic monitoring, whereby progress towards environmental goals is assessed, is a vital element in land monitoring as it provides a means for evaluating the utility of monitoring designs.

Cloud tolerance of remote sensing technologies to measure land surface temperature

USDA-ARS?s Scientific Manuscript database

Conventional means to estimate land surface temperature (LST) from space relies on the thermal infrared (TIR) spectral window and is limited to cloud-free scenes. To also provide LST estimates during periods with clouds, a new method was developed to estimate LST based on passive microwave (MW) obse...

Microwave Remote Sensing and the Cold Land Processes Field Experiment

NASA Technical Reports Server (NTRS)

Kim, Edward J.; Cline, Don; Davis, Bert; Hildebrand, Peter H. (Technical Monitor)

2001-01-01

The Cold Land Processes Field Experiment (CLPX) has been designed to advance our understanding of the terrestrial cryosphere. Developing a more complete understanding of fluxes, storage, and transformations of water and energy in cold land areas is a critical focus of the NASA Earth Science Enterprise Research Strategy, the NASA Global Water and Energy Cycle (GWEC) Initiative, the Global Energy and Water Cycle Experiment (GEWEX), and the GEWEX Americas Prediction Project (GAPP). The movement of water and energy through cold regions in turn plays a large role in ecological activity and biogeochemical cycles. Quantitative understanding of cold land processes over large areas will require synergistic advancements in 1) understanding how cold land processes, most comprehensively understood at local or hillslope scales, extend to larger scales, 2) improved representation of cold land processes in coupled and uncoupled land-surface models, and 3) a breakthrough in large-scale observation of hydrologic properties, including snow characteristics, soil moisture, the extent of frozen soils, and the transition between frozen and thawed soil conditions. The CLPX Plan has been developed through the efforts of over 60 interested scientists that have participated in the NASA Cold Land Processes Working Group (CLPWG). This group is charged with the task of assessing, planning and implementing the required background science, technology, and application infrastructure to support successful land surface hydrology remote sensing space missions. A major product of the experiment will be a comprehensive, legacy data set that will energize many aspects of cold land processes research. The CLPX will focus on developing the quantitative understanding, models, and measurements necessary to extend our local-scale understanding of water fluxes, storage, and transformations to regional and global scales. The experiment will particularly emphasize developing a strong synergism between process-oriented understanding, land surface models and microwave remote sensing. The experimental design is a multi-sensor, multi-scale (1-ha to 160,000 km ^ {2}) approach to providing the comprehensive data set necessary to address several experiment objectives. A description focusing on the microwave remote sensing components (ground, airborne, and spaceborne) of the experiment will be presented.

Linkages between Land Surface Phenology Metrics and Natural and Anthropogenic Events in Drylands (Invited)

NASA Astrophysics Data System (ADS)

de Beurs, K.; Brown, M. E.; Ahram, A.; Walker, J.; Henebry, G. M.

2013-12-01

Tracking vegetation dynamics across landscapes using remote sensing, or 'land surface phenology,' is a key mechanism that allows us to understand ecosystem changes. Land surface phenology models rely on vegetation information from remote sensing, such as the datasets derived from the Advanced Very High Resolution Radiometer (AVHRR), the newer MODIS sensors on Aqua and Terra, and sometimes the higher spatial resolution Landsat data. Vegetation index data can aid in the assessment of variables such as the start of season, growing season length and overall growing season productivity. In this talk we use Landsat, MODIS and AVHRR data and derive growing season metrics based on land surface phenology models that couple vegetation indices with satellite derived accumulated growing degreeday and evapotranspiration estimates. We calculate the timing and the height of the peak of the growing season and discuss the linkage of these land surface phenology metrics with natural and anthropogenic changes on the ground in dryland ecosystems. First we will discuss how the land surface phenology metrics link with annual and interannual price fluctuations in 229 markets distributed over Africa. Our results show that there is a significant correlation between the peak height of the growing season and price increases for markets in countries such as Nigeria, Somalia and Niger. We then demonstrate how land surface phenology metrics can improve models of post-conflict resolution in global drylands. We link the Uppsala Conflict Data Program's dataset of political, economic and social factors involved in civil war termination with an NDVI derived phenology metric and the Palmer Drought Severity Index (PDSI). An analysis of 89 individual conflicts in 42 dryland countries (totaling 892 individual country-years of data between 1982 and 2005) revealed that, even accounting for economic and political factors, countries that have higher NDVI growth following conflict have a lower risk of reverting to civil war. Finally, the patchy and heterogeneous arrangement of vegetation in dryland areas sometimes complicates the extraction of phenological signals using existing remote sensing data. We conclude by demonstrating how the phenological analysis of a range of dryland land cover classes benefits from the availability of synthetic images at Landsat spatial resolution and MODIS time intervals.

Monitoring rice (oryza sativa L.) growth using multifrequency microwave scatterometers

USDA-ARS?s Scientific Manuscript database

Microwave remote sensing can help monitor the land surface water cycle and crop growth. This type of remote sensing has great potential over conventional remote sensing using the visible and infrared regions due to its all-weather day-and-night imaging capabilities. In this investigation, a ground-b...

Satellite remotely-sensed land surface parameters and their climatic effects for three metropolitan regions

USGS Publications Warehouse

Xian, George

2008-01-01

By using both high-resolution orthoimagery and medium-resolution Landsat satellite imagery with other geospatial information, several land surface parameters including impervious surfaces and land surface temperatures for three geographically distinct urban areas in the United States – Seattle, Washington, Tampa Bay, Florida, and Las Vegas, Nevada, are obtained. Percent impervious surface is used to quantitatively define the spatial extent and development density of urban land use. Land surface temperatures were retrieved by using a single band algorithm that processes both thermal infrared satellite data and total atmospheric water vapor content. Land surface temperatures were analyzed for different land use and land cover categories in the three regions. The heterogeneity of urban land surface and associated spatial extents were shown to influence surface thermal conditions because of the removal of vegetative cover, the introduction of non-transpiring surfaces, and the reduction in evaporation over urban impervious surfaces. Fifty years of in situ climate data were integrated to assess regional climatic conditions. The spatial structure of surface heating influenced by landscape characteristics has a profound influence on regional climate conditions, especially through urban heat island effects.

SMERGE: A multi-decadal root-zone soil moisture product for CONUS

NASA Astrophysics Data System (ADS)

Crow, W. T.; Dong, J.; Tobin, K. J.; Torres, R.

2017-12-01

Multi-decadal root-zone soil moisture products are of value for a range of water resource and climate applications. The NASA-funded root-zone soil moisture merging project (SMERGE) seeks to develop such products through the optimal merging of land surface model predictions with surface soil moisture retrievals acquired from multi-sensor remote sensing products. This presentation will describe the creation and validation of a daily, multi-decadal (1979-2015), vertically-integrated (both surface to 40 cm and surface to 100 cm), 0.125-degree root-zone product over the contiguous United States (CONUS). The modeling backbone of the system is based on hourly root-zone soil moisture simulations generated by the Noah model (v3.2) operating within the North American Land Data Assimilation System (NLDAS-2). Remotely-sensed surface soil moisture retrievals are taken from the multi-sensor European Space Agency Climate Change Initiative soil moisture data set (ESA CCI SM). In particular, the talk will detail: 1) the exponential smoothing approach used to convert surface ESA CCI SM retrievals into root-zone soil moisture estimates, 2) the averaging technique applied to merge (temporally-sporadic) remotely-sensed with (continuous) NLDAS-2 land surface model estimates of root-zone soil moisture into the unified SMERGE product, and 3) the validation of the SMERGE product using long-term, ground-based soil moisture datasets available within CONUS.

Estimation of Regional Evapotranspiration Using Remotely Sensed Land Surface Temperature. Part 2: Application of Equilibrium Evaporation Model to Estimate Evapotranspiration by Remote Sensing Technique. [Japan

NASA Technical Reports Server (NTRS)

Kotoda, K.; Nakagawa, S.; Kai, K.; Yoshino, M. M.; Takeda, K.; Seki, K.

1985-01-01

In a humid region like Japan, it seems that the radiation term in the energy balance equation plays a more important role for evapotranspiration then does the vapor pressure difference between the surface and lower atmospheric boundary layer. A Priestley-Taylor type equation (equilibrium evaporation model) is used to estimate evapotranspiration. Net radiation, soil heat flux, and surface temperature data are obtained. Only temperature data obtained by remotely sensed techniques are used.

Machine processing of remotely sensed data; Proceedings of the Fifth Annual Symposium, Purdue University, West Lafayette, Ind., June 27-29, 1979

NASA Technical Reports Server (NTRS)

Tendam, I. M. (Editor); Morrison, D. B.

1979-01-01

Papers are presented on techniques and applications for the machine processing of remotely sensed data. Specific topics include the Landsat-D mission and thematic mapper, data preprocessing to account for atmospheric and solar illumination effects, sampling in crop area estimation, the LACIE program, the assessment of revegetation on surface mine land using color infrared aerial photography, the identification of surface-disturbed features through a nonparametric analysis of Landsat MSS data, the extraction of soil data in vegetated areas, and the transfer of remote sensing computer technology to developing nations. Attention is also given to the classification of multispectral remote sensing data using context, the use of guided clustering techniques for Landsat data analysis in forest land cover mapping, crop classification using an interactive color display, and future trends in image processing software and hardware.

Development of a ground hydrology model suitable for global climate modeling using soil morphology and vegetation cover, and an evaluation of remotely sensed information

NASA Technical Reports Server (NTRS)

Zobler, L.; Lewis, R.

1988-01-01

The long-term purpose was to contribute to scientific understanding of the role of the planet's land surfaces in modulating the flows of energy and matter which influence the climate, and to quantify and monitor human-induced changes to the land environment that may affect global climate. Highlights of the effort include the following: production of geo-coded, digitized World Soil Data file for use with the Goddard Institute for Space Studies (GISS) climate model; contribution to the development of a numerical physically-based model of ground hydrology; and assessment of the utility of remote sensing for providing data on hydrologically significant land surface variables.

Forest inventory with LiDAR and stereo DSM on Washington department of natural resources lands

Treesearch

Jacob L. Strunk; Peter J. Gould

2015-01-01

DNR’s forest inventory group has completed its first version of a new remote-sensing based forest inventory system covering 1.4 million acres of DNR forest lands. We use a combination of field plots, lidar, NAIP, and a NAIP-derived canopy surface DSM. Given that height drives many key inventory variables (e.g. height, volume, biomass, carbon), remote-sensing derived...

Assimilation of Passive and Active Microwave Soil Moisture Retrievals

NASA Technical Reports Server (NTRS)

Draper, C. S.; Reichle, R. H.; DeLannoy, G. J. M.; Liu, Q.

2012-01-01

Root-zone soil moisture is an important control over the partition of land surface energy and moisture, and the assimilation of remotely sensed near-surface soil moisture has been shown to improve model profile soil moisture [1]. To date, efforts to assimilate remotely sensed near-surface soil moisture at large scales have focused on soil moisture derived from the passive microwave Advanced Microwave Scanning Radiometer (AMSR-E) and the active Advanced Scatterometer (ASCAT; together with its predecessor on the European Remote Sensing satellites (ERS. The assimilation of passive and active microwave soil moisture observations has not yet been directly compared, and so this study compares the impact of assimilating ASCAT and AMSR-E soil moisture data, both separately and together. Since the soil moisture retrieval skill from active and passive microwave data is thought to differ according to surface characteristics [2], the impact of each assimilation on the model soil moisture skill is assessed according to land cover type, by comparison to in situ soil moisture observations.

Consequences of land-cover misclassification in models of impervious surface

USGS Publications Warehouse

McMahon, G.

2007-01-01

Model estimates of impervious area as a function of landcover area may be biased and imprecise because of errors in the land-cover classification. This investigation of the effects of land-cover misclassification on impervious surface models that use National Land Cover Data (NLCD) evaluates the consequences of adjusting land-cover within a watershed to reflect uncertainty assessment information. Model validation results indicate that using error-matrix information to adjust land-cover values used in impervious surface models does not substantially improve impervious surface predictions. Validation results indicate that the resolution of the landcover data (Level I and Level II) is more important in predicting impervious surface accurately than whether the land-cover data have been adjusted using information in the error matrix. Level I NLCD, adjusted for land-cover misclassification, is preferable to the other land-cover options for use in models of impervious surface. This result is tied to the lower classification error rates for the Level I NLCD. ?? 2007 American Society for Photogrammetry and Remote Sensing.

Remote sensing of a coupled carbon-water-energy-radiation balances from the Globe to plot scales

NASA Astrophysics Data System (ADS)

Ryu, Y.; Jiang, C.; Huang, Y.; Kim, J.; Hwang, Y.; Kimm, H.; Kim, S.

2016-12-01

Advancements in near-surface and satellite remote sensing technologies have enabled us to monitor the global terrestrial ecosystems at multiple spatial and temporal scales. An emergent challenge is how to formulate a coupled water, carbon, energy, radiation, and nitrogen cycles from remote sensing. Here, we report Breathing Earth System Simulator (BESS), which coupled radiation (shortwave, longwave, PAR, diffuse PAR), carbon (gross primary productivity, ecosystem respiration, net ecosystem exchange), water (evaporation), and energy (latent and sensible heat) balances across the global land at 1 km resolution, 8 daily between 2000 and 2015 using multiple satellite remote sensing. The performance of BESS was tested against field observations (FLUXNET, BSRN) and other independent products (MPI-BGC, MODIS, GLASS). We found that the coupled model, BESS showed on par with, or better performance than the other products which computed land surface fluxes individually. Lastly, we show one plot-level study conducted in a paddy rice to demonstrate how to couple radiation, carbon, water, nitrogen balances with a series of near-surface spectral sensors.

Assessment of surface turbulent fluxes using geostationary satellite surface skin temperatures and a mixed layer planetary boundary layer scheme

NASA Technical Reports Server (NTRS)

Diak, George R.; Stewart, Tod R.

1989-01-01

A method is presented for evaluating the fluxes of sensible and latent heating at the land surface, using satellite-measured surface temperature changes in a composite surface layer-mixed layer representation of the planetary boundary layer. The basic prognostic model is tested by comparison with synoptic station information at sites where surface evaporation climatology is well known. The remote sensing version of the model, using satellite-measured surface temperature changes, is then used to quantify the sharp spatial gradient in surface heating/evaporation across the central United States. An error analysis indicates that perhaps five levels of evaporation are recognizable by these methods and that the chief cause of error is the interaction of errors in the measurement of surface temperature change with errors in the assigment of surface roughness character. Finally, two new potential methods for remote sensing of the land-surface energy balance are suggested which will relay on space-borne instrumentation planned for the 1990s.

Utility of an image-based canopy reflectance modeling tool for remote estimation and LAI and leaf chlorophyll content at regional scales

USDA-ARS?s Scientific Manuscript database

Radiance data recorded by remote sensors function as a unique source for monitoring the terrestrial biosphere and vegetation dynamics at a range of spatial and temporal scales. A key challenge is to relate the remote sensing signal to critical variables describing land surface vegetation canopies su...

Quantifying the Terrestrial Surface Energy Fluxes Using Remotely-Sensed Satellite Data

NASA Astrophysics Data System (ADS)

Siemann, Amanda Lynn

The dynamics of the energy fluxes between the land surface and the atmosphere drive local and regional climate and are paramount to understand the past, present, and future changes in climate. Although global reanalysis datasets, land surface models (LSMs), and climate models estimate these fluxes by simulating the physical processes involved, they merely simulate our current understanding of these processes. Global estimates of the terrestrial, surface energy fluxes based on observations allow us to capture the dynamics of the full climate system. Remotely-sensed satellite data is the source of observations of the land surface which provide the widest spatial coverage. Although net radiation and latent heat flux global, terrestrial, surface estimates based on remotely-sensed satellite data have progressed, comparable sensible heat data products and ground heat flux products have not progressed at this scale. Our primary objective is quantifying and understanding the terrestrial energy fluxes at the Earth's surface using remotely-sensed satellite data with consistent development among all energy budget components [through the land surface temperature (LST) and input meteorology], including validation of these products against in-situ data, uncertainty assessments, and long-term trend analysis. The turbulent fluxes are constrained by the available energy using the Bowen ratio of the un-constrained products to ensure energy budget closure. All final products are within uncertainty ranges of literature values, globally. When validated against the in-situ estimates, the sensible heat flux estimates using the CFSR air temperature and constrained with the products using the MODIS albedo produce estimates closest to the FLUXNET in-situ observations. Poor performance over South America is consistent with the largest uncertainties in the energy budget. From 1984-2007, the longwave upward flux increase due to the LST increase drives the net radiation decrease, and the decrease in the available energy balances the decrease in the sensible heat flux. These datasets are useful for benchmarking climate models and LSM output at the global annual scale and the regional scale subject to the regional uncertainties and performance. Future work should improve the input data, particularly the temperature gradient and Zilitinkevich empirical constant, to reduce uncertainties.

Thermal remote sensing: theory, sensors, and applications

USDA-ARS?s Scientific Manuscript database

Applications of thermal infrared remote sensing for Earth science research are both varied and wide in scope. They range from understanding thermal energy responses that drive land-atmosphere energy exchanges in the hydrologic cycle, to measurement of dielectric surface properties for snow, ice, an...

A two-step framework for reconstructing remotely sensed land surface temperatures contaminated by cloud

NASA Astrophysics Data System (ADS)

Zeng, Chao; Long, Di; Shen, Huanfeng; Wu, Penghai; Cui, Yaokui; Hong, Yang

2018-07-01

Land surface temperature (LST) is one of the most important parameters in land surface processes. Although satellite-derived LST can provide valuable information, the value is often limited by cloud contamination. In this paper, a two-step satellite-derived LST reconstruction framework is proposed. First, a multi-temporal reconstruction algorithm is introduced to recover invalid LST values using multiple LST images with reference to corresponding remotely sensed vegetation index. Then, all cloud-contaminated areas are temporally filled with hypothetical clear-sky LST values. Second, a surface energy balance equation-based procedure is used to correct for the filled values. With shortwave irradiation data, the clear-sky LST is corrected to the real LST under cloudy conditions. A series of experiments have been performed to demonstrate the effectiveness of the developed approach. Quantitative evaluation results indicate that the proposed method can recover LST in different surface types with mean average errors in 3-6 K. The experiments also indicate that the time interval between the multi-temporal LST images has a greater impact on the results than the size of the contaminated area.

Soil-vegetation-atmosphere energy fluxes: Land Surface Temperature evaluation by Terra/MODIS satellite images

NASA Astrophysics Data System (ADS)

Telesca, V.; Copertino, V. A.; Scavone, G.; Pastore, V.; Dal Sasso, S.

2009-04-01

Most of the hydrological models are by now founded on field and satellite data integration. In fact, the use of remote sensing techniques supplies the frequent lack of field-measured variables and parameters required to apply evaluation models of the hydrological cycle components at a regional scale. These components are very sensitive to the climatic and surface features and conditions. Remote sensing represent a complementary contribution to in situ investigation methodologies, furnishing repeated and real time observations. Naturally, the interest of these techniques is tied up to the existence of a solid correlation among the greatness to evaluate and the remote sensing information obtainable from the images. In this context, satellite remote sensing has become a basic tool since it allows the regular monitoring of extensive areas. Different surface variables and parameters can be extracted from the combination of the multi-spectral information contained in a satellite image. Land Surface Temperature (LST) is a fundamental parameter to estimate most of the components of the hydrological cycle and the soil-atmosphere energy balance, such as the net radiation, the sensible heat flux and the actual evapotranspiration. Besides, LST maps can be used in models for the fire monitoring and prevention. The aim of this work is to realize, exploiting the contribution of the remote sensing, some Land Surface Temperature maps, applying different "Split Windows" algorithms and to compare them with the "Day/Night" LST/MODIS, to select the best algorithm to apply in a Two-Source Energy Balance model (STSEB). Integrated into a rainfall/runoff model, it can contribute to cope with problems of land management for the protection from natural hazards. In particular, the energy balance procedure will be included into a model for the ‘in continuous' simulation and the forecast of floods. Another important application of our model is tied up to the forecast of scenarios connected to drought problems. In this context, they can contribute to the planning and the realization of mitigation interventions for the desertification risk.

Geographical Applications of Remote Sensing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Weng, Qihao; Zhou, Yuyu; Quattrochi, Dale

2013-02-28

Data and Information derived through Earth observation technology have been extensively used in geographic studies, such as in the areas of natural and human environments, resources, land use and land cover, human-environment interactions, and socioeconomic issues. Land-use and land-cover change (LULCC), affecting biodiversity, climate change, watershed hydrology, and other surface processes, is one of the most important research topics in geography.

Thermal Infrared Remote Sensing for Analysis of Landscape Ecological Processes: Current Insights and Trends. Chapter 3

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.; Luvall, Jeffrey C.

2014-01-01

NASA or NOAA Earth-observing satellites are not the only space-based TIR platforms. The European Space Agency (ESA), the Chinese, and other countries have in orbit or plan to launch TIR remote sensing systems. Satellite remote sensing provides an excellent opportunity to study land-atmosphere energy exchanges at the regional scale. A predominant application of TIR data has been in inferring evaporation, evapotranspiration (ET), and soil moisture. In addition to using TIR data for ET and soil moisture analysis over vegetated surfaces, there is also a need for using these data for assessment of drought conditions. The concept of ecological thermodynamics provides a quantification of surface energy fluxes for landscape characterization in relation to the overall amount of energy input and output from specific land cover types.

Hydrologic Remote Sensing and Land Surface Data Assimilation.

PubMed

Moradkhani, Hamid

2008-05-06

Accurate, reliable and skillful forecasting of key environmental variables such as soil moisture and snow are of paramount importance due to their strong influence on many water resources applications including flood control, agricultural production and effective water resources management which collectively control the behavior of the climate system. Soil moisture is a key state variable in land surface-atmosphere interactions affecting surface energy fluxes, runoff and the radiation balance. Snow processes also have a large influence on land-atmosphere energy exchanges due to snow high albedo, low thermal conductivity and considerable spatial and temporal variability resulting in the dramatic change on surface and ground temperature. Measurement of these two variables is possible through variety of methods using ground-based and remote sensing procedures. Remote sensing, however, holds great promise for soil moisture and snow measurements which have considerable spatial and temporal variability. Merging these measurements with hydrologic model outputs in a systematic and effective way results in an improvement of land surface model prediction. Data Assimilation provides a mechanism to combine these two sources of estimation. Much success has been attained in recent years in using data from passive microwave sensors and assimilating them into the models. This paper provides an overview of the remote sensing measurement techniques for soil moisture and snow data and describes the advances in data assimilation techniques through the ensemble filtering, mainly Ensemble Kalman filter (EnKF) and Particle filter (PF), for improving the model prediction and reducing the uncertainties involved in prediction process. It is believed that PF provides a complete representation of the probability distribution of state variables of interests (according to sequential Bayes law) and could be a strong alternative to EnKF which is subject to some limitations including the linear updating rule and assumption of jointly normal distribution of errors in state variables and observation.

GPS Remote Sensing Measurements Using Aerosonde UAV

NASA Technical Reports Server (NTRS)

Grant, Michael S.; Katzberg, Stephen J.; Lawrence, R. W.

2005-01-01

In February 2004, a NASA-Langley GPS Remote Sensor (GPSRS) unit was flown on an Aerosonde unmanned aerial vehicle (UAV) from the Wallops Flight Facility (WFF) in Virginia. Using direct and surface-reflected 1.575 GHz coarse acquisition (C/A) coded GPS signals, remote sensing measurements were obtained over land and portions of open water. The strength of the surface-reflected GPS signal is proportional to the amount of moisture in the surface, and is also influenced by surface roughness. Amplitude and other characteristics of the reflected signal allow an estimate of wind speed over open water. In this paper we provide a synopsis of the instrument accommodation requirements, installation procedures, and preliminary results from what is likely the first-ever flight of a GPS remote sensing instrument on a UAV. The correct operation of the GPSRS unit on this flight indicates that Aerosonde-like UAV's can serve as platforms for future GPS remote sensing science missions.

Short-Term Retrospective Land Data Assimilation Schemes

NASA Technical Reports Server (NTRS)

Houser, P. R.; Cosgrove, B. A.; Entin, J. K.; Lettenmaier, D.; ODonnell, G.; Mitchell, K.; Marshall, C.; Lohmann, D.; Schaake, J. C.; Duan, Q.;

Geomorphic Processes and Remote Sensing Signatures of Alluvial Fans in the Kun Lun Mountains, China

NASA Technical Reports Server (NTRS)

Farr, Tom G.; Chadwick, Oliver A.

1996-01-01

The timing of alluvial deposition in arid and semiarid areas is tied to land-surface instability caused by regional climate changes. The distribution pattern of dated deposits provides maps of regional land-surface response to past climate change. Sensitivity to differences in surface roughness and composition makes remote sensing techniques useful for regional mapping of alluvial deposits. Radar images from the Spaceborne Radar Laboratory and visible wavelength images from the French SPOT satellite were used to determine remote sensing signatures of alluvial fan units for an area in the Kun Lun Mountains of northwestern China. These data were combined with field observations to compare surface processes and their effects on remote sensing signatures in northwestern China and the southwestern United States. Geomorphic processes affecting alluvial fans in the two areas include aeolian deposition, desert varnish, and fluvial dissection. However, salt weathering is a much more important process in the Kun Lun than in the southwestern United States. This slows the formation of desert varnish and prevents desert pavement from forming. Thus the Kun Lun signatures are characteristic of the dominance of salt weathering, while signatures from the southwestern United States are characteristic of the dominance of desert varnish and pavement processes. Remote sensing signatures are consistent enough in these two regions to be used for mapping fan units over large areas.

Socioeconomic indicators of heat-related health risk supplemented with remotely sensed data

PubMed Central

Johnson, Daniel P; Wilson, Jeffrey S; Luber, George C

2009-01-01

Background Extreme heat events are the number one cause of weather-related fatalities in the United States. The current system of alert for extreme heat events does not take into account intra-urban spatial variation in risk. The purpose of this study is to evaluate a potential method to improve spatial delineation of risk from extreme heat events in urban environments by integrating sociodemographic risk factors with estimates of land surface temperature derived from thermal remote sensing data. Results Comparison of logistic regression models indicates that supplementing known sociodemographic risk factors with remote sensing estimates of land surface temperature improves the delineation of intra-urban variations in risk from extreme heat events. Conclusion Thermal remote sensing data can be utilized to improve understanding of intra-urban variations in risk from extreme heat. The refinement of current risk assessment systems could increase the likelihood of survival during extreme heat events and assist emergency personnel in the delivery of vital resources during such disasters. PMID:19835578

An Investigation of Land-Atmosphere Coupling from Local to Regional Scales

NASA Astrophysics Data System (ADS)

Brunsell, N. A.; Van Vleck, E.; Rahn, D. A.

2017-12-01

The exchanges of mass and energy between the surface and atmosphere have been shown to depend upon both local and regional climatic influences. However, the degree of control exerted by the land surface on the coupling metrics is not well understood. In particular, we lack an understanding of the relationship between the local microclimate of a site and the regional forces responsible for land-atmosphere coupling. To address this, we investigate a series of metrics calculated from eddy covariance data and ceilometer data, land surface modeling and remotely sensed observations in the central United States to diagnose these interactions and predict the change from one coupling regime (e.g. wet/dry coupling) to another state. The stability of the coupling is quantified using a Lyapunov exponent based methodology. Through the use of a wavelet information theoretic approach, we isolate the roles local energy partitioning, as well as the temperature and moisture gradients on controlling and changing the coupling regime. Taking a multi-scale observational approach, we first examine the relationship at the tower scale. Using land surface models, we quantify to what extent current models are capable of properly diagnosing the dynamics of the coupling regime. In particular, we focus on the role of the surface moisture and vegetation to initiate and maintain precipitation feedbacks. We extend this analysis to the regional scale by utilizing reanalysis and remotely sensed observations. Thus, we are able to quantify the changes in observed coupling patterns with linkages to local interactions to address the question of the local control that the surface exerts over the maintenance of land-atmosphere coupling.

Remote sensing with spaceborne synthetic aperture imaging radars: A review

NASA Technical Reports Server (NTRS)

Cimino, J. B.; Elachi, C.

1983-01-01

A review is given of remote sensing with Spaceborne Synthetic Aperture Radars (SAR's). In 1978, a spaceborne SA was flown on the SEASAT satellite. It acquired high resulution images over many regions in North America and the North Pacific. The acquired data clearly demonstrate the capability of spaceborne SARs to: image and track polar ice floes; image ocean surface patterns including swells, internal waves, current boundaries, weather boundaries and vessels; and image land features which are used to acquire information about the surface geology and land cover. In 1981, another SAR was flown on the second shuttle flight. This Shuttle Imaging Radar (SIR-A) acquired land and ocean images over many areas around the world. The emphasis of the SIR-A experiment was mainly toward geologic mapping. Some of the key results of the SIR-A experiment are given.

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

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.; Luvall, Jeffrey C.; Rickman, Douglas L.; Laymon, Charles A.; Estes, Maurice G., Jr.; Howell, Burgess F.; Arnold, James E. (Technical Monitor)

2002-01-01

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.

Northern Everglades, Florida, satellite image map

USGS Publications Warehouse

Thomas, Jean-Claude; Jones, John W.

2002-01-01

These satellite image maps are one product of the USGS Land Characteristics from Remote Sensing project, funded through the USGS Place-Based Studies Program with support from the Everglades National Park. The objective of this project is to develop and apply innovative remote sensing and geographic information system techniques to map the distribution of vegetation, vegetation characteristics, and related hydrologic variables through space and over time. The mapping and description of vegetation characteristics and their variations are necessary to accurately simulate surface hydrology and other surface processes in South Florida and to monitor land surface changes. As part of this research, data from many airborne and satellite imaging systems have been georeferenced and processed to facilitate data fusion and analysis. These image maps were created using image fusion techniques developed as part of this project.

75 FR 72781 - Buckhorn Exploration Project 2010, Okanogan-Wenatchee National Forest, Okanogan County, WA

Federal Register 2010, 2011, 2012, 2013, 2014

2010-11-26

... prevent unnecessary and undue degradation of public lands and surface resources by initially using remote sensing and other non-surface disturbing prospecting techniques to identify target areas. Exploration...

Remote sensing sensors and applications in environmental resources mapping and modeling

USGS Publications Warehouse

Melesse, Assefa M.; Weng, Qihao; Thenkabail, Prasad S.; Senay, Gabriel B.

2007-01-01

The history of remote sensing and development of different sensors for environmental and natural resources mapping and data acquisition is reviewed and reported. Application examples in urban studies, hydrological modeling such as land-cover and floodplain mapping, fractional vegetation cover and impervious surface area mapping, surface energy flux and micro-topography correlation studies is discussed. The review also discusses the use of remotely sensed-based rainfall and potential evapotranspiration for estimating crop water requirement satisfaction index and hence provides early warning information for growers. The review is not an exhaustive application of the remote sensing techniques rather a summary of some important applications in environmental studies and modeling.

An investigation of satellite sounding products for the remote sensing of the surface energy balance and soil moisture

NASA Technical Reports Server (NTRS)

Diak, George R.

1989-01-01

Improved techniques for the remote sensing of the land surface energy balance (SEB) and soil moisture would greatly improve prediction of climate and weather as well as be of benefit to agriculture, hydrology and many associated fields. Most of the satellite remote sensing methods which were researched to date rely upon satellite-measured infrared surface temperatures or their time changes as a remote sensing signal. Optimistically, only four or five levels of information (wet to dry) in surface heating/evaporation are discernable by surface temperature methods and a good understanding of atmospheric conditions is necessary to bring them to this accuracy level. Skin temperature methods were researched as well as begun work on several new methods for the remote sensing of the SEB, some elements of which are applicable to current and retrospective data sources and some which will rely on instrumentation from the Earth Observing System (EOS) program in the 1990s.

Linking Land Use Changes to Surface Water Quality Variability in Lake Victoria: Some Insights From Remote Sensing (GC41B-1101)

NASA Technical Reports Server (NTRS)

Limaye, Ashutosh; Mugo, Robinson; Wanjohi, James; Farah, Hussein; Wahome, Anastasia; Flores, Africa; Irwin, Dan

2016-01-01

Various land use changes driven by urbanization, conversion of grasslands and woodlands into farmlands, intensification of agricultural practices, deforestation, land fragmentation and degradation are taking place in Africa. In Kenya, agriculture is the main driver of land use conversions. The impacts of these land use changes are observable in land cover maps, and eventually in the hydrological systems. Reduction or change of natural vegetation cover types increases the speed of surface runoff and reduces water and nutrient retention capacities. This can lead to high nutrient inputs into lakes, resulting in eutrophication, siltation and infestation of floating aquatic vegetation. To assess if changes in land use could be contributing to increased phytoplankton blooms and sediment loads into Lake Victoria, we analyzed land use land cover data from Landsat, as well as surface chlorophyll-a and total suspended matter from MODIS-Aqua sensor.

Evapotranspiration from combined reflected solar and emitted terrestrial radiation - Preliminary FIFE results from AVHRR data

NASA Technical Reports Server (NTRS)

Goward, S. N.; Hope, A. S.

1989-01-01

The relation between remotely sensed spectral vegetation indices and thermal IR measurements is studied. Land surface evapotranspiration is evaluated based on this relationship. Analysis of the AVHRR data, obtained in Kansas in 1987, reveal a strong correlation between the spectral vegetation indices and surface temperature and this relation covaries with surface moisture conditions. It is noted that the relation between remotely sensed measurements of canopy green foliage and surface temperature is useful for examining variations in the interface thermal inertia and energy balance Bowen ratio.

Optimal land use/cover classification using remote sensing imagery for hydrological modelling in a Himalayan watershed

NASA Astrophysics Data System (ADS)

Saran, Sameer; Sterk, Geert; Kumar, Suresh

2007-10-01

Land use/cover is an important watershed surface characteristic that affects surface runoff and erosion. Many of the available hydrological models divide the watershed into Hydrological Response Units (HRU), which are spatial units with expected similar hydrological behaviours. The division into HRU's requires good-quality spatial data on land use/cover. This paper presents different approaches to attain an optimal land use/cover map based on remote sensing imagery for a Himalayan watershed in northern India. First digital classifications using maximum likelihood classifier (MLC) and a decision tree classifier were applied. The results obtained from the decision tree were better and even improved after post classification sorting. But the obtained land use/cover map was not sufficient for the delineation of HRUs, since the agricultural land use/cover class did not discriminate between the two major crops in the area i.e. paddy and maize. Therefore we adopted a visual classification approach using optical data alone and also fused with ENVISAT ASAR data. This second step with detailed classification system resulted into better classification accuracy within the 'agricultural land' class which will be further combined with topography and soil type to derive HRU's for physically-based hydrological modelling.

Improving GLOBALlAND30 Artificial Type Extraction Accuracy in Low-Density Residents

NASA Astrophysics Data System (ADS)

Hou, Lili; Zhu, Ling; Peng, Shu; Xie, Zhenlei; Chen, Xu

2016-06-01

GlobalLand 30 is the first 30m resolution land cover product in the world. It covers the area within 80°N and 80°S. There are ten classes including artificial cover, water bodies, woodland, lawn, bare land, cultivated land, wetland, sea area, shrub and snow,. The TM imagery from Landsat is the main data source of GlobalLand 30. In the artificial surface type, one of the omission error happened on low-density residents' part. In TM images, hash distribution is one of the typical characteristics of the low-density residents, and another one is there are a lot of cultivated lands surrounded the low-density residents. Thus made the low-density residents part being blurred with cultivated land. In order to solve this problem, nighttime light remote sensing image is used as a referenced data, and on the basis of NDBI, we add TM6 to calculate the amount of surface thermal radiation index TR-NDBI (Thermal Radiation Normalized Difference Building Index) to achieve the purpose of extracting low-density residents. The result shows that using TR-NDBI and the nighttime light remote sensing image are a feasible and effective method for extracting low-density residents' areas.

Evaluation of various LandFlux evapotranspiration algorithms using the LandFlux-EVAL synthesis benchmark products and observational data

NASA Astrophysics Data System (ADS)

Michel, Dominik; Hirschi, Martin; Jimenez, Carlos; McCabe, Mathew; Miralles, Diego; Wood, Eric; Seneviratne, Sonia

2014-05-01

Research on climate variations and the development of predictive capabilities largely rely on globally available reference data series of the different components of the energy and water cycles. Several efforts aimed at producing large-scale and long-term reference data sets of these components, e.g. based on in situ observations and remote sensing, in order to allow for diagnostic analyses of the drivers of temporal variations in the climate system. Evapotranspiration (ET) is an essential component of the energy and water cycle, which can not be monitored directly on a global scale by remote sensing techniques. In recent years, several global multi-year ET data sets have been derived from remote sensing-based estimates, observation-driven land surface model simulations or atmospheric reanalyses. The LandFlux-EVAL initiative presented an ensemble-evaluation of these data sets over the time periods 1989-1995 and 1989-2005 (Mueller et al. 2013). Currently, a multi-decadal global reference heat flux data set for ET at the land surface is being developed within the LandFlux initiative of the Global Energy and Water Cycle Experiment (GEWEX). This LandFlux v0 ET data set comprises four ET algorithms forced with a common radiation and surface meteorology. In order to estimate the agreement of this LandFlux v0 ET data with existing data sets, it is compared to the recently available LandFlux-EVAL synthesis benchmark product. Additional evaluation of the LandFlux v0 ET data set is based on a comparison to in situ observations of a weighing lysimeter from the hydrological research site Rietholzbach in Switzerland. These analyses serve as a test bed for similar evaluation procedures that are envisaged for ESA's WACMOS-ET initiative (http://wacmoset.estellus.eu). Reference: Mueller, B., Hirschi, M., Jimenez, C., Ciais, P., Dirmeyer, P. A., Dolman, A. J., Fisher, J. B., Jung, M., Ludwig, F., Maignan, F., Miralles, D. G., McCabe, M. F., Reichstein, M., Sheffield, J., Wang, K., Wood, E. F., Zhang, Y., and Seneviratne, S. I. (2013). Benchmark products for land evapotranspiration: LandFlux-EVAL multi-data set synthesis. Hydrology and Earth System Sciences, 17(10): 3707-3720.

Soil moisture status estimation over Three Gorges area with Landsat TM data based on temperature vegetation dryness index

NASA Astrophysics Data System (ADS)

Xu, Lina; Niu, Ruiqing; Li, Jiong; Dong, Yanfang

2011-12-01

Soil moisture is the important indicator of climate, hydrology, ecology, agriculture and other parameters of the land surface and atmospheric interface. Soil moisture plays an important role on the water and energy exchange at the land surface/atmosphere interface. Remote sensing can provide information on large area quickly and easily, so it is significant to do research on how to monitor soil moisture by remote sensing. This paper presents a method to assess soil moisture status using Landsat TM data over Three Gorges area in China based on TVDI. The potential of Temperature- Vegetation Dryness Index (TVDI) from Landsat TM data in assessing soil moisture was investigated in this region. After retrieving land surface temperature and vegetation index a TVDI model based on the features of Ts-NDVI space is established. And finally, soil moisture status is estimated according to TVDI. It shows that TVDI has the advantages of stability and high accuracy to estimating the soil moisture status.

Proceedings of the Eleventh International Symposium on Remote Sensing of Environment, volume 2. [application and processing of remotely sensed data

NASA Technical Reports Server (NTRS)

1977-01-01

Application and processing of remotely sensed data are discussed. Areas of application include: pollution monitoring, water quality, land use, marine resources, ocean surface properties, and agriculture. Image processing and scene analysis are described along with automated photointerpretation and classification techniques. Data from infrared and multispectral band scanners onboard LANDSAT satellites are emphasized.

A thermal-based remote sensing modeling system for estimating evapotranspiration from field to global scales

USDA-ARS?s Scientific Manuscript database

Thermal-infrared remote sensing of land surface temperature provides valuable information for quantifying root-zone water availability, evapotranspiration (ET) and crop condition. This paper describes a robust but relatively simple thermal-based energy balance model that parameterizes the key soil/s...

Assimilation of Satellite-Derived Precipitation into the Regional Atmospheric Model System (RAMS): Its Impacts on the Weather and Hydrology in the Southwest United States

NASA Astrophysics Data System (ADS)

Yi, H.; Gao, X.; Sorooshian, S.

2002-05-01

As one aspect of the study of interactions between the atmosphere, vegetation, soil, and hydrology, there has been on going efforts to assimilate soil moisture data using coupled and uncoupled land surface-atmosphere hydrology models. The assimilation of soil moisture is expected to have influence due to its vital function in regulating runoff, partitioning latent and sensible heat, and through determining groundwater recharge. Soil moisture can provides long-term memory or persistence of the surface boundary condition, influencing large-scale atmospheric circulation over subsequent intervals. Now that the application of satellite remote sensing has become obvious to provide input parameters associated with land surface processes to the numerical models, this study utilizes remotely sensed precipitation data, PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) to assimilate soil moisture and other soil surface characteristics. Compared to the other earlier modeling experiments of seasonal or interannual temporal scale in continental or global spatial scale, this study investigates short term predictability in regional scale with the southwest United States as a study area, which has unique metrological and geographical features that provide special difficulties for mesoscale modeling. Research objectives are to assimilate the PERSIANN precipitation data into the mesoscale model for model initialization, examine the influence and memory of model precipitation errors on the land surface and atmospheric processes, and thereby study the short term predictability of meteorology and hydrology in the Southwest United States.

Land surface Verification Toolkit (LVT)

NASA Technical Reports Server (NTRS)

Kumar, Sujay V.

2017-01-01

LVT is a framework developed to provide an automated, consolidated environment for systematic land surface model evaluation Includes support for a range of in-situ, remote-sensing and other model and reanalysis products. Supports the analysis of outputs from various LIS subsystems, including LIS-DA, LIS-OPT, LIS-UE. Note: The Land Information System Verification Toolkit (LVT) is a NASA software tool designed to enable the evaluation, analysis and comparison of outputs generated by the Land Information System (LIS). The LVT software is released under the terms and conditions of the NASA Open Source Agreement (NOSA) Version 1.1 or later. Land Information System Verification Toolkit (LVT) NOSA.

Area-averaged evapotranspiration over a heterogeneous land surface: aggregation of multi-point EC flux measurements with a high-resolution land-cover map and footprint analysis

NASA Astrophysics Data System (ADS)

Xu, Feinan; Wang, Weizhen; Wang, Jiemin; Xu, Ziwei; Qi, Yuan; Wu, Yueru

2017-08-01

The determination of area-averaged evapotranspiration (ET) at the satellite pixel scale/model grid scale over a heterogeneous land surface plays a significant role in developing and improving the parameterization schemes of the remote sensing based ET estimation models and general hydro-meteorological models. The Heihe Watershed Allied Telemetry Experimental Research (HiWATER) flux matrix provided a unique opportunity to build an aggregation scheme for area-averaged fluxes. On the basis of the HiWATER flux matrix dataset and high-resolution land-cover map, this study focused on estimating the area-averaged ET over a heterogeneous landscape with footprint analysis and multivariate regression. The procedure is as follows. Firstly, quality control and uncertainty estimation for the data of the flux matrix, including 17 eddy-covariance (EC) sites and four groups of large-aperture scintillometers (LASs), were carefully done. Secondly, the representativeness of each EC site was quantitatively evaluated; footprint analysis was also performed for each LAS path. Thirdly, based on the high-resolution land-cover map derived from aircraft remote sensing, a flux aggregation method was established combining footprint analysis and multiple-linear regression. Then, the area-averaged sensible heat fluxes obtained from the EC flux matrix were validated by the LAS measurements. Finally, the area-averaged ET of the kernel experimental area of HiWATER was estimated. Compared with the formerly used and rather simple approaches, such as the arithmetic average and area-weighted methods, the present scheme is not only with a much better database, but also has a solid grounding in physics and mathematics in the integration of area-averaged fluxes over a heterogeneous surface. Results from this study, both instantaneous and daily ET at the satellite pixel scale, can be used for the validation of relevant remote sensing models and land surface process models. Furthermore, this work will be extended to the water balance study of the whole Heihe River basin.

Spatial heterogeneity of leaf area index across scales from simulation and remote sensing

NASA Astrophysics Data System (ADS)

Reichenau, Tim G.; Korres, Wolfgang; Montzka, Carsten; Schneider, Karl

2016-04-01

Leaf area index (LAI, single sided leaf area per ground area) influences mass and energy exchange of vegetated surfaces. Therefore LAI is an input variable for many land surface schemes of coupled large scale models, which do not simulate LAI. Since these models typically run on rather coarse resolution grids, LAI is often inferred from coarse resolution remote sensing. However, especially in agriculturally used areas, a grid cell of these products often covers more than a single land-use. In that case, the given LAI does not apply to any single land-use. Therefore, the overall spatial heterogeneity in these datasets differs from that on resolutions high enough to distinguish areas with differing land-use. Detailed process-based plant growth models simulate LAI for separate plant functional types or specific species. However, limited availability of observations causes reduced spatial heterogeneity of model input data (soil, weather, land-use). Since LAI is strongly heterogeneous in space and time and since processes depend on LAI in a nonlinear way, a correct representation of LAI spatial heterogeneity is also desirable on coarse resolutions. The current study assesses this issue by comparing the spatial heterogeneity of LAI from remote sensing (RapidEye) and process-based simulations (DANUBIA simulation system) across scales. Spatial heterogeneity is assessed by analyzing LAI frequency distributions (spatial variability) and semivariograms (spatial structure). Test case is the arable land in the fertile loess plain of the Rur catchment near the Germany-Netherlands border.

Optimal land use/land cover classification using remote sensing imagery for hydrological modeling in a Himalayan watershed

NASA Astrophysics Data System (ADS)

Saran, Sameer; Sterk, Geert; Kumar, Suresh

2009-10-01

Land use/land cover is an important watershed surface characteristic that affects surface runoff and erosion. Many of the available hydrological models divide the watershed into Hydrological Response Units (HRU), which are spatial units with expected similar hydrological behaviours. The division into HRU's requires good-quality spatial data on land use/land cover. This paper presents different approaches to attain an optimal land use/land cover map based on remote sensing imagery for a Himalayan watershed in northern India. First digital classifications using maximum likelihood classifier (MLC) and a decision tree classifier were applied. The results obtained from the decision tree were better and even improved after post classification sorting. But the obtained land use/land cover map was not sufficient for the delineation of HRUs, since the agricultural land use/land cover class did not discriminate between the two major crops in the area i.e. paddy and maize. Subsequently the digital classification on fused data (ASAR and ASTER) were attempted to map land use/land cover classes with emphasis to delineate the paddy and maize crops but the supervised classification over fused datasets did not provide the desired accuracy and proper delineation of paddy and maize crops. Eventually, we adopted a visual classification approach on fused data. This second step with detailed classification system resulted into better classification accuracy within the 'agricultural land' class which will be further combined with topography and soil type to derive HRU's for physically-based hydrological modeling.

Impact of land surface conditions on the predictability of hydrologic processes and mountain-valley circulations in the North American Monsoon region

NASA Astrophysics Data System (ADS)

Xiang, T.; Vivoni, E. R.; Gochis, D. J.; Mascaro, G.

2015-12-01

Heterogeneous land surface conditions are essential components of land-atmosphere interactions in regions of complex terrain and have the potential to affect convective precipitation formation. Yet, due to their high complexity, hydrologic processes over mountainous regions are not well understood, and are usually parameterized in simple ways within coupled land-atmosphere modeling frameworks. With the improving model physics and spatial resolution of numerical weather prediction models, there is an urgent need to understand how land surface processes affect local and regional meteorological processes. In the North American Monsoon (NAM) region, the summer rainy season is accompanied by a dramatic greening of mountain ecosystems that adds spatiotemporal variability in vegetation which is anticipated to impact the conditions leading to convection, mountain-valley circulations and mesoscale organization. In this study, we present results from a detailed analysis of a high-resolution (1 km) land surface model, Noah-MP, in a large, mountainous watershed of the NAM region - the Rio Sonora (21,264 km2) in Mexico. In addition to capturing the spatial variations in terrain and soil distributions, recently-developed features in Noah-MP allow the model to read time-varying vegetation parameters derived from remotely-sensed vegetation indices; however, this new implementation has not been fully evaluated. Therefore, we assess the simulated spatiotemporal fields of soil moisture, surface temperature and surface energy fluxes through comparisons to remote sensing products and results from coarser land surface models obtained from the North American Land Data Assimilation System. We focus attention on the impact of vegetation changes along different elevation bands on the diurnal cycle of surface energy fluxes to provide a baseline for future analyses of mountain-valley circulations using a coupled land-atmosphere modeling system. Our study also compares limited streamflow observations in the large watershed to simulations using the terrain and channel routing when Noah-MP is run within the WRF-Hydro modeling framework, with the goals of validating the rainfall-runoff partitioning and translating the spatiotemporal mountain processes into improvements in streamflow predictions.

Use of geostationary satellite imagery in optical and thermal bands for the estimation of soil moisture status and land evapotranspiration

NASA Astrophysics Data System (ADS)

Ghilain, N.; Arboleda, A.; Gellens-Meulenberghs, F.

2009-04-01

For water and agricultural management, there is an increasing demand to monitor the soil water status and the land evapotranspiration. In the framework of the LSA-SAF project (http://landsaf.meteo.pt), we are developing an energy balance model forced by remote sensing products, i.e. radiation components and vegetation parameters, to monitor in quasi real-time the evapotranspiration rate over land (Gellens-Meulenberghs et al, 2007; Ghilain et al, 2008). The model is applied over the full MSG disk, i.e. including Europe and Africa. Meteorological forcing, as well as the soil moisture status, is provided by the forecasts of the ECMWF model. Since soil moisture is computed by a forecast model not dedicated to the monitoring of the soil water status, inadequate soil moisture input can occur, and can cause large effects on evapotranspiration rates, especially over semi-arid or arid regions. In these regions, a remotely sensed-based method for the soil moisture retrieval can therefore be preferable, to avoid too strong dependency in ECMWF model estimates. Among different strategies, remote sensing offers the advantage of monitoring large areas. Empirical methods of soil moisture assessment exist using remotely sensed derived variables either from the microwave bands or from the thermal bands. Mainly polar orbiters are used for this purpose, and little attention has been paid to the new possibilities offered by geosynchronous satellites. In this contribution, images of the SEVIRI instrument on board of MSG geosynchronous satellites are used. Dedicated operational algorithms were developed for the LSA-SAF project and now deliver images of land surface temperature (LST) every 15-minutes (Trigo et al, 2008) and vegetations indices (leaf area index, LAI; fraction of vegetation cover, FVC; fraction of absorbed photosynthetically active radiation, FAPAR) every day (Garcia-Haro et al, 2005) over Africa and Europe. One advantage of using products derived from geostationary satellites is the close monitoring of the diurnal variation of the land surface temperature. This feature reinforced the statistical strength of empirical methods. An empirical method linking land surface morning heating rates and the fraction of the vegetation cover, also known as a ‘Triangle method' (Gillies et al, 1997) is examined. This method is expected to provide an estimation of a root-zone soil moisture index. The sensitivity of the method to wind speed, soil type, vegetation type and climatic region is explored. Moreover, the impact of the uncertainty of LST and FVC on the resulting soil moisture estimates is assessed. A first impact study of using remotely sensed soil moisture index in the energy balance model is shown and its potential benefits for operational monitoring of evapotranspiration are outlined. References García-Haro, F.J., F. Camacho-de Coca, J. Meliá, B. Martínez (2005) Operational derivation of vegetation products in the framework of the LSA SAF project. Proceedings of the EUMETSAT Meteorological Satellite Conference Dubrovnik (Croatia) 19-23 Septembre. Gellens-Meulenberghs, F., Arboleda, A., Ghilain, N. (2007) Towards a continuous monitoring of evapotranspiration based on MSG data. Proceedings of the symposium on Remote Sensing for Environmental Monitoring and Change Detection. IAHS series. IUGG, Perugia, Italy, July 2007, 7 pp. Ghilain, N., Arboleda, A. and Gellens-Meulenberghs, F., (2008) Improvement of a surface energy balance model by the use of MSG-SEVIRI derived vegetation parameters. Proceedings of the 2008 EUMETSAT meteorological satellite data user's conference, Darmstadt, Germany, 8th-12th September, 7 pp. Gillies R.R., Carlson T.N., Cui J., Kustas W.P. and Humes K. (1997), Verification of the triangle method for obtaining surface soil water content and energy fluxes from remote measurements of Normalized Difference Vegetation Index (NDVI) and surface radiant temperature, International Journal of Remote Sensing, 18, pp. 3145-3166. Trigo, I.F., Monteiro I.T., Olesen F. and Kabsch E. (2008) An assessment of remotely sensed land surface temperature. Journal of Geophysical Research, 113, D17108, doi:10.1029/2008JD010035.

Influence of crop type specification and spatial resolution on empirical modeling of field-scale Maize and Soybean carbon fluxes in the US Great Plains

NASA Astrophysics Data System (ADS)

McCombs, A. G.; Hiscox, A.; Wang, C.; Desai, A. R.

2016-12-01

A challenge in satellite land surface remote-sensing models of ecosystem carbon dynamics in agricultural systems is the lack of differentiation by crop type and management. This generalization can lead to large discrepancies between model predictions and eddy covariance flux tower observations of net ecosystem exchange of CO2 (NEE). Literature confirms that NEE varies remarkably among different crop types making the generalization of agriculture in remote sensing based models inaccurate. Here, we address this inaccuracy by identifying and mapping net ecosystem exchange (NEE) in agricultural fields by comparing bulk modeling and modeling by crop type, and using this information to develop empirical models for future use. We focus on mapping NEE in maize and soybean fields in the US Great Plains at higher spatial resolution using the fusion of MODIS and LandSAT surface reflectance. MODIS observed reflectance was downscaled using the ESTARFM downscaling methodology to match spatial scales to those found in LandSAT and that are more appropriate for carbon dynamics in agriculture fields. A multiple regression model was developed from surface reflectance of the downscaled MODIS and LandSAT remote sensing values calibrated against five FLUXNET/AMERIFLUX flux towers located on soybean and/or maize agricultural fields in the US Great Plains with multi-year NEE observations. Our new methodology improves upon bulk approximates to map and model carbon dynamics in maize and soybean fields, which have significantly different photosynthetic capacities.

Nasa's Land Remote Sensing Plans for the 1980's

NASA Technical Reports Server (NTRS)

Higg, H. C.; Butera, K. M.; Settle, M.

1985-01-01

Research since the launch of LANDSAT-1 has been primarily directed to the development of analysis techniques and to the conduct of applications studies designed to address resource information needs in the United States and in many other countries. The current measurement capabilities represented by MSS, TM, and SIR-A and B, coupled with the present level of remote sensing understanding and the state of knowledge in the discipline earth sciences, form the foundation for NASA's Land Processes Program. Science issues to be systematically addressed include: energy balance, hydrologic cycle, biogeochemical cycles, biological productivity, rock cycle, landscape development, geological and botanical associations, and land surface inventory, monitoring, and modeling. A global perspective is required for using remote sensing technology for problem solving or applications context. A successful model for this kind of activity involves joint research with a user entity where the user provides a test site and ground truth and NASA provides the remote sensing techniques to be tested.

South Florida Everglades: satellite image map

USGS Publications Warehouse

Jones, John W.; Thomas, Jean-Claude; Desmond, G.B.

2001-01-01

These satellite image maps are one product of the USGS Land Characteristics from Remote Sensing project, funded through the USGS Place-Based Studies Program (http://access.usgs.gov/) with support from the Everglades National Park (http://www.nps.gov/ever/). The objective of this project is to develop and apply innovative remote sensing and geographic information system techniques to map the distribution of vegetation, vegetation characteristics, and related hydrologic variables through space and over time. The mapping and description of vegetation characteristics and their variations are necessary to accurately simulate surface hydrology and other surface processes in South Florida and to monitor land surface changes. As part of this research, data from many airborne and satellite imaging systems have been georeferenced and processed to facilitate data fusion and analysis. These image maps were created using image fusion techniques developed as part of this project.

Preliminary Assessment of Mars Exploration Rover Landing Site Predictions

NASA Technical Reports Server (NTRS)

Golombek, M.; Grant, J.; Parker, T.; Crisp, J.; Squyres, S.; Carr, M.; Haldemann, A.; Arvidson, R.; Ehlmann, B.; Bell, J.

2004-01-01

Selection of the Mars Exploration Rover (MER) landing sites took place over a three year period in which engineering constraints were identified, 155 possible sites were downselected to the final two, surface environments and safety considerations were developed, and the potential science return at the sites was considered. Landing sites in Gusev crater and Meridiani Planum were selected because they appeared acceptably safe for MER landing and roving and had strong morphologic and mineralogical indicators of liquid water in their past and thus appeared capable of addressing the science objectives of the MER missions, which are to determine the aqueous, climatic, and geologic history of sites on Mars where conditions may have been favorable to the preservation of evidence of possible pre-biotic or biotic processes. Engineering constraints important to the selection included: latitude (10 N-15 S) for maximum solar power; elevation (<-1.3 km) for sufficient atmosphere to slow the lander; low horizontal winds, shear and turbulence in the last few kilometers to minimize horizontal velocity; low 10-m scale slopes to reduce airbag spinup and bounce; moderate rock abundance to reduce abrasion or stroke-out of the airbags; and a radar-reflective, load-bearing and trafficable surface safe for landing and roving that is not dominated by fine-grained dust. In selecting the MER landing sites these engineering constraints were addressed via comprehensive evaluation of surface and atmospheric characteristics from existing remote sensing data and models as well as targeted orbital information acquired from Mars Global Surveyor and Mars Odyssey. This evaluation resulted in a number of predictions of the surface characteristics of the sites, which are tested in this abstract. Relating remote sensing signatures to surface characteristics at landing sites allows these sites to be used as ground truth for the orbital data, is essential for selecting and validating landing sites for future missions, and is required for correctly interpreting the surfaces and materials globally present on Mars.

Analysing the Effects of Different Land Cover Types on Land Surface Temperature Using Satellite Data

NASA Astrophysics Data System (ADS)

Şekertekin, A.; Kutoglu, Ş. H.; Kaya, S.; Marangoz, A. M.

2015-12-01

Monitoring Land Surface Temperature (LST) via remote sensing images is one of the most important contributions to climatology. LST is an important parameter governing the energy balance on the Earth and it also helps us to understand the behavior of urban heat islands. There are lots of algorithms to obtain LST by remote sensing techniques. The most commonly used algorithms are split-window algorithm, temperature/emissivity separation method, mono-window algorithm and single channel method. In this research, mono window algorithm was implemented to Landsat 5 TM image acquired on 28.08.2011. Besides, meteorological data such as humidity and temperature are used in the algorithm. Moreover, high resolution Geoeye-1 and Worldview-2 images acquired on 29.08.2011 and 12.07.2013 respectively were used to investigate the relationships between LST and land cover type. As a result of the analyses, area with vegetation cover has approximately 5 ºC lower temperatures than the city center and arid land., LST values change about 10 ºC in the city center because of different surface properties such as reinforced concrete construction, green zones and sandbank. The temperature around some places in thermal power plant region (ÇATES and ZETES) Çatalağzı, is about 5 ºC higher than city center. Sandbank and agricultural areas have highest temperature due to the land cover structure.

Remote Sensing Sensors and Applications in Environmental Resources Mapping and Modelling

PubMed Central

Melesse, Assefa M.; Weng, Qihao; S.Thenkabail, Prasad; Senay, Gabriel B.

2007-01-01

The history of remote sensing and development of different sensors for environmental and natural resources mapping and data acquisition is reviewed and reported. Application examples in urban studies, hydrological modeling such as land-cover and floodplain mapping, fractional vegetation cover and impervious surface area mapping, surface energy flux and micro-topography correlation studies is discussed. The review also discusses the use of remotely sensed-based rainfall and potential evapotranspiration for estimating crop water requirement satisfaction index and hence provides early warning information for growers. The review is not an exhaustive application of the remote sensing techniques rather a summary of some important applications in environmental studies and modeling. PMID:28903290

Land-atmosphere interactions over the continental United States

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zeng, Xubin

This paper briefly discusses four suggested modifications for land surface modeling in climate models. The impact of the modifications on climate simulations is analyzed with the Biosphere-Atmosphere Transfer Scheme (BATS) land surface model. It is found that the modifications can improve BATS simulations. In particular, the sensitivity of BATS to the prescribed value of physical root fraction which cannot be observed from satellite remote sensing or field experiments is improved. These modifications significantly reduce the excessive summer land surface temperature over the continental United States simulated by the National Center for Atmospheric Research Community Climate Model (CCM2) coupled with BATS.more » A land-atmosphere interaction mechanism involving energy and water cycles is proposed to explain the results. 9 refs., 1 fig.« less

An intercomparison of available soil moisture estimates from thermal-infrared and passive microwave remote sensing and land-surface modeling

USDA-ARS?s Scientific Manuscript database

Remotely-sensed soil moisture studies have mainly focused on retrievals using active and passive microwave (MW) sensors whose measurements provided a direct relationship to soil moisture (SM). MW sensors present obvious advantages such as the ability to retrieve through non-precipitating cloud cover...

Phenocams bridge the gap between field and satellite observations in an arid grassland ecosystem

USDA-ARS?s Scientific Manuscript database

Near surface (i.e., camera) and satellite remote sensing metrics have become widely used indicators of plant growing seasons. While robust linkages have been established between field metrics and ecosystem exchange in many land cover types, assessment of how well remotely-derived season start and en...

A thermal-based remote sensing modelling system for estimating crop water use and stress from field to regional scales

USDA-ARS?s Scientific Manuscript database

Thermal-infrared remote sensing of land surface temperature provides valuable information for quantifying root-zone water availability, evapotranspiration (ET) and crop condition. A thermal-based scheme, called the Two-Source Energy Balance (TSEB) model, solves for the soil/substrate and canopy temp...

Toward irrigation retrieval by combining multi-sensor remote sensing data into a land surface model over a semi-arid region

NASA Astrophysics Data System (ADS)

Malbéteau, Y.; Lopez, O.; Houborg, R.; McCabe, M.

2017-12-01

Agriculture places considerable pressure on water resources, with the relationship between water availability and food production being critical for sustaining population growth. Monitoring water resources is particularly important in arid and semi-arid regions, where irrigation can represent up to 80% of the consumptive uses of water. In this context, it is necessary to optimize on-farm irrigation management by adjusting irrigation to crop water requirements throughout the growing season. However, in situ point measurements are not routinely available over extended areas and may not be representative at the field scale. Remote sensing approaches present as a cost-effective technique for mapping and monitoring broad areas. By taking advantage of multi-sensor remote sensing methodologies, such as those provided by MODIS, Landsat, Sentinel and Cubesats, we propose a new method to estimate irrigation input at pivot-scale. Here we explore the development of crop-water use estimates via these remote sensing data and integrate them into a land surface modeling framework, using a farm in Saudi Arabia as a demonstration of what can be achieved at larger scales.

Remote estimation of the surface characteristics and energy balance over an urban-rural area and the effects of surface heat flux on plume spread and concentration. M.S. Thesis; [St. Louis, Missouri, the Land Between the Lakes, Kentucky and Clarksville, Tennessee

NASA Technical Reports Server (NTRS)

Dicristofaro, D. C. (Principal Investigator)

1980-01-01

A one dimensional boundary layer model was used in conjunction with satellite derived infrared surface temperatures to deduce values of moisture availability, thermal inertia, heat and evaporative fluxes. The Penn State satellite image display system, a sophisticated image display facility, was used to remotely sense these various parameters for three cases: St. Louis, Missouri; the Land Between the Lakes, Kentucky; and Clarksville, Tennessee. The urban centers displayed the maximum daytime surface temperatures which correspond to the minimum values of moisture availability. The urban center of St. Louis and the bodies of water displayed the maximum nighttime surface temperatures which correspond to the maximum thermal inertia values. It is shown that moisture availability and thermal inertia are very much responsible for the formation of important temperature variations over the urban rural complex.

Regional surface soil heat flux estimate from multiple remote sensing data in a temperate and semiarid basin

NASA Astrophysics Data System (ADS)

Li, Nana; Jia, Li; Lu, Jing; Menenti, Massimo; Zhou, Jie

2017-01-01

The regional surface soil heat flux (G0) estimation is very important for the large-scale land surface process modeling. However, most of the regional G0 estimation methods are based on the empirical relationship between G0 and the net radiation flux. A physical model based on harmonic analysis was improved (referred to as "HM model") and applied over the Heihe River Basin northwest China with multiple remote sensing data, e.g., FY-2C, AMSR-E, and MODIS, and soil map data. The sensitivity analysis of the model was studied as well. The results show that the improved model describes the variation of G0 well. Land surface temperature (LST) and thermal inertia (Γ) are the two key input variables to the HM model. Compared with in situ G0, there are some differences, mainly due to the differences between remote-sensed LST and the in situ LST. The sensitivity analysis shows that the errors from -7 to -0.5 K in LST amplitude and from -300 to 300 J m-2 K-1 s-0.5 in Γ will cause about 20% errors, which are acceptable for G0 estimation.

Drought monitoring using remote sensing of evapotranspiration

USDA-ARS?s Scientific Manuscript database

Drought assessment is a complex endeavor, requiring monitoring of deficiencies in multiple components of the hydrologic budget. Precipitation anomalies reflect variability in water supply to the land surface, while soil moisture (SM), ground and surface water anomalies reflect deficiencies in moist...

Are the Viking Lander sites representative of the surface of Mars?

NASA Technical Reports Server (NTRS)

Jakosky, B. M.; Christensen, P. R.

1986-01-01

Global remote sensing data of the Martian surface, collected by earth- and satellite-based instruments, are compared with data from the two Viking Landers to determine if the Lander data are representative of the Martian surface. The landing sites are boulder-strewn and feature abundant fine material and evidence of strong eolian forces. One site (VL-1) is in a plains-covered basin which is associated with volcanic activity; the VL-2 site is in the northern plains. Thermal IR, broadband albedo, color imaging and radar remote sensing has been carried out of the global Martian surface. The VL-1 data do not fit a general correlation observed between increases in 70-cm radar cross-sections and thermal inertia. A better fit is found with 12.5-cm cross sections, implying the presence of a thinner or discontinuous duricrust at the VL-1 site, compared to other higher-inertia regions. A thin dust layer is also present at the VL-2 site, based on the Lander reflectance data. The Lander sites are concluded to be among the three observed regions of anomalous reflectivity, which can be expected in low regions selected for the landings. Recommendations are furnished for landing sites of future surface probes in order to choose sites more typical of the global Martian surface.

Long-Term Monitoring of Desert Land and Natural Resources and Application of Remote Sensing Technologies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hamada, Yuki; Rollins, Katherine E.

2016-11-01

Monitoring environmental impacts over large, remote desert regions for long periods of time can be very costly. Remote sensing technologies present a promising monitoring tool because they entail the collection of spatially contiguous data, automated processing, and streamlined data analysis. This report provides a summary of remote sensing products and refinement of remote sensing data interpretation methodologies that were generated as part of the U.S. Department of the Interior Bureau of Land Management Solar Energy Program. In March 2015, a team of researchers from Argonne National Laboratory (Argonne) collected field data of vegetation and surface types from more than 5,000more » survey points within the eastern part of the Riverside East Solar Energy Zone (SEZ). Using the field data, remote sensing products that were generated in 2014 using very high spatial resolution (VHSR; 15 cm) multispectral aerial images were validated in order to evaluate potential refinements to the previous methodologies to improve the information extraction accuracy.« less

Rapid prototyping of soil moisture estimates using the NASA Land Information System

NASA Astrophysics Data System (ADS)

Anantharaj, V.; Mostovoy, G.; Li, B.; Peters-Lidard, C.; Houser, P.; Moorhead, R.; Kumar, S.

2007-12-01

The Land Information System (LIS), developed at the NASA Goddard Space Flight Center, is a functional Land Data Assimilation System (LDAS) that incorporates a suite of land models in an interoperable computational framework. LIS has been integrated into a computational Rapid Prototyping Capabilities (RPC) infrastructure. LIS consists of a core, a number of community land models, data servers, and visualization systems - integrated in a high-performance computing environment. The land surface models (LSM) in LIS incorporate surface and atmospheric parameters of temperature, snow/water, vegetation, albedo, soil conditions, topography, and radiation. Many of these parameters are available from in-situ observations, numerical model analysis, and from NASA, NOAA, and other remote sensing satellite platforms at various spatial and temporal resolutions. The computational resources, available to LIS via the RPC infrastructure, support e- Science experiments involving the global modeling of land-atmosphere studies at 1km spatial resolutions as well as regional studies at finer resolutions. The Noah Land Surface Model, available with-in the LIS is being used to rapidly prototype soil moisture estimates in order to evaluate the viability of other science applications for decision making purposes. For example, LIS has been used to further extend the utility of the USDA Soil Climate Analysis Network of in-situ soil moisture observations. In addition, LIS also supports data assimilation capabilities that are used to assimilate remotely sensed soil moisture retrievals from the AMSR-E instrument onboard the Aqua satellite. The rapid prototyping of soil moisture estimates using LIS and their applications will be illustrated during the presentation.

Monitoring and validating spatio-temporal continuously daily evapotranspiration and its components at river basin scale

NASA Astrophysics Data System (ADS)

Song, L.; Liu, S.; Kustas, W. P.; Nieto, H.

2017-12-01

Operational estimation of spatio-temporal continuously daily evapotranspiration (ET), and the components evaporation (E) and transpiration (T), at watershed scale is very useful for developing a sustainable water resource strategy in semi-arid and arid areas. In this study, multi-year all-weather daily ET, E and T were estimated using MODIS-based (Dual Temperature Difference) DTD model under different land covers in Heihe watershed, China. The remotely sensed ET was validated using ground measurements from large aperture scintillometer systems, with a source area of several kilometers, under grassland, cropland and riparian shrub-forest. The results showed that the remotely sensed ET produced mean absolute percent deviation (MAPD) errors of about 30% during the growing season for all-weather conditions, but the model performed better under clear sky conditions. However, uncertainty in interpolated MODIS land surface temperature input data under cloudy conditions to the DTD model, and the representativeness of LAS measurements for the heterogeneous land surfaces contribute to the discrepancies between the modeled and ground measured surface heat fluxes, especially for the more humid grassland and heterogeneous shrub-forest sites.

Effects of vegetation types on soil moisture estimation from the normalized land surface temperature versus vegetation index space

NASA Astrophysics Data System (ADS)

Zhang, Dianjun; Zhou, Guoqing

2015-12-01

Soil moisture (SM) is a key variable that has been widely used in many environmental studies. Land surface temperature versus vegetation index (LST-VI) space becomes a common way to estimate SM in optical remote sensing applications. Normalized LST-VI space is established by the normalized LST and VI to obtain the comparable SM in Zhang et al. (Validation of a practical normalized soil moisture model with in situ measurements in humid and semiarid regions [J]. International Journal of Remote Sensing, DOI: 10.1080/01431161.2015.1055610). The boundary conditions in the study were set to limit the point A (the driest bare soil) and B (the wettest bare soil) for surface energy closure. However, no limitation was installed for point D (the full vegetation cover). In this paper, many vegetation types are simulated by the land surface model - Noah LSM 3.2 to analyze the effects on soil moisture estimation, such as crop, grass and mixed forest. The locations of point D are changed with vegetation types. The normalized LST of point D for forest is much lower than crop and grass. The location of point D is basically unchanged for crop and grass.

Mapping land water and energy balance relations through conditional sampling of remote sensing estimates of atmospheric forcing and surface states

NASA Astrophysics Data System (ADS)

Farhadi, Leila; Entekhabi, Dara; Salvucci, Guido

2016-04-01

In this study, we develop and apply a mapping estimation capability for key unknown parameters that link the surface water and energy balance equations. The method is applied to the Gourma region in West Africa. The accuracy of the estimation method at point scale was previously examined using flux tower data. In this study, the capability is scaled to be applicable with remotely sensed data products and hence allow mapping. Parameters of the system are estimated through a process that links atmospheric forcing (precipitation and incident radiation), surface states, and unknown parameters. Based on conditional averaging of land surface temperature and moisture states, respectively, a single objective function is posed that measures moisture and temperature-dependent errors solely in terms of observed forcings and surface states. This objective function is minimized with respect to parameters to identify evapotranspiration and drainage models and estimate water and energy balance flux components. The uncertainty of the estimated parameters (and associated statistical confidence limits) is obtained through the inverse of Hessian of the objective function, which is an approximation of the covariance matrix. This calibration-free method is applied to the mesoscale region of Gourma in West Africa using multiplatform remote sensing data. The retrievals are verified against tower-flux field site data and physiographic characteristics of the region. The focus is to find the functional form of the evaporative fraction dependence on soil moisture, a key closure function for surface and subsurface heat and moisture dynamics, using remote sensing data.

New Directions in Land Remote Sensing Policy and International Cooperation

NASA Astrophysics Data System (ADS)

Stryker, Timothy

2010-12-01

Recent changes to land remote sensing satellite data policies in Brazil and the United States have led to the phenomenal growth in the delivery of land imagery to users worldwide. These new policies, which provide free and unrestricted access to land remote sensing data over a standard electronic interface, are expected to provide significant benefits to scientific and operational users, and open up new areas of Earth system science research and environmental monitoring. Freely-available data sets from the China-Brazil Earth Resources Satellites (CBERS), the U.S. Landsat satellites, and other satellite missions provide essential information for land surface monitoring, ecosystems management, disaster mitigation, and climate change research. These missions are making important contributions to the goals and objectives of regional and global terrestrial research and monitoring programs. These programs are in turn providing significant support to the goals and objectives of the United Nations Framework Convention on Climate Change (UN FCCC), the Global Earth Observation System of Systems (GEOSS), and the UN Reduction in Emissions from Deforestation and Degradation (REDD) program. These data policies are well-aligned with the "Data Democracy" initiative undertaken by the international Committee on Earth Observation Satellites (CEOS), through its current Chair, Brazil's National Institute for Space Research (Instituto Nacional de Pesquisas Espaciais, or INPE), and its former chairs, South Africa's Council for Scientific and Industrial Research (CSIR) and Thailand's Geo Informatics and Space Technology Development Agency (GISTDA). Comparable policies for land imaging data are under consideration within Europe and Canada. Collectively, these initiatives have the potential to accelerate and improve international mission collaboration, and greatly enhance the access, use, and application of land surface imagery for environmental monitoring and societal adaption to changing climate conditions.

Assessment and prediction of land ecological environment quality change based on remote sensing-a case study of the Dongting lake area in China

NASA Astrophysics Data System (ADS)

Hu, Wenmin; Wang, Zhongcheng; Li, Chunhua; Zhao, Jin; Li, Yi

2018-02-01

Multi-source remote sensing data is rarely used for the comprehensive assessment of land ecologic environment quality. In this study, a digital environmental model was proposed with the inversion algorithm of land and environmental factors based on the multi-source remote sensing data, and a comprehensive index (Ecoindex) was applied to reconstruct and predict the land environment quality of the Dongting Lake Area to assess the effect of human activities on the environment. The main finding was that with the decrease of Grade I and Grade II quality had a decreasing tendency in the lake area, mostly in suburbs and wetlands. Atmospheric water vapour, land use intensity, surface temperature, vegetation coverage, and soil water content were the main driving factors. The cause of degradation was the interference of multi-factor combinations, which led to positive and negative environmental agglomeration effects. Positive agglomeration, such as increased rainfall and vegetation coverage and reduced land use intensity, could increase environmental quality, while negative agglomeration resulted in the opposite. Therefore, reasonable ecological restoration measures should be beneficial to limit the negative effects and decreasing tendency, improve the land ecological environment quality and provide references for macroscopic planning by the government.

Land surface phenology

USGS Publications Warehouse

Hanes, Jonathan M.; Liang, Liang; Morisette, Jeffrey T.

2013-01-01

Certain vegetation types (e.g., deciduous shrubs, deciduous trees, grasslands) have distinct life cycles marked by the growth and senescence of leaves and periods of enhanced photosynthetic activity. Where these types exist, recurring changes in foliage alter the reflectance of electromagnetic radiation from the land surface, which can be measured using remote sensors. The timing of these recurring changes in reflectance is called land surface phenology (LSP). During recent decades, a variety of methods have been used to derive LSP metrics from time series of reflectance measurements acquired by satellite-borne sensors. In contrast to conventional phenology observations, LSP metrics represent the timing of reflectance changes that are driven by the aggregate activity of vegetation within the areal unit measured by the satellite sensor and do not directly provide information about the phenology of individual plants, species, or their phenophases. Despite the generalized nature of satellite sensor-derived measurements, they have proven useful for studying changes in LSP associated with various phenomena. This chapter provides a detailed overview of the use of satellite remote sensing to monitor LSP. First, the theoretical basis for the application of satellite remote sensing to the study of vegetation phenology is presented. After establishing a theoretical foundation for LSP, methods of deriving and validating LSP metrics are discussed. This chapter concludes with a discussion of major research findings and current and future research directions.

Second Eastern Regional Remote Sensing Applications Conference

NASA Technical Reports Server (NTRS)

Imhoff, M. L. (Editor); Witt, R. G. (Editor); Kugelmann, D. (Editor)

1981-01-01

Participants from state and local governments share experiences in remote sensing applications with one another and with users in the Federal government, universities, and the private sector during technical sessions and forums covering agriculture and forestry; land cover analysis and planning; surface mining and energy; data processing; water quality and the coastal zone; geographic information systems; and user development programs.

Tower-scale performance of four observation-based evapotranspiration algorithms within the WACMOS-ET project

NASA Astrophysics Data System (ADS)

Michel, Dominik; Miralles, Diego; Jimenez, Carlos; Ershadi, Ali; McCabe, Matthew F.; Hirschi, Martin; Seneviratne, Sonia I.; Jung, Martin; Wood, Eric F.; (Bob) Su, Z.; Timmermans, Joris; Chen, Xuelong; Fisher, Joshua B.; Mu, Quiaozen; Fernandez, Diego

2015-04-01

Research on climate variations and the development of predictive capabilities largely rely on globally available reference data series of the different components of the energy and water cycles. Several efforts have recently aimed at producing large-scale and long-term reference data sets of these components, e.g. based on in situ observations and remote sensing, in order to allow for diagnostic analyses of the drivers of temporal variations in the climate system. Evapotranspiration (ET) is an essential component of the energy and water cycle, which cannot be monitored directly on a global scale by remote sensing techniques. In recent years, several global multi-year ET data sets have been derived from remote sensing-based estimates, observation-driven land surface model simulations or atmospheric reanalyses. The LandFlux-EVAL initiative presented an ensemble-evaluation of these data sets over the time periods 1989-1995 and 1989-2005 (Mueller et al. 2013). The WACMOS-ET project (http://wacmoset.estellus.eu) started in the year 2012 and constitutes an ESA contribution to the GEWEX initiative LandFlux. It focuses on advancing the development of ET estimates at global, regional and tower scales. WACMOS-ET aims at developing a Reference Input Data Set exploiting European Earth Observations assets and deriving ET estimates produced by a set of four ET algorithms covering the period 2005-2007. The algorithms used are the SEBS (Su et al., 2002), Penman-Monteith from MODIS (Mu et al., 2011), the Priestley and Taylor JPL model (Fisher et al., 2008) and GLEAM (Miralles et al., 2011). The algorithms are run with Fluxnet tower observations, reanalysis data (ERA-Interim), and satellite forcings. They are cross-compared and validated against in-situ data. In this presentation the performance of the different ET algorithms with respect to different temporal resolutions, hydrological regimes, land cover types (including grassland, cropland, shrubland, vegetation mosaic, savanna, woody savanna, needleleaf forest, deciduous forest and mixed forest) are evaluated at the tower-scale in 24 pre-selected study regions on three continents (Europe, North America, and Australia). References: Fisher, J. B., Tu, K.P., and Baldocchi, D.D. Global estimates of the land-atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites, Remote Sens. Environ. 112, 901-919, 2008. Jiménez, C. et al. Global intercomparison of 12 land surface heat flux estimates. J. Geophys. Res. 116, D02102, 2011. 
 Miralles, D.G. et al. Global land-surface evaporation estimated from satellite-based observations. Hydrol. Earth Syst. Sci. 15, 453-469, 2011. 
 Mu, Q., Zhao, M. & Running, S.W. Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens. Environ. 115, 1781-1800, 2011. 
 Mueller, B., Hirschi, M., Jimenez, C., Ciais, P., Dirmeyer, P. A., Dolman, A. J., Fisher, J. B., Jung, M., Ludwig, F., Maignan, F., Miralles, D. G., McCabe, M. F., Reichstein, M., Sheffield, J., Wang, K., Wood, E. F., Zhang, Y., and Seneviratne, S. I. (2013). Benchmark products for land evapotranspiration: LandFlux-EVAL multi-data set synthesis. Hydrology and Earth System Sciences, 17, 3707-3720. Mueller, B. et al. Benchmark products for land evapotranspiration: LandFlux-EVAL multi-dataset synthesis. Hydrol. Earth Syst. Sci. 17, 3707-3720, 2013. Su, Z. The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes. Hydrol. Earth Syst. Sci. 6, 85-99, 2002.

Applications of remote sensing to watershed management

NASA Technical Reports Server (NTRS)

Rango, A.

1975-01-01

Aircraft and satellite remote sensing systems which are capable of contributing to watershed management are described and include: the multispectral scanner subsystem on LANDSAT and the basic multispectral camera array flown on high altitude aircraft such as the U-2. Various aspects of watershed management investigated by remote sensing systems are discussed. Major areas included are: snow mapping, surface water inventories, flood management, hydrologic land use monitoring, and watershed modeling. It is indicated that technological advances in remote sensing of hydrological data must be coupled with an expansion of awareness and training in remote sensing techniques of the watershed management community.

Multi-temporal analysis of land surface temperature in highly urbanized districts

NASA Astrophysics Data System (ADS)

Kaya, S.; Celik, B.; Sertel, E.; Bayram, B.; Seker, D. Z.

2017-12-01

Istanbul is one of the largest cities around the world with population over 15 million and it has 39 districts. Due to high immigration rate after the 1980s, parallel to the urbanization rapid population increase has occurred in some of these districts. Thus, a significant increase in land surface temperature were monitored and this subject became one of the most popular subject of different researches. Natural landscapes transformed into residential areas with impervious surfaces that causes rise in land surface temperatures which is one of the component of urban heat islands. This study focuses on determining the land use/land cover changes and land surface temperature in highly urbanized districts for last 32 years and examining the relationship between these two parameters using multi-temporal optical and thermal remotely sensed data. In this study, Landsat5 Thematic Mapper and Landsat8 OLI/TIR imagery with acquisition dates June 1984 and June 2016 were used. In order to assess the land use/cover change between 1984 and 2016, Vegetation Impervious Surface-soil (V-I-S) model is used. Each end-member spectra are extracted from ASTER spectral library. Additionally, V-I-S model, NDVI, NDBI and NDBaI indices have been derived for further investigation of land cover changes. The results of the study, presented that in the last 32 years, the amount of impervious surfaces substantially increased along with land surface temperatures.

Regional scale hydrology with a new land surface processes model

NASA Technical Reports Server (NTRS)

Laymon, Charles; Crosson, William

1995-01-01

Through the CaPE Hydrometeorology Project, we have developed an understanding of some of the unique data quality issues involved in assimilating data of disparate types for regional-scale hydrologic modeling within a GIS framework. Among others, the issues addressed here include the development of adequate validation of the surface water budget, implementation of the STATSGO soil data set, and implementation of a remote sensing-derived landcover data set to account for surface heterogeneity. A model of land surface processes has been developed and used in studies of the sensitivity of surface fluxes and runoff to soil and landcover characterization. Results of these experiments have raised many questions about how to treat the scale-dependence of land surface-atmosphere interactions on spatial and temporal variability. In light of these questions, additional modifications are being considered for the Marshall Land Surface Processes Model. It is anticipated that these techniques can be tested and applied in conjunction with GCIP activities over regional scales.

Characterizing Impacts of Land Grabbing on Terrestrial Vegetation and Ecohydrologic change in Mozambique through Multiple-sensor Remote Sensing and Models

NASA Astrophysics Data System (ADS)

Flores, A. N.; Lakshmi, V.; Al-Barakat, R.; Maksimowicz, M.

2017-12-01

Land grabbing, the acquisition of large areas of land by external entities, results from interactions of complex global economic, social, and political processes. These transactions are controversial because they can result in large-scale disruptions to historical land uses, including increased intensity of agricultural practices and significant conversions in land cover. These large-scale disruptions have the potential to impact surface water and energy balance because vegetation controls the partitioning of incoming energy into latent and sensible heat fluxes and precipitation into runoff and infiltration. Because large-scale land acquisitions can impact local ecosystem services, it is important to document changes in terrestrial vegetation associated with these acquisitions to support the assessment of associated impacts on regional surface water and energy balance, spatiotemporal scales of those changes, and interactions and feedbacks with other processes, particularly in the atmosphere. We use remote sensing data from multiple satellite platforms to diagnose and characterize changes in terrestrial vegetation and ecohydrology in Mozambique during periods that bracket periods associated with significant. The Advanced very High Resolution Radiometer (AVHRR) sensor provides long-term continuous data that can document historical seasonal cycles of vegetation greenness. These data are augmented with analyses from Landsat multispectral data, which provides significantly higher spatial resolution. Here we quantify spatiotemporal changes in vegetation are associated with periods of significant land acquisitions in Mozambique. This analysis complements a suite of land-atmosphere modeling experiments designed to deduce potential changes in land surface water and energy budgets associated with these acquisitions. This work advance understanding of how telecouplings between global economic and political forcings and regional hydrology and climate.

Comparison of Land Skin Temperature from a Land Model, Remote Sensing, and In-situ Measurement

NASA Technical Reports Server (NTRS)

Wang, Aihui; Barlage, Michael; Zeng, Xubin; Draper, Clara Sophie

2014-01-01

Land skin temperature (Ts) is an important parameter in the energy exchange between the land surface and atmosphere. Here hourly Ts from the Community Land Model Version 4.0, MODIS satellite observations, and in-situ observations in 2003 were compared. Compared with the in-situ observations over four semi-arid stations, both MODIS and modeled Ts show negative biases, but MODIS shows an overall better performance. Global distribution of differences between MODIS and modeled Ts shows diurnal, seasonal, and spatial variations. Over sparsely vegetated areas, the model Ts is generally lower than the MODIS observed Ts during the daytime, while the situation is opposite at nighttime. The revision of roughness length for heat and the constraint of minimum friction velocity from Zeng et al. [2012] bring the modeled Ts closer to MODIS during the day, and have little effect on Ts at night. Five factors contributing to the Ts differences between the model and MODIS are identified, including the difficulty in properly accounting for cloud cover information at the appropriate temporal and spatial resolutions, and uncertainties in surface energy balance computation, atmospheric forcing data, surface emissivity, and MODIS Ts data. These findings have implications for the cross-evaluation of modeled and remotely sensed Ts, as well as the data assimilation of Ts observations into Earth system models.

A review on remotely sensed land surface temperature anomaly as an earthquake precursor

NASA Astrophysics Data System (ADS)

Bhardwaj, Anshuman; Singh, Shaktiman; Sam, Lydia; Joshi, P. K.; Bhardwaj, Akanksha; Martín-Torres, F. Javier; Kumar, Rajesh

2017-12-01

The low predictability of earthquakes and the high uncertainty associated with their forecasts make earthquakes one of the worst natural calamities, capable of causing instant loss of life and property. Here, we discuss the studies reporting the observed anomalies in the satellite-derived Land Surface Temperature (LST) before an earthquake. We compile the conclusions of these studies and evaluate the use of remotely sensed LST anomalies as precursors of earthquakes. The arrival times and the amplitudes of the anomalies vary widely, thus making it difficult to consider them as universal markers to issue earthquake warnings. Based on the randomness in the observations of these precursors, we support employing a global-scale monitoring system to detect statistically robust anomalous geophysical signals prior to earthquakes before considering them as definite precursors.

Utilizing Higher Resolution Land Surface Remote Sensing Data for Assessing Recent Trends over Asia Monsoon Region

NASA Technical Reports Server (NTRS)

Shen, Suhung; Leptoukh, Gregory

2010-01-01

The slide presentation discusses the integration of 1-kilometer spatial resolution land temperature data from the Moderate Resolution Imaging Spectroradiometer (MODIS), with 8-day temporal resolution, into the NASA Monsoon-Asia Integrated Regional Study (MAIRS) Data Center. The data will be available for analysis and visualization in the Giovanni data system. It discusses the NASA MAIRS Data Center, presents an introduction to the data access tools, and an introduction of Products available from the service, discusses the higher resolution Land Surface Temperature (LST) and presents preliminary results of LST Trends over China.

Spatially Complete Global Surface Albedos Derived from Terra/MODIS Data

NASA Technical Reports Server (NTRS)

King, Michael D.; Moody, Eric G.; Platnick, Steven; Schaaf, Crystal B.

2005-01-01

Spectral land surface albedo is an important parameter for describing the radiative properties of the Earth. Accordingly it reflects the consequences of natural and human interactions, such as anthropogenic, meteorological, and phenological effects, on global and local climatological trends. Consequently, albedos are integral parts in a variety of research areas, such as general circulation models (GCMs), energy balance studies, modeling of land use and land use change, and biophysical, oceanographic, and meteorological studies. Recent production of land surface anisotropy, diffuse bihemispherical (white-sky) albedo and direct beam directional hemispherical (black-sky) albedo from observations acquired by the MODIS instruments aboard NASA's Terra and &la satellite platforms have provided researchers with unprecedented spatial, spectral, and temporal information on the land surface's radiative characteristics. Cloud cover, which curtails retrievals, and the presence of ephemeral and seasonal snow limit the snow-free data to approximately half the global land surfaces on an annual equal-angle basis. This precludes the MOD43B3 albedo products from being used in some remote sensing and ground-based applications, &mate models, and global change research projects.

Estimation of the Relationship Between Remotely Sensed Anthropogenic Heat Discharge and Building Energy Use

NASA Technical Reports Server (NTRS)

Zhou, Yuyu; Weng, Qihao; Gurney, Kevin R.; Shuai, Yanmin; Hu, Xuefei

2012-01-01

This paper examined the relationship between remotely sensed anthropogenic heat discharge and energy use from residential and commercial buildings across multiple scales in the city of Indianapolis, Indiana, USA. The anthropogenic heat discharge was estimated with a remote sensing-based surface energy balance model, which was parameterized using land cover, land surface temperature, albedo, and meteorological data. The building energy use was estimated using a GIS-based building energy simulation model in conjunction with Department of Energy/Energy Information Administration survey data, the Assessor's parcel data, GIS floor areas data, and remote sensing-derived building height data. The spatial patterns of anthropogenic heat discharge and energy use from residential and commercial buildings were analyzed and compared. Quantitative relationships were evaluated across multiple scales from pixel aggregation to census block. The results indicate that anthropogenic heat discharge is consistent with building energy use in terms of the spatial pattern, and that building energy use accounts for a significant fraction of anthropogenic heat discharge. The research also implies that the relationship between anthropogenic heat discharge and building energy use is scale-dependent. The simultaneous estimation of anthropogenic heat discharge and building energy use via two independent methods improves the understanding of the surface energy balance in an urban landscape. The anthropogenic heat discharge derived from remote sensing and meteorological data may be able to serve as a spatial distribution proxy for spatially-resolved building energy use, and even for fossil-fuel CO2 emissions if additional factors are considered.

A Mechanism for Land-Atmosphere Feedback Involving Planetary Wave Structures

NASA Technical Reports Server (NTRS)

Koster, Randal D.; Chang, Yehui; Schubert, Siegfried D.

2014-01-01

While the ability of land surface conditions to influence the atmosphere has been demonstrated in various modeling and observational studies, the precise mechanisms by which land-atmosphere feedback occurs are still largely unknown particularly the mechanisms that allow land moisture state in one region to affect atmospheric conditions in another. Such remote impacts are examined here in the context of atmospheric general circulation model (AGCM) simulations, leading to the identification of one potential mechanism: the phase-locking and amplification of a planetary wave through the imposition of a spatial pattern of soil moisture at the land surface. This mechanism, shown here to be relevant in the AGCM, apparently also operates in nature, as suggested by supporting evidence found in reanalysis data.

Incorporating Land-Use Mapping Uncertainty in Remote Sensing Based Calibration of Land-Use Change Models

NASA Astrophysics Data System (ADS)

Cockx, K.; Van de Voorde, T.; Canters, F.; Poelmans, L.; Uljee, I.; Engelen, G.; de Jong, K.; Karssenberg, D.; van der Kwast, J.

2013-05-01

Building urban growth models typically involves a process of historic calibration based on historic time series of land-use maps, usually obtained from satellite imagery. Both the remote sensing data analysis to infer land use and the subsequent modelling of land-use change are subject to uncertainties, which may have an impact on the accuracy of future land-use predictions. Our research aims to quantify and reduce these uncertainties by means of a particle filter data assimilation approach that incorporates uncertainty in land-use mapping and land-use model parameter assessment into the calibration process. This paper focuses on part of this work, more in particular the modelling of uncertainties associated with the impervious surface cover estimation and urban land-use classification adopted in the land-use mapping approach. Both stages are submitted to a Monte Carlo simulation to assess their relative contribution to and their combined impact on the uncertainty in the derived land-use maps. The approach was applied on the central part of the Flanders region (Belgium), using a time-series of Landsat/SPOT-HRV data covering the years 1987, 1996, 2005 and 2012. Although the most likely land-use map obtained from the simulation is very similar to the original classification, it is shown that the errors related to the impervious surface sub-pixel fraction estimation have a strong impact on the land-use map's uncertainty. Hence, incorporating uncertainty in the land-use change model calibration through particle filter data assimilation is proposed to address the uncertainty observed in the derived land-use maps and to reduce uncertainty in future land-use predictions.

NASA Satellite Captures Super Bowl Cities - Denver, CO

NASA Image and Video Library

2016-02-06

Landsat 7 image of Denver area acquired Nov 3, 2015. Landsat 7 is a U.S. satellite used to acquire remotely sensed images of the Earth's land surface and surrounding coastal regions. It is maintained by the Landsat 7 Project Science Office at the NASA Goddard Space Flight Center in Greenbelt, MD...Landsat satellites have been acquiring images of the Earth’s land surface since 1972. Currently there are more than 2 million Landsat images in the National Satellite Land Remote Sensing Data Archive. For more information visit: landsat.usgs.gov/..To learn more about the Landsat satellite go to:.landsat.gsfc.nasa.gov/ Credit: NASA/GSFC/Landsat 7 NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

NASA Satellite Captures Super Bowl Cities - Santa Clara, CA

NASA Image and Video Library

2017-12-08

Landsat 7 image of the Santa Clara area acquired Nov 16, 2015. Landsat 7 is a U.S. satellite used to acquire remotely sensed images of the Earth's land surface and surrounding coastal regions. It is maintained by the Landsat 7 Project Science Office at the NASA Goddard Space Flight Center in Greenbelt, MD...Landsat satellites have been acquiring images of the Earth’s land surface since 1972. Currently there are more than 2 million Landsat images in the National Satellite Land Remote Sensing Data Archive. For more information visit: landsat.usgs.gov/..To learn more about the Landsat satellite go to:.landsat.gsfc.nasa.gov/ Credit: NASA/GSFC/Landsat 7 NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

NASA Satellite Captures Super Bowl Cities - Boston/Providence [annotated

NASA Image and Video Library

2015-01-30

Landsat 7 image of Boston/Providence area acquired August 25, 2014. Landsat 7 is a U.S. satellite used to acquire remotely sensed images of the Earth's land surface and surrounding coastal regions. It is maintained by the Landsat 7 Project Science Office at the NASA Goddard Space Flight Center in Greenbelt, MD. Landsat satellites have been acquiring images of the Earth’s land surface since 1972. Currently there are more than 2 million Landsat images in the National Satellite Land Remote Sensing Data Archive. For more information visit: landsat.usgs.gov/..To learn more about the Landsat satellite go to:.landsat.gsfc.nasa.gov/ Credit: NASA/GSFC/Landsat 7 NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

NASA Satellite Captures Super Bowl Cities - Seattle

NASA Image and Video Library

2015-01-30

Landsat 7 image of Seattle, Washington acquired August 23, 2014. Landsat 7 is a U.S. satellite used to acquire remotely sensed images of the Earth's land surface and surrounding coastal regions. It is maintained by the Landsat 7 Project Science Office at the NASA Goddard Space Flight Center in Greenbelt, MD. Landsat satellites have been acquiring images of the Earth’s land surface since 1972. Currently there are more than 2 million Landsat images in the National Satellite Land Remote Sensing Data Archive. For more information visit: landsat.usgs.gov/..To learn more about the Landsat satellite go to:.landsat.gsfc.nasa.gov/ Credit: NASA/GSFC/Landsat 7 NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

NASA Satellite Captures Super Bowl Cities - Seattle [annotated

NASA Image and Video Library

2015-01-30

Landsat 7 image of Seattle, Washington acquired August 23, 2014. Landsat 7 is a U.S. satellite used to acquire remotely sensed images of the Earth's land surface and surrounding coastal regions. It is maintained by the Landsat 7 Project Science Office at the NASA Goddard Space Flight Center in Greenbelt, MD. Landsat satellites have been acquiring images of the Earth’s land surface since 1972. Currently there are more than 2 million Landsat images in the National Satellite Land Remote Sensing Data Archive. For more information visit: landsat.usgs.gov/..To learn more about the Landsat satellite go to:.landsat.gsfc.nasa.gov/ Credit: NASA/GSFC/Landsat 7 NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

NASA Satellite Captures Super Bowl Cities - Phoenix

NASA Image and Video Library

2015-01-30

Landsat 7 image of Phoenix, Arizona acquired November 28, 2014. Landsat 7 is a U.S. satellite used to acquire remotely sensed images of the Earth's land surface and surrounding coastal regions. It is maintained by the Landsat 7 Project Science Office at the NASA Goddard Space Flight Center in Greenbelt, MD. Landsat satellites have been acquiring images of the Earth’s land surface since 1972. Currently there are more than 2 million Landsat images in the National Satellite Land Remote Sensing Data Archive. For more information visit: landsat.usgs.gov/..To learn more about the Landsat satellite go to:.landsat.gsfc.nasa.gov/ Credit: NASA/GSFC/Landsat 7 NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

NASA Satellite Captures Super Bowl Cities - Boston/Providence

NASA Image and Video Library

2015-01-30

Landsat 7 image of Boston/Providence area acquired August 25, 2014. Landsat 7 is a U.S. satellite used to acquire remotely sensed images of the Earth's land surface and surrounding coastal regions. It is maintained by the Landsat 7 Project Science Office at the NASA Goddard Space Flight Center in Greenbelt, MD...Landsat satellites have been acquiring images of the Earth’s land surface since 1972. Currently there are more than 2 million Landsat images in the National Satellite Land Remote Sensing Data Archive. For more information visit: landsat.usgs.gov/..To learn more about the Landsat satellite go to:.landsat.gsfc.nasa.gov/ Credit: NASA/GSFC/Landsat 7 NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

NASA Satellite Captures Super Bowl Cities - Phoenix [annotated

NASA Image and Video Library

2015-01-30

Landsat 7 image of Phoenix, Arizona acquired November 28, 2014. Landsat 7 is a U.S. satellite used to acquire remotely sensed images of the Earth's land surface and surrounding coastal regions. It is maintained by the Landsat 7 Project Science Office at the NASA Goddard Space Flight Center in Greenbelt, MD. Landsat satellites have been acquiring images of the Earth’s land surface since 1972. Currently there are more than 2 million Landsat images in the National Satellite Land Remote Sensing Data Archive. For more information visit: landsat.usgs.gov/..To learn more about the Landsat satellite go to:.landsat.gsfc.nasa.gov/ Credit: NASA/GSFC/Landsat 7 NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

NASA Satellite Captures Super Bowl Cities - Charlotte, NC

NASA Image and Video Library

2016-02-06

Landsat 7 image of the Charlotte, NC area acquired Oct 18, 2015. Landsat 7 is a U.S. satellite used to acquire remotely sensed images of the Earth's land surface and surrounding coastal regions. It is maintained by the Landsat 7 Project Science Office at the NASA Goddard Space Flight Center in Greenbelt, MD...Landsat satellites have been acquiring images of the Earth’s land surface since 1972. Currently there are more than 2 million Landsat images in the National Satellite Land Remote Sensing Data Archive. For more information visit: landsat.usgs.gov/..To learn more about the Landsat satellite go to:.landsat.gsfc.nasa.gov/ Credit: NASA/GSFC/Landsat 7 NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

Using hyperspectral remote sensing for land cover classification

NASA Astrophysics Data System (ADS)

Zhang, Wendy W.; Sriharan, Shobha

2005-01-01

This project used hyperspectral data set to classify land cover using remote sensing techniques. Many different earth-sensing satellites, with diverse sensors mounted on sophisticated platforms, are currently in earth orbit. These sensors are designed to cover a wide range of the electromagnetic spectrum and are generating enormous amounts of data that must be processed, stored, and made available to the user community. The Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) collects data in 224 bands that are approximately 9.6 nm wide in contiguous bands between 0.40 and 2.45 mm. Hyperspectral sensors acquire images in many, very narrow, contiguous spectral bands throughout the visible, near-IR, and thermal IR portions of the spectrum. The unsupervised image classification procedure automatically categorizes the pixels in an image into land cover classes or themes. Experiments on using hyperspectral remote sensing for land cover classification were conducted during the 2003 and 2004 NASA Summer Faculty Fellowship Program at Stennis Space Center. Research Systems Inc.'s (RSI) ENVI software package was used in this application framework. In this application, emphasis was placed on: (1) Spectrally oriented classification procedures for land cover mapping, particularly, the supervised surface classification using AVIRIS data; and (2) Identifying data endmembers.

Remote sensing of effects of land use practices on water quality

NASA Technical Reports Server (NTRS)

Graves, D. H.; Colthrap, G. B.

1977-01-01

An intensive study was conducted to determine the utility of manual densitometry and color additive viewing of aircraft and LANDSAT transparencies for monitoring land use and land use change. The relationship between land use and selected water quality parameters was also evaluated. Six watersheds located in the Cumberland Plateau region of eastern Kentucky comprised the study area for the project. Land uses present within the study area were reclaimed surface mining and forestry. Fertilization of one of the forested watersheds also occurred during the study period.

The application of remote sensing to the development and formulation of hydrologic planning models: Executive summary

NASA Technical Reports Server (NTRS)

Castruccio, P. A.; Loats, H. L., Jr.; Fowler, T. R.

1977-01-01

Methods for the reduction of remotely sensed data and its application in hydrologic land use assessment, surface water inventory, and soil property studies are presented. LANDSAT data is used to provide quantitative parameters and coefficients to construct watershed transfer functions for a hydrologic planning model aimed at estimating peak outflow from rainfall inputs.

A hybrid HDRF model of GOMS and SAIL: GOSAIL

NASA Astrophysics Data System (ADS)

Dou, B.; Wu, S.; Wen, J.

2016-12-01

Understanding the surface reflectance anisotropy is the key facet in interpreting the features of land surface from remotely sensed information, which describes the property of land surface to reflect the solar radiation directionally. Most reflectance anisotropy models assumed the nature surface was illuminated only by the direct solar radiation, while the diffuse skylight becomes dominant especially for the over cast sky conditions and high rugged terrain. Correcting the effect of diffuse skylight on the reflectance anisotropy to obtain the intrinsic directional reflectance of land surface is highly desirable for remote sensing applications. This paper developed a hybrid HDRF model of GOMS and SAIL called GOSAIL model for discrete canopies. The accurate area proportions of four scene components are calculated by the GOMS model and the spectral signatures of scene components are provided by the SAIL model. Both the single scattering contribution and the multiple scattering contributions within and between the canopy and background under the clear and diffuse illumination conditions are considered in the GOSAIL model. The HDRF simulated by the 3-D Discrete Anisotropic Radiative Transfer (DART) model and the HDRF measurements over the 100m×100m mature pine stand at the Järvselja, Estonia are used for validating and evaluating the performance of proposed GOSAIL model. The comparison results indicate the GOSAIL model can accurately reproducing the angular feature of discrete canopy for both the clear and overcast atmospheric conditions. The GOSAIL model is promising for the land surface biophysical parameters retrieval (e.g. albedo, leaf area index) over the heterogeneous terrain.

Global change research related to the Earth's energy and hydrologic cycle

NASA Technical Reports Server (NTRS)

Perkey, Donald J.

1994-01-01

The following are discussed: Geophysical Modeling and Processes; Land Surface Processes and Atmospheric Interactions; Remote Sensing Technology and Geophysical Retrievals; and Scientific Data Management and Visual Analysis.

Mapping the Distribution of Potential Land Drought in Batam Island Using the Integration of Remote Sensing and Geographic Information Systems (GIS)

NASA Astrophysics Data System (ADS)

Lubis, M. Z.; Taki, H. M.; Anurogo, W.; Pamungkas, D. S.; Wicaksono, P.; Aprilliyanti, T.

2017-12-01

Potential land drought mapping on Batam is needed to determine the distribution of areas that are very potential to the physical drought of the land. It is because the drought is always threatening on the long dry season. This research integrates remote sensing science with Geographic Information System (GIS). This research aims to map the distribution of land drought potential in Batam Island. The parameters used in this research are land use, Land Surface Temperature (LST), Potential dryness of land on the Batam island. The resulting map indicates the existence of five potential drought classes on the island of Batam. The area of very low drought potential is 2629.45 ha, mostly located in the Sungai Beduk sub-district. High drought potential with an area of 7081.39 ha is located in Sekupang sub-district. The distribution of very high land drought potential is in Batam city and Nongsa sub-district with area of 15600.12 ha. The coefficient of determination (R 2) is 0.6279. This indicates a strong positive relationship between field LST and modelled LST.

Remotely-sensed, nocturnal, dew point correlates with malaria transmission in Southern Province, Zambia: a time-series study

PubMed Central

2014-01-01

Background Plasmodium falciparum transmission has decreased significantly in Zambia in the last decade. The malaria transmission is influenced by environmental variables. Incorporation of environmental variables in models of malaria transmission likely improves model fit and predicts probable trends in malaria disease. This work is based on the hypothesis that remotely-sensed environmental factors, including nocturnal dew point, are associated with malaria transmission and sustain foci of transmission during the low transmission season in the Southern Province of Zambia. Methods Thirty-eight rural health centres in Southern Province, Zambia were divided into three zones based on transmission patterns. Correlations between weekly malaria cases and remotely-sensed nocturnal dew point, nocturnal land surface temperature as well as vegetation indices and rainfall were evaluated in time-series analyses from 2012 week 19 to 2013 week 36. Zonal as well as clinic-based, multivariate, autoregressive, integrated, moving average (ARIMAX) models implementing environmental variables were developed to model transmission in 2011 week 19 to 2012 week 18 and forecast transmission in 2013 week 37 to week 41. Results During the dry, low transmission season significantly higher vegetation indices, nocturnal land surface temperature and nocturnal dew point were associated with the areas of higher transmission. Environmental variables improved ARIMAX models. Dew point and normalized differentiated vegetation index were significant predictors and improved all zonal transmission models. In the high-transmission zone, this was also seen for land surface temperature. Clinic models were improved by adding dew point and land surface temperature as well as normalized differentiated vegetation index. The mean average error of prediction for ARIMAX models ranged from 0.7 to 33.5%. Forecasts of malaria incidence were valid for three out of five rural health centres; however, with poor results at the zonal level. Conclusions In this study, the fit of ARIMAX models improves when environmental variables are included. There is a significant association of remotely-sensed nocturnal dew point with malaria transmission. Interestingly, dew point might be one of the factors sustaining malaria transmission in areas of general aridity during the dry season. PMID:24927747

Remotely-sensed, nocturnal, dew point correlates with malaria transmission in Southern Province, Zambia: a time-series study.

PubMed

Nygren, David; Stoyanov, Cristina; Lewold, Clemens; Månsson, Fredrik; Miller, John; Kamanga, Aniset; Shiff, Clive J

2014-06-13

Plasmodium falciparum transmission has decreased significantly in Zambia in the last decade. The malaria transmission is influenced by environmental variables. Incorporation of environmental variables in models of malaria transmission likely improves model fit and predicts probable trends in malaria disease. This work is based on the hypothesis that remotely-sensed environmental factors, including nocturnal dew point, are associated with malaria transmission and sustain foci of transmission during the low transmission season in the Southern Province of Zambia. Thirty-eight rural health centres in Southern Province, Zambia were divided into three zones based on transmission patterns. Correlations between weekly malaria cases and remotely-sensed nocturnal dew point, nocturnal land surface temperature as well as vegetation indices and rainfall were evaluated in time-series analyses from 2012 week 19 to 2013 week 36. Zonal as well as clinic-based, multivariate, autoregressive, integrated, moving average (ARIMAX) models implementing environmental variables were developed to model transmission in 2011 week 19 to 2012 week 18 and forecast transmission in 2013 week 37 to week 41. During the dry, low transmission season significantly higher vegetation indices, nocturnal land surface temperature and nocturnal dew point were associated with the areas of higher transmission. Environmental variables improved ARIMAX models. Dew point and normalized differentiated vegetation index were significant predictors and improved all zonal transmission models. In the high-transmission zone, this was also seen for land surface temperature. Clinic models were improved by adding dew point and land surface temperature as well as normalized differentiated vegetation index. The mean average error of prediction for ARIMAX models ranged from 0.7 to 33.5%. Forecasts of malaria incidence were valid for three out of five rural health centres; however, with poor results at the zonal level. In this study, the fit of ARIMAX models improves when environmental variables are included. There is a significant association of remotely-sensed nocturnal dew point with malaria transmission. Interestingly, dew point might be one of the factors sustaining malaria transmission in areas of general aridity during the dry season.

Radiative Properties of Smoke and Aerosol Over Land Surfaces

NASA Technical Reports Server (NTRS)

King, Michael D.

2000-01-01

This talk discusses smoke and aerosol's radiative properties with particular attention to distinguishing the measurement over clear sky from clouds over land, sea, snow, etc. surfaces, using MODIS Airborne Simulator data from (Brazil, arctic sea ice and tundra and southern Africa, west Africa, and other ecosystems. This talk also discusses the surface bidirectional reflectance using Cloud Absorption Radiometer, BRDF measurements of Saudi Arabian desert, Persian Gulf, cerrado and rain forests in Brazil, sea ice, tundra, Atlantic Ocean, Great Dismal Swamp, Kuwait oil fire smoke. Recent upgrades to instrument (new TOMS UVA channels at 340 and 380 planned use in Africa (SAFARI 2000) and possibly for MEIDEX will also be discussed. This talk also plans to discuss the spectral variation of surface reflectance over land and the sensitivity of off-nadir view angles to correlation between visible near-infrared reflectance for use in remote sensing of aerosol over land.

Remote sensing of soils, land forms, and land use in the northern great plains in preparation for ERTS applications

NASA Technical Reports Server (NTRS)

Frazee, C. J.; Westin, F. C.; Gropper, J.; Myers, V. I.

1972-01-01

Research to determine the optimum time or season for obtaining imagery to identify and map soil limitations was conducted in the proposed Oahe irrigation project area in South Dakota. The optimum time for securing photographs or imagery is when the soil surface patterns are most apparent. For cultivated areas similar to the study area, May is the optimum time. The fields are cultivated or the planted crop has not yet masked soil surface features. Soil limitations in 59 percent of the field of the flight line could be mapped using the above criteria. The remaining fields cannot be mapped because the vegetation or growing crops do not express features related to soil differences. This suggests that imagery from more than one year is necessary to map completely the soil limitations of Oahe area by remote sensing techniques. Imagery from the other times studied is not suitable for identifying and mapping soil limitations of Oahe area by remote sensing techniques. Imagery from the other times studied is not suitable for identifying and mapping soil limitations because the vegetative cover masked the soil surface and does not reflect soil differences.

Visualizing Airborne and Satellite Imagery

NASA Technical Reports Server (NTRS)

Bierwirth, Victoria A.

2011-01-01

Remote sensing is a process able to provide information about Earth to better understand Earth's processes and assist in monitoring Earth's resources. The Cloud Absorption Radiometer (CAR) is one remote sensing instrument dedicated to the cause of collecting data on anthropogenic influences on Earth as well as assisting scientists in understanding land-surface and atmospheric interactions. Landsat is a satellite program dedicated to collecting repetitive coverage of the continental Earth surfaces in seven regions of the electromagnetic spectrum. Combining these two aircraft and satellite remote sensing instruments will provide a detailed and comprehensive data collection able to provide influential information and improve predictions of changes in the future. This project acquired, interpreted, and created composite images from satellite data acquired from Landsat 4-5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper plus (ETM+). Landsat images were processed for areas covered by CAR during the Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCT AS), Cloud and Land Surface Interaction Campaign (CLASIC), Intercontinental Chemical Transport Experiment-Phase B (INTEXB), and Southern African Regional Science Initiative (SAFARI) 2000 missions. The acquisition of Landsat data will provide supplemental information to assist in visualizing and interpreting airborne and satellite imagery.

Classification of forest land attributes using multi-source remotely sensed data

NASA Astrophysics Data System (ADS)

Pippuri, Inka; Suvanto, Aki; Maltamo, Matti; Korhonen, Kari T.; Pitkänen, Juho; Packalen, Petteri

2016-02-01

The aim of the study was to (1) examine the classification of forest land using airborne laser scanning (ALS) data, satellite images and sample plots of the Finnish National Forest Inventory (NFI) as training data and to (2) identify best performing metrics for classifying forest land attributes. Six different schemes of forest land classification were studied: land use/land cover (LU/LC) classification using both national classes and FAO (Food and Agricultural Organization of the United Nations) classes, main type, site type, peat land type and drainage status. Special interest was to test different ALS-based surface metrics in classification of forest land attributes. Field data consisted of 828 NFI plots collected in 2008-2012 in southern Finland and remotely sensed data was from summer 2010. Multinomial logistic regression was used as the classification method. Classification of LU/LC classes were highly accurate (kappa-values 0.90 and 0.91) but also the classification of site type, peat land type and drainage status succeeded moderately well (kappa-values 0.51, 0.69 and 0.52). ALS-based surface metrics were found to be the most important predictor variables in classification of LU/LC class, main type and drainage status. In best classification models of forest site types both spectral metrics from satellite data and point cloud metrics from ALS were used. In turn, in the classification of peat land types ALS point cloud metrics played the most important role. Results indicated that the prediction of site type and forest land category could be incorporated into stand level forest management inventory system in Finland.

Estimating and validating surface energy fluxes at field scale over a heterogeneous land surfaces based on two-source energy balance model (TSEB)

USDA-ARS?s Scientific Manuscript database

Accurate estimation of surface energy fluxes at field scale over large areas has the potential to improve agricultural water management in arid and semiarid watersheds. Remote sensing may be the only viable approach for mapping fluxes over heterogeneous landscapes. The Two-Source Energy Balance mode...

Interactive Computing and Processing of NASA Land Surface Observations Using Google Earth Engine

NASA Technical Reports Server (NTRS)

Molthan, Andrew; Burks, Jason; Bell, Jordan

2016-01-01

Google's Earth Engine offers a "big data" approach to processing large volumes of NASA and other remote sensing products. h\\ps://earthengine.google.com/ Interfaces include a Javascript or Python-based API, useful for accessing and processing over large periods of record for Landsat and MODIS observations. Other data sets are frequently added, including weather and climate model data sets, etc. Demonstrations here focus on exploratory efforts to perform land surface change detection related to severe weather, and other disaster events.

Biophysical Impacts of Tropical Land Transformation from Forest to Oil Palm and Rubber Plantations in Indonesia

NASA Astrophysics Data System (ADS)

Knohl, Alexander; Meijide, Ana; Fan, Yuanchao; Gunawan, Dodo; Hölscher, Dirk; June, Tania; Niu, Furong; Panferov, Oleg; Ringeler, Andre; Röll, Alexander; Sabajo, Clifton; Tiralla, Nina

2016-04-01

Indonesia currently experiences rapid and large-scale land-use changes resulting in forest loss and the expansion of cash crop plantations such as oil palm and rubber. Such land transformations are associated with changes in surface properties that affect biophysical processes influencing the atmosphere. Yet, the overall effect of such land transformations on the atmosphere at local and regional scale remains unclear. In our study, we combine measurements of microclimate, transpiration via sap-flux, surface energy fluxes via eddy covariance, surface temperature via remote sensing, land surface (CLM) and regional climate modeling (WRF) for Jambi Province in Indonesia. Our microclimatic measurements showed that air temperature within the canopy was on average 0.7-0.8°C higher in monoculture plantations (oil palm and rubber) compared to forest. Remote sensing analysis using MODIS and Landsat revealed a higher canopy surface temperature for oil palm plantations (+1.5°C) compared to forest, but only little differences for rubber plantations. Transpiration (T) and evapotranspiration (ET) as well as the contribution of T to ET of oil palm showed a strong age-dependent increase. The sensible to latent heat flux ratio decreased with age. Overall, rubber plantations showed the lowest transpirations rates (320 mm year-1), oil palm intermediate rates (414 mm year-1), and forest the highest rates (558 mm year-1) indicating substantial differences in water use. Despite the differences in water use and the higher within-canopy and surface temperatures of the plantations compared to the forest, there was only a minor effect of land transformation on the atmosphere at the regional scale (<0.2 °C), irrespectively of the large spatial extend of the transformation. In conclusion, our study shows a strong local scale biophysical impact affecting the conditions at the stand level, which is however mitigated in the atmosphere at the regional level.

Biophysical Impacts of Tropical Land Transformation from Forest to Oil Palm and Rubber Plantations in Indonesia

NASA Astrophysics Data System (ADS)

Knohl, A.; Meijide, A.; Fan, Y.; Hölscher, D.; June, T.; Niu, F.; Panferov, O.; Ringeler, A.; Röll, A.; Sabajo, C.; Tiralla, N.

2015-12-01

Indonesia currently experiences rapid and large-scale land-use changes resulting in forest loss and the expansion of cash crop plantations such as oil palm and rubber. Such land transformations are associated with changes in surface properties that affect biophysical processes influencing the atmosphere. Yet, the overall effect of such land transformations on the atmosphere at local and regional scale remains unclear. In our study, we combine measurements of microclimate, transpiration via sap-flux, surface energy fluxes via eddy covariance, surface temperature via remote sensing, land surface (CLM) and regional climate modeling (WRF) for Jambi Province in Indonesia. Our microclimatic measurements showed that air temperature within the canopy was on average 0.7-0.8°C higher in monoculture plantations (oil palm and rubber) compared to forest. Remote sensing analysis using MODIS and Landsat revealed a higher canopy surface temperature for oil palm plantations (+1.5°C) compared to forest, but only little differences for rubber plantations. Transpiration (T) and evapotranspiration (ET) as well as the contribution of T to ET of oil palm showed a strong age-dependent increase. The sensible to latent heat flux ratio decreased with age. Overall, rubber plantations showed the lowest transpirations rates (320 mm year-1), oil palm intermediate rates (414 mm year-1), and forest the highest rates (558 mm year-1) indicating substantial differences in water use. Despite the differences in water use and the higher within-canopy and surface temperatures of the plantations compared to the forest, there was only a minor effect of land transformation on the atmosphere at the regional scale (<0.2 °C), irrespectively of the large spatial extend of the transformation. In conclusion, our study shows a strong local scale biophysical impact affecting the conditions at the stand level, which is however mitigated in the atmosphere at the regional level.

Retrieval and Mapping of Soil Texture Based on Land Surface Diurnal Temperature Range Data from MODIS

PubMed Central

Wang, De-Cai; Zhang, Gan-Lin; Zhao, Ming-Song; Pan, Xian-Zhang; Zhao, Yu-Guo; Li, De-Cheng; Macmillan, Bob

2015-01-01

Numerous studies have investigated the direct retrieval of soil properties, including soil texture, using remotely sensed images. However, few have considered how soil properties influence dynamic changes in remote images or how soil processes affect the characteristics of the spectrum. This study investigated a new method for mapping regional soil texture based on the hypothesis that the rate of change of land surface temperature is related to soil texture, given the assumption of similar starting soil moisture conditions. The study area was a typical flat area in the Yangtze-Huai River Plain, East China. We used the widely available land surface temperature product of MODIS as the main data source. We analyzed the relationships between the content of different particle soil size fractions at the soil surface and land surface day temperature, night temperature and diurnal temperature range (DTR) during three selected time periods. These periods occurred after rainfalls and between the previous harvest and the subsequent autumn sowing in 2004, 2007 and 2008. Then, linear regression models were developed between the land surface DTR and sand (> 0.05 mm), clay (< 0.001 mm) and physical clay (< 0.01 mm) contents. The models for each day were used to estimate soil texture. The spatial distribution of soil texture from the studied area was mapped based on the model with the minimum RMSE. A validation dataset produced error estimates for the predicted maps of sand, clay and physical clay, expressed as RMSE of 10.69%, 4.57%, and 12.99%, respectively. The absolute error of the predictions is largely influenced by variations in land cover. Additionally, the maps produced by the models illustrate the natural spatial continuity of soil texture. This study demonstrates the potential for digitally mapping regional soil texture variations in flat areas using readily available MODIS data. PMID:26090852

Retrieval and Mapping of Soil Texture Based on Land Surface Diurnal Temperature Range Data from MODIS.

PubMed

Wang, De-Cai; Zhang, Gan-Lin; Zhao, Ming-Song; Pan, Xian-Zhang; Zhao, Yu-Guo; Li, De-Cheng; Macmillan, Bob

2015-01-01

Numerous studies have investigated the direct retrieval of soil properties, including soil texture, using remotely sensed images. However, few have considered how soil properties influence dynamic changes in remote images or how soil processes affect the characteristics of the spectrum. This study investigated a new method for mapping regional soil texture based on the hypothesis that the rate of change of land surface temperature is related to soil texture, given the assumption of similar starting soil moisture conditions. The study area was a typical flat area in the Yangtze-Huai River Plain, East China. We used the widely available land surface temperature product of MODIS as the main data source. We analyzed the relationships between the content of different particle soil size fractions at the soil surface and land surface day temperature, night temperature and diurnal temperature range (DTR) during three selected time periods. These periods occurred after rainfalls and between the previous harvest and the subsequent autumn sowing in 2004, 2007 and 2008. Then, linear regression models were developed between the land surface DTR and sand (> 0.05 mm), clay (< 0.001 mm) and physical clay (< 0.01 mm) contents. The models for each day were used to estimate soil texture. The spatial distribution of soil texture from the studied area was mapped based on the model with the minimum RMSE. A validation dataset produced error estimates for the predicted maps of sand, clay and physical clay, expressed as RMSE of 10.69%, 4.57%, and 12.99%, respectively. The absolute error of the predictions is largely influenced by variations in land cover. Additionally, the maps produced by the models illustrate the natural spatial continuity of soil texture. This study demonstrates the potential for digitally mapping regional soil texture variations in flat areas using readily available MODIS data.

Translation of Land Surface Model Accuracy and Uncertainty into Coupled Land-Atmosphere Prediction

NASA Technical Reports Server (NTRS)

Santanello, Joseph A.; Kumar, Sujay; Peters-Lidard, Christa D.; Harrison, Kenneth W.; Zhou, Shuija

2012-01-01

Land-atmosphere (L-A) Interactions playa critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface heat and moisture budgets, as well as controlling feedbacks with clouds and precipitation that lead to the persistence of dry and wet regimes. Recent efforts to quantify the strength of L-A coupling in prediction models have produced diagnostics that integrate across both the land and PBL components of the system. In this study, we examine the impact of improved specification of land surface states, anomalies, and fluxes on coupled WRF forecasts during the summers of extreme dry (2006) and wet (2007) land surface conditions in the U.S. Southern Great Plains. The improved land initialization and surface flux parameterizations are obtained through the use of a new optimization and uncertainty estimation module in NASA's Land Information System (US-OPT/UE), whereby parameter sets are calibrated in the Noah land surface model and classified according to a land cover and soil type mapping of the observation sites to the full model domain. The impact of calibrated parameters on the a) spinup of the land surface used as initial conditions, and b) heat and moisture states and fluxes of the coupled WRF Simulations are then assessed in terms of ambient weather and land-atmosphere coupling along with measures of uncertainty propagation into the forecasts. In addition, the sensitivity of this approach to the period of calibration (dry, wet, average) is investigated. Finally, tradeoffs of computational tractability and scientific validity, and the potential for combining this approach with satellite remote sensing data are also discussed.

Long-term records of global radiation, carbon and water fluxes derived from multi-satellite data and a process-based model

NASA Astrophysics Data System (ADS)

Ryu, Youngryel; Jiang, Chongya

2016-04-01

To gain insights about the underlying impacts of global climate change on terrestrial ecosystem fluxes, we present a long-term (1982-2015) global radiation, carbon and water fluxes products by integrating multi-satellite data with a process-based model, the Breathing Earth System Simulator (BESS). BESS is a coupled processed model that integrates radiative transfer in the atmosphere and canopy, photosynthesis (GPP), and evapotranspiration (ET). BESS was designed most sensitive to the variables that can be quantified reliably, fully taking advantages of remote sensing atmospheric and land products. Originally, BESS entirely relied on MODIS as input variables to produce global GPP and ET during the MODIS era. This study extends the work to provide a series of long-term products from 1982 to 2015 by incorporating AVHRR data. In addition to GPP and ET, more land surface processes related datasets are mapped to facilitate the discovery of the ecological variations and changes. The CLARA-A1 cloud property datasets, the TOMS aerosol datasets, along with the GLASS land surface albedo datasets, were input to a look-up table derived from an atmospheric radiative transfer model to produce direct and diffuse components of visible and near infrared radiation datasets. Theses radiation components together with the LAI3g datasets and the GLASS land surface albedo datasets, were used to calculate absorbed radiation through a clumping corrected two-stream canopy radiative transfer model. ECMWF ERA interim air temperature data were downscaled by using ALP-II land surface temperature dataset and a region-dependent regression model. The spatial and seasonal variations of CO2 concentration were accounted by OCO-2 datasets, whereas NOAA's global CO2 growth rates data were used to describe interannual variations. All these remote sensing based datasets are used to run the BESS. Daily fluxes in 1/12 degree were computed and then aggregated to half-month interval to match with the spatial-temporal resolution of LAI3g dataset. The BESS GPP and ET products were compared to other independent datasets including MPI-BGC and CLM. Overall, the BESS products show good agreement with the other two datasets, indicating a compelling potential for bridging remote sensing and land surface models.

Mapping Daily Evapotranspiration at Field to Global Scales using Geostationary and Polar Orbiting Satellite Imagery

USDA-ARS?s Scientific Manuscript database

Thermal infrared (TIR) remote sensing of land-surface temperature (LST) provides valuable information about the sub-surface moisture status required for estimating evapotranspiration (ET) and detecting the onset and severity of drought. While empirical indices measuring anomalies in LST and vegetati...

Role of subsurface physics in the assimilation of surface soil moisture observations

USDA-ARS?s Scientific Manuscript database

Soil moisture controls the exchange of water and energy between the land surface and the atmosphere and exhibits memory that may be useful for climate prediction at monthly time scales. Though spatially distributed observations of soil moisture are increasingly becoming available from remotely sense...

Spatially Complete Global Spectral Surface Albedos: Value-Added Datasets Derived from Terra MODIS Land Products

NASA Technical Reports Server (NTRS)

Moody, Eric G.; King, Michael D.; Platnick, Steven; Schaaf, Crystal B.; Gao, Feng

2004-01-01

Land surface albedo is an important parameter in describing the radiative properties of the earth s surface as it represents the amount of incoming solar radiation that is reflected from the surface. The amount and type of vegetation of the surface dramatically alters the amount of radiation that is reflected; for example, croplands that contain leafy vegetation will reflect radiation very differently than blacktop associated with urban areas. In addition, since vegetation goes through a growth, or phenological, cycle, the amount of radiation that is reflected changes over the course of a year. As a result, albedo is both temporally and spatially dependant upon global location as there is a distribution of vegetated surface types and growing conditions. Land surface albedo is critical for a wide variety of earth system research projects including but not restricted to remote sensing of atmospheric aerosol and cloud properties from space, ground-based analysis of aerosol optical properties from surface-based sun/sky radiometers, biophysically-based land surface modeling of the exchange of energy, water, momentum, and carbon for various land use categories, and surface energy balance studies. These projects require proper representation of the surface albedo s spatial, spectral, and temporal variations, however, these representations are often lacking in datasets prior to the latest generation of land surface albedo products.

A cloud mask methodology for high resolution remote sensing data combining information from high and medium resolution optical sensors

NASA Astrophysics Data System (ADS)

Sedano, Fernando; Kempeneers, Pieter; Strobl, Peter; Kucera, Jan; Vogt, Peter; Seebach, Lucia; San-Miguel-Ayanz, Jesús

2011-09-01

This study presents a novel cloud masking approach for high resolution remote sensing images in the context of land cover mapping. As an advantage to traditional methods, the approach does not rely on thermal bands and it is applicable to images from most high resolution earth observation remote sensing sensors. The methodology couples pixel-based seed identification and object-based region growing. The seed identification stage relies on pixel value comparison between high resolution images and cloud free composites at lower spatial resolution from almost simultaneously acquired dates. The methodology was tested taking SPOT4-HRVIR, SPOT5-HRG and IRS-LISS III as high resolution images and cloud free MODIS composites as reference images. The selected scenes included a wide range of cloud types and surface features. The resulting cloud masks were evaluated through visual comparison. They were also compared with ad-hoc independently generated cloud masks and with the automatic cloud cover assessment algorithm (ACCA). In general the results showed an agreement in detected clouds higher than 95% for clouds larger than 50 ha. The approach produced consistent results identifying and mapping clouds of different type and size over various land surfaces including natural vegetation, agriculture land, built-up areas, water bodies and snow.

Remote sensing, land use, and demography - A look at people through their effects on the land

NASA Technical Reports Server (NTRS)

Paul, C. K.; Landini, A. J.

1976-01-01

Relevant causes of failure by the remote sensing community in the urban scene are analyzed. The reasons for the insignificant role of remote sensing in urban land use data collection are called the law of realism, the incompatibility of remote sensing and urban management system data formats is termed the law of nominal/ordinal systems compatibility, and the land use/population correlation dilemma is referred to as the law of missing persons. The study summarizes the three laws of urban land use information for which violations, avoidance, or ignorance have caused the decline of present remote sensing research. Particular attention is given to the rationale for urban land use information and for remote sensing. It is shown that remote sensing of urban land uses compatible with the three laws can be effectively developed by realizing the 10 percent contribution of remote sensing to urban land use planning data collection.

Urban surface energy fluxes based on remotely-sensed data and micrometeorological measurements over the Kansai area, Japan

NASA Astrophysics Data System (ADS)

Sukeyasu, T.; Ueyama, M.; Ando, T.; Kosugi, Y.; Kominami, Y.

2017-12-01

The urban heat island is associated with land cover changes and increases in anthropogenic heat fluxes. Clear understanding of the surface energy budget at urban area is the most important for evaluating the urban heat island. In this study, we develop a model based on remotely-sensed data for the Kansai area in Japan and clarify temporal transitions and spatial distributions of the surface energy flux from 2000 to 2016. The model calculated the surface energy fluxes based on various satellite and GIS products. The model used land surface temperature, surface emissivity, air temperature, albedo, downward shortwave radiation and land cover/use type from the moderate resolution imaging spectroradiometer (MODIS) under cloud free skies from 2000 to 2016 over the Kansai area in Japan (34 to 35 ° N, 135 to 136 ° E). Net radiation was estimated by a radiation budget of upward/downward shortwave and longwave radiation. Sensible heat flux was estimated by a bulk aerodynamic method. Anthropogenic heat flux was estimated by the inventory data. Latent heat flux was examined with residues of the energy budget and parameterization of bulk transfer coefficients. We validated the model using observed fluxes from five eddy-covariance measurement sites: three urban sites and two forested sites. The estimated net radiation roughly agreed with the observations, but the sensible heat flux were underestimated. Based on the modeled spatial distributions of the fluxes, the daytime net radiation in the forested area was larger than those in the urban area, owing to higher albedo and land surface temperatures in the urban area than the forested area. The estimated anthropogenic heat flux was high in the summer and winter periods due to increases in energy-requirements.

Remote-sensing application for facilitating land resource assessment and monitoring for utility-scale solar energy development

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hamada, Yuki; Grippo, Mark A.

2015-01-01

A monitoring plan that incorporates regional datasets and integrates cost-effective data collection methods is necessary to sustain the long-term environmental monitoring of utility-scale solar energy development in expansive, environmentally sensitive desert environments. Using very high spatial resolution (VHSR; 15 cm) multispectral imagery collected in November 2012 and January 2014, an image processing routine was developed to characterize ephemeral streams, vegetation, and land surface in the southwestern United States where increased utility-scale solar development is anticipated. In addition to knowledge about desert landscapes, the methodology integrates existing spectral indices and transformation (e.g., visible atmospherically resistant index and principal components); a newlymore » developed index, erosion resistance index (ERI); and digital terrain and surface models, all of which were derived from a common VHSR image. The methodology identified fine-scale ephemeral streams with greater detail than the National Hydrography Dataset and accurately estimated vegetation distribution and fractional cover of various surface types. The ERI classified surface types that have a range of erosive potentials. The remote-sensing methodology could ultimately reduce uncertainty and monitoring costs for all stakeholders by providing a cost-effective monitoring approach that accurately characterizes the land resources at potential development sites.« less

Improving operational land surface model canopy evapotranspiration in Africa using a direct remote sensing approach

NASA Astrophysics Data System (ADS)

Marshall, M.; Tu, K.; Funk, C.; Michaelsen, J.; Williams, P.; Williams, C.; Ardö, J.; Boucher, M.; Cappelaere, B.; de Grandcourt, A.; Nickless, A.; Nouvellon, Y.; Scholes, R.; Kutsch, W.

2013-03-01

Climate change is expected to have the greatest impact on the world's economically poor. In the Sahel, a climatically sensitive region where rain-fed agriculture is the primary livelihood, expected decreases in water supply will increase food insecurity. Studies on climate change and the intensification of the water cycle in sub-Saharan Africa are few. This is due in part to poor calibration of modeled evapotranspiration (ET), a key input in continental-scale hydrologic models. In this study, a remote sensing model of transpiration (the primary component of ET), driven by a time series of vegetation indices, was used to substitute transpiration from the Global Land Data Assimilation System realization of the National Centers for Environmental Prediction, Oregon State University, Air Force, and Hydrology Research Laboratory at National Weather Service Land Surface Model (GNOAH) to improve total ET model estimates for monitoring purposes in sub-Saharan Africa. The performance of the hybrid model was compared against GNOAH ET and the remote sensing method using eight eddy flux towers representing major biomes of sub-Saharan Africa. The greatest improvements in model performance were at humid sites with dense vegetation, while performance at semi-arid sites was poor, but better than the models before hybridization. The reduction in errors using the hybrid model can be attributed to the integration of a simple canopy scheme that depends primarily on low bias surface climate reanalysis data and is driven primarily by a time series of vegetation indices.

Martian surface materials

NASA Technical Reports Server (NTRS)

Moore, H. J.

1991-01-01

A semiquantitative appreciation for the physical properties of the Mars surface materials and their global variations can be gained from the Viking Lander and remote sensing observations. Analyses of Lander data yields estimates of the mechanical properties of the soil-like surface materials and best guess estimates can be made for the remote sensing signatures of the soil-like materials at the landing sites. Results show that significant thickness of powderlike surface materials with physical properties similar to drift material are present on Mars and probably pervasive in the Tharsis region. It also appears likely that soil-like materials similar to crusty to cloddy material are typical for Mars, and that soil-like material similar to blocky material are common on Mars.

The Radio Frequency Environment at 240-270 MHz with Application to Signal-of-Opportunity Remote Sensing

NASA Technical Reports Server (NTRS)

Piepmeier, Jeffrey R.; Vega, Manuel; Fritts, Matthew; Du Toit, Cornelis; Knuble, Joseph; Lin, Yao-Cheng; Nold, Benjamin; Garrison, James

2017-01-01

Low frequency observations are desired for soil moisture and biomass remote sensing. Long wavelengths are needed to penetrate vegetation and Earths land surface. In addition to the technical challenges of developing Earth observing spaceflight instruments operating at low frequencies, the radio frequency spectrum allocated to remote sensing is limited. Signal-of-opportunity remote sensing offers the chance to use existing signals exploiting their allocated spectrum to make Earth science measurements. We have made observations of the radio frequency environment around 240-270 MHz and discuss properties of desired and undesired signals.

Change detection from remotely sensed images: From pixel-based to object-based approaches

NASA Astrophysics Data System (ADS)

Hussain, Masroor; Chen, Dongmei; Cheng, Angela; Wei, Hui; Stanley, David

2013-06-01

The appetite for up-to-date information about earth's surface is ever increasing, as such information provides a base for a large number of applications, including local, regional and global resources monitoring, land-cover and land-use change monitoring, and environmental studies. The data from remote sensing satellites provide opportunities to acquire information about land at varying resolutions and has been widely used for change detection studies. A large number of change detection methodologies and techniques, utilizing remotely sensed data, have been developed, and newer techniques are still emerging. This paper begins with a discussion of the traditionally pixel-based and (mostly) statistics-oriented change detection techniques which focus mainly on the spectral values and mostly ignore the spatial context. This is succeeded by a review of object-based change detection techniques. Finally there is a brief discussion of spatial data mining techniques in image processing and change detection from remote sensing data. The merits and issues of different techniques are compared. The importance of the exponential increase in the image data volume and multiple sensors and associated challenges on the development of change detection techniques are highlighted. With the wide use of very-high-resolution (VHR) remotely sensed images, object-based methods and data mining techniques may have more potential in change detection.

NOAA National Ocean Service Remote Sensing Applications and Concept of Operations

DTIC Science & Technology

2007-01-01

remote sensing technologies to monitor harmful algal blooms, hypoxia, coral bleaching , contamination, land use changes and bathymetry, and making the...NOAA’s Polar Environmental Satellites are used to help predict the likelihood of mass coral bleaching events. Both intensity and duration of...abnormally warm surface temperatures are used to help predict coral bleaching events. When a temperature anomaly reaches a critically high value or

Evaluation of the global MODIS 30 arc-second spatially and temporally complete snow-free land surface albedo and reflectance anisotropy dataset

NASA Astrophysics Data System (ADS)

Sun, Qingsong; Wang, Zhuosen; Li, Zhan; Erb, Angela; Schaaf, Crystal B.

2017-06-01

Land surface albedo is an essential variable for surface energy and climate modeling as it describes the proportion of incident solar radiant flux that is reflected from the Earth's surface. To capture the temporal variability and spatial heterogeneity of the land surface, satellite remote sensing must be used to monitor albedo accurately at a global scale. However, large data gaps caused by cloud or ephemeral snow have slowed the adoption of satellite albedo products by the climate modeling community. To address the needs of this community, we used a number of temporal and spatial gap-filling strategies to improve the spatial and temporal coverage of the global land surface MODIS BRDF, albedo and NBAR products. A rigorous evaluation of the gap-filled values shows good agreement with original high quality data (RMSE = 0.027 for the NIR band albedo, 0.020 for the red band albedo). This global snow-free and cloud-free MODIS BRDF and albedo dataset (established from 2001 to 2015) offers unique opportunities to monitor and assess the impact of the changes on the Earth's land surface.

To the National Map and beyond

USGS Publications Warehouse

Kelmelis, J.

2003-01-01

Scientific understanding, technology, and social, economic, and environmental conditions have driven a rapidly changing demand for geographic information, both digital and analog. For more than a decade, the U.S. Geological Survey (USGS) has been developing innovative partnerships with other government agencies and private industry to produce and distribute geographic information efficiently; increase activities in remote sensing to ensure ongoing monitoring of the land surface; and develop new understanding of the causes and consequences of land surface change. These activities are now contributing to a more robust set of geographic information called The National Map (TNM). The National Map is designed to provide an up-to-date, seamless, horizontally and vertically integrated set of basic digital geographic data, a frequent monitoring of changes on the land surface, and an understanding of the condition of the Earth's surface and many of the processes that shape it. The USGS has reorganized its National Mapping Program into three programs to address the continuum of scientific activities-describing (mapping), monitoring, understanding, modeling, and predicting. The Cooperative Topographic Mapping Program focuses primarily on the mapping and revision aspects of TNM. The National Map also includes results from the Land Remote Sensing and Geographic Analysis and Monitoring Programs that provide continual updates, new insights, and analytical tools. The National Map is valuable as a framework for current research, management, and operational activities. It also provides a critical framework for the development of distributed, spatially enabled decision support systems.

Using Remotely-Sensed Estimates of Soil Moisture to Infer Soil Texture and Hydraulic Properties across a Semi-arid Watershed

NASA Technical Reports Server (NTRS)

Santanello, Joseph A.; Peters-Lidard, Christa D.; Garcia, Matthew E.; Mocko, David M.; Tischler, Michael A.; Moran, M. Susan; Thoma, D. P.

2007-01-01

Near-surface soil moisture is a critical component of land surface energy and water balance studies encompassing a wide range of disciplines. However, the processes of infiltration, runoff, and evapotranspiration in the vadose zone of the soil are not easy to quantify or predict because of the difficulty in accurately representing soil texture and hydraulic properties in land surface models. This study approaches the problem of parameterizing soils from a unique perspective based on components originally developed for operational estimation of soil moisture for mobility assessments. Estimates of near-surface soil moisture derived from passive (L-band) microwave remote sensing were acquired on six dates during the Monsoon '90 experiment in southeastern Arizona, and used to calibrate hydraulic properties in an offline land surface model and infer information on the soil conditions of the region. Specifically, a robust parameter estimation tool (PEST) was used to calibrate the Noah land surface model and run at very high spatial resolution across the Walnut Gulch Experimental Watershed. Errors in simulated versus observed soil moisture were minimized by adjusting the soil texture, which in turn controls the hydraulic properties through the use of pedotransfer functions. By estimating a continuous range of widely applicable soil properties such as sand, silt, and clay percentages rather than applying rigid soil texture classes, lookup tables, or large parameter sets as in previous studies, the physical accuracy and consistency of the resulting soils could then be assessed. In addition, the sensitivity of this calibration method to the number and timing of microwave retrievals is determined in relation to the temporal patterns in precipitation and soil drying. The resultant soil properties were applied to an extended time period demonstrating the improvement in simulated soil moisture over that using default or county-level soil parameters. The methodology is also applied to an independent case at Walnut Gulch using a new soil moisture product from active (C-band) radar imagery with much lower spatial and temporal resolution. Overall, results demonstrate the potential to gain physically meaningful soils information using simple parameter estimation with few but appropriately timed remote sensing retrievals.

The effect of urban heat island on Izmir's city ecosystem and climate.

PubMed

Corumluoglu, Ozsen; Asri, Ibrahim

2015-03-01

Depending on the researches done on urban landscapes, it is found that the heat island intensity caused by the activities in any city has some impact on the ecosystem of the region and on the regional climate. Urban areas located in arid and semiarid lands somehow represent heat increase when it is compared with the heat in the surrounding rural areas. Thus, cities located amid forested and temperate climate regions show moderate temperatures. The impervious surfaces let the rainfall leave the city lands faster than undeveloped areas. This effect reduces water's cooling effects on these lands. More significantly, if trees and other vegetations are rare in any region, it means less evapotranspiration-the process by which trees "exhale" water. Trees also contribute to the cooling of urban lands by their shade. Land cover and land use maps can easily be produced by processing of remote sensing satellites' images, like processing of Landsat's images. As a result of this process, urban regions can be distinguished from vegetation. Analyzed GIS data produced and supported by these images can be utilized to determine the impact of urban land on energy, water, and carbon balances at the Earth's surface. Here in this study, it is found that remote sensing technique with thermal images is a liable technique to asses where urban heat islands and hot spots are located in cities. As an application area, in Izmir, it was found that the whole city was in high level of surface temperature as it was over 28 °C during the summer times. Beside this, the highest temperature values which go up to 47 °C are obtained at industrial regions especially where the iron-steel factories and the related industrial activities are.

Utilization of combined remote sensing techniques to detect environmental variables influencing malaria vector densities in rural West Africa

PubMed Central

2012-01-01

Introduction The use of remote sensing has found its way into the field of epidemiology within the last decades. With the increased sensor resolution of recent and future satellites new possibilities emerge for high resolution risk modeling and risk mapping. Methods A SPOT 5 satellite image, taken during the rainy season 2009 was used for calculating indices by combining the image's spectral bands. Besides the widely used Normalized Difference Vegetation Index (NDVI) other indices were tested for significant correlation against field observations. Multiple steps, including the detection of surface water, its breeding appropriateness for Anopheles and modeling of vector imagines abundance, were performed. Data collection on larvae, adult vectors and geographic parameters in the field, was amended by using remote sensing techniques to gather data on altitude (Digital Elevation Model = DEM), precipitation (Tropical Rainfall Measurement Mission = TRMM), land surface temperatures (LST). Results The DEM derived altitude as well as indices calculations combining the satellite's spectral bands (NDTI = Normalized Difference Turbidity Index, NDWI Mac Feeters = Normalized Difference Water Index) turned out to be reliable indicators for surface water in the local geographic setting. While Anopheles larvae abundance in habitats is driven by multiple, interconnected factors - amongst which the NDVI - and precipitation events, the presence of vector imagines was found to be correlated negatively to remotely sensed LST and positively to the cumulated amount of rainfall in the preceding 15 days and to the Normalized Difference Pond Index (NDPI) within the 500 m buffer zone around capture points. Conclusions Remotely sensed geographical and meteorological factors, including precipitations, temperature, as well as vegetation, humidity and land cover indicators could be used as explanatory variables for surface water presence, larval development and imagines densities. This modeling approach based on remotely sensed information is potentially useful for counter measures that are putting on at the environmental side, namely vector larvae control via larviciding and water body reforming. PMID:22443452

Utilization of combined remote sensing techniques to detect environmental variables influencing malaria vector densities in rural West Africa.

PubMed

Dambach, Peter; Machault, Vanessa; Lacaux, Jean-Pierre; Vignolles, Cécile; Sié, Ali; Sauerborn, Rainer

2012-03-23

The use of remote sensing has found its way into the field of epidemiology within the last decades. With the increased sensor resolution of recent and future satellites new possibilities emerge for high resolution risk modeling and risk mapping. A SPOT 5 satellite image, taken during the rainy season 2009 was used for calculating indices by combining the image's spectral bands. Besides the widely used Normalized Difference Vegetation Index (NDVI) other indices were tested for significant correlation against field observations. Multiple steps, including the detection of surface water, its breeding appropriateness for Anopheles and modeling of vector imagines abundance, were performed. Data collection on larvae, adult vectors and geographic parameters in the field, was amended by using remote sensing techniques to gather data on altitude (Digital Elevation Model = DEM), precipitation (Tropical Rainfall Measurement Mission = TRMM), land surface temperatures (LST). The DEM derived altitude as well as indices calculations combining the satellite's spectral bands (NDTI = Normalized Difference Turbidity Index, NDWI Mac Feeters = Normalized Difference Water Index) turned out to be reliable indicators for surface water in the local geographic setting. While Anopheles larvae abundance in habitats is driven by multiple, interconnected factors - amongst which the NDVI - and precipitation events, the presence of vector imagines was found to be correlated negatively to remotely sensed LST and positively to the cumulated amount of rainfall in the preceding 15 days and to the Normalized Difference Pond Index (NDPI) within the 500 m buffer zone around capture points. Remotely sensed geographical and meteorological factors, including precipitations, temperature, as well as vegetation, humidity and land cover indicators could be used as explanatory variables for surface water presence, larval development and imagines densities. This modeling approach based on remotely sensed information is potentially useful for counter measures that are putting on at the environmental side, namely vector larvae control via larviciding and water body reforming. © 2012 Dambach et al; licensee BioMed Central Ltd.

Towards Global Simulation of Irrigation in a Land Surface Model: Multiple Cropping and Rice Paddy in Southeast Asia

NASA Technical Reports Server (NTRS)

Beaudoing, Hiroko Kato; Rodell, Matthew; Ozdogan, Mutlu

2010-01-01

Agricultural land use significantly influences the surface water and energy balances. Effects of irrigation on land surface states and fluxes include repartitioning of latent and sensible heat fluxes, an increase in net radiation, and an increase in soil moisture and runoff. We are working on representing irrigation practices in continental- to global-scale land surface simulation in NASA's Global Land Data Assimilation System (GLDAS). Because agricultural practices across the nations are diverse, and complex, we are attempting to capture the first-order reality of the regional practices before achieving a global implementation. This study focuses on two issues in Southeast Asia: multiple cropping and rice paddy irrigation systems. We first characterize agricultural practices in the region (i.e., crop types, growing seasons, and irrigation) using the Global data set of monthly irrigated and rainfed crop areas around the year 2000 (MIRCA2000) dataset. Rice paddy extent is identified using remote sensing products. Whether irrigated or rainfed, flooded fields need to be represented and treated explicitly. By incorporating these properties and processes into a physically based land surface model, we are able to quantify the impacts on the simulated states and fluxes.

Topographic Signatures in Aquarius Radiometer/Scatterometer Response: Initial Results

NASA Technical Reports Server (NTRS)

Utku, C.; LeVine, D. M.

2012-01-01

The effect of topography on remote sensing at L-band is examined using the co-located Aquarius radiometer and scatterometer observations over land. A correlation with slope standard deviation is demonstrated for both the radiometer and scatterometer at topographic scales. Although the goal of Aquarius is remote sensing of sea surface salinity, the radiometer and scatterometer are on continuously and collect data for remote sensing research over land. Research is reported here using the data over land to determine if topography could have impact on the passive remote sensing at L-band. In this study, we report observations from two study regions: North Africa between 15 deg and 30 deg Northern latitudes and Australia less the Tasmania Island. Common to these two regions are the semi-arid climate and low population density; both favorable conditions to isolate the effect of topography from other sources of scatter and emission such as vegetation and urban areas. Over these study regions, topographic scale slopes within each Aquarius pixel are computed and their standard deviations are compared with Aquarius scatterometer and radiometer observations over a 36 day period between days 275 and 311 of 2011.

Application of the Combination Approach for Estimating Evapotranspiration in Puerto Rico

NASA Technical Reports Server (NTRS)

Harmsen, Eric; Luvall, Jeffrey; Gonzalez, Jorge

2005-01-01

The ability to estimate short-term fluxes of water vapor from the land surface is important for validating latent heat flux estimates from high resolution remote sensing techniques. A new, relatively inexpensive method is presented for estimating t h e ground-based values of the surface latent heat flux or evapotranspiration.

Reconstructing spatial-temporal continuous MODIS land surface temperature using the DINEOF method

NASA Astrophysics Data System (ADS)

Zhou, Wang; Peng, Bin; Shi, Jiancheng

2017-10-01

Land surface temperature (LST) is one of the key states of the Earth surface system. Remote sensing has the capability to obtain high-frequency LST observations with global coverage. However, mainly due to cloud cover, there are always gaps in the remotely sensed LST product, which hampers the application of satellite-based LST in data-driven modeling of surface energy and water exchange processes. We explored the suitability of the data interpolating empirical orthogonal functions (DINEOF) method in moderate resolution imaging spectroradiometer LST reconstruction around Ali on the Tibetan Plateau. To validate the reconstruction accuracy, synthetic clouds during both daytime and nighttime are created. With DINEOF reconstruction, the root mean square error and bias under synthetic clouds in daytime are 4.57 and -0.0472 K, respectively, and during the nighttime are 2.30 and 0.0045 K, respectively. The DINEOF method can well recover the spatial pattern of LST. Time-series analysis of LST before and after DINEOF reconstruction from 2002 to 2016 shows that the annual and interannual variabilities of LST can be well reconstructed by the DINEOF method.

Development of a Cost-Effective Airborne Remote Sensing System for Coastal Monitoring

PubMed Central

Kim, Duk-jin; Jung, Jungkyo; Kang, Ki-mook; Kim, Seung Hee; Xu, Zhen; Hensley, Scott; Swan, Aaron; Duersch, Michael

2015-01-01

Coastal lands and nearshore marine areas are productive and rapidly changing places. However, these areas face many environmental challenges related to climate change and human-induced impacts. Space-borne remote sensing systems may be restricted in monitoring these areas because of their spatial and temporal resolutions. In situ measurements are also constrained from accessing the area and obtaining wide-coverage data. In these respects, airborne remote sensing sensors could be the most appropriate tools for monitoring these coastal areas. In this study, a cost-effective airborne remote sensing system with synthetic aperture radar and thermal infrared sensors was implemented to survey coastal areas. Calibration techniques and geophysical model algorithms were developed for the airborne system to observe the topography of intertidal flats, coastal sea surface current, sea surface temperature, and submarine groundwater discharge. PMID:26437413

Development of a Cost-Effective Airborne Remote Sensing System for Coastal Monitoring.

PubMed

Kim, Duk-jin; Jung, Jungkyo; Kang, Ki-mook; Kim, Seung Hee; Xu, Zhen; Hensley, Scott; Swan, Aaron; Duersch, Michael

2015-09-30

Coastal lands and nearshore marine areas are productive and rapidly changing places. However, these areas face many environmental challenges related to climate change and human-induced impacts. Space-borne remote sensing systems may be restricted in monitoring these areas because of their spatial and temporal resolutions. In situ measurements are also constrained from accessing the area and obtaining wide-coverage data. In these respects, airborne remote sensing sensors could be the most appropriate tools for monitoring these coastal areas. In this study, a cost-effective airborne remote sensing system with synthetic aperture radar and thermal infrared sensors was implemented to survey coastal areas. Calibration techniques and geophysical model algorithms were developed for the airborne system to observe the topography of intertidal flats, coastal sea surface current, sea surface temperature, and submarine groundwater discharge.

Use of GLOBE Observations to Derive a Landsat 8 Split Window Algorithm for Urban Heat Island

NASA Astrophysics Data System (ADS)

Fagerstrom, L.; Czajkowski, K. P.

2017-12-01

Surface temperature has been studied to investigate the warming of urban climates, also known as urban heat islands, which can impact urban planning, public health, pollution levels, and energy consumption. However, the full potential of remotely sensed images is limited when analyzing land surface temperature due to the daunting task of correcting for atmospheric effects. Landsat 8 has two thermal infrared sensors. With two bands in the infrared region, a split window algorithm (SWA), can be applied to correct for atmospheric effects. This project used in situ surface temperature measurements from NASA's ground observation program, the Global Learning and Observations to Benefit the Environment (GLOBE), to derive the correcting coefficients for use in the SWA. The GLOBE database provided land surface temperature data that coincided with Landsat 8 overpasses. The land surface temperature derived from Landsat 8 SWA can be used to analyze for urban heat island effect.

An Examination of Body Temperature for the Rocky Intertidal Mussel species, Mytilus californianus, Using Remotely Sensed Satellite Observations

NASA Astrophysics Data System (ADS)

Price, J.; Liff, H.; Lakshmi, V.

2012-12-01

Temperature is considered to be one of the most important physical factors in determining organismal distribution and physiological performance of species in rocky intertidal ecosystems, especially the growth and survival of mussels. However, little is known about the spatial and temporal patterns of temperature in intertidal ecosystems or how those patterns affect intertidal mussel species because of limitations in data collection. We collected in situ temperature at Strawberry Hill, Oregon USA using mussel loggers embedded among the intertidal mussel species, Mytilus californianus. Remotely sensed surface temperatures were used in conjunction with in situ weather and ocean data to determine if remotely sensed surface temperatures can be used as a predictor for changes in the body temperature of a rocky intertidal mussel species. The data used in this study was collected between January 2003 and December 2010. The mussel logger temperatures were compared to in situ weather data collected from a local weather station, ocean data collected from a NOAA buoy, and remotely sensed surface temperatures collected from NASA's sun-synchronous Moderate Resolution Imaging Spectroradiometer aboard the Earth Observing System Aqua and EOS Terra satellites. Daily surface temperatures were collected from four pixel locations which included two sea surface temperature (SST) locations and two land surface temperature (LST) locations. One of the land pixels was chosen to represent the intertidal surface temperature (IST) because it was located within the intertidal zone. As expected, all surface temperatures collected via satellite were significantly correlated to each other and the associated in situ temperatures. Examination of temperatures from the off-shore NOAA buoy and the weather station provide evidence that remotely sensed temperatures were similar to in situ temperature data and explain more variability in mussel logger temperatures than the in situ temperatures. Our results suggest that temperatures (surface temperature and air temperature) are similar across larger spatial scales even when the type of data collection is different. Mussel logger temperatures were strongly correlated to SSTs and were not significantly different than SSTs. Sea surface temperature collected during the Aqua overpass explained 67.1% of the variation in mean monthly mussel logger temperature. When SST, LST, and IST were taken into consideration, nearly 73% of the variation in mussel logger temperature was explained. While in situ monthly air temperature and water temperature explained only 28-33% of the variation in mussel logger temperature. Our results suggests that remotely sensed surface temperatures are reliable and important measurements that can be used to better understand the effects temperature may have on intertidal mussel species in Strawberry Hill, Oregon. Remotely sensed surface temperature could act as a relative indicator of change and may be used to predict general habitat trends and drivers that could directly affect organism body temperature.

Characterizing Mediterranean Land Surfaces as Component of the Regional Climate System by Remote Sensing

NASA Technical Reports Server (NTRS)

Bolle, H.-J.; Koslowsky, D.; Menenti, M.; Nerry, F.; Otterman, Joseph; Starr, D.

1998-01-01

Extensive areas in the Mediterranean region are subject to land degradation and desertification. The high variability of the coupling between the surface and the atmosphere affects the regional climate. Relevant surface characteristics, such as spectral reflectance, surface emissivity in the thermal-infrared region, and vegetation indices, serve as "primary" level indicators for the state of the surface. Their spatial, seasonal and interannual variability can be monitored from satellites. Using relationships between these primary data and combining them with prior information about the land surfaces (such as topography, dominant soil type, land use, collateral ground measurements and models), a second layer of information is built up which specifies the land surfaces as a component of the regional climate system. To this category of parameters which are directly involved in the exchange of energy, momentum and mass between the surface and the atmosphere, belong broadband albedo, thermodynamic surface temperature, vegetation types, vegetation cover density, soil top moisture, and soil heat flux. Information about these parameters finally leads to the computation of sensible and latent heat fluxes. The methodology was tested with pilot data sets. Full resolution, properly calibrated and normalized NOAA-AVHRR multi-annual primary data sets are presently compiled for the whole Mediterranean area, to study interannual variability and longer term trends.

Impact of Calibrated Land Surface Model Parameters on the Accuracy and Uncertainty of Land-Atmosphere Coupling in WRF Simulations

NASA Technical Reports Server (NTRS)

Santanello, Joseph A., Jr.; Kumar, Sujay V.; Peters-Lidard, Christa D.; Harrison, Ken; Zhou, Shujia

2012-01-01

Land-atmosphere (L-A) interactions play a critical role in determining the diurnal evolution of both planetary boundary layer (PBL) and land surface temperature and moisture budgets, as well as controlling feedbacks with clouds and precipitation that lead to the persistence of dry and wet regimes. Recent efforts to quantify the strength of L-A coupling in prediction models have produced diagnostics that integrate across both the land and PBL components of the system. In this study, we examine the impact of improved specification of land surface states, anomalies, and fluxes on coupled WRF forecasts during the summers of extreme dry (2006) and wet (2007) land surface conditions in the U.S. Southern Great Plains. The improved land initialization and surface flux parameterizations are obtained through the use of a new optimization and uncertainty estimation module in NASA's Land Information System (LIS-OPT/UE), whereby parameter sets are calibrated in the Noah land surface model and classified according to a land cover and soil type mapping of the observation sites to the full model domain. The impact of calibrated parameters on the a) spinup of the land surface used as initial conditions, and b) heat and moisture states and fluxes of the coupled WRF simulations are then assessed in terms of ambient weather and land-atmosphere coupling along with measures of uncertainty propagation into the forecasts. In addition, the sensitivity of this approach to the period of calibration (dry, wet, average) is investigated. Finally, tradeoffs of computational tractability and scientific validity, and the potential for combining this approach with satellite remote sensing data are also discussed.

Spatiotemporal Variations in the Difference between Satellite-observed Land Surface Temperature and Station-based Near-surface Air Temperature

NASA Astrophysics Data System (ADS)

Lian, X.

2016-12-01

There is an increasing demand to integrate land surface temperature (LST) into climate research due to its global coverage, which requires a comprehensive knowledge of its distinctive characteristics compared to near-surface air temperature ( ). Using satellite observations and in-situ station-based datasets, we conducted a global-scale assessment of the spatial, seasonal, and interannual variations in the difference between daytime maximum LST and daytime maximum ( , LST - ) during 2003-2014. Spatially, LST is generally higher than over arid and sparsely vegetated regions in the mid-low latitudes, but LST is lower than in the tropical rainforests due to strong evaporative cooling, and in the high-latitude regions due to snow-induced radiative cooling. Seasonally, is negative in tropical regions throughout the year, while it displays a pronounced seasonality in both the mid-latitudes and boreal regions. The seasonality in the mid-latitudes is a result of the asynchronous responses of LST and to the seasonal cycle of radiation and vegetation abundance, whereas in the boreal regions, seasonality is mainly caused by the change in snow cover. At an interannual scale, only a small proportion of the land surface displays a statistically significant trend (P <0.05) due to the short time span of current measurements. Our study identified substantial spatial heterogeneity and seasonality in , as well as its determinant environmental drivers, and thus provides a useful reference for monitoring near-surface temperature changes using remote sensing, particularly in remote regions.

Microwave remote sensing from space

NASA Technical Reports Server (NTRS)

Carver, K. R.; Elachi, C.; Ulaby, F. T.

1985-01-01

Spaceborne microwave remote sensors provide perspectives of the earth surface and atmosphere which are of unique value in scientific studies of geomorphology, oceanic waves and topography, atmospheric water vapor and temperatures, vegetation classification and stress, ice types and dynamics, and hydrological characteristics. Microwave radars and radiometers offer enhanced sensitivities to the geometrical characteristics of the earth's surface and its cover, to water in all its forms - soil and vegetation moisture, ice, wetlands, oceans, and atmospheric water vapor, and can provide high-resolution imagery of the earth's surface independent of cloud cover or sun angle. A brief review of the historical development and principles of active and passive microwave remote sensing is presented, with emphasis on the unique characteristics of the information obtainable in the microwave spectrum and the value of this information to global geoscientific studies. Various spaceborne microwave remote sensors are described, with applications to geology, planetology, oceanography, glaciology, land biology, meteorology, and hydrology. A discussion of future microwave remote sensor technological developments and challenges is presented, along with a summary of future missions being planned by several countries.

Preliminary determination of geothermal working area based on Thermal Infrared and Synthetic Aperture Radar (SAR) remote sensing

NASA Astrophysics Data System (ADS)

Agoes Nugroho, Indra; Kurniawahidayati, Beta; Syahputra Mulyana, Reza; Saepuloh, Asep

2017-12-01

Remote sensing is one of the methods for geothermal exploration. This method can be used to map the geological structures, manifestations, and predict the geothermal potential area. The results from remote sensing were used as guidance for the next step exploration. Analysis of target in remote sensing is an efficient method to delineate geothermal surface manifestation without direct contact to the object. The study took a place in District Merangin, Jambi Province, Indonesia. The area was selected due to existing of Merangin volcanic complex composed by Mounts Sumbing and Hulunilo with surface geothermal manifestations presented by hot springs and hot pools. The location of surface manifestations could be related with local and regional structures of Great Sumatra Fault. The methods used in this study were included identification of volcanic products, lineament extraction, and lineament density quantification. The objective of this study is to delineate the potential zones for sitting the geothermal working site based on Thermal Infrared and Synthetic Aperture Radar (SAR) sensors. The lineament-related to geological structures, was aimed for high lineament density, is using ALOS - PALSAR (Advanced Land Observing Satellite - The Phased Array type L-band Synthetic Aperture Radar) level 1.1. The Normalized Difference Vegetation Index (NDVI) analysis was used to predict the vegetation condition using Landsat 8 OLI-TIRS (The Operational Land Imager - Thermal Infrared Sensor). The brightness temperature was extracted from TIR band to estimate the surface temperature. Geothermal working area identified based on index overlay method from extracted parameter of remote sensing data was located at the western part of study area (Graho Nyabu area). This location was identified because of the existence of high surface temperature about 30°C, high lineament density about 4 - 4.5 km/km2 and low NDVI values less than 0.3.

Comprehensive data set of global land cover change for land surface model applications

NASA Astrophysics Data System (ADS)

Sterling, Shannon; Ducharne, AgnèS.

2008-09-01

To increase our understanding of how humans have altered the Earth's surface and to facilitate land surface modeling experiments aimed to elucidate the direct impact of land cover change on the Earth system, we create and analyze a database of global land use/cover change (LUCC). From a combination of sources including satellite imagery and other remote sensing, ecological modeling, and country surveys, we adapt and synthesize existing maps of potential land cover and layers of the major anthropogenic land covers, including a layer of wetland loss, that are then tailored for land surface modeling studies. Our map database shows that anthropogenic land cover totals to approximately 40% of the Earth's surface, consistent with literature estimates. Almost all (92%) of the natural grassland on the Earth has been converted to human use, mostly grazing land, and the natural temperate savanna with mixed C3/C4 is almost completely lost (˜90%), due mostly to conversion to cropland. Yet the resultant change in functioning, in terms of plant functional types, of the Earth system from land cover change is dominated by a loss of tree cover. Finally, we identify need for standardization of percent bare soil for global land covers and for a global map of tree plantations. Estimates of land cover change are inherently uncertain, and these uncertainties propagate into modeling studies of the impact of land cover change on the Earth system; to begin to address this problem, modelers need to document fully areas of land cover change used in their studies.

Multicriteria analysis for sources of renewable energy using data from remote sensing

NASA Astrophysics Data System (ADS)

Matejicek, L.

2015-04-01

Renewable energy sources are major components of the strategy to reduce harmful emissions and to replace depleting fossil energy resources. Data from remote sensing can provide information for multicriteria analysis for sources of renewable energy. Advanced land cover quantification makes it possible to search for suitable sites. Multicriteria analysis, together with other data, is used to determine the energy potential and socially acceptability of suggested locations. The described case study is focused on an area of surface coal mines in the northwestern region of the Czech Republic, where the impacts of surface mining and reclamation constitute a dominant force in land cover changes. High resolution satellite images represent the main input datasets for identification of suitable sites. Solar mapping, wind predictions, the location of weirs in watersheds, road maps and demographic information complement the data from remote sensing for multicriteria analysis, which is implemented in a geographic information system (GIS). The input spatial datasets for multicriteria analysis in GIS are reclassified to a common scale and processed with raster algebra tools to identify suitable sites for sources of renewable energy. The selection of suitable sites is limited by the CORINE land cover database to mining and agricultural areas. The case study is focused on long term land cover changes in the 1985-2015 period. Multicriteria analysis based on CORINE data shows moderate changes in mapping of suitable sites for utilization of selected sources of renewable energy in 1990, 2000, 2006 and 2012. The results represent map layers showing the energy potential on a scale of a few preference classes (1-7), where the first class is linked to minimum preference and the last class to maximum preference. The attached histograms show the moderate variability of preference classes due to land cover changes caused by mining activities. The results also show a slight increase in the more preferred classes for utilization of sources of renewable energy due to an increase area of reclaimed sites. Using data from remote sensing, such as the multispectral images and the CORINE land cover datasets, can reduce the financial resources currently required for finding and assessing suitable areas.

Land surface phenology of Northeast China during 2000-2015: temporal changes and relationships with climate changes.

PubMed

Zhang, Yue; Li, Lin; Wang, Hongbin; Zhang, Yao; Wang, Naijia; Chen, Junpeng

2017-10-01

As an important crop growing area, Northeast China (NEC) plays a vital role in China's food security, which has been severely affected by climate change in recent years. Vegetation phenology in this region is sensitive to climate change, and currently, the relationship between the phenology of NEC and climate change remains unclear. In this study, we used a satellite-derived normalized difference vegetation index (NDVI) to obtain the temporal patterns of the land surface phenology in NEC from 2000 to 2015 and validated the results using ground phenology observations. We then explored the relationships among land surface phenology, temperature, precipitation, and sunshine hours for relevant periods. Our results showed that the NEC experienced great phenological changes in terms of spatial heterogeneity during 2000-2015. The spatial patterns of land surface phenology mainly changed with altitude and land cover type. In most regions of NEC, the start date of land surface phenology had advanced by approximately 1.0 days year -1 , and the length of land surface phenology had been prolonged by approximately 1.0 days year -1 except for the needle-leaf and cropland areas, due to the warm conditions. We found that a distinct inter-annual variation in land surface phenology related to climate variables, even if some areas presented non-significant trends. Land surface phenology was coupled with climate variables and distinct responses at different combinations of temperature, precipitation, sunshine hours, altitude, and anthropogenic influence. These findings suggest that remote sensing and our phenology extracting methods hold great potential for helping to understand how land surface phenology is sensitive to global climate change.

The CEOS constellation for land surface imaging

USGS Publications Warehouse

Bailey, G.B.; Berger, Marsha; Jeanjean, H.; Gallo, K.P.

2007-01-01

A constellation of satellites that routinely and frequently images the Earth's land surface in consistently calibrated wavelengths from the visible through the microwave and in spatial detail that ranges from sub-meter to hundreds of meters would offer enormous potential benefits to society. A well-designed and effectively operated land surface imaging satellite constellation could have great positive impact not only on the quality of life for citizens of all nations, but also on mankind's very ability to sustain life as we know it on this planet long into the future. The primary objective of the Committee on Earth Observation Satellites (CEOS) Land Surface Imaging (LSI) Constellation is to define standards (or guidelines) that describe optimal future LSI Constellation capabilities, characteristics, and practices. Standards defined for a LSI Constellation will be based on a thorough understanding of user requirements, and they will address at least three fundamental areas of the systems comprising a Land Surface Imaging Constellation: the space segments, the ground segments, and relevant policies and plans. Studies conducted by the LSI Constellation Study Team also will address current and shorter-term problems and issues facing the land remote sensing community today, such as seeking ways to work more cooperatively in the operation of existing land surface imaging systems and helping to accomplish tangible benefits to society through application of land surface image data acquired by existing systems. 2007 LSI Constellation studies are designed to establish initial international agreements, develop preliminary standards for a mid-resolution land surface imaging constellation, and contribute data to a global forest assessment.

Downscaling essential climate variable soil moisture using multisource data from 2003 to 2010 in China

NASA Astrophysics Data System (ADS)

Wang, Hui-Lin; An, Ru; You, Jia-jun; Wang, Ying; Chen, Yuehong; Shen, Xiao-ji; Gao, Wei; Wang, Yi-nan; Zhang, Yu; Wang, Zhe; Quaye-Ballard, Jonathan Arthur

2017-10-01

Soil moisture plays an important role in the water cycle within the surface ecosystem, and it is the basic condition for the growth of plants. Currently, the spatial resolutions of most soil moisture data from remote sensing range from ten to several tens of km, while those observed in-situ and simulated for watershed hydrology, ecology, agriculture, weather, and drought research are generally <1 km. Therefore, the existing coarse-resolution remotely sensed soil moisture data need to be downscaled. This paper proposes a universal and multitemporal soil moisture downscaling method suitable for large areas. The datasets comprise land surface, brightness temperature, precipitation, and soil and topographic parameters from high-resolution data and active/passive microwave remotely sensed essential climate variable soil moisture (ECV_SM) data with a spatial resolution of 25 km. Using this method, a total of 288 soil moisture maps of 1-km resolution from the first 10-day period of January 2003 to the last 10-day period of December 2010 were derived. The in-situ observations were used to validate the downscaled ECV_SM. In general, the downscaled soil moisture values for different land cover and land use types are consistent with the in-situ observations. Mean square root error is reduced from 0.070 to 0.061 using 1970 in-situ time series observation data from 28 sites distributed over different land uses and land cover types. The performance was also assessed using the GDOWN metric, a measure of the overall performance of the downscaling methods based on the same dataset. It was positive in 71.429% of cases, indicating that the suggested method in the paper generally improves the representation of soil moisture at 1-km resolution.

Scaling, soil moisture and evapotranspiration in runoff models

NASA Technical Reports Server (NTRS)

Wood, Eric F.

1993-01-01

The effects of small-scale heterogeneity in land surface characteristics on the large-scale fluxes of water and energy in the land-atmosphere system has become a central focus of many of the climatology research experiments. The acquisition of high resolution land surface data through remote sensing and intensive land-climatology field experiments (like HAPEX and FIFE) has provided data to investigate the interactions between microscale land-atmosphere interactions and macroscale models. One essential research question is how to account for the small scale heterogeneities and whether 'effective' parameters can be used in the macroscale models. To address this question of scaling, the probability distribution for evaporation is derived which illustrates the conditions for which scaling should work. A correction algorithm that may appropriate for the land parameterization of a GCM is derived using a 2nd order linearization scheme. The performance of the algorithm is evaluated.

Remote sensing of vegetation and land-cover change in Arctic Tundra Ecosystems

USGS Publications Warehouse

Stow, Douglas A.; Hope, Allen; McGuire, David; Verbyla, David; Gamon, John A.; Huemmrich, Fred; Houston, Stan; Racine, Charles H.; Sturm, Matthew; Tape, Ken D.; Hinzman, Larry D.; Yoshikawa, Kenji; Tweedie, Craig E.; Noyle, Brian; Silapaswan, Cherie; Douglas, David C.; Griffith, Brad; Jia, Gensuo; Howard E. Epstein,; Walker, Donald A.; Daeschner, Scott; Petersen, Aaron; Zhou, Liming; Myneni, Ranga B.

2004-01-01

The objective of this paper is to review research conducted over the past decade on the application of multi-temporal remote sensing for monitoring changes of Arctic tundra lands. Emphasis is placed on results from the National Science Foundation Land–Air–Ice Interactions (LAII) program and on optical remote sensing techniques. Case studies demonstrate that ground-level sensors on stationary or moving track platforms and wide-swath imaging sensors on polar orbiting satellites are particularly useful for capturing optical remote sensing data at sufficient frequency to study tundra vegetation dynamics and changes for the cloud prone Arctic. Less frequent imaging with high spatial resolution instruments on aircraft and lower orbiting satellites enable more detailed analyses of land cover change and calibration/validation of coarser resolution observations.The strongest signals of ecosystem change detected thus far appear to correspond to expansion of tundra shrubs and changes in the amount and extent of thaw lakes and ponds. Changes in shrub cover and extent have been documented by modern repeat imaging that matches archived historical aerial photography. NOAA Advanced Very High Resolution Radiometer (AVHRR) time series provide a 20-year record for determining changes in greenness that relates to photosynthetic activity, net primary production, and growing season length. The strong contrast between land materials and surface waters enables changes in lake and pond extent to be readily measured and monitored.

Probabilistic Assessment of Soil Moisture using C-band Quad-polarized Remote Sensing Data from RISAT1

NASA Astrophysics Data System (ADS)

Pal, Manali; Suman, Mayank; Das, Sarit Kumar; Maity, Rajib

2017-04-01

Information on spatio-temporal distribution of surface Soil Moisture Content (SMC) is essential in several hydrological, meteorological and agricultural applications. There has been increasing importance of microwave active remote sensing data for large-scale estimation of surface SMC because of its ability to monitor spatial and temporal variation of surface SMC at regional, continental and global scale at a reasonably fine spatial and temporal resolution. The use of Synthetic Aperture Radar (SAR) is highly potential for catchment-scale applications due to high spatial resolution (˜10-20 m) both for vegetated and bare soil surface as well as because of its all-weather and day and night characteristics. However, one prime disadvantage of SAR is that their signal is subjective to SMC along with Land Use Land Cover (LULC) and surface roughness conditions, making the retrieval of SMC from SAR data an "ill-posed" problem. Moreover, the quantification of uncertainty due to inappropriate surface roughness characterization, soil texture, inversion techniques etc. even in the latest established retrieval methods, is little explored. This paper reports a recently developed method to estimate the surface SMC with probabilistic assessment of uncertainty associated with the estimation (Pal et al., 2016). Quad-polarized SAR data from Radar Imaging Satellite1 (RISAT1), launched in 2012 by Indian Space Research Organization (ISRO) and information on LULC regarding bareland and vegetated land (<30 cm height) are used in estimation using the potential of multivariate probabilistic assessment through copulas. The salient features of the study are: 1) development of a combined index to understand the role of all the quad-polarized backscattering coefficients and soil texture information in SMC estimation; 2) applicability of the model for different incidence angles using normalized incidence angle theory proposed by Zibri et al. (2005); and 3) assessment of uncertainty range of the estimated SMC. Supervised Principal Component Analysis (SPCA) is used for development of combined index and Frank copula is found to be the best-fit copula. The developed model is validated with the field soil moisture values over 334 monitoring points within the study area and used for development of a soil moisture map. While the performance is promising, the model is applicable only for bare and vegetated land. References: Pal, M., Maity, R., Suman, M., Das, S.K., Patel, P., and Srivastava, H.S., (2016). "Satellite-Based Probabilistic Assessment of Soil Moisture Using C-Band Quad-Polarized RISAT1 Data." IEEE Transactions on Geoscience and Remote Sensing, In Press, doi:10.1109/TGRS.2016.2623378. Zribi, M., Baghdadi, N., Holah, N., and Fafin, O., (2005)."New methodology for soil surface moisture estimation and its application to ENVISAT-ASAR multi-incidence data inversion." Remote Sensing of Environment, vol. 96, nos. 3-4, pp. 485-496.

Land and atmosphere interactions using satellite remote sensing and a coupled mesoscale/land surface model

NASA Astrophysics Data System (ADS)

Hong, Seungbum

Land and atmosphere interactions have long been recognized for playing a key role in climate and weather modeling. However their quantification has been challenging due to the complex nature of the land surface amongst various other reasons. One of the difficult parts in the quantification is the effect of vegetation which are related to land surface processes such soil moisture variation and to atmospheric conditions such as radiation. This study addresses various relational investigations among vegetation properties such as Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), surface temperature (TSK), and vegetation water content (VegWC) derived from satellite sensors such as Moderate Resolution Imaging Spectroradiometer (MODIS) and EOS Advanced Microwave Scanning Radiometer (AMSR-E). The study provides general information about a physiological behavior of vegetation for various environmental conditions. Second, using a coupled mesoscale/land surface model, we examined the effects of vegetation and its relationship with soil moisture on the simulated land-atmospheric interactions through the model sensitivity tests. The Weather Research and Forecasting (WRF) model was selected for this study, and the Noah land surface model (Noah LSM) implemented in the WRF model was used for the model coupled system. This coupled model was tested through two parameterization methods for vegetation fraction using MODIS data and through model initialization of soil moisture from High Resolution Land Data Assimilation System (HRLDAS). Then, this study evaluates the model improvements for each simulation method.

Scaling of surface energy fluxes using remotely sensed data

NASA Astrophysics Data System (ADS)

French, Andrew Nichols

Accurate estimates of evapotranspiration (ET) across multiple terrains would greatly ease challenges faced by hydrologists, climate modelers, and agronomists as they attempt to apply theoretical models to real-world situations. One ET estimation approach uses an energy balance model to interpret a combination of meteorological observations taken at the surface and data captured by remote sensors. However, results of this approach have not been accurate because of poor understanding of the relationship between surface energy flux and land cover heterogeneity, combined with limits in available resolution of remote sensors. The purpose of this study was to determine how land cover and image resolution affect ET estimates. Using remotely sensed data collected over El Reno, Oklahoma, during four days in June and July 1997, scale effects on the estimation of spatially distributed ET were investigated. Instantaneous estimates of latent and sensible heat flux were calculated using a two-source surface energy balance model driven by thermal infrared, visible-near infrared, and meteorological data. The heat flux estimates were verified by comparison to independent eddy-covariance observations. Outcomes of observations taken at coarser resolutions were simulated by aggregating remote sensor data and estimated surface energy balance components from the finest sensor resolution (12 meter) to hypothetical resolutions as coarse as one kilometer. Estimated surface energy flux components were found to be significantly dependent on observation scale. For example, average evaporative fraction varied from 0.79, using 12-m resolution data, to 0.93, using 1-km resolution data. Resolution effects upon flux estimates were related to a measure of landscape heterogeneity known as operational scale, reflecting the size of dominant landscape features. Energy flux estimates based on data at resolutions less than 100 m and much greater than 400 m showed a scale-dependent bias. But estimates derived from data taken at about 400-m resolution (the operational scale at El Reno) were susceptible to large error due to mixing of surface types. The El Reno experiments show that accurate instantaneous estimates of ET require precise image alignment and image resolutions finer than landscape operational scale. These findings are valuable for the design of sensors and experiments to quantify spatially-varying hydrologic processes.

Using machine learning to produce near surface soil moisture estimates from deeper in situ records at U.S. Climate Reference Network (USCRN) locations: Analysis and applications to AMSR-E satellite validation

USDA-ARS?s Scientific Manuscript database

Surface soil moisture is critical parameter for understanding the energy flux at the land atmosphere boundary. Weather modeling, climate prediction, and remote sensing validation are some of the applications for surface soil moisture information. The most common in situ measurement for these purpo...

Global Validation of MODIS Atmospheric Profile-Derived Near-Surface Air Temperature and Dew Point Estimates

NASA Astrophysics Data System (ADS)

Famiglietti, C.; Fisher, J.; Halverson, G. H.

2017-12-01

This study validates a method of remote sensing near-surface meteorology that vertically interpolates MODIS atmospheric profiles to surface pressure level. The extraction of air temperature and dew point observations at a two-meter reference height from 2001 to 2014 yields global moderate- to fine-resolution near-surface temperature distributions that are compared to geographically and temporally corresponding measurements from 114 ground meteorological stations distributed worldwide. This analysis is the first robust, large-scale validation of the MODIS-derived near-surface air temperature and dew point estimates, both of which serve as key inputs in models of energy, water, and carbon exchange between the land surface and the atmosphere. Results show strong linear correlations between remotely sensed and in-situ near-surface air temperature measurements (R2 = 0.89), as well as between dew point observations (R2 = 0.77). Performance is relatively uniform across climate zones. The extension of mean climate-wise percent errors to the entire remote sensing dataset allows for the determination of MODIS air temperature and dew point uncertainties on a global scale.

Land-Use and Climate : first results from the LUCID experiments ; implications for experimental design in IPCC-AR5

NASA Astrophysics Data System (ADS)

de Noblet, N.; Pitman, A.; Participants, Lucid

2009-04-01

The project "Land-Use and Climate, IDentification of robust impacts" (LUCID) was conceived under the auspices of IGBP-iLEAPS and GEWEX-GLASS, to address the robustness of 'local' and possible remote impacts of land-use induced land-cover changes (LCC). LUCID explores, using methodologies that major climate modelling groups recognise, those impacts of LCC that are robust - that is, above the noise generated by model variability and consistent across a suite of climate models. To start with, seven climate models were run, in ensemble mode (5 realisations per 31-years long experiment), with prescribed observed sea-surface temperatures (SSTs) and sea ice extent (SIc). Pre-industrial and present-day simulations were used to explore the impacts of biogeophysical impacts of human-induced land cover change. The imposed LCC perturbation led to statistically significant changes in latent heat flux and near-surface temperature over the regions of land cover change, but few significant changes in precipitation. Our results show no common remote impacts of land cover change. They also highlight a dilemma for both historical hind-casts and future projections; land cover change is regionally important, but it is not feasible within the time frame of the next IPCC (AR5) assessment to implement this change commonly across multiple models. Further analysis are in progress and will be presented to identify the continental regions where changes in LCC may have been more important than the combined changes in SSTs, SIc and CO2 between the pre-industrial times and nowadays.

Project ATLANTA (Atlanta Land use Analysis: Temperature and Air Quality): Use of Remote Sensing and Modeling to Analyze How Urban Land Use Change Affects Meteorology and Air Quality Through Time

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.; Luvall, Jeffrey C.; Estes, Maurice G., Jr.

1999-01-01

This paper presents an overview of Project ATLANTA (ATlanta Land use ANalysis: Temperature and Air-quality) which is an investigation that seeks to observe, measure, model, and analyze how the rapid growth of the Atlanta, Georgia metropolitan area since the early 1970's has impacted the region's climate and air quality. The primary objectives for this research effort are: (1) To investigate and model the relationships between land cover change in the Atlanta metropolitan, and the development of the urban heat island phenomenon through time; (2) To investigate and model the temporal relationships between Atlanta urban growth and land cover change on air quality; and (3) To model the overall effects of urban development on surface energy budget characteristics across the Atlanta urban landscape through time. Our key goal is to derive a better scientific understanding of how land cover changes associated with urbanization in the Atlanta area, principally in transforming forest lands to urban land covers through time, has, and will, effect local and regional climate, surface energy flux, and air quality characteristics. Allied with this goal is the prospect that the results from this research can be applied by urban planners, environmental managers and other decision-makers, for determining how urbanization has impacted the climate and overall environment of the Atlanta area. Multiscaled remote sensing data, particularly high resolution thermal infrared data, are integral to this study for the analysis of thermal energy fluxes across the Atlanta urban landscape.

Evaluation of CLM4 Solar Radiation Partitioning Scheme Using Remote Sensing and Site Level FPAR Datasets

DOE PAGES

Wang, Kai; Mao, Jiafu; Dickinson, Robert; ...

2013-06-05

This paper examines a land surface solar radiation partitioning scheme, i.e., that of the Community Land Model version 4 (CLM4) with coupled carbon and nitrogen cycles. Taking advantage of a unique 30-year fraction of absorbed photosynthetically active radiation (FPAR) dataset derived from the Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI) data set, multiple other remote sensing datasets, and site level observations, we evaluated the CLM4 FPAR ’s seasonal cycle, diurnal cycle, long-term trends and spatial patterns. These findings show that the model generally agrees with observations in the seasonal cycle, long-term trends, and spatial patterns,more » but does not reproduce the diurnal cycle. Discrepancies also exist in seasonality magnitudes, peak value months, and spatial heterogeneity. Here, we identify the discrepancy in the diurnal cycle as, due to, the absence of dependence on sun angle in the model. Implementation of sun angle dependence in a one-dimensional (1-D) model is proposed. The need for better relating of vegetation to climate in the model, indicated by long-term trends, is also noted. Evaluation of the CLM4 land surface solar radiation partitioning scheme using remote sensing and site level FPAR datasets provides targets for future development in its representation of this naturally complicated process.« less

AVIRIS Land-Surface Mapping in Support of the Boreal Ecosystem-Atmosphere Study (BOREAS)

NASA Technical Reports Server (NTRS)

Roberts, Dar A.; Gamon, John; Keightley, Keir; Prentiss, Dylan; Reith, Ernest; Green, Robert

2001-01-01

A key scientific objective of the original Boreal Ecosystem-Atmospheric Study (BOREAS) field campaign (1993-1996) was to obtain the baseline data required for modeling and predicting fluxes of energy, mass, and trace gases in the boreal forest biome. These data sets are necessary to determine the sensitivity of the boreal forest biome to potential climatic changes and potential biophysical feedbacks on climate. A considerable volume of remotely-sensed and supporting field data were acquired by numerous researchers to meet this objective. By design, remote sensing and modeling were considered critical components for scaling efforts, extending point measurements from flux towers and field sites over larger spatial and longer temporal scales. A major focus of the BOREAS follow-on program is concerned with integrating the diverse remotely sensed and ground-based data sets to address specific questions such as carbon dynamics at local to regional scales. The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) has the potential of contributing to BOREAS through: (1) accurate retrieved apparent surface reflectance; (2) improved landcover classification; and (3) direct assessment of biochemical/biophysical information such as canopy liquid water and chlorophyll concentration through pigment fits. In this paper, we present initial products for major flux tower sites including: (1) surface reflectance of dominant cover types; (2) a land-cover classification developed using spectral mixture analysis (SMA) and Multiple Endmember Spectral Mixture Analysis (MESMA); and (3) liquid water maps. Our goal is to compare these land-cover maps to existing maps and to incorporate AVIRIS image products into models of photosynthetic flux.

Skid Landings of Airplanes on Rocker-Type Fuselages

NASA Technical Reports Server (NTRS)

Mayo, Wilbur L.

1961-01-01

A study is made of the landing of an airplane on a fuselage with "planned" curvature of its lower surface. Initial contact is considered to stop the vertical motion of a point remote from the center of gravity, thus causing rocking on the curved lower surface which converts sinking-speed energy into angular energy in pitch for dissipation by damping forces. Analysis is made of loads and motions for a given fuselage shape, and the contours required to give desired load histories are determined. Most of the calculations involve initial contact at the tail, but there are two cases of unflared landings with initial contact at the nose. The calculations are checked experimentally for the tail - low case.

Application research on land use remote sensing dynamic monitoring: A case study of Anning district, Lanzhou

NASA Astrophysics Data System (ADS)

Zhu, Yunqiang; Zhu, Huazhong; Lu, Heli; Ni, Jianguang; Zhu, Shaoxia

2005-10-01

Remote sensing dynamic monitoring of land use can detect the change information of land use and update the current land use map, which is important for rational utilization and scientific management of land resources. This paper discusses the technological procedure of remote sensing dynamic monitoring of land use including the process of remote sensing images, the extraction of annual change information of land use, field survey, indoor post processing and accuracy assessment. Especially, we emphasize on comparative research on the choice of remote sensing rectifying models, image fusion algorithms and accuracy assessment methods. Taking Anning district in Lanzhou as an example, we extract the land use change information of the district during 2002-2003, access monitoring accuracy and analyze the reason of land use change.

Monitoring Ecosystem Dynamics Ecosystem Using Hyperspectral Reflectance and a Robotic Tram System in Barrow Alaska

NASA Astrophysics Data System (ADS)

Goswami, S.; Gamon, J. A.; Tweedie, C. E.

2012-12-01

Understanding the future state of the earth system requires improved knowledge of ecosystem dynamics and long term observations of how ecosystem structures and functions are being impacted by global change. Improving remote sensing methods is essential for such advancement because satellite remote sensing is the only means by which landscape to continental-scale change can be observed. The Arctic appears to be impacted by climate change more than any other region on Earth. Arctic terrestrial ecosystems comprise only 6% of the land surface area on Earth yet contain an estimated 25% of global soil organic carbon, most of which is stored in permafrost. If projected increases in plant productivity do not offset forecast losses of soil carbon to the atmosphere as greenhouse gases, regional to global greenhouse warming could be enhanced. Soil moisture is an important control of land-atmosphere carbon exchange in arctic terrestrial ecosystems. However, few studies to date have examined using remote sensing, or developed remote sensing methods for observing the complex interplay between soil moisture and plant phenology and productivity in arctic landscapes. This study was motivated by this knowledge gap and addressed the following questions as a contribution to a large scale, multi investigator flooding and draining experiment funded by the National Science Foundation near Barrow, Alaska from 2005 - 2009. 1. How can optical remote sensing be used to monitor the surface hydrology of arctic landscapes? 2. What are the spatio-temporal dynamics of land-surface phenology (NDVI) in the study area and do hydrological treatment has any effect on inter-annual patterns? A new spectral index, the normalized difference surface water index (NDSWI) was developed and tested at multiple spatial and temporal scales. NDSWI uses the 460nm (blue) and 1000nm (IR) bands and was developed to capture surface hydrological dynamics in the study area using the robotic tram system. When applied to high spatial resolution satellite imagery, NDSWI was also able to capture changes in surface hydrology at the landscape scale. Interannual patterns of landsurface phenology (measured with the normalized difference vegetation index - NDVI) unexpectedly lacked marked differences under experimental conditions. Measurement of NDVI was, however, compromised when WTD was above ground level. NDVI and NDSWI were negatively correlated when WTD was above ground level, which held when scaled to MODIS imagery collected from satellite, suggesting that published findings showing a 'greening of the Arctic' may be related to a 'drying of the Arctic' in landscapes dominated by vegetated landscapes where WTD is close to ground level.

Using the USDA Weekly Crop Progress Record to Document Trends in Corn Planting Date From 1979 to 2005

NASA Astrophysics Data System (ADS)

Kucharik, C. J.

2005-12-01

Agriculture is a dominant driver of land surface phenology in the United States Corn Belt. The timing of planting and harvest, along with the rate of plant development, are influenced by crop type, technology, land management decisions, and weather and soil conditions. Collectively, these integrated factors affect the spatial and temporal spectral signature of crops captured by remote sensing. While many studies have used the historical satellite record of vegetation activity to detect changes across the land surface, there has been less emphasis on using ground-based or remote sensing data to depict the contemporary phenology of individual US agro-ecosystems. The objectives of this study were twofold: (1) demonstrate how weekly USDA-NASS 'Crop Progress' data and 'Weekly Weather and Crop Bulletins' could be useful to remote sensing science when characterizing changing land surface phenology over the US; and (2) quantify long-term trends in corn planting progress from 1979 to 2005 across 12 states in the US Corn Belt. Examination of the weekly NASS crop progress data shows that the initiation of corn planting has become significantly (P < 0.01) earlier by 6 to 24 days since 1979, potentially contributing to about 10% to 64% of the linear increase in corn yields during this period. The magnitude of earlier planting date trend varies regionally, and not all of this change can be attributed to an earlier arrival of spring or warmer springtime temperatures. Rather, the change appears to be related to increased farmer planting efficiency in spring attributed to the increased adoption of no-tillage or reduced-tillage practices and plowing soils in fall. Regardless of the exact cause of this trend, we have a legitimate reason to suspect that 'greening' of the Corn Belt since about 1980, according to remote sensing observations, is not entirely due to climate change, but rather arises from human land-use change in combination with climate factors. In the future, crop progress data may provide an ideal blueprint for selecting the ideal MODIS scene (i.e., 8-day period) that can separate various crop phenologies (e.g., corn vs. soybean) at high resolution, and offer a means to help validate or parameterize ecosystem model algorithms.

Improving land surface emissivty parameter for land surface models using portable FTIR and remote sensing observation in Taklimakan Desert

NASA Astrophysics Data System (ADS)

Liu, Yongqiang; Mamtimin, Ali; He, Qing

2014-05-01

Because land surface emissivity (ɛ) has not been reliably measured, global climate model (GCM) land surface schemes conventionally set this parameter as simply assumption, for example, 1 as in the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) model, 0.96 for soil and wetland in the Global and Regional Assimilation and Prediction System (GRAPES) Common Land Model (CoLM). This is the so-called emissivity assumption. Accurate broadband emissivity data are needed as model inputs to better simulate the land surface climate. It is demonstrated in this paper that the assumption of the emissivity induces errors in modeling the surface energy budget over Taklimakan Desert where ɛ is far smaller than original value. One feasible solution to this problem is to apply the accurate broadband emissivity into land surface models. The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument has routinely measured spectral emissivities in six thermal infrared bands. The empirical regression equations have been developed in this study to convert these spectral emissivities to broadband emissivity required by land surface models. In order to calibrate the regression equations, using a portable Fourier Transform infrared (FTIR) spectrometer instrument, crossing Taklimakan Desert along with highway from north to south, to measure the accurate broadband emissivity. The observed emissivity data show broadband ɛ around 0.89-0.92. To examine the impact of improved ɛ to radiative energy redistribution, simulation studies were conducted using offline CoLM. The results illustrate that large impacts of surface ɛ occur over desert, with changes up in surface skin temperature, as well as evident changes in sensible heat fluxes. Keywords: Taklimakan Desert, surface broadband emissivity, Fourier Transform infrared spectrometer, MODIS, CoLM

Remote Sensing of Smoke, Land and Clouds from the NASA ER-2 during SAFARI 2000

NASA Technical Reports Server (NTRS)

King, Michael D.; Platnick, Steven; Moeller, Christopher C.; Revercomb, Henry E.; Chu, D. Allen

2002-01-01

The NASA ER-2 aircraft was deployed to southern Africa between August 17 and September 25, 2000 as part of the Southern Africa Regional Science Initiative (SAFARI) 2000. This aircraft carried a sophisticated array of multispectral scanners, multiangle spectroradiometers, a monostatic lidar, a gas correlation radiometer, upward and downward spectral flux radiometers, and two metric mapping cameras. These observations were obtained over a 3200 x 2800 km region of savanna, woody savanna, open shrubland, and grassland ecosystems throughout southern Africa, and were quite often coordinated with overflights by NASA's Terra and Landsat 7 satellites. The primary purpose of this sophisticated high altitude observing platform was to obtain independent observations of smoke, clouds, and land surfaces that could be used to check the validity of various remote sensing measurements derived by Earth-orbiting satellites. These include such things as the accuracy of the Moderate Resolution Imaging Spectro-radiometer (MODIS) cloud mask for distinguishing clouds and heavy aerosol from land and ocean surfaces, and Terra analyses of cloud optical and micro-physical properties, aerosol properties, leaf area index, vegetation index, fire occurrence, carbon monoxide, and surface radiation budget. In addition to coordination with Terra and Landsat 7 satellites, numerous flights were conducted over surface AERONET sites, flux towers in South Africa, Botswana, and Zambia, and in situ aircraft from the University of Washington, South Africa, and the United Kingdom.

Remote Sensing of Urban Thermal Landscape Characteristics and Their Affects on Local and Regional Meteorology and Air Quality: An Overview of NASA EOS-IDS Project Atlanta

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.; Luvall, Jeffrey C.; Estes, Maurice G., Jr.

1999-01-01

As an entity, the city is a manifestation of human "management" of the land. The act of city-building, however, drastically alters the biophysical environment, which ultimately, impacts local and regional land-atmosphere energy exchange processes. Because of the complexity of both the urban landscape and the attendant energy fluxes that result from urbanization, remote sensing offers the only real way to synoptically quantify these processes. One of the more important land-atmosphere fluxes that occurs over cities relates to the way that thermal energy is partitioned across the heterogeneous urban landscape. The individual land cover and surface material types that comprise the city, such as pavements and buildings, each have their own thermal energy regimes. As the collective urban landscape, the individual thermal energy responses from specific surfaces come together to form the urban heat island phenomena, which prevails as a dome of elevated air temperatures over cities. Although the urban heat island has been known to exist for well over 150 years, it is not understood how differences in thermal energy responses for land covers across the city interact to produce this phenomenon, or how the variability in thermal energy responses from different surface types drive its development. Additionally, it can be hypothesized that as cities grow in size through time, so do their urban heat islands. The interrelationships between urban sprawl and the respective growth of the urban heat island, however, have not been investigated. Moreover, little is known of the consequential effects of urban growth, land cover change, and the urban heat island as they impact local and regional meteorology and air quality.

Land Surface Data Assimilation

NASA Astrophysics Data System (ADS)

Houser, P. R.

2012-12-01

Information about land surface water, energy and carbon conditions is of critical importance to real-world applications such as agricultural production, water resource management, flood prediction, water supply, weather and climate forecasting, and environmental preservation. While ground-based observational networks are improving, the only practical way to observe these land surface states on continental to global scales is via satellites. Remote sensing can make spatially comprehensive measurements of various components of the terrestrial system, but it cannot provide information on the entire system (e.g. evaporation), and the observations represent only an instant in time. Land surface process models may be used to predict temporal and spatial terrestrial dynamics, but these predictions are often poor, due to model initialization, parameter and forcing, and physics errors. Therefore, an attractive prospect is to combine the strengths of land surface models and observations (and minimize the weaknesses) to provide a superior terrestrial state estimate. This is the goal of land surface data assimilation. Data Assimilation combines observations into a dynamical model, using the model's equations to provide time continuity and coupling between the estimated fields. Land surface data assimilation aims to utilize both our land surface process knowledge, as embodied in a land surface model, and information that can be gained from observations. Both model predictions and observations are imperfect and we wish to use both synergistically to obtain a more accurate result. Moreover, both contain different kinds of information, that when used together, provide an accuracy level that cannot be obtained individually. Model biases can be mitigated using a complementary calibration and parameterization process. Limited point measurements are often used to calibrate the model(s) and validate the assimilation results. This presentation will provide a brief background on land surface observation, modeling and data assimilation, followed by a discussion of various hydrologic data assimilation challenges, and finally conclude with several land surface data assimilation case studies.

On the temporal and spatial variability of near-surface soil moisture for the identification of representative in situ soil moisture monitoring stations

USDA-ARS?s Scientific Manuscript database

The high spatio-temporal variability of soil moisture complicates the validation of remotely sensed soil moisture products using in-situ monitoring stations. Therefore, a standard methodology for selecting the most repre- sentative stations for the purpose of validating satellites and land surface ...

Retrieval of an available water-based soil moisture proxy from thermal infrared remote sensing. Part I: Methodology and validation

USDA-ARS?s Scientific Manuscript database

A retrieval of soil moisture is proposed using surface flux estimates from satellite-based thermal infrared (TIR) imagery and the Atmosphere-Land-Exchange-Inversion (ALEXI) model. The ability of ALEXI to provide valuable information about the partitioning of the surface energy budget, which can be l...

Time series decomposition of remotely sensed land surface temperature and investigation of trends and seasonal variations in surface urban heat islands

NASA Astrophysics Data System (ADS)

Quan, Jinling; Zhan, Wenfeng; Chen, Yunhao; Wang, Mengjie; Wang, Jinfei

2016-03-01

Previous time series methods have difficulties in simultaneous characterization of seasonal, gradual, and abrupt changes of remotely sensed land surface temperature (LST). This study proposed a model to decompose LST time series into trend, seasonal, and noise components. The trend component indicates long-term climate change and land development and is described as a piecewise linear function with iterative breakpoint detection. The seasonal component illustrates annual insolation variations and is modeled as a sinusoidal function on the detrended data. This model is able to separate the seasonal variation in LST from the long-term (including gradual and abrupt) change. Model application to nighttime Moderate Resolution Imaging Spectroradiometer (MODIS)/LST time series during 2000-2012 over Beijing yielded an overall root-mean-square error of 1.62 K between the combination of the decomposed trend and seasonal components and the actual MODIS/LSTs. LST decreased (~ -0.086 K/yr, p < 0.1) in 53% of the study area, whereas it increased with breakpoints in 2009 (~0.084 K/yr before and ~0.245 K/yr after 2009) between the fifth and sixth ring roads. The decreasing trend was stronger over croplands than over urban lands (p < 0.05), resulting in an increasing trend in surface urban heat island intensity (SUHII, 0.022 ± 0.006 K/yr). This was mainly attributed to the trends in urban-rural differences in rainfall and albedo. The SUHII demonstrated a concave seasonal variation primarily due to the seasonal variations of urban-rural differences in temperature cooling rate (related to canyon structure, vegetation, and soil moisture) and surface heat dissipation (affected by humidity and wind).

A global analysis of the urban heat island effect based on multisensor satellite data

NASA Astrophysics Data System (ADS)

Xiao, J.; Frolking, S. E.; Milliman, T. E.; Schneider, A.; Friedl, M. A.

2017-12-01

Human population is rapidly urbanizing. In much of the world, cities are prone to hotter weather than surrounding rural areas - so-called `urban heat islands' - and this effect can have mortal consequences during heat waves. During the daytime, when the surface energy balance is driven by incoming solar radiation, the magnitude of urban warming is strongly influenced by surface albedo and the capacity to evaporate water (i.e., there is a strong relationship between vegetated land fraction and the ratio of sensible to latent heat loss or Bowen ratio). At nighttime, urban cooling is often inhibited by the thermal inertia of the built environment and anthropogenic heat exhaust from building and transportation energy use. We evaluated a suite of global remote sensing data sets representing a range of urban characteristics against MODIS-derived land-surface temperature differences between urban and surrounding rural areas. We included two new urban datasets in this analysis - MODIS-derived change in global urban extent and global urban microwave backscatter - along with several MODIS standard products and DMSP/OLS nighttime lights time series data. The global analysis spanned a range of urban characteristics that likely influence the magnitude of daytime and/or nighttime urban heat islands - urban size, population density, building density, state of development, impervious fraction, eco-climatic setting. Specifically, we developed new satellite datasets and synthesizing these with existing satellite data into a global database of urban land surface parameters, used two MODIS land surface temperature products to generate time series of daytime and nighttime urban heat island effects for 30 large cities across the globe, and empirically analyzed these data to determine specifically which remote sensing-based characterizations of global urban areas have explanatory power with regard to both daytime and nighttime urban heat islands.

Earth Survey Applications Division. [a bibliography

NASA Technical Reports Server (NTRS)

Carpenter, L. (Editor)

1981-01-01

Accomplishments of research and data analysis conducted to study physical parameters and processes inside the Earth and on the Earth's surface, to define techniques and systems for remotely sensing the processes and measuring the parameters of scientific and applications interest, and the transfer of promising operational applications techniques to the user community of Earth resources monitors, managers, and decision makers are described. Research areas covered include: geobotany, magnetic field modeling, crustal studies, crustal dynamics, sea surface topography, land resources, remote sensing of vegetation and soils, and hydrological sciences. Major accomplishments include: production of global maps of magnetic anomalies using Magsat data; computation of the global mean sea surface using GEOS-3 and Seasat altimetry data; delineation of the effects of topography on the interpretation of remotely-sensed data; application of snowmelt runoff models to water resources management; and mapping of snow depth over wheat growing areas using Nimbus microwave data.

Digital terrain modeling

NASA Astrophysics Data System (ADS)

Wilson, John P.

2012-01-01

This article examines how the methods and data sources used to generate DEMs and calculate land surface parameters have changed over the past 25 years. The primary goal is to describe the state-of-the-art for a typical digital terrain modeling workflow that starts with data capture, continues with data preprocessing and DEM generation, and concludes with the calculation of one or more primary and secondary land surface parameters. The article first describes some of ways in which LiDAR and RADAR remote sensing technologies have transformed the sources and methods for capturing elevation data. It next discusses the need for and various methods that are currently used to preprocess DEMs along with some of the challenges that confront those who tackle these tasks. The bulk of the article describes some of the subtleties involved in calculating the primary land surface parameters that are derived directly from DEMs without additional inputs and the two sets of secondary land surface parameters that are commonly used to model solar radiation and the accompanying interactions between the land surface and the atmosphere on the one hand and water flow and related surface processes on the other. It concludes with a discussion of the various kinds of errors that are embedded in DEMs, how these may be propagated and carried forward in calculating various land surface parameters, and the consequences of this state-of-affairs for the modern terrain analyst.

Integrated remote sensing for multi-temporal analysis of urban land cover-climate interactions

NASA Astrophysics Data System (ADS)

Savastru, Dan M.; Zoran, Maria A.; Savastru, Roxana S.

2016-08-01

Climate change is considered to be the biggest environmental threat in the future in the South- Eastern part of Europe. In frame of predicted global warming, urban climate is an important issue in scientific research. Surface energy processes have an essential role in urban weather, climate and hydrosphere cycles, as well in urban heat redistribution. This paper investigated the influences of urban growth on thermal environment in relationship with other biophysical variables in Bucharest metropolitan area of Romania. Remote sensing data from Landsat TM/ETM+ and time series MODIS Terra/Aqua sensors have been used to assess urban land cover- climate interactions over period between 2000 and 2015 years. Vegetation abundances and percent impervious surfaces were derived by means of linear spectral mixture model, and a method for effectively enhancing impervious surface has been developed to accurately examine the urban growth. The land surface temperature (Ts), a key parameter for urban thermal characteristics analysis, was also analyzed in relation with the Normalized Difference Vegetation Index (NDVI) at city level. Based on these parameters, the urban growth, and urban heat island effect (UHI) and the relationships of Ts to other biophysical parameters have been analyzed. The correlation analyses revealed that, at the pixel-scale, Ts possessed a strong positive correlation with percent impervious surfaces and negative correlation with vegetation abundances at the regional scale, respectively. This analysis provided an integrated research scheme and the findings can be very useful for urban ecosystem modeling.

Integrating satellite remote sensing data and field data to predict rangeland structural indicators at the continental scale

NASA Astrophysics Data System (ADS)

Zhang, J.; Okin, G.

2016-12-01

Rangelands provide a variety of important ecosystem goods and services across drylands globally. They are also the most important emitters of dust across the globe. Field data collection based on points does not represent spatially continuous information about surface variables and, given the vast size of the world's rangelands, cannot cover even a small fraction of their area. Remote sensing is potentially a labor- and time-saving method to observe important rangeland vegetation variables at both temporal and spatial scales. Information on vegetation cover, bare gap size, and plant height provide key rangeland vegetation variables in arid and semiarid rangelands, in part because they strongly impact dust emission and determine wildlife habitat characteristics. This study reports on relationships between remote sensing in the reflected solar spectrum and field measures related to these three variables, and shows how these relationships can be extended to produce spatially and temporally continuous datasets coupled with quantitative estimates of error. Field data for this study included over 3,800 Assessment, Inventory, and Monitoring (AIM) measurements on Bureau of Land Management (BLM) lands throughout the western US. Remote sensing data were derived from MODIS nadir BRDF-adjusted reflectance (NBAR) and Landsat 8 OLI surface reflectance. Normalized bare gap size, total foliar cover, herbaceous cover and herbaceous height exhibit the greatest predictability from remote sensing variables with physically-reasonable relationships between remote sensing variables and field measures. Data fields produced using these relationships across the western US exhibit good agreement with independent high-resolution imagery.

Estimation of Key Parameters of the Coupled Energy and Water Model by Assimilating Land Surface Data

NASA Astrophysics Data System (ADS)

Abdolghafoorian, A.; Farhadi, L.

2017-12-01

Accurate estimation of land surface heat and moisture fluxes, as well as root zone soil moisture, is crucial in various hydrological, meteorological, and agricultural applications. Field measurements of these fluxes are costly and cannot be readily scaled to large areas relevant to weather and climate studies. Therefore, there is a need for techniques to make quantitative estimates of heat and moisture fluxes using land surface state observations that are widely available from remote sensing across a range of scale. In this work, we applies the variational data assimilation approach to estimate land surface fluxes and soil moisture profile from the implicit information contained Land Surface Temperature (LST) and Soil Moisture (SM) (hereafter the VDA model). The VDA model is focused on the estimation of three key parameters: 1- neutral bulk heat transfer coefficient (CHN), 2- evaporative fraction from soil and canopy (EF), and 3- saturated hydraulic conductivity (Ksat). CHN and EF regulate the partitioning of available energy between sensible and latent heat fluxes. Ksat is one of the main parameters used in determining infiltration, runoff, groundwater recharge, and in simulating hydrological processes. In this study, a system of coupled parsimonious energy and water model will constrain the estimation of three unknown parameters in the VDA model. The profile of SM (LST) at multiple depths is estimated using moisture diffusion (heat diffusion) equation. In this study, the uncertainties of retrieved unknown parameters and fluxes are estimated from the inverse of Hesian matrix of cost function which is computed using the Lagrangian methodology. Analysis of uncertainty provides valuable information about the accuracy of estimated parameters and their correlation and guide the formulation of a well-posed estimation problem. The results of proposed algorithm are validated with a series of experiments using a synthetic data set generated by the simultaneous heat and water (SHAW) model. In addition, the feasibility of extending this algorithm to use remote sensing observations that have low temporal resolution is examined by assimilating the limited number of land surface moisture and temperature observations.

Estimating surface fluxes over middle and upper streams of the Heihe River Basin with ASTER imagery

NASA Astrophysics Data System (ADS)

Ma, W.; Ma, Y.; Hu, Z.; Su, Z.; Wang, J.; Ishikawa, H.

2011-05-01

Land surface heat fluxes are essential measures of the strengths of land-atmosphere interactions involving energy, heat and water. Correct parameterization of these fluxes in climate models is critical. Despite their importance, state-of-the-art observation techniques cannot provide representative areal averages of these fluxes comparable to the model grid. Alternative methods of estimation are thus required. These alternative approaches use (satellite) observables of the land surface conditions. In this study, the Surface Energy Balance System (SEBS) algorithm was evaluated in a cold and arid environment, using land surface parameters derived from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. Field observations and estimates from SEBS were compared in terms of net radiation flux (Rn), soil heat flux (G0), sensible heat flux (H) and latent heat flux (λE) over a heterogeneous land surface. As a case study, this methodology was applied to the experimental area of the Watershed Allied Telemetry Experimental Research (WATER) project, located on the mid-to-upstream sections of the Heihe River in northwest China. ASTER data acquired between 3 May and 4 June 2008, under clear-sky conditions were used to determine the surface fluxes. Ground-based measurements of land surface heat fluxes were compared with values derived from the ASTER data. The results show that the derived surface variables and the land surface heat fluxes furnished by SEBS in different months over the study area are in good agreement with the observed land surface status under the limited cases (some cases looks poor results). So SEBS can be used to estimate turbulent heat fluxes with acceptable accuracy in areas where there is partial vegetation cover in exceptive conditions. It is very important to perform calculations using ground-based observational data for parameterization in SEBS in the future. Nevertheless, the remote-sensing results can provide improved explanations of land surface fluxes over varying land coverage at greater spatial scales.

Land Surface Verification Toolkit (LVT) - A Generalized Framework for Land Surface Model Evaluation

NASA Technical Reports Server (NTRS)

Kumar, Sujay V.; Peters-Lidard, Christa D.; Santanello, Joseph; Harrison, Ken; Liu, Yuqiong; Shaw, Michael

2011-01-01

Model evaluation and verification are key in improving the usage and applicability of simulation models for real-world applications. In this article, the development and capabilities of a formal system for land surface model evaluation called the Land surface Verification Toolkit (LVT) is described. LVT is designed to provide an integrated environment for systematic land model evaluation and facilitates a range of verification approaches and analysis capabilities. LVT operates across multiple temporal and spatial scales and employs a large suite of in-situ, remotely sensed and other model and reanalysis datasets in their native formats. In addition to the traditional accuracy-based measures, LVT also includes uncertainty and ensemble diagnostics, information theory measures, spatial similarity metrics and scale decomposition techniques that provide novel ways for performing diagnostic model evaluations. Though LVT was originally designed to support the land surface modeling and data assimilation framework known as the Land Information System (LIS), it also supports hydrological data products from other, non-LIS environments. In addition, the analysis of diagnostics from various computational subsystems of LIS including data assimilation, optimization and uncertainty estimation are supported within LVT. Together, LIS and LVT provide a robust end-to-end environment for enabling the concepts of model data fusion for hydrological applications. The evolving capabilities of LVT framework are expected to facilitate rapid model evaluation efforts and aid the definition and refinement of formal evaluation procedures for the land surface modeling community.

A Study on Land Suitability for Rice Cultivation in Khordha District of Odisha (India) Using Remote Sensing and GIS

NASA Astrophysics Data System (ADS)

Rath, Sudhansu S.; Panda, Jagabandhu; Annadurai, R.; Nanda, Sachikanta

2018-05-01

With the global population on the rise, it is important to address the increasing demand for food. According to FAO (Food and Agriculture Organization, United Nations), by 2050, the developing countries must double their food production to meet the growing demand. Proper land utilization can be one of the solutions for this problem. In view of this, the current study focussed on land suitability analysis for Khordha district of Odisha (India) for rice crop. This study estimated that the amount of land suitable for rice cropping was 195,731 ha against the currently cultivated land of 122,183.38 ha. Therefore, there was a possibility of more amount of land that could be available for rice cultivation in Khordha district than the currently cultivated area. In order to perform this exercise, the land use and land cover data from IRS (Indian remote sensing satellite), soil nutrient parameters like pH values and nitrogen, potassium, phosphorous and organic carbon contents were considered. In addition, the climatic parameters such as near surface temperature, rainfall and number of rainy days were taken into account. The unused land identified in Khordha district in this study might be utilized for cultivating rice crop in this region.

A Study on Land Suitability for Rice Cultivation in Khordha District of Odisha (India) Using Remote Sensing and GIS

NASA Astrophysics Data System (ADS)

Rath, Sudhansu S.; Panda, Jagabandhu; Annadurai, R.; Nanda, Sachikanta

2018-02-01

With the global population on the rise, it is important to address the increasing demand for food. According to FAO (Food and Agriculture Organization, United Nations), by 2050, the developing countries must double their food production to meet the growing demand. Proper land utilization can be one of the solutions for this problem. In view of this, the current study focussed on land suitability analysis for Khordha district of Odisha (India) for rice crop. This study estimated that the amount of land suitable for rice cropping was 195,731 ha against the currently cultivated land of 122,183.38 ha. Therefore, there was a possibility of more amount of land that could be available for rice cultivation in Khordha district than the currently cultivated area. In order to perform this exercise, the land use and land cover data from IRS (Indian remote sensing satellite), soil nutrient parameters like pH values and nitrogen, potassium, phosphorous and organic carbon contents were considered. In addition, the climatic parameters such as near surface temperature, rainfall and number of rainy days were taken into account. The unused land identified in Khordha district in this study might be utilized for cultivating rice crop in this region.

Analysis of Upper Air, Ground and Remote Sensing Data for the ATLAS Field Campaign in San Juan, Puerto Rico

NASA Technical Reports Server (NTRS)

Gonzalez, Jorge E.; Luvall, Jeff; Rickman, Douglas; Comarazamy, Daniel; Picon, Ana J.

2005-01-01

The Atlas San Juan Mission was conducted in February 2004 with the main objectives of observing the Urban Heat Island of San Juan, providing high resolution data of the land use for El Yunque Rain Forest and for calibrating remote sensors. The mission was coordinated with NASA staff members at Marsha& Stennis, Goddard, and Glenn. The Airborne Thermal and Land Applications Sensor (ATLAS) from NASA/Stennis, that operates in the visual and IR bands, was used as the main sensor and was flown over Puerto Rico in a Lear 23 jet plane. To support the data gathering effort by the ATLAS sensor, remote sensing observations and upper air soundings were conducted along with the deployment of a number of ground based weather stations and temperature sensors. This presentation focuses in the analysis of this complementary data for the Atlas San Juan Mission. Upper air data show that during the days of the mission the Caribbean mid and high atmospheres were relatively dry and highly stable reflecting positive surface lifted index, a necessary condition to conduct this suborbital campaign. Surface wind patterns at levels below 850mb were dominated by the easterly trades, while the jet stream at the edge of the troposphere dominated the westerly wind at levels above 500mb. The jet stream remained at high latitudes reducing the possibility of fronts. In consequence, only 8.4 mm of precipitation were reported during the entire mission. Observation of soundings located about 150 km apart reflected minimum variations of the boundary layer across the Island for levels below 850 meters and a uniform atmosphere for higher levels. The weather stations and the temperature sensors were placed at strategic locations to observe variations across the urban and rural landscapes. Time series plot of the stations' data show that heavily urbanized commercial areas have higher air temperatures than urban and suburban residential areas, and much higher temperatures than rural areas. Temperature differences [dT(U-R)] were obtained by subtracting the values of several stations h m a reference urban station, located m the commercial area of San Juan. These time series show that the UHI peaks during the morning between 10:00am and noon to an average of 4.5 C, a temporal pattern not previously observed in similar studies for continental cities. It is also observed a high variability of the UHI with the precipitation patterns even for short events. These results may be a reflection of a large land use density by low level buildings with an apparent absence of significant heat storage effects in the urban areas, and the importance of the surrounding soil and vegetation moisture in controlling the urban tropical climate. The ATLAS data was used to determine albedo and surface temperature patterns on a 10m scale for the study area. These data were used to calibrate the spatial distribution of the surface temperature when using remote sensing images from MODIS (Moderate Resolution Imaging Spectradiometer). Surface temperatures were estimated using the land surface temperature product MODII-L2 distributed by the Land Process Distributed Active Archive Center(LP DAAC). These results show the maximum, minimum and average temperatures in San Juan and in the entire Island at a resolution of 1 km. The information retrieved from MODIS for land surface temperatures reflected similar temporal and spatial variations as the weather stations and ATLAS measurements with a highest absolute offset of about 5 C due to the differences between surface and air temperatures.

Analysis of Upper Air, Ground and Remote Sensing Data For the ATLAS Field Campaign in San Juan, Puerto Rico

NASA Technical Reports Server (NTRS)

Gonzalez, J. E.; Luvall, J. C.; Rickman, D.; Comarazamy, D. E.; Picon, A.

2004-01-01

The Atlas San Juan Mission was conducted in February 2004 with the main objectives of observing the Urban Heat Island of San Juan, providing high resolution data of the land use for El Yunque Rain Forest and for calibrating remote sensors. The mission was coordinated with NASA staff members at Marshall, Stennis, Goddard, and Glenn. The Airborne Thermal and Land Applications Sensor (ATLAS) from NASA/Stennis, that operates in the visual and IR bands, was used as the main sensor and was flown over Puerto Rico in a Lear 23 jet plane. To support the data gathering effort by the ATLAS sensor, remote sensing observations and upper air soundings were conducted along with the deployment of a number of ground based weather stations and temperature sensors. This presentation focuses in the analysis of this complementary data for the Atlas San Juan Mission. Upper air data show that during the days of the mission the Caribbean mid and high atmospheres were relatively dry and highly stable reflecting positive surface lifted index, a necessary condition to conduct this suborbital campaign. Surface wind patterns at levels below 850mb were dominated by the easterly trades, while the jet stream at the edge of the troposphere dominated the westerly wind at levels above 500mb. The jet stream remained at high latitudes reducing the possibility of fronts. In consequence, only 8.4 mm of precipitation were reported during the entire mission. Observation of soundings located about 150 km apart reflected minimum variations of the boundary layer across the island for levels below 850 meters and a uniform atmosphere for higher levels. The weather stations and the temperature sensors were placed at strategic locations to observe variations across the urban and rural landscapes. Time series plot of the stations' data show that heavily urbanized commercial areas have higher air temperatures than urban and suburban residential areas, and much higher temperatures than rural areas. Temperature differences [dT(U-R)] were obtained by subtracting the values of several stations from a reference urban station, located in the commercial area of San Juan. These time series show that the UHI peaks during the morning between 10:00am and noon to an average of 4.5 C, a temporal pattern not previously observed in similar studies for continental cities. It is also observed a high variability of the UHI with the precipitation patterns even for short events. These results may be a reflection of a large land use density by low level buildings with an apparent absence of significant heat storage effects in the urban areas, and the importance of the surrounding soil and vegetation moisture in controlling the urban tropical climate. The ATLAS data was used to determine albedo and surface temperature patterns on a 10m scale for the study area. These data were used to calibrate the spatial distribution of the surface temperature when using remote sensing images from MODIS (Moderate Resolution Imaging Spectroradiometer). Surface temperatures were estimated using the land surface temperature product MOD11_L2 distributed by the Land Process Distributed Active Archive Center (LP DAAC). These results show the maximum, minimum and average temperatures in San Juan and in the entire Island at a resolution of 1 km. The information retrieved from MODIS for land surface temperatures reflected similar temporal and spatial variations as the weather stations and ATLAS measurements with a highest absolute offset of about 5 C due to the differences between surface and air temperatures.

Trend analysis of time-series phenology of North America derived from satellite data

USGS Publications Warehouse

Reed, B.C.

2006-01-01

Remote sensing information has been used in studies of the seasonal dynamics (phenology) of the land surface since the 1980s. While our understanding of remote sensing phenology is still in development, it is regarded as a key to understanding land-surface processes over large areas. Phenologic metrics, including start of season, end of season, duration of season, and seasonally integrated greenness, were derived from 8 km advanced very high resolution radiometer (AVHRR) data over North America spanning the years 1982-2003. Trend analysis was performed on annual summaries of the metrics to determine areas with increasing or decreasing growing season trends for the time period under study. Results show a trend toward earlier starts of season in limited areas of the mixed boreal forest, and a trend toward later end of season in well-defined areas of New England and southeastern Canada. Results in Saskatchewan, Canada, include a trend toward longer duration of season over a well-defined area, principally as a result of regional changes in land use practices. Changing seasonality appears to be an integrated response to a complex of factors, including climate change, but also, in many places, changes in land use practices. Copyright ?? 2006 by V. H. Winston & Son, Inc. All rights reserved.

Applying Geospatial Techniques to Investigate Boundary Layer Land-Atmosphere Interactions Involved in Tornadogensis

NASA Astrophysics Data System (ADS)

Weigel, A. M.; Griffin, R.; Knupp, K. R.; Molthan, A.; Coleman, T.

2017-12-01

Northern Alabama is among the most tornado-prone regions in the United States. This region has a higher degree of spatial variability in both terrain and land cover than the more frequently studied North American Great Plains region due to its proximity to the southern Appalachian Mountains and Cumberland Plateau. More research is needed to understand North Alabama's high tornado frequency and how land surface heterogeneity influences tornadogenesis in the boundary layer. Several modeling and simulation studies stretching back to the 1970's have found that variations in the land surface induce tornadic-like flow near the surface, illustrating a need for further investigation. This presentation introduces research investigating the hypothesis that horizontal gradients in land surface roughness, normal to the direction of flow in the boundary layer, induce vertically oriented vorticity at the surface that can potentially aid in tornadogenesis. A novel approach was implemented to test this hypothesis using a GIS-based quadrant pattern analysis method. This method was developed to quantify spatial relationships and patterns between horizontal variations in land surface roughness and locations of tornadogenesis. Land surface roughness was modeled using the Noah land surface model parameterization scheme which, was applied to MODIS 500 m and Landsat 30 m data in order to compare the relationship between tornadogenesis locations and roughness gradients at different spatial scales. This analysis found a statistical relationship between areas of higher roughness located normal to flow surrounding tornadogenesis locations that supports the tested hypothesis. In this presentation, the innovative use of satellite remote sensing data and GIS technologies to address interactions between the land and atmosphere will be highlighted.

Validation of the MODIS MOD21 and MOD11 land surface temperature and emissivity products in an arid area of Northwest China

NASA Astrophysics Data System (ADS)

Li, H.; Yang, Y.; Yongming, D.; Cao, B.; Qinhuo, L.

2017-12-01

Land surface temperature (LST) is a key parameter for hydrological, meteorological, climatological and environmental studies. During the past decades, many efforts have been devoted to the establishment of methodology for retrieving the LST from remote sensing data and significant progress has been achieved. Many operational LST products have been generated using different remote sensing data. MODIS LST product (MOD11) is one of the most commonly used LST products, which is produced using a generalized split-window algorithm. Many validation studies have showed that MOD11 LST product agrees well with ground measurements over vegetated and inland water surfaces, however, large negative biases of up to 5 K are present over arid regions. In addition, land surface emissivity of MOD11 are estimated by assigning fixed emissivities according to a land cover classification dataset, which may introduce large errors to the LST product due to misclassification of the land cover. Therefore, a new MODIS LSE&E product (MOD21) is developed based on the temperature emissivity separation (TES) algorithm, and the water vapor scaling (WVS) method has also been incorporated into the MODIS TES algorithm for improving the accuracy of the atmospheric correction. The MOD21 product will be released with MODIS collection 6 Tier-2 land products in 2017. Due to the MOD21 products are not available right now, the MODTES algorithm was implemented including the TES and WVS methods as detailed in the MOD21 Algorithm Theoretical Basis Document. The MOD21 and MOD11 C6 LST products are validated using ground measurements and ASTER LST products collected in an arid area of Northwest China during the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) experiment. In addition, lab emissivity spectra of four sand dunes in the Northwest China are also used to validate the MOD21 and MOD11 emissivity products.

Remote Sensing of Terrestrial Water Storage and Application to Drought Monitoring

NASA Technical Reports Server (NTRS)

Rodell, Matt

2007-01-01

Terrestrial water storage (TWS) consists of groundwater, soil moisture and permafrost, surface water, snow and ice, and wet biomass. TWS variability tends to be dominated by snow and ice in polar and alpine regions, by soil moisture in mid-latitudes, and by surface water in wet, tropical regions such as the Amazon (Rodell and Famiglietti, 2001; Bates et al., 2007). Drought may be defined as a period of abnormally dry weather long enough to cause significant deficits in one or more of the TWS components. Thus, along with observations of the agricultural and socioeconomic impacts, measurements of TWS and its components enable quantification of drought severity. Each of the TWS components exhibits significant spatial variability, while installation and maintenance of sufficiently dense monitoring networks is costly and labor-intensive. Thus satellite remote sensing is an appealing alternative to traditional measurement techniques. Several current remote sensing instruments are able to detect variations in one or more TWS variables, including the Advanced Microwave Scanning Radiometer (AMSR) on NASA's Aqua satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Terra and Aqua. Future satellite missions have been proposed to improve this capability, including the European Space Agency's Soil Moisture Ocean Salinity mission (SMOS) and the Soil Moisture Active Passive (SMAP), Surface Water Ocean Topography (SWOT), and Snow and Cold Land Processes (SCLP) missions recommended by the US National Academy of Science's Decadal Survey for Earth Science (NRC, 2007). However, only one remote sensing technology is able to monitor changes in TWS from the land surface to the base of the deepest aquifer: satellite gravimetry. This paper focuses on NASA's Gravity Recovery and Climate Experiment mission (GRACE; http://www.csr.utexas.edu/grace/) and its potential as a tool for drought monitoring.

Integration of environmental simulation models with satellite remote sensing and geographic information systems technologies: case studies

USGS Publications Warehouse

Steyaert, Louis T.; Loveland, Thomas R.; Brown, Jesslyn F.; Reed, Bradley C.

1993-01-01

Environmental modelers are testing and evaluating a prototype land cover characteristics database for the conterminous United States developed by the EROS Data Center of the U.S. Geological Survey and the University of Nebraska Center for Advanced Land Management Information Technologies. This database was developed from multi temporal, 1-kilometer advanced very high resolution radiometer (AVHRR) data for 1990 and various ancillary data sets such as elevation, ecological regions, and selected climatic normals. Several case studies using this database were analyzed to illustrate the integration of satellite remote sensing and geographic information systems technologies with land-atmosphere interactions models at a variety of spatial and temporal scales. The case studies are representative of contemporary environmental simulation modeling at local to regional levels in global change research, land and water resource management, and environmental simulation modeling at local to regional levels in global change research, land and water resource management and environmental risk assessment. The case studies feature land surface parameterizations for atmospheric mesoscale and global climate models; biogenic-hydrocarbons emissions models; distributed parameter watershed and other hydrological models; and various ecological models such as ecosystem, dynamics, biogeochemical cycles, ecotone variability, and equilibrium vegetation models. The case studies demonstrate the important of multi temporal AVHRR data to develop to develop and maintain a flexible, near-realtime land cover characteristics database. Moreover, such a flexible database is needed to derive various vegetation classification schemes, to aggregate data for nested models, to develop remote sensing algorithms, and to provide data on dynamic landscape characteristics. The case studies illustrate how such a database supports research on spatial heterogeneity, land use, sensitivity analysis, and scaling issues involving regional extrapolations and parameterizations of dynamic land processes within simulation models.

Remote sensing of smoke, land, and clouds from the NASA ER-2 during SAFARI 2000

NASA Astrophysics Data System (ADS)

King, Michael D.; Platnick, Steven; Moeller, Christopher C.; Revercomb, Henry E.; Chu, D. Allen

2003-07-01

The NASA ER-2 aircraft was deployed to southern Africa between 13 August and 25 September 2000 as part of the Southern African Regional Science Initiative (SAFARI) 2000. This aircraft carried a sophisticated array of multispectral scanners, multiangle spectroradiometers, a monostatic lidar, a gas correlation radiometer, upward and downward spectral flux radiometers, and two metric mapping cameras. These observations were obtained over a 3200 × 2800 km region of savanna, woody savanna, open shrubland, and grassland ecosystems throughout southern Africa and were quite often coordinated with overflights by NASA's Terra and Landsat 7 satellites. The primary purpose of this high-altitude observing platform was to obtain independent observations of smoke, clouds, and land surfaces that could be used to check the validity of various remote sensing measurements derived by Earth-orbiting satellites. These include such things as the accuracy of the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask for distinguishing clouds and heavy aerosol from land and ocean surfaces and Terra analyses of cloud optical and microphysical properties, aerosol properties, leaf area index, vegetation index, fire occurrence, carbon monoxide, and surface radiation budget. In addition to coordination with Terra and Landsat 7 satellites, numerous flights were conducted over surface AERONET sites, flux towers in South Africa, Botswana, and Zambia, and in situ aircraft from the University of Washington, South Africa, and the United Kingdom. As a result of this experiment, the MODIS cloud mask was shown to distinguish clouds, cloud shadows, and fires over land ecosystems of southern Africa with a high degree of accuracy. In addition, data acquired from the ER-2 show the vertical distribution and stratification of aerosol layers over the subcontinent and make the first observations of a "blue spike" spectral emission signature associated with air heated by fire advecting over a cooler land surface.

Remote sensing of atmosphere and oceans; Proceedings of Symposium 1 and of the Topical Meeting of the 27th COSPAR Plenary Meeting, Espoo, Finland, July 18-29, 1988

NASA Technical Reports Server (NTRS)

Raschke, E. (Editor); Ghazi, A. (Editor); Gower, J. F. R. (Editor); Mccormick, P. (Editor); Gruber, A. (Editor); Hasler, A. F. (Editor)

1989-01-01

Papers are presented on the contribution of space remote sensing observations to the World Climate Research Program and the Global Change Program, covering topics such as space observations for global environmental monitoring, experiments related to land surface fluxes, studies of atmospheric composition, structure, motions, and precipitation, and remote sensing for oceanography, observational studies of the atmosphere, clouds, and the earth radiation budget. Also, papers are given on results from space observations for meteorology, oceanography, and mesoscale atmospheric and ocean processes. The topics include vertical atmospheric soundings, surface water temperature determination, sea level variability, data on the prehurricane atmosphere, linear and circular mesoscale convective systems, Karman vortex clouds, and temporal patterns of phytoplankton abundance.

Quantifying Arctic Terrestrial Environment Behaviors Using Geophysical, Point-Scale and Remote Sensing Data

NASA Astrophysics Data System (ADS)

Dafflon, B.; Hubbard, S. S.; Ulrich, C.; Peterson, J. E.; Wu, Y.; Wainwright, H. M.; Gangodagamage, C.; Kholodov, A. L.; Kneafsey, T. J.

2013-12-01

Improvement in parameterizing Arctic process-rich terrestrial models to simulate feedbacks to a changing climate requires advances in estimating the spatiotemporal variations in active layer and permafrost properties - in sufficiently high resolution yet over modeling-relevant scales. As part of the DOE Next-Generation Ecosystem Experiments (NGEE-Arctic), we are developing advanced strategies for imaging the subsurface and for investigating land and subsurface co-variability and dynamics. Our studies include acquisition and integration of various measurements, including point-based, surface-based geophysical, and remote sensing datasets These data have been collected during a series of campaigns at the NGEE Barrow, AK site along transects that traverse a range of hydrological and geomorphological conditions, including low- to high- centered polygons and drained thaw lake basins. In this study, we describe the use of galvanic-coupled electrical resistance tomography (ERT), capacitively-coupled resistivity (CCR) , permafrost cores, above-ground orthophotography, and digital elevation model (DEM) to (1) explore complementary nature and trade-offs between characterization resolution, spatial extent and accuracy of different datasets; (2) develop inversion approaches to quantify permafrost characteristics (such as ice content, ice wedge frequency, and presence of unfrozen deep layer) and (3) identify correspondences between permafrost and land surface properties (such as water inundation, topography, and vegetation). In terms of methods, we developed a 1D-based direct search approach to estimate electrical conductivity distribution while allowing exploration of multiple solutions and prior information in a flexible way. Application of the method to the Barrow datasets reveals the relative information content of each dataset for characterizing permafrost properties, which shows features variability from below one meter length scales to large trends over more than a kilometer. Further, we used Pole- and Kite-based low-altitude aerial photography with inferred DEM, as well as DEM from LiDAR dataset, to quantify land-surface properties and their co-variability with the subsurface properties. Comparison of the above- and below-ground characterization information indicate that while some permafrost characteristics correspond with changes in hydrogeomorphological expressions, others features show more complex linkages with landscape properties. Overall, our results indicate that remote sensing data, point-scale measurements and surface geophysical measurements enable the identification of regional zones having similar relations between subsurface and land surface properties. Identification of such zonation and associated permafrost-land surface properties can be used to guide investigations of carbon cycling processes and for model parameterization.

Remote sensing study of the impact of vegetation on thermal environment in different contexts

NASA Astrophysics Data System (ADS)

Xie, Qijiao; Wu, Yingjiao; Zhou, Zhixiang; Wang, Zhengxiang

2018-02-01

Satellite remote sensing technology provides informative data for detecting the land surface temperature (LST) distribution and urban heat island (UHI) effect remotely and regionally. In this study, two Landsat Thematic Mapper (TM) images acquired on September 26, 1987 and September 17, 2013 were used to derive LST and the normalized difference vegetation index (NDVI) values in Wuhan, China. The relationships between NDVI and LST were examined in different contexts, namely built-up area, farmland, grassland and forest. Results showed that negative correlations between the mean NDVI and LST were detected in all observed land covers, which meant that vegetation was efficient in decreasing surface temperatures and mitigating UHI effect. The cooling efficiency of vegetation on thermal environment varied with different contexts. As mean NDVI increased at each 0.1, the decreased LST values in built-up area, farmland, grassland and forest were 1.4 °C, 1.4 °C, 1.1 °C, 1.9 °C in 1987 and 1.4 °C, 1.7 °C, 1.3 °C, 1.8 °C in 2013, respectively. This finding encourages urban planners and greening designers to devote more efforts in protecting urban forests.

Geothermal area detection using Landsat ETM+ thermal infrared data and its mechanistic analysis—A case study in Tengchong, China

NASA Astrophysics Data System (ADS)

Qin, Qiming; Zhang, Ning; Nan, Peng; Chai, Leilei

2011-08-01

Thermal infrared (TIR) remote sensing is an important technique in the exploration of geothermal resources. In this study, a geothermal survey is conducted in Tengchong area of Yunnan province in China using TIR data from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor. Based on radiometric calibration, atmospheric correction and emissivity calculation, a simple but efficient single channel algorithm with acceptable precision is applied to retrieve the land surface temperature (LST) of study area. The LST anomalous areas with temperature about 4-10 K higher than background area are discovered. Four geothermal areas are identified with the discussion of geothermal mechanism and the further analysis of regional geologic structure. The research reveals that the distribution of geothermal areas is consistent with the fault development in study area. Magmatism contributes abundant thermal source to study area and the faults provide thermal channels for heat transfer from interior earth to land surface and facilitate the present of geothermal anomalies. Finally, we conclude that TIR remote sensing is a cost-effective technique to detect LST anomalies. Combining TIR remote sensing with geological analysis and the understanding of geothermal mechanism is an accurate and efficient approach to geothermal area detection.

Exploitation of multi-temporal Earth Observation imagery for monitoring land cover change in mining sites

NASA Astrophysics Data System (ADS)

Petropoulos, G.; Partsinevelos, P.; Mitraka, Z.

2012-04-01

Surface mining has been shown to cause intensive environmental degradation in terms of landscape, vegetation and biological communities. Nowadays, the commercial availability of remote sensing imagery at high spatiotemporal scales, has improved dramatically our ability to monitor surface mining activity and evaluate its impact on the environment and society. In this study we investigate the potential use of Landsat TM imagery combined with diverse classification techniques, namely artificial neural networks and support vector machines for delineating mining exploration and assessing its effect on vegetation in various surface mining sites in the Greek island of Milos. Assessment of the mining impact in the study area is validated through the analysis of available QuickBird imagery acquired nearly concurrently to the TM overpasses. Results indicate the capability of the TM sensor combined with the image analysis applied herein as a potential economically viable solution to provide rapidly and at regular time intervals information on mining activity and its impact to the local environment. KEYWORDS: mining environmental impact, remote sensing, image classification, change detection, land reclamation, support vector machines, neural networks

Radiation fluxes at the FIFE site

NASA Technical Reports Server (NTRS)

Walter-Shea, Elizabeth A.; Blad, Blaine L.; Zara, Pedro; Vining, Roel; Hays, Cynthia J.; Mesarch, Mark A.

1993-01-01

The main objective of the International Satellite Land Surface Climatology Project (ISLSCP) has been stated as 'the development of techniques that may be applied to satellite observations of the radiation reflected and emitted from the Earth to yield quantitative information concerning land surface climatological conditions'. The major field study, FIFE (the First ISLSCP Field Experiment), was conducted in 1987-89 to accomplish this objective. Four intensive field campaigns (IFC's) were carried out in 1987 and one in 1989. Factors contributing to observed reflected radiation from the FIFE site must be understood before the radiation observed by satellites can be used to quantify surface processes. Our last report (Walter-Shea et al., 1992b) focused on slope effects on incoming and outgoing shortwave radiation and net radiation from data collected in 1989. We report here on the final analysis of the slope data as well as results from thermal radiation studies conducted during the FIFE experiment. The specific areas reported are the following: (1) analysis of slope effects on measured reflectance values and estimates of surface albedo; (2) using remotely-measured surface temperatures as a means of estimating sensible heat flux from the Konza Prairie; (3) extracting canopy temperatures from remotely-measured composite surface temperatures; (4) modeling the measured composite temperature of partially vegetated surfaces; and (5) estimating gap distribution in partially vegetated surfaces from reflectance measurements.

Radiation fluxes at the FIFE site. Final report, 1 January 1991-31 July 1992

DOE Office of Scientific and Technical Information (OSTI.GOV)

Walter-Shea, E.A.; Blad, B.L.; Zara, P.

1993-01-01

The main objective of the International Satellite Land Surface Climatology Project (ISLSCP) has been stated as 'the development of techniques that may be applied to satellite observations of the radiation reflected and emitted from the Earth to yield quantitative information concerning land surface climatological conditions'. The major field study, FIFE (the First ISLSCP Field Experiment), was conducted in 1987-89 to accomplish this objective. Four intensive field campaigns (IFC's) were carried out in 1987 and one in 1989. Factors contributing to observed reflected radiation from the FIFE site must be understood before the radiation observed by satellites can be used tomore » quantify surface processes. Our last report (Walter-Shea et al.) focused on slope effects on incoming and outgoing shortwave radiation and net radiation from data collected in 1989. We report here on the final analysis of the slope data as well as results from thermal radiation studies conducted during the FIFE experiment. The specific areas reported are the following: (1) analysis of slope effects on measured reflectance values and estimates of surface albedo; (2) using remotely-measured surface temperatures as a means of estimating sensible heat flux from the Konza Prairie; (3) extracting canopy temperatures from remotely-measured composite surface temperatures; (4) modeling the measured composite temperature of partially vegetated surfaces; and (5) estimating gap distribution in partially vegetated surfaces from reflectance measurements.« less

The Urban Heat Island Impact in Consideration of Spatial Pattern of Urban Landscape and Structure

NASA Astrophysics Data System (ADS)

Kim, J.; Lee, D. K.; Jeong, W.; Sung, S.; Park, J.

2015-12-01

Preceding study has established a clear relationship between land surface temperature and area of land covers. However, only few studies have specifically examined the effects of spatial patterns of land covers and urban structure. To examine how much the local climate is affected by the spatial pattern in highly urbanized city, we investigated the correlation between land surface temperature and spatial patterns of land covers. In the analysis of correlation, we categorized urban structure to four different land uses: Apartment residential area, low rise residential area, industrial area and central business district. Through this study, we aims to examine the types of residential structure and land cover pattern for reducing urban heat island and sustainable development. Based on land surface temperature, we investigated the phenomenon of urban heat island through using the data of remote sensing. This study focused on Daegu in Korea. This city, one of the hottest city in Korea has basin form. We used high-resolution land cover data and land surface temperature by using Landsat8 satellite image to examine 100 randomly selected sample sites of 884.15km2 (1)In each land use, we quantified several landscape-levels and class-level landscape metrics for the sample study sites. (2)In addition, we measured the land surface temperature in 3 year hot summer seasons (July to September). Then, we investigated the pattern of land surface temperature for each land use through Ecognition package. (3)We deducted the Pearson correlation coefficients between land surface temperature and each landscape metrics. (4)We analyzed the variance among the four land uses. (5)Using linear regression, we determined land surface temperature model for each land use. (6)Through this analysis, we aims to examine the best pattern of land cover and artificial structure for reducing urban heat island effect in highly urbanized city. The results of linear regression showed that proportional land cover of grass, tree, water and impervious surfaces well explained the temperature in apartment residential areas. In contrast, the changes in the pattern of water, grass, tree and impervious surfaces were the best to determine the temperature in low rise residential area, central business district and industrial area.

A study of Minnesota land and water resources using remote sensing, volume 13

NASA Technical Reports Server (NTRS)

1980-01-01

Progress in the use of LANDSAT data to classify wetlands in the Upper Mississippi River Valley and efforts to evaluate stress in corn and soybean crops are described. Satellite remote sensing data was used to measure particle concentrations in Lake Superior and several different kinds of remote sensing data were synergistically combined in order to identify near surface bedrock in Minnesota. Data analysis techniques which separate those activities requiring extensive computing form those involving a great deal of user interaction were developed to allow the latter to be done in the user's office or in the field.

The Atlanta Urban Heat Island Mitigation and Air Quality Modeling Project: How High-Resoution Remote Sensing Data Can Improve Air Quality Models

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.; Estes, Maurice G., Jr.; Crosson, William L.; Khan, Maudood N.

2006-01-01

The Atlanta Urban Heat Island and Air Quality Project had its genesis in Project ATLANTA (ATlanta Land use Analysis: Temperature and Air quality) that began in 1996. Project ATLANTA examined how high-spatial resolution thermal remote sensing data could be used to derive better measurements of the Urban Heat Island effect over Atlanta. We have explored how these thermal remote sensing, as well as other imaged datasets, can be used to better characterize the urban landscape for improved air quality modeling over the Atlanta area. For the air quality modeling project, the National Land Cover Dataset and the local scale Landpro99 dataset at 30m spatial resolutions have been used to derive land use/land cover characteristics for input into the MM5 mesoscale meteorological model that is one of the foundations for the Community Multiscale Air Quality (CMAQ) model to assess how these data can improve output from CMAQ. Additionally, land use changes to 2030 have been predicted using a Spatial Growth Model (SGM). SGM simulates growth around a region using population, employment and travel demand forecasts. Air quality modeling simulations were conducted using both current and future land cover. Meteorological modeling simulations indicate a 0.5 C increase in daily maximum air temperatures by 2030. Air quality modeling simulations show substantial differences in relative contributions of individual atmospheric pollutant constituents as a result of land cover change. Enhanced boundary layer mixing over the city tends to offset the increase in ozone concentration expected due to higher surface temperatures as a result of urbanization.

Parameterizing atmosphere-land surface exchange for climate models with satellite data: A case study for the Southern Great Plains CART site

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gao, W.

High-resolution satellite data provide detailed, quantitative descriptions of land surface characteristics over large areas so that objective scale linkage becomes feasible. With the aid of satellite data, Sellers et al. and Wood and Lakshmi examined the linearity of processes scaled up from 30 m to 15 km. If the phenomenon is scale invariant, then the aggregated value of a function or flux is equivalent to the function computed from aggregated values of controlling variables. The linear relation may be realistic for limited land areas having no large surface contrasts to cause significant horizontal exchange. However, for areas with sharp surfacemore » contrasts, horizontal exchange and different dynamics in the atmospheric boundary may induce nonlinear interactions, such as at interfaces of land-water, forest-farm land, and irrigated crops-desert steppe. The linear approach, however, represents the simplest scenario, and is useful for developing an effective scheme for incorporating subgrid land surface processes into large-scale models. Our studies focus on coupling satellite data and ground measurements with a satellite-data-driven land surface model to parameterize surface fluxes for large-scale climate models. In this case study, we used surface spectral reflectance data from satellite remote sensing to characterize spatial and temporal changes in vegetation and associated surface parameters in an area of about 350 {times} 400 km covering the southern Great Plains (SGP) Cloud and Radiation Testbed (CART) site of the US Department of Energy`s Atmospheric Radiation Measurement (ARM) Program.« less

Scaling water and energy fluxes in climate systems - Three land-atmospheric modeling experiments

NASA Technical Reports Server (NTRS)

Wood, Eric F.; Lakshmi, Venkataraman

1993-01-01

Three numerical experiments that investigate the scaling of land-surface processes - either of the inputs or parameters - are reported, and the aggregated processes are compared to the spatially variable case. The first is the aggregation of the hydrologic response in a catchment due to rainfall during a storm event and due to evaporative demands during interstorm periods. The second is the spatial and temporal aggregation of latent heat fluxes, as calculated from SiB. The third is the aggregation of remotely sensed land vegetation and latent and sensible heat fluxes using TM data from the FIFE experiment of 1987 in Kansas. In all three experiments it was found that the surface fluxes and land characteristics can be scaled, and that macroscale models based on effective parameters are sufficient to account for the small-scale heterogeneities investigated.

NASA Earth Resources Survey Symposium. Volume 1-D: Water resources

NASA Technical Reports Server (NTRS)

1975-01-01

Conference papers on water resources and management are summarized. Summaries cover land use, flood control and prediction, watersheds and the effects of snow melt, soil moisture content, and the usefulness of satellite remote sensors in detecting ground and surface water.

Applications of space technology to water resources management

NASA Technical Reports Server (NTRS)

Salomonson, V. V.

1977-01-01

Space technology transfer is discussed in terms of applying visible and infrared remote sensing measurement to water resources management. Mapping and monitoring of snowcovered areas, hydrologic land use, and surface water areas are discussed, using information acquired from LANDSAT and NOAA satellite systems.

An analysis of urban thermal characteristics and associated land cover in Tampa Bay and Las Vegas using Landsat satellite data

USGS Publications Warehouse

Xian, George; Crane, Mike

2006-01-01

Remote sensing data from both Landsat 5 and Landsat 7 systems were utilized to assess urban area thermal characteristics in Tampa Bay watershed of west-central Florida, and the Las Vegas valley of southern Nevada. To quantitatively determine urban land use extents and development densities, sub-pixel impervious surface areas were mapped for both areas. The urban–rural boundaries and urban development densities were defined by selecting certain imperviousness threshold values and Landsat thermal bands were used to investigate urban surface thermal patterns. Analysis results suggest that urban surface thermal characteristics and patterns can be identified through qualitatively based urban land use and development density data. Results show the urban area of the Tampa Bay watershed has a daytime heating effect (heat-source), whereas the urban surface in Las Vegas has a daytime cooling effect (heat-sink). These thermal effects strongly correlated with urban development densities where higher percent imperviousness is usually associated with higher surface temperature. Using vegetation canopy coverage information, the spatial and temporal distributions of urban impervious surface and associated thermal characteristics are demonstrated to be very useful sources in quantifying urban land use, development intensity, and urban thermal patterns.

Coastal and Inland Aquatic Data Products for the Hyperspectral Infrared Imager (HyspIRI)

NASA Technical Reports Server (NTRS)

Abelev, Andrei; Babin, Marcel; Bachmann, Charles; Bell, Thomas; Brando, Vittorio; Byrd, Kristin; Dekker , Arnold; Devred, Emmanuel; Forget, Marie-Helene; Goodman, James;

Climate Impacts of Fire-Induced Land-Surface Changes

NASA Astrophysics Data System (ADS)

Liu, Y.; Hao, X.; Qu, J. J.

2017-12-01

One of the consequences of wildfires is the changes in land-surface properties such as removal of vegetation. This will change local and regional climate through modifying the land-air heat and water fluxes. This study investigates mechanism by developing and a parameterization of fire-induced land-surface property changes and applying it to modeling of the climate impacts of large wildfires in the United States. Satellite remote sensing was used to quantitatively evaluate the land-surface changes from large fires provided from the Monitoring Trends in Burning Severity (MTBS) dataset. It was found that the changes in land-surface properties induced by fires are very complex, depending on vegetation type and coverage, climate type, season and time after fires. The changes in LAI are remarkable only if the actual values meet a threshold. Large albedo changes occur in winter for fires in cool climate regions. The signs are opposite between the first post-fire year and the following years. Summer day-time temperature increases after fires, while nigh-time temperature changes in various patterns. The changes are larger in forested lands than shrub / grassland lands. In the parameterization scheme, the detected post-fire changes are decomposed into trends using natural exponential functions and fluctuations of periodic variations with the amplitudes also determined by natural exponential functions. The final algorithm is a combination of the trends, periods, and amplitude functions. This scheme is used with Earth system models to simulate the local and regional climate effects of wildfires.

Remote Sensing of Atlanta's Urban Sprawl and the Distribution of Land Cover and Surface Temperatures

NASA Technical Reports Server (NTRS)

Laymon, Charles A.; Estes, Maurice G., Jr.; Quattrochi, Dale A.; Arnold, James E. (Technical Monitor)

2001-01-01

Between 1973 and 1992, an average of 20 ha of forest was lost each day to urban expansion of Atlanta, Georgia. Urban surfaces have very different thermal properties than natural surfaces-storing solar energy throughout the day and continuing to release it as sensible heat well after sunset. The resulting heat island effect serves as catalysts for chemical reactions from vehicular exhaust and industrialization leading to a deterioration in air quality. In this study, high spatial resolution multispectral remote sensing data has been used to characterize the type, thermal properties, and distribution of land surface materials throughout the Atlanta metropolitan area. Ten-meter data were acquired with the Advanced Thermal and Land Applications Sensor (ATLAS) on May 11 and 12, 1997. ATLAS is a 15-channel multispectral scanner that incorporates the Landsat TM bands with additional bands in the middle reflective infrared and thermal infrared range. The high spatial resolution permitted discrimination of discrete surface types (e.g., concrete, asphalt), individual structures (e.g., buildings, houses) and their associated thermal characteristics. There is a strong temperature contrast between vegetation and anthropomorphic features. Vegetation has a modal temperature at about 20 C, whereas asphalt shingles, pavement, and buildings have a modal temperature of about 39 C. Broad-leaf vegetation classes are indistinguishable on a thermal basis alone. There is slightly more variability (plus or minus 5 C) among the urban surfaces. Grasses, mixed vegetation and mixed urban surfaces are intermediate in temperature and are characterized by broader temperature distributions with modes of about 29 C. Thermal maps serve as a basis for understanding the distribution of "hotspots", i.e., how landscape features and urban fabric contribute the most heat to the lower atmosphere.

Remote Sensing of Atlanta's Urban Sprawl and the Distribution of Land Cover and Surface Temperature

NASA Technical Reports Server (NTRS)

Laymon, Charles A.; Estes, Maurice G., Jr.; Quattrochi, Dale A.; Goodman, H. Michael (Technical Monitor)

2001-01-01

Between 1973 and 1992, an average of 20 ha of forest was lost each day to urban expansion of Atlanta, Georgia. Urban surfaces have very different thermal properties than natural surfaces-storing solar energy throughout the day and continuing to release it as sensible heat well after sunset. The resulting heat island effect serves as catalysts for chemical reactions from vehicular exhaust and industrialization leading to a deterioration in air quality. In this study, high spatial resolution multispectral remote sensing data has been used to characterize the type, thermal properties, and distribution of land surface materials throughout the Atlanta metropolitan area. Ten-meter data were acquired with the Advanced Thermal and Land Applications Sensor (ATLAS) on May 11 and 12, 1997. ATLAS is a 15-channel multispectral scanner that incorporates the Landsat TM bands with additional bands in the middle reflective infrared and thermal infrared range. The high spatial resolution permitted discrimination of discrete surface types (e.g., concrete, asphalt), individual structures (e.g., buildings, houses) and their associated thermal characteristics. There is a strong temperature contrast between vegetation and anthropomorphic features. Vegetation has a modal temperature at about 20 C, whereas asphalt shingles, pavement, and buildings have a modal temperature of about 39 C. Broad-leaf vegetation classes are indistinguishable on a thermal basis alone. There is slightly more variability (+/-5 C) among the urban surfaces. Grasses, mixed vegetation and mixed urban surfaces are intermediate in temperature and are characterized by broader temperature distributions with modes of about 29 C. Thermal maps serve as a basis for understanding the distribution of "hotspots", i.e., how landscape features and urban fabric contribute the most heat to the lower atmosphere.

Multi-model perspectives and inter-comparison of soil moisture and evapotranspiration in East Africa—an application of Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS)

NASA Astrophysics Data System (ADS)

Pervez, M. S.; McNally, A.; Arsenault, K. R.

2017-12-01

Convergence of evidence from different agro-hydrologic sources is particularly important for drought monitoring in data sparse regions. In Africa, a combination of remote sensing and land surface modeling experiments are used to evaluate past, present and future drought conditions. The Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) routinely simulates daily soil moisture, evapotranspiration (ET) and other variables over Africa using multiple models and inputs. We found that Noah 3.3, Variable Infiltration Capacity (VIC) 4.1.2, and Catchment Land Surface Model based FLDAS simulations of monthly soil moisture percentile maps captured concurrent drought and water surplus episodes effectively over East Africa. However, the results are sensitive to selection of land surface model and hydrometeorological forcings. We seek to identify sources of uncertainty (input, model, parameter) to eventually improve the accuracy of FLDAS outputs. In absence of in situ data, previous work used European Space Agency Climate Change Initiative Soil Moisture (CCI-SM) data measured from merged active-passive microwave remote sensing to evaluate FLDAS soil moisture, and found that during the high rainfall months of April-May and November-December Noah-based soil moisture correlate well with CCI-SM over the Greater Horn of Africa region. We have found good correlations (r>0.6) for FLDAS Noah 3.3 ET anomalies and Operational Simplified Surface Energy Balance (SSEBop) ET over East Africa. Recently, SSEBop ET estimates (version 4) were improved by implementing a land surface temperature correction factor. We re-evaluate the correlations between FLDAS ET and version 4 SSEBop ET. To further investigate the reasons for differences between models we evaluate FLDAS soil moisture with Advanced Scatterometer and SMAP soil moisture and FLDAS outputs with MODIS and AVHRR normalized difference vegetation index. By exploring longer historic time series and near-real time products we will be aiding convergence of evidence for better understanding of historic drought, improved monitoring and forecasting, and better understanding of uncertainties of water availability estimation over Africa

Landsat: A global land-observing program

USGS Publications Warehouse

,

2005-01-01

Landsat represents the world’s longest continuously acquired collection of space-based land remote sensing data. The Landsat Project is a joint initiative of the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA) designed to gather Earth resource data from space. NASA developed and launched the spacecrafts, while the USGS handles the operations, maintenance, and management of all ground data reception, processing, archiving, product generation, and distribution.Landsat satellites have been collecting images of the Earth’s surface for more than thirty years. Landsat’s Global Survey Mission is to repeatedly capture images of the Earth’s land mass, coastal boundaries, and coral reefs, and to ensure that sufficient data are acquired to support the observation of changes on the Earth’s land surface and surrounding environment. NASA launched the first Landsat satellite in 1972, and the most recent one, Landsat 7, in 1999. Landsats 5 and 7 continue to capture hundreds of additional images of the Earth’s surface each day. These images provide a valuable resource for people who work

A Novel Optical Model for Remote Sensing of Near-Surface Soil Moisture

NASA Astrophysics Data System (ADS)

Babaeian, E.; Sadeghi, M.; Jones, S. B.; Tuller, M.

2016-12-01

Common triangle and trapezoid methods that are based on both optical and thermal remote sensing (RS) information have been widely applied in the past to estimate near-surface soil moisture from the soil temperature - vegetation index space (e.g., LST-NDVI). For most cases, this approach assumes a linear relationship between soil moisture and temperature. Though this linearity assumption yields reasonable moisture estimates, it is not always justified as evidenced by laboratory and field measurements. Furthermore, this approach requires optical as well as thermal RS data for definition of the land surface temperature (LST) - vegetation index space, therefore, it is not applicable to satellites that do not provide thermal output such as the ESA Sentinel-2. To overcome these limitations, we propose a novel trapezoid model that only relies on optical NIR and SWIR data. The new model was validated using Sentinel-2 and Landsat-8 data for the semiarid Walnut Gulch (AZ) and sub humid Little Washita (OK) watersheds that vastly differ in land use and surface cover and provide excellent ground-truth moisture information from extensive sensor networks. Preliminary results for 2015-2016 indicate significant potential of the new model with a RMSE smaller than 4% volumetric near-surface moisture content and also confirm the enhanced utility of the high spatially and temporally resolved Sentinel-2 data.

A global, 30-m resolution land-surface water body dataset for 2000

NASA Astrophysics Data System (ADS)

Feng, M.; Sexton, J. O.; Huang, C.; Song, D. X.; Song, X. P.; Channan, S.; Townshend, J. R.

2014-12-01

Inland surface water is essential to terrestrial ecosystems and human civilization. The distribution of surface water in space and its change over time are related to many agricultural, environmental and ecological issues, and are important factors that must be considered in human socioeconomic development. Accurate mapping of surface water is essential for both scientific research and policy-driven applications. Satellite-based remote sensing provides snapshots of Earth's surface and can be used as the main input for water mapping, especially in large areas. Global water areas have been mapped with coarse resolution remotely sensed data (e.g., the Moderate Resolution Imaging Spectroradiometer (MODIS)). However, most inland rivers and water bodies, as well as their changes, are too small to map at such coarse resolutions. Landsat TM (Thematic Mapper) and ETM+ (Enhanced Thematic Mapper Plus) imagery has a 30m spatial resolution and provides decades of records (~40 years). Since 2008, the opening of the Landsat archive, coupled with relatively lower costs associated with computing and data storage, has made comprehensive study of the dynamic changes of surface water over large even global areas more feasible. Although Landsat images have been used for regional and even global water mapping, the method can hardly be automated due to the difficulties on distinguishing inland surface water with variant degrees of impurities and mixing of soil background with only Landsat data. The spectral similarities to other land cover types, e.g., shadow and glacier remnants, also cause misidentification. We have developed a probabilistic based automatic approach for mapping inland surface water bodies. Landsat surface reflectance in multiple bands, derived water indices, and data from other sources are integrated to maximize the ability of identifying water without human interference. The approach has been implemented with open-source libraries to facilitate processing large amounts of Landsat images on high-performance computing machines. It has been applied to the ~9,000 Landsat scenes of the Global Land Survey (GLS) 2000 data collection to produce a global, 30m resolution inland surface water body data set, which will be made available on the Global Land Cover Facility (GLCF) website (http://www.landcover.org).

Downscaling of Remotely Sensed Land Surface Temperature with multi-sensor based products

NASA Astrophysics Data System (ADS)

Jeong, J.; Baik, J.; Choi, M.

2016-12-01

Remotely sensed satellite data provides a bird's eye view, which allows us to understand spatiotemporal behavior of hydrologic variables at global scale. Especially, geostationary satellite continuously observing specific regions is useful to monitor the fluctuations of hydrologic variables as well as meteorological factors. However, there are still problems regarding spatial resolution whether the fine scale land cover can be represented with the spatial resolution of the satellite sensor, especially in the area of complex topography. To solve these problems, many researchers have been trying to establish the relationship among various hydrological factors and combine images from multi-sensor to downscale land surface products. One of geostationary satellite, Communication, Ocean and Meteorological Satellite (COMS), has Meteorological Imager (MI) and Geostationary Ocean Color Imager (GOCI). MI performing the meteorological mission produce Rainfall Intensity (RI), Land Surface Temperature (LST), and many others every 15 minutes. Even though it has high temporal resolution, low spatial resolution of MI data is treated as major research problem in many studies. This study suggests a methodology to downscale 4 km LST datasets derived from MI in finer resolution (500m) by using GOCI datasets in Northeast Asia. Normalized Difference Vegetation Index (NDVI) recognized as variable which has significant relationship with LST are chosen to estimate LST in finer resolution. Each pixels of NDVI and LST are separated according to land cover provided from MODerate resolution Imaging Spectroradiometer (MODIS) to achieve more accurate relationship. Downscaled LST are compared with LST observed from Automated Synoptic Observing System (ASOS) for assessing its accuracy. The downscaled LST results of this study, coupled with advantage of geostationary satellite, can be applied to observe hydrologic process efficiently.

Improving surface-subsurface water budgeting using high resolution satellite imagery applied on a brownfield.

PubMed

Dujardin, J; Batelaan, O; Canters, F; Boel, S; Anibas, C; Bronders, J

2011-01-15

The estimation of surface-subsurface water interactions is complex and highly variable in space and time. It is even more complex when it has to be estimated in urban areas, because of the complex patterns of the land-cover in these areas. In this research a modeling approach with integrated remote sensing analysis has been developed for estimating water fluxes in urban environments. The methodology was developed with the aim to simulate fluxes of contaminants from polluted sites. Groundwater pollution in urban environments is linked to patterns of land use and hence it is essential to characterize the land cover in a detail. An object-oriented classification approach applied on high-resolution satellite data has been adopted. To assign the image objects to one of the land-cover classes a multiple layer perceptron approach was adopted (Kappa of 0.86). Groundwater recharge has been simulated using the spatially distributed WetSpass model and the subsurface water flow using MODFLOW in order to identify and budget water fluxes. The developed methodology is applied to a brownfield case site in Vilvoorde, Brussels (Belgium). The obtained land use map has a strong impact on the groundwater recharge, resulting in a high spatial variability. Simulated groundwater fluxes from brownfield to the receiving River Zenne were independently verified by measurements and simulation of groundwater-surface water interaction based on thermal gradients in the river bed. It is concluded that in order to better quantify total fluxes of contaminants from brownfields in the groundwater, remote sensing imagery can be operationally integrated in a modeling procedure. Copyright © 2010 Elsevier B.V. All rights reserved.

Remote Sensing of Evapotranspiration and Carbon Uptake at Harvard Forest

NASA Technical Reports Server (NTRS)

Min, Qilong; Lin, Bing

2005-01-01

A land surface vegetation index, defined as the difference of microwave land surface emissivity at 19 and 37 GHz, was calculated for a heavily forested area in north central Massachusetts. The microwave emissivity difference vegetation index (EDVI) was estimated from satellite SSM/I measurements at the defined wavelengths and used to estimate land surface turbulent fluxes. Narrowband visible and infrared measurements and broadband solar radiation observations were used in the EDVI retrievals and turbulent flux estimations. The EDVI values represent physical properties of crown vegetation such as vegetation water content of crown canopies. The collocated land surface turbulent and radiative fluxes were empirically linked together by the EDVI values. The EDVI values are statistically sensitive to evapotranspiration fractions (EF) with a correlation coefficient (R) greater than 0.79 under all-sky conditions. For clear skies, EDVI estimates exhibit a stronger relationship with EF than normalized difference vegetation index (NDVI). Furthermore, the products of EDVI and input energy (solar and photosynthetically-active radiation) are statistically significantly correlated to evapotranspiration (R=0.95) and CO2 uptake flux (R=0.74), respectively.

Linking remote sensing, land cover and disease.

PubMed

Curran, P J; Atkinson, P M; Foody, G M; Milton, E J

2000-01-01

Land cover is a critical variable in epidemiology and can be characterized remotely. A framework is used to describe both the links between land cover and radiation recorded in a remotely sensed image, and the links between land cover and the disease carried by vectors. The framework is then used to explore the issues involved when moving from remotely sensed imagery to land cover and then to vector density/disease risk. This exploration highlights the role of land cover; the need to develop a sound knowledge of each link in the predictive sequence; the problematic mismatch between the spatial units of the remotely sensed and epidemiological data and the challenges and opportunities posed by adding a temporal mismatch between the remotely sensed and epidemiological data. The paper concludes with a call for both greater understanding of the physical components of the proposed framework and the utilization of optimized statistical tools as prerequisites to progress in this field.

Land Use and Land Cover Change, Urban Heat Island Phenomenon, and Health Implications: A Remote Sensing Approach

NASA Technical Reports Server (NTRS)

Lo, C. P.; Quattrochi, Dale A.

2003-01-01

Land use and land cover maps of Atlanta Metropolitan Area in Georgia were produced from Landsat MSS and TM images for 1973,1979,1983,1987,1992, and 1997, spanning a period of 25 years. Dramatic changes in land use and land cover have occurred with loss of forest and cropland to urban use. In particular, low-density urban use, which includes largely residential use, has increased by over 119% between 1973 and 1997. These land use and land cover changes have drastically altered the land surface characteristics. An analysis of Landsat images revealed an increase in surface temperature and a decline in NDVI from 1973 to 1997. These changes have forced the development of a significant urban heat island effect and an increase in ground level ozone production to such an extent, that Atlanta has violated EPA's ozone level standard in recent years. The urban heat island initiated precipitation events that were identified between 1996 and 2000 tended to occur near high-density urban areas but outside the I-285 loop that traverses around the Central Business District, i.e. not in the inner city area, but some in close proximity to the highways. The health implications were investigated by comparing the spatial patterns of volatile organic compounds (VOC) and nitrogen oxides (NOx) emissions, the two ingredients that form ozone by reacting with sunlight, with those of rates of cardiovascular and chronic lower respiratory diseases. A clear core-periphery pattern was revealed for both VOC and NOx emissions, but the spatial pattern was more random in the cases of rates of cardiovascular and chronic lower respiratory diseases. Clearly, factors other than ozone pollution were involved in explaining the rates of these diseases. Further research is therefore needed to understand the health geography and its relationship to land use and land cover change as well as urban heat island effect. This paper illustrates the usefulness of a remote sensing approach for this purpose.

Shifting relative importance of climatic constraints on land surface phenology

NASA Astrophysics Data System (ADS)

Garonna, Irene; de Jong, Rogier; Stöckli, Reto; Schmid, Bernhard; Schenkel, David; Schimel, David; Schaepman, Michael E.

2018-02-01

Land surface phenology (LSP), the study of seasonal dynamics of vegetated land surfaces from remote sensing, is a key indicator of global change, that both responds to and influences weather and climate. The effects of climatic changes on LSP depend on the relative importance of climatic constraints in specific regions—which are not well understood at global scale. Understanding the climatic constraints that underlie LSP is crucial for explaining climate change effects on global vegetation phenology. We used a combination of modelled and remotely-sensed vegetation activity records to quantify the interplay of three climatic constraints on land surface phenology (namely minimum temperature, moisture availability, and photoperiod), as well as the dynamic nature of these constraints. Our study examined trends and the relative importance of the three constrains at the start and the end of the growing season over eight global environmental zones, for the past three decades. Our analysis revealed widespread shifts in the relative importance of climatic constraints in the temperate and boreal biomes during the 1982-2011 period. These changes in the relative importance of the three climatic constraints, which ranged up to 8% since 1982 levels, varied with latitude and between start and end of the growing season. We found a reduced influence of minimum temperature on start and end of season in all environmental zones considered, with a biome-dependent effect on moisture and photoperiod constraints. For the end of season, we report that the influence of moisture has on average increased for both the temperate and boreal biomes over 8.99 million km2. A shifting relative importance of climatic constraints on LSP has implications both for understanding changes and for improving how they may be modelled at large scales.

Surface water change as a significant contributor to global evapotranspiration change

NASA Astrophysics Data System (ADS)

Zhan, S.; Song, C.

2017-12-01

Water comprises a critical component of global/regional hydrological and biogeochemical cycles and is essential to all organisms including humans. In the past several decades, climate change has intensified the hydrological cycle, with significant implications for ecosystem services and feedback to regional and global climate. Evapotranspiration (ET) as a linking mechanism between land surface and atmosphere is central to the water cycle and an excellent indicator of the intensity of water cycle. Knowledge of the temporal changes of ET is crucial for accurately estimating global or regional water budgets and better understanding climate and hydrological interactions. While studies have examined changes in global ET, they were conducted using a constant land and surface water (SW) area. However, as many studies have found that global SW is very dynamic and their surface areas have generally been increasing since the 1980s. The conversion from land to water and vice versa significantly changes the local ET since water bodies evaporate at a rate that can be much higher than that of the land. Here, we quantify the global changes in ET caused by such land-water conversion using remotely-sensed SW area and various ET and potential ET products. New SW and lost SW between circa-1985 and circa-2015 were derived from remote sensing and were used to modify the local ET estimates. We found an increase in ET in all continents as consistent with the net increase in SW area. The increasing SW area lead to a global increase in ET by 30.38 ± 5.28 km3/yr. This is a significant contribution when compared to the 92.95 km3/yr/yr increase in ET between 1982-1997 and 103.43 km3/yr/yr decrease between 1998-2008 by Jung et al., (2010) assuming a constant SW. The results enhance our understanding of the water fluxes between the land and atmosphere and supplement land water budget estimates. We conclude that changes in SW lead to a significant change in global ET that cannot be neglected in global ET trend studies and should also be included in global water budget studies.

Implication of relationship between natural impacts and land use/land cover (LULC) changes of urban area in Mongolia

NASA Astrophysics Data System (ADS)

Gantumur, Byambakhuu; Wu, Falin; Zhao, Yan; Vandansambuu, Battsengel; Dalaibaatar, Enkhjargal; Itiritiphan, Fareda; Shaimurat, Dauryenbyek

2017-10-01

Urban growth can profoundly alter the urban landscape structure, ecosystem processes, and local climates. Timely and accurate information on the status and trends of urban ecosystems is critical to develop strategies for sustainable development and to improve the urban residential environment and living quality. Ulaanbaatar city was urbanized very rapidly caused by herders and farmers, many of them migrating from rural places, have played a big role in this urban expansion (sprawl). Today, 1.3 million residents for about 40% of total population are living in the Ulaanbaatar region. Those human activities influenced stronger to green environments. Therefore, the aim of this study is determined to change detection of land use/land cover (LULC) and estimating their areas for the trend of future by remote sensing and statistical methods. The implications of analysis were provided by change detection methods of LULC, remote sensing spectral indices including normalized difference vegetation index (NDVI), normalized difference water index (NDWI) and normalized difference built-up index (NDBI). In addition, it can relate to urban heat island (UHI) provided by Land surface temperature (LST) with local climate issues. Statistical methods for image processing used to define relations between those spectral indices and change detection images and regression analysis for time series trend in future. Remote sensing data are used by Landsat (TM/ETM+/OLI) satellite images over the period between 1990 and 2016 by 5 years. The advantages of this study are very useful remote sensing approaches with statistical analysis and important to detecting changes of LULC. The experimental results show that the LULC changes can image on the present and after few years and determined relations between impacts of environmental conditions.

The influences of land use and land cover on climate; an analysis of the Washington-Baltimore area that couples remote sensing with numerical simulation

USGS Publications Warehouse

Pease, R.W.; Jenner, C.B.; Lewis, J.E.

1980-01-01

The Sun drives the atmospheric heat engine by warming the terrestrial surface which in turn warms the atmosphere above. Climate, therefore, is significantly controlled by complex interaction of energy flows near and at the terrestrial surface. When man alters this delicate energy balance by his use of the land, he may alter his climatic environment as well. Land use climatology has emerged as a discipline in which these energy interactions are studied; first, by viewing the spatial distributions of their surface manifestations, and second, by analyzing the energy exchange processes involved. Two new tools for accomplishing this study are presented: one that can interpret surface energy exchange processes from space, and another that can simulate the complex of energy transfers by a numerical simulation model. Use of a satellite-borne multispectral scanner as an imaging radiometer was made feasible by devising a gray-window model that corrects measurements made in space for the effects of the atmosphere in the optical path. The simulation model is a combination of mathematical models of energy transfer processes at or near the surface. Integration of these two analytical approaches was applied to the Washington-Baltimore area to coincide with the August 5, 1973, Skylab 3 overpass which provided data for constructing maps of the energy characteristics of the Earth's surface. The use of the two techniques provides insights into the relationship of climate to land use and land cover and in predicting alterations of climate that may result from alterations of the land surface.

Unmanned airborne thermal and mutilspectral imagery for estimating evapotranspiration in irrigated vineyards

USDA-ARS?s Scientific Manuscript database

Thermal-infrared remote sensing of land surface temperature (LST) provides valuable information for quantifying rootzone water availability, evapotranspiration (ET) and crop condition. This paper describes the most recent modifications applied to the robust but relatively simple LST-based energy bal...

A Flexible Spatiotemporal Method for Fusing Satellite Images with Different Resolutions

USDA-ARS?s Scientific Manuscript database

Studies of land surface dynamics in heterogeneous landscapes often require remote sensing data with high acquisition frequency and high spatial resolution. However, no single sensor meets this requirement. This study presents a new spatiotemporal data fusion method, the Flexible Spatiotemporal DAta ...

Intercomparison and Uncertainty Assessment of Nine Evapotranspiration Estimates Over South America

NASA Astrophysics Data System (ADS)

Sörensson, Anna A.; Ruscica, Romina C.

2018-04-01

This study examines the uncertainties and the representations of anomalies of a set of evapotranspiration products over climatologically distinct regions of South America. The products, coming from land surface models, reanalysis, and remote sensing, are chosen from sources that are readily available to the community of users. The results show that the spatial patterns of maximum uncertainty differ among metrics, with dry regions showing maximum relative uncertainties of annual mean evapotranspiration, while energy-limited regions present maximum uncertainties in the representation of the annual cycle and monsoon regions in the representation of anomalous conditions. Furthermore, it is found that land surface models driven by observed atmospheric fields detect meteorological and agricultural droughts in dry regions unequivocally. The remote sensing products employed do not distinguish all agricultural droughts and this could be attributed to the forcing net radiation. The study also highlights important characteristics of individual data sets and recommends users to include assessments of sensitivity to evapotranspiration data sets in their studies, depending on region and nature of study to be conducted.

Global discrimination of land cover types from metrics derived from AVHRR pathfinder data

DOE Office of Scientific and Technical Information (OSTI.GOV)

DeFries, R.; Hansen, M.; Townshend, J.

1995-12-01

Global data sets of land cover are a significant requirement for global biogeochemical and climate models. Remotely sensed satellite data is an increasingly attractive source for deriving these data sets due to the resulting internal consistency, reproducibility, and coverage in locations where ground knowledge is sparse. Seasonal changes in the greenness of vegetation, described in remotely sensed data as changes in the normalized difference vegetation index (NDVI) throughout the year, have been the basis for discriminating between cover types in previous attempts to derive land cover from AVHRR data at global and continental scales. This study examines the use ofmore » metrics derived from the NDVI temporal profile, as well as metrics derived from observations in red, infrared, and thermal bands, to improve discrimination between 12 cover types on a global scale. According to separability measures calculated from Bhattacharya distances, average separabilities improved by using 12 of the 16 metrics tested (1.97) compared to separabilities using 12 monthly NDVI values alone (1.88). Overall, the most robust metrics for discriminating between cover types were: mean NDVI, maximum NDVI, NDVI amplitude, AVHRR Band 2 (near-infrared reflectance) and Band 1 (red reflectance) corresponding to the time of maximum NDVI, and maximum land surface temperature. Deciduous and evergreen vegetation can be distinguished by mean NDVI, maximum NDVI, NDVI amplitude, and maximum land surface temperature. Needleleaf and broadleaf vegetation can be distinguished by either mean NDVI and NDVI amplitude or maximum NDVI and NDVI amplitude.« less

Viking Landers and remote sensing

NASA Technical Reports Server (NTRS)

Moore, H. J.; Jakosky, B. M.; Christensen, P. R.

1987-01-01

Thermal and radar remote sensing signatures of the materials in the lander sample fields can be crudely estimated from evaluations of their physical-mechanical properties, laboratory data on thermal conductivities and dielectric constants, and theory. The estimated thermal inertias and dielectric constants of some of the materials in the sample field are close to modal values estimated from orbital and earth-based observations. This suggests that the mechanical properties of the surface materials of much of Mars will not be significantly different that those of the landing sites.

Predictability of Malaria Transmission Intensity in the Mpumalanga Province, South Africa, Using Land Surface Climatology and Autoregressive Analysis

NASA Technical Reports Server (NTRS)

Grass, David; Jasinski, Michael F.; Govere, John

2003-01-01

There has been increasing effort in recent years to employ satellite remotely sensed data to identify and map vector habitat and malaria transmission risk in data sparse environments. In the current investigation, available satellite and other land surface climatology data products are employed in short-term forecasting of infection rates in the Mpumalanga Province of South Africa, using a multivariate autoregressive approach. The climatology variables include precipitation, air temperature and other land surface states computed by the Off-line Land-Surface Global Assimilation System (OLGA) including soil moisture and surface evaporation. Satellite data products include the Normalized Difference Vegetation Index (NDVI) and other forcing data used in the Goddard Earth Observing System (GEOS-1) model. Predictions are compared to long- term monthly records of clinical and microscopic diagnoses. The approach addresses the high degree of short-term autocorrelation in the disease and weather time series. The resulting model is able to predict 11 of the 13 months that were classified as high risk during the validation period, indicating the utility of applying antecedent climatic variables to the prediction of malaria incidence for the Mpumalanga Province.

ANALYSIS OF WATER AND ENERGY FLUXES USING SATELLITE, ENERGY BALANCE MODELING AND OBSERVATIONS (Invited)

NASA Astrophysics Data System (ADS)

Irmak, A.

2009-12-01

Surface energy fluxes, including net radiation (Rn), sensible heat (H), latent heat (LE), and soil heat flux (G) are critical in surface energy balance of any terrain or landscapes. Estimation or measurement of these energy fluxes is important for completing the water balance in terrestrial ecosystems, and therefore accurately predicting the effects of global climate and land use change. The objectives of this study were to (1) use METRICtm (Mapping Evapotranspiration at high Resolution using Internalized Calibration) model for estimating land surface energy fluxes in Nebraska (NE) by utilizing satellite remote sensing data, (2) identify model bias in energy balance components compared with measurements from Bowen Ratio Energy Balance System (BREBS) in a subsurface drip-irrigated maize field in South-central Nebraska, and (3) understand the partitioning of available energy into latent heat for corn and soybean cropping systems at large scale. A total of 15 Landsat images were processed to estimate instantaneous surface energy fluxes at Landsat overpasses with METRIC model. Results showed that the model predictions of the surface energy fluxes and daily evapotranspiration were correlated well with the BREBS measurements. There is a need, however, to test the performance of the model with in-situ observations in other locations with different dataset before utilizing it for crucial water regulatory and policy decisions. The METRICtm approach illustrated how an ‘off-the-shelf’ model can be applied operationally over a significant time period and how that model behaves. The findings makes considerable contribution to our understanding of estimating land surface energy fluxes using remote sensing approach and experimentally describes the operational characteristics of METRICtm and presents its limitations.

Remote sensing of land degradation: experiences from Latin America and the Caribbean.

PubMed

Metternicht, G; Zinck, J A; Blanco, P D; del Valle, H F

2010-01-01

Land degradation caused by deforestation, overgrazing, and inappropriate irrigation practices affects about 16% of Latin America and the Caribbean (LAC). This paper addresses issues related to the application of remote sensing technologies for the identification and mapping of land degradation features, with special attention to the LAC region. The contribution of remote sensing to mapping land degradation is analyzed from the compilation of a large set of research papers published between the 1980s and 2009, dealing with water and wind erosion, salinization, and changes of vegetation cover. The analysis undertaken found that Landsat series (MSS, TM, ETM+) are the most commonly used data source (49% of the papers report their use), followed by aerial photographs (39%), and microwave sensing (ERS, JERS-1, Radarsat) (27%). About 43% of the works analyzed use multi-scale, multi-sensor, multi-spectral approaches for mapping degraded areas, with a combination of visual interpretation and advanced image processing techniques. The use of more expensive hyperspectral and/or very high spatial resolution sensors like AVIRIS, Hyperion, SPOT-5, and IKONOS tends to be limited to small surface areas. The key issue of indicators that can directly or indirectly help recognize land degradation features in the visible, infrared, and microwave regions of the electromagnetic spectrum are discussed. Factors considered when selecting indicators for establishing land degradation baselines include, among others, the mapping scale, the spectral characteristics of the sensors, and the time of image acquisition. The validation methods used to assess the accuracy of maps produced with satellite data are discussed as well.

Estimation of Surface Air Temperature Over Central and Eastern Eurasia from MODIS Land Surface Temperature

NASA Technical Reports Server (NTRS)

Shen, Suhung; Leptoukh, Gregory G.

2011-01-01

Surface air temperature (T(sub a)) is a critical variable in the energy and water cycle of the Earth.atmosphere system and is a key input element for hydrology and land surface models. This is a preliminary study to evaluate estimation of T(sub a) from satellite remotely sensed land surface temperature (T(sub s)) by using MODIS-Terra data over two Eurasia regions: northern China and fUSSR. High correlations are observed in both regions between station-measured T(sub a) and MODIS T(sub s). The relationships between the maximum T(sub a) and daytime T(sub s) depend significantly on land cover types, but the minimum T(sub a) and nighttime T(sub s) have little dependence on the land cover types. The largest difference between maximum T(sub a) and daytime T(sub s) appears over the barren and sparsely vegetated area during the summer time. Using a linear regression method, the daily maximum T(sub a) were estimated from 1 km resolution MODIS T(sub s) under clear-sky conditions with coefficients calculated based on land cover types, while the minimum T(sub a) were estimated without considering land cover types. The uncertainty, mean absolute error (MAE), of the estimated maximum T(sub a) varies from 2.4 C over closed shrublands to 3.2 C over grasslands, and the MAE of the estimated minimum Ta is about 3.0 C.

Hydrologic impacts of land cover variability and change at seasonal to decadal time scales over North America, 1992-2016

NASA Astrophysics Data System (ADS)

Bohn, T. J.; Vivoni, E. R.

2017-12-01

Land cover variability and change have been shown to influence the terrestrial hydrologic cycle by altering the partitioning of moisture and energy fluxes. However, the magnitude and directionality of the relationship between land cover and surface hydrology has been shown to vary substantially across regions. Here, we provide an assessment of the impacts of land cover change on hydrologic processes at seasonal (vegetation phenology) to decadal scales (land cover conversion) in the United States and Mexico. To this end, we combine time series of remotely-sensed land surface characteristics with land cover maps for different decades as input to the Variable Infiltration Capacity hydrologic model. Land surface characteristics (leaf area index, surface albedo, and canopy fraction derived from normalized difference vegetation index) were obtained from the Moderate Resolution Imaging Spectrometer (MODIS) at 8-day intervals over the period 2000-2016. Land cover maps representing conditions in 1992, 2001, and 2011 were derived by homogenizing the National Land Cover Database over the US and the INEGI Series I through V maps over Mexico. An additional map covering all of North America was derived from the most frequent land cover class observed in each pixel of the MODIS MOD12Q1 product during 2001-2013. Land surface characteristics were summarized over land cover fractions at 1/16 degree (6 km) resolution. For each land cover map, hydrologic simulations were conducted that covered the period 1980-2013, using the best-available, hourly meteorological forcings at a similar spatial resolution. Based on these simulations, we present a comparison of the contributions of land cover change and climate variability at seasonal to decadal scales on the hydrologic and energy budgets, identifying the dominant components through time and space. This work also offers a valuable dataset on land cover variability and its hydrologic response for continental-scale assessments and modeling.

Comparing evapotranspiration from Eddy covariance measurements, water budgets, remote sensing, and land surface models over Canada a, b

DOE PAGES

Wang, Shusen; Pan, Ming; Mu, Qiaozhen; ...

2015-07-29

Here, this study compares six evapotranspiration ET products for Canada's landmass, namely, eddy covariance EC measurements; surface water budget ET; remote sensing ET from MODIS; and land surface model (LSM) ET from the Community Land Model (CLM), the Ecological Assimilation of Land and Climate Observations (EALCO) model, and the Variable Infiltration Capacity model (VIC). The ET climatology over the Canadian landmass is characterized and the advantages and limitations of the datasets are discussed. The EC measurements have limited spatial coverage, making it difficult for model validations at the national scale. Water budget ET has the largest uncertainty because of datamore » quality issues with precipitation in mountainous regions and in the north. MODIS ET shows relatively large uncertainty in cold seasons and sparsely vegetated regions. The LSM products cover the entire landmass and exhibit small differences in ET among them. Annual ET from the LSMs ranges from small negative values to over 600 mm across the landmass, with a countrywide average of 256 ± 15 mm. Seasonally, the countrywide average monthly ET varies from a low of about 3 mm in four winter months (November-February) to 67 ± 7 mm in July. The ET uncertainty is scale dependent. Larger regions tend to have smaller uncertainties because of the offset of positive and negative biases within the region. More observation networks and better quality controls are critical to improving ET estimates. Future techniques should also consider a hybrid approach that integrates strengths of the various ET products to help reduce uncertainties in ET estimation.« less

Comparing evapotranspiration from Eddy covariance measurements, water budgets, remote sensing, and land surface models over Canada a, b

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Shusen; Pan, Ming; Mu, Qiaozhen

Here, this study compares six evapotranspiration ET products for Canada's landmass, namely, eddy covariance EC measurements; surface water budget ET; remote sensing ET from MODIS; and land surface model (LSM) ET from the Community Land Model (CLM), the Ecological Assimilation of Land and Climate Observations (EALCO) model, and the Variable Infiltration Capacity model (VIC). The ET climatology over the Canadian landmass is characterized and the advantages and limitations of the datasets are discussed. The EC measurements have limited spatial coverage, making it difficult for model validations at the national scale. Water budget ET has the largest uncertainty because of datamore » quality issues with precipitation in mountainous regions and in the north. MODIS ET shows relatively large uncertainty in cold seasons and sparsely vegetated regions. The LSM products cover the entire landmass and exhibit small differences in ET among them. Annual ET from the LSMs ranges from small negative values to over 600 mm across the landmass, with a countrywide average of 256 ± 15 mm. Seasonally, the countrywide average monthly ET varies from a low of about 3 mm in four winter months (November-February) to 67 ± 7 mm in July. The ET uncertainty is scale dependent. Larger regions tend to have smaller uncertainties because of the offset of positive and negative biases within the region. More observation networks and better quality controls are critical to improving ET estimates. Future techniques should also consider a hybrid approach that integrates strengths of the various ET products to help reduce uncertainties in ET estimation.« less

Remote-sensing-based analysis of landscape change in the desiccated seabed of the Aral Sea--a potential tool for assessing the hazard degree of dust and salt storms.

PubMed

Löw, F; Navratil, P; Kotte, K; Schöler, H F; Bubenzer, O

2013-10-01

With the recession of the Aral Sea in Central Asia, once the world's fourth largest lake, a huge new saline desert emerged which is nowadays called the Aralkum. Saline soils in the Aralkum are a major source for dust and salt storms in the region. The aim of this study was to analyze the spatio-temporal land cover change dynamics in the Aralkum and discuss potential implications for the recent and future dust and salt storm activity in the region. MODIS satellite time series were classified from 2000-2008 and change of land cover was quantified. The Aral Sea desiccation accelerated between 2004 and 2008. The area of sandy surfaces and salt soils, which bear the greatest dust and salt storm generation potential increased by more than 36 %. In parts of the Aralkum desalinization of soils was found to take place within 4-8 years. The implication of the ongoing regression of the Aral Sea is that the expansion of saline surfaces will continue. Knowing the spatio-temporal dynamics of both the location and the surface characteristics of the source areas for dust and salt storms allows drawing conclusions about the potential hazard degree of the dust load. The remote-sensing-based land cover assessment presented in this study could be coupled with existing knowledge on the location of source areas for an early estimation of trends in shifting dust composition. Opportunities, limits, and requirements of satellite-based land cover classification and change detection in the Aralkum are discussed.

Building hydrologic information systems to promote climate resilience in the Blue Nile/Abay higlands

USDA-ARS?s Scientific Manuscript database

Climate adaptation requires information about climate and land-surface conditions – spatially distributed, and at scales of human influence (the field scale). This article describes a project aimed at combining meteorological data, satellite remote sensing, hydrologic modeling, and downscaled clima...

Remote sensing contribution to land surface hydrology

NASA Technical Reports Server (NTRS)

Salomonson, V. V.; Choudhury, B. J.

1990-01-01

Progress that has been made over the past decade in developing technology for hydrological observations from operational aircraft is described. Particular attention is given to research on soil moisture, snow cover, and vegetation. Future missions such as the ESA ERS-1 and Canada's Radarsat mission are considered.

Comparison of prognostic and diagnostic surface flux modeling approaches over the Nile River Basin

USDA-ARS?s Scientific Manuscript database

Regional evapotranspiration (ET) can be estimated using diagnostic remote sensing models, generally based on principles of energy balance, or with spatially distributed prognostic models that simultaneously balance both the energy and water budgets over landscapes using predictive equations for land...

Hydrologic downscaling of soil moisture using global data without site-specific calibration

USDA-ARS?s Scientific Manuscript database

Numerous applications require fine-resolution (10-30 m) soil moisture patterns, but most satellite remote sensing and land-surface models provide coarse-resolution (9-60 km) soil moisture estimates. The Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) model downscales soil moistu...

Land Remote Sensing Overview

NASA Technical Reports Server (NTRS)

Byrnes, Ray

2007-01-01

A general overview of the USGS land remote sensing program is presented. The contents include: 1) Brief overview of USGS land remote sensing program; 2) Highlights of JACIE work at USGS; 3) Update on NASA/USGS Landsat Data Continuity Mission; and 4) Notes on alternative data sources.

Web-based interactive access, analysis and comparison of remotely sensed and in situ measured temperature data

NASA Astrophysics Data System (ADS)

Eberle, Jonas; Urban, Marcel; Hüttich, Christian; Schmullius, Christiane

2014-05-01

Numerous datasets providing temperature information from meteorological stations or remote sensing satellites are available. However, the challenging issue is to search in the archives and process the time series information for further analysis. These steps can be automated for each individual product, if the pre-conditions are complied, e.g. data access through web services (HTTP, FTP) or legal rights to redistribute the datasets. Therefore a python-based package was developed to provide data access and data processing tools for MODIS Land Surface Temperature (LST) data, which is provided by NASA Land Processed Distributed Active Archive Center (LPDAAC), as well as the Global Surface Summary of the Day (GSOD) and the Global Historical Climatology Network (GHCN) daily datasets provided by NOAA National Climatic Data Center (NCDC). The package to access and process the information is available as web services used by an interactive web portal for simple data access and analysis. Tools for time series analysis were linked to the system, e.g. time series plotting, decomposition, aggregation (monthly, seasonal, etc.), trend analyses, and breakpoint detection. Especially for temperature data a plot was integrated for the comparison of two temperature datasets based on the work by Urban et al. (2013). As a first result, a kernel density plot compares daily MODIS LST from satellites Aqua and Terra with daily means from GSOD and GHCN datasets. Without any data download and data processing, the users can analyze different time series datasets in an easy-to-use web portal. As a first use case, we built up this complimentary system with remotely sensed MODIS data and in situ measurements from meteorological stations for Siberia within the Siberian Earth System Science Cluster (www.sibessc.uni-jena.de). References: Urban, Marcel; Eberle, Jonas; Hüttich, Christian; Schmullius, Christiane; Herold, Martin. 2013. "Comparison of Satellite-Derived Land Surface Temperature and Air Temperature from Meteorological Stations on the Pan-Arctic Scale." Remote Sens. 5, no. 5: 2348-2367. Further materials: Eberle, Jonas; Clausnitzer, Siegfried; Hüttich, Christian; Schmullius, Christiane. 2013. "Multi-Source Data Processing Middleware for Land Monitoring within a Web-Based Spatial Data Infrastructure for Siberia." ISPRS Int. J. Geo-Inf. 2, no. 3: 553-576.

Interfacing geographic information systems and remote sensing for rural land-use analysis

NASA Technical Reports Server (NTRS)

Nellis, M. Duane; Lulla, Kamlesh; Jensen, John

1990-01-01

Recent advances in computer-based geographic information systems (GISs) are briefly reviewed, with an emphasis on the incorporation of remote-sensing data in GISs for rural applications. Topics addressed include sampling procedures for rural land-use analyses; GIS-based mapping of agricultural land use and productivity; remote sensing of land use and agricultural, forest, rangeland, and water resources; monitoring the dynamics of irrigation agriculture; GIS methods for detecting changes in land use over time; and the development of land-use modeling strategies.

Using Landsat Thematic Mapper (TM) sensor to detect change in land surface temperature in relation to land use change in Yazd, Iran

NASA Astrophysics Data System (ADS)

Zareie, Sajad; Khosravi, Hassan; Nasiri, Abouzar; Dastorani, Mostafa

2016-11-01

Land surface temperature (LST) is one of the key parameters in the physics of land surface processes from local to global scales, and it is one of the indicators of environmental quality. Evaluation of the surface temperature distribution and its relation to existing land use types are very important to the investigation of the urban microclimate. In arid and semi-arid regions, understanding the role of land use changes in the formation of urban heat islands is necessary for urban planning to control or reduce surface temperature. The internal factors and environmental conditions of Yazd city have important roles in the formation of special thermal conditions in Iran. In this paper, we used the temperature-emissivity separation (TES) algorithm for LST retrieving from the TIRS (Thermal Infrared Sensor) data of the Landsat Thematic Mapper (TM). The root mean square error (RMSE) and coefficient of determination (R2) were used for validation of retrieved LST values. The RMSE of 0.9 and 0.87 °C and R2 of 0.98 and 0.99 were obtained for the 1998 and 2009 images, respectively. Land use types for the city of Yazd were identified and relationships between land use types, land surface temperature and normalized difference vegetation index (NDVI) were analyzed. The Kappa coefficient and overall accuracy were calculated for accuracy assessment of land use classification. The Kappa coefficient values are 0.96 and 0.95 and the overall accuracy values are 0.97 and 0.95 for the 1998 and 2009 classified images, respectively. The results showed an increase of 1.45 °C in the average surface temperature. The results of this study showed that optical and thermal remote sensing methodologies can be used to research urban environmental parameters. Finally, it was found that special thermal conditions in Yazd were formed by land use changes. Increasing the area of asphalt roads, residential, commercial and industrial land use types and decreasing the area of the parks, green spaces and fallow lands in Yazd caused a rise in surface temperature during the 11-year period.

A framework for global diurnally-resolved observations of Land Surface Temperature

NASA Astrophysics Data System (ADS)

Ghent, Darren; Remedios, John

2014-05-01

Land surface temperature (LST) is the radiative skin temperature of the land, and is one of the key parameters in the physics of land-surface processes on regional and global scales. Being a key boundary condition in land surface models, which determine the surface to atmosphere fluxes of heat, water and carbon; thus influencing cloud cover, precipitation and atmospheric chemistry predictions within Global models, the requirement for global diurnal observations of LST is well founded. Earth Observation satellites offer an opportunity to obtain global coverage of LST, with the appropriate exploitation of data from multiple instruments providing a capacity to resolve the diurnal cycle on a global scale. Here we present a framework for the production of global, diurnally resolved, data sets for LST which is a key request from users of LST data. We will show how the sampling of both geostationary and low earth orbit data sets could conceptually be employed to build combined, multi-sensor, pole-to-pole data sets. Although global averages already exist for individual instruments and merging of geostationary based LST is already being addressed operationally (Freitas, et al., 2013), there are still a number of important challenges to overcome. In this presentation, we will consider three of the issues still open in LST remote sensing: 1) the consistency amongst retrievals; 2) the clear-sky bias and its quantification; and 3) merging methods and the propagation of uncertainties. For example, the combined use of both geostationary earth orbit (GEO) and low earth orbit (LEO) data, and both infra-red and microwave data are relatively unexplored but are necessary to make the most progress. Hence this study will suggest what is state-of-the-art and how considerable advances can be made, accounting also for recent improvements in techniques and data quality. The GlobTemperature initiative under the Data User Element of ESA's 4th Earth Observation Envelope Programme (2013-2017), which aims to support the wider uptake of global-scale satellite LST by the research and operational user communities, will be a particularly important element in the development and subsequent provision of global diurnal LST. References Freitas, S.C., Trigo, I.F., Macedo, J., Barroso, C., Silva, R., & Perdigao, R., 2013, Land surface temperature from multiple geostationary satellites, International Journal of Remote Sensing, 34, 3051-3068.

Towards improved characterization of northern wetlands (or other landscapes) by remote sensing - a rapid approach to collect ground truth data

NASA Astrophysics Data System (ADS)

Gålfalk, Magnus; Karlson, Martin; Crill, Patrick; Bastviken, David

2017-04-01

The calibration and validation of remote sensing land cover products is highly dependent on accurate ground truth data, which are costly and practically challenging to collect. This study evaluates a novel and efficient alternative to field surveys and UAV imaging commonly applied for this task. The method consists of i) a light weight, water proof, remote controlled RGB-camera mounted on an extendable monopod used for acquiring wide-field images of the ground from a height of 4.5 meters, and ii) a script for semi-automatic image classification. In the post-processing, the wide-field images are corrected for optical distortion and geometrically rectified so that the spatial resolution is the same over the surface area used for classification. The script distinguishes land surface components by color, brightness and spatial variability. The method was evaluated in wetland areas located around Abisko, northern Sweden. Proportional estimates of the six main surface components in the wetlands (wet and dry Sphagnum, shrub, grass, water, rock) were derived for 200 images, equivalent to 10 × 10 m field plots. These photo plots were then used as calibration data for a regional scale satellite based classification which separates the six wetland surface components using a Sentinel-1 time series. The method presented in this study is accurate, rapid, robust and cost efficient in comparison to field surveys (time consuming) and drone mapping (which require low wind speeds and no rain, suffer from battery limited flight times, have potential GPS/compass errors far north, and in some areas are prohibited by law).

Improving the Representation of Land in Climate Models by Application of EOS Observations

NASA Technical Reports Server (NTRS)

2004-01-01

The PI's IDS current and previous investigation has focused on the applications of the land data toward the improvement of climate models. The previous IDS research identified the key factors limiting the accuracy of climate models to be the representation of albedos, land cover, fraction of landscape covered by vegetation, roughness lengths, surface skin temperature and canopy properties such as leaf area index (LAI) and average stomatal conductance. Therefore, we assembled a team uniquely situated to focus on these key variables and incorporate the remotely sensed measures of these variables into the next generation of climate models.

The Use of a Geographic Information System and Remote Sensing Technology for Monitoring Land Use and Soil Carbon Change in the Subtropical Dry Forest Life Zone of Puerto Rico

NASA Technical Reports Server (NTRS)

Velez-Rodriguez, Linda L. (Principal Investigator)

1996-01-01

Aerial photography, one of the first form of remote sensing technology, has long been an invaluable means to monitor activities and conditions at the Earth's surface. Geographic Information Systems or GIS is the use of computers in showing and manipulating spatial data. This report will present the use of geographic information systems and remote sensing technology for monitoring land use and soil carbon change in the subtropical dry forest life zone of Puerto Rico. This research included the south of Puerto Rico that belongs to the subtropical dry forest life zone. The Guanica Commonwealth Forest Biosphere Reserve and the Jobos Bay National Estuarine Research Reserve are studied in detail, because of their location in the subtropical dry forest life zone. Aerial photography, digital multispectral imagery, soil samples, soil survey maps, field inspections, and differential global positioning system (DGPS) observations were used.

A land data assimilation system for sub-Saharan Africa food and water security applications

PubMed Central

McNally, Amy; Arsenault, Kristi; Kumar, Sujay; Shukla, Shraddhanand; Peterson, Pete; Wang, Shugong; Funk, Chris; Peters-Lidard, Christa D.; Verdin, James P.

2017-01-01

Seasonal agricultural drought monitoring systems, which rely on satellite remote sensing and land surface models (LSMs), are important for disaster risk reduction and famine early warning. These systems require the best available weather inputs, as well as a long-term historical record to contextualize current observations. This article introduces the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), a custom instance of the NASA Land Information System (LIS) framework. The FLDAS is routinely used to produce multi-model and multi-forcing estimates of hydro-climate states and fluxes over semi-arid, food insecure regions of Africa. These modeled data and derived products, like soil moisture percentiles and water availability, were designed and are currently used to complement FEWS NET’s operational remotely sensed rainfall, evapotranspiration, and vegetation observations. The 30+ years of monthly outputs from the FLDAS simulations are publicly available from the NASA Goddard Earth Science Data and Information Services Center (GES DISC) and recommended for use in hydroclimate studies, early warning applications, and by agro-meteorological scientists in Eastern, Southern, and Western Africa. PMID:28195575

A land data assimilation system for sub-Saharan Africa food and water security applications

USGS Publications Warehouse

McNally, Amy; Arsenault, Kristi; Kumar, Sujay; Shukla, Shraddhanand; Peterson, Pete; Wang, Shugong; Funk, Chris; Peters-Lidard, Christa; Verdin, James

2017-01-01

Seasonal agricultural drought monitoring systems, which rely on satellite remote sensing and land surface models (LSMs), are important for disaster risk reduction and famine early warning. These systems require the best available weather inputs, as well as a long-term historical record to contextualize current observations. This article introduces the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), a custom instance of the NASA Land Information System (LIS) framework. The FLDAS is routinely used to produce multi-model and multi-forcing estimates of hydro-climate states and fluxes over semi-arid, food insecure regions of Africa. These modeled data and derived products, like soil moisture percentiles and water availability, were designed and are currently used to complement FEWS NET’s operational remotely sensed rainfall, evapotranspiration, and vegetation observations. The 30+ years of monthly outputs from the FLDAS simulations are publicly available from the NASA Goddard Earth Science Data and Information Services Center (GES DISC) and recommended for use in hydroclimate studies, early warning applications, and by agro-meteorological scientists in Eastern, Southern, and Western Africa.

A land data assimilation system for sub-Saharan Africa food and water security applications.

PubMed

McNally, Amy; Arsenault, Kristi; Kumar, Sujay; Shukla, Shraddhanand; Peterson, Pete; Wang, Shugong; Funk, Chris; Peters-Lidard, Christa D; Verdin, James P

2017-02-14

Seasonal agricultural drought monitoring systems, which rely on satellite remote sensing and land surface models (LSMs), are important for disaster risk reduction and famine early warning. These systems require the best available weather inputs, as well as a long-term historical record to contextualize current observations. This article introduces the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), a custom instance of the NASA Land Information System (LIS) framework. The FLDAS is routinely used to produce multi-model and multi-forcing estimates of hydro-climate states and fluxes over semi-arid, food insecure regions of Africa. These modeled data and derived products, like soil moisture percentiles and water availability, were designed and are currently used to complement FEWS NET's operational remotely sensed rainfall, evapotranspiration, and vegetation observations. The 30+ years of monthly outputs from the FLDAS simulations are publicly available from the NASA Goddard Earth Science Data and Information Services Center (GES DISC) and recommended for use in hydroclimate studies, early warning applications, and by agro-meteorological scientists in Eastern, Southern, and Western Africa.

Data Descriptor: A Land Data Assimilation System for Sub-Saharan Africa Food and Water Security Applications

NASA Technical Reports Server (NTRS)

McNally, Amy; Arsenault, Krist; Kumar, Sujay; Shukla, Shraddhanand; Peter, Pete; Wang, Shugong; Funk, Chris; Peters-Lidard, Christa D.; Verdin, James

2017-01-01

Seasonal agricultural drought monitoring systems, which rely on satellite remote sensing and land surface models (LSMs), are important for disaster risk reduction and famine early warning. These systems require the best available weather inputs, as well as a long-term historical record to contextualize current observations. This article introduces the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), a custom instance of the NASA Land Information System (LIS) framework. The FLDAS is routinely used to produce multi-model and multi-forcing estimates of hydro-climate states and fluxes over semi-arid, food insecure regions of Africa. These modeled data and derived products, like soil moisture percentiles and water availability, were designed and are currently used to complement FEWSNETs operational remotely sensed rainfall, evapotranspiration, and vegetation observations. The 30+ years of monthly outputs from the FLDAS simulations are publicly available from the NASA Goddard Earth Science Data and Information Services Center (GES DISC) and recommended for use in hydroclimate studies, early warning applications, and by agro-meteorological scientists in Eastern, Southern, and Western Africa.

A land data assimilation system for sub-Saharan Africa food and water security applications

NASA Astrophysics Data System (ADS)

McNally, Amy; Arsenault, Kristi; Kumar, Sujay; Shukla, Shraddhanand; Peterson, Pete; Wang, Shugong; Funk, Chris; Peters-Lidard, Christa D.; Verdin, James P.

2017-02-01

Seasonal agricultural drought monitoring systems, which rely on satellite remote sensing and land surface models (LSMs), are important for disaster risk reduction and famine early warning. These systems require the best available weather inputs, as well as a long-term historical record to contextualize current observations. This article introduces the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), a custom instance of the NASA Land Information System (LIS) framework. The FLDAS is routinely used to produce multi-model and multi-forcing estimates of hydro-climate states and fluxes over semi-arid, food insecure regions of Africa. These modeled data and derived products, like soil moisture percentiles and water availability, were designed and are currently used to complement FEWS NET's operational remotely sensed rainfall, evapotranspiration, and vegetation observations. The 30+ years of monthly outputs from the FLDAS simulations are publicly available from the NASA Goddard Earth Science Data and Information Services Center (GES DISC) and recommended for use in hydroclimate studies, early warning applications, and by agro-meteorological scientists in Eastern, Southern, and Western Africa.

An Experimental Global Monitoring System for Rainfall-triggered Landslides using Satellite Remote Sensing Information

NASA Technical Reports Server (NTRS)

Hong, Yang; Adler, Robert F.; Huffman, George J.

2006-01-01

Landslides triggered by rainfall can possibly be foreseen in real time by jointly using rainfall intensity-duration thresholds and information related to land surface susceptibility. However, no system exists at either a national or a global scale to monitor or detect rainfall conditions that may trigger landslides due to the lack of extensive ground-based observing network in many parts of the world. Recent advances in satellite remote sensing technology and increasing availability of high-resolution geospatial products around the globe have provided an unprecedented opportunity for such a study. In this paper, a framework for developing an experimental real-time monitoring system to detect rainfall-triggered landslides is proposed by combining two necessary components: surface landslide susceptibility and a real-time space-based rainfall analysis system (http://trmm.gsfc.nasa.aov). First, a global landslide susceptibility map is derived from a combination of semi-static global surface characteristics (digital elevation topography, slope, soil types, soil texture, and land cover classification etc.) using a GIs weighted linear combination approach. Second, an adjusted empirical relationship between rainfall intensity-duration and landslide occurrence is used to assess landslide risks at areas with high susceptibility. A major outcome of this work is the availability of a first-time global assessment of landslide risk, which is only possible because of the utilization of global satellite remote sensing products. This experimental system can be updated continuously due to the availability of new satellite remote sensing products. This proposed system, if pursued through wide interdisciplinary efforts as recommended herein, bears the promise to grow many local landslide hazard analyses into a global decision-making support system for landslide disaster preparedness and risk mitigation activities across the world.

Evening Transition by a River Sampled Using a Remotely-Piloted Multicopter

NASA Astrophysics Data System (ADS)

Wrenger, B.; Cuxart, J.

2017-12-01

Measurements made with instruments aboard a remotely-piloted multicopter flying across the Weser river in Germany provide information on the thermal structure of the boundary layer over the river and adjacent land, in this case in summer for late afternoon, the evening transition and early night on a clear calm day. The river has a characteristic width of 100 m. The stratification over the land and river are of opposite signs at the lower levels, except during part of the evening transition. The multicopter allows to qualitatively estimate the evolution of the thermal contrast between both surfaces, showing that the river banks experience very significant daytime cooling and nocturnal warming due to river-bank circulations, with the change of sign taking place well before sunset.

Study of the wide area of a lake with remote sensing

NASA Astrophysics Data System (ADS)

Lazaridou, Maria A.; Karagianni, Aikaterini C.

2016-08-01

Water bodies are particularly important for environment and development issues. Their study requires multiple information. Remote sensing has been proven useful in the above study. This paper concerns the wide area of Lake Orestiada in the region of Western Macedonia in Greece. The area is of particular interest because Lake Orestiada is included in the Natura 2000 network and is surrounded by diverse landcovers as built up areas and agricultural land. Multispectral and thermal Landsat 5 satellite images of two time periods are being used. Their processing is being done by Erdas Imagine software. The general physiognomy of the area and the lake shore are examined after image enhancement techniques and image interpretation. Directions of the study concern geomorphological aspects, land covers, estimation of surface temperature as well as changes through time.

Applying ECOSTRESS Diurnal Cycle Land Surface Temperature and Evapotranspiration to Agricultural Soil and Water Management

NASA Astrophysics Data System (ADS)

Pestana, S. J.; Halverson, G. H.; Barker, M.; Cooley, S.

2016-12-01

Increased demand for agricultural products and limited water supplies in Guanacaste, Costa Rica have encouraged the improvement of water management practices to increase resource use efficiency. Remotely sensed evapotranspiration (ET) data can contribute by providing insights into variables like crop health and water loss, as well as better inform the use of various irrigation techniques. EARTH University currently collects data in the region that are limited to costly and time-intensive in situ observations and will greatly benefit from the expanded spatial and temporal resolution of remote sensing measurements from the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS). In this project, Moderate Resolution Imaging Spectroradiometer (MODIS) Priestly-Taylor Jet Propulsion Laboratory (PT-JPL) data, with a resolution of 5 km per pixel, was used to demonstrate to our partners at EARTH University the application of remotely sensed ET measurements. An experimental design was developed to provide a method of applying future ECOSTRESS data, at the higher resolution of 70 m per pixel, to research in managing and implementing sustainable farm practices. Our investigation of the diurnal cycle of land surface temperature, net radiation, and evapotranspiration will advance the model science for ECOSTRESS, which will be launched in 2018 and installed on the International Space Station.

Bridging the Global Precipitation and Soil Moisture Active Passive Missions: Variability of Microwave Surface Emissivity from In situ and Remote Sensing Perspectives

NASA Astrophysics Data System (ADS)

Zheng, Y.; Kirstetter, P.; Hong, Y.; Turk, J.

2016-12-01

The overland precipitation retrievals from satellite passive microwave (PMW) sensors such as the Global Precipitation Mission (GPM) microwave imager (GMI) are impacted by the land surface emissivity. The estimation of PMW emissivity faces challenges because it is highly variable under the influence of surface properties such as soil moisture, surface roughness and vegetation. This study proposes an improved quantitative understanding of the relationship between the emissivity and surface parameters. Surface parameter information is obtained through (i) in-situ measurements from the International Soil Moisture Network and (ii) satellite measurements from the Soil Moisture Active and Passive mission (SMAP) which provides global scale soil moisture estimates. The variation of emissivity is quantified with soil moisture, surface temperature and vegetation at various frequencies/polarization and over different types of land surfaces to sheds light into the processes governing the emission of the land. This analysis is used to estimate the emissivity under rainy conditions. The framework built with in-situ measurements serves as a benchmark for satellite-based analyses, which paves a way toward global scale emissivity estimates using SMAP.

Use of remote sensing techniques for inventorying and planning utilization of land resources in South Dakota

NASA Technical Reports Server (NTRS)

Myers, V. I.; Frazee, C. J.; Rusche, A. E.; Moore, D. G.; Nelson, G. D.; Westin, F. C.

1974-01-01

The basic procedures for interpreting remote sensing imagery to rapidly develop general soils and land use inventories were developed and utilized in Pennington County, South Dakota. These procedures and remote sensing data products were illustrated and explained to many user groups, some of whom are interested in obtaining similar data. The general soils data were integrated with land soils data supplied by the county director of equalization to prepare a land value map. A computer print-out of this map indicating a land value for each quarter section is being used in tax reappraisal of Pennington County. The land use data provided the land use planners with the present use of land in Pennington County. Additional uses of remote sensing applications are also discussed including tornado damage assessment, hail damage evaluation, and presentation of soil and land value information on base maps assembled from ERTS-1 imagery.

Monitoring of "all-weather" evapotranspiration using optical and passive microwave remote sensing imagery over the River Source Region in Southwest China

NASA Astrophysics Data System (ADS)

Ma, Y.; Liu, S.

2017-12-01

Accurate estimation of surface evapotranspiration (ET) with high quality is one of the biggest obstacles for routine applications of remote sensing in eco-hydrological studies and water resource management at basin scale. However, many aspects urgently need to deeply research, such as the applicability of the ET models, the parameterization schemes optimization at the regional scale, the temporal upscaling, the selecting and developing of the spatiotemporal data fusion method and ground-based validation over heterogeneous land surfaces. This project is based on the theoretically robust surface energy balance system (SEBS) model, which the model mechanism need further investigation, including the applicability and the influencing factors, such as local environment, and heterogeneity of the landscape, for improving estimation accuracy. Due to technical and budget limitations, so far, optical remote sensing data is missing due to frequent cloud contamination and other poor atmospheric conditions in Southwest China. Here, a multi-source remote sensing data fusion method (ESTARFM: Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) method will be proposed through blending multi-source remote sensing data acquired by optical, and passive microwave remote sensors on board polar satellite platforms. The accurate "all-weather" ET estimation will be carried out for daily ET of the River Source Region in Southwest China, and then the remotely sensed ET results are overlapped with the footprint-weighted images of EC (eddy correlation) for ground-based validation.

Impact of land use changes on hydrology of Mt. Kilimanjaro. The case of Lake Jipe catchment

NASA Astrophysics Data System (ADS)

Ngugi, Keziah; Ogindo, Harun; Ertsen, Maurits

2015-04-01

Mt. Kilimanjaro is an important water tower in Kenya and Tanzania. Land degradation and land use changes have contributed to dwindling surface water resources around Mt. Kilimanjaro. This study focuses on Lake Jipe catchment of about 451Km2 (Ndetei 2011) which is mainly drained by River Lumi, a tributary of river Pangani. River Lumi starts from Mt. Kilimanjaro and flows North east wards to cross the border from Tanzania to Kenya eventually flowing into Lake Jipe which is a trans-boundary lake. The main purpose of this study was to investigate historical land use changes and relate this to reduction in surface water resources. The study will propose measures that could restore the catchment thereby enhancing surface water resources feeding Lake Jipe. A survey was conducted to document community perspectives of historical land use changes. This information was corroborated using Landsat remote sensed images spanning the period 1985-2013 to determine changes in the land cover due to human activities on Lake Jipe Catchment. River Lumi flow data was obtained from Water Resources Management Authority and analyzed for flow trends. The dwindling extent of the Lake was obtained from the community's perspective survey and by Landsat images. Community survey and remote sensing indicated clearing of the forest on the mountain and conversion of the same to crop production fields; damming of river Lumi in Tanzania, conversion of bush land to crop production fields further downstream of river Lumi and irrigation. There is heavy infestation of the invasive species Prosopis juliflora which had aggressively colonized grazing land and blocked irrigation canals. Other land use changes include land fragmentation due to subdivision. Insecure land tenure was blamed for failure by farmers to develop soil and water conservation infrastructure. Available River gauging data showed a general decline in river flow. Heavy flooding occurred during rainy seasons. Towards Lake Jipe after the river gauging station, several springs discharge into river Lumi and the river becomes permanent. The community believes Lake Jipe is a dying lake and will be gone in the coming years unless interventions to save it are implemented. Most of river Lumi water was delivered directly into the lakes outlet, river Ruvu, thus by-passing Lake Jipe. This was due to siltation that blocked river Lumis mouth. Consequently, lake Jipes volume and surface area have reduced dramatically from over the years. Drying of Lake Jipe will affect a lot of people who depend on the lake and the ecosystem. Addressing the problems requires re-afforestation measures and soil and moisture conservation. The severe runoff need to be dammed especially on the Kenyan side to create artificial surface water resources. River Lumi should be trained to discharge into the lake. Land tenure security need to be improved as incentives for proper land utilization. New farming methods to increase land productivity will encourage farmers to practice soil and water conservation measure.

An energy balance approach for mapping crop waterstress and yield impacts over the Czech Republic

USDA-ARS?s Scientific Manuscript database

There is a growing demand for timely, spatially distributed information regarding crop condition and water use to inform agricultural decision making and yield forecasting efforts. Remote sensing of land-surface temperature has proven valuable for mapping evapotranspiration (ET) and crop stress from...

Spatially Complete Global Surface Albedos Derived from Terra/MODIS Data

NASA Technical Reports Server (NTRS)

King, Michael D.; Moody, Eric G.; Schaaf, Crystal B.; Platnick, Steven

2006-01-01

Spectral land surface albedo is an important parameter for describing the radiative properties of the Earth. Accordingly it reflects the consequences of natural and human interactions, such as anthropogenic, meteorological, and phenological effects, on global and local climatological trends. Consequently, albedos are integral parts in a variety of research areas, such as general circulation models (GCMs), energy balance studies, modeling of land use and land use change, and biophysical, oceanographic, and meteorological studies. , Over five years of land surface anisotropy, diffuse bihemispherical (white-sky) albedo and direct beam directional hemispherical (black-sky) albedo from observations acquired by the MODIS instruments aboard NASA s Terra and Aqua satellite platforms have provided researchers with unprecedented spatial, spectral, and temporal information on the land surface s radiative characteristics. However, roughly 30% of the global land surface, on an annual equal-angle basis, is obscured due to persistent and transient cloud cover, while another 207% is obscured due to ephemeral and seasonal snow effects. This precludes the MOD43B3 albedo products from being directly used in some remote sensing and ground-based applications, climate models, and global change research projects. To provide researchers with the requisite spatially complete global snow-free land surface albedo dataset, an ecosystem-dependent temporal interpolation technique was developed to fill missing or lower quality data and snow covered values from the official MOD43B3 dataset with geophysically realistic values. The method imposes pixel-level and local regional ecosystem-dependent phenological behavior onto retrieved pixel temporal data in such a way as to maintain pixel-level spatial and spectral detail and integrity. The phenological curves are derived from statistics based on the MODIS MOD12Q1 IGBP land cover classification product geolocated with the MOD43B3 data.

Assessing the Effects of Irrigation on Land Surface Processes Utilizing CLM.PF in Los Angeles, California

NASA Astrophysics Data System (ADS)

Reyes, B.; Vahmani, P.; Hogue, T. S.; Maxwell, R. M.

2013-05-01

Irrigation can significantly alter land surface properties including increases in evapotranspiration (ET) and latent heat flux and a decrease in land surface temperatures that have a wide range of effects on the hydrologic cycle. However, most irrigation in land surface modeling studies has generally been limited to large-scale cropland applications while ignoring the, relatively, much smaller use of irrigation in urban areas. Although this assumption may be valid in global studies, as we seek to apply models at higher resolutions and at more local scales, irrigation in urban areas can become a key factor in land-atmosphere interactions. Landscape irrigation can account for large portions of residential urban water use, especially in semi-arid environments (e.g. ~50% in Los Angeles, CA). Previous modeling efforts in urbanized semi-arid regions have shown that disregarding irrigation leads to inaccurate representation of the energy budget. The current research models a 49.5-km2 (19.11-mi2) domain near downtown Los Angeles in the Ballona Creek watershed at a high spatial and temporal resolution using a coupled hydrologic (ParFlow) and land surface model (CLM). Our goals are to (1) provide a sensitivity analysis for urban irrigation parameters including sensitivity to total volume and timing of irrigation, (2) assess the effects of irrigation on varying land cover types on the energy budget, and (3) evaluate if residential water use data is useful in providing estimates for irrigation in land surface modeling. Observed values of land surface parameters from remote sensing products (Land Surface Temperature and ET), water use data from the Los Angeles Department of Water and Power (LADWP), and modeling results from an irrigated version of the NOAH-Urban Canopy Model are being used for comparison and evaluation. Our analysis provides critical information on the degree to which urban irrigation should be represented in high-resolution, semi-arid urban land surface modeling of the region. This research also yields robust upper-boundary conditions for further analysis and modeling in Los Angeles.

Actual daily evapotranspiration estimated from MERIS and AATSR data over the Chinese Loess Plateau

NASA Astrophysics Data System (ADS)

Liu, R.; Wen, J.; Wang, X.; Wang, L.; Tian, H.; Zhang, T. T.; Shi, X. K.; Zhang, J. H.; Lu, Sh. N.

2009-02-01

The Loess Plateau is located in north of China and has a significant impact on the climate and ecosystem evolvement over the East Asian continent. Based on the land surface energy balance theory, the potential of using Medium Resolution Imaging Spectrometer (onboard sensor of the Environmental Satellite) remote sensing data on 7, 11 and 27 June 2005 is explored. The "split-window" algorithm is used to retrieve surface temperature from the Advanced the Along-Track Scanning Radiometer, another onboard senor of the Environmental Satellite. Then the near surface net radiation, sensible heat flux and soil heat flux are estimated by using the developed algorithm. We introduce a simple algorithm to predict the heat flux partitioning between the soil and vegetation. Combining the sunshine hours, air temperature, sunshine duration and wind speed measured by weather stations, a model for estimating daily ET is proposed. The instantaneous ET is also converted to daily value. Comparison of latent heats flux retrieved by remote sensing data with ground observation from eddy covariance flux system during Loess Plateau land surface process field Experiment, the maximum and minimum error of this approach are 10.96% and 4.80% respectively, the cause of the bias is also explored and discussed.

In situ mineralogical-chemical analysis of Martian materials at landing/roving sites by active and passive remote sensing methods

NASA Technical Reports Server (NTRS)

Neukum, G.; Lehmann, F.; Regner, P.; Jaumann, R.

1988-01-01

Remote sensing of the Martian surface from the ground and from orbiting spacecraft has provided some first-order insight into the mineralogical-chemical composition and the weathering state of Martian surface materials. Much more detailed information can be gathered from performing such measurements in situ at the landing sites or from a rover in combination with analogous measurements from orbit. Measurements in the wavelength range of approximately 0.3 to 12.0 micrometers appear to be suitable to characterize much of the physical, mineralogical, petrological, and chemical properties of Martian surface materials and the weathering and other alteration processes that have acted on them. It is of particular importance to carry out measurements at the same time over a broad wavelength range since the reflectance signatures are caused by different effects and hence give different and complementing information. It appears particularly useful to employ a combination of active and passive methods because the use of active laser spectroscopy allows the obtaining of specific information on thermal infrared reflectance of surface materials. It seems to be evident that a spectrometric survey of Martian materials has to be focused on the analysis of altered and fresh mafic materials and rocks, water-bearing silicates, and possibly carbonates.

Spatial Heterogeneity of Leaf Area Index (LAI) and Its Temporal Course on Arable Land: Combining Field Measurements, Remote Sensing and Simulation in a Comprehensive Data Analysis Approach (CDAA).

PubMed

Reichenau, Tim G; Korres, Wolfgang; Montzka, Carsten; Fiener, Peter; Wilken, Florian; Stadler, Anja; Waldhoff, Guido; Schneider, Karl

2016-01-01

The ratio of leaf area to ground area (leaf area index, LAI) is an important state variable in ecosystem studies since it influences fluxes of matter and energy between the land surface and the atmosphere. As a basis for generating temporally continuous and spatially distributed datasets of LAI, the current study contributes an analysis of its spatial variability and spatial structure. Soil-vegetation-atmosphere fluxes of water, carbon and energy are nonlinearly related to LAI. Therefore, its spatial heterogeneity, i.e., the combination of spatial variability and structure, has an effect on simulations of these fluxes. To assess LAI spatial heterogeneity, we apply a Comprehensive Data Analysis Approach that combines data from remote sensing (5 m resolution) and simulation (150 m resolution) with field measurements and a detailed land use map. Test area is the arable land in the fertile loess plain of the Rur catchment on the Germany-Belgium-Netherlands border. LAI from remote sensing and simulation compares well with field measurements. Based on the simulation results, we describe characteristic crop-specific temporal patterns of LAI spatial variability. By means of these patterns, we explain the complex multimodal frequency distributions of LAI in the remote sensing data. In the test area, variability between agricultural fields is higher than within fields. Therefore, spatial resolutions less than the 5 m of the remote sensing scenes are sufficient to infer LAI spatial variability. Frequency distributions from the simulation agree better with the multimodal distributions from remote sensing than normal distributions do. The spatial structure of LAI in the test area is dominated by a short distance referring to field sizes. Longer distances that refer to soil and weather can only be derived from remote sensing data. Therefore, simulations alone are not sufficient to characterize LAI spatial structure. It can be concluded that a comprehensive picture of LAI spatial heterogeneity and its temporal course can contribute to the development of an approach to create spatially distributed and temporally continuous datasets of LAI.

Spatial Heterogeneity of Leaf Area Index (LAI) and Its Temporal Course on Arable Land: Combining Field Measurements, Remote Sensing and Simulation in a Comprehensive Data Analysis Approach (CDAA)

PubMed Central

Korres, Wolfgang; Montzka, Carsten; Fiener, Peter; Wilken, Florian; Stadler, Anja; Waldhoff, Guido; Schneider, Karl

2016-01-01

The ratio of leaf area to ground area (leaf area index, LAI) is an important state variable in ecosystem studies since it influences fluxes of matter and energy between the land surface and the atmosphere. As a basis for generating temporally continuous and spatially distributed datasets of LAI, the current study contributes an analysis of its spatial variability and spatial structure. Soil-vegetation-atmosphere fluxes of water, carbon and energy are nonlinearly related to LAI. Therefore, its spatial heterogeneity, i.e., the combination of spatial variability and structure, has an effect on simulations of these fluxes. To assess LAI spatial heterogeneity, we apply a Comprehensive Data Analysis Approach that combines data from remote sensing (5 m resolution) and simulation (150 m resolution) with field measurements and a detailed land use map. Test area is the arable land in the fertile loess plain of the Rur catchment on the Germany-Belgium-Netherlands border. LAI from remote sensing and simulation compares well with field measurements. Based on the simulation results, we describe characteristic crop-specific temporal patterns of LAI spatial variability. By means of these patterns, we explain the complex multimodal frequency distributions of LAI in the remote sensing data. In the test area, variability between agricultural fields is higher than within fields. Therefore, spatial resolutions less than the 5 m of the remote sensing scenes are sufficient to infer LAI spatial variability. Frequency distributions from the simulation agree better with the multimodal distributions from remote sensing than normal distributions do. The spatial structure of LAI in the test area is dominated by a short distance referring to field sizes. Longer distances that refer to soil and weather can only be derived from remote sensing data. Therefore, simulations alone are not sufficient to characterize LAI spatial structure. It can be concluded that a comprehensive picture of LAI spatial heterogeneity and its temporal course can contribute to the development of an approach to create spatially distributed and temporally continuous datasets of LAI. PMID:27391858

Bringing the Coastal Zone into Finer Focus

NASA Astrophysics Data System (ADS)

Guild, L. S.; Hooker, S. B.; Kudela, R. M.; Morrow, J. H.; Torres-Perez, J. L.; Palacios, S. L.; Negrey, K.; Dungan, J. L.

2015-12-01

Measurements over extents from submeter to 10s of meters are critical science requirements for the design and integration of remote sensing instruments for coastal zone research. Various coastal ocean phenomena operate at different scales (e.g. meters to kilometers). For example, river plumes and algal blooms have typical extents of 10s of meters and therefore can be resolved with satellite data, however, shallow benthic ecosystem (e.g., coral, seagrass, and kelp) biodiversity and change are best studied at resolutions of submeter to meter, below the pixel size of typical satellite products. The delineation of natural phenomena do not fit nicely into gridded pixels and the coastal zone is complicated by mixed pixels at the land-sea interface with a range of bio-optical signals from terrestrial and water components. In many standard satellite products, these coastal mixed pixels are masked out because they confound algorithms for the ocean color parameter suite. In order to obtain data at the land/sea interface, finer spatial resolution satellite data can be achieved yet spectral resolution is sacrificed. This remote sensing resolution challenge thwarts the advancement of research in the coastal zone. Further, remote sensing of benthic ecosystems and shallow sub-surface phenomena are challenged by the requirements to sense through the sea surface and through a water column with varying light conditions from the open ocean to the water's edge. For coastal waters, >80% of the remote sensing signal is scattered/absorbed due to the atmospheric constituents, sun glint from the sea surface, and water column components. In addition to in-water measurements from various platforms (e.g., ship, glider, mooring, and divers), low altitude aircraft outfitted with high quality bio-optical radiometer sensors and targeted channels matched with in-water sensors and higher altitude platform sensors for ocean color products, bridge the sea-truth measurements to the pixels acquired from satellite and high altitude platforms. We highlight a novel NASA airborne calibration, validation, and research capability for addressing the coastal remote sensing resolution challenge.

Rock Statistics at the Mars Pathfinder Landing Site, Roughness and Roving on Mars

NASA Technical Reports Server (NTRS)

Haldemann, A. F. C.; Bridges, N. T.; Anderson, R. C.; Golombek, M. P.

1999-01-01

Several rock counts have been carried out at the Mars Pathfinder landing site producing consistent statistics of rock coverage and size-frequency distributions. These rock statistics provide a primary element of "ground truth" for anchoring remote sensing information used to pick the Pathfinder, and future, landing sites. The observed rock population statistics should also be consistent with the emplacement and alteration processes postulated to govern the landing site landscape. The rock population databases can however be used in ways that go beyond the calculation of cumulative number and cumulative area distributions versus rock diameter and height. Since the spatial parameters measured to characterize each rock are determined with stereo image pairs, the rock database serves as a subset of the full landing site digital terrain model (DTM). Insofar as a rock count can be carried out in a speedier, albeit coarser, manner than the full DTM analysis, rock counting offers several operational and scientific products in the near term. Quantitative rock mapping adds further information to the geomorphic study of the landing site, and can also be used for rover traverse planning. Statistical analysis of the surface roughness using the rock count proxy DTM is sufficiently accurate when compared to the full DTM to compare with radar remote sensing roughness measures, and with rover traverse profiles.

Use of remote sensing technology for inventorying and planning utilization of land resources in South Dakota

NASA Technical Reports Server (NTRS)

1974-01-01

A comprehensive land use planning process model is being developed in Meade County, South Dakota, using remote sensing technology. The proper role of remote sensing in the land use planning process is being determined by interaction of remote sensing specialists with local land use planners. The data that were collected by remote sensing techniques are as follows: (1) level I land use data interpreted at a scale of 1:250,000 from false color enlargement prints of ERTS-1 color composite transparencies; (2) detailed land use data interpreted at a scale of 1:24,000 from enlargement color prints of high altitude RB-57 photography; and (3) general soils map interpreted at a scale of 1:250,000 from false color enlargement prints of ERTS-1 color composite transparencies. In addition to use of imagery as an interpretation aid, the utility of using photographs as base maps was demonstrated.

Eco-hydrological Wireless Sensor Network and upscaling method research in the Heihe River Basin, China

NASA Astrophysics Data System (ADS)

Jin, Rui; kang, Jian

2017-04-01

Wireless Sensor Networks are recognized as one of most important near-surface components of GEOSS (Global Earth Observation System of Systems), with flourish development of low-cost, robust and integrated data loggers and sensors. A nested eco-hydrological wireless sensor network (EHWSN) was installed in the up- and middle-reaches of the Heihe River Basin, operated to obtain multi-scale observation of soil moisture, soil temperature and land surface temperature from 2012 till now. The spatial distribution of EHWSN was optimally designed based on the geo-statistical theory, with the aim to capture the spatial variations and temporal dynamics of soil moisture and soil temperature, and to produce ground truth at grid scale for validating the related remote sensing products and model simulation in the heterogeneous land surface. In terms of upscaling research, we have developed a set of method to aggregate multi-point WSN observations to grid scale ( 1km), including regression kriging estimation to utilize multi-resource remote sensing auxiliary information, block kriging with homogeneous measurement errors, and bayesian-based upscaling algorithm that utilizes MODIS-derived apparent thermal inertia. All the EHWSN observation are organized as datasets to be freely published at http://westdc.westgis.ac.cn/hiwater. EHWSN integrates distributed observation nodes to achieve an automated, intelligent and remote-controllable network that provides superior integrated, standardized and automated observation capabilities for hydrological and ecological processes research at the basin scale.

Hydrological Response to Land Cover Changes and Human Activities in Arid Regions Using a Geographic Information System and Remote Sensing

PubMed Central

Mahmoud, Shereif H.; Alazba, A. A.

2015-01-01

The hydrological response to land cover changes induced by human activities in arid regions has attracted increased research interest in recent decades. The study reported herein assessed the spatial and quantitative changes in surface runoff resulting from land cover change in the Al-Baha region of Saudi Arabia between 1990 and 2000 using an ArcGIS-surface runoff model and predicted land cover and surface runoff depth in 2030 using Markov chain analysis. Land cover maps for 1990 and 2000 were derived from satellite images using ArcGIS 10.1. The findings reveal a 26% decrease in forest and shrubland area, 28% increase in irrigated cropland, 1.5% increase in sparsely vegetated land and 0.5% increase in bare soil between 1990 and 2000. Overall, land cover changes resulted in a significant decrease in runoff depth values in most of the region. The decrease in surface runoff depth ranged from 25-106 mm/year in a 7020-km2 area, whereas the increase in such depth reached only 10 mm/year in a 243-km2 area. A maximum increase of 73 mm/year was seen in a limited area. The surface runoff depth decreased to the greatest extent in the central region of the study area due to the huge transition in land cover classes associated with the construction of 25 rainwater harvesting dams. The land cover prediction revealed a greater than twofold increase in irrigated cropland during the 2000-2030 period, whereas forest and shrubland are anticipated to occupy just 225 km2 of land area by 2030, a significant decrease from the 747 km2 they occupied in 2000. Overall, changes in land cover are predicted to result in an annual increase in irrigated cropland and dramatic decline in forest area in the study area over the next few decades. The increase in surface runoff depth is likely to have significant implications for irrigation activities. PMID:25923712

Coarse Scale In Situ Albedo Observations over Heterogeneous Land Surfaces and Validation Strategy

NASA Astrophysics Data System (ADS)

Xiao, Q.; Wu, X.; Wen, J.; BAI, J., Sr.

2017-12-01

To evaluate and improve the quality of coarse-pixel land surface albedo products, validation with ground measurements of albedo is crucial over the spatially and temporally heterogeneous land surface. The performance of albedo validation depends on the quality of ground-based albedo measurements at a corresponding coarse-pixel scale, which can be conceptualized as the "truth" value of albedo at coarse-pixel scale. The wireless sensor network (WSN) technology provides access to continuously observe on the large pixel scale. Taking the albedo products as an example, this paper was dedicated to the validation of coarse-scale albedo products over heterogeneous surfaces based on the WSN observed data, which is aiming at narrowing down the uncertainty of results caused by the spatial scaling mismatch between satellite and ground measurements over heterogeneous surfaces. The reference value of albedo at coarse-pixel scale can be obtained through an upscaling transform function based on all of the observations for that pixel. We will devote to further improve and develop new method that that are better able to account for the spatio-temporal characteristic of surface albedo in the future. Additionally, how to use the widely distributed single site measurements over the heterogeneous surfaces is also a question to be answered. Keywords: Remote sensing; Albedo; Validation; Wireless sensor network (WSN); Upscaling; Heterogeneous land surface; Albedo truth at coarse-pixel scale

Supplementing land-use statistics with landscape metrics: some methodological considerations.

PubMed

Herzog, F; Lausch, A

2001-11-01

Landscape monitoring usually relies on land-use statistics which reflect the share of land-sue/land cover types. In order to understand the functioning of landscapes, landscape pattern must be considered as well. Indicators which address the spatial configuration of landscapes are therefore needed. The suitability of landscape metrics, which are computed from the type, geometry and arrangement of patches, is examined. Two case studies in a surface mining region show that landscape metrics capture landscape structure but are highly dependent on the data model and on the methods of data analysis. For landscape metrics to become part of policy-relevant sets of environmental indicators, standardised procedures for their computation from remote sensing images must be developed.

Quality Evaluation of Land-Cover Classification Using Convolutional Neural Network

NASA Astrophysics Data System (ADS)

Dang, Y.; Zhang, J.; Zhao, Y.; Luo, F.; Ma, W.; Yu, F.

2018-04-01

Land-cover classification is one of the most important products of earth observation, which focuses mainly on profiling the physical characters of the land surface with temporal and distribution attributes and contains the information of both natural and man-made coverage elements, such as vegetation, soil, glaciers, rivers, lakes, marsh wetlands and various man-made structures. In recent years, the amount of high-resolution remote sensing data has increased sharply. Accordingly, the volume of land-cover classification products increases, as well as the need to evaluate such frequently updated products that is a big challenge. Conventionally, the automatic quality evaluation of land-cover classification is made through pixel-based classifying algorithms, which lead to a much trickier task and consequently hard to keep peace with the required updating frequency. In this paper, we propose a novel quality evaluation approach for evaluating the land-cover classification by a scene classification method Convolutional Neural Network (CNN) model. By learning from remote sensing data, those randomly generated kernels that serve as filter matrixes evolved to some operators that has similar functions to man-crafted operators, like Sobel operator or Canny operator, and there are other kernels learned by the CNN model that are much more complex and can't be understood as existing filters. The method using CNN approach as the core algorithm serves quality-evaluation tasks well since it calculates a bunch of outputs which directly represent the image's membership grade to certain classes. An automatic quality evaluation approach for the land-cover DLG-DOM coupling data (DLG for Digital Line Graphic, DOM for Digital Orthophoto Map) will be introduced in this paper. The CNN model as an robustness method for image evaluation, then brought out the idea of an automatic quality evaluation approach for land-cover classification. Based on this experiment, new ideas of quality evaluation of DLG-DOM coupling land-cover classification or other kinds of labelled remote sensing data can be further studied.

Mapping Surface Cover Parameters Using Aggregation Rules and Remotely Sensed Cover Classes. Version 1.9

NASA Technical Reports Server (NTRS)

Arain, Altaf M.; Shuttleworth, W. James; Yang, Z-Liang; Michaud, Jene; Dolman, Johannes

1997-01-01

A coupled model, which combines the Biosphere-Atmosphere Transfer Scheme (BATS) with an advanced atmospheric boundary-layer model, was used to validate hypothetical aggregation rules for BATS-specific surface cover parameters. The model was initialized and tested with observations from the Anglo-Brazilian Amazonian Climate Observational Study and used to simulate surface fluxes for rain forest and pasture mixes at a site near Manaus in Brazil. The aggregation rules are shown to estimate parameters which give area-average surface fluxes similar to those calculated with explicit representation of forest and pasture patches for a range of meteorological and surface conditions relevant to this site, but the agreement deteriorates somewhat when there are large patch-to-patch differences in soil moisture. The aggregation rules, validated as above, were then applied to remotely sensed 1 km land cover data set to obtain grid-average values of BATS vegetation parameters for 2.8 deg x 2.8 deg and 1 deg x 1 deg grids within the conterminous United States. There are significant differences in key vegetation parameters (aerodynamic roughness length, albedo, leaf area index, and stomatal resistance) when aggregate parameters are compared to parameters for the single, dominant cover within the grid. However, the surface energy fluxes calculated by stand-alone BATS with the 2-year forcing, data from the International Satellite Land Surface Climatology Project (ISLSCP) CDROM were reasonably similar using aggregate-vegetation parameters and dominant-cover parameters, but there were some significant differences, particularly in the western USA.

First Results of the Land Atmosphere Feedback Experiment

NASA Astrophysics Data System (ADS)

Wulfmeyer, V.; Turner, D. D.

2017-12-01

The Land-Atmosphere Feedback Experiment (LAFE) deployed several state-of-the-art scanning lidar and remote sensing systems to the ARM SGP site during August 2017. A novel synergy of remote sensing systems was applied for simultaneous measurements of land-surface fluxes and horizontal and vertical transport processes in the atmospheric boundary layer (ABL). The impact of spatial inhomogeneities of the soil-vegetation continuum on LA feedback was studied using the scanning capability of the instrumentation as well as soil, vegetation, and surface flux measurements. The synergy of remote sensing and in-situ instruments consisted of three components: 1) The SGP water-vapor and temperature Raman lidar, the SGP Doppler lidar, the University of Hohenheim (UHOH) Doppler lidar, and the NCAR water-vapor DIAL to measure mean profiles and gradients of moisture, temperature, and horizontal wind. Due to their high vertical and temporal resolutions, also profiles of higher-order turbulent moments in the water vapor and wind fields as well as of profiles of the latent heat flux, the sensible heat flux, TKE, and momentum flux were observed. 2) A novel scanning lidar system synergy consisting of the NOAA High-Resolution Doppler lidar, the UHOH water-vapor differential absorption lidar, and the UHOH temperature rotational Raman lidar. These systems performed coordinated range-height indicator (RHI) scans from just above the canopy level to the lower troposphere including the interfacial layer at the ABL top. This component was augmented by three energy balance closure towers of NOAA and one EBC station of UHOH. 3) The University of Wisconsin SPARC and the University of Oklahoma CLAMPS systems operating two vertically pointing atmospheric emitted radiance interferometers and two Doppler lidar systems scanning cross track to the central RHI for determining the surface friction velocity and the horizontal variability of temperature, moisture, and wind. NOAA ARL also provided UAS and aircraft measurements (Navajo Piper) in accordance with the surface scans. Thus, both the variability of surface fluxes and CBL dynamics and thermodynamics over the SGP site was studied for the first time. This is essential for advanced observation and understanding of LA feedback. First results are presented at the conference.

Remote microscopy and volumetric imaging on the surface of icy satellites

NASA Astrophysics Data System (ADS)

Soto, Alejandro; Nowicki, Keith; Howett, Carly; Feldkhun, Daniel; Retherford, Kurt D.

2017-10-01

With NASA PIDDP support we have applied recent advancements in Fourier-domain microscopy to develop an instrument capable of microscopic imaging from meter-scale distances for use on a planetary lander on the surface of an icy satellite or other planetary bodies. Without moving parts, our instrument projects dynamic patterns of laser light onto a distant target using a lightweight large-aperture reflector, which then collects the light scattered or fluoresced by the target on a fast photon-bucket detector. Using Fourier Transform based techniques, we reconstruct an image from the detected light. The remote microscope has been demonstrated to produce 2D images with better than 15 micron lateral resolution for targets at a distance of 5 meters and is capable of linearly proportionally higher resolution at shorter distances. The remote microscope is also capable of providing three-dimensional (3D) microscopic imaging capabilities, allowing future surface scientists to explore the morphology of microscopic features in surface ices, for example. The instrument enables microscopic in-situ imaging during day or night without the use of a robotic arm, greatly facilitating the surface operations for a lander or rover while expanding the area of investigation near a landing site for improved science targeting. We are developing this remote microscope for in-situ planetary exploration as a collaboration between the Southwest Research Institute, LambdaMetrics, and the University of Colorado.

Directional radiance measurements: Challenges in the sampling of landscapes

NASA Technical Reports Server (NTRS)

Deering, D. W.

1994-01-01

Most earth surfaces, particularly those supporting natural vegetation ecosystems, constitute structurally and spectrally complex surfaces that are distinctly non-Lambertian reflectors. Obtaining meaningful measurements of the directional radiances of landscapes and obtaining estimates of the complete bidirectional reflectance distribution functions of ground targets with complex and variable landscape and radiometric features are challenging tasks. Reasons for the increased interest in directional radiance measurements are presented, and the issues that must be addressed when trying to acquire directional radiances for vegetated land surfaces from different types of remote sensing platforms are discussed. Priority research emphases are suggested, concerning field measurements of directional surface radiances and reflectances for future research. Primarily, emphasis must be given to the acquisition of more complete and directly associated radiometric and biometric parameter data sets that will empower the exploitation of the 'angular dimension' in remote sensing of vegetation through enabling the further development and rigorous validation of state of the art plant canopy models.

Remote sensing for environmental protection of the eastern Mediterranean rugged mountainous areas, Lebanon

NASA Astrophysics Data System (ADS)

Khawlie, M.; Awad, M.; Shaban, A.; Bou Kheir, R.; Abdallah, C.

Lying along the eastern Mediterranean coast with elevated mountain chains higher than 2500 m straddling its terrain, Lebanon is a country of natural beauty and is thus attracting tourism. However, with a population density exceeding 800/km 2 and a rugged steep sloping land, problems abound in the country calling for holistic-approach studies. Only remote sensing, whose use is new in Lebanon can secure such needed studies within a scientific and pragmatic framework. The paper demonstrates for the concerned themes, the innovative use of remote sensing in such a difficult terrain, giving three examples of major environmental problems in the coastal mountains. Only few studies have so far focused on those mountains, notably application of remote sensing. The rugged mountainous terrain receives considerable rain, but the water is quickly lost running on the steep slopes, or infiltrating through fractures and the karstic conduits into the subsurface. Field investigations are difficult to achieve, therefore, remote sensing helps reveal various surface land features important in reflecting water feeding into the subsurface. Optical, radar and thermal infrared remotely sensed data cover a wide spectrum serving that purpose. A map of preferential groundwater accumulation potential is produced. It can serve for better water exploitation as well as protection. Because the terrain is karstic and rugged, the subsurface water flow is difficult to discern. Any pollution at a certain spot would certainly spread around. This constitutes the second example of environmental problems facing the mountainous areas in Lebanon. An integrated approach using remote sensing and geographic information systems (GIS) gives good results in finding out the likelihood of how pollution, or contaminants, can selectively move in the subsurface. A diagnostic analysis with a GIS-type software acts as a guide producing indicative maps for the above purpose. The third example given deals with the problem of losing soil, which is a very vital source in such mountainous land. With steep slopes, torrential rain and improper human interference, run-off is high and water-soil erosion is continuously deteriorating the land cover. Remote sensing can facilitate studying the factors enhancing the process, such as soil type, slope gradient, drainage, geology and land cover. Digital elevation models created from SAR imagery contribute significantly to assessing vulnerability of hydric-soil erosion over such a difficult terrain. GIS layers of the above factors are integrated with erosional criteria to produce a risk map of soil erosion. Results indicate that 36% of the Lebanese terrain is under threat of high-level erosion, and 52% of that is concentrated in the rugged mountainous regions.

Spatiotemporal variations in the difference between satellite-observed daily maximum land surface temperature and station-based daily maximum near-surface air temperature

NASA Astrophysics Data System (ADS)

Lian, Xu; Zeng, Zhenzhong; Yao, Yitong; Peng, Shushi; Wang, Kaicun; Piao, Shilong

2017-02-01

There is an increasing demand to integrate land surface temperature (LST) into climate research due to its global coverage, which requires a comprehensive knowledge of its distinctive characteristics compared to near-surface air temperature (Tair). Using satellite observations and in situ station-based data sets, we conducted a global-scale assessment of the spatial and seasonal variations in the difference between daily maximum LST and daily maximum Tair (δT, LST - Tair) during 2003-2014. Spatially, LST is generally higher than Tair over arid and sparsely vegetated regions in the middle-low latitudes, but LST is lower than Tair in tropical rainforests due to strong evaporative cooling, and in the high-latitude regions due to snow-induced radiative cooling. Seasonally, δT is negative in tropical regions throughout the year, while it displays a pronounced seasonality in both the midlatitudes and boreal regions. The seasonality in the midlatitudes is a result of the asynchronous responses of LST and Tair to the seasonal cycle of radiation and vegetation abundance, whereas in the boreal regions, seasonality is mainly caused by the change in snow cover. Our study identified substantial spatial heterogeneity and seasonality in δT, as well as its determinant environmental drivers, and thus provides a useful reference for monitoring near-surface air temperature changes using remote sensing, particularly in remote regions.

Research on Land Use Changes in Panjin City Basing on Remote Sensing Data

NASA Astrophysics Data System (ADS)

Ding, Hua; Li, Ru Ren; Shuang Sun, Li; Wang, Xin; Liu, Yu Mei

2018-05-01

Taking Landsat remote sensing image as the main data source, the research on land use changes in Panjin City in 2005 to 2015 is made with the support of remote sensing platform and GIS platform in this paper; the range of land use changes and change rate are analyzed through the classification of remote sensing image; the dynamic analysis on land changes is made with the help of transfer matrix of land use type; the quantitative calculation on all kinds of dynamic change features of land changes is made by utilizing mathematical model; and the analysis on driving factors of land changes of image is made at last. The research results show that, in recent ten years, the area of cultivated land in Panjin City decreased, the area of vegetation increased, and meanwhile the area of road increased drastically, the settlement place decreased than ever, and water area changed slightly.

Visualization and Analysis of Multi-scale Land Surface Products via Giovanni Portals

NASA Technical Reports Server (NTRS)

Shen, Suhung; Kempler, Steven J.; Gerasimov, Irina V.

2013-01-01

Large volumes of MODIS land data products at multiple spatial resolutions have been integrated into the Giovanni online analysis system to support studies on land cover and land use changes,focused on the Northern Eurasia and Monsoon Asia regions through the LCLUC program. Giovanni (Goddard Interactive Online Visualization ANd aNalysis Infrastructure) is a Web-based application developed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), providing a simple and intuitive way to visualize, analyze, and access Earth science remotely-sensed and modeled data.Customized Giovanni Web portals (Giovanni-NEESPI andGiovanni-MAIRS) have been created to integrate land, atmospheric,cryospheric, and societal products, enabling researchers to do quick exploration and basic analyses of land surface changes, and their relationships to climate, at global and regional scales. This presentation shows a sample Giovanni portal page, lists selected data products in the system, and illustrates potential analyses with imagesand time-series at global and regional scales, focusing on climatology and anomaly analysis. More information is available at the GES DISCMAIRS data support project portal: http:disc.sci.gsfc.nasa.govmairs.

Estimation of Soil Moisture from Optical and Thermal Remote Sensing: A Review

PubMed Central

Zhang, Dianjun; Zhou, Guoqing

2016-01-01

As an important parameter in recent and numerous environmental studies, soil moisture (SM) influences the exchange of water and energy at the interface between the land surface and atmosphere. Accurate estimate of the spatio-temporal variations of SM is critical for numerous large-scale terrestrial studies. Although microwave remote sensing provides many algorithms to obtain SM at large scale, such as SMOS and SMAP etc., resulting in many data products, they are almost low resolution and not applicable in small catchment or field scale. Estimations of SM from optical and thermal remote sensing have been studied for many years and significant progress has been made. In contrast to previous reviews, this paper presents a new, comprehensive and systematic review of using optical and thermal remote sensing for estimating SM. The physical basis and status of the estimation methods are analyzed and summarized in detail. The most important and latest advances in soil moisture estimation using temporal information have been shown in this paper. SM estimation from optical and thermal remote sensing mainly depends on the relationship between SM and the surface reflectance or vegetation index. The thermal infrared remote sensing methods uses the relationship between SM and the surface temperature or variations of surface temperature/vegetation index. These approaches often have complex derivation processes and many approximations. Therefore, combinations of optical and thermal infrared remotely sensed data can provide more valuable information for SM estimation. Moreover, the advantages and weaknesses of different approaches are compared and applicable conditions as well as key issues in current soil moisture estimation algorithms are discussed. Finally, key problems and suggested solutions are proposed for future research. PMID:27548168

Estimation of Soil Moisture from Optical and Thermal Remote Sensing: A Review.

PubMed

Zhang, Dianjun; Zhou, Guoqing

2016-08-17

As an important parameter in recent and numerous environmental studies, soil moisture (SM) influences the exchange of water and energy at the interface between the land surface and atmosphere. Accurate estimate of the spatio-temporal variations of SM is critical for numerous large-scale terrestrial studies. Although microwave remote sensing provides many algorithms to obtain SM at large scale, such as SMOS and SMAP etc., resulting in many data products, they are almost low resolution and not applicable in small catchment or field scale. Estimations of SM from optical and thermal remote sensing have been studied for many years and significant progress has been made. In contrast to previous reviews, this paper presents a new, comprehensive and systematic review of using optical and thermal remote sensing for estimating SM. The physical basis and status of the estimation methods are analyzed and summarized in detail. The most important and latest advances in soil moisture estimation using temporal information have been shown in this paper. SM estimation from optical and thermal remote sensing mainly depends on the relationship between SM and the surface reflectance or vegetation index. The thermal infrared remote sensing methods uses the relationship between SM and the surface temperature or variations of surface temperature/vegetation index. These approaches often have complex derivation processes and many approximations. Therefore, combinations of optical and thermal infrared remotely sensed data can provide more valuable information for SM estimation. Moreover, the advantages and weaknesses of different approaches are compared and applicable conditions as well as key issues in current soil moisture estimation algorithms are discussed. Finally, key problems and suggested solutions are proposed for future research.

Investigation of remote sensing to detect near-surface groundwater on irrigated lands

NASA Technical Reports Server (NTRS)

Ryland, D. W.; Schmer, F. A.; Moore, D. G.

1975-01-01

The application of remote sensing techniques was studied for detecting areas with high water tables in irrigated agricultural lands. Aerial data were collected by the LANDSAT-1 satellite and aircraft over the Kansas/Bostwick Irrigation District in Republic and Jewell Counties, Kansas. LANDSAT-1 data for May 12 and August 10, 1973, and aircraft flights (midday and predawn) on August 10 and 11, 1973, and June 25 and 26, 1974, were obtained. Surface and water table contour maps and active observation well hydrographs were obtained from the Bureau of Reclamation for use in the analysis. Results of the study reveal that LANDSAT-1 data (May MSS band 6 and August MSS band 7) correlate significantly (0.01 level) with water table depth for 144 active observation wells located throughout the Kansas/Bostwick Irrigation District. However, a map of water table depths of less than 1.83 meters prepared from the LANDSAT-1 data did not compare favorably with a map of seeped lands of less than 1.22 m (4 feet) to the water table. Field evaluation of the map is necessary for a complete analysis. Analysis of three fields on a within or single-field basis for the 1973 LANDSAT-1 data also showed significant correlation results.

A Novel Concept for Observing Land-Surface-Atmosphere Feedback Based on a Synergy of Scanning Lidar Systems

NASA Astrophysics Data System (ADS)

Wulfmeyer, V.; Turner, D. D.; Mauder, M.; Behrendt, A.; Ingwersen, J.; Streck, T.

2015-12-01

Improved simulations of land-surface-atmosphere interaction are fundamental for improving weather forecast and climate models. This requires observations of 2D fields of surface fluxes and the 3D structure of the atmospheric boundary layer simultaneously. A novel strategy is introduced for studying land-surface exchange and entrainment processes in the convective boundary layer (CBL) over complex terrain by means of a new generation of remote sensing systems. The sensor synergy consists of scanning Doppler lidar (DL), water-vapor differential absorption lidar (WVDIAL), and temperature rotational Raman lidar (TRRL) systems supported by surface in-situ measurements. The 2D measurements of surface fluxes are realized by the operation of a DL, a WVDIAL, and a TRRL along the same line-of-sight (LOS) in a range-height-indicator (RHI) mode whereas the other DL is performing a series of cross track RHI scans along this LOS. This new setup enables us to determine the friction velocity as well as surface sensible and latent heat fluxes by closing the complete set of Monin-Obukhov similarity relationships under a variety of surface layer stability conditions and different land cover and soil properties. As this closure is performed at all DL crossing points along the LOS, this is a strategy towards a 2D mapping of surface fluxes entirely based on remote sensing systems. Further details are presented at the conference. The second configuration is the simultaneous vertical profiling of vertical wind, humidity and temperature by DL, WVDIAL and TRRL so that latent heat and sensible heat flux profiles as well as a variety of different turbulent moments can be measured in the CBL. Consequently, by alternating of RHI scanning and vertical pointing modes, entrainment fluxes and surface fluxes can be measured almost simultaneously. This novel strategy has been realized for the first time during the Surface Atmospheric Boundary Layer Exchange (SABLE) campaign in the Kraichgau region, north of the Black Forest low mountain region, in Southern Germany in August 2014 (see https://klimawandel.uni-hohenheim.de/start?&L=1). A further refined design of this experiment is planned at the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) site in summer 2016.

Albedo and land surface temperature shift in hydrocarbon seepage potential area, case study in Miri Sarawak Malaysia

NASA Astrophysics Data System (ADS)

Suherman, A.; Rahman, M. Z. A.; Busu, I.

2014-02-01

The presence of hydrocarbon seepage is generally associated with rock or mineral alteration product exposures, and changes of soil properties which manifest with bare development and stress vegetation. This alters the surface thermodynamic properties, changes the energy balance related to the surface reflection, absorption and emission, and leads to shift in albedo and LST. Those phenomena may provide a guide for seepage detection which can be recognized inexpensively by remote sensing method. District of Miri is used for study area. Available topographic maps of Miri and LANDSAT ETM+ were used for boundary construction and determination albedo and LST. Three land use classification methods, namely fixed, supervised and NDVI base classifications were employed for this study. By the intensive land use classification and corresponding statistical comparison was found a clearly shift on albedo and land surface temperature between internal and external seepage potential area. The shift shows a regular pattern related to vegetation density or NDVI value. In the low vegetation density or low NDVI value, albedo of internal area turned to lower value than external area. Conversely in the high vegetation density or high NDVI value, albedo of internal area turned to higher value than external area. Land surface temperature of internal seepage potential was generally shifted to higher value than external area in all of land use classes. In dense vegetation area tend to shift the temperature more than poor vegetation area.

The observed cooling effect of desert blooms based on high-resolution Moderate Resolution Imaging Spectroradiometer products

NASA Astrophysics Data System (ADS)

He, Bin; Huang, Ling; Liu, Junjie; Wang, Haiyan; Lż, Aifeng; Jiang, Weiguo; Chen, Ziyue

2017-05-01

Desert greening through planting or irrigation is a potential approach to mitigate desertification and climate warming, but its influence on regional climate is unclear due to scarcity of observations. "Desert blooms," which are natural phenomena usually associated with the El Niño-Southern Oscillation, regularly occur in the world's driest desert, the Atacama Desert. This sudden conversion of land cover likely has a large impact on regional climate through alteration of local energy budgets and provides a unique opportunity to study the potential climatic and environmental consequences of desert greening. Here we evaluated the land surface effects of blooms in the Atacama Desert using vegetation and climate data acquired from remote sensing. The rapid vegetation growth during blooms led to an increase in evapotranspiration and a decrease in albedo. These two processes caused a 0.31°C ± 0.05°C decrease in daytime land surface temperature. During nighttime, we observed a 0.02°C ± 0.02°C increase in land surface temperature due to enhanced heat capacity associated with blooms. This asymmetric diurnal variation in land surface temperature produced a net decrease in daily land surface temperature of 0.29°C ± 0.07°C. Our observations demonstrate the potential benefits of desert blooms on local climate. Results from this study also provide new evidence for plausible climate consequences expected from local "desert greening" strategies.

Neighborhood Landscape Spatial Patterns and Land Surface Temperature: An Empirical Study on Single-Family Residential Areas in Austin, Texas.

PubMed

Kim, Jun-Hyun; Gu, Donghwan; Sohn, Wonmin; Kil, Sung-Ho; Kim, Hwanyong; Lee, Dong-Kun

2016-09-02

Rapid urbanization has accelerated land use and land cover changes, and generated the urban heat island effect (UHI). Previous studies have reported positive effects of neighborhood landscapes on mitigating urban surface temperatures. However, the influence of neighborhood landscape spatial patterns on enhancing cooling effects has not yet been fully investigated. The main objective of this study was to assess the relationships between neighborhood landscape spatial patterns and land surface temperatures (LST) by using multi-regression models considering spatial autocorrelation issues. To measure the influence of neighborhood landscape spatial patterns on LST, this study analyzed neighborhood environments of 15,862 single-family houses in Austin, Texas, USA. Using aerial photos, geographic information systems (GIS), and remote sensing, FRAGSTATS was employed to calculate values of several landscape indices used to measure neighborhood landscape spatial patterns. After controlling for the spatial autocorrelation effect, results showed that larger and better-connected landscape spatial patterns were positively correlated with lower LST values in neighborhoods, while more fragmented and isolated neighborhood landscape patterns were negatively related to the reduction of LST.

Neighborhood Landscape Spatial Patterns and Land Surface Temperature: An Empirical Study on Single-Family Residential Areas in Austin, Texas

PubMed Central

Kim, Jun-Hyun; Gu, Donghwan; Sohn, Wonmin; Kil, Sung-Ho; Kim, Hwanyong; Lee, Dong-Kun

2016-01-01

Rapid urbanization has accelerated land use and land cover changes, and generated the urban heat island effect (UHI). Previous studies have reported positive effects of neighborhood landscapes on mitigating urban surface temperatures. However, the influence of neighborhood landscape spatial patterns on enhancing cooling effects has not yet been fully investigated. The main objective of this study was to assess the relationships between neighborhood landscape spatial patterns and land surface temperatures (LST) by using multi-regression models considering spatial autocorrelation issues. To measure the influence of neighborhood landscape spatial patterns on LST, this study analyzed neighborhood environments of 15,862 single-family houses in Austin, Texas, USA. Using aerial photos, geographic information systems (GIS), and remote sensing, FRAGSTATS was employed to calculate values of several landscape indices used to measure neighborhood landscape spatial patterns. After controlling for the spatial autocorrelation effect, results showed that larger and better-connected landscape spatial patterns were positively correlated with lower LST values in neighborhoods, while more fragmented and isolated neighborhood landscape patterns were negatively related to the reduction of LST. PMID:27598186

Thermal Infrared Remote Sensing for Analysis of Landscape Ecological Processes: Methods and Applications

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.; Luvall, Jeffrey C.

1998-01-01

Thermal Infrared (TIR) remote sensing data can provide important measurements of surface energy fluxes and temperatures, which are integral to understanding landscape processes and responses. One example of this is the successful application of TIR remote sensing data to estimate evapotranspiration and soil moisture, where results from a number of studies suggest that satellite-based measurements from TIR remote sensing data can lead to more accurate regional-scale estimates of daily evapotranspiration. With further refinement in analytical techniques and models, the use of TIR data from airborne and satellite sensors could be very useful for parameterizing surface moisture conditions and developing better simulations of landscape energy exchange over a variety of conditions and space and time scales. Thus, TIR remote sensing data can significantly contribute to the observation, measurement, and analysis of energy balance characteristics (i.e., the fluxes and redistribution of thermal energy within and across the land surface) as an implicit and important aspect of landscape dynamics and landscape functioning. The application of TIR remote sensing data in landscape ecological studies has been limited, however, for several fundamental reasons that relate primarily to the perceived difficulty in use and availability of these data by the landscape ecology community, and from the fragmentation of references on TIR remote sensing throughout the scientific literature. It is our purpose here to provide evidence from work that has employed TIR remote sensing for analysis of landscape characteristics to illustrate how these data can provide important data for the improved measurement of landscape energy response and energy flux relationships. We examine the direct or indirect use of TIR remote sensing data to analyze landscape biophysical characteristics, thereby offering some insight on how these data can be used more robustly to further the understanding and modeling of landscape ecological processes.

Assimilation of Remotely Sensed Evaporative Fraction for Improved Agricultural Irrigation Water Management

NASA Astrophysics Data System (ADS)

Lei, F.; Crow, W. T.; Kustas, W. P.; Yang, Y.; Anderson, M. C.

2017-12-01

Improving the water usage efficiency and maintaining water use sustainability is challenging under rapidly changed natural environments. For decades, extensive field investigations and conceptual/physical numerical modeling have been developed to quantify and track surface water and energy fluxes at different spatial and temporal scales. Meanwhile, with the development of satellite-based sensors, land surface eco-hydrological parameters can be retrieved remotely to supplement ground-based observations. However, both models and remote sensing retrievals contain various sources of errors and an accurate and spatio-temporally continuous simulation and forecasting system at the field-scale is crucial for the efficient water management in agriculture. Specifically, data assimilation technique can optimally integrate measurements acquired from various sources (including in-situ and remotely-sensed data) with numerical models through consideration of different types of uncertainties. In this presentation, we will focus on improving the estimation of water and energy fluxes over a vineyard in California, U.S. A high-resolution remotely-sensed Evaporative Fraction (EF) product from the Atmosphere-Land Exchange Inverse (ALEXI) model will be incorporated into a Soil Vegetation Atmosphere Transfer (SVAT) model via a 2-D data assimilation method. The results will show that both the accuracy and spatial variability of soil water content and evapotranspiration in SVAT model can be enhanced through the assimilation of EF data. Furthermore, we will demonstrate that by taking the optimized soil water flux as initial condition and combining it with weather forecasts, future field water status can be predicted under different irrigation scenarios. Finally, we will discuss the practical potential of these advances by leveraging our numerical experiment for the design of new irrigation strategies and water management techniques.

Towards the Consideration of Surface and Environment variables for a Microwave Precipitation Algorithm Over Land

NASA Astrophysics Data System (ADS)

Wang, N. Y.; You, Y.; Ferraro, R. R.; Guch, I.

2014-12-01

Microwave satellite remote sensing of precipitation over land is a challenging problem due to the highly variable land surface emissivity, which, if not properly accounted for, can be much greater than the precipitation signal itself, especially in light rain/snow conditions. Additionally, surfaces such as arid land, deserts and snow cover have brightness temperatures characteristics similar to precipitation Ongoing work by NASA's GPM microwave radiometer team is constructing databases for the GPROF algorithm through a variety of means, however, there is much uncertainty as to what is the optimal information needed for the wide array of sensors in the GPM constellation, including examination of regional conditions. The at-launch database focuses on stratification by emissivity class, surface temperature and total precipitable water (TPW). We'll perform sensitivity studies to determine the potential role of environmental factors such as land surface temperature, surface elevation, and relative humidity and storm morphology such as storm vertical structure, height, and ice thickness to improve precipitation estimation over land, including rain and snow. In other words, what information outside of the satellite radiances can help describe the background and subsequent departures from it that are active precipitating regions? It is likely that this information will be a function of the various precipitation regimes. Statistical methods such as Principal Component Analysis (PCA) will be utilized in this task. Databases from a variety of sources are being constructed. They include existing satellite microwave measurements of precipitating and non-precipitating conditions, ground radar precipitation rate estimates, surface emissivity climatology from satellites, surface temperature and TPW from NWP reanalysis. Results from the analysis of these databases with respect to the microwave precipitation sensitivity to the variety of environmental conditions in different climate regimes will be discussed.

Towards an improved Land Surface Phenology mapping using a new MODIS product: A case study of Bavarian Forest National Park

NASA Astrophysics Data System (ADS)

Misra, Gourav; Buras, Allan; Asam, Sarah; Menzel, Annette

2017-04-01

Past work in remote sensing of land surface phenology have mapped vegetation cycles at multiple scales. Much has been discussed and debated about the uncertainties associated with the selection of data, data processing and the eventual conclusions drawn. Several studies do however provide evidence of strong links between different land surface phenology (LSP) metrics with specific ground phenology (GP) (Fisher and Mustard, 2007; Misra et al., 2016). Most importantly the use of high temporal and spatial resolution remote sensing data and ground truth information is critical for such studies. In this study, we use a higher temporal resolution 4 day MODIS NDVI product developed by EURAC (Asam et al., in prep) for the Bavarian Forest National Park during 2002-2015 period and extract various phenological metrics covering different phenophases of vegetation (start of season / sos and end of season / eos). We found the LSP-sos to be more strongly linked to the elevation of the area than LSP-eos which has been cited to be harder to detect (Stöckli et al., 2008). The LSP metrics were also correlated to GP information at 4 different stations covering elevations ranging from approx. 500 to 1500 metres. Results show that among the five dominant species in the area i.e. European ash, Norway spruce, European beech, Norway maple and orchard grass, only particular GP observations for some species show stronger correlations with LSP than others. Spatial variations in the LSP-GP correlations were also observed, with certain areas of the National Park showing positive correlations and others negative. An analysis of temporal trends of LSP also indicates the possibility to detect those areas in the National Park that were affected by extreme events. Further investigations are planned to explain the heterogeneity in the derived LSP metrics using high resolution ground truth data and multivariate statistical analyses. Acknowledgement: This research received funding from the Bavarian State Ministry of the Environment and Consumer Protection. References: 1. Fisher, J.I.; Mustard, J.F. Cross-scalar satellite phenology from ground, Landsat, and MODIS data. Remote Sens. Environ. 2007, 109, 261-273. 2. Misra, G.; Buras, A.; Menzel, A. Effects of Different Methods on the Comparison be-tween Land Surface and Ground Phenology—A Methodological Case Study from South-Western Germany. Remote Sens. 2016, 8, 753. 3. Asam, S.; Callegari, M.; Fiore, G.; Matiu, M.; De Gregorio, L.; Jacob, A.; Staab, J.; Men-zel, A.; Notarnicola, C. Analysis of spatiotemporal variations of climate, snow cover and plant phenology over the Alps. 2017 (in preparation). 4. Stöckli, R.; Rutishauser, T.; Dragoni, D.; O'Keefe, J.; Thornton, P. E.; Jolly, M.; Lu, L.; Denning, A. S. Remote sensing data assimilation for a prognostic phenology model. Journal of Geophysical Research: Biogeosciences. 2008, 113.

Remote sensing for oceanography, hydrology and agriculture; Proceedings of Symposia A5, A3 and A9 of the COSPAR 29th Plenary Meeting, Washington, Aug. 28-Sept. 5, 1992

NASA Technical Reports Server (NTRS)

Gower, J. F. R. (Editor); Salomonson, V. V. (Editor); Engman, E. T. (Editor); Ormsby, J. P. (Editor); Gupta, R. K. (Editor)

1993-01-01

New results from satellite studies of the ocean and radar mapping of the earth are presented. Atttention is given to data from the ERS-1 satellite. Synthetic aperture radar mapping of land surface features and sea ice, radar backscatter measurements, and orbit altitude measurements are discussed. The use of remote sensing in hydrology, soil moisture determination, precipitation measurement, agricultural meteorology, and crop growth estimation is reviewed.

Applications of TIMS data in agricultural areas and related atmospheric considerations

NASA Technical Reports Server (NTRS)

Pelletier, R. E.; Ochoa, M. C.

1986-01-01

While much of traditional remote sensing in agricultural research was limited to the visible and reflective infrared, advances in thermal infrared remote sensing technology are adding a dimension to digital image analysis of agricultural areas. The Thermal Infrared Multispectral Scanner (TIMS) an airborne sensor having six bands over the nominal 8.2 to 12.2 m range, offers the ability to calculate land surface emissivities unlike most previous singular broadband sensors. Preliminary findings on the utility of the TIMS for several agricultural applications and related atmospheric considerations are discussed.

Updating representation of land surface-atmosphere feedbacks in airborne campaign modeling analysis

NASA Astrophysics Data System (ADS)

Huang, M.; Carmichael, G. R.; Crawford, J. H.; Chan, S.; Xu, X.; Fisher, J. A.

2017-12-01

An updated modeling system to support airborne field campaigns is being built at NASA Ames Pleiades, with focus on adjusting the representation of land surface-atmosphere feedbacks. The main updates, referring to previous experiences with ARCTAS-CARB and CalNex in the western US to study air pollution inflows, include: 1) migrating the WRF (Weather Research and Forecasting) coupled land surface model from Noah to improved/more complex models especially Noah-MP and Rapid Update Cycle; 2) enabling the WRF land initialization with suitably spun-up land model output; 3) incorporating satellite land cover, vegetation dynamics, and soil moisture data (i.e., assimilating Soil Moisture Active Passive data using the ensemble Kalman filter approach) into WRF. Examples are given of comparing the model fields with available aircraft observations during spring-summer 2016 field campaigns taken place at the eastern side of continents (KORUS-AQ in South Korea and ACT-America in the eastern US), the air pollution export regions. Under fair weather and stormy conditions, air pollution vertical distributions and column amounts, as well as the impact from land surface, are compared. These help identify challenges and opportunities for LEO/GEO satellite remote sensing and modeling of air quality in the northern hemisphere. Finally, we briefly show applications of this system on simulating Australian conditions, which would explore the needs for further development of the observing system in the southern hemisphere and inform the Clean Air and Urban Landscapes (https://www.nespurban.edu.au) modelers.

Consequences of land use and land cover change

USGS Publications Warehouse

Slonecker, E. Terrence; Barnes, Christopher; Karstensen, Krista; Milheim, Lesley E.; Roig-Silva, Coral M.

2013-01-01

The U.S. Geological Survey (USGS) Climate and Land Use Change Mission Area is one of seven USGS mission areas that focuses on making substantial scientific "...contributions to understanding how Earth systems interact, respond to, and cause global change". Using satellite and other remotely sensed data, USGS scientists monitor patterns of land cover change over space and time at regional, national, and global scales. These data are analyzed to understand the causes and consequences of changing land cover, such as economic impacts, effects on water quality and availability, the spread of invasive species, habitats and biodiversity, carbon fluctuations, and climate variability. USGS scientists are among the leaders in the study of land cover, which is a term that generally refers to the vegetation and artificial structures that cover the land surface. Examples of land cover include forests, grasslands, wetlands, water, crops, and buildings. Land use involves human activities that take place on the land. For example, "grass" is a land cover, whereas pasture and recreational parks are land uses that produce a cover of grass.

Physical Properties of the MER and Beagle II Landing Sites on Mars

NASA Astrophysics Data System (ADS)

Jakosky, B. M.; Pelkey, S. M.; Mellon, M. T.; Putzig, N.; Martinez-Alonso, S.; Murphy, N.; Hynek, B.

2003-12-01

The ESA Beagle II and the NASA Mars Exploration Rover spacecraft are scheduled to land on the martian surface in December 2003 and January 2004, respectively. Mission operations and success depends on the physical properties of the surfaces on which they land. Surface structural characteristics such as the abundances of loose, unconsolidated fine material, of fine material that has been cemented into a duricrust, and of rocks affect the ability to safely land and to successfully sample and traverse the surface. Also, physical properties affect surface and atmospheric temperatures, which affect lander and rover functionality. We are in the process of analyzing surface temperature information for these sites, derived from MGS TES and Odyssey THEMIS daytime and nighttime measurements. Our approach is to: (i) remap thermal inertia using TES data at ~3-km resolution, to obtain the most complete coverage possible; (ii) interpret physical properties from TES coverage in conjunction with other remote-sensing data sets; (iii) map infrared brightness using daytime and nighttime THEMIS data at 100-m resolution, and do qualitative analysis of physical properties and processes; and (iv) derive thermal inertia from THEMIS nighttime data in conjunction with daytime albedo measurements derived from TES, THEMIS, and MOC observations. In addition, we will use measured temperatures and derived thermal inertia to predict surface temperatures for the periods of the missions.

SMAP Soil Moisture Disaggregation using Land Surface Temperature and Vegetation Data

NASA Astrophysics Data System (ADS)

Fang, B.; Lakshmi, V.

2016-12-01

Soil moisture (SM) is a key parameter in agriculture, hydrology and ecology studies. The global SM retrievals have been providing by microwave remote sensing technology since late 1970s and many SM retrieval algorithms have been developed, calibrated and applied on satellite sensors such as AMSR-E (Advanced Microwave Scanning Radiometer for the Earth Observing System), AMSR-2 (Advanced Microwave Scanning Radiometer 2) and SMOS (Soil Moisture and Ocean Salinity). Particularly, SMAP (Soil Moisture Active/Passive) satellite, which was developed by NASA, was launched in January 2015. SMAP provides soil moisture products of 9 km and 36 km spatial resolutions which are not capable for research and applications of finer scale. Toward this issue, this study applied a SM disaggregation algorithm to disaggregate SMAP passive microwave soil moisture 36 km product. This algorithm was developed based on the thermal inertial relationship between daily surface temperature variation and daily average soil moisture which is modulated by vegetation condition, by using remote sensing retrievals from AVHRR (Advanced Very High Resolution Radiometer, MODIS (Moderate Resolution Imaging Spectroradiometer), SPOT (Satellite Pour l'Observation de la Terre), as well as Land Surface Model (LSM) output from NLDAS (North American Land Data Assimilation System). The disaggregation model was built at 1/8o spatial resolution on monthly basis and was implemented to calculate and disaggregate SMAP 36 km SM retrievals to 1 km resolution in Oklahoma. The SM disaggregation results were also validated using MESONET (Mesoscale Network) and MICRONET (Microscale Network) ground SM measurements.

Alaska at the Crossroads of Migration: Space Based Ornithology

NASA Technical Reports Server (NTRS)

Deppe, Jill; Wessels, Konrad; Smith, James A.

2007-01-01

Understanding bird migration on a global scale is one of the most compelling and challenging problems of modern biology with major implications for human health and conservation biology. Revolutionary advances in remote sensing now provide us with near real-time measurements of atmospheric and land surface conditions at high spatial resolution over entire continents. We use spatially-explicit, individual based bird migration models driven by numerical weather prediction models of atmospheric conditions, dynamic habitat suitability maps derived from remotely sensed land surface conditions, biophysiological models, and biological field data to simulate migration routes, timing, energy budgets, and survival of individual birds and populations. Long-distance migratory birds travel annually between breeding grounds in Alaska and wintering grounds in Latin Amierica. Approximately 25% of these species are potential vectors of Avian Influenza. Alaska is at the crossroads of Asian and New World migratory flyways and is likely to be a point of introduction of Asian H5N1 AI into the western hemisphere. If/when an infected bird is detected, a pressing question will be where was this bird several days ago, and where is it likely to go after it was released from the survey site? Answers to such questions will increase effectiveness of AI surveillance and mitigation measures. From a conservation perspective, Alaska's diverse landscape provides breeding sites for many migrants, and climatic and land surface changes along migratory flyways in the western hemisphere may reduce bird survival and physical condition upon arrival at Alaskan breeding territories, success and migrant populations.

Satellite estimation of incident photosynthetically active radiation using ultraviolet reflectance

NASA Technical Reports Server (NTRS)

Eck, Thomas F.; Dye, Dennis G.

1991-01-01

A new satellite remote sensing method for estimating the amount of photosynthetically active radiation (PAR, 400-700 nm) incident at the earth's surface is described and tested. Potential incident PAR for clear sky conditions is computed from an existing spectral model. A major advantage of the UV approach over existing visible band approaches to estimating insolation is the improved ability to discriminate clouds from high-albedo background surfaces. UV spectral reflectance data from the Total Ozone Mapping Spectrometer (TOMS) were used to test the approach for three climatically distinct, midlatitude locations. Estimates of monthly total incident PAR from the satellite technique differed from values computed from ground-based pyranometer measurements by less than 6 percent. This UV remote sensing method can be applied to estimate PAR insolation over ocean and land surfaces which are free of ice and snow.

All-weather Land Surface Temperature Estimation from Satellite Data

NASA Astrophysics Data System (ADS)

Zhou, J.; Zhang, X.

2017-12-01

Satellite remote sensing, including the thermal infrared (TIR) and passive microwave (MW), provides the possibility to observe LST at large scales. For better modeling the land surface processes with high temporal resolutions, all-weather LST from satellite data is desirable. However, estimation of all-weather LST faces great challenges. On the one hand, TIR remote sensing is limited to clear-sky situations; this drawback reduces its usefulness under cloudy conditions considerably, especially in regions with frequent and/or permanent clouds. On the other hand, MW remote sensing suffers from much greater thermal sampling depth (TSD) and coarser spatial resolution than TIR; thus, MW LST is generally lower than TIR LST, especially at daytime. Two case studies addressing the challenges mentioned previously are presented here. The first study is for the development of a novel thermal sampling depth correction method (TSDC) to estimate the MW LST over barren land; this second study is for the development of a feasible method to merge the TIR and MW LSTs by addressing the coarse resolution of the latter one. In the first study, the core of the TSDC method is a new formulation of the passive microwave radiation balance equation, which allows linking bulk MW radiation to the soil temperature at a specific depth, i.e. the representative temperature: this temperature is then converted to LST through an adapted soil heat conduction equation. The TSDC method is applied to the 6.9 GHz channel in vertical polarization of AMSR-E. Evaluation shows that LST estimated by the TSDC method agrees well with the MODIS LST. Validation is based on in-situ LSTs measured at the Gobabeb site in western Namibia. The results demonstrate the high accuracy of the TSDC method: it yields a root-mean squared error (RMSE) of 2 K and ignorable systematic error over barren land. In the second study, the method consists of two core processes: (1) estimation of MW LST from MW brightness temperature and (2) three-time-scale decomposition of LST. The method is applied to two MW sensors (i.e. AMSR-E and AMSR2) and MODIS in northeast China and its surrounding area, with dominating land covers of forest and cropland. By comparing against the in-situ LST and surface air temperature, we find the merged LST has similar accuracy to the MODIS LST in version 6 and good image quality.

Remote sensing of aerosols over land surfaces from POLDER-ADEOS-1 polarized measurements

NASA Astrophysics Data System (ADS)

Deuzé, J. L.; BréOn, F. M.; Devaux, C.; Goloub, P.; Herman, M.; Lafrance, B.; Maignan, F.; Marchand, A.; Nadal, F.; Perry, G.; Tanré, D.

2001-03-01

The polarization measurements achieved by the POLDER instrument on ADEOS-1 are used for the remote sensing of aerosols over land surfaces. The key advantage of using polarized observations is their ability to systematically correct for the ground contribution, whereas the classical approach using natural light fails. The estimation of land surface polarizing properties from POLDER has been examined in a previous paper. Here we consider how the optical thickness δ0 and Ångstrom exponent α of aerosols are derived from the polarized light backscattered by the particles. The inversion scheme is detailed, and illustrative results are presented. Maps of the retrieved optical thickness allow for detection of large aerosol features, and in the case of small aerosols, the δ0 and α retrievals are consistent with correlative ground-based measurements. However, because polarized light stems mainly from small particles, the results are biased for aerosol distributions containing coarser modes of particles. To overcome this limitation, an aerosol index defined as the product AI = δ0α is proposed. Theoretical analysis and comparison with ground-based measurements suggest that AI is approximately the same when using δ0, and α is related to the entire aerosol size distribution or derived from the polarized light originating from the small polarizing particles alone. This invariance is specially assessed by testing the continuity of AI across coastlines, given the unbiased properties of aerosol retrieval over ocean. Although reducing the information concerning the aerosols, this single parameter allows a link between the POLDER aerosol surveys over land and ocean. POLDER aerosol index global maps enable the monitoring of major aerosol sources over continental areas.

Impact of vegetation dynamics on hydrological processes in a semi-arid basin by using a land surface-hydrology coupled model

NASA Astrophysics Data System (ADS)

Jiao, Yang; Lei, Huimin; Yang, Dawen; Huang, Maoyi; Liu, Dengfeng; Yuan, Xing

2017-08-01

Land surface models (LSMs) are widely used to understand the interactions between hydrological processes and vegetation dynamics, which is important for the attribution and prediction of regional hydrological variations. However, most LSMs have large uncertainties in their representations of eco-hydrological processes due to deficiencies in hydrological parameterizations. In this study, the Community Land Model version 4 (CLM4) LSM was modified with an advanced runoff generation and flow routing scheme, resulting in a new land surface-hydrology coupled model, CLM-GBHM. Both models were implemented in the Wudinghe River Basin (WRB), which is a semi-arid basin located in the middle reaches of the Yellow River, China. Compared with CLM, CLM-GBHM increased the Nash Sutcliffe efficiency for daily river discharge simulation (1965-1969) from -0.03 to 0.23 and reduced the relative bias in water table depth simulations (2010-2012) from 32.4% to 13.4%. The CLM-GBHM simulations with static, remotely sensed and model-predicted vegetation conditions showed that the vegetation in the WRB began to recover in the 2000s due to the Grain for Green Program but had not reached the same level of vegetation cover as regions in natural eco-hydrological equilibrium. Compared with a simulation using remotely sensed vegetation cover, the simulation with a dynamic vegetation model that considers only climate-induced change showed a 10.3% increase in evapotranspiration, a 47.8% decrease in runoff, and a 62.7% and 71.3% deceleration in changing trend of the outlet river discharge before and after the year 2000, respectively. This result suggests that both natural and anthropogenic factors should be incorporated in dynamic vegetation models to better simulate the eco-hydrological cycle.

Thermal signatures of urban land cover types: High-resolution thermal infrared remote sensing of urban heat island in Huntsville, AL

NASA Technical Reports Server (NTRS)

Lo, Chor Pang

1996-01-01

The main objective of this research is to apply airborne high-resolution thermal infrared imagery for urban heat island studies, using Huntsville, AL, a medium-sized American city, as the study area. The occurrence of urban heat islands represents human-induced urban/rural contrast, which is caused by deforestation and the replacement of the land surface by non-evaporating and non-porous materials such as asphalt and concrete. The result is reduced evapotranspiration and more rapid runoff of rain water. The urban landscape forms a canopy acting as a transitional zone between the atmosphere and the land surface. The composition and structure of this canopy have a significant impact on the thermal behavior of the urban environment. Research on the trends of surface temperature at rapidly growing urban sites in the United States during the last 30 to 50 years suggests that significant urban heat island effects have caused the temperatures at these sites to rise by 1 to 2 C. Urban heat islands have caused changes in urban precipitation and temperature that are at least similar to, if not greater than, those predicted to develop over the next 100 years by global change models. Satellite remote sensing, particularly NOAA AVHRR thermal data, has been used in the study of urban heat islands. Because of the low spatial resolution (1.1 km at nadir) of the AVHRR data, these studies can only examine and map the phenomenon at the macro-level. The present research provides the rare opportunity to utilize 5-meter thermal infrared data acquired from an airplane to characterize more accurately the thermal responses of different land cover types in the urban landscape as input to urban heat island studies.

Impact of vegetation dynamics on hydrological processes in a semi-arid basin by using a land surface-hydrology coupled model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiao, Yang; Lei, Huimin; Yang, Dawen

Land surface models (LSMs) are widely used to understand the interactions between hydrological processes and vegetation dynamics, which is important for the attribution and prediction of regional hydrological variations. However, most LSMs have large uncertainties in their representations of ecohydrological processes due to deficiencies in hydrological parameterizations. In this study, the Community Land Model version 4 (CLM4) LSM was modified with an advanced runoff generation and flow routing scheme, resulting in a new land surface-hydrology coupled model, CLM-GBHM. Both models were implemented in the Wudinghe River Basin (WRB), which is a semi-arid basin located in the middle reaches of themore » Yellow River, China. Compared with CLM, CLM-GBHM increased the Nash Sutcliffe efficiency for daily river discharge simulation (1965–1969) from 0.03 to 0.23 and reduced the relative bias in water table depth simulations (2010–2012) from 32.4% to 13.4%. The CLM-GBHM simulations with static, remotely sensed and model-predicted vegetation conditions showed that the vegetation in the WRB began to recover in the 2000s due to the Grain for Green Program but had not reached the same level of vegetation cover as regions in natural eco-hydrological equilibrium. Compared with a simulation using remotely sensed vegetation cover, the simulation with a dynamic vegetation model that considers only climate-induced change showed a 10.3% increase in evapotranspiration, a 47.8% decrease in runoff, and a 62.7% and 71.3% deceleration in changing trend of the outlet river discharge before and after the year 2000, respectively. This result suggests that both natural and anthropogenic factors should be incorporated in dynamic vegetation models to better simulate the eco-hydrological cycle.« less

The Synergistic Use of NASA's A-Train Observations to Characterize the Planetary Boundary Layer and Enable Improved Understanding and Prediction of Land-Atmosphere Interactions

NASA Astrophysics Data System (ADS)

Zavodsky, B.; Santanello, J. A.; Friedl, M. A.; Susskind, J.; Palm, S. P.

2010-12-01

The planetary boundary layer (PBL) serves as a short-term memory of land-atmosphere (L-A) interactions through the diurnal integration of surface fluxes and subsequent evolution of PBL fluxes and states. Recent advances in satellite remote sensing offer the ability to monitor PBL and land surface properties at increasingly high spatial and temporal resolutions and, consequently, have the potential to provide valuable information on the terrestrial energy and water cycle across a range of scales. In this study, we evaluate the retrieval of PBL structure and temperature and moisture properties from measurements made by NASA's Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), Moderate Resolution Imaging Spectroradiometer (MODIS) , and Atmospheric Infrared Sounder (AIRS) instruments aboard the 'A-Train' constellation. The global coverage of these sensors greatly improves upon the coarse network of synoptic radiosonde and intermittent satellite and ground remote sensing currently available, and combining the high vertical and spectral resolution of these sensors allows for PBL retrievals to be evaluated in the context of their relationship with the land surface. Results include an evaluation of CALIPSO, MODIS, and AIRS temperature and humidity retrievals using radiosonde data, focusing on how well PBL properties (e.g. PBL height, temperature, humidity, and stability) can be discerned from each sensor under a range of conditions. Overall, this research is timely in assessing the potential for merging complimentary information from independent sensors, and provides a unique opportunity to evaluate and apply NASA data to answer fundamental questions regarding observation, understanding, and prediction of L-A interactions and coupling.

Linking land use changes to surface water quality variability in Lake Victoria: some insights from remote sensing

NASA Astrophysics Data System (ADS)

Mugo, R. M.; Limaye, A. S.; Nyaga, J. W.; Farah, H.; Wahome, A.; Flores, A.

2016-12-01

The water quality of inland lakes is largely influenced by land use and land cover changes within the lake's catchment. In Africa, some of the major land use changes are driven by a number of factors, which include urbanization, intensification of agricultural practices, unsustainable farm management practices, deforestation, land fragmentation and degradation. Often, the impacts of these factors are observable on changes in the land cover, and eventually in the hydrological systems. When the natural vegetation cover is reduced or changed, the surface water flow patterns, water and nutrient retention capacities are also changed. This can lead to high nutrient inputs into lakes, leading to eutrophication, siltation and infestation of floating aquatic vegetation. To assess the relationship between land use and land cover changes in part of the Lake Victoria Basin, a series of land cover maps were derived from Landsat imagery. Changes in land cover were identified through change maps and statistics. Further, the surface water chlorophyll-a concentration and turbidity were derived from MODIS-Aqua data for Lake Victoria. Chlrophyll-a and turbidity are good proxy indicators of nutrient inputs and siltation respectively. The trends in chlorophyll-a and turbidity concentrations were analyzed and compared to the land cover changes over time. Certain land cover changes related to agriculture and urban development were clearly identifiable. While these changes might not be solely responsible for variability in chlrophyll-a and turbidity concentrations in the lake, they are potentially contributing factors to this problem. This work illustrates the importance of addressing watershed degradation while seeking to solve water quality related problems.

Development of a generalized multi-pixel and multi-parameter satellite remote sensing algorithm for aerosol properties

NASA Astrophysics Data System (ADS)

Hashimoto, M.; Nakajima, T.; Takenaka, H.; Higurashi, A.

2013-12-01

We develop a new satellite remote sensing algorithm to retrieve the properties of aerosol particles in the atmosphere. In late years, high resolution and multi-wavelength, and multiple-angle observation data have been obtained by grand-based spectral radiometers and imaging sensors on board the satellite. With this development, optimized multi-parameter remote sensing methods based on the Bayesian theory have become popularly used (Turchin and Nozik, 1969; Rodgers, 2000; Dubovik et al., 2000). Additionally, a direct use of radiation transfer calculation has been employed for non-linear remote sensing problems taking place of look up table methods supported by the progress of computing technology (Dubovik et al., 2011; Yoshida et al., 2011). We are developing a flexible multi-pixel and multi-parameter remote sensing algorithm for aerosol optical properties. In this algorithm, the inversion method is a combination of the MAP method (Maximum a posteriori method, Rodgers, 2000) and the Phillips-Twomey method (Phillips, 1962; Twomey, 1963) as a smoothing constraint for the state vector. Furthermore, we include a radiation transfer calculation code, Rstar (Nakajima and Tanaka, 1986, 1988), numerically solved each time in iteration for solution search. The Rstar-code has been directly used in the AERONET operational processing system (Dubovik and King, 2000). Retrieved parameters in our algorithm are aerosol optical properties, such as aerosol optical thickness (AOT) of fine mode, sea salt, and dust particles, a volume soot fraction in fine mode particles, and ground surface albedo of each observed wavelength. We simultaneously retrieve all the parameters that characterize pixels in each of horizontal sub-domains consisting the target area. Then we successively apply the retrieval method to all the sub-domains in the target area. We conducted numerical tests for the retrieval of aerosol properties and ground surface albedo for GOSAT/CAI imager data to test the algorithm for the land area. In this test, we simulated satellite-observed radiances for a sub-domain consisting of 5 by 5 pixels by the Rstar code assuming wavelengths of 380, 674, 870 and 1600 [nm], atmospheric condition of the US standard atmosphere, and the several aerosol and ground surface conditions. The result of the experiment showed that AOTs of fine mode and dust particles, soot fraction and ground surface albedo at the wavelength of 674 [nm] are retrieved within absolute value differences of 0.04, 0.01, 0.06 and 0.006 from the true value, respectively, for the case of dark surface, and also, for the case of blight surface, 0.06, 0.03, 0.04 and 0.10 from the true value, respectively. We will conduct more tests to study the information contents of parameters needed for aerosol and land surface remote sensing with different boundary conditions among sub-domains.

Geology of Lunar Landing Sites and Origin of Basin Ejecta from a Clementine Perspective

NASA Technical Reports Server (NTRS)

Jolliff, Bradley L.; Haskin, Larry A.

1998-01-01

The goals of this research were to examine Clementine multispectral data covering the Apollo landing sites in order to: (1) provide ground truth for the remotely sensed observations, (2) extend our understanding of the Apollo landing sites to the surrounding regions using the empirically calibrated Clementine data, and (3) investigate the composition and distribution of impact-basin ejecta using constraints based upon the remotely sensed data and the Apollo samples. Our initial efforts (in collaboration with P. Lucey and coworkers) to use the Apollo soil compositions to "calibrate" information derived from the remotely sensed data resulted in two extremely useful algorithms for computing estimates of the concentrations of FeO and TiO2 from the UV-VIS 5-band data. In this effort, we used the average surface soil compositions from 37 individual Apollo and 3 Luna sample stations that could be resolved using the Clementine data. We followed this work with a detailed investigation of the Apollo 17 landing site, where the sampling traverses were extensive and the spectral and compositional contrast between different soils covers a wide range. We have begun to investigate the nature and composition of basin ejecta by comparing the thick deposits on the rim of Imbrium in the vicinity of the Apollo 15 site and those occurring southeast of the Serenitatis basin, in the Apollo 17 region. We continue this work under NAG5-6784, "Composition, Lithology, and Heterogeneity of the lunar crust using remote sensing of impact-basin uplift structures and ejecta as probes. The main results of our work are given in the following brief summaries of major tasks. Detailed accounts of these results are given in the attached papers, manuscripts, and extended abstracts.

Using remotely-sensed multispectral imagery to build age models for alluvial fan surfaces

NASA Astrophysics Data System (ADS)

D'Arcy, Mitch; Mason, Philippa J.; Roda Boluda, Duna C.; Whittaker, Alexander C.; Lewis, James

2016-04-01

Accurate exposure age models are essential for much geomorphological field research, and generally depend on laboratory analyses such as radiocarbon, cosmogenic nuclide, or luminescence techniques. These approaches continue to revolutionise geomorphology, however they cannot be deployed remotely or in situ in the field. Therefore other methods are still needed for producing preliminary age models, performing relative dating of surfaces, or selecting sampling sites for the laboratory analyses above. With the widespread availability of detailed multispectral imagery, a promising approach is to use remotely-sensed data to discriminate surfaces with different ages. Here, we use new Landsat 8 Operational Land Imager (OLI) multispectral imagery to characterise the reflectance of 35 alluvial fan surfaces in the semi-arid Owens Valley, California. Alluvial fans are useful landforms to date, as they are widely used to study the effects of tectonics, climate and sediment transport processes on source-to-sink sedimentation. Our target fan surfaces have all been mapped in detail in the field, and have well-constrained exposure ages ranging from modern to ~ 125 ka measured using a high density of 10Be cosmogenic nuclide samples. Despite all having similar granitic compositions, the spectral properties of these surfaces vary systematically with their exposure ages. Older surfaces demonstrate a predictable shift in reflectance across the visible and short-wave infrared spectrum. Simple calculations, such as the brightness ratios of different wavelengths, generate sensitive power law relationships with exposure age that depend on post-depositional alteration processes affecting these surfaces. We investigate what these processes might be in this dryland location, and evaluate the potential for using remotely-sensed multispectral imagery for developing surface age models. The ability to remotely sense relative exposure ages has useful implications for preliminary mapping, selecting sampling sites for laboratory-based exposure age techniques, and correlating existing age constraints to un-sampled surfaces.

Combining observations in the reflective solar and thermal domains for improved carbon and energy flux estimation

USDA-ARS?s Scientific Manuscript database

This study investigates the utility of integrating remotely sensed estimates of leaf chlorophyll (Cab) into a therma-based Two-Source Energy Balance (TSEB) model that estimates land-surface CO2 and energy fluxes using an analytical, light-use-efficiency (LUE) based model of canopy resistance. The LU...

A flexible spatiotemporal method for fusing satellite images with different resolutions

Treesearch

Xiaolin Zhu; Eileen H. Helmer; Feng Gao; Desheng Liu; Jin Chen; Michael A. Lefsky

2016-01-01

Studies of land surface dynamics in heterogeneous landscapes often require remote sensing datawith high acquisition frequency and high spatial resolution. However, no single sensor meets this requirement. This study presents a new spatiotemporal data fusion method, the Flexible Spatiotemporal DAta Fusion (FSDAF) method, to generate synthesized frequent high spatial...

Utilization of point soil moisture measurements for field scale soil moisture averages and variances in agricultural landscapes

USDA-ARS?s Scientific Manuscript database

Soil moisture is a key variable in understanding the hydrologic processes and energy fluxes at the land surface. In spite of new technologies for in-situ soil moisture measurements and increased availability of remotely sensed soil moisture data, scaling issues between soil moisture observations and...

A review of the applications of ASCAT soil moisture products

USDA-ARS?s Scientific Manuscript database

Remote sensing of soil moisture has reached a level of good maturity and accuracy for which the retrieved products are ready to use in real-world applications. Due to the importance of soil moisture in the partitioning of the water and energy fluxes between the land surface and the atmosphere, a wid...

SEBAL-A: A remote sensing ET algorithm that accounts for advection with limited data. Part II: Test for transferability

USDA-ARS?s Scientific Manuscript database

Because the Surface Energy Balance Algorithm for Land (SEBAL) tends to underestimate ET under conditions of advection, the model was modified by incorporating an advection component as part of the energy usable for crop evapotranspiration (ET). The modification involved the estimation of advected en...

The Goddard Snow Radiance Assimilation Project: An Integrated Snow Radiance and Snow Physics Modeling Framework for Snow/cold Land Surface Modeling

NASA Technical Reports Server (NTRS)

Kim, E.; Tedesco, M.; Reichle, R.; Choudhury, B.; Peters-Lidard C.; Foster, J.; Hall, D.; Riggs, G.

2006-01-01

Microwave-based retrievals of snow parameters from satellite observations have a long heritage and have so far been generated primarily by regression-based empirical "inversion" methods based on snapshots in time. Direct assimilation of microwave radiance into physical land surface models can be used to avoid errors associated with such retrieval/inversion methods, instead utilizing more straightforward forward models and temporal information. This approach has been used for years for atmospheric parameters by the operational weather forecasting community with great success. Recent developments in forward radiative transfer modeling, physical land surface modeling, and land data assimilation are converging to allow the assembly of an integrated framework for snow/cold lands modeling and radiance assimilation. The objective of the Goddard snow radiance assimilation project is to develop such a framework and explore its capabilities. The key elements of this framework include: a forward radiative transfer model (FRTM) for snow, a snowpack physical model, a land surface water/energy cycle model, and a data assimilation scheme. In fact, multiple models are available for each element enabling optimization to match the needs of a particular study. Together these form a modular and flexible framework for self-consistent, physically-based remote sensing and water/energy cycle studies. In this paper we will describe the elements and the integration plan. All modules will operate within the framework of the Land Information System (LIS), a land surface modeling framework with data assimilation capabilities running on a parallel-node computing cluster. Capabilities for assimilation of snow retrieval products are already under development for LIS. We will describe plans to add radiance-based assimilation capabilities. Plans for validation activities using field measurements will also be discussed.

[Evaluation of eco-environmental quality based on artificial neural network and remote sensing techniques].

PubMed

Li, Hongyi; Shi, Zhou; Sha, Jinming; Cheng, Jieliang

2006-08-01

In the present study, vegetation, soil brightness, and moisture indices were extracted from Landsat ETM remote sensing image, heat indices were extracted from MODIS land surface temperature product, and climate index and other auxiliary geographical information were selected as the input of neural network. The remote sensing eco-environmental background value of standard interest region evaluated in situ was selected as the output of neural network, and the back propagation (BP) neural network prediction model containing three layers was designed. The network was trained, and the remote sensing eco-environmental background value of Fuzhou in China was predicted by using software MATLAB. The class mapping of remote sensing eco-environmental background values based on evaluation standard showed that the total classification accuracy was 87. 8%. The method with a scheme of prediction first and classification then could provide acceptable results in accord with the regional eco-environment types.

Developing a Dynamic SPARROW Water Quality Decision Support System Using NASA Remotely-Sensed Products

NASA Astrophysics Data System (ADS)

Al-Hamdan, M. Z.; Smith, R. A.; Hoos, A.; Schwarz, G. E.; Alexander, R. B.; Crosson, W. L.; Srikishen, J.; Estes, M., Jr.; Cruise, J.; Al-Hamdan, A.; Ellenburg, W. L., II; Flores, A.; Sanford, W. E.; Zell, W.; Reitz, M.; Miller, M. P.; Journey, C. A.; Befus, K. M.; Swann, R.; Herder, T.; Sherwood, E.; Leverone, J.; Shelton, M.; Smith, E. T.; Anastasiou, C. J.; Seachrist, J.; Hughes, A.; Graves, D.

2017-12-01

The USGS Spatially Referenced Regression on Watershed Attributes (SPARROW) surface water quality modeling system has been widely used for long term, steady state water quality analysis. However, users have increasingly requested a dynamic version of SPARROW that can provide seasonal estimates of nutrients and suspended sediment to receiving waters. The goal of this NASA-funded project is to develop a dynamic decision support system to enhance the southeast SPARROW water quality model and finer-scale dynamic models for selected coastal watersheds through the use of remotely-sensed data and other NASA Land Information System (LIS) products. The spatial and temporal scale of satellite remote sensing products and LIS modeling data make these sources ideal for the purposes of development and operation of the dynamic SPARROW model. Remote sensing products including MODIS vegetation indices, SMAP surface soil moisture, and OMI atmospheric chemistry along with LIS-derived evapotranspiration (ET) and soil temperature and moisture products will be included in model development and operation. MODIS data will also be used to map annual land cover/land use in the study areas and in conjunction with Landsat and Sentinel to identify disturbed areas that might be sources of sediment and increased phosphorus loading through exposure of the bare soil. These data and others constitute the independent variables in a regression analysis whose dependent variables are the water quality constituents total nitrogen, total phosphorus, and suspended sediment. Remotely-sensed variables such as vegetation indices and ET can be proxies for nutrient uptake by vegetation; MODIS Leaf Area Index can indicate sources of phosphorus from vegetation; soil moisture and temperature are known to control rates of denitrification; and bare soil areas serve as sources of enhanced nutrient and sediment production. The enhanced SPARROW dynamic models will provide improved tools for end users to manage water quality in near real time and for the formulation of future scenarios to inform strategic planning. Time-varying SPARROW outputs will aid water managers in decision making regarding allocation of resources in protecting aquatic habitats, planning for harmful algal blooms, and restoration of degraded habitats, stream segments, or lakes.

Spatially Distributed Assimilation of Remotely Sensed Leaf Area Index and Potential Evapotranspiration for Hydrologic Modeling in Wetland Landscapes

NASA Astrophysics Data System (ADS)

Rajib, A.; Evenson, G. R.; Golden, H. E.; Lane, C.

2017-12-01

Evapotranspiration (ET), a highly dynamic flux in wetland landscapes, regulates the accuracy of surface/sub-surface runoff simulation in a hydrologic model. Accordingly, considerable uncertainty in simulating ET-related processes remains, including our limited ability to incorporate realistic ground conditions, particularly those involved with complex land-atmosphere feedbacks, vegetation growth, and energy balances. Uncertainty persists despite using high resolution topography and/or detailed land use data. Thus, a good hydrologic model can produce right answers for wrong reasons. In this study, we develop an efficient approach for multi-variable assimilation of remotely sensed earth observations (EOs) into a hydrologic model and apply it in the 1700 km2 Pipestem Creek watershed in the Prairie Pothole Region of North Dakota, USA. Our goal is to employ EOs, specifically Leaf Area Index (LAI) and Potential Evapotranspiration (PET), as surrogates for the aforementioned processes without overruling the model's built-in physical/semi-empirical process conceptualizations. To do this, we modified the source code of an already-improved version of the Soil and Water Assessment Tool (SWAT) for wetland hydrology (Evenson et al. 2016 HP 30(22):4168) to directly assimilate remotely-sensed LAI and PET (obtained from the 500 m and 1 km Moderate Resolution Imaging Spectroradiometer (MODIS) gridded products, respectively) into each model Hydrologic Response Unit (HRU). Two configurations of the model, one with and one without EO assimilation, are calibrated against streamflow observations at the watershed outlet. Spatio-temporal changes in the HRU-level water balance, based on calibrated outputs, are evaluated using MODIS Actual Evapotranspiration (AET) as a reference. It is expected that the model configuration having remotely sensed LAI and PET, will simulate more realistic land-atmosphere feedbacks, vegetation growth and energy balance. As a result, this will decrease simulated water balance uncertainties compared to the default model configuration.

(abstract) Two-Dimensional Surface Current in the Coastal Environment Using Vector Along-Track Interferometry

NASA Technical Reports Server (NTRS)

Rodriguez, Ernesto; Imel, David; Houshmand, Bijan; Carande, Richard

1994-01-01

The structure of surface currents in the coastal environment can be very complex as it is governed by a multitude of factors such as local bathymetry, sea state, etc. Knowledge of the structure of coastal currents is a key requirement in the ability to carry out safe maneuvers and landings in an unknown coastal situation. Furthermore, it is desirable to have the ability to obtain such information by remote sensing and in a timely manner. We present a remote sensing technique which has the potential to meet certain specific requisites. We will present a theoretical discussion of the measurement technique, then will demonstrate the technique using data previously acquired and compare the results against conventional along-track interferometric measurements.

Impacts of Irrigation on Daily Extremes in the Coupled Climate System

NASA Technical Reports Server (NTRS)

Puma, Michael J.; Cook, Benjamin I.; Krakauer, Nir; Gentine, Pierre; Nazarenka, Larissa; Kelly, Maxwell; Wada, Yoshihide

2014-01-01

Widespread irrigation alters regional climate through changes to the energy and water budgets of the land surface. Within general circulation models, simulation studies have revealed significant changes in temperature, precipitation, and other climate variables. Here we investigate the feedbacks of irrigation with a focus on daily extremes at the global scale. We simulate global climate for the year 2000 with and without irrigation to understand irrigation-induced changes. Our simulations reveal shifts in key climate-extreme metrics. These findings indicate that land cover and land use change may be an important contributor to climate extremes both locally and in remote regions including the low-latitudes.

Landsat: A Global Land-Imaging Project

USGS Publications Warehouse

Headley, Rachel

2010-01-01

Across nearly four decades since 1972, Landsat satellites continuously have acquired space-based images of the Earth's land surface, coastal shallows, and coral reefs. The Landsat Program, a joint effort of the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA), was established to routinely gather land imagery from space; consequently, NASA develops remote-sensing instruments and spacecraft, then launches and validates the satellites. The USGS then assumes ownership and operation of the satellites, in addition to managing all ground-data reception, archiving, product generation, and distribution. The result of this program is a visible, long-term record of natural and human-induced changes on the global landscape.

Landsat: a global land imaging program

USGS Publications Warehouse

Byrnes, Raymond A.

2012-01-01

Landsat satellites have continuously acquired space-based images of the Earth's land surface, coastal shallows, and coral reefs across four decades. The Landsat Program, a joint effort of the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA), was established to routinely gather land imagery from space. In practice, NASA develops remote-sensing instruments and spacecraft, launches satellites, and validates their performance. The USGS then assumes ownership and operation of the satellites, in addition to managing all ground-data reception, archiving, product generation, and distribution. The result of this program is a visible, long-term record of natural and human-induced changes on the global landscape.

Earth Observations from the International Space Station: Benefits for Humanity

NASA Technical Reports Server (NTRS)

Stefanov, William L.

2015-01-01

The International Space Station (ISS) is a unique terrestrial remote sensing platform for observation of the Earth's land surface, oceans, and atmosphere. Unlike automated remote-sensing platforms it has a human crew; is equipped with both internal and externally-mounted active and passive remote sensing instruments; and has an inclined, low-Earth orbit that provides variable views and lighting (day and night) over 95 percent of the inhabited surface of the Earth. As such, it provides a useful complement to autonomous, sun-synchronous sensor systems in higher altitude polar orbits. Beginning in May 2012, NASA ISS sensor systems have been available to respond to requests for data through the International Charter, Space and Major Disasters, also known as the "International Disaster Charter" or IDC. Data from digital handheld cameras, multispectral, and hyperspectral imaging systems has been acquired in response to IDC activations and delivered to requesting agencies through the United States Geological Survey. The characteristics of the ISS for Earth observation will be presented, including past, current, and planned NASA, International Partner, and commercial remote sensing systems. The role and capabilities of the ISS for humanitarian benefit, specifically collection of remotely sensed disaster response data, will be discussed.

Land-atmosphere coupling manifested in warm-season observations on the U.S. southern great plains

DOE PAGES

Phillips, Thomas J.; Klein, Stephen A.

2014-01-28

This study examines several observational aspects of land-atmosphere coupling on daily average time scales during warm seasons of the years 1997 to 2008 at the Department of Energy Atmospheric Radiation Measurement Program’s Southern Great Plains (SGP) Central Facility site near Lamont, Oklahoma. Characteristics of the local land-atmosphere coupling are inferred by analyzing the covariability of selected land and atmospheric variables that include precipitation and soil moisture, surface air temperature, relative humidity, radiant and turbulent fluxes, as well as low-level cloud base height and fractional coverage. For both the energetic and hydrological aspects of this coupling, it is found that large-scalemore » atmospheric forcings predominate, with local feedbacks of the land on the atmosphere being comparatively small much of the time. The weak land feedbacks are manifested by 1) the inability of soil moisture to comprehensively impact the coupled land-atmosphere energetics, and 2) the limited recycling of local surface moisture under conditions where most of the rainfall derives from convective cells that originate at remote locations. There is some evidence, nevertheless, of the local land feedback becoming stronger as the soil dries out in the aftermath of precipitation events, or on days when the local boundary-layer clouds are influenced by thermal updrafts known to be associated with convection originating at the surface. Finally, we also discuss potential implications of these results for climate-model representation of regional land-atmosphere coupling.« less

The U.S. Geological Survey Land Remote Sensing Program

USGS Publications Warehouse

,

2003-01-01

In 2002, the U. S. Geological Survey (USGS) launched a program to enhance the acquisition, preservation, and use of remotely sensed data for USGS science programs, as well as for those of cooperators and customers. Remotely sensed data are fundamental tools for studying the Earth's land surface, including coastal and near-shore environments. For many decades, the USGS has been a leader in providing remotely sensed data to the national and international communities. Acting on its historical topographic mapping mission, the USGS has archived and distributed aerial photographs of the United States for more than half a century. Since 1972, the USGS has acquired, processed, archived, and distributed Landsat and other satellite and airborne remotely sensed data products to users worldwide. Today, the USGS operates and manages the Landsats 5 and 7 missions and cooperates with the National Aeronautics and Space Administration (NASA) to define and implement future satellite missions that will continue and expand the collection of moderate-resolution remotely sensed data. In addition to being a provider of remotely sensed data, the USGS is a user of these data and related remote sensing technology. These data are used in natural resource evaluations for energy and minerals, coastal environmental surveys, assessments of natural hazards (earthquakes, volcanoes, and landslides), biological surveys and investigations, water resources status and trends analyses and studies, and geographic and cartographic applications, such as wildfire detection and tracking and as a source of information for The National Map. The program furthers these distinct but related roles by leading the USGS activities in providing remotely sensed data while advancing applications of such data for USGS programs and a wider user community.

Spatial Surface PM2.5 Concentration Estimates for Wildfire Smoke Plumes in the Western U.S. Using Satellite Retrievals and Data Assimilation Techniques

NASA Astrophysics Data System (ADS)

Loria Salazar, S. M.; Holmes, H.

2015-12-01

Health effects studies of aerosol pollution have been extended spatially using data assimilation techniques that combine surface PM2.5 concentrations and Aerosol Optical Depth (AOD) from satellite retrievals. While most of these models were developed for the dark-vegetated eastern U.S. they are being used in the semi-arid western U.S. to remotely sense atmospheric aerosol concentrations. These models are helpful to understand the spatial variability of surface PM2.5concentrations in the western U.S. because of the sparse network of surface monitoring stations. However, the models developed for the eastern U.S. are not robust in the western U.S. due to different aerosol formation mechanisms, transport phenomena, and optical properties. This region is a challenge because of complex terrain, anthropogenic and biogenic emissions, secondary organic aerosol formation, smoke from wildfires, and low background aerosol concentrations. This research concentrates on the use and evaluation of satellite remote sensing to estimate surface PM2.5 concentrations from AOD satellite retrievals over California and Nevada during the summer months of 2012 and 2013. The aim of this investigation is to incorporate a spatial statistical model that uses AOD from AERONET as well as MODIS, surface PM2.5 concentrations, and land-use regression to characterize spatial surface PM2.5 concentrations. The land use regression model uses traditional inputs (e.g. meteorology, population density, terrain) and non-traditional variables (e.g. FIre Inventory from NCAR (FINN) emissions and MODIS albedo product) to account for variability related to smoke plume trajectories and land use. The results will be used in a spatially resolved health study to determine the association between wildfire smoke exposure and cardiorespiratory health endpoints. This relationship can be used with future projections of wildfire emissions related to climate change and droughts to quantify the expected health impact.

Geometric-Optical Modeling of Directional Thermal Radiance for Improvement of Land Surface Temperature Retrievals from MODIS, ASTER, and Landsat-7 Instruments

NASA Technical Reports Server (NTRS)

Li, Xiaowen; Friedl, Mark; Strahler, Alan

2002-01-01

The general objectives of this project were to improve understanding of the directional emittance properties of land surfaces in the thermal infrared (TIR) region of the electro-magnetic spectrum. To accomplish these objectives our research emphasized a combination of theoretical model development and empirical studies designed to improve land surface temperature (LST) retrievals from space-borne remote sensing instruments. Following the proposal, the main tasks for this project were to: (1) Participate in field campaigns; (2) Acquire and process field, aircraft, and ancillary data; (3) Develop and refine models of LST emission; (4) Develop algorithms for LST retrieval; and (5) Explore LST retrieval methods for use in energy balance models. In general all of these objectives were addressed, and for the most part achieved. The main results from this project are described in the publications arising from this effort. We summarize our efforts related to each of the objectives.

The characteristics and interpretability of land surface change and implications for project design

USGS Publications Warehouse

Sohl, Terry L.; Gallant, Alisa L.; Loveland, Thomas R.

2004-01-01

The need for comprehensive, accurate information on land-cover change has never been greater. While remotely sensed imagery affords the opportunity to provide information on land-cover change over large geographic expanses at a relatively low cost, the characteristics of land-surface change bring into question the suitability of many commonly used methodologies. Algorithm-based methodologies to detect change generally cannot provide the same level of accuracy as the analyses done by human interpreters. Results from the Land Cover Trends project, a cooperative venture that includes the U.S. Geological Survey, Environmental Protection Agency, and National Aeronautics and Space Administration, have shown that land-cover conversion is a relatively rare event, occurs locally in small patches, varies geographically and temporally, and is spectrally ambiguous. Based on these characteristics of change and the type of information required, manual interpretation was selected as the primary means of detecting change in the Land Cover Trends project. Mixtures of algorithm-based detection and manual interpretation may often prove to be the most feasible and appropriate design for change-detection applications. Serious examination of the expected characteristics and measurability of change must be considered during the design and implementation phase of any change analysis project.

Viking landing sites, remote-sensing observations, and physical properties of Martian surface materials

USGS Publications Warehouse

Moore, H.J.; Jakosky, B.M.

1989-01-01

Important problems that confront future scientific exploration of Mars include the physical properties of Martian surface materials and the geologic processes that formed the materials. The design of landing spacecraft, roving vehicles, and sampling devices and the selection of landing sites, vehicle traverses, and sample sites will be, in part, guided by the physical properties of the materials. Four materials occur in the sample fields of the Viking landers: (1) drift, (2) crusty to cloddy, (3) blocky, and (4) rock. The first three are soillike. Drift materials is weak, loose, and porous. We estimate that it has a dielectric constant near 2.4 and a thermal inertia near 1 ?? 10-3 to 3 ?? 10-3 (cal cm-2 sec 1 2 K-1) because of its low bulk density, fine grain size, and small cohesion. Crusty to cloddy material is expected to have a dielectric constant near 2.8 and a thermal inertia near 4 ?? 10-3 to 7 ?? 10-3 because of its moderate bulk density and cementation of grains. Blocky material should have a dielectric constant near 3.3 and a thermal inertia near 7 ?? 10-3 to 9 ?? 10-3 because of its moderate bulk density and cementation. Common basaltic rocks have dielectric constans near 8 and thermal inertias near 30 ?? 10-3 to 60 ?? 10-3. Comparisons of estimated dielectric constants and thermal inertias of the materials at the landing sites with those obtained remotely by Earth-based radars and Viking Orbiter thermal sensors suggest that the materials at the landing sites are good analogs for materials elsewhere on Mars. Correlation of remotely estimated dielectric constant and thermal inertias indicates two modal values for paired values of dielectric constants and thermal inertias near (A) 2 and 2 ?? 10-3 and (B) 3 and 6 ?? 10-3, respectively. These two modes are comparable to the dielectric constants and thermal inertias for drift and crusty to cloddy material, respectively. Dielectric constants and thermal inertias for blocky material are larger but conistent with values in the northern plains. Our interprertations are compatible with an aeolian origin for drift and similar materials elsewhere on Mars. The postulate that moderate dielectric constants and thermal inertias larger than 3 or 4 ?? 10-3 are produced by cementation of soillike materials is partly consistent with the data. The average dielectric constant and thermal inertia and their correlation with one another suggest that most of the surface of Mars should present few difficulties to future surface exploration, but some surfaces may present difficulties for spacecraft that are not suitably designed. ?? 1989.

The U.S. Geological Survey Land Remote Sensing Program

USGS Publications Warehouse

,

2007-01-01

The fundamental goals of the U.S. Geological Survey's Land Remote Sens-ing (LRS) Program are to provide the Federal Government and the public with a primary source of remotely sensed data and applications and to be a leader in defining the future of land remote sensing, nationally and internationally. Remotely sensed data provide information that enhance the understand-ing of ecosystems and the capabilities for predicting ecosystem change. The data promote an understanding of the role of the environment and wildlife in human health issues, the requirements for disaster response, the effects of climate variability, and the availability of energy and mineral resources. Also, as land satellite systems acquire global coverage, the program coordinates a network of international receiving stations and users of the data. It is the responsibility of the program to assure that data from land imaging satellites, airborne photography, radar, and other technologies are available to the national and global science communities.

Spectral Behavior of a Linearized Land-Atmosphere Model: Applications to Hydrometeorology

NASA Astrophysics Data System (ADS)

Gentine, P.; Entekhabi, D.; Polcher, J.

2008-12-01

The present study develops an improved version of the linearized land-atmosphere model first introduced by Lettau (1951). This model is used to investigate the spectral response of land-surface variables to a daily forcing of incoming radiation at the land-surface. An analytical solution of the problem is found in the form of temporal Fourier series and gives the atmospheric boundary-layer and soil profiles of state variables (potential temperature, specific humidity, sensible and latent heat fluxes). Moreover the spectral dependency of surface variables is expressed as function of land-surface parameters (friction velocity, vegetation height, aerodynamic resistance, stomatal conductance). This original approach has several advantages: First, the model only requires little data to work and perform well: only time series of incoming radiation at the land-surface, mean specific humidity and temperature at any given height are required. These inputs being widely available over the globe, the model can easily be run and tested under various conditions. The model will also help analysing the diurnal shape and frequency dependency of surface variables and soil-ABL profiles. In particular, a strong emphasis is being placed on the explanation and prediction of Evaporative Fraction (EF) and Bowen Ratio diurnal shapes. EF is shown to remain a diurnal constant under restricting conditions: fair and dry weather, with strong solar radiation and no clouds. Moreover, the EF pseudo-constancy value is found and given as function of surface parameters, such as aerodynamic resistance and stomatal conductance. Then, application of the model for the conception of remote-sensing tools, according to the temporal resolution of the sensor, will also be discussed. Finally, possible extensions and improvement of the model will be discussed.

Simulation and Analysis of Topographic Effect on Land Surface Albedo over Mountainous Areas

NASA Astrophysics Data System (ADS)

Hao, D.; Wen, J.; Xiao, Q.

2017-12-01

Land surface albedo is one of the significant geophysical variables affecting the Earth's climate and controlling the surface radiation budget. Topography leads to the formation of shadows and the redistribution of incident radiation, which complicates the modeling and estimation of the land surface albedo. Some studies show that neglecting the topography effect may lead to significant bias in estimating the land surface albedo for the sloping terrain. However, for the composite sloping terrain, the topographic effects on the albedo remain unclear. Accurately estimating the sub-topographic effect on the land surface albedo over the composite sloping terrain presents a challenge for remote sensing modeling and applications. In our study, we focus on the development of a simplified estimation method for land surface albedo including black-sky albedo (BSA) and white-sky albedo (WSA) of the composite sloping terrain at a kilometer scale based on the fine scale DEM (30m) and quantitatively investigate and understand the topographic effects on the albedo. The albedo is affected by various factors such as solar zenith angle (SZA), solar azimuth angle (SAA), shadows, terrain occlusion, and slope and aspect distribution of the micro-slopes. When SZA is 30°, the absolute and relative deviations between the BSA of flat terrain and that of rugged terrain reaches 0.12 and 50%, respectively. When the mean slope of the terrain is 30.63° and SZA=30°, the absolute deviation of BSA caused by SAA can reach 0.04. The maximal relative and relative deviation between the WSA of flat terrain and that of rugged terrain reaches 0.08 and 50%. These results demonstrate that the topographic effect has to be taken into account in the albedo estimation.

Changes in Land Surface Water Dynamics since the 1990s and Relation to Population Pressure

NASA Technical Reports Server (NTRS)

Prigent, C.; Papa, F.; Aires, F.; Jimenez, C.; Rossow, W. B.; Matthews, E.

2012-01-01

We developed a remote sensing approach based on multi-satellite observations, which provides an unprecedented estimate of monthly distribution and area of land-surface open water over the whole globe. Results for 1993 to 2007 exhibit a large seasonal and inter-annual variability of the inundation extent with an overall decline in global average maximum inundated area of 6% during the fifteen-year period, primarily in tropical and subtropical South America and South Asia. The largest declines of open water are found where large increases in population have occurred over the last two decades, suggesting a global scale effect of human activities on continental surface freshwater: denser population can impact local hydrology by reducing freshwater extent, by draining marshes and wetlands, and by increasing water withdrawals. Citation: Prigent, C., F. Papa, F. Aires, C. Jimenez, W. B. Rossow, and E. Matthews (2012), Changes in land surface water dynamics since the 1990s and relation to population pressure, in section 4, insisting on the potential applications of the wetland dataset.

Surface reflectance retrieval from satellite and aircraft sensors: Results of sensor and algorithm comparisons during FIFE

DOE Office of Scientific and Technical Information (OSTI.GOV)

Markham, B.L.; Halthore, R.N.; Goetz, S.J.

1992-11-30

This work is part of the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE), an international land-surface-atmosphere experiment aimed at improving the way climate models represent energy, water, heat, and carbon exchanges, and improving the utilization of satellite based remote sensing to monitor such parameters. This paper reports on comparison of measurement systems which were deployed to measure surface reflectance factors, from aircraft or satellites. These instruments look over the general range of 0.4 to 2.5[mu]m. Instruments studied include Landsat 5 thematic mapper (TM), the SPOT 1 high-resolution visible sensor (HRV) 1, the NS001 thematic mapper simulator,more » and the modular multispectral radiometers (MMRs). The study looked at the radiometric consistency of the different instruments, and the adequacy of the atmospheric correction routines applied to data analysis.« less

Monitoring Urbanization-Related Land Cover Change on the U.S. Great Plains and Impacts on Remotely Sensed Vegetation Dynamics

NASA Astrophysics Data System (ADS)

Krehbiel, C. P.; Jackson, T.; Henebry, G. M.

2014-12-01

Earth is currently in an era of rapid urban growth with >50% of global population living in urban areas. Urbanization occurs alongside urban population growth, as cities expand to meet the demands of increasing population. Consequently, there is a need for remote sensing research to detect, monitor, and measure urbanization and its impacts on the biosphere. Here we used MODIS and Landsat data products to (1) detect urbanization-related land cover changes, (2) investigate urbanization-related impacts on land surface phenology (LSP) across rural to urban gradients and (3) explore fractional vegetation and impervious surface area regionally across the US Great Plains and within 14 cities in this region. We used the NLCD Percent Impervious Surface Area (%ISA) and Land Cover Type (LCT) products from 2001, 2006, and 2011 for 30m classification of the peri-urban environment. We investigated the impacts of urbanization-related land cover change on urban LSP at 30m resolution using the NDVI product from Web Enabled Landsat Data (http://weld.cr.usgs.gov) with accumulated growing degree-days calculated from first-order weather stations. We fitted convex quadratic LSP models to a decade (2003-2012) of observations to yield these phenometrics: modeled peak NDVI, time (thermal and calendar) to modeled peak, duration of season (DOS), and model fit. We compared our results to NDVI from MODIS NBAR (500m) and we explored the utility of 4 μm radiance (MODIS band 23) at 1 km resolution to characterize fractional vegetation dynamics in and around urbanized areas. Across all 14 cities we found increases in urbanized area (>25 %ISA) exceeding 10% from 2001-2011. Using LSP phenometrics, we were able to detect changes from cropland to suburban LCTs. In general we found negative relationships between DOS and distance from city center. We found a distinct seasonal cycle of MIR radiance over cropland LCTs due to the spectral contrast between bare soils and green vegetation.

A dataset mapping the potential biophysical effects of vegetation cover change

NASA Astrophysics Data System (ADS)

Duveiller, Gregory; Hooker, Josh; Cescatti, Alessandro

2018-02-01

Changing the vegetation cover of the Earth has impacts on the biophysical properties of the surface and ultimately on the local climate. Depending on the specific type of vegetation change and on the background climate, the resulting competing biophysical processes can have a net warming or cooling effect, which can further vary both spatially and seasonally. Due to uncertain climate impacts and the lack of robust observations, biophysical effects are not yet considered in land-based climate policies. Here we present a dataset based on satellite remote sensing observations that provides the potential changes i) of the full surface energy balance, ii) at global scale, and iii) for multiple vegetation transitions, as would now be required for the comprehensive evaluation of land based mitigation plans. We anticipate that this dataset will provide valuable information to benchmark Earth system models, to assess future scenarios of land cover change and to develop the monitoring, reporting and verification guidelines required for the implementation of mitigation plans that account for biophysical land processes.

A dataset mapping the potential biophysical effects of vegetation cover change

PubMed Central

Duveiller, Gregory; Hooker, Josh; Cescatti, Alessandro

2018-01-01

Changing the vegetation cover of the Earth has impacts on the biophysical properties of the surface and ultimately on the local climate. Depending on the specific type of vegetation change and on the background climate, the resulting competing biophysical processes can have a net warming or cooling effect, which can further vary both spatially and seasonally. Due to uncertain climate impacts and the lack of robust observations, biophysical effects are not yet considered in land-based climate policies. Here we present a dataset based on satellite remote sensing observations that provides the potential changes i) of the full surface energy balance, ii) at global scale, and iii) for multiple vegetation transitions, as would now be required for the comprehensive evaluation of land based mitigation plans. We anticipate that this dataset will provide valuable information to benchmark Earth system models, to assess future scenarios of land cover change and to develop the monitoring, reporting and verification guidelines required for the implementation of mitigation plans that account for biophysical land processes. PMID:29461538

Downscaling MODIS Land Surface Temperature for Urban Public Health Applications

NASA Technical Reports Server (NTRS)

Al-Hamdan, Mohammad; Crosson, William; Estes, Maurice, Jr.; Estes, Sue; Quattrochi, Dale; Johnson, Daniel

2013-01-01

This study is part of a project funded by the NASA Applied Sciences Public Health Program, which focuses on Earth science applications of remote sensing data for enhancing public health decision-making. Heat related death is currently the number one weather-related killer in the United States. Mortality from these events is expected to increase as a function of climate change. This activity sought to augment current Heat Watch/Warning Systems (HWWS) with NASA remotely sensed data, and models used in conjunction with socioeconomic and heatrelated mortality data. The current HWWS do not take into account intra-urban spatial variation in risk assessment. The purpose of this effort is to evaluate a potential method to improve spatial delineation of risk from extreme heat events in urban environments by integrating sociodemographic risk factors with estimates of land surface temperature (LST) derived from thermal remote sensing data. In order to further improve the consideration of intra-urban variations in risk from extreme heat, we also developed and evaluated a number of spatial statistical techniques for downscaling the 1-km daily MODerate-resolution Imaging Spectroradiometer (MODIS) LST data to 60 m using Landsat-derived LST data, which have finer spatial but coarser temporal resolution than MODIS. In this paper, we will present these techniques, which have been demonstrated and validated for Phoenix, AZ using data from the summers of 2000-2006.

Perspectives of methods of laser monitoring of the atmosphere and sea surface

NASA Astrophysics Data System (ADS)

Pashayev, Arif; Tunaboylu, Bahadir; Usta, Metin; Sadixov, Ilham; Allahverdiyev, Kerim

2016-01-01

Laser monitoring (remote sensing) may be considered as the science of collecting and interpreting information about the atmosphere, earth and sea using sensors on earth, on platforms in our atmosphere (airplanes, balloons) or in space (satellites) without being in direct physical contact with them. Remote sensing by LIDARs (Light Identification Detection and Ranging) has wide applications as technique to probe the Earth's atmosphere, ocean and land surfaces. LIDARs are widely used to get knowledge of spatial and temporal variations in meteorological quantities (e.g. temperature, humidity, clouds and aerosol properties) and to monitor the changes in these quantities on different timescales. Subject of the present work is quite wide. It is rather difficult to perform analysis and to provide full knowledge about existing information. In the present work, in addition to the literature data, the information will be provided also about KA-09 aerosol LIDAR developed at the Marmara Research Centre of TÜBITAK (Turkish Scientific and technological Research Council) and also about KA-14 LIDAR developed at the National Aviation Academy of Azerbaijan for remote sensing of contaminations on water surfaces taking place during oil-gas production. The main goal of this paper is to give students insight in different remote sensing instruments and techniques (including their perspectives) that are used for the derivation of meteorological quantities and obtaining the information about water surface.

Microclimatic conditions in different land-use systems in Sumatra, Indonesia

NASA Astrophysics Data System (ADS)

Meijide, Ana; Tiralla, Nina; Sabajo, Clifton; Panferov, Oleg; Gunawan, Dodo; Knohl, Alexander

2015-04-01

Over the last decades, Indonesia has experienced an unprecedented transformation of the land surface through deforestation and conversion from forest to other land-uses such as oil palm and rubber plantations. These transformations are expected to affect not only biodiversity and carbon storage, but also the biophysical conditions of the land surface, i.e. air and surface temperature, surface albedo, air humidity, and soil moisture. There is, however, a lack of quantitative information characterizing these differences with a systematic experimental design. We report results from micrometeorological measurements in four different land-use types (forest, rubber plantation, jungle rubber, and oil palm plantation, n=4) in two different landscapes in Jambi Province in Sumatra/Indonesia as well as remote sensing data from Landsat. Preliminary results show differences on the average within-canopy air temperature, with lowest values in the forest (24.70°C ± 0.01°C) and highest in oil palm and rubber plantations (25.45°C ± 0.02°C and 25.55°C ± 0.01°C respectively). The temperature ranges also varied between different land uses, from 6.27°C in the forest and up to 9.40 °C in oil palm between the 5 and 95% percentile. Relative air humidity followed an inverse trend to air temperature, with rubber and oil palm plantations being on average 6.29 and 5.37 % drier than the forest. Soil temperature was up to 1°C warmer in oil palm than in forest plots, while soil moisture was more influenced by the soil type in the different landscapes than by the land uses. In conclusion, our data demonstrate that land transformation in Indonesia results in distinctly different microclimatic conditions across land-use types.

Monitoring cover crops using radar remote sensing in southern Ontario, Canada

NASA Astrophysics Data System (ADS)

Shang, J.; Huang, X.; Liu, J.; Wang, J.

2016-12-01

Information on agricultural land surface conditions is important for developing best land management practices to maintain the overall health of the fields. The climate condition supports one harvest per year for the majority of the field crops in Canada, with a relative short growing season between May and September. During the non-growing-season months (October to the following April), many fields are traditionally left bare. In more recent year, there has been an increased interest in planting cover crops. Benefits of cover crops include boosting soil organic matters, preventing soil from erosion, retaining soil moisture, and reducing surface runoff hence protecting water quality. Optical remote sensing technology has been exploited for monitoring cover crops. However limitations inherent to optical sensors such as cloud interference and signal saturation (when leaf area index is above 2.5) impeded its operational application. Radar remote sensing on the other hand is not hindered by unfavorable weather conditions, and the signal continues to be sensitive to crop growth beyond the saturation point of optical sensors. It offers a viable means for capturing timely information on field surface conditions (with or without crop cover) or crop development status. This research investigated the potential of using multi-temporal RADARSAT-2 C-band synthetic aperture radar (SAR) data collected in 2015 over multiple fields of winter wheat, corn and soybean crops in southern Ontario, Canada, to retrieve information on the presence of cover crops and their growth status. Encouraging results have been obtained. This presentation will report the methodology developed and the results obtained.

Applying remote sensing and GIS techniques in solving rural county information needs

NASA Technical Reports Server (NTRS)

Johannsen, Chris J.; Fernandez, R. Norberto; Lozano-Garcia, D. Fabian

1992-01-01

The project designed was to acquaint county government officials and their clientele with remote sensing and GIS products that contain information about land conditions and land use. Other users determined through the course of this project were federal agencies working at the county level, agricultural businesses and others in need of spatial information. The specific project objectives were: (1) to investigate the feasibility of using remotely sensed data to identify and quantify specific land cover categories and conditions for purposes of tax assessment, cropland area measurements and land use evaluation; (2) to investigate the use of satellite remote sensing data as an aid in assessing soil management practices; and (3) to evaluate the use of remotely sensed data to assess soil resources and conditions which affect productivity.

Remote sensing requirements as suggested by watershed model sensitivity analyses

NASA Technical Reports Server (NTRS)

Salomonson, V. V.; Rango, A.; Ormsby, J. P.; Ambaruch, R.

1975-01-01

A continuous simulation watershed model has been used to perform sensitivity analyses that provide guidance in defining remote sensing requirements for the monitoring of watershed features and processes. The results show that out of 26 input parameters having meaningful effects on simulated runoff, 6 appear to be obtainable with existing remote sensing techniques. Of these six parameters, 3 require the measurement of the areal extent of surface features (impervious areas, water bodies, and the extent of forested area), two require the descrimination of land use that can be related to overland flow roughness coefficient or the density of vegetation so as to estimate the magnitude of precipitation interception, and one parameter requires the measurement of distance to get the length over which overland flow typically occurs. Observational goals are also suggested for monitoring such fundamental watershed processes as precipitation, soil moisture, and evapotranspiration. A case study on the Patuxent River in Maryland shows that runoff simulation is improved if recent satellite land use observations are used as model inputs as opposed to less timely topographic map information.

Diurnal Temperature Regime in the Regolith Surface Layer of the Lagado Planitia Region on Phobos: Model Predictions for Different Seasons

NASA Astrophysics Data System (ADS)

Kuzmin, R. O.; Zabalueva, E. V.

2018-03-01

The paper contains the data on the thermal and physical characteristic of the surface regolith of the Martian satellite Phobos obtained from the spaceborne remote sensing (with the Mariner 9, Viking, and Mars Global Surveyor orbiters and the Phobos-2 spacecraft) and the results of the numerical modeling of the thermal regime in the surface regolith (on diurnal and seasonal scales) performed for the prospective landing site in the Lagado Planitia region located in the anti-Martian hemisphere of Phobos.

Investigation of Long-Term Impacts of Urbanization when Considering Global Warming for a Coastal Tropical Region

NASA Technical Reports Server (NTRS)

Gonalez, Jorge E.; Comarazamy, Daniel E.; Luvall, Jeffrey C.; Rickman, Douglas L.; Smith, T.

2010-01-01

The overachieving goal of this project is to gain a better understanding of the climate impacts caused by the combined effects of land cover and land use (LCLU) changes and increasing global concentrations of green house gases (GHG) in tropical coastal areas, regions where global, regional and local climate phenomena converge, taking as the test case the densely populated northeast region of the Caribbean island of Puerto Rico. The research uses an integrated approach of high-resolution remote sensing information linked to a high resolution Regional Atmospheric Modeling System (RAMS), which was employed to perform ensembles of climate simulations (combining 2-LCLU and 2-GHG concentration scenarios). Reconstructed agricultural maps are used to define past LCLU, and combined with reconstructed sea surface temperatures (SST) for the same period form the PAST climate scenario (1951-1956); while the PRESENT scenario (2000-2004) was additionally supported by high resolution remote sensing data (10-m-res). The climate reconstruction approach is validated with available observed climate data from surface weather stations for both periods of time simulated. The selection of the past and present climate scenarios considers large-scale biases (i.e. ENSO/NAO) as reflected in the region of interest. Direct and cross comparison of the results is allowing quantifying single, combined, and competitive effects. Results indicate that global GHG have dominant effects on minimum temperatures (following regional tendencies), while urban sprawl dominates maximum temperatures. To further investigate impacts of land use the Bowen Ratio and the thermal response number (TRN) are analyzed. The Bowen ratio indicates that forestation of past agricultural high areas have an overwhelmingly mitigation effect on increasing temperatures observed in different LCLU scenarios, but when abandoned agricultural lands are located in plains, the resulting shrub/grass lands produce higher surface temperatures. The TRN (J/m^2/degC) is a surface property defined as the ratio of the surface net radiation to the rate of change in surface temperature, expresses how those fluxes are reacting to radiant energy inputs. Natural vegetated surfaces have a greater TRN than urban and barren surfaces because the net radiation processed by them is mostly used for latent heat and thermal storage heat rather than sensible heat (heating the air). Significant changes in TRN were observed in the metropolitan area of San Juan for the two analyzed periods reflecting a reduction of this variable in the present from the past consistent with increasing in thermal mass, or intense urbanization.

Detection of land degradation with polarimetric SAR

NASA Technical Reports Server (NTRS)

Ray, Terrill W.; Farr, Tom G.; Van Zyl, Jakob J.

1992-01-01

Multispectral radar polarimeter data were collected over the Manix Basin Area of the Mojave desert using an airborne SAR. An analysis of the data reveals unusual polarization responses which are attributed to the formation of wind ripples on the surfaces of fields that have been abandoned for more than 5 years. This hypothesis has been confirmed through field observations, and a second-order perturbation model is shown to effectively model the polarization responses. The results demonstrate the usefulness of remote sensing techniques for the study of land degradation at synoptic scales.

Detecting geothermal anomalies and evaluating LST geothermal component by combining thermal remote sensing time series and land surface model data

USGS Publications Warehouse

Romaguera, Mireia; Vaughan, R. Greg; Ettema, J.; Izquierdo-Verdiguier, E.; Hecker, C. A.; van der Meer, F.D.

2018-01-01

This paper explores for the first time the possibilities to use two land surface temperature (LST) time series of different origins (geostationary Meteosat Second Generation satellite data and Noah land surface modelling, LSM), to detect geothermal anomalies and extract the geothermal component of LST, the LSTgt. We hypothesize that in geothermal areas the LSM time series will underestimate the LST as compared to the remote sensing data, since the former does not account for the geothermal component in its model.In order to extract LSTgt, two approaches of different nature (physical based and data mining) were developed and tested in an area of about 560 × 560 km2 centered at the Kenyan Rift. Pre-dawn data in the study area during the first 45 days of 2012 were analyzed.The results show consistent spatial and temporal LSTgt patterns between the two approaches, and systematic differences of about 2 K. A geothermal area map from surface studies was used to assess LSTgt inside and outside the geothermal boundaries. Spatial means were found to be higher inside the geothermal limits, as well as the relative frequency of occurrence of high LSTgt. Results further show that areas with strong topography can result in anomalously high LSTgt values (false positives), which suggests the need for a slope and aspect correction in the inputs to achieve realistic results in those areas. The uncertainty analysis indicates that large uncertainties of the input parameters may limit detection of LSTgt anomalies. To validate the approaches, higher spatial resolution images from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data over the Olkaria geothermal field were used. An established method to estimate radiant geothermal flux was applied providing values between 9 and 24 W/m2 in the geothermal area, which coincides with the LSTgt flux rates obtained with the proposed approaches.The proposed approaches are a first step in estimating LSTgt at large spatial coverage from remote sensing and LSM data series, and provide an innovative framework for future improvements.

Lunar Reconnaissance Orbiter Camera Observations Relating to Science and Landing Site Selection in South Pole-Aitken Basin for a Robotic Sample Return Mission

NASA Technical Reports Server (NTRS)

Jolliff, B. L.; Clegg-Watkins, R. N.; Petro, N. E.; Lawrence, S. L.

2016-01-01

The Moon's South Pole-Aitken basin (SPA) is a high priority target for Solar System exploration, and sample return from SPA is a specific objective in NASA's New Frontiers program. Samples returned from SPA will improve our understanding of early lunar and Solar System events, mainly by placing firm timing constraints on SPA formation and the post-SPA late-heavy bombardment (LHB). Lunar Reconnaissance Orbiter Camera (LROC) images and topographic data, especially Narrow Angle Camera (NAC) scale (1-3 mpp) morphology and digital terrain model (DTM) data are critical for selecting landing sites and assessing landing hazards. Rock components in regolith at a given landing site should include (1) original SPA impact-melt rocks and breccia (to determine the age of the impact event and what materials were incorporated into the melt); (2) impact-melt rocks and breccia from large craters and basins (other than SPA) that represent the post-SPA LHB interval; (3) volcanic basalts derived from the sub-SPA mantle; and (4) older, "cryptomare" (ancient buried volcanics excavated by impact craters, to determine the volcanic history of SPA basin). All of these rock types are sought for sample return. The ancient SPA-derived impact-melt rocks and later-formed melt rocks are needed to determine chronology, and thus address questions of early Solar System dynamics, lunar history, and effects of giant impacts. Surface compositions from remote sensing are consistent with mixtures of SPA impactite and volcanic materials, and near infrared spectral data distinguish areas with variable volcanic contents vs. excavated SPA substrate. Estimating proportions of these rock types in the regolith requires knowledge of the surface deposits, evaluated via morphology, slopes, and terrain ruggedness. These data allow determination of mare-cryptomare-nonmare deposit interfaces in combination with compositional and mineralogical remote sensing to establish the types and relative proportions of materials expected at a given site. Remote sensing compositions, e.g., FeO, also constrain the relative abundances of components. Landing-site assessments use crater and boulder distributions, and slope and terrain rugge

Comparison of in-situ measurements and satellite-derived surface emissivity over Italian volcanic areas

NASA Astrophysics Data System (ADS)

Silvestri, Malvina; Musacchio, Massimo; Cammarano, Diego; Fabrizia Buongiorno, Maria; Amici, Stefania; Piscini, Alessandro

2016-04-01

In this work we compare ground measurements of emissivity collected during dedicated fields campaign on Mt. Etna and Solfatara of Pozzuoli volcanoes and acquired by means of Micro-FTIR (Fourier Thermal Infrared spectrometer) instrument with the emissivity obtained by using single ASTER data (Advanced Spaceborne Thermal Emission and Reflection Radiometer, ASTER 05) and the ASTER emissivity map extract from ASTER Global Emissivity Database (GED), released by LP DAAC on April 2, 2014. The database was developed by the National Aeronautics and Space Administration's (NASA) Jet Propulsion Laboratory (JPL), California Institute of Technology. The database includes land surface emissivity derived from ASTER data acquired over the contiguous United States, Africa, Arabian Peninsula, Australia, Europe, and China. Through this analysis we want to investigate the differences existing between the ASTER-GED dataset (average from 2000 to 2008 seasoning independent) and fall in-situ emissivity measurement. Moreover the role of different spatial resolution characterizing ASTER and MODIS, 90mt and 1km respectively, by comparing them with in situ measurements, is analyzed. Possible differences can be due also to the different algorithms used for the emissivity estimation, Temperature and Emissivity Separation algorithm for ASTER TIR band( Gillespie et al, 1998) and the classification-based emissivity method (Snyder and al, 1998) for MODIS. Finally land surface temperature products generated using ASTER-GED and ASTER 05 emissivity are also analyzed. Gillespie, A. R., Matsunaga, T., Rokugawa, S., & Hook, S. J. (1998). Temperature and emissivity separation from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images. IEEE Transactions on Geoscience and Remote Sensing, 36, 1113-1125. Snyder, W.C., Wan, Z., Zhang, Y., & Feng, Y.-Z. (1998). Classification-based emissivity for land surface temperature measurement from space. International Journal of Remote Sensing, 19, 2753-2574.

Land Surface Modeling Applications for Famine Early Warning

NASA Astrophysics Data System (ADS)

McNally, A.; Verdin, J. P.; Peters-Lidard, C. D.; Arsenault, K. R.; Wang, S.; Kumar, S.; Shukla, S.; Funk, C. C.; Pervez, M. S.; Fall, G. M.; Karsten, L. R.

2015-12-01

AGU 2015 Fall Meeting Session ID#: 7598 Remote Sensing Applications for Water Resources Management Land Surface Modeling Applications for Famine Early Warning James Verdin, USGS EROS Christa Peters-Lidard, NASA GSFC Amy McNally, NASA GSFC, UMD/ESSIC Kristi Arsenault, NASA GSFC, SAIC Shugong Wang, NASA GSFC, SAIC Sujay Kumar, NASA GSFC, SAIC Shrad Shukla, UCSB Chris Funk, USGS EROS Greg Fall, NOAA Logan Karsten, NOAA, UCAR Famine early warning has traditionally required close monitoring of agro-climatological conditions, putting them in historical context, and projecting them forward to anticipate end-of-season outcomes. In recent years, it has become necessary to factor in the effects of a changing climate as well. There has also been a growing appreciation of the linkage between food security and water availability. In 2009, Famine Early Warning Systems Network (FEWS NET) science partners began developing land surface modeling (LSM) applications to address these needs. With support from the NASA Applied Sciences Program, an instance of the Land Information System (LIS) was developed to specifically support FEWS NET. A simple crop water balance model (GeoWRSI) traditionally used by FEWS NET took its place alongside the Noah land surface model and the latest version of the Variable Infiltration Capacity (VIC) model, and LIS data readers were developed for FEWS NET precipitation forcings (NOAA's RFE and USGS/UCSB's CHIRPS). The resulting system was successfully used to monitor and project soil moisture conditions in the Horn of Africa, foretelling poor crop outcomes in the OND 2013 and MAM 2014 seasons. In parallel, NOAA created another instance of LIS to monitor snow water resources in Afghanistan, which are an early indicator of water availability for irrigation and crop production. These successes have been followed by investment in LSM implementations to track and project water availability in Sub-Saharan Africa and Yemen, work that is now underway. Adoption of LSM and data assimilation technology has enabled FEWS NET to take greater advantage of remote sensing observations to robustly estimate key agro-climatological states, like soil moisture and snow water equivalent, building confidence in our understanding of conditions in data sparse regions of the world.

Do Surface Energy Fluxes Reveal Land Use/Land Cover Change in South Florida?: A Remote Sensing Perspective

NASA Astrophysics Data System (ADS)

Kandel, H. P.; Melesse, A. M.

2017-12-01

Series of changes on land use/ land cover in South Florida resulting from drainage and development activities during early to mid-20th followed by restoration measures since late-20th century have had prominent impacts on hydrologic regime and energy fluxes in the region. Previous results from numerical modeling and MODIS-based analysis have shown a shift in dominance of heat fluxes: from latent to sensible along the axes of urbanization, and an opposite along the axes of restoration. This study implements a slightly modified version of surface energy balance algorithm (SEBAL) on cloud-masked Landsat imageries archived over the period of 30-years combined with ground-meteorological data for South Florida using spatial analysis model in ArcGIS and calculates energy flux components: sensible heat flux, latent heat flux, and ground heat flux. The study finally computes variation of Bowen's ratio (BR) and daily evapotranspiration (ET) rate over various land covers for different years. Coexistences are apparent between increased BR and increased intensity of urbanization, and between increased daily ET rates and improved best management practices in agricultural areas. An increase in mean urban BR from 1.67 in 1984 to 3.06 in 2010 show plausible link of BR with urban encroachment of open lands, and expulsion of additional heat by increased population/automobiles/factories/air conditioning units. Likewise, increase in mean agricultural daily ET rates from 0.21 mm/day to 3.60 mm/day between 1984 to 2010 probably shows the effects of improved moisture conditions on the northern farm lands as the results of restoration practices. Once new observed data become available to corroborate these results, remote sensing methods-owing to their greater spatial and temporal details-can be used as assessment measures both for the progress of restoration evaluation and for the extent detection of human-induced climate change.

Investigation of the applications of GEOS-3 radar altimeter data in remote sensing of land and sea features

NASA Technical Reports Server (NTRS)

Miller, L. S.

1977-01-01

A number of GEOS-3 passes over the Atlantic Ocean and Southeastern U.S. are examined. Surface-truth and radar altimeter data comparisons are given in terms of surface correlation length, signal fluctuation characteristics, and altitude tracker dynamic response. Detailed analyses are given regarding spatial resolution and its dependency on angular backscatter behavior. These analyses include data from passes over ocean (diffuse scatter), land (large body scatter), and mirror-like inland water areas (pseudo-specular scatter). Altimeter data are examined for a pass over a large reservoir and marsh area of differing water levels; this geometry represents a stepchange in altitude which is usable in determination of the transient response of the tracker. The extent to which pulse-length limited operation pertains over-land is examined. A Wiener filter altitude algorithm is discussed which permits specification of tracker variance and geoidal spectral characteristics during operation.

An Earth Observation Land Data Assimilation System for Data from Multiple Wavelength Domains: Water and Energy Balance Components

NASA Astrophysics Data System (ADS)

Quaife, T. L.; Davenport, I. J.; Lines, E.; Styles, J.; Lewis, P.; Gurney, R. J.

2012-12-01

Satellite observations offer a spatially and temporally synoptic data source for constraining models of land surface processes, but exploitation of these data for such purposes has been largely ad-hoc to date. In part this is because traditional land surface models, and hence most land surface data assimilation schemes, have tended to focus on a specific component of the land surface problem; typically either surface fluxes of water and energy or biogeochemical cycles such as carbon and nitrogen. Furthermore the assimilation of satellite data into such models tends to be restricted to a single wavelength domain, for example passive microwave, thermal or optical, depending on the problem at hand. The next generation of land surface schemes, such as the Joint UK Land Environment Simulator (JULES) and the US Community Land Model (CLM) represent a broader range of processes but at the expense of increasing overall model complexity and in some cases reducing the level of detail in specific processes to accommodate this. Typically, the level of physical detail used to represent the interaction of electromagnetic radiation with the surface is not sufficient to enable prediction of intrinsic satellite observations (reflectance, brightness temperature and so on) and consequently these are not assimilated directly into the models. A seemingly attractive alternative is to assimilate high-level products derived from satellite observations but these are often only superficially related to the corresponding variables in land surface models due to conflicting assumptions between the two. This poster describes the water and energy balance modeling components of a project funded by the European Space Agency to develop a data assimilation scheme for the land surface and observation operators to translate between models and the intrinsic observations acquired by satellite missions. The rationale behind the design of the underlying process model is to represent the physics of the water and energy balance in as parsimonious manner as possible, using a force-restore approach, but describing the physics of electromagnetic radiation scattering at the surface sufficiently well that it is possible to assimilate the intrinsic observations made by remote sensing instruments. In this manner the initial configuration of the resulting scheme will be able to make optimal use of available satellite observations at arbitrary wavelengths and geometries. Model complexity can then be built up from this point whilst ensuring consistency with satellite observations.

Temporal and spatial changes of land use and landscape in a coal mining area in Xilingol grassland

NASA Astrophysics Data System (ADS)

Guan, Chunzhu; Zhang, Baolin; Li, Jiannan; Zhao, Junling

2017-01-01

Coal mining, particularly surface mining, inevitably disturbs land. According to Landsat images acquired over Xilingol grassland in 2005, 2009 and 2015, land uses were divided into seven classes, i. e., open stope, stripping area, waste-dump area, mine industrial area, farmland, urban area and the original landscape (grassland), using supervised classification and human-computer interactive interpretation. The overall classification accuracies were 97.72 %, 98.43 % and 96.73 %, respectively; the Kappa coefficients were 0.95, 0.97 and 0.95, respectively. Analysis on LUCC (Land Use and Cover Change) showed that surface coal mining disturbed grassland ecosystem: grassland decreased by 8661.15 hm2 in 2005-2015. The area and proportion of mining operation areas (open stope, stripping area, waste-dump area, mine industrial field) increased, but those of grassland decreased continuously. Transfer matrix of land use changes showed that waste-dump had the largest impacts in mining disturbance, and that effective reclamation of waste-dump areas would mitigate eco-environment destruction, as would be of great significance to protect fragile grassland eco-system. Six landscape index showed that landscape fragmentation increased, and the influences of human activity on landscape was mainly reflected in the expansion of mining area and urban area. Remote sensing monitoring of coal surface mining in grassland would accurately demonstrate the dynamics and trend of LUCC, providing scientific supports for ecological reconstruction in surface mining area.

Monitoring Drought at Continental Scales Using Thermal Remote Sensing of Evapotranspiration (Invited)

NASA Astrophysics Data System (ADS)

Anderson, M. C.; Hain, C.; Mecikalski, J. R.; Kustas, W. P.

2009-12-01

Thermal infrared (TIR) remote sensing of land-surface temperature (LST) provides valuable information about the sub-surface moisture status: soil surface temperature increases with decreasing water content, while moisture depletion in the plant root zone leads to stomatal closure, reduced transpiration, and elevated canopy temperatures that can be effectively detected from space. Empirical indices measuring anomalies in LST and vegetation amount (e.g., as quantified by the Normalized Difference Vegetation Index; NDVI) have demonstrated utility in monitoring drought conditions over large areas, but may provide ambiguous results when vegetation growth is limited by energy (radiation, air temperature) rather than moisture. A more physically based interpretation of LST and NDVI and their relationship to sub-surface moisture conditions can be obtained with a surface energy balance model driven by TIR remote sensing. In this approach, moisture stress can be quantified in terms of the reduction of evapotranspiration (ET) from the potential rate (PET) expected under non-moisture limiting conditions. The Atmosphere-Land Exchange Inverse (ALEXI) model couples a two-source (soil+canopy) land-surface model with an atmospheric boundary layer model in time-differencing mode to routinely and robustly map fluxes across the U.S. continent at 5-10km resolution using thermal band imagery from the Geostationary Operational Environmental Satellites (GOES). Finer resolution flux maps can be generated through spatial disaggregation using TIR data from polar orbiting instruments such as Landsat (60-120m) and MODIS (1km). A derived Evaporative Stress Index (ESI), given by 1-ET/PET, shows good correspondence with standard drought metrics and with patterns of antecedent precipitation, but can be produced at significantly higher spatial resolution due to limited reliance on ground observations. Because the ESI does not use precipitation data as input, it provides an independent means for assessing standard meteorologically-based drought indicators, and may be more robust in regions with limited monitoring networks. In this study, monthly maps of ESI anomalies for 2000-2008 are compared to standard drought indices and to drought classifications in the U.S. Drought Monitor. The ESI shows better skill in ranking drought severity than do precipitation-based indices composited over comparable time intervals. The thermal remote sensing inputs to ALEXI detect drought conditions even under the dense forest cover along the East Coast of the United States, where microwave soil moisture retrievals typically lose sensitivity. On the other hand, microwave observations are not constrained by cloud cover and provide better temporal continuity, but typically at significantly lower spatial resolution. A merged TIR-microwave moisture anomaly product may have potential for optimizing both spatial and temporal coverage in continental-scale drought monitoring.

Evaluating the Utility of Satellite Soil Moisture Retrievals over Irrigated Areas and the Ability of Land Data Assimilation Methods to Correct for Unmodeled Processes

NASA Technical Reports Server (NTRS)

Kumar, S. V.; Peters-Lidard, C. D.; Santanello, J. A.; Reichle, R. H.; Draper, C. S.; Koster, R. D.; Nearing, G.; Jasinski, M. F.

2015-01-01

Earth's land surface is characterized by tremendous natural heterogeneity and human-engineered modifications, both of which are challenging to represent in land surface models. Satellite remote sensing is often the most practical and effective method to observe the land surface over large geographical areas. Agricultural irrigation is an important human-induced modification to natural land surface processes, as it is pervasive across the world and because of its significant influence on the regional and global water budgets. In this article, irrigation is used as an example of a human-engineered, often unmodeled land surface process, and the utility of satellite soil moisture retrievals over irrigated areas in the continental US is examined. Such retrievals are based on passive or active microwave observations from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), the Advanced Microwave Scanning Radiometer 2 (AMSR2), the Soil Moisture Ocean Salinity (SMOS) mission, WindSat and the Advanced Scatterometer (ASCAT). The analysis suggests that the skill of these retrievals for representing irrigation effects is mixed, with ASCAT-based products somewhat more skillful than SMOS and AMSR2 products. The article then examines the suitability of typical bias correction strategies in current land data assimilation systems when unmodeled processes dominate the bias between the model and the observations. Using a suite of synthetic experiments that includes bias correction strategies such as quantile mapping and trained forward modeling, it is demonstrated that the bias correction practices lead to the exclusion of the signals from unmodeled processes, if these processes are the major source of the biases. It is further shown that new methods are needed to preserve the observational information about unmodeled processes during data assimilation.

Consistency of Estimated Global Water Cycle Variations Over the Satellite Era

NASA Technical Reports Server (NTRS)

Robertson, F. R.; Bosilovich, M. G.; Roberts, J. B.; Reichle, R. H.; Adler, R.; Ricciardulli, L.; Berg, W.; Huffman, G. J.

2013-01-01

Motivated by the question of whether recent indications of decadal climate variability and a possible "climate shift" may have affected the global water balance, we examine evaporation minus precipitation (E-P) variability integrated over the global oceans and global land from three points of view-remotely sensed retrievals / objective analyses over the oceans, reanalysis vertically-integrated moisture convergence (MFC) over land, and land surface models forced with observations-based precipitation, radiation and near-surface meteorology. Because monthly variations in area-averaged atmospheric moisture storage are small and the global integral of moisture convergence must approach zero, area-integrated E-P over ocean should essentially equal precipitation minus evapotranspiration (P-ET) over land (after adjusting for ocean and land areas). Our analysis reveals considerable uncertainty in the decadal variations of ocean evaporation when integrated to global scales. This is due to differences among datasets in 10m wind speed and near-surface atmospheric specific humidity (2m qa) used in bulk aerodynamic retrievals. Precipitation variations, all relying substantially on passive microwave retrievals over ocean, still have uncertainties in decadal variability, but not to the degree present with ocean evaporation estimates. Reanalysis MFC and P-ET over land from several observationally forced diagnostic and land surface models agree best on interannual variations. However, upward MFC (i.e. P-ET) reanalysis trends are likely related in part to observing system changes affecting atmospheric assimilation models. While some evidence for a low-frequency E-P maximum near 2000 is found, consistent with a recent apparent pause in sea-surface temperature (SST) rise, uncertainties in the datasets used here remain significant. Prospects for further reducing uncertainties are discussed. The results are interpreted in the context of recent climate variability (Pacific Decadal Oscillation, Atlantic Meridional Overturning), and efforts to distinguish these modes from longer-term trends.

Climate Responses to Changes in Land-surface Properties due to Wildfires

NASA Astrophysics Data System (ADS)

Liu, Y.; Hao, X.; Qu, J. J.

2015-12-01

Wildfires can feedback the atmosphere by impacting atmospheric radiation transfer and cloud microphysics through emitting smoke particles and the land-air heat and water fluxes through modifying land-surface properties. While the impacts through smoke particles have been extensively investigated recently, very few studies have been conducted to examine the impacts through land-surface property change. This study is to fill this gap by examining the climate responses to the changes in land-surface properties induced by several large wildfires in the United States. Satellite remote sensing tools including MODIS and Landsat are used to quantitatively evaluate the land-surface changes characterized by reduced vegetation coverage and increased albedo over long post-fire periods. Variations in air and soil temperature and moisture of the burned areas are also monitored. Climate modeling is conducted to simulate climate responses and understand the related physical processes and interactions. The preliminary results indicate noticeable changes in water and heat transfers from the ground to the atmosphere through several mechanisms. Larger albedo reduces solar radiation absorbed on the ground, leading to less energy for latent and sensible heat fluxes. With smaller vegetation coverage, water transfer from the soil to the atmosphere through transpiration is reduced. Meanwhile, the Bowen ratio becomes larger after burning and therefore more solar energy absorbed on the ground is converted into sensible heat instead of being used as latent energy for water phase change. In addition, reduced vegetation coverage reduces roughness and increases wind speed, which modify dynamic resistances to water and heat movements. As a result of the changes in the land-air heat and water fluxes, clouds and precipitation as well as other atmospheric processes are affected by wildfires.

LandingNav: a precision autonomous landing sensor for robotic platforms on planetary bodies

NASA Astrophysics Data System (ADS)

Katake, Anup; Bruccoleri, Chrisitian; Singla, Puneet; Junkins, John L.

2010-01-01

Increased interest in the exploration of extra terrestrial planetary bodies calls for an increase in the number of spacecraft landing on remote planetary surfaces. Currently, imaging and radar based surveys are used to determine regions of interest and a safe landing zone. The purpose of this paper is to introduce LandingNav, a sensor system solution for autonomous landing on planetary bodies that enables landing on unknown terrain. LandingNav is based on a novel multiple field of view imaging system that leverages the integration of different state of the art technologies for feature detection, tracking, and 3D dense stereo map creation. In this paper we present the test flight results of the LandingNav system prototype. Sources of errors due to hardware limitations and processing algorithms were identified and will be discussed. This paper also shows that addressing the issues identified during the post-flight test data analysis will reduce the error down to 1-2%, thus providing for a high precision 3D range map sensor system.

São Paulo urban heat islands have a higher incidence of dengue than other urban areas.

PubMed

Araujo, Ricardo Vieira; Albertini, Marcos Roberto; Costa-da-Silva, André Luis; Suesdek, Lincoln; Franceschi, Nathália Cristina Soares; Bastos, Nancy Marçal; Katz, Gizelda; Cardoso, Vivian Ailt; Castro, Bronislawa Ciotek; Capurro, Margareth Lara; Allegro, Vera Lúcia Anacleto Cardoso

2015-01-01

Urban heat islands are characterized by high land surface temperature, low humidity, and poor vegetation, and considered to favor the transmission of the mosquito-borne dengue fever that is transmitted by the Aedes aegypti mosquito. We analyzed the recorded dengue incidence in Sao Paulo city, Brazil, in 2010-2011, in terms of multiple environmental and socioeconomic variables. Geographical information systems, thermal remote sensing images, and census data were used to classify city areas according to land surface temperature, vegetation cover, population density, socioeconomic status, and housing standards. Of the 7415 dengue cases, a majority (93.1%) mapped to areas with land surface temperature >28°C. The dengue incidence rate (cases per 100,000 inhabitants) was low (3.2 cases) in high vegetation cover areas, but high (72.3 cases) in low vegetation cover areas where the land surface temperature was 29±2°C. Interestingly, a multiple cluster analysis phenogram showed more dengue cases clustered in areas of land surface temperature >32°C, than in areas characterized as low socioeconomic zones, high population density areas, or slum-like areas. In laboratory experiments, A. aegypti mosquito larval development, blood feeding, and oviposition associated positively with temperatures of 28-32°C, indicating these temperatures to be favorable for dengue transmission. Thus, among all the variables studied, dengue incidence was most affected by the temperature. Copyright © 2014 Elsevier Editora Ltda. All rights reserved.

Species–specific contributions to moderate resolution vegetation indices derived from sub-decimeter aerial photography – Prospects for phenological monitoring

USDA-ARS?s Scientific Manuscript database

High spatial heterogeneity in ground cover, large amounts of exposed bare soil, and modest cover from shrubs and grasses in arid and semi-arid ecosystems challenge the integration of field observations of phenology and remotely sensed data to monitor changes in land surface phenology. This research ...

A FIRST APPROXIMATION OF IMPERVIOUS SURFACES ACROSS THE DANUBE RIVER BASIN USING THE CORINE LAND COVER DATABASE AND POTENTIAL RELATIONSHIPS TO FLOODING RISKS

EPA Science Inventory

This last year, broad geographic areas in Europe experienced significant levels of flooding causing extensive loss of human lives and property. In North America, the US Environmental Protection Agency has been using GIS and remotely sensed data to assess the distribution and exte...

Prospects for phenological monitoring in an arid southwestern U.S. rangeland using field observations with hyperspatial and moderate resolution imagery

USDA-ARS?s Scientific Manuscript database

Relating field observations of plant phenological events to remotely sensed depictions of land surface phenology remains a challenge to the vertical integration of data from disparate sources. This research conducted at the Jornada Basin Long-Term Ecological Research site in southern New Mexico cap...

Prospects for phenological monitoring in an arid southwestern U.S. rangeland using field observations with hyperspatial and moderate resolution imagery

USDA-ARS?s Scientific Manuscript database

Relating field observations of plan phonological events to remotely sensed depictions of land surface phenology remains a challenge to the vertical integration of data from disparate sources. This research conducted at the Jornada Basin Long-Term Ecological Research site in southern New Mexico capit...

Monitoring land surface albedo and vegetation dynamics using high spatial and temporal resolution synthetic data from Landsat and MODIS BRDF/albedo product

USDA-ARS?s Scientific Manuscript database

Climate warming over the past half century has led to observable changes in vegetation phenology and growing season length; which can be measured globally using remote sensing derived vegetation indices. Previous studies in mid- and high northern latitude systems show temperature driven earlier spri...

Assessing the effects of land use spatial structure on urban heat islands using HJ-1B remote sensing imagery in Wuhan, China

NASA Astrophysics Data System (ADS)

Wu, Hao; Ye, Lu-Ping; Shi, Wen-Zhong; Clarke, Keith C.

2014-10-01

Urban heat islands (UHIs) have attracted attention around the world because they profoundly affect biological diversity and human life. Assessing the effects of the spatial structure of land use on UHIs is essential to better understanding and improving the ecological consequences of urbanization. This paper presents the radius fractal dimension to quantify the spatial variation of different land use types around the hot centers. By integrating remote sensing images from the newly launched HJ-1B satellite system, vegetation indexes, landscape metrics and fractal dimension, the effects of land use patterns on the urban thermal environment in Wuhan were comprehensively explored. The vegetation indexes and landscape metrics of the HJ-1B and other remote sensing satellites were compared and analyzed to validate the performance of the HJ-1B. The results have showed that land surface temperature (LST) is negatively related to only positive normalized difference vegetation index (NDVI) but to Fv across the entire range of values, which indicates that fractional vegetation (Fv) is an appropriate predictor of LST more than NDVI in forest areas. Furthermore, the mean LST is highly correlated with four class-based metrics and three landscape-based metrics, which suggests that the landscape composition and the spatial configuration both influence UHIs. All of them demonstrate that the HJ-1B satellite has a comparable capacity for UHI studies as other commonly used remote sensing satellites. The results of the fractal analysis show that the density of built-up areas sharply decreases from the hot centers to the edges of these areas, while the densities of water, forest and cropland increase. These relationships reveal that water, like forest and cropland, has a significant effect in mitigating UHIs in Wuhan due to its large spatial extent and homogeneous spatial distribution. These findings not only confirm the applicability and effectiveness of the HJ-1B satellite system for studying UHIs but also reveal the impacts of the spatial structure of land use on UHIs, which is helpful for improving the planning and management of the urban environment.

The impact of CO2 fertilization and historical land use/land cover change on regional climate extremes

NASA Astrophysics Data System (ADS)

Findell, Kirsten; Berg, Alexis; Gentine, Pierre; Krasting, John; Lintner, Benjamin; Malyshev, Sergey; Santanello, Joseph; Shevliakova, Elena

2017-04-01

Recent research highlights the role of land surface processes in heat waves, droughts, and other extreme events. Here we use an earth system model (ESM) from the Geophysical Fluid Dynamics Laboratory (GFDL) to investigate the regional impacts of historical anthropogenic land use/land cover change (LULCC) and the vegetative response to changes in atmospheric CO2 on combined extremes of temperature and humidity. A bivariate assessment allows us to consider aridity and moist enthalpy extremes, quantities central to human experience of near-surface climate conditions. We show that according to this model, conversion of forests to cropland has contributed to much of the upper central US and central Europe experiencing extreme hot, dry summers every 2-3 years instead of every 10 years. In the tropics, historical patterns of wood harvesting, shifting cultivation and regrowth of secondary vegetation have enhanced near surface moist enthalpy, leading to extensive increases in the occurrence of humid conditions throughout the tropics year round. These critical land use processes and practices are not included in many current generation land models, yet these results identify them as critical factors in the energy and water cycles of the midlatitudes and tropics. Current work is targeted at understanding how CO2 fertilization of plant growth impacts water use efficiency and surface flux partitioning, and how these changes influence temperature and humidity extremes. We use this modeling work to explore how remote sensing can be used to determine how different forest ecosystems in different climatological regimes are responding to enhanced CO2 and a warming world.

Comparative Perspectives on Recent Trends in Land Surface Dynamics in the Grasslands of North and South America

NASA Astrophysics Data System (ADS)

Henebry, G. M.; Valle De Carvalho E Oliveira, P.; Zheng, B.; de Beurs, K.; Owsley, B.

2015-12-01

In our current era of intensive earth observation the time is ripe to shift away from studies relying on single sensors or single products to the synergistic use of multiple sensors and products at complementary spatial, temporal, and spectral scales. The use of multiple time series can not only reveal hotspots of change in land surface dynamics, but can indicate plausible proximate causes of the changes and suggest their possible consequences. Here we explore recent trends in the land surface dynamics of exemplary semi-arid grasslands in the western hemisphere, including the shortgrass prairie of eastern Colorado and New Mexico, the sandhills prairie of Nebraska, the "savana gramineo-lenhosa" variety of cerrado in central Brazil, and the pampas of Argentina. Observational datasets include (1) NBAR-based vegetation indices, land surface temperature, and evapotranspiration from MODIS, (2) air temperature, water vapor, and vegetation optical depth from AMSR-E and AMSR2, (3) surface air temperature, water vapor, and relative humidity from AIRS, and (4) surface shortwave, longwave, and total net flux from CERES. The spatial resolutions of these nested data include 500 m, 1000 m, 0.05 degree, 25 km, and 1 degree. We apply the nonparametric Seasonal Kendall trend test to each time series independently to identify areas of significant change. We then examine polygons of co-occurrence of significant change in two or more types of products using the surface radiation and energy budgets as guides to interpret the multiple changes. Changes occurring across broad areas are more likely to be of climatic origin; whereas, changes that are abrupt in space and time and of limited area are more likely anthropogenic. Results illustrate the utility of considering multiple remote sensing products as complementary views of land surface dynamics.

Annotated bibliography of remote sensing methods for monitoring desertification

USGS Publications Warehouse

Walker, A.S.; Robinove, Charles J.

1981-01-01

Remote sensing techniques are valuable for locating, assessing, and monitoring desertification. Remotely sensed data provide a permanent record of the condition of the land in a format that allows changes in land features and condition to be measured. The annotated bibliography of 118 items discusses remote sensing methods that may be applied to desertification studies.

Modeling landscape evapotranspiration by integrating land surface phenology and a water balance algorithm

USGS Publications Warehouse

Senay, Gabriel B.

2008-01-01

The main objective of this study is to present an improved modeling technique called Vegetation ET (VegET) that integrates commonly used water balance algorithms with remotely sensed Land Surface Phenology (LSP) parameter to conduct operational vegetation water balance modeling of rainfed systems at the LSP’s spatial scale using readily available global data sets. Evaluation of the VegET model was conducted using Flux Tower data and two-year simulation for the conterminous US. The VegET model is capable of estimating actual evapotranspiration (ETa) of rainfed crops and other vegetation types at the spatial resolution of the LSP on a daily basis, replacing the need to estimate crop- and region-specific crop coefficients.

Pre-seismic anomalies in remotely sensed land surface temperature measurements: The case study of 2003 Boumerdes earthquake

NASA Astrophysics Data System (ADS)

Bellaoui, Mebrouk; Hassini, Abdelatif; Bouchouicha, Kada

2017-05-01

Detection of thermal anomaly prior to earthquake events has been widely confirmed by researchers over the past decade. One of the popular approaches for anomaly detection is the Robust Satellite Approach (RST). In this paper, we use this method on a collection of six years of MODIS satellite data, representing land surface temperature (LST) images to predict 21st May 2003 Boumerdes Algeria earthquake. The thermal anomalies results were compared with the ambient temperature variation measured in three meteorological stations of Algerian National Office of Meteorology (ONM) (DELLYS-AFIR, TIZI-OUZOU, and DAR-EL-BEIDA). The results confirm the importance of RST as an approach highly effective for monitoring the earthquakes.

NASA applications project in Miami County, Indiana

NASA Technical Reports Server (NTRS)

Johannsen, Chris J.; Fernandez, R. Norberto; Lozano-Garcia, D. Fabian

1990-01-01

This project was designed to acquaint county government officials and their clientele with remote sensing and geographic information systems (GIS) products that contain information about land conditions and land use. The specific project objectives are: (1) to investigate the feasibility of using remotely sensed data to identify and quantify specific land cover categories and conditions for purposes of tax assessment, cropland area measurements, and land use evaluation; (2) to evaluate the use of remotely sensed data to assess soil resources and conditions which affect productivity; (3) to investigate the use of satellite remote sensing data as an aid in assessing soil management practices; and (4) to evaluate the market potential of products derived from the above projects.

Environmental Public Health Applications Using Remotely Sensed Data.

PubMed

Al-Hamdan, Mohammad Z; Crosson, William L; Economou, Sigrid A; Estes, Maurice G; Estes, Sue M; Hemmings, Sarah N; Kent, Shia T; Puckett, Mark; Quattrochi, Dale A; Rickman, Douglas L; Wade, Gina M; McClure, Leslie A

2014-01-01

We describe a remote sensing and GIS-based study that has three objectives: (1) characterize fine particulate matter (PM 2.5 ), insolation and land surface temperature using NASA satellite observations, EPA ground-level monitor data and North American Land Data Assimilation System (NLDAS) data products on a national scale; (2) link these data with public health data from the REasons for Geographic And Racial Differences in Stroke (REGARDS) national cohort study to determine whether these environmental risk factors are related to cognitive decline, stroke and other health outcomes; and (3) disseminate the environmental datasets and public health linkage analyses to end users for decision-making through the Centers for Disease Control and Prevention (CDC) Wide-ranging Online Data for Epidemiologic Research (WONDER) system. This study directly addresses a public health focus of the NASA Applied Sciences Program, utilization of Earth Sciences products, by addressing issues of environmental health to enhance public health decision-making.

MODIS Cloud Products Derived from Terra and Aqua During CRYSTAL-FACE

NASA Technical Reports Server (NTRS)

King, Michael D.; Platnick, S.; Riedi, J. C.; Ackerman, S. A.; Menzel, W. P.

2003-01-01

The Moderate Resolution Imaging Spectroradiometer (MODIS), developed as part of the Earth Observing System (EOS) and launched on Terra in December 1999 and Aqua in May 2002, is designed to meet the scientific needs for satellite remote sensing of clouds, aerosols, water vapor, and land and ocean surface properties. During the CRYSTAL-FACE experiment, numerous aircraft coordinated both in situ and remote sensing observations with the Terra and Aqua spacecraft. In this paper we will emphasize the optical, microphysical, and physical properties of both liquid water and ice clouds obtained from an analysis of the satellite observations over Florida and the Gulf of Mexico during July 2002. We will present the frequency distribution of liquid water and ice cloud microphysical properties throughout the region, separating the results over land and ocean. Probability distributions of effective radius and cloud optical thickness will also be shown.

Environmental Public Health Applications Using Remotely Sensed Data

PubMed Central

Al-Hamdan, Mohammad Z.; Crosson, William L.; Economou, Sigrid A.; Estes, Maurice G.; Estes, Sue M.; Hemmings, Sarah N.; Kent, Shia T.; Puckett, Mark; Quattrochi, Dale A.; Rickman, Douglas L.; Wade, Gina M.; McClure, Leslie A.

2012-01-01

We describe a remote sensing and GIS-based study that has three objectives: (1) characterize fine particulate matter (PM2.5), insolation and land surface temperature using NASA satellite observations, EPA ground-level monitor data and North American Land Data Assimilation System (NLDAS) data products on a national scale; (2) link these data with public health data from the REasons for Geographic And Racial Differences in Stroke (REGARDS) national cohort study to determine whether these environmental risk factors are related to cognitive decline, stroke and other health outcomes; and (3) disseminate the environmental datasets and public health linkage analyses to end users for decision-making through the Centers for Disease Control and Prevention (CDC) Wide-ranging Online Data for Epidemiologic Research (WONDER) system. This study directly addresses a public health focus of the NASA Applied Sciences Program, utilization of Earth Sciences products, by addressing issues of environmental health to enhance public health decision-making. PMID:24910505

Comparison of Atmospheric Parameters Derived from In-Situ and Hyper-/Multispectral Remote Sensing Data of Beautiful Bavarian Lakes

NASA Astrophysics Data System (ADS)

Riedel, S.; Gege, P.; Schneider, M.; Pfug, B.; Oppelt, N.

2016-08-01

Atmospheric correction is a critical step and can be a limiting factor in the extraction of aquatic ecosystem parameters from remote sensing data of coastal and lake waters. Atmospheric correction models commonly in use for open ocean water and land surfaces can lead to large errors when applied to hyperspectral images taken from satellite or aircraft. The main problems arise from uncertainties in aerosol parameters and neglecting the adjacency effect, which originates from multiple scattering of upwelling radiance from the surrounding land. To better understand the challenges for developing an atmospheric correction model suitable for lakes, we compare atmospheric parameters derived from Sentinel- 2A and airborne hyperspectral data (HySpex) of two Bavarian lakes (Klostersee, Lake Starnberg) with in-situ measurements performed with RAMSES and Ibsen spectrometer systems and a Microtops sun photometer.

Urban land use: Remote sensing of ground-basin permeability

NASA Technical Reports Server (NTRS)

Tinney, L. R.; Jensen, J. R.; Estes, J. E.

1975-01-01

A remote sensing analysis of the amount and type of permeable and impermeable surfaces overlying an urban recharge basin is discussed. An effective methodology for accurately generating this data as input to a safe yield study is detailed and compared to more conventional alternative approaches. The amount of area inventoried, approximately 10 sq. miles, should provide a reliable base against which automatic pattern recognition algorithms, currently under investigation for this task, can be evaluated. If successful, such approaches can significantly reduce the time and effort involved in obtaining permeability data, an important aspect of urban hydrology dynamics.

Book Review: Book review

NASA Astrophysics Data System (ADS)

Clevers, Jan G. P. W.

2016-09-01

For many years a good introductory book for undergraduate and postgraduate students on remote sensing of the Earth's land surface, which was not starting with an emphasis on traditional photographic techniques, was missing. In 2010 the first edition of the book Fundamentals of Satellite Remote Sensing by Emilio Chuvieco and Alfredo Huete was published by CRC Press and it was filling this gap. Now the second edition by Emilio Chuvieco was published by CRC Press. This second edition is made more attractive by the use of colour and including colour illustrations instead of the black-and-white ones in the first edition.

The application of remotely sensed data to pedologic and geomorphic mapping on alluvial fan and playa surfaces in Saline Valley, California

NASA Technical Reports Server (NTRS)

Miller, D. A.; Petersen, G. W.; Kahle, A. B.

1986-01-01

Arid and semiarid regions yield excellent opportunities for the study of pedologic and geomorphic processes. The dominance of rock and soil exposure over vegetation not only provides the ground observer with observational possibilities but also affords good opportunities for measurement by aircraft and satellite remote sensor devices. Previous studies conducted in the area of pedologic and geomorphic mapping in arid regions with remotely sensed data have utilized information obtained in the visible to near-infrared portion of the spectrum. Thermal Infrared Multispectral Scanner (TIMS) and Thematic Mapping (TM) data collected in 1984 are being used in comjunction with maps compiled during a Bureau of Land Management (BLM) soil survey to aid in a detailed mapping of alluvial fan and playa surfaces within the valley. The results from this study may yield valuable information concerning the application of thermal data and thermal/visible data combinations to the problem of dating pedologic and geomorphic features in arid regions.

Estimation of Regional Evapotranspiration Using Remotely Sensed Land Surface Temperature. Part 1: Measurement of Evapotranspiration at the Environmental Research Center and Determination of Priestley-taylor Parameter

NASA Technical Reports Server (NTRS)

Kotada, K.; Nakagawa, S.; Kai, K.; Yoshino, M. M.; Takeda, K.; Seki, K.

1985-01-01

In order to study the distribution of evapotranspiration in the humid region using remote sensing technology, the parameter (alpha) in the Priestley-Taylor model was determined. The daily means of the parameter alpha = 1.14 can be available from summer to autumn and alpha = to approximately 2.0 in winter. The results of the satellite and the airborne sensing done on 21st and 22nd January, 1983, are described. Using the vegetation distribution in the Tsukuba Academic New Town, as well as the radiation temperature obtained by remote sensing and the radiation data observed at the ground surface, the evapotranspiration was calculated for each vegetation type by the Priestley-Taylor method. The daily mean evapotranspiration on 22nd January, 1983, was approximately 0.4 mm/day. The differences in evapotranspiration between the vegetation types were not detectable, because the magnitude of evapotranspiration is very little in winter.

Composition and potential origin of marine debris stranded in the Western Indian Ocean on remote Alphonse Island, Seychelles.

PubMed

Duhec, Aurélie V; Jeanne, Richard F; Maximenko, Nikolai; Hafner, Jan

2015-07-15

The abundance, composition, and potential sources of marine debris were investigated on remote Alphonse Island, during the austral winter 2013. A total of 4743 items, weighing 142 kg, were removed from 1 km of windward beach, facing the prevailing southeasterly trade winds. Our study demonstrates the prevalence of plastic debris as a world-wide marine contaminant. Characteristics of the debris suggest it originated primarily from land-based sources. To determine their potential geographic sources we used the Surface Current from Diagnostic model of near-surface ocean currents, forced by satellite sea level and wind data. While preliminary evidence indicated the Southeast Asia to be the main source of the flotsam, the model highlighted Somalia as another potential primary source. Our study concludes that most of the collected debris entered the sea as a result of inadequate waste management and demonstrates how anthropogenic waste can negatively impact even the most remote environments. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Advanced Understanding of Convection Initiation and Optimizing Cloud Seeding by Advanced Remote Sensing and Land Cover Modification over the United Arab Emirates

NASA Astrophysics Data System (ADS)

Wulfmeyer, V.; Behrendt, A.; Branch, O.; Schwitalla, T.

2016-12-01

A prerequisite for significant precipitation amounts is the presence of convergence zones. These are due to land surface heterogeneity, orography as well as mesoscale and synoptic scale circulations. Only, if these convergence zones are strong enough and interact with an upper level instability, deep convection can be initiated. For the understanding of convection initiation (CI) and optimal cloud seeding deployment, it is essential that these convergence zones are detected before clouds are developing in order to preempt the decisive microphysical processes for liquid water and ice formation. In this presentation, a new project on Optimizing Cloud Seeding by Advanced Remote Sensing and Land Cover Modification (OCAL) is introduced, which is funded by the United Arab Emirates Rain Enhancement Program (UAEREP). This project has two research components. The first component focuses on an improved detection and forecasting of convergence zones and CI by a) operation of scanning Doppler lidar and cloud radar systems during two seasonal field campaigns in orographic terrain and over the desert in the UAE, and b) advanced forecasting of convergence zones and CI with the WRF-NOAHMP model system. Nowcasting to short-range forecasting of convection will be improved by the assimilation of Doppler lidar and the UAE radar network data. For the latter, we will apply a new model forward operator developed at our institute. Forecast uncertainties will be assessed by ensemble simulations driven by ECMWF boundaries. The second research component of OCAL will study whether artificial modifications of land surface heterogeneity are possible through plantations or changes of terrain, leading to an amplification of convergence zones. This is based on our pioneering work on high-resolution modeling of the impact of plantations on weather and climate in arid regions. A specific design of the shape and location of plantations can lead to the formation of convergence zones, which can strengthen convergent flows already existing in the region of interest, thus amplifying convection and precipitation. We expect that this method can be successfully applied in regions with pre-existing land-surface heterogeneity and orography such as coastal areas with land-sea breezes and the Al Hajar Mountain range.

A Simple Downscaling Algorithm for Remotely Sensed Land Surface Temperature

NASA Astrophysics Data System (ADS)

Sandholt, I.; Nielsen, C.; Stisen, S.

2009-05-01

The method is illustrated using a combination of MODIS NDVI data with a spatial resolution of 250m and 3 Km Meteosat Second Generation SEVIRI LST data. Geostationary Earth Observation data carry a large potential for assessment of surface state variables. Not the least the European Meteosat Second Generation platform with its SEVIRI sensor is well suited for studies of the dynamics of land surfaces due to its high temporal frequency (15 minutes) and its red, Near Infrared (NIR) channels that provides vegetation indices, and its two split window channels in the thermal infrared for assessment of Land Surface Temperature (LST). For some applications the spatial resolution in geostationary data is too coarse. Due to the low statial resolution of 4.8 km at nadir for the SEVIRI sensor, a means of providing sub pixel information is sought for. By combining and properly scaling two types of satellite images, namely data from the MODIS sensor onboard the polar orbiting platforms TERRA and AQUA and the coarse resolution MSG-SEVIRI, we exploit the best from two worlds. The vegetation index/surface temperature space has been used in a vast number of studies for assessment of air temperature, soil moisture, dryness indices, evapotranspiration and for studies of land use change. In this paper, we present an improved method to derive a finer resolution Land Surface Temperature (LST). A new, deterministic scaling method has been applied, and is compared to existing deterministic downscaling methods based on LST and NDVI. We also compare our results from in situ measurements of LST from the Dahra test site in West Africa.

Future of Land Remote Sensing: What is Needed

NASA Technical Reports Server (NTRS)

Goward, Samuel N.

2007-01-01

A viewgraph presentation describing the future of land remote sensing and the new technologies needed for clear views of the Earth is shown. The contents include: 1) Viewing the Earth; 2) Multi-Imagery; 3) May Missions and Sensors; 4) What is Needed; 5) Things to Think About; 6) Global Land Remote Sensing in Landsat 7 Era; 7) Seasonality; 8) Cloud Contamination; 9) NRC Decadal Study; 10) Atmospheric Attenuation; 11) Geo-Registration; 12) Orthorectification Required; 13) Band Registration with OLI; and 14) Things to Do. A viewgraph presentation describing the future of land remote sensing and the new technologies needed for clear views of the Earth is shown. The contents include: 1) Viewing the Earth; 2) Multi-Imagery; 3) May Missions and Sensors; 4) What is Needed; 5) Things to Think About; 6) Global Land Remote Sensing in Landsat 7 Era; 7) Seasonality; 8) Cloud Contamination; 9) NRC Decadal Study; 10) Atmospheric Attenuation; 11) Geo-Registration; 12) Orthorectification Required; 13) Band Registration with OLI; and 14) Things to Do.

Landsat: A global land-imaging mission

USGS Publications Warehouse

,

2012-01-01

Across four decades since 1972, Landsat satellites have continuously acquired space-based images of the Earth's land surface, coastal shallows, and coral reefs. The Landsat Program, a joint effort of the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA), was established to routinely gather land imagery from space. NASA develops remote-sensing instruments and spacecraft, then launches and validates the performance of the instruments and satellites. The USGS then assumes ownership and operation of the satellites, in addition to managing all ground reception, data archiving, product generation, and distribution. The result of this program is a long-term record of natural and human induced changes on the global landscape.

A new multi-angle remote sensing framework for scaling vegetation properties from tower-based spectro-radiometers to next generation "CubeSat"-satellites.

NASA Astrophysics Data System (ADS)

Hilker, T.; Hall, F. G.; Dyrud, L. P.; Slagowski, S.

2014-12-01

Frequent earth observations are essential for assessing the risks involved with global climate change, its feedbacks on carbon, energy and water cycling and consequences for live on earth. Often, satellite-remote sensing is the only practical way to provide such observations at comprehensive spatial scales, but relationships between land surface parameters and remotely sensed observations are mostly empirical and cannot easily be scaled across larger areas or over longer time intervals. For instance, optically based methods frequently depend on extraneous effects that are unrelated to the surface property of interest, including the sun-server geometry or background reflectance. As an alternative to traditional, mono-angle techniques, multi-angle remote sensing can help overcome some of these limitations by allowing vegetation properties to be derived from comprehensive reflectance models that describe changes in surface parameters based on physical principles and radiative transfer theory. Recent results have shown in theoretical and experimental research that multi-angle techniques can be used to infer and scale the photosynthetic rate of vegetation, its biochemical and structural composition robustly from remote sensing. Multi-angle remote sensing could therefore revolutionize estimates of the terrestrial carbon uptake as scaling of primary productivity may provide a quantum leap in understanding the spatial and temporal complexity of terrestrial earth science. Here, we introduce a framework of next generation tower-based instruments to a novel and unique constellation of nano-satellites (Figure 1) that will allow us to systematically scale vegetation parameters from stand to global levels. We provide technical insights, scientific rationale and present results. We conclude that future earth observation from multi-angle satellite constellations, supported by tower based remote sensing will open new opportunities for earth system science and earth system modeling.

Modelling the angular effects on satellite retrieved LST at global scale using a land surface classification

NASA Astrophysics Data System (ADS)

Ermida, Sofia; DaCamara, Carlos C.; Trigo, Isabel F.; Pires, Ana C.; Ghent, Darren

2017-04-01

Land Surface Temperature (LST) is a key climatological variable and a diagnostic parameter of land surface conditions. Remote sensing constitutes the most effective method to observe LST over large areas and on a regular basis. Although LST estimation from remote sensing instruments operating in the Infrared (IR) is widely used and has been performed for nearly 3 decades, there is still a list of open issues. One of these is the LST dependence on viewing and illumination geometry. This effect introduces significant discrepancies among LST estimations from different sensors, overlapping in space and time, that are not related to uncertainties in the methodologies or input data used. Furthermore, these directional effects deviate LST products from an ideally defined LST, which should represent to the ensemble of directional radiometric temperature of all surface elements within the FOV. Angular effects on LST are here conveniently estimated by means of a kernel model of the surface thermal emission, which describes the angular dependence of LST as a function of viewing and illumination geometry. The model is calibrated using LST data as provided by a wide range of sensors to optimize spatial coverage, namely: 1) a LEO sensor - the Moderate Resolution Imaging Spectroradiometer (MODIS) on-board NASA's TERRA and AQUA; and 2) 3 GEO sensors - the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on-board EUMETSAT's Meteosat Second Generation (MSG), the Japanese Meteorological Imager (JAMI) on-board the Japanese Meteorological Association (JMA) Multifunction Transport SATellite (MTSAT-2), and NASA's Geostationary Operational Environmental Satellites (GOES). As shown in our previous feasibility studies the sampling of illumination and view angles has a high impact on the obtained model parameters. This impact may be mitigated when the sampling size is increased by aggregating pixels with similar surface conditions. Here we propose a methodology where land surface is stratified by means of a cluster analysis using information on land cover type, fraction of vegetation cover and topography. The kernel model is then adjusted to LST data corresponding to each cluster. It is shown that the quality of the cluster based kernel model is very close to the pixel based one. Furthermore, the reduced number of parameters (limited to the number of identified clusters, instead of a pixel-by-pixel model calibration) allows improving the kernel model trough the incorporation of a seasonal component. The application of the here discussed procedure towards the harmonization of LST products from multi-sensors is on the framework of the ESA DUE GlobTemperature project.

Towards a Near Real-Time Satellite-Based Flux Monitoring System for the MENA Region

NASA Astrophysics Data System (ADS)

Ershadi, A.; Houborg, R.; McCabe, M. F.; Anderson, M. C.; Hain, C.

2013-12-01

Satellite remote sensing has the potential to offer spatially and temporally distributed information on land surface characteristics, which may be used as inputs and constraints for estimating land surface fluxes of carbon, water and energy. Enhanced satellite-based monitoring systems for aiding local water resource assessments and agricultural management activities are particularly needed for the Middle East and North Africa (MENA) region. The MENA region is an area characterized by limited fresh water resources, an often inefficient use of these, and relatively poor in-situ monitoring as a result of sparse meteorological observations. To address these issues, an integrated modeling approach for near real-time monitoring of land surface states and fluxes at fine spatio-temporal scales over the MENA region is presented. This approach is based on synergistic application of multiple sensors and wavebands in the visible to shortwave infrared and thermal infrared (TIR) domain. The multi-scale flux mapping and monitoring system uses the Atmosphere-Land Exchange Inverse (ALEXI) model and associated flux disaggregation scheme (DisALEXI), and the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) in conjunction with model reanalysis data and multi-sensor remotely sensed data from polar orbiting (e.g. Landsat and MODerate resolution Imaging Spectroradiometer (MODIS)) and geostationary (MSG; Meteosat Second Generation) satellite platforms to facilitate time-continuous (i.e. daily) estimates of field-scale water, energy and carbon fluxes. Within this modeling system, TIR satellite data provide information about the sub-surface moisture status and plant stress, obviating the need for precipitation input and a detailed soil surface characterization (i.e. for prognostic modeling of soil transport processes). The STARFM fusion methodology blends aspects of high frequency (spatially coarse) and spatially fine resolution sensors and is applied directly to flux output fields to facilitate daily mapping of fluxes at sub-field scales. A complete processing infrastructure to automatically ingest and pre-process all required input data and to execute the integrated modeling system for near real-time agricultural monitoring purposes over targeted MENA sites is being developed, and initial results from this concerted effort will be discussed.

Comparing land surface phenology derived from satellite and GPS network microwave remote sensing.

PubMed

Jones, Matthew O; Kimball, John S; Small, Eric E; Larson, Kristine M

2014-08-01

The land surface phenology (LSP) start of season (SOS) metric signals the seasonal onset of vegetation activity, including canopy growth and associated increases in land-atmosphere water, energy and carbon (CO2) exchanges influencing weather and climate variability. The vegetation optical depth (VOD) parameter determined from satellite passive microwave remote sensing provides for global LSP monitoring that is sensitive to changes in vegetation canopy water content and biomass, and insensitive to atmosphere and solar illumination constraints. Direct field measures of canopy water content and biomass changes desired for LSP validation are generally lacking due to the prohibitive costs of maintaining regional monitoring networks. Alternatively, a normalized microwave reflectance index (NMRI) derived from GPS base station measurements is sensitive to daily vegetation water content changes and may provide for effective microwave LSP validation. We compared multiyear (2007-2011) NMRI and satellite VOD records at over 300 GPS sites in North America, and their derived SOS metrics for a subset of 24 homogenous land cover sites to investigate VOD and NMRI correspondence, and potential NMRI utility for LSP validation. Significant correlations (P<0.05) were found at 276 of 305 sites (90.5 %), with generally favorable correspondence in the resulting SOS metrics (r (2)=0.73, P<0.001, RMSE=36.8 days). This study is the first attempt to compare satellite microwave LSP metrics to a GPS network derived reflectance index and highlights both the utility and limitations of the NMRI data for LSP validation, including spatial scale discrepancies between local NMRI measurements and relatively coarse satellite VOD retrievals.

Landsat Evapotranspiration for Historical Field-scale Water Use (1984-2015) in the Upper Rio Grande River Basin

NASA Astrophysics Data System (ADS)

Senay, G. B.; Schauer, M.; Singh, R. K.; Friedrichs, M.

2017-12-01

Field-scale water use maps derived from evapotranspiration (ET) can characterize water use patterns and the impacts of water management decisions. This project generated historical (1984-2015) Landsat-based ET maps for the entire Upper Rio Grande basin which makes this one of the largest regions in the United States with remotely sensed historical ET at Landsat resolution. More than 10,000 Landsat images spanning 32 years were processed using the Operational Simplified Surface Energy Balance (SSEBop) model which integrates weather data and remotely sensed images to estimate monthly and annual ET. Time-series analysis focused on three water-intensive study areas within the basin: the San Luis Valley in Colorado, irrigated fields along the Rio Grande River near Albuquerque, NM, and irrigated fields near Las Cruces, NM. Preliminary analysis suggests land use changes result in declining water use in irrigated areas of the basin which corresponds with increases in land surface temperatures. Time-series analysis of water use patterns at multiple temporal and spatial scales demonstrates the impact of water management decisions on the availability of water in the basin. Comparisons with cropland data from the USDA (NASS CDL) demonstrate how water use for particular crop types changes over time in response to land use changes and shifts in water management. This study illustrates a useful application of "Big Data" earth observation science for quantifying impacts of climate and land use changes on water availability within the United States as well as applications in planning water resource allocation, managing water rights, and sustaining agricultural production in the Upper Rio Grande basin.

Integrating remote sensing, field observations, and ground temperature modeling to help address permafrost-related societal challenges around native village of Telida, Interior Alaska

NASA Astrophysics Data System (ADS)

Panda, S. K.; Kholodov, A. L.; Romanovsky, V. E.; Hanson, T.

2017-12-01

The dramatic landscape changes caused by thawing permafrost are wreaking havoc in remote Arctic villages. These changes are directly affecting land use, subsistence access, and adding to the high cost of infrastructure maintenance in remote villages; in some instances, threatening the safety of people who practice subsistence in remote sites. The goal of this project is to study the status and distribution of near-surface permafrost surrounding the Telida village in the Upper Kuskokwim region, and ultimately forecast how permafrost distribution and temperature may evolve in the near-future to help the Telida community better prepare in addressing the engineering, environmental, and subsistence access issues that are emerging with warmer climate and degrading permafrost. We collected baseline data on permafrost distribution, ground temperature, and thaw depth around Telida village during two visits to the study site (August 2016 and 2017) and conducted snow survey in February 2017 in all major landcover types. We mapped landcover, soil, and near-surface permafrost distribution for a 30 square mile area surrounding the Telida village using SPOT pan-sharpened image products (pixel size: 2.5 m) and ground observations. We identified seven major landcover types surrounding the Telida village: Open spruce, Mixed forest, Closed spruce, Wetland, Tall shrub, Deciduous forest, and Low shrub. Open spruce is the most dominant landcover type occupying 33% of the study area. Near-surface permafrost underlies less than 53% of the study area; it is present under Open spruce (active layer thickness: 59±7 cm; permafrost surface temperature: -0.6 °C) and Low shrub landcover types (active layer thickness: 60 cm; permafrost surface temperature: -0.07 °C), sporadic under Closed spruce landcover type, and absent under other landcover types. The mean annual permafrost surface temperature ranges from 0 to -1 °C and coldest permafrost is found under Open spruce landcover type. Permafrost around Telida village is warm and at the verge of thawing. Our ground temperature modeling effort will provide critical knowledge on how permafrost status and distribution around Telida village may evolve in the near future which we hope will help the Telida community in addressing the emerging engineering, environmental, and land use challenges.

Exploring the mid-infrared region for urban remote sensing: seasonal and view angle effects

NASA Astrophysics Data System (ADS)

Krehbiel, C. P.; Kovalskyy, V.; Henebry, G. M.

2013-12-01

Spanning 3-5 microns, the mid-infrared (MIR) region is the mixing zone between reflected sunlight and emitted earthlight in roughly equal proportions. While the MIR has been utilized in atmospheric remote sensing, its potential in terrestrial remote sensing--particularly urban remote sensing, has yet to be realized. One major advantage of the MIR is the ability to penetrate most anthropogenic haze and smog. Green vegetation appears MIR-dark, urban building materials appear MIR-grey, and bare soil and dried vegetation appear MIR-bright. Thus, there is an intrinsic seasonality in MIR radiance dynamics due both to surface type differences and to seasonal change in insolation. These factors merit exploration into the potential applications of the MIR for monitoring urban change. We investigated MIR radiance dynamics in relation to (1) the spectral properties of land cover types, (2) time of year and (3) sensor view zenith angle (VZA). We used Aqua MODIS daily swaths for band 23 (~ 4.05 μm) at 1 km spatial resolution from 2009-2010 and the NLCD Percent Impervious Surface Area (%ISA) 30 m product from 2001 and 2006. We found the effects of time of year, sensor VZA, and %ISA to be three principal factors influencing MIR radiance dynamics. We focused on analyzing the relationship between MIR radiance and %ISA over eight major cities in the Great Plains of the USA. This region is characterized by four distinct seasons, relatively flat terrain, and isolated urban centers situated within a vegetated landscape. We used west-east transects beginning in the agricultural areas outside of each city, passing through the urban core and extending back out into the agricultural periphery to observe the spatial pattern of MIR radiance and how it changes seasonally. Sensor VZA influences radiance dynamics by affecting the proportion of surface elements detected--especially pertinent at the coarse spatial resolution (~1 km) of MODIS. For example, smaller VZAs (<30°) capture more spatial detail than larger VZAs (>30°). Larger VZAs detect a larger proportion of crop canopies and less soil surface, and thus generally exhibit lower radiance and less variation than smaller VZAs. Future work should focus on how best to account for (1) land surface phenology, (2) the proportion of impervious surface, and (3) sensor viewing geometry to generate high signal-to-noise ratio composites and advance change detection and urban growth monitoring.

Classifying land cover from an object-oriented approach - applied to LANDSAT 8 at the regional scale of the Lake Tana Basin (Ethiopia)

NASA Astrophysics Data System (ADS)

Lemma, Hanibal; Frankl, Amaury; Poesen, Jean; Adgo, Enyew; Nyssen, Jan

2017-04-01

Object-oriented image classification has been gaining prominence in the field of remote sensing and provides a valid alternative to the 'traditional' pixel based methods. Recent studies have proven the superiority of the object-based approach. So far, object-oriented land cover classifications have been applied either at limited spatial coverages (ranging 2 to 1091 km2) or by using very high resolution (0.5-16 m) imageries. The main aim of this study is to drive land cover information for large area from Landsat 8 OLI surface reflectance using the Estimation of Scale Parameter (ESP) tool and the object oriented software eCognition. The available land cover map of Lake Tana Basin (Ethiopia) is about 20 years old with a courser spatial scale (1:250,000) and has limited use for environmental modelling and monitoring studies. Up-to-date and basin wide land cover maps are essential to overcome haphazard natural resources management, land degradation and reduced agricultural production. Indeed, object-oriented approach involves image segmentation prior to classification, i.e. adjacent similar pixels are aggregated into segments as long as the heterogeneity in the spectral and spatial domains is minimized. For each segmented object, different attributes (spectral, textural and shape) were calculated and used for in subsequent classification analysis. Moreover, the commonly used error matrix is employed to determine the quality of the land cover map. As a result, the multiresolution segmentation (with parameters of scale=30, shape=0.3 and Compactness=0.7) produces highly homogeneous image objects as it is observed in different sample locations in google earth. Out of the 15,089 km2 area of the basin, cultivated land is dominant (69%) followed by water bodies (21%), grassland (4.8%), forest (3.7%) and shrubs (1.1%). Wetlands, artificial surfaces and bare land cover only about 1% of the basin. The overall classification accuracy is 80% with a Kappa coefficient of 0.75. With regard to individual classes, the classification show higher Producer's and User's accuracy (above 84%) for cultivated land, water bodies and forest, but lower (less than 70%) for shrubs, bare land and grassland. Key words: accuracy assessment, eCognition, Estimation of Scale Parameter, land cover, Landsat 8, remote sensing

Estimation of Land Surface Temperature from GCOM-W1 AMSR2 Data over the Chinese Landmass

NASA Astrophysics Data System (ADS)

Zhou, Ji; Dai, Fengnan; Zhang, Xiaodong

2016-04-01

As one of the most important parameter at the interface between the earth's surface and atmosphere, land surface temperature (LST) plays a crucial role in many fields, such as climate change monitoring and hydrological modeling. Satellite remote sensing provides the unique possibility to observe LST of large regions at diverse spatial and temporal scales. Compared with thermal infrared (TIR) remote sensing, passive microwave (PW) remote sensing has a better ability in overcoming the influences of clouds; thus, it can be used to improve the temporal resolution of current satellite TIR LST. However, most of current methods for estimation LST from PW remote sensing are empirical and have unsatisfied generalization. In this study, a semi-empirical method is proposed to estimate LST from the observation of the Advanced Microwave Scanning Radiometer 2 (AMSR2) on board the Global Change Observation Mission 1st-WATER "SHIZUKU" satellite (GCOM-W1). The method is based on the PW radiation transfer equation, which is simplified based on (1) the linear relationship between the emissivities of horizontal and vertical polarization channels at the same frequency and (2) the significant relationship between atmospheric parameters and the atmospheric water vapor content. An iteration approach is used to best fit the pixel-based coefficients in the simplified radiation transfer equation of the horizontal and vertical polarization channels at each frequency. Then an integration approach is proposed to generate the ensemble estimation from estimations of multiple frequencies for different land cover types. This method is trained with the AMSR2 brightness temperature and MODIS LST in 2013 over the entire Chinese landmass and then it is tested with the data in 2014. Validation based on in situ LSTs measured in northwestern China demonstrates that the proposed method has a better accuracy than the polarization radiation method, with a root-mean squared error of 3 K. Although the proposal method is applied to AMSR2 data, it has good ability to extend to other satellite PW sensors, such as the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) on board the Aqua satellite and the Special Sensor Microwave/Imager (SSM/I) on board the Defense Meteorological Satellite Program (DMSP) satellite. It would be beneficial in providing LST to applications at continental and global scales.

An Overview of the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE)

NASA Astrophysics Data System (ADS)

Sellers, P. J.; Hall, F. G.; Asrar, G.; Strebel, D. E.; Murphy, R. E.

1992-11-01

In the summer of 1983 a group of scientists working in the fields of meteorology, biology, and remote sensing met to discuss methods for modeling and observing land-surface—atmosphere interactions on regional and global scales. They concluded, first, that the existing climate models contained poor representations of the processes controlling the exchanges of energy, water, heat, and carbon between the land surface and the atmosphere and, second, that satellite remote sensing had been underutilized as a means of specifying global fields of the governing biophysical parameters. Accordingly, a multiscale, multidisciplinary experiment, FIFE, was initiated to address these two issues. The objectives of FIFE were specified as follows: (1) Upscale integration of models: The experiment was designed to test the soil-plant-atmosphere models developed by biometeorologists for small-scale applications (millimeters to meters) and to develop methods to apply them at the larger scales (kilometers) appropriate to atmospheric models and satellite remote sensing. (2) Application of satellite remote sensing: Even if the first goal were achieved to yield a "perfect" model of vegetation-atmosphere exchanges, it would have very limited applications without a global observing system for initialization and validation. As a result, the experiment was tasked with exploring methods for using satellite data to quantify important biophysical states and rates for model input. The experiment was centered on a 15 × 15 km grassland site near Manhattan, Kansas. This area became the focus for an extended monitoring program of satellite, meteorological, biophysical, and hydrological data acquisition from early 1987 through October 1989 and a series of 12- to 20-day intensive field campaigns (IFCs), four in 1987 and one in 1989. During the IFCs the fluxes of heat, moisture, carbon dioxide, and radiation were measured with surface and airborne equipment in coordination with measurements of surface and atmospheric parameters and satellite overpasses. The resulting data are held in a single integrated data base and continue to be analyzed by the participating scientists and others. The first two sections of this paper recount the history and scientific background leading up to FIFE; the third and fourth sections review the experiment design, the scientific teams and equipment involved, and the actual execution of the experiment; the fifth section provides an overview of the contents of this special issue; the sixth section summarizes the management and resources of the project; and the last section lists the acknowledgments.

The impact of urban morphology and land cover on the sensible heat flux retrieved by satellite and in-situ observations

NASA Astrophysics Data System (ADS)

Gawuc, L.; Łobocki, L.; Kaminski, J. W.

2017-12-01

Land surface temperature (LST) is a key parameter in various applications for urban environments research. However, remotely-sensed radiative surface temperature is not equivalent to kinetic nor aerodynamic surface temperature (Becker and Li, 1995; Norman and Becker, 1995). Thermal satellite observations of urban areas are also prone to angular anisotropy which is directly connected with the urban structure and relative sun-satellite position (Hu et al., 2016). Sensible heat flux (Qh) is the main component of surface energy balance in urban areas. Retrieval of Qh, requires observations of, among others, a temperature gradient. The lower level of temperature measurement is commonly replaced by remotely-sensed radiative surface temperature (Chrysoulakis, 2003; Voogt and Grimmond, 2000; Xu et al., 2008). However, such replacement requires accounting for the differences between aerodynamic and radiative surface temperature (Chehbouni et al., 1996; Sun and Mahrt, 1995). Moreover, it is important to avoid micro-scale processes, which play a major role in the roughness sublayer. This is due to the fact that Monin-Obukhov similarity theory is valid only in dynamic sublayer. We will present results of the analyses of the impact of urban morphology and land cover on the seasonal changes of sensible heat flux (Qh). Qh will be retrieved by two approaches. First will be based on satellite observations of radiative surface temperature and second will be based on in-situ observations of kinetic road temperature. Both approaches will utilize wind velocity, and air temperature observed in-situ. We will utilize time series of MODIS LST observations for the period of 2005-2014 as well as simultaneous in-situ observations collected by road weather network (9 stations). Ground stations are located across the city of Warsaw, outside the city centre in low-rise urban structure. We will account for differences in urban morphology and land cover in the proximity of ground stations. We will utilize DEM and Urban Atlas LULC database and freely available visible aerial and satellite imagery. All the analyses will be conducted for single pixels, which will be closest to the locations of the ground stations (nearest neighbour approach). Appropriate figures showing the seasonal variability of Qh will be presented.

Apollo 16 landing site: Summary of earth based remote sensing data, part W

NASA Technical Reports Server (NTRS)

Zisk, S. H.; Masursky, H.; Milton, D. J.; Schaber, G. G.; Shorthill, R. W.; Thompson, T. W.

1972-01-01

Infrared and radar studies of the Apollo 16 landing site are summarized. Correlations and comparisons between earth based remote sensing data, IR observations, and other data are discussed in detail. Remote sensing studies were devoted to solving two problems: (1) determining the physical difference between Cayley and Descartes geologic units near the landing site; and (2) determining the nature of the bright unit of Descartes mountain material.

Completing the Picture: Importance of Considering Participatory Mapping for REDD+ Measurement, Reporting and Verification (MRV)

PubMed Central

Rafanoharana, Serge; Boissière, Manuel; Wijaya, Arief; Wardhana, Wahyu

2016-01-01

Remote sensing has been widely used for mapping land cover and is considered key to monitoring changes in forest areas in the REDD+ Measurement, Reporting and Verification (MRV) system. But Remote Sensing as a desk study cannot capture the whole picture; it also requires ground checking. Therefore, complementing remote sensing analysis using participatory mapping can help provide information for an initial forest cover assessment, gain better understanding of how local land use might affect changes, and provide a way to engage local communities in REDD+. Our study looked at the potential of participatory mapping in providing complementary information for remotely sensed maps. The research sites were located in different ecological and socio-economic contexts in the provinces of Papua, West Kalimantan and Central Java, Indonesia. Twenty-one maps of land cover and land use were drawn with local community participation during focus group discussions in seven villages. These maps, covering a total of 270,000ha, were used to add information to maps developed using remote sensing, adding 39 land covers to the eight from our initial desk assessment. They also provided additional information on drivers of land use and land cover change, resource areas, territory claims and land status, which we were able to correlate to understand changes in forest cover. Incorporating participatory mapping in the REDD+ MRV protocol would help with initial remotely sensed land classifications, stratify an area for ground checks and measurement plots, and add other valuable social data not visible at the RS scale. Ultimately, it would provide a forum for local communities to discuss REDD+ activities and develop a better understanding of REDD+. PMID:27977685

76 FR 65529 - Agency Information Collection Activities: Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-21

... National Land Remote Sensing Education, Outreach and Research Activity (NLRSEORA). As required by the... Drive MS 517, Reston, VA, 20192 (mail) . SUPPLEMENTARY INFORMATION: Title: National Land Remote Sensing... Remote Sensing Program, therefore it is more appropriate to refer to this effort as an activity rather...

Reduction of Topographic Effect for Curve Number Estimated from Remotely Sensed Imagery

NASA Astrophysics Data System (ADS)

Zhang, Wen-Yan; Lin, Chao-Yuan

2016-04-01

The Soil Conservation Service Curve Number (SCS-CN) method is commonly used in hydrology to estimate direct runoff volume. The CN is the empirical parameter which corresponding to land use/land cover, hydrologic soil group and antecedent soil moisture condition. In large watersheds with complex topography, satellite remote sensing is the appropriate approach to acquire the land use change information. However, the topographic effect have been usually found in the remotely sensed imageries and resulted in land use classification. This research selected summer and winter scenes of Landsat-5 TM during 2008 to classified land use in Chen-You-Lan Watershed, Taiwan. The b-correction, the empirical topographic correction method, was applied to Landsat-5 TM data. Land use were categorized using K-mean classification into 4 groups i.e. forest, grassland, agriculture and river. Accuracy assessment of image classification was performed with national land use map. The results showed that after topographic correction, the overall accuracy of classification was increased from 68.0% to 74.5%. The average CN estimated from remotely sensed imagery decreased from 48.69 to 45.35 where the average CN estimated from national LULC map was 44.11. Therefore, the topographic correction method was recommended to normalize the topographic effect from the satellite remote sensing data before estimating the CN.

Monitoring land use/cover changes on the Romanian Black Sea Coast

NASA Astrophysics Data System (ADS)

Zoran, L. F. V.; Dida, A. I.; Zoran, M. A.

2014-10-01

Remotely sensed satellite data are critical to understanding the coastal zones' physical and social systems interaction, complementing ground based methods and providing accurate wide range, objective and comparable, at widely-varying scales, synoptically data. For some environmental agreements remote sensing may provide the only viable means of compliance verification because the phenomena are monitored occurs over large and inaccessible geographic areas. The main aim of this paper was the assessment of coastal zone land cover/use changes based on fusion technique of satellite remote sensing imagery. The evaluation of coastal zone landscapes was based upon different sub-functions which refer to landscape features such as water, soil, land-use, buildings, groundwater, biotope types. A newly proposed sub-pixel mapping algorithm was applied to a set of multispectral and multitemporal satellite data for Danube Delta, Constantza and Black Sea coastal zone areas in Romania. A land cover classification and subsequent environmental quality analysis for change detection was done based on Landsat TM , Landsat ETM, QuickBird satellite images over 1990 to 2013 period of time. Spectral signatures of different terrain features have been used to separate and classify surface units of coastal zone and sub-coastal zone area.The change in the position of the coastline in Constantza area was examined in relation with the urban expansion. A distinction was made between landfill/sedimentation processes on the one hand and dredging/erosion processes on the other. We considered the Romanian Black Sea coastal zone dynamics in connection with the spatio-temporal variation of physical and biogeochemical processes and their influences on the environmental state in the near-shore area.

Sensitivity of atmospheric correction to loading and model of the aerosol

NASA Astrophysics Data System (ADS)

Bassani, Cristiana; Braga, Federica; Bresciani, Mariano; Giardino, Claudia; Adamo, Maria; Ananasso, Cristina; Alberotanza, Luigi

2013-04-01

The physically-based atmospheric correction requires knowledge of the atmospheric conditions during the remotely data acquisitions [Guanter et al., 2007; Gao et al., 2009; Kotchenova et al. 2009; Bassani et al., 2010]. The propagation of solar radiation in the atmospheric window of visible and near-infrared spectral domain, depends on the aerosol scattering. The effects of solar beam extinction are related to the aerosol loading, by the aerosol optical thickness @550nm (AOT) parameter [Kaufman et al., 1997; Vermote et al., 1997; Kotchenova et al., 2008; Kokhanovsky et al. 2010], and also to the aerosol model. Recently, the atmospheric correction of hyperspectral data is considered sensitive to the micro-physical and optical characteristics of aerosol, as reported in [Bassani et al., 2012]. Within the framework of CLAM-PHYM (Coasts and Lake Assessment and Monitoring by PRISMA HYperspectral Mission) project, funded by Italian Space Agency (ASI), the role of the aerosol model on the accuracy of the atmospheric correction of hyperspectral image acquired over water target is investigated. In this work, the results of the atmospheric correction of HICO (Hyperspectral Imager for the Coastal Ocean) images acquired on Northern Adriatic Sea in the Mediterranean are presented. The atmospheric correction has been performed by an algorithm specifically developed for HICO sensor. The algorithm is based on the equation presented in [Vermote et al., 1997; Bassani et al., 2010] by using the last generation of the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) radiative transfer code [Kotchenova et al., 2008; Vermote et al., 2009]. The sensitive analysis of the atmospheric correction of HICO data is performed with respect to the aerosol optical and micro-physical properties used to define the aerosol model. In particular, a variable mixture of the four basic components: dust- like, oceanic, water-soluble, and soot, has been considered. The water reflectance, obtained from the atmospheric correction with variable model and fixed loading of the aerosol, has been compared. The results highlight the requirements to define the aerosol characteristics, loading and model, to simulate the radiative field in the atmosphere system for an accurate atmospheric correction of hyperspectral data, improving the accuracy of the results for surface reflectance process over water, a dark-target. As conclusion, the aerosol model plays a crucial role for an accurate physically-based atmospheric correction of hyperspectral data over water. Currently, the PRISMA mission provides valuable opportunities to study aerosol and their radiative effects on the hyperspectral data. Bibliography Guanter, L.; Estellès, V.; Moreno, J. Spectral calibration and atmospheric correction of ultra-fine spectral and spatial resolution remote sensing data. Application to CASI-1500 data. Remote Sens. Environ. 2007, 109, 54-65. Gao, B.-C.; Montes, M.J.; Davis, C.O.; Goetz, A.F.H. Atmospheric correction algorithms for hyperspectral remote sensing data of land and ocean. Remote Sens. Environ. 2009, 113, S17-S24. Kotchenova, S. Atmospheric correction for the monitoring of land surfaces. J. Geophys. Res. 2009, 113, D23. Bassani C.; Cavalli, R.M.; Pignatti S. Aerosol optical retrieval and surface reflectance from airborne remote sensing data over land. Sens. 2010, 10, 6421-6438. Kaufman, Y. J., Tanrè, D., Gordon H. R., Nakajima T., Lenoble J., Frouin R., Grassl H., Herman B.M., King M., and Teillet P.M.: Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer, J. Geophys. Res., 102(D14), 17051-17067, 1997. Vermote, E.F.; Tanrè , D.; Deuzè´ , J.L.; Herman M.; Morcrette J.J. Second simulation of the satellite signal in the solar spectrum, 6S: An overview. IEEE Trans. Geosci. Remote Sens. 1997, 35, 675-686. Kotchenova, S.Y.; Vermote, E.F.; Levy, R.; Lyapustin, A. Radiative transfer codes for atmospheric correction and aerosol retrieval: Intercomparison study. Appl. Optics 2008, 47, 2215-2226. Kokhanovsky A.A., Deuzè J.L., Diner D.J., Dubovik O., Ducos F., Emde C., Garay M.J., Grainger R.G., Heckel A., Herman M., Katsev I.L., Keller J., Levy R., North P.R.J., Prikhach A.S., Rozanov V.V., Sayer A.M., Ota Y., Tanrè D., Thomas G.E., Zege E.P. The inter-comparison of major satellite aerosol retrieval algorithms using simulated intensity and polarization characteristics of reflected light. Atmos. Meas. Tech., 3, 909-932, 2010. Bassani C.; Cavalli, R.M.; Antonelli, P. Influence of aerosol and surface reflectance variability on hyperspectral observed radiance. Atmos. Meas. Tech. 2012, 5, 1193-1203. Vermote , E.F.; Kotchenova, S. Atmospheric correction for the monitoring of land surfaces. J. Geophys. Res. 2009, 113, D23.

Deriving temporally continuous soil moisture estimations at fine resolution by downscaling remotely sensed product

NASA Astrophysics Data System (ADS)

Jin, Yan; Ge, Yong; Wang, Jianghao; Heuvelink, Gerard B. M.

2018-06-01

Land surface soil moisture (SSM) has important roles in the energy balance of the land surface and in the water cycle. Downscaling of coarse-resolution SSM remote sensing products is an efficient way for producing fine-resolution data. However, the downscaling methods used most widely require full-coverage visible/infrared satellite data as ancillary information. These methods are restricted to cloud-free days, making them unsuitable for continuous monitoring. The purpose of this study is to overcome this limitation to obtain temporally continuous fine-resolution SSM estimations. The local spatial heterogeneities of SSM and multiscale ancillary variables were considered in the downscaling process both to solve the problem of the strong variability of SSM and to benefit from the fusion of ancillary information. The generation of continuous downscaled remote sensing data was achieved via two principal steps. For cloud-free days, a stepwise hybrid geostatistical downscaling approach, based on geographically weighted area-to-area regression kriging (GWATARK), was employed by combining multiscale ancillary variables with passive microwave remote sensing data. Then, the GWATARK-estimated SSM and China Soil Moisture Dataset from Microwave Data Assimilation SSM data were combined to estimate fine-resolution data for cloudy days. The developed methodology was validated by application to the 25-km resolution daily AMSR-E SSM product to produce continuous SSM estimations at 1-km resolution over the Tibetan Plateau. In comparison with ground-based observations, the downscaled estimations showed correlation (R ≥ 0.7) for both ascending and descending overpasses. The analysis indicated the high potential of the proposed approach for producing a temporally continuous SSM product at fine spatial resolution.

Remotely Sensed Based Lake/Reservoir Routing in Congo River Basin

NASA Astrophysics Data System (ADS)

Raoufi, R.; Beighley, E.; Lee, H.

2017-12-01

Lake and reservoir dynamics can influence local to regional water cycles but are often not well represented in hydrologic models. One challenge that limits their inclusion in models is the need for detailed storage-discharge behavior that can be further complicated in reservoirs where specific operation rules are employed. Here, the Hillslope River Routing (HRR) model is combined with a remotely sensed based Reservoir Routing (RR) method and applied to the Congo River Basin. Given that topographic data are often continuous over the entire terrestrial surface (i.e., does not differentiate between land and open water), the HRR-RR model integrates topographic derived river networks and catchment boundaries (e.g., HydroSHEDs) with water boundary extents (e.g., Global Lakes and Wetlands Database) to develop the computational framework. The catchments bordering lakes and reservoirs are partitioned into water and land portions, where representative flowpath characteristics are determined and vertical water balance and lateral routings is performed separately on each partition based on applicable process models (e.g., open water evaporation vs. evapotranspiration). To enable reservoir routing, remotely sensed water surface elevations and extents are combined to determine the storage change time series. Based on the available time series, representative storage change patterns are determined. Lake/reservoir routing is performed by combining inflows from the HRR-RR model and the representative storage change patterns to determine outflows. In this study, a suite of storage change patterns derived from remotely sensed measurements are determined representative patterns for wet, dry and average conditions. The HRR-RR model dynamically selects and uses the optimal storage change pattern for the routing process based on these hydrologic conditions. The HRR-RR model results are presented to highlight the importance of lake attenuation/routing in the Congo Basin.

Classification of simple vegetation types using POLSAR image data

NASA Technical Reports Server (NTRS)

Freeman, A.

1993-01-01

Mapping basic vegetation or land cover types is a fairly common problem in remote sensing. Knowledge of the land cover type is a key input to algorithms which estimate geophysical parameters, such as soil moisture, surface roughness, leaf area index or biomass from remotely sensed data. In an earlier paper, an algorithm for fitting a simple three-component scattering model to POLSAR data was presented. The algorithm yielded estimates for surface scatter, double-bounce scatter and volume scatter for each pixel in a POLSAR image data set. In this paper, we show how the relative levels of each of the three components can be used as inputs to simple classifier for vegetation type. Vegetation classes include no vegetation cover (e.g. bare soil or desert), low vegetation cover (e.g. grassland), moderate vegetation cover (e.g. fully developed crops), forest and urban areas. Implementation of the approach requires estimates for the three components from all three frequencies available using the NASA/JPL AIRSAR, i.e. C-, L- and P-bands. The research described in this paper was carried out by the Jet Propulsion Laboratory, California Institute of Technology under a contract with the National Aeronautics and Space Administration.

Investigation of the 3D temperature distribution patterns above the Antarctic Peninsula using remote sensing data - A contribution for polar climate monitoring

NASA Astrophysics Data System (ADS)

Wachter, Paul; Höppner, Kathrin; Jacobeit, Jucundus; Diedrich, Erhard

2015-04-01

West Antarctica and the Antarctic Peninsula are in the focus of current studies on a changing environment and climate of the polar regions. A recently founded Junior Researchers Group at the German Aerospace Center (DLR) is studying changing processes in cryosphere and atmosphere above the Antarctic Peninsula. It is the aim of the group to make use of long-term remote sensing data sets of the land and ice surfaces and the atmosphere in order to characterize environmental changes in this highly sensitive region. One of the PhD projects focuses on the investigation of the 3D temperature distribution patterns above the Antarctic Peninsula. Temperature data sets ranging from MODIS land surface temperatures up to middle atmosphere data of AURA/MLS will be evaluated over the last approx. 12 years. This 3-dimensional view allows comprehensive investigations of the thermal structure and spatio-temporal characteristics of the southern polar atmosphere. Tropospheric data sets will be analyzed by multivariate statistical methods and will allow the identification of dominant atmospheric circulation patterns as well as their temporal variability. An overview of the data sets and first results will be presented.

Thermophysical properties of the MER and Beagle II landing site regions on Mars

NASA Astrophysics Data System (ADS)

Jakosky, Bruce M.; Hynek, Brian M.; Pelkey, Shannon M.; Mellon, Michael T.; Martínez-Alonso, Sara; Putzig, Nathaniel E.; Murphy, Nate; Christensen, Philip R.

2006-08-01

We analyzed remote-sensing observations of the Isidis Basin, Gusev Crater, and Meridiani Planum landing sites for Beagle II, MER-A Spirit, and MER-B Opportunity spacecraft, respectively. We emphasized the thermophysical properties using daytime and nighttime radiance measurements from the Mars Global Surveyor (MGS) Thermal Emission Spectrometer and Mars Odyssey Thermal Emission Imaging System (THEMIS) and thermal inertias derived from nighttime data sets. THEMIS visible images, MGS Mars Orbiter Camera (MOC) narrow-angle images, and MGS Mars Orbiter Laser Altimeter (MOLA) data are incorporated as well. Additionally, the remote-sensing data were compared with ground-truth at the MER sites. The Isidis Basin surface layer has been shaped by aeolian processes and erosion by slope winds coming off of the southern highlands and funneling through notches between massifs. In the Gusev region, surface materials of contrasting thermophysical properties have been interpreted as rocks or bedrock, duricrust, and dust deposits; these are consistent with a complex geological history dominated by volcanic and aeolian processes. At Meridiani Planum the many layers having different thermophysical and erosional properties suggest periodic deposition of differing sedimentological facies possibly related to clast size, grain orientation and packing, or mineralogy.

Hydrological Relevant Parameters from Remote Sensing - Spatial Modelling Input and Validation Basis

NASA Astrophysics Data System (ADS)

Hochschild, V.

2012-12-01

This keynote paper will demonstrate how multisensoral remote sensing data is used as spatial input for mesoscale hydrological modeling as well as for sophisticated validation purposes. The tasks of Water Resources Management are subject as well as the role of remote sensing in regional catchment modeling. Parameters derived from remote sensing discussed in this presentation will be land cover, topographical information from digital elevation models, biophysical vegetation parameters, surface soil moisture, evapotranspiration estimations, lake level measurements, determination of snow covered area, lake ice cycles, soil erosion type, mass wasting monitoring, sealed area, flash flood estimation. The actual possibilities of recent satellite and airborne systems are discussed, as well as the data integration into GIS and hydrological modeling, scaling issues and quality assessment will be mentioned. The presentation will provide an overview of own research examples from Germany, Tibet and Africa (Ethiopia, South Africa) as well as other international research activities. Finally the paper gives an outlook on upcoming sensors and concludes the possibilities of remote sensing in hydrology.

Validating modeled soil moisture with in-situ data for agricultural drought monitoring in West Africa

NASA Astrophysics Data System (ADS)

McNally, A.; Yatheendradas, S.; Jayanthi, H.; Funk, C. C.; Peters-Lidard, C. D.

2011-12-01

The declaration of famine in Somalia on July 21, 2011 highlights the need for regional hydroclimate analysis at a scale that is relevant for agropastoral drought monitoring. A particularly critical and robust component of such a drought monitoring system is a land surface model (LSM). We are currently enhancing the Famine Early Warning Systems Network (FEWS NET) monitoring activities by configuring a custom instance of NASA's Land Information System (LIS) called the FEWS NET Land Data Assimilation System (FLDAS). Using the LIS Noah LSM, in-situ measurements, and remotely sensed data, we focus on the following question: How can Noah be best parameterized to accurately simulate hydroclimate variables associated with crop performance? Parameter value testing and validation is done by comparing modeled soil moisture against fortuitously available in-situ soil moisture observations in the West Africa. Direct testing and application of the FLDAS over African agropastoral locations is subject to some issues: [1] In many regions that are vulnerable to food insecurity ground based measurements of precipitation, evapotranspiration and soil moisture are sparse or non-existent, [2] standard landcover classes (e.g., the University of Maryland 5 km dataset), do not include representations of specific agricultural crops with relevant parameter values, and phenologies representing their growth stages from the planting date and [3] physically based land surface models and remote sensing rain data might still need to be calibrated or bias-corrected for the regions of interest. This research aims to address these issues by focusing on sites in the West African countries of Mali, Niger, and Benin where in-situ rainfall and soil moisture measurements are available from the African Monsoon Multidisciplinary Analysis (AMMA). Preliminary results from model experiments over Southern Malawi, validated with Normalized Difference Vegetation Index (NDVI) and maize yield data, show that the ability to detect a drought signal in modeled soil moisture and actual evapotranspiration was sensitive to parameters like minimum stomatal resistance, green vegetation fraction, and minimum threshold for transpiration stress. In addition to improving our understanding and representation of the land surface physics in agropastoral drought, this study moves us closer to confidently validating LSM estimates with remotely sensed data (e.g. MODIS NDVI), essential in regions that lack ground based measurements. Ultimately, these improved information products serve to better inform decision makers about seasonal food production and anticipate the need for relief, as well as guide climate change adaptation strategies, potentially saving millions of lives.

Integrating Statistical and Expert Knowledge to Develop Phenoregions for the Continental United States

NASA Astrophysics Data System (ADS)

Hoffman, F. M.; Kumar, J.; Hargrove, W. W.

2013-12-01

Vegetated ecosystems typically exhibit unique phenological behavior over the course of a year, suggesting that remotely sensed land surface phenology may be useful for characterizing land cover and ecoregions. However, phenology is also strongly influenced by temperature and water stress; insect, fire, and storm disturbances; and climate change over seasonal, interannual, decadal and longer time scales. Normalized difference vegetation index (NDVI), a remotely sensed measure of greenness, provides a useful proxy for land surface phenology. We used NDVI for the conterminous United States (CONUS) derived from the Moderate Resolution Spectroradiometer (MODIS) at 250 m resolution to develop phenological signatures of emergent ecological regimes called phenoregions. By applying a unsupervised, quantitative data mining technique to NDVI measurements for every eight days over the entire MODIS record, annual maps of phenoregions were developed. This technique produces a prescribed number of prototypical phenological states to which every location belongs in any year. To reduce the impact of short-term disturbances, we derived a single map of the mode of annual phenological states for the CONUS, assigning each map cell to the state with the largest integrated NDVI in cases where multiple states tie for the highest frequency. Since the data mining technique is unsupervised, individual phenoregions are not associated with an ecologically understandable label. To add automated supervision to the process, we applied the method of Mapcurves, developed by Hargrove and Hoffman, to associate individual phenoregions with labeled polygons in expert-derived maps of biomes, land cover, and ecoregions. Utilizing spatial overlays with multiple expert-derived maps, this "label-stealing"' technique exploits the knowledge contained in a collection of maps to identify biome characteristics of our statistically derived phenoregions. Generalized land cover maps were produced by combining phenoregions according to their degree of spatial coincidence with expert-developed land cover or biome regions. Goodness-of-fit maps, which show the strength the spatial correspondence, were also generated.

Land-Atmosphere Interactions in Cold Environments (LATICE): The role of Atmosphere - Biosphere - Cryosphere - Hydrosphere interactions in a changing climate

NASA Astrophysics Data System (ADS)

Burkhart, J. F.; Tallaksen, L. M.; Stordal, F.; Berntsen, T.; Westermann, S.; Kristjansson, J. E.; Etzelmuller, B.; Hagen, J. O.; Schuler, T.; Hamran, S. E.; Lande, T. S.; Bryn, A.

2015-12-01

Climate change is impacting the high latitudes more rapidly and significantly than any other region of the Earth because of feedback processes between the atmosphere and the underlying surface. A warmer climate has already led to thawing of permafrost, reducing snow cover and a longer growing season; changes, which in turn influence the atmospheric circulation and the hydrological cycle. Still, many studies rely on one-way coupling between the atmosphere and the land surface, thereby neglecting important interactions and feedbacks. The observation, understanding and prediction of such processes from local to regional and global scales, represent a major scientific challenge that requires multidisciplinary scientific effort. The successful integration of earth observations (remote and in-situ data) and model development requires a harmonized research effort between earth system scientists, modelers and the developers of technologies and sensors. LATICE, which is recognized as a priority research area by the Faculty of Mathematics and Natural Sciences at the University of Oslo, aims to advance the knowledge base concerning land atmosphere interactions and their role in controlling climate variability and climate change at high northern latitudes. The consortium consists of an interdisciplinary team of experts from the atmospheric and terrestrial (hydrosphere, cryosphere and biosphere) research groups, together with key expertise on earth observations and novel sensor technologies. LATICE addresses critical knowledge gaps in the current climate assessment capacity through: Improving parameterizations of processes in earth system models controlling the interactions and feedbacks between the land (snow, ice, permafrost, soil and vegetation) and the atmosphere at high latitudes, including the boreal, alpine and artic zone. Assessing the influence of climate and land cover changes on water and energy fluxes. Integrating remote earth observations with in-situ data and suitable models to allow studies of finer-scale processes governing land-atmosphere interactions. Addressing observational challenges through the development of novel observational products and networks.

Advanced Russian Mission Laplace-P to Study the Planetary System of Jupiter: Scientific Goals, Objectives, Special Features and Mission Profile

NASA Astrophysics Data System (ADS)

Martynov, M. B.; Merkulov, P. V.; Lomakin, I. V.; Vyatlev, P. A.; Simonov, A. V.; Leun, E. V.; Barabanov, A. A.; Nasyrov, A. F.

2017-12-01

The advanced Russian project Laplace-P is aimed at developing and launching two scientific spacecraft (SC)— Laplace-P1 ( LP1 SC) and Laplace-P2 ( LP2 SC)—designed for remote and in-situ studies of the system of Jupiter and its moon Ganymede. The LP1 and LP2 spacecraft carry an orbiter and a lander onboard, respectively. One of the orbiter's objectives is to map the surface of Ganymede from the artificial satellite's orbit and to acquire the data for the landing site selection. The main objective of the lander is to carry out in-situ investigations of Ganymede's surface. The paper describes the scientific goals and objectives of the mission, its special features, and the LP1 and LP2 mission profiles during all of the phases—from the launch to the landing on the surface of Ganymede.

Remote sensing of ephemeral water bodies in western Niger

USGS Publications Warehouse

Verdin, J.P.

1996-01-01

Research was undertaken to evaluate the feasibility of monitoring the small ephemeral water bodies of the Sahel with the 1.1 km resolution data of the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR). Twenty-one lakes of western Niger with good ground observation records were selected for examination. Thematic Mapper images from 1988 were first analysed to determine surface areas and temperature differences between water and adjacent land. Six AVHRR scenes from the 1988-89 dry season were then studied. It was found that a lake can be monitored until its surface area drops below 10 ha, in most cases. Furthermore, with prior knowledge of the location and shape of a water body, its surface area can be estimated from AVHRR band 5 data to within about 10 ha. These results are explained by the sharp temperature contrast between water and land, on the order of 13?? C.

Apollo 17 Lunar Surface Experiments package

NASA Image and Video Library

1972-05-10

S72-37260 (November 1972) --- The remote antenna for the Lunar Seismic Profiling Experiment, Numbered S-203, a component of the Apollo Lunar Surface Experiments Package which will be carried on the Apollo 17 lunar landing mission. LSPE components are four geophones similar to those used in earlier active seismic experiments an electronics package in the ALSEP central station, and eight explosive packages which will be deployed during the geology traverse. The four geophones will be placed one in the center and at each corner of a 90-meter equilateral triangle. Explosive charges placed on the surface will generate seismic waves of varying strengths to provide data on the structural profile of the landing site. After the charges have been fired by ground command, the experiment will settle down into a passive listening mode, detecting moonquakes, meteorite impacts and the thump caused by the Lunar Module ascent stage impact. The antenna is of the telescoping type.

Advances in U.S. Land Imaging Capabilities

NASA Astrophysics Data System (ADS)

Stryker, T. S.

2017-12-01

Advancements in Earth observations, cloud computing, and data science are improving everyday life. Information from land-imaging satellites, such as the U.S. Landsat system, helps us to better understand the changing landscapes where we live, work, and play. This understanding builds capacity for improved decision-making about our lands, waters, and resources, driving economic growth, protecting lives and property, and safeguarding the environment. The USGS is fostering the use of land remote sensing technology to meet local, national, and global challenges. A key dimension to meeting these challenges is the full, free, and open provision of land remote sensing observations for both public and private sector applications. To achieve maximum impact, these data must also be easily discoverable, accessible, and usable. The presenter will describe the USGS Land Remote Sensing Program's current capabilities and future plans to collect and deliver land remote sensing information for societal benefit. He will discuss these capabilities in the context of national plans and policies, domestic partnerships, and international collaboration. The presenter will conclude with examples of how Landsat data is being used on a daily basis to improve lives and livelihoods.

On the relationship between land surface infrared emissivity and soil moisture

NASA Astrophysics Data System (ADS)

Zhou, Daniel K.; Larar, Allen M.; Liu, Xu

2018-01-01

The relationship between surface infrared (IR) emissivity and soil moisture content has been investigated based on satellite measurements. Surface soil moisture content can be estimated by IR remote sensing, namely using the surface parameters of IR emissivity, temperature, vegetation coverage, and soil texture. It is possible to separate IR emissivity from other parameters affecting surface soil moisture estimation. The main objective of this paper is to examine the correlation between land surface IR emissivity and soil moisture. To this end, we have developed a simple yet effective scheme to estimate volumetric soil moisture (VSM) using IR land surface emissivity retrieved from satellite IR spectral radiance measurements, assuming those other parameters impacting the radiative transfer (e.g., temperature, vegetation coverage, and surface roughness) are known for an acceptable time and space reference location. This scheme is applied to a decade of global IR emissivity data retrieved from MetOp-A infrared atmospheric sounding interferometer measurements. The VSM estimated from these IR emissivity data (denoted as IR-VSM) is used to demonstrate its measurement-to-measurement variations. Representative 0.25-deg spatially-gridded monthly-mean IR-VSM global datasets are then assembled to compare with those routinely provided from satellite microwave (MW) multisensor measurements (denoted as MW-VSM), demonstrating VSM spatial variations as well as seasonal-cycles and interannual variability. Initial positive agreement is shown to exist between IR- and MW-VSM (i.e., R2 = 0.85). IR land surface emissivity contains surface water content information. So, when IR measurements are used to estimate soil moisture, this correlation produces results that correspond with those customarily achievable from MW measurements. A decade-long monthly-gridded emissivity atlas is used to estimate IR-VSM, to demonstrate its seasonal-cycle and interannual variation, which is spatially coherent and consistent with that from MW measurements, and, moreover, to achieve our objective of investigating the relationship between land surface IR emissivity and soil moisture.

15 CFR 960.9 - License term.

Code of Federal Regulations, 2010 CFR

2010-01-01

... SERVICE LICENSING OF PRIVATE REMOTE SENSING SYSTEMS Licenses § 960.9 License term. (a) Each license for... licensee to: (1) Provide data to the National Satellite Land Remote Sensing Data Archive for the basic data set; (2) Make data available to the National Satellite Land Remote Sensing Data Archive that the...

Spice In Martian Soil

NASA Astrophysics Data System (ADS)

Seiferlin, K.; Spohn, T.; Spice Team

The Netlander mission offers a unique opportunity to study the surface and the inte- rior of Mars at four different locations at the same time. In addition to real "network"- science, where the presence of four stations is a 'must' to address global science as- pects, local, landing site-related instruments can more than double our knowledge of the surface of Mars, compared to the three landing sites (Viking 1 and 2, Pathfinder) we are currently familiar with. The SPICE instrument will characterize the soil at the landing sites. Force sensors integrated into the seismometer legs (three per station) will determine the mechanical strength of the soil. Thermal sensors will measure the local soil temperature, the thermal inertia and the thermal diffusivity independently, thus allowing us to determine the thermal conductivity and the volumetric heat capac- ity of the soil. These properties will tell us about (1) soil cementation ("duricrust"), (2) volatile exchange with the atmosphere, (3) grain size, (4) near-surface stratigra- phy, and (5) will finally provide ground truth for remote sensing data such as that from Mars Global Surveyor's thermal emission spectrometer.

Mars Sample Return without Landing on the Surface

NASA Technical Reports Server (NTRS)

Jurewicz, A. J. G.; Jones, Steven M.; Yen, A. S.

2000-01-01

Many in the science community want a Mars sample return in the near future, with the expectation that it will provide in-depth information, significantly beyond what we know from remote sensing, limited in-situ measurements, and work with Martian meteorites. Certainly, return of samples from the Moon resulted in major advances in our understanding of both the geologic history of our planetary satellite, and its relationship to Earth. Similar scientific insights would be expected from analyses of samples returned from Mars. Unfortunately, Mars-lander sample-return missions have been delayed, for the reason that NASA needs more time to review the complexities and risks associated with that type of mission. A traditional sample return entails a complex transfer-chain, including landing, collection, launch, rendezvous, and the return to Earth, as well as an evaluation of potential biological hazards involved with bringing pristine Martian organics to Earth. There are, however, means of returning scientifically-rich samples from Mars without landing on the surface. This paper discusses an approach for returning intact samples of surface dust, based on known instrument technology, without using an actual Martian lander.

Sea-land segmentation for infrared remote sensing images based on superpixels and multi-scale features

NASA Astrophysics Data System (ADS)

Lei, Sen; Zou, Zhengxia; Liu, Dunge; Xia, Zhenghuan; Shi, Zhenwei

2018-06-01

Sea-land segmentation is a key step for the information processing of ocean remote sensing images. Traditional sea-land segmentation algorithms ignore the local similarity prior of sea and land, and thus fail in complex scenarios. In this paper, we propose a new sea-land segmentation method for infrared remote sensing images to tackle the problem based on superpixels and multi-scale features. Considering the connectivity and local similarity of sea or land, we interpret the sea-land segmentation task in view of superpixels rather than pixels, where similar pixels are clustered and the local similarity are explored. Moreover, the multi-scale features are elaborately designed, comprising of gray histogram and multi-scale total variation. Experimental results on infrared bands of Landsat-8 satellite images demonstrate that the proposed method can obtain more accurate and more robust sea-land segmentation results than the traditional algorithms.

Observing the Vertical Dimensions of Singapore's Urban Heat Island

NASA Astrophysics Data System (ADS)

Chow, W. T. L.; Ho, D. X. Q.

2015-12-01

In numerous cities, measurements of urban warmth in most urban heat island (UHI) studies are generally constrained towards surface or near-surface (<2 m above ground) levels across horizontal variations in land use and land cover. However, there has been hitherto limited attention towards the measurement of vertical temperature profiles extending from the urban surface through to the urban boundary layer. Knowledge of these profiles, through how they vary over different local urban morphologies, and develop with respect to synoptic meteorological conditions, are important towards several aspects of UHI research; these include validating modelling urban canopy lapse rate profiles or estimating the growth of urban plumes. In this study, we utilised temperature sensors attached onto remote controlled aerial quadcopter platforms to measure urban temperature and humidity profiles in Singapore, which is a rapidly urbanizing major tropical metropolis. These profiles were measured from the surface to ~100 m above ground level, a height which includes all of the urban canopy and parts of the urban boundary layer. Initial results indicate significant variations in stability measured over different land uses (e.g. urban park, high-rise residential, commercial); these profiles are also temporally dynamic, depending on the time of day and larger-scale weather conditions.

Investigation of Seasonal Landscape Freeze/Thaw Cycles in Relation to Cloud Structure in the High Northern Latitudes

NASA Technical Reports Server (NTRS)

Smith, Cosmo

2011-01-01

The seasonal freezing and thawing of Earth's cryosphere (the portion of Earth's surface permanently or seasonally frozen) has an immense impact on Earth's climate as well as on its water, carbon and energy cycles. During the spring, snowmelt and the transition between frozen and non-frozen states lowers Earth's surface albedo. This change in albedo causes more solar radiation to be absorbed by the land surface, raising surface soil and air temperatures as much as 5 C within a few days. The transition of ice into liquid water not only raises the surface humidity, but also greatly affects the energy exchange between the land surface and the atmosphere as the phase change creates a latent energy dominated system. There is strong evidence to suggest that the thawing of the cryosphere during spring and refreezing during autumn is correlated to local atmospheric conditions such as cloud structure and frequency. Understanding the influence of land surface freeze/thaw cycles on atmospheric structure can help improve our understanding of links between seasonal land surface state and weather and climate, providing insight into associated changes in Earth's water, carbon, and energy cycles that are driven by climate change.Information on both the freeze/thaw states of Earth's land surface and cloud characteristics is derived from data sets collected by NOAA's Special Sensor Microwave/Imager (SSM/I), the Advanced Microwave Scanning Radiometer on NASA's Earth Observing System(AMSR-E), NASA's CloudSat, and NASA's SeaWinds-on-QuickSCAT Earth remote sensing satellite instruments. These instruments take advantage of the microwave spectrum to collect an ensemble of atmospheric and land surface data. Our analysis uses data from radars (active instruments which transmit a microwave signal toward Earth and measure the resultant backscatter) and radiometers (passive devices which measure Earth's natural microwave emission) to accurately characterize salient details on Earth's surface and atmospheric states. By comparing the cloud measurements and the surface freeze-thaw data sets, a correlation between the two phenomena can be developed.

Three Decades of Remote Sensing Based Tropical Forests Phenological Patterns and Trends

NASA Astrophysics Data System (ADS)

Didan, K.

2010-12-01

The faint climatic seasonality of tropical rain forests is believed to be the reason these biomes lack strong and detectable seasonality. Forest seasonality is a critical element of ecosystem functions. It moderates the echo-hydrology, carbon, and nutrient exchange of the area. While deciduous forests exhibit distinct and strong seasonality, tropical forests do not, yet they play a large role in the cycling of energy and mass. Tropical forests represent a large percentage of vegetated land and their importance to the Earth system stems from their biological diversity, their habitat role, their role in regulating global weather, and the role they play in carbon storage. While Tropical forests are well buffered by their sheer size, their vulnerability to climate change is exacerbated by the human pressure. All of this begs the questions of what are the patterns and characteristic of tropical forests phenology and are there any detectable trends over the last three decades of synoptic remote sensing. These three decades comprise different episodes of droughts and an ever increasing level of human encroachment. In so far understanding the function and dynamic of these biomes, field studies continue to play a major role, but synoptic remote sensing is emerging as a viable tool to addressing the spatial and temporal scale associated with this problem. Recent studies of Brazilian rainforest with synoptic remote sensing point to a sizable seasonal signal coincident with the dry season. However, these studies were not extensive in time or space and did not look at other rainforests. Using data from AVHRR and MODIS, we generated a 30 year record of the 2 bands Enhance Vegetation Index (EVI2), and analyzed the patterns and trends of land surface phenology across all tropical forests using the homogeneous phenology cluster approach. We chose EVI because of its superior performance over these dense forests, and we selected the homogeneous phenology cluster approach to abate the noise associated with single pixel approaches. This approach reasonably assumes that land surface seasonality is homogeneous over areas with similar climate, soil, elevation gradient, aspect, and land cover. Our goal was to establish the patterns and characteristic of the land surface phenology of these biomes and to analyze their spatio-temporal trends. Strong variability was observed across the forests of South America, Central Africa and South East Asia. The South American forest exhibited the faintest seasonal signal with the strongest response to droughts. These forests show a more distinct and ever more evident dry season response. The Tropical forest of central Africa shows a more evident seasonal signal and a trend where the season is breaking into a bimodal growth mode, with two distinct and separate growth periods. The Tropical forest of South East Asia shows a more fragmented seasonality that most likely is the result of human pressure and its impact on land cover. Although we have not looked at the underlying climatic factors, we hypothesize that the seasonality of these forests are mostly shaped by the climatic patterns which would suggest that these trends will become more evident as climate continues to change.

Development of a tree classifier for discrimination of surface mine activity from Landsat digital data

NASA Technical Reports Server (NTRS)

Solomon, J. L.; Miller, W. F.; Quattrochi, D. A.

1979-01-01

In a cooperative project with the Geological Survey of Alabama, the Mississippi State Remote Sensing Applications Program has developed a single purpose, decision-tree classifier using band-ratioing techniques to discriminate various stages of surface mining activity. The tree classifier has four levels and employs only two channels in classification at each level. An accurate computation of the amount of disturbed land resulting from the mining activity can be made as a product of the classification output. The utilization of Landsat data provides a cost-efficient, rapid, and accurate means of monitoring surface mining activities.

Land Cover Indicators for U.S. National Climate Assessments

NASA Astrophysics Data System (ADS)

Channan, S.; Thomson, A. M.; Collins, K. M.; Sexton, J. O.; Torrens, P.; Emanuel, W. R.

2014-12-01

Land is a critical resource for human habitat and for the vast majority of human activities. Many natural resources are derived from terrestrial ecosystems or otherwise extracted from the landscape. Terrestrial biodiversity depends on land attributes as do people's perceptions of the value of land, including its value for recreation or tourism. Furthermore, land surface properties and processes affect weather and climate, and land cover change and land management affect emissions of greenhouse gases. Thus, land cover with its close association with climate is so pervasive that a land cover indicator is of fundamental importance to U.S. national climate assessments and related research. Moderate resolution remote sensing products (MODIS) were used to provide systematic data on annual distributions of land cover over the period 2001-2012. Selected Landsat observations and data products further characterize land cover at higher resolution. Here we will present the prototype for a suite of land cover indicators including land cover maps as well as charts depicting attributes such as composition by land cover class, statistical indicators of landscape characteristics, and tabular data summaries indispensable for communicating the status and trends of U.S. land cover at national, regional and state levels.

Heat Capacity Mapping Mission investigation no. 25 (Tellus project)

NASA Technical Reports Server (NTRS)

Deparatesi, S. G. (Principal Investigator); Reiniger, P. (Editor)

1982-01-01

The TELLUS pilot project, utilizing 0.5 to 1.1 micron and 10.5 to 12.5 micron day and/or night imagery from the Heat Capacity Mapping Mission, is described. The application of remotely sensed data to synoptic evaluation of evapotranspiration and moisture in agricultural soils was considered. The influence of topography, soils, land use, and meteorology on surface temperature distribution was evaluated. Anthropogenic heat release was investigated. Test areas extended from semi-arid land in southern Italy to polders in the Netherlands, and from vine-growing hills in the Rhineland to grasslands in Buckinghamshire.

Remotely Sensed Index of Deforestation/Urbanization for use in Climate Models

NASA Technical Reports Server (NTRS)

Carlson, Toby N.

1996-01-01

The purpose of this investigation is to use a new method for deriving land surface parameters from a combination of thermal infrared and vegetation index measurements from satellites (Landsat-TM, and NOAA-AVHRR) and to integrate these parameters with more conventional data bases. We have completed an investigation of urbanization in the State College, PA area and have begun work in Chester County, PA, and Costa Rica. Our basic hypothesis is that changes in land use, including deforestation, exert a profound influence on local microclimates whose effects may greatly exceed in importance those occurring on larger scales.

Application of computer processed multispectral data to the discrimination of land collapse (sinkhole) prone areas in Florida

NASA Technical Reports Server (NTRS)

Coker, A. E.; Marshall, R.; Thomson, N. S.

1977-01-01

Data were collected near Bartow, Florida, for the purpose of studying land collapse phenomena using remote sensing techniques. Data obtained using the multispectral scanner system consisted of various combinations of 18 spectral bands ranging from 0.4-14.0 microns and several types of photography. The multispectral data were processed on a special-purpose analog computer in order to detect moisture-stressed vegetation and to enhance terrain surface temperatures. The processed results were printed on film to show the patterns of distribution of the proposed hydrogeologic indicators.

The new Landsat 8 potential for remote sensing of colored dissolved organic matter (CDOM)

USGS Publications Warehouse

Slonecker, Terry; Jones, Daniel K.; Pellerin, Brian A.

2016-01-01

Due to a combination of factors, such as a new coastal/aerosol band and improved radiometric sensitivity of the Operational Land Imager aboard Landsat 8, the atmospherically-corrected Surface Reflectance product for Landsat data, and the growing availability of corrected fDOM data from U.S. Geological Survey gaging stations, moderate-resolution remote sensing of fDOM may now be achievable. This paper explores the background of previous efforts and shows preliminary examples of the remote sensing and data relationships between corrected fDOM and Landsat 8 reflectance values. Although preliminary results before and after Hurricane Sandy are encouraging, more research is needed to explore the full potential of Landsat 8 to continuously map fDOM in a number of water profiles.

The new Landsat 8 potential for remote sensing of colored dissolved organic matter (CDOM).

PubMed

Slonecker, E Terrence; Jones, Daniel K; Pellerin, Brian A

2016-06-30

Due to a combination of factors, such as a new coastal/aerosol band and improved radiometric sensitivity of the Operational Land Imager aboard Landsat 8, the atmospherically-corrected Surface Reflectance product for Landsat data, and the growing availability of corrected fDOM data from U.S. Geological Survey gaging stations, moderate-resolution remote sensing of fDOM may now be achievable. This paper explores the background of previous efforts and shows preliminary examples of the remote sensing and data relationships between corrected fDOM and Landsat 8 reflectance values. Although preliminary results before and after Hurricane Sandy are encouraging, more research is needed to explore the full potential of Landsat 8 to continuously map fDOM in a number of water profiles. Published by Elsevier Ltd.

Detection of geothermal anomalies in Tengchong, Yunnan Province, China from MODIS multi-temporal night LST imagery

NASA Astrophysics Data System (ADS)

Li, H.; Kusky, T. M.; Peng, S.; Zhu, M.

2012-12-01

Thermal infrared (TIR) remote sensing is an important technique in the exploration of geothermal resources. In this study, a geothermal survey is conducted in Tengchong area of Yunnan province in China using multi-temporal MODIS LST (Land Surface Temperature). The monthly night MODIS LST data from Mar. 2000 to Mar. 2011 of the study area were collected and analyzed. The 132 month average LST map was derived and three geothermal anomalies were identified. The findings of this study agree well with the results from relative geothermal gradient measurements. Finally, we conclude that TIR remote sensing is a cost-effective technique to detect geothermal anomalies. Combining TIR remote sensing with geological analysis and the understanding of geothermal mechanism is an accurate and efficient approach to geothermal area detection.

Vegetation controls on the biophysical surface properties at global scale

NASA Astrophysics Data System (ADS)

Forzieri, Giovanni; Cescatti, Alessandro

2016-04-01

Leaf area index (LAI) plays an important role in determining resistances to heat, moisture and momentum exchanges between the land surface and atmosphere. Exploring how variations in LAI may induce changes in the surface energy balance is a key to understanding vegetation-climate interactions and for predicting biophysical climate impacts associated to changes in land cover. To this end, we analyzed remote sensing-observed dynamics in LAI, surface energy fluxes and climate drivers at global scale. We investigated the link between interannual variability of LAI and the components of the surface energy budget under diverse climate gradients. Results show that a 25% increase in annual LAI may induce up to 2% increase in available surface energy, as consequence of higher short wave absorption due to reduced albedos, up to 20% increase and 10% decrease in latent and sensible heat, respectively, leading to a decrease of the Bowen ratio in densely vegetated canopies. Opposite patterns are found for a reduction in LAI of similar magnitude. Such changes are strongly modulated by concurrent year-to-year variations and climatological means of air temperature, precipitation and snow cover as well as by land cover-specific physiological processes. Boreal and semi-arid regions appear to be mostly exposed to large changes in biophysical surface processes induced by interannual fluctuations in LAI. The combination of the emergent patters translates into variations in the long-wave outgoing radiation that reflect the surface warming/cooling associated to LAI changes. These findings provide a deeper understanding of the vegetation control on biophysical surface properties and define a set of observational-based diagnostics of LAI-dependent land surface-atmosphere interactions.

Observations of land-atmosphere interactions using satellite data

NASA Astrophysics Data System (ADS)

Green, Julia; Gentine, Pierre; Konings, Alexandra; Alemohammad, Hamed; Kolassa, Jana

2016-04-01

Observations of land-atmosphere interactions using satellite data Julia Green (1), Pierre Gentine (1), Alexandra Konings (1,2), Seyed Hamed Alemohammad (3), Jana Kolassa (4) (1) Columbia University, Earth and Environmental Engineering, NY, NY, USA, (2) Stanford University, Environmental Earth System Science, Stanford, CA, USA, (3) Massachusetts Institute of Technology, Civil and Environmental Engineering, Cambridge, MA, USA, (4) National Aeronautics and Space Administration/Goddard Space Flight Center, Greenbelt, MD, USA. Previous studies of global land-atmosphere hotspots have often relied solely on data from global models with the consequence that they are sensitive to model error. On the other hand, by only analyzing observations, it can be difficult to distinguish causality from mere correlation. In this study, we present a general framework for investigating land-atmosphere interactions using Granger Causality analysis applied to remote sensing data. Based on the near linear relationship between chlorophyll sun induced fluorescence (SIF) and photosynthesis (and thus its relationship with transpiration), we use the GOME-2 fluorescence direct measurements to quantify the surface fluxes between the land and atmosphere. By using SIF data to represent the flux, we bypass the need to use soil moisture data from FLUXNET (limited spatially and temporally) or remote sensing (limited by spatial resolution, canopy interference, measurement depth, and radio frequency interference) thus eliminating additional uncertainty. The Granger Causality analysis allows for the determination of the strength of the two-way causal relationship between SIF and several climatic variables: precipitation, radiation and temperature. We determine that warm regions transitioning from water to energy limitation exhibit strong feedbacks between the land surface and atmosphere due to their high sensitivity to climate and weather variability. Tropical rainforest regions show low magnitudes of causal feedback likely due to other factors influencing the land surface such as phenological controls (e.g. leaf area index), nutrient limitations or soil texture. These results were then compared to CMIP5 GCM results using GPP in place of SIF. GCM results varied greatly between models as well as with the observational data analysis indicating deficiencies in the representation of certain modeled phenomena such as low level clouds and boundary layer development. This study highlights the need for GCM improvement to more accurately capture the feedbacks between the land and atmosphere. These results have the potential to improve our understanding of the underlying mechanisms between land and atmosphere coupling, which could ultimately be used to improve weather and climate predictions.

Non-radiative processes dominate land surface signals in the climate system

NASA Astrophysics Data System (ADS)

Bright, R. M.; Davin, E.; O'Halloran, T. L.; Pongratz, J.; Zhao, K.; Cescatti, A.

2016-12-01

Perturbations to the surface energy budget linked to land cover/land management changes (LCMC) are rarely included in land-climate assessments although they have long been recognized as important drivers of local climate change. At local scales, climate forcings from LCMC depend strongly on changes to surface energy redistribution by various non-radiative mechanisms, dampening or even outweighing the local radiative effect of an albedo change. The extent to which these mechanisms are locally relevant for different types of LCMC across the world remains largely unquantified. Here, we combine extensive records of remote sensing and in-situ observations to quantify local forcings for nine common real-world LCMC perturbations, identifying their underlying physical mechanisms and analyzing their spatial patterns at the global scale. We find that throughout the densely populated regions, non-radiative forcings dominate the local surface temperature response in 8 of 9 LCMC scenarios. Further, the observed local response to re-/afforestation is an annual cooling in all regions south of the upper conterminous United States, Western Europe, and Indo-China. Given that the global response to re-/afforestation in these regions is likely a cooling, projects here can be seen as attractive mitigation measures. Our results - gridded to a 1° x 1° resolution - can be directly used to evaluate climate models or compute indicators providing a more comprehensive picture of the trade-offs between local and global climate forcings linked to land sector projects and policies.

Recent trends (2003-2013) of land surface heat fluxes on the southern side of the central Himalayas, Nepal

NASA Astrophysics Data System (ADS)

Amatya, Pukar Man; Ma, Yaoming; Han, Cunbo; Wang, Binbin; Devkota, Lochan Prasad

2015-12-01

Novice efforts have been made in order to study the regional distribution of land surface heat fluxes on the southern side of the central Himalayas utilizing high-resolution remotely sensed products, but these have been on instantaneous scale. In this study the Surface Energy Balance System model is used to obtain annual averaged maps of the land surface heat fluxes for 11 years (2003-2013) and study their annual trends on the central Himalayan region. The maps were derived at 5 km resolution using monthly input products ranging from satellite derived to Global Land Data Assimilation System meteorological data. It was found that the net radiation flux is increasing as a result of decreasing precipitation (drier environment). The sensible heat flux did not change much except for the northwestern High Himalaya and High Mountains. In northwestern High Himalaya sensible heat flux is decreasing because of decrease in wind speed, ground-air temperature difference, and increase in winter precipitation, whereas in High Mountains it is increasing due to increase in ground-air temperature difference and high rate of deforestation. The latent heat flux has an overall increasing trend with increase more pronounced in the lower regions compared to high elevated regions. It has been reported that precipitation is decreasing with altitude in this region. Therefore, the increasing trend in latent heat flux can be attributed to increase in net radiation flux under persistent forest cover and irrigation land used for agriculture.

A land use and land cover classification system for use with remote sensor data

USGS Publications Warehouse

Anderson, James R.; Hardy, Ernest E.; Roach, John T.; Witmer, Richard E.

1976-01-01

The framework of a national land use and land cover classification system is presented for use with remote sensor data. The classification system has been developed to meet the needs of Federal and State agencies for an up-to-date overview of land use and land cover throughout the country on a basis that is uniform in categorization at the more generalized first and second levels and that will be receptive to data from satellite and aircraft remote sensors. The proposed system uses the features of existing widely used classification systems that are amenable to data derived from remote sensing sources. It is intentionally left open-ended so that Federal, regional, State, and local agencies can have flexibility in developing more detailed land use classifications at the third and fourth levels in order to meet their particular needs and at the same time remain compatible with each other and the national system. Revision of the land use classification system as presented in U.S. Geological Survey Circular 671 was undertaken in order to incorporate the results of extensive testing and review of the categorization and definitions.

Spatio-temporal footprints of urbanisation in Surat, the Diamond City of India (1990-2009).

PubMed

Sharma, Richa; Ghosh, Aniruddha; Joshi, Pawan Kumar

2013-04-01

Urbanisation is a ubiquitous phenomenon with greater prominence in developing nations. Urban expansion involves land conversions from vegetated moisture-rich to impervious moisture-deficient land surfaces. The urban land transformations alter biophysical parameters in a mode that promotes development of heat islands and degrades environmental health. This study elaborates relationships among various environmental variables using remote sensing dataset to study spatio-temporal footprint of urbanisation in Surat city. Landsat Thematic Mapper satellite data were used in conjugation with geo-spatial techniques to study urbanisation and correlation among various satellite-derived biophysical parameters, [Normalised Difference Vegetation Index, Normalised Difference Built-up Index, Normalised Difference Water Index, Normalised Difference Bareness Index, Modified NDWI and land surface temperature (LST)]. Land use land cover was prepared using hierarchical decision tree classification with an accuracy of 90.4 % (kappa = 0.88) for 1990 and 85 % (kappa = 0.81) for 2009. It was found that the city has expanded over 42.75 km(2) within a decade, and these changes resulted in elevated surface temperatures. For example, transformation from vegetation to built-up has resulted in 5.5 ± 2.6 °C increase in land surface temperature, vegetation to fallow 6.7 ± 3 °C, fallow to built-up is 3.5 ± 2.9 °C and built-up to dense built-up is 5.3 ± 2.8 °C. Directional profiling for LST was done to study spatial patterns of LST in and around Surat city. Emergence of two new LST peaks for 2009 was observed in N-S and NE-SW profiles.

Remote sensing of forest dynamics and land use in Amazonia

NASA Astrophysics Data System (ADS)

Toomey, Michael Paul

The rich, vast Amazonian ecosystem is directly and indirectly threatened by human activities; remote sensing serves as an essential tool for monitoring, understanding and mitigating these threats. A multi-faceted body of work is described here, addressing three major issues that employ and advance remote sensing techniques for the study of Amazonia and other tropical rainforest regions. In Chapter 2, canopy reflectance modeling and satellite observations were used to quantify the effect of epiphylls on remote sensing of humid forests. Modeling simulations demonstrated sensitivity of canopy-level near infrared and green reflectance to epiphylls on leaves. Time series of Moderate Resolution Imaging Spectrometer (MODIS) data corroborated the modeling results, suggesting a degree of coupling between epiphyll cover and vegetation indices which must be accounted for when using optical remote sensing in humid forests. In Chapter 4, 11 years (2000--2010) of MODIS land surface temperature (LST) data covering the entire Amazon basin were used to ascertain the role of heat stress during droughts in 2005 and 2010. Preliminary accuracy assessments showed that LST data provided reasonably accurate estimates of daytime air temperatures (RMSE = 1.45°C; Chapter 3). There were moderate to strong correlations between LST-based air temperature estimates and tower measurements (mean r = 0.64), illustrating a sensitivity to temporal variability. During both droughts, MODIS LST data detected anomalously high daytime and nighttime canopy temperatures throughout drought-affected regions. Multivariate linear models of LST and precipitation anomalies explained 65.1% of the variability in forest biomass losses, as determined from a wide network of forest inventory plots. These results suggest that models should incorporate both heat and moisture to predict drought effects on tropical forests. In Chapter 5, I performed high spatial and temporal resolution modeling of carbon stocks and fluxes in the state of Rondonia, Brazil for the period 1985--2009. Based on this analysis, Rondonia contributed ˜4% of pan-tropical humid forest deforestation emissions while carbon uptake by secondary forest was negligible due to limited spatial extent and high turnover rates. Spatial analysis of land cover change demonstrated the necessity for fine resolution carbon monitoring in tropical regions dominated by non-mechanized, smallholder land uses.

A sensitivity study of the coupled simulation of the Northeast Brazil rainfall variability

NASA Astrophysics Data System (ADS)

Misra, Vasubandhu

2007-06-01

Two long-term coupled ocean-land-atmosphere simulations with slightly different parameterization of the diagnostic shallow inversion clouds in the atmospheric general circulation model (AGCM) of the Center for Ocean-Land-Atmosphere Studies (COLA) coupled climate model are compared for their annual cycle and interannual variability of the northeast Brazil (NEB) rainfall variability. It is seen that the solar insolation affected by the changes to the shallow inversion clouds results in large scale changes to the gradients of the SST and the surface pressure. The latter in turn modulates the surface convergence and the associated Atlantic ITCZ precipitation and the NEB annual rainfall variability. In contrast, the differences in the NEB interannual rainfall variability between the two coupled simulations is attributed to their different remote ENSO forcing.

Earth survey applications division: Research leading to the effective use of space technology in applications relating to the Earth's surface and interior

NASA Technical Reports Server (NTRS)

Carpenter, L. (Editor)

1980-01-01

Accomplishments and future plans are described for the following areas: (1) geology - geobotanical indicators and geopotential data; (2) modeling magnetic fields; (3) modeling the structure, composition, and evolution of the Earth's crust; (4) global and regional motions of the Earth's crust and earthquake occurrence; (5) modeling geopotential from satellite tracking data; (6) modeling the Earth's gravity field; (7) global Earth dynamics; (8) sea surface topography, ocean dynamics; and geophysical interpretation; (9) land cover and land use; (10) physical and remote sensing attributes important in detecting, measuring, and monitoring agricultural crops; (11) prelaunch studies using LANDSAT D; (12) the multispectral linear array; (13) the aircraft linear array pushbroom radiometer; and (14) the spaceborne laser ranging system.

Remotely Sensed Thermal Anomalies in Western Colorado

DOE Data Explorer

Khalid Hussein

2012-02-01

This layer contains the areas identified as areas of anomalous surface temperature from Landsat satellite imagery in Western Colorado. Data was obtained for two different dates. The digital numbers of each Landsat scene were converted to radiance and the temperature was calculated in degrees Kelvin and then converted to degrees Celsius for each land cover type using the emissivity of that cover type. And this process was repeated for each of the land cover types (open water, barren, deciduous forest and evergreen forest, mixed forest, shrub/scrub, grassland/herbaceous, pasture hay, and cultivated crops). The temperature of each pixel within each scene was calculated using the thermal band. In order to calculate the temperature an average emissivity value was used for each land cover type within each scene. The NLCD 2001 land cover classification raster data of the zones that cover Colorado were downloaded from USGS site and used to identify the land cover types within each scene. Areas that had temperature residual greater than 2o, and areas with temperature equal to 1o to 2o, were considered Landsat modeled very warm and warm surface exposures (thermal anomalies), respectively. Note: 'o' is used in this description to represent lowercase sigma.

A Comparison of Methods for a Priori Bias Correction in Soil Moisture Data Assimilation

NASA Technical Reports Server (NTRS)

Kumar, Sujay V.; Reichle, Rolf H.; Harrison, Kenneth W.; Peters-Lidard, Christa D.; Yatheendradas, Soni; Santanello, Joseph A.

2011-01-01

Data assimilation is being increasingly used to merge remotely sensed land surface variables such as soil moisture, snow and skin temperature with estimates from land models. Its success, however, depends on unbiased model predictions and unbiased observations. Here, a suite of continental-scale, synthetic soil moisture assimilation experiments is used to compare two approaches that address typical biases in soil moisture prior to data assimilation: (i) parameter estimation to calibrate the land model to the climatology of the soil moisture observations, and (ii) scaling of the observations to the model s soil moisture climatology. To enable this research, an optimization infrastructure was added to the NASA Land Information System (LIS) that includes gradient-based optimization methods and global, heuristic search algorithms. The land model calibration eliminates the bias but does not necessarily result in more realistic model parameters. Nevertheless, the experiments confirm that model calibration yields assimilation estimates of surface and root zone soil moisture that are as skillful as those obtained through scaling of the observations to the model s climatology. Analysis of innovation diagnostics underlines the importance of addressing bias in soil moisture assimilation and confirms that both approaches adequately address the issue.

Regulating services as measures of ecological resilience on DoD lands

USGS Publications Warehouse

Angermeier, Paul; Villamagna, Amy M.

2015-01-01

Knowledge of the capacity and flow of ecosystem services can help DoD land managers make decisions that enhance cost-effectiveness, minimize environmental damage, and maximize resources available for military missions. We demonstrated a methodology to quantify and map selected regulating services (RS), which helps land managers envision tradeoffs. Our objectives were to 1) estimate current capacity of and demand for selected RS within DoD lands, 2) examine the effects of future DoD land management and climate changes on the capacity and flow of these RS, and 3) project how land-use and climate changes in nearby lands affect future demand for RS. Our approach incorporates widely accepted models and equations, remote sensing, GIS analysis, and stakeholder involvement. Required data include land cover/use, soil type, precipitation, and air temperature. We integrated data into the a) Surface Curve Number Method and b) Revised Universal Soil Loss Equation to estimate capacity of sediment, nitrogen (N) and surface-water regulation. Capacities and flows of RS vary greatly across landscapes and are likely to vary as climate changes or development occurs. Analyses of RS capacity and flow can help managers and planners prioritize actions in the context of best management practices and compatible use buffers. Staff surveys indicated that our approach was informative and easy to use. Implementation may be most limited by on-installation personnel time.