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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS1998

Effect of land-use practice on soil moisture variability for soils covered with dense forest vegetation of Puerto Rico

Little is known about the landuse management effect on soil moisture and soil pH distribution on a landscape covered with dense tropical forest vegetation. This study was conducted at three locations where the history of the landuse management is different. Soil moisture was measured using a 6-cm three-rod Time Domain Reflectometery (TDR) probe. Disturbed soil samples were taken from the top 5-cm at the up, mid, and foothill landscape position from the same spots where soil moisture was measured. The results showed that soil moisture varies with landscape position and depth at all three locations. Soil pH and moisture variability were found to be affected by the change in landuse management and landscape position. Soil moisture distribution usually expected to be relatively higher in the foothill (P3) area of these forests than the uphill (P1) position. However, our results indicated that in the Luquillo and Guanica site the surface soil moisture was significantly higher for P1 than P3 position. These suggest that the surface and subsurface drainage in these two sites may have been poor due to the nature of soil formation and type.

Tsegaye, T.↗
NASA NTRS1997

Interannual Variability in Soil Trace Gas (CO2, N2O, NO) Fluxes and Analysis of Controllers

Interannual variability in flux rates of biogenic trace gases must be quantified in order to understand the differences between short-term trends and actual long-term change in biosphere-atmosphere interactions. We simulated interannual patterns (1983-1988) of global trace gas fluxes from soils using the NASA Ames model version of CASA (Carnegie-Ames-Stanford Approach) in a transient simulation mode. This ecosystem model has been recalibrated for simulations driven by satellite vegetation index data from the NOAA Advanced Very High Resolution Radiometer (AVHRR) over the mid-1980s. The predicted interannual pattern of soil heterotropic CO2 emissions indicates that relatively large increases in global carbon flux from soils occurred about three years following the strong El Nino Southern Oscillation (ENSO) event of 1983. Results for the years 1986 and 1987 showed an annual increment of +1 Pg (1015 g) C-CO2 emitted from soils, which tended to dampen the estimated global increase in net ecosystem production with about a two year lag period relative to plant carbon fixation. Zonal discrimination of model results implies that 80-90 percent of the yearly positive increments in soil CO2 emission during 1986-87 were attributable to soil organic matter decomposition in the low-latitudes (between 30 N and 30 S). Soils of the northern middle-latitude zone (between 30 N and 60 N) accounted for the residual of these annual increments. Total annual emissions of nitrogen trace gases (N2O and NO) from soils were estimated to vary from 2-4 percent over the time period modeled, a level of variability which is consistent with predicted interannual fluctuations in global soil CO2 fluxes. Interannual variability of precipitation in tropical and subtropical zones (30 N to 20 S appeared to drive the dynamic inverse relationship between higher annual emissions of NO versus emissions of N2O. Global mean emission rates from natural (heterotrophic) soil sources over the period modeled (1983-1988) were estimated at 57.1 Pg C-CO2yr-1, 9.8Tg (1012 g) N-NO yr-1, and 9.7 Tg N-N2O yr-1. Chemical fertilizer contributions to global soil N gas fluxes were estimated at between 1.3 to 7.3 Tg N-NO yr-1, and 1.2 to 4.0 Tg N-N2O yr-1.

Potter, C.↗
NASA NTRS2018

Satellite Observations and Modeling to Understand the Lower Mekong River Basin Streamflow Variability

In this work, we have used the Soil & Water Assessment Tool (SWAT) to examine streamflow variability of the Lower Mekong River Basin (LMRB) associated with changes in the Upper Mekong River Basin (UMRB) inflows. Two hypothetical experiments were formulated and evaluated for the LMRB, where we conducted runoff simulations with multiple inflow changes that include upstream runoff yield increase and decrease scenarios. Streamflow variability of the LMRB was quantified by two streamflow metrics that explain flow variability and predictability, and high flow disturbance. The model experiments were performed for the Lower Mekong River Basin with identical climate, soil, and other watershed characteristics data. Remote sensing precipitation (Tropical Rainfall Measurement Mission, TRMM, and Global Precipitation Measurement mission, GPM), meteorological data as well as spatial data that include a digital elevation model, newly developed soil information (Harmonized World Soil Database, HWSD), and land use and land cover were processed as input to the LMRB model simulations. Observed daily streamflow data along the Lower Mekong River from Chiang Sean, Thailand to Kratie, Cambodia were used for calibration and validation. Our work results suggest that the Lower Mekong River streamflow is highly variable and has a low predictability (Colwell index of about 32%). We found that releasing more water from upstream Mekong during rainfall months by 30% would result in a reduction in the Lower Mekong streamflow predictability by about 21%. This reduction in predictability is mainly attributed to a decrease in the Contingency index. Our work shows that the ability to predict floods/droughts at the Lower Mekong River would be reduced if there is any anticipated change (i.e., increase/decrease) from UMRB releases. Our results also show that releasing more flows from the upstream Mekong would also affect flood duration and the frequency of flood occurrences downstream. The results of this work thus help to quantify the sensitivity of streamflow variability at the Lower Mekong River Basin to upstream anthropogenic changes.

Streamflow Predictability↗
NASA NTRS2010

Remote Characterization of Biomass Measurements: Case Study of Mangrove Forests

Accurately quantifying forest biomass is of crucial importance for climate change studies. By quantifying the amount of above and below ground biomass and consequently carbon stored in forest ecosystems, we are able to derive estimates of carbon sequestration, emission and storage and help close the carbon budget. Mangrove forests, in addition to providing habitat and nursery grounds for over 1300 animal species, are also an important sink of biomass. Although they only constitute about 3% of the total forested area globally, their carbon storage capacity -- in forested biomass and soil carbon -- is greater than that of tropical forests (Lucas et al, 2007). In addition, the amount of mangrove carbon -- in the form of litter and leaves exported into offshore areas is immense, resulting in over 10% of the ocean's dissolved organic carbon originating from mangroves (Dittmar et al, 2006) The measurement of forest above ground biomass is carried out on two major scales: on the plot scale, biomass can be measured using field measurements through allometric equation derivation and measurements of forest plots. On the larger scale, the field data are used to calibrate remotely sensed data to obtain stand-wide or even regional estimates of biomass. Currently, biomass can be calculated using average stand biomass values and optical data, such as aerial photography or satellite images (Landsat, Modis, Ikonos, SPOT, etc.). More recent studies have concentrated on deriving forest biomass values using radar (JERS, SIR-C, SRTM, Airsar) and/or lidar (ICEsat/GLAS, LVIS) active remote sensing to retrieve more accurate and detailed measurements of forest biomass. The implementation of a generation of new active sensors (UAVSar, DesdynI, Alos/Palsar, TerraX) has prompted the development of new tecm'liques of biomass estimation that use the combination of multiple sensors and datasets, to quantify past, current and future biomass stocks. Focusing on mangrove forest biomass estimation, this book chapter has 3 main objectives: a) To describe in detail the field methodologies used to derive accurate estimates of biomass in mangrove forests b) To explain how mangrove forest biomass can be measured using several remote sensing techniques and datasets c) To give a detailed explanation of the measurement challenges and errors that arise in each estimate of forest biomass

Fatoyinbo, Temilola E.↗
NASA NTRS1976

First concept for a tropical area monitoring project

The first concept of a tropical area monitoring project is presented. The project would develop an operational system capable of monitoring land areas by machine processing of satellite data. LANDSAT images would be processed within a controlled isolable unit to detect changes in forest cover, rangeland, soil integrity, and other factors important to conservation of tropical ecology. An introductory developmental effort is described to demonstrate the use of LANDSAT data in this application. The independent unit, which functions as a user organization within the development project, assures that the technology will be transferable to a user organization through well defined, easily monitored interfaces with the rest of the world.

Source record↗
NASA NTRS2004

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

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.

Gonzalez, J. E.↗
NASA NTRS2005

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

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.

Gonzalez, Jorge E.↗
NASA NTRS2015

Integrated Assessment of Climate Change Impacts on Maize Farms and Farm Household Incomes in South India: A Case Study from Tamil Nadu: Chapter - 9

South India is characterized by a wide variety of landscapes, soils and climatic zones. It is comprised of tropical, semi-arid, humid-moist, and high-altitude environments, which support a diversity of agricultural systems. Our study focused on the state of Tamil Nadu, which is characterized by a generally tropical climate, and receive rainfall during both the southwest monsoon season (SWM, June to September) and the northeast monsoon (NEM, September to December). Agriculture continues to be an important sector in the state economy, as more than 56 of the people depend on agriculture and allied sectors for their livelihood. Analysis of land-use patterns in Tamil Nadu reveals that in the past decade there has been a reduction in net sown area and current fallow, while the share of cultivable wastelands has increased. The area under cereals, pulses, and oilseeds had marginally declined, although area under commercial crops like turmeric, sugar-cane, banana, fruits, and vegetables has shown an increasing trend. The production performance of major crops like cereals, pulses, and oilseeds has not shown any significant increase. Demand and supply gap of important crops in Tamil Nadu for the year 2010 indicates that the state is lagging far behind in the production of various crops.

agriculture↗
NASA NTRS2020

Satellite Monitoring of Global Surface Soil Organic Carbon Dynamics Using the SMAP Level 4 Carbon Product

Soil organic carbon (SOC) is an important metric of soil health and the terrestrial carbon balance. Short‐term climate variations affect SOC through changes in temperature and moisture, which control vegetation growth and soil decomposition. We evaluated a satellite data‐driven carbon model, operating under the NASA Soil Moisture Active‐Passive (SMAP) mission, as a means of monitoring global surface SOC dynamics. The SMAP Level 4 Carbon (L4C) product estimates a daily global carbon budget including surface (0‐ to 5‐cm depth) SOC. We found that the L4C mean latitudinal SOC distribution is generally consistent with alternative assessments from static soil inventory records and dynamic global vegetation models (r ≥ 0.89). Within forest systems, based on inventory data, L4C SOC is most similar in magnitude to litterfall but is correlated with coarse woody debris ( urn:x-wiley:jgrg:media:jgrg21790:jgrg21790-math-0001) and total SOC ( urn:x-wiley:jgrg:media:jgrg21790:jgrg21790-math-0002). L4C SOC is sensitive to seasonal and annual climate variability, with mean residence times that range from 1.5 years in the wet tropics to 17 years in the cold tundra. Incorporating soil moisture retrievals from the SMAP L‐band (1.4 GHz) microwave radiometer within the L4C algorithm provides enhanced soil moisture sensitivity under low‐to‐moderate vegetation cover (<5 kg/sq.m vegetation water content). The L‐band soil moisture had the greatest impact on the L4C carbon budget in semiarid regions, which span almost 60% of the globe and account for substantial variability in the terrestrial carbon sink. The L4C operational product enables prognostic investigations into effects of recent climate trends and anomalies (e.g., droughts and pluvials) on shallow soil carbon dynamics.

K. Arthur Endsley↗
NASA NTRS2022

Assimilation of SMAP Products for Improving Streamflow Simulations over Tropical Climate Region—Is Spatial Information More Important Than Temporal Information?

Streamflow is one of the key variables in the hydrological cycle. Simulation and forecasting of streamflow are challenging tasks for hydrologists, especially in sparsely gauged areas. Coarse spatial resolution remote sensing soil moisture products (equal to or larger than 9 km) are often assimilated into hydrological models to improve streamflow simulation in large catchments. This study uses the Ensemble Kalman Filter (EnKF) technique to assimilate SMAP soil moisture products at the coarse spatial resolution of 9 km (SMAP 9 km), and downscaled SMAP soil moisture product at the higher spatial resolution of 1 km (SMAP 1 km), into the Soil and Water Assessment Tool (SWAT) to investigate the usefulness of different spatial and temporal resolutions of remotely sensed soil moisture products in streamflow simulation and forecasting. The experiment was set up for eight catchments across the tropical climate of Vietnam, with varying catchment areas from 267 to 6430 km^2 during the period 2017–2019. We comprehensively evaluated the EnKF-based SWAT model in simulating streamflow at low, average, and high flow. Our results indicated that high-spatial resolution of downscaled SMAP 1 km is more beneficial in the data assimilation framework in aiding the accuracy of streamflow simulation, as compared to that of SMAP 9 km, especially for the small catchments. Our analysis on the impact of observation resolution also indicates that the improvement in the streamflow simulation with data assimilation is more significant at catchments where downscaled SMAP 1 km has fewer missing observations. This study is helpful for adding more understanding of performances of soil moisture data assimilation based hydrological modelling over the tropical climate region, and exhibits the potential use of remote sensing data assimilation in hydrology.

soil moisture↗
NASA NTRS2009

Integrating and Visualizing Tropical Cyclone Data Using the Real Time Mission Monitor

The Real Time Mission Monitor (RTMM) is a visualization and information system that fuses multiple Earth science data sources, to enable real time decision-making for airborne and ground validation experiments. Developed at the NASA Marshall Space Flight Center, RTMM is a situational awareness, decision-support system that integrates satellite imagery, radar, surface and airborne instrument data sets, model output parameters, lightning location observations, aircraft navigation data, soundings, and other applicable Earth science data sets. The integration and delivery of this information is made possible using data acquisition systems, network communication links, network server resources, and visualizations through the Google Earth virtual globe application. RTMM is extremely valuable for optimizing individual Earth science airborne field experiments. Flight planners, scientists, and managers appreciate the contributions that RTMM makes to their flight projects. A broad spectrum of interdisciplinary scientists used RTMM during field campaigns including the hurricane-focused 2006 NASA African Monsoon Multidisciplinary Analyses (NAMMA), 2007 NOAA-NASA Aerosonde Hurricane Noel flight, 2007 Tropical Composition, Cloud, and Climate Coupling (TC4), plus a soil moisture (SMAP-VEX) and two arctic research experiments (ARCTAS) in 2008. Improving and evolving RTMM is a continuous process. RTMM recently integrated the Waypoint Planning Tool, a Java-based application that enables aircraft mission scientists to easily develop a pre-mission flight plan through an interactive point-and-click interface. Individual flight legs are automatically calculated "on the fly". The resultant flight plan is then immediately posted to the Google Earth-based RTMM for interested scientists to view the planned flight track and subsequently compare it to the actual real time flight progress. We are planning additional capabilities to RTMM including collaborations with the Jet Propulsion Laboratory in the joint development of a Tropical Cyclone Integrated Data Exchange and Analysis System (TC IDEAS) which will serve as a web portal for access to tropical cyclone data, visualizations and model output.

Goodman, H. Michael↗
NASA NTRS2021

The Inland Maintenance and Reintensification of Tropical Storm Bill (2015). Part II: Precipitation Microphysics

Tropical Storm Bill produced over 400 mmof rainfall to portions of southern Oklahoma from 16-20 June 2015, adding to the catastrophic urban and river flooding that occurred throughout the region in the month prior to landfall. The unprecedented excessive precipitation event that occurred across Oklahoma and Texas during May and June 2015 resulted in anomalously high soil moisture and latent heat fluxes over the region, acting to increase the available boundary layer moisture. Tropical Storm Bill progressed inland over the region of anomalous soil moisture and latent heat fluxes which helped maintain polarimetric radar signatures associated with tropical, warm rain events. Vertical profiles of polarimetric radar variables such as Z(H), Z(DR), K(DP), and ρ(hv) were analyzed in time and space over Texas and Oklahoma. The profiles suggest that Tropical Storm Bill maintained warm rain signatures and collision-coalescence processes as it tracked hundreds of kilometers inland away from the landfall point consistent with tropical cyclone precipitation characteristics. Dual-frequency precipitation radar observations from the NASA GPM DPR were also analyzed post-landfall and showed similar signatures of collision-coalescence while Bill moved over north Texas, southern Oklahoma, eastern Missouri, and western Kentucky.

Noah S. Brauer↗
NASA NTRS2007

Evaluating the Capacity of Global CO2 Flux and Atmospheric Transport Models to Incorporate New Satellite Observations

As we enter the new era of satellite remote sensing for CO2 and other carbon cyclerelated quantities, advanced modeling and analysis capabilities are required to fully capitalize on the new observations. Model estimates of CO2 surface flux and atmospheric transport are required for initial constraints on inverse analyses, to connect atmospheric observations to the location of surface sources and sinks, and ultimately for future projections of carbon-climate interactions. For application to current, planned, and future remotely sensed CO2 data, it is desirable that these models are accurate and unbiased at time scales from less than daily to multi-annual and at spatial scales from several kilometers or finer to global. Here we focus on simulated CO2 fluxes from terrestrial vegetation and atmospheric transport mutually constrained by analyzed meteorological fields from the Goddard Modeling and Assimilation Office for the period 1998 through 2006. Use of assimilated meteorological data enables direct model comparison to observations across a wide range of scales of variability. The biospheric fluxes are produced by the CASA model at lxi degrees on a monthly mean basis, modulated hourly with analyzed temperature and sunlight. Both physiological and biomass burning fluxes are derived using satellite observations of vegetation, burned area (as in GFED-2), and analyzed meteorology. For the purposes of comparison to CO2 data, fossil fuel and ocean fluxes are also included in the transport simulations. In this presentation we evaluate the model's ability to simulate CO2 flux and mixing ratio variability in comparison to in situ observations at sites in Northern mid latitudes and the continental tropics. The influence of key process representations is inferred. We find that the model can resolve much of the hourly to synoptic variability in the observations, although there are limits imposed by vertical resolution of boundary layer processes. The seasonal cycle and its interannual variations generally respond adequately, but discrepancies in the tropics suggest the need for a refinement of the soil moisture dependence of the respiration flux in CASA. Examples and inferences for interpretation of satellite data will be discussed. In general, the fidelity of these simulations leads us to anticipate incorporation of real-time, highly resolved remote sensing and other observations into quantitative analyses that will reduce uncertainty in the terrestrial CO2 sink and revolutionize our understanding of the key processes controlling atmospheric CO2 and its evolution with time.

Kawa, S. R.↗
NASA NTRS2015

Monitoring Atmospheric CO2 From Space: Challenge & Approach

Atmospheric CO2 is the key radiative forcing for the Earth's climate and may contribute a major part of the Earth's warming during the past 150 years. Advanced knowledge on the CO2 distributions and changes can lead considerable model improvements in predictions of the Earth's future climate. Large uncertainties in the predictions have been found for decades owing to limited CO2 observations. To obtain precise measurements of atmospheric CO2, certain challenges have to be overcome. For an example, global annual means of the CO2 are rather stable, but, have a very small increasing trend that is significant for multi-decadal long-term climate. At short time scales (a second to a few hours), regional and subcontinental gradients in the CO2 concentration are very small and only in an order of a few parts per million (ppm) compared to the mean atmospheric CO2 concentration of about 400 ppm, which requires atmospheric CO2 space monitoring systems with extremely high accuracy and precision (about 0.5 ppm or 0.125%) in spatiotemporal scales around 75 km and 10-s. It also requires a decadal-scale system stability. Furthermore, rapid changes in high latitude environments such as melting ice, snow and frozen soil, persistent thin cirrus clouds in Amazon and other tropical areas, and harsh weather conditions over Southern Ocean all increase difficulties in satellite atmospheric CO2 observations. Space lidar approaches using Integrated Path Differential Absorption (IPDA) technique are considered to be capable of obtaining precise CO2 measurements and, thus, have been proposed by various studies including the 2007 Decadal Survey (DS) of the U.S. National Research Council. This study considers to use the Intensity-Modulated Continuous-Wave (IM-CW) lidar to monitor global atmospheric CO2 distribution and variability from space. Development and demonstration of space lidar for atmospheric CO2 measurements have been made through joint adventure of NASA Langley Research Center and Exelis, Inc. As prototype space IPDA lidars, airborne laser absorption lidar systems operating in 1.57 CO2 absorption band have been developed and tested through lab, ground-based range, and flight campaigns. Very encouraging results have been obtained. The signal-to-noise ratio (SNR) for clear sky IPDA measurements of CO2 differential absorption optical depth (DAOD) for a 10-s integration over vegetated areas with about 10 km range was found to be as high as 1300, resulting in an error 0.077% or equivalent CO2 mixing ratio (XCO2) column precision of ~0.3 ppm. Precise range measurements using the IM-CW lidar approach were also achieved, and the uncertainties have been shown to be at sub meter level. Based on the airborne lidar development, space lidar and atmospheric CO2 observations are simulated. It shows that with the IM-CW approach, accurate atmospheric CO2 measurements can be achieved from space, and a space mission such as that proposed by the DS will meet science goals of atmospheric CO2 monitoring.

Lin, Bing↗
NASA NTRS1989

Nitrate in the atmospheric boundary layer of the tropical South Pacific - Implications regarding sources and transport

Weekly bulk aerosol samples collected at three sites in the tropical South Pacific from 1983 to 1987 are analyzed. The mean nitrate concentrations obtained for the sites range from 0.107 to 0.117 microg/cu m. The results suggest that the region is minimally affected by the transport of soil material and pollutants from the continents. Measurements from sites in the tropical North Pacific show mean nitrate concentrations that are about three times higher than those in the South Pacific, showing that the North Pacific is significantly impacted by the transport of material from Asia and North America. The relationships between the nitrate concentrations to other constituents at American Samoa are discussed, including nonseasalt sulfate, Pb-210, and Be-7.

Savoie, Dennis L.↗
NASA NTRS1980

Remote sensing data applied to the evaluation of soil erosion caused by land-use. Ribeirao Anhumas Basin Area: A case study

Formerly covered by a tropical forest, the study area was deforested in the early 40's for coffee plantation and cattle raising, which caused intense gully erosion problems. To develop a method to analyze the relationship between land use and soil erosion, visual interpretations of aerial photographs (scale 1:25.000), MSS-LANDSAT imagery (scale 1:250,000), as well as automatic interpretation of computer compatible tapes by IMAGE-100 system were carried out. From visual interpretation the following data were obtained: land use and cover tapes, slope classes, ravine frequency, and a texture sketch map. During field work, soil samples were collected for texture and X-ray analysis. The texture sketch map indicate that the areas with higher slope angles have a higher susceptibilty to the development of gullies. Also, the over carriage of pastureland, together with very friable lithologies (mainly sandstone) occuring in that area, seem to be the main factors influencing the catastrophic extension of ravines in the study site.

Dejesusparada, N.↗
NASA NTRS2005

Global Partitioning of NOx Sources Using Satellite Observations: Relative Roles of Fossil Fuel Combustion, Biomass Burning and Soil Emissions

This document contains the following abstract for the paper "Global partitioning of NOx sources using satellite observations: Relative roles of fossil fuel combustion, biomass burning and soil emissions." Satellite observations have been used to provide important new information about emissions of nitrogen oxides. Nitrogen oxides (NOx) are significant in atmospheric chemistry, having a role in ozone air pollution, acid deposition and climate change. We know that human activities have led to a three- to six-fold increase in NOx emissions since pre-industrial times, and that there are three main surface sources of NOx: fuel combustion, large-scale fires, and microbial soil processes. How each of these sources contributes to the total NOx emissions is subject to some doubt, however. The problem is that current NOx emission inventories rely on bottom-up approaches, compiling large quantities of statistical information from diverse sources such as fuel and land use, agricultural data, and estimates of burned areas. This results in inherently large uncertainties. To overcome this, Lyatt Jaegle and colleagues from the University of Washington, USA, used new satellite observations from the Global Ozone Monitoring Experiment (GOME) instrument. As the spatial and seasonal distribution of each of the sources of NOx can be clearly mapped from space, the team could provide independent topdown constraints on the individual strengths of NOx sources, and thus help resolve discrepancies in existing inventories. Jaegle's analysis of the satellite observations, presented at the recent Faraday Discussion on "Atmospheric Chemistry", shows that fuel combustion dominates emissions at northern mid-latitudes, while fires are a significant source in the Tropics. Additionally, she discovered a larger than expected role for soil emissions, especially over agricultural regions with heavy fertilizer use. Additional information is included in the original extended abstract.

Jaegle, Lyatt↗
NASA NTRS1995

NASA Tropical Rainfall Measurement Mission (TRMM): Effects of tropical rainfall on upper ocean dynamics, air-sea coupling and hydrologic cycle

This was a Tropical Rainfall Measurement Mission (TRMM) modeling, analysis and applications research project. Our broad scientific goals addressed three of the seven TRMM Priority Science Questions, specifically: What is the monthly average rainfall over the tropical ocean areas of about 10(exp 5) sq km, and how does this rain and its variability affect the structure and circulation of the tropical oceans? What is the relationship between precipitation and changes in the boundary conditions at the Earth's surface (e.g., sea surface temperature, soil properties, vegetation)? How can improved documentation of rainfall improve understanding of the hydrological cycle in the tropics?

Lagerloef, Gary↗