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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Vegetation Monitoring Optimization with Normalized Difference Vegetation Index and Evapotranspiration Using Remote Sensing Measurements and Land Surface Models Over East Africa

The majority of people in East Africa rely on the agro-pastoral system for their livelihood, which is highly vulnerable to droughts and flooding. Agro-pastoral droughts are endemic to the region and are considered the main natural hazard that contributes to food insecurity. Drought begins with rainfall deficit, gradually leading to soil moisture deficit, higher land surface temperature, and finally impacts to vegetation growth. Therefore, monitoring vegetation conditions is essential in understanding the progression of drought, potential effects on food security, and providing early warning information needed for drought mitigation decisions. Because vegetation processes couple the land and atmosphere, monitoring of vegetation conditions requires consideration of both water provision and demand. While there is consensus in using either the Normalized Difference Vegetation Index (NDVI) or evapotranspiration(ET) for vegetation monitoring, a comprehensive assessment optimizing the use of both has not yet been done. Moreover, the evaluation methods for understanding the relationships between NDVI and ET for vegetation monitoring are also limited. Taking these gaps into account we have developed a framework to optimize vegetation monitoring using both NDVI and ET by identifying where they perform the best by using triple collocation and cross-correlation methods. We estimated the random error structure in Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI; ET from the Operational Simplified Surface Energy Balance (SSEBop) model; and ET from land surface models (LSMs). LSM ET and SSEBop ET have been found to be better indicators for vegetation monitoring during extreme drought events, while NDVI could provide better information on vegetation condition during wetter than normal conditions. The random error structures of these variables suggest that LSM ET is most likely to provide important information for vegetation monitoring over low and high ends of the vegetation fraction areas. Over moderate vegetative areas, any of these variables could provide important vegetation information for drought characterization and food security assessments. While this study provides a framework for optimizing vegetation monitoring for drought and food security assessments over East Africa, the framework can be adopted to optimize vegetation monitoring over any other drought and food insecure region of the world.

triple collocation↗
NASA NTRS2004

The Hurricane-Flood-Landslide Continuum: Forecasting Hurricane Effects at Landfall

Hurricanes, typhoons, and cyclones strike Central American, Caribbean, Southeast Asian and Pacific Island nations even more frequently than the U.S. The global losses of life and property from the floods, landslides and debris flows caused by cyclonic storms are staggering. One of the keys to reducing these losses, both in the U.S. and internationally, is to have better forecasts of what is about to happen from several hours to days before the event. Particularly in developing nations where science, technology and communication are limited, advance-warning systems can have great impact. In developing countries, warnings of even a few hours or days can mitigate or reduce catastrophic losses of life. With the foregoing needs in mind, we propose an initial project of three years total duration that will aim to develop and transfer a warning system for a prototype region in the Central Caribbean, specifically the islands of Puerto Rico and Hispanola. The Hurricane-Flood-Landslide Continuum will include satellite observations to track and nowcast dangerous levels of precipitation, atmospheric and hydrological models to predict near-future runoff, and streamflow changes in affected regions, and landslide models to warn when and where landslides and debris flows are imminent. Since surface communications are likely to be interrupted during these crises, the project also includes the capability to communicate disaster information via satellite to vital government officials in Puerto Rico, Haiti, and Dominican Republic.

Negri, A.↗
NASA NTRS2011

NASA Soil Moisture Active Passive (SMAP) Mission Formulation

The Soil Moisture Active Passive (SMAP) Mission is one of the first Earth observation satellites being formulated by NASA in response to the 2007 National Research Council s Earth Science Decadal Survey [1]. SMAP s measurement objectives are high-resolution global measurements of near-surface soil moisture and its freeze-thaw state. These measurements would allow significantly improved estimates of water, energy and carbon transfers between the land and atmosphere. The soil moisture control of these fluxes is a key factor in the performance of atmospheric models used for weather forecasts and climate projections. Soil moisture measurements are also of great importance in assessing flooding and monitoring drought. Knowledge gained from SMAP s planned observations can help mitigate these natural hazards, resulting in potentially great economic and societal benefits. SMAP measurements would also yield high resolution spatial and temporal mapping of the frozen or thawed condition of the surface soil and vegetation. Observations of soil moisture and freeze/thaw timing over the boreal latitudes will contribute to reducing a major uncertainty in quantifying the global carbon balance and help resolve an apparent missing carbon sink over land. The SMAP mission would utilize an L-band radar and radiometer sharing a rotating 6-meter mesh reflector antenna (see Figure 1) [2]. The radar and radiometer instruments would be carried onboard a 3-axis stabilized spacecraft in a 680 km polar orbit with an 8-day repeating ground track. The instruments are planned to provide high-resolution and high-accuracy global maps of soil moisture at 10 km resolution and freeze/thaw at 3 km resolution, every two to three days (see Table 1 for a list of science data products). The mission is adopting a number of approaches to identify and mitigate potential terrestrial radio frequency interference (RFI). These approaches are being incorporated into the radiometer and radar flight hardware and ground processing designs.

Entekhabi, Dara↗
NASA NTRS2011

The NASA Soil Moisture Active Passive (SMAP) Mission - Algorithm and Cal/Val Activities and Synergies with SMOS and Other L-Band Missions

NASA's Soil Moisture Active Passive (SMAP) mission, planned for launch in late 2014, has as its key measurement objective the frequent, global mapping of near-surface soil moisture and its freeze-thaw state. SMAP soil moisture and freeze/thaw measurements at 10 km and 3 km resolutions respectively, would enable significantly improved estimates of water, energy and carbon transfers between the land and atmosphere. Soil moisture control of these fluxes is a key factor in the performance of atmospheric models used for weather forecasts and climate projections Soil moisture measurements are also of great importance in assessing floods and for monitoring drought. In addition, observations of soil moisture and freeze/thaw timing over the boreal latitudes can help reduce uncertainties in quantifying the global carbon balance. The SMAP measurement concept utilizes an L-band radar and radiometer sharing a rotating 6-meter mesh reflector antenna. The SMAP radiometer and radar flight hardware and ground processing designs are incorporating approaches to identify and mitigate potential terrestrial radio frequency interference (RFI). The radar and radiometer instruments are planned to operate in a 680 km polar orbit, viewing the surface at a constant 40-degree incidence angle with a 1000-km swath width, providing 3-day global coverage. Data from the instruments would yield global maps of soil moisture and freeze/thaw state to be provided at 10 km and 3 km resolutions respectively, every two to three days. Plans are to provide also a radiometer-only soil moisture product at 40-km spatial resolution. This product and the underlying brightness temperatures have characteristics similar to those provided by the Soil Moisture and Ocean Salinity (SMOS) mission. As a result, there are unique opportunities for common data product development and continuity between the two missions. SMAP also has commonalities with other satellite missions having L-band radiometer and/or radar sensors applicable to soil moisture measurement, such as Aquarius, SAO COM, and ALOS-2. The algorithms and data products for SMAP are being developed in the SMAP Science Data System (SDS) Testbed. The algorithms are developed and evaluated in the SDS Testbed using simulated SMAP observations as well as observational data from current airborne and spaceborne L-band sensors including SMOS. The SMAP project is developing a Calibration and Validation (Cal/Val) Plan that is designed to support algorithm development (pre-launch) and data product validation (post-launch). A key component of the Cal/Val Plan is the identification, characterization, and instrumentation of sites that can be used to calibrate and validate the sensor data (Level I) and derived geophysical products (Level 2 and higher). In this presentation we report on the development status of the SMAP data product algorithms, and the planning and implementation of the SMAP Cal/Val program. Several components of the SMAP algorithm development and Cal/Val plans have commonality with those of SMOS, and for this reason there are shared activities and resources that can be utilized between the missions, including in situ networks, ancillary data sets, and long-term monitoring sites.

Njoku, Eni↗
NASA NTRS2014

Assimilation of SMOS Retrieved Soil Moisture into the Land Information System

Soil moisture is a crucial variable for weather prediction because of its influence on evaporation and surface heat fluxes. It is also of critical importance for drought and flood monitoring and prediction and for public health applications such as monitoring vector-borne diseases. Land surface modeling benefits greatly from regular updates with soil moisture observations via data assimilation. Satellite remote sensing is the only practical observation type for this purpose in most areas due to its worldwide coverage. The newest operational satellite sensor for soil moisture is the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) instrument aboard the Soil Moisture and Ocean Salinity (SMOS) satellite. The NASA Short-term Prediction Research and Transition Center (SPoRT) has implemented the assimilation of SMOS soil moisture observations into the NASA Land Information System (LIS), an integrated modeling and data assimilation software platform. We present results from assimilating SMOS observations into the Noah 3.2 land surface model within LIS. The SMOS MIRAS is an L-band radiometer launched by the European Space Agency in 2009, from which we assimilate Level 2 retrievals [1] into LIS-Noah. The measurements are sensitive to soil moisture concentration in roughly the top 2.5 cm of soil. The retrievals have a target volumetric accuracy of 4% at a resolution of 35-50 km. Sensitivity is reduced where precipitation, snowcover, frozen soil, or dense vegetation is present. Due to the satellite's polar orbit, the instrument achieves global coverage twice daily at most mid- and low-latitude locations, with only small gaps between swaths.

Blankenship, Clay B.↗
NASA NTRS2020

Evaluating Flood Forecasting System Performance in Cambodia

Every year, Cambodia experiences flooding as a result of monsoon rains and typhoons. Flood forecasting systems are designed to enable people to mitigate economic and social impacts from these events. However, in order for forecasts to be used effectively, an assessment of their accuracy is needed. This study demonstrates the performance of regional and global flood forecasting systems over the 2019 flood season. To do this, we assess the flood forecast accuracy at different forecast lead times and gauge locations in Cambodia. We then compare the flood forecast performance to satellite-based flood maps produced by the Hydrological Remote Sensing Analysis of Floods (HYDRAFloods) tool currently being co-developed by SERVIR-Mekong in collaboration with the Myanmar Department of Disaster Management. This assessment of the flood forecasting systems’ performance and comparison to flood extents helps (1) provide valuable information to forecasters and disaster managers as they make improvements to their models, and (2) provides support to forecast users as they evaluate the strengths and weaknesses of different systems for taking action.

Nauman, Claire↗
NASA NTRS2008

Modeling Bird Migration in Changing Habitats: Space-based Ornithology using Satellites and GIS

Understanding bird migration and avian biodiversity is one of the most compelling and challenging problems of modern biology with major implications for human health and conservation biology. Migration and conservation efforts cross national boundaries and are subject to numerous international agreements and treaties presenting challenges in both geographic space and time. Space based technology, coupled with geographic information systems, yields new opportunities to shed light on the distribution and movement of organisms on the planet and their sensitivity to human disturbances and environmental changes. At NASA, we are creating ecological forecasting tools for science and application users to address the consequences of loss of wetlands, flooding, drought or other natural disasters such as hurricanes on avian biodiversity and bird migration. In our work, we use individual organism biophysical models and drive these models with satellite observations and numerical weather predictions of the spatio-temporal gradients in climate and habitat. Geographic information system technology comprises one component of our overall simulation framework, especially for characterizing the changing habitats and conditions encountered by en-route migratory birds. Simulation provides a tool for studying bird migration across multiple scales and can be linked to mechanistic processes describing the time and energy budget states of migrating birds. Such models yield an understanding of how a migratory flyway and its component habitats function as a whole and link stop-over ecology with biological conservation and management. We present examples of our simulation of shorebirds, principally, pectoral sandpipers, along the central flyways of the United States and Canada from the Gulf of Mexico to Alaska.

Smith, James A.↗
NASA NTRS2011

Hyperresolution Global Land Surface Modeling: Meeting a Grand Challenge for Monitoring Earth's Terrestrial Water

Monitoring Earth's terrestrial water conditions is critically important to many hydrological applications such as global food production; assessing water resources sustainability; and flood, drought, and climate change prediction. These needs have motivated the development of pilot monitoring and prediction systems for terrestrial hydrologic and vegetative states, but to date only at the rather coarse spatial resolutions (approx.10-100 km) over continental to global domains. Adequately addressing critical water cycle science questions and applications requires systems that are implemented globally at much higher resolutions, on the order of 1 km, resolutions referred to as hyperresolution in the context of global land surface models. This opinion paper sets forth the needs and benefits for a system that would monitor and predict the Earth's terrestrial water, energy, and biogeochemical cycles. We discuss six major challenges in developing a system: improved representation of surface-subsurface interactions due to fine-scale topography and vegetation; improved representation of land-atmospheric interactions and resulting spatial information on soil moisture and evapotranspiration; inclusion of water quality as part of the biogeochemical cycle; representation of human impacts from water management; utilizing massively parallel computer systems and recent computational advances in solving hyperresolution models that will have up to 10(exp 9) unknowns; and developing the required in situ and remote sensing global data sets. We deem the development of a global hyperresolution model for monitoring the terrestrial water, energy, and biogeochemical cycles a grand challenge to the community, and we call upon the international hydrologic community and the hydrological science support infrastructure to endorse the effort.

Wood, Eric F.↗
NASA NTRS1990

Radar detection of flooding beneath the forest canopy - A review

Synthetic aperture radar remote sensing is a promising tool for detection of flooding on forested floodplains. The brigtht appearance of flooded forests on radar images results from double-bounce reflections between smooth water surfaces and tree trunks or branches. Enhanced backscattering at L-band has been shown to occur in a wide variety of forest types, including cypress-tupelo swamps, temperate bottomland hardwoods, spruce bogs, mangroves and tropical floodplain forests. Lack of enhancement is a function of both stand density and branching structure. According to models and measurements, the magnitude of the enhancement is about 3 to 10 dB. Steep incidence angles (20-30 deg) are optimal for detection of flooding, since some forest types exhibit bright returns only at steeper angles. P-band should prove useful for floodwater mapping in dense stands, and multifrequency polarimetric analysis should allow flooded forests to be distinguished from marshes.

Hess, Laura L.↗
NASA NTRS2015

Evaluating the Sensitivity of Agricultural Model Performance to Different Climate Inputs: Supplemental Material

Projections of future food production necessarily rely on models, which must themselves be validated through historical assessments comparing modeled and observed yields. Reliable historical validation requires both accurate agricultural models and accurate climate inputs. Problems with either may compromise the validation exercise. Previous studies have compared the effects of different climate inputs on agricultural projections but either incompletely or without a ground truth of observed yields that would allow distinguishing errors due to climate inputs from those intrinsic to the crop model. This study is a systematic evaluation of the reliability of a widely used crop model for simulating U.S. maize yields when driven by multiple observational data products. The parallelized Decision Support System for Agrotechnology Transfer (pDSSAT) is driven with climate inputs from multiple sources reanalysis, reanalysis that is bias corrected with observed climate, and a control dataset and compared with observed historical yields. The simulations show that model output is more accurate when driven by any observation-based precipitation product than when driven by non-bias-corrected reanalysis. The simulations also suggest, in contrast to previous studies, that biased precipitation distribution is significant for yields only in arid regions. Some issues persist for all choices of climate inputs: crop yields appear to be oversensitive to precipitation fluctuations but under sensitive to floods and heat waves. These results suggest that the most important issue for agricultural projections may be not climate inputs but structural limitations in the crop models themselves.

simulation↗
NASA NTRSDate not supplied

LIS-Hydro: A Comprehensive Framework for Flood Prediction, Analysis, and Management

A subset of agencies from across the U.S. Federal enterprise are collaborating on the development of a fully-integrated hydro-modeling and streamflow prediction system (i.e., LIS-Hydro). The hydrological products and services will, for the very first time, establish a routinely available authoritative source of global water intelligence information supporting war-fighters, planners, and decision makers at all echelons and services of the U.S. military, Federal government, and intelligence community. Completion of the project and sustainment of the operational capability by Air Force Weather will establish a national asset to assist federal agencies in implementing government-wide strategies around water resources (U.S. Global Water Strategy, 2017). A summary of the interagency scientific collaboration in addressing some of the key gaps and needs identified during a 2017 joint NASA, USACE/ERDC, and U.S. Air Force co-sponsored workshop will be presented.

Jerry W Wegiel↗
NASA NTRS2005

Space-based Remote Sensing: A Tool for Studying Bird Migration Across Multiple Scales

The study of bird migration on a global scale is one of the compelling and challenging problems of modern biology with major implications for human health and conservation biology. Migration and conservation efforts cross national boundaries and are subject to numerous international agreements and treaties. Space based technology offers new opportunities to shed understanding on the distribution and migration of organisms on the planet and their sensitivity to human disturbances and environmental changes. Our working hypothesis is that individual organism biophysical models of energy and water balance, driven by satellite measurements of spatio-temporal gradients in climate and habitat, will help us to explain the variability in avian species richness and distribution. Further, these models provide an ecological forecasting tool for science and application users to visualize the possible consequences of loss of wetlands, flooding, or other natural disasters such as hurricanes on avian biodiversity and bird migration.

Smith, James A.↗
NASA NTRS2015

The Constrained Vapor Bubble Experiment - Interfacial Flow Region

Internal heat transfer coefficient of the CVB correlated to the presence of the interfacial flow region. Competition between capillary and Marangoni flow caused Flooding and not a Dry-out region. Interfacial flow region growth is arrested at higher power inputs. 1D heat model confirms the presence of interfacial flow region. 1D heat model confirms the arresting phenomena of interfacial flow region Visual observations are essential to understanding.

microgravity↗
NASA NTRS2020

Satellite Gravimetry Improves Seasonal Streamflow Forecast Initialization in Africa

West Africa is one of the poorest regions in the world and highly vulnerable to extreme hydrological events due to the lack of reliable monitoring and forecast systems. For the first time, we demonstrate that initial hydrological conditions informed by satellite-based terrestrial water storage (TWS) estimates improve seasonal streamflow forecasts. TWS variability detected by the Gravity Recovery and Climate Experiment (GRACE) satellites is assimilated into a land surface model during 2003–2016 and used to initialize 6-month hindcasts (i.e., forecasts of past events) during West Africa's wet seasons. We find that GRACE data assimilation (DA) generally increases groundwater and soil moisture storage in the region, resulting in increased evapotranspiration and reduced total runoff. Total runoff is particularly lower at the headwaters of the Niger River, positively impacting streamflow simulations and hindcast initializations. Compared to simulations without GRACE-DA, hindcasts are notably improved at locations draining from large basin areas, in particular, over the Niger River basin, which is consistent with GRACE's coarse spatial resolution. The long memory of groundwater and deep soil moisture, two main TWS components updated by GRACE-DA, is reflected in prolonged improvements in the streamflow hindcasts. Model accuracy at Niamey, Niger, the most populated city where streamflow observations are available, improved up to 33% during the flood season. These new findings directly contribute to ongoing developments in food security, flood potential forecast, and water-related disaster warning systems for Africa.

Augusto Getirana↗
NASA NTRS2014

Colombia Mi Pronostico Flood Application: Updating and Improving the Mi Pronostico Flood Web Application to Include an Assessment of Flood Risk

Colombia is a country with highly variable terrain, from the Andes Mountains to plains and coastal areas, many of these areas are prone to flooding disasters. To identify these risk areas NASA's Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) was used to construct a digital elevation model (DEM) for the study region. The preliminary risk assessment was applied to a pilot study area, the La Mosca River basin. Precipitation data from the National Aeronautics and Space Administration (NASA) Tropical Rainfall Measuring Mission (TRMM)'s near-real-time rainfall products as well as precipitation data from the Instituto de Hidrologia, Meteorologia y Estudios Ambientales (the Institute of Hydrology, Meteorology and Environmental Studies, IDEAM) and stations in the La Mosca River Basin were used to create rainfall distribution maps for the region. Using the precipitation data and the ASTER DEM, the web application, Mi Pronóstico, run by IDEAM, was updated to include an interactive map which currently allows users to search for a location and view the vulnerability and current weather and flooding conditions. The geospatial information was linked to an early warning system in Mi Pronóstico that can alert the public of flood warnings and identify locations of nearby shelters.

Rushley, Stephanie↗
NASA NTRS1978

HCMM energy budget data as a model input for assessing regions of high potential groundwater pollution

The author has identified the following significant results. In early April 1978, heavy spring runoff from snowmelt caused significant flooding along a portion of the Big Sioux River Basin in southeastern South Dakota. The flooded area was visible from surrounding areas on a May 15 HCMM IR test image. On May 15, the flood waters had receded but an area of anomalous residual high soil moisture remained. The high soil moisture area was not visible on a HCMM day visible test image of the same scene, or on LANDSAT imagery. To evaluate the effect of water table depth on surface temperatures, thermal scanner data collected on September 5 and 6, 1978 at approximate HCMM overpass times at an altitude of 3650 m were analyzed. Apparent surface temperatures measured by the scanner included emittance contributions from soil surface and the land cover. Results indicated that the shallow water tables produced a damping of the amplitude of the diurnal surface temperature wave.

Moore, D. G.↗
NASA NTRS2006

An Efficient Approach for the Reliability Analysis of Phased-Mission Systems with Dependent Failures

We consider the reliability analysis of phased-mission systems with common-cause failures in this paper. Phased-mission systems (PMS) are systems supporting missions characterized by multiple, consecutive, and nonoverlapping phases of operation. System components may be subject to different stresses as well as different reliability requirements throughout the course of the mission. As a result, component behavior and relationships may need to be modeled differently from phase to phase when performing a system-level reliability analysis. This consideration poses unique challenges to existing analysis methods. The challenges increase when common-cause failures (CCF) are incorporated in the model. CCF are multiple dependent component failures within a system that are a direct result of a shared root cause, such as sabotage, flood, earthquake, power outage, or human errors. It has been shown by many reliability studies that CCF tend to increase a system's joint failure probabilities and thus contribute significantly to the overall unreliability of systems subject to CCF.We propose a separable phase-modular approach to the reliability analysis of phased-mission systems with dependent common-cause failures as one way to meet the above challenges in an efficient and elegant manner. Our methodology is twofold: first, we separate the effects of CCF from the PMS analysis using the total probability theorem and the common-cause event space developed based on the elementary common-causes; next, we apply an efficient phase-modular approach to analyze the reliability of the PMS. The phase-modular approach employs both combinatorial binary decision diagram and Markov-chain solution methods as appropriate. We provide an example of a reliability analysis of a PMS with both static and dynamic phases as well as CCF as an illustration of our proposed approach. The example is based on information extracted from a Mars orbiter project. The reliability model for this orbiter considers the various phases of Launch, Cruise, Mars Orbit Insertion, and Orbit. Some of the CCF for the orbiter in this mission include environmental effects, such as micrometeoroids, human operator errors, and software errors.

reliability analysis↗
NASA NTRS2013

A Canonical Repsonse of Precipitation Characteristics to Global Warming from CMIP5 Models

In this study, we find from analyses of projections of 14 CMIP5 models a robust, canonical global response in rainfall characteristics to a warming climate. Under a scenario of 1% increase per year of CO2 emission, the model ensemble projects globally more heavy precipitation (+7+/-2.4%/K1), less moderate precipitation (-2.5+/-0.6%/K), more light precipitation (+1.8+/-1.3%/K1), and increased length of dry (no-rain) periods (+4.7+/-2.1%/K). Regionally, a majority of the models project a consistent response with more heavy precipitation over climatologically wet regions of the deep tropics, especially the equatorial Pacific Ocean and the Asian monsoon regions, and more dry periods over the land areas of the subtropics and the tropical marginal convective zones. Our results suggest that increased CO2 emissions induce a global adjustment in circulation and moisture availability manifested in basic changes in global precipitation characteristics, including increasing risks of severe floods and droughts in preferred geographic locations worldwide.

CMIP5↗