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NASA NTRS2003

Transferring Knowledge from Observations and Models to Decision Makers: An Overview and Challenges

Over the last 25 years, a tremendous progress has been made in the Earth science space-based remote sensing observations, technologies and algorithms. Such advancements have improved the predictability by providing lead-time and accuracy of forecast in weather, climate, natural hazards, and natural resources. It has further reduced or bounded the overall uncertainties by partially improving our understanding of planet Earth as an integrated system that is governed by non-linear and chaotic behavior. Many countries such US, European Community, Japan, China and others have invested billions of dollars in developing and launching space-based assets in the low earth (LEO) and geostationary (GEO) orbits. However, the wealth of this scientific knowledge that has potential of extracting monumental socio-economic benefits from such large investments have been slow in reaching to public and decision makers. For instance, there are a number of areas such as energy forecasting, aviation safety, agricultural competitiveness, disaster management, security, air quality and public health can directly take advantage. Nevertheless, we all live in a global economy that depends on access to the best available Earth Science information for all inhabitants of this planet. This paper surveys and examines a number such applications in terms of their architecture, maturity and economic applicability as they apply to the societal needs. A detailed analysis is also presented of various challenges and issues that pertain to a number of areas such as: (1) difficulties in making a speedy transition of data and information from observations and models to relevant Decision Support Systems (DSS) or tools, (2) data and models inter-operability issues, (3) limitations of spatial, spectral and temporal resolution, (4) communication limitations as dictated by the availability of image processing and data compression techniques. Additionally, the most critical element amongst all is the organizational and management boundaries that must be resolved at local, state, national and international levels to implement and realize free flow of such vital information. This paper also makes attempts to address this topic and discuss possible approaches to deal with this quandary.

Habib, Shahid↗
NASA NTRS2013

Where Does the Irrigation Water Go? An Estimate of the Contribution of Irrigation to Precipitation Using MERRA

Irrigation is an important human activity that may impact local and regional climate, but current climate model simulations and data assimilation systems generally do not explicitly include it. The European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim) shows more irrigation signal in surface evapotranspiration (ET) than the Modern-Era Retrospective Analysis for Research and Applications (MERRA) because ERA-Interim adjusts soil moisture according to the observed surface temperature and humidity while MERRA has no explicit consideration of irrigation at the surface. But, when compared with the results from a hydrological model with detailed considerations of agriculture, the ET from both reanalyses show large deficiencies in capturing the impact of irrigation. Here, a back-trajectory method is used to estimate the contribution of irrigation to precipitation over local and surrounding regions, using MERRA with observation-based corrections and added irrigation-caused ET increase from the hydrological model. Results show substantial contributions of irrigation to precipitation over heavily irrigated regions in Asia, but the precipitation increase is much less than the ET increase over most areas, indicating that irrigation could lead to water deficits over these regions. For the same increase in ET, precipitation increases are larger over wetter areas where convection is more easily triggered, but the percentage increase in precipitation is similar for different areas. There are substantial regional differences in the patterns of irrigation impact, but, for all the studied regions, the highest percentage contribution to precipitation is over local land.

ERA-Interim↗
NASA NTRS2017

The Past and Future Changes in Climate of the Rice-Wheat Cropping Zone in Punjab, Pakistan

Agriculture is ranked top among Pakistan economic sectors vulnerable to the potential impacts of climate change. The agricultural production system is directly affected by weather inputs (temperature, solar radiation, and rainfall) that are projected to change in future (following increases in carbon dioxide and other greenhouse gasses). Although Climatic extremes such as drought, floods and heat waves, are expected to increase with detrimental consequences for agriculture and livestock production, yet changes in mean climates also pose challenges to sustainable development. This study presents climate change results for five districts within the major rice-wheat productivity zone of Punjab province in Pakistan. The results are focused on RCP8.5 mid-century (2040-2069) scenarios derived from five global climate models (GCMs) output and the Kharif (June-October) and Rabi (November-April) seasons. Analysis of recent historical weather data of Sialkot and Sheikhupura districts shows an increase in minimum temperatures and maximum temperatures and a large variation in rainfall. This temperature change and variability in rainfall are expected to enhance further as we approach 2050s. The mean maximum temperature is projected to increase by 2-2.5 ºC during the rice growing (Kharif) season and 2.4-2.7ºC during the wheat growing (Rabi) season. Rainfall during the rice growing season is more uncertain, with projections indicating an increase of 25%-35 % in the study region, while a minimal change is expected during the Rabi season. Climax of the rice growing season corresponds with the projected increase in monsoon intensity, leaving no doubt about the crop water demand satisfaction. However, a simultaneous increase in the day and night temperature may affect the growth and development of some critical phenological stages and thus could impact final yield.

Climate Change↗
NASA NTRS2010

DEM Solutions Develops Answers to Modeling Lunar Dust and Regolith

With the proposed return to the Moon, scientists like NASA-KSC's Dr. Calle are concerned for a number of reasons. We will be staying longer on the planet's surface, future missions may include dust-raising activities, such as excavation and handling of lunar soil and rock, and we will be sending robotic instruments to do much of the work for us. Understanding more about the chemical and physical properties of lunar dust, how dust particles interact with each other and with equipment surfaces and the role of static electricity build-up on dust particles in the low-humidity lunar environment is imperative to the development of technologies for removing and preventing dust accumulation, and successfully handling lunar regolith. Dr. Calle is currently working on the problems of the electrostatic phenomena of granular and bulk materials as they apply to planetary surfaces, particularly to those of Mars and the Moon, and is heavily involved in developing instrumentation for future planetary missions. With this end in view, the NASA Kennedy Space Center's Innovative Partnerships Program Office partnered with OEM Solutions, Inc. OEM Solutions is a global leader in particle dynamics simulation software, providing custom solutions for use in tackling tough design and process problems related to bulk solids handling. Customers in industries such as pharmaceutical, chemical, mineral, and materials processing as well as oil and gas production, agricultural and construction, and geo-technical engineering use OEM Solutions' EDEM(TradeMark) software to improve the design and operation of their equipment while reducing development costs, time-to-market and operational risk. EDEM is the world's first general-purpose computer-aided engineering (CAE) tool to use state-of-the-art discrete element modeling technology for the simulation and analysis of particle handling and manufacturing operations. With EDEM you'can quickly and easily create a parameterized model of your granular solids system. Computer-aided design (CAD) models of real particles can be imported to obtain an accurate representation of their shape. EDEM(TradeMark) uses particle-scale behavior models to simulate bulk solids behavior. In addition to particle size and shape, the models can account for physical properties of particles along with interaction between particles and with equipment surfaces and surrounding media, as needed to define the physics of a particular process.

Dunn, Carol Anne↗
NASA NTRS2020

Kenya Food Security & Agriculture II: Utilizing NASA Earth Observations to Enhance Drought Warning Systems and Develop Capacity to Use the RHEAS Model in Kenya

Twenty-three counties in Kenya experience frequent drought, which damages agricultural productivity and threatens the health and well being of millions. NASA DEVELOP partnered with NASA SERVIR, the Regional Centre for Mapping of Resources for Development (RCMRD) and Kenya’s National Drought Management Authority (NDMA) to enhance drought-detection capacity using NASA Earth observations. Currently, the NDMA publishes monthly Early Warning Bulletins with drought conditions for each arid or semi-arid county in Kenya. These bulletins utilize the Vegetation Condition Index (VCI) from the Moderate Resolution ImagingSpectroradiometer (MODIS) as well as a variety of biophysical, social and economic drought indicators, but do not allow for advanced forecasting. To improve drought-monitoring capabilities, the team created a Combined Drought Indicator (CDI) using the Regional Hydrologic Extremes Assessment System (RHEAS) model and data from Aqua and Terra MODIS and the National Centers for Environmental Prediction. The CDI combined precipitation anomalies, soil moisture anomalies, evaporative stress, and VCI according to weights determined by principal component analysis. To evaluate the performance of the CDI across Kenya, the team compiled a dataset of drought events from historical reports and the NDMA’s records. Results suggested that the CDI detected drought earlier thanVCI alone. However, more validation is needed to ensure that the CDI accurately and consistently detects drought earlier than current warning systems. In order to better understand the behavior of individual indices and the potential for earlier drought detection, the team also conducted a time-lag analysis. The team found that VCI responds to drought, on average, one month later than most other indices included in the CDI, suggesting that incorporating additional indices could improve early drought warning systems in Kenya.

Food Security & Agriculture↗
NASA NTRS2020

Kenya Food Security & Agriculture II: Utilizing NASA Earth Observations to Enhance Drought Warning Systems and Develop Capacity to Use the RHEAS Model in Kenya

Twenty-three counties in Kenya experience frequent drought, which damages agricultural productivity and threatens the health and well being of millions. NASA DEVELOP partnered with NASA SERVIR, the Regional Centre for Mapping of Resources for Development (RCMRD) and Kenya’s National Drought Management Authority (NDMA) to enhance drought-detection capacity using NASA Earth observations. Currently, the NDMA publishes monthly Early Warning Bulletins with drought conditions for each arid or semi-arid county in Kenya. These bulletins utilize the Vegetation Condition Index (VCI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) as well as a variety of biophysical, social and economic drought indicators, but do not allow for advanced forecasting. To improve drought-monitoring capabilities, the team created a Combined Drought Indicator (CDI) using the Regional Hydrologic Extremes Assessment System (RHEAS) model and data from Aqua and Terra MODIS and the National Centers for Environmental Prediction.The CDI combined precipitation anomalies, soil moisture anomalies, evaporative stress, and VCI according to weights determined by principal component analysis. To evaluate the performance of the CDI across Kenya, the team compiled a dataset of drought events from historical reports and the NDMA’s records. Results suggested that the CDI detected drought earlier than VCI alone. However, more validation is needed to ensure that the CDI accurately and consistently detects drought earlier than current warning systems. In order to better understand the behavior of individual indices and the potential for earlier drought detection, the team also conducted a time-lag analysis. The team found that VCI responds to drought, on average, one month later than most other indices included in the CDI, suggesting that incorporating additional indices could improve early drought warning systems in Kenya.

Food Security & Agriculture↗
NASA NTRS1977

Multispectral system analysis through modeling and simulation

The design and development of multispectral remote sensor systems and associated information extraction techniques should be optimized under the physical and economic constraints encountered and yet be effective over a wide range of scene and environmental conditions. Direct measurement of the full range of conditions to be encountered can be difficult, time consuming, and costly. Simulation of multispectral data by modeling scene, atmosphere, sensor, and data classifier characteristics is set forth as a viable alternative, particularly when coupled with limited sets of empirical measurements. A multispectral system modeling capability is described. Use of the model is illustrated for several applications - interpretation of remotely sensed data from agricultural and forest scenes, evaluating atmospheric effects in LANDSAT data, examining system design and operational configuration, and development of information extraction techniques.

Malila, W. A.↗
NASA NTRS1977

Multispectral system analysis through modeling and simulation

The design and development of multispectral remote sensor systems and associated information extraction techniques should be optimized under the physical and economic constraints encountered and yet be effective over a wide range of scene and environmental conditions. Direct measurement of the full range of conditions to be encountered can be difficult, time consuming, and costly. Simulation of multispectral data by modeling scene, atmosphere, sensor, and data classifier characteristics is set forth as a viable alternative, particularly when coupled with limited sets of empirical measurements. A multispectral system modeling capability is described. Use of the model is illustrated for several applications - interpretation of remotely sensed data from agricultural and forest scenes, evaluating atmospheric effects in Landsat data, examining system design and operational configuration, and development of information extraction techniques.

Malila, W. A.↗
NASA NTRS1972

SLAR image interpretation keys for geographic analysis

A means for side-looking airborne radar (SLAR) imagery to become a more widely used data source in geoscience and agriculture is suggested by providing interpretation keys as an easily implemented interpretation model. Interpretation problems faced by the researcher wishing to employ SLAR are specifically described, and the use of various types of image interpretation keys to overcome these problems is suggested. With examples drawn from agriculture and vegetation mapping, direct and associate dichotomous image interpretation keys are discussed and methods of constructing keys are outlined. Initial testing of the keys, key-based automated decision rules, and the role of the keys in an information system for agriculture are developed.

Coiner, J. C.↗
NASA NTRS2013

Advancing Drought Understanding, Monitoring and Prediction

Having the capacity to monitor droughts in near-real time and providing accurate drought prediction from weeks to seasons in advance can greatly reduce the severity of social and economic damage caused by drought, a leading natural hazard for North America. The congressional mandate to establish the National Integrated Drought Information System (NIDIS; Public Law 109-430) in 2006 was a major impulse to develop, integrate, and provide drought information to meet the challenges posed by this hazard. Significant progress has been made on many fronts. On the research front, efforts by the broad scientific community have resulted in improved understanding of North American droughts and improved monitoring and forecasting tools. We now have a better understanding of the droughts of the twentieth century including the 1930s "Dust Bowl"; we have developed a broader array of tools and datasets that enhance the official North American Drought Monitor based on different methodologies such as state-of-the-art land surface modeling (e.g., the North American Land Data Assimilation System) and remote sensing (e.g., the evaporative stress index) to better characterize the occurrence and severity of drought in its multiple manifestations. In addition, we have new tools for drought prediction [including the new National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2, for operational prediction and an experimental National Multimodel Ensemble] and have explored diverse methodologies including ensemble hydrologic prediction approaches. Broad NIDIS-inspired progress is influencing the development of a Global Drought Information System (GDIS) under the auspices of the World Climate Research Program. Despite these advances, current drought monitoring and forecasting capabilities still fall short of users' needs, especially the need for skillful and reliable drought forecasts at regional and local scales. To tackle this outstanding challenging problem, focused and coordinated research efforts are needed, drawing from excellence across the broad drought research community. To meet this challenge, National Oceanic and Atmospheric Administration (NOAA)'s Drought Task Force was established in October 2011 with the ambitious goal of achieving significant new advances in the ability to understand, monitor, and predict drought over North America. The Task Force (duration of October 2011-September 2014) is an initiative of NOAA's Climate Program Office Modeling, Analysis, Predictions, and Projections (MAPP) program in partnership with NIDIS. It brings together over 30 leading MAPP-funded drought scientists from multiple academic and federal institutions [involves scientists from NOAA's research laboratories and centers, the National Aeronautics and Space Administration (NASA), U.S. Department of Agriculture, National Center for Atmospheric Research (NCAR), and many universities] in a concerted research effort that builds on individual MAPP research projects. These projects span the wide spectrum of drought research needed to make fundamental advances, from those aimed at the basic understanding of drought mechanisms to those aimed at testing new drought monitoring and prediction tools for operational and service purposes (as part of NCEP's Climate Test Bed). The Drought Task Force provides focus and coordination to MAPP drought research activities and also facilitates synergies with other national and international drought research efforts, including those by the GDIS.

GDIS↗
NASA NTRS2010

Evaluating the Utility of Remotely-Sensed Soil Moisture Retrievals for Operational Agricultural Drought Monitoring

Soil moisture is a fundamental data source used by the United States Department of Agriculture (USDA) International Production Assessment Division (IPAD) to monitor crop growth stage and condition and subsequently, globally forecast agricultural yields. Currently, the USDA IPAD estimates surface and root-zone soil moisture using a two-layer modified Palmer soil moisture model forced by global precipitation and temperature measurements. However, this approach suffers from well-known errors arising from uncertainty in model forcing data and highly simplified model physics. Here we attempt to correct for these errors by designing and applying an Ensemble Kalman filter (EnKF) data assimilation system to integrate surface soil moisture retrievals from the NASA Advanced Microwave Scanning Radiometer (AMSR-E) into the USDA modified Palmer soil moisture model. An assessment of soil moisture analysis products produced from this assimilation has been completed for a five-year (2002 to 2007) period over the North American continent between 23degN - 50degN and 128degW - 65degW. In particular, a data denial experimental approach is utilized to isolate the added utility of integrating remotely-sensed soil moisture by comparing EnKF soil moisture results obtained using (relatively) low-quality precipitation products obtained from real-time satellite imagery to baseline Palmer model runs forced with higher quality rainfall. An analysis of root-zone anomalies for each model simulation suggests that the assimilation of AMSR-E surface soil moisture retrievals can add significant value to USDA root-zone predictions derived from real-time satellite precipitation products.

Bolten, John D.↗
NASA NTRS1978

Enhanced solar energy options using earth-orbiting mirrors

A system of orbiting space reflectors is described, analyzed, and shown to economically provide nearly continuous insolation to preselected ground sites, producing benefits hitherto lacking in conventional solar farms and leading to large reductions in energy costs for such installations. Free-flying planar mirrors of about 1 sq km are shown to be optimum and can be made at under 10 g/sq m of surface, thus minimizing material needs and space transportation costs. Models are developed for both the design of such mirrors and for the analysis of expected ground insolation as a function of orbital parameters, time, and site location. Various applications (agricultural, solar-electric production, weather enhancement, etc.) are described.

Gilbreath, W. P.↗
NASA NTRS1999

Growing wheat in Biosphere 2 under elevated CO2: observations and modeling

Spring wheat (Triticum aestivum L., cv. Yecora Rojo) was grown in the intensive agricultural biome (IAB) of Biosphere 2 during the l995-l996 winter/spring season. Environmental conditions were characterized by a day/night temperature regime of 27/17 degrees C, relative humidity (RH) levels around 45%, mean atmospheric CO2 concentration of 450 ppmv, and natural light conditions with mean intensities about half of outside levels. Weekly samples of above-ground plant matter were collected throughout the growing season and phenological events recorded. A computer model, CERES-Wheat, previously tested under both field and controlled conditions, was used to simulate the observed crop growth and to help in data analysis. We found that CERES-Wheat simulated the data collected at Biosphere 2 to within 10% of observed, thus suggesting that wheat growth inside the IAB was comparable to that documented in other environments. The model predicts phenological stages and final dry matter (DM) production within l0% of the observed data. Measured DM production rates, normalized for light absorbed by the crop. suggested photosynthetic efficiencies intermediate between those observed under optimal field conditions and those recorded in NASA-Controlled Ecological Life-Support Systems (CELSS). We suggest that such a difference can be explained primarily in terms of low light levels inside the IAB, with additional effects due to elevated CO2 concentrations and diffuse light fractions.

Non-NASA Center↗
NASA NTRS2011

NASA Tech Briefs, November 2011

The topics include: 1) Flight Test Results from the Rake Airflow Gage Experiment on the F-15B; 2) Telemetry and Science Data Software System; 3) CropEx Web-Based Agricultural Monitoring and Decision Support; 4) High-Performance Data Analysis Tools for Sun-Earth Connection Missions; 5) Experiment in Onboard Synthetic Aperture Radar Data Processing; 6) Microfabrication of a High-Throughput Nanochannel Delivery/Filtration System; 7) Improved Design and Fabrication of Hydrated-Salt Pills; 8) Monolithic Flexure Pre-Stressed Ultrasonic Horns; 9) Cryogenic Quenching Process for Electronic Part Screening; 10) Broadband Via-Less Microwave Crossover Using Microstrip-CPW Transitions; 11) Wheel-Based Ice Sensors for Road Vehicles; 12) G-DYN Multibody Dynamics Engine; 13) Multibody Simulation Software Testbed for Small-Body Exploration and Sampling; 14) Propulsive Reaction Control System Model; 15) Licklider Transmission Protocol Implementation; 16) Core Recursive Hierarchical Image Segmentation; 17) Two-Stage Centrifugal Fan; 18) Combined Structural and Trajectory Control of Variable-Geometry Planetary Entry Systems; 19) Pressure Regulator With Internal Ejector Circulation Pump, Flow and Pressure Measurement Porting, and Fuel Cell System Integration Options; 20) Temperature-Sensitive Coating Sensor Based on Hematite; 21) Standardization of a Volumetric Displacement Measurement for Two-Body Abrasion Scratch Test Data Analysis; 22) Detection of Carbon Monoxide Using Polymer-Carbon Composite Films; 23) Substituted Quaternary Ammonium Salts Improve Low-Temperature Performance of Double-Layer Capacitors; 24) Sustainably Sourced, Thermally Resistant, Radiation Hard Biopolymer; 25) Integrated Lens Antennas for Multi-Pixel Receivers; 26) 180-GHz Interferometric Imager; 27) Maturation of Structural Health Management Systems for Solid Rocket Motors; 28) Validating Phasing and Geometry of Large Focal Plane Arrays; 29) Transverse Pupil Shifts for Adaptive Optics Non-Common Path Calibration; 30) Qualification of Fiber Optic Cables for Martian Extreme Temperature Environments; 31) Solid-State Spectral Light Source System; 32) Multiple-Event, Single-Photon Counting Imaging Sensor; 33) Surface Modeling to Support Small-Body Spacecraft Exploration and Proximity Operations; and 34) Achieving Exact and Constant Turnaround Ratio in a DDS-Based Coherent Transponder.

Source record↗
NASA NTRS2021

USA Crop Yield Estimation with MODIS NDVI: Are Remotely Sensed Models Better Than Simple Trend Analyses?

Crop yield forecasting is performed monthly during the growing season by the United States Department of Agriculture’s National Agricultural Statistics Service. The underpinnings are long-established probability surveys reliant on farmers’ feedback in parallel with biophysical measurements. Over the last decade though, satellite imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) has been used to corroborate the survey information. This is facilitated through the Global Inventory Modeling and Mapping Studies/Global Agricultural Monitoring system, which provides open access to pertinent real-time normalized difference vegetation index (NDVI) data. Hence, two relatively straightforward MODIS-based modeling methods are employed operationally. The first model constitutes mid-season timing based on the maximum peak NDVI value, while the second is reflective of late-season timing by integrating accumulated NDVI over a threshold value. Corn model results nationally show the peak NDVI method provides a R^(2) of 0.88 and a coefficient of variation (CV) of 3.5%. The accumulated method, using an optimally derived 0.58 NDVI threshold, improves the performance to 0.93 and 2.7%, respectively. Both these models outperform simple trend analysis, which is 0.48 and 7.4%, correspondingly. For soybeans the R^(2) results of the peak NDVI model are 0.62, and 0.73 for the accumulated using a 0.56 threshold. CVs are 6.8% and 5.7%, respectively. Spring wheat’s R2performance with the accumulated NDVI model is 0.60 but just 0.40 with peak NDVI. The soybean and spring wheat models perform similarly to trend analysis. Winter wheat and upland cotton show poor model performance, regardless of method. Ultimately, corn yield forecasting derived from MODIS imagery is robust, and there are circumstances when forecasts for soybeans and spring wheat have merit too.

crop yield↗
NASA NTRS2018

Enhancing the USDA FAS Crop Forecasting System Using SMAP L3 Soil Moisture Observations

One of the U.S. Department of Agriculture-Foreign Agricultural Services (USDA-FAS) mission objectives is to provide current information on global crop supply and demand estimates. Crop growth and development is especially susceptible to the amount of water present in the root-zone portion of the soil profile. Therefore, accurate knowledge of the root-zone soil moisture (RZSM) is an essential for USDA-FAS global crop assessments. This paper focusses on the possibility of enhancing the USDA-FAS's RZSM estimates through the integration of passive-based soil moisture observations derived from the Soil Moisture Active Passive (SMAP) mission into the USDA-FAS Palmer model. Lag-correlation analysis, which explores the agreement between changes in RZSM and crop status indicated that the satellite-based observations can enhance the model-only estimates.

crop growth↗
NASA NTRS2019

Evaluating the Operational Application of SMAP for Global Agricultural Drought Monitoring

Over the past two decades, remote sensing has made possible the routine global monitoring of surface soil moisture. Regionalagricultural drought monitoring is one of the most logicalapplication areas for such monitoring. However, remote sensing alone provides soil moisture information for only the top few centimetersof the soil profile, while agricultural drought monitoring requires knowledge of the amount of water present in the entireroot zone. The assimilation of remotely sensed soil moisture productsinto continuous soil water balance models provides a way ofaddressing this shortcoming. Here, we describe the assimilationof NASA's soil moisture active passive (SMAP) surface soil moisture data into the United States Department of Agriculture Foreign Agricultural Service (USDA FAS) Palmer model and assess the impactof SMAP on USDA FAS drought monitoring capabilities. Theassimilation of SMAP is specifically designed to enhance the model skill and the USDA FAS drought capabilities by correcting for randomerrors inherent in its rainfall forcing data. The performanceof this SMAP-based assimilation system is evaluated using two approaches.At global scale, the accuracy of the system is assessed by examining the lagged correlation agreement between soil moistureand the normalized difference vegetation index (NDVI). Additional regional-scale evaluation using in situ-based soil moisture estimatesis carried out at seven of the SMAP core Cal/Val sites located in theUSA. Both types of analysis demonstrate the value of assimilating SMAP into the USDA FAS Palmer model and its potential to enhance operational USDA FAS root-zone soil moisture information.

Mladenova, Iliana E.↗
NASA NTRS2011

Evaluating the Use of Remote Sensing Data in the USAID Famine Early Warning Systems Network

The US Agency for International Development (USAID) s Famine Early Warning System Network (FEWS NET) provides monitoring and early warning support to decision makers responsible for responding to food insecurity emergencies on three continents. FEWS NET uses satellite remote sensing and ground observations of rainfall and vegetation in order to provide information on drought, floods and other extreme weather events to decision makers. Previous research has presented results from a professional review questionnaire with FEWS NET expert end-users whose focus was to elicit Earth observation requirements. The review provided FEWS NET operational requirements and assessed the usefulness of additional remote sensing data. Here we analyzed 1342 food security update reports from FEWS NET. The reports consider the biophysical, socioeconomic, and contextual influences on the food security in 17 countries in Africa from 2000-2009. The objective was to evaluate the use of remote sensing information in comparison with other important factors in the evaluation of food security crises. The results show that all 17 countries use rainfall information, agricultural production statistics, food prices and food access parameters in their analysis of food security problems. The reports display large scale patterns that are strongly related to history of the FEWS NET program in each country. We found that rainfall data was used 84% of the time, remote sensing of vegetation 28% of the time, and gridded crop models 10%, reflecting the length of use of each product in the regions. More investment is needed in training personnel on remote sensing products to improve use of data products throughout the FEWS NET system.

Brown, Molly E.↗