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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Data Fusion for Urban Air Quality Assessment & Forecasting

This presentation provides an overview for our funded project with NASA's Health and Air Quality Applied Sciences Program. The project will expand an existing air quality data fusion tool implemented in Google Earth Engine (GEE) by our project team members at Sonoma Technology, Inc. (STI), a private air quality data company. We will expand the capabilities of this tool using new methods developed by the NASA GMAO which will give it the capability of providing sub-city scale resolution and hourly frequency estimates and forecasts of three key air quality indicators: surface-level particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3). We will combine a variety of Earth Observations including satellite data, global air quality forecasts, and local data from regulatory-grade monitors and/or low cost sensors. We will implement the new data fusion capabilities into the existing GEE tool in consultation with our end-users to best address their needs for sub-city scale air quality estimates and forecasts.

K Emma Knowland↗
NASA NTRSDate not supplied

Utilizing Remote Sensing to Detect and Assess the Impacts of Estuarine Breach Events for Improved Coastal Wetland Monitoring and Management

Estuaries are extremely dynamic environments that provide a host of vital ecosystem services. California’s Marine Life Protection Act protects such ecosystems by creating Marine Protected Areas (MPAs). California has approximately 440,000 acres of estuarine habitat as well as 23 Estuarine Marine Protected Areas (EMPAs). Thus, in situ data collection is often difficult due to time and resource constraints. This project used remote sensing to gather data that examined the health and dynamics of California EMPAs to supplement ground-based field measurements. Through the use of Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS), Sentinel-2 MultiSpectral Instrument (MSI), and Sentinel-1 C-band Synthetic Aperture Radar (C-SAR), this project assessed mouth state, inundation extent, turbidity, temperature, and tidal measurements for observable estuaries. The Normalized Difference Water Index (NDWI) from Sentinel-2 MSI captured estuary mouth state and inundation extent. Landsat 8 OLI and Sentinel-2 MSI detected differences in water quality metrics that correlated to changes in estuary mouth state (i.e., open or closed) through algorithms in Google Earth Engine (GEE). The GEE California Estuary Assessment (CEA) tool was created with partner input throughout development and culminated in a graphical user interface tailored to management needs. The CEA tool will allow the partners, the Ocean Protection Council, Moss Landing Marine Laboratories’ Central Coast Wetlands Group, the Southern California Coastal Water Research Project, and University of California Los Angeles (UCLA) and Davis (UCD), to better understand estuary dynamics and facilitate informed management decisions through satellite-based Earth observations.

Alex Gunnerson↗
NASA NTRS2023

A Global Land Cover Training Dataset From 1984 to 2020

State-of-the-art cloud computing platforms such as Google Earth Engine (GEE) enable regional-to-global land cover and land cover change mapping with machine learning algorithms. However, collection of high-quality training data, which is necessary for accurate land cover mapping, remains costly and labor-intensive. To address this need, we created a global database of nearly 2 million training units spanning the period from 1984 to 2020 for seven primary and nine secondary land cover classes. Our training data collection approach leveraged GEE and machine learning algorithms to ensure data quality and biogeographic representation. We sampled the spectral-temporal feature space from Landsat imagery to efficiently allocate training data across global ecoregions and incorporated publicly available and collaborator-provided datasets to our database. To reflect the underlying regional class distribution and post-disturbance landscapes, we strategically augmented the database. We used a machine learning-based cross-validation procedure to remove potentially mis-labeled training units. Our training database is relevant for a wide array of studies such as land cover change, agriculture, forestry, hydrology, urban development, among many others.

Radost Stanimirova↗
DOE OSTI2026

A&L Annual Report: GRM-MAPS

Exploding Bridge Wire (EBW) is an important design for initiating detonators relevant to several LLNL systems. In such designs, a metal bridge wire is in direct contact with a porous secondary high explosive (HE), typically pressed to around 50% of its crystalline density. Initiation is believed to occur through shock waves generated when the bridge wire is made to explode via the rapid passing of a strong current pulse. Permeametry is an important characterization tool for the porous powdered HE (PETN, HMX, etc.) component in such devices. Such powders are well known to coarsen with age, with an accompanying deterioration in performance, which includes increased function time and sensitivity. Among various material characteristics, one that has been shown to correlate the most with such performance parameters is the flow-permeable surface area (FSSA), measured using air-permeametry. Commercially available permeametry apparatuses, such as that from Fisher Scientific or a more modern version from Micromeritics suffer from a few drawbacks, most notably as related to monitoring age-related changes in FSSA. This is because it requires re-compaction of the aged powder into the permeametry tube, which in most cases leads to an unwanted increase in FSSA. To address this and a few other shortcomings, we have been developing a modern version of the permeameter, Gee-Reinstein-Maiti Modern Air-Permeametry System (GRM-MAPS). In this report, we summarize progress made in FY25 toward perfecting and calibrating the instrument, which we aim at deploying within surveillance in FY26.

36 MATERIALS SCIENCE↗
NASA NTRS1972

Tethered body problems and relative motion orbit determination

Selected problems dealing with orbiting tethered body systems have been studied. In addition, a relative motion orbit determination program was developed. Results from these tasks are described and discussed. The expected tethered body motions were examined, analytically, to ascertain what influence would be played by the physical parameters of the tether, the gravity gradient and orbit eccentricity. After separating the motion modes these influences were determined; and, subsequently, the effects of oscillations and/or rotations, on tether force, were described. A study was undertaken, by examining tether motions, to see what type of control actions would be needed to accurately place a mass particle at a prescribed position relative to a main vehicle. Other applications for tethers were studied. Principally these were concerned with the producing of low-level gee forces by means of stabilized tether configurations; and, the initiation of free transfer trajectories from tether supported vehicle relative positions.

Eades, J. B., Jr.↗
NASA NTRS2003

Boundary-Layer Separation Control under Low-Pressure Turbine Airfoil Conditions using Glow-Discharge Plasma Actuators

Modem low-pressure turbines, in general, utilize highly loaded airfoils in an effort to improve efficiency and to lower the number of airfoils needed. Typically, the airfoil boundary layers are turbulent and fully attached at takeoff conditions, whereas a substantial fraction of the boundary layers on the airfoils may be transitional at cruise conditions due to the change of density with altitude. The strong adverse pressure gradients on the suction side of these airfoils can lead to boundary-layer separation at the latter low Reynolds number conditions. Large separation bubbles, particularly those which fail to reattach, cause a significant degradation of engine efficiency. A component efficiency drop of the order 2% may occur between takeoff and cruise conditions for large commercial transport engines and could be as large as 7% for smaller engines at higher altitude. An efficient means of of separation elimination/reduction is, therefore, crucial to improved turbine design. Because the large change in the Reynolds number from takeoff to cruise leads to a distinct change in the airfoil flow physics, a separation control strategy intended for cruise conditions will need to be carefully constructed so as to incur minimum impact/penalty at takeoff. A complicating factor, but also a potential advantage in the quest for an efficient strategy, is the intricate interplay between separation and transition for the situation at hand. Volino gives a comprehensive discussion of several recent studies on transition and separation under low-pressure-turbine conditions, among them one in the present facility. Transition may begin before or after separation, depending on the Reynolds number and other flow conditions. If the transition occurs early in the boundary layer then separation may be reduced or completely eliminated. Transition in the shear layer of a separation bubble can lead to rapid reattachment. This suggests using control mechanisms to trigger and enhance early transition. Gad-el-Hak provides a review of various techniques for flow control in general and Volino discusses recent studies on separation control under low-pressure-turbine conditions utilizing passive as well as active devices. As pointed out by Volino, passive devices optimized for separation control at low Reynolds numbers tend to increase losses at high Reynolds numbers, Active devices have the attractive feature that they can be utilized only in operational regimes where they are needed and when turned off would not affect the flow. The focus in the present paper is an experimental Separation is induced on a flat plate installed in a closed-circuit wind tunnel by a shaped insert on the opposite wall. The flow conditions represent flow over the suction surface of a modem low-pressure-turbine airfoil ('Pak-B'). The Reynolds number, based on wetted plate length and nominal exit velocity, is varied from 50,000 to 300,000, covering cruise to takeoff conditions. Low (0.2%) and high (2.5%) Gee-stream turbulence intensities are set using passive grids. A spanwise-oriented phased-plasma-array actuator, fabricated on a printed circuit board, is surface- flush-mounted upstream of the separation point and can provide forcing in a wide frequency range. Static surface pressure measurements and hot-wire anemometry of the base and controlled flows are performed and indicate that the glow-discharge plasma actuator is an effective device for separation control. of active separation control using glow discharge plasma actuators.

Hultgren, Lennart S.↗
NASA NTRS2008

Future NTP Development Synergy Leveraged from Current J-2X Engine Development

This paper is a discussion of how the many long-lead development elements required for the realization of a future nuclear thermal propulsion (NTP) system can be effectively leveraged from the ongoing work being conducted on the J-2X engine program for the Constellation Program. Development studies conducted to date for NTP forward planning have identified a number of technical areas that will require advancement to acceptable technology readiness levels (TRLs) before they can be utilized in NTP system development. These include high-temperature, high-area ratio nozzle extension; long-life, low-NPSP. turbomachinery; and low-boiloff propellant management; and a qualified nuclear fuel element. The current J-2X program is working many of these areas that can be leveraged to support NTP development in a highly compatible and synergistic fashion. In addition to supporting technical development, there are other programmatic issues being worked in the J-2X program that can be leveraged by a future NTP development program. These include compliance with recently-evolved space system requirements such as human-rating, fault tolerance and fracture control. These and other similar mandatory system requirements have been adopted by NASA and can result in a significant technical impact beyond elevation of the root technologies required by NTP. Finally, the exploitation of experience, methodologies, and procedures developed by the J-2X program in the areas of verification, qualification, certification, altitude simulation testing, and facility definition will be especially applicable to a future NTP system. The similarities in system mission (in-space propulsion) and operational environment (vacuum, zero-gee) between J-2X and NTP make this highly synergistic. Thus, it can be $hown that the collective benefit of leveraging experience and technologies developed during the J-2X program can result in significant savings in development cost and schedule for NTP.

Ballard, Richard O.↗
NASA NTRS2010

The Successful Implementation of NASA Orbital Debris Requirements for the Retirement of TDRS-1

TDRS-1 was decommissioned on October 28th 2009 following more than 26 years of operation. The Grand Old Dame of the TDRSS constellation wa s launched aboard the maiden voyage of the Space Shuttle Challenger ( STS-6) in April 1983. TDRS-1 survived a malfunction of the Inertial Upper Stage eventually utilizing its own propulsion system to success fully reach its assigned station in geosynchronous orbit. The anomalo us beginning of the TDRS-1 mission was not without lasting consequenc es as the primary reaction control system (A-side) was completely di sabled with an apparent propellant leak and the secondary system (B-s ide) suffered damage to its negative roll thruster rendering the thru ster inoperable. Following decommissioning the challenge to completin g a successful TDRS-1 end-of-mission (EOM) was the implementation of the stringent orbital debris requirements of NPR 8715.6 with a parti ally functioning spacecraft not originally designed to meet those req uirements. The TDRS-1 EOM had three key goals: 1) removal of the spac ecraft from geosynchronous orbit; 2) depletion of the remaining prope llant; and 3) passivation of all other sources of energy storage or generation. The TDRS-1 EOM approach was one of minimizing risks while accomplishing the goals above. The orbit raising portion of EOM was accomplished using deltavelocity operations already proven during pre vious stationchanging maneuvers. The propellant depletion approach wa s necessarily more aggressive as over 20 hours of burn time was requ ired to deplete the remaining fuel. A novel approach utilizing a spin ning, thrusting, passively controlled spacecraft configuration was ut ilized to achieve reasonable burn durations that met schedule constra ints. This nonstandard configuration required careful analysis of ele ctrical, thermal, and communication subsystems. The configuration wa s thoroughly simulated prior to the start of operations and carefully characterized during the initial spin period and first burn. Passiva tion was by definition a unique operation not previously performed wi thin the TDRS 1-7 constellation. Use was made of a TDRS spacecraft si mulator to verify the operational procedure to mitigate risks and pro vide crew training. TDRS-1 orbit raising maneuvers commenced on June 5th 2010 and completed on June 14th with an apogee 370 km and a peri gee 352 km above geosynchronous altitude. 127 kg of propellant were e stimated to be remaining in the tanks at the completion of orbit rais ing. TDRS-1 was placed in its spinning orientation on June 16th and the first fuel depletion burn was performed the following day. A seri es of 10 depletion burns were performed ending on June 26th when both propellant tank pressures experienced dramatic drops. Final passivat ion was performed on June 27 th deactivating electronics, removing t he batteries from the bus and solar arrays, and disabling the space-t o-ground communications equipment. The Second TDRS Ground Terminal (S TGT) continued to open loop track TDRS-1 for several days attempting command reacquisition several times a day. All attempts were unsucce ssful confirming passivation was achieved. The TDRS-1 orbit at passiv ation was an orbital debris compliant 36,319 x 36,128 km in height. W hile differing spacecraft designs may preclude mimicking the exact TD RS-1 EOM approach, the TDRS-1 campaign serves to demonstrate that pr e-NPR 8715.6 designs can be made to meet the requirements resulting i n a reduced orbit debris environment for future missions

Mirczak, Walter↗
NASA NTRS2012

A Robust Multi-Scale Modeling System for the Study of Cloud and Precipitation Processes

During the past decade, numerical weather and global non-hydrostatic models have started using more complex microphysical schemes originally developed for high resolution cloud resolving models (CRMs) with 1-2 km or less horizontal resolutions. These microphysical schemes affect the dynamic through the release of latent heat (buoyancy loading and pressure gradient) the radiation through the cloud coverage (vertical distribution of cloud species), and surface processes through rainfall (both amount and intensity). Recently, several major improvements of ice microphysical processes (or schemes) have been developed for cloud-resolving model (Goddard Cumulus Ensemble, GCE, model) and regional scale (Weather Research and Forecast, WRF) model. These improvements include an improved 3-ICE (cloud ice, snow and graupel) scheme (Lang et al. 2010); a 4-ICE (cloud ice, snow, graupel and hail) scheme and a spectral bin microphysics scheme and two different two-moment microphysics schemes. The performance of these schemes has been evaluated by using observational data from TRMM and other major field campaigns. In this talk, we will present the high-resolution (1 km) GeE and WRF model simulations and compared the simulated model results with observation from recent field campaigns [i.e., midlatitude continental spring season (MC3E; 2010), high latitude cold-season (C3VP, 2007; GCPEx, 2012), and tropical oceanic (TWP-ICE, 2006)].

Tao, Wei-Kuo↗
NASA NTRS2019

Thermal Design of Astrobee Perching Arm

This paper presents the thermal design of actuators in the perching arm of Astrobee robot that will operate inside the International Space Station (ISS) in starting 2019. Since the crew's safety is of the utmost importance on the ISS, all materials used in the Astrobee robot should meet the touch temperature requirements according to the ISS safety standards to protect crew from skin burns. The Astrobee perching arm consists of 2-DOF arm servo motors and 1-DOF gripper DC motor, which are capable of overheating when stalled, particularly given the lack of gravity-driven thermal convection in the ISS zero-gee environment. Thermal properties of two types of actuators are verified by monitoring the touch temperature in worst-case operations with no thermal protection. Then, the proper thermal protection designs have been conducted and installed to guarantee the safety in all conditions.

Park, In-Won↗
NASA NTRS2020

Washington Health & Air Quality: Quantifying Air Quality Parameters and Validating Air Pollution Sources Impacting the Health of Puget Sound Residents Through the Use of NASA and ESA Remote Sensing Data

In the Puget Sound region of Washington, high levels of air pollutants put residents’ health at risk by increasing their likelihood of developing critical respiratory conditions. This project used remotely-sensed data to investigate aerosol optical depth (AOD) from NASA satellite sensors including the Terra and Aqua MODerate Resolution Imaging Spectroradiometer (MODIS) and European Space Agency Copernicus Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI). The team visualized the most recent data in Google Earth Engine (GEE) API to display air pollution trends in Washington State, which will support the Puget Sound Clean Air Agency’s (PSCAA) decision-making processes. The team performed linear regressions using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm to form a relationship between ground-level microscopic particles (PM2.5) and AOD in the Puget Sound region, validating the relationship using concentration readings taken from Environmental Protection Agency (EPA) air quality monitors. The team utilized estimated PM2.5 and other satellite data to produce a web-based tool and to evaluate the effectiveness of using such a tool for near real-time air quality monitoring within a particular region. The team found that the tool provides useful supplementary data that fills in the gaps of the PSCAA’s air monitoring network.

Health & Air Quality↗
NASA NTRS2020

Washington Health & Air Quality: Quantifying Air Quality Parameters and Validating Air Pollution Sources Impacting the Health of Puget Sound Residents Through the Use of NASA and ESA Remote Sensing Data

In the Puget Sound region of Washington, high levels of air pollutants put residents’ health at risk by increasing their likelihood of developing critical respiratory conditions. This project used remotely-sensed data to investigate aerosol optical depth (AOD) from NASA satellite sensors including the Terra and Aqua MODerate resolution Imaging Spectroradiometer (MODIS) and European Space Agency Copernicus Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI). The team visualized the most recent data in Google Earth Engine (GEE) API to display air pollution trends from Northern California to British Columbia, which will support the Puget Sound Clean Air Agency’s (PSCAA) decision-making processes. The team performed linear regressions using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm to form a relationship between ground-level microscopic particles (PM2.5) and AOD in the Puget Sound region, validating the relationship using concentration readings taken from Environmental Protection Agency (EPA) air quality monitors. The team utilized estimated PM2.5 and other satellite data to produce a web-based tool and to evaluate the effectiveness of using such a tool for near real-time air quality monitoring within a particular region. The team found that the tool provides useful supplementary data that fills in the gaps of the PSCAA’s air monitoring network.

Health & Air Quality↗
NASA NTRS2020

Exploring Spatiotemporal Relations Between Soil Moisture, Precipitation, and Streamflow for a Large Set of Watersheds Using Google Earth Engine

An understanding of streamflow variability and its response to changes in climate conditions is essential for water resource planning and management practices that will help to mitigate the impacts of extreme events such as floods and droughts on agriculture and other human activities. This study investigated the relationship between precipitation, soil moisture, and streamflow over a wide range of watersheds across the United States using Google Earth Engine (GEE). The correlation analyses disclosed a strong association between precipitation, soil moisture, and streamflow, however, soil moisture was found to have a higher correlation with the streamflow relative to precipitation. Results indicated different strength of the association depends on the watershed classes and lag times assessments. The perennial watersheds showed higher coherence compared to intermittent watersheds. Previous month precipitation and soil moisture have a stronger influence on the current month streamflow, particularly in the snow-dominated watersheds. Monthly streamflow forecasting models were developed using an autoregressive integrated moving average (ARIMA) and support vector machine (SVM). The results showed that the SVM model generally performed better than the ARIMA model. Overall streamflow forecasting model performance varied considerably among watershed classes, and perennial watersheds tend to exhibit better predictably compared to intermittent watersheds due to lower streamflow variability. The SVM models with precipitation and streamflow inputs performed better than those with streamflow input only. Results indicated that the inclusion of antecedent root-zone soil moisture improved the streamflow forecasting in most of the watersheds, and the largest improvements occurred in the intermittent watersheds. In conclusion, this work demonstrated that knowing the relationship between precipitation, soil moisture, and streamflow in different watershed classes will enhance the understanding of the hydrologic process and can be effectively utilized in improving streamflow forecasting for better satellite-based water resource management strategies.

Nazmus Sazib↗
NASA NTRS2020

Pacific Northwest Health & Air Quality - Utilizing NASA Earth Observations to Analyze Air Quality Impacts from Wildfires in the Pacific Northwest

The Pacific Northwest region of the United States and Canada has become more vulnerable to intense wildfire regimes due to years of fire suppression and climatic changes. Smoke from fires exposes communities to hazardous aerosols and pollutants known to trigger asthma symptoms and exacerbate other respiratory and cardiovascular diseases. In partnership with The Nature Conservancy’s Washington Chapter and the Puget Sound Clean Air Agency, NASA DEVELOP investigated the impacts of wildfire smoke on air quality from 2008 to 2020 using NASA Earth observations. To explore the various dimensions of smoke and its relation to air quality, the team looked at the vertical extent of smoke plumes and the resulting changes in air quality. The team evaluated the potential relationship between plume height of wildfire smoke and fire radiative power using the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Aqua and Terra satellites and Terra’s Multi-angle Imaging SpectroRadiometer (MISR) using the MISR INteractive eXplorer (MINX). The team determined that there was no regional relationship between fire radiative power and smoke plume height. To investigate changes in air quality resulting from wildfire smoke, the team utilized data from NASA’s Fire Information for Resource Management System, from the European Space Agency’s Sentinel-5P Tropospheric Monitoring Instrument (TROPOMI), and true color imagery from Landsat 8 Operational Land Imager (OLI). The team created a Google Earth Engine-based (GEE) web tool to visualize changes in atmospheric pollutants and aerosol optical depth. Results of case study fires showed varying increases in pollutant concentrations when compared to a baseline map. The end products provided the partners with tools to quantify plume height using MINX and to visualize recent air quality patterns relating to variations in wildfire extent and severity in the Pacific Northwest

Ani Matevosian↗
NASA NTRS2020

Cloud-Computing and Machine Learning in Support of Country-Level Land Cover and Ecosystem Extent Mapping in Liberia and Gabon

Liberia and Gabon joined the Gaborone Declaration for Sustainability in Africa (GDSA), established in 2012, with the goal of incorporating the value of nature intonational decision making by estimating the multiple services obtained from ecosystems using the natural capital accounting framework. In this study, we produced 30-m resolution 10 classes land cover maps for the 2015 epoch for Liberia and Gabon using the Google Earth Engine (GEE) cloud platform to support the ongoing natural capital accounting efforts in these nations. We pro-pose an integrated method of pixel-based classification using Landsat 8 data, the Random Forest(RF) classifier and ancillary data to produce high quality land cover products to fit abroad range of applications, including natural capital accounting. Our approach focuses on a pre-classification filtering (Masking Phase) based on spectral signature and ancillary data to reduce the number of pixels prone to be misclassified; therefore, increasing the quality of the final product. The proposed approach yields an overall accuracy of 83% and 81% for Liberia and Gabon, respectively, out performing prior land cover products for these countries in both thematic content and accuracy. Our approach, while relatively simple and highly replicable, was able to produce high quality land cover products to fill an observational gap in up to date land cover data at national scale for Liberia and Gabon.

Celio de Sousa↗
NASA NTRS2021

Large-Scale High-Resolution Coastal Mangrove Forests Mapping Across West Africa With Machine Learning Ensemble and Satellite Big Data

Coastal mangrove forests provide important ecosystem goods and services, including carbon sequestration, biodiversity conservation, and hazard mitigation. However, they are being destroyed at an alarming rate by human activities. To characterize mangrove forest changes, evaluate their impacts, and support relevant protection and restoration decision making, accurate and up-to-date mangrove extent mapping at large spatial scales is essential. Available large-scale mangrove extent data products use a single machine learning method commonly with 30 m Landsat imagery, and significant inconsistencies remain among these data products. With huge amounts of satellite data involved and the heterogeneity of land surface characteristics across large geographic areas, finding the most suitable method for large-scale high-resolution mangrove mapping is a challenge. The objective of this study is to evaluate the performance of a machine learning ensemble for mangrove forest mapping at 20 m spatial resolution across West Africa using Sentinel-2 (optical) and Sentinel-1 (radar) imagery. The machine learning ensemble integrates three commonly used machine learning methods in land cover and land use mapping, including Random Forest (RF), Gradient Boosting Machine (GBM), and Neural Network (NN). The cloud-based big geospatial data processing platform Google Earth Engine (GEE) was used for pre-processing Sentinel-2 and Sentinel-1 data. Extensive validation has demonstrated that the machine learning ensemble can generate mangrove extent maps at high accuracies for all study regions in West Africa (92%–99% Producer’s Accuracy, 98%–100% User’s Accuracy, 95%–99% Overall Accuracy). This is the first-time that mangrove extent has been mapped at a 20 m spatial resolution across West Africa. The machine learning ensemble has the potential to be applied to other regions of the world and is therefore capable of producing high-resolution mangrove extent maps at global scales periodically.

coastal environment↗
NASA NTRS2021

Argentina Food Security & Agriculture: Crop Monitoring and Forecasting for Argentina using NASA Satellite Observations

Early harvest information helps guide agricultural commodity assessments in Argentina, providing valuable planning information to identify potentially food-insecure regions, anticipate transportation and storage demands, predict price fluctuations, and project commodity trends. However, crop yield estimates are currently subjective, based on interviews with qualified informants (i.e., farmers, agribusiness actors). In partnership with the Buenos Aires Grain Exchange, we leveraged Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Soil Moisture Active Passive (SMAP), and Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM IMERG) NASA Earth observations to develop a Google Earth Engine (GEE) toolset to monitor vegetation growth. The first component of the toolset produces spatial and temporal maps of temperature, precipitation, soil moisture, and the Normalized Difference Vegetation Index (NDVI), allowing users to visualize the influence of the region’s climate and weather. Next, we developed an autoregressive model to predict NDVI several months in advance. Lastly, we created a linear regression model of crop yield and NDVI for soybeans, corn, and wheat, and input the forecasted NDVI to generate a predicted crop yield output. The NDVI forecasting model produced accurate predictions at two, four, and six months when examining the most recent growing season. In the crop yield model, soybeans exhibited moderately strong correlation, wheat had consistent weak correlation, and corn varied from weak to strong correlation depending on zone. This information is vital for vegetation growth monitoring by identifying areas of high growth and allocating resources to areas of lower growth to efficiently maximize crop yields.

DEVELOP Tech Paper↗
NASA NTRSDate not supplied

Evaluating SAR Radiometric Terrain Correction products: Optimal products for applied users

Operational applications for Synthetic Aperture Radar (SAR) are under development around the world, driven by the free-and-open access of SAR C-band observations that Sentinel-1 of Copernicus has been providing since 2014. Groups like SERVIR, a joint initiative between NASA and USAID, are at the forefront of remote sensing applied uses, and have made many significant contributions to lower the barrier to access, process, and apply SAR for ecosystem services. A takeaway from the SERVIR experience in using SAR is the need to use the appropriate SAR polarimetric product. Radiometric Terrain Correction (RTC) is a key entry-level product for multiple applications that range from ecosystems to hazards. Many software packages exist to create RTC products from SLC or GRD-type Level-1 SAR data, some of which were released only recently, e.g. Interferometric SAR Computing Environment (ISCE) added an RTC module in April 2020. In addition, new versions of open source softwares are expected to address known issues from previous versions, such as Sentinel-1 Toolbox from the European Space Agency (SNAP-7). Despite the growing availability of RTC software solutions, little work has been done to identify differences between RTC products from different softwares. And to address the question, which open-source software produces the most accurate RTC product? This work evaluates Sentinel-1 RTC products created with three different softwares and approaches, including SNAP-7, ISCE-2, and a pseudo RTC product derived from GEE. The GAMMA-derived RTC product, a known optimal RTC and implemented by Alaska Satellite Facility (ASF), is used as a reference. Time series stacks over ten different sites representing varied terrain and ecosystems are evaluated. Products are evaluated for geolocation quality, absolute radiometric calibration, and for the fidelity of the radiometric terrain flattening. The results provide direct guidance and recommendations about the quality of the RTC products obtained from open source methods. This understanding is key to develop operational applications that rely on SAR Sentinel-1 data that need affordable and scalable solutions.

Africa Flores-Anderson↗