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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Use of Autonomous Vehicles in Emergency Situations - Wildfire Planning and Mitigation

Unmanned vehicles can be useful in emergency situations for many applications such as surveillance, access to harsh environments, and delivery of supplies. However, efforts to use drones in these situations has not been well-coordinated. This project is looking at multiple aspects of the problem with sub-teams addressing: establishing a method and database network to identify and communicate resources for deployment during major incidents and disasters; identifying how drones and other aviation assets can be useful in wildfire-specific emergencies, including mitigation and prevention planning; and considering safety, rules, and behavior of autonomous vehicles.

Autonomous Vehicles↗
NASA NTRS2022

Idaho Wildfires II: Assessing the Relationship Between Drought Indicators and Wildfire Risk to Enhance Hazard Modeling and Inform Mitigation Planning

The western United States has experienced twenty years of increased and prolonged drought which have exacerbated wildfire hazards. These jeopardize population centers through increased risks to ecosystem services, local economies, and livelihoods. The Idaho Office of Emergency Management, Water Resources, and Department of Lands are seeking methods to dynamically monitor these conditions and update models that inform hazard mitigation planning and resource allocation. Towards this, these agencies partnered with NASA DEVELOP to produce drought-enhanced wildfire hazard models. Part of a two-term project, the two teams revised the state’s static wildfire hazard model with refined data layers and remotely-sensed data to reflect dynamic ecosystem responses to drought conditions and wildfire potential. Our team distinguished between rangeland and forestland ecosystems, and investigated relationships between drought metrics and vegetation condition using TerrSet Earth Trends Modeler. This analysis determined that total precipitation at a 5-month lag interval (r 2 = 0.72) along with the Evaporative Stress Index (r 2 = 0.69); and precipitation at a 5-month interval (r 2 = 0.42) were important drivers in rangeland and forestland, respectively. These driver variables were incorporated into a temporally dynamic wildfire hazard map. Our team used linear regression to correlate hazard ratings with wildfire frequency. For the year 2020, neither the enhanced hazard model (p < 0.10, r 2 = 0.01) nor the state’s static model (p < 0.05, r 2 = 0.03) were strongly correlated with actual wildfire frequency as they expressed an inverse relationship between wildfire hazard and frequency. This suggests wildfire occurrence is complex and not necessarily driven by the variables used.

Wildfire↗
NASA NTRSDate not supplied

Assessing Drought and Fire Conditions, Trends, and Susceptibility to Inform State Mitigation Efforts and Bolster Monitoring Protocol in North Central Idaho

Escalating severity and frequency of drought and wildfire call for effective and cost-efficient mitigation planning and monitoring protocols. The Palouse ecoregion, an agricultural epicenter in North-central Idaho, is of particular concern as both drought and wildfire present substantial economic threats. The DEVELOP team implemented Earth observation data to assist the Idaho Office of Emergency Management, Idaho Department of Water Resources, and Idaho Department of Lands in updating the state’s Hazard Mitigation Plan by enhancing their drought and fire monitoring capabilities. The team utilized Landsat 8 Operational Land Imager (OLI), and Aqua and Terra’s Moderate Resolution Imaging Spectroradiometer (MODIS), along with ancillary datasets, to assess drought indicators and map hazard susceptibility. The team upgraded the state’s current fire hazard model by updating existing data layers and adding drought indicator data to support partners’ continued assessment of fire hazard conditions. The team observed Evaporative Demand Drought Index (EDDI) spikes during the highest fire occurrence and burned area years in the study period: 2015 and 2021. Models from dry, high fire occurrence and burned area year 2015 outperformed models from mesic, low fire occurrence and burned area year 2016. The increased understanding of drought conditions and fire susceptibility in this ecosystem will assist partners in improving land management practices.

Ford Freyberg↗
NASA NTRS2022

Evaluation and Improvement of System-of-Systems Resilience in a Simulation of Wildfire Emergency Response

Because of the increasing threat that wildfires pose, there is interest in leveraging new technologies to improve firefighting. Specifically, Unmanned Aerial Systems (UAS) and UAS Traffic Management (UTM) promise to improve firefighters’ situational awareness, coordination, communications, safety, and strategy. While these technologies could be beneficial, there has been little formal investigation into how much benefit would occur and whether these benefits would outweigh hazards introduced by these systems. To better understand the impacts of these technologies, this paper presents a high-level dynamic simulation for evaluating wildfire response performance and resilience incorporating fire propagation, surveillance and communication, response planning, and the resulting mitigation actions. This simulation is then used to study the impact of communications and surveillance improvement, considering (1) the effect on fire containment and ground crew injuries and (2) the effect of introduced and existing disruptive fault scenarios. Simulating this model over a large number of scenarios finds that these changes can improve containment and reduce ground crew injuries. While these improvements generalize over both existing and introduced single-fault scenarios and thus result in a more resilient system, they could be negated if the introduced communications infrastructure is prone to full-scale outages.

modeling↗
NASA NTRS2020

Gila Water Resources III - Modeling the Impacts of Post-fire Restoration Methods on Vegetation Recovery in the Gila National Forest

In recent years, wildfires in New Mexico’s Gila National Forest have become increasingly common and more severe. Wildfires can have powerful impacts on hydrology and soil stability, including erosion, flooding, and debris-flows that threaten lives and infrastructure downstream. Vegetation restoration treatments like seeding and mulching can mitigate these effects and facilitate ecosystem recovery. Understanding the effectiveness of various restoration methods is vital to planning a cost-effective and successful post-fire recovery strategy. The immediate response to a fire on US Forest Service land is coordinated by a Burned Area Emergency Response (BAER) team, a group responsible for mitigating immediate post-fire risks to human life, property, and critical natural and cultural resources. This study created a proof-of-concept methodology for a decision-support tool designed to help BAER teams identify the restoration treatments most likely to succeed in a given burned area. Leveraging random forest regression, Google Earth Engine, and Landsat 7 and 8 Earth observations, this study modeled vegetation recovery after the 2013 Silver Fire for seeded areas, seeded/mulched areas, and untreated areas. Treatment type and initial burn severity were the largest drivers of vegetation recovery across the landscape. Seeded/mulched areas showed higher recovery levels than untreated areas three months post-fire, but by four years post-fire, treated and untreated areas displayed similar recovery levels. To produce a robust predictive tool for the Gila National Forest, the model should be trained on many more fires and incorporate post-fire weather conditions into the process. Such a model will help partners ensure efficient resource use and plan effective post-fire restoration strategies.

DEVELOP Project Summary↗
NASA NTRS2022

Exploring Applications of Machine Learning for Wildfire Monitoring and Detection using Unmanned Aerial Vehicles

Wildfires are increasing in frequency and severity around the world, including the United States. The losses caused by wildfires could be mitigated if high-risk areas, hotspots, and flare-ups could be monitored continuously, such as through the use of Unmanned Aerial Vehicles (UAVs). This paper documents exploratory efforts using machine learning to determine efficient flight paths for UAVs and to detect wildfires using image classification. On path planning, three machine learning techniques—Genetic Algorithm, Simulated Annealing, and Dynamic Programming—were explored. Genetic Algorithm was found to be an effective approach for path planning for wildfire monitoring and surveillance by UAVs. For a scenario of 25 locations in a circular arrangement, the algorithm was able to return the optimal path. The accuracy and execution time was found to be sensitive to the algorithm hyperparameters selected, which was especially evident in scenarios with hundreds or thousands of locations. Simulated Annealing was also found to be an effective approach for UAV path planning, with a major benefit of avoiding getting trapped in local minima and being straightforward to implement. Like Genetic Algorithm, the performance of Simulated Annealing was also found to be sensitive to the algorithm hyperparameters selected. By comparison, Dynamic Programming guarantees optimality for any number of locations, but it was found to be less practical in terms of execution time for scenarios with more than about a couple dozen locations. On wildfire detection, image classification using deep learning with a convolutional neural network was explored. Transfer learning was found to be a useful technique to efficiently train deep learning models. Also, it was determined that GPU processing can increase training speed by an order of magnitude, which enables significantly faster development. For a validation test set of 500 images, there were only two false negatives and zero false positives. These results demonstrate that detecting wildfires in static cameras using machine learning is feasible and establish a baseline for using images captured by UAVs in flight for wildfire detection.

Wildfire management↗
NASA NTRS2009

Monitoring Disasters by Use of Instrumented Robotic Aircraft

Efforts are under way to develop data-acquisition, data-processing, and data-communication systems for monitoring disasters over large geographic areas by use of uninhabited aerial systems (UAS) robotic aircraft that are typically piloted by remote control. As integral parts of advanced, comprehensive disaster- management programs, these systems would provide (1) real-time data that would be used to coordinate responses to current disasters and (2) recorded data that would be used to model disasters for the purpose of mitigating the effects of future disasters and planning responses to them. The basic idea is to equip UAS with sensors (e.g., conventional video cameras and/or multispectral imaging instruments) and to fly them over disaster areas, where they could transmit data by radio to command centers. Transmission could occur along direct line-of-sight paths and/or along over-the-horizon paths by relay via spacecraft in orbit around the Earth. The initial focus is on demonstrating systems for monitoring wildfires; other disasters to which these developments are expected to be applicable include floods, hurricanes, tornadoes, earthquakes, volcanic eruptions, leaks of toxic chemicals, and military attacks. The figure depicts a typical system for monitoring a wildfire. In this case, instruments aboard a UAS would generate calibrated thermal-infrared digital image data of terrain affected by a wildfire. The data would be sent by radio via satellite to a data-archive server and image-processing computers. In the image-processing computers, the data would be rapidly geo-rectified for processing by one or more of a large variety of geographic-information- system (GIS) and/or image-analysis software packages. After processing by this software, the data would be both stored in the archive and distributed through standard Internet connections to a disaster-mitigation center, an investigator, and/or command center at the scene of the fire. Ground assets (in this case, firefighters and/or firefighting equipment) would also be monitored in real time by use of Global Positioning System (GPS) units and radio communication links between the assets and the UAS. In this scenario, the UAS would serve as a data-relay station in the sky, sending packets of information concerning the locations of assets to the image-processing computer, wherein this information would be incorporated into the geo-rectified images and maps. Hence, the images and maps would enable command-center personnel to monitor locations of assets in real time and in relation to locations affected by the disaster. Optionally, in case of a disaster that disrupted communications, the UAS could be used as an airborne communication relay station to partly restore communications to the affected area. A prototype of a system of this type was demonstrated in a project denoted the First Response Experiment (Project FiRE). In this project, a controlled outdoor fire was observed by use of a thermal multispectral scanning imager on a UAS that delivered image data to a ground station via a satellite uplink/ downlink telemetry system. At the ground station, the image data were geo-rectified in nearly real time for distribution via the Internet to firefighting managers. Project FiRE was deemed a success in demonstrating several advances essential to the eventual success of the continuing development effort.

Wegener, Steven S.↗
NASA NTRS2005

An Agent-Based Interface to Terrestrial Ecological Forecasting

The latest generation of NASA Earth Observing System (EOS) satellites has brought a new dimension to continuous monitoring of the living part of the Earth System, the biosphere. EOS data can now provide weekly global measures of vegetation productivity and ocean chlorophyll, and many related biophysical factors such as land cover changes or snowmelt rates. However, the highest economic value would come from forecasting impending conditions of the biosphere, to allow decision makers to mitigate dangers or exploit positive trends. NASA's strategic plan for the Earth Science Enterprise i d e n a s ecological forecasting as a focus for research. Ecological forecasting predicts the effects of changes in the physical, chemical and biological environment on ecosystem activity. Possible applications of such a system include predicting shortfalls or bumper crops of agricultural production, populations of threatened or invasive species or wildfire danger in time to allow improves preparation and logistical efficiency. Petabytes of remote sensing data are now available to help measure, understand and forecast changes in the Earth system, but using these data effectively can be surprisingly hard. The volume and variety of data files and formats are daunting. Simple data management activities, such as locating and transferring files, changing file formats, gridding point data, and scaling and reprojecting gridded data, can consume far more personnel time and resources than the actual data analysis. Some scientists commit to a particular data source or resolution just because using anything different would be more effort that it's worth. Better tools can help, but most of the tools developed to date are little more than shell scripts; they lack the flexibility to meet the diverse needs of users and are difficult to extend to handle changes in available data sources.

Golden, Keith↗
NASA NTRS2021

Colorado Ecological Forecasting: Monitoring Post-fire Cheatgrass (Bromus tectorum) Distribution to Inform Management Planning

Cheatgrass (Bromus tectorum) is a species of concern across the western United States as it has the potential to outcompete native plant species, reduce biodiversity, and diminish nutrient availability for ungulates. Furthermore, because cheatgrass can quickly dominate disturbed landscapes it has the potential to exacerbate wildfire risk by increasing fuel loads. In 2020, the Cameron Peak fire burned more than 200,000 acres on the Arapaho and Roosevelt National Forests in Colorado. These issues are of imminent concern for our partners at the Forest Service (USFS), as they are tasked with wildfire risk and invasive species mitigation. Disturbances such as wildfires can substantially increase the rate and extent of cheatgrass spread. Current cheatgrass mitigation methods rely on field crews to physically locate cheatgrass on the landscape, which takes time, money, and extensive manpower. Here, we developed two Random Forest models within the Software for Assisted Habitat Modeling (SAHM) using remote sensing predictors derived from Sentinel-2 MultiSpectral Instrument (MSI) and Shuttle Radar Topography Mission (SRTM). The first model identified suitable cheatgrass habitat while the other detected cheatgrass presence during the 2021 growing season. Topographic variables were found to be the most important in driving the habitat suitability model. Cheatgrass detection was also found to be possible within a short timespan with limited imagery surrounding a phenological shift of the plant. Maps produced from these models provide natural resource managers the ability to implement early detection and rapid response to prevent the spread of cheatgrass to new locations.

DEVELOP Tech Paper↗
NASA NTRSDate not supplied

Proposition to Optimize Fire Stations for Wildfires

The rising trend in wildfire occurrence and severity has put a strain on wildfire management organizations by spreading out limited resources to meet increasing demand. There has been extensive prior research and data collection to determine the areas of highest risk and to predict regional wildfire damages based on historical trends. Our group aims to utilize this data to best determine fire station placement, optimizing where resources are allocated to reduce the time and investment needed to effectively mitigate wildfires. Using existing research, we are able to calculate optimal fire station locations by utilizing a single-objective facility location problem algorithm incentivized for cost reduction. Further work would be needed to refine the algorithm to accommodate for more realistic factors including access to water and roadways as well as better accounting for the costs involved, but our work serves as a proof of concept and lays the foundation for future research. Implementation of this algorithm would allow public fire planning agencies (such as CAL FIRE and Forest Service) to shift resources to where they would be most effective.

Wildfires↗
NASA NTRS2012

Information Systems Coordinate Emergency Management

The rescue crews have been searching for the woman for nearly a week. Hurricane Katrina devastated Hancock County, the southernmost point in Mississippi, and the woman had stayed through the storm in her beach house. There is little hope of finding her alive; the search teams know she is gone because the house is gone. Late at night in the art classroom of the school that is serving as the county s emergency operations center, Craig Harvey is discussing the search with the center s commander. Harvey is the Chief Operating Officer of a unique company called NVision Solutions Inc., based at NASA s Stennis Space Center in Bay St. Louis, only a couple of miles away. He and his entire staff have set up a volunteer operation in the art room, supporting the emergency management efforts using technology and capabilities the company developed through its NASA partnerships. As he talks to the commander, Harvey feels an idea taking shape that might lead them to the woman s location. Working with surface elevation data and hydrological principles, Harvey creates a map showing how the floodwaters from the storm would have flowed along the topography of the region around the woman s former home. Using the map, search crews find the woman s body in 15 minutes. Recovering individuals who have been lost is a sad reality of emergency management in the wake of a disaster like Hurricane Katrina in 2005. But the sooner answers can be provided, the sooner a community s overall recovery can take place. When damage is extensive, resources are scattered, and people are in dire need of food, shelter, and medical assistance, the speed and efficiency of emergency operations can be the key to limiting the impact of a disaster and speeding the process of recovery. And a key to quick and effective emergency planning and response is geographic information. With a host of Earth-observing satellites orbiting the globe at all times, NASA generates an unmatched wealth of data about our ever-changing planet. This information can be captured, analyzed, and visualized by geographic information systems (GIS) to produce maps, charts, and other tools that can reveal information essential to a wide variety of applications including emergency management. Knowing precise, real-time information about the size, location, environmental conditions, and resulting damage of an event like a flood or wildfire as well as the location and numbers of emergency responders and other resources contributes directly to the effectiveness of disaster mitigation. The need for such information is also evident when responding to homeland security threats, such as a terrorist attack. Recognizing the value of its geospatial information resources for this and other purposes, in 1998 Stennis and the state of Mississippi partnered to form what became the Enterprise for Innovative Geospatial Solutions (EIGS) industry cluster, supporting the growth of remote sensing and GIS-based research and business. As part of EIGS, several companies partnered with NASA through dual use and Small Business Innovation Research (SBIR) contracts. Among those was NVision.

Source record↗
NASA NTRS2022

Supporting Hazard Analysis for Wildfire Response Using fmdtools and MIKA

The System Wide Safety (SWS) Safety Demonstrator (SD) Series drives development of an increasingly capable In-Time Aviation Safety Management System (IASMS) focusing on humanitarian applications, starting with wildfire response (SD-1). The goals of this report are to (1) provide an early hazard analysis and mitigation evaluation of wildfire response to support these efforts and (2) provide a demonstration of capabilities of the Fault Model Design Tools (fmdtools) and Manager for Intelligent Knowledge Access (MIKA) tools. fmdtools provides a modeling, simulation, and resiliency analysis framework in which a wildfire response model, the System Modeling and Analysis of Resiliency in Scalable Traffic Management for Emergency Response Operations (SMARt-STEReO), is built. MIKA is an intelligent knowledge manager with several capabilities, including assisting in hazard analysis by extracting and analyzing hazards from historical incident reports. The following topics are covered in the report: Understanding Wildfire Hazard Dynamics. We provide a description and simulated examples of how hazards occur in the SMARt-STEReO model of wildfire response and their effect on its outcome. This provides a common mental model and focuses the analysis presented in the remainder of the report. Wildfire Hazard Identification. MIKA identifies wildfire hazards from three relevant datasets: the ICS-209-PLUS, SAFECOM, and SAFENET. Hazards are manually organized into a taxonomy and MIKA analyzes each hazard’s effects, likelihood, severity, and risk. Evaluating Mitigation Strategies. The SMARt-STEReO wildfire response model built in fmdtools evaluates a subset of identified hazards. Specifically, we simulate the effect of communications faults and equipment faults on operator safety, the effect of changing winds and flammability, and a scenario with multiple ignition points and heavy smoke. Tool Limitations and Usage Considerations. We provide a discussion of appropriate tool use cases as well as limitations and considerations for usage. The tool findings are used to synthesize recommendations for wildfire response operations, which can be captured as part of an IASMS. Key recommendations are as follows: Hazards are identified from a broad spectrum of sources including aircraft subsystems, operational sources, and ground crew operations. Highest risk operational environment hazards identified are Evacuations. The highest risk manned aerial operations hazard categorized is Jumper Operations Mishap. Ground crew hazards that are highest risk are Burns, Cargo Operations Overhead, Dehydration, Entrapment, Falling Objects, Heart Attacks, Heat Exhaustion, Inadequate Training or Certification, Vehicle Breakdown, and Vehicle Collision. Modelled containment failures arise from a mismatch between the difficulty of the firefighting scenario and the capacity (e.g., speed, effectiveness, awareness) of the response. In firefighting scenarios where containment is possible (e.g., because the fire does not spread too quickly), these mismatches can occur because of a change in environmental conditions (e.g., wind, flammability, etc) or because of planning, equipment, or communications faults. Improvements to communications increase the capacity of the firefighting response by reducing the time needed to respond to the fire. While surveillance does not increase this capacity by itself, it increases operator safety by increasing state awareness, enabling firefighters to evade approaching fires. Increasing both has a synergistic effect. In general, these performance and resilience increases generalize over fault scenarios as well as unforeseen changes to circumstances (i.e., wind, aridity, etc.). However, these improvements need to be designed so as not to make the system prone to persistent large-scale communications outages, which can reduce performance.

Hazard analysis↗
NASA NTRSDate not supplied

NASA Research to Expand UAS Operations for Disaster Response

Natural disasters can result in the loss of life and cost governments and private industry billions to recover each year. Over the past decade the rate and severity of natural disasters such as wildfires and hurricanes have resulted in increasingly negative impacts to communities, public health, natural ecosystems, and the economy. To help reduce these impacts, NASA’s Aeronautics Research Mission Directorate is working to advance technologies and enable the safe and efficient inclusion of novel aviation applications to better assist in disaster response. To execute on these efforts, NASA’s Advanced Capabilities for Emergency Response Operations (ACERO) and System-Wide Safety (SWS) projects have developed coordinated strategic research plans focused on aviation operations for disaster response. The ACERO project will be a multi-year effort that focuses on enabling the use of uncrewed aircraft systems (UAS) to improve firefighter safety and efficiency and enable the use of UAS to conduct new missions such as logistics and aerial suppression. The ACERO project will demonstrate technologies that support the Second Shift concept, enabling UAS and ground technologies to support aerial suppression in degraded visual conditions (e.g., heavy smoke, nighttime). The SWS project will be a multi-year effort that focuses on addressing the key safety barriers that are preventing the authorization of UAS operations in a variety of increasingly complex disaster response applications: post-hurricane response, medical courier, and urban disaster response. The SWS project will demonstrate an In-Time Aviation Safety Management System (IASMS) designed to effectively monitor, assess, and mitigate safety risks associated with hazards to UAS operations for disaster response. This paper will provide a deeper insight into NASA’s research and development plans and discuss how solutions developed in partnership with industry stakeholders and federal agencies will improve disaster response across the globe.

disaster response↗
NASA NTRS2022

Chapter 10 - Remote Sensing Measurements of Aerosol Properties

Satellite instruments have proven especially capable at monitoring the quantity of airborne particles in columns of atmosphere, globally. This chapter describes the principles of satellite measurements and retrieval algorithms, and surveys current instruments and their capabilities. We outline the issues associated with retrieval algorithms, such as surface characterization and aerosol proximity to clouds, and the challenges with interpretation of the results. The relationship between measured aerosol properties and climate-relevant aerosol properties simulated in models is outlined, as well as how measurements are used to evaluate models. Most space-based aerosol instruments are passive sensors that measure reflected sunlight at multiple wavelengths, some at multiple viewing angles. A few are active sensors that send out their own laser light and measure the returned signal. Except when clouds are present, the excess amount of light scattered back to space, beyond that expected from the surface and atmospheric gas, is attributed to aerosol. Satellite measurements are used in many ways in aerosol research. They often provide the only method for monitoring hazardous phenomena such as major wildfire and volcanic eruption plumes, especially in remote areas. Stable, long-term, near-global-scale satellite data records make it possible to identify regional and global aerosol trends. Aerosol radiative effects on climate can be quantified on a near-global scale and used to estimate the strength of aerosol–radiation and aerosol–cloud interactions as well as to evaluate climate model simulations of these interactions. Aerosol-type mapping from satellite imagery is helpful for source attribution, model validation, and to constrain particle light-absorption properties that are essential for radiative forcing calculations. The range of aerosol properties retrieved from satellite observations has grown considerably since the first global estimates of aerosol optical depth (τ a) over ocean were made in the late 1970s. Methods for retrieving particle size and light-absorption properties were explored in the 1990s using multispectral, multi-angle observations, and polarization in visible and near-infrared wavelengths. Sensitivity to particle light absorption, primarily from black or brown carbon content, improved with the inclusion of UV channels, and sensitivity to very thin aerosol layers in the upper troposphere and lower stratosphere was advanced with the use of limb-sounding instruments and active sensors. There are limitations to every measurement technique, including satellite aerosol remote sensing. For wide-swath, passive instruments, aerosol retrievals near clouds can present substantial challenges as far as 15 km away due to cloud-scattered light contaminating the signal. In nearly all cases, retrievals over bright snow and ice surfaces are precluded because surface reflectance uncertainties can overwhelm the aerosol signal. Similarly, meteorological cloud is identified and masked out where possible. Data from passive sensors also lack vertical resolution except those that view toward the limb or where multi-angle imagery is acquired over plumes from wildfires, erupting volcanoes, and wind-blown dust. Yet, passive sensors provide vastly more coverage than the active instruments that mitigate these issues. Particle microphysical information is qualitative from all remote sensing techniques, relying on proxies to infer particle composition, hygroscopicity, and the amount of light-absorbing material. Further, particles smaller than about 200 nm diameter cannot be distinguished from atmospheric gas molecules with remote sensing, which hinders studies of cloud condensation nuclei and their effects on clouds. Most satellite instruments dedicated to aerosol observations are in low-Earth, near-polar, sun-synchronous orbits, which means they cross the equator at the same local time each day. Most are set on cycles that repeat approximately every 16 days, which makes it difficult to monitor aerosol evolution locally. Geostationary satellites make it possible to observe changes occurring from minutes to hours over regions up to 8000 km in size, but lack coverage of high latitudes, and often provide more limited constraints on aerosol properties. Ground-truth data are vital for satellite aerosol-retrieval validation. The AErosol RObotic NETwork (AERONET) of sun photometers was created in 1993 and has become an established global network of over 350 instruments for validating satellite measurements. The network, as well as global networks of ground-based lidars, solar flux radiometers and other sun photometers, are widely used for evaluating global satellite retrievals and model simulations. NASA's Earth Observing System (EOS) program beginning in 1999 led to improvements in reliability, spatial resolution, and spectral resolution (and hence, to improved particle size discrimination and light absorption properties). Satellite payloads include advanced broad-swath and multi-angle imagers, along with the first space-based active sensor focused largely on long-term aerosol monitoring. Since about 2002, Europe's SENTINEL and operational meteorological satellite fleets are also providing sustained aerosol observations, with planned continuation until at least 2030. Satellite remote sensing instruments offer valuable data for evaluating aerosol representations in global climate models. They have been used to assess aerosol optical and physical properties, trends and distributions, and are applied increasingly as direct model constraints in data assimilation to create global aerosol reanalysis products. Aerosol optical depth is the most common quantity adopted for routine model evaluation, including multiwavelength data to loosely constrain particle-size distributions. These evaluations of multiple models have revealed general biases in their regional aerosol amounts and seasonal patterns of transport and removal. Although satellite measurements have near-global coverage, substantial errors can be introduced into the model observation comparison unless attention is paid to spatial and temporal collocation, cloud screening, subgrid-scale variability, and measurement uncertainties that vary with retrieval conditions.

aerosol properties↗