Mapping and managing Earth’s mineral resources demands advanced techniques for characterizing surface composition, a challenge that can be effectively addressed by spaceborne Earth observation. Thermal infrared (TIR) sensors hosted on orbital platforms provide a powerful tool for regional scale (~1000s km2), high-resolution (≤100m) identification of mineral composition and surface thermal properties. In this study, we demonstrate the potential of multispectral TIR image data acquired by the ECOSTRESS and ASTER spaceborne sensors with near-global coverage to produce the first mineral maps of the Earth’s arid and semi-arid regions. TIR data complement Visible to ShortWave InfraRed (VSWIR) data because most important rock forming minerals do not have features in the VSWIR. Thus, integrating TIR-derived mineralogy is essential to comprehensively map the surface composition and interpret the geology. The mapping results were validated at three sites—the Algodones Dunes (quartz), White Sands Dunes (gypsum), and Mehdi Ridge (calcite)—showing strong spatial and abundance agreement with laboratory data from field samples and reference literature. These results confirm the reliability of high spatial resolution multispectral TIR data in capturing major surface mineral distributions.

One of the Designated Observables (DOs) identified in the Decadal Survey for Earth SFcience and Applications from Space was Surface Biology and Geology (SBG). NASA has formulated this and several of the other DOs into the Earth System Observatory, which provides a framework from which to answer many of the questions posed by the Decadal Survey and address the goals of the DOs. The SBG mission concept, now in formulation, has an overarching goal of acquiring global hyperspectral visible to shortwave infrared (VSWIR; 0.38–2.5 μm) and multispectral mid and thermal infrared (MIR: 3–5 μm; TIR: 8–12 μm) image data at high spatial resolution (~30 m in the VSWIR and ~ 60 m in the TIR). The VSWIR and TIR are separate instruments on separate platforms and thus will have different characteristics such as local overpass and temporal revisit times as a function of the individual scientific objectives. The SBG-TIR is a joint-endeavor between NASA and ASI in Italy, with the instrument being built at the NASA Jet Propulsion Laboratory (JPL). It will have a wide swath width (935 km) resulting in a three-day equatorial revisit time. During Phase A development, the TIR spectral resolution was increased from five to six bands (plus the original two planned for the MIR). The addition of a 10.3 μm band vastly improves the capability of surface mineralogy mapping and aerosol detection in sulfur dioxide (SO2) plumes. For the first time, an Earth-orbiting TIR mission is planning an operational surface mineralogy (SM) L3 product. This product uses the L2 TIR surface emissivity data as input together with a spectral library of the most common Earth surface minerals to produce mineral and weight percent silica (WPS) maps of the Earth’s arid lands. Here, we describe the current SM algorithm testing and development, initial results, and plans for ongoing work prior to the planned 2028 launch.

The 2017-2027 Decadal Survey for Earth Science and Applications from Space identified Surface Biology and Geology (SBG) as a key Designated Observable to be addressed with a future Earth mission concept. The thermal infrared (TIR) aspect of this new mission includes, for the first time, the development and production of thermal and volcanic standard data products: Level-3 Elevated Temperature Features (ETF) and Level-4 Volcanic Activity (VA). Both of these require extensive trade space analysis, development, testing, validation, and automation before the launch planned for 2028. The ETF product must rapidly and accurately detect thermal change in some of Earth’s most dynamic processes (i.e., volcanic activity, wildland fires) with a low rate of false positives. Over twenty prior algorithms were investigated, with a subset selected for initial testing, leading to the development of the final ETF algorithm architecture. The VA product must detect and quantify volcanic activity based on thermal, gas, and ash emissions. A modified ETF and the Plume Tracker software are leveraged to assess the level of activity at the world’s active volcanoes and over time establish long-term trends (years) in eruptive activity. Development is still ongoing but advances have been achieved to automate the processing of these volcanic products to be used for the first time in a NASA operational mission.

Twenty-one years of ASTER global thermal infrared (TIR) acquisitions provide a large amount of data for volcano monitoring. These data, with high spatial and spectral resolution, enable routine investigations of volcanoes in remote and inaccessible regions, including those with no ground-based monitoring. However, the dataset is too large to be manually analyzed on a global basis. Here, we systematically process the data over several volcanoes using a deep learning algorithm to automatically extract volcanic thermal anomalies. We explore the application of a Convolutional Neural Network (CNN), specifically UNET, to detect subtle to intense anomalies exploiting the spatial relationships of the volcanic features. We employ a supervised UNET network trained with the largest (1500) labeled dataset of ASTER TIR images from five different volcanoes, namely Etna (Italy), Popocatépetl (Mexico), Lascar (Chile), Fuego (Guatemala), and Kliuchevskoi (Russia). We show that our approach achieves high accuracy (93%) with excellent generalization capabilities. The effectiveness of our model for detecting the full range of thermal emission is shown for volcanoes with very different styles of activity and tested at Vulcano (Italy). The results demonstrate the potential applicability of the proposed approach to the development of automated thermal analysis systems at the global-scale using future TIR data such as the planned NASA SBG mission.

High resolution (i.e., sub-100 m) visible/near infrared (VNIR) and thermal infrared (TIR) orbital data are vital for the scientific study of numerous volcanic processes including eruption detection, thermal baseline monitoring, measuring degassing rates, and compositional change assessment. However, these data are rarely captured at a temporal cadence required to model trends in dynamic systems nor create quantitative forecasts about future activity. Sensor-webs created to improve the repeat time of these high resolution sensors rely on early detection by lower resolution instruments and/or data from ground-based systems each with much better temporal resolution. The ASTER Urgent Request Protocol (URP) is one such sensor-web created to increase data collection of the world's most active volcanoes. It has operated since 2004 acquiring an additional 6,200 scenes of more than 100 different volcanoes. The URP is a combined effort of university and government (NASA, USGS) partners, and the data are used operationally for monitoring, crisis response, and to inform future mission development.

We have developed a new portable, ground-based imaging system primarily to investigate the thermal properties of volcanic processes during lava propagation and cooling, although it is potentially applicable to any dynamic geologic process. The miniature multispectral thermal camera (MMT-Cam) is an imaging system that acquires six wavelength bands of thermal infrared (TIR) data between 8 and 12 μm nearly simultaneously. The spatial and temporal resolutions of the camera system are high, 640 × 512 pixels, and 1 s, respectively. The imaging system is calibrated using full-aperture blackbody experiments at a range of temperatures from 283 to 1023 K to account for all instrumental, optical, and transmission effects. In addition, the baseline drift due to changing internal camera temperature is measured and removed. As a result, the accurate multispectral TIR image data are acquired of dynamic surfaces such as propagating and cooling lava flow surfaces captured at the critical temporal (seconds) and thermal (initial rapid cooling) scales. These improved acquisition parameters provide valuable data for both compositional and textural spatiotemporal variability analyses of these volcanic surfaces. Furthermore, the MMT-Cam specifications are designed to be comparable to current and proposed Earth-orbiting TIR instruments to better evaluate the potential for future TIR data sets to deliver similar data sets.

A new airborne facility instrument for Earth science applications is introduced. The Mineral and Gas Identifier (MAGI) is a wide-swath (programmable up to ±42° off nadir) moderate spectral resolution thermal-infrared (TIR) imaging spectrometer that spans the 7.1- to 12.7-μm spectral window in 32 uniform and contiguous channels. Its spectral resolution enables improved discrimination of rock and mineral types, greatly expanded gas-detection capability, and generally more accurate land-surface temperature retrievals. The instrument design arose from trade studies between spectral resolution, spectral range, and instrument sensitivity and has now been validated by flight data acquired with the completed sensor. It offers a potential prototype for future space-based TIR instruments, which will require much higher spectral resolution than is currently available in order to address more detailed climate, anthropogenic, and solid Earth science questions.

This document summarizes ongoing activities associated with a research project funded by the national aeronautics and space administration (NASA) focusing on volcanic change detection through the use of satellite imagery. This work includes systems development as well as improvements in data analysis methods. Participating organizations include the NASA land processes distributed active archive center (LP DAAC) at the U.S. geological survey (USGS) center for earth resources observation and science (EROS), the Advanced spaceborne thermal emission and reflection radiometer (ASTER) science team, the Alaska volcano observatory (AVO) at the USGS Alaska science center, the jet propulsion laboratory/California Institute of Technology (JPL/CalTech), the University of Pittsburgh, and the University of Alaska Fairbanks.