On-orbit calibration and performance of the EMIT imaging spectrometer

The Earth surface Mineral dust source InvesTigation (EMIT) is a remote visible to shortwave infrared (VSWIR) imaging spectrometer that has been operating onboard the International Space Station since July 2022. This article describes EMIT's on-orbit spectroradiometric calibration and validation. Accurate spectroscopy is vital to achieve consistent mapping results with orbital imaging spectrometers. EMIT takes a unique approach to this challenge, with just six optical elements, no shutter, and no onboard calibration systems. Its simple design focuses on uniformity and stability to enable vicarious spectroradiometric calibration. Our experiments demonstrate that this approach is successful, approaching the fidelity of manual field spectroscopy in some cases, and enabling new and more accurate products across diverse Earth science disciplines. EMIT achieves several notable firsts for an instrument of its class. It demonstrates successful on-orbit adjustments of Focal Plane Array (FPA) alignment with sub-micron precision. It offers spectral uniformity better than 98%. Optical artifacts in the measurement channels are at least three orders of magnitude below the primary solar-reflected surface signals. Its noise performance enables percent-level discrimination in the depths of mineral absorption features. In these aspects, EMIT satisfies the stringent performance needs for the next generation of VSWIR imaging spectrometers to observe the Earth's ecosystems, geology, and water resources.EMIT is supported by the National Aeronautics and Space Administration Earth Venture Instrument program, under the Earth Science Division of the Science Mission Directorate. K. Dana Chadwick is supported by the NASA Applied Sciences Program. Carlos P´ erez García- Pando and María Gonçalves Ageitos acknowledge support from the European Research Council (ERC) Consolidator Grant FRAGMENT (grant agreement No. 773051), and the AXA Chair on Sand and Dust Storms at the Barcelona Supercomputing Center funded by the AXA Research Fund. Martina Klose has received funding through the Helmholtz Association’s Initiative and Networking Fund (grant agreement No. VH-NG-1533). We thank Jeffrey Czapla-Myers and the University of Arizona team for their maintenance and operation of the Railroad Valley automated calibration facility. This research was performed at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. We acknowledge the support and assistance of NASA’s International Space Station Program. The USGS authors’ contribution to this published Work was prepared by U.S. federal government employees as part of their official duties and constitutes a “work of the United States government,” and is considered to be in the public domain and therefore domestic copyright does not apply. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Copyright 2024 California Institute of Technology. All rights reserved. US Government Support Acknowledged.Peer ReviewedArticle signat per 56 autors: David R. Thompson, Robert O. Green, Christine Bradley, Philip G. Brodrick, Natalie Mahowald, Eyal Ben Dor, Matthew Bennett, Michael Bernas, Nimrod Carmon, K. Dana Chadwick, Roger N. Clark, Red Willow Coleman, Evan Cox, Ernesto Diaz, Michael L. Eastwood, Regina Eckert, Bethany L. Ehlmann, Paul Ginoux, María Gonçalves Ageitos, Kathleen Grant, Luis Guanter, Daniela Heller Pearlshtien, Mark Helmlinger, Harrison Herzog, Todd Hoefen, Yue Huang, Abigail Keebler, Olga Kalashnikova, Didier Keymeulen, Raymond Kokaly, Martina Klose, Longlei Li, Sarah R. Lundeen, John Meyer, Elizabeth Middleton, Ron L. Miller, Pantazis Mouroulis, Bogdan Oaida, Vincenzo Obiso, Francisco Ochoa, Winston Olson-Duvall, Gregory S. Okin, Thomas H. Painter, Carlos Pérez García-Pando, Randy Pollock, Vincent Realmuto, Lucas Shaw, Peter Sullivan, Gregg Swayze, Erik Thingvold, Andrew K. Thorpe, Suresh Vannan, Catalina Villarreal, Charlene Ung, Daniel W. Wilson, Sander Zandbergen.Objectius de Desenvolupament Sostenible::13 - Acció per al ClimaPostprint (published version

Investigating the Use of Generative Adversarial Networks (GANs) for Pansharpening Thermal Satellite Imagery

Remotely sensed satellite imagery can be an important data source for observing and measuring environmental changes in urban areas. For example, thermal satellite imagery can be used to identify urban heat islands (UHIs) and provide motivation for urban cooling strategies. However, the spatial resolution of thermal satellite imagery is insufficient for many urban biodiversity applications (e.g., tree planting and shifting ranges of flora and fauna in response to climate change) because of the inherent heterogeneity and complexity of cities. In order to improve the usability of this imagery, artificial intelligence techniques can increase the spatial resolution of the thermal imagery without losing valuable spectral information. One such technique is pansharpening, which fuses high-spatial resolution panchromatic (single band gray-scale) images and lower-spatial resolution multispectral or thermal infrared images. This project uses a modified generative adversarial network (GAN) to pansharpen lower resolution, remotely sensed thermal satellite imagery using high spatial resolution red-green-blue (RGB) imagery. A focus on developing higher spatial resolution maps of heat across cities could enable such broader-scale investigations into the impacts of UHIs on biodiversity. This thesis develops a novel training dataset with patch-pairs of co-located thermal (70 m) and RGB imagery (3 m). Using this novel dataset, this thesis assesses whether a pansharpening model (PanColorGAN) trained on RGB and panchromatic imagery can be successfully applied to the thermal-optical patch-pairs to solve the thermal pansharpening problem. In addition, this thesis will determine if a pansharpening model (pix2pix) trained on the thermal-optical patch-pairs produces higher quality results than the model with pre-trained weights (PanColorGAN). This approach will be applied to five cities with variable climate-urban environments across the United States: Austin, TX, Boulder, CO, Chicago, IL, Los Angeles, CA, and Washington, D.C. The visual and quantitative results indicated that the PanColorGAN framework with pre-trained weights produced higher quality thermal images than the pix2pix framework trained on the thermal-optical patch-pair dataset. While thermal-optical pansharpening successfully recovered many of the spatial details of the high-resolution RGB imagery, it failed to fully retain the valuable spectral information from the thermal imagery

Comparison of Thermal Infrared-Derived Maps of Irrigated and Non-Irrigated Vegetation in Urban and Non-Urban Areas of Southern California

It is important to understand the distribution of irrigated and non-irrigated vegetation in rapidly expanding urban areas that are experiencing climate-induced changes in water availability, such as Los Angeles, California. Mapping irrigated vegetation in Los Angeles is necessary for developing sustainable water use practices and accurately accounting for the megacity’s carbon exchange and water balance changes. However, pre-existing maps of irrigated vegetation are largely limited to agricultural regions and are too coarse to resolve heterogeneous urban landscapes. Previous research suggests that irrigation has a strong cooling effect on vegetation, especially in semi-arid environments. The July 2018 launch of the ECOsystem Spaceborne Thermal Radiometer on Space Station (ECOSTRESS) offers an opportunity to test this hypothesis using retrieved land surface temperature (LST) data in complex, heterogeneous urban/non-urban environments. In this study, we leverage Landsat 8 optical imagery and 30 m sharpened afternoon summertime ECOSTRESS LST, then apply very high-resolution (0.6–10 m) vegetation fraction weighting to produce a map of irrigated and non-irrigated vegetation in Los Angeles. This classification was compared to other classifications using different combinations of sensors in order to offer a preliminary accuracy and uncertainty assessment. This approach verifies that ECOSTRESS LST data provides an accurate map (98.2% accuracy) of irrigated urban vegetation in southern California that has the potential to reduce uncertainties in regional carbon and hydrological cycle models

A Simplified Framework for High-Resolution Urban Vegetation Classification with Optical Imagery in the Los Angeles Megacity

High spatial resolution maps of Los Angeles, California are needed to capture the heterogeneity of urban land cover while spanning the regional domain used in carbon and water cycle models. We present a simplified framework for developing a high spatial resolution map of urban vegetation cover in the Southern California Air Basin (SoCAB) with publicly available satellite imagery. This method uses Sentinel-2 (10–60 × 10–60 m) and National Agriculture Imagery Program (NAIP) (0.6 × 0.6 m) optical imagery to classify urban and non-urban areas of impervious surface, tree, grass, shrub, bare soil/non-photosynthetic vegetation, and water. Our approach was designed for Los Angeles, a geographically complex megacity characterized by diverse Mediterranean land cover and a mix of high-rise buildings and topographic features that produce strong shadow effects. We show that a combined NAIP and Sentinel-2 classification reduces misclassified shadow pixels and resolves spatially heterogeneous vegetation gradients across urban and non-urban regions in SoCAB at 0.6–10 m resolution with 85% overall accuracy and 88% weighted overall accuracy. Results from this study will enable the long-term monitoring of land cover change associated with urbanization and quantification of biospheric contributions to carbon and water cycling in cities