Remote VSWIR Imaging spectroscopy for Global Discovery and Conservation Science Remote VSWIR Imaging spectroscopy for Global Discovery and Conservation Science 7. Objectives and Strategic ValueWe will apply the JPL VSWIR optimal estimation prototype, an advanced new Bayesian imaging spectrometer atmospheric correction algorithm, for terrestrial ecosystem observations. We will: 1. Provide a standard, open source atmospheric correction that is easy to use by terrestrial ecologists, obviating sophisticated post-processing and commercial licenses. 2. Enable dramatic improvements in state of art ecosystem mapping performance. Optimal estimation methods have improved spectrum quality metrics by a factor of >7 on diverse validation datasets (Thompson et al., RSE 2019). 3. Demonstrate formal uncertainty quantification and propagation which are traditionally lacking in imaging spectrometer atmospheric correction (Thompson et al., RSE 2018). These advantages will support accurate and bias-free global maps of terrestrial species, functional traits, and ecosystems critical for the anticipated Surface Biology and Geology (SBG) investigation designated in the National Academies 2017 Earth Science and Applications Decadal Survey. The JPL-led SBG architecture study is nearing a NASA Mission Concept review, so it is the key time to demonstrate these technologies. Strategic Value of Collaboration The Arizona State University School of Earth and Space Exploration (ASU SESE) recently announced a new Center for Global Discovery and Conservation Science. The center, under direction of Dr. Gregory Asner, has broad expertise in remote sensing of global ecosystems. Their focus on imaging spectroscopy for ecosystem science is an ideal opportunity for collaboration. This SURP is the first project in the long-term partnership. ASU will conduct their own imaging spectroscopy investigations, contributing additional algorithms and field data where appropriate. Partnership with the new ASU center is in JPLs long-term interest for the success of future SBG efforts, and we anticipate a growing population of student talent contributing to JPL research and benefiting from exposure to JPL instrument and algorithm expertise. Strategic Value to Future Missions This research will significantly advance the following Earth surface studies: (1) Historical AVIRIS-C, PRISM, AVIRIS-NG, and Hyperion archives; (2) reanalysis of data for CORAL, ABoVE, AVIRIS-NG India, and the HyspIRI precursor campaign; (3) upcoming sub-orbital and orbital investigations including recently-selected EVS-3 missions Delta-X and S-MODE, and the designated Surface Biology and Geology (SBG) investigation. Establishing community standard atmospheric corrections will significantly lower the barrier of entry to other university groups using these data, and further raising JPLs profile in the community as we prepare for the flood of future SBG-related measurements. 8. Background Remote Visible / ShortWave InfraRed (VSWIR) imaging spectrometers map spectral radiance from 380 - 2500 nm. Surface reflectance features reveal the chemistry and composition of Earths terrestrial ecosystems. USA measurements come mainly from airborne platforms at three institutions: JPL; NSF (using JPL spectrometers); and Arizona State University by Co-I Asner. This makes JPL and the Asner laboratory the critical pathfinders for an anticipated orbital investigation recommended by the National Academies NASA Earth Science Decadal Survey (2017). Moving to orbit will challenge conventional algorithms - particularly atmospheric correction required to estimate ecosystem properties. Minor errors in reflectance estimates can become systematic biases at the global scale. Earths biodiversity concentrates in tropical areas that are (a) underrepresented in current atmospheric correction research and (b) challenging due to high aerosol and water vapor loads, which thwart traditional corrections that use fragile heuristics and band ratio theresholds. Rigorous algorithms developed at JPL offer a solution (Thompson et al., 2018). Bayesian Maximum A Posteriori (MAP) optimization optimizes a model combining surface, atmosphere, and instrument. Our prior research (Thompson et al., 2018, 2019a, 2019b) adopts the Rodgers et al. formalism (Rogers et al., 2000) known as Optimal Estimation (OE). OE is part of a broad family of iterative probabilistic model inversion methods, used for decades by atmospheric remote sensing missions ( OCO-2, SCIAMACHY, MAIA, etc.). It finds parameters of the surface and atmosphere that are most probable given the measurement while accounting for noise and the strength of background knowledge. This combines measurement information with Bayesian statistical priors over climatology. It transparently captures the benefits of conventional heuristic aerosol retrievals, exploiting similar properties while adding rigor and robustness. Another advantage is the ability to incorporate information distributed across the entire VSWIR spectral range without pre-selecting atmospheric retrieval windows. These yield significant accuracy improvements for challenging atmospheres (Thompson et al., 2019). It enables principled accounting for uncertainty in measurement data and propagation of predicted uncdertainty to downstream algorithms. However, while OE methods have been validated in multiple field cases, our current models are unproven for terrestrial ecology. Topographical corrections may be needed to account for illumination variability due to slope and horizon obscuration. Ecosystem trait estimates can be vulnerable to the balance between different levels of diffuse and direct solar illumination, making them particularly sensitive to such effects. In addition, there is the critical question of how the information in tree canopies is preserved or diluted across scales. Single pixel measurements from airborne data can be acquired at 1 m sampling, which can provide information about individual organisms and even filter out shaded parts of the tree canopy. In contrast, global-scale measurements will generally acquire spectra at 30 sampling. The degree to which critical canopy data, such as functional traits, species, and phenotype, are preserved in the coarser pixels is still unknown. Most airborne investigations of these phenomena, spearheaded by the Asner lab at ASU, use filtering methods to focus on dense, well-lit canopy pixels. The coarser orbital pixels risk contamination of this signal by substrate and shaded areas of the canopy. Finally, observations of traits over time may be subject to highly variable atmospheric conditions that complicate the identification of trends across different biomes and environments. Dealing with this variance willbe a significant challenge for orbital investigations.
|Effective start/end date||11/9/21 → 9/25/23|
- NASA: Headquarters: $52,500.00
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