Methane (CH4) is a prominent greenhouse gas responsible for about 20% of all atmospheric radiative forcing. As we notice trends in increasing global temperatures, understanding and detecting these emissions has become increasingly important. This requires the creation of robust greenhouse gas plume detectors. Previous work at the NASA Jet Propulsion Laboratory has shown Convolutional Neural Networks (CNN) to be an appropriate solution to map methane sources from future imaging spectrometer missions, such as Carbon Mapper. However, current models suffer from a high rate of false positives due to false enhancements in the detected images.We have compiled datasets from two Airborne Visible/Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) California campaigns. We then trained a GoogleNet CNN Classifier model on each campaign. The baseline current model uses a Unimodal column-wise matched filter (CMF). This results in a model known to be sensitive to false enhancements, such as water/water vapor, bright/dark surfaces, or confuser materials with similar absorption wavelengths to methane. We first note improvements between the Unimodal CMF model and a new Surface-Controlled CMF model, whose dataset matches that of the Unimodal CMF model, but removes enhancements not matching the absorption wavelength of methane. From this, we note minimal improvement (1% increase in F1 score). We then experiment with various auxiliary products measuring albedo (rgbmu, SWALB), vegetation (NDVI, ENDVI), and water (h2o, NDWI) indices designed to combat issues known to produce false enhancements. After training on these new input representations for both campaigns, we noticed a significant improvement in the multi-channel model’s results. We observe an increase in the F1 score for classifying positive tiles from 0.78 to 0.86 when trained using auxiliary albedo indices, showing promise for future use of auxiliary products in improving methane plume detectors.

Mapping global vertical vegetation structure (VS) is critical for the quantification of global carbon stocks. While orbital LIDAR measurements of NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission provide direct estimates of vertical VS, they only cover 4% of the global land surface. Recent works have produced global contiguous maps of canopy height using convolutional neural network (CNN) models and satellite imagery. However, these models faced some challenges estimating high canopy heights (>30 m) and identified some tiling artifacts (Lang et al., 2022; Potapov et al., 2020).We present various methods to address these limitations. First, we remove tiling artifacts by using overlapping tiles when producing maps and removing zero-padding from CNN architectures. Second, we compare the benefits and limitations of a few different methods to improve high canopy height estimation. Among the methods is histogram matching predicted heights to nearby LIDAR measurements. Another is learning a calibration model to correct each pixel based on similar known measurements nearby. Finally, we borrow recent advances in deep depth completion from the autonomous driving field to create an integrated model that uses known values to improve the prediction map. To demonstrate these methods, we map global vertical VS with a CNN model at 1km resolution using observations from Landsat, L-band synthetic aperture radar observations from the Advanced Land Observing Satellite (ALOS) PALSAR-2, and surface topography. For model training, GEDI relative height metrics are filtered and aggregated into 1km grids. We also use measurements from the Ice, Cloud and land Elevation Satellite 2 (ICESat-2), inter-calibrated with GEDI using co-located measurements. Finally, we apply the aforementioned corrective methods to the product, reporting global RMSE and MAE metrics, as well as visual qualitative observations. These results open the path for unsaturated global vertical VS products at higher 500 m, 200 m, and 100 m resolutions.

Identification of global methane (CH4) sources is critical to the quantification and mitigation of this greenhouse gas. Future imaging spectrometer missions, such as Carbon Mapper, will provide global, spatially resolved observations that will make it possible to accurately map methane sources. However, the sheer data volume of these missions make manual source identification infeasible, and expected artifacts in matched filter methane plume identification challenge simple thresholding. Recent works have demonstrated the feasibility of Convolutional Neural Networks (CNNs) for plume detection; however, in the past, these models have suffered from high false positive rates and were limited in their training and evaluation to individual flight campaigns.We have assembled quality-controlled tiled datasets from three Airborne Visible/Infrared Imaging Spectrometer Next Generation (AVIRIS-NG) campaigns: a 2020 California campaign (“COVID”), a 2019 Texas Permian Basin campaign (“Permian”), and another 2018 California campaign (“CACH4”). These datasets are notable for their diversity of surface conditions, spatial resolutions, and source types (oil & gas, energy, waste, livestock). Labeled methane sources in these datasets have been manually verified, and flightlines with systematic artifacts have been filtered out. We trained a GoogLeNet CNN classifier model on each of these campaigns to evaluate intra- and inter- campaign performance. We also trained a model on all three campaigns and evaluated its performance on each dataset. We observed an F1 performance of 0.7 or greater for each model trained and evaluated on its own dataset. We also observed that the model trained on all three datasets often outperforms individual models on multiple metrics. Finally, we converted the model into a fully convolutional network (FCN) for methane plume saliency map generation. We plan to extend this work to datasets acquired by the Global Airborne Observatory (GAO) and prepare a model for deployment for the Carbon Mapper orbital data product pipeline.

Identification of global methane (CH4) sources is critical for the quantification and mitigation of this potent greenhouse gas. In preparation for Carbon Mapper, a future spaceborne imaging spectrometer mission, our work so far has focused on developing a robust methane plume detection method with AVIRIS-NG Columnwise Matched Filter (CMF) data. While we have previously demonstrated robust classification of plume source presence in ~800m square tiles, a deployed Science Data System (SDS) pipeline will require heat map or mask products that highlight the location of methane plume sources in entire flightlines and scenes.We present two methods for implementing pixel-wise methane plume detection. First, we convert our existing GoogLeNet Convolutional Neural Network (CNN) classifier into a Fully Convolutional Network (FCN) segmentation model with a novel implementation of shift-and-stitch, in which the final fully connected layer is replaced by a one-by-one convolutional layer. This allows us to produce a saliency map of methane plumes in scenes of arbitrary sizes without re-training the existing classification model. While heatmaps produced by this method lack pixel-wise precision, they are sufficiently localized to direct attention for further review.Second, we propose a new hybrid segmentation model based on the popular U-Net architecture. Notably, we backpropagate both segmentation and classification losses during training, which significantly reduces the number of false positive plume detections due to false enhancements and artifacts. Additionally, we successfully use algorithm-generated weakly-labeled segmentation masks, mitigating the need for expensive human-generated segmentation annotations. We report and compare the scene-level detection performance of these two methods on previously curated datasets from “COVID” (2020 California), “CACH4” (2018 California), and “Permian” (2019 Texas) AVIRIS-NG Campaigns.

Ocean worlds such as Europa and Enceladus are high priority targets in the search for past or extant life beyond Earth. Evidence of life may be preserved in samples of surface ice by processes such as deposition from active plumes or thermal convection. Terrestrial life produces unique distributions of organic molecules that translate into recognizable biosignatures. Identification and quantification of these organic compounds can be achieved by separation science such as capillary electrophoresis coupled to mass spectrometry (CE-MS). However, the data generated by such an instrument can be multiple orders of magnitude larger than what can be transmitted back to Earth during an ocean worlds mission. This requires onboard science data analysis capabilities that summarize and prioritize CE-MS observations with limited compute resources. In response, the Autonomous Capillary Electrophoresis Mass-spectra Examination (ACME) onboard science autonomy system was created for application to the Ocean Worlds Life Surveyor (OWLS) instrument suite. ACME is able to compress raw mass spectra by two to three orders of magnitude while preserving most of its scientifically relevant information content. This summarization is achieved by the extraction of raw data surrounding autonomously identified ion peaks and the detection and parameterization of unique background regions. Prioritization of the summarized observations is then enabled by providing estimates of scientific utility, the uniqueness of an observation relative to previous observations, and the presence of key target compound signatures.

The Ocean Worlds Life Surveyor (OWLS) is a field prototype instrument suite designed to autonomously search for evidence of water-based life, developed in preparation for potential future missions to ocean worlds such as Enceladus and Europa. One instrument included in this suite is a Capillary Electrophoresis-Electrospray Ionization Mass Spectrometer (CE-ESI MS), which can detect the presence of organic molecules and other potential biosignature compounds. Due to the extreme energy costs involved in communication from these distant worlds, a mission’s downlink bandwidth is insufficient to return raw data from even a single recorded dataset. We developed two onboard capabilities to address this constraint: compression via knowledge summarization, and prioritization for the most scientifically useful observations. To summarize and prioritize the data generated by the CE-ESI MS, we developed the Autonomous CE-ESI Mass-Spectra Examination (ACME) system. ACME performs content summarization while ensuring that scientifically valuable signals are retained. First, ACME identifies and characterizes potential peaks in the mass spectra, each of which may indicate the presence of a specific compound. Then, ACME uses a decision tree model trained on expert-labeled data and peak properties such as width and signal-to-noise ratio to filter only for peaks of likely scientific interest. Finally, ACME produces a series of Autonomous Science Data Products (ASDPs): crops of small regions of the raw mass spectra data around each peak, a summary of the background noise to provide context and justification for its decisions, estimates of the scientific utility of the observation, and a brief description of its contents to enable downlink prioritization based on known science targets of interest as well as diversity sampling. Typical data sizes of the peak locations, crops, and background noise summary satisfy the mission downlink bandwidth constraints with an average compression ratio of 900:1. ACME was validated on lab- and field-collected data to confirm that scientists are able to successfully analyze and make valid scientific conclusions using only ACME’s ASDPs, compared to analyzing the raw data directly.

Despite methane’s important role as a greenhouse gas, the contribution of individual sources to rising methane concentrations in Earth’s atmosphere is poorly understood. This is in part due to the lack of frequent measurements on a global scale, required to accurately quantify fugitive methane sources. Future missions such as Earth Surface Mineral Dust Source Investigation (EMIT), Surface Biology and Geology (SBG), and Carbon Mapper promise to provide global, spatially resolved spectroscopy observations that will allow us to map methane sources. However, the detection and attribution of individual methane sources is challenged by retrieval artifacts and noise in retrieved methane concentrations. Additionally, manual methane plume detection is not scalable to global space-borne observations due to the sheer volume of data generated. A robust automated system to detect methane plumes is needed. We evaluated the performance and sensitivity of several methane plume detection methods on 30m to 60m hyperspectral imagery, downsampled from airborne campaigns with AVIRIS-NG. To aid the training of the plume detection models, we explored supplementing downsampled airborne imagery with Large Eddy Simulations (LES) of methane plumes. We compared baseline methods such as thresholding and random forest classifiers, as well as state-of-the-art deep learning methods such as convolutional neural networks (CNNs) for classification and conditional adversarial networks (pix2pix) for plume segmentation.

Methane’s high heat trapping potential has made it a priority for quantification and mitigation efforts worldwide. Ground-based surveys and in-situ measurement techniques to quantify natural and fugitive methane emission sources are time-consuming, expensive, and often lead to sparse measurements. Failure to accurately quantify emissions at the point-source scale have thus led to poorly constrained emission estimates. Airborne imaging spectrometers such as the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) and the Global Airborne Observatory (GAO) have been employed to map the often stochastic and intermittent point-source emissions from a diverse set of source types including oil and gas, dairy, etc. A matched filter is applied to the methane-absorption relevant spectral features of the instrument’s radiance cube. Machine learning models are then trained to recognize methane plumes from these column-matched filter methane maps. However, current Convolutional Neural Network (CNN) models suffer from a high false-positive rate and poorly generalize to new scenes. False-positive detections are primarily due to methane absorption-mimicking surface spectroscopic features, as well as a lack of training data. To supplement the available training data, we utilize Large Eddy Simulations (LES) of methane point-source emissions to train a Convolutional Neural Network (CNN) on a plume-classification task. We observe a significant distribution shift between LES and AVIRIS-NG plumes, primarily caused by high LES plume enhancements. Through a series of image transforms verified through an adversarial approach using a discriminator network, we minimize the distribution shift between synthetic LES plumes and plumes observed by AVIRIS-NG and GAO. CNNs trained on a mixture of LES and real-world plumes, and tested on flightlines from multiple campaigns exhibit an error reduction compared to previous models. The reduction in false-positive plume detections demonstrates that supplementing the limited training data of real methane plumes with LES provides an avenue to make automatic detection more robust for future airborne and spaceborne missions such as SBG, EMIT, and Carbon Mapper.