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NASA NTRS2012

Miniaturized Laser Heterodyne Radiometer (LHR) for Measurements of Greenhouse Gases in the Atmospheric Column

This passive laser heterodyne radiometer (LHR) instrument simultaneously measures multiple trace gases in the atmospheric column including carbon dioxide (CO2) and methane (CH4), and resolves their concentrations at different altitudes. This instrument has been designed to operate in tandem with the passive aerosol sensor currently used in AERONET (an established network of more than 450 ground aerosol monitoring instruments worldwide). Because aerosols induce a radiative effect that influences terrestrial carbon exchange, simultaneous detection of aerosols with these key carbon cycle gases offers a uniquely comprehensive measurement approach. Laser heterodyne radiometry is a technique for detecting weak signals that was adapted from radio receiver technology. In a radio receiver, a weak input signal from a radio antenna is mixed with a stronger local oscillator signal. The mixed signal (beat note, or intermediate frequency) has a frequency equal to the difference between the input signal and the local oscillator. The intermediate frequency is amplified and sent to a detector that extracts the audio from the signal. In the LHR instrument described here, sunlight that has undergone absorption by the trace gas is mixed with laser light at a frequency matched to a trace gas absorption feature in the infrared (IR). Mixing results in a beat signal in the RF (radio frequency) region that can be related to the atmospheric concentration. For a one-second integration, the estimated column sensitivities are 0.1 ppmv for CO2, and <1 ppbv for CH4. In addition to producing a standalone ground measurement product, this instrument could be used to calibrate/validate four Earth observing missions: ASCENDS (Active Sensing of CO2 Emissions over Nights, Days, and Seasons), OCO-2 (Orbiting Carbon Observatory), OCO-3, and GOSAT (Greenhouse gases Observational SATellite). The only network that currently measures CO2 and CH4 in the atmospheric column is TCCON (Total Carbon Column Observing Network), and only two of its 16 operational sites are in the United States. TCCON data is used for validation of GOSAT data, and will be used for OCO-2 validation. While these Fourier-transform spectrometers (FTS) can measure the largest range of trace gases, the network is severely limited due to the high cost and extreme size of these instruments (these occupy small buildings and require personnel for operation). The LHR/AERONET instrument offers a significantly smaller (carry-on luggage size) autonomous instrument that can be incorporated into AERONET s much larger (450 instruments) global network.

Steel, Emily↗
NASA NTRS2012

NASA's Carbon Monitoring System Flux-Pilot Project: A Multi-Component Analysis System for Carbon-Cycle Research and Monitoring

The importance of greenhouse gas increases for climate motivates NASA s observing strategy for CO2 from space, including the forthcoming Orbiting Carbon Observatory (OCO-2) mission. Carbon cycle monitoring, including attribution of atmospheric concentrations to regional emissions and uptake, requires a robust modeling and analysis infrastructure to optimally extract information from the observations. NASA's Carbon-Monitoring System Flux-Pilot Project (FPP) is a prototype for such analysis, combining a set of unique tools to facilitate analysis of atmospheric CO2 along with fluxes between the atmosphere and the terrestrial biosphere or ocean. NASA's analysis system is unique, in that it combines information and expertise from the land, oceanic, and atmospheric branches of the carbon cycle and includes some estimates of uncertainty. Numerous existing space-based missions provide information of relevance to the carbon cycle. This study describes the components of the FPP framework, assessing the realism of computed fluxes, thus providing the basis for research and monitoring applications. Fluxes are computed using data-constrained terrestrial biosphere models and physical ocean models, driven by atmospheric observations and assimilating ocean-color information. Use of two estimates provides a measure of uncertainty in the fluxes. Along with inventories of other emissions, these data-derived fluxes are used in transport models to assess their consistency with atmospheric CO2 observations. Closure is achieved by using a four-dimensional data assimilation (inverse) approach that adjusts the terrestrial biosphere fluxes to make them consistent with the atmospheric CO2 observations. Results will be shown, illustrating the year-to-year variations in land biospheric and oceanic fluxes computed in the FPP. The signals of these surface-flux variations on atmospheric CO2 will be isolated using forward modeling tools, which also incorporate estimates of transport error. The results will be discussed in the context of interannual variability of observed atmospheric CO2 distributions.

Pawson, S.↗
NASA NTRS2013

Scatter-Reducing Sounding Filtration Using a Genetic Algorithm and Mean Monthly Standard Deviation

Retrieval algorithms like that used by the Orbiting Carbon Observatory (OCO)-2 mission generate massive quantities of data of varying quality and reliability. A computationally efficient, simple method of labeling problematic datapoints or predicting soundings that will fail is required for basic operation, given that only 6% of the retrieved data may be operationally processed. This method automatically obtains a filter designed to reduce scatter based on a small number of input features. Most machine-learning filter construction algorithms attempt to predict error in the CO2 value. By using a surrogate goal of Mean Monthly STDEV, the goal is to reduce the retrieved CO2 scatter rather than solving the harder problem of reducing CO2 error. This lends itself to improved interpretability and performance. This software reduces the scatter of retrieved CO2 values globally based on a minimum number of input features. It can be used as a prefilter to reduce the number of soundings requested, or as a post-filter to label data quality. The use of the MMS (Mean Monthly Standard deviation) provides a much cleaner, clearer filter than the standard ABS(CO2-truth) metrics previously employed by competitor methods. The software's main strength lies in a clearer (i.e., fewer features required) filter that more efficiently reduces scatter in retrieved CO2 rather than focusing on the more complex (and easily removed) bias issues.

Mandrake, Lukas↗
NASA NTRS2015

Probes Measure Gases for Environmental Research

NASA's Orbiting Carbon Observatory-2 satellite will make the first space-based measurements of carbon dioxide in Earth's atmosphere. In support of the mission, Goddard Space Flight Center will fly air missions from Wallops Flight Facility to gather finer-grained data in areas of interest. Goddard started working with Blacksburg, Virginia-based Aeroprobe Corporation through the SBIR program in 2008 to develop sensors for such flights, and the company has since commercialized the resulting product.

Source record↗
NASA NTRS2011

A Panchromatic Imaging Fourier Transform Spectrometer for the NASA Geostationary Coastal and Air Pollution Events Mission

This paper summarizes the design and development of the Panchromatic Imaging Fourier Transform Spectrometer (PanFTS) for the NASA Geostationary Coastal and Air Pollution Events (GEO-CAPE) Mission. The PanFTS instrument will advance the understanding of the global climate and atmospheric chemistry by measuring spectrally resolved outgoing thermal and reflected solar radiation. With continuous spectral coverage from the near-ultraviolet through the thermal infrared, this instrument is designed to measure pollutants, greenhouse gases, and aerosols as called for by the U.S. National Research Council Decadal Survey; Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond1. The PanFTS instrument is a hybrid instrument based on spectrometers like the Tropospheric Emissions Spectrometer (TES) that measures thermal emission, and those like the Orbiting Carbon Observatory (OCO), and the Ozone Monitoring Instrument (OMI) that measure scattered solar radiation. Simultaneous measurements over the broad spectral range from IR to UV is accomplished by a two sided interferometer with separate optical trains and detectors for the ultraviolet-visible and infrared spectral domains. This allows each side of the instrument to be independently optimized for its respective spectral domain. The overall interferometer design is compact because the two sides share a single high precision cryogenic optical path difference mechanism (OPDM) and metrology laser as well as a number of other instrument systems including the line-of-sight pointing mirror, the data management system, thermal control system, electrical system, and the mechanical structure. The PanFTS breadboard instrument has been tested in the laboratory and demonstrated the basic functionality for simultaneous measurements in the visible and infrared. It is set to begin operations in the field at the California Laboratory for Atmospheric Remote Sensing (CLARS) observatory on Mt. Wilson measuring the atmospheric chemistry across the Los Angeles basin. Development has begun on a flight size PanFTS engineering model (EM) that addresses all critical scaling issues and demonstrates operation over the full spectral range of the flight instrument which will show the PanFTS instrument design is mature.

Imaging spectrometer(↗
NASA NTRS2016

B33C-0612: Evaluation of Simulated Biospheric Carbon Dioxide Fluxes and Atmospheric Concentrations Using Global in Situ Observations

Atmospheric mixing ratios of carbon dioxide (CO2) are largely controlled by anthropogenic emission sources and biospheric sources/sinks. Global biospheric fluxes of CO2 are controlled by complex processes facilitating the exchange of carbon between terrestrial ecosystems and the atmosphere. These processes which play a key role in these terrestrial ecosystem-atmosphere carbon exchanges are currently not fully understood, resulting in large uncertainties in the quantification of biospheric CO2 fluxes. Current models with these inherent deficiencies have difficulties simulating the global carbon cycle with high accuracy. We are developing a new modeling platform, GEOS-Chem-CASA by integrating the year-specific NASA-CASA (National Aeronautics and Space Administration - Carnegie Ames Stanford Approach) biosphere model with the GEOS-Chem (Goddard Earth Observation System-Chemistry) chemical transport model to improve the simulation of atmosphere-terrestrial ecosystem carbon exchange. We use NASA-CASA to explicitly represent the exchange of CO2 between terrestrial ecosystem and atmosphere by replacing the baseline GEOS-Chem land net CO2 flux and forest biomass burning CO2 emissions. We will present the estimation and evaluation of these "bottom-up" land CO2 fluxes, simulated atmospheric mixing ratios, and forest disturbance changes over the last decade. In addition, we will present our initial comparison of atmospheric column-mean dry air mole fraction of CO2 predicted by the model and those retrieved from NASA's OCO-2 (Orbiting Carbon Observatory-2) satellite instrument and model-predicted surface CO2 mixing ratios with global in situ observations. This evaluation is the first step necessary for our future work planned to constrain the estimates of biospheric carbon fluxes through "top-down" inverse modeling, which will improve our understanding of the processes controlling atmosphere-terrestrial ecosystem greenhouse gas exchanges, especially over regions which lack in situ observations.

in situ observations↗
NASA NTRS2016

Comparison of Sun-Induced Chlorophyll Fluorescence Estimates Obtained from Four Portable Field Spectroradiometers

Remote Sensing of Sun-Induced Chlorophyll Fluorescence (SIF) is a research field of growing interest because it offers the potential to quantify actual photosynthesis and to monitor plant status. New satellite missions from the European Space Agency, such as the Earth Explorer 8 FLuorescence EXplorer (FLEX) mission-scheduled to launch in 2022 and aiming at SIF mapping-and from the National Aeronautics and Space Administration (NASA) such as the Orbiting Carbon Observatory-2 (OCO-2) sampling mission launched in July 2014, provide the capability to estimate SIF from space. The detection of the SIF signal from airborne and satellite platform is difficult and reliable ground level data are needed for calibration/validation. Several commercially available spectroradiometers are currently used to retrieve SIF in the field. This study presents a comparison exercise for evaluating the capability of four spectroradiometers to retrieve SIF. The results show that an accurate far-red SIF estimation can be achieved using spectroradiometers with an ultrafine resolution (less than 1 nm), while the red SIF estimation requires even higher spectral resolution (less than 0.5 nm). Moreover, it is shown that the Signal to Noise Ratio (SNR) plays a significant role in the precision of the far-red SIF measurements.

Julitta, Tommaso↗
NASA NTRS2017

The 2015-2016 El Nino and the Response of the Carbon Cycle: Findings from NASA's OCO-2 Mission

The El Nino Southern Oscillation (ENSO) is the most important mode of tropical climate variability on interannual to decadal time scales. Correlations between atmospheric CO2 growth rate and ENSO activity are relatively well known but the magnitude of this correlation, the contribution from tropical marine vs. terrestrial flux components, and the causal mechanisms, are poorly constrained in space and time. The launch of NASA's Orbiting Carbon Observatory-2 (OCO-2) mission in July 2014 was rather timely given the development of strong ENSO conditions over the tropical Pacific Ocean in 2015-2016. In this presentation, we will discuss how the high-density observations from OCO-2 provided us with a novel dataset to resolve the linkages between El Nino and atmospheric CO2. Along with information from in situ observations of ÎpCO2 from NOAA's Tropical Atmosphere Ocean (TAO) project and atmospheric CO2 from the Scripps CO2 Program, and other remote-sensing missions, we are able to piece together the time dependent response of atmospheric CO2 concentrations over the Tropics. Our findings confirm the hypothesis from studies following the 1997-1998 El Nino event that an early reduction in CO2 outgassing from the tropical Pacific Ocean is later reversed by enhanced net CO2 emissions from the terrestrial biosphere. This implies that a component of the interannual variability (IAV) in the growth rate of atmospheric CO2, which has typically been used to constrain the climate sensitivity of tropical land carbon fluxes, is strongly influenced and modified by ocean fluxes during the early phase of the ENSO event. Our analyses shed further light on the understanding of the marine vs. terrestrial partitioning of tropical carbon fluxes during El Nino events, their relative contributions to the global atmospheric CO2 growth rate, and provide clues about the sensitivity of the carbon cycle to climate forcing on interannual time scales.

CO2↗
NASA NTRS2018

The 2015-2016 El Nino and the Response of the Carbon Cycle: Findings from NASA's OCO-2 Mission

The El Nino Southern Oscillation (ENSO) is the most important mode of tropical climate variability on interannual to decadal time scales. Correlations between atmospheric CO2 growth rate and ENSO activity are relatively well known but the magnitude of this correlation, the contribution from tropical marine vs. terrestrial flux components, and the causal mechanisms, are poorly constrained in space and time. The launch of NASA's Orbiting Carbon Observatory-2 (OCO-2) mission in July 2014 was rather timely given the development of strong ENSO conditions over the tropical Pacific Ocean in 2015-2016. In this presentation, we will discuss how the high-density observations from OCO-2 provided us with a novel dataset to resolve the linkages between El Nino and atmospheric CO2. Along with information from in situ observations of CO2 from NOAA's Tropical Atmosphere Ocean (TAO) project and atmospheric CO2 from the Scripps CO2 Program, and other remote-sensing missions, we are able to piece together the time dependent response of atmospheric CO2 concentrations over the Tropics. Our findings confirm the hypothesis from studies following the 1997-1998 El Nino event that an early reduction in CO2 outgassing from the tropical Pacific Ocean is later reversed by enhanced net CO2 emissions from the terrestrial biosphere. This implies that a component of the interannual variability (IAV) in the growth rate of atmospheric CO2, which has typically been used to constrain the climate sensitivity of tropical land carbon fluxes, is strongly influenced and modified by ocean fluxes during the early phase of the ENSO event. Our analyses shed further light on the understanding of the marine vs. terrestrial partitioning of tropical carbon fluxes during El Nino events, their relative contributions to the global atmospheric CO2 growth rate, and provide clues about the sensitivity of the carbon cycle to climate forcing on interannual time scales.

OCO-2↗
NASA NTRS2018

Joint Observation Estimates of XCO2

Localized, correlated biases exist in OCO-2 (Orbiting Carbon Observatory-2) data. These biases do not improve with averaging. These have been noted by O'Dell (various OCO-2 telecons), Worden (2017), Kulawik (2018). We propose to utilize information from adjacent observations to reduce this bias. This works on the assumption that grouping the observations allows the system to find a better, global minimum. Pooling adjacent observations could allow more accurate characterization of interferents (e.g. retrieving additional albedo parameters), reducing entanglement with CO2.

Kulawik, Susan S.↗
NASA NTRS2018

Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS): Final Report of the ASCENDS Ad Hoc Science Definition Team

Improved remote sensing observations of atmospheric carbon dioxide (CO2) are critically needed to quantify, monitor, and understand the Earth's carbon cycle and its evolution in a changing climate. The processes governing ocean and terrestrial carbon uptake remain poorly understood,especially in dynamic regions with large carbon stocks and strong vulnerability to climate change,for example, the tropical land biosphere, the northern hemisphere high latitudes, and the Southern Ocean. Because the passive spectrometers used by GOSAT (Greenhouse gases Observing SATellite) and OCO-2 (Orbiting Carbon Observatory-2) require sunlit and cloud-free conditions,current observations over these regions remain infrequent and are subject to biases. These short comings limit our ability to understand and predict the processes controlling the carbon cycle on regional to global scales.In contrast, active CO2 remote-sensing techniques allow accurate measurements to be taken day and night, over ocean and land surfaces, in the presence of thin or scattered clouds, and at all times of year. Because of these benefits, the National Research Council recommended the National Aeronautics and Space Administration (NASA) Active Sensing of CO2 Emissions over Nights,Days, and Seasons (ASCENDS) mission in the 2007 report Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond. The ability of ASCENDS to collect low-bias observations in these key regions is expected to address important gaps in our knowledge of the contemporary carbon cycle.The ASCENDS ad hoc Science Definition Team (SDT), comprised of carbon cycle modeling and active remote sensing instrument teams throughout the United States (US), worked to develop the mission's requirements and advance its readiness from 2008 through 2018. Numerous scientific investigations were carried out to identify the benefit and feasibility of active CO2 remote sensing measurements for improving our understanding of CO2 sources and sinks. This report summarizes their findings and recommendations based on mission modeling studies, analysis of ancillary meteorological data products, development and demonstration of candidate technologies, anddesign studies of the ASCENDS mission concept.

Kawa, S. Randolph↗
NASA NTRS2018

A Global Spatially Contiguous Solar-Induced Fluorescence (CSIF) Dataset Using Neural Networks

Satellite-retrieved solar-induced chlorophyll fluorescence (SIF) has shown great potential to monitor the photosynthetic activity of terrestrial ecosystems. However, several issues, including low spatial and temporal resolution of the gridded datasets and high uncertainty of the individual retrievals, limit the applications of SIF. In addition, inconsistency in measurement footprints also hinders the direct comparison between gross primary production (GPP) from eddy covariance (EC) flux towers and satellite-retrieved SIF. In this study, by training a neural network (NN) with surface reflectance from the MODerate-resolution Imaging Spectroradiometer (MODIS) and SIF from Orbiting Carbon Observatory-2 (OCO-2), we generated two global spatially contiguous SIF (CSIF) datasets at moderate spatiotemporal (0.05° 4-day) resolutions during the MODIS era, one for clear-sky conditions (2000–2017) and the other one in all-sky conditions (2000–2016). The clear-sky instantaneous CSIF (CSIF(sub clear-inst)) shows high accuracy against the clear-sky OCO-2 SIF and little bias across biome types. The all-sky daily average CSIF (CSIF(sub all-daily)) dataset exhibits strong spatial, seasonal and interannual dynamics that are consistent with daily SIF from OCO-2 and the Global Ozone Monitoring Experiment-2 (GOME-2). An increasing trend (0.39 %) of annual average CSIFall-daily is also found, confirming the greening of Earth in most regions. Since the difference between satellite-observed SIF and CSIF is mostly caused by the environmental down-regulation on SIF(sub yield), the ratio between OCO-2 SIF and CSIF(sub clear-inst) can be an effective indicator of drought stress that is more sensitive than the normalized difference vegetation index and enhanced vegetation index. By comparing CSIF(sub all-daily) with GPP estimates from 40 EC flux towers across the globe, we find a large cross-site variation (c.v. = 0.36) of the GPP–SIF relationship with the highest regression slopes for evergreen needleleaf forest. However, the cross-biome variation is relatively limited (c.v. = 0.15). These two contiguous SIF datasets and the derived GPP–SIF relationship enable a better understanding of the spatial and temporal variations of the GPP across biomes and climate.

Zhang, Yao↗
NASA NTRS2018

Southern California Megacity CO2, CH4, and CO Flux Estimates Using Ground- and Space-Based Remote Sensing and a Lagrangian Model

We estimate the overall CO2, CH4, and CO flux from the South Coast Air Basin using an inversion that couples Total Carbon Column Observing Network (TCCON) and Orbiting Carbon Observatory-2 (OCO-2) observations, with the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model and the Open-source Data Inventory for Anthropogenic CO2 (ODIAC). Using TCCON data we estimate the direct net CO2 flux from the So-CAB to be 104±26 Tg CO2 yr(exp -1) for the study period of July 2013–August 2016. We obtain a slightly higher estimate of 120±30 Tg CO2 yr(exp -1) using OCO-2 data. These CO2 emission estimates are on the low end of previous work. Our net CH4 (360±90 Gg CH4 y(exp -1)) flux estimate is in agreement with central values from previous top-down studies going back to 2010 (342–440 Gg CH4 yr(exp -1)). CO emissions are estimated at 487±122 Gg CO yr(exp -1), much lower than previous top-down estimates (1440 Gg CO yr(exp -1)). Given the decreasing emissions of CO, this finding is not unexpected. We perform sensitivity tests to estimate how much errors in the prior, errors in the covariance, different inversion schemes, or a coarser dynamical model influence the emission estimates. Overall, the uncertainty is estimated to be 25%, with the largest contribution from the dynamical model. Lessons learned here may help in future inversions of satellite data over urban areas.

Total Carbon Column Observing Network (TCCON)↗
NASA NTRS2021

Emerging satellite observations for diurnal cycling of ecosystem processes

Diurnal cycling of plant carbon uptake and water use, and their responses to water and heat stresses, provide direct insight into assessing ecosystem productivity, agricultural production and management practices, carbon and water cycles, and feedbacks to the climate. Temperature, light, atmospheric water demand, soil moisture and leaf water potential vary over the course of the day, leading to diurnal variations in stomatal conductance, photosynthesis and transpiration. Earth observations from polar-orbiting satellites are incapable of studying these diurnal variations. Here, we review the emerging satellite observations that have the potential for studying how plant functioning and ecosystem processes vary over the course of the diurnal cycle. The recently launched ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) and Orbiting Carbon Observatory-3 (OCO-3) provide land surface temperature, evapotranspiration (ET), gross primary production (GPP) and solar-induced chlorophyll fluorescence data at different times of day. New generation operational geostationary satellites such as Himawari-8 and the GOES-R series can provide continuous, high-frequency data of land surface temperature, solar radiation, GPP and ET. Future satellite missions such as GeoCarb, TEMPO and Sentinel-4 are also planned to have diurnal sampling capability of solar-induced chlorophyll fluorescence. We explore the unprecedented opportunities for characterizing and understanding how GPP, ET and water use efficiency vary over the course of the day in response to temperature and water stresses, and management practices. We also envision that these emerging observations will revolutionize studies of plant functioning and ecosystem processes in the context of climate change and that these observations and findings can inform agricultural and forest management and lead to improvements in Earth system models and climate projections.

Plant functioning↗
NASA NTRS2020

Establishing Launch Readiness of NASA ISS Payload OCO-3

The Orbiting Carbon Observatory – 3 (OCO-3) is a NASA Earth-monitoring payload, designed and built by the Jet Propulsion Laboratory (JPL) to perform space-based observation of variations of global carbon dioxide (CO2) by sampling its emission sources and gradients from the unique vantage point of the International Space Station (ISS). This paper describes the development and results of the end-to-end verification, validation and calibration activities that were performed to meet OCO-3's science measurement, functional and performance requirements prior to launch. The pre-launch activities encompassed a suite of tests and analyses that were performed after payload integration and during launch vehicle integration that helped the OCO-3 project demonstrate its launch and mission readiness in terms of ISS compatibility, ground-based instrument calibrations, flight dynamics and environmental integrity and pointing knowledge and accuracy.

Srivastava, Priyanka↗
NASA NTRS2021

Covid-19 Impact Monitoring for Climate Environment (Greenhouse Gases)

To track atmospheric CO2 changes resulting from the lockdowns, observations collected by the NASA Orbiting Carbon Observatory-2 (OCO-2) satellite and Japan’s Greenhouse gases Observing SATellite (GOSAT) in 2020 were compared to results collected in previous years. The OCO-2 results were used to search for changes on regional scales over the globe. Targeted observations from GOSAT were used to track changes in large urban areas, such as Beijing and Tokyo. Both types of observations yielded key insights into the CO2 changes accompanying the economic disruptions caused by the COVID-19 lockdowns. ESA, NASA and JAXA developed the dashboard jointly to monitor the climate impacts of COVID-19.

greenhouse gases↗
NASA NTRS2023

Anomalous Net Biome Exchange Over Amazonian Rainforests Induced by the 2015/16 El Niño: Temperature-dominated Total Flux but Soil Dryness-Shaped Spatial Pattern

The magnitude and spatial pattern of anomalous net biome exchange (NBE) induced by the 2015/16 El Niño over Amazonian rainforests remain uncertain. We here investigated them using multi-model posterior NBE products in the Orbiting Carbon Observatory-2 (OCO-2) version 10 modeling intercomparison project. Results suggest that relative to the annual NBE average in 2017/18, larger anomalous carbon release occurred over the eastern and northern Amazonian rainforests in 2015/16, with a total flux of approximately 0.4 PgC yr−1 after assimilating satellite-observed column CO2 concentrations (XCO2) over land. We further find that this anomalous spatial pattern was predominantly determined by soil dryness, while the total positive NBE anomaly was dominated by higher temperature with its contribution of approximately 68～70%. We believe that atmospheric inversions assimilating more satellite-observed XCO2 in future can provide us more comprehensive understanding how Amazonian rainforests cope with the abiotic stresses induced by El Niño events.

Jun Wang↗
NASA NTRS2011

Space-Time Data fusion for Remote Sensing Applications

NASA has been collecting massive amounts of remote sensing data about Earth's systems for more than a decade. Missions are selected to be complementary in quantities measured, retrieval techniques, and sampling characteristics, so these datasets are highly synergistic. To fully exploit this, a rigorous methodology for combining data with heterogeneous sampling characteristics is required. For scientific purposes, the methodology must also provide quantitative measures of uncertainty that propagate input-data uncertainty appropriately. We view this as a statistical inference problem. The true but notdirectly- observed quantities form a vector-valued field continuous in space and time. Our goal is to infer those true values or some function of them, and provide to uncertainty quantification for those inferences. We use a spatiotemporal statistical model that relates the unobserved quantities of interest at point-level to the spatially aggregated, observed data. We describe and illustrate our method using CO2 data from two NASA data sets.

carbon dioxide↗