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NASA NTRS2021

Groundwater Recharge Estimated by Land Surface Models: An Evaluation in the Conterminous U.S.

Estimating diffuse recharge of precipitation is fundamental to assessing groundwater sustainability. Diffuse recharge is also the process through which climate and climate change directly affect groundwater. In this study, we evaluated diffuse recharge over the conterminous U.S. simulated by a suite of land surface models (LSMs) that were forced using a common set of meteorological input data. Simulated annual recharge exhibited spatial patterns that were similar among the LSMs, with the highest values in the eastern U.S. and Pacific Northwest. However, the magnitudes of annual recharge varied significantly among the models and were associated with differences in simulated ET, runoff and snow. Evaluation against two independent datasets did not answer the question of whether the ensemble mean performs the best, due to inconsistency between those datasets. The amplitude and timing of seasonal maximum recharge differed among the models, influenced strongly by model physics governing deep soil moisture drainage rates and, in cold regions, snowmelt. Evaluation using in situ soil moisture observations suggested that true recharge peaks 1-3 months later than simulated recharge, indicating systematic biases in simulating deep soil moisture. However, recharge from lateral flows and through preferential flows cannot be inferred from soil moisture data, and the seasonal cycle of simulated groundwater storage actually compared well with in situ groundwater observations. Long-term trends in recharge were not consistently correlated with either precipitation trends or temperature trends. This study highlights the need to employ dynamic flow models in LSMs, among other improvements, to enable more accurate simulation of recharge.

snowmelt↗

NASA NTRS2021

Extreme Lake-Effect Snow from a GPM Microwave Imager Perspective: Observational Analysis and Precipitation Retrieval Evaluation

This study focuses on the ability of the Global Precipitation Measurement (GPM) passive microwave sensors to detect and provide quantitative precipitation estimates (QPE) for extreme lake-effect snowfall events over the United States lower Great Lakes region. GPM Microwave Imager (GMI) high frequency channels can clearly detect intense shallow convective snowfall events. However, GMI Goddard PROfiling (GPROF) QPE retrievals produce inconsistent results when compared against the Multi-Radar/Multi-Sensor (MRMS) ground-based radar reference dataset. While GPROF retrievals adequately capture intense snowfall rates and spatial patterns of one event, GPROF systematically underestimates intense snowfall rates in another event. Furthermore, GPROF produces abundant light snowfall rates that do not conform with MRMS observations. Ad-hoc precipitation rate thresholds are suggested to partially mitigate GPROF’s overproduction of light snowfall rates. The sensitivity and retrieval efficiency of GPROF to key parameters (2-meter temperature, total precipitable water, and background surface type) used to constrain the GPROF a-priori retrieval database are investigated. Results demonstrate that typical lake-effect snow environmental and surface conditions, especially coastal surfaces, are underpopulated in the database and adversely affect GPROF retrievals. For the two presented case studies, using snow cover a-priori database in the locations of originally deemed as coastline improves retrieval. This study suggests that it is particularly important to have more accurate GPROF surface classifications and better representativeness of the a-priori databases to improve intense lake-effect snow detection and retrieval performance.

Lisa Milani↗

NASA NTRS2020

Arctic Snow Depth and Sea Ice Thickness From ICESat‐2 and CryoSat‐2 Freeboards: A First Examination

We present a first examination of Arctic sea ice snow depth estimates from differencing satellite lidar (ICESat‐2) and radar (CryoSat‐2) freeboards. These estimates cover the period between 14 October 2018 and the end of April 2019. Snow depth is related to freeboard differences by the refractive index/bulk density of the snow layer—the only free parameter in the approach. Area‐averaged snow depth ranges from 9 cm (on first‐year ice: 5 cm, multiyear ice: 14 cm) in late October to 19 cm (first‐year ice: 17 cm, multiyear ice: 27 cm) in April; on average, this snow is thinner over FYI. Spatial patterns and gradients of snow depth estimates compare well with reconstructions using snowfall from ERA‐Interim and ERA5, although snowfall from ERA5 is systematically higher. For all months, the results suggest that ~50% of the total freeboard is comprised of snow. Retrievals are within a few centimeters of snow depth data acquired by Operation IceBridge in April 2019. Sources of uncertainties associated with this freeboard‐differencing approach are discussed. Further, sea ice thicknesses calculated using the retrieved snow depth and a modified climatology are contrasted. Comparatively, the snow depth and calculated ice thickness using a modified climatology are higher by ~5 cm and 0.33 m, although these differences are not uniform throughout the season. Snow accumulation was slower between October and December but increased between December and January, unlike the modified climatology, which exhibited a monotonic accumulation for all months. Future opportunities for assessment and improvement of these estimates are discussed.

Arctic Ocean↗

NASA NTRS2020

An AeroCom–AeroSat study: intercomparison of satellite AOD datasets for aerosol model evaluation

To better understand and characterize current uncertainties in the important observational constraint of climate models of aerosol optical depth (AOD), we evaluate and intercompare 14 satellite products, representing nine different retrieval algorithm families using observations from five different sensors on six different platforms. The satellite products (super-observations consisting of 1°×1° daily aggregated retrievals drawn from the years 2006, 2008 and 2010) are evaluated with AErosol RObotic NETwork (AERONET) and Maritime Aerosol Network (MAN) data. Results show that different products exhibit different regionally varying biases (both under- and overestimates) that may reach ±50 %, although a typical bias would be 15 %–25 % (depending on the product). In addition to these biases, the products exhibit random errors that can be 1.6 to 3 times as large. Most products show similar performance, although there are a few exceptions with either larger biases or larger random errors. The intercomparison of satellite products extends this analysis and provides spatial context to it. In particular, we show that aggregated satellite AOD agrees much better than the spatial coverage (often driven by cloud masks) within the 1°×1° grid cells. Up to ∼50 % of the difference between satellite AOD is attributed to cloud contamination. The diversity in AOD products shows clear spatial patterns and varies from 10 % (parts of the ocean) to 100 % (central Asia and Australia). More importantly, we show that the diversity may be used as an indication of AOD uncertainty, at least for the better performing products. This provides modellers with a global map of expected AOD uncertainty in satellite products, allows assessment of products away from AERONET sites, can provide guidance for future AERONET locations and offers suggestions for product improvements. We account for statistical and sampling noise in our analyses. Sampling noise, variations due to the evaluation of different subsets of the data, causes important changes in error metrics. The consequences of this noise term for product evaluation are discussed.

Aerosol↗

NASA NTRS2019

Boreal canopy surfaces from spaceborne stereogrammetry

Surface elevation estimates from high resolution spaceborne image (HRSI) stereogrammetry are used to examine fine-scaled structure of boreal forest canopies. These data can depict detailed spatial patterns of vertical forest structure at remote sites across the circumpolar domain where these estimates would otherwise be unavailable. This work examines where these estimates are most effective at describing vertical forest structure to explain which canopy surfaces they represent. We evaluated the variation in canopy surface estimates captured from four general types of HRSI digital surface models (DSMs) across the full range of boreal canopy cover. These DSMs, classified into 4 types by grouping them according to the acquisition's (1) sun elevation angle (low or high) and (2) seasonality-driven ground surface condition (snow presence/absence), vary with acquisition characteristics and the details of this variation continues to be studied. We explored some of this variation by comparing the distributions of differences in boreal canopy percentile heights derived from reference small footprint lidar in Tanana Valley, Alaska with canopy surface elevations derived from these 4 types of HRSI DSMs. We examined how canopy surface estimates from HRSI DSMs differ according to acquisition characteristics and canopy cover, and ultimately which canopy surfaces are represented in these DSMs. Our results help clarify which boreal canopy surfaces are representative of those captured with HRSI DSMs. They show that in the Tanana Valley (1) DSMs grouped by sun elevation angle and ground surface condition provide different surface estimates of boreal canopies; (2) the two DSM types that appear to most differently capture boreal forest canopy surfaces are DSMs from snow-free images acquired at sun elevation angles <30° (Low sun elev. & snow-free) and those with snow-cover at sun elevation angles ≥30° (High sun elev. & snow-free); (3) DSMs with snow most often do not capture upper canopy surfaces; (4) the “Low sun elev. & snow-free” DSMs resolve surfaces that are most representative of upper canopy surfaces (dense forests >60% cover, 70th–80th percentile heights); and (5) in the most dense forests (>80% cover) where canopy gaps are least likely to bias downward the average surface estimates, the snow-free DSM types are representative of 70th - 80th percentile heights (“Low sun elev. & snow-free”) and 60th–70th percentile heights (“High sun elev. & snow-free”). The combination of horizontal structure (canopy cover) and acquisition characteristics affect the boreal vertical structure (canopy surface height) estimates from spaceborne stereogrammetry. These effects should be considered when analyzing products derived from HRSI DSMs, and as part of a comprehensive approach to spaceborne remote sensing of circumpolar boreal forests.

forest structure↗

NASA NTRS2019

Inter-annual variation of tropical cyclones simulated by GEOS-5 AGCM with modified convection scheme

The Goddard Earth Observing System version 5 (GEOS-5) global climate model with a 50-km horizontal resolution is forced by observed sea surface temperature (SST) to examine the fidelity of the seasonal-mean and inter-annual variation of tropical cyclones (TCs) in the western North Pacific (WNP) and the North Atlantic (NATL). The standard Relaxed Arakawa Schubert (RAS) deep convection scheme is modified to improve the representation of TCs, where the scheme implements a stochastic limit of the cumulus entrainment rate. The modification drives mid- and upper-tropospheric cooling and low- to mid-tropospheric drying in the background state, which tends to increase atmospheric instability. This enables the model to increase convective variability on an intra-seasonal timescale and improve the simulation of intense storms. Five-member ensemble runs with the modified RAS scheme for 12 years (1998–2009) exhibit realistic spatial distributions in the climatological-mean TC development area and their pathways over WNP and NATL. The GCM is able to reproduce the inter-annual variation of accumulated cyclone energy (ACE) by prescribing yearly varying observed SST even though the individual TC intensity is still underpredicted. A sensitivity of TC activity to El Niño–Southern Oscillation (ENSO) phase is also reproduced realistically over WNP in terms of the spatial pattern changes in the main development region and TC pathways. However, the model exhibits a notable deficiency in NATL in reproducing the observed inter-annual variation of TC activity and the sensitivity to the ENSO.

accumulated cyclone energy↗

NASA NTRS2020

Forecasting Global Fire Emissions on Subseasonal to Seasonal (S2S) Time Scales

Fire emissions of gases and aerosols alter atmospheric composition and have substantial impacts on climate, ecosystem function, and human health. Warming climate and human expansion in fire‐prone landscapes exacerbate fire impacts and call for more effective management tools. Here we developed a global fire forecasting system that predicts monthly emissions using past fire data and climate variables for lead times of 1 to 6 months. Using monthly fire emissions from the Global Fire Emissions Database (GFED) as the prediction target, we fit a statistical time series model, the Autoregressive Integrated Moving Average model with eXogenous variables (ARIMAX), in over 1,300 different fire regions. Optimized parameters were then used to forecast future emissions. The forecast system took into account information about region‐specific seasonality, long‐term trends, recent fire observations, and climate drivers representing both large‐scale climate variability and local fire weather. We cross‐validated the forecast skill of the system with different combinations of predictors and forecast lead times. The reference model, which combined endogenous and exogenous predictors with a 1 month forecast lead time, explained 52% of the variability in the global fire emissions anomaly, considerably exceeding the performance of a reference model that assumed persistent emissions during the forecast period. The system also successfully resolved detailed spatial patterns of fire emissions anomalies in regions with significant fire activity. This study bridges the gap between the efforts of near‐real‐time fire forecasts and seasonal fire outlooks and represents a step toward establishing an operational global fire, smoke, and carbon cycle forecasting system.

vapor pressure↗

NASA NTRS2020

Object-Based Comparison of Data-Driven and Physics-Driven Satellite Estimates of Extreme Rainfall

The Global Precipitation Measurement (GPM) constellation of spaceborne sensors provides a variety of direct and indirect measurements of precipitation processes. Such observations can be employed to derive spatially and temporally consistent gridded precipitation estimates either via data-driven retrieval algorithms or by assimilation into physically based numerical weather models. We compare the data-driven Integrated Multisatellite Retrievals for GPM (IMERG) and the assimilation-enabled NASA-Unified Weather Research and Forecasting (NU-WRF) model against Stage IV reference precipitation for four major extreme rainfall events in the southeastern United States using an object-based analysis framework that decomposes gridded precipitation fields into storm objects. As an alternative to conventional ‘‘grid-by-grid analysis,’’ the object-based approach provides a promising way to diagnose spatial properties of storms, trace them through space and time, and connect their accuracy to storm types and input data sources. The evolution of two tropical cyclones are generally captured by IMERG and NU-WRF, while the less organized spatial patterns of two mesoscale convective systems pose challenges for both. NU-WRF rain rates are generally more accurate, while IMERG better captures storm location and shape. Both show higher skill in detecting large, intense storms compared to smaller, weaker storms. IMERG’s accuracy depends on the input microwave and infrared data sources; NU-WRF does not appear to exhibit this dependence. Findings highlight that an object-oriented view can provide deeper insights into satellite precipitation performance and that the satellite precipitation community should further explore the potential for ‘‘hybrid’’ data-driven and physics-driven estimates in order to make optimal usage of satellite observations.

extreme events↗

NASA NTRS2021

A Three-Step Semi Analytical Algorithm (3SAA) for Estimating Inherent Optical Properties Over Oceanic, Coastal, and Inland Waters From Remote Sensing Reflectance

We present a three-step inverse model (3SAA) for estimating the inherent optical properties (IOPs) of surface waters from the remote sensing reflectance spectra, Rrs(). The derived IOPs include the total (a()), phytoplankton (aphy()), and colored detrital matter (acdm()), absorption coefficients, and the total (bb()) and particulate (bbp()) backscattering coefficients. The first step uses an improved neural network approach to estimate the diffuse attenuation coefficient of downwelling irradiance from Rrs. a() and bbp() are then estimated using the LS2 model (Loisel et al., 2018), which does not require spectral assumptions on IOPs and hence can assess a() and bb() at any wavelength at which Rrs() is measured. Then, an inverse optimization algorithm is combined with an optical water class (OWC) approach to assess aphy() and acdm() from anw().The proposed model is evaluated using an in situ dataset collected in open oceanic, coastal, and inland waters. Comparisons with other standard semi-analytical algorithms (QAA and GSM), as well as match-up exercises, have also been performed. The applicability of the algorithm on OLCI observations was assessed through the analysis of global IOPs spatial patterns derived from 3SAA and GSM. The good performance of 3SAA is manifested by median absolute percentage differences (MAPD) of 13%, 23%, 34% and 34% for bbp(443), anw(443), aphy(443) and acdm(443), respectively for oceanic waters. Due to the absence of spectral constraints on IOPs in the inversion of total IOPs, and the adoption of an OWC-based approach, the performance of 3SAA is only slightly degraded in bio-optical complex inland waters.

ocean color↗

NASA NTRSDate not supplied

MONITORING ECO-HYDROLOGICAL SPRING ONSET OVER ALASKA AND NORTHERN CANADA WITH COMPLEMENTARY SATELLITE REMOTE SENSING DATA

Youngwook Kim↗

NASA NTRS2021

Remote Sensing of Wetland Area Loss and Gain in the Western Barataria Basin (Louisiana, U.S.A.) since Hurricane Katrina

The goal of this study was to analyze the recent record of Landsat satellite imagery to better understand spatial patterns and infer processes of change in marshland vegetation cover in coastal Louisiana wetlands. The study area covered subbasins of the western Barataria Basin near the Bayou Lafourche in southeastern Louisiana. Bayou Lafourche's estuaries have seen some of the highest rates of wetland conversion to open water of any coastal zone in the United States. The Landsat Normalized Difference Water Index (NDWI) was used in this study to map changes in land/water fractions at a 30-m pixel resolution between October 2005 (post-Katrina) and October 2018. Validation boat surveys were conducted in July 2020 to visually verify the presence or absence of (>1 m) emergent vegetation growing on marshlands across several subbasins, with photographs collected at more than 150 target locations along the route. Based on NDWI change, a total of 42.3 km2 was estimated to fall into the category of wetland loss since 2005. The leading subbasins for these recent wetland-to-water conversions made up most of the southwestern portion of the study area just north of Port Fourchon. The consistently low distance detected between the majority of wetland loss cells and locations of historical oil and gas wells implies that effects of well drilling and extraction have had a continuing negative impact on marshland degradation in the basin. Conversely, a total of 34 km2 was estimated to fall into the category of recent wetland gain, with much of the new vegetation growth verified to have been restored within several shoreline protection and dredging projects. It was concluded that the positive contributions made by such shoreline enhancement and marshland nourishment projects in Lafourche Parish to offset wetland losses over the past 15 years should not be underestimated.

Remote Sensing↗

NASA NTRS2022

NASA GEOS Composition Forecast Modeling System GEOS-CF v1.0: Stratospheric Composition

The NASA Goddard Earth Observing System (GEOS) Composition Forecast (GEOS-29CF) provides recent estimates and five-day forecasts of atmospheric composition to the public in near-real time. To do this, the GEOS Earth system model is coupled with the GEOS-Chem tropospheric-stratospheric unified chemistry extension (UCX) to represent composition from the surface to the top of the GEOS atmosphere (0.01 hPa). The GEOS-CF system is described, including updates made to the GEOS-Chem UCX mechanism within GEOS-CF for improved representation of stratospheric chemistry. Comparisons are made against balloon, lidar and satellite observations for stratospheric composition, including measurements of ozone (O3) and important nitrogen and chlorine species related to stratospheric O3recovery. The GEOS-CF nudges the stratospheric O3towards the GEOS Forward Processing (GEOS FP) assimilated O3product; as a result the stratospheric O3in the GEOS-CF historical estimate agrees well with observations. During abnormal dynamical and chemical environments such as the 2020 polar vortexes, the GEOS-CF O3forecasts are more realistic than GEOS FP O3forecasts because of the inclusion of the complex GEOS-Chem UCX stratospheric chemistry. Overall, the spatial patterns of the GEOS-CF simulated concentrations of stratospheric composition agree well with satellite observations. However, there are notable biases – such as low NOx and HNO3 in the polar regions and generally low HCl throughout the stratosphere – and future improvements to the chemistry mechanism and emissions are discussed. GEOS-CF is a new tool for the research community and instrument teams observing trace gases in the stratosphere and troposphere, providing near-real-time three-dimensional gridded information on atmospheric composition.

GEOS-CF↗

NASA NTRS2022

Changes in Land Use Enhance the Sensitivity of Tropical Ecosystems to Fire-Climate Extremes

The Pantanal, the largest contiguous wetland in the world with a high diversity of ecosystems and habitat for several endangered species, was impacted by record-breaking wildfires in 2020. In this study, we integrate satellite and modeling data that enable exploration of natural and human contributing factors to the unprecedented 2020 fires. We demonstrate that the fires were fueled by an exceptional multi-year drought, but dry conditions solely could not explain the spatial patterns of burning. Our analysis reveals how human-caused fires exacerbated drought effects on natural ecosystem within the Pantanal, with large burned fractions primarily over natural (52%), and low cattle density areas (44%) in 2020. The post-fire ecosystem and hydrology changes also had strong ecological effects, with vegetation productivity less than − 1.5 σ over more than 30% of the natural and conservation areas. In contrast to more managed areas, there was a clear decrease in evaporation (by ~ 9%) and an increase in runoff (by ~ 5%) over the natural areas, with long-term impacts on ecosystem recovery and fire risk. This study provides the first tropical evidence outside rainforests of the synergy between climate, land management and fires, and the associated impacts on the ecosystem and hydrology over the largest contiguous wetlands in the world.

Sujay Kumar↗

NASA NTRS2022

Low-Level Jets and the Convergence of Mars Data Assimilation Algorithms

Data assimilation is an increasingly popular technique in Mars atmospheric science, but its effect on the mean states of the underlying atmosphere models has not been thoroughly examined. The robustness of results to the choice of model and assimilation algorithm also warrants further study. We investigate these issues using two Mars general circulation models (MGCMs), with particular emphasis on zonal wind and temperature fields. When temperature retrievals from the Mars Global Surveyor Thermal Emission Spectrometer (TES) are assimilated into the U.K.-Laboratoire de M ́et ́eorologie Dynamique (UK-LMD) MGCM to create the Mars Analysis Correction Data Assimilation (MACDA) reanalysis, low-level zonal jets in the winter northern hemisphere shift equatorward and weaken relative to a free-running control simulation from the same MGCM. The Ensemble Mars Atmosphere Reanalysis System (EMARS) reanalysis, which is also based on TES temperature retrievals, also shows jet weakening (but less if any shifting) relative to a control simulation performed with the underlying Geophysical Fluid Dynamics Laboratory (GFDL) MGCM. Examining higher levels of the atmosphere, monthly mean three-dimensional temperature and zonal wind fields are in generally better agreement between the two reanalyses than between the two control simulations. In conjunction with information about the MGCMs’ physical parametrizations, intercomparisons between the various reanalyses and control simulations suggest that overall the EMARS control run is plausibly less biased (relative to the true state of the Martian atmosphere) than the MACDA control run. Implications for future observational studies are discussed. Plain Language Summary An increasingly popular way to study Martian weather and climate is to combine atmospheric temperature observations with a computer model (specifically, a global climate model). The process of combining model and observations is called “data assimilation”, and the resulting merged data set is called a “reanalysis”. One advantage of reanalyses is that they include variables (such as wind) that are not directly observed. For scientific and practical applications we want these variables to be reasonably accurate— however, it is not clear how well data assimilation algorithms compute them. Our study investigates this issue using two Mars reanalyses and two model simulations that do not assimilate temperature data. We focus on slowly-varying atmospheric phenomena (timescales from 10 Mars days to a season). Assimilating temperature data into two different global climate models changes the strength and/or spatial pattern of east-west winds at low altitudes. Furthermore, monthly mean three-dimensional temperature and east-west wind fields agree better between reanalyses than between non-assimilating model simulations. This suggests that the data assimilation process is basically successful. One non-assimilating model simulation has less realistic representations of atmospheric physical processes than the other—we argue that this plausibly gives it larger biases relative to the true state of the atmosphere.

Todd A Mooring↗

NASA NTRS2022

Future Climate Change Under SSP Emission Scenarios with GISS-E2.1

This paper presents the response to anthropogenic forcing in the GISS-E2.1 climate models for the 21st century Shared Socioeconomic Pathways emission scenarios within the Coupled Model Intercomparison Project Phase 6 (CMIP6). The experiments were performed using an updated and improved version of the NASA Goddard Institute for Space Studies (GISS) coupled general circulation model that includes two different versions for atmospheric composition: A non-interactive version (NINT) with prescribed composition and a tuned aerosol indirect effect and the One-Moment Aerosol model (OMA) version with fully interactive aerosols which includes a parameterized first indirect aerosol effect on clouds. The effective climate sensitivities are 3.0°C and 2.9°C for the NINT and OMA models, respectively. Each atmospheric version is coupled to two different ocean general circulation models: The GISS ocean model (E2.1-G) and HYCOM (E2.1-H). We describe the global mean responses for all future scenarios and spatial patterns of change for surface air temperature and precipitation for four of the marker scenarios: SSP1-2.6, SSP2-4.5, SSP4-6.0, and SSP5-8.5. By 2100, global mean warming ranges from 1.5°C to 5.2°C relative to 1,850–1,880 mean temperature. Two high-mitigation scenarios SSP1-1.9 and SSP1-2.6 limit the surface warming to below 2°C by the end of the 21st century, except for the NINT E2.1-H model that simulates 2.2°C of surface warming. For the high emission scenario SSP5-8.5, the range is 4.6–5.2°C at 2100. Due to about 15% larger effective climate sensitivity and stronger transient climate response in both NINT and OMA CMIP6 models compared to CMIP5 versions, there is a stronger warming by 2100 in the SSP emission scenarios than in the comparable Representative Concentration Pathway (RCP) scenarios in CMIP5. Changes in sea ice area are highly correlated to global mean surface air temperature anomalies and show steep declines in both hemispheres, with the largest sea ice area decreases occurring during September in the Northern Hemisphere in both E2.1-G (−1.21 × 106 km2/°C) and E2.1-H models (−0.94 × 106 km2/°C). Both coupled models project decreases in the Atlantic overturning stream function by 2100. The largest decrease of 56%–65% in the 21st century overturning stream function is produced in the warmest scenario SSP5-8.5 in the E2.1-G model, comparable to the reduction in the corresponding CMIP5 GISS-E2 RCP8.5 simulation. Both low-end scenarios SSP1-1.9 and SSP1-2.6 also simulate substantial reductions of the overturning (9%–37%) with slow recovery of about 10% by the end of the 21st century (relative to the maximum decrease at the middle of the 21st century).

climate models↗

NASA NTRS2021

Monitoring Eco-Hydrological Spring Onset Over Alaska and Northern Canada With Complementary Satellite Remote Sensing Data

More than half of the global land area undergoes seasonal freeze/thaw (FT) transitions in spring. Spatial patterns and timing of spring thawing influence eco-hydrological processes and landscape moisture availability over arctic and boreal ecosystems. The seasonal progression of spring thawing coincides with warmer temperatures, snowmelt, and a rapid increase in soil moisture, which initiates the growing season for ecosystem productivity. In this study, we utilize complementary satellite observations to determine the pattern and order of occurrence in landscape thawing, soil moisture increase, and ecosystem productivity that collectively define the eco-hydrological spring onset across Alaska and Northern Canada. Satellite data utilized include landscape FT status from SMAP and AMSR-2, OCO-2 derived solar-induced chlorophyll fluorescence (GOSIF), and gross primary production (GPP) and soil moisture from SMAP. The resulting spring onset maps showed spring thawing as the precursor to growing season onset, indicated by a rapid rise in available soil moisture and GPP. Our results indicated an average spring transition period of 3±2 (SD) weeks between initial landscape thawing and growing season onset. A rapid increase in soil moisture generally followed landscape thawing but occurred before the subsequent seasonal rise in GPP. Spring onset generally occurred earlier in boreal forest (DOY 102±14) than arctic tundra (DOY 124±22).

Derksen, Chris↗

NASA NTRS2022

Evaluating Approaches Relating Ecosystem Productivity with Desis Spectral Information

Data from the DLR Earth Sensing Imaging Spectrometer (DESIS), mounted on the International Space Station (ISS), were used to develop and test algorithms for remotely retrieving ecosystem productivity. Twenty DESIS images were used from three widely separated forested study sites representing deciduous and conifer forests. Gross primary production (GPP) values from eddy covariance flux towers at the sites were matched with DESIS spectral reflectances collected on the same days. Multiple algorithms were successful relating spectral reflectance with GPP, including: spectral vegetation indices (SVI) sensitive to chlorophyll content, SVI used in a photosynthetic light-use efficiency model framework, spectral shape characteristics through spectral derivatives and absorption feature analysis, and statistical models leading to multiband hyperspectral indices from partial least squares regression. Successful algorithms were able to achieve R2 better than 0.7 using a diverse set of observations combining data from different sites from multiple years and at multiple times during the year. The demonstrated robustness of the algorithms provides some confidence in using DESIS imagery to map spatial patterns of GPP.

K F Huemmrich↗

NASA NTRS2022

Evaluating Approaches Relating Ecosystem Productivity with DESIS Spectral Information

Gross Primary Productivity (GPP)↗