Using the space-borne NASA scatterometer (NSCAT) to determine the frozen and thawed seasons

We hypothesize that the strong sensitivity of radar backscatter to surface dielectric properties, and hence to the phase (solid or liquid) of any water near the surface should make space-borne radar observations a powerful tool for large-scale spatial monitoring of the freeze/thaw state of the land surface, and thus ecosystem growing season length. We analyzed the NASA scatterometer (NSCAT) backscatter from September 1996 to June 1997, along with temperature and snow depth observations and ecosystem modeling, for three BOREAS sites in central Canada. Because of its short wavelength (2.14 cm), NSCAT was sensitive to canopy and surface water. NSCAT had 25 km spatial resolution and approximately twice-daily temporal coverage at the BOREAS latitude. At the northern site the NSCAT signal showed strong seasonality, with backscatter around −8 dB in winter and −12 dB in early summer and fall. The NSCAT signal for the southern sites had less seasonality. At all three sites there was a strong decrease in backscatter during spring thaw (4–6 dB). At the southern deciduous site, NSCAT backscatter rose from −11 to −9.2 dB during spring leaf-out. All sites showed 1–2 dB backscatter shifts corresponding to changes in landscape water state coincident with brief midwinter thaws, snowfall, and extreme cold (Tmax\u3c−25°C). Freeze/thaw detection algorithms developed for other radar instruments gave reasonable results for the northern site but were not successful at the two southern sites. We developed a change detection algorithm based on first differences of 5-day smoothed NSCAT backscatter measurements. This algorithm had some success in identifying the arrival of freezing conditions in the autumn and the beginning of thaw in the spring. Changes in surface freeze/thaw state generally coincided with the arrival and departure of the seasonal snow cover and with simulated shifts in the directions of net carbon exchange at each of the study sites

Application of Spaceborne Synthetic Aperture Radar to Monitoring Seasonal Ecological and Hydrologic Processes in Boreal Forest

Freezehaw transitions in boreal landscapes drive critical dynamics in ecosystem and hydrologic activity. A capability for accurate, repeated, and reliable monitoring of landscape freezekhaw dynamics would improve our ability to quantify the interannual variability ofboreal hydrology and river runoff/flood dynamics and to assess the period of photosynthetic activity in boreal and arctic ecosystems, thus improving estimates of annual carbon budgets and of the interannual variability of regional carbon fluxes. Results from BOREAS experiments have indicated that the boreal forest has a net annual carbon flux near zero. A first step in assessing and monitoring year-to-year changes in the boreal carbon flux is to determine the annual variation in growing season length. Weapply imagery from the ERS spaceborne Synthetic Aperture Radars (SARs) to estimate landscape freezekhaw dynamics over selected areas of the BOREAS region of Canada. A temporal series of freeze/thaw maps are derived that provide fractional estimates of frozen and thawed landscape. The inferred landscape freezehhaw state is validated against temperature measurements obtained from a distributed temperature monitoring network and from meteorological observations. We examine the relationships of radar-estimated thaw patterns with topography and landcover. SAR-derived timing ofspring thaw is comparred with initiation of streamflow. Ecological process models are used to estimate Net Ecosystem Exchange (NEE) and Net Primary Productivity (NPP) on the landscape scale. Model results are comparred with timing and spatial distribution of freeze and thaw events. As the timing of spring thaw is a major factor influencing the net annual cabon flux, we seek to incorportate the radar-based measure of landscape freezeithaw dynamics as direct input to ecological process models to provide a capability for improved ecosystem carbon flux estimates at regional scales using spaceborne rad

Global net carbon exchange and intra-annual atmospheric CO2 concentrations predicted by an Ecosystem Process Model and Three-Dimensional Atmospheric Transport Model

A generalized terrestrial ecosystem process model, BIOME-BGC (for BIOME BioGeoChemical Cycles), was used to simulate the global fluxes of CO2 resulting from photosynthesis, autotrophic respiration, and heterotrophic respiration. Daily meteorological data for the year 1987, gridded to 1° by 1°, were used to drive the model simulations. From the maximum value of the normalized difference vegetation index (NDVI) for 1987, the leaf area index for each grid cell was computed. Global NPP was estimated to be 52 Pg C, and global Rh was estimated to be 66 Pg C. Model predictions of the stable carbon isotopic ratio 13C/12C for C3 and C4 vegetation were in accord with values published in the literature, suggesting that our computations of total net photosynthesis, and thus NPP, are more reliable than Rh. For each grid cell, daily Rh was adjusted so that the annual total was equal to annual NPP, and the resulting net carbon fluxes were used as inputs to a three-dimensional atmospheric transport model (TM2) using wind data from 1987. We compared the spatial and seasonal patterns of NPP with a diagnostic NDVI model, where NPP was derived from biweekly NDVI data and Rh was tuned to fit atmospheric CO2 observations from three northern stations. To an encouraging degree, predictions from the BIOME-BGC model agreed in phase and amplitude with observed atmospheric CO2 concentrations for 20° to 55°N, the zone in which the most complete data on ecosystem processes and meteorological input data are available. However, in the tropics and high northern latitudes, disagreements between simulated and measured CO2 concentrations indicated areas where the model could be improved. We present here a methodology by which terrestrial ecosystem models can be tested globally, not by comparisons to homogeneous-plot data, but by seasonal and spatial consistency with a diagnostic NDVI model and atmospheric CO2 observations

FLUXNET: A New Tool to Study the Temporal and Spatial Variability of Ecosystem-Scale Carbon Dioxide, Water Vapor, and Energy Flux Densities

FLUXNET is a global network of micrometeorological flux measurement sites that measure the exchanges of carbon dioxide, water vapor, and energy between the biosphere and atmosphere. At present over 140 sites are operating on a long–term and continuous basis. Vegetation under study includes temperate conifer and broadleaved (deciduous and evergreen) forests, tropical and boreal forests, crops, grasslands, chaparral, wetlands, and tundra. Sites exist on five continents and their latitudinal distribution ranges from 70°N to 30°S. FLUXNET has several primary functions. First, it provides infrastructure for compiling, archiving, and distributing carbon, water, and energy flux measurement, and meteorological, plant, and soil data to the science community. (Data and site information are available online at the FLUXNET http://www–eosdis.ornl.gov/FLUXNET/.) Second, the project supports calibration and flux intercomparison activities. This activity ensures that data from the regional networks are intercomparable. And third, FLUXNET supports the synthesis, discussion, and communication of ideas and data by supporting project scientists, workshops, and visiting scientists. The overarching goal is to provide information for validating computations of net primary productivity, evaporation, and energy absorption that are being generated by sensors mounted on the NASA Terra satellite. Data being compiled by FLUXNET are being used to quantify and compare magnitudes and dynamics of annual ecosystem carbon and water balances, to quantify the response of stand–scale carbon dioxide and water vapor flux densities to controlling biotic and abiotic factors, and to validate a hierarchy of soil–plant–atmosphere trace gas exchange models. Findings so far include 1) net CO2 exchange of temperate broadleaved forests increases by about 5.7 g C m–2 day–1 for each additional day that the growing season is extended; 2) the sensitivity of net ecosystem CO2 exchange to sunlight doubles if the sky is cloudy rather than clear; 3) the spectrum of CO2 flux density exhibits peaks at timescales of days, weeks, and years, and a spectral gap exists at the month timescale; 4) the optimal temperature of net CO2 exchange varies with mean summer temperature; and 5) stand age affects carbon dioxide and water vapor flux densities

Global satellite monitoring of climate-induced vegetation disturbances

Terrestrial disturbances are accelerating globally, but their full impact is not quantified because we lack an adequate monitoring system. Remote sensing offers a means to quantify the frequency and extent of disturbances globally. Here, we review the current application of remote sensing to this problem and offer a framework for more systematic analysis in the future. We recommend that any proposed monitoring system should not only detect disturbances, but also be able to: identify the proximate cause(s); integrate a range of spatial scales; and, ideally, incorporate process models to explain the observed patterns and predicted trends in the future. Significant remaining challenges are tied to the ecology of disturbances. To meet these challenges, more effort is required to incorporate ecological principles and understanding into the assessments of disturbance worldwide

Radar remote sensing of the spring thaw transition across a boreal landscape

The seasonal transition of the boreal forest between frozen and non-frozen conditions affects a number of ecosystem processes that cycle between winter dormant and summer active states. The relatively short Ku-band wavelength (2.14 cm) of the space-borne NASA scatterometer (NSCAT) is sensitive to changes in dielectric properties, associated with large-scale changes in the relative abundance and phase (frozen or thawed) of canopy and surface water. We used a temporal change detection analysis of NSCAT daily radar backscatter measurements to characterize the 1997 seasonal spring thaw transition period across the 106 km2 BOREAS study region of central Canada. In the spring, air temperature transitions from frozen to non-frozen conditions and surface observations of seasonal snow cover depletion were generally coincident with decreases in radar backscatter of more than 2.9 dB, regardless of regional landcover characteristics. We used a temporal classification of NSCAT daily differences from 5-day smoothed backscatter values to derive three simple indices describing the initiation, primary event and completion of the spring thaw transition period. Several factors had a negative impact on the relative accuracy of NSCAT-based results, including periodic gaps in NSCAT daily time-series information and a large (i.e., \u3e2 cm day−1) spring rainfall event. However, these results were generally successful in capturing the seasonal transition of the region from frozen to non-frozen conditions, based on comparisons with regional weather station network information. These results illustrate the potential for improved assessment of springtime phenology and associated ecosystem dynamics across high latitude regions, where field based and optical remote-sensing methods are substantially degraded by frequent cloud cover, low solar illumination and sparse surface weather station networks

Application of the NASA Scatterometer (NSCAT) for Determining the Daily Frozen and Nonfrozen Landscape of Alaska

The seasonal transition of the land surface between frozen and nonfrozen conditions affects a number of terrestrial processes that cycle between wintertime dormant and summertime active states. The relatively short (2.14 cm) Ku-band of the space-borne NASA scatterometer (NSCAT) is sensitive to changes in dielectric properties, associated with large-scale shifts in the relative abundance and phase (frozen or thawed) of canopy and surface water. We used a temporal change detection analysis of NSCAT daily radar backscatter measurements to classify surface freeze/thaw state across a 1.4 million km2region of Alaska from January to June 1997. In the spring, radar backscatter measurements showed pronounced decreases (1.6–4.9 dB) relative to reference frozen state conditions, which corresponded with sustained maximum daily air temperature measurements above 0.0°C and total decreases in measured snow depths from 28% to 61% of seasonal maximum values. We classified the daily frozen and nonfrozen area for the region based on the sign (+/−) of the radar backscatter temporal difference relative to frozen and nonfrozen reference conditions. These results compared favorably (e.g., r2=0.881; p⩽0.0001) with frozen area estimates derived from the regional weather station network. NSCAT-derived estimates of the timing and spatial variation in regional thaw during spring were also generally consistent with seasonal increases in river discharge for five major Alaska basins. The NSCAT sensor appears to be responsive to changes in dielectric properties associated with surface freeze/thaw transitions over broad boreal and arctic landscapes. Further study involving longer time-series information, alternative radar wavelengths, and finer spatial scales is needed, however, to resolve the various components (i.e., soil, vegetation, snow) of the regional radar freeze/thaw signature for improved monitoring of the circumpolar high latitudes

Evaluating the role of land cover and climate uncertainties in computing gross primary production in Hawaiian Island ecosystems

<div><p>Gross primary production (GPP) is the Earth’s largest carbon flux into the terrestrial biosphere and plays a critical role in regulating atmospheric chemistry and global climate. The Moderate Resolution Imaging Spectrometer (MODIS)-MOD17 data product is a widely used remote sensing-based model that provides global estimates of spatiotemporal trends in GPP. When the MOD17 algorithm is applied to regional scale heterogeneous landscapes, input data from coarse resolution land cover and climate products may increase uncertainty in GPP estimates, especially in high productivity tropical ecosystems. We examined the influence of using locally specific land cover and high-resolution local climate input data on MOD17 estimates of GPP for the State of Hawaii, a heterogeneous and discontinuous tropical landscape. Replacing the global land cover data input product (MOD12Q1) with Hawaii-specific land cover data reduced statewide GPP estimates by ~8%, primarily because the Hawaii-specific land cover map had less vegetated land area compared to the global land cover product. Replacing coarse resolution GMAO climate data with Hawaii-specific high-resolution climate data also reduced statewide GPP estimates by ~8% because of the higher spatial variability of photosynthetically active radiation (PAR) in the Hawaii-specific climate data. The combined use of both Hawaii-specific land cover and high-resolution Hawaii climate data inputs reduced statewide GPP by ~16%, suggesting equal and independent influence on MOD17 GPP estimates. Our sensitivity analyses within a heterogeneous tropical landscape suggest that refined global land cover and climate data sets may contribute to an enhanced MOD17 product at a variety of spatial scales.</p></div