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

Sensitivity of boreal forest regional water flux and net primary production simulations to sub-grid-scale land cover complexity

We use a general ecosystem process model (BIOME-BGC) coupled with remote sensing information to evaluate the sensitivity of boreal forest regional evapotranspiration (ET) and net primary production (NPP) to land cover spatial scale. Simulations were conducted over a 3 year period (1994–1996) at spatial scales ranging from 30 to 50 km within the BOREAS southern modeling subarea. Simulated fluxes were spatially complex, ranging from 0.1 to 3.9 Mg C ha−1 yr−1 and from 18 to 29 cm yr−1. Biomass and leaf area index heterogeneity predominantly controlled this complexity, while biophysical differences between deciduous and coniferous vegetation were of secondary importance. Spatial aggregation of land cover characteristics resulted in mean monthly NPP estimation bias from 25 to 48% (0.11–0.20 g C m−2 d−1) and annual estimation errors from 2 to 14% (0.04–0.31 Mg C ha−1 yr−1). Error was reduced at longer time intervals because coarse scale overestimation errors during spring were partially offset by underestimation of fine scale results during summer and winter. ET was relatively insensitive to land cover spatial scale with an average bias of less than 5% (0.04 kg m−2 d−1). Factors responsible for differences in scaling behavior between ET and NPP included compensating errors for ET calculations and boreal forest spatial and temporal NPP complexity. Careful consideration of landscape spatial and temporal heterogeneity is necessary to identify and mitigate potential error sources when using plot scale information to understand regional scale patterns. Remote sensing data integrated within an ecological process model framework provides an efficient mechanism to evaluate scaling behavior, interpret patterns in coarse resolution data, and identify appropriate scales of operation for various processes

BIOME-BGC simulations of stand hydrologic process for BOREAS

BIOME-BGC is a general ecosystem model designed to simulate hydrologic and biogeochemical processes across multiple scales. The objectives of this investigation were to compare BIOME-BGC estimates of hydrologic processes with observed data for different boreal forest stands and investigate factors that control simulated water fluxes. Model results explained 62 and 98% of the respective variances in observed daily evapotranspiration and soil water; simulations of the onset of spring thaw and the dates of snowpack disappearance and accumulation also generally tracked observations. Differences between observed and simulated evapotranspiration were attributed to model assumptions of constant, growing season, overstory leaf areas that did not account for phenological changes and understory effects on stand daily water fluxes. Vapor pressure deficit and solar radiation accounted for 58–74% of the variances in simulated daily evapotranspiration during the growing season, though low air temperature and photosynthetic light levels were found to be the major limiting factors regulating simulated canopy conductances to water vapor. Humidity and soil moisture were generally not low enough to induce physiological water stress in black spruce stands, though low soil water potentials resulted in approximate 34% reductions in simulated mean daily canopy conductances for aspen and jack pine stands. The sensitivity of evapotranspiration simulations to leaf area (LAI) was less than expected because of opposing responses of transpiration and evaporation to LAI. The results of this investigation identify several components within boreal forest stands that are sensitive to climate change

Impacts of large-scale oscillations on pan-Arctic terrestrial net primary production

Analyses of regional climate oscillations and satellite remote sensing derived net primary production (NPP) and growing season dynamics for the pan-Arctic region indicate that the oscillations influence NPP by regulating seasonal patterns of low temperature and moisture constraints to photosynthesis. Early-spring (Feb–Apr) patterns of the Arctic Oscillation (AO) are proportional to growing season onset (r = −0.653; P = 0.001), while growing season patterns of the Pacific Decadal Oscillation (PDO) are proportional to plant-available moisture constraints to NPP (Im) (r = −0.471; P = 0.023). Relatively strong, negative PDO phases from 1988–1991 and 1998–2002 coincided with prolonged regional droughts indicated by a standardized moisture stress index. These severe droughts resulted in widespread reductions in NPP, especially for relatively drought prone boreal forest and grassland/cropland ecosystems. The influence of AO and PDO patterns on northern vegetation productivity appears to be decreasing and increasing, respectively, as low temperature constraints to plant growth relax and NPP becomes increasingly limited by available water supply under a warming climate

A continuous satellite-derived global record of land surface evapotranspiration from 1983 to 2006

We applied a satellite remote sensing–based evapotranspiration (ET) algorithm to assess global terrestrial ET from 1983 to 2006. The algorithm quantifies canopy transpiration and soil evaporation using a modified Penman-Monteith approach with biome-specific canopy conductance determined from the normalized difference vegetation index (NDVI) and quantifies open water evaporation using a Priestley-Taylor approach. These algorithms were applied globally using advanced very high resolution radiometer (AVHRR) GIMMS NDVI, NCEP/NCAR Reanalysis (NNR) daily surface meteorology, and NASA/GEWEX Surface Radiation Budget Release−3.0 solar radiation inputs. We used observations from 34 FLUXNET tower sites to parameterize an NDVI-based canopy conductance model and then validated the global ET algorithm using measurements from 48 additional, independent flux towers. Two sets of monthly ET estimates at the tower level, driven by in situ meteorological measurements and meteorology interpolated from coarse resolution NNR meteorology reanalysis, agree favorably (root mean square error (RMSE) = 13.0–15.3 mm month−1; R2 = 0.80–0.84) with observed tower fluxes from globally representative land cover types. The global ET results capture observed spatial and temporal variations at the global scale and also compare favorably (RMSE = 186.3 mm yr−1; R2 = 0.80) with ET inferred from basin-scale water balance calculations for 261 basins covering 61% of the global vegetated area. The results of this study provide a relatively long term global ET record with well-quantified accuracy for assessing ET climatologies, terrestrial water, and energy budgets and long-term water cycle changes

Sensitivity of Pan-Arctic Terrestrial Net Primary Productivity Simulations to Daily Surface Meterology From NCEP-NCAR and ERA-40 Reanalyses

We applied a terrestrial net primary production (NPP) model driven by satellite remote sensing observations of vegetation properties and daily surface meteorology from the 45-year ECMWF Re-Analysis (ERA-40) and the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP-NCAR) reanalysis (NNR) to assess NPP spatial and temporal variability for the pan-Arctic basin and Alaska from 1982 to 2000. Sensitivity analysis of the production efficiency model (PEM) to uncertainties in surface meteorological inputs indicate that ERA-40 solar radiation and NNR solar radiation and surface temperatures are the primary sources of PEM-based NPP uncertainty for the region. Considerable positive bias in solar radiation inputs relative to surface observation networks resulted in overprediction of annual NPP by approximately 35.2 and 61.6% using ERA-40 and NNR inputs, respectively. Despite these uncertainties, both reanalysis products captured the major annual anomalies and trends in surface meteorology for the domain. The two reanalysis products also produced similar NPP spatial patterns for 74.7% of the domain, and similar annual anomalies and temporal trends, though there were significant regional differences particularly for Eurasia. A simple correction method based on a sensitivity experiment between reanalysis and surface station meteorological measurements produced generally consistent NPP results that were considerably smaller than PEM simulations derived from uncorrected reanalysis drivers. The results of this study identify major sources of uncertainty in reanalysis-based surface meteorology, and associated impacts on regional NPP simulations of the northern high latitudes

Satellite assessment of land surface evapotranspiration for the pan-Arctic domain

Regional evapotranspiration (ET), including water loss from plant transpiration and soil evaporation, is essential to understanding interactions between land-atmosphere surface energy and water balances. Vapor pressure deficit (VPD) and surface air temperature are key variables for stomatal conductance and ET estimation. We developed an algorithm to estimate ET using the Penman-Monteith approach driven by Moderate Resolution Imaging Spectroradiometer (MODIS)-derived vegetation data and daily surface meteorological inputs including incoming solar radiation, air temperature, and VPD. The model was applied using alternate daily meteorological inputs, including (1) site level weather station observations, (2) VPD and air temperature derived from the Advanced Microwave Scanning Radiometer (AMSR-E) on the EOS Aqua satellite, and (3) Global Modeling and Assimilation Office (GMAO) reanalysis meteorology-based surface air temperature, humidity, and solar radiation data. Model performance was assessed across a North American latitudinal transect of six eddy covariance flux towers representing northern temperate grassland, boreal forest, and tundra biomes. Model results derived from the three meteorology data sets agree well with observed tower fluxes (r \u3e 0.7; P \u3c 0.003; root mean square error of latent heat flux \u3c30 W m−2) and capture spatial patterns and seasonal variability in ET. The MODIS-AMSR-E–derived ET results also show similar accuracy to ET results derived from GMAO, while ET estimation error was generally more a function of algorithm parameterization than differences in meteorology drivers. Our results indicate significant potential for regional mapping and monitoring daily land surface ET using synergistic information from satellite optical IR and microwave remote sensing

Satellite observations of annual variability in terrestrial carbon cycles and seasonal growing seasons at high northern latitudes

Global satellite remote sensing records show evidence of recent vegetation greening and an advance in the onset of the growing season at high latitudes. We apply a terrestrial net primary production (NPP) model driven by satellite observations of vegetation properties and daily surface meteorology from an atmospheric GCM to assess spatial patterns, annual variability, and recent trends in vegetation productivity across Alaska and northwest Canada. We compare these results with regional observations of the timing of growing season onset derived from satellite passive microwave remote sensing measurements from the Special Sensor Microwave Imager, SSM/I. Our results show substantial variability in annual NPP for the region that appears to be driven largely by variations in canopy photosynthetic leaf area and average summer air temperatures. Variability in maximum canopy leaf area and NPP also correspond closely to remote sensing observations of the timing of the primary seasonal thaw event in spring. Relatively early spring thawing appears to enhance NPP, while delays in seasonal thawing and growing season onset reduce annual vegetation productivity. Our results indicate that advances in seasonal thawing and spring and summer warming for the region associated with global change are promoting a general increase in NPP

Satellite-based model detection of recent climate-driven changes in northern high-latitude vegetation productivity

We applied a satellite remote sensing based production efficiency model (PEM) using an integrated AVHRR and MODIS FPAR/LAI time series with a regionally corrected NCEP/NCAR reanalysis daily surface meteorology and NASA/GEWEX Surface Radiation Budget shortwave solar radiation inputs to assess annual terrestrial net primary productivity (NPP) for the pan-Arctic basin and Alaska from 1983 to 2005. Our results show that low temperature constraints on Boreal-Arctic NPP are decreasing by 0.43% per year (P \u3c 0.001), whereas a positive trend in vegetation moisture constraints of 0.49% per year (P = 0.04) are offsetting the potential benefits of longer growing seasons and contributing to recent disturbances in NPP. The PEM simulations of NPP seasonality, annual anomalies and trends are similar to stand inventory network measurements of boreal aspen stem growth (r = 0.56; P = 0.007) and atmospheric CO2 measurement based estimates of the timing of growing season onset (r = 0.78; P \u3c 0.001). Our results indicate that summer drought led to marked NPP decreases in much of the boreal forest region after the late-1990s. However, seasonal low temperatures are still a dominant limitation on regional NPP. Despite recent drought events, mean annual NPP for the pan-Arctic region showed a positive growth trend of 0.34% per year (20.27 TgC/a; P = 0.002) from 1983 to 2005. Drought induced NPP decreases may become more frequent and widespread as regional ecosystems adjust to a warmer, drier atmosphere, though the occurrence and severity of drought events will depend on future patterns of plant-available moisture

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