Earth Observing System. Volume 1, Part 2: Science and Mission Requirements. Working Group Report Appendix

Areas of global hydrologic cycles, global biogeochemical cycles geophysical processes are addressed including biological oceanography, inland aquatic resources, land biology, tropospheric chemistry, oceanic transport, polar glaciology, sea ice and atmospheric chemistry

Behavior of multitemporal and multisensor passive microwave indices in Southern Hemisphere ecosystems

©2014. American Geophysical Union. All Rights Reserved. This study focused on the time series analysis of passive microwave and optical satellite data collected from six Southern Hemisphere ecosystems in Australia and Argentina. The selected ecosystems represent a wide range of land cover types, including deciduous open forest, temperate forest, tropical and semiarid savannas, and grasslands. We used two microwave indices, the frequency index (FI) and polarization index (PI), to assess the relative contributions of soil and vegetation properties (moisture and structure) to the observations. Optical-based satellite vegetation products from the Moderate Resolution Imaging Spectroradiometer were also included to aid in the analysis. We studied the X and Ka bands of the Advanced Microwave Scanning Radiometer-EOS and Wind Satellite, resulting in up to four observations per day (1:30, 6:00, 13:30, and 18:00-h). Both the seasonal and hourly variations of each of the indices were examined. Environmental drivers (precipitation and temperature) and eddy covariance measurements (gross ecosystem productivity and latent energy) were also analyzed. It was found that in moderately dense forests, FI was dependent on canopy properties (leaf area index and vegetation moisture). In tropical woody savannas, a significant regression (R2) was found between FI and PI with precipitation (R2->-0.5) and soil moisture (R2->-0.6). In the areas of semiarid savanna and grassland ecosystems, FI variations found to be significantly related to soil moisture (R2->-0.7) and evapotranspiration (R2->-0.5), while PI varied with vegetation phenology. Significant differences (p-<-0.01) were found among FI values calculated at the four local times. Key Points Passive microwave indices can be used to estimate vegetation moisture Microwave observations were supported by flux data Passive microwave indices could be used to estimate evapotranspiratio

Temporal stability of soil moisture and radar backscatter observed by the advanced Synthetic Aperture Radar (ASAR)

The high spatio-temporal variability of soil moisture is the result of atmospheric forcing and redistribution processes related to terrain, soil, and vegetation characteristics. Despite this high variability, many field studies have shown that in the temporal domain soil moisture measured at specific locations is correlated to the mean soil moisture content over an area. Since the measurements taken by Synthetic Aperture Radar (SAR) instruments are very sensitive to soil moisture it is hypothesized that the temporally stable soil moisture patterns are reflected in the radar backscatter measurements. To verify this hypothesis 73 Wide Swath (WS) images have been acquired by the ENVISAT Advanced Synthetic Aperture Radar (ASAR) over the REMEDHUS soil moisture network located in the Duero basin, Spain. It is found that a time-invariant linear relationship is well suited for relating local scale (pixel) and regional scale (50 km) backscatter. The observed linear model coefficients can be estimated by considering the scattering properties of the terrain and vegetation and the soil moisture scaling properties. For both linear model coefficients, the relative error between observed and modelled values is less than 5 % and the coefficient of determination (R-2) is 86 %. The results are of relevance for interpreting and downscaling coarse resolution soil moisture data retrieved from active (METOP ASCAT) and passive (SMOS, AMSR-E) instruments

Assessment of multi-temporal, multi-sensor radar and ancillary spatial data for grasslands monitoring in Ireland using machine learning approaches

Accurate inventories of grasslands are important for studies of carbon dynamics, biodiversity conservation and agricultural management. For regions with persistent cloud cover the use of multi-temporal synthetic aperture radar (SAR) data provides an attractive solution for generating up-to-date inventories of grasslands. This is even more appealing considering the data that will be available from upcoming missions such as Sentinel-1 and ALOS-2. In this study, the performance of three machine learning algorithms; Random Forests (RF), Support Vector Machines (SVM) and the relatively underused Extremely Randomised Trees (ERT) is evaluated for discriminating between grassland types over two large heterogeneous areas of Ireland using multi-temporal, multi-sensor radar and ancillary spatial datasets. A detailed accuracy assessment shows the efficacy of the three algorithms to classify different types of grasslands. Overall accuracies ≥ 88.7% (with kappa coefficient of 0.87) were achieved for the single frequency classifications and maximum accuracies of 97.9% (kappa coefficient of 0.98) for the combined frequency classifications. For most datasets, the ERT classifier outperforms SVM and RF

POTENTIAL CONTRASTS IN CO2 AND CH4 FLUX RESPONSE UNDER CHANGING CLIMATE CONDITIONS: A SATELLITE REMOTE SENSING DRIVEN ANALYSIS OF THE NET ECOSYSTEM CARBON BUDGET FOR ARCTIC AND BOREAL REGIONS

The impact of warming on the net ecosystem carbon budget (NECB) in Arctic-boreal regions remains highly uncertain. Heightened CH4 emissions from Arctic-boreal ecosystems could shift the northern NECB from an annual carbon sink further towards net carbon source. Northern wetland CH4 fluxes may be particularly sensitive to climate warming, increased soil temperatures and duration of the soil non-frozen period. Changes in northern high latitude surface hydrology will also impact the NECB, with surface and soil wetting resulting from thawing permafrost landscapes and shifts in precipitation patterns; summer drought conditions can potentially reduce vegetation productivity and land sink of atmospheric CO2 but also moderate the magnitude of CH4 increase. The first component of this work develops methods to assess seasonal variability and longer term trends in Arctic-boreal surface water inundation from satellite microwave observations, and quantifies estimate uncertainty. The second component of this work uses this information to improve understanding of impacts associated with changing environmental conditions on high latitude wetland CH4 emissions. The third component focuses on the development of a satellite remote sensing data informed Terrestrial Carbon Flux (TCF) model for northern wetland regions to quantify daily CH4 emissions and the NECB, in addition to vegetation productivity and landscape CO2 respiration loss. Finally, the fourth component of this work features further enhancement of the TCF model by improving representation of diverse tundra and boreal wetland ecosystem land cover types. A comprehensive database for tower eddy covariance CO2 and CH4 flux observations for the Arctic-boreal region was developed to support these efforts, providing an assessment of the TCF model ability to accurately quantify contemporary changes in regional terrestrial carbon sink/source strength

Remote Sensing of Environmental Changes in Cold Regions

This Special Issue gathers papers reporting recent advances in the remote sensing of cold regions. It includes contributions presenting improvements in modeling microwave emissions from snow, assessment of satellite-based sea ice concentration products, satellite monitoring of ice jam and glacier lake outburst floods, satellite mapping of snow depth and soil freeze/thaw states, near-nadir interferometric imaging of surface water bodies, and remote sensing-based assessment of high arctic lake environment and vegetation recovery from wildfire disturbances in Alaska. A comprehensive review is presented to summarize the achievements, challenges, and opportunities of cold land remote sensing

Assessing the utility of geospatial technologies to investigate environmental change within lake systems

Over 50% of the world's population live within 3. km of rivers and lakes highlighting the on-going importance of freshwater resources to human health and societal well-being. Whilst covering c. 3.5% of the Earth's non-glaciated land mass, trends in the environmental quality of the world's standing waters (natural lakes and reservoirs) are poorly understood, at least in comparison with rivers, and so evaluation of their current condition and sensitivity to change are global priorities. Here it is argued that a geospatial approach harnessing existing global datasets, along with new generation remote sensing products, offers the basis to characterise trajectories of change in lake properties e.g., water quality, physical structure, hydrological regime and ecological behaviour. This approach furthermore provides the evidence base to understand the relative importance of climatic forcing and/or changing catchment processes, e.g. land cover and soil moisture data, which coupled with climate data provide the basis to model regional water balance and runoff estimates over time. Using examples derived primarily from the Danube Basin but also other parts of the World, we demonstrate the power of the approach and its utility to assess the sensitivity of lake systems to environmental change, and hence better manage these key resources in the future

Tundra Snow Cover Properties from \u3cem\u3eIn-Situ\u3c/em\u3e Observation and Multi-Scale Passive Microwave Remote Sensing

Tundra snow cover is important to monitor as it influences local, regional, and global scale surface water balance, energy fluxes, and ecosystem and permafrost dynamics. Moreover, recent global circulation models (GCM) predict a pronounced shift in high latitude winter precipitation and mean annual air temperature due to the feedback between air temperature and snow extent. At regional and hemispheric scales, the estimation of snow extent, snow depth and, snow water equivalent (SWE) is important because high latitude snow cover both forces and reacts to atmospheric circulation patterns. Moreover, snow cover has implications on soil moisture dynamics, the depth, formation and growth of the permafrost active layer, the vegetation seasonality, and the respiration of C02. In Canada, daily snow depth observations are available from 1955 to present for most meteorological stations. Moreover, despite the abundance and dominance of a northern snow cover, most, if not all, long term snow monitoring sites are located south of 550N. Stations in high latitudes are extremely sparse and coastally biased. In Arctic regions, it can be logistically difficult and very expensive to acquire both spatially and temporally extensive in-situ snow data. Thus, the possibility of using satellite remote sensing to estimate snow cover properties is appealing for research in remote northern regions. Remote sensing techniques have been employed to monitor the snow since the 1960s when the visible light channels were used to map snow extent. Since then, satellite remote sensing has expanded to provide information on snow extent, depth, wetness, and SWE. However, the utility of satellite sensors to provide useful, operational tundra snow cover data depends on sensor parameters and data resolution. Passive microwave data are the only currently operational sources for providing estimates of dry snow extent, SWE and snow depth. Currently, no operational passive microwave algorithms exist for the spatially expansive tundra and high Arctic regions. The heterogeneity of sub-satellite grid tundra snow and terrain are the main limiting factors in using conventional SWE retrieval algorithm techniques. Moreover, there is a lack of in-situ data for algorithm development and testing. The overall objective of this research is to improve operational capabilities for estimating end of winter, pre-melt tundra SWE in a representative tundra study area using satellite passive microwave data. The study area for the project is located in the Daring-Exeter-Yamba portion of the Upper-Coppermine River Basin in the Northwest Territories. The size, orientation and boundaries of the study area were defined based on the satellite EASE grid (25 x 25 km) centroid located closest to the Tundra Ecosystem Research Station operated by the Government of the Northwest Territories. Data were collected during intensive late winter field campaigns in 2004, 2005, 2006, 2007, 2008, and 2009. During each field campaign, snow depth, density and stratigraphy were recorded at sites throughout the study area. During the 2005 and 2008 seasons, multi-scale airborne passive microwave radiometer data were also acquired. During the 2007 season, ground based passive microwave radiometer data were acquired. For each year, temporally coincident AMSR-E satellite Tb were obtained. The spatial distribution of snow depth, density and SWE in the study area is controlled by the interaction of blowing snow with terrain and land cover. Despite the spatial heterogeneity of snow cover, several inter-annual consistencies were identified. Tundra snow density is consistent when considered on a site-by-site basis and among different terrain types. A regional average density of 0.294 g/cm3 was derived from the six years of measurements. When applied to site snow depths, there is little difference in SWE derived from either the site or the regional average density. SWE is more variable from site to site and year to year than density which requires the use of a terrain based Classification to better quantify regional SWE. The variability in SWE was least on lakes and flat tundra, while greater on slopes and plateaus. Despite the variability, the interannual ratios of SWE among different terrain types does not change that much. The variability (CV) in among terrain categories was quite similar. The overall weighted mean CV for the study area was 0.40, which is a useful regional generalization. The terrain and landscape based classification scheme was used to generalize and extrapolate tundra SWE. Deriving a weighted mean SWE based on the spatial proportion of landscape and terrain features was shown as a method for generalizing the regional distribution of tundra SWE. The SWE data from each year were compared to AMSR-E satellite Tb. Within each season and among each of the seasons, there was little difference in 19 GHz Tb. However, there was always a large decrease in 37 GHz Tb from early November through April. The change in ΔTb37-19 throughout each season showed that the Tb at 37 GHz is sensitive to parameters which evolve over a winter season. A principal component analysis (PCA) showed that there are differences in ΔTb37-19 among different EASE grids and that land cover may have an influence on regional Tb. However, the PCA showed little relationship between end of season ΔTb37-19 and lake fraction. A good relationship was found between ΔTb37-19 and in-situ SWE. A quadratic function was fitted to explain 89 percent of the variance in SWE from the ΔTb37-19. The quadratic relationship provides a good fit between the data; however, the nature of the relationship is opposite to the expected linear relationship between ΔTb37-19 and SWE. Airborne Tb data were used to examine how different snow, land cover and terrain properties influence microwave emission. In flat tundra, there was a significant relationship between SWE and high resolution ΔTb37-19. On lakes and slopes, no strong relationships were found between SWE and high resolution ΔTb37-19. Due to the complexity of snow and terrain in high resolution footprints, it was a challenge to isolate a relationship between SWE and Tb. However, as the airborne footprint size increased the amplitude of variability in Tb decrease considerably to the point that Tb in large footprints is not sensitive to local scale variability in SWE. As such, most of the variability evident in the high and mid resolution airborne data will not persist at the EASE grid scale. Despite the many challenges, algorithm development should be possible at the satellite scale. The AMSR-E ΔTb37-19 changes from year to year in response to differences in snow cover properties. However, the multiple years of in-situ snow data remain the most important contribution in linking Tb with SWE

Mapping gains and losses in woody vegetation across global tropical drylands

Woody vegetation in global tropical drylands is of significant importance for both the interannual variability of the carbon cycle and local livelihoods. Satellite observations over the past decades provide a unique way to assess the vegetation long-term dynamics across biomes worldwide. Yet, the actual changes in the woody vegetation are always hidden by interannual fluctuations of the leaf density, because the most widely used remote sensing data are primarily related to the photosynthetically active vegetation components. Here, we quantify the temporal trends of the nonphotosynthetic woody components (i.e., stems and branches) in global tropical drylands during 2000–2012 using the vegetation optical depth (VOD), retrieved from passive microwave observations. This is achieved by a novel method focusing on the dry season period to minimize the influence of herbaceous vegetation and using MODerate resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data to remove the interannual fluctuations of the woody leaf component. We revealed significant trends (P < 0.05) in the woody component (VODwood) in 35% of the areas characterized by a nonsignificant trend in the leaf component (VODleaf modeled from NDVI), indicating pronounced gradual growth/decline in woody vegetation not captured by traditional assessments. The method is validated using a unique record of ground measurements from the semiarid Sahel and shows a strong agreement between changes in VODwood and changes in ground observed woody cover (r2 = 0.78). Reliability of the obtained woody component trends is also supported by a review of relevant literatures for eight hot spot regions of change. The proposed approach is expected to contribute to an improved assessment of, for example, changes in dryland carbon pools