scholarly journals Spatiotemporal dynamics of wetted soils across a polar desert landscape

2014 ◽  
Vol 27 (2) ◽  
pp. 197-209 ◽  
Author(s):  
Zachary L. Langford ◽  
Michael N. Gooseff ◽  
Derrick J. Lampkin
Keyword(s):  
Remote Sensing ◽  
Regional Climate ◽  
Temporal Distribution ◽  
Remote Sensing Data ◽  
Dry Valleys ◽  
Cold Desert ◽  
Land System ◽  
Spatial Consistency ◽  
Free Region ◽  
Desert Landscape

AbstractLiquid water is scarce across the landscape of the McMurdo Dry Valleys (MDV), Antarctica, a 3800 km2ice-free region, and is chiefly associated with soils that are adjacent to streams and lakes (i.e. wetted margins) during the annual thaw season. However, isolated wetted soils have been observed at locations distal from water bodies. The source of water for the isolated patches of wet soil is potentially generated by a combination of infiltration from melting snowpacks, melting of pore ice at the ice table, and melting of buried segregation ice formed during winter freezing. High resolution remote sensing data gathered several times per summer in the MDV region were used to determine the spatial and temporal distribution of wet soils. The spatial consistency with which the wet soils occurred was assessed for the 2009–10 to 2011–12 summers. The remote sensing analyses reveal that cumulative area and number of wet soil patches varies among summers. The 2010–11 summer provided the most wetted soil area (10.21 km2) and 2009–10 covered the least (5.38 km2). These data suggest that wet soils are a significant component of the MDV cold desert land system and may become more prevalent as regional climate changes.

scholarly journals Snow depth derived from passive microwave remote-sensing data in China

2008 ◽  
Vol 49 ◽  
pp. 145-154 ◽  
Author(s):  
Tao Che ◽  
Xin Li ◽  
Rui Jin ◽  
Richard Armstrong ◽  
Tingjun Zhang
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Snow Depth ◽  
Temporal Distribution ◽  
Remote Sensing Data ◽  
Passive Microwave ◽  
Spatial And Temporal Distribution ◽  
Sensing Data ◽  
Seasonal Snow ◽  
Snow Mass

AbstractIn this study, we report on the spatial and temporal distribution of seasonal snow depth derived from passive microwave satellite remote-sensing data (e.g. SMMR from 1978 to 1987 and SMM/ I from 1987 to 2006) in China. We first modified the Chang algorithm and then validated it using meteorological observation data, considering the influences from vegetation, wet snow, precipitation, cold desert and frozen ground. Furthermore, the modified algorithm is dynamically adjusted based on the seasonal variation of grain size and snow density. Snow-depth distribution is indirectly validated by MODIS snow-cover products by comparing the snow-extent area from this work. The final snow-depth datasets from 1978 to 2006 show that the interannual snow-depth variation is very significant. The spatial and temporal distribution of snow depth is illustrated and discussed, including the steady snow-cover regions in China and snow-mass trend in these regions. Though the areal extent of seasonal snow cover in the Northern Hemisphere indicates a weak decrease over a long period, there is no clear trend in change of snow-cover area extent in China. However, snow mass over the Qinghai–Tibetan Plateau and northwestern China has increased, while it has weakly decreased in northeastern China. Overall, snow depth in China during the past three decades shows significant interannual variation, with a weak increasing trend.

Biophysical remote sensing of arctic environments

2003 ◽  
Vol 27 (1) ◽  
pp. 44-68 ◽  
Author(s):  
Gita J. Laidler ◽  
Paul Treitz
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Spatial Scales ◽  
Temporal Distribution ◽  
Remote Sensing Data ◽  
Vegetation Mapping ◽  
General Purpose ◽  
The Arctic ◽  
Natural Systems ◽  
Tundra Ecosystem

Various remote sensing studies have been conducted to investigate methods and applications of vegetation mapping and analysis in arctic environments. The general purpose of these studies is to extract information on the spatial and temporal distribution of vegetation as required for tundra ecosystem and climate change studies. Because of the recent emphasis on understanding natural systems at large spatial scales, there has been an increasing interest in deriving biophysical variables from satellite data. Satellite remote sensing offers potential for extrapolating, or ‘scaling up’ biophysical measures derived from local sites, to landscape and even regional scales. The most common investigations include mapping spatial vegetation patterns or assessing biophysical tundra characteristics, using medium resolution satellite data. For instance, Landsat TM data have been shown to be useful for broad vegetation mapping and analysis, but not accurately representative of smaller vegetation communities or local spatial variation. It is anticipated, that high spatial resolution remote sensing data, now available from commercial remote sensing satellites, will provide the necessary sampling scale to link field data to remotely sensed reflectance data. As a result, it is expected that these data will improve the representation of biophysical variables over sparsely vegetated regions of the Arctic.

Remote characterization of photosynthetic communities in the Fryxell basin of Taylor Valley, Antarctica

2020 ◽  
Vol 32 (4) ◽  
pp. 255-270 ◽  
Author(s):  
Mark R. Salvatore ◽  
Schuyler R. Borges ◽  
John E. Barrett ◽  
Eric R. Sokol ◽  
Lee F. Stanish ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Microbial Mats ◽  
Remote Sensing Data ◽  
Spectral Unmixing ◽  
Ecological Communities ◽  
Dry Valleys ◽  
Spectral Signatures ◽  
Taylor Valley ◽  
Infrared Spectral

AbstractWe investigate the spatial distribution, spectral properties and temporal variability of primary producers (e.g. communities of microbial mats and mosses) throughout the Fryxell basin of Taylor Valley, Antarctica, using high-resolution multispectral remote-sensing data. Our results suggest that photosynthetic communities can be readily detected throughout the Fryxell basin based on their unique near-infrared spectral signatures. Observed intra- and inter-annual variability in spectral signatures are consistent with short-term variations in mat distribution, hydration and photosynthetic activity. Spectral unmixing is also implemented in order to estimate mat abundance, with the most densely vegetated regions observed from orbit correlating spatially with some of the most productive regions of the Fryxell basin. Our work establishes remote sensing as a valuable tool in the study of these ecological communities in the McMurdo Dry Valleys and demonstrates how future scientific investigations and the management of specially protected areas could benefit from these tools and techniques.

scholarly journals New lessons on the Sudd hydrology learned from remote sensing and climate modeling

2006 ◽  
Vol 10 (4) ◽  
pp. 507-518 ◽  
Author(s):  
Y. A. Mohamed ◽  
H. H. G. Savenije ◽  
W. G. M. Bastiaanssen ◽  
B. J .J. M. van den Hurk
Keyword(s):  
Remote Sensing ◽  
Regional Climate Model ◽  
Land Surface ◽  
Evaporation Rate ◽  
Climate Model ◽  
Regional Climate ◽  
Remote Sensing Data ◽  
Open Water ◽  
Groundwater Table ◽  
Water Evaporation

Abstract. Despite its local and regional importance, hydro-meteorological data on the Sudd (one of Africa's largest wetlands) is very scanty. This is due to the physical and political situation of this area of Sudan. The areal size of the wetland, the evaporation rate, and the influence on the micro and meso climate are still unresolved questions of the Sudd hydrology. The evaporation flux from the Sudd wetland has been estimated using thermal infrared remote sensing data and a parameterization of the surface energy balance (SEBAL model). It is concluded that the actual spatially averaged evaporation from the Sudd wetland over 3 years of different hydrometeorological characteristics varies between 1460 and 1935 mm/yr. This is substantially less than open water evaporation. The wetland area appears to be 70% larger than previously assumed when the Sudd was considered as an open water body. The temporal analysis of the Sudd evaporation demonstrated that the variation of the atmospheric demand in combination with the inter-annual fluctuation of the groundwater table results into a quasi-constant evaporation rate in the Sudd, while open water evaporation depicts a clear seasonal variability. The groundwater table characterizes a distinct seasonality, confirming that substantial parts of the Sudd are seasonal swamps. The new set of spatially distributed evaporation parameters from remote sensing form an important dataset for calibrating a regional climate model enclosing the Nile Basin. The Regional Atmospheric Climate Model (RACMO) provides an insight not only into the temporal evolution of the hydro-climatological parameters, but also into the land surface climate interactions and embedded feedbacks. The impact of the flooding of the Sudd on the Nile hydroclimatology has been analysed by simulating two land surface scenarios (with and without the Sudd wetland). The paper presents some of the model results addressing the Sudd's influence on rainfall, evaporation and runoff of the river Nile, as well as the influence on the microclimate. The paper presents a case study that confirms the feasibility of using remote sensing data (with good spatial and poor temporal coverage) in conjunction with a regional climate model. The combined model provides good temporal and spatial representation in a region characterized by extremely scarce ground data.

scholarly journals An assessment of aerosol optical properties from remote-sensing observations and regional chemistry–climate coupled models over Europe

2018 ◽  
Vol 18 (7) ◽  
pp. 5021-5043 ◽  
Author(s):  
Laura Palacios-Peña ◽  
Rocío Baró ◽  
Alexander Baklanov ◽  
Alessandra Balzarini ◽  
Dominik Brunner ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Optical Properties ◽  
Atmospheric Aerosols ◽  
Climate Models ◽  
Regional Climate ◽  
Chemical Properties ◽  
Remote Sensing Data ◽  
Regional Climate Models ◽  
Aerosol Optical Properties ◽  
Coupled Models

Abstract. Atmospheric aerosols modify the radiative budget of the Earth due to their optical, microphysical and chemical properties, and are considered one of the most uncertain climate forcing agents. In order to characterise the uncertainties associated with satellite and modelling approaches to represent aerosol optical properties, mainly aerosol optical depth (AOD) and Ångström exponent (AE), their representation by different remote-sensing sensors and regional online coupled chemistry–climate models over Europe are evaluated. This work also characterises whether the inclusion of aerosol–radiation (ARI) or/and aerosol–cloud interactions (ACI) help improve the skills of modelling outputs.Two case studies were selected within the EuMetChem COST Action ES1004 framework when important aerosol episodes in 2010 all over Europe took place: a Russian wildfire episode and a Saharan desert dust outbreak that covered most of the Mediterranean Sea. The model data came from different regional air-quality–climate simulations performed by working group 2 of EuMetChem, which differed according to whether ARI or ACI was included or not. The remote-sensing data came from three different sensors: MODIS, OMI and SeaWIFS. The evaluation used classical statistical metrics to first compare satellite data versus the ground-based instrument network (AERONET) and then to evaluate model versus the observational data (both satellite and ground-based data).Regarding the uncertainty in the satellite representation of AOD, MODIS presented the best agreement with the AERONET observations compared to other satellite AOD observations. The differences found between remote-sensing sensors highlighted the uncertainty in the observations, which have to be taken into account when evaluating models. When modelling results were considered, a common trend for underestimating high AOD levels was observed. For the AE, models tended to underestimate its variability, except when considering a sectional approach in the aerosol representation. The modelling results showed better skills when ARI+ACI interactions were included; hence this improvement in the representation of AOD (above 30 % in the model error) and AE (between 20 and 75 %) is important to provide a better description of aerosol–radiation–cloud interactions in regional climate models.

Estimating wildfire risk on a Mojave Desert landscape using remote sensing and field sampling

2013 ◽  
Vol 22 (6) ◽  
pp. 770 ◽  
Author(s):  
Peter F. Van Linn ◽  
Kenneth E. Nussear ◽  
Todd C. Esque ◽  
Lesley A. DeFalco ◽  
Richard D. Inman ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Data ◽  
Mojave Desert ◽  
Risk Model ◽  
Model Performance ◽  
Remote Sensing Data ◽  
Fire Risk ◽  
Wildfire Risk ◽  
Fuel Loads ◽  
Desert Landscape

Predicting wildfires that affect broad landscapes is important for allocating suppression resources and guiding land management. Wildfire prediction in the south-western United States is of specific concern because of the increasing prevalence and severe effects of fire on desert shrublands and the current lack of accurate fire prediction tools. We developed a fire risk model to predict fire occurrence in a north-eastern Mojave Desert landscape. First we developed a spatial model using remote sensing data to predict fuel loads based on field estimates of fuels. We then modelled fire risk (interactions of fuel characteristics and environmental conditions conducive to wildfire) using satellite imagery, our model of fuel loads, and spatial data on ignition potential (lightning strikes and distance to roads), topography (elevation and aspect) and climate (maximum and minimum temperatures). The risk model was developed during a fire year at our study landscape and validated at a nearby landscape; model performance was accurate and similar at both sites. This study demonstrates that remote sensing techniques used in combination with field surveys can accurately predict wildfire risk in the Mojave Desert and may be applicable to other arid and semiarid lands where wildfires are prevalent.

scholarly journals New lessons on the Sudd hydrology learned from remote sensing and climate modeling

2005 ◽  
Vol 2 (4) ◽  
pp. 1503-1535 ◽  
Author(s):  
Y. A. Mohamed ◽  
H. H. G. Savenije ◽  
W. G. M. Bastiaanssen ◽  
B. J. J. M. van den Hurk
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Climate Model ◽  
Meteorological Data ◽  
Regional Climate ◽  
Remote Sensing Data ◽  
Open Water ◽  
Water Evaporation ◽  
Thermal Infrared Remote Sensing ◽  
The Impact

Abstract. Despite its local and regional importance, hydro-meteorological data on the Sudd (one of Africa's largest wetlands) is very scanty. This is due to the physical and political situation of this area of Sudan. The areal size of the wetland, the evaporation rate, and the influence on the micro and meso climate are still unresolved questions of the Sudd hydrology. The evaporation flux from the Sudd wetland has been estimated using thermal infrared remote sensing data and a parameterization of the surface energy balance (SEBAL model). It is concluded that the actual spatially averaged evaporation from the Sudd wetland over 3 years of different hydrometeorological characteristics varies between 1460 and 1935 mm/yr. This is substantially less than open water evaporation. The wetland area appears to be 70% larger than previously assumed when the Sudd was considered as an open water body. The groundwater table characterizes a distinct seasonality, confirming that substantial parts of the Sudd are seasonal swamps. The new set of spatially distributed evaporation parameters from remote sensing form an important dataset for calibrating a regional climate model enclosing the Nile Basin. The Regional Atmospheric Climate Model (RACMO) provides an insight not only into the temporal evolution of the hydro-climatological parameters, but also into the land surface climate interactions and embedded feedbacks. The impact of the flooding of the Sudd on the Nile hydroclimatology has been analysed by simulating two land surface scenarios (with and without the Sudd wetland). The paper presents some of the model results addressing the Sudd's influence on rainfall, evaporation and runoff of the river Nile, as well as the influence on the microclimate.

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