The development of new algorithms for remote sensing of snow conditions based on data from the catchment of Øvre Heimdalsvatn and the vicinity

2010 ◽  
pp. 35-46
Author(s):  
Rune Solberg ◽  
Hans Koren ◽  
Jostein Amlien ◽  
Eirik Malnes ◽  
Dagrun Vikhamar Schuler ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Snow Conditions ◽  
New Algorithms

The development of new algorithms for remote sensing of snow conditions based on data from the catchment of Øvre Heimdalsvatn and the vicinity

Hydrobiologia ◽  
2010 ◽  
Vol 642 (1) ◽  
pp. 35-46 ◽  
Author(s):  
Rune Solberg ◽  
Hans Koren ◽  
Jostein Amlien ◽  
Eirik Malnes ◽  
Dagrun Vikhamar Schuler ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Snow Conditions ◽  
New Algorithms

scholarly journals Simulation of the microwave emission of multi-layered snowpacks using the Dense Media Radiative transfer theory: the DMRT-ML model

2013 ◽  
Vol 6 (4) ◽  
pp. 1061-1078 ◽  
Author(s):  
G. Picard ◽  
L. Brucker ◽  
A. Roy ◽  
F. Dupont ◽  
M. Fily ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Thermal Emission ◽  
Remote Sensing Data ◽  
Microwave Emission ◽  
Alpine Regions ◽  
Wide Range ◽  
Dense Media ◽  
Wet Snow ◽  
Snow Conditions

Abstract. DMRT-ML is a physically based numerical model designed to compute the thermal microwave emission of a given snowpack. Its main application is the simulation of brightness temperatures at frequencies in the range 1–200 GHz similar to those acquired routinely by space-based microwave radiometers. The model is based on the Dense Media Radiative Transfer (DMRT) theory for the computation of the snow scattering and extinction coefficients and on the Discrete Ordinate Method (DISORT) to numerically solve the radiative transfer equation. The snowpack is modeled as a stack of multiple horizontal snow layers and an optional underlying interface representing the soil or the bottom ice. The model handles both dry and wet snow conditions. Such a general design allows the model to account for a wide range of snow conditions. Hitherto, the model has been used to simulate the thermal emission of the deep firn on ice sheets, shallow snowpacks overlying soil in Arctic and Alpine regions, and overlying ice on the large ice-sheet margins and glaciers. DMRT-ML has thus been validated in three very different conditions: Antarctica, Barnes Ice Cap (Canada) and Canadian tundra. It has been recently used in conjunction with inverse methods to retrieve snow grain size from remote sensing data. The model is written in Fortran90 and available to the snow remote sensing community as an open-source software. A convenient user interface is provided in Python.

scholarly journals The Use of Sentinel-3 Imagery to Monitor Cyanobacterial Blooms

Environments ◽  
2019 ◽  
Vol 6 (6) ◽  
pp. 60 ◽  
Author(s):  
Igor Ogashawara
Keyword(s):  
Remote Sensing ◽  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Lake Erie ◽  
Water Governance ◽  
Spectral Band ◽  
Cyanobacterial Blooms ◽  
Chl A ◽  
Cyanobacterial Harmful Algal Blooms ◽  
New Algorithms

Cyanobacterial harmful algal blooms (CHABs) have been a concern for aquatic systems, especially those used for water supply and recreation. Thus, the monitoring of CHABs is essential for the establishment of water governance policies. Recently, remote sensing has been used as a tool to monitor CHABs worldwide. Remote monitoring of CHABs relies on the optical properties of pigments, especially the phycocyanin (PC) and chlorophyll-a (chl-a). The goal of this study is to evaluate the potential of recent launch the Ocean and Land Color Instrument (OLCI) on-board the Sentinel-3 satellite to identify PC and chl-a. To do this, OLCI images were collected over the Western part of Lake Erie (U.S.A.) during the summer of 2016, 2017, and 2018. When comparing the use of traditional remote sensing algorithms to estimate PC and chl-a, none was able to accurately estimate both pigments. However, when single and band ratios were used to estimate these pigments, stronger correlations were found. These results indicate that spectral band selection should be re-evaluated for the development of new algorithms for OLCI images. Overall, Sentinel 3/OLCI has the potential to be used to identify PC and chl-a. However, algorithm development is needed.

scholarly journals Remote Sensing of Snow Cover Using Spaceborne SAR: A Review

2019 ◽  
Vol 11 (12) ◽  
pp. 1456 ◽  
Author(s):  
Ya-Lun S. Tsai ◽  
Andreas Dietz ◽  
Natascha Oppelt ◽  
Claudia Kuenzer
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Meteorological Data ◽  
Weather Condition ◽  
Natural Phenomenon ◽  
Snow Cover Extent ◽  
Wet Snow ◽  
Snow Conditions ◽  
Interferometric Sar ◽  
Processing Techniques

The importance of snow cover extent (SCE) has been proven to strongly link with various natural phenomenon and human activities; consequently, monitoring snow cover is one the most critical topics in studying and understanding the cryosphere. As snow cover can vary significantly within short time spans and often extends over vast areas, spaceborne remote sensing constitutes an efficient observation technique to track it continuously. However, as optical imagery is limited by cloud cover and polar darkness, synthetic aperture radar (SAR) attracted more attention for its ability to sense day-and-night under any cloud and weather condition. In addition to widely applied backscattering-based method, thanks to the advancements of spaceborne SAR sensors and image processing techniques, many new approaches based on interferometric SAR (InSAR) and polarimetric SAR (PolSAR) have been developed since the launch of ERS-1 in 1991 to monitor snow cover under both dry and wet snow conditions. Critical auxiliary data including DEM, land cover information, and local meteorological data have also been explored to aid the snow cover analysis. This review presents an overview of existing studies and discusses the advantages, constraints, and trajectories of the current developments.

Recent developments in estimating land surface biogeophysical variables from optical remote sensing

2007 ◽  
Vol 31 (5) ◽  
pp. 501-516 ◽  
Author(s):  
Shunlin Liang
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
State Of The Art ◽  
Optical Remote Sensing ◽  
Nonlinear Inversion ◽  
Spatial And Temporal Scales ◽  
Recent Developments ◽  
Ill Posed ◽  
Earth System Models ◽  
New Algorithms

Earth system models and many other applications require biogeophysical variables, and remote sensing is the only means by which to estimate them at the appropriate spatial and temporal scales. Developing advanced inversion methods to solve ill-posed multidimensional nonlinear inversion problems is critical and very challenging. This article reviews state-of-the-art algorithms for estimating land surface biogeophysical variables in optical remote sensing (from the visible to the thermal infrared spectrum) to stimulate the development of new algorithms and to utilize existing ones.

scholarly journals Microwave Remote Sensing of Snowpack Properties: Potential and Limitations

1987 ◽  
Vol 18 (1) ◽  
pp. 1-20 ◽  
Author(s):  
P. Y. Bernier
Keyword(s):  
Remote Sensing ◽  
Snow Water Equivalent ◽  
Microwave Remote Sensing ◽  
Ground Truth ◽  
Active Sensors ◽  
Ground Truth Data ◽  
Snow Water ◽  
Snow Conditions ◽  
Snowpack Properties ◽  
Microwave Sensors

This review explores from a user's viewpoint the possibilities and limitations of microwave-based techniques for the remote sensing of snowpack properties. Mapping of dry snowpacks and detection of melt onset can be achieved with combinations of readings taken at different frequencies with passive microwave sensors. A combination of readings from both passive and active sensors coupled with ground truth data will be required to estimate snow water equivalent under most snow conditions. Snowpack structure and overlying vegetation still present major problems in the estimation of snowpack water equivalent from microwave remote sensing devices.

scholarly journals Simulation of the microwave emission of multi-layered snowpacks using the dense media radiative transfer theory: the DMRT-ML model

2012 ◽  
Vol 5 (4) ◽  
pp. 3647-3694 ◽  
Author(s):  
G. Picard ◽  
L. Brucker ◽  
A. Roy ◽  
F. Dupont ◽  
M. Fily ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Thermal Emission ◽  
Remote Sensing Data ◽  
Microwave Emission ◽  
Alpine Regions ◽  
Wide Range ◽  
Dense Media ◽  
Wet Snow ◽  
Snow Conditions

Abstract. DMRT-ML is a physically-based numerical model designed to compute the thermal microwave emission of a given snowpack. Its main application is the simulation of brightness temperatures at frequencies in the range 1–200 GHz similar to those acquired routinely by space-based microwave radiometers. The model is based on the Dense Media Radiative Transfer (DMRT) theory for the computation of the snow scattering and extinction coefficients and on the Discrete Ordinate Method (DISORT) to numerically solve the radiative transfer equation. The snowpack is modeled as a stack of multiple horizontal snow layers and an optional underlying interface representing the soil or the bottom ice. The model handles both dry and wet snow conditions. Such a general design allows the user to account for a wide range of snow conditions. Hitherto, the model has been used to simulate the thermal emission of the deep firn on ice sheets, shallow snowpacks overlying soil in Arctic and Alpine regions, and overlying ice on the large ice-sheet margins and glaciers. DMRT-ML has thus been validated in three very different conditions: Antarctica, Barnes Ice Cap (Canada) and Canadian tundra. It has been recently used in conjunction with inverse methods to retrieve snow grain size from remote sensing data. The model is written in Fortran90 and available to the snow remote sensing community as an open-source software.

Assessment of the snow conditions in the major watersheds of Afghanistan using multispectral and passive microwave remote sensing

2012 ◽  
Vol 26 (17) ◽  
pp. 2631-2642 ◽  
Author(s):  
Steven F. Daly ◽  
Carrie M. Vuyovich ◽  
Elias J. Deeb ◽  
Stephen D. Newman ◽  
Timothy B. Baldwin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Microwave Remote Sensing ◽  
Passive Microwave ◽  
Passive Microwave Remote Sensing ◽  
Snow Conditions
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