scholarly journals Small scale spatial variability of bare-ice albedo at Jamtalferner, Austria

2020 ◽  
Author(s):  
Lea Hartl ◽  
Lucia Felbauer ◽  
Gabriele Schwaizer ◽  
Andrea Fischer
Keyword(s):  
Satellite Data ◽  
Full Range ◽  
Temporal Variations ◽  
Small Scale ◽  
Landsat 8 ◽  
Dry Ice ◽  
In Situ Data ◽  
Depth Analysis ◽  
The Difference

Abstract. As Alpine glaciers recede, they are quickly becoming snow free in summer and, accordingly, spatial and temporal variations in ice albedo increasingly affect the melt regime. To accurately model future developments, such as deglaciation patterns, it is important to understand the processes governing broadband and spectral albedo at a local scale. However, little in situ data of ice albedo exits. As a contribution to this knowledge gap, we present spectral reflectance data from 325 to 1075 nm collected along several profile lines in the ablation zone of Jamtalferner, Austria. Measurements were timed to closely coincide with a Sentinel 2 and Landsat 8 overpass and are compared to the respective ground reflectance products. The brightest spectra have a maximum reflectance of up to 0.7 and consist of clean, dry ice. In contrast, reflectance does not exceed 0.2 at dark spectra where liquid water and/or fine grained debris are present. Spectra can roughly be grouped into dry ice, wet ice, and dirt/rocks, although transitions between types are fluid. Neither satellite captures the full range of in situ reflectance values. The difference between ground and satellite data is not uniform across satellite bands, between Landsat and Sentinel, and to some extent between ice surface types (underestimation of reflectance for bright surfaces, overestimation for dark surfaces). We wish to highlight the need for further, systematic measurements of in situ spectral albedo, its variability in time and space, and in- depth analysis of time-synchronous satellite data.

scholarly journals Small-scale spatial variability in bare-ice reflectance at Jamtalferner, Austria

2020 ◽  
Vol 14 (11) ◽  
pp. 4063-4081
Author(s):  
Lea Hartl ◽  
Lucia Felbauer ◽  
Gabriele Schwaizer ◽  
Andrea Fischer
Keyword(s):  
Satellite Data ◽  
Spectral Reflectance ◽  
Full Range ◽  
Feedback Mechanism ◽  
Small Scale ◽  
Landsat 8 ◽  
Dry Ice ◽  
Depth Analysis ◽  
Reflectance Data

Abstract. As Alpine glaciers become snow-free in summer, more dark, bare ice is exposed, decreasing local albedo and increasing surface melting. To include this feedback mechanism in models of future deglaciation, it is important to understand the processes governing broadband and spectral albedo at a local scale. However, few in situ reflectance data have been measured in the ablation zones of mountain glaciers. As a contribution to this knowledge gap, we present spectral reflectance data (hemispherical–conical–reflectance factor) from 325 to 1075 nm collected along several profile lines in the ablation zone of Jamtalferner, Austria. Measurements were timed to closely coincide with a Sentinel-2 and Landsat 8 overpass and are compared to the respective ground reflectance (bottom-of-atmosphere) products. The brightest spectra have a maximum reflectance of up to 0.7 and consist of clean, dry ice. In contrast, reflectance does not exceed 0.2 for dark spectra where liquid water and/or fine-grained debris are present. Spectra can roughly be grouped into dry ice, wet ice, and dirt or rocks, although gradations between these groups occur. Neither satellite captures the full range of in situ reflectance values. The difference between ground and satellite data is not uniform across satellite bands, between Landsat and Sentinel, and to some extent between ice surface types (underestimation of reflectance for bright surfaces, overestimation for dark surfaces). We highlight the need for further, systematic measurements of in situ spectral reflectance properties, their variability in time and space, and in-depth analysis of time-synchronous satellite data.

Small scale spatial variability of spectral Albedo on Jamtalferner, Austria

2020 ◽  
Author(s):  
Lea Hartl ◽  
Lucia Felbauer ◽  
Gabriele Schwaizer ◽  
Andrea Fischer
Keyword(s):  
Satellite Data ◽  
Liquid Water ◽  
Spectral Reflectance ◽  
Correlation Coefficients ◽  
Landsat 8 ◽  
Dry Ice ◽  
Ablation Area ◽  
In Situ Data ◽  
Sentinel 2

<p>Glacier albedo is one of the most important and most variable parameters affecting surface energy balance and directly impacts ice loss. We present preliminary results from a study aiming to quantify the range and variability of spectral reflectance on a glacier terminus and assess the effects of liquid water and impurities on ablation area reflectance. In a second step of the analysis, in-situ data is compared with Landsat 8 and Sentinel 2 surface reflectance products.</p><p>In-situ spectral reflectance data was collected for wavelengths from 350-1000nm, using a hand-held ASD spectroradiometer. 246 spectra were gathered along 16 profile lines.</p><p>The “brightest” profile has a maximum reflectance of 0.7 and consists of clean, dry ice. At several “dark” profiles, reflectance does not exceed 0.2. At these profiles, liquid water is present, often mixed with fine grained debris. Individual spectra can roughly be grouped into dry ice, wet ice, and dirt/rocks. However, transitions between groups are fluid and in practice these categories cannot always be clearly separated. The spread of reflectance values per profile is generally lower for darker profiles. The reflectance spectra for clean ice exhibit the typical shape found in literature, with highest reflectance values in the lower third of our wavelength range and declining values for wavelengths greater than approximately 580nm. For wet ice surfaces, the spectra follow roughly the same shape as for dry ice, but are strongly dampened in amplitude, with reflectance typically below 0.2.</p><p>For the comparison of in-situ and satellite data, we use a Sentinel 2A scene acquired the same day as the ground measurements and a Landsat 8 scene from the previous day. Both scenes are cloud free over the study area. The wavelength range of the in-situ data overlaps with Landsat 8 bands 1-5 and Sentinel 2 bands 1-9 and 8A, respectively.</p><p>Neither satellite captures the full range of in-situ reflectance values. In all bands in which both satellites overlap, Sentinel values are shifted up against Landsat, in the sense that the maximum values of the Sentinel data are closer to the maximum values measured on the ground, while the minimum Landsat data are closer to the minimum ground values. Comparing the mean of the spectral reflectances measured on the ground with the associated satellite band values yields Pearson correlation coefficients from 0.53 to 0.62 for Landsat and 0.3 to 0.65 for Sentinel. Correlation coefficients decrease significantly for lower resolution satellite bands.</p><p>When binning ground measurements by the associated satellite pixel, the difference between the median/mean ground value and the satellite value tends to decrease with increasing number of ground measurements mapped to unique satellite pixels. While this is expected, the relationship is not obviously linear for our data and differs between the satellites and different bands.</p><p>Further in-situ measurements and analysis of satellite data will be carried out to improve understanding of processes governing ablation area reflectance, satellite derived ablation area reflectance products, and the modelling of feedback mechanisms.</p>

STORM ICE OIL WIND WAVE WATCH SYSTEM (SIOWS): WEB GIS APPLICATION FOR MONITORING THE ARCTIC

2017 ◽  
Author(s):  
Alexander Myasoedov ◽  
Alexander Myasoedov ◽  
Sergey Azarov ◽  
Sergey Azarov ◽  
Ekaterina Balashova ◽  
...  
Keyword(s):  
Satellite Data ◽  
Wind Wave ◽  
Arctic Region ◽  
Web Gis ◽  
The Arctic ◽  
Flexible Tool ◽  
In Situ Data ◽  
Current State ◽  
Watch System

Working with satellite data, has long been an issue for users which has often prevented from a wider use of these data because of Volume, Access, Format and Data Combination. The purpose of the Storm Ice Oil Wind Wave Watch System (SIOWS) developed at Satellite Oceanography Laboratory (SOLab) is to solve the main issues encountered with satellite data and to provide users with a fast and flexible tool to select and extract data within massive archives that match exactly its needs or interest improving the efficiency of the monitoring system of geophysical conditions in the Arctic. SIOWS - is a Web GIS, designed to display various satellite, model and in situ data, it uses developed at SOLab storing, processing and visualization technologies for operational and archived data. It allows synergistic analysis of both historical data and monitoring of the current state and dynamics of the "ocean-atmosphere-cryosphere" system in the Arctic region, as well as Arctic system forecasting based on thermodynamic models with satellite data assimilation.

scholarly journals RETRIEVAL OF SEA SURFACE TEMPERATURE OVER POTERAN ISLAND WATER OF INDONESIA WITH LANDSAT 8 TIRS IMAGE: A PRELIMINARY ALGORITHM

2015 ◽  
Vol XL-2/W4 ◽  
pp. 87-90 ◽  
Author(s):  
M. A. Syariz ◽  
L. M. Jaelani ◽  
L. Subehi ◽  
A. Pamungkas ◽  
E. S. Koenhardono ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Brightness Temperature ◽  
Sea Surface ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
In Situ Data ◽  
The North ◽  
Thermal Band

The Sea Surface Temperature (SST) retrieval from satellites data Thus, it could provide SST data for a long time. Since, the algorithms of SST estimation by using Landsat 8 Thermal Band are sitedependence, we need to develop an applicable algorithm in Indonesian water. The aim of this research was to develop SST algorithms in the North Java Island Water. The data used are in-situ data measured on April 22, 2015 and also estimated brightness temperature data from Landsat 8 Thermal Band Image (band 10 and band 11). The algorithm was established using 45 data by assessing the relation of measured in-situ data and estimated brightness temperature. Then, the algorithm was validated by using another 40 points. The results showed that the good performance of the sea surface temperature algorithm with coefficient of determination (<i>R</i><sup>2</sup>) and Root Mean Square Error (<i>RMSE</i>) of 0.912 and 0.028, respectively.

scholarly journals OPTMIZATION OF BIO-OPTICAL MODEL PARAMETERS FOR TURBID LAKE WATER QUALITY ESTIMATION USING LANDSAT 8 AND WASI-2D

2020 ◽  
Vol XLII-3/W11 ◽  
pp. 67-72
Author(s):  
A. Manuel ◽  
A. C. Blanco ◽  
A. M. Tamondong ◽  
R. Jalbuena ◽  
O. Cabrera ◽  
...  
Keyword(s):  
Water Quality ◽  
Chlorophyll A ◽  
The Philippines ◽  
Quality Data ◽  
Model Parameters ◽  
Landsat 8 ◽  
Water Quality Data ◽  
Chlorophyll A Concentration ◽  
In Situ Data

Abstract. Laguna Lake, the Philippines’ largest freshwater lake, has always been historically, economically, and ecologically significant to the people living near it. However, as it lies at the center of urban development in Metro Manila, it suffers from water quality degradation. Water quality sampling by current field methods is not enough to assess the spatial and temporal variations of water quality in the lake. Regular water quality monitoring is advised, and remote sensing addresses the need for a synchronized and frequent observation and provides an efficient way to obtain bio-optical water quality parameters. Optimization of bio-optical models is done as local parameters change regionally and seasonally, thus requiring calibration. Field spectral measurements and in-situ water quality data taken during simultaneous satellite overpass were used to calibrate the bio-optical modelling tool WASI-2D to get estimates of chlorophyll-a concentration from the corresponding Landsat-8 images. The initial output values for chlorophyll-a concentration, which ranges from 10–40 μg/L, has an RMSE of up to 10 μg/L when compared with in situ data. Further refinements in the initial and constant parameters of the model resulted in an improved chlorophyll-a concentration retrieval from the Landsat-8 images. The outputs provided a chlorophyll-a concentration range from 5–12 μg/L, well within the usual range of measured values in the lake, with an RMSE of 2.28 μg/L compared to in situ data.

An integrated decision support system using satellite and in-situ data for coastal area hazard mitigation and resilience to natural disasters

2021 ◽  
Author(s):  
Christos Kontopoulos ◽  
Nikos Grammalidis ◽  
Dimitra Kitsiou ◽  
Vasiliki Charalampopoulou ◽  
Anastasios Tzepkenlis ◽  
...  
Keyword(s):  
Decision Support ◽  
Surface Water ◽  
Decision Support System ◽  
Real Time ◽  
Support System ◽  
Satellite Data ◽  
Hazard Mitigation ◽  
Google Earth ◽  
Landsat 8

&lt;p&gt;Nowadays, the importance of coastal areas is greater than ever, with approximately 10% of the global population living in these areas. These zones are an intermediate space between sea and land and are exposed to a variety of natural (e.g. ground deformation, coastal erosion, flooding, tornados, sea level rise, etc.) and anthropogenic (e.g. excessive urbanisation) hazards. Therefore, their conservation and proper sustainable management is deemed crucial both for economic and environmental purposes. The main goal of the Greece-China bilateral research project &amp;#8220;EPIPELAGIC: ExPert Integrated suPport systEm for coastaL mixed urbAn &amp;#8211; industrial &amp;#8211; critical infrastructure monitorinG usIng Combined technologies&amp;#8221; is the design and deployment of an integrated Decision Support System (DSS) for hazard mitigation and resilience. The system exploits near-real time data from both satellite and in-situ sources to efficiently identify and produce alerts for important risks (e.g. coastal flooding, soil erosion, degradation, subsidence), as well as to monitor other important changes (e.g. urbanization, coastline). To this end, a robust methodology has been defined by fusing satellite data (Optical/multispectral, SAR, High Resolution imagery, DEMs etc.) and in situ real-time measurements (tide gauges, GPS/GNSS etc.). For the satellite data pre-processing chain, image composite/mosaic generation techniques will be implemented via Google Earth Engine (GEE) platform in order to access Sentinel 1, Sentinel 2, Landsat 5 and Landsat 8 imagery for the studied time period (1991-2021). These optical and SAR composites will be stored into the main database of the EPIPELAGIC server, after all necessary harmonization and correction techniques, along with other products that are not yet available in GEE (e.g. ERS or Sentinel-1 SLC products) and will have to be locally processed. A Machine Learning (ML) module, using data from this main database will be trained to extract additional high-level information (e.g. coastlines, surface water, urban areas, etc.). Both conventional (e.g. Otsu thresholding, Random Forest, Simple Non-Iterative Clustering (SNIC) algorithm, etc.) and deep learning approaches (e.g. U-NET convolutional networks) will be deployed to address problems such as surface water detection and land cover/use classification. Additionally, in-situ or auxiliary/cadastral datasets will be used as ground truth data. Finally, a Decision Support System (DSS), will be developed to periodically monitor the evolution of these measurements, detect significant changes that may indicate impending risks and hazards, and issue alarms along with suggestions for appropriate actions to mitigate the detected risks. Through the project, the extensive use of Explainable Artificial Intelligence (xAI) techniques will also be investigated in order to provide &amp;#8220;explainable recommendations&amp;#8221; that will significantly facilitate the users to choose the optimal mitigation approach. The proposed integrated monitoring solutions is currently under development and will be applied in two Areas of Interest, namely Thermaic Gulf in Thessaloniki, Greece, and the Yellow River Delta in China. They are expected to provide valuable knowledge, methodologies and modern techniques for exploring the relevant physical mechanisms and offer an innovative decision support tool. Additionally, all project related research activities will provide ongoing support to the local culture, society, economy and environment in both involved countries, Greece and China.&lt;/p&gt;

scholarly journals Surface waters properties in the Laptev and the East-Siberian Seas in summer 2018 from in situ and satellite data

2019 ◽  
Author(s):  
Anastasiia Tarasenko ◽  
Alexandre Supply ◽  
Nikita Kusse-Tiuz ◽  
Vladimir Ivanov ◽  
Mikhail Makhotin ◽  
...  
Keyword(s):  
Surface Water ◽  
Satellite Data ◽  
Water Masses ◽  
Sea Surface ◽  
Ekman Transport ◽  
Water Displacement ◽  
Wind Speeds ◽  
In Situ Data ◽  
Ocean Salinity

Abstract. Variability of surface water masses of the Laptev and the East-Siberian seas in August–September 2018 is studied using in situ and satellite data. In situ data was collected during ARKTIKA-2018 expedition and then completed with satellite estimates of sea surface temperature (SST) and salinity (SSS), sea surface height, satellite-derived wind speeds and sea ice concentrations. Derivation of SSS is still challenging in high latitude regions, and the quality of Soil Moisture and Ocean Salinity (SMOS) SSS retrieval was improved by applying a threshold on SSS weekly error. The validity of SST and SSS products is demonstrated using ARKTIKA-2018 continuous thermosalinograph measurements and CTD casts. The surface gradients and mixing of river and sea waters in the free of ice and ice covered areas is described with a special attention to the marginal ice zone. The Ekman transport was calculated to better understand the pathway of surface water displacement. T-S diagram using surface satellite estimates shows a possibility to investigate the surface water masses transformation in detail.

scholarly journals Estimating daily surface NO<sub>2</sub> concentrations from satellite data - A case study over Hong Kong using land use regression models

2016 ◽  
Author(s):  
Jasdeep S Anand ◽  
Paul S Monks
Keyword(s):  
Land Use ◽  
Hong Kong ◽  
Satellite Data ◽  
Monitoring Network ◽  
Fine Scale ◽  
Land Use Regression ◽  
Vertical Column ◽  
Mixed Effect ◽  
In Situ Data

Abstract. Land Use Regression (LUR) models have been used in epidemiology to determine the fine-scale spatial variation in air pollutants such as nitrogen dioxide (NO2) in cities and larger regions. However, they are often limited in their temporal resolution, which may potentially be rectified by employing the synoptic coverage provided by satellite measurements. In this work a mixed effects LUR model is developed to model daily surface NO2 concentrations over the Hong Kong SAR during 2005-2015. In-situ measurements from the Hong Kong Air Quality Monitoring Network, along with tropospheric vertical column density (VCD) data from the OMI, GOME-2A and SCIAMACHY satellite instruments were combined with fine-scale land use parameters to provide the spatiotemporal information necessary to predict daily surface concentrations. Cross-validation with the in-situ data shows that the mixed effect LUR model using OMI data has a high predictive power (adj. R2 = 0.84), especially when compared with surface concentrations derived using the MACC-II reanalysis model dataset (adj. R2 = 0.11). Time series analysis shows no statistically significant trend in NO2 concentrations during 2005-2015, despite a reported decline in NOx emissions. This study demonstrates the utility in combining satellite data with LUR models to derive daily maps of ambient surface NO2 for use in exposure studies.

Geo-spatial Modeling for Automated Demarcation of Snow Avalanche Hazard Areas Using Landsat-8 Satellite Images and In Situ Data

2019 ◽  
Vol 47 (3) ◽  
pp. 513-526 ◽  
Author(s):  
Dhiraj Kumar Singh ◽  
Varunendra Dutta Mishra ◽  
Hemendra Singh Gusain ◽  
Neena Gupta ◽  
Arun Kumar Singh
Keyword(s):  
Spatial Modeling ◽  
Satellite Images ◽  
Snow Avalanche ◽  
Landsat 8 ◽  
Avalanche Hazard ◽  
In Situ Data

scholarly journals Mapping of Snow Depth by Blending Satellite and In-Situ Data Using Two-Dimensional Optimal Interpolation—Application to AMSR2

2019 ◽  
Vol 11 (24) ◽  
pp. 3049 ◽  
Author(s):  
Cezar Kongoli ◽  
Jeffrey Key ◽  
Thomas M. Smith
Keyword(s):  
North America ◽  
Snow Depth ◽  
Weather Prediction ◽  
Weighted Average ◽  
Optimal Interpolation ◽  
Horizontal Distance ◽  
Two Dimensional ◽  
In Situ Data ◽  
The Difference

The development of a snow depth product over North America is investigated by applying two-dimensional optimal interpolation to passive microwave satellite-derived and in-situ measured snow depth. At each snow-covered satellite footprint, the technique computes a snow depth increment as the weighted average of data increments, and updates the satellite-derived snow depth accordingly. Data increments are computed as the difference between the in-situ-measured and satellite snow depth at station locations surrounding the satellite footprint. Calculation of optimal weights is based on spatial lag autocorrelation of snow depth increments, modelled as functions of horizontal distance and elevation difference between pairs of observations. The technique is applied to Advanced Microwave Scanning Radiometer 2 (AMSR2) snow depth and in-situ snow depth obtained from the Global Historical Climatology Network. The results over North America during January–February 2017 indicate that the technique greatly enhances the performance of the satellite estimates, especially over mountain terrain, albeit with an accuracy inferior to that over low elevation areas. Moreover, the technique generates more accurate output compared to that from NOAA’s Global Forecast System, with implications for improving the utilization of satellite data in snow assessments and numerical weather prediction.

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