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.

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.

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>

scholarly journals Feasibility Study of Transparency Estimation in Lake Nakaumi by SGLI using in-situ Spectral Reflectance Data

2009 ◽  
Vol 65 (1) ◽  
pp. 1046-1050
Author(s):  
Yuji SAKUNO ◽  
Yasushi MIYAMOTO ◽  
Toshiaki KOZU ◽  
Toyoshi SHIMOMAI ◽  
Tsuneo MATSUNAGA ◽  
...  
Keyword(s):  
Feasibility Study ◽  
Spectral Reflectance ◽  
Lake Nakaumi ◽  
Reflectance 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

<p>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 “EPIPELAGIC: ExPert Integrated suPport systEm for coastaL mixed urbAn – industrial – critical infrastructure monitorinG usIng Combined technologies” 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 “explainable recommendations” 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.</p>

scholarly journals Estudio del proceso de evaporación en el Salar Tres Quebradas por medio de medidas in situ y datos de satélite

2019 ◽  
pp. 1
Author(s):  
F. Carmona ◽  
R. Rivas ◽  
A.M.G. Faramiñán ◽  
C. Mancino ◽  
M. Bayala ◽  
...  
Keyword(s):  
Satellite Data ◽  
Landsat 8 ◽  
Flow Models ◽  
Spectral Signatures ◽  
Class A ◽  
Salt Flats ◽  
Level Function ◽  
Evaporation Pan ◽  
Numerical Flow Modeling

<p>The calculation of evaporation (<em>Ev</em>) is a fundamental process on the planning of investment for nonmetallic mining in salt flats. Dispose to reliable estimates of evaporation allows to reduce one of the main uncertainties of the flow models in this type of basin. This paper focuses on the calculation of <em>Ev</em> in the Tres Quebradas salt flat, Catamarca (Argentina), applying Priestley-Taylor model whit satellite data. Study area comprises the Tres Quebradas and Verde lagoons, and a central evaporite zone. Satellite data (CERES and OLI-LandSat 8), meteorological information, brine density measurements, evaporation measurements, and spectral signatures to calculations were used. The lagoons evaporation was estimated and by means of a Class A evaporation pan validated. The evaporation control in evaporite zones also was studied using a phreatic level function. <em>Ev</em> values of 1302 mm year<sup>–1</sup> and 1249 mm year<sup>–1</sup> for the Tres Quebradas and Verde lagoons were obtained, respectively, similar to Class A evaporation pan values measured. In the case of evaporite zones, an average annual value of 152 mm year<sup>–1</sup> was estimated, regulated by the phreatic level. In summary, an average annual of system water loss by evaporation of 1.31±0.32 m<sup>3</sup> s<sup>–1</sup> was obtained, where more than 80% corresponds to the Tres Quebradas and Verde lagoons, and the rest to the central evaporite zone. The results achieved are consistent and will be used as input data in the numerical flow modeling to the estimation of the lithium brine reserve of the salt flats.</p>

scholarly journals Spectral Reflectance in the Spatial-temporal Dynamic of Turbidity, Itaipu Reservoir, Brazil

2021 ◽  
Vol 44 ◽  
Author(s):  
Douglas Stefanello Facco ◽  
Laurindo Antonio Guasselli ◽  
Luis Fernando Chimelo Ruiz ◽  
João Paulo Delapasse Simioni ◽  
Daiane Gerhardt Dick
Keyword(s):  
Sediment Transport ◽  
Spectral Reflectance ◽  
Storage Capacity ◽  
Space Time ◽  
Landsat 8 ◽  
Time Dynamics ◽  
High Turbidity ◽  
Useful Life ◽  
And Storage ◽  
Reflectance Data

Water quality and the useful life of reservoirs and dams are influenced by the entry of suspended solids, in addition to reducing theirtransparency and storage capacity. It is primary to monitor and analyses its space-time dynamics. Thus, the objective of this work isto characterize the dynamics of the Itaipu Reservoir waters from turbidity, rainfall and spectral reflectance data. To characterize thedynamics, the reservoir was divided into 18 aquatic compartments between upstream and downstream, using precipitation data fromthe TRMM sensor and Landsat 8 images in different precipitation situations. NDWI, MNDWI and NDTI water spectral indexes werecalculated from Landsat 8 images. The results showed high correlation between the NDTI index and the turbidity (R² = 0.91). Then theNDTI images were reclassified into low, medium and high turbidity. A strong correlation between turbidity and 4 Band correspondingto the spectral range of red (R² = 0.94) was also obtained. The precipitation has a determinant influence, being the Paraná River, in theperiods of greater precipitation, the main agent in sediment transport. The space-time dynamics showed that the lateral compartmentsof the reservoir have less influence on sediment transport. In this sense, our analysis brought new elements to understand the turbidityvariation in these Itaipu Reservoir compartments, as well as the spectral reflectance dynamics in the space-time characterization relatedto turbidity.

scholarly journals Evaluation Analysis of Landsat Level-1 and Level-2 Data Products Using In Situ Measurements

2020 ◽  
Vol 12 (16) ◽  
pp. 2597
Author(s):  
Cibele Teixeira Pinto ◽  
Xin Jing ◽  
Larry Leigh
Keyword(s):  
In Situ Measurements ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Spectral Bands ◽  
Data Products ◽  
Landsat 7 ◽  
Level 1 ◽  
Level 2 ◽  
Reflectance Data

Landsat Level-1 products are delivered as quantized and calibrated scaled Digital Numbers (DN). The Level-1 DN data can be rescaled to Top-of-Atmosphere (TOA) reflectance applying radiometric rescaling coefficients. Currently, the Level-1 product is the standard data product of the Landsat sensors. The more recent Level-2 data products contain surface reflectance values, i.e., reflectance as it would be measured at ground level in the absence of atmospheric effects; in the near future, these products are anticipated to become the standard products of the Landsat sensors. The purpose of this paper is to present a radiometric performance evaluation of Level-1 and Level-2 data products for the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) sensors. TOA reflectance and derived surface reflectance values from both data products were evaluated and compared to in situ measurements from eight test sites located in Turkey, Brazil, Chile, the United States, France, and Namibia. The results indicate an agreement between the ETM+ and OLI Level-1 TOA reflectance data and the in situ measurements of 3.9% to 6.5% and 3.9% to 6.0%, respectively, across all spectral bands. Agreement between the in situ measurements and both Level-2 surface reflectance data products were consistently decreased in the shorter wavelength bands, and slightly better in the longer wavelength bands. The mean percent absolute error for Level-2 surface reflectance data ranged from 3.3% to 10% for both Landsat sensors. The significant decay in agreement with the data collected in situ in the short wavelength spectral bands with Level-2 data suggests issues with retrieval of aerosol concentration at some sites. In contrast, the results indicate a reasonably accurate estimate of water vapor in the mid-infrared spectrum. Lastly, despite the less reliable performance of Level-2 data product in the visible spectral region (compared with Level-1 data) in both sensors, the Landsat-8 OLI Level-2 showed an improvement of surface reflectance product over all spectral bands in common with the Landsat-7 ETM+ Level-2 data.

scholarly journals Can single empirical algorithms accurately predict inland shallow water quality status from high resolution, multi-sensor, multi-temporal satellite data?

2015 ◽  
Vol XL-7/W3 ◽  
pp. 1511-1516 ◽  
Author(s):  
I. Theologou ◽  
M. Patelaki ◽  
K. Karantzalos
Keyword(s):  
Water Quality ◽  
High Resolution ◽  
Satellite Data ◽  
Landsat 8 ◽  
Landsat 7 ◽  
Multi Temporal ◽  
Water Quality Status ◽  
Quality Status ◽  
Empirical Algorithms

Assessing and monitoring water quality status through timely, cost effective and accurate manner is of fundamental importance for numerous environmental management and policy making purposes. Therefore, there is a current need for validated methodologies which can effectively exploit, in an unsupervised way, the enormous amount of earth observation imaging datasets from various high-resolution satellite multispectral sensors. To this end, many research efforts are based on building concrete relationships and empirical algorithms from concurrent satellite and in-situ data collection campaigns. We have experimented with Landsat 7 and Landsat 8 multi-temporal satellite data, coupled with hyperspectral data from a field spectroradiometer and in-situ ground truth data with several physico-chemical and other key monitoring indicators. All available datasets, covering a 4 years period, in our case study Lake Karla in Greece, were processed and fused under a quantitative evaluation framework. The performed comprehensive analysis posed certain questions regarding the applicability of single empirical models across multi-temporal, multi-sensor datasets towards the accurate prediction of key water quality indicators for shallow inland systems. Single linear regression models didn’t establish concrete relations across multi-temporal, multi-sensor observations. Moreover, the shallower parts of the inland system followed, in accordance with the literature, different regression patterns. Landsat 7 and 8 resulted in quite promising results indicating that from the recreation of the lake and onward consistent per-sensor, per-depth prediction models can be successfully established. The highest rates were for chl-a (r<sup>2</sup>=89.80%), dissolved oxygen (r<sup>2</sup>=88.53%), conductivity (r<sup>2</sup>=88.18%), ammonium (r<sup>2</sup>=87.2%) and pH (r<sup>2</sup>=86.35%), while the total phosphorus (r<sup>2</sup>=70.55%) and nitrates (r<sup>2</sup>=55.50%) resulted in lower correlation rates.

scholarly journals Optimal Spatiotemporal Scales to Aggregate Satellite Ocean Color Data for Nearshore Reefs and Tropical Coastal Waters: Two Case Studies

2021 ◽  
Vol 8 ◽  
Author(s):  
Erick F. Geiger ◽  
Scott F. Heron ◽  
William J. Hernández ◽  
Jamie M. Caldwell ◽  
Kim Falinski ◽  
...  
Keyword(s):  
Water Quality ◽  
Puerto Rico ◽  
Satellite Data ◽  
Ocean Color ◽  
Local Analysis ◽  
Small Scale ◽  
Space And Time ◽  
Data Gaps ◽  
Color Data

Remotely sensed ocean color data are useful for monitoring water quality in coastal environments. However, moderate resolution (hundreds of meters to a few kilometers) satellite data are underutilized in these environments because of frequent data gaps from cloud cover and algorithm complexities in shallow waters. Aggregating satellite data over larger space and time scales is a common method to reduce data gaps and generate a more complete time series, but potentially smooths out the small-scale, episodic changes in water quality that can have ecological influences. By comparing aggregated satellite estimates of Kd(490) with related in-water measurements, we can understand the extent to which aggregation methods are viable for filling gaps while being able to characterize ecologically relevant water quality conditions. In this study, we tested a combination of six spatial and seven temporal scales for aggregating data from the VIIRS instrument at several coral reef locations in Maui, Hawai‘i and Puerto Rico and compared these with in situ measurements of Kd(490) and turbidity. In Maui, we found that the median value of a 5-pixels, 7-days spatiotemporal cube of satellite data yielded a robust result capable of differentiating observations across small space and time domains and had the best correlation among spatiotemporal cubes when compared with in situ Kd(490) across 11 nearshore sites (R2 = 0.84). We also found long-term averages (i.e., chronic condition) of VIIRS data using this aggregation method follow a similar spatial pattern to onshore turbidity measurements along the Maui coast over a three-year period. In Puerto Rico, we found that the median of a 13-pixels, 13-days spatiotemporal cube of satellite data yielded the best overall result with an R2 = 0.54 when compared with in situ Kd(490) measurements for one nearshore site with measurement dates spanning 2016–2019. As spatiotemporal cubes of different dimensions yielded optimum results in the two locations, we recommend local analysis of spatial and temporal optima when applying this technique elsewhere. The use of satellite data and in situ water quality measurements provide complementary information, each enhancing understanding of the issues affecting coastal ecosystems, including coral reefs, and the success of management efforts.

scholarly journals ANALYSIS OF IN-SITU SPECTRAL REFLECTANCE OF SAGO AND OTHER PALMS: IMPLICATIONS FOR THEIR DETECTION IN OPTICAL SATELLITE IMAGES

2018 ◽  
Vol IV-3 ◽  
pp. 185-191
Author(s):  
Jojene Rendon Santillan ◽  
Meriam Makinano-Santillan
Keyword(s):  
Optical Sensors ◽  
Spectral Reflectance ◽  
Satellite Images ◽  
Spectral Response ◽  
Coconut Oil ◽  
Landsat 8 ◽  
Electromagnetic Spectrum ◽  
Palm Species ◽  
Optical Satellite Images

We present a characterization, comparison and analysis of in-situ spectral reflectance of Sago and other palms (coconut, oil palm and nipa) to ascertain on which part of the electromagnetic spectrum these palms are distinguishable from each other. The analysis also aims to reveal information that will assist in selecting which band to use when mapping Sago palms using the images acquired by these sensors. The datasets used in the analysis consisted of averaged spectral reflectance curves of each palm species measured within the 345&amp;ndash;1045&amp;thinsp;nm wavelength range using an Ocean Optics USB4000-VIS-NIR Miniature Fiber Optic Spectrometer. This in-situ reflectance data was also resampled to match the spectral response of the 4 bands of ALOS AVNIR-2, 3 bands of ASTER VNIR, 4 bands of Landsat 7 ETM+, 5 bands of Landsat 8, and 8 bands of Worldview-2 (WV2). Examination of the spectral reflectance curves showed that the near infra-red region, specifically at 770, 800 and 875 nm, provides the best wavelengths where Sago palms can be distinguished from other palms. The resampling of the in-situ reflectance spectra to match the spectral response of optical sensors made possible the analysis of the differences in reflectance values of Sago and other palms in different bands of the sensors. Overall, the knowledge learned from the analysis can be useful in the actual analysis of optical satellite images, specifically in determining which band to include or to exclude, or whether to use all bands of a sensor in discriminating and mapping Sago palms.

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