Remote sensing and in situ observations of marine slicks associated with inhomogeneous coastal currents

2011 ◽  
Author(s):  
S. Ermakov ◽  
I. Kapustin ◽  
I. Sergievskaya
Keyword(s):  
Remote Sensing ◽  
Coastal Currents ◽  
In Situ Observations

scholarly journals Estudo do ciclo diário da Camada Limite Planetária durante a estação cChuvosa da Amazônia (GOAmazon 2014/15)

2018 ◽  
Vol 40 ◽  
pp. 63 ◽  
Author(s):  
Rayonil Gomes Carneiro ◽  
Alice Henkes ◽  
Gilberto Fisch ◽  
Camilla Kassar Borges
Keyword(s):  
Remote Sensing ◽  
Boundary Layer ◽  
Planetary Boundary Layer ◽  
Remote Sensing Data ◽  
Great Depth ◽  
Wet Season ◽  
Daily Cycle ◽  
Convective Boundary ◽  
In Situ Observations

In the present study, the evolution the diurnal cycle of planetary boundary layer in the wet season at Amazon region during a period of intense observations carried out in the GOAmazon Project 2014/2015 (Green Ocean Amazon).The analysis includes radiosonde and remote sensing data. In general case, the results of the daily cycle in the wet season indicate a Nocturnal boundary layer with a small oscillation in its depth and with a tardy erosion. The convective boundary layer did not present great depth, responding to the low values of sensible heat of the wet season. A comparison between the different techniques(in situ observations and remote sensing)  for estimating the planetary boundary layer is also presented.

Characterization of Remote Sensing Albedo Over Sloped Surfaces Based on DART Simulations and In Situ Observations

2018 ◽  
Vol 123 (16) ◽  
pp. 8599-8622 ◽  
Author(s):  
Shengbiao Wu ◽  
Jianguang Wen ◽  
Dongqin You ◽  
Dalei Hao ◽  
Xingwen Lin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
In Situ Observations

scholarly journals Three-phase Evolution of a Coronal Hole. I. 360° Remote Sensing and In Situ Observations

2018 ◽  
Vol 861 (2) ◽  
pp. 151 ◽  
Author(s):  
Stephan G. Heinemann ◽  
Manuela Temmer ◽  
Stefan J. Hofmeister ◽  
Astrid M. Veronig ◽  
Susanne Vennerstrøm
Keyword(s):  
Remote Sensing ◽  
Coronal Hole ◽  
Phase Evolution ◽  
Three Phase ◽  
In Situ Observations

scholarly journals An Ocean-Colour Time Series for Use in Climate Studies: The Experience of the Ocean-Colour Climate Change Initiative (OC-CCI)

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4285 ◽  
Author(s):  
Shubha Sathyendranath ◽  
Robert Brewin ◽  
Carsten Brockmann ◽  
Vanda Brotas ◽  
Ben Calton ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Marine Ecosystem ◽  
Ocean Colour ◽  
Remote Sensing Reflectance ◽  
Resolution Imaging ◽  
In Situ Observations ◽  
Climate Studies ◽  
Reflectance Data

Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel.

scholarly journals Integrative and comprehensive Understanding on Polar Environments (iCUPE): the concept and initial results

2020 ◽  
Author(s):  
Tuukka Petäjä ◽  
Ella-Maria Duplissy ◽  
Ksenia Tabakova ◽  
Julia Schmale ◽  
Barbara Altstädter ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Natural Resources ◽  
Satellite Remote Sensing ◽  
Integrated Approach ◽  
The Arctic ◽  
Polar Regions ◽  
Comprehensive Understanding ◽  
In Situ Observations ◽  
Initial Results

Abstract. The role of polar regions increases in terms of megatrends such as globalization, new transport routes, demography and use of natural resources consequent effects of regional and transported pollutant concentrations. We set up the ERA-PLANET Strand 4 project iCUPE – integrative and Comprehensive Understanding on Polar Environments to provide novel insights and observational data on global grand challenges with an Arctic focus. We utilize an integrated approach combining in situ observations, satellite remote sensing Earth Observations (EO) and multi-scale modeling to synthesize data from comprehensive long-term measurements, intensive campaigns and satellites to deliver data products, metrics and indicators to the stakeholders concerning the environmental status, availability and extraction of natural resources in the polar areas. The iCUPE work consists of thematic state-of-the-art research and provision of novel data in atmospheric pollution, local sources and transboundary transport, characterization of arctic surfaces and their changes, assessment of concentrations and impacts of heavy metals and persistent organic pollutants and their cycling, quantification of emissions from natural resource extraction and validation and optimization of satellite Earth Observation (EO) data streams. In this paper we introduce the iCUPE project and summarize initial results arising out of integration of comprehensive in situ observations, satellite remote sensing and multiscale modeling in the Arctic context.

Assessment of meteorological and agricultural droughts using in-situ observations and remote sensing data

2019 ◽  
Vol 222 ◽  
pp. 125-138 ◽  
Author(s):  
Depeng Zuo ◽  
Siyang Cai ◽  
Zongxue Xu ◽  
Dingzhi Peng ◽  
Guangyuan Kan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
In Situ Observations

Linking Remote-Sensing and In Situ Observations of Coronal Mass Ejections Using STEREO

Solar Physics ◽  
2011 ◽  
Vol 270 (2) ◽  
pp. 561-573 ◽  
Author(s):  
L. Rodriguez ◽  
M. Mierla ◽  
A. N. Zhukov ◽  
M. West ◽  
E. Kilpua
Keyword(s):  
Remote Sensing ◽  
Coronal Mass Ejections ◽  
In Situ Observations

scholarly journals Supplementing Oscat winds with Saral Altika observations for cyclone studies

2014 ◽  
Vol XL-8 ◽  
pp. 1059-1064 ◽  
Author(s):  
K. Niharika ◽  
H. S. V. Usha Sundari ◽  
A. V. V. Prasad ◽  
E. V. S. Sita Kumari ◽  
V. K. Dadhwal ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Life Cycle ◽  
Wind Speed ◽  
Storm Surge ◽  
Disaster Management ◽  
Management Practices ◽  
In Situ Observations ◽  
Remote Sensing Techniques ◽  
Microwave Sensors

Accurate prediction of life cycle of cyclone is very critical to the disaster management practices. Since the cyclones originate over the oceans where in situ observations are limited, we have to resort to the remote sensing techniques. Both optical and microwave sensors help studying the cyclones. While scatterometer provide wind vectors, altimeters can give only wind speed. In this paper we present how altimeter measurements can supplement the scatterometer observations in determining the radius of maximum winds (RMW). Sustained maximum winds, indicator for the intensity of the cyclone, are within the eye wall of a cyclone at a distance of RMW. This parameter is also useful in predicting right time of the storm surge. In this paper we used the wind speed estimations from AltiKa, an altimeter operating at Ka band.

scholarly journals Information – based uncertainty decomposition in dual channel microwave remote sensing of soil moisture

2020 ◽  
Author(s):  
Bonan Li ◽  
Stephen P. Good
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Mutual Information ◽  
Microwave Remote Sensing ◽  
Reference Network ◽  
Brightness Temperatures ◽  
Dual Channel ◽  
The Us ◽  
In Situ Observations

Abstract. NASA's Soil Moisture Active-Passive (SMAP) mission characterizes global spatiotemporal patterns in surface soil moisture using dual L-band microwave retrievals of horizontal, TBh, and vertical, TBv, polarized microwave brightness temperatures through a modeled relationship between vegetation opacity and surface scattering albedo (i.e. tau-omega model). Although this model has been validated against in situ soil moisture measurements across sparse validations sites, there is lack of systematic characterization of where and why SMAP estimates deviate from the in situ observations. Here, soil moisture observations from the US Climate Reference Network are used within a mutual information framework to decompose the overall retrieval uncertainty from SMAPs Modified Dual Channel Algorithm (MDCA) into random uncertainty derived from raw data itself and model uncertainty derived from the model’s inherent structure. The results shown that, on average, 12 % of the uncertainty in SMAP soil moisture estimates is caused by the loss of information in the MDCA model itself while the remainder is induced by inadequacy of TBh and TBv observations. We find the fraction of algorithm induced uncertainty is negatively correlated (pearson r of −0.48) with correlations between in-situ observations and MDCA estimates. A decomposition of mutual information between TBh, TBv and MDCA soil moisture shows that on average 55 % of the mutual information is redundantly shared by TBh and TBv, while the information provided uniquely from both TBh and TBv is 15 %. The fraction of information redundantly provided by TBh and TBv was found to be tightly correlated (pearson r = −0.7) to how well the MDCA output correlated to in situ observations. Thus, MDCA overall quality improves as TBh and TBv provide more redundant information for the MDCA. This suggests the informational redundancy between these remotely sensed observations can be used as independent metric to assess the overall quality of algorithms using these data streams. This study provides a baseline approach that can also be applied to evaluate other remote sensing models and understand informational loss as satellite retrievals are translated to end user products.

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