Taking advantage of Multisensor Synergy: New discovery and analysis tools

2020 ◽  
Author(s):  
Lucile Gaultier ◽  
Fabrice Collard ◽  
Ziad El Khoury Hanna ◽  
Gilles Guitton ◽  
Sylvain Herlédan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Interaction Analysis ◽  
Remote Sensing Data ◽  
Open Ocean ◽  
Online Portal ◽  
In Situ Data ◽  
Real Time Analysis ◽  
Wide Range ◽  
Sample Frequency

<p>Numerous new satellites and sensors have arised during the past decade. This satellite constellation has never been so dense and diverse. It provides a wide range of view angles to the ocean surface from the coast to the open ocean, at various scales and from physical to biological processes. Sentinel 1-2-3 program covers various sensors such as SAR, Optical, radiometer or altimeter with a repeat subcycle of only a few days, yet the repeat frequency for each sensor alone is not enough to monitor meso to submeso scales.</p><p>In the other hand, in-situ data are sparse in space but offers a high sample frequency and therefore complementary to remote sensing<br>observations. Handling consistently these huge heterogeneous datasets in a simple, fast and convenient way is now possible using the free and open Ocean Virtual Laboratory online portal or its standalone version. These tools are starting to be widely used by the scientific community to better discover, understand and monitor oceanic processes. We will demonstrate the potential and functionalities of these tools using various test cases:</p><p>Collocating Sentinel 1-2-3 for wave current interaction analysis<br>Creating synoptic charts of fronts and eddies, highlighting strong and energetic ocean currents<br>Campaign at sea planning and real time analysis of in-situ / remote sensing data. <br>Validation and comparison of currents (derived from satellite and models) with a Lagrangian approach using SEAScope stand alone interactive tool. </p><p><br>Online tool is available at https://ovl.oceandatalab.com and standalone version at https://seascope.oceandatalab.com. A splinter-meeting will<br>be organised at the conference to provide hands-on demonstration. </p>

scholarly journals Trophic status assessment of small turbid lakes comparing remote sensing and in situ data: case study at lower Danube floodplain

2020 ◽  
Vol 32 ◽  
pp. 53-63
Author(s):  
Stefan Kazakov ◽  
Valko Biserkov ◽  
Luchezar Pehlivanov ◽  
Stoyan Nedkov
Keyword(s):  
Remote Sensing ◽  
Chlorophyll A ◽  
Satellite Data ◽  
Suspended Matter ◽  
Trophic Status ◽  
Remote Sensing Data ◽  
Water Quality Monitoring ◽  
Floodplain Lakes ◽  
In Situ Data

The aim of the study was to compare in situ and remote sensing data, in order to assess the applicability of satellite images in water quality monitoring of floodplain lakes. Two indicators of trophic status were compared: chlorophyll a and total suspended matter. Two lakes on Lower Danube floodplain were selected: Srebarna and Malak Preslavets. Data were obtained in July and August 2018. Sentinel 2 MSI L1c images were analyzed in SeNtinel Application Platform (SNAP), (v. 6.0). According to in situ data, Srebarna Lake indicated status of eutrophication, while Malak Preslavets experienced hypertrophic conditions. Satellite data indicated eutrophic conditions for both lakes. Comparing the results from in situ and satellite data, chlorophyll a showed higher correlation (r = 0.66) and comparable results. On the other hand, significantly overestimation of suspended matter according to satellite data were found, as well weaker correlation (r = 0.57) between both methods. Remote sensing i.e. Sentinel products are emerging as a powerful tool in environmental observation. Although weather conditions could have significant impact on environmental dynamic especially in floodplain lakes, combining and comparing of different methods could improve the preciseness of the methodology as well as assessment reliability.

scholarly journals Compact models for adaptive sampling in marine robotics

2019 ◽  
Vol 39 (1) ◽  
pp. 127-142
Author(s):  
Trygve Olav Fossum ◽  
John Ryan ◽  
Tapan Mukerji ◽  
Jo Eidsvik ◽  
Thom Maughan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Adaptive Sampling ◽  
Remote Sensing Data ◽  
Compact Set ◽  
Regional Patterns ◽  
In Situ Data ◽  
Ocean Science ◽  
Autonomous Surface Vehicle ◽  
Compact Models

Finding high-value locations for in situ data collection is of substantial importance in ocean science, where diverse bio-physical processes interact to create dynamically evolving phenomena. These cover a variable spatial extent, and are sparse and difficult to predict. Autonomous robotic platforms can sustain themselves in harsh conditions with persistent presence, but require deployment at the correct place and time. To that end, we consider the use of remote sensing data for building compact models that can improve skill in predicting sub-mesoscale features and inform onboard sampling. The model enables prediction of regional patterns based on sparse in situ data, a capability that is essential in regions where use of satellite remote sensing in real time is often limited by cloud cover. Our model is based on classification of sea-surface temperature (SST) images, but the technique is general across any remotely sensed parameter. Images having similar magnitude and spatial patterns are grouped into a compact set of conditional means representing the dominant states. The classification is unsupervised and uses a combination of dictionary learning and hierarchical clustering. The method is demonstrated using SST images from Monterey Bay, California. The consistency of the classification result is verified and compared with oceanographic forcing using historical wind measurements. The established model is then shown to work in a real application using measurements from an autonomous surface vehicle (ASV), together with forecast and sampling strategies. Finally an analysis of the model prediction error is presented and compared across different paths and survey duration.

scholarly journals REMOTE SENSING OF WATER BALANCE IN PANTANAL

2019 ◽  
Vol 46 (3) ◽  
pp. 20
Author(s):  
Adriana Aparecida Moreira ◽  
Alice César Fassoni-Andrade ◽  
Anderson Luis Ruhoff ◽  
Rodrigo Cauduro Dias de Paiva
Keyword(s):  
Remote Sensing ◽  
Water Balance ◽  
Water Storage ◽  
Remote Sensing Data ◽  
Northern Region ◽  
Sensing Data ◽  
In Situ Data ◽  
Storage Change ◽  
Hydrological Variables

Pantanal, located in the Upper Paraguay basin, is the world’s largest tropical wetland. The maintenance of this ecosystem depends on the water balance since precipitation is seasonal and high losses of water occur due to the high evapotranspiration. Water balance assessment using in situ data is still a challenge due to the large extension of the area and the complexity to be represented. In this study, the water balance in the Upper Paraguay basin was investigated based on hydrological variables derived from remote sensing data. Precipitation, evapotranspiration, and water storage change data were estimated with accuracy by the water balance, but the same was not possible for the discharge. However, high uncertainties in the estimates were verified, mainly during the rainy season. The remote sensing data allowed the identification of the seasonality of hydrological variables in the Pantanal system and in the different regions of the basin: Chaco, Pantanal and Planalto. Water deficit in the basin was observed from March/April to September as well as a positive water balance due to precipitation during the rest of the year. The spatial analysis of the basin showed that in the northern region, the precipitation, the evapotranspiration, and the water storage variation are higher than in the southern region. Results demonstrated that remote sensing data can help in the comprehension of hydrological systems operation, especially in large wetland regions.

scholarly journals In-situ multispectral and bathymetric measurements over a supraglacial lake in western Greenland using a remotely controlled watercraft

2011 ◽  
Vol 5 (2) ◽  
pp. 445-452 ◽  
Author(s):  
M. Tedesco ◽  
N. Steiner
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Attenuation Factor ◽  
Greenland Ice Sheet ◽  
Remote Sensing Data ◽  
Lake Depth ◽  
In Situ Data ◽  
Lake Bottom ◽  
Bottom Albedo

Abstract. Supraglacial lakes form from meltwater on the Greenland ice sheet in topographic depressions on the surface, affecting both surface and sub-glacial processes. As the reflectance in the visible and near-infrared regions of a column of water is modulated by its height, retrieval techniques using spaceborne remote sensing data (e.g. Landsat, MODIS) have been proposed in the literature for the detection of lakes and estimation of their volume. These techniques require basic assumptions on the spectral properties of the water as well as the bottom of the lake, among other things. In this study, we report results obtained from the analysis of concurrent in-situ multi-spectral and depth measurements collected over a supraglacial lake during early July 2010 in West Greenland (Lake Olivia, 69°36'35" N, 49°29'40" W) and aim to assess some of the underlying hypotheses in remote sensing based bathymetric approaches. In particular, we focus our attention on the analysis of the lake bottom albedo and of the water attenuation coefficient. The analysis of in-situ data (collected by means of a remotely controlled boat equipped with a GPS, a sonar and a spectrometer) highlights the exponential trend of the water-leaving reflectance with lake depth. The values of the attenuation factor obtained from in-situ data are compared with those computed using approaches proposed in the literature. Also, the values of the lake bottom albedo from in-situ measurements are compared with those obtained from the analysis of reflectance of shallow waters. Finally, we quantify the error between in-situ measured and satellite-estimated lake depth values for the lake under study.

scholarly journals Deep Learning for Monitoring Agricultural Drought in South Asia Using Remote Sensing Data

2021 ◽  
Vol 13 (9) ◽  
pp. 1715
Author(s):  
Foyez Ahmed Prodhan ◽  
Jiahua Zhang ◽  
Fengmei Yao ◽  
Lamei Shi ◽  
Til Prasad Pangali Sharma ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
South Asia ◽  
Model Performance ◽  
Remote Sensing Data ◽  
Agricultural Drought ◽  
Gradient Boosting ◽  
Complex Nature ◽  
Sensing Data

Drought, a climate-related disaster impacting a variety of sectors, poses challenges for millions of people in South Asia. Accurate and complete drought information with a proper monitoring system is very important in revealing the complex nature of drought and its associated factors. In this regard, deep learning is a very promising approach for delineating the non-linear characteristics of drought factors. Therefore, this study aims to monitor drought by employing a deep learning approach with remote sensing data over South Asia from 2001–2016. We considered the precipitation, vegetation, and soil factors for the deep forwarded neural network (DFNN) as model input parameters. The study evaluated agricultural drought using the soil moisture deficit index (SMDI) as a response variable during three crop phenology stages. For a better comparison of deep learning model performance, we adopted two machine learning models, distributed random forest (DRF) and gradient boosting machine (GBM). Results show that the DFNN model outperformed the other two models for SMDI prediction. Furthermore, the results indicated that DFNN captured the drought pattern with high spatial variability across three penology stages. Additionally, the DFNN model showed good stability with its cross-validated data in the training phase, and the estimated SMDI had high correlation coefficient R2 ranges from 0.57~0.90, 0.52~0.94, and 0.49~0.82 during the start of the season (SOS), length of the season (LOS), and end of the season (EOS) respectively. The comparison between inter-annual variability of estimated SMDI and in-situ SPEI (standardized precipitation evapotranspiration index) showed that the estimated SMDI was almost similar to in-situ SPEI. The DFNN model provides comprehensive drought information by producing a consistent spatial distribution of SMDI which establishes the applicability of the DFNN model for drought monitoring.

scholarly journals Flux rope axis geometry of magnetic clouds deduced from in situ data

2013 ◽  
Vol 8 (S300) ◽  
pp. 265-268
Author(s):  
Miho Janvier ◽  
Pascal Démoulin ◽  
Sergio Dasso
Keyword(s):  
Interplanetary Medium ◽  
Magnetic Cloud ◽  
Flux Rope ◽  
Magnetic Clouds ◽  
Direct Integration ◽  
Axis Orientation ◽  
In Situ Data ◽  
Geometrical Features ◽  
Wide Range

AbstractMagnetic clouds (MCs) consist of flux ropes that are ejected from the low solar corona during eruptive flares. Following their ejection, they propagate in the interplanetary medium where they can be detected by in situ instruments and heliospheric imagers onboard spacecraft. Although in situ measurements give a wide range of data, these only depict the nature of the MC along the unidirectional trajectory crossing of a spacecraft. As such, direct 3D measurements of MC characteristics are impossible. From a statistical analysis of a wide range of MCs detected at 1 AU by the Wind spacecraft, we propose different methods to deduce the most probable magnetic cloud axis shape. These methods include the comparison of synthetic distributions with observed distributions of the axis orientation, as well as the direct integration of observed probability distribution to deduce the global MC axis shape. The overall shape given by those two methods is then compared with 2D heliospheric images of a propagating MC and we find similar geometrical features.

Real-Time Analysis Of In-Situ Spectroscopic Ellipsometric Data During Mbe Growth Of III-V Semiconductors

1991 ◽  
Vol 222 ◽  
Author(s):  
B. Johs ◽  
J. L. Edwards ◽  
K. T. Shiralagi ◽  
R. Droopad ◽  
K. Y. Choi ◽  
...  
Keyword(s):  
Real Time ◽  
Optical Constants ◽  
Growth Parameters ◽  
Ex Situ ◽  
Time Analysis ◽  
Mbe Growth ◽  
In Situ Data ◽  
Real Time Analysis

ABSTRACTA modular spectroscopic ellipsometer, capable of both in-situ and ex-situ operation, has been used to measure important growth parameters of GaAs/AIGaAs structures. The ex-situ measurements provided layer thicknesses and compositions of the grown structures. In-situ ellipsometric measurements allowed the determination of growth rates, layer thicknesses, and high temperature optical constants. By performing a regression analysis of the in-situ data in real-time, the thickness and composition of an AIGaAs layer were extracted during the MBE growth of the structure.

Brazilian aviation nowcasting system: current stage and some results

2021 ◽  
Author(s):  
Vinícius Almeida ◽  
Gutemberg França ◽  
Francisco Albuquerque Neto ◽  
Haroldo Campos Velho ◽  
Manoel Almeida ◽  
...  
Keyword(s):  
Rio De Janeiro ◽  
Remote Sensing Data ◽  
Weather Forecast ◽  
Weather Data ◽  
Vertical Acceleration ◽  
In Situ Data ◽  
Forecasting System ◽  
Under Construction ◽  
Convective Weather

<p>Emphasizes some aspects of the aviation forecasting system under construction for use by the integrated meteorological center (CIMAER) in Brazil. It consists of a set of hybrid models based on determinism and machine learning that use remote sensing data (such as lighting sensor, SODAR, satellite and soon RADAR), in situ data (from the surface weather station and radiosonde) and aircraft data (such as retransmission of aircraft weather data and vertical acceleration). The idea is to gradually operationalize the system to assist CIMAER´s meteorologists in generating their nowcasting, for example, of visibility, ceiling, turbulence, convective weather, ice, etc. with objectivity and precision. Some test results of the developed nowcasting models are highlighted as examples of nowcasting namely: a) visibility and ceiling up to 1h for Santos Dumont airport; b) 6-8h convective weather forecast for the Rio de Janeiro area and the São Paulo-Rio de Janeiro route. Finally, the steps in development and the futures are superficially covered.</p>

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