scholarly journals Spatial scales of optical variability in the coastal ocean: Implications for remote sensing and in situ sampling

2016 ◽  
Vol 121 (6) ◽  
pp. 4194-4208 ◽  
Author(s):  
Wesley J. Moses ◽  
Steven G. Ackleson ◽  
Johnathan W. Hair ◽  
Chris A. Hostetler ◽  
W. David Miller
Keyword(s):  
Remote Sensing ◽  
Spatial Scales ◽  
Coastal Ocean ◽  
Optical Variability

In situ optical variability and relationships in the Santa Barbara Channel: implications for remote sensing

2006 ◽  
Vol 45 (15) ◽  
pp. 3593 ◽  
Author(s):  
Grace C. Chang ◽  
Andrew H. Barnard ◽  
Scott McLean ◽  
Peter J. Egli ◽  
Casey Moore ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Optical Variability ◽  
Santa Barbara Channel ◽  
Santa Barbara

scholarly journals Coastal observing and forecasting system for the German Bight – estimates of hydrophysical states

Ocean Science ◽  
2011 ◽  
Vol 7 (5) ◽  
pp. 569-583 ◽  
Author(s):  
E. V. Stanev ◽  
J. Schulz-Stellenfleth ◽  
J. Staneva ◽  
S. Grayek ◽  
J. Seemann ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
German Bight ◽  
Numerical Models ◽  
Coastal Ocean ◽  
Hf Radar ◽  
Optimal Interpolation ◽  
Surface Currents ◽  
Arctic Seas

Abstract. A coastal observing system for Northern and Arctic Seas (COSYNA) aims at construction of a long-term observatory for the German part of the North Sea, elements of which will be deployed as prototype modules in Arctic coastal waters. At present a coastal prediction system deployed in the area of the German Bight integrates near real-time measurements with numerical models in a pre-operational way and provides continuously state estimates and forecasts of coastal ocean state. The measurement suite contributing to the pre-operational set up includes in situ time series from stationary stations, a High-Frequency (HF) radar system measuring surface currents, a FerryBox system and remote sensing data from satellites. The forecasting suite includes nested 3-D hydrodynamic models running in a data-assimilation mode, which are forced with up-to-date meteorological forecast data. This paper reviews the present status of the system and its recent upgrades focusing on developments in the field of coastal data assimilation. Model supported data analysis and state estimates are illustrated using HF radar and FerryBox observations as examples. A new method combining radial surface current measurements from a single HF radar with a priori information from a hydrodynamic model is presented, which optimally relates tidal ellipses parameters of the 2-D current field and the M2 phase and magnitude of the radials. The method presents a robust and helpful first step towards the implementation of a more sophisticated assimilation system and demonstrates that even using only radials from one station can substantially benefit state estimates for surface currents. Assimilation of FerryBox data based on an optimal interpolation approach using a Kalman filter with a stationary background covariance matrix derived from a preliminary model run which was validated against remote sensing and in situ data demonstrated the capabilities of the pre-operational system. Data assimilation significantly improved the performance of the model with respect to both SST and SSS and demonstrated a good skill not only in the vicinity of the Ferry track, but also over larger model areas. The examples provided in this study are considered as initial steps in establishing new coastal ocean products enhanced by the integrated COSYNA-observations and numerical modelling.

scholarly journals Utility of Satellite Remote Sensing for Land–Atmosphere Coupling and Drought Metrics

2017 ◽  
Vol 18 (3) ◽  
pp. 863-877 ◽  
Author(s):  
Joshua K. Roundy ◽  
Joseph A. Santanello
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Prediction Models ◽  
Water Cycle ◽  
Spatial Scales ◽  
Complex Nature ◽  
Sufficient Information ◽  
Tropospheric Temperature ◽  
In Situ Data

Abstract Feedbacks between the land and the atmosphere can play an important role in the water cycle, and a number of studies have quantified land–atmosphere (LA) interactions and feedbacks through observations and prediction models. Because of the complex nature of LA interactions, the observed variables are not always available at the needed temporal and spatial scales. This work derives the Coupling Drought Index (CDI) solely from satellite data and evaluates the input variables and the resultant CDI against in situ data and reanalysis products. NASA’s Aqua satellite and retrievals of soil moisture and lower-tropospheric temperature and humidity properties are used as input. Overall, the Aqua-based CDI and its inputs perform well at a point, spatially, and in time (trends) compared to in situ and reanalysis products. In addition, this work represents the first time that in situ observations were utilized for the coupling classification and CDI. The combination of in situ and satellite remote sensing CDI is unique and provides an observational tool for evaluating models at local and large scales. Overall, results indicate that there is sufficient information in the signal from simultaneous measurements of the land and atmosphere from satellite remote sensing to provide useful information for applications of drought monitoring and coupling metrics.

scholarly journals Essential Biodiversity Variables: Integrating In-Situ Observations and Remote Sensing Through Modeling

2020 ◽  
pp. 485-501 ◽  
Author(s):  
Néstor Fernández ◽  
Simon Ferrier ◽  
Laetitia M. Navarro ◽  
Henrique M. Pereira
Keyword(s):  
Remote Sensing ◽  
Data Integration ◽  
Spatial Scales ◽  
Data Sets ◽  
Biodiversity Monitoring ◽  
Multiple Sources ◽  
Biodiversity Data ◽  
Essential Biodiversity Variables ◽  
Critical Components

AbstractEssential biodiversity variables (EBVs) are designed to support the detection and quantification of biodiversity change and to define priorities in biodiversity monitoring. Unlike most primary observations of biodiversity phenomena, EBV products should provide information readily available to produce policy-relevant biodiversity indicators, ideally at multiple spatial scales, from global to subnational. This information is typically complex to produce from a single set of data or type of observation, thus requiring approaches that integrate multiple sources of in situ and remote sensing (RS) data. Here we present an up-to-date EBV concept for biodiversity data integration and discuss the critical components of workflows for EBV production. We argue that open and reproducible workflows for data integration are critical to ensure traceability and reproducibility so that each EBV endures and can be updated as novel biodiversity models are adopted, new observation systems become available, and new data sets are incorporated. Fulfilling the EBV vision requires strengthening efforts to mobilize massive amounts of in situ biodiversity data that are not yet publicly available and taking full advantage of emerging RS technologies, novel biodiversity models, and informatics infrastructures, in alignment with the development of a globally coordinated system for biodiversity monitoring.

scholarly journals Seasonal evolution of soil and plant parameters on the agricultural Gebesee test site: a database for the set-up and validation of EO-LDAS and satellite-aided retrieval models

2018 ◽  
Vol 10 (1) ◽  
pp. 525-548 ◽  
Author(s):  
Sina C. Truckenbrodt ◽  
Christiane C. Schmullius
Keyword(s):  
Remote Sensing ◽  
Reference Data ◽  
Spatial Scales ◽  
Test Site ◽  
Earth Observation ◽  
Reference Database ◽  
Observation Data ◽  
Retrieval Models ◽  
In Situ Data

Abstract. Ground reference data are a prerequisite for the calibration, update, and validation of retrieval models facilitating the monitoring of land parameters based on Earth Observation data. Here, we describe the acquisition of a comprehensive ground reference database which was created to test and validate the recently developed Earth Observation Land Data Assimilation System (EO-LDAS) and products derived from remote sensing observations in the visible and infrared range. In situ data were collected for seven crop types (winter barley, winter wheat, spring wheat, durum, winter rape, potato, and sugar beet) cultivated on the agricultural Gebesee test site, central Germany, in 2013 and 2014. The database contains information on hyperspectral surface reflectance factors, the evolution of biophysical and biochemical plant parameters, phenology, surface conditions, atmospheric states, and a set of ground control points. Ground reference data were gathered at an approximately weekly resolution and on different spatial scales to investigate variations within and between acreages. In situ data collected less than 1 day apart from satellite acquisitions (RapidEye, SPOT 5, Landsat-7 and -8) with a cloud coverage  ≤  25 % are available for 10 and 15 days in 2013 and 2014, respectively. The measurements show that the investigated growing seasons were characterized by distinct meteorological conditions causing interannual variations in the parameter evolution. Here, the experimental design of the field campaigns, and methods employed in the determination of all parameters, are described in detail. Insights into the database are provided and potential fields of application are discussed. The data will contribute to a further development of crop monitoring methods based on remote sensing techniques. The database is freely available at PANGAEA (https://doi.org/10.1594/PANGAEA.874251).

scholarly journals Cometary Comae-Surface Links

2020 ◽  
Vol 216 (8) ◽  
Author(s):  
Raphael Marschall ◽  
Yuri Skorov ◽  
Vladimir Zakharov ◽  
Ladislav Rezac ◽  
Selina-Barbara Gerig ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Scales ◽  
Dust Emission ◽  
Data Set ◽  
Water Ice ◽  
Erosion Processes ◽  
State Of Research ◽  
Large Effort ◽  
Cometary Activity

AbstractA comet is a highly dynamic object, undergoing a permanent state of change. These changes have to be carefully classified and considered according to their intrinsic temporal and spatial scales. The Rosetta mission has, through its contiguous in-situ and remote sensing coverage of comet 67P/Churyumov-Gerasimenko (hereafter 67P) over the time span of August 2014 to September 2016, monitored the emergence, culmination, and winding down of the gas and dust comae. This provided an unprecedented data set and has spurred a large effort to connect in-situ and remote sensing measurements to the surface. In this review, we address our current understanding of cometary activity and the challenges involved when linking comae data to the surface. We give the current state of research by describing what we know about the physical processes involved from the surface to a few tens of kilometres above it with respect to the gas and dust emission from cometary nuclei. Further, we describe how complex multidimensional cometary gas and dust models have developed from the Halley encounter of 1986 to today. This includes the study of inhomogeneous outgassing and determination of the gas and dust production rates. Additionally, the different approaches used and results obtained to link coma data to the surface will be discussed. We discuss forward and inversion models and we describe the limitations of the respective approaches. The current literature suggests that there does not seem to be a single uniform process behind cometary activity. Rather, activity seems to be the consequence of a variety of erosion processes, including the sublimation of both water ice and more volatile material, but possibly also more exotic processes such as fracture and cliff erosion under thermal and mechanical stress, sub-surface heat storage, and a complex interplay of these processes. Seasons and the nucleus shape are key factors for the distribution and temporal evolution of activity and imply that the heliocentric evolution of activity can be highly individual for every comet, and generalisations can be misleading.

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