scholarly journals Spatial Distribution of Pigment Concentration Around the East Korean Warm Current Region Derived from Satellite Data - Satellite Observation in May 1980 -

2002 ◽  
Vol 35 (3) ◽  
pp. 265-272 ◽  
Author(s):  
Sang Woo Kim ◽  
Sei-ich Saitoh ◽  
Dong Sun Kim
Keyword(s):  
Spatial Distribution ◽  
Satellite Data ◽  
Satellite Observation ◽  
Pigment Concentration ◽  
Warm Current ◽  
Current Region ◽  
East Korean Warm Current

scholarly journals Remotely sensed latent heat fluxes for improving model predictions of soil moisture: a case study

2010 ◽  
Vol 7 (4) ◽  
pp. 6179-6205
Author(s):  
J. M. Schuurmans ◽  
F. C. van Geer ◽  
M. F. P. Bierkens
Keyword(s):  
Spatial Distribution ◽  
Soil Moisture ◽  
Data Assimilation ◽  
Latent Heat ◽  
Satellite Data ◽  
Hydrological Model ◽  
Remotely Sensed ◽  
Heat Fluxes ◽  
Model Errors ◽  
Small Catchment

Abstract. This paper investigates whether the use of remotely sensed latent heat fluxes improves the accuracy of spatially-distributed soil moisture predictions by a hydrological model. By using real data we aim to show the potential and limitations in practice. We use (i) satellite data of both ASTER and MODIS for the same two days in the summer of 2006 that, in association with the Surface Energy Balance Algorithm for Land (SEBAL), provides us the spatial distribution of daily ETact and (ii) an operational physically based distributed (25 m×25 m) hydrological model of a small catchment (70 km2) in The Netherlands that simulates the water flow in both the unsaturated and saturated zone. Firstly, model outcomes of ETact are compared to the processed satellite data. Secondly, we perform data assimilation that updates the modelled soil moisture. We show that remotely sensed ETact is useful in hydrological modelling for two reasons. Firstly, in the procedure of model calibration: comparison of modeled and remotely sensed ETact together with the outcomes of our data assimilation procedure points out potential model errors (both conceptual and flux-related). Secondly, assimilation of remotely sensed ETact results in a realistic spatial adjustment of soil moisture, except for the area with forest and deep groundwater levels. As both ASTER and MODIS images were available for the same days, this study provides also an excellent opportunity to compare the worth of these two satellite sources. It is shown that, although ASTER provides much better insight in the spatial distribution of ETact due to its higher spatial resolution than MODIS, they appeared in this study just as useful.

Satellite Parametric Description to Ontology Concepts and Semantic Classification of Satellite Data

2019 ◽  
pp. 1198-1222
Author(s):  
Sunitha Abburu ◽  
Nitant Dube
Keyword(s):  
Satellite Data ◽  
Phase 1 ◽  
Research Work ◽  
Data Classification ◽  
Data Retrieval ◽  
Satellite Observation ◽  
Two Phase ◽  
Storage And Retrieval ◽  
Kalpana Satellite

Current satellite data retrieval systems retrieves data using latitude, longitude, date, time and sensor parameters like wind, cloud etc. To achieve concept based satellite data retrieval like Storm, Hurricane, Overcast and Frost etc., requires ontological concept descriptions using satellite observation parameters and concept based classification of satellite data. The current research work has designed and implemented a two phase methodology to achieve this. The phase 1 defines ontology concepts through satellite observation parameters and phase 2 describes ontology concept based satellite data classification. The efficiency of the methodology is been tested by taking the Kalpana satellite data from MOSDAC and weather ontology. This achieves concept based retrieval of satellite data, application interoperability and strengthen the ontologies. The current methodology is implemented and results in concept based satellite data classification, storage and retrieval.

Investigations of the Tasman Sea by satellite altimetry

1984 ◽  
Vol 35 (6) ◽  
pp. 619 ◽  
Author(s):  
R Coleman
Keyword(s):  
Spatial Distribution ◽  
Satellite Data ◽  
Satellite Altimetry ◽  
Sea Surface ◽  
Altimeter Data ◽  
Winter Period ◽  
East Australian Current ◽  
Tasman Sea ◽  
Topographic Influence ◽  
Surface Variability

Altimeter data obtained over a period of 3.6 years (from April 1975 to November 1978) and over the winter period July-September 1978 from the GEOS-3 and SEASAT satellites were used to study the spatial distribution of mesoscale sea-surface variability in the Tasman Sea. Satellite data generally agreed with existing hydrographic measurements. Patterns of higher sea-surface variability were shown to be associated with the East Australian Current and eddy areas. Though the Tasman Front is known to be present at certain times of the year, it is concluded that it is not a permanent feature across the Tasman Sea. Low variability levels in the mid-Tasman Sea are seemingly dictated by the Lord Howe Rise, thus suggesting some sort of topographic influence.

Satellite Parametric Description to Ontology Concepts and Semantic Classification of Satellite Data

2016 ◽  
Vol 12 (2) ◽  
pp. 53-75 ◽  
Author(s):  
Sunitha Abburu ◽  
Nitant Dube
Keyword(s):  
Satellite Data ◽  
Phase 1 ◽  
Research Work ◽  
Data Classification ◽  
Data Retrieval ◽  
Satellite Observation ◽  
Two Phase ◽  
Semantic Classification ◽  
Kalpana Satellite

Current satellite data retrieval systems retrieves data using latitude, longitude, date, time and sensor parameters like wind, cloud etc. To achieve concept based satellite data retrieval like Storm, Hurricane, Overcast and Frost etc., requires ontological concept descriptions using satellite observation parameters and concept based classification of satellite data. The current research work has designed and implemented a two phase methodology to achieve this. The phase 1 defines ontology concepts through satellite observation parameters and phase 2 describes ontology concept based satellite data classification. The efficiency of the methodology is been tested by taking the Kalpana satellite data from MOSDAC and weather ontology. This achieves concept based retrieval of satellite data, application interoperability and strengthen the ontologies. The current methodology is implemented and results in concept based satellite data classification, storage and retrieval.

scholarly journals Estimation of Terrestrial Global Gross Primary Production (GPP) with Satellite Data-Driven Models and Eddy Covariance Flux Data

2018 ◽  
Vol 10 (9) ◽  
pp. 1346 ◽  
Author(s):  
Joanna Joiner ◽  
Yasuko Yoshida ◽  
Yao Zhang ◽  
Gregory Duveiller ◽  
Martin Jung ◽  
...  
Keyword(s):  
Primary Production ◽  
Eddy Covariance ◽  
Satellite Data ◽  
Gross Primary Production ◽  
Satellite Observation ◽  
Data Driven ◽  
Spatial And Temporal Variability ◽  
Light Use Efficiency ◽  
High Productivity ◽  
Use Efficiency

We estimate global terrestrial gross primary production (GPP) based on models that use satellite data within a simplified light-use efficiency framework that does not rely upon other meteorological inputs. Satellite-based geometry-adjusted reflectances are from the MODerate-resolution Imaging Spectroradiometer (MODIS) and provide information about vegetation structure and chlorophyll content at both high temporal (daily to monthly) and spatial (∼1 km) resolution. We use satellite-derived solar-induced fluorescence (SIF) to identify regions of high productivity crops and also evaluate the use of downscaled SIF to estimate GPP. We calibrate a set of our satellite-based models with GPP estimates from a subset of distributed eddy covariance flux towers (FLUXNET 2015). The results of the trained models are evaluated using an independent subset of FLUXNET 2015 GPP data. We show that variations in light-use efficiency (LUE) with incident PAR are important and can be easily incorporated into the models. Unlike many LUE-based models, our satellite-based GPP estimates do not use an explicit parameterization of LUE that reduces its value from the potential maximum under limiting conditions such as temperature and water stress. Even without the parameterized downward regulation, our simplified models are shown to perform as well as or better than state-of-the-art satellite data-driven products that incorporate such parameterizations. A significant fraction of both spatial and temporal variability in GPP across plant functional types can be accounted for using our satellite-based models. Our results provide an annual GPP value of ∼140 Pg C year - 1 for 2007 that is within the range of a compilation of observation-based, model, and hybrid results, but is higher than some previous satellite observation-based estimates.

Clouds over the Tropial Atlantic during July and August 1970

1974 ◽  
Vol 55 (6) ◽  
pp. 587-596 ◽  
Author(s):  
R. Madden ◽  
L. Sapp ◽  
E. Zipser
Keyword(s):  
Spatial Distribution ◽  
Satellite Data ◽  
Relative Frequency ◽  
Black Body ◽  
Tropical Atlantic ◽  
Body Temperatures ◽  
Cloud Clusters ◽  
Weather Observations ◽  
High Clouds

Nimbus IV radiance measurements in the 10.5–12.5-μm window channel from the tropical Atlantic during July and August 1970 are studied. The relative frequency of cold, and presumably high clouds, is determined. The average spatial distribution of equivalent black-body temperatures associated with 38 cloud clusters is reported. Ship cloud and weather observations are studied in conjunction with the satellite data. By combining the evidence provided by the satellite and ship observations, a model of typical clouds and weather associated with cloud clusters is proposed for further study.

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