scholarly journals Digital Northern Great Plains: A Web-Based System Delivering Near Real Time Remote Sensing Data for Precision Agriculture

2010 ◽  
Vol 2 (3) ◽  
pp. 861-873 ◽  
Author(s):  
Xiaodong Zhang ◽  
Santhosh Seelan ◽  
George Seielstad
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Great Plains ◽  
Precision Agriculture ◽  
Remote Sensing Data ◽  
Northern Great Plains ◽  
Web Based ◽  
Sensing Data ◽  
Web Based System

A Web-Based System for Classification of Remote Sensing Data

2013 ◽  
Vol 6 (4) ◽  
pp. 1934-1948 ◽  
Author(s):  
Angel Ferran ◽  
Sergio Bernabe ◽  
Pablo G. Rodriguez ◽  
Antonio Plaza
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Web Based ◽  
Sensing Data ◽  
Web Based System

scholarly journals Giovanni: The Bridge Between Data and Science

Eos ◽  
2017 ◽  
Author(s):  
Zhong Liu ◽  
James Acker
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Earth Science ◽  
Remote Sensing Data ◽  
Data Sets ◽  
Science Data ◽  
Web Based ◽  
Sensing Data ◽  
Daunting Task ◽  
Earth Science Data

Using satellite remote sensing data sets can be a daunting task. Giovanni, a Web-based tool, facilitates access, visualization, and exploration for many of NASA’s Earth science data sets.

scholarly journals Hydrological real-time modeling using remote sensing data

2010 ◽  
Vol 7 (6) ◽  
pp. 8809-8835
Author(s):  
P. Meier ◽  
A. Frömelt ◽  
W. Kinzelbach
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Real Time ◽  
River Basin ◽  
Time Lag ◽  
Remote Sensing Data ◽  
Modeling Framework ◽  
Sensing Data ◽  
Soil Storage ◽  
Time Modeling

Abstract. Reliable real-time forecasts of the discharge can provide valuable information for the management of a river basin system. Sequential data assimilation using the Ensemble Kalman Filter provides a both efficient and robust tool for a real-time modeling framework. One key parameter in a hydrological system is the soil moisture which recently can be characterized by satellite based measurements. A forecasting framework for the prediction of discharges is developed and applied to three different sub-basins of the Zambezi River Basin. The model is solely based on remote sensing data providing soil moisture and rainfall estimates. The soil moisture product used is based on the back-scattering intensity of a radar signal measured by the radar scatterometer on board the ERS satellite. These soil moisture data correlate well with the measured discharge of the corresponding watershed if the data are shifted by a time lag which is dependent on the size and the dominant runoff process in the catchment. This time lag is the basis for the applicability of the soil moisture data for hydrological forecasts. The conceptual model developed is based on two storage compartments. The processes modeled include evaporation losses, infiltration and percolation. The application of this model in a real-time modeling framework yields good results in watersheds where the soil storage is an important factor. For the largest watershed a forecast over almost six weeks can be provided. However, the quality of the forecast increases significantly with decreasing prediction time. In watersheds with little soil storage and a quick response to rainfall events the performance is relatively poor.

scholarly journals Remote radiological assessment in the marine environment: A pilot study based on Cs-137 measurements and satellite observations in the Aegean Sea

2019 ◽  
Vol 24 ◽  
pp. 191
Author(s):  
G. Mavrokefalou ◽  
H. Florou ◽  
O. Sykioti
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Satellite Data ◽  
Sea Water ◽  
Remote Sensing Data ◽  
Aegean Sea ◽  
137Cs Activity ◽  
Sea Surface ◽  
Sensing Data ◽  
Activity Concentrations

A program concept has been developed to utilize sea parameters like sea surface temperature (SST), ocean colour (OC) and sea surface salinity (SSS), in order to explore their potential relations with 137Cs activity concentrations in sea water. These relations are expected to lead to the creation of an innovative tool based on remote sensing data and in real time 137Cs measurements, for the remote radioactivity detection of the Greek marine ecosystem both for routine control and emergency recordings. The presented results are a preliminary effort of the tool’s development. Remote sensing data have been acquired from MIRAS and MODIS instruments on-board ESA-SMOS and NASA-TERRA/AQUA satellites respectively. Satellite data comprise of SST and OC measurements. The ERL’s data of 137Cs activity concentrations (204 measurements) in seawater have been used for the period March 2012 to February 2015. Therefore, a) map analyses in a GIS including interpolation and integration of 83 real time measurements corrected with the effective half live of 7.2 y according to the monthly data of satelites and spatial linear regression have been implemented for the Aegean Sea, b) additional temporal analyses using linear and polynomial regression have been performed for the area of Souda- Crete, for which the most frequent measurements of 137Cs activity concentration in sea water have been measured in ERL. In this study, the first derived results on the correlation between SST measurements with 137Cs activity concentrations are presented, whereas the respective correlation with OC is being under invstigation. Further investigations include multivariate polynomial analyses into the Geographic Information System (GIS) platform with more extensive sampling and satellite data from new systems, whereas comparative correlations of 137Cs with seawater parameters derived by conventional means will be performed.

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