Real-Time Evaluation of Remote Sensing Data on Board of Satellites

2011 ◽  
Author(s):  
Kurt Schwenk ◽  
Katharina Goetz ◽  
Maria von Schoenermark ◽  
Felix Huber
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Time Evaluation

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.

scholarly journals Estimating Near Real-Time Hourly Evapotranspiration Using Numerical Weather Prediction Model Output and GOES Remote Sensing Data in Iowa

2020 ◽  
Vol 12 (14) ◽  
pp. 2337
Author(s):  
Wonsook S. Ha ◽  
George R. Diak ◽  
Witold F. Krajewski
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Numerical Weather Prediction Model ◽  
Sensing Data ◽  
Numerical Weather ◽  
Spatio Temporal

This study evaluates the applicability of numerical weather prediction output supplemented with remote sensing data for near real-time operational estimation of hourly evapotranspiration (ET). Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR) systems were selected to provide forcing data for a Penman-Monteith model to calculate the Actual Evapotranspiration (AET) over Iowa. To investigate how the satellite-based remotely sensed net radiation ( R n ) estimates might potentially improve AET estimates, Geostationary Operational Environmental Satellite derived R n (GOES- R n ) data were incorporated into each dataset for comparison with the RAP and HRRR R n -based AET evaluations. The authors formulated a total of four AET models—RAP, HRRR, RAP-GOES, HRRR-GOES, and validated the respective ET estimates against two eddy covariance tower measurements from central Iowa. The implementation of HRRR-GOES for AET estimates showed the best results among the four models. The HRRR-GOES model improved statistical results, yielding a correlation coefficient of 0.8, a root mean square error (mm hr−1) of 0.08, and a mean bias (mm hr−1) of 0.02 while the HRRR only model results were 0.64, 0.09, and 0.04, respectively. Despite limited in situ observational data to fully test a proposed AET estimation, the HRRR-GOES model clearly showed potential utility as a tool to predict AET at a regional scale with high spatio-temporal resolution.

scholarly journals Gridded Flood Depth Estimates from Satellite Derived Inundations

2021 ◽  
Author(s):  
Seth Bryant ◽  
Heather McGrath ◽  
Mathieu Boudreault
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Cost Allocation ◽  
Remote Sensing Data ◽  
Flood Events ◽  
Sensing Data ◽  
Flood Depth ◽  
Emergency Managers ◽  
Minimal Data ◽  
Allocation Algorithms

Abstract. Canada's RADARSAT missions improve the potential to study past flood events; however, existing tools to derive flood depths from this remote-sensing data do not correct for errors, leading to poor estimates. To provide more accurate gridded depth estimates of historical flooding, a new tool is proposed that integrates Height Above Nearest Drainage and Cost Allocation algorithms. This tool is tested against two trusted, hydraulically derived, gridded depths of recent floods in Canada. This validation shows the proposed tool outperforms existing tools and can provide more accurate estimates from minimal data without the need for complex physics-based models or expert judgement. With improvements in remote-sensing data, the tool proposed here can provide flood researchers and emergency managers accurate depths in near-real time.

Using a Remote Sensing data based toolkit to monitor vine water use and water status for real time irrigation scheduling in California vineyards

2020 ◽  
Author(s):  
Maria Mar Alsina ◽  
Kyle Knipper ◽  
Martha Anderson ◽  
WIlliam Kustas ◽  
Nicolas Bambach ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Water Use ◽  
Water Status ◽  
Irrigation Management ◽  
Remote Sensing Data ◽  
Irrigation Scheduling ◽  
Area Index ◽  
Pilot Experiment ◽  
Sensing Data

<p>Grapevines are one of the major drivers of agriculture in California, representing a production equivalent to $6.25 billion in 2018. Water is scarce, and increasingly intense and prolonged drought periods, like one that recently occurred in the 2012-2016 period, may happen with greater frequency. Consequently, there is a need to develop irrigation management decision tools to help growers maximize water use while maintaining productivity. Furthermore, grapevines are deficit irrigated, and a correct management of the vine water status during the season is key to achieve the target yield and quality. Traditionally, viticulturists use visual clues and/or leaf level indicators of vine water status to regulate the water deficit along the season. However, these methods are time-consuming and only provide discrete data that do not represent the often-high spatial variability of vineyards.  Remote sensing techniques may represent a fast real-time decision-making tool for irrigation management, able to extensively cover multiple vineyards with low human or economic investments. <br>While growers currently calculate the vine water demands using the reference evapotranspiration from a weather station located in the region and a crop coefficient, usually from literature, they don't have any means to measure or estimate the actual water used by the vines. Knowing the actual evapotranspiration (ET) in real-time and at a sub-field scale would provide essential information to monitor vine water status and adjust the irrigation amounts to the real water needs. The aim of the GRAPEX (Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment) project, has been to provide growers with an irrigation toolkit that integrates the spatial distribution of vine water use and water status. The project focuses on grapevines, but it will be easily extrapolated to orchards and other crop types.<br>We present the results of a pilot experiment where we applied the scientific developments of the GRAPEX project into a practical tool that growers can use for irrigation management. We run this pilot experiment over 6 commercial grapevine blocks, located in Cloverdale, Sonoma, CA. During the 2019 growing season, we provided the viticulturists with weekly maps of actual ET calculated using the DisALEXI model, Sentinel-2 Normalized Difference Vegetation and Normalized Vegetation Water Indices as well as local weather data, forecasted ET and soil moisture. The data were delivered weekly in a dashboard, including spatial and tabular views, as well as an irrigation recommendation derived from the past week's vine water use and water status data. Along with the remote sensing data, we took periodic measurements of leaf area index, leaf water potential, and gas exchange to evaluate the irrigation practices. We compared the irrigation prescription based on the provided data with the grower's practices. The total season irrigation ranged between 70 and 120 mm depending on the block, and our irrigation recommendations deviated between 10 and 30 mm from the growers' practices, also depending on the block. This analyzes the performance of the ET toolkit in assisting irrigation scheduling for improving water use efficiency of the vineyard blocks.</p>

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