Keywords
remote sensing, data assimilation, soil moisture, model-data integration
Location
Session C1: VI Data Mining for Environmental Sciences Session
Start Date
13-7-2016 10:30 AM
End Date
13-7-2016 10:50 AM
Abstract
This talk presents recent work on data fusion methods applied to the estimation of soil moisture by means of remote sensing. Particularly in research dealing with landscapes and agriculture there is a need for spatially and temporally resolved estimation of soil moisture from local to regional level and beyond. The methods to evaluate the water content of the soil range from direct measurements, optical and microwave remote sensing to modeling based on precipitation measurements. Usually the availability of the specific data defines the used method. The uncertainties connected to each way of estimation vary with weather conditions, region and data quality. Combination of continually improved measurements like the RADOLAN network by the German Meteorological Service (DWD) that can feed a model with precipitation data of very high resolution and the addition of knowledge from radar-satellites is one example of promising developments. The author presents his ongoing work in combining the different methods to reduce uncertainties, fill gaps in data and understanding the scalability of soil moisture measurements. The key idea is to treat remote sensing and simulations as equal partners in the model-data-integration. The strengths and weaknesses of each approach is briefly presented. The presentation the develops ideas on how to combine the different information in a meaningful to gain greater insight into the real state of the system with regard to soil moisture.
Included in
Civil Engineering Commons, Data Storage Systems Commons, Environmental Engineering Commons, Hydraulic Engineering Commons, Other Civil and Environmental Engineering Commons
Developement of Data Fusion Methods for an Improved Estimation of Soil Moisture
Session C1: VI Data Mining for Environmental Sciences Session
This talk presents recent work on data fusion methods applied to the estimation of soil moisture by means of remote sensing. Particularly in research dealing with landscapes and agriculture there is a need for spatially and temporally resolved estimation of soil moisture from local to regional level and beyond. The methods to evaluate the water content of the soil range from direct measurements, optical and microwave remote sensing to modeling based on precipitation measurements. Usually the availability of the specific data defines the used method. The uncertainties connected to each way of estimation vary with weather conditions, region and data quality. Combination of continually improved measurements like the RADOLAN network by the German Meteorological Service (DWD) that can feed a model with precipitation data of very high resolution and the addition of knowledge from radar-satellites is one example of promising developments. The author presents his ongoing work in combining the different methods to reduce uncertainties, fill gaps in data and understanding the scalability of soil moisture measurements. The key idea is to treat remote sensing and simulations as equal partners in the model-data-integration. The strengths and weaknesses of each approach is briefly presented. The presentation the develops ideas on how to combine the different information in a meaningful to gain greater insight into the real state of the system with regard to soil moisture.
