Investigation of the impact of the nonlinear relations among soil moisture products over data fusion process

2017-04-28
Afshar, Mehdi
Bulut, Burak
Yılmaz, Mustafa Tuğrul
Soil moisture is one of the terrestrial essential climate variables that has critical role in the water, energy, and carbon cycles. There are different ways available for the retrieval of this essential variable (e.g., remote sensing, hydrological models, insitu measurements, and etc.). However, the time series of these retrievals often contain systematic differences, which need to be removed via different rescaling approaches before these data sets could be used in data fusion type studies. In this study, the added utility of nonlinear rescaling methods relative to linear methods in the framework of data fusion has been explored. Nonlinear rescaling methods implemented in this study include: multivariate adaptive regression splines (MARS), Support vector machines (SVM), and artificial neural network (ANN), while the linear methods include linear regression, variance-matching, and triple collocation. Land Parameter Retrieval Model (LPRM) and NOAH soil moisture datasets are rescaled into the space of in-situ measurements obtained over four United States Department of Agriculture (USDA) Agricultural Research Service (ARS) watersheds and later merged using a simple linear weighting method. Validation of the fused products using linear and nonlinear methods show that on average, fusing of nonlinearly rescaled LPRM and NOAH soil moisture products yields 3 percent correlation (i.e., against the in situ data) improvement against nonlinearly rescaled NOAH soil moisture product, while this improvement is more than 5 percent when the fused product is compared against the linearly rescaled NOAH product.
EGU General Assembly 2017

Suggestions

Investigation of added utility of nonlinear techniques in rescaling soil moisture datasets
Hesami Afshar, Mahdi; Yılmaz, Mustafa Tuğrul; Department of Civil Engineering (2019)
Soil moisture plays a key role in weather forecasting, hydrologic modeling, climate change studies and water resource management. There are multiple ways to estimate this essential variable (i.e., remote sensing, modeling, station-based observations) and clear benefits associated with merging independent estimates. However, the time series of these products generally contain systematic differences that must be removed through rescaling before the application of data merging approaches (e.g., data assimilati...
Evaluation of the consistency of station-based soil moisture measurements with hydrological model and remote sensing observations over Turkey
Bulut, Burak; Yılmaz, Mustafa Tuğrul; Department of Civil Engineering (2015)
Soil moisture is a critical parameter for many subjects like climate, drought, water and energy balance, weather prediction; yet the number of studies involving soil moisture has been limited in Turkey. Soil moisture parameter can be obtained using several different methods. Among the values obtained via different methods, station-based observations have the greatest potential to provide the most accurate soil moisture information, even though station based observations have the representativeness errors ov...
Investigation of Turkey's carbon dioxide problem by numerical modeling
Can, Ali; Tokdemir, Turgut; Department of Engineering Sciences (2006)
CO2 emission is very important, because it is responsible for about 60% of the "Greenhouse Effect". The major objectives of this study were to prepare a CO2 emission inventory of Turkey based on districts and provinces by using the fuel consumption data with respect to its sources, to find the CO2 uptake rate of forests in Turkey based on provinces and districts, and to estimate the ground level concentration of CO2 across Turkey using U.S. EPA's ISCLT3 model for the preparation of ground level concentratio...
Investigation of 8-year-long composition Record in the Eastern Mediterranean Precipitation
Işıkdemir, Özlem; Tuncel, Süleyman Gürdal; Department of Environmental Engineering (2006)
Measurement of chemical composition of precipitation is important both to understand acidification of terrestrial and aquatic ecosystems and neutralization process in the atmosphere. Such data are scarce in the Mediterranean region. In this study, chemical composition of daily, wet-only, 387 number of rain water samples collected between 1991 and 1999 were investigated to determine levels, temporal variation and long-term trends in concentrations of major ions and trace elements between 1991 and 1999. Sampl...
Using cosmic-ray neutron probes in validating satellite soil moisture products and land surface models
Duygu, Mustafa Berk; Akyürek, Sevda Zuhal (MDPI AG, 2019-01-01)
Soil moisture content is one of the most important parameters of hydrological studies. Cosmic-ray neutron sensing is a promising proximal soil moisture sensing technique at intermediate scale and high temporal resolution. In this study, we validate satellite soil moisture products for the period of March 2015 and December 2018 by using several existing Cosmic Ray Neutron Probe (CRNP) stations of the COSMOS database and a CRNP station that was installed in the south part of Turkey in October 2016. Soil moist...
Citation Formats
M. Afshar, B. Bulut, and M. T. Yılmaz, “Investigation of the impact of the nonlinear relations among soil moisture products over data fusion process,” presented at the EGU General Assembly 2017, 2017, Accessed: 00, 2021. [Online]. Available: meetingorganizer.copernicus.org/EGU2017/EGU2017-16160.pdf.