Retrieval of fractional snow covered area from MODIS data by multivariate adaptive regression splines

2018-02-01
KUTER, SEMİH
Akyürek, Sevda Zuhal
Weber, Gerhard-Wilhelm
In this paper, a novel approach to estimate fractional snow cover (FSC) from MODIS data in a complex and heterogeneous Alpine terrain is represented by using a state-of-the-art nonparametric spline regression method, namely, multivariate adaptive regression splines (MARS). For this purpose, twenty MODIS - Landsat 8 image pairs acquired between April 2013 and December 2016 over European Alps are used. Fifteen of the image pairs are employed during model training and five images are reserved as an independent test dataset. MARS models are trained by using MODIS top-of-atmosphere reflectance values of bands 1-7, normalized difference snow index, normalized difference vegetation index and land cover class as predictor variables. Reference FSC maps are generated from higher spatial resolution Landsat 8 binary snow cover maps. Multilayer feedforward artificial neural network (ANN) models are also trained by using the same input data. During the training and the testing, the effects of the training data size and the sampling type on the predictive performance of ANN and MARS models are investigated. An additional search is also conducted to reveal whether the choice of the transfer function used in the output layer of ANN has a significant contribution to the network's FSC mapping performance. The final ANN and MARS FSC products are at 500 m spatial resolution. The results on the independent test scenes indicate that the developed ANN models with linear and hyperbolic tangent transfer functions in the output layer and the MARS models are in good agreement with reference FSC data with the same average values of R = 0.93. In contrast, the standard MODIS snow fraction product, namely, MOD10 FSC, exhibits slightly poorer performance with average R = 0.88. The proposed MARS approach is statistically proven to have the same performance with ANN, yet it is computationally more efficient in model building.
REMOTE SENSING OF ENVIRONMENT

Suggestions

Integration of environmental variables with satellite images in regional scale vegetation classification
Domaç, Ayşegül; Süzen, Mehmet Lütfi; Bilgin, Cemal Can (Informa UK Limited, 2006-04-01)
The difficulty of collecting information at conventional field studies and relatively coarse spatial and spectral resolution of Landsat images forced the use of environmental variables as ancillary data in vegetation mapping. The aim of this study is to increase the accuracy of species level vegetation classification incorporating environmental variables in the Amanos Mountains region of southern central Turkey. In the first part of the study, ordinary vegetation classification is attained by using a maximu...
Evaluating a mesoscale atmosphere model and a satellite-based algorithm in estimating extreme rainfall events in northwestern Turkey
Yücel, İsmail (Copernicus GmbH, 2014-01-01)
Quantitative precipitation estimates are obtained with more uncertainty under the influence of changing climate variability and complex topography from numerical weather prediction (NWP) models. On the other hand, hydrologic model simulations depend heavily on the availability of reliable precipitation estimates. Difficulties in estimating precipitation impose an important limitation on the possibility and reliability of hydrologic forecasting and early warning systems. This study examines the performance o...
Evaluation of displacement coefficient method for seismically retrofitted buildings with various ductility capacities
Dicleli, Murat (Wiley, 2014-07-25)
This research study is aimed at evaluating the accuracy of the displacement coefficient method (DCM) of FEMA 440 and associated nonlinear static procedure (NLSP) for actual buildings with soft story mechanism and various ductility capacities. The DCM and associated NLSP are evaluated using two existing seismically vulnerable buildings with soft story mechanism. The buildings are first retrofitted using a ductile steel-brace-link system to represent those with good ductility capacity and then retrofitted wit...
STATISTICAL PARAMETERS CHARACTERIZING THE SPATIAL VARIABILITY OF SELECTED SOIL HYDRAULIC-PROPERTIES
Ünlü, Kahraman; BIGGAR, JW; MORKOC, F (Wiley, 1990-11-01)
The knowledge of the statistical parameters of the variance, sigma-2, and the correlation scale, lambda, characterizing the spatial structures of the log of the saturated hydraulic conductivity, lnK(s), pore size distribution parameter alpha, and the specific water capacity, C, is required in stochastic modeling in order to understand the overall response of large-scale heterogenous unsaturated flow systems. These parameters are estimated assuming second-order stationarity and an exponential semivariogram ...
Mathematical modeling of steam-assisted gravity drainage
Akın, Serhat (Elsevier BV, 2006-03-01)
A mathematical model for gravity drainage in heavy-oil reservoirs and tar sands during steam injection in linear geometry is proposed. The mathematical model is based on experimental observations that the steam zone shape is an inverted triangle with the vertex fixed at the bottom production well. Both temperature and asphaltene content dependence of viscosity of the drained heavy oil and their impact on heavy oil production are considered. The developed model has been validated using experimental data pres...
Citation Formats
S. KUTER, S. Z. Akyürek, and G.-W. Weber, “Retrieval of fractional snow covered area from MODIS data by multivariate adaptive regression splines,” REMOTE SENSING OF ENVIRONMENT, pp. 236–252, 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/35825.