ESTIMATION OF SUBPIXEL SNOW-COVERED AREA BY NONPARAMETRIC REGRESSION SPLINES

Download

index.pdf

Date

2016-10-17

Author

KUTER, SEMİH
Akyürek, Sevda Zuhal
Weber, G. -W.

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

154
views

0
downloads

Measurement of the areal extent of snow cover with high accuracy plays an important role in hydrological and climate modeling. Remotely-sensed data acquired by earth-observing satellites offer great advantages for timely monitoring of snow cover. However, the main obstacle is the tradeoff between temporal and spatial resolution of satellite imageries. Soft or subpixel classification of low or moderate resolution satellite images is a preferred technique to overcome this problem. The most frequently employed snow cover fraction methods applied on Moderate Resolution Imaging Spectroradiometer (MODIS) data have evolved from spectral unmixing and empirical Normalized Difference Snow Index (NDSI) methods to latest machine learning-based artificial neural networks (ANNs). This study demonstrates the implementation of subpixel snow-covered area estimation based on the state-of-the-art nonparametric spline regression method, namely, Multivariate Adaptive Regression Splines (MARS). MARS models were trained by using MODIS top of atmospheric reflectance values of bands 1-7 as predictor variables. Reference percentage snow cover maps were generated from higher spatial resolution Landsat ETM+ binary snow cover maps. A multilayer feed-forward ANN with one hidden layer trained with backpropagation was also employed to estimate the percentage snow-covered area on the same data set. The results indicated that the developed MARS model performed better than the ANN model with an average RMSE of 0.1656 over the test areas; whereas the average RMSE of the ANN model was 0.3868.

Subject Keywords

Remote Sensing, Snow Cover, MARS, Artificial Neural Network, MODIS, Landsat

URI

https://hdl.handle.net/11511/36610

DOI

https://doi.org/10.5194/isprs-archives-xlii-2-w1-31-2016

Collections

Department of Civil Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Soft Classification of Satellite Data for Snow Mapping by using Multivariate Adaptive Regression Splines Kuter, Semih; Akyürek, Sevda Zuhal; Weber, William (2016-07-03) Measurement of the areal extent of snow cover with high accuracy plays an important role in hydrological and climate modeling. Remotely-sensed data acquired by earth-observing satellites offer great advantages for timely monitoring of snow cover. However, the main obstacle is the trade-off between the temporal and spatial resolution of the satellite imageries. Soft or sub-pixel classification of low or moderate resolution satellite images is a preferred technique to overcome this problem. In this presentati...
Cross-Country Assessment of H-SAF Snow Products by Sentinel-2 Imagery Validated against In-Situ Observations and Webcam Photography Piazzi, Gaia; Tanis, Cemal Melih; KUTER, SEMİH; Simsek, Burak; Puca, Silvia; Toniazzo, Alexander; Takala, Matias; Akyürek, Sevda Zuhal; Gabellani, Simone; Arslan, Ali Nadir (2019-03-15) Information on snow properties is of critical relevance for a wide range of scientific studies and operational applications, mainly for hydrological purposes. However, the ground-based monitoring of snow dynamics is a challenging task, especially over complex topography and under harsh environmental conditions. Remote sensing is a powerful resource providing snow observations at a large scale. This study addresses the potential of using Sentinel-2 high-resolution imagery to assess moderate-resolution snow p...
Fractional Snow Cover Mapping by Artificial Neural Networks and Support Vector Machines ÇİFTÇİ, BORA BERKAY; KUTER, SEMİH; Akyürek, Sevda Zuhal; WEBER, GERHARD WİEHELM (Copernicus GmbH; 2017-10-15) Snow is an important land cover whose distribution over space and time plays a significant role in various environmental processes. Hence, snow cover mapping with high accuracy is necessary to have a real understanding for present and future climate, water cycle, and ecological changes. This study aims to investigate and compare the design and use of artificial neural networks (ANNs) and support vector machines (SVMs) algorithms for fractional snow cover (FSC) mapping from satellite data. ANN and SVM models...
Special issue on remote sensing of snow and its applications Arslan, Ali Nadir; Akyürek, Sevda Zuhal (2019-06-01) Snow cover is an essential climate variable directly affecting the Earth's energy balance. Snow cover has a number of important physical properties that exert an influence on global and regional energy, water, and carbon cycles. Remote sensing provides a good understanding of snow cover and enable snow cover information to be assimilated into hydrological, land surface, meteorological, and climate models for predicting snowmelt runoff, snow water resources, and to warn about snow-related natural hazards. Th...
Determination of snow water equivalent over eastern part of Turkey using passive microwave data Beşer, Özgür; Şorman, Ali Ünal; Department of Civil Engineering (2011) The assimilation process to produce daily Snow Water Equivalent (SWE) maps is modified by using Helsinki University of Technology (HUT) snow emission model and AMSR-E passive microwave data. The characteristics of HUT emission model is analyzed in-depth and discussed with respects to the extinction coefficient function. A new extinction coefficient function for the HUT model is proposed for snow over mountainous areas. Performance of the modified model is checked against original and other modified cases ag...

Citation Formats

S. KUTER, S. Z. Akyürek, and G.-W. Weber, “ESTIMATION OF SUBPIXEL SNOW-COVERED AREA BY NONPARAMETRIC REGRESSION SPLINES,” 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/36610.