English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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 lowor moderate resolution satellite images is a preferred technique to overcome this problem. The most frequently employed snow coverfraction methods applied on Moderate Resolution Imaging Spectroradiometer (MODIS) data have evolved from spectral unmixingand 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-artnonparametric spline regression method, namely, Multivariate Adaptive Regression Splines (MARS). MARS models were trained byusing MODIS top of atmospheric reflectance values of bands 1-7 as predictor variables. Reference percentage snow cover maps weregenerated from higher spatial resolution Landsat ETM+ binary snow cover maps. A multilayer feed-forward ANN with one hiddenlayer trained with backpropagation was also employed to estimate the percentage snow-covered area on the same data set. The resultsindicated that the developed MARS model performed better than t

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

ESTIMATION OF SUBPIXEL SNOW-COVERED AREA BY NONPARAMETRIC REGRESSION SPLINES