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

Kuter, Semih; Akyürek, Zuhal; Weber, Gerhard‐Wilhelm

doi:10.1016/j.rse.2017.11.021

Cited by 83 publications

(39 citation statements)

References 89 publications

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“…The cornerstone of SNOWL is the snow line and the land line. The snow line is the snow-covered elevation where all pixels above it are covered by snow, and the land line is the minimum elevation where snow exists (Krajčí et al, 2014(Krajčí et al, , 2016Lei et al, 2012). On that account, all the cloudy pixels above the snow line are labeled as snow, and all the cloudy pix-els below the land line are labeled as land by SNOWL.…”

Section: The Snow Line Mapping Approach (Snowl)mentioning

confidence: 99%

“…In addition, some other SCA products with a higher resolution can also be considered as the true ground data. For example, the Landsat TM/ETM+/OLI FSC products have been used to validate the MODIS FSC products (Liang et al, 2017;Crawford, 2015;Kuter et al, 2018). However, when the MODIS snow products are covered by cloud, the optically based products with a higher spatial resolution will also be covered by cloud.…”

Section: In Situ and Remote Sensing Data Based Evaluationmentioning

confidence: 99%

“…In the MODIS C6 products, the binary SCA products have been substituted by the NDSI, and the FSC product is not supplied at all (Hall and Riggs, 2016a, b;Riggs et al, 2017). Research work has demon-strated that the has a strong spatial and temporal agreement with Landsat TM/ETM+ (Kuter et al, 2018). Increasing research is thus moving toward MODIS C6 data (Malmros et al, 2018;Huang et al, 2018).…”

Section: Future Directionsmentioning

confidence: 99%

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The recent developments in cloud removal approaches of MODIS snow cover product

Jing

Shen

et al. 2019

Hydrol. Earth Syst. Sci.

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Abstract. The snow cover products of optical remote sensing systems play an important role in research into global climate change, the hydrological cycle, and the energy balance. Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products are the most popular datasets used in the community. However, for MODIS, cloud cover results in spatial and temporal discontinuity for long-term snow monitoring. In the last few decades, a large number of cloud removal methods for MODIS snow cover products have been proposed. In this paper, our goal is to make a comprehensive summarization of the existing algorithms for generating cloud-free MODIS snow cover products and to expose the development trends. The methods of generating cloud-free MODIS snow cover products are classified into spatial methods, temporal methods, spatio-temporal methods, and multi-source fusion methods. The spatial methods and temporal methods remove the cloud cover of the snow product based on the spatial patterns and temporal changing correlation of the snowpack, respectively. The spatio-temporal methods utilize the spatial and temporal features of snow jointly. The multi-source fusion methods utilize the complementary information among different sources among optical observations, microwave observations, and station observations.

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Section: The Snow Line Mapping Approach (Snowl)mentioning

confidence: 99%

Section: In Situ and Remote Sensing Data Based Evaluationmentioning

confidence: 99%

Section: Future Directionsmentioning

confidence: 99%

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The recent developments in cloud removal approaches of MODIS snow cover product

Jing

Shen

et al. 2019

Hydrol. Earth Syst. Sci.

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“…Given the different selections of NDSI threshold, Zhang et al evaluated the C6 products across China [38]. For cloud removal, some studies have first converted the NDSI to SCA or FSC, and then removed the clouds, as in the C5 products [39][40][41]. In addition, using eight-day MOD10C2 data, Li et al [42] confirmed that the snow cover patterns on the TP exhibit significant spatio-temporal heterogeneity, and that the snow cover fraction decreased slightly by~1.1% from 2001 to 2014.…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Fusion Framework to Generate a Spatio–Temporally Continuous MODIS NDSI Product over the Tibetan Plateau

Jing

Shen

et al. 2019

Remote Sensing

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The Tibetan Plateau (TP) is an important component of the global environmental system, on which the snow cover greatly affects the regional climate and ecology. Moderate resolution imaging spectroradiometer (MODIS) snow cover products have been demonstrated to be appropriate for investigating the snow cover over the TP. However, they are subject to cloud obscuration, and the TP’s extremely complex terrain makes the snow monitoring difficult. Therefore, in this paper, we propose a two-stage spatio–temporal fusion framework for the cloud removal of MODIS C6 snow products, including an adjusted Terra and Aqua combination (TAC) and a spatio–temporal fusion based on Gaussian kernel function and error correction (STF-GKF-EC). To the best of our knowledge, this is the first time that a spatio–temporally continuous daily 500-m MODIS normalized difference snow index (NDSI) product has been generated for the TP, which greatly improves the spatial and temporal resolutions of the current snow cover products. The main stage, STF-GKF-EC, adaptively weights the spatial and temporal correlations by the Gaussian kernel function, and further takes the rapid changes of snow cover into consideration through the error correction. The experiments indicated that STF-GKF-EC removes clouds completely, achieving an overall accuracy (OA) and mean absolute error (MAE) of 91.48% and 3.88, respectively. Based on the cloud-removed results, during 2001–2017, as far as the intra-annual variation is concerned, a large proportion of the snow cover appears between October and May, with a peak in February/March, and the variation is mainly controlled by temperature. For the inter-annual variation, an obvious increasing trend of 0.68/year for NDSI is observed before 2005, followed by a slight decreasing trend of 0.16/year, in which precipitation is a better explanation factor than temperature.

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“…Multivariate adaptive regression splines (MARS) are a regression technique that incorporate nonlinear and interacting relationships by fitting an adaptive nonlinear regression model using multiple piecewise linear basis functions hierarchically organized in successive splits over the predictor variable space (Friedman, ). MARS models have been applied to model groundwater level fluctuations (Rezaie‐balf et al, ), river water pollution (Kisi & Parmar, ), snow cover (Kuter et al, ), and species distributions (Elith & Leathwick, ; Leathwick et al, ). Although RF and MARS have been applied for similar purposes, they represent contrasting techniques, partly because predictions from MARS are not constrained by the training data.…”

Section: Introductionmentioning

confidence: 99%

Inside or Outside: Quantifying Extrapolation Across River Networks

Booker

Whitehead

2018

Water Resources Research

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Regression techniques are often used to predict responses across landscapes or under scenarios describing changes in climate, management, or land cover. The ability of random forests (RFs) and multivariate adaptive regression splines (MARS) to predict flow variability, low flow, Escherichia coli, and a macroinvertebrate community index was compared. Cross validation was applied to test predictive performance across an induced spectrum of interpolation to extrapolation by splitting each data set into two geographical, environmental, and random groups. RF and MARS both represent nonlinear and interacting patterns but showed contrasting ability to interpolate and extrapolate. RF always performed better than MARS when interpolating within environmental space or extrapolating in geographical space. RF models for all four responses were transferable in geographic space but not to environmental conditions outside the training data. Neither technique was successful when extrapolating across environmental gradients, although RF out‐performed MARS, despite RF predictions being constrained by the training data. New methods to quantify interpolation versus extrapolation for predictions are demonstrated. Degree of extrapolation is calculated by transforming both the training data and new predictors in response turnover space. A decline in cross‐validation performance was related to an increase in degree of extrapolation regardless of whether extrapolating in geographical or environmental space. Degree of extrapolation is valuable. It identifies those predictions that are more reliable because they represent interpolation versus those that are more uncertain that represent extrapolation. For example, high degree of extrapolation under climate or land cover change indicates increased risk of producing misleading predictions from both RF and MARS.

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

Cited by 83 publications

References 89 publications

The recent developments in cloud removal approaches of MODIS snow cover product

The recent developments in cloud removal approaches of MODIS snow cover product

A Two-Stage Fusion Framework to Generate a Spatio–Temporally Continuous MODIS NDSI Product over the Tibetan Plateau

Inside or Outside: Quantifying Extrapolation Across River Networks

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