An Effective Method for Snow-Cover Mapping of Dense Coniferous Forests in the Upper Heihe River Basin Using Landsat Operational Land Imager Data

Wang, Xiaoyan; Wang, Jian; Jiang, Zhiyong; Li, Hongyi; Hao, Xiaohua

doi:10.3390/rs71215882

Cited by 60 publications

(41 citation statements)

References 23 publications

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“…The presence of forest in seasonally snow-covered regions, especially in the northern hemisphere, creates great challenges for the accurate FSC retrieval from satellites, as forest canopy can block sensor's view of snow cover, either almost totally, or at least partially. Many studies have been conducted to overcome the presence of forest [8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Automated Webcam Monitoring of Fractional Snow Cover in Northern Boreal Conditions

et al. 2017

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Abstract:Fractional snow cover (FSC) is an important parameter to estimate snow water equivalent (SWE) and surface albedo important to climatic and hydrological applications. The presence of forest creates challenges to retrieve FSC accurately from satellite data, as forest canopy can block the sensor's view of snow cover. In addition to the challenge related to presence of forest, in situ data of FSC-necessary for algorithm development and validation-are very limited. This paper investigates the estimation of FSC using digital imagery to overcome the obstacle caused by forest canopy, and the possibility to use this imagery in the validation of FSC derived from satellite data. FSC is calculated here using an algorithm based on defining a threshold value according to the histogram of an image, to classify a pixel as snow-covered or snow-free. Images from the MONIMET camera network, producing a continuous image series in Finland, are used in the analysis of FSC. The results obtained from automated image analysis of snow cover are compared with reference data estimated by visual inspection of same images. The results show the applicability and usefulness of digital imagery in the estimation of fractional snow cover in forested areas, with a Root Mean Squared Error (RMSE) in the range of 0.1-0.3 (with the full range of 0-1).

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Section: Introductionmentioning

confidence: 99%

Automated Webcam Monitoring of Fractional Snow Cover in Northern Boreal Conditions

et al. 2017

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“…Besides the more general issues of cloud cover and temporal gaps between images, the identification of snow cover beneath dark forest canopies and areas of shadow (especially due to steep topography) constitute the main challenges when mapping snow extents using the NDSI (Wang et al, 2015). The present study was no exception in this regard.…”

Section: Snow Observationsmentioning

confidence: 91%

Efficient multi-objective calibration and uncertainty analysis of distributed snow simulations in rugged alpine terrain

Thornton

Mariethoz

Brauchli

et al. 2019

Preprint

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Abstract. In steep and complex mountainous terrain, robust simulations of snow accumulation and ablation are crucial to a wide range of applications, especially those related to hydrology and ecology. Whilst new opportunities exist to integrate high-resolution spatio-temporal observations in the estimation of uncertain parameters in (a.k.a. “calibration” of) sophisticated, process-rich snow models, they have not yet been fully exploited. Here, with a view towards improving representations of snow and ultimately meltwater dynamics in rugged topography, a novel approach to the calibration of a high-resolution energy balance-based snow model that additionally accounts for gravitational snow redistribution is presented. Several important but uncertain parameters are estimated using an efficient, gradient-based method with respect to two complementary types of snow observations – snow extent maps derived from Landsat 8 images, and snow water equivalent (SWE) time-series reconstructed at two contrasting locations. When assessed on a per-pixel basis over 17 days that together encompass practically the full range of possible snow cover conditions, snow patterns were reproduced with a mean accuracy of 85 %. The spatial performance metrics obtained compare favourably with those previously reported, whilst the temporal evolution of SWE at the stations was also satisfactorily simulated. Uncertainty and data worth analyses revealed that: i) the propensity for model predictions to be erroneous was substantially reduced by the calibration process, ii) pre-calibration uncertainty was largely associated with two parameters that were introduced to modify the longwave component of the energy balance, but this uncertainty was greatly diminished by calibration, and iii) the lower elevation SWE time-series was particularly valuable despite the comparatively small number of observations at this site. Alongside a gridded snowmelt dataset, commensurate estimates of firn melt, ice melt, liquid precipitation, and potential evapotranspiration were also produced. Our study demonstrates the growing potential of combining observation technologies and state-of-the-art inverse approaches to both constrain and quantify the uncertainty associated with simulations of alpine snow dynamics.

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“…The accuracy of snow detection from satellite data is, in general, significantly higher in open areas than in forested areas. Indeed, trees increase the complexity of the scene by masking the snow on the ground and altering the radiance measured by the MODIS satellite [45], [46]. Since elevation also strongly affects quantity and distribution patterns of precipitation and snow, we analyzed the snow cover area (SCA) for different land use (i.e.…”

Section: B Validation With Ground Datamentioning

confidence: 99%

“…The model requires a forest cover map and surface area proportions as input; the reflectance values of snow and forest are derived from in situ reflectance measurements. Wang et al [9] introduced the Normalized Difference Forest Snow Index (NDFSI) to distinguish snow-covered from snow-free evergreen coniferous forests. The index is based on the analysis of the spectral signature of both landcover types in the near-infrared (NIR) and shortwave infrared (SWIR) bands.…”

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confidence: 99%

A Novel Data Fusion Technique for Snow Cover Retrieval

Gregorio

Callegari

Marín

et al. 2019

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This paper presents a novel data fusion technique for improving the snow cover monitoring for a mesoscale Alpine region, in particular in those areas where the two information sources disagree. The presented methodological innovation consists in the integration of remote sensing data products and the numerical simulation results by means of a machine learning classifier (Support Vector Machine), capable to extract information from their quality measures. This differs from the existing approaches where remote sensing is only used for model tuning or data assimilation. The technique has been tested to generate a time series of about 1300 snow maps for the period between October 2012 and July 2016. The results show an average agreement between the fused product and the reference ground data of 96%, compared to 90% of the MODIS data product and 92% of the numerical model simulation. Moreover, one of the most important results is observed from the analysis of snow cover area (SCA) time series, where the fused product seems to overcome the well know underestimation of snow in forest of the MODIS product, by accurately reproducing the SCA peaks of winter season.

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An Effective Method for Snow-Cover Mapping of Dense Coniferous Forests in the Upper Heihe River Basin Using Landsat Operational Land Imager Data

Cited by 60 publications

References 23 publications

Automated Webcam Monitoring of Fractional Snow Cover in Northern Boreal Conditions

Automated Webcam Monitoring of Fractional Snow Cover in Northern Boreal Conditions

Efficient multi-objective calibration and uncertainty analysis of distributed snow simulations in rugged alpine terrain

A Novel Data Fusion Technique for Snow Cover Retrieval

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