Comparison of Snow Indexes in Estimating Snow Cover Fraction in a Mountainous Area in Northwestern China

Lin, Junjie; Xia, Feng; Xiao, Pengfeng; Li, Hui; Wang, Jiangeng; Li, Yun

doi:10.1109/lgrs.2011.2179634

Cited by 22 publications

(10 citation statements)

References 10 publications

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“…Based on the investigation, the linear and exponential relationship between in-situ snow depth and calculated snow cover fraction (figure) calculated Using 30 corresponding data the value of a, b, c and d is estimated using the least square method. Equation (7)(8)(9) indicate the estimated formulas for linear, first order exponential function and second order exponential function respectively. In order to evaluate the estimated parameters of 50 in-situ snow depth data with them Corresponded snow cover fraction used to calculate root mean square error (RMSE).…”

Section: Resultsmentioning

confidence: 99%

Estimation of the Surface Area Covered by Snow and the Resulting Runoff Using Landsat Satellite Images

2023

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Snow is one of the most important sources of water in most parts of the world, which supplies about a third of the water needed for agricultural activities, drinking, and underground water sources. The runoff caused by melted snow can be destructive, also the numerous volume of snow can lead to an avalanche, so it's important to estimate it. The area of the snow research is around Goose Lake in California, the USA where the ground snow measuring station is (Latitude: 41.92999, Longitude: -120.4168117). The snow can be measured and calculated using the Daily Satellite imagery of the Landsat for five years' period (2017-2022) for about four months (December-March) (total of about 40 images. The information from ground snow measuring stations was used to evaluate the final results. The accuracy assessment shows 76% accuracy.

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Section: Resultsmentioning

confidence: 99%

Estimation of the Surface Area Covered by Snow and the Resulting Runoff Using Landsat Satellite Images

2023

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“…The FVC of a given pixel was derived by the method of Carlson and Ripley (1997) [69] using the 8-days cycle vegetation index data derived from GK2A/AMI. The snow cover fraction was calculated for the pixels covered with snow [70], and this was considered in retrieving the daily GK2A LSE.…”

Section: Methodsmentioning

confidence: 99%

Development of a Land Surface Temperature Retrieval Algorithm from GK2A/AMI

Choi

Suh

2020

Remote Sensing

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Land surface temperature (LST) is an important geophysical element for understanding Earth systems and land–atmosphere interactions. In this study, we developed a nonlinear split-window LST retrieval algorithm for the observation area of GEO-KOMPSAT-2A (GK2A), the next-generation geostationary satellite in Korea. To develop the GK2A LST retrieval algorithm, radiative transfer model simulation data, considering various impacting factors, were constructed. The LST retrieval algorithm was developed with a total of six equations as per day/night and atmospheric conditions (dry/normal/wet), considering the effects of diurnal variation of LST and atmospheric conditions on LST retrieval. The emissivity of each channel required for LST retrieval was calculated in real-time with the vegetation cover method using the consecutive 8-day cycle vegetation index provided by GK2A. The indirect validation of the results of GK2A LST with Moderate Resolution Imaging Spectroradiometer (MODIS) LST Collection 6 showed a high correlation coefficient (0.969), slightly warm bias (+1.227 K), and root mean square error (RMSE) (2.281 K). Compared to the MODIS LST, the GK2A LST showed a warm bias greater than +1.8 K during the day, but a relatively small bias (<+0.7 K) at night. Based on the results of the validation with in situ measurements from the Tateno station of the Baseline Surface Radiation Network, the correlation coefficient was 0.95, bias was +0.523 K, and RMSE was 2.021 K.

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“…If an NDSI threshold for snow cover is set to 0.4 (see Fig. 1), then all snow cover with lower values will be excluded from the snow map, which may eliminate a significant amount of snow (e.g., Jain et al, 2008;Hassan et al, 2012;Lin et al, 2012). (1) The solid line is the NDSI with the VIS reflectance increasing from 0 to 100 % in increments of 1 % while the SWIR reflectance is constant at 10 %.…”

Section: Normalized Difference Snow Index (Ndsi) As the Standard For mentioning

confidence: 99%

Overview of NASA's MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) snow-cover Earth System Data Records

Riggs

Hall

Román³

2017

Earth Syst. Sci. Data

104

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Abstract. Knowledge of the distribution, extent, duration and timing of snowmelt is critical for characterizing the Earth's climate system and its changes. As a result, snow cover is one of the Global Climate Observing System (GCOS) essential climate variables (ECVs). Consistent, long-term datasets of snow cover are needed to study interannual variability and snow climatology. The NASA snow-cover datasets generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua spacecraft and the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) are NASA Earth System Data Records (ESDR). The objective of the snow-cover detection algorithms is to optimize the accuracy of mapping snow-cover extent (SCE) and to minimize snow-cover detection errors of omission and commission using automated, globally applied algorithms to produce SCE data products. Advancements in snow-cover mapping have been made with each of the four major reprocessings of the MODIS data record, which extends from 2000 to the present. MODIS Collection 6 (C6; https://nsidc.org/data/modis/data_summaries) and VIIRS Collection 1 (C1; https://doi.org/10.5067/VIIRS/VNP10.001) represent the state-of-the-art global snow-cover mapping algorithms and products for NASA Earth science. There were many revisions made in the C6 algorithms which improved snow-cover detection accuracy and information content of the data products. These improvements have also been incorporated into the NASA VIIRS snow-cover algorithms for C1. Both information content and usability were improved by including the Normalized Snow Difference Index (NDSI) and a quality assurance (QA) data array of algorithm processing flags in the data product, along with the SCE map. The increased data content allows flexibility in using the datasets for specific regions and end-user applications. Though there are important differences between the MODIS and VIIRS instruments (e.g., the VIIRS 375 m native resolution compared to MODIS 500 m), the snow detection algorithms and data products are designed to be as similar as possible so that the 16+ year MODIS ESDR of global SCE can be extended into the future with the

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Comparison of Snow Indexes in Estimating Snow Cover Fraction in a Mountainous Area in Northwestern China

Cited by 22 publications

References 10 publications

Estimation of the Surface Area Covered by Snow and the Resulting Runoff Using Landsat Satellite Images

Estimation of the Surface Area Covered by Snow and the Resulting Runoff Using Landsat Satellite Images

Development of a Land Surface Temperature Retrieval Algorithm from GK2A/AMI

Overview of NASA's MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) snow-cover Earth System Data Records

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