Accuracy assessment of MODIS, NOAA and IRS data in snow cover mapping under Himalayan conditions

Jain, Sanjay K.; Goswami, Ajanta; Saraf, A. K.

doi:10.1080/01431160801908129

Cited by 106 publications

(46 citation statements)

References 21 publications

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“…Numerous studies have been performed on the mapping of seasonal snow cover using different methods and optical sensors globally (Dozier et al, 1981;Dozier 1984Dozier , 1989Hall et al, 1995Hall et al, , 2002Klein et al, 1998;Vikhamar and Solberg, 2003;Salomonson and Appel, 2004;Painter et al, 2009). In addition, many studies have been conducted on snow cover mapping in Himalaya using optical satellite sensors (Gupta et al, 2005;Kulkarni et al, 2006Kulkarni et al, , 2010Kulkarni et al, , 2011Jain et al, 2008;Negi et al, 2009a;Immerzeel et al, 2009). Apart from mapping, monitoring of snow properties such as snow grain size, albedo, snow pollution (contamination by soot, soil, etc.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of snow grain size and albedo of western Himalayan snow cover using satellite data

Negi

Kokhanovsky

2011

The Cryosphere

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Abstract. In the present study we describe the retrievals of snow grain size and spectral albedo (plane and spherical albedo) for western Himalayan snow cover using Hyperion sensor data. The asymptotic radiative transfer (ART) theory was explored for the snow retrievals. To make the methodology operational only five spectral bands (440, 500, 1050, 1240 and 1650 nm) of Hyperion were used for snow parameters retrieval. The bi-spectral method (440 nm in the visible and 1050/1240 nm in the NIR region) was used to retrieve snow grain size. Spectral albedos were retrieved using satellite reflectances and estimated grain size. A good agreement was observed between retrieved snow parameters and ground observed snow-meteorological conditions. The satellite retrieved grain sizes were compared with field spectroradiometer retrieved grain sizes and close results were found for lower Himalayan snow. The wavelength 1240 nm was found to be more suitable compared to 1050 nm for grain size retrieval along the steep slopes. The methodology was able to retrieve the spatial variations in snow parameters in different parts of western Himalaya which are due to snow climatic and terrain conditions of Himalaya. This methodology is of importance for operational snow cover and glacier monitoring in Himalayan region using space-borne and airborne sensors.

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Section: Introductionmentioning

confidence: 99%

Retrieval of snow grain size and albedo of western Himalayan snow cover using satellite data

Negi

Kokhanovsky

2011

The Cryosphere

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“…Validation of MODIS snow products and its very reasonably good agreement with the ground observation data from various regions are well known from the literatures of (Klein and Bernett, 2003); (Parajka and Bloeschl 2006); (Wang et al, 2008). (Jain et al, 2008) concluded that MODIS data could be effectively used for SCA estimation under Himalayan rugged topographic and harsh climatic conditions. Review of many other works also reveals how the MODIS snow-cover products have been used and also includes details regarding snow cover mapping accuracy.…”

Section: Introductionmentioning

confidence: 54%

Hydrological Modelling and data assimilation of Satellite Snow Cover Area using a Land Surface Model, VIC

Naha¹,

Thakur²,

Aggarwal³

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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NBSSLUP soil map and land use land cover map of ISRO-GBP project for year 2014 were used for generating the soil parameters and vegetation parameters respectively. The threshold temperature i.e. the minimum rain temperature is -0.5°C and maximum snow temperature is about +0.5°C at which VIC can generate snow fluxes. Hydrological simulations were done using both NCEP and IMD based meteorological Forcing datasets, but very few snow fluxes were obtained using IMD data met forcing, whereas NCEP based met forcing has given significantly better snow fluxes throughout the simulation years as the temperature resolution as given by IMD data is 0.5°C and rainfall resolution of 0.25°C. The simulated discharge has been validated using observed data from BBMB (Bhakra Beas Management Board) and coefficient of Correlation(R 2 ) measured for (2003)(2004)(2005)(2006) was 0.67 and 0.61 for the year 2006.But as VIC does not consider snowmelt runoff as a part of the total discharge, snowmelt runoff has been estimated for the simulation both with and without D.A. The snow fluxes as generated from VIC gives basin average estimates of Snow Cover, SWE, Snow Depth and Snow melt. It has been observed to be overestimated when model predicted snow cover is compared with MODIS SCA of 500 m resolution from MOD10A2 for each year. So MODIS 8-day snow cover area has been assimilated directly into the model state as well as by using EnKF after every 8 days for the year 2006.D.I Technique performed well as compared to EnKF. R 2 between Model SCA and MODIS SCA is estimated as 0.73 after D.I with Root Mean Square Error (RMSE) of +0.19. After direct Insertion of D.A, SCA has been reduced comparatively which resulted in 7% reduction of annual snowmelt contribution to total discharge.The assimilation of MODIS SCA data hence improved the snow cover area (SCA) fraction and finally updated other snow components.

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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%

“…These data screens were revised in MODIS C6 based on the evaluation of C5 products and studies of the use of C5 products reported in the literature. The accuracy of the heritage MODIS C5 daily snow-cover product has been reported in a diversity of landscapes, with different methods of comparison (e.g., Hall and Riggs, 2007;Jain et al, 2008;Huang et al, 2011;Arsenault et al, 2012;Rittger et al, 2013;Marchane et al, 2015). A notable increase in accuracy of snow-cover detection is observed in the MODIS C6 products on mountain ranges in the Northern Hemisphere during spring and summer as compared to C5, which resulted from the surface temperature screen (Sect.…”

Section: Accuracy Uncertainty and Errormentioning

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

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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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Accuracy assessment of MODIS, NOAA and IRS data in snow cover mapping under Himalayan conditions

Cited by 106 publications

References 21 publications

Retrieval of snow grain size and albedo of western Himalayan snow cover using satellite data

Retrieval of snow grain size and albedo of western Himalayan snow cover using satellite data

Hydrological Modelling and data assimilation of Satellite Snow Cover Area using a Land Surface Model, VIC

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

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