Assessment of daily MODIS snow cover products to monitor snow cover dynamics over the Moroccan Atlas mountain range

Marchane, Ahmed; Jarlan, Lionel; Hanich, Lahoucine; Boudhar, Abdelghani; Gascoin, Simon; Tavernier, A.; Filali, N.; Page, Michel Le; Hagolle, Olivier; Berjamy, Brahim

doi:10.1016/j.rse.2015.01.002

Cited by 112 publications

(122 citation statements)

References 53 publications

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“…In general, a single snow event covers the upper part of the mountainous areas, but snowmelt starts shortly after accumulation. Similar cycles are also monitored in some other mountainous regions, i.e., Moroccan Atlas [16] and the Tibetan Plateau [18]. This behavior results in a characteristic depletion curve that is characterized by an exponential shape.…”

Section: Time Seriessupporting

confidence: 68%

“…Temporal filtering: In a second step, a forward (pixel value of the closest clear-sky observation in the forward time direction) and a backward (pixel value of closest clear-sky observation in the backward time direction) gap-filling procedure [16,17,50] is carried out. Foppa and Seiz [50] used a period of 7 days in both directions for the European Alps.…”

Section: Post-processingmentioning

confidence: 99%

“…Especially the MODIS sensor with 36 spectral bands and a reasonable resolution of 250 m-1000 m is an appropriate tool to monitor snow cover in mountains [14]. The MODIS snow product (MOD10A1) [15] was used by [16] to monitor the snow dynamic of the Moroccan Atlas mountain range. They improved the original snow mapping applying a spatio-temporal filter algorithm to reduce cloud-affected pixels considering three days in backward direction.…”

Section: Introductionmentioning

confidence: 99%

“…Snowfall was also occasionally detected in October/November, but only lasted for one day [25]. Grab and Linde [24] presented a study on snow cover distribution in Lesotho, which was based on an 8-year long MODIS data series but without any gap-filling techniques to reduce cloud cover as shown by [13,16,18,19]. The high snowfall variability and its spatial distribution require a time series that should be as long as possible to support more reliable analyses.…”

Section: Introductionmentioning

confidence: 99%

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Snow Extent Variability in Lesotho Derived from MODIS Data (2000–2014)

2016

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Abstract:In Lesotho, snow cover is not only highly relevant to the climate system, but also affects socio-economic factors such as water storage for irrigation or hydro-electricity. However, while sound knowledge of annual and inter-annual snow dynamics is strongly required by local stakeholders, in-situ snow information remains limited. In this study, satellite data are used to generate a time series of snow cover and to provide the missing information on a national scale. A snow retrieval method, which is based on MODIS data and considers the concept of a normalized difference snow index (NDSI), has been implemented. Monitoring gaps due to cloud cover are filled by temporal and spatial post-processing. The comparison is based on the use of clear sky reference images from Landsat-TM and ENVISAT-MERIS. While the snow product is considered to be of good quality (mean accuracy: 68%), a slight bias towards snow underestimation is observed. Based on the daily product, a consistent time series of snow cover for Lesotho from 2000-2014 was generated for the first time. Analysis of the time series showed that the high annual variability of snow coverage and the short duration of single snow events require daily monitoring with a gap-filling procedure.

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Section: Time Seriessupporting

confidence: 68%

Section: Post-processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Snow Extent Variability in Lesotho Derived from MODIS Data (2000–2014)

2016

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“…The C6 MODIS SCE product suite is available from the National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC). Collection 5 (C5) consists of a mature suite of MODIS land and cryospheric science algorithms that include SCE maps with an accuracy range of up to 95 % under clear skies (Tong et al, 2009;Gao et al, 2010a, b;Arsenault et al, 2012;Gascoin et al, 2015;Marchane et al, 2015;Mir et al, 2015;Hawotte et al, 2016). Notable revisions have been made in the Terra and Aqua MODIS snow-cover algorithms and data products in C6 compared to C5 (Riggs et al, 2016a, b, c) to correct known errors, improve accuracy and increase data content.…”

Section: Introductionmentioning

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

108

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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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Winter climate change and fine root biogenic silica in sugar maple trees (Acer saccharum): Implications for silica in the Anthropocene

2017

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Winter temperatures are projected to increase over the next century, leading to reductions in winter snowpack and increased frequency of soil freezing in many northern forest ecosystems. Here we examine biogenic silica (BSi) concentrations in sugar maple (Acer saccharum) fine roots collected from a snow manipulation experiment at Hubbard Brook Experimental Forest (New Hampshire, USA). Increased soil freezing significantly lowered the BSi content of sugar maple fine roots potentially decreasing their capacity to take up water and dissolved nutrients. The reduced silica uptake (8 ± 1 kmol silica km−2) by sugar maple fine roots is comparable to silica export from temperate forest watersheds. We estimate that fine roots account for 29% of sugar maple BSi, despite accounting for only 4% of their biomass. These results suggest that increased frequency of soil freezing will reduce silica uptake by temperate tree roots, thereby changing silica availability in downstream receiving waters.

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Assessment of daily MODIS snow cover products to monitor snow cover dynamics over the Moroccan Atlas mountain range

Cited by 112 publications

References 53 publications

Snow Extent Variability in Lesotho Derived from MODIS Data (2000–2014)

Snow Extent Variability in Lesotho Derived from MODIS Data (2000–2014)

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

Winter climate change and fine root biogenic silica in sugar maple trees (Acer saccharum): Implications for silica in the Anthropocene

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