An improved Terra–Aqua MODIS snow cover and Randolph Glacier Inventory 6.0 combined product (MOYDGL06*) for high-mountain Asia between 2002 and 2018

Abstract. Snow is a significant component of the ecosystem and water resources in high-mountain Asia (HMA). Therefore, accurate, continuous, and long-term snow monitoring is indispensable for the water resources management and economic development. The present study improves the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua satellites 8 d (“d” denotes “day”) composite snow cover Collection 6 (C6) products, named MOD10A2.006 (Terra) and MYD10A2.006 (Aqua), for HMA with a multistep approach. The primary purpose of this study was to reduce uncertainty in the Terra–Aqua MODIS snow cover products and generate a combined snow cover product. For reducing underestimation mainly caused by cloud cover, we used seasonal, temporal, and spatial filters. For reducing overestimation caused by MODIS sensors, we combined Terra and Aqua MODIS snow cover products, considering snow only if a pixel represents snow in both the products; otherwise it is classified as no snow, unlike some previous studies which consider snow if any of the Terra or Aqua product identifies snow. Our methodology generates a new product which removes a significant amount of uncertainty in Terra and Aqua MODIS 8 d composite C6 products comprising 46 % overestimation and 3.66 % underestimation, mainly caused by sensor limitations and cloud cover, respectively. The results were validated using Landsat 8 data, both for winter and summer at 20 well-distributed sites in the study area. Our validated adopted methodology improved accuracy by 10 % on average, compared to Landsat data. The final product covers the period from 2002 to 2018, comprising a combination of snow and glaciers created by merging Randolph Glacier Inventory version 6.0 (RGI 6.0) separated as debris-covered and debris-free with the final snow product MOYDGL06*. We have processed approximately 746 images of both Terra and Aqua MODIS snow containing approximately 100 000 satellite individual images. Furthermore, this product can serve as a valuable input dataset for hydrological and glaciological modelling to assess the melt contribution of snow-covered areas. The data, which can be used in various climatological and water-related studies, are available for end users at https://doi.org/10.1594/PANGAEA.901821 (Muhammad and Thapa, 2019).

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A combined Terra/Aqua MODIS snow-cover and RGI6.0 glacier product (MOYDGL06*) for the High Mountain Asia between 2002 and 2018

10.5194/essd-2019-78 ◽

2019 ◽

Author(s):

Sher Muhammad ◽

Amrit Thapa

Keyword(s):

Water Resources ◽

Snow Cover ◽

Cloud Cover ◽

Ground Truth ◽

High Mountain ◽

Landsat 8 ◽

Spatial Filters ◽

Moderate Resolution ◽

Moderate Resolution Imaging Spectroradiometer ◽

Improved Accuracy

Abstract. Snow is a significant component of the ecosystem and water resources in the High Mountain Asia (HMA). Accurate, continuous and long-term snow monitoring is necessary for water resources management and economic development. In this study, we improved Moderate-resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua snow–cover for HMA by a multi-step approach. The primary purpose of this study was to reduce uncertainty in MODIS snow cover. For reducing underestimation mainly caused by cloud cover, we used seasonal, temporal, and spatial filters. For reducing overestimation caused by MODIS sensor, we combined MODIS Terra and Aqua snow-cover products considering snow only if a pixel is snow in both the products otherwise no snow, unlike some previous studies considering snow if any of the Terra or Aqua product is snow. Our methodology generates a new product which removes a significant amount of uncertainty in raw MODIS 8-day composite product comprising 46 % overestimation and 3.66 % underestimation, mainly caused by sensor limitations and cloud cover, respectively. The results were validated using Landsat 8 data as ground truth, both for winter and summer at twenty well-distributed sites in the study area. Our validation results show that the adopted methodology improved accuracy on average by 10 %, mainly reducing the snow overestimation. The final product covers the period from 2002 to 2018, as a combination of snow and glaciers created by merging RGI6.0 glacier boundaries separately debris-covered and debris-free to the final snow product namely MOYDGL06*. Each of the Terra and Aqua datasets contains seven hundred and forty-six image files derived initially from approximately one hundred thousand satellite individual images. The data is available for researchers to use for various climate and water-related studies. The data is available at https://doi.org/10.1594/PANGAEA.901821 (Muhammad and Thapa, 2019).

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Daily Terra–Aqua MODIS cloud-free snow and Randolph Glacier Inventory 6.0 combined product (M*D10A1GL06) for high-mountain Asia between 2002 and 2019

Earth System Science Data ◽

10.5194/essd-13-767-2021 ◽

2021 ◽

Vol 13 (2) ◽

pp. 767-776

Author(s):

Sher Muhammad ◽

Amrit Thapa

Keyword(s):

Snow Cover ◽

Cloud Cover ◽

Regional Climate ◽

Regional Climate Change ◽

Training Data ◽

High Mountain ◽

Optical Remote Sensing ◽

Glacier Inventory ◽

Moderate Resolution Imaging Spectroradiometer ◽

The Individual

Abstract. Snow is a dominant water resource in high-mountain Asia (HMA) and crucial for mountain communities and downstream populations. Snow cover monitoring is significant to understand regional climate change, managing meltwater, and associated hazards/disasters. The uncertainties in passive optical remote-sensing snow products, mainly underestimation caused by cloud cover and overestimation associated with sensors' limitations, hamper the understanding of snow dynamics. We reduced the biases in Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua daily snow data and generated a combined daily snow product for high-mountain Asia between 2002 and 2019. An improved MODIS 8 d composite MOYDGL06* product was used as a training data for reducing the underestimation and overestimation of snow in daily products. The daily MODIS Terra and Aqua images were improved by implementing cloud removal algorithms followed by gap filling and reduction in overestimated snow beyond the respective 8 d composite snow extent of the MOYDGL06* product. The daily Terra and Aqua snow products were combined and merged with the Randolph Glacier Inventory version 6.0 (RGI 6.0) described as M*D10A1GL06 to make a more complete cryosphere product with 500 m spatial resolution. The pixel values in the daily combined product are preserved and reversible to the individual Terra and Aqua improved products. We suggest a weight of 0.5 and 1 to snow pixels in either or both Terra and Aqua products, respectively, for deriving snow cover statistics from our final snow product. The values 200, 242, and 252 indicate snow pixels in both Terra and Aqua and have a weight of 1, whereas pixels with snow in one of the Terra or Aqua products have a weight of 0.5. On average, the M*D10A1GL06 product reduces 39.1 % of uncertainty compared to the MOYDGL06* product. The uncertainties due to cloud cover (underestimation) and sensor limitations, mainly larger solar zenith angle (SZA) (overestimation) reduced in this product, are approximately 32.9 % and 6.2 %, respectively. The data in this paper are mainly useful for observation and simulation of climate, hydro-glaciological forcings, calibration, validation, and other water-related studies. The data are available at https://doi.org/10.1594/PANGAEA.918198 (Muhammad, 2020) and the algorithm source code at https://doi.org/10.5281/zenodo.3862058 (Thapa, 2020).

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Brief Communication: Updated GAMDAM Glacier Inventory over the High Mountain Asia

10.5194/tc-2018-139 ◽

2018 ◽

Cited By ~ 1

Author(s):

Akiko Sakai

Keyword(s):

Snow Cover ◽

Cloud Cover ◽

High Mountain ◽

Glacier Area ◽

Landsat Images ◽

Glacier Inventory ◽

Final Version ◽

Seasonal Snow ◽

Steep Slopes ◽

Seasonal Snow Cover

Abstract. The first version of the Glacier Area Mapping for Discharge from the Asian Mountains (GAMDAM) glacier inventory was the first methodologically consistent glacier inventory covering High Mountain Asia, and it underestimated glacier area because it did not include steep slopes covered with ice or snow and shadowed areas. During the process of revising the GAMDAM glacier inventory, source Landsat images were carefully selected to find images free of shadows, cloud cover, and seasonal snow cover taken from 1990 to 2010. Then, more than 90 % of the glacier area in the final version of the GAMDAM glacier inventory was delineated based on summer Landsat images. The total glacier area was 100,693±15,103 km2 and included 134,770 glaciers using 453 Landsat image scenes.

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An improved Terra–Aqua MODIS daily cloud-free snow and Randolph Glacier Inventory 6.0 combined product (M*D10A1GL06) for high-mountain Asia between 2002 and 2019

10.5194/essd-2020-141 ◽

2020 ◽

Author(s):

Sher Muhammad ◽

Amrit Thapa

Keyword(s):

Snow Cover ◽

Regional Climate ◽

Probabilistic Approach ◽

Regional Climate Change ◽

High Mountain ◽

Optical Remote Sensing ◽

Glacier Inventory ◽

Moderate Resolution Imaging Spectroradiometer ◽

Cloud Removal ◽

The Individual

Abstract. Snow is a dominant water resource in High Mountain Asia (HMA) and crucial for the mountain communities and downstream population. Snow cover monitoring is significant to understand regional climate change, managing meltwater, and associated hazards/disasters. The uncertainties in passive optical remote sensing snow products mainly underestimation caused by cloud-cover and overestimation associated with sensorsˈ limitations hamper the understand snow dynamics. We reduced the biases in Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua daily snow data and generated a combined daily snow product for High Mountain Asia between 2002 and 2019. An improved MODIS 8-day composite MOYDGL06* product was used as a base for reducing the underestimation and overestimation of snow in daily products. The daily MODIS Terra and Aqua images were improved by the corresponding 8-day composite image of the MOYDGL06* product by implementing cloud removal algorithms followed by gap filling and reduction in overestimated snow beyond the respective 8-day composite snow extent. The daily Terra and Aqua snow products were combined and merged with the Randolph Glacier Inventory (RGI) Version 6.0 to make a more complete cryosphere product. The pixel values in the daily combined product are preserved and reversible to the individual Terra and Aqua improved products. We suggest a probabilistic approach for deriving snow cover statistics from our final snow product. The pixels with values 200, 242, and 252 indicate snow in both Terra and Aqua and has a 100 % probability, whereas pixels with snow in one of the Terra or Aqua products have a 50 % probability. The data associated with this paper are available for the end-users mainly useful for observation and simulation of climate, hydro-glaciological forcings, calibration, validation, and other water-related studies. The data are available at https://doi.pangaea.de/10.1594/PANGAEA.918198 (Muhammad, 2020) and the algorithm source code at https://doi.org/10.5281/zenodo.3862058 (Thapa, 2020).

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Observing Snow Cover and Water Resource Changes in the High Mountain Asia Region in Comparison with Global Mountain Trends over 2000–2018

Remote Sensing ◽

10.3390/rs12233913 ◽

2020 ◽

Vol 12 (23) ◽

pp. 3913

Author(s):

Claudia Notarnicola

Keyword(s):

Water Resources ◽

Confidence Interval ◽

Snow Cover ◽

Water Storage ◽

High Mountain ◽

Global Level ◽

Total Water ◽

Mountain Areas ◽

Snow Cover Extent ◽

The Impact

The quantification of snow cover changes and of the related water resources in mountain areas has a key role for understanding the impact on several sectors such as ecosystem services, tourism and energy production. By using NASA-Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2000 to 2018, this study analyzes changes in snow cover in the High Mountain Asia region and compares them with global mountain areas. Globally, snow cover extent and duration are declining with significant trends in around 78% of mountain areas, and the High Mountain Asia region follows similar trends in around 86% of the areas. As an example, Shaluli Shan area in China shows significant negative trends for both snow cover extent and duration, with −11.4% (confidence interval: −17.7%, −5.5%) and −47.3 days (confidence interval: −70.4 days, −24.4 days) at elevations >5500 m a.s.l. respectively. In spring, an earlier snowmelt of −13.5 days (confidence interval: −24.3 days, −2.0 days) in 4000–5500 m a.s.l. is detected. On the other side, Tien Shan area shows an earlier snow onset of −28.8 days (confidence interval: −44.3 days, −8.2 days) between 2500 and 4000 m a.s.l., governed by decreasing temperature and increasing snowfall. In the current analysis, the Tibetan Plateau shows no significant changes. Regarding water resources, by using Gravity Recovery and Climate Experiment (GRACE) data it was found that around 50% of areas in the High Mountain Asia region and 30% at global level are suffering from significant negative temporal trends of total water storage (including groundwater, soil moisture, surface water, snow, and ice) in the period 2002–2015. In the High Mountain Asia region, this negative trend involves around 54% of the areas during spring period, while at a global level this percentage lies between 25% and 30% for all seasons. Positive trends for water storage are detected in a maximum 10% of the areas in High Mountain Asia region and in around 20% of the areas at global level. Overall snow mass changes determine a significant contribution to the total water storage changes up to 30% of the areas in winter and spring time over 2002–2015.

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The Spatial–Spectral–Environmental Extraction Endmember Algorithm and Application in the MODIS Fractional Snow Cover Retrieval

Remote Sensing ◽

10.3390/rs12223693 ◽

2020 ◽

Vol 12 (22) ◽

pp. 3693

Author(s):

Hongyu Zhao ◽

Xiaohua Hao ◽

Jian Wang ◽

Hongyi Li ◽

Guanghui Huang ◽

...

Keyword(s):

Snow Cover ◽

Absolute Error ◽

Satellite Observation ◽

Landsat 8 ◽

Endmember Extraction ◽

Analysis Model ◽

Fractional Snow Cover ◽

Extraction Algorithm ◽

Moderate Resolution ◽

Moderate Resolution Imaging Spectroradiometer

Endmember extraction is a primary and indispensable component of the spectral mixing analysis model applicated to quantitatively retrieve fractional snow cover (FSC) from satellite observation. In this study, a new endmember extraction algorithm, the spatial–spectral–environmental (SSE) endmember extraction algorithm, is developed, in which spatial, spectral and environmental information are integrated together to automatically extract different types of endmembers from moderate resolution imaging spectroradiometer (MODIS) images. Then, combining the linear spectral mixture analysis model (LSMA), the SSE endmember extraction algorithm is practically applied to retrieve FSC from standard MODIS surface reflectance products in China. The new algorithm of MODIS FSC retrieval is named as SSEmod. The accuracy of SSEmod is quantitatively validated with 16 higher spatial-resolution FSC maps derived from Landsat 8 binary snow cover maps. Averaged over all regions, the average root-mean-square-error (RMSE) and mean absolute error (MAE) are 0.136 and 0.092, respectively. Simultaneously, we also compared the SSEmod with MODImLAB, MODSCAG and MOD10A1. In all regions, the average RMSE of SSEmod is improved by 2.3%, 2.6% and 5.3% compared to MODImLAB for 0.157, MODSCAG for 0.157 and MOD10A1 for 0.189. Therefore, our SSE endmember extraction algorithm is reliable for the MODIS FSC retrieval and may be also promising to apply other similar satellites in view of its accuracy and efficiency.

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Brief communication: Updated GAMDAM glacier inventory over high-mountain Asia

The Cryosphere ◽

10.5194/tc-13-2043-2019 ◽

2019 ◽

Vol 13 (7) ◽

pp. 2043-2049 ◽

Cited By ~ 3

Author(s):

Akiko Sakai

Keyword(s):

Snow Cover ◽

Landsat Imagery ◽

High Mountain ◽

Glacier Area ◽

Landsat Images ◽

Glacier Inventory ◽

Seasonal Snow ◽

Seasonal Snow Cover

Abstract. The original Glacier Area Mapping for Discharge from the Asian Mountains (GAMDAM) glacier inventory was the first methodologically consistent dataset for high-mountain Asia. Nonetheless, the GAMDAM inventory underestimated glacier area, as it did not include steep ice- and snow-covered slopes or shaded components. During revision of the inventory, Landsat imagery free of shadow, cloud, and seasonal snow cover was selected for the period 1990–2010, after which >90 % of the glacier area was delineated. The updated GAMDAM inventory, comprised of 453 Landsat images, includes 134 770 glaciers with a total area of 100 693±11 790 km2.

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Evaluation of VIIRS and MODIS Snow Cover Fraction in High-Mountain Asia Using Landsat 8 OLI

Frontiers in Remote Sensing ◽

10.3389/frsen.2021.647154 ◽

2021 ◽

Vol 2 ◽

Author(s):

Karl Rittger ◽

Kat J. Bormann ◽

Edward H. Bair ◽

Jeff Dozier ◽

Thomas H. Painter

Keyword(s):

Snow Cover ◽

Infrared Imaging ◽

Mean Squared Error ◽

Probability Of Detection ◽

Thematic Mapper ◽

High Mountain ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Fractional Snow Cover ◽

Visible Wavelengths

We present the first application of the Snow Covered Area and Grain size model (SCAG) to the Visible Infrared imaging Radiometer Suite (VIIRS) and assess these retrievals with finer‐resolution fractional snow cover maps from Landsat 8 Operational Land Imager (OLI). Because Landsat 8 OLI avoids saturation issues common to Landsat 1–7 in the visible wavelengths, we re-assess the accuracy of the SCAG fractional snow cover maps from Moderate Resolution Imaging Spectroradiometer (MODIS) that were previously evaluated using data from earlier Landsat sensors. Use of the fractional snow cover maps from Landsat 8 OLI shows a negative bias of −0.5% for MODSCAG and −1.3% for VIIRSCAG, whereas previous MODSCAG evaluations found a bias of −7.6% in the Himalaya. We find similar root mean squared error (RMSE) values of 0.133 and 0.125 for MODIS and VIIRS, respectively. The Recall statistic (probability of detection) for cells with more than 15% snow cover in this challenging steep topography was found to be 0.90 for both MODSCAG and VIIRSCAG, significantly higher than previous evaluations based on Landsat 5 Thematic Mapper (TM) and 7 Enhanced Thematic Mapper Plus (ETM+). In addition, daily retrievals from MODIS and VIIRS are consistent across gradients of elevation, slope, and aspect. Different native resolutions of the gridded products at 1 km and 500 m for VIIRS and MODIS, respectively, result in snow cover maps showing a slightly different distribution of values with VIIRS having more mixed pixels and MODIS having 7% more pure snow pixels. Despite the resolution differences, the snow maps from both sensors produce similar total snow-covered areas and snow-line elevations in this region, with R2 values of 0.98 and 0.88, respectively. We find that the SCAG algorithm performs consistently across various spatial resolutions and that fractional snow cover maps from the VIIRS instruments aboard Suomi NPP, JPPS–1, and JPPS–2 can be a suitable replacement as MODIS sensors reach their ends of life.

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Daily MODIS Snow Cover Maps for the European Alps from 2002 onwards at 250 m Horizontal Resolution Along with a Nearly Cloud-Free Version

Data ◽

10.3390/data5010001 ◽

2019 ◽

Vol 5 (1) ◽

pp. 1 ◽

Cited By ~ 1

Author(s):

Michael Matiu ◽

Alexander Jacob ◽

Claudia Notarnicola

Keyword(s):

Snow Cover ◽

Programming Languages ◽

Cross Validation ◽

Horizontal Resolution ◽

Data Sets ◽

European Alps ◽

Spatial Filters ◽

Moderate Resolution ◽

Moderate Resolution Imaging Spectroradiometer ◽

Python Programming

Snow cover dynamics impact a whole range of systems in mountain regions, from society to economy to ecology; and they also affect downstream regions. Monitoring and analyzing snow cover dynamics has been facilitated with remote sensing products. Here, we present two high-resolution daily snow cover data sets for the entire European Alps covering the years 2002 to 2019, and with automatic updates. The first is based on moderate resolution imaging spectroradiometer (MODIS) and its implementation is specifically tailored to the complex terrain, exploiting the highest possible resolution available of 250 m. The second is a nearly cloud-free product derived from the first using temporal and spatial filters, which reduce average cloud cover from 41.9% to less than 0.1%. Validation has been performed using an extensive network of 312 ground stations, and for the cloud filtering also with cross-validation. Average overall accuracies were 93% for the initial and 91.5% for the cloud-filtered product using the ground stations; and 95.3% for the cross-validation of the cloud-filter. The data can be accessed online and via the R and python programming languages. Possible applications of the data include but are not limited to hydrology, cryosphere and climate.

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New generation geostationary satellite observations support seasonality in greenness of the Amazon evergreen forests

Nature Communications ◽

10.1038/s41467-021-20994-y ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Hirofumi Hashimoto ◽

Weile Wang ◽

Jennifer L. Dungan ◽

Shuang Li ◽

Andrew R. Michaelis ◽

...

Keyword(s):

Cloud Cover ◽

Seasonal Patterns ◽

Optical Remote Sensing ◽

Amazon Forest ◽

Evergreen Forests ◽

Geostationary Satellites ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

New Generation

AbstractAssessing the seasonal patterns of the Amazon rainforests has been difficult because of the paucity of ground observations and persistent cloud cover over these forests obscuring optical remote sensing observations. Here, we use data from a new generation of geostationary satellites that carry the Advanced Baseline Imager (ABI) to study the Amazon canopy. ABI is similar to the widely used polar orbiting sensor, the Moderate Resolution Imaging Spectroradiometer (MODIS), but provides observations every 10–15 min. Our analysis of NDVI data collected over the Amazon during 2018–19 shows that ABI provides 21–35 times more cloud-free observations in a month than MODIS. The analyses show statistically significant changes in seasonality over 85% of Amazon forest pixels, an area about three times greater than previously reported using MODIS data. Though additional work is needed in converting the observed changes in seasonality into meaningful changes in canopy dynamics, our results highlight the potential of the new generation geostationary satellites to help us better understand tropical ecosystems, which has been a challenge with only polar orbiting satellites.

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