scholarly journals Overview of NASA's MODIS and VIIRS Snow-Cover Earth System Data Records

2017 ◽  
Author(s):  
George A. Riggs ◽  
Dorothy K. Hall ◽  
Miguel O. Román
Keyword(s):  
Snow Cover ◽  
Information Content ◽  
Infrared Imaging ◽  
Global Climate ◽  
Detection Accuracy ◽  
Earth System ◽  
Detection Algorithms ◽  
Snow Cover Extent ◽  
Data Products ◽  
System Data

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 (Suomi-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) and VIIRS Collection 1 (C1) 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 S-NPP VIIRS snow products and with products from future Joint Polar Satellite System (JPSS) platforms. These NASA datasets are archived and accessible through the NASA Distributed Active Archive Center (DAAC) at the National Snow and Ice Data Center (NSIDC) in Boulder, Colorado. DOIs of the referenced datasets: MODIS Collection 5 doi: http://dx.doi.org/10.5067/ACYTYZB9BEOS doi: http://dx.doi.org/10.5067/R90VAMI75N22 doi: http://dx.doi.org/10.5067/63NQASRDPDB0 doi: http://dx.doi.org/10.5067/ZFAEMQGSR4XD doi: http://dx.doi.org/10.5067/EI5HGLM2NNHN doi: http://dx.doi.org/10.5067/EW53FPU9NAS6 MODIS Collection 6 doi: http://dx.doi.org/10.5067/MODIS/MOD10_L2.006 doi: http://dx.doi.org/10.5067/MODIS/MYD10_L2.006 doi: http://dx.doi.org/10.5067/MODIS/MOD10A1.006 doi: http://dx.doi.org/10.5067/MODIS/MYD10A1.006 doi: http://dx.doi.org/10.5067/MODIS/MOD10C1.006 doi: http://dx.doi.org/10.5067/MODIS/MYD10C1.006 VIIRS Collection 1 doi:10.5067/VIIRS/VNP10.001

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

2017 ◽  
Vol 9 (2) ◽  
pp. 765-777 ◽  
Author(s):  
George A. Riggs ◽  
Dorothy K. Hall ◽  
Miguel O. Román
Keyword(s):  
Snow Cover ◽  
Information Content ◽  
Infrared Imaging ◽  
Global Climate ◽  
Detection Accuracy ◽  
Earth System ◽  
Detection Algorithms ◽  
Snow Cover Extent ◽  
Data Products ◽  
System Data

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 S-NPP VIIRS snow products and with products from future Joint Polar Satellite System (JPSS) platforms. These NASA datasets are archived and accessible through the NASA Distributed Active Archive Center at the National Snow and Ice Data Center in Boulder, Colorado.

scholarly journals Continuity of MODIS and VIIRS Snow Cover Extent Data Products for Development of an Earth Science Data Record

2020 ◽  
Vol 12 (22) ◽  
pp. 3781
Author(s):  
George Riggs ◽  
Dorothy Hall
Keyword(s):  
Snow Cover ◽  
Infrared Imaging ◽  
Earth Science ◽  
Satellite System ◽  
Science Data ◽  
Snow Cover Extent ◽  
Data Record ◽  
Data Products ◽  
Moderate Resolution ◽  
Earth Science Data

An Earth Observing System global snow cover extent data products record at moderate spatial resolution (375–500 m) began in February 2000 with the Moderate-resolution Imaging Spectroradiometer (MODIS) instrument onboard the Terra satellite. The record continued with the Aqua MODIS in July 2002, the Suomi-National Polar Platform (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) in January 2012 and continues with the Joint Polar Satellite System-1 (JPSS-1) VIIRS, launched in November of 2017. The objective of this work is to develop a snow cover extent Earth Science Data Record (ESDR) using different satellites, sensors and algorithms. There are many issues to understand when data from different algorithms and sensors are used over a decade-scale time period to create a continuous dataset. Issues may also arise with sensor degradation and even differences in sensor band locations. In this paper we describe development of an ESDR derived from existing MODIS and VIIRS data products and demonstrate continuity among the products. The MODIS and VIIRS snow cover detection algorithms produce very similar daily snow cover maps, with 90–97% agreement in snow cover extent (SCE) in different landscapes. Differences in SCE between products ranged from 2–15% and are attributable to convolved factors of viewing geometry, pixel spread across a scan and time of observation. Compared at a common grid size of 1 km, there is a mean of 95% agreement in SCE and a difference range of 1–10% between the MODIS and VIIRS SCE maps. Mapping sensor observations to a coarser resolution grid reduces the effect of the factors convolved in the 500 m tile to tile comparisons. We conclude that the MODIS and VIIRS SCE data products are reliable constituents of a moderate-resolution ESDR.

scholarly journals Spatiotemporal Variations in Snow Cover and Hydrological Effects in the Upstream Region of the Shule River Catchment, Northwestern China

2021 ◽  
Vol 13 (16) ◽  
pp. 3212
Author(s):  
Youyan Jiang ◽  
Wentao Du ◽  
Jizu Chen ◽  
Wenxuan Sun
Keyword(s):  
Snow Cover ◽  
Global Climate ◽  
Snow Accumulation ◽  
Upstream Region ◽  
Slope Aspect ◽  
Spatiotemporal Variations ◽  
River Catchment ◽  
Ice Melt ◽  
Inland River ◽  
Snow Cover Extent

Precipitation and snow/ice melt water are the primary water sources in inland river basins in arid areas, and these are sensitive to global climate change. A dataset of snow cover in the upstream region of the Shule River catchment was established using MOD10A2 data from 2000 to 2019, and the spatiotemporal variations in the snow cover and its meteorological, runoff, and topographic impacts were analyzed. The results show that the spatial distribution of the snow cover is highly uneven owing to altitude differences. The snow cover in spring and autumn is mainly concentrated along the edges of the region, whereas that in winter and summer is mainly distributed in the south. Notable differences in snow accumulation and melting are observed at different altitudes, and the annual variation in the snow cover extent shows bimodal characteristics. The correlation between the snow cover extent and runoff is most significant in April. The snow cover effectively replenishes the runoff at higher altitudes (3300–4900 m), but this contribution weakens with increasing altitude (>4900 m). The regions with a high snow cover frequency are mostly concentrated at high altitudes. Regions with slopes of <30° show a strong correlation with the snow cover frequency, which decreases for slopes of >45°. The snow cover frequency and slope aspect show symmetrical changes.

scholarly journals Detection and Attribution of Observed Changes in Northern Hemisphere Spring Snow Cover

2013 ◽  
Vol 26 (18) ◽  
pp. 6904-6914 ◽  
Author(s):  
David E. Rupp ◽  
Philip W. Mote ◽  
Nathaniel L. Bindoff ◽  
Peter A. Stott ◽  
David A. Robinson
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Climate Models ◽  
Global Climate ◽  
Coupled Model ◽  
Internal Variability ◽  
Global Climate Models ◽  
Anthropogenic Forcing ◽  
Detection And Attribution ◽  
Snow Cover Extent

Abstract Significant declines in spring Northern Hemisphere (NH) snow cover extent (SCE) have been observed over the last five decades. As one step toward understanding the causes of this decline, an optimal fingerprinting technique is used to look for consistency in the temporal pattern of spring NH SCE between observations and simulations from 15 global climate models (GCMs) that form part of phase 5 of the Coupled Model Intercomparison Project. The authors examined simulations from 15 GCMs that included both natural and anthropogenic forcing and simulations from 7 GCMs that included only natural forcing. The decline in observed NH SCE could be largely explained by the combined natural and anthropogenic forcing but not by natural forcing alone. However, the 15 GCMs, taken as a whole, underpredicted the combined forcing response by a factor of 2. How much of this underprediction was due to underrepresentation of the sensitivity to external forcing of the GCMs or to their underrepresentation of internal variability has yet to be determined.

scholarly journals MODIS Cloud-Gap Filled Snow-Cover Products: Advantages and Uncertainties

2019 ◽  
Author(s):  
Dorothy K. Hall ◽  
George A. Riggs ◽  
Nicolo E. DiGirolamo ◽  
Miguel O. Román
Keyword(s):  
United States ◽  
Snow Cover ◽  
Environmental Science ◽  
Hydrological Modeling ◽  
Infrared Imaging ◽  
Regional Climate ◽  
Science Data ◽  
Snow Cover Extent ◽  
Data Record ◽  
Terra Modis

Abstract. MODerate resolution Imaging Spectroradiometer (MODIS) cryosphere products that have been available since the launch of the Terra MODIS in 2000 and the Aqua MODIS in 2002 include snow-cover extent (swath, daily and eight-day composites) and daily snow albedo. These products are used in hydrological modeling and studies of local and regional climate, and are increasingly being used to study regional hydrological and climatological changes over time. Reprocessing of the complete snow-cover data record, from Collection 5 (C5) to Collection 6 (C6) and Collection 6.1 (C6.1), has led to improvements in the MODIS product suite. Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Collection 1 (C1) snow-cover products have been available since 2011, and are currently being reprocessed for Collection 2 (C2). To address the need for a cloud-reduced or cloud-free daily snow product for both MODIS and VIIRS, a new daily cloud-gap filled snow-cover product was developed for MODIS C6.1 and VIIRS C2 processing. MOD10A1F (Terra) and MYD10A1F (Aqua) are daily, 500-m resolution cloud-gap filled (CGF) snow-cover map products from MODIS. VNP10A1F is the 375-m resolution CGF snow map from VIIRS. The CGF maps provide daily cloud-free snow maps, along with cloud-persistence maps showing the age of the snow or non-snow observation in each pixel. Work is ongoing to evaluate and document uncertainties in the MODIS and VIIRS standard daily CGF snow-cover products. Analysis of the MOD/MYD10A1F products for study areas in the western United States shows excellent results in terms of accuracy of snow-cover mapping. When there are frequent clear-sky episodes, MODIS is able to capture enough clear views of the surface to produce accurate snow-cover information and snow maps. Even in the extensively-cloud-covered northeastern United States during winter months, snow maps from MODIS CGF products are useful, though the snow maps are likely to miss some snow, particularly during the spring snowmelt period when snow may fall and melt within a day or two, before the clouds clear from the storm that deposited the snow. Comparisons between the Terra and Aqua CGF snow maps have revealed differences that are related to cloud masking in the two algorithms. We conclude that the MODIS Terra CGF is the more accurate MODIS snow-cover product, and should therefore be the basis of an Environmental Science Data Record that will extend the CGF data record from the Terra MODIS beginning in 2000 through the VIIRS era, at least through the early 2030s.

scholarly journals Multi-Scale Feature Pyramid Network: A Heavily Occluded Pedestrian Detection Network Based on ResNet

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1820
Author(s):  
Xiaotao Shao ◽  
Qing Wang ◽  
Wei Yang ◽  
Yun Chen ◽  
Yi Xie ◽  
...  
Keyword(s):  
Semantic Information ◽  
Detection System ◽  
Pedestrian Detection ◽  
Detection Accuracy ◽  
The Public ◽  
Scale Feature ◽  
Detection Algorithms ◽  
Multi Scale ◽  
Art Works ◽  
Feature Pyramid

The existing pedestrian detection algorithms cannot effectively extract features of heavily occluded targets which results in lower detection accuracy. To solve the heavy occlusion in crowds, we propose a multi-scale feature pyramid network based on ResNet (MFPN) to enhance the features of occluded targets and improve the detection accuracy. MFPN includes two modules, namely double feature pyramid network (FPN) integrated with ResNet (DFR) and repulsion loss of minimum (RLM). We propose the double FPN which improves the architecture to further enhance the semantic information and contours of occluded pedestrians, and provide a new way for feature extraction of occluded targets. The features extracted by our network can be more separated and clearer, especially those heavily occluded pedestrians. Repulsion loss is introduced to improve the loss function which can keep predicted boxes away from the ground truths of the unrelated targets. Experiments carried out on the public CrowdHuman dataset, we obtain 90.96% AP which yields the best performance, 5.16% AP gains compared to the FPN-ResNet50 baseline. Compared with the state-of-the-art works, the performance of the pedestrian detection system has been boosted with our method.

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