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

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.

Fractional snow-cover mapping using an improved endmember extraction algorithm

2014 ◽  
Vol 8 (1) ◽  
pp. 084691 ◽  
Author(s):  
Ying Zhang ◽  
Xiaodong Huang ◽  
Xiaohua Hao ◽  
Jie Wang ◽  
Wei Wang ◽  
...  
Keyword(s):  
Snow Cover ◽  
Endmember Extraction ◽  
Fractional Snow Cover ◽  
Extraction Algorithm

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

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).

scholarly journals Consequences of the 2019 Greenland Ice Sheet Melt Episode on Albedo

2021 ◽  
Vol 13 (2) ◽  
pp. 227
Author(s):  
Arthur Elmes ◽  
Charlotte Levy ◽  
Angela Erb ◽  
Dorothy K. Hall ◽  
Ted A. Scambos ◽  
...  
Keyword(s):  
Radiative Forcing ◽  
Surface Albedo ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Surface Energy Budget ◽  
Landsat 8 ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Seasonal Snow Cover

In mid-June 2019, the Greenland ice sheet (GrIS) experienced an extreme early-season melt event. This, coupled with an earlier-than-average melt onset and low prior winter snowfall over western Greenland, led to a rapid decrease in surface albedo and greater solar energy absorption over the melt season. The 2019 melt season resulted in significantly more melt than other recent years, even compared to exceptional melt years previously identified in the moderate-resolution imaging spectroradiometer (MODIS) record. The increased solar radiation absorbance in 2019 warmed the surface and increased the rate of meltwater production. We use two decades of satellite-derived albedo from the MODIS MCD43 record to show a significant and extended decrease in albedo in Greenland during 2019. This decrease, early in the melt season and continuing during peak summer insolation, caused increased radiative forcing of the ice sheet of 2.33 Wm−2 for 2019. Radiative forcing is strongly influenced by the dramatic seasonal differences in surface albedo experienced by any location experiencing persistent and seasonal snow-cover. We also illustrate the utility of the newly developed Landsat-8 albedo product for better capturing the detailed spatial heterogeneity of the landscape, leading to a more refined representation of the surface energy budget. While the MCD43 data accurately capture the albedo for a given 500 m pixel, the higher spatial resolution 30 m Landsat-8 albedos more fully represent the detailed landscape variations.

Assessment of Arctic Cloud Cover Anomalies in Atmospheric Reanalysis Products Using Satellite Data

2016 ◽  
Vol 29 (17) ◽  
pp. 6065-6083 ◽  
Author(s):  
Yinghui Liu ◽  
Jeffrey R. Key
Keyword(s):  
Cloud Cover ◽  
Climate Models ◽  
Global Climate ◽  
Correlation Coefficients ◽  
Cloud Amount ◽  
Satellite Observation ◽  
Skill Score ◽  
Global Climate Models ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract Cloud cover is one of the largest uncertainties in model predictions of the future Arctic climate. Previous studies have shown that cloud amounts in global climate models and atmospheric reanalyses vary widely and may have large biases. However, many climate studies are based on anomalies rather than absolute values, for which biases are less important. This study examines the performance of five atmospheric reanalysis products—ERA-Interim, MERRA, MERRA-2, NCEP R1, and NCEP R2—in depicting monthly mean Arctic cloud amount anomalies against Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations from 2000 to 2014 and against Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) observations from 2006 to 2014. All five reanalysis products exhibit biases in the mean cloud amount, especially in winter. The Gerrity skill score (GSS) and correlation analysis are used to quantify their performance in terms of interannual variations. Results show that ERA-Interim, MERRA, MERRA-2, and NCEP R2 perform similarly, with annual mean GSSs of 0.36/0.22, 0.31/0.24, 0.32/0.23, and 0.32/0.23 and annual mean correlation coefficients of 0.50/0.51, 0.43/0.54, 0.44/0.53, and 0.50/0.52 against MODIS/CALIPSO, indicating that the reanalysis datasets do exhibit some capability for depicting the monthly mean cloud amount anomalies. There are no significant differences in the overall performance of reanalysis products. They all perform best in July, August, and September and worst in November, December, and January. All reanalysis datasets have better performance over land than over ocean. This study identifies the magnitudes of errors in Arctic mean cloud amounts and anomalies and provides a useful tool for evaluating future improvements in the cloud schemes of reanalysis products.

scholarly journals Aerosol Characteristics and Their Impact on the Himalayan Energy Budget

2021 ◽  
Vol 14 (1) ◽  
pp. 179
Author(s):  
Kesar Chand ◽  
Jagdish Chandra Kuniyal ◽  
Shruti Kanga ◽  
Raj Paul Guleria ◽  
Gowhar Meraj ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Radiative Forcing ◽  
Satellite Observation ◽  
Transfer Model ◽  
Hysplit Model ◽  
Aerosol Radiative Forcing ◽  
Alpine Regions ◽  
Himalayan Glaciers ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

The extensive work on the increasing burden of aerosols and resultant climate implications shows a matter of great concern. In this study, we investigate the aerosol optical depth (AOD) variations in the Indian Himalayan Region (IHR) between its plains and alpine regions and the corresponding consequences on the energy balance on the Himalayan glaciers. For this purpose, AOD data from Moderate Resolution Imaging Spectroradiometer (MODIS, MOD-L3), Aerosol Robotic Network (AERONET), India, and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) were analyzed. Aerosol radiative forcing (ARF) was assessed using the atmospheric radiation transfer model (RTM) integrated into AERONET inversion code based on the Discrete Ordinate Radiative Transfer (DISORT) module. Further, air mass trajectory over the entire IHR was analyzed using a hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. We estimated that between 2001 and 2015, the monthly average ARF at the surface (ARFSFC), top of the atmosphere (ARFTOA), and atmosphere (ARFATM) were −89.6 ± 18.6 Wm−2, −25.2 ± 6.8 Wm−2, and +64.4 ± 16.5 Wm−2, respectively. We observed that during dust aerosol transport days, the ARFSFC and TOA changed by −112.2 and −40.7 Wm−2, respectively, compared with low aerosol loading days, thereby accounting for the decrease in the solar radiation by 207% reaching the surface. This substantial decrease in the solar radiation reaching the Earth’s surface increases the heating rate in the atmosphere by 3.1-fold, thereby acting as an additional forcing factor for accelerated melting of the snow and glacier resources of the IHR.

scholarly journals CALIPSO IIR Version 2 Level 1b calibrated radiances: analysis and reduction of residual biases in the Northern Hemisphere

2018 ◽  
Vol 11 (4) ◽  
pp. 2485-2500 ◽  
Author(s):  
Anne Garnier ◽  
Thierry Trémas ◽  
Jacques Pelon ◽  
Kam-Pui Lee ◽  
Delphine Nobileau ◽  
...  
Keyword(s):  
Satellite Observation ◽  
Calibration Procedure ◽  
Latitude Range ◽  
Brightness Temperatures ◽  
Depth Analysis ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Individual ◽  
Semi Empirical ◽  
Global Bias

Abstract. Version 2 of the Level 1b calibrated radiances of the Imaging Infrared Radiometer (IIR) on board the Cloud-Aerosol Lidar and Infrared Satellite Observation (CALIPSO) satellite has been released recently. This new version incorporates corrections of small but systematic seasonal calibration biases previously revealed in Version 1 data products mostly north of 30∘ N. These biases – of different amplitudes in the three IIR channels 8.65 µm (IIR1), 10.6 µm (IIR2), and 12.05 µm (IIR3) – were made apparent by a striping effect in images of IIR inter-channel brightness temperature differences (BTDs) and through seasonal warm biases of nighttime IIR brightness temperatures in the 30–60∘ N latitude range. The latter were highlighted through observed and simulated comparisons with similar channels of the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Aqua spacecraft. To characterize the calibration biases affecting Version 1 data, a semi-empirical approach is developed, which is based on the in-depth analysis of the IIR internal calibration procedure in conjunction with observations such as statistical comparisons with similar MODIS/Aqua channels. Two types of calibration biases are revealed: an equalization bias affecting part of the individual IIR images and a global bias affecting the radiometric level of each image. These biases are observed only when the temperature of the instrument increases, and they are found to be functions of elapsed time since night-to-day transition, regardless of the season. Correction coefficients of Version 1 radiances could thus be defined and implemented in the Version 2 code. As a result, the striping effect seen in Version 1 is significantly attenuated in Version 2. Systematic discrepancies between nighttime and daytime IIR–MODIS BTDs in the 30–60∘ N latitude range in summer are reduced from 0.2 K in Version 1 to 0.1 K in Version 2 for IIR1–MODIS29. For IIR2–MODIS31 and IIR3–MODIS32, they are reduced from 0.4 K to close to zero, except for IIR3–MODIS32 in June, where the night-minus-day difference is around −0.1 K.

scholarly journals SnowCloudMetrics: Snow Information for Everyone

2020 ◽  
Vol 12 (20) ◽  
pp. 3341
Author(s):  
Ryan L. Crumley ◽  
Ross T. Palomaki ◽  
Anne W. Nolin ◽  
Eric A. Sproles ◽  
Eugene J. Mar
Keyword(s):  
Snow Cover ◽  
Data Storage ◽  
Image Data ◽  
Global Scale ◽  
Google Earth ◽  
Mountain Environments ◽  
Resource Managers ◽  
Moderate Resolution ◽  
Snow Telemetry ◽  
Moderate Resolution Imaging Spectroradiometer

Snow is a critical component of the climate system, provides fresh water for millions of people globally, and affects forest and wildlife ecology. Snowy regions are typically data sparse, especially in mountain environments. Remotely-sensed snow cover data are available globally but are challenging to convert into accessible, actionable information. SnowCloudMetrics is a web portal for on-demand production and delivery of snow information including snow cover frequency (SCF) and snow disappearance date (SDD) using Google Earth Engine (GEE). SCF and SDD are computed using the Moderate Resolution Imaging Spectroradiometer (MODIS) Snow Cover Binary 500 m (MOD10A1) product. The SCF and SDD metrics are assessed using 18 years of Snow Telemetry records at more than 750 stations across the Western U.S. SnowCloudMetrics provides users with the capacity to quickly and efficiently generate local-to-global scale snow information. It requires no user-side data storage or computing capacity, and needs little in the way of remote sensing expertise. SnowCloudMetrics allows users to subset by year, watershed, elevation range, political boundary, or user-defined region. Users can explore the snow information via a GEE map interface and, if desired, download scripts for access to tabular and image data in non-proprietary formats for additional analyses. We present global and hemispheric scale examples of SCF and SDD. We also provide a watershed example in the transboundary, snow-dominated Amu Darya Basin. Our approach represents a new, user-driven paradigm for access to snow information. SnowCloudMetrics benefits snow scientists, water resource managers, climate scientists, and snow related industries providing SCF and SDD information tailored to their needs, especially in data sparse regions.

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