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

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.

scholarly journals Using Air Temperature to Quantitatively Predict the MODIS Fractional Snow Cover Retrieval Errors over the Continental United States

2014 ◽  
Vol 15 (2) ◽  
pp. 551-562 ◽  
Author(s):  
Jiarui Dong ◽  
Mike Ek ◽  
Dorothy Hall ◽  
Christa Peters-Lidard ◽  
Brian Cosgrove ◽  
...  
Keyword(s):  
United States ◽  
Snow Cover ◽  
Air Temperature ◽  
Snow Water Equivalent ◽  
Probability Of Detection ◽  
Joint Analysis ◽  
Snow Cover Area ◽  
Fractional Snow Cover ◽  
Retrieval Errors

Abstract Understanding and quantifying satellite-based, remotely sensed snow cover uncertainty are critical for its successful utilization. The Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover errors have been previously recognized to be associated with factors such as cloud contamination, snowpack grain sizes, vegetation cover, and topography; however, the quantitative relationship between the retrieval errors and these factors remains elusive. Joint analysis of the MODIS fractional snow cover (FSC) from Collection 6 (C6) and in situ air temperature and snow water equivalent measurements provides a unique look at the error structure of the MODIS C6 FSC products. Analysis of the MODIS FSC dataset over the period from 2000 to 2005 was undertaken over the continental United States (CONUS) with an extensive observational network. When compared to MODIS Collection 5 (C5) snow cover area, the MODIS C6 FSC product demonstrates a substantial improvement in detecting the presence of snow cover in Nevada [30% increase in probability of detection (POD)], especially in the early and late snow seasons; some improvement over California (10% POD increase); and a relatively small improvement over Colorado (2% POD increase). However, significant spatial and temporal variations in accuracy still exist, and a proxy is required to adequately predict the expected errors in MODIS C6 FSC retrievals. A relationship is demonstrated between the MODIS FSC retrieval errors and temperature over the CONUS domain, captured by a cumulative double exponential distribution function. This relationship is shown to hold for both in situ and modeled daily mean air temperature. Both of them are useful indices in filtering out the misclassification of MODIS snow cover pixels and in quantifying the errors in the MODIS C6 product for various hydrological applications.

Latest Advances in Fractional Snow Cover Mapping on MODIS Data by Machine Learning Algorithms

2020 ◽  
Author(s):  
Semih Kuter ◽  
Zuhal Akyurek
Keyword(s):  
Machine Learning ◽  
Snow Cover ◽  
General Circulation ◽  
Snow Water Equivalent ◽  
Machine Learning Algorithms ◽  
Multivariate Adaptive Regression Splines ◽  
Support Vector ◽  
Landsat 8 ◽  
European Alps ◽  
Fractional Snow Cover

<p>Spatial extent of snow has been declared as an essential climate variable. Accurate modeling of snow cover is crucial for the better prediction of snow water equivalent and, consequently, for the success of general circulation and weather forecasting models as well as climate change and hydrological studies. This presentation mainly focuses on the representation of the latest findings of our efforts in fractional snow cover mapping on MODIS images by data-driven machine learning methodologies. For this purpose, a dataset composed of 20 MODIS - Landsat 8 image pairs acquired between Apr 2013 and Dec 2016 over European Alps were employed. Artificial neural networks (ANN), multivariate adaptive regression splines (MARS), support vector regression (SVR) and random forest (RF) models were trained and tested by using reference FSC maps generated from higher spatial resolution Landsat 8 binary snow maps. ANN, MARS, SVR and RF models exhibited quite good performance with average R ≈ 0.93, whereas the agreement between the reference FSC maps and the MODIS’ own product MOD10A1 (C5) was slightly poorer with R ≈ 0.88.</p>

scholarly journals Comparison between Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) Assessment of Vegetation Indices

2017 ◽  
Vol 1 (2) ◽  
pp. 355-366
Author(s):  
E. O. Makinde ◽  
A. D. Obigha
Keyword(s):  
Statistical Analysis ◽  
Land Cover ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Thematic Mapper ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Water Index ◽  
Landsat 7 ◽  
Index 2

The Landsat system has contributed significantly to the understanding of the Earth observation for over forty years. Since May 2013, data from Landsat 8 has been available online for download, with substantial differences from its predecessors, having an extended number of spectral bands and narrower bandwidths. The objectives of this research were majorly to carry out a cross comparison analysis between vegetation indices derived from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) and also performed statistical analysis on the results derived from the vegetation indices. Also, this research carried out a change detection on four land cover classes present within the study area, as well as projected the land cover for year 2030. The methods applied in this research include, carrying out image classification on the Landsat imageries acquired between 1984 – 2016 to ascertain the changes in the land cover types, calculated the mean values of differenced vegetation indices derived from the four land covers between Landsat 7 ETM+ and Landsat 8 OLI. Statistical analysis involving regression and correlation analysis were also carried out on the vegetation indices derived between the two sensors, as well as scatter plot diagrams with linear regression equation and coefficients of determination (R2). The results showed no noticeable differences between Landsat 7 and Landsat 8 sensors, which demonstrates high similarities. This was observed because Global Environmental Monitoring Index (GEMI), Improved Modified Triangular Vegetation Index 2 (MTVI2), Normalized Burn Ratio (NBR), Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Leaf Area Index (LAI) and Land Surface Water Index (LSWI) had smaller standard deviations. However, Renormalized Difference Vegetation Index (RDVI), Anthocyanin Reflectance Index 1 (ARI1) and Anthocyanin Reflectance Index 2 (ARI2) performed relatively poorly because their standard deviations were high. the correlation analysis of the vegetation indices that both sensors had a very high linear correlation coefficient with R2 greater than 0.99. It was concluded from this research that Landsat 7 ETM+ and Landsat 8 OLI can be used as complimentary data.

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

2020 ◽  
Vol 12 (1) ◽  
pp. 345-356 ◽  
Author(s):  
Sher Muhammad ◽  
Amrit Thapa
Keyword(s):  
Water Resources ◽  
Snow Cover ◽  
Cloud Cover ◽  
High Mountain ◽  
Landsat 8 ◽  
Spatial Filters ◽  
Glacier Inventory ◽  
Input Dataset ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Improved Accuracy

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).

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.

scholarly journals Snow-Covered Area Retrieval from Himawari–8 AHI Imagery of the Tibetan Plateau

2019 ◽  
Vol 11 (20) ◽  
pp. 2391
Author(s):  
Gongxue Wang ◽  
Lingmei Jiang ◽  
Jiancheng Shi ◽  
Xiaojing Liu ◽  
Jianwei Yang ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Snow Cover ◽  
Temporal Resolution ◽  
Estimation Method ◽  
High Temporal Resolution ◽  
Landsat 8 ◽  
The Tibetan Plateau ◽  
Snow Covered Area ◽  
Covered Area ◽  
Fractional Snow Cover

Daily snow-covered area retrieval using the imagery in solar reflective bands often encounters extensive data gaps caused by cloud obscuration. With the inception of geostationary satellites carrying advanced multispectral imagers of high temporal resolution, such as Japan’s geostationary weather satellite Himawari–8, considerable progress can now be made towards spatially-complete estimation of daily snow-covered area. We developed a dynamic snow index (normalized difference snow index for vegetation-free background and normalized difference forest–snow index for vegetation background) fractional snow cover estimation method using Himawari–8 Advanced Himawari Imager (AHI) observations of the Tibetan Plateau. This method estimates fractional snow cover with the pixel-by-pixel linear relationship of snow index observations acquired under snow-free and snow-covered conditions. To achieve reliable snow-covered area mapping with minimal cloud contamination, the daily fractional snow cover can be represented as the composite of the high temporal resolution fractional snow cover estimates during daytime. The comparison against reference fractional snow cover data from Landsat–8 Operational Land Imager (OLI) showed that the root–mean–square error (RMSE) of the Himawari–8 AHI fractional snow cover ranged from 0.07 to 0.16, and that the coefficient of determination (R2) reached 0.81–0.96. Results from the 2015/2016 and 2016/2017 winters indicated that the daily composite of Himawari–8 observations obtained a 14% cloud percentage over the Tibetan Plateau, which was less than the cloud percentage (27%) from the combination of Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua.

scholarly journals Análise multitemporal de uso, ocupação e cobertura da terra na zona Leste da cidade de Caxias/Maranhão/Brasil

2021 ◽  
Vol 14 (3) ◽  
pp. 1415
Author(s):  
Patricia Barbosa Pereira ◽  
Hikaro Kayo de Brito Nunes ◽  
Francisco De Assis da Silva Araújo
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Urban Area ◽  
Thematic Mapper ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Land Occupation ◽  
Multi Temporal ◽  
Landsat 5 Tm ◽  
The City

Com o avanço da quantidade de habitantes no espaço urbano surgem novas formas de modificações no ambiente, e, assim, há o favorecimento da intensificação do processo de antropização, como a supressão da cobertura vegetal, a descaracterização do relevo e danos aos cursos d’água. Frente a isso, o objetivo deste estudo é analisar e quantificar, em escala multitemporal, a dinâmica de uso, ocupação e cobertura da terra da cidade de Caxias/MA com foco na zona Leste por meio de ferramentas obtidas junto ao Sensoriamento Remoto. A metodologia utilizada foi pesquisa bibliográfica, documental e cartográfica. Os mapas temáticos foram confeccionados através da interpretação de imagens obtidas dos satélites Landsat 5 TM (Thematic Mapper) para o ano de 2000 e o Landsat 8 OLI (Operational Land Imager) para 2017, por meio do plugin SCP (Semi-Automatic Classification) do software QGIS 2.18.8. Com os resultados obtidos, constatou-se que, a vegetação secundária continuou representando a maior área, apesar da área urbana ter crescido (de 33% a 35%). Isso é caraterizado devido à grande área verde no bairro Pai Geraldo e no bairro Baixinha onde está localizada uma fazenda. Diante dos dados e com as etapas de sensoriamento remoto, de campo e de laboratório, este estudo representou uma análise de uso, ocupação e cobertura da terra ocorrida, onde, a partir dela constatou-se as mais diversas atividades desenvolvidas na área, relacionando, ainda, com distintos riscos e impactos socioambientais. Assim, reforça-se a necessidade de novos estudos e a contribuição do sensoriamento remoto para o alcance dos objetivos.  Analyze multi-temporal the dynamics of use, land occupation and coverage the on east Zone of the city of Caxias/MA/Maranhão/Brazil  A B S T R A C TWith the advancement of the amount of people in the urban area there are new forms of changes in environment, and thus there favoring intensifying anthropization process as suppression of vegetation, the relief adulteration and damage to water courses. Faced with this, the general objective of this study was to analyze in multi-temporal scale, the dynamics of use, land occupation and coverage of the city of Caxias/MA with a focus on east Zone East through tools obtained from the Remote Sensing. The methodology used was literature, documentary and cartographic. Thematic maps were made by interpreting images obtained from the Landsat 5 TM (Thematic Mapper) satellites for the year 2000 and the Landsat 8 OLI (Operational Land Imager) for 2017, using the SCP (Semi-Automatic Classification) plugin of the QGIS software 2.18.8. Through the results obtained, it was found that in the zona Leste secondary vegetation continued to represent the largest area, despite the urban area having grown (from 33% to 35%). It is characterized because of the large green area in the Pai Geraldo district and Baixinha where it is located a farm. In the face and the remote sensing steps, field and laboratory, this study represents an analysis of use, occupation and land cover occurred in two areas of the city of Caxias/MA where, from there it was found the most diverse activities in the area, also with different risks and environmental impacts. Thus, it reinforces the need for further studies and remote sensing to the achievement of goals.Keywords: land cover; remote sensing; East zone; Caxias/MA.

scholarly journals Enhancement of snow cover change detection with sparse representation and dictionary learning

2014 ◽  
Vol XL-8 ◽  
pp. 543-548 ◽  
Author(s):  
D. Varade ◽  
O. Dikshit
Keyword(s):  
Sparse Representation ◽  
Snow Cover ◽  
Reference Data ◽  
Matching Pursuit ◽  
Support Vector ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Visual Interpretation ◽  
The Difference ◽  
Change Map

Sparse representation and decoding is often used for denoising images and compression of images with respect to inherent features. In this paper, we adopt a methodology incorporating sparse representation of a snow cover change map using the K-SVD trained dictionary and sparse decoding to enhance the change map. The pixels often falsely characterized as "changes" are eliminated using this approach. The preliminary change map was generated using differenced NDSI or S3 maps in case of Resourcesat-2 and Landsat 8 OLI imagery respectively. These maps are extracted into patches for compressed sensing using Discrete Cosine Transform (DCT) to generate an initial dictionary which is trained by the K-SVD approach. The trained dictionary is used for sparse coding of the change map using the Orthogonal Matching Pursuit (OMP) algorithm. The reconstructed change map incorporates a greater degree of smoothing and represents the features (snow cover changes) with better accuracy. The enhanced change map is segmented using kmeans to discriminate between the changed and non-changed pixels. The segmented enhanced change map is compared, firstly with the difference of Support Vector Machine (SVM) classified NDSI maps and secondly with a reference data generated as a mask by visual interpretation of the two input images. The methodology is evaluated using multi-spectral datasets from Resourcesat-2 and Landsat-8. The k-hat statistic is computed to determine the accuracy of the proposed approach.

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).

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