scholarly journals Landsat 8 OLI Broadband Albedo Validation in Antarctica and Greenland

2021 ◽  
Vol 13 (4) ◽  
pp. 799
Author(s):  
Giacomo Traversa ◽  
Davide Fugazza ◽  
Antonella Senese ◽  
Massimo Frezzotti
Keyword(s):  
Narrow Band ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Landsat 8 ◽  
Polar Regions ◽  
Landsat 8 Oli ◽  
Anomalous Data ◽  
The Antarctic ◽  
Good Agreement

The albedo is a fundamental component of the processes that govern the energy budget, and particularly important in the context of climate change. However, a satellite-based high-resolution (30 m) albedo product which can be used in the polar regions up to 82.5° latitude during the summer seasons is lacking. To cover this gap, in this study we calculate satellite-based broadband albedo from Landsat 8 OLI and validate it against broadband albedo measurements from in situ stations located on the Antarctic and Greenland icesheets. The model to derive the albedo from raw satellite data includes an atmospheric and topographic correction and conversion from narrow-band to broadband albedo, and at each step different options were taken into account, in order to provide the best combination of corrections. Results, after being cleaned from anomalous data, show a good agreement with in situ albedo measurements, with a mean absolute error between in situ and satellite albedo of 0.021, a root mean square error of 0.026, a standard deviation of 0.015, a correlation coefficient of 0.995 (p < 0.01) and a bias estimate of −0.005. Considering the structure of the model, it could be applied to data from previous sensors of the Landsat family and help construct a record to analyze albedo variations in the polar regions.

scholarly journals DETEKSI SEBARAN MUATAN PADATAN TERSUSPENSI DENGAN MODEL EMPIRIS DAN MODEL SEMI-ANALITIK DI PERAIRAN BEKASI

2020 ◽  
Vol 12 (2) ◽  
pp. 341-351
Author(s):  
Pingkan Mayestika Afgatiani ◽  
Maryani Hartuti ◽  
Syarif Budhiman
Keyword(s):  
Time Series ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Google Earth ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Mean Square ◽  
Itu Model ◽  
Google Earth Engine

Salah satu parameter dalam kualitas air adalah muatan padatan tersuspensi (MPT). Muatan padatan tersuspensi terdiri dari lumpur, pasir dan jasad renik yang disebabkan pengikisan tanah yang terbawa ke badan air. Penelitian ini bertujuan untuk mendeteksi sedimen tersuspensi di perairan Bekasi. Landsat 8 digunakan untuk analisis padatan tersuspensi dengan platform Google Earth Engine dengan membandingkan antara model empiris dan semi-analitik. Alur studi ini meliputi deliniasi wilayah non air menggunakan data citra surface reflectance, analisis MPT, dan visualisasi. Selanjutnya dilakukan validasi dengan data in situ, pemilihan model dan implementasi time series. Hasil deteksi MPT tertampil dengan tampilan warna yang berbeda sesuai dengan konsentrasinya. Hasil uji validasi dengan data in situ menunjukkan nilai Normalized Mean Absolute Error (NMAE) model semi-analitik lebih mendekati syarat minimum yaitu sebesar 66,8%, berbeda jauh dengan model empiris sebesar 43768%. Nilai Root Mean Square Error (RMSE) pun terlihat bahwa model semi-analitik menghasilkan nilai yang jauh lebih kecil sebesar 51,4 dan model empiris sebesar 58577,2. Hal ini menunjukkan bahwa model semi-analitik memiliki nilai yang lebih baik dalam mendeteksi sebaran MPT. Analisis time series menunjukkan bahwa persebaran MPT tahun 2015 – 2019 di perairan pesisir memiliki sebaran MPT yang sangat tinggi, karena banyaknya tambak dan muara sungai. Oleh karena itu, model semi-analitik lebih direkomendasikan untuk mengestimasi konsentrasi MPT dibandingkan dengan model empiris.

scholarly journals Implementation of a simple thermodynamic sea ice scheme, SICE version 1.0-38h1, within the ALADIN–HIRLAM numerical weather prediction system version 38h1

2018 ◽  
Vol 11 (8) ◽  
pp. 3347-3368 ◽  
Author(s):  
Yurii Batrak ◽  
Ekaterina Kourzeneva ◽  
Mariken Homleid
Keyword(s):  
Sea Ice ◽  
Numerical Weather Prediction ◽  
Mean Absolute Error ◽  
Weather Prediction ◽  
Absolute Error ◽  
The Arctic ◽  
Polar Regions ◽  
Form Drag ◽  
Numerical Weather ◽  
Ice Surface Temperature

Abstract. Sea ice is an important factor affecting weather regimes, especially in polar regions. A lack of its representation in numerical weather prediction (NWP) systems leads to large errors. For example, in the HARMONIE–AROME model configuration of the ALADIN–HIRLAM NWP system, the mean absolute error in 2 m temperature reaches 1.5 ∘C after 15 forecast hours for Svalbard. A possible reason for this is that the sea ice properties are not reproduced correctly (there is no prognostic sea ice temperature in the model). Here, we develop a new simple sea ice scheme (SICE) and implement it in the ALADIN–HIRLAM NWP system in order to improve the forecast quality in areas influenced by sea ice. The new parameterization is evaluated using HARMONIE–AROME experiments covering the Svalbard and Gulf of Bothnia areas for a selected period in March–April 2013. It is found that using the SICE scheme improves the forecast, decreasing the value of the 2 m temperature mean absolute error on average by 0.5 ∘C in areas that are influenced by sea ice. The new scheme is sensitive to the representation of the form drag. The 10 m wind speed bias increases on average by 0.4 m s−1 when the form drag is not taken into account. Also, the performance of SICE in March–April 2013 and December 2015–December 2016 was studied by comparing modelling results with the sea ice surface temperature products from MODIS and VIIRS. The warm bias (of approximately 5 ∘C) of the new scheme is indicated for areas of thick ice in the Arctic. Impacts of the SICE scheme on the modelling results and possibilities for future improvement of sea ice representation in the ALADIN–HIRLAM NWP system are discussed.

Characteristics of Landsat 8 OLI-derived NDVI by comparison with multiple satellite sensors and in-situ observations

2015 ◽  
Vol 164 ◽  
pp. 298-313 ◽  
Author(s):  
Yinghai Ke ◽  
Jungho Im ◽  
Junghee Lee ◽  
Huili Gong ◽  
Youngryel Ryu
Keyword(s):  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Satellite Sensors ◽  
In Situ Observations

scholarly journals REMOTE ESTIMATION OF TOTAL SUSPENDED SOLID (TSS) TRANSPORT AFFECTED BY TIDAL BORE “BONO” OF KAMPAR BIG RIVER ESTUARY USING LANDSAT 8 OLI IMAGERY

2017 ◽  
Vol 42 (1) ◽  
pp. 37-45 ◽  
Author(s):  
Ulung Jantama Wisha ◽  
Ruzana Dhiauddin ◽  
Gunardi Kusumah
Keyword(s):  
River Estuary ◽  
Total Suspended Solid ◽  
Suspended Solid ◽  
Tidal Range ◽  
Primary Data ◽  
Natural Phenomenon ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Tidal Bore

The Kampar River estuary has a unique tidal bore, namely Bono. A tidal bore is a natural phenomenon caused by the tidal flow which meet the flow of the river. Tidal bore "Bono" has an impact on the transport of suspended particles which is pretty much along the Kampar River. The purpose of this study is to determine the estimated concentration of total suspended solid in the river as the result of the transport by Bono in Kampar River estuary by Landsat 8 OLI. The primary data are Landsat 8 OLI sensor – on Path 126 and Row 60, recording date was on 23 -04-2016, which was analyzed spatially – and TSS in situ. The secondary data are tide forecasting data and topographical map of Indonesia. Distribution of total suspended solid indicates sediment transport and its distribution by TSS values ranged between 10-150 mg.L-1 and TSS in situ value ranged between 42-241 mg.L-1. Tidal range ranged from 0.78 to 4.2 m and current velocity ranged from 0-0.9 m.s-1, which generate tidal bore extending from the mouth to the river body, resulting in suspended particle transport along the river. TSS concentration is higher in the river estuary.

scholarly journals Determining PM10 Model in Hanoi Using Landsat 8 Oli and Ground-measured Dust Data

2018 ◽  
Vol 34 (1) ◽  
Author(s):  
Nguyen Nhu Hung ◽  
Tran Van Anh ◽  
Pham Quang Vinh ◽  
Nguyen Thanh Binh ◽  
Vu Van Hoang
Keyword(s):  
Air Pollutants ◽  
Satellite Image ◽  
Calculated Data ◽  
Regression Function ◽  
Measured Data ◽  
Landsat 8 ◽  
Mean Square ◽  
Correlation Model ◽  
Landsat 8 Oli

PM10 (Particulate matter 10 is a dust with aerodynamic diameters of 0.001 ÷ 10μm) is one of the air pollutants affecting human health. In this study, we conducted a modeling study to identify PM10 dust in the air by using Landsat 8 OLI satellite image, along with PM10 ground-measured data using the machine DustTrak II . Conduct regression analysis to determine the correlation model. Here, we used 16 in-situ measurement points. In that, 10 points were used to determine the regression function and 6 other points were used to test the regression model. Results were evaluated based on correlation coefficient (R) and Root Mean Square Error (RMSE) between measured and calculated data.

scholarly journals An Optimal Two Bands Ratio Model to Monitor Chlorophyll-a in Urban Lake Using Landsat 8 Data

2020 ◽  
Vol 143 ◽  
pp. 02003
Author(s):  
Qi Chen ◽  
Mutao Huang ◽  
Kaiyuan Bai ◽  
Xiaojuan Li
Keyword(s):  
Chlorophyll A ◽  
Environmental Issue ◽  
Inland Waters ◽  
Landsat 8 ◽  
Ratio Model ◽  
Landsat 8 Oli ◽  
Band Ratio ◽  
Chl A ◽  
Quality Indexes

Chlorophyll-a (Chl-a) estimation in inland waters is an essential environmental issue. This study aimed to identify a band ratio model for Chl-a simulation using Landsat 8 OLI data and in situ Chl-a measuring in Lake Donghu. The band B1and B2, respectively at the wavelength of 443 nm and 483 nm, in the band ratio model [B1/B2] performed best in Chl-a estimation with the R2 of 0.6215. K-means cluster analysis based on water quality indexes (Chl-a, pH, DO, TN, TP, COD, Turbidity) was conducted to further improve the accuracy of inversion model. The MAPE of the optimal [B1/B2] algorithm has decreased by 4.81% and 39.87% respectively for 17 December 2017 (R2=0.7669, N=42) and 26 March 2018 (R2=0.9156, N=45).

scholarly journals Evaluation of Greenland near surface air temperature datasets

2017 ◽  
Vol 11 (4) ◽  
pp. 1591-1605 ◽  
Author(s):  
J. E. Jack Reeves Eyre ◽  
Xubin Zeng
Keyword(s):  
Air Temperature ◽  
Mean Absolute Error ◽  
Ice Sheet ◽  
Surface Air Temperature ◽  
Absolute Error ◽  
Near Surface ◽  
Satellite Retrieval ◽  
Highly Correlated ◽  
Global Reanalysis

Abstract. Near-surface air temperature (SAT) over Greenland has important effects on mass balance of the ice sheet, but it is unclear which SAT datasets are reliable in the region. Here extensive in situ SAT measurements ( ∼  1400 station-years) are used to assess monthly mean SAT from seven global reanalysis datasets, five gridded SAT analyses, one satellite retrieval and three dynamically downscaled reanalyses. Strengths and weaknesses of these products are identified, and their biases are found to vary by season and glaciological regime. MERRA2 reanalysis overall performs best with mean absolute error less than 2 °C in all months. Ice sheet-average annual mean SAT from different datasets are highly correlated in recent decades, but their 1901–2000 trends differ even in sign. Compared with the MERRA2 climatology combined with gridded SAT analysis anomalies, thirty-one earth system model historical runs from the CMIP5 archive reach  ∼  5 °C for the 1901–2000 average bias and have opposite trends for a number of sub-periods.

Extending the Ice Watch system as a citizen science project for the collection of In-Situ sea ice observations

2020 ◽  
Author(s):  
Ole Jakob Hegelund ◽  
Alistair Everett ◽  
Ted Cheeseman ◽  
Penelope Wagner ◽  
Nick Hughes ◽  
...  
Keyword(s):  
Sea Ice ◽  
Citizen Science ◽  
European Space Agency ◽  
Earth Observation ◽  
Machine Learning Algorithms ◽  
The Arctic ◽  
Polar Regions ◽  
User Engagement ◽  
The Antarctic

&lt;p&gt;The Ice Watch program coordinates routine visual observations of sea-ice including icebergs and meteorological parameters. The development and use of the Arctic Shipborne Sea Ice Standardization Tool (ASSIST) software has enabled the program to collect over 6 800 records from numerous ship voyages and it is complementary to the Antarctic Sea-ice Processes and Climate (ASPeCt) in the Antarctic. These observations will enhance validation and calibration of data from the Copernicus Sentinel satellites and other Earth Observation missions where the lack of routine spatially and temporally coincident data from the Polar Regions hinders the development of automatic classification products. A critical piece of information for operations and research, photographic records of observations, is often missing. As mobile phones are nearly ubiquitous and feature high-quality cameras, capable of recording accurate ancillary timing and positional information we are developing the IceWatchApp to aid users in supplementing observations with a photographic record.&lt;/p&gt;&lt;p&gt;The IceWatchApp has been funded by the Citizen Science Earth Observation Lab (CSEOL) programme of the European Space Agency and the Polar Citizen Science Collective, which has successfully implemented similar observation projects within atmospherics, biology and marine geosciences, is collaborating in its development. The image database will aid the training of machine learning algorithms for automatic sea ice type classification and provide a mechanism for crowd-sourcing identification through an &amp;#8220;ask a scientist&amp;#8221; feedback feature. The app will also have the capability to provide near real-time satellite and Copernicus services products back to the user, thereby educating them on Earth Observation, and giving them an improved understanding of the surrounding environment.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords&lt;/strong&gt;: Polar regions, Arctic, Antarctic, data collection, In-Situ measurements, remote sensing, Sea Ice, user engagement, citizen science, Earth Observation.&lt;br&gt;&lt;strong&gt;Abstract&lt;/strong&gt;: to session 35413&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

scholarly journals MODELOS EMPÍRICOS PARA INFERÊNCIA DE COMPONENTES OPTICAMENTE ATIVOS EM AMBIENTES LÓTICOS E SEMILÊNTICOS DO RIO TIBAGI/PR, APLICADOS À IMAGENS LANDSAT 8/OLI

2018 ◽  
Vol 43 ◽  
pp. 75
Author(s):  
Paulo Henrique Marques De Castro ◽  
Adriana Castreghini de Freitas Pereira ◽  
Mirian Vizintim Fernandes Barros
Keyword(s):  
Landsat 8 ◽  
Landsat 8 Oli

A questão da qualidade e escassez da água no planeta tem ganhado destaque em pesquisas científicas, e demais publicações com conotação de alerta e de conscientização. Especialistas apontam que a crise da água no século XXI é muito mais de gerenciamento do que uma crise real de escassez e estresse hídrico (HOEKSTRA & MEKONNEN, 2012). Entretanto, existem pesquisadores (ELLIOTT, et al., 2014;  RULLI, et al.,  2013) que apontam que a crise é um misto de problemas ambientais com agravantes relacionados à economia e ao desenvolvimento social. Pesquisas cientificas atuais utilizam o sensoriamento remoto para monitorar corpos d’água em relação aos componentes opticamente ativos (COAs), a partir de modelos empíricos para inferência detes; garantindo uma maior representatividade espacial da variável, além de redução de custos e otmização de tempo. Nesse sentido, o objetivo da pesquisa foi o de gerar modelos empíricos para estimativa de COAs da água, exequíveis em imagens orbitais multiespectrais de média resolução do satélite Landsat 8/OLI. Para tanto, foram adquiridas imagens simultaneamente à mensuração de variáveis limnológicas “in situ”, em pontos devidamente georreferenciados de um ambiente aquático semilêntico e outro lótico. Tais pontos foram distribuidos em uma represa a partir do modelo amostral de Faixas Concêntricas, e no rio a partir de Margens Paralelas, de acordo com Castro et al., (2017), ambos localizados no rio Tibagi, responsável por grande parte do abastecimento público de água de alguns municípios do Estado do Paraná. Os dados limnológicos e espectrais obtidos foram correlacionados, e a partir de regressão linear múltipla os resultados apresentaram modelos empíricos adequados, com média do valor de resposta (R2) de 46%, para a estimativa de clorofila-a, total de sólidos em suspensão e turbidez; em ambiente semilêntico do rio Tibagi.

scholarly journals Retrieval of Urban Aerosol Optical Depth from Landsat 8 OLI in Nanjing, China

2021 ◽  
Vol 13 (3) ◽  
pp. 415
Author(s):  
Yangyang Jin ◽  
Zengzhou Hao ◽  
Jian Chen ◽  
Dong He ◽  
Qingjiu Tian ◽  
...  
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Absolute Error ◽  
Meteorological Conditions ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Large Area ◽  
The North ◽  
Deep Blue ◽  
Seasonal Characteristics

Aerosol is an essential parameter for assessing the atmospheric environmental quality, and accurate monitoring of the aerosol optical depth (AOD) is of great significance in climate research and environmental protection. Based on Landsat 8 Operational Land Imager (OLI) images and MODIS09A1 surface reflectance products under clear skies with limited cloud cover, we retrieved the AODs in Nanjing City from 2017 to 2018 using the combined Dark Target (DT) and Deep Blue (DB) methods. The retrieval accuracy was validated by in-situ CE-318 measurements and MOD04_3K aerosol products. Furthermore, we analyzed the spatiotemporal distribution of the AODs and discussed a case of high AOD distribution. The results showed that: (1) Validated by CE-318 and MOD04_3K data, the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE) of the retrieved AODs were 0.874 and 0.802, 0.134 and 0.188, and 0.099 and 0.138, respectively. Hence, the combined DT and DB algorithms used in this study exhibited a higher performance than the MOD04_3K-obtained aerosol products. (2) Under static and stable meteorological conditions, the average annual AOD in Nanjing was 0.47. At the spatial scale, the AODs showed relatively high values in the north and west, low in the south, and the lowest in the center. At the seasonal scale, the AODs were highest in the summer, followed by spring, winter, and autumn. Moreover, changes were significantly higher in the summer than in the other three seasons, with little differences among spring, autumn, and winter. (3) Based on the spatial and seasonal characteristics of the AOD distribution in Nanjing, a case of high AOD distribution caused by a large area of external pollution and local meteorological conditions was discussed, indicating that it could provide extra details of the AOD distribution to analyze air pollution sources using fine spatial resolution like in the Landsat 8 OLI.

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