High resolution LST climatology in an urban area using Landsat 8 thermal data

2020 ◽  
Author(s):  
Sorin Cheval ◽  
Alexandru Dumitrescu ◽  
Vlad Amihăesei
Keyword(s):  
Missing Data ◽  
High Resolution ◽  
Urban Area ◽  
Land Surface ◽  
Missing Values ◽  
Urban Climate ◽  
Landsat 8 ◽  
Data Set ◽  
Area Of Interest ◽  
Spatial Coverage

<p>The Landsat 8 satellites retrieve land surface temperature (LST) values at 30-m spatial resolution since 2013, but the urban climate studies frequently use a limited number of images due to the problems related to missing data over the area of interest. This paper proposes a procedure for building a long-term LST data set in an urban area using the high-resolution Landsat 8 imagery. The methodology is demonstrated on 94 images available through 2013-2018 over Bucharest (Romania). The raw images contain between 1.1% and 58.4% missing data. Based on an Empirical Orthogonal Filling (EOF) procedure, the LST missing values were reconstructed by means of the function dineof implemented in sinkr R packages. The output was used for exploring the LST climatology in the area of interest. The gap filling procedure was validated by comparing artificial gaps created in the real data sets. At the best of our knowledge, this is the first study using full spatial coverage high resolution remote sensing data for investigating the urban climate. The validation pursued the comparison between LST and Ta at 3 WMO stations monitoring the climate of Bucharest, and returned strong correlation coefficients (R2 > 0.9). Further research may be envisaged aiming to update the data set with more recent LST information and to combine data from various sources in order to build a more robust urban LST climatology.</p><p>This work was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CCCDI -<br>UEFISCDI, project number COFUND-SUSCROP-SUSCAP-2, within PNCDI III.</p>

scholarly journals Exploratory Analysis of Urban Climate Using a Gap-Filled Landsat 8 Land Surface Temperature Data Set

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5336
Author(s):  
Sorin Cheval ◽  
Alexandru Dumitrescu ◽  
Vlad-Alexandru Amihaesei
Keyword(s):  
Surface Temperature ◽  
Urban Area ◽  
Spatial Resolution ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Climate ◽  
Temperature Data ◽  
Landsat 8 ◽  
Data Set ◽  
Surface Temperature Data

The Landsat 8 satellites have retrieved land surface temperature (LST) resampled at a 30-m spatial resolution since 2013, but the urban climate studies frequently use a limited number of images due to the problems related to missing data over the city of interest. This paper endorses a procedure for building a long-term gap-free LST data set in an urban area using the high-resolution Landsat 8 imagery. The study is applied on 94 images available through 2013–2018 over Bucharest (Romania). The raw images containing between 1.1% and 58.4% missing LST data were filled in using the Data INterpolating Empirical Orthogonal Functions (DINEOF) algorithm implemented in the sinkr R packages. The resulting high-spatial-resolution gap-filled land surface temperature data set was used to explore the LST climatology over Bucharest (Romania) an urban area, at a monthly, seasonal, and annual scale. The performance of the gap-filling method was checked using a cross-validation procedure, and the results pledge for the development of an LST-based urban climatology.

scholarly journals Handling Complex Missing Data Using Random Forest Approach for an Air Quality Monitoring Dataset: A Case Study of Kuwait Environmental Data (2012 to 2018)

2021 ◽  
Vol 18 (3) ◽  
pp. 1333
Author(s):  
Ahmad R. Alsaber ◽  
Jiazhu Pan ◽  
Adeeba Al-Hurban 
Keyword(s):  
Air Quality ◽  
Missing Data ◽  
Random Forest ◽  
Missing Values ◽  
Imputation Method ◽  
Environmental Data ◽  
Environmental Research ◽  
Quality Data ◽  
Data Set ◽  
Air Quality Data

In environmental research, missing data are often a challenge for statistical modeling. This paper addressed some advanced techniques to deal with missing values in a data set measuring air quality using a multiple imputation (MI) approach. MCAR, MAR, and NMAR missing data techniques are applied to the data set. Five missing data levels are considered: 5%, 10%, 20%, 30%, and 40%. The imputation method used in this paper is an iterative imputation method, missForest, which is related to the random forest approach. Air quality data sets were gathered from five monitoring stations in Kuwait, aggregated to a daily basis. Logarithm transformation was carried out for all pollutant data, in order to normalize their distributions and to minimize skewness. We found high levels of missing values for NO2 (18.4%), CO (18.5%), PM10 (57.4%), SO2 (19.0%), and O3 (18.2%) data. Climatological data (i.e., air temperature, relative humidity, wind direction, and wind speed) were used as control variables for better estimation. The results show that the MAR technique had the lowest RMSE and MAE. We conclude that MI using the missForest approach has a high level of accuracy in estimating missing values. MissForest had the lowest imputation error (RMSE and MAE) among the other imputation methods and, thus, can be considered to be appropriate for analyzing air quality data.

scholarly journals Continuous high-resolution midlatitude-belt simulations for July–August 2013 with WRF

2017 ◽  
Vol 10 (5) ◽  
pp. 2031-2055 ◽  
Author(s):  
Thomas Schwitalla ◽  
Hans-Stefan Bauer ◽  
Volker Wulfmeyer ◽  
Kirsten Warrach-Sagi
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Large Scale ◽  
Temperature Fields ◽  
Surface Model ◽  
Limited Area ◽  
Data Set ◽  
Latitude Belt ◽  
Simulated Precipitation ◽  
The Impact

Abstract. Increasing computational resources and the demands of impact modelers, stake holders, and society envision seasonal and climate simulations with the convection-permitting resolution. So far such a resolution is only achieved with a limited-area model whose results are impacted by zonal and meridional boundaries. Here, we present the setup of a latitude-belt domain that reduces disturbances originating from the western and eastern boundaries and therefore allows for studying the impact of model resolution and physical parameterization. The Weather Research and Forecasting (WRF) model coupled to the NOAH land–surface model was operated during July and August 2013 at two different horizontal resolutions, namely 0.03 (HIRES) and 0.12° (LOWRES). Both simulations were forced by the European Centre for Medium-Range Weather Forecasts (ECMWF) operational analysis data at the northern and southern domain boundaries, and the high-resolution Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) data at the sea surface.The simulations are compared to the operational ECMWF analysis for the representation of large-scale features. To analyze the simulated precipitation, the operational ECMWF forecast, the CPC MORPHing (CMORPH), and the ENSEMBLES gridded observation precipitation data set (E-OBS) were used as references.Analyzing pressure, geopotential height, wind, and temperature fields as well as precipitation revealed (1) a benefit from the higher resolution concerning the reduction of monthly biases, root mean square error, and an improved Pearson skill score, and (2) deficiencies in the physical parameterizations leading to notable biases in distinct regions like the polar Atlantic for the LOWRES simulation, the North Pacific, and Inner Mongolia for both resolutions.In summary, the application of a latitude belt on a convection-permitting resolution shows promising results that are beneficial for future seasonal forecasting.

scholarly journals Derivation and analysis of a high-resolution estimate of global permafrost zonation

2011 ◽  
Vol 5 (3) ◽  
pp. 1547-1582
Author(s):  
S. Gruber
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Permafrost Region ◽  
Large Area ◽  
Data Set ◽  
Regional Patterns ◽  
Actual Surface ◽  
Interesting Insight ◽  
Air Temperatures ◽  
Discontinuous Permafrost

Abstract. Permafrost underlies much of Earths' surface and interacts with climate, eco-systems and human systems. It is a complex phenomenon controlled by climate and (sub-) surface properties and reacts to change with variable delay. Heterogeneity and sparse data challenge the modeling of its spatial distribution. Currently, there is no data set to adequately inform global studies of permafrost. The available data set for the Northern Hemisphere is frequently used for model evaluation, but its quality and consistency are difficult to assess. A global model of permafrost extent and dataset of permafrost zonation are presented and discussed, extending earlier studies by including the Southern Hemisphere, by consistent data and methods, and most importantly, by attention to uncertainty and scaling. Established relationships between air temperature and the occurrence of permafrost are re-formulated into a model that is parametrized using published estimates. It is run with a high-resolution (<1 km) global elevation data and air temperatures based on the NCAR-NCEP reanalysis and CRU TS 2.0. The resulting data provides more spatial detail and a consistent extrapolation to remote regions, while aggregated values resemble previous studies. The estimated uncertainties affect regional patterns and aggregate number, but provide interesting insight. The permafrost area, i.e. the actual surface area underlain by permafrost, north of 60° S is estimated to be 13–18 × 106 km2 or 9–14 % of the exposed land surface. The global permafrost area including Antarctic and sub-sea permafrost is estimated to be 16–21 × 106 km2. The global permafrost region, i.e. the exposed land surface below which some permafrost can be expected, is estimated to be 22 ± 3 × 106 km2. A large proportion of this exhibits considerable topography and spatially-discontinuous permafrost, underscoring the importance of attention to scaling issues and heterogeneity in large-area models.

scholarly journals Mosaicking Weather Radar Retrievals from an Operational Heterogeneous Network at C and X Band for Precipitation Monitoring in Italian Central Apennines

2022 ◽  
Vol 14 (2) ◽  
pp. 248
Author(s):  
Stefano Barbieri ◽  
Saverio Di Fabio ◽  
Raffaele Lidori ◽  
Francesco L. Rossi ◽  
Frank S. Marzano ◽  
...  
Keyword(s):  
Atmospheric Precipitation ◽  
Weather Radar ◽  
Geographic Area ◽  
Single Frequency ◽  
Estimation Accuracy ◽  
Central Apennines ◽  
Data Set ◽  
Area Of Interest ◽  
Spatial Coverage ◽  
Network Pattern

Meteorological radar networks are suited to remotely provide atmospheric precipitation retrieval over a wide geographic area for severe weather monitoring and near-real-time nowcasting. However, blockage due to buildings, hills, and mountains can hamper the potential of an operational weather radar system. The Abruzzo region in central Italy’s Apennines, whose hydro-geological risks are further enhanced by its complex orography, is monitored by a heterogeneous system of three microwave radars at the C and X bands with different features. This work shows a systematic intercomparison of operational radar mosaicking methods, based on bi-dimensional rainfall products and dealing with both C and X bands as well as single- and dual-polarization systems. The considered mosaicking methods can take into account spatial radar-gauge adjustment as well as different spatial combination approaches. A data set of 16 precipitation events during the years 2018–2020 in the central Apennines is collected (with a total number of 32,750 samples) to show the potentials and limitations of the considered operational mosaicking approaches, using a geospatially-interpolated dense network of regional rain gauges as a benchmark. Results show that the radar-network pattern mosaicking, based on the anisotropic radar-gauge adjustment and spatial averaging of composite data, is better than the conventional maximum-value merging approach. The overall analysis confirms that heterogeneous weather radar mosaicking can overcome the issues of single-frequency fixed radars in mountainous areas, guaranteeing a better spatial coverage and a more uniform rainfall estimation accuracy over the area of interest.

scholarly journals A Global High‐Resolution Data Set of Soil Hydraulic and Thermal Properties for Land Surface Modeling

2019 ◽  
Vol 11 (9) ◽  
pp. 2996-3023 ◽  
Author(s):  
Yongjiu Dai ◽  
Qinchuan Xin ◽  
Nan Wei ◽  
Yonggen Zhang ◽  
Wei Shangguan ◽  
...  
Keyword(s):  
Thermal Properties ◽  
High Resolution ◽  
Land Surface ◽  
Surface Modeling ◽  
Data Set ◽  
High Resolution Data ◽  
Land Surface Modeling ◽  
Soil Hydraulic ◽  
Resolution Data

Detecting drought effects on tree mortality in forests of Franconia (Germany)

2020 ◽  
Author(s):  
Johannes Heisig ◽  
Cyrus Samimi
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Water Levels ◽  
Landsat 8 ◽  
Sensing Data ◽  
Dead Trees ◽  
Spatial Coverage ◽  
Data Products ◽  
Dead Vegetation

&lt;p&gt;Central European forests face challenges with climate changing much faster than they can adapt. Extremely hot and dry summers like in 2018 deprive forests of soil moisture, leaving them with low ground water levels. While individuals with deep and well-established root systems survive, young individuals and shallow-rooted species perish.&lt;/p&gt;&lt;p&gt;In southern Germany, die-off of single trees or small groups got noticeable recently. Such effects of harsher conditions rarely occur over large areas, but more in a spotted, irregular manner. This makes the phenomenon difficult to detect and to estimate its extent. The share of trees lately deteriorated may be larger than expected and represent a considerable portion of forests. Therefore, we see the great need for monitoring. Remote sensing data is suitable to examine inaccessible areas at a large scale. To quantify mortality of individual trees among a majority of vital ones, sensor platforms and respective data have to fulfill certain criteria regarding spatial, temporal and spectral resolution. Dead trees can be distinguished from others due to discoloration and defoliation. This change in appearance affects the spectral response, even in pixels larger than the tree&amp;#8217;s extent.&lt;/p&gt;&lt;p&gt;This study aims at recommending a suitable spatial scale for space-borne multispectral imagery products to achieve this task. We evaluate commercial and free remote sensing data products and their ability to estimate fractional cover of dead vegetation. Satellite data employed in this study comes from Landsat 8 (30 m), Sentinel-2 (10 m), RapidEye (6.5 m) and PlanetScope (3 m). Classification performance is tested against high-resolution multispectral aerial imagery (17 cm) acquired with a Micasense RedEdge-M camera.&lt;/p&gt;&lt;p&gt;High-resolution Micasense images are capable of detecting single dead trees, even after downgrading the resolution from 17 cm to 3 m. For all data products tested, fraction of dead trees per pixel did not differ significantly among land cover types (dead vegetation, vital vegetation, pavement, open soil). This indicates that individual dead trees may not be detectable in vital forest stands. The finding even seems to be valid for a resolution of 3 m (PlanetScope), which is identical to the downgraded Micasense data. In the near future the detection of this phenomenon might profit from technical developments towards even higher spatial detail of space-borne sensors. Alternatively, high resolution images from aerial campaigns, manned or unmanned, could bridge this gap when flight time and spatial coverage are increased significantly and facilitating policies are in place.&lt;/p&gt;

HRSC-A data: a new high-resolution data set with multipurpose applications in physical geography

2007 ◽  
Vol 31 (2) ◽  
pp. 179-197 ◽  
Author(s):  
J.-C. Otto ◽  
K. Kleinod ◽  
O. König ◽  
M. Krautblatter ◽  
M. Nyenhuis ◽  
...  
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Research Training ◽  
Spatial Scales ◽  
Three Dimensional ◽  
Physical Geography ◽  
Vegetation Monitoring ◽  
Data Set ◽  
High Resolution Data ◽  
Resolution Data

The analysis and interpretation of remote sensing data facilitates investigation of land surface complexity on large spatial scales. We introduce here a geometrically high-resolution data set provided by the airborne High Resolution Stereo Camera (HRSC-A). The sensor records digital multispectral and panchromatic stereo bands from which a very high-resolution ground elevation model can be produced. After introducing the basic principles of the HRSC technique and data, applications of HRSC data within the multidisciplinary Research Training Group 437 are presented. Applications include geomorphologic mapping, geomorphometric analysis, mapping of surficial grain-size distribution, rock glacier kinematic analysis, vegetation monitoring and three-dimensional landform visualization. A final evaluation of the HRSC data based on three years of multipurpose usage concludes this presentation. A combination of image and elevation data opens up various possibilities for visualization and three-dimensional analysis of the land surface, especially in geomorphology. Additionally, the multispectral imagery of the HRSC data has potential for land cover mapping and vegetation monitoring. We consider HRSC data a valuable source of high-resolution terrain information with high applicability in physical geography and earth system science.

scholarly journals Weighted Maximum Likelihood Correlation Coefficient to Handle Missing Values and Outliers in Data Set

2021 ◽  
Vol 20 ◽  
pp. 415-430
Author(s):  
Juthaphorn Sinsomboonthong ◽  
Saichon Sinsomboonthong
Keyword(s):  
Missing Data ◽  
Maximum Likelihood ◽  
Sample Size ◽  
Correlation Coefficient ◽  
Missing Values ◽  
Mean Squared Error ◽  
Correlation Coefficients ◽  
Median Percentage ◽  
Data Set ◽  
Median Correlation

The proposed estimator, namely weighted maximum likelihood (WML) correlation coefficient, for measuring the relationship between two variables to concern about missing values and outliers in the dataset is presented. This estimator is proven by applying the conditional probability function to take care of some missing values and pay more attention to values near the center. However, outliers in the dataset are assigned a slight weight. These using techniques will give the robust proposed method when the preliminary assumptions are not met data analysis. To inspect about the quality of the proposed estimator, the six methods—WML, Pearson, median, percentage bend, biweight mid, and composite correlation coefficients—are compared the properties in two criteria, i.e. the bias and mean squared error, via the simulation study. The results of generated data are illustrated that the WML estimator seems to have the best performance to withstand the missing values and outliers in dataset, especially for the tiny sample size and large percentage of outliers regardless of missing data levels. However, for the massive sample size, the median correlation coefficient seems to have the good estimator when linear relationship levels between two variables are approximately over 0.4 irrespective of outliers and missing data levels

scholarly journals Incoming data quality control in high-resolution urban climate simulations: a Hong Kong–Shenzhen area urban climate simulation as a case study using the WRF/Noah LSM/SLUCM model (Version 3.7.1)

2020 ◽  
Vol 13 (12) ◽  
pp. 6349-6360
Author(s):  
Zhiqiang Li ◽  
Bingcheng Wan ◽  
Yulun Zhou ◽  
Hokit Wong
Keyword(s):  
Quality Control ◽  
Hong Kong ◽  
High Resolution ◽  
Data Quality ◽  
Land Surface ◽  
Urban Climate ◽  
Data Quality Control ◽  
Climate Simulations ◽  
Arw Model ◽  
Hong Kong Sar

Abstract. The growth of computational power unleashed the potential of high-resolution urban climate simulations using limited-area models in recent years. This trend empowered us to deepen our understanding of urban-scale climatology with much finer spatial–temporal details. However, these high-resolution models would also be particularly sensitive to model uncertainties, especially in urbanizing cities where natural surface texture is changed artificially into impervious surfaces with extreme rapidity. These artificial changes always lead to dramatic changes in the land surface process. While models capturing detailed meteorological processes are being refined continuously, the input data quality has been the primary source of biases in modeling results but has received inadequate attention. To address this issue, we first examine the quality of the incoming static data in two cities in China, i.e., Shenzhen and Hong Kong SAR, provided by the WRF ARW model, a widely applied state-of-the-art mesoscale numerical weather simulation model. Shenzhen has gone through an unprecedented urbanization process in the past 30 years, and Hong Kong SAR is another well-urbanized city. A significant proportion of the incoming data is outdated, highlighting the necessity of conducting incoming data quality control in the region of Shenzhen and Hong Kong SAR. Therefore, we proposed a sophisticated methodology to develop a high-resolution land surface dataset in this region. We conducted urban climate simulations in this region using both the developed land surface dataset and the original dataset utilizing the WRF ARW model coupled with Noah LSM/SLUCM and evaluated the performance of modeling results. The performance of modeling results using the developed high-resolution urban land surface datasets is significantly improved compared to modeling results using the original land surface dataset in this region. This result demonstrates the necessity and effectiveness of the proposed methodology. Our results provide evidence of the effects of incoming land surface data quality on the accuracy of high-resolution urban climate simulations and emphasize the importance of the incoming data quality control.

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