Validation of Physical Radiative Transfer Equation-Based Land Surface Temperature Using Landsat 8 Satellite Imagery and SURFRAD in-situ Measurements

Land Surface Temperature (LST) is a key criterion in the physics of the Earth surface that controls the interactions between the land and atmosphere. The objective of this study is to evaluate the performance of physics-based Radiative Transfer Equation (RTE) method on LST retrieval using Landsat 8 satellite imagery and simultaneous in-situ LST data. In order to validate the satellite-based LST, in-situ LST measurements were obtained from Surface Radiation Budget Network (SURFRAD) stations simultaneous with satellite data acquisitions. In the study, four SURFRAD stations (BND, FPK, TBL and GWN) and five images for each SURFRAD station, totally twenty cloud-free images, were used for RTE-based LST validation. RTE method uses the atmospheric parameters acquired from radiosounding data simultaneous with satellite pass; however, these parameters were retrieved from NASA's atmospheric correction parameter calculator since radiosounding data are not available every time. Thus, this situation is another contribution of this study. As a result of the validation process of all data, the statistical measures, namely, coefficient of determination (R2 ), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and RMSE-observations standard deviation ratio (RSR) were calculated as 0.96, 3.12 K, 2.30 K and 0.33, respectively. However, the accuracy of RTE method on LST retrieval increased (R2 = 0.97, RMSE = 2.17 K, MAE = 1.44 K and RSR = 0.25) after removing TBL station from the analysis, since LST differences in this station were high for all scenes. RSR (ranging from 0 to high positive vlues) is an important measure for model evaluation, and the lower RSR value means high performance of the model. The obtained results revealed that physics-based RTE method is an effective and practical way for LST retrieval from Landsat 8 data despite using interpolated atmospheric parameters instead of radiosounding data. © 2019 Elsevier LtdNational Oceanic and Atmospheric Administration U.S. Geological SurveyThe author thanks USGS for providing Landsat 8 satellite imagery free of charge. Besides, the author thanks NOAA for providing in-situ LST measurements form SURFRAD stations publicly via the FTP server (ftp://aftp.cmdl.noaa.gov/data/radiation/surfrad/)

Monitoring thermal anomaly and radiative heat flux using thermal infrared satellite imagery – A case study at Tuzla geothermal region

Geothermal energy, which is renewable, reliable and environmentally friendly, is one of the most important energy resources. Thus, it is crucial to explore geothermal areas in order to reduce the use of other energy sources that are detrimental to the environment and ecology. Thermal Infrared (TIR) remote sensing is an effective way to detect thermal anomalies in geothermal areas and volcanoes, since it is cost and time effective, and offers to work on a large scale compared to geophysical methods. The aim of this study is to investigate thermal anomalies in Tuzla geothermal region using daytime and nighttime TIR data with reference to Land Surface Temperature (LST) and Radiative Heat Flux (RHF). Many geophysical studies have been conducted in this region; however, it can also be studied with TIR remote sensing for further exploration. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data, acquired on 15.06.2012 and 15.07.2017 as daytime image and 15.09.2013 and 31.12.2017 as nighttime image, were utilized as satellite imagery. In addition to ASTER data, we proposed a multi-sensor based LST retrieval for nighttime using Landsat 8 data for emissivity acquisition. In order to evaluate the accuracy of LST images, cross-validation method was utilized with reference to Moderate Resolution Imaging Spectroradiometer (MODIS) LST products. The coefficient of determination (r2) and Root Mean Square Error (RMSE) were considered as statistical metrics and the lowest result was obtained as 90% and 1.76 K, respectively. As a result of the analyses, it was observed that nighttime LST presented better results for thermal anomalies in that geothermal area than daytime LST. Considering geothermal anomaly, the geothermal area had higher LST values even though it held identical or same NDVI values as compared to non-geothermal surroundings. In addition, the net radiative heat loss values were calculated as 17.83 MW and 121.28 MW for 2013 and 2017, respectively. The obtained results proved that TIR remote sensing could be utilized in the studies of geothermal area exploration. © 2018 Elsevier Lt

Discovering the changes in land surface temperature caused by the conversion of agricultural lands to residential and urban use

Airbus;Aselsan;et al.;Roketsan;STM Engineering Technology Consultancy;Turkish Aerospace9th International Conference on Recent Advances in Space Technologies, RAST 2019 --11 June 2019 through 14 June 2019 -- --The aim of this study is to understand the changes in Land Surface Temperature (LST) caused by the conversion of agricultural lands to residential and urban use. Landsat-5 and Landsat-8 satellite data acquired on 30.09.1996 and 27.09.2018, respectively, were utilized to obtain LST images. Single Channel Algorithm was used as LST retrieval method. The study was conducted in eight test sites where farmlands converted to residential and urban areas. These test sites are located in Ceyhan city, which is one of the districts of Adana province in Southern Turkey, and the total area size not only test sites of the study area is 28.51 km2. LST images were cross-validated with MODIS LST products and RMSEs were calculated as 2.5 K and 2.2 K for 1996 and 2018 LST images, respectively. Considering long term variations, the conversion of farmlands to residential and urban areas caused a distinctive increase in LST, which may lead to the changes in the regional climate patterns. This study showed that decision makers and city planners can consider LST images for a sustainable environment. © 2019 IEEE.Firat University Scientific Research Projects Management Unit: FBA-2018-10799ACKNOWLEDGMENT This study was supported by the Scientific Research Projects Unit of Cukurova University with the project number of FBA-2018-10799. Besides, the authors thank the U.S. Geological Survey for providing the satellite imagery free of charge

Application of Long Short-Term Memory neural network model for the reconstruction of MODIS Land Surface Temperature images

Land surface temperature (LST) is an important parameter that supplies information about the skin temperature of the Earth surface. Remote sensing satellite systems with thermal bands can be used to obtain LST information. One of these satellite systems, namely, Moderate Resolution Imaging Spectroradiometer (MODIS) is mostly utilized in LST studies. One of the problems of obtaining LST from the MODIS data is missing pixels because of the effects such as cloud coverage. This drawback can be encountered by applying Long Short-Term Memory (LSTM) network with one-step-ahead prediction of MODIS data to reconstruct daily LST through the previous data. In this study, LSTM network was applied to the daytime and nighttime MODIS time-series, separately. MODIS LST data (MYD11A1) have the spatial resolution of 1 km × 1 km with 1-day temporal resolution. The selected data range from Day of Year (DOY) 1 in 2017 (01 January 2017) to DOY 59 in 2019 (28 February 2019). MODIS images were processed for the reconstruction of daily LST images concerning an agricultural region in Ceyhan, Adana, Turkey. 82% of data were chosen as the training data while the remaining data were used for testing purposes. The data were reconstructed by feeding the network adding the new data in a moving window in each prediction step. The produced Root Mean Square Error (RMSE) map regarding all reconstruction errors from daytime and nighttime images varied between 2 K to 9 K and 1 K–5 K, respectively. Besides, the coefficients of determination (R2) at a selected pixel of time-series analysis were obtained as 0.894 and 0.905 for daytime and nighttime LST image, respectively. The results revealed that the LSTM network could be used to fix the missing pixels in LST images. © 2019 Elsevier Lt

Modeling and predicting seasonal ionospheric variations in Turkey using artificial neural network (ANN)

The aim of this study was to model and predict seasonal ionospheric total electron content (TEC) using artificial neural network (ANN). Within this scope, GPS observations acquired from ANKR GPS station (Turkey) in 2015 were utilized to model TEC variations. Considering all data for each season, training and testing data were set as 80% and 10%, respectively, and the rest of the data were used to estimate TEC values using extracted mathematical models of ANN method. Day of Year (DOY), hour, F107 cm index (solar activity), Kp index and DsT index (magnetic storm index) were considered as the input parameters in ANN. The performances of ANN models were evaluated using RMSE and R statistical metrics for each season. As a result of the analyses, considering the prediction results, ANN presented more successful predictions of TEC values in winter and autumn than summer and spring with RMSE 3.92 TECU and 3.97 TECU, respectively. On the other hand the R value of winter data set (0.74) was lower than the autumn data set (0.88) while the RMSE values were opposite. This situation can be caused by the accuracy and precision of data sets. The results showed that the ANN model predicted GPS-TEC in a good agreement for ANKR station. © 2019, Springer Nature B.V

Evaluation of spatio-temporal variability in Land Surface Temperature: A case study of Zonguldak, Turkey

The aim of this study is to analyze spatio-temporal variability in Land Surface Temperature (LST) in and around the city of Zonguldak as a result of the growing urbanization and industrialization during the last decade. Three Landsat 5 data and one Landsat 8 data acquired on different dates were exploited in acquiring LST maps utilizing mono-window algorithm. The outcomes obtained from this study indicate that there exists a significant temperature rise in the region for the time period between 1986 and 2015. Some cross sections were selected in order to examine the relationship between the land use and LST changes in more detail. The mean LST difference between 1986 and 2015 in ERDEMIR iron and steel plant (6.8 °C), forestland (3 °C), city and town centers (4.2 °C), municipal rubbish tip (-3.9 °C), coal dump site (12.2 °C), and power plants’ region (7 °C) were presented. In addition, the results indicated that the mean LST difference between forestland and city centers was approximately 5 °C, and the difference between forestland and industrial enterprises was almost 8 °C for all years. Spatio-temporal variability in LST in Zonguldak was examined in that study and due to the increase in LST, policy makers and urban planners should consider LST and urban heat island parameters for sustainable development. © 2015, Springer International Publishing Switzerland

The acquisition of impervious surface area from LANDSAT 8 satellite sensor data using urban indices: a comparative analysis

Rapid and irregular urbanization is an essential issue in terms of environmental assessment and management. The dynamics of landscape patterns should be observed and analyzed by local authorities for a sustainable environment. The aim of this study is to determine which spectral urban index, originated from old Landsat missions, represents impervious area better when new generation Earth observation satellite Landsat 8 data are used. Two datasets of Landsat 8, acquired on 2 September 2013 and 10 September 2016, were utilized to investigate the consistency of the results. In this study, commonly used urban indices namely normalized difference built-up index (NDBI), index-based built-up index (IBI), urban index (UI), and enhanced built-up and bareness index (EBBI) were utilized to extract impervious areas. The accuracy assessment of urban indices was conducted by comparing the results with pan-sharpened images, which were classified using maximum likelihood classification (MLC) method. The kappa values of MLC, IBI, NDBI, EBBI, and UI for 2013 dataset were 0.89, 0.79, 0.71, 0.59, and 0.49, respectively, and the kappa values of MLC, IBI, NDBI, EBBI, and UI for 2016 dataset were 0.90, 0.78, 0.70, 0.56, and 0.47, respectively. In addition, area information was extracted from indices and classified images, and the obtained outcomes showed that IBI presented better results than the other urban indices, and UI extracted impervious areas worse than the other indices in both selected cases. Consequently, Landsat 8 satellite data can be considered as an important source to extract and monitor impervious surfaces for the sustainable development of cities. © 2018, Springer International Publishing AG, part of Springer Nature

PIXEL-BASED CLASSIFICATION ANALYSIS OF LAND USE LAND COVER USING SENTINEL-2 AND LANDSAT-8 DATA

The aim of this study is to conduct accuracy analyses of Land Use Land Cover (LULC) classifications derived from Sentinel-2 and Landsat-8 data, and to reveal which dataset present better accuracy results. Zonguldak city and its near surrounding was selected as study area for this case study. Sentinel-2 Multispectral Instrument (MSI) and Landsat-8 the Operational Land Imager (OLI) data, acquired on 6 April 2016 and 3 April 2016 respectively, were utilized as satellite imagery in the study. The RGB and NIR bands of Sentinel-2 and Landsat-8 were used for classification and comparison. Pan-sharpening process was carried out for Landsat-8 data before classification because the spatial resolution of Landsat-8 (30m) is far from Sentinel-2 RGB and NIR bands (10m). LULC images were generated using pixel-based Maximum Likelihood (MLC) supervised classification method. As a result of the accuracy assessment, kappa statistics for Sentinel-2 and Landsat-8 data were 0.78 and 0.85 respectively. The obtained results showed that Sentinel-2 MSI presents more satisfying LULC images than Landsat-8 OLI data. However, in some areas of Sea class Landsat-8 presented better results than Sentinel-2

Analysis of land use land cover classification results derived from sentinel-2 image

17th International Multidisciplinary Scientific GeoConference, SGEM 2017 -- 29 June 2017 through 5 July 2017 -- -- 130787In this study, object-based Land Use Land Cover (LULC) classification performance of Sentinel-2 image has been tested by comparing other medium resolution satellite dataset of Zonguldak test field. The test field covering a small area around Zonguldak is located in the Western Black Sea region of Turkey. It is noted for being one of the main coal mining areas in the world. For the purpose of the study, pan-sharpened Landsat 8 image was used because of its nearly similar ground sampling distance (GSD). The RGB and NIR bands of Sentinel-2 were used for classification and comparison. As a first step, Landsat-8 pan-sharpened image was created using High Pass Filtering (HPF) pan sharp algorithm in ERDAS software package. Following this, resulted images were handled by the eCognition v4.0.6 software with the main steps of segmentation and classification. After determining the optimal segmentation parameters correctly, classification of main Land use/Land cover classes were compared with by Landsat-8 derived LULC classes. Furthermore, the results were verified visually using high resolution satellite image Worldview-2. The accuracy assessment as Kappa statistics for Sentinel-2 and Landsat-8 are 0.74 and 0.66, respectively. The obtained results revealed that Sentinel-2 LULC images give better results than Landsat-8. © SGEM2017. All Rights Reserved

Index-Based Identification of Surface Water Resources Using Sentinel-2 Satellite Imagery

2nd International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2018 --19 October 2018 through 21 October 2018 -- --Water is a key variable for the sustainability of the world. Surface water resources are of prime importance for all living creatures. Thus these resources should be monitored for proper water planning. In this study, we aimed to identify which spectral water index will represent water body better when the data of new imaging satellite, namely Sentinel-2, are used. Since these indices were originated from old Landsat missions, it is important to investigate the performances of these indices with other data resources. Çatalan and Yedigöze dam reservoirs were considered as the study area. Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI) were utilized as spectral indices. The obtained results showed that NDWI presented water body better than MNDWI when using Sentinel-2 data. As a statistical metric, the kappa values of NDWI and MNDW were obtained as 0.88 and 0.83 respectively. The results have revealed that remote sensing technology and remotely sensed images are important resources for surface water monitoring and management. © 2018 IEEE