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/)

Potential of global thresholding methods for the identification of surface water resources using Sentinel-2 satellite imagery and normalized difference water index

WOS: 000492872000003The aim of this study is to investigate the performance of 15 automatic thresholding methods, namely Huang and Wang's fuzzy thresholding method, intermode thresholding method, isodata thresholding method, Li and Tam's thresholding method, maximum entropy thresholding method, mean thresholding method, minimum error thresholding method, minimum thresholding method, moment-preserving thresholding method, Otsu' s thresholding method, percentile (p-tile) thresholding method, Renyi' s entropy thresholding method, Shanbhag's thresholding method, triangle thresholding method, and Yen's thresholding method, for mapping water body using Sentinel-2 data based on normalized difference water index. Three different types of surface water bodies, such as a natural lake (Lake Burdur), a dam reservoir (Aslantas Dam Reservoir), and a part of a river (Aras River), are chosen to reveal the potential of 15 thresholding methods. The reference water body maps of each test site were generated by manual digitization of high-resolution Google Earth images. The thresholding methods were assessed using the statistical measures, namely overall accuracy (OA), Kappa coefficient, producer's accuracy, user's accuracy, and misclassification error (ME). The accuracy analyses of 15 thresholding methods were carried out separately for each test site, and then the overall accuracies were calculated to determine the best method. The obtained OA results showed that minimum thresholding method was the best method among these 15 algorithms with 0.0008 ME, 99.92% OA, and 0.9758 Kappa coefficient. On the other hand, Shanbhag' s method provided the lowest overall accuracies as 0.3133 ME, 68.67% OA, and 0.3190 Kappa coefficient. (C) 2019 Society of Photo-Optical Instrumentation Engineers (SPIE

Composites of Functionalized Multi-Walled Carbon Nanotube and Sodium Alginate for Tactile Sensing Applications

Flexible–tactile sensors are predicted to soon be extensively used in wearable devices. Various materials in flexible-sensor fabrication offer sensing properties with multiple capabilities. There is a crucial research opportunity in the field of flexible–tactile sensors for these materials, including nanocomposites. While the nanocomposites’ electrical properties mainly depend on nanofillers, the mechanical properties are determined by their polymer components. Carbon nanotubes (CNTs) are one of the most promising materials among nanofillers due to their high electrical conductivity, thermal stability, and durability. However, CNTs should be processed to increase the binding capacity of the polymer structure. In this study, the nanocomposite used for sensor manufacturing consisted of acid-functionalized CNTs and sodium alginate as the nanofiller and the polymer material, respectively. The sensor material was cross-linked using calcium chloride and glycerin was involved in the sensor fabrication to test its effect on sensing and flexibility. It is critical to note that sodium alginate and glycerin are biocompatible and biodegradable substances. In the scope of this study, the impedance changes of the fabricated tactile sensors were examined in the 100 Hz–10 MHz frequency range and equivalent circuits of the sensors were created. Additionally, impedance changes were obtained when alternating forces were applied to the sensors. The results showed that the frequency responses of the sensors differed from each other in different frequency ranges. In addition, each sensor had different sensing mechanisms in specific frequency ranges and the sensor made with glycerin had higher flexibility but less sensitivity

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

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

WOS: 000458467900020Geothermal 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 (r(2)) 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

Sensitivity Analysis and Validation of Daytime and Nighttime Land Surface Temperature Retrievals from Landsat 8 Using Different Algorithms and Emissivity Models

Land Surface Temperature (LST) is a substantial element indicating the relationship between the atmosphere and the land. This study aims to examine the efficiency of different LST algorithms, namely, Single Channel Algorithm (SCA), Mono Window Algorithm (MWA), and Radiative Transfer Equation (RTE), using both daytime and nighttime Landsat 8 data and in-situ measurements. Although many researchers conducted validation studies of daytime LST retrieved from Landsat 8 data, none of them considered nighttime LST retrieval and validation because of the lack of Land Surface Emissivity (LSE) data in the nighttime. Thus, in this paper, we propose using a daytime LSE image, whose acquisition is close to nighttime Thermal Infrared (TIR) data (the difference ranges from one day to four days), as an input in the algorithm for the nighttime LST retrieval. In addition to evaluating the three LST methods, we also investigated the effect of six Normalized Difference Vegetation Index (NDVI)-based LSE models in this study. Furthermore, sensitivity analyses were carried out for both in-situ measurements and LST methods for satellite data. Simultaneous ground-based LST measurements were collected from Atmospheric Radiation Measurement (ARM) and Surface Radiation Budget Network (SURFRAD) stations, located at different rural environments of the United States. Concerning the in-situ sensitivity results, the effect on LST of the uncertainty of the downwelling and upwelling radiance was almost identical in daytime and nighttime. Instead, the uncertainty effect of the broadband emissivity in the nighttime was half of the daytime. Concerning the satellite observations, the sensitivity of the LST methods to LSE proved that the variation of the LST error was smaller than daytime. The accuracy of the LST retrieval methods for daytime Landsat 8 data varied between 2.17 K Root Mean Square Error (RMSE) and 5.47 K RMSE considering all LST methods and LSE models. MWA with two different LSE models presented the best results for the daytime. Concerning the nighttime accuracy of the LST retrieval, the RMSE value ranged from 0.94 K to 3.34 K. SCA showed the best results, but MWA and RTE also provided very high accuracy. Compared to daytime, all LST retrieval methods applied to nighttime data provided highly accurate results with the different LSE models and a lower bias with respect to in-situ measurements

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