Many investigations have shown Thermal data obtained from sensors have extensive applications in the study of earth features. But based on the author's information, the use of Thermal Remote Sensing has not been widespread yet. Temperature is important factor in understanding some physico-chemical and biological properties, which can be used for describing the state of materials. Therefore, decision for selecting Thermal Remotelysensed data and sensor types is a crucial step in the environmental studies. So, it is necessary that the content of Thermal band information and their applications be evaluated. In this study, the advantages of Thermal Remote Sensing and its applications in the study of sustainable development, vegetation, climate, air quality, soil and seismology are discussed. Based on the obtained results it may be concluded that Thermal Remote Sensing data are powerful tools for environmental studies.

The earth landscape is always changing due to human activities and natural phenomena. Therefore, in order to optimize the management of the natural areas, knowledge of the trend and extent of land cover / land use changes is considered necessary, and the estimation of these changes is of great importance. Reviewing these changes through satellite images and predicting and evaluating their potential through modeling can help environmental planners and natural resource managers to make more informed decisions. In the present study, quantitative detection and evaluation of changes in vegetation was performed in the areas with combat desertification projects, Shahdad and Bam in Kerman province and Garmsar in Semnan province, during a 30-year period within 1987, 2002 and 2017. The NDVI vegetation index and land use maps were produced using the ETM + TM and OLI satellite images in the three corresponding periods for the vegetation lands/non-vegetation lands, and agricultural lands. The Kappa coefficient of 0. 83 to 0. 86, 0. 91 to 0. 92, and 0. 94 to 0. 95 was calculated for 1987, 2002, and 2017, respectively, and the total accuracy was between 88 % and 97 %. After providing the land use maps in different years, the monitoring of land use changes was investigated using the change detection methods. According to the trend of changes during the studied periods, our results showed that the vegetation lands in these three areas had an increasing trend, and the non-vegetation lands were turned to vegetation lands over time. Moreover, an increasing trend was found for the agricultural lands during these three periods. Finally, the cost-effectiveness of projects implemented in the studied areas was calculated and evaluated.

Population growth and urbanization development are the major factors in increasing land surface temperature (LST) in urban areas which lead to Urban Heat Island (UHI). Green covers play an important role in improving the comfort level of citizens and achieving a sustainable urban environment through decreasing temperature, increasing humidity, and finally dwindling UHI. The current study aims to analyze and evaluate the changes of green covers and LST in Isfahan Metropolitan Area (IMA), Iran, from 1998 to 2014. This study emphasizes the impact of green covers on IMA temperature patterns. Accordingly, Normalized Difference Vegetation Index (NDVI) threshold method was applied to obtain the land surface emissivity (LSE). In addition, Planck’ s law for TM image and Split Window (SW) algorithm for OLI/TIRS image were utilized in order to retrieve LST. It was validated with data collected from 5 stations within the city. Temporal and spatial changes in IMA’ s LST were then analyzed using statistical methods, Mann-Kendall analysis, and Urban-Heat-Island Ration Index (URI). The result indicates that LST in IMA had an increasing trend over the study period and its intensity, generally, concentrated in the northwest and the northeast of the city, the bed of dried Zayandeh-Rood and green covers along the river bank which destroyed. Also, there was an increasing trend in URI from 0. 25 in 1998 to 0. 312 in 2014. All in all, it can be concluded Mann-Kendall trend test and URI were appropriate outfits to analyze satellite images in order to identify the Spatio-temporal change of UHI.

An earthquake is the movement of the surface of the Earth resulting from a sudden release of energy in the Earth's lithosphere that creates seismic waves. Earthquakes are one of the most unpredictable and dangerous natural phenomena that cause many financial and human losses every year. Due to the great importance of this natural crisis, several studies have been conducted to investigate this phenomenon. Many of these studies show that the earthquakes phenomenon is highly related to the deformation of the earth, rising ground temperatures, gases and aerosols, and electromagnetic disturbances in the atmosphere. The land surface temperature is highly dependent on the interactions of the earth's surface layers. When an earthquake occurs, stresses and activities in the fault range increase, causing significant temperature changes compared to normal temperatures. These temperature changes manifest themselves as anomalies in place or time. Regarding the materials and methods, in this research, using MODIS Thermal products and shapefile of Iran’, s faults, seven earthquakes with the intensity of more than 6 Ms have been investigated. First the preprocessing was performed on LST data so that Thermal noise signals caused by seasonal changes be removed from the original data. This was done by using a linear model made from the previous year data which no seismic activities were reported during its 40 days of investigation. Then, using the formation of a three-dimensional picture of time-temperature-distance in the earthquake-related fault as input, two methods for detecting Thermal anomalies have been investigated on the data. The mean standard deviation method, which is a threshold method using two parameters, and the interquartile method, which is similar to the previous method but uses different statistical parameters as input, are the two algorithms used in this research. Finally, using the results of the best method for detecting anomalies, severity parameter of each earthquake is estimated using artificial neural networks. Regarding the results and discussion, it should be noted that the results of anomaly detection algorithms show that both methods of Thermal anomaly detection have detected Thermal anomalies related to each earthquake on the day of the earthquake in a radius closest to the fault. In some cases like fahraj earthquake some anomalies were detected aside the anomaly detected on the day of the earthquake. However, results of the mean-standard deviation method gives more false alarms as an earthquake Thermal anomaly than the interquartile method. Although these anomalies could be related to the earthquake it cannot be a certain fact. So in order to have a better outcome we use the results of interquartile anomaly detection method as input for training of artificial neural network. The results in mathematical modeling have a relatively high accuracy in the case of seismic intensity parameter using artificial neural network with the total accuracy of 0. 73. These results indicate that the best accuracy belongs to Azgalah and the one with least accuracy belongs to fahraj study case. Although the number of earthquakes studied for neural network training has been relatively small, but the availability of large amounts of data on each earthquake has provided appropriate accuracy. In conclusion, this study shows that Thermal anomalies is one of the most significant precursors for earthquake’, s investigations. Using the relevant fault and anomalies with respect to the buffer zones in different distances can help us increase the accuracy dramatically. Since many previous studies that investigated Thermal anomalies connected to the earthquakes, explored areas around the epicenter, in this study we show that the corresponding fault is just as important as epicenter. Finally, it should be noted that the indicator of surface temperature changes and Thermal anomalies alone cannot be sufficient to fully investigate the parameters of the earthquake or have the necessary accuracy to analyze the earthquake. However, due to the low volume of Thermal data and the simplicity of working with them, it is recommended that they be used for initial earthquake surveys, and if it is partially confirmed for further analysis, use other methods and indicators that require the application of heavy and complex algorithms and processes. It is also possible to combine the results of this precursor with the results of other precursors to achieve sufficient accuracy. Regarding the results and discussion, it should be noted that the results of anomaly detection algorithms show that both methods of Thermal anomaly detection have detected Thermal anomalies related to each earthquake on the day of the earthquake in a radius closest to the fault. In some cases like fahraj earthquake some anomalies were detected aside the anomaly detected on the day of the earthquake. However, results of the mean-standard deviation method gives more false alarms as an earthquake Thermal anomaly than the interquartile method. Although these anomalies could be related to the earthquake it cannot be a certain fact. So in order to have a better outcome we use the results of interquartile anomaly detection method as input for training of artificial neural network. The results in mathematical modeling have a relatively high accuracy in the case of seismic intensity parameter using artificial neural network with the total accuracy of 0. 73. These results indicate that the best accuracy belongs to Azgalah and the one with least accuracy belongs to fahraj study case. Although the number of earthquakes studied for neural network training has been relatively small, but the availability of large amounts of data on each earthquake has provided appropriate accuracy. In conclusion, this study shows that Thermal anomalies is one of the most significant precursors for earthquake’, s investigations. Using the relevant fault and anomalies with respect to the buffer zones in different distances can help us increase the accuracy dramatically. Since many previous studies that investigated Thermal anomalies connected to the earthquakes, explored areas around the epicenter, in this study we show that the corresponding fault is just as important as epicenter. Finally, it should be noted that the indicator of surface temperature changes and Thermal anomalies alone cannot be sufficient to fully investigate the parameters of the earthquake or have the necessary accuracy to analyze the earthquake. However, due to the low volume of Thermal data and the simplicity of working with them, it is recommended that they be used for initial earthquake surveys, and if it is partially confirmed for further analysis, use other methods and indicators that require the application of heavy and complex algorithms and processes. It is also possible to combine the results of this precursor with the results of other precursors to achieve sufficient accuracy.

Monitoring seasonal changes of Meighan wetland using SAR, Thermal and optical Remote Sensing imagesAbstractThe aim of this study is to monitor the seasonal changes of Meighan wetland located in Markazi province in Iran. This is a multi-sensor approach,Sentinel-1 and Landsat 8 images were captured from May 2019 to January 2020. Modified Normalized Difference Water Index (MNDWI) and Land surface temperature were computed based on spectral bands of Landsat 8. Backscattering values in VH and VV polarimetric bands of Sentinel 1 images were also considered. Different wetland land cover classes were extracted based on these three measures. The results of each season were further compared with the classification output with support vector machines. The wetland main water body reaches its maximum extent in May 2019 (61. 18 square kilometers) and its minimum extent is reported in August 2019 with an extent of 19. 25 square kilometers. The outputs of the support vector machine classification were more compatible with MNDWI index. The results of this study show that the multi-sensor approach can efficiently be used in monitoring seasonal changes of wetland. Introduction Wetlands are one of the natural ecosystems that play an important role in plant and animal diversity conservation. Wetlands are very sensitive to environmental changes because they are located in an intermediate zone between land and marine ecosystems. Their constant monitoring is of great importance especially in wetlands with seasonal changes pattern. The Wetland ecosystems are influenced by anthropogenic and natural factors. Drought, reduced rainfall, unsustainable management of water resources, overexploitation, and dam construction threaten wetlands. Field surveying and mapping of natural resources are generally not cost-effective because these methods are expensive and time-consuming. Also, it is not possible to repeat it periodically with a constant interval. Therefore, the use of Remote Sensing data such as optics and radar data is necessary in the study of natural resources. However, natural landscapes are complex and composed of various land cover types. Optical multispectral images are not always able to classify such a landscape, perfectly. This source of data is also affected by atmospheric conditions,the presence of clouds or fog block capturing these images. SAR sensors unlike optics sensors are capable of capturing images in all weather conditions. In fact, the use of each satellite image has advantages and disadvantages and in many applications they complement each other. Multi-sensor approaches beneficiate from the capabilities of different satellite images. Researches have shown that a multi-sensor approach in natural resources studies, especially wetlands is of great value. The multi-source approach and the seasonal variations discussed in this study have not been followed in any research on Meighan wetland. The benefits of Sentinel-1 characteristics,such as suitable spatial and radiometric resolutions and free access highlight the finding of this research. Materials and methods Meighan wetland is located in the center of Iran in Markazi province. This wetland has ecological and economical importance in the region. In the last two decades, one road is constructed on it and divided it into two parts,this changes the wetland into a calm environment and subsequently the evaporation has been increased. In this study, the seasonal changes of Meighan wetland were investigated using Landsat 8 and Sentinel-1 images. The images in each season were selected in such a way that the minimum possible difference exist between their acquisition date. The preprocessing steps were done independently on each optic and SAR image. Sentinel-1 SAR images have been calibrated and the digital numbers were converted into the corresponding backscattering values (in decibel) in each polarimetric band. Although, from spectral reflectance values in different Landsat bands, Modified Normalized Difference Water Index (MNDWI) were calculated in each season. Land surface temperatures were also calculated from Thermal bands. Five different land cover classes are observed in the wetland and its surroundings,main water body of the wetland, shallow water zone, saline soil, surrounding area and remaining land covers (known as others). These areas were also extracted based on MNDWI index, land surface temperature (LST) and backscattering values in VH and VV sentinel-1 polarimetric bands. Then, the whole area is classified by the support vector machine classifier. In the last step, the extracted regions from different methods were compared with the land cover classification results in each season. The differences and similarities of the extracted areas were discussed further. Results and discussion The findings of this study show that the main wetland body reaches its maximum extent in May 2019 based on the SVM classification results. In this month, MNDWI index-based results were closer to the one obtained with the support vector machine classification. The support vector machine classification results and MNDWI index achieved similar results in the delineation of the wetland water zone, the shallow water zone and saline soil. In August 2019, the wetland water area was reduced based on the support vector machine classification. In May 2019 and January 2020, when the wetland water area was larger in comparison to other months, the results of the MNDWI index are close to the results of the support vector machine classification. The extracted area of shallow water class and saline soil class show the highest difference between classification results and MNDWI results. The same results have been obtained by comparison of extracted area based on the backscattering values of VH and VV polarimetric bands and MNDWI index,the maximum differences are observed in shallow water and saline soil classes. This could be related to the sensitivity of SAR backscattering values to moisture content. Over the year, the moisture content varies in response to temperature, rainfall, and evapotranspiration. The changes in moisture content affect the dielectric constant of the material. The dielectric constant governs the magnitude of backscattering values. The moisture changes cause variation in SAR backscattering values over the year. Conclusion Long-term wetland change detection is frequently studied with optical Remote Sensing images. Although, wetlands show the seasonal pattern in response to temperature and rainfall changes over the year, however, wetland seasonal variations are not fully explored. In this study, Sentinel 1 and Landsat8 images covering the study area were captured over the year. The results of the present study showed that the seasonal variation of wetland can be monitored based on a multi-sensor approach. In May 2019, the Meighan main water body reached the highest extent and the smallest area was observed in August 2019. In addition, in January 2020, the wetland water area increased again. Also some differences are observed between the extracted areas based on the MNDWI index, VH and VV polarizations, and the support vector machine classification results in different seasons. These differences are observed more in the spring. The performance of MNDWI index in wetland water area extraction in most seasons is very close to the classification results of the support vector machine. This shows the high capabilities of MNDWI spectral index in monitoring wetlands. In addition, the main water body of the wetland can be well separated by backscattering values of VH and VV Sentinel 1 polarimetric bands.

Soil moisture is one of the important environmental parameters. Traditional methods of field measurement of soil moisture cannot adequately reflect spatial variability of soil moisture. Remote Sensing has a widespread role in this. Landsat 8 satellite data and Sentinel-1 radar satellite from Tehran were provided. 72 soil samples were taken at the same time by satellite passing from the area and used in the validation phase with the necessary processing on satellite images and utilizes four different groups of indicators: 1) SAVI, NDVI, MI, NDWI 2) bands of Landsat 8 3) filters Radar 4) LST to modeling soil moisture. The investigation of the accuracy of functions and the introduction of the most accurate models was done by calculating the regression relations between these criteria and the ground points. The results of comparison of relations in the final step introduced two multivariate regression models to estimate the moisture content of the proposed area. The results showed that the proposed models have a good correlation coefficient of R2 = 62 and R2 = 73. Also, among the indicators in the four groups, SAVI, Band 1, Band 11, Li and Least filters have the highest correlation for estimating soil moisture content.

Spatiotemporal estimation and monitoring of soil moisture based on Remote Sensing observations (optical and Thermal) is challenging due to its physical nature in high vegetation conditions, necessitating improving and increasing the accuracy of soil moisture estimation in these areas. Therefore, this research aimed to develop a new approach to estimating surface soil moisture in agricultural fields with dense vegetation using machine learning algorithms by incorporating optical and Thermal Remote Sensing data and soil physical properties. For this objective, 16 Landsat-8 satellite images and more than 430 control locations were used during the sugarcane crop’s growth period in 2018-2019 at the Hakim Farabi Sugarcane Agro-Industrial company in the Khuzestan province of Iran. A set of 10 scenarios of various unique combinations of the available input variables were developed and then evaluated by five machine learning algorithms, including multiple linear regression (MLR), decision tree-based algorithms (CART and M5P), and ensemble learning-based algorithms (gradient-boosted regression trees (GBRT) and random forest regression (RFR)). According to the results, the highest correlation between input variables and surface soil moisture was observed in Soil Wetness Index (SWI) and Normalized Soil Moisture Index (NSMI) with R values of 0.79 and 0.69, respectively. Also, the highest accuracy of machine learning algorithms based on R2, RMSE, and MAE results was obtained in GBRT (0.99, 0.011, and 0.006) and RFR (0.99, 0.014, and 0.007), respectively. In general, the findings of this research show the importance of using variables based on Landsat-8 Remote Sensing data in combination with ensemble learning algorithms that can be independent of any ground measurements.