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Near-surface air temperature is a key factor in many studies and its spatiotemporal patterns are highly dependent on the ground surface characteristics and vary over time and space. So Land Surface Temperature (LST) is an important parameter for air temperature estimation. In this study, it is tried to model the air temperature by deploying some of the parameters that affect it. The parameters that have been taken into account in this study include land surface temperature, Normalized Difference Vegetation Index (NDVI), Vapor Pressure (VP) and Lifted Index (LI) as a measure of atmospheric stability. To assess the impact of each of these parameters, different linear regression models, were tested. Support Vector Regression (SVR) and hybrid artificial neural network methods were also performed. To model and evaluate the time series data of Georgia State in The United States of America over 1 year have been used. The NDVI, Total Precipitable Water (TPW), LST and LI parameters are products of MODIS. VP is calculated by using a logarithmic model from the TPW. Finally, it was found out that the LST and VP have positive effects, LI has negative and NDVI had a slightly positive impact on the air temperature at 2 meters height. The achieved accuracy in the linear model when all parameters are involved was 2.29 °C with a correlation coefficient of R=0.96. Next, the SVR model was examined and applied to the linear model taking all parameters into account. It was found that it does not end up to any significant increase in accuracy but certainly increases the computation time. The accuracy of this model was about 2.25 °C with a correlation coefficient of 0.96. Finally, a hybrid artificial neural network was examined. It was found that it increases the accuracy but certainly increases the computation time. The achieved accuracy of this model was about 2.14 °C with a correlation coefficient of 0.96. [ABSTRACT FROM AUTHOR]

An abstract of the study "Enhancement of a Semi-empirical Model using Genetic Algorithm for Estimation of Near Surface Particulate Matter (PM10) Concentration in City of Tehran Using Satellite Images and Weather Data" by A. Hejazi and colleagues is presented.

The reflectance spectrum of green leaves is considerably affected by their biochemical and biophysical properties. It is possible to extract biochemical information from a continuous vegetation spectrum produced using hyperspectral sensors. The numerous absorption features present in the vegetation spectrum carry a considerable amount of information related to the content and the structure of the leaves and stems. In the present study, we tried to introduce a method for relative quantification of vegetation leaves protein contents using EO-1 Hyperion datasets through an innovative index named PALI (Protein Absorption Lines Index). The results of applying PALI to AVIRIS data also showed its robustness. However, applying PALI index for Hyperion images can only show the vegetation leaves protein contents of a pixel relative to its neighboring pixels and not absolute values. Nonetheless, it is assumed that absolute measurements will be possible if one can calibrate this index with field data. [ABSTRACT FROM AUTHOR]

Classification of vegetation according to their species composition is one of the most important tasks in the application of remote sensing in precision agriculture. To prepare an algorithm for such a mandate, there is a need for ground truth. Field operation is very costly and time consuming. Therefore, some other method must be developed, such as extracting information from the satellite images, which is comparatively cheaper and faster. In this study, we first introduced a simple method for Determination of the Vegetation Specie in full cover pixels (DVS) using their laboratory measured spectral reflectance curves. Then, based on these pixels, a hybrid method for vegetation field classification, which we call SCANN (Spectral Characteristics and Artificial Neural Network), is introduced. In this method, different vegetation spectral reflectance characteristics at the three extremes of green, red, and near-infrared along with an artificial neural network method were used. Comparing the results of DVS with those of field collected data showed near 100% accuracy. Based on the results of DVS, the results of SCANN showed an overall accuracy of more than 94%. This method is suggested for unsupervised classification using Hyperspectral images. [ABSTRACT FROM AUTHOR]

Emissivity mapping of the Earth's surface is the prerequisite to thermal remote sensing. A precise determination of a surfaces temperature is dependent upon the availability of precise emissivity data for that surface. The present study area is a part of sugarcane plantation fields in the west part of Khuzestan province. In this work, Temperature Emissivity Separation algorithm (TES) was applied to five different ASTER L1B images. It was found out that TES method overestimates temperature in all the five thermal bands, and underestimates the emissivities as compared to the laboratory values. The differences in the emissivity values (as compared to laboratory values) varied from 10% in band 10 to 3% in band 14. The main reasons for these discrepancies were a lack of proper calibration of the thermal bands, the possible presence of radiometric noises in the calculation of the emissivity Maximum Minimum Differences (MMD) as well as mixed pixels. To overcome these uncertainties in the TES algorithm, an Improved TES method (ITES) was introduced. In the ITES method, the surface exiting thermal fluxes were simulated. The emissivities of four different reference surfaces, along with air temperature measured at nearby weather stations (believed to represent LST of full vegetated pixels) and the band 14 temperature, were employed as inputs. The results show noticeable improvements in the predicted emissivity to around 1% for band 10 and less than 1% for bands 13 and 14 as compared to the corresponding laboratory values. The root mean square error (RMSE) of emissivities for full vegetation cover was less than 0.015 and less than 0.01 for partial vegetated cover, bare soil, and sea water surface. Finally, emissivity maps for one sample image, employing the five thermal bands, were produced. It is believed that these maps can be used in other satellite images as layers of emissivity values for the purpose of a proper estimation of surface temperatures. [ABSTRACT FROM AUTHOR]