Remote sensing of land surface temperature (LST) from the thermal band data of Landsat Thematic Mapper (TM) still remains unused in comparison with the extensive studies of its visible and near-infrared (NIR) bands for various applications. The brightness temperature can be computed from the digital number (DN) of TM6 data using the equation provided by the National Aeronautics and Space Administration (NASA). However, a proper algorithm for retrieving LST from the only one thermal band of the sensor still remains unavailable due to many diYculties in the atmospheric correction. Based on thermal radiance transfer equation, an attempt has been made in the paper to develop a mono-window algorithm for retrieving LST from Landsat TM6 data. Three parameters are required for the algorithm: emissivity, transmittance and eVective mean atmospheric temperature. Method about determination of atmo- spheric transmittance is given in the paper through the simulation of atmospheric conditions with LOWTRAN 7 program. A practicable approach of estimating eVective mean atmospheric temperature from local meteorological observation is also proposed in the paper when the in situ atmospheric proéle data is unavailable at the satellite pass, which is generally the case in the real world especially for the images in the past. Sensitivity analysis of the algorithm indicates that the possible error of ground emissivity, which is diYcult to estimate, has relatively insigniécant impact on the probable LST estimation error dT, which is sensible to the possible error of transmittance dt 6 and mean atmospheric temperature dT a . Validation of the simulated data for various situations of seven typical atmospheres indicates that the algorithm is able to provide an accurate LST retrieval from TM6 data. The LST diVerence between the retrieved and the simulated ones is less than 0.4°C for most situations. Application of the algorithm to the sand dunes across the Israel-Egypt border results in a reasonable LST

The successful launch of the Landsat 8 satellite with two thermal infrared bands on February 11, 2013, for continuous Earth observation provided another opportunity for remote sensing of land surface temperature (LST). However, calibration notices issued by the United States Geological Survey (USGS) indicated that data from the Landsat 8 Thermal Infrared Sensor (TIRS) Band 11 have large uncertainty and suggested using TIRS Band 10 data as a single spectral band for LST estimation. In this study, we presented an improved mono-window (IMW) algorithm for LST retrieval from the Landsat 8 TIRS Band 10 data. Three essential parameters (ground emissivity, atmospheric transmittance and effective mean atmospheric temperature) were required for the IMW algorithm to retrieve LST. A new method was proposed to estimate the parameter of effective mean atmospheric temperature from local meteorological data. The other two essential parameters could be both estimated through the so-called land cover approach. Sensitivity analysis conducted for the IMW algorithm revealed that the possible error in estimating the required atmospheric water vapor content has the most significant impact on the probable LST estimation error. Under moderate errors in both water vapor content and ground emissivity, the algorithm had an accuracy of ~1.4 K for LST retrieval. Validation of the IMW algorithm using the simulated datasets for various situations indicated that the LST difference between the retrieved and the simulated ones was 0.67 K on average, with an RMSE of 0.43 K. Comparison of our IMW algorithm with the single-channel (SC) algorithm for three main atmosphere profiles indicated that the average error and RMSE of the IMW algorithm were −0.05 K and 0.84 K, respectively, which were less than the −2.86 K and 1.05 K of the SC algorithm. Application of the IMW algorithm to Nanjing and its vicinity in east China resulted in a reasonable LST estimation for the region. Spatial variation of the extremely hot weather, a frequently-occurring phenomenon of an abnormal heat flux process in summer along the Yangtze River Basin, had been thoroughly analyzed. This successful application suggested that the IMW algorithm presented in the study could be used as an efficient method for LST retrieval from the Landsat 8 TIRS Band 10 data.

Land surface temperature (LST) is one of the most important variables measured by satellite remote sensing. Public domain data are available from the newly operational Landsat-8 Thermal Infrared Sensor (TIRS). This paper presents an adjustment of the split window algorithm (SWA) for TIRS that uses atmospheric transmittance and land surface emissivity (LSE) as inputs. Various alternatives for estimating these SWA inputs are reviewed, and a sensitivity analysis of the SWA to misestimating the input parameters is performed. The accuracy of the current development was assessed using simulated Modtran data. The root mean square error (RMSE) of the simulated LST was calculated as 0.93 °C. This SWA development is leading to progress in the determination of LST by Landsat-8 TIRS.

The response of vegetation to regional climate change was quantified between 1982 and 2010 in the Mongolian plateau by integrating the Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI) (1982–2006) and the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI (2000–2010). Average NDVI values for the growing season (April–October) were extracted from the AVHRR and MODIS NDVI datasets after cross-calibrating and consistency checking the dataset, based on the overlapping period of 2000–2006. Correlations between NDVI and climatic variables (temperature and precipitation) were analyzed to understand the impact of climate change on vegetation dynamics in the plateau. The results indicate that the growing-season NDVI generally exhibited an upward trend with both temperature and precipitation before the mid- or late 1990s. However, a downward trend in the NDVI with significantly decreased precipitation has been observed since the mid- or late 1990s. This is an apparent reversal in the NDVI trend from 1982 to 2010. Pixel-based analysis further indicated that the timing of the NDVI trend reversal varied across different regions and for different vegetation types. We found that approximately 66% of the plateau showed an increasing trend before the reversal year, whereas 60% showed a decreasing trend afterwards. The vegetation decline in the last decade is mostly attributable to the recent tendency towards a hotter and drier climate and the associated widespread drought stress. Monitoring precipitation stress and associated vegetation dynamics will be important for raising the alarm and performing risk assessments for drought disasters and other related natural disasters like sandstorms.