Landsat 8地表温度产品验证与分析

Landsat系列卫星热波段具有60~120m的空间分辨率，对各种环境监测起到了重要的作用。随着Landsat系列卫星在全球范围内地表温度（land surface temperature，LST）产品的发布，其验证工作也随之展开，然而对于长时间序列的精度验证工作仍然缺乏。以黑河流域中游为研究区，利用研究区内湿地站（SD）、戈壁站（GB）和大满超级站（CJZ）三个气象站的地面测量数据对2013-2016年清晰无云的31景Landsat 8地表温度产品进行了验证与分析，并将Landsat 8地表温度产品与广泛使用的普适性单通道算法（JMS）反演结果进行了对比。结果表明，Landsat 8地表温度产品与普适性单通道算法反演结果精度均较高，在各个站点处R2均优于0.949。基于所有站点分析，Landsat 8地表温度产品精度稍高于普适性单通道算法反演结果。     

Detection of surface temperature anomaly of the Sea of Marmara

Monitoring sea surface temperature (SST) over a long-term and detecting the anomalies highly contribute to understanding the prevailing water quality of the sea. Earth observation satellite images are the key data sources that offer the long-term SST detection in a cost and time effective way. Since the Sea of Marmara in Türkiye is surrounded by the highly populated provinces, the water quality of the sea has gained importance for scientific and public communities over the years. This article emphasizes on the significance of detecting SST trend and corresponding anomalies of the Sea of Marmara over the past 32 years. To address the SST variations of the Sea of Marmara in time, a comprehensive set of both field and satellite data regarding SSTs were obtained within the context of this study. The SST trend and its anomalies between the years 1990 and 2021 were detected by applying Seasonal-Trend decomposition procedure based on LOESS (STL) method to NOAA OISST V2 data. On the other hand, spatial SST distribution was detected with Landsat-8, Sentinel-3 and NOAA OISST V2 satellite data. SST results were verified with the in-situ data within the scope of accuracy assessment. The results showed that SST time-series data performed an increasing trend and had anomalies mostly during the spring months in the recent years.     

Integration of Landsat-8 and Sentinel-1 dataset to extract geological lineaments in complex formations of Tepal mountain area,Shahrood, north Iran

Recently, the detection and extraction of geological lineaments have become an essential analytical technique to find relationships between the characteristics and occurrence of hydrogeology, and tectonic studies. The use of remote sensing, with the progressive development of image enhancement techniques, provides an opportunity to produce more reliable and comprehensive lineament maps. In this paper, semi-automatic approach based on Landsat 8 and Sentinel 1 radar data is proposed for lineaments extraction and validation. The combined method of linear filtering and automatic line module ensures a high degree of accuracy resulting in a lineament map. Based on identified lineaments, Sentinel1 is more capable of detecting edges than Landsat8, but the primary orientation lineaments extracted from Landsat8 and Sentinel1 were different. So, by combining band6 of Landsat8, and VV and VH polarization of Sentinel1, the area lineaments were extracted with high accuracy. Rose diagram showed the extracted lineaments' orientation is in good compliance with the region's existing faults. Also, the formations' lineament length density has good consistent with the density of the faults in the geological map.