Intra-seasonal variation of surface air temperature in Nepal Himalayas

Intra-seasonal variation of surface air temperature observed by the automatic weather station at Syangpoche in Khumbu region, Nepal Himalayas, is analyzed. In the monsoon season, temperature was nearly constant with large decrease in insolation due to monsoon clouds. On the other hand, large intra-seasonal variation existed in the winter with increase in temperature associated with passing synoptic scale high-pressure system which disturb local circulation pattern as well as decrease in temperature due to the nighttime strong radiative cooling under the condition of snow covers. Monsoon clouds and deep valley system caused unique surface temperature variation.

Indonesia Land Surface Temperature Variation during 2001 – 2020

Indonesia, as the largest archipelago in the world, still depends on agriculture and fisheries activities. Climate change will affect agricultural yields and fish catches, so research on climate change is essential for Indonesia and neighboring countries. Land Surface Temperature (LST) is a critical indicator in climate science to measure the temperature of an area. This study examines the LST variation and seasonal pattern with 189 sub-regions covering 5 main islands of the Indonesia archipelago with 105 pixels (95 km) distance apart. The data was downloaded from NASA MODIS and covered a 20-year from 2001 to 2020. Overall, Indonesia has had a stable LST with a total average increase of 0.009oC (95% confidence interval: -0.041,0.059 oC).

Avaliação da variação da temperatura superficial na lagoa rasa de Itapeva mediante a imagens de satélite

The role of temperature in water is fundamental for the community aquatic dynamics once it regulates several processes on different scales. The spatial and temporal variability of water temperature can be assessed by satellite images, which allows a better understanding of ecosystems. In this work, we evaluated the surface temperature variation of Itapeva Lake, located in Rio Grande do Sul, Brazil, between 1985 and 2017, using MOD11A1 product and images Landsat 5, 7 and 8. An homogeneous seasonal variation pattern was identify between the two sensors used. The information provided by MODIS and Landsat has a coefficient R2 = 0.91 and RMSE = 2.32 ° C. The analysis between the Landsat series adjusted data and the original data allowed the smoothing of maximum and minimum temperatures of water, reducing biased records. Water temperature for the summer and autumn months increases, while for the winter season the regime decrease. However, the surface temperature response may be better understood by involving climatic variables in the study.

Assessment of Land Surface Temperature Variation in Anaiyur Catchment Using Remote Sensing Algorithm

Land Surface Temperature (LST) is one of the important indicators to understand the spatial changes and surface processes on the earth surface that leads to actual assessment of environmental quality from local to global scales. In this paper, the thermal infrared bands of the Landsat 8 data were used to retrieve Land Surface Temperature for Anaiyur catchment located at Ramanathapuram district. Two images of April 05, 2017 and August 22, 2019 were used in this study to assess the land surface temperature. The results showed that LST from April, 2017 has higher temperature than August, 2019 because of the different season. The period of images taken were based on two different seasons. Overall, Remote sensing algorithms were effective for monitoring and analysing spatially and temporal variation of Land Surface Temperature.