The recent slowdown in global warming has initiated a reanalysis of temperature data in some mountainous regions for understanding the consequences and impact that a hiatus has on the climate system. Spatiotemporal temperature variability is analyzed over the Tibetan Plateau because of its sensitivity to climate change with a station network updated to 2014, and its linkages to remote sensing–based variability of MODIS daytime and nighttime temperature are investigated. Results indicate the following: 1) Almost all stations have experienced a notable warming in the time interval 1961–2014, with most obvious warming in winter, which depends on the selected time intervals. 2) There is no clear shift from a predominant warming to a near stagnation during the most recent period (2001–present). 3) Uniform altitudinal dependence of temperature change trends could not be confirmed for all regions, time intervals, and seasons, but sometimes an altitude threshold around 3 km is apparent. 4) Most of the meteorological stations are associated with MODIS temperature warming pixels, and thus regional cooling is missing when considering only the locations of meteorological stations. In summarizing, previous studies based on station observations do not provide a complete picture for the temperature change over the Tibetan Plateau. Remote sensing–based analyses have the potential to find early signals of regional climate changes and assess the impact of global climate changes in complex regional, seasonal, and altitudinal environments.

The relationship between the land cover (LC) characteristics and the land surface temperature (LST) is significant for surface urban heat island (SUHI) study and for sustainability research. To better understand how the land surface temperature (LST) responds to LC, two urban areas, Vienna and Madrid, with different climatic conditions are selected and compared, using Landsat-8 OLI data and urban atlas data. To determine a suitable scale for analyzing the relationship between LC and LST, a correlation analysis at different sizes of spatial analytical scales is applied. To demonstrate the LC composition effects on LST, a regression analysis of the whole study area and in the specific circumstance is undertaken. The results show that: (1) In the summer, Vienna presents high temperature in the urban areas and low temperature in the surrounding rural areas, while Madrid displays the opposite appearance, being relatively cooler in the urban areas as compared to the rural areas, with the main different factors affecting elevated urban LST; (2) Suitable analytical scales are suggested in studying the LC–LST relationship between different LC characteristics in the two study areas; (3) Negative effects on the LST appear when the area of cooling sources, such as water or urban greenery, reaches 10% at a 990 × 990 m² scale in Vienna. Built-up area is the main factor affecting elevated urban LST where such areas cover the majority at a 990 × 990 m² scale in Madrid. These findings provide a valuable view regarding how to balance the urban surface thermal environment through urban planning.

To complement geographical presentation of remote sensing vegetation information, the authors apply Budyko’s physical state space diagram to analyze functional climate relations. As an example, the authors use Interim ECMWF Re-Analysis (ERA-Interim) global weather data to provide the statistics (1982–2006) of climate states in a two-dimensional state space spanned by water demand (net radiation N) versus water/energy limitation (dryness ratio D of net radiation over precipitation). Embedding remote sensing–based Global Inventory Modeling and Mapping Studies (GIMMS) data [normalized difference vegetation index (NDVI) > 0.1] shows the following results: (i) A bimodal frequency distribution of unit areas (pixels) is aligned near D ~ 1 but separated meridionally, associated with higher and lower net radiation. (ii) Vegetation states are represented as (N, D, NDVI) triplets that reveal temperate and tropical forests crossing the border (D ~ 1) separating energy- and water-limited climates but unexpectedly show that they also exist in marginal regions (few pixels) of large dryness. (iii) Interannual variability of dryness is lowest where the largest climate mean NDVI values of greenness (forests) occur. The authors conclude that the combined (N, D, NDVI) analysis based on climate means has shown that tropical and temperate forests (NDVI > 0.6) are (i) not restricted to the energy-limited domain D < 1 (extending into the water-limited surface climate regime) and (ii) associated with low interannual variability of dryness. Thus, measures of interannual variability may be included in Budyko’s classical framework of geobotanic analysis of surface climates.