Satellite-derived Land Surface Temperature (LST) dynamics have been increasingly used to study various geophysical processes. This review provides an extensive overview of the applications of LST in the context of global change. By filtering a selection of relevant keywords, a total of 164 articles from 14 international journals published during the last two decades were analyzed based on study location, research topic, applied sensor, spatio-temporal resolution and scale and employed analysis methods. It was revealed that China and the USA were the most studied countries and those that had the most first author affiliations. The most prominent research topic was the Surface Urban Heat Island (SUHI), while the research topics related to climate change were underrepresented. MODIS was by far the most used sensor system, followed by Landsat. A relatively small number of studies analyzed LST dynamics on a global or continental scale. The extensive use of MODIS highly determined the study periods: A majority of the studies started around the year 2000 and thus had a study period shorter than 25 years. The following suggestions were made to increase the utilization of LST time series in climate research: The prolongation of the time series by, e.g., using AVHRR LST, the better representation of LST under clouds, the comparison of LST to traditional climate change measures, such as air temperature and reanalysis variables, and the extension of the validation to heterogenous sites.

This special issue is focused on the assessment of algorithms for the observation of Earth's climate from environmental satellites. Climate data records derived by remote sensing are increasingly a key source of insight into the workings of and changes in Earth's climate system. Producers of data sets must devote considerable effort and expertise to maximise the true climate signals in their products and minimise effects of data processing choices and changing sensors. A key choice is the selection of algorithm(s) for classification and/or retrieval of the climate variable. Within the European Space Agency Climate Change Initiative, science teams undertook systematic assessment of algorithms for a range of essential climate variables. The papers in the special issue report some of these exercises (for ocean colour, aerosol, ozone, greenhouse gases, clouds, soil moisture, sea surface temperature and glaciers). The contributions show that assessment exercises must be designed with care, considering issues such as the relative importance of different aspects of data quality (accuracy, precision, stability, sensitivity, coverage, etc.), the availability and degree of independence of validation data and the limitations of validation in characterising some important aspects of data (such as long-term stability or spatial coherence). As well as requiring a significant investment of expertise and effort, systematic comparisons are found to be highly valuable. They reveal the relative strengths and weaknesses of different algorithmic approaches under different observational contexts, and help ensure that scientific conclusions drawn from climate data records are not influenced by observational artifacts, but are robust. (C) 2015 Elsevier Inc. All rights reserved.

Climate change is associated with earth radiation budget that depends upon incoming solar radiation, surface albedo and radiative forcing by greenhouse gases. Human activities are contributing to climate change by causing changes in Earth's atmosphere (greenhouse gases, aerosols) and biosphere (deforestation, urbanization, irrigation). Long term and precise measurements from calibrated global observation constellation is a vital component in climate system modelling. Space based records of biosphere, cryosphere, hydrosphere and atmosphere over more than three decades are providing important information on climate change. Space observations are an important source of climate variables due to multi scale simultaneous observation (local, regional, and global scales) capability with temporal revisit in tune with requirements of land, ocean and atmospheric processes. Essential climatic variables that can be measured from space include atmosphere (upper air temperature, water vapour, precipitation, clouds, aerosols, GHGs etc.), ocean (sea ice, sea level, SST, salinity, ocean colour etc.) and land (snow, glacier, albedo, biomass, LAI/fAPAR, soil moisture etc.). India's Earth Observation Programme addresses various aspects of land, ocean and atmospheric applications. The present and planned missions such as Resourcesat-1, Oceansat-2, RISAT, Megha-Tropiques, INSAT-3D, SARAL, Resourcesat-2, Geo-HR Imager and series of Environmental satellites (I-STAG) would help in understanding the issues related to climate changes. The paper reviews observational needs, space observation systems and studies that have been carried out at ISRO (Indian Space Research Organization) towards mapping/detecting the indicators of climate change, monitoring the agents of climate change and understanding the impact of climate change, in national perspectives. Studies to assess glacier retreat, changes in polar ice cover, timberline change and coral bleaching are being carried out towards monitoring of climate change indicators. Spatial methane inventories from paddy rice, livestock and wetlands have been prepared and seasonal pattern of CO(2), and CO have been analysed. Future challenges in space observations include design and placement of adequate and accurate multi-platform observational systems to monitor all parameters related to various interaction processes and generation of long term calibrated climate data records pertaining to land ocean and atmosphere.

The international EU-funded SIBERIA project (1998-2000) aimed at the production of an extensive forest map using spaceborne SAR data acquired by the ERS and JERS, satellites. For a large geographical region (900.000 km(2)) located in the Central Siberian Plateau, one-day ERS coherence and JERS backscatter were used to retrieve growing stock volume. A classification algorithm based on peaks in the coherence and backscatter histograms was used. Four volume classes, water and open land were considered. An independent test in 10 areas showed an accuracy above 80%. The produced forest map serves as a tool for the sustainable management of Siberian natural resources and for a better understanding of the role of boreal forests in climate change. The objective of the international EU-funded SIBERIA-II project (2002-2005) is to demonstrate the viability of full carbon accounting, including all greenhouse gasses, with a multi-sensor approach over a 2 million-km2 area in Siberia. Having recently started, a general overview of the aims and the objectives of the project is given. Using several satellite observations available and the SIBERIA database, the first step consists in the generation of several Earth Observation (EO) products (such as biomass, phenological parameters, soil moisture, snow cover etc). Together with land-cover information from local institutions, these products will be input to two dynamic vegetation models for full regional carbon accounting. To increase knowledge, additional products such as Afforestation-Reforestafion-Deforestation and fire scars maps are planned.