Applications of Land Surface Temperature
Satellite-based Land surface temperature (LST) measurements have a wide variety of applications, including:
  • Climate change (including  detection of climate change signals over inland water bodies)
  • Land/atmosphere feedbacks
  • Modelling studies
  • Land cover changes
  • Crop management
  • Water management
  • Fire monitoring
  • Geological applications
  • Other applications
For more details and examples see the (A)ATSR Exploitation Plan for LST and references therein.
LST for Climate change applications

LST retrieved from Earth Observation satellites can be used to monitor urban climate, both in obtaining boundary conditions of the atmosphere, but also in understanding the environmental conditions necessary to sustain human life. Urbanization causes the local air and surface temperature to rise several degrees higher than the temperatures of the surrounding rural areas creating what is known as the urban heat island (UHI) effect. The main contributing factors are changes in the physical characteristics of the surface, such as thermal capacity, heat conductivity, and albedo.

AATSR- and AVHRR-based long time series of LST acquired over large inland water bodies have also been used to determine trends of surface water temperatures for a large set of lakes worldwide (Schneider and Hook, 2010). The study found that the mean nighttime surface water temperature has been rapidly warming for the period 1985-2009 with an average rate of 0.045 ± 0.011 °C/yr and rates as high as 0.10 ± 0.01 °C/yr.

LST for Land/atmosphere feedbacks

Anthropogenic climate changes, such as increased surface temperature, and changes in precipitation and net radiative heating influence the state of the land surface in terms of soil moisture, albedo and roughness. Theses changes to the land surface in turn drive important feedbacks to the atmosphere. LST plays an important role in the energy balance of the Earth, being a determining factor in the partitioning of available energy into sensible and latent heat fluxes, and heat flux into the ground. More energy is partitioned into latent heat for higher vegetative cover, whereas higher sensible heat is more typical of sparsely vegetated surfaces. Because water vapour transports energy to the atmosphere, latent heat flux is positively related to evapotranspiration. This constitutes an important climate system feedback between the land-surface and the atmosphere because soil moisture anomalies can translate into precipitation anomalies through the evapotranspiration rate. This feedback on the precipitation regime could influence the occurrence and persistence of pluvial and drought conditions. This in turn influences the distribution of vegetation, thereby altering surface albedo and subsequent surface evaporation.

LST for fire monitoring
The emissions from biomass burning contribute significantly to the quantities of CO2 and trace gases in the atmosphere. Fire also plays an important role in land cover change processes such as deforestation. It is thus important to monitor the land surface that is burned on both regional and global scales. Thermal instruments can be used to monitor the high temperatures of active fires; with satellite-derived LST also having an important role to play in monitoring the fire regime, in applications such as burned area mapping and fuel moisture derivation.

LST data from ATSR-2, AVHRR and MODIS have been widely used for mapping burned area, showing good accuracy. The theory behind this application is based on charcoal and ash absorbing more energy than vegetation. This together with the loss of cooling from transpiring vegetation and reduction in soil moisture, at least in the short-term after fire, means that burned areas tend to have higher temperatures than the surrounding vegetation cover that was not affected. 

Another fire detection application is the estimation of live fuel moisture content (FMC), which is considered one of the most important variables in fire occurrence, propagation, and fire risk monitoring and is defined as the ratio between as the percentage of water weight over sample dry weight. Recent studies have experimented with the combined use of LST and NDVI, with these showing statistically stronger correlations with FMC than either of the two variables alone.

This image shows global fire detections (red) made between July 1996 and August 2010 by the ATSR-2 and AATSR instruments. Credits: ESA

Fuel Moisture Content derived from NDVI:LST ratio over the mixed tree/grass landscapes of Africa in the 3rd Quarter of 2007
LST for modelling studies
Comparisons of satellite-derived LST with model estimations can provide a valuable insight into the performance of the current LST products and improve the accuracy of both EO and model predictions. While parameterizations affect the simulated data, the forcing data is equally important. For example, there are wide variations in the amount and properties of clouds produced in models, which affect LST. Such uncertainties in the simulations of LST can feedback into the partitioning into sensible and latent fluxes. This can lead to large differences between the model simulations of LST and remotely sensed observations.

High quality observations can provide a constraint on these model simulations with the aim of reducing uncertainty. As such there has been much recent interest in integrating satellite observations into climate models, which can take advantage of the strengths of both satellite observations and model predictions. This technique is known as data assimilation, whereby the correction applied to the model is derived from the respective weightings of the uncertainties of both the model predictions and the observations. With LST being integral to the surface energy budget it is one of the most promising choices for constraining models. Indeed, LST products have been extensively used as inputs into assimilation routines to help improve the estimate of model state and prognostic variables. These are in turn used to improve the understanding and quantifications of surface fluxes, and water availability.

This image shows a time series of open loop modeling vs. model run following data assimilation of mean daily LST for the continental land mass covering the entire year 2007. Observations are plotted for comparison (Ghent et al., 2010)

This image shows a time series of open loop modeling vs. model run following LST data assimilation for values over a region of West Africa from 1st January – 31st May 2007 of mean daily soil moisture. ERS scatterometer surface soil moisture observations are plotted for comparison (Ghent et al., 2010)
LST and land cover change
Because of its sensitivity to soil moisture and vegetation cover LST is important in applications such as desertification, land cover mapping and change detection. Amidst the growing demand for land cover mapping at both global and regional scales, some studies have reported that vegetation indices, such as NDVI, could not provide sufficient information for land cover mapping as a result of the influence of soil moisture and its physical properties, and surface temperature; whereas vegetation indices containing data acquired in the middle infrared and TIR channels performed better.
LST for geological applications

The use of thermal imagery from Earth Observation is unique in contributing to the identification of surface materials and features such as geothermal anomalies, and rock types. Other geological applications of satellite-derived LST include: earthquake precursor detection and monitoring; detection and monitoring of the onset and progression of volcanic activity, including airborne volcanic ash plumes and low temperature thermal anomalies; aquatic thermal plume detection, associated for example with shallow undersea volcanic eruptions; differentiation of rock lithologies, which can be important for mineral exploration and geotechnical engineering; and geothermal resource exploration.

The image shows a decorrelation stretch of the Death Valley, CA, area derived from data of the North American ASTER Land Surface Emissivity Database (from Hulley and Hook, 2009)
LST for crop management
The use of remote sensing data, including LST, has proved to be important in several aspects of crop management, such as stress detection, monitoring crop growth, forecasting yield, and in irrigation scheduling. The use of LST to monitor water stress in plants, and hence its application in crop management, is based upon the relationship between canopy temperature and transpiration. In other words, as a plant transpires the leaves cool so that the surface temperature of the canopy is lower than the surrounding air temperature. If water is limiting and transpiration is decreased, then as the leaves absorb radiation the surface temperature of the canopy increases above the surrounding air temperature.
LST for water management
The use of remotely sensed TIR data is a valuable indicator of the surface moisture and evapotranspiration. Soil moisture availability can be assessed by using LST data to derive a property known as thermal inertia (TI), which describes the resistance of a property to temperature variations based on its material density, thermal conductivity, and specific heat. Water bodies have a higher TI than dry soil, and exhibit a lower diurnal temperature fluctuation. This fluctuation range is reduced when water content of soils increases, with their TI increasing proportionately. Although TI can be derived from the temperature diffusion equation, a simpler formulation is the apparent thermal inertia (ATI) which can be derived directly from remote sensing imagery.

In addition to monitoring vegetation water stress, and deriving soil moisture, remotely sensed LST products can be utilized for a multitude of water management applications: the assessment of agricultural and urban water consumption; the negotiation and monitoring of water rights; the assessment of water losses from riparian systems and reservoirs; the assessment of aquifer depletion rates; the monitoring of sediment transport within rivers and into estuaries; and the assessment of water quality and alternative water management practices.

In situ LST compared against in situ soil moisture over 5 consecutive days over a bare soil plot in Kruger National Park, South Africa