MultiCAST - Land surface temperature

Land surface temperature

GENERAL INFORMATION

Land Surface Temperature (LST) is a fundamental parameter in remote sensing, providing valuable insights into the thermal characteristics of the Earth’s surface. It encompasses the temperature of various land surfaces, including natural elements like soil, vegetation, and water bodies, and human-made structures such as buildings and roads. LST is a crucial indicator of surface energy exchange, land-atmosphere interactions, and environmental conditions.

In remote sensing, LST is pivotal in several fields, including climate change analysis, urban heat islands, drought assessment, and public health studies. It is primarily obtained through the utilization of Thermal Infrared (TIR) remote sensing techniques, enabling measurements over extensive spatial scales. By capturing land-atmosphere interactions, LST has garnered significant attention from geoscientists in recent decades. The rapid advancements in Earth observation technologies have further enhanced the importance and applicability of remotely sensed LST in various domains.

LST holds recognition as one of the most critical Earth system data and an essential climate variable. Its applications have expanded beyond its traditional use as a climate change indicator. LST is a key metric for evaluating energy redistribution at the land-atmosphere interface, identifying plant water stress, monitoring drought conditions, assessing land cover and land use changes, investigating urban heat island effects, studying heat stress, and conducting epidemiological analyses. The versatility and wide-ranging applications of LST underscore its significance in understanding and managing various environmental and societal phenomena.

CHARACTERISTICS OF REMOTELY SENSED LST

Remote sensing data used to retrieve LST are typically acquired by sensors that measure the thermal radiance emitted by the Earth’s surface. Accurate LST retrieval from remote sensing depends on aspects such as atmospheric effects, sensor parameters such as spectral range and viewing angle, and surface parameters such as emissivity and geometry. Here are some key characteristics of remotely sensed thermal data and LST:

Thermal Infrared (TIR) Bands: Sensors equipped with TIR bands capture thermal radiation emitted by the land surface in the long-wave infrared portion of the electromagnetic spectrum. These bands are sensitive to temperature variations and provide valuable information for LST retrieval.
The wavelengths of Thermal or longwave infrared (TIR or LWIR) light are normally between 8,000 and 15,000 nanometers (8-15 micrometers). Most of the energy in this part of the spectrum is emitted (not reflected) by the Earth as heat so that it can be observed day and night.
Emissivity: Emissivity refers to the efficiency with which a surface emits thermal radiation. Different land cover types have varying emissivity values, which influence the accuracy of LST retrieval. Correcting for emissivity variations is crucial to obtain accurate LST estimates.
Atmospheric Effects: The Earth’s atmosphere interacts with the thermal radiation emitted by the land surface, leading to atmospheric attenuation and absorption. Gases like water vapor and carbon dioxide can affect the propagation of thermal radiation. Atmospheric correction techniques are employed to account for these effects and retrieve accurate LST values.
Diurnal and Seasonal Variations: LST exhibits diurnal and seasonal variations due to the influence of solar radiation, cloud cover, vegetation dynamics, and other factors. Long-term time series analysis of LST data can provide insights into the Earth’s surface energy budget and climate patterns.

REMOTE SENSING TECHNIQUES TO RETRIEVE LST

Various techniques are used to retrieve LST from remote sensing data. These techniques rely on the principles of thermal radiative transfer and mathematical algorithms. Here are some common techniques employed for LST retrieval:

Radiative Transfer Models (RTMs): RTMs simulate the interaction between thermal radiation and the Earth’s atmosphere-surface system. These models account for atmospheric conditions, surface emissivity, and other parameters to estimate LST accurately. RTMs are often combined with satellite-derived atmospheric profiles and ancillary data to retrieve LST.
Split-Window Algorithms: Split-Window algorithms utilize the differences in brightness temperatures measured by two or more thermal bands. These algorithms exploit the differential absorption characteristics of atmospheric gases to estimate LST. They are commonly used for LST retrieval without atmospheric profiles or ancillary data.
Land Surface Emissivity Spectral Library: Emissivity is crucial in LST retrieval. Spectral libraries are developed to characterize the emissivity properties of different land cover types across the thermal spectrum. These libraries are combined with measured or modeled emissivity values to improve the accuracy of LST retrieval.
Temperature-Vegetation Index (TVX) Methods: TVX methods combine thermal information with vegetation indices, such as the Normalized Difference Vegetation Index (NDVI), to account for the influence of vegetation on LST. These methods exploit the negative correlation between temperature and vegetation cover to estimate LST accurately in vegetated areas.
Single-Channel Algorithm (SCA): The Single-Channel Algorithm (SCA) is another technique used for retrieving LST using satellite imagery. This algorithm estimates LST using only one thermal band, and it requires ancillary data such as atmospheric water vapor content and surface emissivity to correct for atmospheric effects. The SCA is often combined with other techniques, such as the Split-Window Algorithm, to improve the accuracy of LST retrieval.

MAIN SATELLITES USED FOR LST

Several satellites are used to acquire remote sensing data for LST retrieval. Here are some of the main satellites used in LST studies:

Landsat series: The Landsat satellites, such as Landsat 5, 7, 8, and 9, provide valuable thermal data in the thermal infrared (TIR) range. These satellites have thermal sensors, such as the Thermal Infrared Sensor (TIRS) on Landsat 8 and 9, which capture thermal radiation for LST retrieval.
MODIS (Moderate Resolution Imaging Spectroradiometer): MODIS sensors aboard the Terra and Aqua satellites acquire data in multiple spectral bands, including TIR bands. MODIS data are widely used for global LST monitoring due to their high temporal resolution and moderate spatial resolution.
GOES (Geostationary Operational Environmental Satellite): GOES satellites are geostationary satellites that provide continuous monitoring of a specific region, such as North America or East Asia. GOES sensors include TIR bands that are used for regional-scale LST studies and weather monitoring.
Sentinel-3: The Sentinel-3 satellites, operated by the European Space Agency (ESA), carry the Sea and Land Surface Temperature Radiometer (SLSTR) instrument, which is specifically designed for LST retrieval. Sentinel-3 SLSTR provides high-quality thermal data with a spatial resolution of 1 km and a revisit time of 1-2 days, enabling detailed LST monitoring for land and coastal areas.
ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer): The ASTER instrument aboard the Terra satellite is primarily designed for high-resolution land surface temperature measurements. ASTER data include TIR bands that are used for detailed LST mapping and analysis. ASTER provides thermal data with a spatial resolution ranging from 15 to 90 meters, depending on the spectral band.

These satellites, with their diverse capabilities in acquiring TIR data, contribute to the availability of remote sensing data for LST retrieval, supporting a wide range of applications in climate studies, environmental monitoring, and land surface analysis.

MAIN TECHNICAL FEATURES OF SATELLITES

Satellites used for LST retrieval vary in terms of their technical features, which influence the quality and applicability of the acquired data. Here are some main technical features to consider:

Spatial Resolution: Satellite sensors have different spatial resolutions, ranging from tens of meters to several kilometers. For example, the Landsat series of satellites provide thermal data with a spatial resolution of 60 meters for Landsat 7 and 100 meters for Landsat 8 and 9, while MODIS sensors aboard the Terra and Aqua satellites provide thermal data with a spatial resolution of 1 km. The higher spatial resolution allows for the detection of finer-scale LST variations, while coarser resolution facilitates regional or global LST analysis.
Spectral Range: The spectral range of satellite sensors determines their sensitivity to different wavelengths. TIR bands are crucial for LST retrieval, but multispectral sensors with additional bands provide complementary information for improved atmospheric correction and emissivity estimation. For example, the ASTER instrument aboard the Terra satellite provides thermal data in five TIR bands, while MODIS sensors aboard the Terra and Aqua satellites provide thermal data in two TIR bands.
Radiometric Accuracy: Radiometric accuracy refers to the sensor’s ability to measure and differentiate small changes in thermal radiance. Higher radiometric accuracy enhances the precision of LST retrieval and facilitates the detection of subtle temperature variations. For example, Landsat 9 has an improved radiometric accuracy compared to Landsat 8, with a 14-bit radiometric resolution compared to Landsat 8’s 12-bit radiometric resolution.
Temporal Resolution: Temporal resolution refers to the frequency with which a satellite revisits the same location on Earth and acquires new data. For example, the Landsat series of satellites have a 16-day revisit time, while MODIS sensors aboard the Terra and Aqua satellites provide daily global coverage. The higher temporal resolution allows for more frequent LST observations, which can be useful for monitoring rapidly changing phenomena such as wildfires or urban heat islands.

MAIN APPLICATIONS

LST data obtained through remote sensing have various applications in various fields. Some of the main applications include:

Urban Heat Island (UHI) Analysis: LST data can be used to assess the spatial distribution and intensity of the Surface UHI, where urban areas experience higher temperatures than surrounding rural areas. This information helps in urban planning, energy management, and mitigation strategies.
Climate Studies: LST data contribute to climate studies by providing insights into surface energy budgets, temperature anomalies, and long-term temperature trends. Monitoring LST helps understand climate change impacts on terrestrial ecosystems and water resources.
Agriculture: LST is valuable for crop monitoring and yield estimation. It provides information on crop stress, water requirements, and phenological stages. LST data assist in precision agriculture practices, irrigation scheduling, and assessing drought conditions.
Hydrology: LST data play a significant role in hydrological modeling and water resource management. They help estimate evapotranspiration rates, detect water bodies’ thermal anomalies, and monitor river and lake temperatures for aquatic ecosystem analysis.
Ecosystem Monitoring: LST contributes to ecosystem monitoring by assessing vegetation health, detecting forest fires, and analyzing habitat suitability for various species. It aids in biodiversity studies, conservation efforts, and monitoring the impacts of land use and land cover changes.