Google Earth Engine Open-Source Code for Land Surface Temperature Estimation from the Landsat Series

Land Surface Temperature (LST) is increasingly important for various studies assessing land surface conditions, e.g., studies of urban climate, evapotranspiration, and vegetation stress. The Landsat series of satellites have the potential to provide LST estimates at a high spatial resolution, which is particularly appropriate for local or small-scale studies. Numerous studies have proposed LST retrieval algorithms for the Landsat series, and some datasets are available online. However, those datasets generally require the users to be able to handle large volumes of data. Google Earth Engine (GEE) is an online platform created to allow remote sensing users to easily perform big data analyses without increasing the demand for local computing resources. However, high spatial resolution LST datasets are currently not available in GEE. Here we provide a code repository that allows computing LSTs from Landsat 4, 5, 7, and 8 within GEE. The code may be used freely by users for computing Landsat LST as part of any analysis within GEE

TIRS-2 and Future Thermal Instrument Band Study and Stray Light Study

Landsat thermal instruments have been a significant source of data for thermal remote sensing applications, and future Landsat missions will continue this tradition. This work was designed to help inform the requirements for several parameters of future Landsat thermal instruments, and assess the impact that these parameters can have on the retrieved Land Surface Temperature (LST). Two main studies were conducted in this research. The first will investigate the impact that uncertainty in the spectral response of the bands will have on the LST product using the Split Window Algorithm. The main parameters that will be tested are the center and width of he bands. The second study will investigate the impact of stray light on LST, including different magnitudes of stray light and different combinations of in-field and out-of-field targets. The results of the band study showed that shifting of the bands seems to be have a larger impact on the LST than widening of the bands. Small shifts of only +/- 50 nm can cause errors of over 1 K in the LST. This study also showed that atmospheres with more water vapor content will have more effected than those with lower water vapor. The stray light study showed that using the stray light coefficients from TIRS-2 will not have a significant impact, when compared to the residual errors associated with the Split Window Algorithm

Remote Sensing Monitoring of Land Surface Temperature (LST)

This book is a collection of recent developments, methodologies, calibration and validation techniques, and applications of thermal remote sensing data and derived products from UAV-based, aerial, and satellite remote sensing. A set of 15 papers written by a total of 70 authors was selected for this book. The published papers cover a wide range of topics, which can be classified in five groups: algorithms, calibration and validation techniques, improvements in long-term consistency in satellite LST, downscaling of LST, and LST applications and land surface emissivity research

Land Surface Temperature Product Validation Best Practice Protocol Version 1.0 - October, 2017

The Global Climate Observing System (GCOS) has specified the need to systematically generate andvalidate Land Surface Temperature (LST) products. This document provides recommendations on goodpractices for the validation of LST products. Internationally accepted definitions of LST, emissivity andassociated quantities are provided to ensure the compatibility across products and reference data sets. Asurvey of current validation capabilities indicates that progress is being made in terms of up-scaling and insitu measurement methods, but there is insufficient standardization with respect to performing andreporting statistically robust comparisons.Four LST validation approaches are identified: (1) Ground-based validation, which involvescomparisons with LST obtained from ground-based radiance measurements; (2) Scene-based intercomparisonof current satellite LST products with a heritage LST products; (3) Radiance-based validation,which is based on radiative transfer calculations for known atmospheric profiles and land surface emissivity;(4) Time series comparisons, which are particularly useful for detecting problems that can occur during aninstrument's life, e.g. calibration drift or unrealistic outliers due to undetected clouds. Finally, the need foran open access facility for performing LST product validation as well as accessing reference LST datasets isidentified