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

Abstract

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.

Toward a Comprehensive Dam Monitoring: On-Site and Remote-Retrieved Forcing Factors and Resulting Displacements (GNSS and PS–InSAR)

“…correlation was ≈ 0.96, the Root-Mean-Square Error between measured and sine model was ≈ 1.70 K, which is coherent with the error in inferring surface temperature applying mono-window algorithms, ±2-3 K, as reported by [77]). The standard deviation, σXY, of the differences between measured and interpolated values was ≈ 1.64 K. The resulting amplitude ΔT in 2015 was 9.80 K, the average temperature was 293.16 K, and the peak (303 K) was modelled on DOY 217.…”

“…Since water surface temperature follows a sinusoidal behaviour [78], remotely sensed T W (black dots in Figure 5) were interpolated via a sinusoidal function (dashed line) to facilitate the interpretation of the phenomenon. A sinusoidal curve is well matched to the sparse temperature measurements (the Pearson correlation was ≈ 0.96, the Root-Mean-Square Error between measured and sine model was ≈ 1.70 K, which is coherent with the error in inferring surface temperature applying mono-window algorithms, ±2-3 K, as reported by [77]). The standard deviation, σ XY , of the differences between measured and interpolated values was ≈ 1.64 K. The resulting amplitude ∆T in 2015 was 9.80 K, the average temperature T was 293.16 K, and the peak (303 K) was modelled on DOY 217.…”

“…Water surface temperature, T W (K), is here estimated by radiometrically processing a Landsat 8 TIRS time series. In particular, a time series of T W for the central pixel of the reservoir was retrieved by applying the Statistical Mono-Window (SMW) algorithm developed by the Climate Monitoring Satellite Application Facility (CM-SAF) [76]; in particular, the Google Earth Engine Open Source Code developed by Ermida et al [77] was employed. Water surface temperature follows a sinusoidal behaviour, T W = T + ∆T × sin(ωt + ϕ), which is a function of the yearly average of T W , T (K); the yearly amplitude of T W , ∆T(K); the phase difference ≥φ (−); the time t, here assumed as the DOY; and the yearly angular velocity of revolution of the earth (1.7214 × 10 −5 rad DOY −1 ).…”

Linking Urban Sprawl and Surface Urban Heat Island in the Teresina–Timon Conurbation Area in Brazil

“…The LST estimates were accessed from thermal infrared (TIR) channels of Landsat series satellites are primarily applicable for local and small-scale studies. Then, we adapted the GEE code from Ermida et al [68], using the values of surface emissivity for the years 2000, 2010, and 2019.…”

A Multidisciplinary Approach for Multi-risk Analysis and Monitoring of Influence of SODs and RODs on Historic Centres: The ResCUDE Project

“… Monitoring techniques able to identify/measure subsidence/displacement phenomena and temperatures on large areas. Multi-temporal SAR datasets, where available, will provide useful information on both the spatial and temporal patterns of displacements, if present, through the generation of time series [ 20 ] using the DInSAR technique, while thermal image acquired from satellite [ 21 ] will allow the production of Land Surface Temperature (LST) maps. Satellite dataset will be considered also to assess the albedo value at large scale [ 22 ].…”