A New Approach to Defining Uncertainties for MODIS Land Surface Temperature

Abstract

The accuracy of land surface temperature (LST) observations is critical to many applications. Any observation of LST is subject to incomplete knowledge, so an accurate assessment of the uncertainty budget is critical. We present a comprehensive and consistent approach to determining an uncertainty budget for LST products. We apply this approach to the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on-board the Aqua satellite. In order to generate the uncertainty model, a new implementation of the generalised split-window algorithm is applied, in which retrieval coefficients are categorised by viewing angle and water vapour. Validation of the LST against in situ data shows a mean absolute bias of 0.37 K for daytime and 0.73 K for nighttime. The average standard deviation per site is 1.53 K for daytime and 1.21 K for nighttime. Uncertainties from the implemented model are estimates in their own right and are also validated. We do this by comparing the standard deviation of the differences between the satellite and in situ LSTs, and the total uncertainties of the validation matchups. We show that the uncertainty model provides a good fit. Our approach offers a framework for quantifying uncertainties for LST that is equally applicable across different sensors and different retrieval approaches.

Intra‐day variability of temperature and its near‐surface gradient with elevation over mountainous terrain: Comparing MODIS land surface temperature data with coarse and fine scale near‐surface measurements

“…Therefore, LST from satellite data should be used with caution. The information should be verified with secondary data from stations or sensors because coarsescale satellite data can introduce errors in the characterization of the temperature patterns that can then be propagated to hydrological or agricultural models (Hulley et al, 2012;Ghent et al, 2019). Sometimes when temperature data are limited, satellite data are extrapolated between different scales, usually using geostatistical estimations including secondary information as elevation or NSTGE (Monestiez et al, 2001;Naseer et al, 2019).…”

Land Surface Temperature Retrieval from Landsat 5, 7, and 8 over Rural Areas: Assessment of Different Retrieval Algorithms and Emissivity Models and Toolbox Implementation

“…Accurate LST retrieval from TIR data depends on atmospheric effects, sensor parameters, i.e., spectral range and viewing angle, and surface parameters such as emissivity and geometry [32][33][34][35][36][37][38]. Since emissivity and atmospheric effects are two fundamental factors to derive LST from thermal data, many researchers have proposed different approaches for LST retrieval considering these factors [39][40][41][42][43][44][45][46].…”

Assessment of the Urban Impact on Surface and Screen-Level Temperature in the ALADIN-Climate Driven SURFEX Land Surface Model for Budapest

“…Upon SURFEX limitations, it is worth noting that satellite LST products are burdened with some considerable uncertainties [19,51] as well. For example, LST observations are valid only during clear-sky conditions, that limits the meteorological conditions to be potentially studied.…”

“…In addition, satellite cloud-screening algorithms are often imperfect, causing biases in LST estimation [19]. Moreover, large viewing angles enhance atmospheric radiation extinction that can result in biases in observed LST [19,51,52]. Lastly, the anisotropy of LST (caused by the high level of surface heterogeneity particularly in urban areas) modifies the viewing angle of the satellite sensor above certain objects, hence leads to the uncertainty of observation as well [19].…”