Spatio‐temporal prediction of land surface temperature using semantic kriging

Abstract

Spatio‐temporal prediction and forecasting of land surface temperature (LST) are relevant. However, several factors limit their usage, such as missing pixels, line drops, and cloud cover in satellite images. Being measured close to the Earth's surface, LST is mainly influenced by the land use/land cover (LULC) distribution of the terrain. This article presents a spatio‐temporal interpolation method which semantically models LULC information for the analysis of LST. The proposed spatio‐temporal semantic kriging (ST‐SemK) approach is presented in two variants: non‐separable ST‐SemK (ST‐SemKNSep) and separable ST‐SemK (ST‐SemKSep). Empirical studies have been carried out with derived Landsat 7 ETM+ satellite images of LST for two spatial regions: Kolkata, India and Dallas, Texas, U.S. It has been observed that semantically enhanced spatio‐temporal modeling by ST‐SemK yields more accurate prediction results than spatio‐temporal ordinary kriging and other existing methods.

A Comparative Study of Machine Learning and Spatial Interpolation Methods for Predicting House Prices

“…Cokriging refers to a family of methods that incorporate associated variables into the kriging models. Cokriging has the same variants as kriging-simple, ordinary, and universal-and it tends to perform better than its univariate counterparts, as briefly dis-cussed in Section 2 (see, for example, [14]). However, in this study, none of the ancillary variables available (i.e., those presented in Table 1) was sufficiently correlated with the target variable.…”

“…For example, Wu and Li [13] argued that the temperature could be more accurately interpolated when the variable 'altitude' was included in the model, along with latitude and longitude. Bhattacharjee et al [14] also demonstrated that a cokriging model incorporating a range of weather variables into the temperature prediction could produce more precise estimations than a model relying only on distances.…”