Spatio-Temporal Dual Kriging with Adaptive Coefficient Drift Function

Abstract

Research on spatio-temporal geostatistical modeling remains a critical challenge in numerous scientific and engineering disciplines. This paper introduces a novel extension of dual kriging, called spatio-temporal dual kriging (ST-DK), in which drift functions with fixed and adaptive coefficients are established. The approach appears to be effective in modeling complex spatio-temporal dynamics, particularly when relevant auxiliary variables exert substantial influence on the target variable. To illustrate its performance, we compare the ST-DK model with the classical spatio-temporal regression kriging (ST-RK) and geographically and temporally weighted regression (GTWR) models for estimating temperature and air pressure data from Thailand in 2018. Our findings demonstrate that both the ST-DK and ST-RK models when utilizing adaptive coefficients outperform their fixed coefficient counterparts. Furthermore, the ST-DK method consistently exhibits superior performance compared to the ST-RK and GTWR methods.

1. Introduction

Spatio-temporal interpolation is a computational technique that estimates the value of an interested variable at a given location and time, provided there is available information in the sample at other locations and times. It is a combination of spatial interpolation, which deals with the estimation of values in geographic space, and temporal interpolation, which focuses on estimating values over a specific time period. A fundamental assumption that underlies spatio-temporal interpolation is that close observations exhibit greater similarity than distant ones [1]. Spatio-temporal interpolation methods can be categorized into two distinct approaches: reduction and extension approaches [2,3,4]. Within the reduction approach, time is considered an independent dimension distinct from the spatial dimensions. This methodology is a two-step process in which data are first interpolated in space at each time step, and the resulting time series at each spatial location is subsequently interpolated. In contrast, the extension approach is formulated to simultaneously interpolate in both the spatial and time dimensions. The reduction methods, such as shape function-based methods, inverse distance weighted (IDW), tension spline, and global polynomial interpolations, offer more straightforward and less computationally demanding procedures. However, their suitability may be limited in scenarios where the temporal component of the data plays a significant role [5,6]. On the contrary, the extension methods, such as parallel IDW, random forests, geographically and temporally weighted regression (GTWR), and spatio-temporal kriging methods, are more complex and computationally intensive. Nonetheless, such techniques possess enhanced capabilities for handling situations where the temporal component is substantial and can potentially yield higher accuracy, particularly in cases where the spatio-temporal relationships are complex [7].

Spatio-temporal kriging methodologies have emerged as powerful and effective geostatistical tools applied in several fields, including environmental sciences [8], pollution demography [9], and meteorology [10,11,12]. These methods expand upon spatial kriging techniques by incorporating both spatial and temporal information and enabling the construction of an optimal linear estimator that minimizes estimation variance under the constraint of unbiasedness. This estimator is a linear combination of observed spatio-temporal values and their corresponding weights. The two most commonly employed kriging techniques in the spatio-temporal domain are spatio-temporal simple kriging (ST-SK) and spatio-temporal ordinary kriging (ST-OK). These methods do not utilize any additional information or auxiliary variables during the estimation process. Their weights are determined by the covariance function or the variogram, which depends on the assumed degree of stationarity ascribed to the underlying random function. The ST-SK technique assumes second-order stationarity in the random function, characterized by a constant and known mean and a known covariance function dependent only on the separation vector between a pair of measured points in space and time. The ST-OK method, on the other hand, necessitates the assumption of intrinsic stationarity in the random function. This implies that the mean of the random function is constant, and the variance of the difference is the same between the random function at two geographic locations lagged by the same distance [13,14].

An alternative spatio-temporal kriging incorporates additional variables, referred to as auxiliary variables, into the kriging model to enhance its predictive abilities, particularly when integrating relevant auxiliary variables that influence the target variable. The spatio-temporal regression kriging (ST-RK) technique involves decomposing the target variable into two distinct components: a deterministic trend component and a stochastic residual component [8]. The trend component encapsulates the systematic variation in the target variable, which can be modeled using auxiliary variables. Conversely, the residual component represents the remaining unexplained variability, which can be modeled using either ST-OK or ST-SK. Similar to the ST-RK method, the target variable in the spatio-temporal kriging with external drift (ST-KED) process can be divided into deterministic drift and stochastic residual terms. However, unlike the ST-RK method, ST-KED weights are directly derived from the kriging equations, which are formulated under the unbiasedness and minimum error variance conditions [15].

In recent years, these two techniques have been successfully applied in diverse applications. The ST-RK method has been used to predict chlorophyll a [8], nitrogen dioxide (NO₂) concentrations [9], rainfall [10], and mean daily temperature [11]. The ST-KED modeling technique has been employed to interpolate soil water content [16], rainfall data [17], daily precipitation, and temperature [18]. Furthermore, extensive research has consistently shown that ST-KED outperforms ST-OK regarding prediction accuracy and reliability [16,17,18]. Notwithstanding its superior performance, the ST-KED method is impeded by its computational complexity. Therefore, this study aimed to tackle this limitation by developing an efficient algorithm for the ST-KED, thereby enhancing its applicability in broader practical scenarios.

In spatial kriging methods, kriging with external drift (KED) is computationally expensive because it requires solving a new kriging system to determine the weights of the linear estimator for each interpolated point. In contrast, dual kriging (DK), a technique derived from KED, provides a unified approach to determining the weights of the estimator for all estimated points. Hence, DK is an advancement compared with KED that alleviates its computational challenges [19,20]. Although KED and DK produce the same estimated values, there is currently a dearth of research on spatio-temporal interpolation.

Motivated by this observation, we developed a spatio-temporal dual kriging (ST-DK) algorithm to reduce the computational burden compared with that of the ST-KED method by expanding upon the principles of spatial KED interpolation. The proposed ST-DK algorithm was designed to accommodate both the fixed and adaptive coefficients of drift functions.

The remainder of this paper is structured as follows: Section 2 provides background information on ST-RK and ST-KED. Section 3 describes the construction of the proposed ST-DK methodology for fixed and adaptive coefficients of trend functions. In Section 4, a detailed description of the data and the selection criteria for auxiliary variables is presented. Subsequently, the performance of the proposed ST-DK method is evaluated using meteorological data, including temperature and air pressure measurements. The method is then compared against the ST-RK and GTWR models. Conclusions and discussions are drawn in Section 5.

2. Methodological Framework of Spatio-Temporal Kriging Incorporating Auxiliary Variables

Suppose that $\left\{Z\right(s,t)$, $(s,t)\in D\times \mathcal{T}\}$ is a real-value stochastic process or random function on the space–time domain $D\times \mathcal{T}$, where $D\subset {\mathbb{R}}^d$ and $\mathcal{T}\subset \mathbb{R}$ or $\mathbb{Z}$. The random variable $Z(s,t)$ can be disaggregated into two components [21,22]:

$$\begin{array}{c}\hfill Z(s,t)=\mu (s,t)+ϵ(s,t),\end{array}$$

(1)

where $\mu (s,t)$ denotes the deterministic drift function, also known as the trend function, and $ϵ(s,t)$ is a stochastic residual with a mean of zero.

The drift function can typically be represented as a linear combination of the functions evaluated at a spatio-temporal point $(s,t)$, as expressed by the following equation [10,22,23]:

$$\begin{array}{c}\hfill \mu (s,t)=\sum _{l=0}^L{\alpha }_l{f}_l\left(X(s,t)\right),\end{array}$$

(2)

where $X(s,t)={\left[X_1(s,t),...,X_{\eta }(s,t)\right]}^T$ is the vector of $\eta$ auxiliary variables at point $(s,t)$, and ${\alpha }_l$ is a nonzero constant coefficient of a known deterministic function $f_l$, which is represented in terms of auxiliary variables for $l=0,1,\dots ,L$. The function $f_l$ is often referred to as a basis function in certain studies [24,25,26].

Given the assumption of second-order stationarity for the residual, the spatio-temporal covariance function $\mathbf{C}({\mathbf{h}}_s,h_t)$ exists. This covariance function quantifies the dependence between any pair of residuals separated by a spatial lag ${\mathbf{h}}_s$ and temporal lag $h_t$, defined as follows [27]:

$$\begin{array}{c}\hfill \mathbf{C}({\mathbf{h}}_s,h_t)=\mathrm{Cov}\left[ϵ(s+{\mathbf{h}}_s,t+h_t),ϵ(s,t)\right],\end{array}$$

(3)

where $(s+{\mathbf{h}}_s,t+h_t)$, $(s,t)\in D\times \mathcal{T}$.

Furthermore, the spatio-temporal variogram of the residual is given by:

$$\begin{array}{cc}\hfill \gamma ({\mathbf{h}}_s,h_t)& =\frac{1}{2}\mathrm{Var}\left[ϵ(s+{\mathbf{h}}_s,t+h_t)-ϵ(s,t)\right],\hfill \\ \hfill & =\frac{1}{2}\mathbb{E}{\left[ϵ(s+{\mathbf{h}}_s,t+h_t)-ϵ(s,t)\right]}^2,\hfill \end{array}$$

(4)

which results in

$$\begin{array}{cc}\hfill \mathbf{C}({\mathbf{h}}_s,h_t)& =\mathbf{C}(\mathbf{0},0)-\gamma ({\mathbf{h}}_s,h_t),\hfill \end{array}$$

(5)

where $\mathbf{C}(\mathbf{0},0)$ is the global sill value of the spatio-temporal variogram.

The classical empirical spatio-temporal variogram estimator [28], which estimates the theoretical spatio-temporal variogram function (presented in Equation (4)), can be formally defined as:

$$\begin{array}{c}\hfill {\gamma }^{*}({\mathbf{h}}_s,h_t)=\frac{1}{2\left|N_s\left({\mathbf{h}}_s\right)\right|\left|N_t\left(h_t\right)\right|}\sum _{s_i,s_u\in N_s\left({\mathbf{h}}_s\right)}\sum _{t_j,t_v\in N_t\left(h_t\right)}{\left(ϵ(s_i,t_j)-ϵ(s_u,t_v)\right)}^2,\end{array}$$

(6)

where $N_s\left({\mathbf{h}}_s\right)$ represents the set of all spatial location pairs separated by the spatial lag vector ${\mathbf{h}}_s$ and $N_t\left(h_t\right)$ denotes the set of all time point pairs separated by the temporal lag $h_t$. The sizes of these sets, $\left|N_s\left({\mathbf{h}}_s\right)\right|$ and $\left|N_t\left(h_t\right)\right|$, indicate the number of distinct pairs in each set.

Several spatio-temporal covariance models have been proposed in the literature, including the sum model [29], the metric model [30], and the product model [27]. The present study focused on the product-sum model, which is expressed as follows [31]:

$$\begin{array}{c}\hfill \mathbf{C}({\mathbf{h}}_s,h_t)=k_1{\mathbf{C}}_s\left({\mathbf{h}}_s\right){\mathbf{C}}_t\left(h_t\right)+k_2{\mathbf{C}}_s\left({\mathbf{h}}_s\right)+k_3{\mathbf{C}}_t\left(h_t\right),\end{array}$$

(7)

where ${\mathbf{C}}_s$ and ${\mathbf{C}}_t$ are spatial and temporal covariance functions, respectively. The spatial and temporal covariance functions are defined as:

$$\begin{array}{cc}\hfill {\mathbf{C}}_s\left({\mathbf{h}}_s\right)& =\mathbf{C}({\mathbf{h}}_s,0)={\mathbf{C}}_s\left(\mathbf{0}\right)-{\gamma }_s\left({\mathbf{h}}_s\right),\hfill \end{array}$$

(8)

$$\begin{array}{cc}\hfill {\mathbf{C}}_t\left(h_t\right)& =\mathbf{C}(\mathbf{0},h_t)={\mathbf{C}}_t\left(0\right)-{\gamma }_t\left(h_t\right),\hfill \end{array}$$

(9)

where ${\mathbf{C}}_s\left(\mathbf{0}\right)$ and ${\mathbf{C}}_t\left(\mathbf{0}\right)$ are the values of the sill of the spatial variogram, ${\gamma }_s$, and temporal variogram, ${\gamma }_t$, respectively. This study used the exponential model [32], a parametric variogram, to represent both the spatial and temporal variograms, which were fit to the corresponding empirical variograms using weighted least squares [33]. The interior-point algorithm [34], via the fmincon function of MATLAB software (Version 2018a), was employed to determine the unknown parameters of the variograms.

Based on the relationship between the covariance function and variogram established in Equations (5) and (7)–(9), the spatio-temporal variogram can be expressed as:

$$\begin{array}{c}\hfill \gamma ({\mathbf{h}}_s,h_t)=[k_1{\mathbf{C}}_t\left(0\right)+k_2]{\gamma }_s\left({\mathbf{h}}_s\right)+[k_1{\mathbf{C}}_s\left(\mathbf{0}\right)+k_3]{\gamma }_t\left(h_t\right)-k_1{\gamma }_s\left({\mathbf{h}}_s\right){\gamma }_t\left(h_t\right),\end{array}$$

(10)

where

$$\begin{array}{cc}\hfill k_1& =\frac{{\mathbf{C}}_s\left(\mathbf{0}\right)+{\mathbf{C}}_t\left(0\right)-\mathbf{C}(\mathbf{0},0)}{{\mathbf{C}}_s\left(\mathbf{0}\right){\mathbf{C}}_t\left(0\right)},\hfill \\ \hfill k_2& =\frac{\mathbf{C}(\mathbf{0},0)-{\mathbf{C}}_t\left(0\right)}{{\mathbf{C}}_s\left(\mathbf{0}\right)},\hfill \\ \hfill k_3& =\frac{\mathbf{C}(\mathbf{0},0)-{\mathbf{C}}_s\left(\mathbf{0}\right)}{{\mathbf{C}}_t\left(0\right)}.\hfill \end{array}$$

The parameters of the spatio-temporal variogram model can be determined by fitting the empirical variogram provided in Equation (7). Subsequently, the spatio-temporal covariance function of the residuals is derived by leveraging the relationship between the variogram and covariance function as provided in Equation (5).

The objective of spatio-temporal kriging with a drift function is to estimate the value of the target variable at an unobserved spatio-temporal point $(s_0,t_0)$, represented as $Z(s_0,t_0)$, where $s_0\in D$ and $t_0\in \mathcal{T}$. The estimation is achieved using the principles of unbiasedness and minimum error variance. Two classical spatio-temporal kriging methods, spatio-temporal regression kriging (ST-RK) and spatio-temporal kriging with external drift (ST-KED), are covered in detail in the following.

2.1. Spatio-Temporal Regression Kriging

The ST-RK estimator briefly described in the previous section consists of drift function and residual components, in which the implementation of each term is obtained independently. The drift function is conventionally modeled using multiple linear regression (MLR), while the residuals are interpolated using spatio-temporal simple kriging (ST-SK). Given a set of $n\times m$ observed values of the target variables and auxiliary variables at distinct spatio-temporal points $(s_1,t_1),\dots ,(s_n,t_m)$, the ST-RK estimator at the point $(s_0,t_0)$ denoted as $Z_{\mathrm{ST-RK}}(s_0,t_0)$, can be written as [23]:

$$\begin{array}{cc}\hfill Z_{\mathrm{ST-RK}}(s_0,t_0)& =\mu (s_0,t_0)+{ϵ}_{\mathrm{ST-SK}}(s_0,t_0),\hfill \\ \hfill & =\sum _{l=0}^L{\alpha }_l{f}_l\left(X(s_0,t_0)\right)+\sum _{j=1}^m\sum _{i=1}^n{\lambda }_{ij}^{{ϵ}_0}ϵ(s_i,t_j),\hfill \\ \hfill & ={\mathit{\alpha }}^T{\mathit{F}}_0+{\mathit{ϵ}}^T{\mathbf{\Lambda }}^{{ϵ}_0},\hfill \end{array}$$

(11)

where $\mathit{\alpha }={\left[{\alpha }_0,\dots ,{\alpha }_L\right]}^T$ is a vector of coefficients,

${\mathit{F}}_0={\left[f_0\left(X(s_0,t_0)\right),\dots ,f_L\left(X(s_0,t_0)\right)\right]}^T$ is a vector of the basis functions,

$\mathit{ϵ}={\left[ϵ(s_1,t_1),\dots ,ϵ(s_n,t_m)\right]}^T$ is a residual vector, and

${\mathbf{\Lambda }}^{{ϵ}_0}={\left[{\lambda }_{11}^{{ϵ}_0},\dots ,{\lambda }_{nm}^{{ϵ}_0}\right]}^T$ is the ST-SK weight vector.

The optimal weight vector for the ST-SK method is determined by minimizing the prediction error variance under the constraint of unbiasedness. This minimization problem is solved utilizing the Lagrange multiplier method, resulting in a system of linear equations known as the ST-SK system [35]:

$$\begin{array}{c}\hfill \sum _{j=1}^m\sum _{i=1}^n{\lambda }_{ij}^{{ϵ}_0}\mathbf{C}\left(s_u-s_i,t_v-t_j\right)=\mathbf{C}\left(s_u-s_0,t_v-t_0\right),\mathrm{for}u=1,\dots ,n\mathrm{and}v=1,\dots ,m.\end{array}$$

(12)

The ST-SK equations can be expressed in the following matrix form:

$$\begin{array}{c}\hfill \mathbf{\Omega }{\mathbf{\Lambda }}^{{ϵ}_0}={\mathbf{\Omega }}_0,\end{array}$$

(13)

where $\mathbf{\Omega }=\left[\begin{array}{ccc}\mathbf{C}\left(s_1-s_1,t_1-t_1\right)& \cdots & \mathbf{C}\left(s_1-s_n,t_1-t_m\right)\\ ⋮& \ddots & ⋮\\ \mathbf{C}\left(s_n-s_1,t_m-t_1\right)& \cdots & \mathbf{C}\left(s_n-s_n,t_m-t_m\right)\end{array}\right]$ and

       ${\mathbf{\Omega }}_0=\left[\begin{array}{c}\mathbf{C}\left(s_1-s_0,t_1-t_0\right)\\ ⋮\\ \mathbf{C}\left(s_n-s_0,t_m-t_0\right)\end{array}\right]$.

2.2. Spatio-Temporal Kriging with External Drift

Let $Z_{\mathrm{ST-KED}}(s_0,t_0)$ be the estimated value of the target variable at the point $(s_0,t_0)$. The ST-KED estimator is expressed as a linear combination of input data, $Z(s_i,t_j)$ for $i=1,\dots ,n$ and $j=1,\dots ,m$ [35]:

$$\begin{array}{cc}\hfill Z_{\mathrm{ST-KED}}(s_0,t_0)& =\sum _{j=1}^m\sum _{i=1}^n{\lambda }_{ij}Z(s_i,t_j),\hfill \end{array}$$

(14)

$$\begin{array}{cc}\hfill & ={\mathit{Z}}^T\mathbf{\Lambda },\hfill \end{array}$$

(15)

where $\mathit{Z}={\left[Z(s_1,t_1),\dots ,Z(s_n,t_m)\right]}^T$ is the vector of observed data for the target variable, and $\mathbf{\Lambda }={\left[{\lambda }_{11},\dots ,{\lambda }_{nm}\right]}^T$ is the ST-KED weight vector.

The optimal weight vector for the ST-KED technique can be found using the Lagrange multiplier method to minimize the prediction error variance while satisfying the unbiasedness constraint. This approach is based on the ST-KED estimator and the form of the target variable Z, as represented in Equation (1). The ST-KED system is written as [15]:

$$\begin{array}{c}\hfill \left[\begin{array}{ccc}\mathbf{\Omega }& |& \mathit{F}\\ --& +& --\\ {\mathit{F}}^T& |& {\mathbf{0}}_{L+1}\end{array}\right]\left[\begin{array}{c}\mathbf{\Lambda }\\ --\\ \mathit{N}\end{array}\right]=\left[\begin{array}{c}{\mathbf{\Omega }}_0\\ --\\ {\mathit{F}}_0\end{array}\right],\end{array}$$

(16)

where ${\mathbf{0}}_{L+1}$ is the $(L+1)\times (L+1)$ zero matrix,

$\mathit{N}={\left[{\eta }_0,\dots ,{\eta }_L\right]}^T$ represents the vector of Lagrange multipliers,

$\mathit{F}=\left[\begin{array}{c}{\mathit{F}}_1\\ ⋮\\ {\mathit{F}}_m\end{array}\right]$, and

${\mathit{F}}_j=\left[\begin{array}{ccc}{f}_0\left(X(s_1,t_j)\right)& \cdots & f_L\left(X(s_1,t_j)\right)\\ ⋮& \ddots & ⋮\\ f_0\left(X(s_n,t_j)\right)& \cdots & f_L\left(X(s_n,t_j)\right)\end{array}\right]$, for $j=1,\dots ,m$.

Since the ST-KED weights need to be recalculated for each unobserved data point, this leads to a significant increase in the required computational resources. Employing a single weight matrix to estimate the target variable across all ungauged locations can effectively address this challenge. The spatio-temporal dual kriging (ST-DK) method is therefore proposed to overcome this limitation.

3. Methodology of Spatio-Temporal Dual Kriging

The formulation of ST-DK interpolation is inspired by the principles of DK [36]. The ST-DK algorithm can be described as a refinement of the ST-KED method, in which the ST-DK weight coefficient matrix is performed only once for every unobserved site. This section presents the derivation of both the fixed and adaptive coefficients of the ST-DK method. To simplify subsequent references, we use FST-DK and AST-DK to designate the ST-DK method with a fixed coefficient drift function and the ST-DK method with an adaptive coefficient drift function, respectively.

3.1. Spatio-Temporal Dual Kriging with Fixed Coefficient Drift Function

Given the ST-KED system represented in Equation (16), where the coefficient matrix is assumed to be invertible, the solution can be expressed in the following form [36,37]:

$$\begin{array}{c}\hfill \left[\begin{array}{c}\mathbf{\Lambda }\\ --\\ \mathit{N}\end{array}\right]=\left[\begin{array}{ccc}\mathit{U}& |& \mathit{V}\\ --& +& --\\ {\mathit{V}}^T& |& \mathit{W}\end{array}\right]\left[\begin{array}{c}{\mathbf{\Omega }}_0\\ --\\ {\mathit{F}}_0\end{array}\right],\end{array}$$

(17)

where $\mathit{U}$, $\mathit{V}$, and $\mathit{W}$ are matrices with dimensions of $nm\times nm$, $nm\times (L+1)$, and $(L+1)\times (L+1)$, respectively. Substituting the weight solution of Equation (17) for the weight vector $\mathbf{\Lambda }$ in Equation (15) yields the following expression for the estimated value:

$$\begin{array}{c}\hfill Z_{\mathrm{ST-KED}}(s_0,t_0)={\mathit{Z}}^T{\mathit{VF}}_0+{\mathit{Z}}^T\mathit{U}{\mathbf{\Omega }}_0.\end{array}$$

(18)

To establish a formal representation of the dual formulation of ST-KED, two matrices are introduced:

$$\begin{array}{c}\hfill {\mathit{a}}^T=\left[a_0,\dots ,a_L\right]={\mathit{Z}}^T\mathit{V}\mathrm{and}{\mathit{b}}^T=\left[b_{11},\dots ,b_{nm}\right]={\mathit{Z}}^T\mathit{U}.\end{array}$$

(19)

Consequently, Equation (18) undergoes a transformation, resulting in a novel form known as the FST-DK estimator, as depicted below:

$$\begin{array}{cc}\hfill Z_{\mathrm{FST-DK}}(s_0,t_0)& =\sum _{l=0}^L{a}_l{f}_l\left(X(s_0,t_0)\right)+\sum _{j=1}^m\sum _{i=1}^n{b}_{ij}\mathbf{C}\left(s_i-s_0,t_j-t_0\right),\hfill \\ \hfill & ={\mathit{a}}^T{\mathit{F}}_0+{\mathit{b}}^T{\mathbf{\Omega }}_0.\hfill \end{array}$$

(20)

As a result, the vectors $\mathit{a}$ and $\mathit{b}$ become the weight vectors of the FST-DK estimator, exhibiting invariance with respect to the target point $(s_0,t_0)$. According to Equation (19), the weight vectors $\mathit{a}$ and $\mathit{b}$ can be represented in matrix form as:

$$\begin{array}{c}\hfill \left[\begin{array}{c}\mathit{b}\\ --\\ \mathit{a}\end{array}\right]=\left[\begin{array}{ccc}\mathit{U}& |& \mathit{A}\\ --& +& --\\ {\mathit{V}}^T& |& \mathit{B}\end{array}\right]\left[\begin{array}{c}\mathit{Z}\\ --\\ {\underline{\mathbf{0}}}_{L+1}\end{array}\right],\end{array}$$

(21)

where $\mathit{A}$, $\mathit{B}$ are arbitrary matrices with dimensions of $nm\times (L+1)$ and $(L+1)\times (L+1)$, respectively, and ${\underline{\mathbf{0}}}_{L+1}$ represents the zero vector of size $L+1$.

Given the substitutions $\mathit{A}=\mathit{V}$ and $\mathit{B}=\mathit{W}$, the vectors of coefficients $\mathit{a}$ and $\mathit{b}$ are solutions of the FST-DK system,

$$\begin{array}{c}\hfill \left[\begin{array}{ccc}\mathbf{\Omega }& |& \mathit{F}\\ --& +& --\\ {\mathit{F}}^T& |& {\mathbf{0}}_{L+1}\end{array}\right]\left[\begin{array}{c}\mathit{b}\\ --\\ \mathit{a}\end{array}\right]=\left[\begin{array}{c}\mathit{Z}\\ --\\ {\underline{\mathbf{0}}}_{L+1}\end{array}\right].\end{array}$$

(22)

The estimated value of the target variable at the unobserved point $(s_0,t_0)$ is therefore obtained by substituting the vectors $\mathit{a}$ and $\mathit{b}$ into Equation (20).

3.2. Spatio-Temporal Dual Kriging with Adaptive Coefficient Drift Function

In the previous case, the coefficient ${\alpha }_l$, where $l=0,1,\dots ,L$, of the drift function $\mu (s,t)$ in Equation (2) remain constant across all time steps. This property of the coefficients makes it simple to use in practice, as it does not require us to keep track of its value at each time step. Although this can be a significant advantage, especially when dealing with large datasets, it is a trade-off between estimation accuracy and computational cost. To address this limitation and incorporate a more flexible drift function, this study developed a novel drift function approach that incorporates dynamically adjusted coefficients. The proposed drift function, denoted by ${\mu }_A$, is hereby introduced as follows:

$$\begin{array}{c}\hfill {\mu }_A(s,t)=\sum _{l=0}^L\sum _{\tau =t_1}^{t_m}{\alpha }_{l\tau }{f}_{l\tau }\left(X(s,t)\right),\end{array}$$

(23)

where $f_{l\tau }$ is the l-th basis function of the auxiliary variables at time step $\tau$, with ${\alpha }_{l\tau }\in \mathbb{R}-\left\{0\right\}$ being a constant coefficient. Furthermore, $t_1$ and $t_m$ represent the initial and final time steps, respectively.

For $l=0,1,\dots ,L$, function $f_{l\tau }$ is represented in terms of auxiliary variables and is defined by:

$$\begin{array}{c}\hfill f_{l\tau }\left(X(s,t)\right)=\left\{\begin{array}{cc}{f}_l\left(X(s,t)\right),& \tau =t,\\ 0,& \tau \ne t.\end{array}\right.\end{array}$$

(24)

This expression of $f_{l\tau }$ indicates that the drift function ${\mu }_A$ can change over time step $\tau$.

Incorporating these adaptive coefficients into the drift function of ST-KED, the value at the target point $(s_0,t_0)$ is estimated using the ST-KED estimator, as presented in Equation (14). This estimator is denoted as ${\widehat{Z}}_{\mathrm{ST-KED}}(s_0,t_0)$. Moreover, the matrix equation that arises from employing the Lagrange multiplier method to minimize the variance of prediction error under the constraint of unbiasedness is expressed as follows (refer to Appendix A for a detailed explanation of the derivation process):

$$\begin{array}{c}\hfill \left[\begin{array}{ccc}\mathbf{\Omega }& |& \mathcal{F}\\ --& +& --\\ {\mathcal{F}}^T& |& {\mathbf{0}}_{(L+1)m}\end{array}\right]\left[\begin{array}{c}\mathbf{\Lambda }\\ --\\ \mathcal{N}\end{array}\right]=\left[\begin{array}{c}{\mathbf{\Omega }}_0\\ --\\ {\mathcal{F}}_0\end{array}\right],\end{array}$$

(25)

where $\mathcal{N}={\left[{\eta }_{0t_1},\dots ,{\eta }_{L{t}_m}\right]}^T$ is a vector of the Lagrange multiplier,

$\mathcal{F}=\left[\begin{array}{cccc}{\mathit{F}}_1& {\mathbf{0}}_{n\times (L+1)}& \cdots & {\mathbf{0}}_{n\times (L+1)}\\ {\mathbf{0}}_{n\times (L+1)}& {\mathit{F}}_2& \cdots & {\mathbf{0}}_{n\times (L+1)}\\ ⋮& ⋮& \ddots & ⋮\\ {\mathbf{0}}_{n\times (L+1)}& {\mathbf{0}}_{n\times (L+1)}& \cdots & {\mathit{F}}_m\end{array}\right]$,

${\mathbf{0}}_{n\times (L+1)}$ represents the $n\times (L+1)$ zero matrix, and

${\mathcal{F}}_0=\left[\begin{array}{c}{f}_{0t_1}\left(X\left(s_0,t_0\right)\right)\\ ⋮\\ f_{L{t}_m}\left(X\left(s_0,t_0\right)\right)\end{array}\right]$; see details in Appendix A.

By applying the same procedure as in Section 2.2 and Section 3.1, the AST-DK estimator takes the following form:

$$\begin{array}{cc}\hfill Z_{\mathrm{AST-DK}}(s_0,t_0)& =\sum _{l=0}^L\sum _{\tau =t_1}^{t_m}{a}_{l\tau }{f}_{l\tau }\left(X(s_0,t_0)\right)+\sum _{j=1}^m\sum _{i=1}^n{b}_{ij}\mathbf{C}\left(s_i-s_0,t_j-t_0\right),\hfill \\ \hfill & ={\widehat{\mathit{a}}}^T{\mathcal{F}}_0+{\mathit{b}}^{\mathit{T}}{\mathbf{\Omega }}_0,\hfill \end{array}$$

(26)

where $\widehat{\mathit{a}}$ and $\mathit{b}$ are the vector of coefficients such that ${\widehat{\mathit{a}}}^T=\left[a_{0t_1},\dots ,a_{L{t}_m}\right]$ and ${\mathit{b}}^T=\left[b_{11},\dots ,b_{nm}\right]$.

Analogous to the approach outlined in Section 3.1, we can derive the AST-DK system for determining the vectors $\widehat{\mathit{a}}$ and $\mathit{b}$, which is given by

$$\begin{array}{c}\hfill \left[\begin{array}{ccc}\mathbf{\Omega }& |& \mathcal{F}\\ --& +& --\\ {\mathcal{F}}^T& |& {\mathbf{0}}_{(L+1)m}\end{array}\right]\left[\begin{array}{c}\mathbf{b}\\ --\\ \widehat{\mathit{a}}\end{array}\right]=\left[\begin{array}{c}\mathit{Z}\\ --\\ {\underline{\mathbf{0}}}_{(L+1)m}\end{array}\right].\end{array}$$

(27)

To evaluate the effectiveness of the proposed AST-DK techniques, these methods were applied to interpolate meteorological data, encompassing temperature and air pressure. Additionally, the performance of these methods was compared to that of ST-RK with a fixed coefficient drift function (denoted FST-RK), ST-RK with adaptive coefficient drift function method (denoted AST-RK), and geographically and temporally weighted regression (GTWR).

The GTWR model is an advanced statistical approach that extends the geographically weighted regression (GWR) model. Its objective is to simultaneously integrate spatial and temporal information to delineate the relationship between a target variable and its auxiliary variables. This technique involves specifying the regression model, followed by selecting optimal spatial and temporal bandwidths for constructing a weight matrix. Estimating values at specific target points is achieved through a weighted regression process utilizing the determined weight matrix [38,39,40,41].

4. Application of the Proposed ST-DK Techniques for Temperature and Air Pressure Interpolations in Thailand

4.1. Study Area and Data Description

Our domain of study covered the entire country of Thailand, which lies between $5^{\circ }{37}^{\prime }$ N latitude and ${20}^{\circ }{27}^{\prime }$ N and ${97}^{\circ }{22}^{\prime }$ E and ${105}^{\circ }{37}^{\prime }$ E longitude. The total area of the country is 513,115 square kilometers, with a coastline of 3219 km [42,43]. The present study leveraged observational data from the Hydro-Informatics Institute (HII) [44] including temperature (°C), air pressure (hPa), relative humidity (%RH), and the digital elevation model (DEM) (metre). A comprehensive network of 226 weather stations covered the study area, as depicted in Figure 1. The performances of the kriging schemes were evaluated based on monthly mean data over the period of January 2018 to December 2018. Prior to the interpolation process, data cleaning and preprocessing procedures were conducted to ensure data quality.

4.2. Selection of Auxiliary Variables

The estimation accuracy of the ST-DK, ST-RK, and GTWR methods mainly depends on the careful selection of auxiliary variables for which a strong correlation with target variables is found [45]. This stage involves an interaction assessment between external factors and target variables using the Pearson correlation coefficient [46]. Suppose the absolute value of the correlation coefficient exceeds a predefined threshold of 0.5 (as established in [47]). In this case, the candidate variable is considered statistically significant and deemed suitable to include in both the basis functions of the drift terms and the model of the GTWR. In this work, air pressure, relative humidity, and DEM were considered potential auxiliary variables for temperature interpolation, whereas temperature, relative humidity, and DEM were those for the air pressure estimation.

4.3. Accuracy Assessment

A comparison of the performances between the proposed ST-DK and the traditional ST-RK methods was conducted in the case of the fixed and adaptive coefficient drift functions. Moreover, the performance of the GTWR model was also included for comparison in addition to the AST-DK and AST-RK methods. The accuracy of all algorithms was evaluated using a 10-fold cross-validation technique. The process was performed by dividing the entire dataset into ten equal-sized folds. Each fold was then used once as the test set, while the remaining nine folds served as the training set [48]. After each of the 10 folds was the left-out fold once, the overall accuracy was computed by averaging the prediction error values obtained from the test folds. The root mean square error (RMSE) [49] and mean absolute percentage error (MAPE) [50] were the performance evaluation statistics, which can be written as follows:

$$\mathrm{RMSE}=\sqrt{\frac{1}{N}\sum _{n=1}^N{\left(Z_n-Z_n^{*}\right)}^2}$$

and

$$\mathrm{MAPE}=\frac{1}{N}\sum _{n=1}^N\frac{|Z_n-Z_n^{*}|}{Z_n}\times 100,$$

where N is the number of the observed data in each fold, and $Z_n$ and $Z_n^{*}$ are the n-th actual and estimated values, respectively.

4.4. Results

4.4.1. Comparison of FST-DK with FST-RK

Table 1. Pearson correlation coefficients between candidate auxiliary variables and target variables, calculated using the entire dataset.

Variable	Temperature	Air Pressure
Temperature	1.0000	0.5775
Air Pressure	0.5775	1.0000
Relative Humidity	−0.2264	0.2204
DEM	−0.4284	−0.7577

presents the correlation coefficients between the target and potential auxiliary variables. It was found that air pressure correlated with temperature more significantly than the others, with a correlation coefficient of 0.5775. On the other hand, a significant negative correlation was observed between air pressure and DEM. Consequently, air pressure and temperature were selected as each other’s auxiliary variables, with DEM being another auxiliary variable for air pressure estimation.

The drift function used in the temperature estimation process takes the following form:

$$\begin{array}{c}\hfill \mu (s,t)={\alpha }_0+{\alpha }_1{X}_1(s,t),\end{array}$$

(28)

where $X_1(s,t)$ is the air pressure at point $(s,t)$. By comparing the given form with Equation (2), we observe that $f_0\left(X_1(s,t)\right)=1$ and $f_1\left(X_1(s,t)\right)=X_1(s,t)$ for all $(s,t)$ points.

The drift function for air pressure estimation is represented as:

$$\begin{array}{c}\hfill \mu (s,t)={\alpha }_0+{\alpha }_1{X}_2(s,t)+{\alpha }_2{X}_3(s,t),\end{array}$$

(29)

where $X_2(s,t)$ and $X_3(s,t)$ denote temperature and DEM at point $(s,t)$, respectively. Suppose that $X(s,t)={\left[X_2(s,t),X_3(s,t)\right]}^T$ and through a direct comparison with Equation (2), it follows that $f_0\left(X(s,t)\right)=1$, $f_1\left(X(s,t)\right)=X_2(s,t)$, and $f_2\left(X(s,t)\right)=X_3(s,t)$. All unknown parameters of the drift functions were estimated through ordinary least squares (OLS) [51].

The RMSE and MAPE values attained from the FST-DK and FST-RK methods are summarized in

Table 2. Comparison of temperature and air pressure estimation errors for the FST-RK and FST-DK methods.

Target Variable	Auxiliary Variable	Error	FST-RK	FST-DK
Temperature	Air Pressure	RMSE MAPE	0.8717 2.4435	0.8948 2.4940
Air Pressure	Temperature, DEM	RMSE MAPE	13.6485 0.7581	11.5184 0.7242

. Regarding accuracy measures, FST-RK exhibited superior performance in temperature estimation compared to FST-DK. The FST-RK method had RMSE and MAPE values that are lower by 2.5816% and 2.0249%, respectively. On the contrary, the FST-DK excelled in air pressure estimation, achieving a lower RMSE (15.6068%) and MAPE (4.4717%) than the FST-RK method. Additionally, the results presented in Table 2 indicate elevated MAPE values for temperature estimation. These outcomes could be attributed to the influence of the moderate correlation between temperature and air pressure.

4.4.2. Comparison of AST-DK with AST-RK and GTWR

Since the AST-DK and AST-RK trends were assumed to change over time in the present case, the procedure to compute overall correlation coefficients, therefore, differed from the fixed coefficient case. Here, monthly correlation coefficients between variables are averaged to obtain an overall correlation. A highly correlated relationship is observed between temperature and air pressure, with a correlation coefficient exceeding 0.8. The DEM variable is also included in the drift function for both temperature and air pressure estimations, as the absolute values of their correlation coefficients with DEM are greater than 0.5. When employing the GTWR methodology, air pressure and DEM are treated as auxiliary variables for the prediction of temperature, while temperature and DEM are utilized as auxiliary variables for the prediction of air pressure (

Table 3. Monthly means of Pearson correlation coefficients between candidate auxiliary and target variables.

Variable	Temperature	Air Pressure
Temperature	1.0000	0.8021
Air Pressure	0.8021	1.0000
Relative Humidity	−0.0710	0.3432
DEM	−0.5907	−0.7608

).

Therefore, we defined the drift function based on the selected auxiliary variables for temperature estimation. In this work, we defined two such functions, utilizing the variable definitions stated in the previous subsection. The first function is formulated as:

$$\begin{array}{c}\hfill {\mu }_A(s,t)=\sum _{\tau =1}^{12}{\alpha }_{0\tau }{f}_{0\tau }\left(X_1(s,t)\right)+\sum _{\tau =1}^{12}{\alpha }_{1\tau }{f}_{1\tau }\left(X_1(s,t)\right),\end{array}$$

(30)

where $f_{0\tau }\left(X_1(s,t)\right)=\left\{\begin{array}{cc}1,& \tau =t,\\ 0,& \tau \ne t\end{array}\right.$ and $f_{1\tau }\left(X_1(s,t)\right)=\left\{\begin{array}{cc}{X}_1(s,t),& \tau =t,\\ 0,& \tau \ne t\end{array}\right.$ for $t=1,2,\dots ,12$.

Furthermore, suppose that $\mathbf{X}(s,t)={\left[X_1(s,t),X_3(s,t)\right]}^T$, a second drift function is expressed as:

$$\begin{array}{c}\hfill {\mu }_A(s,t)=\sum _{\tau =1}^{12}{\alpha }_{0\tau }{f}_{0\tau }\left(\mathbf{X}(s,t)\right)+\sum _{\tau =1}^{12}{\alpha }_{1\tau }{f}_{1\tau }\left(\mathbf{X}(s,t)\right)+\sum _{\tau =1}^{12}{\alpha }_{2\tau }{f}_{2\tau }\left(\mathbf{X}(s,t)\right),\end{array}$$

(31)

where $f_{0\tau }\left(\mathbf{X}(s,t)\right)=\left\{\begin{array}{cc}1,& \tau =t,\\ 0,& \tau \ne t,\end{array}\right.$$f_{1\tau }\left(\mathbf{X}(s,t)\right)=\left\{\begin{array}{cc}{X}_1(s,t),& \tau =t,\\ 0,& \tau \ne t,\end{array}\right.$ and

     $f_{2\tau }\left(\mathbf{X}(s,t)\right)=\left\{\begin{array}{cc}{X}_3(s,t),& \tau =t,\\ 0,& \tau \ne t,\end{array}\right.$ for $t=1,2,\dots ,12$.

For air pressure estimation, the drift function is formulated as:

$$\begin{array}{c}\hfill {\mu }_A(s,t)=\sum _{\tau =1}^{12}{\alpha }_{0\tau }{f}_{0\tau }\left(X(s,t)\right)+\sum _{\tau =1}^{12}{\alpha }_{1\tau }{f}_{1\tau }\left(X(s,t)\right)+\sum _{\tau =1}^{12}{\alpha }_{2\tau }{f}_{2\tau }\left(X(s,t)\right),\end{array}$$

(32)

where $f_{0\tau }\left(X(s,t)\right)=\left\{\begin{array}{cc}1,& \tau =t,\\ 0,& \tau \ne t,\end{array}\right.$$f_{1\tau }\left(X(s,t)\right)=\left\{\begin{array}{cc}{X}_2(s,t),& \tau =t,\\ 0,& \tau \ne t,\end{array}\right.$ and

     $f_{2\tau }\left(X(s,t)\right)=\left\{\begin{array}{cc}{X}_3(s,t),& \tau =t,\\ 0,& \tau \ne t,\end{array}\right.$ for $t=1,2,\dots ,12$.

At each monthly time step, the unknown coefficients are identified through the application of the OLS method, as in the previous subsection.

Moreover, the two functions for temperature estimation in the GTWR model are expressed as:

$$\begin{array}{c}\hfill {\mu }_{GWR}(s,t)={\beta }_0(s,t)+{\beta }_1(s,t)X_1(s,t),\end{array}$$

(33)

and

$$\begin{array}{c}\hfill {\mu }_{GWR}(s,t)={\beta }_0(s,t)+{\beta }_1(s,t)X_1(s,t)+{\beta }_2(s,t)X_3(s,t),\end{array}$$

(34)

where ${\beta }_0$, ${\beta }_1$, and ${\beta }_2$ are unknown parameters that are estimated using the locally weighted least squares method [52,53]. The weighting function implemented in this study was the Gaussian spatio-temporal kernel function [40,41]. A detailed description of the GTWR modeling can be found in references [38,39].

For air pressure estimation, the drift function for kriging is formulated as:

$$\begin{array}{c}\hfill {\mu }_A(s,t)=\sum _{\tau =1}^{12}{\alpha }_{0\tau }{f}_{0\tau }\left(X(s,t)\right)+\sum _{\tau =1}^{12}{\alpha }_{1\tau }{f}_{1\tau }\left(X(s,t)\right)+\sum _{\tau =1}^{12}{\alpha }_{2\tau }{f}_{2\tau }\left(X(s,t)\right),\end{array}$$

(35)

where $f_{0\tau }\left(X(s,t)\right)=\left\{\begin{array}{cc}1,& \tau =t,\\ 0,& \tau \ne t,\end{array}\right.$$f_{1\tau }\left(X(s,t)\right)=\left\{\begin{array}{cc}{X}_2(s,t),& \tau =t,\\ 0,& \tau \ne t,\end{array}\right.$ and

     $f_{2\tau }\left(X(s,t)\right)=\left\{\begin{array}{cc}{X}_3(s,t),& \tau =t,\\ 0,& \tau \ne t,\end{array}\right.$ for $t=1,2,\dots ,12$.

Within the GTWR framework, the air pressure model is formally defined as follows:

$$\begin{array}{c}\hfill {\mu }_{GWR}(s,t)={\beta }_0(s,t)+{\beta }_1(s,t)X_2(s,t)+{\beta }_2(s,t)X_3(s,t).\end{array}$$

(36)

Table 4. Performance comparison of the AST-DK, AST-RK, and GTWR methods for temperature and air pressure estimations.

Target Variable	Auxiliary Variable	Error	AST-RK	AST-DK	GTWR
Temperature	Air Pressure	RMSE MAPE	0.8660 2.4302	0.8542 2.4099	1.4278 4.3972
Temperature	Air Pressure, DEM	RMSE MAPE	0.8955 2.4803	0.8336 2.3488	1.4211 4.3585
Air Pressure	Temperature, DEM	RMSE MAPE	11.9122 0.7121	10.5835 0.6799	12.2437 0.8044

demonstrates the estimation performance of the AST-DK, AST-RK, and GTWR methods for temperature and air pressure based on the functions given in Equations (30)–(32). As evidenced by the RMSE and MAPE metrics, the AST-DK method with one and two auxiliary variables consistently outperforms the AST-RK and GTWR algorithms for estimating both target variables. In particular, the AST-DK model yielded an RMSE of 0.8336 for temperature estimation, representing improvements of 6.9123% and 41.3412% over the AST-RK and GTWR methods, respectively. Similarly, the AST-DK model achieved an RMSE of 10.5835 for air pressure, representing a 7.4256% improvement over the AST-RK method and a 15% improvement over the GTWR technique. Notably, the AST-RK and AST-DK methods with one auxiliary variable surpassed the accuracy of the fixed coefficient approaches presented in Table 2. Furthermore, all four spatio-temporal kriging methods demonstrated superior performance to the GTWR model.

Additionally, the role of the adaptive coefficient drift function in the ST-DK method became apparent when considering temperature estimation with a single auxiliary variable. In the case of fixed coefficients, the ST-RK model yielded higher accuracy estimates than ST-DK, but the opposite scenario occurred for the adaptive coefficient schemes.

As both AST-RK and AST-DK approaches provide superior accuracy compared with other interpolation models, the spatial distributions of predicted temperature and air pressure achieved by these two methods are only presented for illustration purposes. The maps were gridded at a resolution of 0.05 degrees, corresponding to an approximate spatial area of 5.5 km² per grid cell. To estimate unobserved values of temperature and air pressure within these grid cells, ordinary kriging (OK) was implemented for prediction of unknown auxiliary variable values at the locations lacking measured data. All maps were created using QGIS software (Version 3.34.0).

Thailand’s weather is divided into three main seasons; the summer season (March to May), the rainy season (May to October), and the winter season (November to February) [54,55]. One month of each season (March, July, and November) was used to represent seasonal meteorological fluctuations across the country. The temperature variations across Thailand are visualized in Figure 2. As can be seen in the figures, stronger temperature gradients were produced by the ST-DK method for all three representative months. A more dispersed distribution of high temperatures can be markedly observed in March from the north to the center regions in Figure 2a.

A comparison of air pressure distributions obtained from the AST-RK and AST-DK methods is presented in Figure 3. The results of both methods show similar distribution patterns for March and November. A notable difference can be found in July, in which a relatively low air pressure produced by the AST-RK model was identified in the northern area compared with the AST-DK approach.

5. Conclusions and Discussions

This paper introduces the ST-DK technique, a novel methodological approach designed as a computationally efficient alternative to the ST-KED method for spatio-temporal interpolation. The ST-DK model leverages the ability to utilize fixed coefficients within its drift functions while offering increased flexibility to incorporate adaptive coefficients. This capability enables the ST-DK to model complex spatio-temporal dynamics effectively. The results of the case study revealed that the ST-DK and ST-RK methods with adaptive coefficient drift functions (AST-DK and AST-RK, respectively) had superior performance in the estimation of both temperature and air pressure compared to their counterparts employing fixed coefficients (FST-DK and FST-RK) and GTWR model. Furthermore, all four spatio-temporal kriging techniques provided better estimation accuracy than the GTWR method. Notably, the AST-DK approach consistently outperformed the AST-RK method in the estimation of both temperature and air pressure. The advantages of the ST-DK model in estimation over the ST-RK method may be attributed to the fact that correlated residuals are considered in the DK trend process, which is not the case for the RK model. Despite the satisfactory results of this study, higher computational resources for a large number of unobserved locations and estimated time steps can be a challenge. To handle the issue of the growing dimensionality of the ST-DK system, specialized solvers designed for large linear systems are used to exploit the sparsity and symmetry of the ST-DK coefficient matrix [56,57,58]. Another practical way to boost simulation efficiency is by localizing the ST-DK computations to a neighborhood of each unobserved site, resulting in a model size reduction. Possible future research endeavors include the identification of an optimal drift function and the development of techniques for determining the optimal coefficients within adaptive coefficient drift functions tailored for the ST-DK framework.