# Comparison of Multiple Linear Regression, Cubist Regression, and Random Forest Algorithms to Estimate Daily Air Surface Temperature from Dynamic Combinations of MODIS LST Data

^{1}

^{2}

^{*}

## Abstract

**:**

_{a}) using MODIS land surface temperature data (MODIS LST). Among these methods, the most common used method is statistical modeling, and the most applied algorithms are linear/multiple linear regression models (LM). There are only a handful of studies using machine learning algorithm models such as random forest (RF) or cubist regression (CB). In particular, there is no study comparing different combinations of four MODIS LST datasets with or without auxiliary data using different algorithms such as multiple linear regression, random forest, and cubist regression for daily T

_{a-max}, T

_{a-min}, and T

_{a-mean}estimation. Our study examines the mentioned combinations of four MODIS-LST datasets and shows that different combinations and differently applied algorithms produce various T

_{a}estimation accuracies. Additional analysis of daily data from three climate stations in the mountain area of North West of Vietnam for the period of five years (2009 to 2013) with four MODIS LST datasets (AQUA daytime, AQUA nighttime, TERRA daytime, and TERRA nighttime) and two additional auxiliary datasets (elevation and Julian day) shows that CB and LM should be applied if MODIS LST data is used solely. If MODIS LST is used together with auxiliary data, especially in mountainous areas, CB or RF is highly recommended. This study proved that the very high accuracy of T

_{a}estimation (R

^{2}> 0.93/0.80/0.89 and RMSE ~1.5/2.0/1.6 °C of T

_{a-max}, T

_{a-min}, and T

_{a-mean}, respectively) could be achieved with a simple combination of four LST data, elevation, and Julian day data using a suitable algorithm.

## 1. Introduction

_{a}) with high spatial and temporal resolution plays an important role in various applications, such as crop growth monitoring and simulations [1], hydrological, ecological, and environmental studies [2,3,4], weather forecasting [5,6], and climate change [7,8]. It is used as a key input variable and directly affects the accuracy of these applications. Traditionally, T

_{a}is usually measured by weather stations (often at 2 m above the ground) and usually limited in spatial coverage. Especially in mountainous areas of Vietnam, weather station coverage is extremely sparse.

_{a}and LST because of the complex surface energy budget and multiple related variables between them.

_{a}using satellite data such as the temperature–vegetation index method—TVX [9,10,11], surface energy-balance-based methods [12], and statistical methods [13,14,15,16,17,18] using different satellite datasets such as Landsat—ETM+ [19,20], AVHRR [21], or MODIS LST [11,14,22,23]. Among these satellite data, the most used is MODIS LST because it is freely available and can be obtained easily [18]. In addition, MODIS satellite provides four LST datasets daily, including: TERRA daytime (LST

_{td}), TERRA nighttime (LST

_{tn}), AQUA daytime (LST

_{ad}), and AQUA nighttime (LST

_{an}), which overpass local time at around 10 a.m., 10 p.m., 1 p.m., and 1 a.m. (our study area), respectively.

_{a}estimation studies; however, studies using machine learning techniques such as cubist regression (CB) or random forests (RF) are very rare (as far as we know, only [18,24,25,26]. However, all of these studies used MODIS LST integrating auxiliary data and estimated only T

_{a-max}or T

_{a-mean}. Furthermore, their conclusions are also different. Meyer et al. [26] stated that RF algorithms show the weakest results among linear regression, generalized boosted regression models (GBM), and Cubist regression. In contrast, Xu et al. [25] concluded that RF outperforms the linear regression. Zhang et al. [18] divided their data record into two groups (group S1 contains all four MODIS LST under good quality and group S2 had at least one LST with poor quality). The results based on the two datasets are different: in group S1, RF shows the best results in almost all combinations, but in group S2 the best algorithm is the Cubist regression. As a final result, the best algorithm for daily T

_{a-max}, T

_{a-min}, and T

_{a-mean}estimation remains unknown.

_{ad}, LST

_{an}, LST

_{td}, and LST

_{tn}. Therefore, it is important to compare the dynamic combination of one to four LST data that are available at different times and locations as well as the most suitable algorithm to apply for T

_{a}estimation. Furthermore, a rising question using LST MODIS solely is the kind of relationship (linear or nonlinear) between LST and T

_{a}, especially in mountainous areas.

^{4}− 1) possible dynamic combinations of four LST with or without auxiliary data for daily T

_{a}estimation using three different algorithms: multiple linear regression (LM), cubist regression (CB), and random forests models (RF). Finally, the accuracies of these T

_{a}-estimated are evaluated by comparison with T

_{a}-measured data, which are collected from weather stations. Root mean square error (RMSE) and coefficient of determination (R

^{2}) were used as the model evaluation scores.

## 2. Materials and Methods

#### 2.1. Study Area and Weather Station Data

^{2}(Figure 1). The study area presents a rural and mountainous region in northwest Vietnam with a sparse distribution of weather stations. There are only four weather stations (Figure 1) within these two provinces. However, due to the lack of data measurement, we chose only three stations, Sin Ho, Dien Bien, and Lai Chau, for this study (Table 1). In each station, the T

_{a}data were recorded hourly. T

_{a-max}and T

_{a-min}are the highest (maximum) and lowest (minimum) air surface temperatures that occur on a diurnal cycle (24 h cycle), respectively; T

_{a-mean}was calculated by averaging all 24 hourly measurements in a day. Generally, T

_{a-max}occurs after solar noon from one to two hours, and T

_{a-min}usually occurs shortly before dawn. In this study, we collected daily T

_{a-max}, T

_{a-min}, and T

_{a-mean}from 2009 to 2013 from the Vietnam Institute of Meteorology, Hydrology, and the Environment (IMHEN).

#### 2.2. Data

#### 2.2.1. MODIS LST

_{ad}), AQUA nighttime (LST

_{an}), TERRA daytime (LST

_{td}), and TERRA nighttime (LST

_{tn}).

_{a}[13,14,29]. It should be considered that eight-day-average LST is calculated by averaging all valid data under clear sky conditions, the number of participant data points varying from one to eight days depending on availability. Meanwhile, eight-day-average T

_{a}is calculated by averaging the data under changing sky conditions. Therefore, if we compare eight-day-average LST and eight-day-average T

_{a}, the sampling may introduce uncertainty [22]. Taking this difference into consideration, in this study we decided to use daily LST under clear sky conditions instead of eight-day-average LST data.

#### 2.2.2. MODIS Land Cover

_{a}estimation in northern Vietnam: station elevation (el) and Julian day data. Elevations of stations were obtained from the Vietnam Institute of Meteorology, Hydrology and Environment (IMHEN). The Julian day (jd) was extracted from the NASA server [32].

#### 2.3. Methods

#### 2.3.1. Calculating LST of Weather–Station–Location

- A total of 3652 MODIS images (MOD11A1 and MYD11A1, h27v06, Collection 5, from 1 January 2009 to 31 December 2013, over northern Vietnam) in HDF (Hierarchical Data Format) format were reprojected to WGS_1984_UTM_zone_48N using the nearest neighbor resampling method with the MODIS Re-Projection Tool. The corresponding layers (LST_Day_1km, LST_Night_1km, Daytime LST observation time, and Nighttime LST observation time) were extracted in TIF format. However, Daytime and Nighttime LST observation time were used in order to identify the approximate overpass time of MODIS at local time.
- MODIS LST data for the pixels in which the weather stations are located are extracted from 7304 TIF format MODIS images (3652 daytime and 3652 nighttime images) using batch processing of extract multi value to points in ArcGIS 10.3.
- All these LST data (DN value) were converted to Celsius temperature using the following equation:°C = 0.02 * DN − 273.15,
- Removing outlier data: MODIS LST products are not available for a location (pixel) if clouds are present [27]. However, there are some pixels that are lightly covered or contaminated by clouds. These pixels are not removed because the contamination is very small and cannot be detected by the cloud-removing mask algorithm [33,34]. To avoid this kind of data, we studied and developed a similar method that was used in [35]. This approach includes two steps: First, we simply filter and remove all unrealistic LST data that had values greater than 100 °C and/or below −50 °C. Second, we calculated the difference between T
_{a-max}versus LST daytime and T_{a-min}versus LST nighttime. Then, we applied statistical outlier removal based on these differences’ histograms to detect and remove data with unusually large differences (the histogram does not follow a normal distribution).

#### 2.3.2. Estimation Air Temperature Using MODIS LST Data

- Dynamic Combination of MODIS LST data

_{a}, we used all possible combinations of four LST data (LST

_{ad}, LST

_{an}, LST

_{td}, and LST

_{tn}). These 15-combinations are shown in Table 2.

- Algorithms used

_{a}estimation using MODIS LST [14,17,22,25,36,37]. Although it was found that the correlation between LST and T

_{a}is high, this relationship may not actually be linear [18]. Therefore, our current knowledge might be incomplete if we do not try machine learning algorithms. Machine learning algorithms promise a better estimation of T

_{a}using MODIS LST because they can handle non-linearity and highly correlated predictor variables [26,38,39]. Furthermore, based on the conceptual designs of machine learning algorithms, they are able to deal with data that have a different relationship between predictor and response variables under different conditions such as season, elevation, and land cover characteristic [26].

_{a}estimation research and showed very good results in the research of Meyer et al. [26] and Zhang et al. [18].

_{a}and assess the accuracy of estimation, three different methods were employed: linear regression (LM), cubist regression (CB) and random forests (RF). All methods are performed in the R statistical software.

#### 2.3.3. Comparison of Different Combination and Algorithms

- Assessment Criteria

^{2}) and the root mean square error (RMSE) that were calculated from the measured and estimated T

_{a}values from three algorithms: LM, CB, and RF.

- Comparison

## 3. Results

#### 3.1. The Relationship between T_{a} and LST MODIS

_{a}estimation, we first test the relationship between T

_{a}and LST MODIS of all three weather stations.

_{a}and LST of daytime and nighttime. It was found that: The coefficients of determination were high, ranging from 0.43 to 0.72, and the correlation between LST nighttime and T

_{a-min}were higher than LST daytime and T

_{a-max}at Dien Bien and Sin Ho stations. However, at Lai Chau station, the correlation between LST daytime versus T

_{a-max}was slightly higher than nighttime versus T

_{a-min}. This indicates that the relationship between MODIS LST and T

_{a}of this study area is complex.

_{a-min}versus LST

_{an}and LST

_{tn}were quite similar at all three stations. However, the relationships between T

_{a-max}versus LST daytime were different; at Lai Chau station most T

_{a-max}values are higher than the LST

_{ad}and LST

_{td}values (most of the points lie above the red line). Meanwhile, at Dien Bien station, T

_{a-max}is quite similar to LST

_{ad}but T

_{a-max}was higher than LST

_{td}. At Sin Ho station, there is not much difference between T

_{a}versus LST but there are a lot of data points lying outside the “±5 lines”.

#### 3.2. Different Combinations of MODIS LST for T_{a} Estimation

_{a}and LST are strong for both Terra LST and Aqua LST of daytime and nighttime. Furthermore, in Section 1 we also showed that there are plenty of studies using MODIS LST data for T

_{a}estimation using the LM method.

#### 3.2.1. Combinations Using One LST Variable

^{2}) and root mean square error (RMSE) of combinations C01–C04 using three algorithms (LM, CB, and RF) with Dataset A and Dataset B, respectively. It can be clearly seen that there is a large difference between Figure 3a (using LST solely) and Figure 3b (using LST with elevation and Julian day data). At Figure 3a, LM and CB show similar results and higher accuracy than the RF algorithm in all four combinations (C01–C04). In contrast, Figure 3b shows similar results for CB and RF in all four combinations and slightly higher values than with the LM algorithm. It is suggested that when one LST is used with an auxiliary data for T

_{a}estimation, RF and CB performance are better than LM.

^{2}and lower value of RMSE). It can be stated that nighttime LST was better than daytime for deriving daily T

_{a}. This result is consistent with [17,47]. Regarding the two datasets used, in all combinations (C01–C04) the accuracies of T

_{a}estimation using Dataset B are much higher than when using Dataset A.

_{a-min}and T

_{a-mean}estimation, Figure 3a shows that the combinations using LST nighttime (C02 and C04) have significantly higher accuracy than the combinations using LST daytime (C01 and C03). However, these differences are not clearly shown in Figure 3b (except for in the LM results).

_{a-max}estimation than TERRA daytime (C03). However, at night AQUA and TERRA show similar results for T

_{a}estimation. The results of both daytime and nighttime of TERRA and AQUA are consistent and similar in T

_{a}estimation (Figure 3b).

#### 3.2.2. Combinations Using Two-LST Variables

_{a}. As shown in Table 2, we applied six possible combinations of LST for T

_{a}estimation.

_{a}estimation using Dataset A and B are higher than the one-LST-combination (Figure 3a,b). Figure 4a shows that the difference between the three algorithms is not as large as in the results shown in Figure 3a (except for C07).

_{a}estimations using Dataset B are still higher than using Dataset A.

_{ad}+ LST

_{an}; and C06, combined LST

_{td}+ LST

_{tn}), there are similar results for T

_{a}estimations between them. It is indicated that the overpass times of AQUA and TERRA do not significantly affect the result of T

_{a}estimation when we combine daytime and nighttime LST. This is true for all three methods (LM, CB, and RF). These results are also consistent with previous studies [15,17,47], which used LM as the statistical model for T

_{a}estimation.

^{2}approximately 0.6, 0.5 and 0.35; RMSE approximately 3.5, 3.2, and 3.7 °C for T

_{a-max}, T

_{a-mean}, and T

_{a-min}, respectively). In addition, among the three algorithms, RF shows the lowest results with lower R

^{2}and higher RMSE. In contrast, the results of Dataset B are absolutely different (Figure 4b, panel row 3). The results of C07 (using Dataset B) are similar to the five other two-LST-combined (R

^{2}approximately 0.88, 0.80, and 0.73; RMSE approximately 1.8, 1.9, and 2.5 °C for T

_{a-max}, T

_{a-mean}, and T

_{a-min}, respectively, except for the results of LM) and much higher than using Dataset A. Among the three algorithms, the lowest result for T

_{a}estimation is LM (Figure 4b). Meanwhile, CB and RF show higher results, especially for T

_{a-min}and T

_{a-mean}estimation. It should be noted that C07 is the combination of TERRA and AQUA daytime LST, which is the most complicated in the relationship between T

_{a}and LST in comparison to the rest of the combinations. The difference between the results of Datasets A and B indicates that elevation and Julian day (i.e., season) also affect the relationship between LST and T

_{a}. This is consistent with the results from [15,23,48,49]. The high accuracy of T

_{a}estimation using the RF and CB algorithms in Figure 4b also indicates that RF and CB can account for the complicated relationship between predictor and response variables under different conditions, especially in mountainous area. This finding is consistent with the studies by Zhang et al. [18] and Xu et al. [25].

#### 3.2.3. Combinations Using Three-LST Variables

_{a}estimation and the differences in accuracy between the three different algorithms are not significant (p-value > 0.05). However, the results of T

_{a}estimation using Dataset B are much higher than using Dataset A. In both datasets, the results of T

_{a-max}and T

_{a-mean}are always better than T

_{a-min}(except C12 and C14 of Dataset A). This can be explained by the fact that, because of two LST nighttime variables (LST

_{tn}and LST

_{an}) in C12 and C14, the accuracy of T

_{a-min}estimation could be increased. However, in Dataset B, by introducing the two variables elevation and Julian day, the accuracy of all T

_{a-max}, T

_{a-min}, and T

_{a-mean}estimations has increased (T

_{a-max}and T

_{a-mean}is increased more significantly than T

_{a-min}when elevation and Julian day data were introduced).

#### 3.2.4. Combinations Using Four-LST Variables

_{a}estimation in both Dataset A and B. However, the results of Dataset B (R

^{2}approximately 0.93, 0.89 and 0.8, RMSE approximately 1.5, 1.6, and around 2.0 °C for T

_{a-max}, T

_{a-mean}, and T

_{a-min}, respectively) are much higher than the results of Dataset A (R

^{2}approximately 0.84, 0.88, and 0.75; RMSE roughly 2.2, 1.7, and 2.2 °C for T

_{a-max}, T

_{a-mean}, and T

_{a-min}, respectively).

## 4. Discussion

#### 4.1. Model Calibration and Validation

_{a}estimation using MODIS LST. First, we randomly divide the data of all 15 combinations into two datasets: calibration and validation (70% and 30%, respectively). Next, we fitted the model using a calibration dataset, and then we applied the fitted model to the validation dataset and the entire dataset. Finally, we assessed the accuracies of validation data, full data, and cross-validation.

_{a}estimation using Dataset B (right-hand panel) are slightly higher than with Dataset A (left-hand panel). It could be suggested that when LST data alone were used (without auxiliary data), the accuracy of T

_{a}estimation could be affected by a change in season or the elevation of the weather station. This is consistent with previous studies [17,36]. In the CB method (Figure 8), the results of validation, full data, and cross-validation are also consistent with each other. However, in both algorithms LM and CB, the results of Dataset A and Dataset B showed a significant difference, especially the combinations 1, 3, and 7 (C01, C03, and C07), where there is only LST daytime data. It is suggested that if LST nighttime is not available then the accuracy of T

_{a}estimation could be improved by adding auxiliary data. Comparing Figure 7 and Figure 8, it can be clearly seen that CB produces better results for T

_{a}estimation than LM.

_{a}estimation using MODIS LST. It is also clearly seen that the results of T

_{a}estimation using Dataset B are much higher than Dataset A, especially the combinations C01, C03, and C07. Again, the results of RF confirm that auxiliary data (i.e., elevation and Julian day) together with the RF algorithm can increase the accuracy of T

_{a}estimation, especially in the case of missing LST nighttime data (i.e., combinations C01, C03, and C07).

#### 4.2. Effects of Different Combinations and Statistical Model Applications

^{2}and RMSE.

_{a-max}, T

_{a-min}, and T

_{a-mean}estimations, the results of the LM and CB algorithms are similar and higher than RF. However, from C10 to C15, the differences between the three algorithms are not clear. The results of combinations C01, C03, and C07 are much lower than the rest of the combinations in all three algorithms.

_{a}and LST becomes more complicated. That is why simple models like C01, C03, and C07 (of Dataset A) cannot handle this relationship well. The results of all combinations (C01 to C15) were quite similar when the CB and RF algorithms were applied.

_{a}estimation (slightly higher than RF and much higher than LM). This is consistent with the studies of [18,25]. It should be remembered that Xu et al. [25] used MODIS LST and many other auxiliary variables like NDVI, longitude, latitude, etc. In this case, it could be explained by the complex terrain of the study area. It is suggested that the differences in topography, land surface properties, solar radiation, and many other factors could affect the relationships between T

_{a}and LST [14,50,51,52]. Therefore, a linear regression model, considered as a single global model, could not handle the complicated relationship between T

_{a}and the abovementioned variables under different conditions [25]. In contrast, CB and RF can account for the nonlinear and complicated relationship between the predictor and response variables under different conditions. That is why, in this mountainous study area, the cubist regression and random forest algorithms always show better results than LM in all 15 combinations (Figure 10, right panel).

_{a-min}estimation, follow by T

_{a-mean}and T

_{a-max}estimation.

## 5. Conclusions

_{a}estimation (R

^{2}> 0.93/0.80/0.89 and RMSE ~1.5/2.0/1.6 °C of T

_{a-max}, T

_{a-min}, and T

_{a-mean}, respectively) could be achieved with a simple combination of four LST data, elevation, and Julian day data using a suitable algorithm.

_{a}estimation, guaranteeing higher accuracy. Depending on LST data availability, it could be used in any combination from C02, C04, and C05 to C15 (except C07 and C09) to achieve the highest results solely with MODIS LST using any of the three mentioned algorithms. However, when MODIS LST and auxiliary (elevation and Julian day) are available, any combination (C01–C15) can be applied with the CB or RF algorithm.

_{a-max}, T

_{a-min}, and T

_{a-mean}, using Dataset A, T

_{a-mean}was estimated with the highest accuracy, followed by T

_{a-min}and T

_{a-max}. However, the difference between T

_{a-max}and T

_{a-min}was not significant. Considering Dataset B, T

_{a-max}was estimated with the highest accuracy, followed by T

_{a-mean}and T

_{a-min}. This means that the highest improvement for T

_{a-max}is made by introducing elevation and Julian day data, followed by T

_{a-mean}and T

_{a-min}. However, the difference between T

_{a-max}and T

_{a-mean}was not significant.

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## Appendix A

Combination | a_{0} | a_{1} | a_{2} | a_{3} | a_{4} | |
---|---|---|---|---|---|---|

T_{a-min} Estimation | C01 | −0.4567 | 0.6037 | |||

C02 | −0.2678 | 1.0020 | ||||

C03 | −1.1905 | 0.7170 | ||||

C04 | −1.7601 | 1.0184 | ||||

C05 | 0.1561 | −0.0656 | 1.0647 | |||

C06 | −3.6700 | 0.0382 | 1.0329 | |||

C07 | −4.1084 | 0.4906 | 0.2442 | |||

C08 | −1.4168 | 1.0031 | 0.0277 | |||

C09 | −2.1783 | −0.0425 | 1.0769 | |||

C10 | −2.2857 | 0.4799 | 0.5784 | |||

C11 | −2.8733 | −0.0336 | 0.0347 | 1.0349 | ||

C12 | −2.4495 | 0.5464 | −0.0378 | 0.5552 | ||

C13 | −1.0977 | −0.0344 | 0.9997 | 0.0496 | ||

C14 | −0.5283 | −0.1538 | 0.6645 | 0.5408 | ||

C15 | −1.6045 | −0.0714 | 0.6659 | −0.0020 | 0.4556 | |

T_{a-max} Estimation | C01 | 0.7418 | 0.9849 | |||

C02 | 8.4402 | 1.1748 | ||||

C03 | 6.1865 | 0.9026 | ||||

C04 | 5.8675 | 1.2125 | ||||

C05 | −0.0367 | 0.5587 | 0.7505 | |||

C06 | 4.3759 | 0.1263 | 1.1694 | |||

C07 | −0.0708 | 1.0098 | −0.0068 | |||

C08 | 8.5918 | 1.1432 | 0.0458 | |||

C09 | −0.7751 | 0.4757 | 0.8778 | |||

C10 | 5.5651 | 0.3821 | 0.9083 | |||

C11 | 1.0850 | 0.5573 | −0.2434 | 0.9824 | ||

C12 | 7.5080 | 0.4518 | −0.1274 | 0.9481 | ||

C13 | 3.7089 | 0.6542 | 0.8513 | −0.3246 | ||

C14 | −1.1526 | 0.4704 | 0.3212 | 0.6027 | ||

C15 | 3.2723 | 0.5978 | 0.4074 | −0.4465 | 0.7015 | |

T_{a-mean} Estimation | C01 | −0.3329 | 0.7579 | |||

C02 | 3.0973 | 1.0630 | ||||

C03 | 0.9103 | 0.8154 | ||||

C04 | 1.1378 | 1.0888 | ||||

C05 | −0.4702 | 0.2122 | 0.9074 | |||

C06 | −1.6236 | 0.1523 | 1.0316 | |||

C07 | −3.2005 | 0.6693 | 0.1964 | |||

C08 | 1.3821 | 1.0028 | 0.1121 | |||

C09 | −2.2374 | 0.1935 | 0.9691 | |||

C10 | 0.7231 | 0.4639 | 0.6828 | |||

C11 | −2.3500 | 0.2016 | 0.0036 | 0.9516 | ||

C12 | −0.0214 | 0.5094 | 0.0377 | 0.6325 | ||

C13 | −0.2995 | 0.2344 | 0.8818 | −0.0172 | ||

C14 | −1.5659 | 0.1396 | 0.5060 | 0.5450 | ||

C15 | −1.1587 | 0.1968 | 0.5392 | −0.0702 | 0.4952 |

_{0}is the intercept of each model (combination), a

_{1}–a

_{4}are parameters of LST variables with the same order as shown in Table 2.

## Appendix B

Combination | a_{0} | a_{1} | a_{2} | a_{3} | a_{4} | Elevation | Julian Day | |
---|---|---|---|---|---|---|---|---|

T_{a-min} Estimation | C01 | 4.1126 | 0.4728 | −0.0029 | 0.0066 | |||

C02 | 0.3258 | 0.9505 | −0.0008 | 0.0057 | ||||

C03 | 3.1331 | 0.6298 | −0.0042 | 0.0046 | ||||

C04 | −1.6854 | 0.9873 | −0.0005 | 0.0050 | ||||

C05 | 0.4293 | −0.0318 | 0.9822 | −0.0007 | 0.0050 | |||

C06 | −4.5116 | 0.1075 | 0.9595 | −0.0006 | 0.0053 | |||

C07 | 1.7992 | 0.0921 | 0.5553 | −0.0041 | 0.0042 | |||

C08 | −1.4452 | 0.9067 | 0.0887 | −0.0009 | 0.0054 | |||

C09 | −2.3238 | 0.0098 | 0.9868 | −0.0007 | 0.0049 | |||

C10 | −2.7464 | 0.4843 | 0.5678 | −0.0003 | 0.0056 | |||

C11 | −3.0229 | −0.0266 | 0.1405 | 0.8891 | −0.0011 | 0.0045 | ||

C12 | −3.2945 | 0.5450 | −0.0031 | 0.5286 | −0.0003 | 0.0053 | ||

C13 | −0.7924 | −0.0512 | 0.8894 | 0.1366 | −0.0010 | 0.0044 | ||

C14 | −0.7538 | −0.1054 | 0.6304 | 0.4971 | −0.0005 | 0.0044 | ||

C15 | −1.8881 | −0.0530 | 0.6303 | 0.0650 | 0.3815 | −0.0007 | 0.0041 | |

T_{a-max} Estimation | C01 | 10.4393 | 0.7387 | −0.0043 | 0.0007 | |||

C02 | 18.4850 | 0.8308 | −0.0045 | −0.0048 | ||||

C03 | 15.9842 | 0.7267 | −0.0066 | −0.0048 | ||||

C04 | 13.5620 | 0.9526 | −0.0032 | −0.0040 | ||||

C05 | 10.5450 | 0.4115 | 0.5496 | −0.0038 | −0.0010 | |||

C06 | 12.3927 | 0.3408 | 0.6526 | −0.0044 | −0.0055 | |||

C07 | 11.3235 | 0.3628 | 0.4616 | −0.0058 | −0.0027 | |||

C08 | 16.0125 | 0.5685 | 0.3214 | −0.0052 | −0.0056 | |||

C09 | 6.3793 | 0.4536 | 0.6361 | −0.0031 | −0.0007 | |||

C10 | 15.0605 | 0.3058 | 0.6539 | −0.0038 | −0.0045 | |||

C11 | 8.8810 | 0.2941 | 0.1853 | 0.5654 | −0.0041 | −0.0032 | ||

C12 | 15.3856 | 0.3071 | 0.2084 | 0.4250 | −0.0048 | −0.0060 | ||

C13 | 13.7642 | 0.1994 | 0.4982 | 0.2098 | −0.0049 | −0.0043 | ||

C14 | 8.8056 | 0.3859 | 0.2534 | 0.4067 | −0.0037 | −0.0008 | ||

C15 | 12.8016 | 0.2253 | 0.3015 | 0.1265 | 0.3065 | −0.0047 | −0.0044 | |

T_{a-mean} Estimation | C01 | 5.4211 | 0.6044 | −0.0030 | 0.0035 | |||

C02 | 6.6191 | 0.9322 | −0.0017 | 0.0003 | ||||

C03 | 7.1288 | 0.6996 | −0.0048 | −0.0001 | ||||

C04 | 3.4497 | 1.0007 | −0.0011 | 0.0006 | ||||

C05 | 2.4255 | 0.1864 | 0.8229 | −0.0013 | 0.0016 | |||

C06 | 0.6160 | 0.2471 | 0.8388 | −0.0016 | 0.0003 | |||

C07 | 4.1489 | 0.2239 | 0.5289 | −0.0043 | 0.0007 | |||

C08 | 3.8781 | 0.7786 | 0.2243 | −0.0020 | −0.0002 | |||

C09 | −0.5674 | 0.2103 | 0.8726 | −0.0011 | 0.0019 | |||

C10 | 3.1115 | 0.4446 | 0.6126 | −0.0011 | 0.0004 | |||

C11 | −0.1692 | 0.1288 | 0.1705 | 0.7716 | −0.0016 | 0.0009 | ||

C12 | 2.0954 | 0.4650 | 0.1490 | 0.4676 | −0.0015 | −0.0002 | ||

C13 | 2.9232 | 0.0875 | 0.7353 | 0.1781 | −0.0019 | 0.0001 | ||

C14 | 0.6955 | 0.1371 | 0.4779 | 0.4833 | −0.0011 | 0.0014 | ||

C15 | 1.5190 | 0.0943 | 0.4956 | 0.1184 | 0.3575 | −0.0016 | 0.0001 |

_{0}is the intercept of each model (combination), a

_{1}–a

_{4}are parameters of LST variables with the same order as shown in Table 2.

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**Figure 1.**Location of the weather stations and range of elevation (

**a**) and land cover (

**b**) from MODIS MCD12Q1 data in 2010 of the study area.

**Figure 2.**The relationship between LST (x-axis) and T

_{a-max}(first and third columns), T

_{a-min}(second and last columns) of all meteorological stations from 2009 to 2013. The dashed line indicates that the difference between T

_{a}and LST is over ±5 °C (±5 line). The red line indicates the 1:1 line.

**Figure 3.**(

**a**) Cross-validation results for one-LST-combination (C01–C04) using Dataset A, and multiple comparisons of the three algorithms. The x-axis shows the value of R

^{2}and RMSE (°C), the y-axis shows the model types. The box and whiskers plots show the distributions of R

^{2}and RMSE; (

**b**) Cross-validation results for one-LST-combination (C01–C04) using Dataset B, and multiple comparisons of the three algorithms. The x-axis shows the values of R

^{2}and RMSE (°C); the y-axis shows the model types. The box and whiskers plots show the distributions of R

^{2}and RMSE.

**Figure 4.**(

**a**) Cross-validation results for two-LST-combinations (C05–C10) using Dataset A and multiple comparisons of the three algorithms. The x-axis shows the value of R

^{2}and RMSE (°C); the y-axis shows the model types. The box and whiskers plots show the distributions of R

^{2}and RMSE; (

**b**) Cross-validation results for two-LST-combinations (C05–C10) using Dataset B and multiple comparisons of the three algorithms. The x-axis shows the values of R

^{2}and RMSE (°C); the y-axis shows the model types. The box and whiskers plots show the distributions of R

^{2}and RMSE.

**Figure 5.**(

**a**) Cross-validation results for three-LST-combinations (C11–C14) using Dataset A and multiple comparisons of the three algorithms. The x-axis shows the values of R

^{2}and RMSE (°C); the y-axis shows the model types. The box and whiskers plots show the distributions of R

^{2}and RMSE; (

**b**) Cross-validation results for three-LST-combinations (C11–C14) using Dataset B and multiple comparisons of the three algorithms. The x-axis shows the value of R

^{2}and RMSE (°C); the y-axis shows the model types. The box and whiskers plots show the distributions of R

^{2}and RMSE.

**Figure 6.**Cross-validation results for four-LST-combinations (C15) using Dataset A (upper rows) and B (lower rows) and multiple comparisons of the three algorithms. The x-axis shows the values of R

^{2}and RMSE (°C); the y-axis shows the model types. The box and whiskers plots show the distributions of R

^{2}and RMSE.

**Figure 7.**Comparison of accuracy (R

^{2}and RMSE) when applying the LM algorithm to the validation dataset (_val), the full dataset (_all), and a cross-validation (_cv) of all combinations. The x-axis shows the combination number. The y-axis shows the values of RMSE (°C) and R

^{2}.

**Figure 8.**Comparison of accuracy (R

^{2}and RMSE) when applying the CB algorithm to the validation dataset (_val), the full dataset (_all), and a cross-validation (_cv) of all combinations. The x-axis shows the combination number. The y-axis shows the values of RMSE (°C) and R

^{2}.

**Figure 9.**Comparison of accuracy (R

^{2}and RMSE) when applying the RF algorithm to the validation dataset (_val), the full dataset (_all), and a cross-validation (_cv) of all combinations. The x-axis shows the combination number. The y-axis shows the values of RMSE (°C) and R

^{2}.

**Figure 10.**Different performance of the algorithms LM (red), CB (green), and RF (blue) through 15 combinations of Dataset A and Dataset B. The x-axis shows the combination number. The y-axis shows the values of RMSE (°C) and R

^{2}.

No. | Station | Lat (°) | Long (°) | Elevation (m) | Land Cover |
---|---|---|---|---|---|

1 | Sin Ho | 22.37 | 103.25 | 1534 | Forest |

2 | Dien Bien | 21.37 | 103.00 | 475 | Crop land |

3 | Lai Chau | 22.07 | 103.15 | 243 | Forest |

No. | Combination | SinHo | DienBien | LaiChau | Total | |||
---|---|---|---|---|---|---|---|---|

C01 | LST_{ad} | 488 | 572 | 571 | 1631 | |||

C02 | LST_{an} | 420 | 321 | 261 | 1002 | |||

C03 | LST_{td} | 427 | 500 | 507 | 1434 | |||

C04 | LST_{tn} | 562 | 593 | 528 | 1683 | |||

C05 | LST_{ad} | +LST_{an} | 254 | 219 | 190 | 663 | ||

C06 | LST_{td} | +LST_{tn} | 255 | 286 | 298 | 839 | ||

C07 | LST_{ad} | +LST_{td} | 297 | 318 | 348 | 963 | ||

C08 | LST_{an} | +LST_{td} | 231 | 193 | 176 | 600 | ||

C09 | LST_{ad} | +LST_{tn} | 283 | 348 | 340 | 971 | ||

C10 | LST_{an} | +LST_{tn} | 294 | 224 | 193 | 711 | ||

C11 | LST_{td} | +LST_{tn} | +LST_{ad} | 195 | 200 | 229 | 624 | |

C12 | LST_{td} | +LST_{tn} | +LST_{an} | 176 | 132 | 131 | 439 | |

C13 | LST_{ad} | +LST_{an} | +LST_{td} | 184 | 138 | 137 | 459 | |

C14 | LST_{ad} | +LST_{an} | +LST_{tn} | 198 | 159 | 143 | 500 | |

C15 | LST_{ad} | +LST_{an} | +LST_{td} | +LST_{tn} | 141 | 92 | 100 | 333 |

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**MDPI and ACS Style**

Noi, P.T.; Degener, J.; Kappas, M. Comparison of Multiple Linear Regression, Cubist Regression, and Random Forest Algorithms to Estimate Daily Air Surface Temperature from Dynamic Combinations of MODIS LST Data. *Remote Sens.* **2017**, *9*, 398.
https://doi.org/10.3390/rs9050398

**AMA Style**

Noi PT, Degener J, Kappas M. Comparison of Multiple Linear Regression, Cubist Regression, and Random Forest Algorithms to Estimate Daily Air Surface Temperature from Dynamic Combinations of MODIS LST Data. *Remote Sensing*. 2017; 9(5):398.
https://doi.org/10.3390/rs9050398

**Chicago/Turabian Style**

Noi, Phan Thanh, Jan Degener, and Martin Kappas. 2017. "Comparison of Multiple Linear Regression, Cubist Regression, and Random Forest Algorithms to Estimate Daily Air Surface Temperature from Dynamic Combinations of MODIS LST Data" *Remote Sensing* 9, no. 5: 398.
https://doi.org/10.3390/rs9050398