# Revealing the Driving Mechanisms of Land Surface Temperature Spatial Heterogeneity and Its Sensitive Regions in China Based on GeoDetector

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials

#### 2.1. Satellite Products

#### 2.2. Reanalysis Data

#### 2.3. Climate Type Data

## 3. Methods

#### 3.1. GeoDetector

#### 3.1.1. Factor Detector

_{h}and N are the number of cells in stratum h and the whole area, respectively; and ${\sigma}_{h}^{2}$ and ${\sigma}^{2}$ are the variances of response variable values in stratum h and the whole area, respectively. SSW and SST are the sum of variance within stratum and the total variance of the whole area, respectively. The range of q-value is from 0 to 1, and a larger value indicates more significant spatial heterogeneity of response variable; if the stratification is generated by the driving variable, a larger q-value indicates a stronger explanatory power of the driving variable on the response variable, and vice versa [47]. A simple transformation of the q value satisfies the non-central F distribution [26]:

#### 3.1.2. Interaction Detector

#### 3.1.3. Risk Detector

#### 3.2. Data Discretization Methods

## 4. Results

#### 4.1. Data Discretization

#### 4.2. Selection of Optimal Spatial Unit Scale

#### 4.3. Impact of Individual Factor on the Spatial Heterogeneity of LST

#### 4.4. Effect of the Joint Factor on the Spatial Heterogeneity of LST

#### 4.5. Determine the Regions of the LST That Are Vulnerable to Drivers

## 5. Discussions

## 6. Conclusions

- (1)
- The factor detector showed that the explanatory ability of the drivers (TA, WV, CLIMATE, DEM, AOD, RN, NDVI, PRE, ET, and SM) indicates that TA has the greatest driving effect in the selected years, and the driving strength is increasing at a rate of 0.003/year. WV is second only to TA and also shows a strong driving effect on LST spatial heterogeneity with a change rate of 0.004/year. LULC has no driving effect on LST spatial heterogeneity due to the spatial unit scale.
- (2)
- The interaction detector revealed that the effect of the interaction is significantly greater than the effect of any single factor, which indicates that the spatial heterogeneity of LST is the result of multi-factor interactions. Similarly to the individual effect, TA has the strongest joint effect with other factors, especially the interaction with LULC, with a mean q-value of 0.78.
- (3)
- The risk detector found that the sensitive areas of LST determined by the driving factor have a similar spatial distribution pattern. However, variations in the high-sensitivity regions exist from year to year. During the study period, LST was driven by AOD over the widest area, with an average share of 15.8%, followed by WV, with an average share of 11.5%. Overall, the high-sensitivity areas determined by most drivers showed a decreasing trend.

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Spatial distribution of (

**a**) annual mean LST, (

**b**) annual mean air temperature, (

**c**) annual mean NDVI, (

**d**) annual mean soil moisture, (

**e**) annual mean net surface radiation, (

**f**) annual mean precipitation, (

**g**) annual mean AOD, (

**h**) annual mean evaporation, (

**i**) annual mean water vapor, (

**j**) land cover, (

**k**) elevation, and (

**l**) climate types.

**Figure 2.**Parameter optimization (

**a**) process and (

**b**) results of discretization of spatial data on potential drivers of LST in 2003 based on the GeoDetector.

**Figure 4.**Contribution of individual driver to spatial differentiation in LST (q-value) in China in 2003, 2008, 2013, and 2018.

**Figure 5.**Interactions between drivers of spatial differentiation on LST in (

**a**) 2003, (

**b**) 2008, (

**c**) 2013, and (

**d**) 2018.

**Figure 6.**Mean values of LST in different subregions of each driver in (

**a**) 2003, (

**b**) 2008, (

**c**) 2013, and (

**d**) 2018. Red bar represents the high value of the mean LST in the subarea, which means the area greatly affected by the driver, blue bar represents the low-value area, and gray bar represents the medium of the intensity affected by the driver.

**Figure 7.**Spatial distribution of high-sensitivity (red), medium-sensitivity (grey), and low-sensitivity (blue) areas for surface temperature based on the drivers in (

**a**) 2003, (

**b**) 2008, (

**c**) 2013, and (

**d**) 2018.

**Figure 8.**Percentage of highly sensitive regions of the LST in response to drivers. The upper triangle in the figure indicates an increasing trend in highly sensitive areas from 2003 to 2018, the lower triangle indicates a decreasing trend, and the circle indicates no change.

Judgment Criteria | Interaction Type |
---|---|

q(X1∩X2) = q(X1) + q(X2) | Independent |

q(X1∩X2) > q(X1) + q(X2) | Nonlinear enhance |

q(X1∩X2) < Min(q(X1), q(X2)) | Nonlinear weaken |

Min(q(X1), q(X2)) < q(X1∩X2) < Max(q(X1), q(X2)) | Univariate weaken |

q(X1∩X2) > Max(q(X1), q(X2)) | Bivariate enhance |

**Table 2.**Optimization methods and dispersion intervals for spatial data discretization parameters for potential drivers of LST in 2003.

Variables | TA(K) | NDVI | SM(cm^{3}/cm^{3}) | RN(W/cm^{2}) | PRE(mm) | AOD | ET(mm) | WV(cm) | DEM(km) |
---|---|---|---|---|---|---|---|---|---|

Discretization Methods | SD | NB | SD | SD | QU | QU | QU | SD | NB |

Discrete interval | 262.49 | 0.00 | 0.08 | 26.93 | 1.11 | 0.00 | 23.05 | 0.35 | 0.00 |

269.69 | 0.17 | 0.18 | 51.47 | 22.83 | 0.01 | 75.43 | 0.48 | 539.69 | |

273.83 | 0.30 | 0.21 | 59.95 | 32.30 | 0.02 | 98.81 | 0.69 | 1262.52 | |

277.97 | 0.41 | 0.24 | 68.43 | 42.14 | 0.02 | 113.31 | 0.90 | 2199.13 | |

282.12 | 0.52 | 0.28 | 76.91 | 61.66 | 0.03 | 126.64 | 1.10 | 3403.75 | |

286.26 | 0.64 | 0.31 | 85.39 | 93.01 | 0.04 | 145.09 | 1.31 | 4545.05 | |

290.40 | 0.83 | 0.34 | 93.87 | 203.67 | 0.06 | 180.45 | 1.52 | 6000.00 | |

297.44 | 0.41 | 132.29 | 0.26 | 357.08 | 2.43 |

**Table 3.**Optimization methods and dispersion intervals for spatial data discretization parameters for potential drivers of LST in 2008.

Variables | TA(K) | NDVI | SM(cm^{3}/cm^{3}) | RN(W/cm^{2}) | PRE(mm) | AOD | ET(mm) | WV(cm) | DEM(km) |
---|---|---|---|---|---|---|---|---|---|

Discretization Methods | NB | EI | SD | QU | QU | QU | QU | QU | NB |

Discrete interval | 261.75 | 0.01 | 0.04 | 9.46 | 3.99 | 0.00 | 15.33 | 0.26 | 0.00 |

268.24 | 0.12 | 0.17 | 53.08 | 25.64 | 0.01 | 75.52 | 0.43 | 565.33 | |

272.65 | 0.24 | 0.20 | 61.58 | 42.41 | 0.01 | 94.71 | 0.69 | 1244.37 | |

276.80 | 0.36 | 0.23 | 72.70 | 53.63 | 0.03 | 109.64 | 0.81 | 2186.64 | |

281.60 | 0.48 | 0.26 | 84.03 | 68.84 | 0.04 | 124.11 | 0.94 | 3326.08 | |

286.17 | 0.60 | 0.30 | 93.52 | 92.09 | 0.07 | 141.82 | 1.13 | 4465.60 | |

290.81 | 0.71 | 0.33 | 135.44 | 210.34 | 0.11 | 175.99 | 1.48 | 5864.00 | |

297.18 | 0.83 | 0.40 | 0.35 | 357.94 | 2.54 |

**Table 4.**Optimization methods and dispersion intervals for spatial data discretization parameters for potential drivers of LST in 2013.

Variables | TA(K) | NDVI | SM(cm^{3}/cm^{3}) | RN(W/cm^{2}) | PRE(mm) | AOD | ET(mm) | WV(cm) | DEM(km) |
---|---|---|---|---|---|---|---|---|---|

Discretization Methods | SD | QU | NB | NB | SD | QU | SD | SD | NB |

Discrete interval | 259.93 | 0.00 | 0.04 | 9.92 | 5.06 | 0.00 | 14.24 | 0.30 | −89.00 |

269.88 | 0.16 | 0.15 | 46.41 | 16.22 | 0.01 | 56.65 | 0.43 | 508.09 | |

273.92 | 0.26 | 0.19 | 58.45 | 33.84 | 0.02 | 79.79 | 0.65 | 1241.16 | |

277.97 | 0.34 | 0.23 | 67.86 | 51.46 | 0.03 | 102.93 | 0.86 | 2274.81 | |

282.01 | 0.42 | 0.26 | 77.90 | 69.07 | 0.05 | 126.07 | 1.07 | 3503.69 | |

286.06 | 0.49 | 0.30 | 88.97 | 86.69 | 0.07 | 149.21 | 1.28 | 4616.17 | |

290.10 | 0.58 | 0.34 | 100.88 | 104.31 | 0.10 | 172.35 | 1.49 | 6030.00 | |

297.32 | 0.87 | 0.41 | 137.66 | 212.16 | 0.33 | 373.79 | 2.46 |

**Table 5.**Optimization methods and dispersion intervals for spatial data discretization parameters for potential drivers of LST in 2018.

Variables | TA(K) | NDVI | SM(cm^{3}/cm^{3}) | RN(W/cm^{2}) | PRE(mm) | AOD | ET(mm) | WV(cm) | DEM(km) |
---|---|---|---|---|---|---|---|---|---|

Discretization Methods | NB | NB | SD | QU | SD | QU | QU | QU | NB |

Discrete interval | 263.11 | 0.01 | 0.06 | 28.68 | 2.59 | 0 | 21.47 | 0.21 | 0.00 |

269.56 | 0.16 | 0.15 | 54.86 | 11.39 | 0.01 | 63.81 | 0.44 | 624.20 | |

274.15 | 0.27 | 0.22 | 62.68 | 32.68 | 0.03 | 86.28 | 0.77 | 1388.16 | |

278.74 | 0.36 | 0.29 | 70.94 | 53.97 | 0.06 | 111.19 | 0.96 | 2393.28 | |

283.55 | 0.45 | 0.35 | 76.10 | 75.25 | 0.10 | 135.76 | 1.27 | 3496.70 | |

287.80 | 0.55 | 0.43 | 84.31 | 96.54 | 0.26 | 178.07 | 1.61 | 4495.55 | |

291.64 | 0.65 | 98.87 | 117.83 | 308.53 | 2.63 | 5710.00 | |||

298.42 | 0.84 | 135.60 | 209.62 |

**Table 6.**Statistics on the contribution of individual driver to spatial differentiation in LST (q-values) in China in 2003, 2008, 2013, and 2018.

q-Value | |||||
---|---|---|---|---|---|

2003 | 2008 | 2013 | 2018 | slope | |

TA | 0.72 | 0.74 | 0.73 | 0.77 | 0.003 |

NDVI | 0.33 | 0.35 | 0.31 | 0.42 | 0.005 |

SM | 0.24 | 0.28 | 0.26 | 0.33 | 0.005 |

RN | 0.34 | 0.47 | 0.36 | 0.37 | 0.000 |

PRE | 0.23 | 0.36 | 0.36 | 0.45 | 0.013 |

AOD | 0.32 | 0.53 | 0.43 | 0.43 | 0.005 |

ET | 0.26 | 0.30 | 0.23 | 0.32 | 0.002 |

WV | 0.69 | 0.64 | 0.71 | 0.74 | 0.004 |

LULC | Non-significant | ||||

DEM | 0.46 | 0.32 | 0.49 | 0.47 | 0.004 |

CLIMATE | 0.52 | 0.50 | 0.56 | 0.65 | 0.009 |

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## Share and Cite

**MDPI and ACS Style**

Yu, Y.; Fang, S.; Zhuo, W.
Revealing the Driving Mechanisms of Land Surface Temperature Spatial Heterogeneity and Its Sensitive Regions in China Based on GeoDetector. *Remote Sens.* **2023**, *15*, 2814.
https://doi.org/10.3390/rs15112814

**AMA Style**

Yu Y, Fang S, Zhuo W.
Revealing the Driving Mechanisms of Land Surface Temperature Spatial Heterogeneity and Its Sensitive Regions in China Based on GeoDetector. *Remote Sensing*. 2023; 15(11):2814.
https://doi.org/10.3390/rs15112814

**Chicago/Turabian Style**

Yu, Yanru, Shibo Fang, and Wen Zhuo.
2023. "Revealing the Driving Mechanisms of Land Surface Temperature Spatial Heterogeneity and Its Sensitive Regions in China Based on GeoDetector" *Remote Sensing* 15, no. 11: 2814.
https://doi.org/10.3390/rs15112814