|Table of Contents|

Estimation of Near-surface Air Temperature in Xi'an City of Shaanxi Province, China Based on Random Forest(PDF)

《地球科学与环境学报》[ISSN:1672-6561/CN:61-1423/P]

Issue:
2022年第01期
Page:
102-113
Research Field:
环境与可持续发展
Publishing date:

Info

Title:
Estimation of Near-surface Air Temperature in Xi'an City of Shaanxi Province, China Based on Random Forest
Author(s):
FENG Rui1;  YANG Li-ping2*;  HOU Cheng-lei3;  WANG Tong2;  ZHANG Jing2;  XIAO Shun4
(1. School of Earth Science and Resources, Chang'an University, Xi'an 710054, Shaanxi, China; 2. School of Geological Engineering and Geomatics, Chang'an University, Xi'an 710054, Shaanxi, China; 3. College of Land Resources and Surveying & Mapping Engineering, Shandong Agriculture and Engineering University, Jinan 250100, Shandong, China; 4. School of Geography and Tourism, Shaanxi Normal University, Xi'an 710119, Shaanxi, China)
Keywords:
near-surface air temperature;  random forest;  estimation;  land surface temperature;  heat island effect;  altitude;  Landsat 8;  Xi'an
PACS:
P407; TP79
DOI:
10.19814/j.jese.2021.08027
Abstract:
With the continuously expanding of the urban areas and the dramatic population growth, serious urban climate and thermal environment issues have occurred and attracted extensive attention. High-precision and large-scale remote sensing monitoring of near-surface air temperature in Xi'an city can provide reference for the improvement of urban climate, the alleviation of heat island effect, as well as the creation of a suitable living environment. Traditional near-surface air temperature monitoring approaches are confined when dealing with complicated relationships among multiple factors. Taking Xi'an city as the study area, the random forest(RF)models, which can integrate multiple factors and simulate complicated and non-linear mapping relationship, were adopted. Through comprehensive analysis of various parameters derived from Landsat 8 imagery and SRTM DEM data, several near-surface air temperature estimation RF models were constructed, and the optimal model was confirmed by performance comparison and evaluation. Furthermore, near-surface air temperature in Xi'an city on May 16, 2016 was estimated, and the spatial distribution pattern was discussed. The results show that among all the near-surface air temperature influencing factors, altitude has the highest contribution to the estimated temperature of the RF models, followed by land surface temperature(LST); both of them are crucial for the estimation of near-surface air temperature. For all the RF models, the training set R2 is higher than 0.916, and the RMSE is lower than 0.467 ℃; all the validation set R2 is higher than 0.726, and the RMSE is lower than 0.840 ℃; the training set R2 of all RF models is higher than that of the validation set, while the training set RMSE is lower compared with the validation set; the training set R2 of the optimal model is 0.934 with the RMSE being 0.425 ℃, and the validation set R2 is 0.795 with a RMSE value of 0.783 ℃; the R2 and RMSE of the air temperature estimation accuracy are 0.792 and 1.055 ℃, respectively. Near-surface air temperature in the central urban districts of Xi'an city is higher than that of suburban areas, and the mean minimum temperature, the mean maximum temperature, as well as the mean temperature are higher than those of suburban areas by 1.54 ℃, 0.01 ℃ and 1.76 ℃, respectively. The near-surface air temperature in the study area is low in the south and high in the north, and presents obvious spatial heterogeneity. Air temperature gradually decreases from the central urban districts through suburbs to the southern mountainous areas, indicating a prominent urban heat island effect.

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Last Update: 2022-02-25