Please wait a minute...
 
Remote Sensing for Natural Resources    2024, Vol. 36 Issue (2) : 229-238     DOI: 10.6046/zrzyyg.2023038
|
Analyzing the spatio-temporal evolution of surface temperatures in the Yanhe River basin based on the changes in surface parameters
LI Weiyang1(), SHI Haijing2(), NIE Weiting1, YANG Xinyuan1
1. School of Resources and Environment, Northwest A&F University, Yangling 712100, China
2. Institute of Soil and Water Conservation, Northwest A&F University, Yangling 712100, China
Download: PDF(8301 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

Surface temperature, a significant parameter of surface energy balance and land surface processes, is closely associated with the changes in surface parameters. With the Yanhe River basin in the Loess Plateau as a study area, this study derived the surface temperatures through inversion using the atmospheric correction method based on the Landsat OLI/TIRS images of 2015, 2018, and 2020. Moreover, by extracting the normalized difference build-up index (NDBI), normalized differential vegetation index (NDVI), and normalized difference moisture index (NDMI), this study analyzed the relationships of surface temperatures with surface parameters and land use types, as well as the spatio-temporal variations of surface temperatures. The results demonstrate that the correlation coefficients between the inverted and verified values of surface temperatures in 2015, 2018, and 2020 were 0.569, 0.675, and 0.632, respectively, all exceeding 0.5, suggesting certain accuracy and feasibility. Concerning the spatio-temporal variations of surface temperatures, the low-, sub-medium-, and sub-high-temperature zones exhibited decreased areas, whereas medium- and high-temperature zones manifested significantly and slightly increased areas, respectively, suggesting that the surface temperatures tended to increase towards medium and high temperatures. In terms of the relationship with land use types, the surface temperatures of underlying surface cover types increased in the order of water area, forest land, grassland, cultivated land, and construction land. The quantitative relationship reveals significant correlations between changes in surface temperatures and surface parameters of the Yanhe River basin. Specifically, the changes in surface temperatures were positively correlated with the NDBI but negatively correlated with the NDVI and the NDMI. From the perspective of geographical environment factors, surface temperatures decreased with increasing altitude. Different slopes exhibited distinct surface temperatures, which were the highest on flat slopes and lower on steeper slopes. Additionally, different slope aspects manifested significantly different surface temperatures, which were significantly higher on sunny slopes compared to shady slopes. The findings of this study will serve as a reference for exploring surface water thermal environments in complex areas.
Keywords surface temperature      remote sensing      temperature transfer matrix      correlation analysis      Yanhe River basin     
ZTFLH:  TP79  
Issue Date: 14 June 2024
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Weiyang LI
Haijing SHI
Weiting NIE
Xinyuan YANG
Cite this article:   
Weiyang LI,Haijing SHI,Weiting NIE, et al. Analyzing the spatio-temporal evolution of surface temperatures in the Yanhe River basin based on the changes in surface parameters[J]. Remote Sensing for Natural Resources, 2024, 36(2): 229-238.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2023038     OR     https://www.gtzyyg.com/EN/Y2024/V36/I2/229
Fig.1  Location of study area
等级 划分依据①
高温区 T S > μ + S t d
次高温区 μ + 0.5 S t d < T S ≤ μ + S t d
中温区 μ - 0.5 S t d < T S ≤ μ + 0.5 S t d
次中温区 μ - S t d < T S ≤ μ - 0.5 S t d
低温区 T S < μ - S t d
Tab.1  Temperature classification basis
Fig.2  ArcMap randomly generates sample points
日期 最小值 最大值 平均值 标准差
2015-07-01 24.80 45.61 35.14 3.12
2018-05-22 17.05 26.85 20.45 1.52
2020-05-11 18.23 34.27 26.51 3.06
Tab.2  Temperature changes in the Yanhe River Basin from 2015 to 2020(℃)
Fig.3  Temperature grade distribution in Yanhe River Basin in different years
2018年
2015年		 低温区 次中温区 中温区 次高温区 高温区 总计
低温区 70 525.80 28 347.93 26 502.39 1 450.08 524.88 127 351.08
次中温区 25 225.74 31 915.98 55 129.95 5 688.27 1 629.54 119 589.48
中温区 24 373.44 38 485.35 144 142.47 50 651.64 27 076.60 284 729.49
次高温区 3 529.08 7 680.42 41 143.50 29 623.86 29 656.80 111 633.66
高温区 1 456.83 4 027.68 30 612.78 29 816.82 58 754.50 124 668.63
总计 125 110.89 110 457.36 297 531.09 117 230.67 117 642.32 767 972.34
Tab.3  Temperature transfer matrix from 2015 to 2018(hm2)
2020年
2018年		 低温区 次中温区 中温区 次高温区 高温区 总计
低温区 78 520.59 25 666.65 19 162.17 1 434.60 326.88 125 110.89
次中温区 27 076.41 35 266.68 41 553.27 5 112.90 1 448.19 110 457.45
中温区 17 520.75 49 070.25 164 683.71 44 563.14 21 693.24 297 531.09
次高温区 27.63 1 087.47 47 982.33 38 024.91 30 108.33 117 230.67
高温区 16.65 55.98 14 379.84 3 1732.20 71 457.66 117 642.33
总计 123 162.03 111 147.03 287 761.32 120 867.75 125 034.30 767 972.43
Tab.4  Temperature transfer matrix from 2018 to 2020(hm2)
土地利用类型 2015年 2018年 2020年
耕地 2.497 67 2.652 77 2.602 38
林地 4.507 34 3.775 78 4.213 32
草地 2.809 73 2.622 07 2.857 82
水域 4.884 62 3.763 21 4.234 66
建设用地 2.215 39 1.542 80 2.026 44
Tab.5  The temperature conditions of each land type in different years were scored
年份 回归分析①
2015年 L S T = 22.6651 e 0.993 N D B I R 2 = 0.495 L S T = - 34.4369 N D M I + 54.508 R 2 = 0.491 L S T = - 24.0171 N D V I + 53.895 R 2 = 0.449
2018年 L S T = 16.261 e 0.431 N D B I R 2 = 0.207 L S T = - 8.8673 N D M I + 24.661 R 2 = 0.203 L S T = - 6.4295 N D V I + 25.464 R 2 = 0.195
2020年 L S T = 13.224 e 1.363 N D B I R 2 = 0.489 L S T = - 34.78 N D M I + 43.726 R 2 = 0.473 L S T = - 23.9976 N D V I + 44.660 R 2 = 0.469
Tab.6  The quantitative relationship between NDBI,NDVI,NDMI and LST
Fig.4  Change of NDBI in different years
Fig.5  Change of NDVI in different years
Fig.6  Change of NDMI in different years
Fig.7  Topographic factor classification map of Yanhe River Basin
海拔等级 坡度分级 坡向分类
高程/m 平均
温度/℃		 坡度 平均
温度/℃		 坡向 平均
温度/℃
[495,1 000) 35.42 平坡 35.82 阳坡 35.71
[1 000,1 250) 35.03 缓坡 35.33 半阳坡 35.49
[1 250,1 500) 34.67 斜坡 35.28 平缓坡 35.26
[1 500,1 795) 34.56 陡坡 35.06 半阴坡 35.07
急坡 34.93 阴坡 34.73
Tab.7  Classification and statistics of LST and topographic factors in 2015
海拔等级 坡度分级 坡向分类
高程/m 平均
温度/℃		 坡度 平均
温度/℃		 坡向 平均
温度/℃
[495,1 000) 21.48 平坡 21.18 阳坡 21.23
[1 000,1 250) 20.55 缓坡 20.51 半阳坡 20.67
[1 250,1 500) 20.05 斜坡 20.43 平缓坡 20.52
[1 500,1 795) 20.01 陡坡 20.38 半阴坡 20.39
急坡 20.34 阴坡 20.16
Tab.8  Classification and statistics of LST and topographic factors in 2018
海拔等级 坡度分级 坡向分类
高程/m 平均
温度/℃		 坡度 平均
温度/℃		 坡向 平均
温度/℃
[495,1 000) 27.27 平坡 27.38 阳坡 27.22
[1 000,1 250) 26.88 缓坡 26.75 半阳坡 27.09
[1 250,1 500) 26.42 斜坡 26.54 平缓坡 26.72
[1 500,1 795) 26.29 陡坡 26.41 半阴坡 26.35
急坡 26.27 阴坡 25.85
Tab.9  Classification and statistics of LST and topographic factors in 2020
[1] Zhao L, Lee X, Schultz N M. A wedge strategy for mitigation of urban warming in future climate scenarios[J]. Atmospheric Chemistry and Physics, 2017, 17(14):9067-9080.
[2] Chen Y Y, Duan S B, Ren H Z, et al. Algorithm development for land surface temperature retrieval:Application to Chinese Gaofen-5 data[J]. Remote Sensing, 2017, 9(2):161.
[3] Jiang Y, Weng Q. Estimation of hourly and daily evapotranspiration and soil moisture using downscaled LST over various urban surfaces[J]. GIScience and Remote Sensing, 2017, 54(1):95-117.
[4] Anderson M C, Norman J M, Kustas W P, et al. A thermal-based remote sensing technique for routine mapping of land-surface carbon,water and energy fluxes from field to regional scales[J]. Remote Sensing of Environment, 2008, 112(12):4227-4241.
[5] Mia M B, Fujimitsu Y, Nishijima J. Monitoring of thermal activity at the Hatchobaru-Otake geothermal area in Japan using multi-Source satellite images:With comparisons of methods,and solar and seasonal effects[J]. Remote Sensing, 2018, 10(9):1430.
[6] 侯宇初, 张冬有. 基于Landsat8遥感影像的地表温度反演方法对比研究[J]. 中国农学通报, 2019, 35(10):142-147.
doi: 10.11924/j.issn.1000-6850.casb18120100
[6] Hou Y C, Zhang D Y. Comparison study on land surface temperature retrieval algorithms based on Landsat8 remote sensing image[J]. Chinese Agricultural Science Bulletin, 2019, 35(10):142-147.
[7] Jimenez-Munoz J C, Cristobal J, Sobrino J A, et al. Revision of the single-channel algorithm for land surface temperature retrieval from Landsat thermal-infrared data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(1):339-349.
[8] Jin M J, Li J M, Wang C L, et al. A practical split-window algorithm for retrieving land surface temperature from Landsat8 data and a case study of an urban area in China[J]. Remote Sensing, 2015, 7(4):4371-4390.
[9] Ren H Z, Du C, Liu R Y, et al. Atmospheric water vapor retrieval from Landsat8 thermal infrared images[J]. Journal of Geophysical Research:Atmospheres, 2015, 120(5):1723-1738.
[10] Maulana J, Bioresita F. Monitoring of land surface temperature in Surabaya,Indonesia from 2013-2021 using Landsat8 imagery and Google Earth Engine[J]. IOP Conference Series:Earth and Environmental Science, 2023, 1127(1):012027.
[11] Kimothi S, Thapliyal A, Gehlot A, et al. Spatio-temporal fluctuations analysis of land surface temperature (LST) using Remote Sensing data (LANDSAT TM5/8) and multifractal technique to characterize the urban heat Islands (UHIs)[J]. Sustainable Energy Technologies and Assessments, 2023, 55:102956
[12] Galve J M, Sánchez J M, García-Santos V, et al. Assessment of land surface temperature estimates from Landsat8-TIRS in a high-contrast semiarid agroecosystem,algorithms intercomparison[J]. Remote Sensing, 2022, 14(8):1843.
[13] Bradtke K. Landsat8 data as a source of high resolution sea surface temperature maps in the Baltic Sea[J]. Remote Sensing, 2021, 13(22):4619-4619.
[14] Guha S, Govil H. Annual assessment on the relationship between land surface temperature and six remote sensing indices using Landsat data from 1988 to 2019[J]. Geocarto International, 2022, 37(15):4292-4311
[15] Mallick J, Singh C K, Shashtri S, et al. Land surface emissivity retrieval based on moisture index from LANDSAT TM satellite data over heterogeneous surfaces of Delhi city[J]. International Journal of Applied Earth Observation and Geoinformation, 2012, 19:348-358.
[16] Rehman A, Qin J, Pervez A, et al. Land-Use/Land Cover changes contribute to land surface temperature:A case study of the upper Indus basin of Pakistan[J]. Sustainability, 2022, 14(2):934.
[17] 钱乐祥, 丁圣彦. 珠江三角洲土地覆盖变化对地表温度的影响[J]. 地理学报, 2005, 60(5):761-770.
[17] Qian L X, Ding S Y. Influence of land cover change on land surface temperature in Zhujiang delta[J]. Acta Geographica Sinica, 2005, 60(5):761-770.
doi: 10.11821/xb200505007
[18] SimÓ G, García-Santos V, JimÉnez M A, et al. Landsat and local land surface temperatures in a heterogeneous terrain compared to MODIS values[J]. Remote Sensing, 2016, 8(10):849.
[19] Peng X, Wu W, Zheng Y, et al. Correlation analysis of land surface temperature and topographic elements in Hangzhou,China[J]. Scientific Reports, 2020, 10:10451.
[20] 赵伟, 李爱农, 张正健, 等. 基于Landsat8热红外遥感数据的山地地表温度地形效应研究[J]. 遥感技术与应用, 2016, 31(1):63-73.
[20] Zhao W, Li A N, Zhang Z J, et al. A study on land surface temperature terrain effect over mountainous area based on Landsat8 Thermal infrared data[J]. Remote Sensing Technology and Application, 2016, 31(1):63-73.
[21] 袁毓婕, 高学睿, 黄可静, 等. 基于RHESSys模型的延河流域水文要素定量模拟[J]. 南水北调与水利科技(中英文), 2023, 21(1):116-126.
[21] Yuan Y J, Gao X R, Huang K J, et al. Quantitative simulation of hydrological elements based on RHESSys model in Yanhe River basin[J]. South-to-North Water Transfers and Water Science & Technology, 2023, 21(1):116-126.
[22] 韩磊, 朱会利, 刘钊. 延安市退耕还林前后土地利用动态变化分析[J]. 西北师范大学学报(自然科学版), 2017, 53(5):101-108.
[22] Han L, Zhu H L, Liu Z. Analysis on land use dynamic changes of pre and post returning farmland to forestland in Yan’an City[J]. Journal of Northwest Normal University(Natural Science), 2017, 53(5):101-108.
[23] 刘强, 穆兴民, 赵广举, 等. 延河流域水沙变化及其对降水和土地利用变化的响应[J]. 干旱区资源与环境, 2021, 35(7):129-135.
[23] Liu Q, Mu X M, Zhao G J, et al. Runoff and sediment changes and their responses to precipitation and land use change in the Yanhe river basin[J]. Journal of Arid Land Resources and Environment, 2021, 35(7):129-135.
[24] 吴志刚, 江滔, 樊艳磊, 等. 基于Landsat8数据的地表温度反演及分析研究——以武汉市为例[J]. 工程地球物理学报, 2016, 13(1):135-142.
[24] Wu Z G, Jiang T, Fan Y L, et al. Land surface temperature retrieval and result analysis based on Landsat8 data in Wuhan City[J]. Chinese Journal of Engineering Geophysics, 2016, 13(1):135-142.
[25] Zhong X, Zhao L H, Wang J, et al. A retrieval method for land surface temperatures based on UAV broadband thermal infrared images via the three-dimensional look-up table[J]. Building and Environment, 2022, 226:109793.
[26] Qin Z, Karnieli A, Berliner P. A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region[J]. International Journal of Remote Sensing, 2001, 22(18):3719-3746.
[27] Kachar H, Vafsian A R, Modiri M, et al. Evaluation of spatial and temporal distribution changes of LST using Landsat images (case study:Tehran)[J]. The International Archives of the Photogrammetry,Remote Sensing and Spatial Information Sciences, 2015, XL-1/W5:351-356.
[28] 刘思煜, 吴焱, 许娟, 等. 延安市地表热环境与下垫面指数的关系研究[J]. 西北林学院学报, 2022, 37(6):207-214+265.
[28] Liu S Y, Wu Y, Xu J, et al. Relationship between surface thermal environment and underlying surface index in Yan’an City[J]. Journal of Northwest Forestry University, 2022, 37(6):207-214,265.
[29] 赵禾苗, 阿里木江·卡斯木. 基于地理探测器的喀什市地表热场空间分异及影响因素分析[J]. 生态与农村环境学报, 2022, 38(2):147-156.
[29] Zhao H M, Alimujiang K. Analysis of spatial differentiation and influencing factors of surface thermal field in Kashgar City based on geographic detector[J]. Journal of Ecology and Rural Environment, 2022, 38(2):147-156.
[30] Kuang W, Liu Y, Dou Y, et al. What are hot and what are not in an urban landscape:Quantifying and explaining the land surface temperature pattern in Beijing,China[J]. Landscape Ecology, 2015, 30(2):357-373.
[31] 查勇, 倪绍祥, 杨山. 一种利用TM图像自动提取城镇用地信息的有效方法[J]. 遥感学报, 2003, 7(1):37-40,82.
[31] Zha Y, Ni S X, Yang S. An effective approach to automatically extract urban land-use from TM Imagery[J]. National Remote Sensing Bulletin, 2003, 7(1):37-40,82.
[32] 郭宇, 王宏伟, 张喆, 等. 南京市热环境与地表覆被的时空尺度效应及驱动机制研究[J]. 生态环境学报, 2020, 29(7):1403-1411.
doi: 10.16258/j.cnki.1674-5906.2020.07.015
[32] Guo Y, Wang H W, Zhang Z, et al. Spatio-temporal scale effect and driving mechanism of thermal environment and land surface cover in Nanjing[J]. Ecology and Environmental Sciences, 2020, 29(7):1403-1411.
[33] 钱乐祥, 崔海山. 归一化水汽指数与地表温度的关系[J]. 地理研究, 2008, 27(6):1358-1366,1483.
[33] Qian L X, Cui H S. Relationship between normalized difference moisture index and land surface temperature[J]. Geographical Research, 2008, 27(6):1358-1366,1483.
doi: 10.11821/yj2008060015
[1] HE Haixia, LI Bo. Characteristics and risk analysis of the flash flood occurring in Datong of Qinghai Province on August 18, 2022[J]. Remote Sensing for Natural Resources, 2024, 36(2): 135-141.
[2] LIU Jiafeng, ZHANG Wenkai, DU Xiaomin, JI Xinyang, YANG Jinzhong, FAN Jinghui, SUN Xiyong, TONG Jing. Evolutionary process and inundation risk identification of water bodies in the beach area of the Yellow River estuary from 1976 to 2020[J]. Remote Sensing for Natural Resources, 2024, 36(2): 151-159.
[3] SONG Qi, GAO Xiaohong, SONG Yuting, LI Qiaoli, CHEN Zhen, LI Runxiang, ZHANG Hao, CAI Sangjie. Estimation of soil organic carbon content in farmland based on UAV hyperspectral images: A case study of farmland in the Huangshui River basin[J]. Remote Sensing for Natural Resources, 2024, 36(2): 160-172.
[4] GAO Sheng, CHEN Fulong, SHI Pilong, ZHOU Wei, ZHU Meng, LUO Yansong, YANG Qingxia, WANG Qin. Fine-scale remote sensing monitoring and interpretation of large-scene vegetation health in the Jiuzhai Valley biosphere reserve: A case study of the Changhai pilot zone[J]. Remote Sensing for Natural Resources, 2024, 36(2): 188-197.
[5] XING Yu, WANG Jingya, YANG Jinzhong, CHEN Dong, DU Xiaomin, GUO Jingkai, SONG Licong. Distributions and existing problems of mining land of abandoned open-pit mines in China[J]. Remote Sensing for Natural Resources, 2024, 36(2): 21-26.
[6] MA Xiaoxue, JIAO Runcheng, CAO Ying, NAN Yun, WANG Shengyu, GUO Xuefei, ZHAO Danning, YAN Chi, NI Xuan. Application of integrated remote sensing monitoring technology for geological hazards in major engineering construction:A case study of the Yanqing competition area of the Beijing 2022 Olympic Winter Games[J]. Remote Sensing for Natural Resources, 2024, 36(2): 248-256.
[7] CHEN Yan, YUAN Jing, TANG Chunhua, SUN Chao, TANG Xiao, WANG Mingyou. A remote sensing methodology for predicting geothermal resources in the Wugongshan uplift zone[J]. Remote Sensing for Natural Resources, 2024, 36(2): 27-38.
[8] SONG Shuangshuang, XIAO Kaifei, LIU Zhaohua, ZENG Zhaoliang. A YOLOv5-based target detection method using high-resolution remote sensing images[J]. Remote Sensing for Natural Resources, 2024, 36(2): 50-59.
[9] ZHAO Binru, NIU Siwen, WANG Liyan, YANG Xiaotong, JIAO Hongbo, WANG Zike. An intelligent color enhancement method for high-resolution remote sensing images of the coastal zone of an island[J]. Remote Sensing for Natural Resources, 2024, 36(2): 70-79.
[10] ZHAO Heting, LI Xiaojun, XU Xinyu, GAI Junfei. An ICM-based adaptive pansharpening algorithm for hyperspectral images[J]. Remote Sensing for Natural Resources, 2024, 36(2): 97-104.
[11] LIN Dan, LI Qiucen, CHEN Zhikui, ZHONG Fangming, LI Lifang. Research advances and challenges in multi-label classification of remote sensing images[J]. Remote Sensing for Natural Resources, 2024, 36(2): 10-20.
[12] CAI Jian’ao, MING Dongping, ZHAO Wenyi, LING Xiao, ZHANG Yu, ZHANG Xingxing. Integrated remote sensing-based hazard identification and disaster-causing mechanisms of landslides in Zayu County[J]. Remote Sensing for Natural Resources, 2024, 36(1): 128-136.
[13] SONG Yingxu, ZOU Yujia, YE Runqing, HE Zhixia, WANG Ningtao. Dynamic analysis of landslide hazards in the Three Gorges Reservoir area based on Google Earth Engine[J]. Remote Sensing for Natural Resources, 2024, 36(1): 154-161.
[14] YAO Lingyun, WANG Li, NIU Zheng, YIN Ziqi, FU Yuwen. Urban expansion in the Changsha-Zhuzhou-Xiangtan urban agglomeration and its urban heat island effect from 2000 to 2018[J]. Remote Sensing for Natural Resources, 2024, 36(1): 162-168.
[15] LU Yanling, HUANG Yaqi, ZHOU Junfen, WANG Jie, WEI Jingshan. Analyzing multidimensional rural poverty alleviation at the county level in Guangxi based on remote sensing data of nighttime light[J]. Remote Sensing for Natural Resources, 2024, 36(1): 169-178.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech