Relationship between land surface temperature and normalized difference water index on various land surfaces: A seasonal analysis

Year 2021, , 165 - 173, 15.10.2021

Subhanil Guha , Himanshu Govil

https://doi.org/10.26833/ijeg.821730

Cited By: 9

Abstract

The present study examines the seasonal relationship between land surface temperature (LST) and normalized difference water index (NDWI) on various land surfaces in Raipur City of India by using a series of Landsat images for four specific seasons since 1991-92. The LST is retrieved using the mono-window algorithm technique. The results show that the LST of the study area is noticeably affected by surface composition. The best correlation (correlation coefficient r = 0.42) between the LST and NDWI is achieved in the post-monsoon season, followed by the monsoon season (r = 0.33), pre-monsoon season (r = 0.25), and winter season (r = 0.04). There is a moderate negative correlation (r = -0.49, -0.33, -0.31, and -0.25 in the pre-monsoon, monsoon, post-monsoon, and winter season, respectively) generated between the LST and NDWI on water bodies. On green vegetation, this LST-NDWI correlation is moderate positive (r = 0.67, 0.43, 0.50, and 0.25 in the pre-monsoon, monsoon, post-monsoon, and winter season, respectively). On human settlement and barren land surface, the correlation is weak positive (r = 0.24, 0.21, 0.27, and 0.15 in the pre-monsoon, monsoon, post-monsoon, and winter season, respectively). The output of the research work can be used in the town planning section of any urban agglomeration.

Keywords

Landsat, Land surface, LST, NDWI, Raipur

References

• Barsi J, Schott J, Hook S, Raqueno N, Markham B & Radocinski R (2014). Landsat-8 thermal infrared sensor (TIRS) vicarious radiometric calibration. Remote Sensing, 6(11), 11607-11626.
• Carlson T N & Ripley D A (1997). On the Relation between NDVI, Fractional Vegetation Cover, and Leaf Area Index. Remote Sensing of Environment, 62, 241-252. https://doi.org/10.1016/S0034-4257(97)00104-1
• Chen X L, Zhao H M, Li P X & Yi Z Y (2006). Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sensing of Environment, 104(2), 133–146. https://doi.org/10.1016/j.rse.2005.11.016
• Choudhury D, Das K, & Das A (2019). Assessment of land use land cover changes and its impact on variations of land surface temperature in Asansol-Durgapur Development Region. Egyptian Journal of Remote Sensing and Space Sciences, 22(2), 203-218. https://doi.org/10.1016/j.ejrs.2018.05.004
• Coll C, Galve J M, Sanchez J M & Caselles V. 2010. Validation of Landsat-7/ETM+ thermal-band calibration and atmospheric correction with ground-based measurements. IEEE Transactions on Geoscience and Remote Sensing, 48(1), 547–555. https://doi.org/10.1109/TGRS.2009.2024934
• Essa W, Verbeiren B, Van der Kwast J, Van de Voorde T & Batelaan O (2012). Evaluation of the DisTrad thermal sharpening methodology for urban areas. International Journal of Applied Earth Observation and Geoinformation, 19, 163-172. https://doi.org/10.1016/j.jag.2012.05.010
• Ghobadi Y., Pradhan B., Shafri H.Z.M. & Kabiri K. 2014. Assessment of spatial relationship between land surface temperature and land use/cover retrieval from multi-temporal remote sensing data in South Karkheh Sub-basin, Iran. Arabian Journal of Geosciences, 8(1), 525–537. https://doi: 10.1007/s12517-013-1244-3.
• Govil H, Guha S, Dey A & Gill N (2019). Seasonal evaluation of downscaled land surface temperature: A case study in a humid tropical city. Heliyon, 5(6), e01923. https://doi.org/ 10.1016/j.heliyon.2019.e01923
• Govil H, Guha S, Diwan P, Gill N & Dey A (2020). Analyzing Linear Relationships of LST with NDVI and MNDISI Using Various Resolution Levels of Landsat 8 OLI and TIRS Data. Data Management, Analytics and Innovation. Advances in Intelligent Systems and Computing, 1042. Springer, Singapore, 171-184. https://doi.org/10.1007/978-981-32-9949-8_13
• Guha S, Govil H & Besoya M (2020c). An investigation on seasonal variability between LST and NDWI in an urban environment using Landsat satellite data. Geomatics, Natural Hazards and Risk, 11(1), 1319-1345. https://doi.org/10.1080/19475705.2020.1789762
• Guha S, Govil H & Mukherjee S (2017). Dynamic analysis and ecological evaluation of urban heat islands in Raipur city, India. Journal of Applied Remote Sensing, 11(3), 036020. https://doi:10.1117/1.JRS.11.036020
• Guha S, Govil H, Dey A & Gill N (2020a). A case study on the relationship between land surface temperature and land surface indices in Raipur City, India. Geografisk Tidsskrift-Danish Journal of Geography, 120(1), 35-50. https://doi.org/10.1080/00167223.2020.1752272
• Guha S, Govil H, Gill N & Dey A (2020b). Analytical study on the relationship between land surface temperature and land use/land cover indices. Annals of GIS, 26(2), 201-216. https://doi.org/10.1080/19475683.2020.1754291
• Hao X, Li W & Deng H (2016). The oasis effect and summer temperature rise in arid regions-case study in Tarim Basin. Scientific Reports, 6, 35418. https://doi.org/10.1038/srep35418
• Hou G L, Zhang H Y, Wang Y Q, Qiao Z H & Zhang Z X (2010). Retrieval and Spatial Distribution of Land Surface Temperature in the Middle Part of Jilin Province Based on MODIS Data. Scientia Geographica sinica, 30, 421-427.
• Li J (2006). Estimating land surface temperature from Landsat-5 TM. Remote Sensing Technology and Application, 21, 322-326.
• Li W F, Cao Q W, Kun L, & Wu J S (2017). Linking potential heat source and sink to urban heat island: Heterogene-ous effects of landscape pattern on land surface temperature. Science of the Total Environment, 586, 457–465. https://doi.org/10.1016/j.scitotenv.2017.01.191
• Markham B L & Barker J K (1985). Spectral characteristics of the LANDSAT thematic mapper sensors. International Journal of Remote Sensing, 6(5), 697–716. https://doi.org/10.1080/01431168508948492
• McFeeters S K (1996). The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425-1432. https://doi.org/10.1080/01431169608948714
• McFeeters S K (2013). Using the Normalized Difference Water Index (NDWI) within a Geographic Information System to Detect Swimming Pools for Mosquito Abatement: A Practical Approach. Remote Sensing, 5(7), 3544-3561. https://doi.org/10.3390/rs5073544
• Qin Z, Karnieli A & Barliner P (2001). A Mono-Window Algorithm for Retrieving Land Surface Temperature from Landsat TM Data and Its Application to the Israel-Egypt Border Region. International Journal of Remote Sensing, 22(18), 3719-3746. https://doi:10.1080/01431160010006971
• Sobrino J A, Jimenez-Munoz J C & Paolini L (2004). Land surface temperature retrieval from Landsat TM5. Remote Sensing of Environment, 9, 434–440. https://doi:10.1016/j.rse.2004.02.003
• Sobrino J A, Raissouni N & Li Z (2001). A comparative study of land surface emissivity retrieval from NOAA data. Remote Sensing of Environment, 75(2), 256–266. https://doi.org/10.1016/S0034-4257(00)00171-1
• Sun Q, Tan J & Xu Y (2010). An ERDAS image processing method for retrieving LST and describing urban heat evolution: A case study in the Pearl River Delta Region in South China. Environmental Earth Science, 59, 1047-1055.
• Tomlinson C J, Chapman L, Trones J E & Baker C (2011). Remote sensing land surface temperature for meteorology and climatology: a review. Meteorological Application, 118, 296–306. https://doi.org/10.1002/met.287
• URL-1: https://censusindia.gov.in/2011
• URL-2: http://www.surveyofindia.gov.in
• URL-3: https://www.earthexplorer.usgs.gov
• Vlassova L, Perez-Cabello F, Nieto H, Martín P, Riaño D, & De La Riva J (2014). Assessment of methods for land surface temperature retrieval from Landsat-5 TM images applicable to multiscale tree-grass ecosystem modeling. Remote Sensing, 6(5), 4345-4368.
• Wu C, Li J, Wang C, Song C, Chen Y, Finka M & Rosa D L (2019). Understanding the relationship between urban blue infrastructure and land surface temperature. Science of the Total Environment, 694, 133742. https://doi.org/10.1016/j.scitotenv.2019.133742
• Wukelic G E, Gibbons D E, Martucci L M & Foote H P (1989). Radiometric calibration of Landsat Thematic Mapper thermal band. Remote Sensing of Environment, 28, 339–347. https://doi.org/10.1016/0034-4257(89)90125-9
• Yang J & Qiu J (1996). The empirical expressions of the relation between precipitable water and ground water vapor pressure for some areas in China. Scientia Atmospherica Sinica, 20, 620-626.
• Yuan X, Wang W, Cui J, Meng F, Kurban A & De Maeyer P (2017). Vegetation changes and land surface feedbacks drive shifts in local temperatures over Central Asia. Scientific Reports, 7(1), 3287. https://doi.org/10.1038/s41598-017-03432-2
• Zanter K (2019). Landsat 8 (L8) Data Users Handbook; EROS: Sioux Falls, SD, USA.
• Zhang X, Estoque R C & Murayama Y (2017). An urban heat island study in Nanchang City, China based on land surface temperature and social-ecological variables. Sustainable Cities and Society, 32, 557-568. https://doi.org/10.1016/j.scs.2017.05.005