Trend Analysis of Land surface temperature using Remote Sensing Data in Iran

جغرافیا و مخاطرات محیطی

Article 6, Volume 10, Issue 2 - Serial Number 38, July 2021, Pages 93-109   

Document Type: Research Article

DOI: 10.22067/geoeh.2021.69462.1035

Authors

Ali Reza Karbalaee^{* 1}; Zahra Beygam Hejazizadeh¹; Seyed Abolfazl Masoodian²; Mohammad Keikhosravi kiany³

¹Department of Climatology, Faculty of Geographical Sciences, Kharazmi University, Tehran, Iran

²Department of Physical Geography, Faculty of Geographical Sciences and Planning, University of Isfahan, Isfahan

³Department of Physical Geography, Faculty of Geographical Sciences and Planning, University of Isfahan, Isfahan.iran

Abstract

Land Surface temperature is an appropriate manifestation of energy balance in the surface and greenhouse effect; because key parameterization in the physics of land surface processes is on a regional and especially global scale. It has been almost two decades since the MODIS sensor measured the surface temperature. Analysis of Land surface temperature (LST) Variation can help us understand the behavior of this key metering meter. The purpose of this study is to investigate the trend of land surface albedo in Iran. For this purpose; The Land surface temperature data of the Modis Terra sensor (MOD11A1) was used daily for the period 1379-1389 at a spatial resolution of 1. 1 km. After extracting the data in Iran, the Land surface temperature trend of Iran in 1884077 cells was examined using the Mann-Kendall test. The results of this study showed that in all seasons, increasing and decreasing trends in Land surface temperature are observed; however, the largest range of increasing trends of Land surface temperature in winter can be seen in January, February, and March. The results showed that in winter, the most significant increasing trends are seen in the heights, which can be related to the reduction of snow cover and reduced albedo in these areas. Using the digital elevation model of Iran, the Land surface temperature trend was calculated at altitudes of 100 meters, which showed that in January month, all height levels have an increasing trend in surface temperature, which is significant at the level of 0.05(the 95th confidence level).

Keywords

Land Surface temperature; trend; Mann-Kendall test; Iran

References

حجازی زاده، زهرا؛ بزمی، نسرین؛ رحیمی، علیرضا؛ طولابی نژاد، میثم؛ بساک، عاطفه؛ 1396. مدلسازیِ فضایی - زمانیِ آلبدو در گسترۀ ایران زمین. نشریه تحقیقات کاربردی علوم جغرافیایی. شماره ۱۷، ۱-۱۷

خلیلی، علی؛ بذرافشان، جواد؛ 1383.  تحلیل روند تغییرات بارندگی‌های سالانه، فصلی و ماهانه پنج ایستگاه قدیمی ایران در یکصد و شانزده سال گذشته، مجله بیابان. شماره 9.  33-25.

کربلائی، علیرضا؛ 1399. رفتارسنجی زمانی مکانی سپیدایی در ایران. رساله دکتری دانشگاه خوارزمی. استاد راهنما دکتر زهرا حجازی زاده. استاد مشاور سید ابوالفضل مسعودیان.

کیخسروی کیانی، محمد صادق؛ 1395. آب و هواشناسی پوشش برف در ایران با بهره‌گیری از داده‌های دور سنجی. رساله دکتری جغرافیایی طبیعی گرایش آب و هواشناسی دانشگاه اصفهان. استاد راهنما: سید ابوالفضل مسعودیان

مرادی، مسعود؛ صلاحی، برومند؛ مسعودیان، سید ابوالفضل؛ 1395. پهنه بندی دمای رویۀ زمین ایران با داده‌های مودیس. مخاطرات محیط طبیعی. شماره 5.  101-116.

مردادی، مسعود؛ 1395. بررسی آب و هواشناختی دمای سطح زمین در گستره ایران با بهره‌گیری از داده‌های مودیس. رساله دکتری. دانشکده ادبیات و علوم انسانی گروه جغرافیای طبیعی دانشگاه محقق اردبیلی. استاد راهنما: سید ابوالفضل مسعودیان

مسعودیان، سید ابوالفضل؛ 1398. پیامدهای تغییر توزیع فراوانی دمای کمینه ایران. کنفرانس بین المللی تغییر اقلیم، پیامدها، سازگاری و تعدیل ۱۳۹۸  خرداد ۲۱. www.icciam.khu.ac.ir

مسعودیان، سید ابوالفضل؛ ترکی، مسلم؛ 1398. واکاوی تغییرات زمانی و مکانی جزیرة گرمایی کلان‌شهر اهواز به کمک داده‌های مودیس. جغرافیا و برنامه ریزی محیطی. شماره 30. 75-92.

مسعودیان، سیدابوالفضل؛ منتظری، مجید؛ 1399. رفتار زمانی – مکانی جزیرۀ گرمایی کلانشهر اصفهان. مخاطرات محیط طبیعی. شماره 9، 35-46.

 

Abera, TA, Heiskanen J, Maeda EE, Pellikka PK., 2000. Land surface temperature trend and its drivers in East Africa. Journal of Geophysical Research: Atmospheres. 125:e2020JD033446.

Anniballe  R, Bonafoni S, Pichierri M., 2014. Spatial and temporal trends of the surface and air heat island over Milan using MODIS data. Remote Sensing of Environment, 150:163-171.

Bai L, Long D, Yan L.,  2019. Estimation of surface soil moisture with downscaled land surface temperatures using a data fusion approach for heterogeneous agricultural land. Water Resources Research, 55: 1105-1128.

Coll C, Caselles V, Galve JM, Valor E, Niclos R, Sánchez JM, Rivas R., 2005. Ground measurements for the validation of land surface temperatures derived from AATSR and MODIS data.  Remote sensing of Environment, 97:288-300.

 Coll C, Caselles V., 1997. A split‐window algorithm for land surface temperature from advanced very high resolution radiometer data: validation and algorithm comparison. Journal of Geophysical Research: Atmospheres, 102(D14): 16697-16713.

Coll C, Wan Z, Galve JM., 2009. Temperature‐based and radiance‐based validations of the V5 MODIS land surface temperature product. Journal of Geophysical Research: Atmospheres, 114(D20).

Cui YY, De Foy B., 2012. Seasonal variations of the urban heat island at the surface and the near-surface and reductions due to urban vegetation in Mexico City. Journal of Applied Meteorology and Climatology, 51: 855-868.

Donohoe A, Armour KC, Pendergrass AG, Battisti DS., 2014. Shortwave and longwave radiative contributions to global warming under increasing CO2. Proceedings of the National Academy of Sciences, 111:16700-16705.

 Firoozi F, Mahmoudi P, Jahanshahi SM, Tavousi T, Liu Y, Liang Z., 2020. Modeling changes trend of time series of land surface temperature (LST) using satellite remote sensing productions (case study: Sistan plain in east of Iran). Arabian Journal of Geosciences, 13: 1-14.

Forzieri G, Alkama R, Miralles DG, Cescatti A., 2017. Satellites reveal contrasting responses of regional climate to the widespread greening of Earth. Science, 356: 1180-1184.

Jin Jin M, Dickinson RE., 1999. Interpolation of surface radiative temperature measured from polar orbiting satellites to a diurnal cycle: 1. without clouds. Journal of Geophysical Research: Atmospheres, 104: 2105-2116.

Jin M., 2000. Interpolation of surface radiative temperature measured from polar orbiting satellites to a diurnal cycle: 2. Cloudy‐pixel treatment. Journal of Geophysical Research: Atmospheres, 105: 4061-4076.

Jin M., 2004. Analysis of land skin temperature using AVHRR observations. Bulletin of the American Meteorological Society, 85: 587-600.

Karbalaee  A R, Hedjazizadeh Z,  Masoodian S A., 2021.Spatiotemporal variations of albedo using MODIS and PCA analysis in Iran. Theor Appl Climatol 145: 245–260. https:// doi.org/10.1007/s00704-021-03596-y

Kendall M G, 1975. Rank Correlation Measures, Charles Griffin, London: 272.

Liu Y, Hiyama T, & Yamaguchi Y., 2006. Scaling of land surface temperature using satellite data: A case examination on ASTER and MODIS products over a heterogeneous terrain area. Remote Sensing of Environment, 105(2), 115-128.

LUINTEL N, MA W, MA Y, WANG B, SUBBA S., 2019. Spatial and temporal variation of daytime and nighttime MODIS land surface temperature across Nepal. Atmospheric and Oceanic Science Letters, 12: 305-312.

Muro J, Strauch  A, Heinemann S, Steinbach S, Thonfeld F, Waske B, Diekkrüger B., 2018. Land surface temperature trends as indicator of land use changes in wetlands. International journal of applied earth observation and geoinformation, 70: 62-71.

Niclos R, Valiente J A, Barberà M J, Caselles V., 2013. Land surface air temperature retrieval from EOS-MODIS images. IEEE Geoscience and remote sensing letters, 11: 1380-1384.

 NourEldeen N, Mao K, Yuan Z, Shen X, Xu T, Qin Z., 2020. Analysis of the spatiotemporal change in land surface temperature for a long-term sequence in Africa (2003–2017). Remote Sensing, 12: 488.

Oku Y, Ishikawa H, Haginoya, S, Ma Y., 2006.  Recent trends in land surface temperature on the Tibetan Plateau. Journal of Climate, 19:  2995-3003.

Panwar M, Agarwal A, Devadas V., 2018. Analyzing land surface temperature trends using non-parametric approach: A case of Delhi, India. Urban climate, 24: 19-25.

Peñuelas J, Filella I., 2009. Phenology feedbacks on climate change. Science, 324: 887-888.

Pfeifroth U, Sanchez‐Lorenzo A, Manara V, Trentmann J,  Hollmann  R., 2018. Trends and variability of surface solar radiation in Europe based on surface‐and satellite‐based data records. Journal of Geophysical Research: Atmospheres, 123: 1735-1754.

Salama M S, Van der Velde  R, Zhong L, Ma Y, Ofwono M, Su Z., 2012. Decadal variations of land surface temperature anomalies observed over the Tibetan Plateau by the Special Sensor Microwave Imager (SSM/I) from 1987 to 2008. Climatic Change, 114: 769-781.

Sun D, Pinker R T., 2004. Case study of soil moisture effect on land surface temperature retrieval. IEEE Geoscience and remote sensing letters, 1: 127-130.

Tomlinson C J, Chapman L, Thornes JE, Baker C J., 2012. Derivation of Birmingham's summer surface urban heat island from MODIS satellite images. International Journal of Climatology, 32: 214-224.

Wan Z., 1999. MODIS land-surface temperature algorithm theoretical basis document (LST ATBD). Institute for Computational Earth System Science, Santa Barbara: 75.

Yu Y, Privette J L, Pinheiro A C., 2007. Evaluation of split-window land surface temperature algorithms for generating climate data records. IEEE Transactions on Geoscience and Remote Sensing, 46: 179-192.

Zhang X, Vincent L A, Hogg W D, Niitsoo A., 2000. Temperature and precipitation trends in Canada during the 20th century. Atmosphere-ocean, 38: 395-429.

Zhao W, He J, Wu Y, Xiong D, Wen F, Li A., 2019. An analysis of land surface temperature trends in the central Himalayan region based on MODIS products. Remote Sensing, 11: 900.

 Zhu Z, Piao S, Myneni R B, Huang M, Zeng, Z., Canadell, J. G  Zeng N., 2016. Greening of the Earth and its drivers. Nature climate change, 6: 791-795.