Detection of land surface albedo changes over Iran using remote sensing data

IF 2.3 4区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES
Omid Reza Kefayat Motlagh, Mohammad Darand
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引用次数: 0

Abstract

Albedo is one of the key parameters in climatic studies. Investigating its temporal and spatial behavior can be a tool for understanding environmental changes. The MODIS sensor continuously produces the land surface albedo on a global scale and with the appropriate spatial resolution and makes it available to researchers. In this study, to analyze Iran's surface albedo trend, first, the daily albedo data of the MODIS on Iran in the period from January 1, 2001 to December 30, 2021 with a spatial resolution of 500 m were prepared from the NASA website. After the necessary pre-processing, the long-term seasonal and annual trend of Iran's albedo was calculated at the 90% confidence level using the non-parametric Mann–Kendall test. The findings showed that the albedo trend is positive in the lowland interior areas of Iran and negative in the highland areas. Since the decreasing trend of albedo in highland areas indicates the reduction of snow cover in these areas, this issue can challenge the life and water resources of these areas that rely on the accumulation of snow.

Abstract Image

利用遥感数据探测伊朗陆地表面反照率的变化
反照率是气候研究的关键参数之一。调查其时间和空间行为可作为了解环境变化的工具。MODIS 传感器以适当的空间分辨率连续生成全球范围的陆地表面反照率,并提供给研究人员。在本研究中,为了分析伊朗的地表反照率趋势,首先从美国国家航空航天局网站上获取了 2001 年 1 月 1 日至 2021 年 12 月 30 日期间 MODIS 对伊朗的每日反照率数据,空间分辨率为 500 米。经过必要的预处理后,利用非参数 Mann-Kendall 检验法计算了伊朗反照率的长期季节和年度趋势,置信度为 90%。结果表明,伊朗内陆低地的反照率趋势为正,高地为负。由于高原地区的反照率呈下降趋势,表明这些地区的积雪面积减少,这个问题会对这些地区依赖积雪的生命和水资源构成挑战。
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来源期刊
Meteorological Applications
Meteorological Applications 地学-气象与大气科学
CiteScore
5.70
自引率
3.70%
发文量
62
审稿时长
>12 weeks
期刊介绍: The aim of Meteorological Applications is to serve the needs of applied meteorologists, forecasters and users of meteorological services by publishing papers on all aspects of meteorological science, including: applications of meteorological, climatological, analytical and forecasting data, and their socio-economic benefits; forecasting, warning and service delivery techniques and methods; weather hazards, their analysis and prediction; performance, verification and value of numerical models and forecasting services; practical applications of ocean and climate models; education and training.
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