به‌کارگیری یک الگوریتم ادغام-ریزمقیاس‌سازی داده‌های چندماهواره‌ای به‌منظور تولید نقشه‌های روزانه ابرآزاد سطح برف و آب معادل برف (مطالعه موردی: حوضه کارون)

نویسندگان

چکیده

در پژوهش حاضر، امکان تولید نقشه‌های ابرآزاد روزانه و هشت روزه سطح برف (SC) و عمق آب معادل برف (SWE) با تفکیک مکانی ??? متر از طریق یک الگوریتم ادغام-ریزمقیاس‌سازی که مبتنی بر ترکیب مزیت تفکیک مکانی مطلوب تصاویر حاصل از سنجنده‌های نوری MODIS نصب شده بر روی ماهواره‌های Terra و Aqua (به‌ترتیب، MODIS Terra و MODIS Aqua) با مزیت پشت‌نمایی پوشش ابر سنجنده میکروویو AMSR E می‌باشد، مورد بررسی قرار گرفت. این الگوریتم برای حوضه کارون، سال آبی ????-????، به‌کار گرفته شد. ادغام نقشه‌های SC حاصل از سنجنده‌های MODIS Terra و MODIS Aqua (TAC) موجب شد میانگین سهم پیکسل‌های ابری در نقشه TAC طی دوره مطالعاتی در مقایسه با نقشه‌های MODIS Terra و MODIS Aqua به‌ترتیب، به‌میزان ??/?? و ??/?? درصد کاهش یابد. متوسط دقت کلی نقشه‌های SC در شرایط آسمان صاف (مقایسه به‌ازای پیکسل‌های غیرابری) برای تصاویر حاصل از سنجنده‌های MODIS Terra و MODIS Aqua به‌ترتیب، برابر با ??/?? و ??/?? درصد بود. متوسط دقت کلی نقشه‌های SC در شرایط جوی عام (مقایسه به‌ازای پیکسل‌های ابری و غیرابری)، شامل نقشه‌های MODIS Terra، MODIS Aqua، TAC و MAC SC (حاصل از ترکیب نقشه TAC و AMSR E) به‌ترتیب، برابر با ??/??، ??/??، ??/?? و ??/?? درصد بود.

کلیدواژه‌ها


عنوان مقاله [English]

Application of a multi-satellite data fusion-disaggregation algorithm to obtain daily cloud-free snow cover and snow water equivalent maps (Case study: Karun watershed, Ahwaz, Iran)

نویسندگان [English]

  • Farzin Parchami-Araghi
  • Fatemeh Samipour
چکیده [English]

Snow cover, as an important component of land cover, is one of the most active natural and plays an important role in hydrological processes and climate. Variability in snow covered area has a significant influence on water and energy cycles, as well as socioeconomic and environmental repercussions. Frequent and long-term snow observation, accurate snow cover (SC) mapping and snow water equivalent (SWE) estimation are crucial for operational flood control, water delay planning, and resource management in snowmelt-dominated basins. Today, satellite-derived snow products obtained from visible and infrared imagery, as well as passive microwaves are available on the Internet, with few recently availability in near-real time . Optical sensors (e.g., Landsat, Advanced Very High Resolution Radiometer-AVHRR, Moderate Resolution Imaging Spectroradiometer-MODIS, Systeme Probatoire d’Observation de la Tarre-SPOT) have been well developed to provide snow information with good temporal and/or spatial resolution. But, cloud cover is a major factor in optical remote sensing that limits our capability to map the Earth’s surface. It is often not an easy task to collect a time-series cloud-free images for a particular area of interest, using optical remote sensing, which limits their wider applications for SC monitoring. Space-borne passive-microwave radiometers (e.g., Scanning Multichannel Microwave Radiometer-SMMR, Special Sensor Microwave/Imager-SSM/I, Advanced Microwave Scanning Radiometer-Earth Observing System-AMSR-E), can penetrate cloud to detect microwave energy emitted by snow and ice and provide information on SC and SWE. These passive microwave data are well suited to snow cover monitoring because of the characteristics such as all-weather imaging, large swath width with frequent overpass times. But, the coarse spatial resolution (e.g., ?? km of AMSR-E daily SWE product) hinders their applications in operational hydrological modeling. It seems that the combination of MODIS and AMSR-E can take advantage of both high spatial resolution of optical data and cloud transparency of passive microwave data.
In this study, daily cloud-free SC and SWE maps at the ???-m resolution were produced for Karun watershed, Ahwaz, Iran (January ??–??, ????). The daily MODIS-Terra, MODIS-Aqua, and AMSR-E snow data products were used via a fusion-disaggregation algorithm. The developed SC and SWE maps were evaluated, using total accuracy of snow mapping in clear-sky (Os) and all-sky (Oa) conditions, underestimation (UEc) and Overestimation (OEc) of snow covered area in clear-sky condition, snow accuracy in clear-sky (Sc) or in all-sky (Sa) conditions, and no snow accuracy in clear-sky (NSc) or in all-sky (NSa) conditions.
The results of this study showed that the combination of MODIS-Terra and MODIS-Aqua considerably reduced the cloud coverage in such high resolution optical data. Although MAC-SC and MAC-SWE products have been developed to have ??? m spatial resolution, the massive and continuous cloud cover (larger than ?? km in size) in the MODIS-Terra and MODIS-Aqua and, hence, TAC products were simply replaced by the coarse AMSR-E pixels. In this case, although those cloud coverages were removed in the MAC-SC and MAC-SWE products, the actual resolution of the snow or no-snow pixels kept ?? km and such pixels had the false spatial resolution of ??? m. The SWE redistribution of AMSR-E based on MAC-SC products enhanced (to some extent) the spatial resolution of the AMSR-E SWE products. However, there was no measured data to evaluate the accuracy of the enhanced SWE products. It can be concluded that for pixels with scattered cloud cover (less than ?? km in size) in the TAC products, the MAC-SC and MAC-SWE products indeed improve the spatial resolution of those pixels to ??? m, while for massive cloud cover (larger than ?? km in size), the actual resolution of those pixels in the MAC-SC and MAC-SWE products are ?? km, even in ??? m pixel size. Despite of these limitations, the MAC-SC and MAC-SWE maps are suitable for hydrological, meteorological modeling on a daily basis in the study area.

کلیدواژه‌ها [English]

  • AMSR-E
  • Aqua
  • Confusion matrix
  • MODIS
  • Remote sensing
  • Terra