Influence of the 17 April 2006 Asian dust storm on Moderate Resolution Imaging Spectroradiometer data for land cover identification

This study explores the use of satellite-based LULC (Land Use / Land Cover) data while simultaneously correcting potential evapotranspiration (PET) input with Leaf Area Index (LAI) to increase the performance of a physically distributed hydrologic model. The mesoscale hydrologic model (mHM) was selected for this purpose due to its unique features. Since LAI input informs the model about vegetation dynamics, we incorporated the LAI based PET correction option together with multi-year LULC data. The Globcover land cover data was selected for the single land cover cases, and hybrid of CORINE (coordination of information on the environment) and MODIS (Moderate Resolution Imaging Spectroradiometer) land cover datasets were chosen for the cases with multiple land cover datasets. These two datasets complement each other since MODIS has no separate forest class but more frequent (yearly) observations than CORINE. Calibration period spans from 1990 to 2006 and corresponding NSE (Nash-Sutcliffe Efficiency) values varies between 0.23 and 0.42, while the validation period spans from 2007 to 2010 and corresponding NSE values are between 0.13 and 0.39. The results revealed that the best performance is obtained when multiple land cover datasets are provided to the model and LAI data is used to correct PET, instead of default aspect-based PET correction in mHM. This study suggests that to minimize errors due to parameter uncertainties in physically distributed hydrologic models, adequate information can be supplied to the model with care taken to avoid over-parameterizing the model.

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DUST STORM DETECTION USING MODIS DATA OVER THE MIDDLE EAST

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w18-1147-2019 ◽

2019 ◽

Vol XLII-4/W18 ◽

pp. 1147-1151

Author(s):

A. Zandkarimi ◽

P. Fatehi

Keyword(s):

Remote Sensing ◽

Middle East ◽

Dust Storm ◽

Dust Storms ◽

Time Data ◽

Sensing Technology ◽

Moderate Resolution ◽

Resolution Imaging ◽

Data Source ◽

Moderate Resolution Imaging Spectroradiometer

Abstract. Dust storms are one of the common phenomena in the arid and semi-arid regions which cause many economic and environmental losses also affect human health. Therefore, it is necessary to be able to detect dust storms. Several methods exist for dust monitoring, such as Ground-based measurements, satellite remote sensing, video surveillance, wireless sensors. Remote sensing technology provides wide coverage, high spectral and temporal resolutions, even near real-time data, which can offer a valuable data source for dust storm monitoring. We used an algorithm based on Moderate Resolution Imaging Spectroradiometer (MODIS) images for detecting dust storm over the Middle East. The proposed algorithm uses the brightness temperature using multi-bands. The performance of the algorithm was evaluated using the ground-based observations of synoptic stations. The results showed that by applying the algorithm, the dust area can be clearly separated, especially in the regions that cloud is mixed with dust and achieved overall accuracy was ~78%.

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