Spatial uncertainty in multi-year mean phenology based on remote sensing data

identification of spatial variations of snow-covered days over iran based on remote sensing data

1. introduction glaciers and snow covers in the mountains play an important role in the water budget of many areas of the world (ramage and isacks, 2003). in high altitudes and mountainous regions, snowmelt is a great contributor to the yearly runoff. the snow melt supplies 1/6 of people's needed water but due to global warming these glaciers may be at risk (barnett, et al. 2005). for the regio...

Remote sensing data assimilation for a prognostic phenology model

[1] Predicting the global carbon and water cycle requires a realistic representation of vegetation phenology in climate models. However most prognostic phenology models are not yet suited for global applications, and diagnostic satellite data can be uncertain and lack predictive power. We present a framework for data assimilation of Fraction of Photosynthetically Active Radiation absorbed by ve...

Air temperature estimation based on environmental parameters using remote sensing data

 This study is aimed at estimating monthly mean air temperature (Ta) using the MODIS Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), latitude, altitude, slope gradient and land use data during 2001-2015. The results showed that despite some spatial similarities between annual spatial patterns of Ta and LST, their variations are significantly different, so that the...

Investigation of periodic changes of the Oman Sea coastline using remote sensing data and spatial analysis

Extended abstract   1- Introduction Coastal environments are one of the most sensitive environmental systems under the influence of dominant hydrodynamic processes. Coastal changes and evolution are occurring very fast. Coastal areas are now gradually becoming known as severe natural and man-made disturbances, including sea levels rising, coastal erosion and sedimentation, and over-exploitat...

Remote sensing based on hyperspectral data analysis