Peter Romanov

Sea-ice monitoring over the Caspian Sea using geostationary satellite data

A new technique is proposed for sea-ice mapping using observations from geostationary satellite over the Caspian Sea. A two end-member linear-mixture approach has been employed. A neural-network-based approach was used to simulate water and ice reflectances for all possible sun–satellite geometries. The ice-mapping technique incorporates an advanced cloud-detection algorithm with adaptive threshold values. The average percentage of cloud reduction because of the daily compositing ranged from 22% to 25%. Daily maps of ice distribution and concentration with minimal cloud coverage were produced for the winter seasons of 2007 and 2008. The retrieved ice distribution demonstrated a good agreement with Moderate Resolution Imaging Spectroradiometer (MODIS) images and National Oceanic Atmospheric Administration (NOAA) Interactive Multisensor Snow and Ice Mapping System (IMS) snow and ice charts. The obtained correlation coefficients with IMS charts for 2007 and 2008 were 0.92 and 0.83, respectively. The technique has been proposed as one of the candidate ice-mapping techniques for the future Geostationary Operational Environmental Satellite-R Series (GOES-R) Advance Baseline Imager (ABI) instrument.

Analysis of vegetation index NDVI anisotropy to improve the accuracy of the GOES-R green vegetation fraction product

Green Vegetation Fraction (GVF) is the fraction of area within the instrument footprint occupied by green vegetation. Information on GVF is needed to estimate the surface energy balance in numerical weather prediction (NWP) and climate models. For the Geostationary Operational Environmental Satellite-R Series (GOES-R) Advanced Baseline Imager (ABI) algorithm development, a normalized difference vegetation index (NDVI) based linear mixture algorithm has been chosen to convert NDVI into GVF. The GVF algorithm has been developed and tested using a proxy dataset from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor onboard the European Meteosat Second Generation (MSG) geostationary satellite. Studies of SEVIRI data have shown that NDVI strongly depends upon the viewing and illumination geometry of observations, especially over dense vegetation. If not corrected, this angular anisotropy of NDVI causes substantial spurious diurnal variations in the derived GVF. An empirical kernel-driven model to correct NDVI for angular anisotropy has been developed and implemented in the GVF algorithm. Its kernel weights for the GVF algorithm were also determined empirically from the SEVIRI clear-sky data. The preliminary validation estimates show that the models performance is good.

The NASA NEESPI Data Portal to Support Studies of Climate and Environmental Changes in Non-boreal Europe

NASA NEESPI (Northern Eurasia Earth Science Partnership Initiative) data portal is a NASA funded project that focuses on collecting satellite remote sensing data, providing tools, information, and services in support of NEESPI scientific objectives (Leptoukh, et al., 2007). The data can be accessed online through anonymous ftp, through an advanced data searching and ordering system Mirador that uses keywords to find data quickly in a Google-like interface, and through the Goddard Interactive Online Visualization ANd aNalysis Infrastructure (Giovanni). The portal provides preprocessed data from different satellite sensors and numerical models to the same spatial and temporal resolution and the same projection so that the data can be used easily to perform inter-comparison or relationship studies. In addition, it provides parameter and spatially subsetted data for regional studies. Studies of regional carbon, hydrology, aerosols in non-boreal Europe and their interactions with global climate are very challenging research topics. The NASA NEESPI data portal makes many satellite data available for such studies, including information on land cover types, fire, vegetation index, aerosols, land surface temperature, soil moisture, precipitation, snow/ice, and other parameters. This paper will introduce the features and products available in the system, focusing on the online data 1 tool, Giovanni NEESPI. An example that explores different data through Giovanni NEESPI in temperate region of non-boreal Europe will be presented.

Satellite-Derived Information on Snow Cover for Agriculture Applications in Ukraine

This paper demonstrates how NOAA interactive satellite-based maps of snow cover can be used in the assessment of unfavorable agricultural conditions in Ukraine. The focus was on two events, the extensive winterkill in winter of 2002–2003 and the drought in the early 2007. Both events had a strong adverse effect on the yield and on the production of major grain crops. The analysis of NOAA daily snow maps has revealed an extremely short duration of snow in Ukraine in winter of 2006–2007. This is indicative of lower winter-time precipitation that contributed to the soil dryness in spring 2007. To identify potential crop freeze damage we have estimated the minimum temperature of snow-free land surface from snow charts combined with satellite land surface temperature retrievals. Temperatures below –18°C indicating potential winterkill were observed in the Central and Eastern Ukraine in December 2002.