Abstract. ERS-1/ERS-2 synthetic aperture radar interferometry was used to study the 1997 eruption of Okmok volcano in Alaska. First, we derived an accurate digital elevation model (DEM) using a tandem ERS-1/ERS-2 image pair and the preexisting DEM. Second, by studying changes in interferometric coherence we found that the newly erupted lava lost radar coherence for 5-17 months after the eruption. This suggests changes in the surface backscattering characteristics and was probably related to cooling and compaction processes. Third, the atmospheric delay anomalies in the deformation interferograms were quantitatively assessed. Atmospheric delay anomalies in some of the interferograms were significant and consistently smaller than one to two fringes in magnitude. For this reason, repeat observations are important to confidently interpret small geophysical signals related to volcanic activities. Finally, using two-pass differential interferometry, we analyzed the preeruptive inflation, coeruptive deflation, and posteruptive inflation and confirmed the observations using independent image pairs. We observed more than 140 cm of subsidence associated with the 1997 eruption. This subsidence occurred between 16 months before the eruption and 5 months after the eruption, was preceded by-•18 cm of uplift between 1992 and 1995 centered in the same location, and was followed by •10 cm of uplift between September 1997 and 1998. The best fitting model suggests the magma reservoir resided at 2.7 km depth beneath the center of the caldera, which was -5 km from the eruptive vent. We estimated the volume of the erupted material to be 0.055 km 3 and the average thickness of the erupted lava to be---7.4 m.

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ABSTRACT:The ASTER Global Digital Elevation Model Version 2 (GDEM v2) was evaluated over the conterminous United States in a manner similar to the validation conducted for the original GDEM Version 1 (v1) in 2009. The absolute vertical accuracy of GDEM v2 was calculated by comparison with more than 18,000 independent reference geodetic ground control points from the National Geodetic Survey. The root mean square error (RMSE) measured for GDEM v2 is 8.68 meters. This compares with the RMSE of 9.34 meters for GDEM v1. Another important descriptor of vertical accuracy is the mean error, or bias, which indicates if a DEM has an overall vertical offset from true ground level. The GDEM v2 mean error of -0.20 meters is a significant improvement over the GDEM v1 mean error of -3.69 meters. The absolute vertical accuracy assessment results, both mean error and RMSE, were segmented by land cover to examine the effects of cover types on measured errors. The GDEM v2 mean errors by land cover class verify that the presence of aboveground features (tree canopies and built structures) cause a positive elevation bias, as would be expected for an imaging system like ASTER. In open ground classes (little or no vegetation with significant aboveground height), GDEM v2 exhibits a negative bias on the order of 1 meter. GDEM v2 was also evaluated by differencing with the Shuttle Radar Topography Mission (SRTM) dataset. In many forested areas, GDEM v2 has elevations that are higher in the canopy than SRTM.

Abstract:The Advanced Very High Resolution Radiometer (AVHRR) sensor provides a unique global remote sensing dataset that ranges from the 1980s to the present. Over the years, several efforts have been made on the calibration of the different instruments to establish a consistent land surface reflectance time-series and to augment the AVHRR data record with data from other sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS). In this paper, we present a summary of all the corrections applied to the AVHRR surface reflectance and NDVI Version 4 Product, developed in the framework of the National Oceanic and Atmospheric Administration (NOAA) Climate Data Record (CDR) program. These corrections result from assessment of the geolocation, improvement of cloud masking, and calibration monitoring. Additionally, we evaluate the performance of the surface reflectance over the AERONET sites by a cross-comparison with MODIS, which is an already validated product, and evaluation of a downstream leaf area index (LAI) product. We demonstrate the utility of this long time-series by estimating the winter wheat yield over the USA. The methods developed by Becker-Reshef et al. (2010) and Franch et al. (2015) are applied to both the MODIS and AVHRR data. Comparison of the results from both sensors during the MODIS-era shows the consistency of the dataset with similar errors of 10%. When applying the methods to AVHRR historical data from the 1980s, the results have errors equivalent to those derived from MODIS.