This study evaluates the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover product over the territory of Austria. The aims are (a) to analyse the spatial and temporal variability of the MODIS snow product classes, (b) to examine the accuracy of the MODIS snow product against in situ snow depth data, and (c) to identify the main factors that may influence the MODIS classification ...

The availability of water surface inundation with high spatial resolution is of fundamental importance in several applications such as hydrology, meteorology and ecology. Medium spatial resolution sensors, like MODerate-resolution Imaging Spectroradiometer (MODIS), exhibit a significant potential to study inundation dynamics over large areas because of their high temporal resolution. However, t...

[1] This paper compares seasonal and spatial variations of MODIS albedos with those from the Community Land Model (CLM2). MODIS surface albedo data from September 2000 to August 2002 were used to investigate the model biases. To assess how inaccuracies in the land surface data used in CLM2 contribute to the model albedo biases, we created a new land surface dataset using the highest quality rep...

Deriving date-specific phenometrics from different multi-day MODIS composite data can potentially yield very different results. The objectives of this study were therefore to quantify the differences between (i) using 8-day vs. 16-day MODIS data and (ii) using the date-flag in the 8-day MOD09 data when estimating key phenological metrics for Skukuza, South Africa. After applying Savitsky-Golay ...

An algorithm based on the radiance transfer model (RM) and a dynamic learning neural network (NN) for estimating water vapor content from moderate resolution imaging spectrometer (MODIS) 1B data is developed in this paper. The MODTRAN4 is used to simulate the sun-surface-sensor process with different conditions. The dynamic learning neural network is used to estimate water vapor content. Analys...

Time series of satellite data can be employed for mapping the development of vegetation in space and time. However, noise induced by cloud contamination and atmospheric variability affects data quality. Science Datasets is an integral part of the MODIS Land production chain that focuses on evaluating and documenting the scientific quality of products. This study aims at the reconstruction of ti...

There is a trend to acquire high accuracy land-cover maps using multi-source classification methods, most of which are based on data fusion, especially pixel-or feature-level fusions. A probabilistic graphical model (PGM) approach is proposed in this research for 30 m resolution land-cover mapping with multi-temporal Landsat and MODerate Resolution Imaging Spectroradiometer (MODIS) data. Indepe...

[1] Cloud microphysical observations collected in situ during the VAMOS Ocean-Cloud-Atmosphere-Land Study Regional Experiment within the Chile-Peru stratocumulus cloud deck during October–November 2008 were used to assess MODIS Level 2 cloud property retrievals. The in situ aircraft-derived cloud property values were constructed from the drop size distributions measured by the Cloud Droplet Pro...

Phase 1I of the Scientific Data Purchase (SDP) has provided NASA investigators access to data from four different satellite and airborne data sources. The Moderate Resolution Imaging Spectrometer (MODIS) land discipline team (MODLAND) sought to utilize these data in support of land product validation activities with a lbcus on tile EOS Land Validation Core Sites. These sites provide a globally ...

By using the Gulang Heterogeneous Underlying Surface Layer Experiment (GHUSLE) data, the accuracy of land surface temperature (LST) in Northwest China retrieved by the Moderate-Resolution Imaging Spectroradiometer(MODIS) and Visible and InfraRed Radiometer(VIRR) data is verified. Furthermore, a new LST algorithm for heterogeneous underlying surface is developed and the LST retrieved by the two ...