Jordan Borak

The MODIS Land product quality assessment approach

The correct interpretation of scientific information from global, long-term series of remote sensing products requires the ability to discriminate between product artifacts and changes in the Earth processes being monitored. A suite of global land surface products is made from Moderate Resolution Imaging Spectroradiometer (MODIS) instrument data. Quality assessment (QA) is an integral part of this production chain and focuses on evaluating and documenting the scientific quality of the products with respect to their intended performance. This paper describes the QA approach adopted by the MODIS Land (MODLAND) Science Team and coordinated by the MODIS Land Data Operational Product Evaluation (LDOPE) facility. The described methodology represents a new approach for assessing and ensuring the performance of land remote sensing products that are generated on a systematic basis.

NCA-LDAS land analysis: Development and performance of a multisensor, multivariate land data assimilation system for the National Climate Assessment

AbstractThis article describes one of the first successful examples of multisensor, multivariate land data assimilation, encompassing a large suite of soil moisture, snow depth, snow cover, and irr...

Use of Satellite-Derived Aerodynamic Roughness for Improved Meteorological and Atmospheric Transport Modeling

One example of an application of EOS science data is the computation of aerodynamic roughness for momentum and its ultimate application to meteorological and atmospheric transport modeling. Currently, data products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and other sensors such as Landsat and ASTER are being used to estimate vegetation and urban (non-vegetated) aerodynamic roughness fields at local to regional scales. The goal is to incorporate those satellite-based roughnesses into both numerical meteorological models and atmospheric transport and dispersion models, to improve understanding and forecasting of wind and pressure fields, as well as plume dispersion. Once achieved, the results should be of practical benefit to society in the areas of improved weather and climate forecasting and better prediction of the dispersion of pollutants and other hazardous releases to the atmosphere.