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Privette, Jeffrey L.

Publications and source records attributed to Privette, Jeffrey L..

A Conceptual Enterprise Framework for Managing Scientific Data Stewardship

Scientific data stewardship is an important part of long-term preservation and the use/reuse of digital research data. It is critical for ensuring trustworthiness of data, products, and services, which is important for decision-making. Recent U.S. federal government directives and scientific organization guidelines have levied specific requirements, increasing the need for a more formal approach to ensuring that stewardship activities support compliance verification and reporting. However, many science data centers lack an integrated, systematic, and holistic framework to support such efforts. The current business- and process-oriented stewardship frameworks are too costly and lengthy for most data centers to implement. They often do not explicitly address the federal stewardship requirements and/or the uniqueness of geospatial data. This work proposes a data-centric conceptual enterprise framework for managing stewardship activities, based on the philosophy behind the Plan-Do-Check-Act (PDCA) cycle, a proven industrial concept. This framework, which includes the application of maturity assessment models, allows for quantitative evaluation of how organizations manage their stewardship activities and supports informed decision-making for continual improvement towards full compliance with federal, agency, and user requirements.

Scientific data stewardship↗

Assessment of Biases in MODIS Surface Reflectance Due to Lambertian Approximation

Using MODIS data and the AERONET-based Surface Reflectance Validation Network (ASRVN), this work studies errors of MODIS atmospheric correction caused by the Lambertian approximation. On one hand, this approximation greatly simplifies the radiative transfer model, reduces the size of the look-up tables, and makes operational algorithm faster. On the other hand, uncompensated atmospheric scattering caused by Lambertian model systematically biases the results. For example, for a typical bowl-shaped bidirectional reflectance distribution function (BRDF), the derived reflectance is underestimated at high solar or view zenith angles, where BRDF is high, and is overestimated at low zenith angles where BRDF is low. The magnitude of biases grows with the amount of scattering in the atmosphere, i.e., at shorter wavelengths and at higher aerosol concentration. The slope of regression of Lambertian surface reflectance vs. ASRVN bidirectional reflectance factor (BRF) is about 0.85 in the red and 0.6 in the green bands. This error propagates into the MODIS BRDF/albedo algorithm, slightly reducing the magnitude of overall reflectance and anisotropy of BRDF. This results in a small negative bias of spectral surface albedo. An assessment for the GSFC (Greenbelt, USA) validation site shows the albedo reduction by 0.004 in the near infrared, 0.005 in the red, and 0.008 in the green MODIS bands.

Wang, Yujie↗

Southern Africa Validation of NASA's Earth Observing System (SAVE EOS)

Southern Africa Validation of EOS (SAVE) is 4-year, multidisciplinary effort to validate operational and experimental products from Terra-the flagship satellite of NASA's Earth Observing System (EOS). At test sites from Zambia to South Africa, we are measuring soil, vegetation and atmospheric parameters over a range of ecosystems for comparison with products from Terra, Landsat 7, AVHRR and SeaWiFS. The data are also employed to parameterize and improve vegetation process models. Fixed-point and mobile "transect" sampling are used to collect the ground data. These are extrapolated over larger areas with fine-resolution multispectral imagery. We describe the sites, infrastructure, and measurement strategies developed underSAVE, as well as initial results from our participation in the first Intensive Field Campaign of SAFARI 2000. We also describe SAVE's role in the Kalahari Transect Campaign (February/March 2000) in Zambia and Botswana.

Privette, Jeffrey L.↗