Facilitate Visualization and Distribution of NASA\u27s Environmental Science Data through Open Standards and Open Source Software for Geospatial

This paper introduces the utilization of open standards and open source software for visualization and distribution of geospatial environmental science data at the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC). The ORNL DAAC is one of the NASA Earth Observing System Data and Information System (EOSDIS) data centers. A big challenge for the ORNL DAAC (https://daac.ornl.gov) is to efficiently manage over a thousand heterogeneous environmental data, collected through field campaigns, aircraft/satellite observations, and model simulations. ORNL DAAC also has to provide tools to easily find, visualize, and access the heterogeneous data. To address this challenge, the ORNL DAAC has leveraged Open Geospatial Consortium (OGC) standards and open source software to develop the Spatial Data Access Tool (SDAT, https://webmap.ornl.gov/ogc). SDAT is a suite of open standards-based web mapping, subsetting, and transformation services and applications that allow users to visualize and download geospatial data in customized spatial/temporal extents, formats, and projections. The open source MapServer/Geospatial Data Abstraction Library (GDAL) powers the backend OGC Web services of SDAT. Open source Javascript libraries, including OpenLayers, GeoExt, and proj4js, were used to create the SDAT Web User Interface and MapWidget, a light-weight Javascript library that allows SDAT visualization to be easily embedded on any webpage. SDAT also provides KML files to enable interactive data visualization in the popular Google Earth application or any KML-compatible client. SDAT provides a common framework and standard service interfaces for ORNL DAAC data holdings. SDAT user interface hides their heterogeneity from end users, and promotes their usage. SDAT facilitates integration of ORNL DAAC data resources with other related data systems. In 2016, SDAT served more than 2 million mapping requests and 72 thousand customized data downloads from over 2500 distinct data users

SoilSCAPE: A unified web portal for soil moisture

<p>Soil moisture imposes a strong constraint on the transpiration and carbon fixation of vegetation and the loss of terrestrial carbon through soil respiration. However, despite its importance in regulating ecosystem fluxes, sources of high-quality and high spatial and temporal resolution soil moisture estimates are rare. The SoilSCAPE unified soil moisture portal brings together data from a wireless sensor network of over 150 sensor nodes and harmonizes this high-resolution soil moisture data with a variety of related data to address key science questions of the NASA Soil Moisture Active Passive (SMAP) Tier 1 and the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) Earth Venture-1 missions.</p> <p>One primary goal of the AirMOSS mission is to use soil moisture data to reduce the uncertainty in the estimates of net ecosystem exchange (NEE) for North America. To understand the controls on NEE and their dependence on vegetation phenology and other landscape factors, the localized SoilSCAPE sensor data must be linked to remotely-sensed data from AirMOSS and tower-based AmeriFlux observations, as well as quantitative information on landscape attributes. The SoilSCAPE unified soil moisture portal aims to solve a significant information system challenge by seamlessly integrating and visualizing heterogeneous data (i.e. size of data granules, sensing volumes, spatial resolution, temporal refresh rate, etc.) and metadata and making them accessible to the user community through a single interface.</p

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