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STARFM

    The STARFM algorithm uses comparisons of one or more pairs of observed Landsat/MODIS maps, collected on the same day, to predict maps at Landsat-scale on other MODIS observation dates. STARFM was initially developed at the NASA Goddard Space Flight Center by Dr. Feng Gao. This version (v1.2) has been greatly improved in computing efficiency (e.g. one run for multiple dates and parallel computing) for large-area processing (Gao et al., 2015). Additional improvements (e.g. Landsat and MODIS images co-registration, daily MODIS nadir BRDF-adjusted reflectance) in the operational data fusion system (Wang et al., 2014) are beyond the STARFM program and are not included in this package. Improvement and continuous maintenance are being undertaken in the USDA-ARS Hydrology and Remote Sensing Laboratory (HRSL), Beltsville, MD by Dr. Feng Gao.

    AROP

      The automated registration and orthorectification package (AROP) uses precisely registered and orthorectified Landsat data (e.g., GeoCover or recently released free Landsat Level 1T data from the USGS EROS data center) as the base image to co-register, orthorectify and reproject (if needs) the warp images from other data sources, and thus make geo-referenced time-series images consistent in the geographic extent, spatial resolution, and projection. The co-registration, orthorectification and reprojection processes were integrated and thus image is only resampled once. This package has been tested on the Landsat Multi-spectral Scanner (MSS), TM, Enhanced TM Plus (ETM+) and Operational Land Imager (OLI), Terra ASTER, CBERS CCD, IRS-P6 AWiFS, and Sentinel-2 Multispectral Instrument (MSI) data.

      RF-CLASS: Remote-sensing-based Flood Crop Loss Assessment Service System

        The Remote-sensing-based Flood Crop Loss Assessment Service System (RF-CLASS) is an Earth Observation (EO) based flood crop loss assessment cyber-service system operated by the Center for Spatial Information Science and Systems (CSISS), George Mason University. RF-CLASS supports flood-related crop statistics and insurance decision-making.

        National Land Cover Database 2011 (NLCD 2011)

          National Land Cover Database 2011 (NLCD 2011) is the most recent national land cover product created by the Multi-Resolution Land Characteristics (MRLC) Consortium, providing the capability to assess national land cover changes and trends across the United States from 2001 to 2011 at a spatial resolution of 30 meters, based primarily on a decision-tree classification of circa 2011 Landsat satellite data.

          USDA Forest Service Remote Sensing Applications Center and Geospatial Technology and Applications Center

            The Forest Service's Remote Sensing Applications Center (RSAC) is in Salt Lake City, Utah, co-located with the agency's Geospatial Service and Technology Center. Guided by national steering committees and field sponsors, RSAC provides national assistance to agency field units in applying the most advanced geospatial technology toward improved monitoring and mapping of natural resources. RSAC's principal goal is to develop and implement less costly ways for the Forest Service to obtain needed forest resource information.

            Farm Service Agency Aerial Photography Imagery Products and Programs

              APFO is home to one of the country's largest aerial film libraries. We currently house more than 70,000 rolls of film (10 million plus images). Our film dates from 1955 to the present. We have coverage of most of the United States and its territories. Historic aerial images play a more vital role today than ever before with environmental assessments, change detection, and property boundary disputes.

              NUOnet (Nutrient Use and Outcome Network) database

                The Nutrient Uptake and Outcomes (NUOnet) database will be able to help establish baselines on nutrient use efficiencies; processes contributing to nutrient losses; and processes contributing to optimal crop yield, nutritional and organoleptic quality. This national database could be used to calculate many different environmental indicators from a comprehensive understanding of nutrient stocks and flows.