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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.

    WISDEM

      WISDEM simulates the variation in multi-species weed populations over time in response to crop rotation, tillage system, and specific weed management tactics and the consequent crop yield loss due to weed competition. Population dynamics of individual weed species are predicted from a limited number of parameters that can be derived from literature sources and expert opinion.

      WeedSite

        Software for learning about the benefits of site-specific weed management compared to a uniform herbicide application. No GIS software is needed. The benefits are predicted from weed maps drawn by the user.

        SWAGMAN-Whatif

          An interactive computer program was developed to simulate the interactions among the above factors. It shows how changing one factor impacts the outcome of the other factors for a single growing season. The user selects a climate, a crop, and soil characteristics from menu lists, and then sets the water table depth and quality, irrigation (river or well) water quality and then develops an irrigation schedule. On execution, the relative yield reductions due to over irrigation, under irrigation, and salinity, water table rise or fall and surface runoff are shown numerically for the growing season. Soil water content, soil salinity, water table depth changes and rain and irrigation events during the season are also shown graphically.

          WATSUIT

            Predicts the salinity, sodicity, and toxic-solute concentration of the soil-water within a simulated crop root zone resulting from the use of a particular irrigation water of given composition and at a specified leaching fraction. It can be used to evaluate the effect of a given salinity level (or solute concentration) on crop yield and of a given sodicity level on soil permeability.

            CPM - Cotton Production Model

              A new process-based cotton model, CPM, has been developed to simulate the growth and development of upland cotton (Gossypium hirsutum L.) throughout the growing season with minimal data input. CPM predicts final cotton yield for any combination of soil, weather, cultivar and sequence of management actions.

              USDA-ARS Colorado Maize Water Productivity Dataset 2012-2013

                The USDA-Agricultural Research Service carried out an experiment on water productivity in response to seasonal timing of irrigation of maize (*Zea mays* L.) at the Limited Irrigation Research Farm (LIRF) facility in northeastern Colorado (40°26’ N, 104°38’ W) starting in 2012. Twelve treatments involved different water availability targeted at specific growth-stages. This dataset includes data from the first two years, which were complete years with intact treatments. Data includes canopy growth and development (canopy height, canopy cover and LAI), irrigation, precipitation, and soil water storage measured periodically through the season; daily estimates of crop evapotranspiration; and seasonal measurement of crop water use, harvest index and crop yield. Hourly and daily weather data are also provided from the CoAgMET, Colorado’s network of meteorological information.