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    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.

    Nearest Neighbor Soil Water Retention Estimator

      The k-nearest neighbor (k-NN) technique is a non-parametric technique that can be used to make predictions of discrete (class-type) as well as continuous variables. The k-NN technique and many of its derivatives belong to the group of .lazy learning algorithms.. It is lazy, as it passively stores the development data set until the time of application; all calculations are performed only when estimations need to be generated.


        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.

        SolarCalQ - Version 1

          The purpose of the SolarCalQ Version 1 JAVA model is to simulate the spectral quality of incident solar radiation for any location on the globe, down to one minute time steps.

          The Ogallala Agro-Climate Tool

            The Ogallala Agro-Climate Tool is a Visual Basic application that estimates irrigation demand and crop water use over the Ogallala Aquifer region.


              Root Zone Water Quality Model 2 (RZWQM2) is a whole-system model for studying crop production and environmental quality under current and changing climate conditions. It emphasizes the effects of agricultural management practices on physical, chemical and biological processes. RZWQM2 is a one-dimensional model with a pseudo 2-dimensional drainage flow. Crop simulation options include the generic plant growth model, DSSAT-CSM 4.0 and HERMES SUCROS models. It also can simulate surface energy balance with components from the SHAW model and water erosion from the GLEAMS model. An automated parameter estimation algorithm (PEST) was added to RZWQM2 for objective model calibration and uncertainty analysis.


                PhenologyMMS is a simulation model that outlines and quantifies the developmental sequence of different crops under varying levels of water deficits, provides developmental information relevant to each crop, and is intended to be used either independently or inserted into existing crop growth models.


                  HIRO2 (Hortonian Infiltration and Run-Off/On) is a spatially distributed rainfall-runoff model for event-based studies of space-time watershed processes. A grid-based routing hierarchy was defined over the watershed using the D-infinity contributing area algorithm. Computation of ponding time was included to handle variable run-on and rainfall intensity. The Green-Ampt model was adopted to calculate surface infiltration, and the kinematic wave model was used to route Hortonian runoff and channel flow. The model can handle input rainfall, soil parameters, surface roughness, and other properties that vary in space and time.

                  Non-dominated Sorting Genetic Algorithm-II

                    This code is implements the nondominated sorting genetic algorithm (NSGA-II) in the R statistical programming language. The function is theoretically applicable to any number of objectives without modification. The function automatically detects the number of objectives from the population matrix used in the function call. NSGA-II has been applied in ARS research for automatic calibration of hydrolgic models (whittaker link) and economic optimization (whittaker link).