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Rangeland Analysis Platform: Monitor rangelands across the USA

    The Rangeland Analysis Platform ( rangelands.app) is a free online application that provides simple and fast access to geospatial vegetation data for U.S. rangelands. The tool was developed to provide landowners, resource managers, conservationists, and scientists access to data that can inform land management planning, decision making, and the evaluation of outcomes. The Rangeland Analysis Platform (RAP) uses innovative cloud computing technology to provide maps and analysis opportunities straight to your desktop, delivered securely and instantaneously.

    Farm Service Agency Online Data Resources

      As directed by the OPEN (Open, Public, Electronic, and Necessary) Government Data Act and through its commitment to United States agriculturalists and interested public, FSA provides numerous data resources through reports, visualizations, and other formats. Visit the FSA Online Data Resources page to view the table that provides links to pages with USDA Farm Services Agency (FSA) data. Use the search feature for FSA data resources by category or program.

      IMAP: Image Mapping & Analytics for Phenotyping

        A set of PYTHON programs to implement image processing of ground and aerial images by offering via graphical user interface (GUI) 1) plot-level metrics extraction through a series of algorithms for image conversion, band math, radiometric/geometric calibrations, segmentation, masking, adaptive region of interest (ROI), gridding, heatmap, and batch process, 2) GIS interface for GeoTIFF pixels to Lat/Lon, UTM conversion, read/write shapefile, Lat/Lon to ROI, grid to polygon, and 3) utility GUI functions for zooming, panning, rotation, images to video, file I/O, and histogram.

        Metadata for: Climate-driven prediction of land water storage anomalies: An outlook for water resources monitoring across the conterminous United States

          These research data are associated with the manuscript entitled “Climate-driven prediction of land water storage anomalies: An outlook for water resources monitoring across the conterminous United States” (https://doi.org/10.1016/j.jhydrol.2020.125053). The study focused on the conterminous United States (CONUS) which extends over a region of contrasting climates with an uneven distribution of freshwater resources. Under climate change, an exacerbation of the contrast between dry and wet regions is expected across the CONUS and could drastically affect local ecosystems, agriculture practices, and communities. Hence, efforts to better understand long-term spatial and temporal patterns of freshwater resources are needed to plan and anticipate responses. Since 2002, the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) satellite observations provide estimates of large-scale land water storage changes with an unprecedented accuracy. However, the limited lifetime and observation gaps of the GRACE mission have sparked research interest for GRACE-like data reconstruction. This study developed a predictive modeling approach to quantify monthly land liquid water equivalence thickness anomaly (LWE) using climate variables including total precipitation (PRE), number of wet day (WET), air temperature (TMP), and potential evapotranspiration (PET). The approach builds on the achievements of the GRACE mission by determining LWE footprints using a multivariate regression on principal components model with lag signals. The performance evaluation of the model with a lag signals consideration shows 0.5 ≤ R2 ≤ 0.8 for 41.2% of the CONUS. However, the model’s predictive power is unevenly distributed. The model could be useful for predicting and monitoring freshwater resources anomalies for the locations with high model performances. The processed data used as inputs in the study are here provided including the GIS files of the different maps reported. Data reported in the csv files are 0.5-degree gridded monthly time-series of Land water Equivalence anomalies (USlwe163.csv), Potential evapotranspiration (USpet163.csv), Precipitation (USpre163.csv), above-ground air temperature (UStmp163.csv), and number of wet days (USwet163.csv) for 163 consecutive months over the period 2002 to 2017.

          Long-Term Agroecosystem Research Network regions, 2018 version

            The Long-Term Agroecosystem Research Network, consisting of 18+ research locations, is conducting research on the sustainable intensification of agroecosystems. To enable coordinated network level research, a spatial framework is required to facilitate analysis. This dataset contains a geodatabase of three new maps describing regional boundaries for the LTAR Network titled "Long-Term Agroecosystem Research Network regions, 2018 version.”

            LTAR Walnut Gulch Experimental Watershed DAP GIS Layers

            NAL Geospatial Catalog
              The USDA-ARS Southwest Watershed Research Center (SWRC) operates the Walnut Gulch Experimental Watershed (WGEW) in southeastern Arizona as an outdoor laboratory for studying semiarid rangeland hydrologic, ecosystem, climate, and erosion processes.

              Kellogg Biological Station (KBS) Long Term Ecological Research (LTER) Data Catalog

              NAL Geospatial Catalog
                The Kellogg Biological Station (KBS) Long Term Ecological Research (LTER) Data Catalog is a collection of data resources, including data sets, aerial photos, satellite imagery, GIS data, and national LTER network-wide data. You are welcome to examine and use the data as you wish for research and educational needs. However, data are copyrighted and use in a publication requires permission as detailed in KBS LTER's terms of use, which can be found at http://lter.kbs.msu.edu/data/terms-of-use/

                United States Drought Monitor

                  The U.S. Drought Monitor is a map released every Thursday, showing parts of the U.S. that are in drought. The map uses five classifications: abnormally dry (D0), showing areas that may be going into or are coming out of drought, and four levels of drought: moderate (D1), severe (D2), extreme (D3) and exceptional (D4).