Continuous tower-based measurements of the ecosystem-atmosphere exchange of CO2 and CH4 are presented, recorded over the period 2012–2018 and reported at a 30-minute time step at a sub-boreal forest in the northeastern United States. Additionally, we describe a five-year (2012–2016) dataset of chamber-based measurements of soil fluxes of CO2, CH4, and N2O (2013–2016 only), conducted each year from May to November. These data can be used for process studies, for biogeochemical and land surface model validation and benchmarking, and for regional-to-global upscaling and budgeting analyses.
Data from: Identification and functional characterization of immunity-suppressing, candidate effector proteins in the parasitic weed Phelipanche aegyptiaca
All source data from the referenced paper (Figures 1b and Table 1). 22 Excel files of data from each experimental block of the reactive oxygen species assay, 1 Excel file of the combined data from the bacterial growth enhancement assay, 1 Excel file of the RT-qPCR data.
LandPKS (Land Potential Knowledge System): Mobile App for Extension, Land-Use and Project Planning, M&E and On-Farm Research
LandPKS comprises a free modular mobile phone app connected to cloud-based storage, global databases, and models, downloadable from Google Play or the iTunes App Store; a system for storing and accessing user data; and a system for sharing data, information and knowledge. LandPKS is being developed to help users determine the sustainable potential of their land, including its restoration potential, based on its unique soil, topography and climate. The land potential assessments will be updated based on new evidence regarding the success or failure of new management and restoration systems on different soils.
A multi-trophic study to assess ecosystem recovery following energy development for oil and gas extraction in northern U.S. Great Plains rangelands is reported. Soil factors, plant species composition and cover, and nematode trophic structuring between reclaimed oil and gas well sites are compared.
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.
The Snowmelt-Runoff Model (WinSRM) is designed to simulate and forecast daily streamflow in mountain basins where snowmelt is a major runoff factor.
WinTR-55 is a single-event rainfall-runoff small watershed hydrologic model. The model generates hydrographs from both urban and agricultural areas and at selected points along the stream system. Hydrographs are routed downstream through channels and/or reservoirs. Multiple sub-areas can be modeled within the watershed.
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.