2,365 research outputs found

    Yield prediction by analysis of multispectral scanner data

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    A preliminary model describing the growth and grain yield of wheat was developed. The modeled growth characteristics of the wheat crop were used to compute wheat canopy reflectance using a model of vegetation canopy reflectance. The modeled reflectance characteristics were compared with the corresponding growth characteristics and grain yield in order to infer their relationships. It appears that periodic wheat canopy reflectance characteristics potentially derivable from earth satellites will be useful in forecasting wheat grain yield

    Investigations of vegetation and soils information contained in LANDSAT Thematic Mapper and Multispectral Scanner data

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    An extension of the TM tasseled cap transformation to reflectance factor data is presented, and the basic concepts underlying the tasseled cap transformations are described. The ratio of TM bands 5 and 7, and TM tasseled cap wetness, are both shown to offer promise of direct detection of available soil moisture. Some effects of organic matter and other soil characteristics or constituents on TM tasseled cap spectral response are also considered

    Labeling research in support of through-the-season area estimation

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    The development of LANDSAT-based through-the-season labeling procedures for corn and soybeans is discussed. A model for predicting labeling accuracy within key time periods throughout the growing season is outlined. Two methods for establishing the starting point of one key time period, viz., early season, are described. In addition, spectral-temporal characteristics for separating crops in the early season time period are discussed

    Wheat yield forecasts using LANDSAT data

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    Several considerations of winter wheat yield prediction using LANDSAT data were discussed. In addition, a simple technique which permits direct early season forecasts of wheat production was described

    Forecasts of winter wheat yield and production using LANDSAT data

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    There are no author-identified significant results in this report

    Analysis of scanner data for crop inventories

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    There are no author-identified significant results in this report

    Application of remote sensing to selected problems within the state of California

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    Specific case studies undertaken to demonstrate the usefulness of remote sensing technology to resource managers in California are highlighted. Applications discussed include the mapping and quantization of wildland fire fuels in Mendocino and Shasta Counties as well as in the Central Valley; the development of a digital spectral/terrain data set for Colusa County; the Forsythe Planning Experiment to maximize the usefulness of inputs from LANDSAT and geographic information systems to county planning in Mendocino County; the development of a digital data bank for Big Basin State Park in Santa Cruz County; the detection of salinity related cotton canopy reflectance differences in the Central Valley; and the surveying of avocado acreage and that of other fruits and nut crops in Southern California. Special studies include the interpretability of high altitude, large format photography of forested areas for coordinated resource planning using U-2 photographs of the NASA Bucks Lake Forestry test site in the Plumas National Forest in the Sierra Nevada Mountains

    Application of remote sensing to selected problems within the state of California

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    There are no author-identified significant results in this report

    Wheat productivity estimates using LANDSAT data

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    The author has identified the following significant results. Large area LANDSAT yield estimates were generated. These results were compared with estimates computed using a meteorological yield model (CCEA). Both of these estimates were compared with Kansas Crop and Livestock Reporting Service (KCLRS) estimates of yield, in an attempt to assess the relative and absolute accuracy of the LANDSAT and CCEA estimates. Results were inconclusive. A large area direct wheat prediction procedure was implemented. Initial results have produced a wheat production estimate comparable with the KCLRS estimate
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