International Journal of Remote Sensing · 1994 · 86 citations · 21 references
AgricultureAgronomyCrop SciencesCrop ModelingYield MappingYield MonitoringLeaf Area Index EstimationRemote Sensing For Crop ModelingRemote Sensing For Yield EstimationSpatial Crop ModelingVegetation IndicesYield PredictionCrop Biomass EstimationCrop Productivity AssessmentCrop Yield MappingEmpirical Leaf Area Index ModelingField Variability AssessmentWithin-field VariabilityYield Map AnalysisYield Variability Modeling
Abstract This study used Landsat-5 Thematic Mapper (TM) data to develop empirical models for determining soybean and corn crop yield, leaf area index, wet biomass, dry biomass and plant height. Ground-truth data was obtained from more than 50 commercial farms in Ohio, USA, during 1988 and 1989. Several significant linear, non-linear, logarithmic, exponential, and power models were developed. The best soybean models generally comprised of information from the commonly-used bands 3 and 4. TM data for the most significant soybean models explained 69 to 76 per cent of the between field variability in wet biomass, dry biomass, and plant height, 63 per cent of the variability in leaf area index, and 35 per cent of the variability in yield. The best corn models incorporated band 5 and/or band 7 along with band 4. The most significant corn models explained 80 per cent of the variability in wet biomass, 66 to 67 per cent of the variability in dry biomass, plant height, and leaf area index, and 52 per cent of the variability in yield. A new cubed ratio vegetation index, (TM4/TM5)3, was found to be particularly useful for modelling corn characteristics.
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Vegetation indices in crop assessments
C. L. Wiegand, A. J. Richardson, D. E. Escobar et al. · Remote Sensing of Environment · 1991 · 404 citations
Agriculture, Agronomy, Crop Sciences +14
R. B. Macdonald, F. G. Hall · Science · 1980 · 330 citations