Journal of Agronomy and Crop Science · 2018 · 40 citations · 44 references
AgricultureCrop SciencesPlant SciencesCereal ScienceCrop ManagementCrop SensingCold Stress PhysiologyCrop Stress DetectionPlant Stress DetectionProximal Crop SensingRemote Sensing For Yield EstimationVegetation IndicesCanopy Spectral ReflectanceCrop Damage DetectionCrop Damage ReductionField Evaluation Under Frost StressFrost Damage AssessmentFrost Injury AssessmentSpectral IndicesYield Loss Estimation From Frost
Abstract Frost can significantly reduce production of field crops grown in Mediterranean‐type environments, where significant economic losses for Australian wheat occur annually. If non‐destructive sensors could make rapid, spatial assessment of frost damage, this could limit economic losses through timely management decisions. This paper reports on a methodology for imposing frost treatments to wheat under field conditions and the utility of canopy reflectance data for detecting early crop damage. Purpose‐built chambers using stepped additions of dry ice allowed for a range of frost scenarios to be tested when applied at wheat heading and anthesis. For frost treatments applied at anthesis, grain number and yield were reduced by 8.8% and 7.2%, respectively, for every degree Celsius below zero (down to −4°C). This effect was additive over two consecutive nights. For cold load equivalent, there was a 2.2% and 1.9% reduction in grain number and yield, respectively, per °C hr (below 0°C). For wheat, spectral indices PRI and NDVI (reflectance) and FRF _G and SFR _G (fluorescence) showed significant relationships, with cold load applied for heading treatments. Next steps include targeting frost intensities equivalent to cold loads between 20 and 80°C hr and testing the utility of these proposed indices in a commercial paddock setting.
44
R: A Language and Environment for Statistical Computing
R Core Team · 2000 · 353K citations · Full text
Natural Sciences, Computer Science, Statistical Computing +10
Overview of the radiometric and biophysical performance of the MODIS vegetation indices
Alfredo Huete, Kamel Didan, Tomoaki Miura et al. · Remote Sensing of Environment · 2002 · 9.6K citations
Agriculture, Ecology, Geography +14