Monitoring Oats and Winter Wheat Within-Field Spatial Variability by Satellite Images

Abstract

Remote sensing is a methodology using different tools to monitor and predict yields. Spatial variability of crops can be monitored through sampling of vegetation indices derived from the entire crop growth; spatial variability can be used to plan further agronomic management. This paper evaluates the suitability of vegetation indices derived from satellite Landsat and EO-1 data that compare yield, topography wetness index, solar radiation, and meteorological data over a relatively small field (11.5 ha). Time series images were selected from 2006, 2010, and 2014, when oat was grown, and from 2005, 2011 and 2013, when winter wheat was grown. The images were selected from the entire growing season of the crops. An advantage of this method is the availability of these images and their easy application in deriving vegetation indices. It was confirmed that Landsat and EO-1 images in combination with meteorological data are useful for yield component prediction. Spatial resolution of 30 m was sufficient to evaluate a field of 11.5 ha.

Assessing the Within-Field Heterogeneity Using Rapid-Eye NDVI Time Series Data

“…Heterogeneous growing conditions within a field plot can benefit from modern precision agriculture in terms of spatially optimized management [1] and illustrate the high importance of acknowledging the interaction between the soil conditions, topography and cereal production [2][3][4][5][6][7]. For an efficient and non-destructive assessment of the heterogeneity of soil conditions of arable land and plant growth characteristics, the use of remote sensing data is becoming increasingly established [8,9]. Plant growth across landscapes or within a single field can show temporally consistent spatial patterns, such as low-and high-yielding areas.…”