Yield variability prediction by remote sensing sensors with different spatial resolution

Abstract

Currently, remote sensing sensors are very popular for crop monitoring and yield prediction. This paper describes how satellite images with moderate (Landsat satellite data) and very high (QuickBird and WorldView-2 satellite data) spatial resolution, together with GreenSeeker hand held crop sensor, can be used to estimate yield and crop growth variability. Winter barley (2007 and 2015) and winter wheat (2009 and 2011) were chosen because of cloud-free data availability in the same time period for experimental field from Landsat satellite images and QuickBird or WorldView-2 images. Very high spatial resolution images were resampled to worse spatial resolution. Normalised difference vegetation index was derived from each satellite image data sets and it was also measured with GreenSeeker handheld crop sensor for the year 2015 only. Results showed that each satellite image data set can be used for yield and plant variability estimation. Nevertheless, better results, in comparison with crop yield, were obtained for images acquired in later phenological phases, e.g. in 2007 - BBCH 59 - average correlation coefficient 0.856, and in 2011 - BBCH 59-0.784. GreenSeeker handheld crop sensor was not suitable for yield estimation due to different measuring method.

Assessing Multiple Years’ Spatial Variability of Crop Yields Using Satellite Vegetation Indices

“…The relationships (r and R 2 values) between VIs and final GY in the three cited sources [80,81,83] are no better, in general, than those found in the present study (Table 8). In this light, the above discussed data of F a (Table 8) may be considered encouraging and set the premise for a larger use of remote (satellite) imagery in the interpretation and subsequent management of crop spatial variation.…”

“…In soybean [81], the combined SR and SAVI together with soil elevation concurred to delineate management zones reflecting significant differences in final yield and soil properties. More generally, in winter cereals (barley and wheat), NDVI from Landsat as well as higher resolution satellites (QuickBird and WorldView-2) proved effective in describing yield spatial variability, especially at the onset of the reproductive stage [83]. The same happened in winter oilseed rape with the Enhanced Moisture Stress Index [80].…”

Monitoring of Movement on the Farm Using WiFi Technology

“…Annually linked factors may include anomalies in planting, emergence, or weather conditions. Seasonally linked factors can include plant diseases, weed development, severe climatic events, or irrigation system malfunctions (Bégué et al, 2008;Kumhálová and Matějková, 2017).…”

Different Remote Sensing Data in Relative Biomass Determination and in Precision Fertilization Task Generation for Cereal Crops

“…There is a growing number of research comparing different dates of remote sensing data such as Vincini et al (2014), where they compared Sentinel-2 data from three different growing stages of winter wheat canopies and Kumhálová and Matějková (2017) where they studied winter wheat and winter barley, and concluded that images acquired in later phenological phases conduct better results. Data processing may take from few minutes to a few days, even weeks, but other aspect is that how to adapt that data to the work execution.…”