Medium-resolution multispectral satellite imagery in precision agriculture: mapping precision canola (Brassica napus L.) yield using Sentinel-2 time series.
- Resource Type
- Article
- Authors
- Nguyen, Lan H.; Robinson, Samuel; Galpern, Paul
- Source
- Precision Agriculture. Jun2022, Vol. 23 Issue 3, p1051-1071. 21p.
- Subject
- *RAPESEED
*REMOTE-sensing images
*TIME series analysis
*CANOLA
*PRECISION farming
*CROP yields
- Language
- ISSN
- 1385-2256
Remote sensing imagery has been a key data source for precision agriculture. However, high-resolution and/or hyperspectral imagery have typically been favored for their greater information content. This study aims to demonstrate the capability of medium-resolution imagery in precision agriculture by developing an example of canola yield mapping using Sentinel-2 data in central Alberta. Two simple empirical models for mapping precision canola yield are tested: one using random forest regression and a second using functional linear regression. Both take as input freely-available Sentinel-2 time series images and use these to predict precision yield gathered by a yield monitor. The models were able to predict crop yield to within 12–16% accuracy of the reference yield. These results also demonstrate that a time series of medium-resolution multispectral imagery can capture small-scale variation in crop yields. The proposed methods can be applied to other areas or cropping systems to improve understanding of crop growth at both the field-level and regional-level. [ABSTRACT FROM AUTHOR]