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Medium-resolution multispectral satellite imagery in precision agriculture: map** precision canola (Brassica napus L.) yield using Sentinel-2 time series

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Abstract

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 develo** an example of canola yield map** using Sentinel-2 data in central Alberta. Two simple empirical models for map** 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 crop** systems to improve understanding of crop growth at both the field-level and regional-level.

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Acknowledgements

This research has been made possible by Alberta Canola Producers Commission, Manitoba Canola Growers Association, and Eyes High Postdoctoral Research Program at University of Calgary. We thank the producer who provided us with the precision canola yield dataset and valuable insight into the underlying field-level patterns of yield. We elect not to name them to maintain confidentiality. We also thank Laurel Thompson at Lakeland College in Vermillion, Alberta.

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  1. Department of Biological Science, University of Calgary, Calgary, AB, T3B 4S3, Canada

    Lan H. Nguyen, Samuel Robinson & Paul Galpern

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  1. Lan H. Nguyen
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Appendix

Appendix

NDVI time series filtering

At any particular location (pixel), let X be a vector containing n values of days-of-year (DOYs) and Y be a vector containing n values of the GEE-retrieved NDVI observed at DOYs in X.

Rule 1 If a large change in NDVI is detected within a searching window of 14 days, a pixel is considered as cloud contaminated.

Let ∆Y be an n x n matrix containing distances between a particular Y value and all values in the Y vector (∆Yi = yi—Y with i indicates a value position in X, Y vectors and a row position in ∆Y). At a random position j, ∆Yj is split into two part: ∆Yj1—distances between yj and Y values to the left of j (start → j), and ∆Yj2—distances between yj and Y values to the right of j position in Y (j → end).

If min(∆Yj1) < − 0.1 & min(∆Yj2) < − 0.1 & (min(∆Yj1) + min(∆Yj2)) < − 0.3, the NDVI observation at j is considered as cloud contaminated.

Rule 2 If a very large change in NDVI is detected between three consecutive valid observations (no matter how far they are from each other in terms of DOY), a pixel is considered as cloud contaminated.

Let x1, x2, x3 be three consecutive DOYs with GEE-filtered NDVI observations—y1, y2, y3.

If (y1−y2) ≥ 0.15 & (y2−y3) ≤ − 0.15 & (y1 + y3) ≥ 0.35, NDVI at x2 is considered as cloud contaminated.

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Nguyen, L.H., Robinson, S. & Galpern, P. Medium-resolution multispectral satellite imagery in precision agriculture: map** precision canola (Brassica napus L.) yield using Sentinel-2 time series. Precision Agric 23, 1051–1071 (2022). https://doi.org/10.1007/s11119-022-09874-7

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