Preprint Article Version 1 This version is not peer-reviewed

Improving Yield Mapping Accuracy Using Remote Sensing

Version 1 : Received: 23 January 2019 / Approved: 29 January 2019 / Online: 29 January 2019 (04:55:05 CET)

How to cite: Trevisan, R.G.; Shiratsuchi, L..S.; Bullock, D.S.; Martin, N.F. Improving Yield Mapping Accuracy Using Remote Sensing. Preprints 2019, 2019010287 (doi: 10.20944/preprints201901.0287.v1). Trevisan, R.G.; Shiratsuchi, L..S.; Bullock, D.S.; Martin, N.F. Improving Yield Mapping Accuracy Using Remote Sensing. Preprints 2019, 2019010287 (doi: 10.20944/preprints201901.0287.v1).

Abstract

The objective of this work was to investigate the use of remotely sensed vegetation indices to improve the quality of yield maps. The method was applied to the yield data of twelve cornfields from the Data Intensive Farm Management project. The results revealed the need to time shift the yield values up to three seconds to better match the sensor readings with the geographic coordinates. The residuals of the yield prediction model were used to identify points with unlikely yield values for that location, as an alternative to traditional approaches using local spatial statistics, without any assumption of spatial dependence or stationarity. The temporal and spatial distribution of the standardized coefficients for each experimental unit highlighted the presence of trends in the data. At least five out of the twelve fields presented trends that could have been induced by data collection.

Subject Areas

on-farm precision experimentation; normalized difference vegetation index; data filtering; error correction

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