Exploring remotely sensed technologies for monitoring wheat potassium and phosphorus using field spectroscopy

Given the importance of potassium (K) and phosphorus (P) contents to wheat yield and grain quality, and the very little experience that has been gained on nutritional monitoring of other than nitrogen using remotely sensed technologies, a study was undertaken to explore the possibility of identifying these mineral stresses using spectral data. Canopy spectra and biophysical data were collected from commercial and experimental fields in India and Israel. Traditional and newly developed vegetation indices, together with Partial Least Squares (PLS) regression models, were calculated in order to predict potassium and phosphorus contents from the wheat canopy spectral data. Results show that the application of PLS and specific narrow bands vegetation indices reached significant levels of accuracy in the retrieval of K and P levels, in comparison to traditional broad band indices. Additionally, it was observed that a significant improvement is obtained when the mineral total content is considered instead of the relative content. Therefore it was suggested that the biomass should also be retrieved from the spectral data. Finally, as very different crop conditions were included in this study, it was possible to confirm that the level of accuracy in the retrieval of K and P levels is related to the quality and variability of the data used for calibrating the models.

Research highlights▶ An algorithm was searched for monitoring K and P wheat condition from remotely sensed data. ▶ Vegetation indices and Partial Least Square Regression (PLS) models were considered. ▶ A proposed vegetation index including 1645 and 1715 nm retrieved the potassium concentration. ▶ The accuracy of this index was affected by the biomass variability of the dataset. ▶ PLS models were able to retrieve K and P concentration with high levels of accuracy.