Temporal paradox in soil potassium estimations using spaceborne multispectral imagery

•Earlier images performed better in soil TK estimations than the more recent ones.•The spatial distribution of potassium is strongly controlled by deposition pattern.•The distribution of potassium exhibited minimal changes over the past 30 years.•The increase of SOM contents masked the true spectral response of soil TK. The time difference between the field sampling and acquired spaceborne imagery has always been ignored in satellite-based soil analyses. In this study, the impacts of the time difference on soil nutrient predictions and the underlying mechanism for these predictions were investigated using a case study from the North China Plain. Soil total potassium (TK) was sampled in 2016 and was subsequently analyzed, with the soil TK content then predicted using the original reflectances from eight Landsat TM/OLI images that spanned the 1986–2016 period as inputs to multiple linear regression (MLR) and artificial neural network (ANN) models. The results reveal a temporal paradox, where earlier satellite imagery yields higher accuracy in the predictions than does the more recent imagery. Historical soil nutrient data sets were used to explain this temporal paradox, which indicated that both an internal and external factor influenced the soil TK predictions. The internal factor, the deposition pattern across the study region, was found to strongly control the spatial distribution of potassium and exhibited minimal changes over the past 30 years, which influenced the good soil TK predictions using the early satellite imagery. The external factor, the soil organic matter (SOM), which has a stronger impact on the spectral reflectances than the soil TK, indicated that the increase and regional uniformization of SOM contents caused by Chinese agricultural development from the 1980s to the early 2000s masked the true spectral response of soil TK and explained the decline in the prediction accuracy of soil TK with time. This study reveals a potential limitation in remote-sensing-based soil TK predictions, and indicates that early satellite imagery should be considered as a potentially important factor in future research in order to accurately assess soil nutrient.