Selection of NPK specific spectral bands using Hyperspectral imagery and ensemble machine learning approach over agricultural lands in Morocco

Abstract

Evaluation of soil's available Nitrogen, Phosphorus, and Potassium (NPK) has gained new prospects with the recent availability of hyperspectral remote sensing imagery (i.e., PRISMA satellite). Such an evaluation may be crucial for developing soil recommendations as well as placing variable rate fertilization into practice. However, retrieving soil nutrient information using a single prediction model is difficult due to the complexity of the continuous representation of soil dynamics. For instance, the high collinearity of the hyperspectral spectral can affect the prediction and therefore the accuracy as well as the interpretability of the retrieval model. Therefore, the spectral dimension needs to be reduced through a selection of specific spectral bands or regions that are most helpful in describing NPK. This study assesses the efficiency of PRISMA hyperspectral imagery to identify the most informative hyperspectral bands responding to NPK content in agricultural soils. To do so, the spectral band selection process of soil NPK-specific bands was performed on visible near-infrared (VNIR) and shortwave near-infrared (SWIR) hyperspectral data regions, using a multimethod modeling approach consisting of Partial Least Squares (PLSR), Principal Component Regression (PCR), and Gaussian Processes Regression (GPR) regression models. In this context, NPK soil sample locations (n = 200) were collected over heterogeneous agricultural bare lands in Morocco and analyzed against PRISMA hyperspectral datasets with 205 bands along the 400–2500 nm range of the Electromagnetic spectrum (VIS-NIR-SWIR). NPK soil concentrations were retrieved from a historical soil analysis database encompassing many agricultural perimeters in Morocco between 2019 and 2021. The used multi-method resulted in a selection of optimal bands or regions over the VNIR and SWIR sensitive to and potential for mapping soil NPK concentrations. A Preliminary set of bands that achieved the highest importance values for NPK, respectively have been identified and are being considered for scientific publication. They will be presented together with each of the multimethod approach performances (i.e., RMSE, R2) during EGU General Assembly 2023. Some of these selected bands agree with the absorption features of NPK reported in the literature, whereas others are being reported for the first time; particularly for P absorption traits that are challenging to identify. The resulting specific absorption features of NPK could be enhanced following further transformations of the hyperspectral signal. Ultimately, the selection of optimal band and regions is of importance for the quantification of soil NPK and are expected to help deepen our understanding of the spectral response of soil NPK content and to implement further recommendations tool for variable rate fertilization applications.Keywords— hyperspectral imaging, agricultural soils, variable rate fertilization, precision agriculture, ensemble machine learning, remote sensing.