Spectral and Textural Features for Predicting Soil Phosphorus Using Vis-NIR Point Data and Multispectral UAV Imagery: A Case Study From a Long-Term Experiment
[en] Soil nutrient status assessment is a key aspect of crop management. Unlike the labor- and time-intensive conventional approach, precision farming techniques are expanding to ensure the uniformity of soil nutrients, enhance production, and alleviate economic pressure. Aims: In this study, the potentials of visible and near-infrared spectroscopy (Vis-NIRS), as non-imaging technology and multispectral imagery mounted on unmanned aerial vehicle (UAV) to predict plant-available (AP) and total phosphorus (TP) (P) were studied and compared. Materials & Methods: Soil samples were taken from a long-term experiment with contrasting fertilization treatments, and their spectra were recorded. Additionally, drone multispectral images were taken before and after soil tillage and seedbed preparation. Results: The predicted available P content by Vis-NIRS was characterized by a cross-validation determination coefficient of R2cv = 0.82 and validation determination coefficient of R2v = 0.74, whereas the root mean square error for cross-validation (RMSEcv) and validation (RMSEv) were, respectively, 11.23 and 14.09 mg kg−1. The random forest (RF) model based on the textural and spectral features from multispectral images taken after seedbed preparation had the highest performances to predict plant-available P (R2v = 0.68, RMSEv = 13.65 mg kg−1, and RPIQv = 2.98), whereas the lowest prediction accuracy was obtained for total P prediction model after seedbed preparation (R2v = 0.40, RMSEv = 67.91, and RPIQv = 0.6). The effective wavelengths were around 450, 580, and 700 nm for predicting the available P fraction. Before soil tillage, the vegetation indices ranked high in the RF prediction models for available phosphorus (AP) and TP as compared to those developed after using tillage image-derived indices. In contrast, red-edge, red, and green bands, in addition to texture indices, were the most important predictors of soil available P following seedbed preparation. Conclusion: Our study suggests that soil tillage and seedbed preparation incorporate vegetation cover and alter soil roughness, resulting in a more homogeneous, smoother surface and higher accuracy for soil P prediction using UAV multispectral imagery.
Disciplines :
Agriculture & agronomy
Author, co-author :
El-Mejjaouy, Yousra ; Université de Liège - ULiège > Département GxABT > Biosystems Dynamics and Exchanges (BIODYNE) ; Plant Stress Physiology Laboratory, University Mohammed VI Polytechnic (UM6P)—AgroBioSciences, Benguerir, Morocco
Bastin, Jean-François ; Université de Liège - ULiège > TERRA Research Centre > Biodiversité, Ecosystème et Paysage (BEP)
Baeten, Vincent; Valorisation of Agricultural Products Department, Walloon Agricultural Research Centre (CRA-W), Gembloux, Belgium
Meersmans, Jeroen ; Université de Liège - ULiège > Département GxABT > Echanges Eau - Sol - Plantes
Oukarroum, Abdallah; Gembloux Agro-Bio Tech, Biodiversity and Landscape Unit, University of Liege, Gembloux, Belgium
Dumont, Benjamin ; Université de Liège - ULiège > TERRA Research Centre > Plant Sciences
Mercatoris, Benoît ; Université de Liège - ULiège > TERRA Research Centre > Biosystems Dynamics and Exchanges (BIODYNE)
Language :
English
Title :
Spectral and Textural Features for Predicting Soil Phosphorus Using Vis-NIR Point Data and Multispectral UAV Imagery: A Case Study From a Long-Term Experiment
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