Agricultural soil properties mapping from PRISMA and EnMap data: exploiting multitemporal bare soil approaches
Author(s)
Date Issued
2024
Type
conferenceObject
Abstract
Understanding the properties of agricultural soils is essential for optimizing food production and the overall efficiency of agricultural systems. In this study, multiple bare soil PRISMA and EnMAP
hyperspectral images acquired over agricultural fields in Northern Italy were used to retrieve Soil Organic Matter (SOM), Calcium carbonate (CaCO3), and texture (Silt, Clay, Sand) topsoil properties
using Machine Learning Regression Algorithms. Result shows that Partial Least Squares Regression (PLSR) was found to be a highly consistent and successful algorithm for prediction. For Clay, the
best results were achieved using the PRISMA sensor, Relevance Vector Machine algorithm, and Derivative of first-order preprocessing technique, resulting in an R2 of 0.82 and Root Mean Square
Error (RME) of 6.72%. For Silt, the EnMap sensor, PLSR algorithm, and Derivative of first-order preprocessing technique yielded an R2 of 0.81 and RME of 5.49%. For SOM, the PRISMA sensor and
PLSR algorithm provided the best results, with a R2 of 0.77 and RME of 1.84%. Lastly, for CaCO3, the best results were obtained by PRISMA using a Standard Normal Variate preprocessing technique
applied to absorbance smoothed with Savitzky-Golay filter, yielding an R2 of 0.58 and RME of 2.60%.