Assessing the Information Potential of MIR Spectral Signatures for Prediction of Multiple Soil Properties Based on Data from the AfSIS Phase I Project.

ABSTRACT: The aim of the study was to assess the predictive potential of mid-infrared (MIR) spectral response in the estimation of 60 soil properties. It is important to know the accuracy limitations in estimating various soil characteristics using various models in conditions of high spatial variability of the environment. To fully assess this potential, three types of algorithms were used in modeling, i.e., partial least squares (PLSR), one-dimensional convolutional neural network (1DCNN), and generalized regression neural network (GRNN). The research used data from 19 sub-Saharan African countries collected as part of the Africa Soil Information Service (AfSIS) Phase I project. The repositories provide 18,250 MIR reflectance recordings and nearly two thousand analytical data records from the dete

SUBMITTER: Gruszczynski S 

PROVIDER: S-EPMC9691257 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature