Linear Multi-Task Learning for Predicting Soil Properties Using Field Spectroscopy

Haijun Qi,Shaowen Li,Arnon Karnieli,Tarin Paz-Kagan

doi:10.3390/rs9111099

Abstract

Field spectroscopy has been suggested to be an efficient method for predicting soil properties using quantitative mathematical models in a rapid and non-destructive manner. Traditional multivariate regression algorithms usually regard the modeling of each soil property as a single task, which means only one response variable is considered as the output during modeling. Therefore, these algorithms are less suitable for the prediction of several key soil properties with low concentrations or unobvious spectral absorption signals. In the current study, we investigated the performance of a linear multi-task learning (LMTL) algorithm based on a regularized dirty model for modeling and predicting several key soil properties using field spectroscopy (350–2500 nm) as an integrated approach. We tested seven key soil properties including available nitrogen (N), phosphorus (P) and potassium (K), pH, water content (WC), organic matter (OM), and electrical conductivity (EC) in drylands. The model performances of LMTL models were compared with the commonly used single-task algorithm of the partial least squares regression (PLS-R). Our results show that the LMTL models outperformed the PLS-R models with the advantage of shared features; the ratio of performance to deviation (RPD) values in the validation set improved by 10.24%, 4.93%, 25.77%, 11.76%, 6.74%, 53.13%, and 3.15% for N, P, K, pH, WC, OM, and EC, respectively. The best prediction was obtained for OM with RPD = 2.29, indicating high accuracy (RPD > 2). The prediction results of N, P, WC, and pH were categorized as of moderate accuracy (1.4 < RPD < 2), while K and EC were categorized as of poor accuracy (RPD < 1.4). However, the explanatory power of the LMTL models was moderate due to fewer features being selected by the regularization algorithm of the LMTL approach, which should be further studied in the soil spectral analysis. Our results highlight the use of LMTL in field spectroscopy analysis that can improve the generalization performance of regression models for predicting soil properties.

Highlights

The assessment and monitoring key soil properties are important processes for quantifying soil quality and developing tools for soil management in general and precision agriculture in particular
Our study illustrates that linear multi-task learning (LMTL) algorithms can improve the prediction accuracies of seven key soil properties by field VNIR/SWIR spectroscopy in the drylands
Our results demonstrate that: (1) The used features for predicting different soil properties are correlated and most of them are attributed to soil Fe oxides, water content (WC), organic matter (OM) and clay minerals

Summary

Introduction

The assessment and monitoring key soil properties are important processes for quantifying soil quality and developing tools for soil management in general and precision agriculture in particular. Conventional laboratory methods for detecting soil properties and quality are expensive and time-consuming. Namely reflectance spectroscopy, has been proposed as a rapid, non-destructive, reproducible, and cost-effective analytical method for assessing soil properties and quality [1]. Field spectroscopy conducts in-situ soil spectral measurements directly and omits the steps of soil sampling and soil pretreatment, making it much faster and more effective than laboratory-based spectroscopy. It is much more suitable for soil mapping, large-scale monitoring and making real-time predictions [2]. One way to improve field spectroscopy accuracy in predicting soil properties is by applying advanced multivariate regression algorithms [3]

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