Next Article in Journal
A Novel De-Noising Method for Improving the Performance of Full-Waveform LiDAR Using Differential Optical Path
Next Article in Special Issue
Combining Partial Least Squares and the Gradient-Boosting Method for Soil Property Retrieval Using Visible Near-Infrared Shortwave Infrared Spectra
Previous Article in Journal
Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry
Previous Article in Special Issue
Evaluation of the U.S. Geological Survey Landsat Burned Area Essential Climate Variable across the Conterminous U.S. Using Commercial High-Resolution Imagery
Open AccessArticle

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

School of Information and Computer Science, Anhui Agricultural University, Hefei 230036, China
The Remote Sensing Laboratory, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer Campus 84990, Israel
The Department of Sensing, Information and Mechanization Engineering, Institute of Agricultural Engineering, Agricultural Research Organization (ARO), Volcani Center, Rishon LeZion 7528809, Israel
Authors to whom correspondence should be addressed.
Remote Sens. 2017, 9(11), 1099;
Received: 18 September 2017 / Revised: 23 October 2017 / Accepted: 23 October 2017 / Published: 30 October 2017
(This article belongs to the Special Issue Quantitative Remote Sensing of Land Surface Variables)
PDF [1818 KB, uploaded 30 October 2017]


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. View Full-Text
Keywords: visible-near infrared and shortwave infrared (VNIR/SWIR); soil measurement; dirty model; partial least squares regression (PLS-R); regularization; shared features visible-near infrared and shortwave infrared (VNIR/SWIR); soil measurement; dirty model; partial least squares regression (PLS-R); regularization; shared features

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material


Share & Cite This Article

MDPI and ACS Style

Qi, H.; Paz-Kagan, T.; Karnieli, A.; Li, S. Linear Multi-Task Learning for Predicting Soil Properties Using Field Spectroscopy. Remote Sens. 2017, 9, 1099.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top