Next Article in Journal
Remote-Sensing Hydraulic Characterization of Channel Habitat Units in a Tropical Montane River: Bladen River, Belize
Next Article in Special Issue
A Land Product Characterization System for Comparative Analysis of Satellite Data and Products
Previous Article in Journal
Research on the Parallelization of the DBSCAN Clustering Algorithm for Spatial Data Mining Based on the Spark Platform
Previous Article in Special Issue
Linear Multi-Task Learning for Predicting Soil Properties Using Field Spectroscopy
Open AccessArticle

Combining Partial Least Squares and the Gradient-Boosting Method for Soil Property Retrieval Using Visible Near-Infrared Shortwave Infrared Spectra

Institute for Cartography, TU Dresden, 01062 Dresden, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2017, 9(12), 1299; https://doi.org/10.3390/rs9121299
Received: 12 October 2017 / Revised: 5 December 2017 / Accepted: 10 December 2017 / Published: 12 December 2017
(This article belongs to the Special Issue Quantitative Remote Sensing of Land Surface Variables)
Soil spectroscopy has experienced a tremendous increase in soil property characterisation, and can be used not only in the laboratory but also from the space (imaging spectroscopy). Partial least squares (PLS) regression is one of the most common approaches for the calibration of soil properties using soil spectra. Besides functioning as a calibration method, PLS can also be used as a dimension reduction tool, which has scarcely been studied in soil spectroscopy. PLS components retained from high-dimensional spectral data can further be explored with the gradient-boosted decision tree (GBDT) method. Three soil sample categories were extracted from the Land Use/Land Cover Area Frame Survey (LUCAS) soil library according to the type of land cover (woodland, grassland, and cropland). First, PLS regression and GBDT were separately applied to build the spectroscopic models for soil organic carbon (OC), total nitrogen content (N), and clay for each soil category. Then, PLS-derived components were used as input variables for the GBDT model. The results demonstrate that the combined PLS-GBDT approach has better performance than PLS or GBDT alone. The relative important variables for soil property estimation revealed by the proposed method demonstrated that the PLS method is a useful dimension reduction tool for soil spectra to retain target-related information. View Full-Text
Keywords: PLS; gradient-boosted decision trees; soil spectroscopy; LUCAS PLS; gradient-boosted decision trees; soil spectroscopy; LUCAS
Show Figures

Figure 1

MDPI and ACS Style

Liu, L.; Ji, M.; Buchroithner, M. Combining Partial Least Squares and the Gradient-Boosting Method for Soil Property Retrieval Using Visible Near-Infrared Shortwave Infrared Spectra. Remote Sens. 2017, 9, 1299.

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.

Article Access Map by Country/Region

1
Back to TopTop