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Predicting Spatial Variations in Soil Nutrients with Hyperspectral Remote Sensing at Regional Scale

Rapid acquisition of the spatial distribution of soil nutrients holds great implications for farmland soil productivity safety, food security and agricultural management. To this end, we collected 1297 soil samples and measured the content of soil total nitrogen (TN), soil available phosphorus (AP)...

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Autores principales: Song, Ying-Qiang, Zhao, Xin, Su, Hui-Yue, Li, Bo, Hu, Yue-Ming, Cui, Xue-Sen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6163195/
https://www.ncbi.nlm.nih.gov/pubmed/30217092
http://dx.doi.org/10.3390/s18093086
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author Song, Ying-Qiang
Zhao, Xin
Su, Hui-Yue
Li, Bo
Hu, Yue-Ming
Cui, Xue-Sen
author_facet Song, Ying-Qiang
Zhao, Xin
Su, Hui-Yue
Li, Bo
Hu, Yue-Ming
Cui, Xue-Sen
author_sort Song, Ying-Qiang
collection PubMed
description Rapid acquisition of the spatial distribution of soil nutrients holds great implications for farmland soil productivity safety, food security and agricultural management. To this end, we collected 1297 soil samples and measured the content of soil total nitrogen (TN), soil available phosphorus (AP) and soil available potassium (AK) in Zengcheng, north of the Pearl River Delta, China. Hyperspectral remote sensing images (115 bands) of the Chinese Environmental 1A satellite were used as auxiliary variables and dimensionality reduction was performed using Pearson correlation analysis and principal component analysis. The TN, AP and AK of soil were predicted in the study area based on auxiliary variables after dimensionality reduction, along with stepwise linear regression (SLR), support vector machine (SVM), random forest (RF) and back-propagation neural network (BPNN) models; 324 independent points were used to verify the predictive performance. The BPNN model, which demonstrated the best predictive accuracy among all methods, combined ordinary kriging (OK) with mapping the spatial variations of soil nutrients. Results show that the BPNN model with double hidden layers had better predictive accuracy for soil TN (root mean square error (RMSE) = 0.409 mg kg(−1), R(2) = 44.24%), soil AP (RMSE = 40.808 mg kg(−1), R(2) = 42.91%) and soil AK (RMSE = 67.464 mg kg(−1), R(2) = 48.53%) compared with the SLR, SVM and RF models. The back propagation neural network-ordinary kriging (BPNNOK) model showed the best predictive results of soil TN (RMSE = 0.292 mg kg(−1), R(2) = 68.51%), soil AP (RMSE = 29.62 mg kg(−1), R(2) = 69.30%) and soil AK (RMSE = 49.67 mg kg(−1) and R(2) = 70.55%), indicating the best fitting ability between hyperspectral remote sensing bands and soil nutrients. According to the spatial mapping results of the BPNNOK model, concentrations of soil TN (north-central), soil AP (central and southwest) and soil AK (central and southeast) were respectively higher in the study area. The most important bands (464–517 nm) for soil TN (b10, b14, b20 and b21), soil AP (b3, b19 and b22) and soil AK (b4, b11, b12 and b25) exhibited the best response and sensitivity according to the SLR, SVM, RF and BPNN models. It was concluded that the application of hyperspectral images (visible-near-infrared data) with BPNNOK model was found to be an efficient method for mapping and monitoring soil nutrients at the regional scale.
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spelling pubmed-61631952018-10-10 Predicting Spatial Variations in Soil Nutrients with Hyperspectral Remote Sensing at Regional Scale Song, Ying-Qiang Zhao, Xin Su, Hui-Yue Li, Bo Hu, Yue-Ming Cui, Xue-Sen Sensors (Basel) Article Rapid acquisition of the spatial distribution of soil nutrients holds great implications for farmland soil productivity safety, food security and agricultural management. To this end, we collected 1297 soil samples and measured the content of soil total nitrogen (TN), soil available phosphorus (AP) and soil available potassium (AK) in Zengcheng, north of the Pearl River Delta, China. Hyperspectral remote sensing images (115 bands) of the Chinese Environmental 1A satellite were used as auxiliary variables and dimensionality reduction was performed using Pearson correlation analysis and principal component analysis. The TN, AP and AK of soil were predicted in the study area based on auxiliary variables after dimensionality reduction, along with stepwise linear regression (SLR), support vector machine (SVM), random forest (RF) and back-propagation neural network (BPNN) models; 324 independent points were used to verify the predictive performance. The BPNN model, which demonstrated the best predictive accuracy among all methods, combined ordinary kriging (OK) with mapping the spatial variations of soil nutrients. Results show that the BPNN model with double hidden layers had better predictive accuracy for soil TN (root mean square error (RMSE) = 0.409 mg kg(−1), R(2) = 44.24%), soil AP (RMSE = 40.808 mg kg(−1), R(2) = 42.91%) and soil AK (RMSE = 67.464 mg kg(−1), R(2) = 48.53%) compared with the SLR, SVM and RF models. The back propagation neural network-ordinary kriging (BPNNOK) model showed the best predictive results of soil TN (RMSE = 0.292 mg kg(−1), R(2) = 68.51%), soil AP (RMSE = 29.62 mg kg(−1), R(2) = 69.30%) and soil AK (RMSE = 49.67 mg kg(−1) and R(2) = 70.55%), indicating the best fitting ability between hyperspectral remote sensing bands and soil nutrients. According to the spatial mapping results of the BPNNOK model, concentrations of soil TN (north-central), soil AP (central and southwest) and soil AK (central and southeast) were respectively higher in the study area. The most important bands (464–517 nm) for soil TN (b10, b14, b20 and b21), soil AP (b3, b19 and b22) and soil AK (b4, b11, b12 and b25) exhibited the best response and sensitivity according to the SLR, SVM, RF and BPNN models. It was concluded that the application of hyperspectral images (visible-near-infrared data) with BPNNOK model was found to be an efficient method for mapping and monitoring soil nutrients at the regional scale. MDPI 2018-09-13 /pmc/articles/PMC6163195/ /pubmed/30217092 http://dx.doi.org/10.3390/s18093086 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Song, Ying-Qiang
Zhao, Xin
Su, Hui-Yue
Li, Bo
Hu, Yue-Ming
Cui, Xue-Sen
Predicting Spatial Variations in Soil Nutrients with Hyperspectral Remote Sensing at Regional Scale
title Predicting Spatial Variations in Soil Nutrients with Hyperspectral Remote Sensing at Regional Scale
title_full Predicting Spatial Variations in Soil Nutrients with Hyperspectral Remote Sensing at Regional Scale
title_fullStr Predicting Spatial Variations in Soil Nutrients with Hyperspectral Remote Sensing at Regional Scale
title_full_unstemmed Predicting Spatial Variations in Soil Nutrients with Hyperspectral Remote Sensing at Regional Scale
title_short Predicting Spatial Variations in Soil Nutrients with Hyperspectral Remote Sensing at Regional Scale
title_sort predicting spatial variations in soil nutrients with hyperspectral remote sensing at regional scale
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6163195/
https://www.ncbi.nlm.nih.gov/pubmed/30217092
http://dx.doi.org/10.3390/s18093086
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