Estimating the soil salinity over partially vegetated surfaces from multispectral remote sensing image using non-negative matrix factorization
Multispectral remote sensing technique has been extensively applied in recent years for the detection of soil salinity on bare soil; however, multispectral remote sensing is restricted in areas covered with vegetation, largely due to the mixed pixel problem. In the present study, non-negative matrix...
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| Published in: | Geoderma Vol. 354; p. 113887 |
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| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
15.11.2019
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| ISSN: | 0016-7061, 1872-6259 |
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| Abstract | Multispectral remote sensing technique has been extensively applied in recent years for the detection of soil salinity on bare soil; however, multispectral remote sensing is restricted in areas covered with vegetation, largely due to the mixed pixel problem. In the present study, non-negative matrix factorization (NMF) was implemented to separate soil spectral signal from mixed pixels of Landsat 5 Thematic Mapper (TM) to further estimate the soil salinity in a partially vegetated area. Four methods, namely, partial least squares regression (PLSR), least-squares support vector machine (LS-SVM), back propagation neural network (BPNN), and random forest (RF), were applied and compared. The results showed that the NMF-separated soil spectra could greatly improve the prediction accuracy compared with the mixed spectra, and among the four modeling methods, RF performed better than the rest of the methods, with the averaged results of determination of the prediction R2p = 0.67, a root mean square error of the prediction RMSEp = 0.73 ms cm−1, and the ratio of the standard deviation to RMSEp RPD = 1.61 after 100 times of random sampling and modeling. This approach could propose a new method for accurate and timely monitoring of soil salinity in a partially vegetation-covered area.
•Vegetation decreased the prediction accuracy of soil salinity.•NMF was used initially on multispectral remote sending image to alleviate vegetation effects.•Prediction accuracy of soil salinity was greatly improved from original models. |
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| AbstractList | Multispectral remote sensing technique has been extensively applied in recent years for the detection of soil salinity on bare soil; however, multispectral remote sensing is restricted in areas covered with vegetation, largely due to the mixed pixel problem. In the present study, non-negative matrix factorization (NMF) was implemented to separate soil spectral signal from mixed pixels of Landsat 5 Thematic Mapper (TM) to further estimate the soil salinity in a partially vegetated area. Four methods, namely, partial least squares regression (PLSR), least-squares support vector machine (LS-SVM), back propagation neural network (BPNN), and random forest (RF), were applied and compared. The results showed that the NMF-separated soil spectra could greatly improve the prediction accuracy compared with the mixed spectra, and among the four modeling methods, RF performed better than the rest of the methods, with the averaged results of determination of the prediction R2p = 0.67, a root mean square error of the prediction RMSEp = 0.73 ms cm−1, and the ratio of the standard deviation to RMSEp RPD = 1.61 after 100 times of random sampling and modeling. This approach could propose a new method for accurate and timely monitoring of soil salinity in a partially vegetation-covered area. Multispectral remote sensing technique has been extensively applied in recent years for the detection of soil salinity on bare soil; however, multispectral remote sensing is restricted in areas covered with vegetation, largely due to the mixed pixel problem. In the present study, non-negative matrix factorization (NMF) was implemented to separate soil spectral signal from mixed pixels of Landsat 5 Thematic Mapper (TM) to further estimate the soil salinity in a partially vegetated area. Four methods, namely, partial least squares regression (PLSR), least-squares support vector machine (LS-SVM), back propagation neural network (BPNN), and random forest (RF), were applied and compared. The results showed that the NMF-separated soil spectra could greatly improve the prediction accuracy compared with the mixed spectra, and among the four modeling methods, RF performed better than the rest of the methods, with the averaged results of determination of the prediction R2p = 0.67, a root mean square error of the prediction RMSEp = 0.73 ms cm−1, and the ratio of the standard deviation to RMSEp RPD = 1.61 after 100 times of random sampling and modeling. This approach could propose a new method for accurate and timely monitoring of soil salinity in a partially vegetation-covered area. •Vegetation decreased the prediction accuracy of soil salinity.•NMF was used initially on multispectral remote sending image to alleviate vegetation effects.•Prediction accuracy of soil salinity was greatly improved from original models. |
| ArticleNumber | 113887 |
| Author | Zhang, Fangfang Wang, Changkun Liu, Jie Xu, Aiai Pan, Kai Wu, Shiwen Pan, Xianzhang Liu, Ya |
| Author_xml | – sequence: 1 givenname: Ya surname: Liu fullname: Liu, Ya organization: Jinling Institute of Technology, Nanjing 211169, China – sequence: 2 givenname: Fangfang surname: Zhang fullname: Zhang, Fangfang organization: State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China – sequence: 3 givenname: Changkun surname: Wang fullname: Wang, Changkun organization: State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China – sequence: 4 givenname: Shiwen surname: Wu fullname: Wu, Shiwen organization: State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China – sequence: 5 givenname: Jie surname: Liu fullname: Liu, Jie organization: State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China – sequence: 6 givenname: Aiai surname: Xu fullname: Xu, Aiai organization: State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China – sequence: 7 givenname: Kai surname: Pan fullname: Pan, Kai organization: State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China – sequence: 8 givenname: Xianzhang surname: Pan fullname: Pan, Xianzhang email: panxz@issas.ac.cn organization: State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China |
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| Keywords | Mixed pixel Multispectral imaging Non-negative matrix factorization Soil salinity Prediction |
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