VisNIR spectra of dried ground soils predict properties of soils scanned moist and intact
In this paper we investigated the possibility of using the VisNIR spectra of dry ground soils to predict properties of soils scanned in their natural physical state with unknown water content. We regard this as an important nexus between large soil spectral libraries (based on dried and ground soil...
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description | In this paper we investigated the possibility of using the VisNIR spectra of dry ground soils to predict properties of soils scanned in their natural physical state with unknown water content. We regard this as an important nexus between large soil spectral libraries (based on dried and ground soil samples) and in-field use of VisNIR where variable soil moisture and secondary structure affect predictions of soil properties of interest. External parameter orthogonalization — EPO — was used in developing a partial least squares regression (PLSR) model to predict clay and organic C contents of soil samples with variable moisture contents. The Texas Soil Spectral Library based on spectra of dried and ground soil samples along with spectra of intact and dried and ground soil cores from Central Texas USA were used for EPO–PLSR model calibration and validation.
Using EPO, three matrices were developed to project VisNIR reflectance spectra to a subspace insensitive to soil moisture. The first matrix P1 was developed using spectra collected from rewetted samples that had been dried and ground. The second and third matrices, P2 and P3 were developed using spectra from soil core samples that were in their natural physical state when scanned and either field-moist (P2), or air-dried (P3). The results showed that EPO–PLSR successfully removed the effect of moisture without knowing the moisture at time of scanning and substantially improved the prediction of clay content compared to organic C content. For clay content, the validation results were as follows: No correction, R2=0.63, RMSEP=355gkg−1; P1 correction, R2=0.73, RMSEP=141gkg−1; and P2 correction, R2=0.77, RMSEP=90gkg−1. For organic C content, the validation statistics were: No correction, R2=0.49, RMSEP=9.4gkg−1; P1 correction, R2=0.51, RMSEP=7.5gkg−1; and P2 correction, R2=0.53, RMSEP=7.3gkg−1. Corrections of soil intactness alone had the following results for clay content: No correction RMSEP=125gkg−1 and P3 correction RMSEP=97gkg−1, and for organic C content: No correction RMSEP=7.5gkg−1, and P3 correction RMSEP=7.4gkg−1. Model results using the P2 matrix were consistently better than using P1. Particularly in predicting clay, P2 reduced the bias, non-unity, and lack of correlation, possibly because P2 accounted for the effect of natural aggregation of the soil in addition to soil moisture. Improvements for correction for intactness alone were small in clay and not significant in organic C content. We conclu |
doi_str_mv | 10.1016/j.geoderma.2014.01.011 |
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Using EPO, three matrices were developed to project VisNIR reflectance spectra to a subspace insensitive to soil moisture. The first matrix P1 was developed using spectra collected from rewetted samples that had been dried and ground. The second and third matrices, P2 and P3 were developed using spectra from soil core samples that were in their natural physical state when scanned and either field-moist (P2), or air-dried (P3). The results showed that EPO–PLSR successfully removed the effect of moisture without knowing the moisture at time of scanning and substantially improved the prediction of clay content compared to organic C content. For clay content, the validation results were as follows: No correction, R2=0.63, RMSEP=355gkg−1; P1 correction, R2=0.73, RMSEP=141gkg−1; and P2 correction, R2=0.77, RMSEP=90gkg−1. For organic C content, the validation statistics were: No correction, R2=0.49, RMSEP=9.4gkg−1; P1 correction, R2=0.51, RMSEP=7.5gkg−1; and P2 correction, R2=0.53, RMSEP=7.3gkg−1. Corrections of soil intactness alone had the following results for clay content: No correction RMSEP=125gkg−1 and P3 correction RMSEP=97gkg−1, and for organic C content: No correction RMSEP=7.5gkg−1, and P3 correction RMSEP=7.4gkg−1. Model results using the P2 matrix were consistently better than using P1. Particularly in predicting clay, P2 reduced the bias, non-unity, and lack of correlation, possibly because P2 accounted for the effect of natural aggregation of the soil in addition to soil moisture. Improvements for correction for intactness alone were small in clay and not significant in organic C content. We concluded that it is feasible to apply the EPO algorithm to employ VisNIR models from dried ground spectral libraries for prediction of soil properties based on field scans of soils in the natural physical state and at variable water contents.
•Lab-based VisNIR models can be used to predict properties of moist intact spectra.•The EPO algorithm removed the effect of soil moisture in VisNIR spectra.•Prior information on soil moisture is not needed for the EPO.•Removing the effect of moisture and intactness gave the best result.•The matrix used to remove the soil moisture effects was similar to another study.•The EPO correction has a more effect on the clay prediction than organic C.</description><identifier>ISSN: 0016-7061</identifier><identifier>EISSN: 1872-6259</identifier><identifier>DOI: 10.1016/j.geoderma.2014.01.011</identifier><identifier>CODEN: GEDMAB</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Agronomy. Soil science and plant productions ; Biological and medical sciences ; Calibration ; Clay ; Earth sciences ; Earth, ocean, space ; Exact sciences and technology ; External parameter orthogonalization ; Fundamental and applied biological sciences. Psychology ; Grounds ; Libraries ; Mathematical models ; Moisture content ; Organic carbon ; Partial least squares regression ; Proximal sensing ; Regression ; Soil spectral library ; Soils ; Spectra ; Surficial geology</subject><ispartof>Geoderma, 2014-06, Vol.221-222, p.61-69</ispartof><rights>2014 Elsevier B.V.</rights><rights>2015 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a431t-46d03a0ac3e0f8abad19d6421eb3193d89172da4eb69693d73891ee16d477a5f3</citedby><cites>FETCH-LOGICAL-a431t-46d03a0ac3e0f8abad19d6421eb3193d89172da4eb69693d73891ee16d477a5f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0016706114000202$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65534</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=28361181$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Ge, Yufeng</creatorcontrib><creatorcontrib>Morgan, Cristine L.S.</creatorcontrib><creatorcontrib>Ackerson, Jason P.</creatorcontrib><title>VisNIR spectra of dried ground soils predict properties of soils scanned moist and intact</title><title>Geoderma</title><description>In this paper we investigated the possibility of using the VisNIR spectra of dry ground soils to predict properties of soils scanned in their natural physical state with unknown water content. We regard this as an important nexus between large soil spectral libraries (based on dried and ground soil samples) and in-field use of VisNIR where variable soil moisture and secondary structure affect predictions of soil properties of interest. External parameter orthogonalization — EPO — was used in developing a partial least squares regression (PLSR) model to predict clay and organic C contents of soil samples with variable moisture contents. The Texas Soil Spectral Library based on spectra of dried and ground soil samples along with spectra of intact and dried and ground soil cores from Central Texas USA were used for EPO–PLSR model calibration and validation.
Using EPO, three matrices were developed to project VisNIR reflectance spectra to a subspace insensitive to soil moisture. The first matrix P1 was developed using spectra collected from rewetted samples that had been dried and ground. The second and third matrices, P2 and P3 were developed using spectra from soil core samples that were in their natural physical state when scanned and either field-moist (P2), or air-dried (P3). The results showed that EPO–PLSR successfully removed the effect of moisture without knowing the moisture at time of scanning and substantially improved the prediction of clay content compared to organic C content. For clay content, the validation results were as follows: No correction, R2=0.63, RMSEP=355gkg−1; P1 correction, R2=0.73, RMSEP=141gkg−1; and P2 correction, R2=0.77, RMSEP=90gkg−1. For organic C content, the validation statistics were: No correction, R2=0.49, RMSEP=9.4gkg−1; P1 correction, R2=0.51, RMSEP=7.5gkg−1; and P2 correction, R2=0.53, RMSEP=7.3gkg−1. Corrections of soil intactness alone had the following results for clay content: No correction RMSEP=125gkg−1 and P3 correction RMSEP=97gkg−1, and for organic C content: No correction RMSEP=7.5gkg−1, and P3 correction RMSEP=7.4gkg−1. Model results using the P2 matrix were consistently better than using P1. Particularly in predicting clay, P2 reduced the bias, non-unity, and lack of correlation, possibly because P2 accounted for the effect of natural aggregation of the soil in addition to soil moisture. Improvements for correction for intactness alone were small in clay and not significant in organic C content. We concluded that it is feasible to apply the EPO algorithm to employ VisNIR models from dried ground spectral libraries for prediction of soil properties based on field scans of soils in the natural physical state and at variable water contents.
•Lab-based VisNIR models can be used to predict properties of moist intact spectra.•The EPO algorithm removed the effect of soil moisture in VisNIR spectra.•Prior information on soil moisture is not needed for the EPO.•Removing the effect of moisture and intactness gave the best result.•The matrix used to remove the soil moisture effects was similar to another study.•The EPO correction has a more effect on the clay prediction than organic C.</description><subject>Agronomy. Soil science and plant productions</subject><subject>Biological and medical sciences</subject><subject>Calibration</subject><subject>Clay</subject><subject>Earth sciences</subject><subject>Earth, ocean, space</subject><subject>Exact sciences and technology</subject><subject>External parameter orthogonalization</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>Grounds</subject><subject>Libraries</subject><subject>Mathematical models</subject><subject>Moisture content</subject><subject>Organic carbon</subject><subject>Partial least squares regression</subject><subject>Proximal sensing</subject><subject>Regression</subject><subject>Soil spectral library</subject><subject>Soils</subject><subject>Spectra</subject><subject>Surficial geology</subject><issn>0016-7061</issn><issn>1872-6259</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><recordid>eNqFkcFqHDEMhk1poNukr1DmUshltpY965m5NYQ0CYQGQlvoyWhtTfAyO95Ys4G8fbVs2mtAIGR9v2R-KfUZ9BI0uK-b5SPlSGWLS6OhWWqQgHdqAV1ramdW_Xu10ELWrXbwQX1k3kjZaqMX6s_vxD9uHyreUZgLVnmoYkkUq8eS91OsOKeRq12hmMIsOe-ozIn4AB57HHCaRLDNiecKRZOmGcN8pk4GHJk-veZT9ev71c_Lm_ru_vr28uKuxsbCXDcuaosagyU9dLjGCH10jQFaW-ht7HpoTcSG1q53UrdWXojAxaZtcTXYU3V-nCufe9oTz36bONA44kR5zx5kmIWV0c3bqFDW6R6MoO6IhpKZCw1-V9IWy4sH7Q-2-43_Z7s_2O41SIAIv7zuQHFmHApOIfF_temsA-gO3LcjR-LNc6LiOSSaghhd5BQ-5vTWqr_t9Jt1</recordid><startdate>20140601</startdate><enddate>20140601</enddate><creator>Ge, Yufeng</creator><creator>Morgan, Cristine L.S.</creator><creator>Ackerson, Jason P.</creator><general>Elsevier B.V</general><general>Elsevier</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7UA</scope><scope>C1K</scope><scope>F1W</scope><scope>H96</scope><scope>L.G</scope><scope>8FD</scope><scope>FR3</scope><scope>KR7</scope></search><sort><creationdate>20140601</creationdate><title>VisNIR spectra of dried ground soils predict properties of soils scanned moist and intact</title><author>Ge, Yufeng ; Morgan, Cristine L.S. ; Ackerson, Jason P.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a431t-46d03a0ac3e0f8abad19d6421eb3193d89172da4eb69693d73891ee16d477a5f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Agronomy. Soil science and plant productions</topic><topic>Biological and medical sciences</topic><topic>Calibration</topic><topic>Clay</topic><topic>Earth sciences</topic><topic>Earth, ocean, space</topic><topic>Exact sciences and technology</topic><topic>External parameter orthogonalization</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>Grounds</topic><topic>Libraries</topic><topic>Mathematical models</topic><topic>Moisture content</topic><topic>Organic carbon</topic><topic>Partial least squares regression</topic><topic>Proximal sensing</topic><topic>Regression</topic><topic>Soil spectral library</topic><topic>Soils</topic><topic>Spectra</topic><topic>Surficial geology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ge, Yufeng</creatorcontrib><creatorcontrib>Morgan, Cristine L.S.</creatorcontrib><creatorcontrib>Ackerson, Jason P.</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Water Resources Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><jtitle>Geoderma</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ge, Yufeng</au><au>Morgan, Cristine L.S.</au><au>Ackerson, Jason P.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>VisNIR spectra of dried ground soils predict properties of soils scanned moist and intact</atitle><jtitle>Geoderma</jtitle><date>2014-06-01</date><risdate>2014</risdate><volume>221-222</volume><spage>61</spage><epage>69</epage><pages>61-69</pages><issn>0016-7061</issn><eissn>1872-6259</eissn><coden>GEDMAB</coden><abstract>In this paper we investigated the possibility of using the VisNIR spectra of dry ground soils to predict properties of soils scanned in their natural physical state with unknown water content. We regard this as an important nexus between large soil spectral libraries (based on dried and ground soil samples) and in-field use of VisNIR where variable soil moisture and secondary structure affect predictions of soil properties of interest. External parameter orthogonalization — EPO — was used in developing a partial least squares regression (PLSR) model to predict clay and organic C contents of soil samples with variable moisture contents. The Texas Soil Spectral Library based on spectra of dried and ground soil samples along with spectra of intact and dried and ground soil cores from Central Texas USA were used for EPO–PLSR model calibration and validation.
Using EPO, three matrices were developed to project VisNIR reflectance spectra to a subspace insensitive to soil moisture. The first matrix P1 was developed using spectra collected from rewetted samples that had been dried and ground. The second and third matrices, P2 and P3 were developed using spectra from soil core samples that were in their natural physical state when scanned and either field-moist (P2), or air-dried (P3). The results showed that EPO–PLSR successfully removed the effect of moisture without knowing the moisture at time of scanning and substantially improved the prediction of clay content compared to organic C content. For clay content, the validation results were as follows: No correction, R2=0.63, RMSEP=355gkg−1; P1 correction, R2=0.73, RMSEP=141gkg−1; and P2 correction, R2=0.77, RMSEP=90gkg−1. For organic C content, the validation statistics were: No correction, R2=0.49, RMSEP=9.4gkg−1; P1 correction, R2=0.51, RMSEP=7.5gkg−1; and P2 correction, R2=0.53, RMSEP=7.3gkg−1. Corrections of soil intactness alone had the following results for clay content: No correction RMSEP=125gkg−1 and P3 correction RMSEP=97gkg−1, and for organic C content: No correction RMSEP=7.5gkg−1, and P3 correction RMSEP=7.4gkg−1. Model results using the P2 matrix were consistently better than using P1. Particularly in predicting clay, P2 reduced the bias, non-unity, and lack of correlation, possibly because P2 accounted for the effect of natural aggregation of the soil in addition to soil moisture. Improvements for correction for intactness alone were small in clay and not significant in organic C content. We concluded that it is feasible to apply the EPO algorithm to employ VisNIR models from dried ground spectral libraries for prediction of soil properties based on field scans of soils in the natural physical state and at variable water contents.
•Lab-based VisNIR models can be used to predict properties of moist intact spectra.•The EPO algorithm removed the effect of soil moisture in VisNIR spectra.•Prior information on soil moisture is not needed for the EPO.•Removing the effect of moisture and intactness gave the best result.•The matrix used to remove the soil moisture effects was similar to another study.•The EPO correction has a more effect on the clay prediction than organic C.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.geoderma.2014.01.011</doi><tpages>9</tpages></addata></record> |
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subjects | Agronomy. Soil science and plant productions Biological and medical sciences Calibration Clay Earth sciences Earth, ocean, space Exact sciences and technology External parameter orthogonalization Fundamental and applied biological sciences. Psychology Grounds Libraries Mathematical models Moisture content Organic carbon Partial least squares regression Proximal sensing Regression Soil spectral library Soils Spectra Surficial geology |
title | VisNIR spectra of dried ground soils predict properties of soils scanned moist and intact |
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