Laboratory Visible and Near-Infrared Spectroscopy with Genetic Algorithm-Based Partial Least Squares Regression for Assessing the Soil Phosphorus Content of Upland and Lowland Rice Fields in Madagascar
As a laboratory proximal sensing technique, the capability of visible and near-infrared (Vis-NIR) diffused reflectance spectroscopy with partial least squares (PLS) regression to determine soil properties has previously been demonstrated. However, the evaluation of the soil phosphorus (P) content&am...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-03-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2072-4292/11/5/506 |
| _version_ | 1818975079487242240 |
|---|---|
| author | Kensuke Kawamura Yasuhiro Tsujimoto Tomohiro Nishigaki Andry Andriamananjara Michel Rabenarivo Hidetoshi Asai Tovohery Rakotoson Tantely Razafimbelo |
| author_facet | Kensuke Kawamura Yasuhiro Tsujimoto Tomohiro Nishigaki Andry Andriamananjara Michel Rabenarivo Hidetoshi Asai Tovohery Rakotoson Tantely Razafimbelo |
| author_sort | Kensuke Kawamura |
| collection | DOAJ |
| description | As a laboratory proximal sensing technique, the capability of visible and near-infrared (Vis-NIR) diffused reflectance spectroscopy with partial least squares (PLS) regression to determine soil properties has previously been demonstrated. However, the evaluation of the soil phosphorus (P) content—a major nutrient constraint for crop production in the tropics—is still a challenging task. PLS regression with waveband selection can improve the predictive ability of a calibration model, and a genetic algorithm (GA) has been widely applied as a suitable method for selecting wavebands in laboratory calibrations. To develop a laboratory-based proximal sensing method, this study investigated the potential to use GA-PLS regression analyses to estimate oxalate-extractable P in upland and lowland soils from laboratory Vis-NIR reflectance data. In terms of predictive ability, GA-PLS regression was compared with iterative stepwise elimination PLS (ISE-PLS) regression and standard full-spectrum PLS (FS-PLS) regression using soil samples collected in 2015 and 2016 from the surface of upland and lowland rice fields in Madagascar (n = 103). Overall, the GA-PLS model using first derivative reflectance (FDR) had the best predictive accuracy (R2 = 0.796) with a good prediction ability (residual predictive deviation (RPD) = 2.211). Selected wavebands in the GA-PLS model did not perfectly match wavelengths of previously known absorption features of soil nutrients, but in most cases, the selected wavebands were within 20 nm of previously known wavelength regions. Bootstrap procedures (N = 10,000 times) using selected wavebands also confirmed the improvements in accuracy and robustness of the GA-PLS model compared to those of the ISE-PLS and FS-PLS models. These results suggest that soil oxalate-extractable P can be predicted from Vis-NIR spectroscopy and that GA-PLS regression has the advantage of tuning optimum bands for PLS regression, contributing to a better predictive ability. |
| first_indexed | 2024-12-20T15:50:15Z |
| format | Article |
| id | doaj.art-47ed36a546ea4e57848075cb5426044a |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-20T15:50:15Z |
| publishDate | 2019-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-47ed36a546ea4e57848075cb5426044a2022-12-21T19:34:43ZengMDPI AGRemote Sensing2072-42922019-03-0111550610.3390/rs11050506rs11050506Laboratory Visible and Near-Infrared Spectroscopy with Genetic Algorithm-Based Partial Least Squares Regression for Assessing the Soil Phosphorus Content of Upland and Lowland Rice Fields in MadagascarKensuke Kawamura0Yasuhiro Tsujimoto1Tomohiro Nishigaki2Andry Andriamananjara3Michel Rabenarivo4Hidetoshi Asai5Tovohery Rakotoson6Tantely Razafimbelo7Japan International Research Center for Agricultural Sciences (JIRCAS), 1-1 Ohwashi, Tsukuba, Ibaraki 305-8686, JapanJapan International Research Center for Agricultural Sciences (JIRCAS), 1-1 Ohwashi, Tsukuba, Ibaraki 305-8686, JapanJapan International Research Center for Agricultural Sciences (JIRCAS), 1-1 Ohwashi, Tsukuba, Ibaraki 305-8686, JapanLaboratoire des Radio-Isotopes, Université d’Antananarivo, BP 3383, Route d’Andraisoro, 101 Antananarivo, MadagascarLaboratoire des Radio-Isotopes, Université d’Antananarivo, BP 3383, Route d’Andraisoro, 101 Antananarivo, MadagascarJapan International Research Center for Agricultural Sciences (JIRCAS), 1-1 Ohwashi, Tsukuba, Ibaraki 305-8686, JapanLaboratoire des Radio-Isotopes, Université d’Antananarivo, BP 3383, Route d’Andraisoro, 101 Antananarivo, MadagascarLaboratoire des Radio-Isotopes, Université d’Antananarivo, BP 3383, Route d’Andraisoro, 101 Antananarivo, MadagascarAs a laboratory proximal sensing technique, the capability of visible and near-infrared (Vis-NIR) diffused reflectance spectroscopy with partial least squares (PLS) regression to determine soil properties has previously been demonstrated. However, the evaluation of the soil phosphorus (P) content—a major nutrient constraint for crop production in the tropics—is still a challenging task. PLS regression with waveband selection can improve the predictive ability of a calibration model, and a genetic algorithm (GA) has been widely applied as a suitable method for selecting wavebands in laboratory calibrations. To develop a laboratory-based proximal sensing method, this study investigated the potential to use GA-PLS regression analyses to estimate oxalate-extractable P in upland and lowland soils from laboratory Vis-NIR reflectance data. In terms of predictive ability, GA-PLS regression was compared with iterative stepwise elimination PLS (ISE-PLS) regression and standard full-spectrum PLS (FS-PLS) regression using soil samples collected in 2015 and 2016 from the surface of upland and lowland rice fields in Madagascar (n = 103). Overall, the GA-PLS model using first derivative reflectance (FDR) had the best predictive accuracy (R2 = 0.796) with a good prediction ability (residual predictive deviation (RPD) = 2.211). Selected wavebands in the GA-PLS model did not perfectly match wavelengths of previously known absorption features of soil nutrients, but in most cases, the selected wavebands were within 20 nm of previously known wavelength regions. Bootstrap procedures (N = 10,000 times) using selected wavebands also confirmed the improvements in accuracy and robustness of the GA-PLS model compared to those of the ISE-PLS and FS-PLS models. These results suggest that soil oxalate-extractable P can be predicted from Vis-NIR spectroscopy and that GA-PLS regression has the advantage of tuning optimum bands for PLS regression, contributing to a better predictive ability.http://www.mdpi.com/2072-4292/11/5/506Madagascaroxalate-extractable soil Ppartial least squares regressionsoil fertilityspectral assessmentswaveband selection |
| spellingShingle | Kensuke Kawamura Yasuhiro Tsujimoto Tomohiro Nishigaki Andry Andriamananjara Michel Rabenarivo Hidetoshi Asai Tovohery Rakotoson Tantely Razafimbelo Laboratory Visible and Near-Infrared Spectroscopy with Genetic Algorithm-Based Partial Least Squares Regression for Assessing the Soil Phosphorus Content of Upland and Lowland Rice Fields in Madagascar Remote Sensing Madagascar oxalate-extractable soil P partial least squares regression soil fertility spectral assessments waveband selection |
| title | Laboratory Visible and Near-Infrared Spectroscopy with Genetic Algorithm-Based Partial Least Squares Regression for Assessing the Soil Phosphorus Content of Upland and Lowland Rice Fields in Madagascar |
| title_full | Laboratory Visible and Near-Infrared Spectroscopy with Genetic Algorithm-Based Partial Least Squares Regression for Assessing the Soil Phosphorus Content of Upland and Lowland Rice Fields in Madagascar |
| title_fullStr | Laboratory Visible and Near-Infrared Spectroscopy with Genetic Algorithm-Based Partial Least Squares Regression for Assessing the Soil Phosphorus Content of Upland and Lowland Rice Fields in Madagascar |
| title_full_unstemmed | Laboratory Visible and Near-Infrared Spectroscopy with Genetic Algorithm-Based Partial Least Squares Regression for Assessing the Soil Phosphorus Content of Upland and Lowland Rice Fields in Madagascar |
| title_short | Laboratory Visible and Near-Infrared Spectroscopy with Genetic Algorithm-Based Partial Least Squares Regression for Assessing the Soil Phosphorus Content of Upland and Lowland Rice Fields in Madagascar |
| title_sort | laboratory visible and near infrared spectroscopy with genetic algorithm based partial least squares regression for assessing the soil phosphorus content of upland and lowland rice fields in madagascar |
| topic | Madagascar oxalate-extractable soil P partial least squares regression soil fertility spectral assessments waveband selection |
| url | http://www.mdpi.com/2072-4292/11/5/506 |
| work_keys_str_mv | AT kensukekawamura laboratoryvisibleandnearinfraredspectroscopywithgeneticalgorithmbasedpartialleastsquaresregressionforassessingthesoilphosphoruscontentofuplandandlowlandricefieldsinmadagascar AT yasuhirotsujimoto laboratoryvisibleandnearinfraredspectroscopywithgeneticalgorithmbasedpartialleastsquaresregressionforassessingthesoilphosphoruscontentofuplandandlowlandricefieldsinmadagascar AT tomohironishigaki laboratoryvisibleandnearinfraredspectroscopywithgeneticalgorithmbasedpartialleastsquaresregressionforassessingthesoilphosphoruscontentofuplandandlowlandricefieldsinmadagascar AT andryandriamananjara laboratoryvisibleandnearinfraredspectroscopywithgeneticalgorithmbasedpartialleastsquaresregressionforassessingthesoilphosphoruscontentofuplandandlowlandricefieldsinmadagascar AT michelrabenarivo laboratoryvisibleandnearinfraredspectroscopywithgeneticalgorithmbasedpartialleastsquaresregressionforassessingthesoilphosphoruscontentofuplandandlowlandricefieldsinmadagascar AT hidetoshiasai laboratoryvisibleandnearinfraredspectroscopywithgeneticalgorithmbasedpartialleastsquaresregressionforassessingthesoilphosphoruscontentofuplandandlowlandricefieldsinmadagascar AT tovoheryrakotoson laboratoryvisibleandnearinfraredspectroscopywithgeneticalgorithmbasedpartialleastsquaresregressionforassessingthesoilphosphoruscontentofuplandandlowlandricefieldsinmadagascar AT tantelyrazafimbelo laboratoryvisibleandnearinfraredspectroscopywithgeneticalgorithmbasedpartialleastsquaresregressionforassessingthesoilphosphoruscontentofuplandandlowlandricefieldsinmadagascar |