Combining visible near‐infrared spectroscopy and water vapor sorption for soil specific surface area estimation
Abstract The soil specific surface area (SSA) is a fundamental property governing a range of soil processes relevant to engineering, environmental, and agricultural applications. A method for SSA determination based on a combination of visible near‐infrared spectroscopy (vis‐NIRS) and vapor sorption...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Vadose Zone Journal |
| Online Access: | https://doi.org/10.1002/vzj2.20007 |
| _version_ | 1819086113257553920 |
|---|---|
| author | Maria Knadel Lis Wollesen deJonge Markus Tuller Hafeez Ur Rehman Peter Weber Jensen Per Moldrup Mogens H. Greve Emmanuel Arthur |
| author_facet | Maria Knadel Lis Wollesen deJonge Markus Tuller Hafeez Ur Rehman Peter Weber Jensen Per Moldrup Mogens H. Greve Emmanuel Arthur |
| author_sort | Maria Knadel |
| collection | DOAJ |
| description | Abstract The soil specific surface area (SSA) is a fundamental property governing a range of soil processes relevant to engineering, environmental, and agricultural applications. A method for SSA determination based on a combination of visible near‐infrared spectroscopy (vis‐NIRS) and vapor sorption isotherm measurements was proposed. Two models for water vapor sorption isotherms (WSIs) were used: the Tuller–Or (TO) and the Guggenheim–Anderson–de Boer (GAB) model. They were parameterized with sorption isotherm measurements and applied for SSA estimation for a wide range of soils (N = 270) from 27 countries. The generated vis‐NIRS models were compared with models where the SSA was determined with the ethylene glycol monoethyl ether (EGME) method. Different regression techniques were tested and included partial least squares (PLS), support vector machines (SVM), and artificial neural networks (ANN). The effect of dataset subdivision based on EGME values on model performance was also tested. Successful calibration models for SSATO and SSAGAB were generated and were nearly identical to that of SSAEGME. The performance of models was dependent on the range and variation in SSA values. However, the comparison using selected validation samples indicated no significant differences in the estimated SSATO, SSAGAB, and SSAEGME, with an average standardized RMSE (SRMSE = RMSE/range) of 0.07, 0.06 and 0.07, respectively. Small differences among the regression techniques were found, yet SVM performed best. The results of this study indicate that the combination of vis‐NIRS with the WSI as a reference technique for vis‐NIRS models provides SSA estimations akin to the EGME method. |
| first_indexed | 2024-12-21T21:15:05Z |
| format | Article |
| id | doaj.art-b646a5f20af8450cae6388c0c9c45589 |
| institution | Directory Open Access Journal |
| issn | 1539-1663 |
| language | English |
| last_indexed | 2024-12-21T21:15:05Z |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Vadose Zone Journal |
| spelling | doaj.art-b646a5f20af8450cae6388c0c9c455892022-12-21T18:50:01ZengWileyVadose Zone Journal1539-16632020-01-01191n/an/a10.1002/vzj2.20007Combining visible near‐infrared spectroscopy and water vapor sorption for soil specific surface area estimationMaria Knadel0Lis Wollesen deJonge1Markus Tuller2Hafeez Ur Rehman3Peter Weber Jensen4Per Moldrup5Mogens H. Greve6Emmanuel Arthur7Dep. of Agroecology Aarhus Univ. Blichers Allé 20 DK‐8830 Tjele DenmarkDep. of Agroecology Aarhus Univ. Blichers Allé 20 DK‐8830 Tjele DenmarkDep. of Environmental Science The Univ. of Arizona 1177 E. 4th St. Tucson AZ 85721 USADep. of Agroecology Aarhus Univ. Blichers Allé 20 DK‐8830 Tjele DenmarkDep. of Agroecology Aarhus Univ. Blichers Allé 20 DK‐8830 Tjele DenmarkDep. of Civil Engineering Aalborg Univ. Thomas Manns Vej 23 Aalborg Ø 9200 DenmarkDep. of Agroecology Aarhus Univ. Blichers Allé 20 DK‐8830 Tjele DenmarkDep. of Agroecology Aarhus Univ. Blichers Allé 20 DK‐8830 Tjele DenmarkAbstract The soil specific surface area (SSA) is a fundamental property governing a range of soil processes relevant to engineering, environmental, and agricultural applications. A method for SSA determination based on a combination of visible near‐infrared spectroscopy (vis‐NIRS) and vapor sorption isotherm measurements was proposed. Two models for water vapor sorption isotherms (WSIs) were used: the Tuller–Or (TO) and the Guggenheim–Anderson–de Boer (GAB) model. They were parameterized with sorption isotherm measurements and applied for SSA estimation for a wide range of soils (N = 270) from 27 countries. The generated vis‐NIRS models were compared with models where the SSA was determined with the ethylene glycol monoethyl ether (EGME) method. Different regression techniques were tested and included partial least squares (PLS), support vector machines (SVM), and artificial neural networks (ANN). The effect of dataset subdivision based on EGME values on model performance was also tested. Successful calibration models for SSATO and SSAGAB were generated and were nearly identical to that of SSAEGME. The performance of models was dependent on the range and variation in SSA values. However, the comparison using selected validation samples indicated no significant differences in the estimated SSATO, SSAGAB, and SSAEGME, with an average standardized RMSE (SRMSE = RMSE/range) of 0.07, 0.06 and 0.07, respectively. Small differences among the regression techniques were found, yet SVM performed best. The results of this study indicate that the combination of vis‐NIRS with the WSI as a reference technique for vis‐NIRS models provides SSA estimations akin to the EGME method.https://doi.org/10.1002/vzj2.20007 |
| spellingShingle | Maria Knadel Lis Wollesen deJonge Markus Tuller Hafeez Ur Rehman Peter Weber Jensen Per Moldrup Mogens H. Greve Emmanuel Arthur Combining visible near‐infrared spectroscopy and water vapor sorption for soil specific surface area estimation Vadose Zone Journal |
| title | Combining visible near‐infrared spectroscopy and water vapor sorption for soil specific surface area estimation |
| title_full | Combining visible near‐infrared spectroscopy and water vapor sorption for soil specific surface area estimation |
| title_fullStr | Combining visible near‐infrared spectroscopy and water vapor sorption for soil specific surface area estimation |
| title_full_unstemmed | Combining visible near‐infrared spectroscopy and water vapor sorption for soil specific surface area estimation |
| title_short | Combining visible near‐infrared spectroscopy and water vapor sorption for soil specific surface area estimation |
| title_sort | combining visible near infrared spectroscopy and water vapor sorption for soil specific surface area estimation |
| url | https://doi.org/10.1002/vzj2.20007 |
| work_keys_str_mv | AT mariaknadel combiningvisiblenearinfraredspectroscopyandwatervaporsorptionforsoilspecificsurfaceareaestimation AT liswollesendejonge combiningvisiblenearinfraredspectroscopyandwatervaporsorptionforsoilspecificsurfaceareaestimation AT markustuller combiningvisiblenearinfraredspectroscopyandwatervaporsorptionforsoilspecificsurfaceareaestimation AT hafeezurrehman combiningvisiblenearinfraredspectroscopyandwatervaporsorptionforsoilspecificsurfaceareaestimation AT peterweberjensen combiningvisiblenearinfraredspectroscopyandwatervaporsorptionforsoilspecificsurfaceareaestimation AT permoldrup combiningvisiblenearinfraredspectroscopyandwatervaporsorptionforsoilspecificsurfaceareaestimation AT mogenshgreve combiningvisiblenearinfraredspectroscopyandwatervaporsorptionforsoilspecificsurfaceareaestimation AT emmanuelarthur combiningvisiblenearinfraredspectroscopyandwatervaporsorptionforsoilspecificsurfaceareaestimation |