Using Reflectance Spectroscopy and Artificial Neural Network to Assess Water Infiltration Rate into the Soil Profile
We explored the effect of raindrop energy on both water infiltration into soil and the soil's NIR-SWIR spectral reflectance (1200–2400 nm). Seven soils with different physical and morphological properties from Israel and the US were subjected to an artificial rainstorm. The spectral properties...
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| Format: | Article |
| Language: | English |
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Wiley
2012-01-01
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| Series: | Applied and Environmental Soil Science |
| Online Access: | http://dx.doi.org/10.1155/2012/439567 |
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| author | Naftali Goldshleger Alexandra Chudnovsky Eyal Ben-Dor |
| author_facet | Naftali Goldshleger Alexandra Chudnovsky Eyal Ben-Dor |
| author_sort | Naftali Goldshleger |
| collection | DOAJ |
| description | We explored the effect of raindrop energy on both water infiltration into soil and the soil's NIR-SWIR spectral reflectance (1200–2400 nm). Seven soils with different physical and morphological properties from Israel and the US were subjected to an artificial rainstorm. The spectral properties of the crust formed on the soil surface were analyzed using an artificial neural network (ANN). Results were compared to a study with the same population in which partial least-squares (PLS) regression was applied. It was concluded that both models (PLS regression and ANN) are generic as they are based on properties that correlate with the physical crust, such as clay content, water content and organic matter. Nonetheless, better results for the connection between infiltration rate and spectral properties were achieved with the non-linear ANN technique in terms of statistical values (RMSE of 17.3% for PLS regression and 10% for ANN). Furthermore, although both models were run at the selected wavelengths and their accuracy was assessed with an independent external group of samples, no pre-processing procedure was applied to the reflectance data when using ANN. As the relationship between infiltration rate and soil reflectance is not linear, ANN methods have the advantage for examining this relationship when many soils are being analyzed. |
| format | Article |
| id | doaj-art-f2b5b6bbdd1341d797a0ebfa487d53d2 |
| institution | Kabale University |
| issn | 1687-7667 1687-7675 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied and Environmental Soil Science |
| spelling | doaj-art-f2b5b6bbdd1341d797a0ebfa487d53d22025-02-03T01:30:57ZengWileyApplied and Environmental Soil Science1687-76671687-76752012-01-01201210.1155/2012/439567439567Using Reflectance Spectroscopy and Artificial Neural Network to Assess Water Infiltration Rate into the Soil ProfileNaftali Goldshleger0Alexandra Chudnovsky1Eyal Ben-Dor2Soil Erosion Research Station, Soil Conservation and Drainage Division, Ministry of Agriculture, c/o Rupin Institute, Emek-Hefer 40250, IsraelDepartment of Environmental Sciences, Weizmann Institute of Science, Rehovot 76100, IsraelDepartment of Geography and the Human Environment, Tel-Aviv University, Remote Sensing and GIS Laboratory, P.O. Box 39040, Ramat Aviv, Tel Aviv 69978, IsraelWe explored the effect of raindrop energy on both water infiltration into soil and the soil's NIR-SWIR spectral reflectance (1200–2400 nm). Seven soils with different physical and morphological properties from Israel and the US were subjected to an artificial rainstorm. The spectral properties of the crust formed on the soil surface were analyzed using an artificial neural network (ANN). Results were compared to a study with the same population in which partial least-squares (PLS) regression was applied. It was concluded that both models (PLS regression and ANN) are generic as they are based on properties that correlate with the physical crust, such as clay content, water content and organic matter. Nonetheless, better results for the connection between infiltration rate and spectral properties were achieved with the non-linear ANN technique in terms of statistical values (RMSE of 17.3% for PLS regression and 10% for ANN). Furthermore, although both models were run at the selected wavelengths and their accuracy was assessed with an independent external group of samples, no pre-processing procedure was applied to the reflectance data when using ANN. As the relationship between infiltration rate and soil reflectance is not linear, ANN methods have the advantage for examining this relationship when many soils are being analyzed.http://dx.doi.org/10.1155/2012/439567 |
| spellingShingle | Naftali Goldshleger Alexandra Chudnovsky Eyal Ben-Dor Using Reflectance Spectroscopy and Artificial Neural Network to Assess Water Infiltration Rate into the Soil Profile Applied and Environmental Soil Science |
| title | Using Reflectance Spectroscopy and Artificial Neural Network to Assess Water Infiltration Rate into the Soil Profile |
| title_full | Using Reflectance Spectroscopy and Artificial Neural Network to Assess Water Infiltration Rate into the Soil Profile |
| title_fullStr | Using Reflectance Spectroscopy and Artificial Neural Network to Assess Water Infiltration Rate into the Soil Profile |
| title_full_unstemmed | Using Reflectance Spectroscopy and Artificial Neural Network to Assess Water Infiltration Rate into the Soil Profile |
| title_short | Using Reflectance Spectroscopy and Artificial Neural Network to Assess Water Infiltration Rate into the Soil Profile |
| title_sort | using reflectance spectroscopy and artificial neural network to assess water infiltration rate into the soil profile |
| url | http://dx.doi.org/10.1155/2012/439567 |
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