Estimation of Soil Organic Matter on Paddy Field using Remote Sensing Method
Soil organic matter (SOM) is one of the important parameters in agriculture management, thus estimating its distribution on the land will be essential. Remote sensing can be utilized to map the SOM distribution in the large-scale area. The objective of this research was to determine the estimation o...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Sebelas Maret University
2019-12-01
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| Series: | Sains Tanah: Journal of Soil Science and Agroclimatology |
| Subjects: | |
| Online Access: | https://jurnal.uns.ac.id/tanah/article/view/35395 |
| _version_ | 1819079198674780160 |
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| author | Luthfan Nur Habibi Komariah Komariah Dwi Priyo Ariyanto Jauhari Syamsiyah Takashi S.T. Tanaka |
| author_facet | Luthfan Nur Habibi Komariah Komariah Dwi Priyo Ariyanto Jauhari Syamsiyah Takashi S.T. Tanaka |
| author_sort | Luthfan Nur Habibi |
| collection | DOAJ |
| description | Soil organic matter (SOM) is one of the important parameters in agriculture management, thus estimating its distribution on the land will be essential. Remote sensing can be utilized to map the SOM distribution in the large-scale area. The objective of this research was to determine the estimation of SOM distribution on the paddy field in Sukoharjo Regency, Indonesia using Landsat 8 OLI imagery. The sampling points were determined by purposive sampling based on an overlay of land use classification map of paddy field, NDSI (Normalized Difference Soil Index) map, and soil type map. The analysis method was used simple linear regression (SLR) and multiple linear regression (MLR) between SOM content and a digital number of Landsat 8 OLI imagery. The SLR analysis resulted that all band except band 1 and 5 of Landsat 8 OLI Imagery have the capability to estimating SOM. The MLR model based on best subset analysis resulted in the combination of bands 3, 4, 6, and 7 was the best model for estimating SOM distribution (R2=0.399). The MLR model was used to create SOM distribution map on paddy field in Sukoharjo Regency and resulted in the SOM range of the area is distributed from very low (<1%) to moderate (2.1–4.2%) with the largest area was on low level (1–2%) about 11,028 ha. The result indicates that Landsat 8 OLI Imagery could be used for mapping the SOM distribution. |
| first_indexed | 2024-12-21T19:25:11Z |
| format | Article |
| id | doaj.art-3d85cde3d72a42608bdb5a9522bcd8e8 |
| institution | Directory Open Access Journal |
| issn | 1412-3606 2356-1424 |
| language | English |
| last_indexed | 2024-12-21T19:25:11Z |
| publishDate | 2019-12-01 |
| publisher | Sebelas Maret University |
| record_format | Article |
| series | Sains Tanah: Journal of Soil Science and Agroclimatology |
| spelling | doaj.art-3d85cde3d72a42608bdb5a9522bcd8e82022-12-21T18:52:50ZengSebelas Maret UniversitySains Tanah: Journal of Soil Science and Agroclimatology1412-36062356-14242019-12-0116215916810.20961/stjssa.v16i2.3539524949Estimation of Soil Organic Matter on Paddy Field using Remote Sensing MethodLuthfan Nur Habibi0Komariah Komariah1Dwi Priyo Ariyanto2Jauhari Syamsiyah3Takashi S.T. Tanaka4Department of Soil Science, Faculty of Agriculture, Sebelas Maret UniversityDepartment of Soil Science, Faculty of Agriculture, Sebelas Maret UniversityDepartment of Soil Science, Faculty of Agriculture, Sebelas Maret UniversityDepartment of Soil Science, Faculty of Agriculture, Sebelas Maret UniversityFaculty of Applied Biological Sciences, Gifu UniversitySoil organic matter (SOM) is one of the important parameters in agriculture management, thus estimating its distribution on the land will be essential. Remote sensing can be utilized to map the SOM distribution in the large-scale area. The objective of this research was to determine the estimation of SOM distribution on the paddy field in Sukoharjo Regency, Indonesia using Landsat 8 OLI imagery. The sampling points were determined by purposive sampling based on an overlay of land use classification map of paddy field, NDSI (Normalized Difference Soil Index) map, and soil type map. The analysis method was used simple linear regression (SLR) and multiple linear regression (MLR) between SOM content and a digital number of Landsat 8 OLI imagery. The SLR analysis resulted that all band except band 1 and 5 of Landsat 8 OLI Imagery have the capability to estimating SOM. The MLR model based on best subset analysis resulted in the combination of bands 3, 4, 6, and 7 was the best model for estimating SOM distribution (R2=0.399). The MLR model was used to create SOM distribution map on paddy field in Sukoharjo Regency and resulted in the SOM range of the area is distributed from very low (<1%) to moderate (2.1–4.2%) with the largest area was on low level (1–2%) about 11,028 ha. The result indicates that Landsat 8 OLI Imagery could be used for mapping the SOM distribution.https://jurnal.uns.ac.id/tanah/article/view/35395landsat 8 olindsipcaprecision agricultureregression |
| spellingShingle | Luthfan Nur Habibi Komariah Komariah Dwi Priyo Ariyanto Jauhari Syamsiyah Takashi S.T. Tanaka Estimation of Soil Organic Matter on Paddy Field using Remote Sensing Method Sains Tanah: Journal of Soil Science and Agroclimatology landsat 8 oli ndsi pca precision agriculture regression |
| title | Estimation of Soil Organic Matter on Paddy Field using Remote Sensing Method |
| title_full | Estimation of Soil Organic Matter on Paddy Field using Remote Sensing Method |
| title_fullStr | Estimation of Soil Organic Matter on Paddy Field using Remote Sensing Method |
| title_full_unstemmed | Estimation of Soil Organic Matter on Paddy Field using Remote Sensing Method |
| title_short | Estimation of Soil Organic Matter on Paddy Field using Remote Sensing Method |
| title_sort | estimation of soil organic matter on paddy field using remote sensing method |
| topic | landsat 8 oli ndsi pca precision agriculture regression |
| url | https://jurnal.uns.ac.id/tanah/article/view/35395 |
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