Assimilating satellite imagery and visible-near infrared spectroscopy to model and map soil loss by water erosion in Australia
Soil loss causes environmental degradation and reduces agricultural productivity over large areas of the world. Here, we use the latest earth observation data and soil visible-near infrared (vis-NIR) spectroscopy to estimate the factors of the Revised Universal Soil Loss Equation (RUSLE) and to mode...
| Main Authors: | , , , , , |
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| Format: | Journal Article |
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Pergamon
2016
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| Online Access: | http://hdl.handle.net/20.500.11937/74056 |
| _version_ | 1848763168270057472 |
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| author | Teng, H. Viscarra Rossel, Raphael Shi, Z. Behrens, T. Chappell, A. Bui, E. |
| author_facet | Teng, H. Viscarra Rossel, Raphael Shi, Z. Behrens, T. Chappell, A. Bui, E. |
| author_sort | Teng, H. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Soil loss causes environmental degradation and reduces agricultural productivity over large areas of the world. Here, we use the latest earth observation data and soil visible-near infrared (vis-NIR) spectroscopy to estimate the factors of the Revised Universal Soil Loss Equation (RUSLE) and to model soil loss by water erosion in Australia. We estimate rainfall erosivity (R) using the Tropical Rainfall Measuring Mission (TRMM); slope length and steepness (L and S) using a 3-arcsec Shuttle Radar Topography Mission (SRTM) digital elevation model; cover management (C) and control practice (P) using the national dynamic land cover dataset (DLCD) of Australia derived from the moderate-resolution imaging spectroradiometer (MODIS); and soil erodibility (K) using vis-NIR estimates of the contents of sand, silt, clay and organic carbon in Australian soil. We model K using a machine-learning algorithm with environmental predictors selected to best capture the factors that influence erodibility and produced a digital map of K. We use the derived RUSLE factors to estimate soil loss at 1-km resolution across the whole of Australia. We found that the potential gross average soil loss by water erosion in Australian is 1.86 t ha-1 y-1 (95% confidence intervals of 1.78 and 1.93 t ha-1 y-1), equivalent to a total of 1242 × 106 tonnes of soil lost annually (95% confidence intervals of 1195 and 1293 t × 106 y-1). Our estimates of erosion are generally smaller than previous continental estimates using the RUSLE, but particularly in croplands, which might indicate that soil conservation practices effectively reduced erosion in Australia. However we also identify localized regions with large erosion in northern Australia and northeastern Queensland. Erosion in these areas carries sediments laden with nitrogen, phosphorus and pollutants from agricultural production into the sea, negatively affecting marine ecosystems. We used the best available data and our results provide better estimates compared to previous assessments. Our approach will be valuable for other large, sparsely sampled areas of the world where assessments of soil erosion are needed. |
| first_indexed | 2025-11-14T10:59:10Z |
| format | Journal Article |
| id | curtin-20.500.11937-74056 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:59:10Z |
| publishDate | 2016 |
| publisher | Pergamon |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-740562019-08-15T05:22:24Z Assimilating satellite imagery and visible-near infrared spectroscopy to model and map soil loss by water erosion in Australia Teng, H. Viscarra Rossel, Raphael Shi, Z. Behrens, T. Chappell, A. Bui, E. Soil loss causes environmental degradation and reduces agricultural productivity over large areas of the world. Here, we use the latest earth observation data and soil visible-near infrared (vis-NIR) spectroscopy to estimate the factors of the Revised Universal Soil Loss Equation (RUSLE) and to model soil loss by water erosion in Australia. We estimate rainfall erosivity (R) using the Tropical Rainfall Measuring Mission (TRMM); slope length and steepness (L and S) using a 3-arcsec Shuttle Radar Topography Mission (SRTM) digital elevation model; cover management (C) and control practice (P) using the national dynamic land cover dataset (DLCD) of Australia derived from the moderate-resolution imaging spectroradiometer (MODIS); and soil erodibility (K) using vis-NIR estimates of the contents of sand, silt, clay and organic carbon in Australian soil. We model K using a machine-learning algorithm with environmental predictors selected to best capture the factors that influence erodibility and produced a digital map of K. We use the derived RUSLE factors to estimate soil loss at 1-km resolution across the whole of Australia. We found that the potential gross average soil loss by water erosion in Australian is 1.86 t ha-1 y-1 (95% confidence intervals of 1.78 and 1.93 t ha-1 y-1), equivalent to a total of 1242 × 106 tonnes of soil lost annually (95% confidence intervals of 1195 and 1293 t × 106 y-1). Our estimates of erosion are generally smaller than previous continental estimates using the RUSLE, but particularly in croplands, which might indicate that soil conservation practices effectively reduced erosion in Australia. However we also identify localized regions with large erosion in northern Australia and northeastern Queensland. Erosion in these areas carries sediments laden with nitrogen, phosphorus and pollutants from agricultural production into the sea, negatively affecting marine ecosystems. We used the best available data and our results provide better estimates compared to previous assessments. Our approach will be valuable for other large, sparsely sampled areas of the world where assessments of soil erosion are needed. 2016 Journal Article http://hdl.handle.net/20.500.11937/74056 10.1016/j.envsoft.2015.11.024 Pergamon restricted |
| spellingShingle | Teng, H. Viscarra Rossel, Raphael Shi, Z. Behrens, T. Chappell, A. Bui, E. Assimilating satellite imagery and visible-near infrared spectroscopy to model and map soil loss by water erosion in Australia |
| title | Assimilating satellite imagery and visible-near infrared spectroscopy to model and map soil loss by water erosion in Australia |
| title_full | Assimilating satellite imagery and visible-near infrared spectroscopy to model and map soil loss by water erosion in Australia |
| title_fullStr | Assimilating satellite imagery and visible-near infrared spectroscopy to model and map soil loss by water erosion in Australia |
| title_full_unstemmed | Assimilating satellite imagery and visible-near infrared spectroscopy to model and map soil loss by water erosion in Australia |
| title_short | Assimilating satellite imagery and visible-near infrared spectroscopy to model and map soil loss by water erosion in Australia |
| title_sort | assimilating satellite imagery and visible-near infrared spectroscopy to model and map soil loss by water erosion in australia |
| url | http://hdl.handle.net/20.500.11937/74056 |