Knowledge Resource Center for Ecological Environment in Arid Area
| DOI | 10.1016/j.geoderma.2020.114890 |
| High resolution middle eastern soil attributes mapping via open data and cloud computing | |
| Poppiel, Raul Roberto; Melo Dematte, Jose Alexandre; Rosin, Nicolas Augusto; Campos, Lucas Rabelo; Tayebi, Mahboobeh; Bonfatti, Benito Roberto; Ayoubi, Shamsollah; Tajik, Samaneh; Afshar, Farideh Abbaszadeh; Jafari, Azam; Hamzehpour, Nikou; Taghizadeh-Mehrjardi, Ruhollah; Ostovari, Yaser; Asgari, Najmeh; Naimi, Salman; Nabiollahi, Kamal; Fathizad, Hassan; Zeraatpisheh, Mojtaba; Javaheri, Fatemeh; Doustaky, Maryam; Naderi, Mehdi; Dehghani, Somayeh; Atash, Saeedeh; Farshadirad, Akram; Mirzaee, Salman; Shahriari, Ali; Ghorbani, Maryam; Rahmati, Mehdi | |
| Corresponding Author | Dematte, JAM (corresponding author), Univ Sao Paulo, Luiz de Queiroz Coll Agr, Dept Soil Sci, Padua Dias Av 11,Postal Box 09, BR-13416900 Sao Paulo, Brazil. |
| Journal | GEODERMA
 |
| ISSN | 0016-7061 |
| EISSN | 1872-6259 |
| Year Published | 2021 |
| Volume | 385 |
| Abstract in English | Soil presents a high vulnerability to the environmental degradation processes especially in arid and semiarid regions, requiring research that leads to its understanding. To date, there are no detailed soil maps covering a large extension of the Middle East region, especially for calcium carbonate content. Thus, we used topsoil data (0-20 cm) from more than 5000 sites for mapping near 3,338,000 square km of the Middle East. To do this, we used covariates obtained from remote sensing data and random forest (RF) algorithm. Around 65% of the soil information was acquired from Iranian datasets and the remaining from the World Soil Information Service dataset. By using 30 covariates layers-soil, climate, relief, parent material and age features- we then trained and tuned RF regression models-in R software- and used the optimal ones (according to the minimum root mean square error) for making spatial predictions-within Google Earth Engine- of topsoil attributes and associated uncertainties at 30 m resolution. All covariates were relatively important for mapping topsoil attributes, ranging from 4% to 98%. Annual precipitation, temperature annual range and elevation were the most important ones (>31%). Overall, the prediction models trained by RF explained around 40-66% of the variation present in topsoil attributes. The ratio of the performance to interquartile distance (RPIQ) ranged between 1.59 and 2.83, suggesting accurate models. Our predicted maps indicated that sandy and loamy soils with poor organic carbon levels, alkaline reaction and high calcium carbonate content were widespread in middle eastern topsoils. Our framework overcomes some limitations related to high computational requirements and enables accurate predictions of topsoil attributes. Our maps presented correct pedological correspondences and had realistic spatial representations and interesting levels of uncertainties. |
| Keyword in English | Soil science Pedometry Random forest Google earth engine Uncertainty |
| Subtype | Article |
| Language | 英语 |
| Indexed By | SCI-E |
| WOS ID | WOS:000609999200039 |
| WOS Keyword | ORGANIC-MATTER ; RANDOM FOREST ; UNCERTAINTY ; REGION |
| WOS Subject | Soil Science |
| WOS Research Area | Agriculture |
| Document Type | 期刊论文 |
| Identifier | http://119.78.100.177/qdio/handle/2XILL650/347923 |
| Affiliation | [Poppiel, Raul Roberto; Melo Dematte, Jose Alexandre; Rosin, Nicolas Augusto; Campos, Lucas Rabelo; Tayebi, Mahboobeh] Univ Sao Paulo, Luiz de Queiroz Coll Agr, Dept Soil Sci, Padua Dias Av 11,Postal Box 09, BR-13416900 Sao Paulo, Brazil; [Bonfatti, Benito Roberto] Univ Estado Minas Gerais, 700 Colorado St, BR-37902092 Passos, MG, Brazil; [Ayoubi, Shamsollah; Tajik, Samaneh; Asgari, Najmeh; Naimi, Salman] Isfahan Univ Technol, Coll Agr, Dept Soil Sci, Esfahan 8415683111, Iran; [Afshar, Farideh Abbaszadeh] Univ Jiroft, Fac Agr, Dept Soil Sci, Jiroft 7867161167, Iran; [Jafari, Azam] Shahid Bahonar Univ Kerman, Coll Agr, Dept Soil Sci, Kerman 7616913439, Iran; [Hamzehpour, Nikou; Rahmati, Mehdi] Univ Maragheh, Dept Soil Sci & Engn, Fac Agr, Postal Box 83111-55181, Maragheh, Iran; [Taghizadeh-Mehrjardi, Ruhollah] Univ Tubingen, Dept Geosci Soil Sci & Geomorphol, Rumelinstr 19-23, Tubingen, Germany; [Ostovari, Yaser] Tech Univ Munich, TUM Sch Life Sci Weihenstephan, Res Dept Ecol & Ecosyst Management, Chair So... |
| Recommended Citation GB/T 7714 | Poppiel, Raul Roberto,Melo Dematte, Jose Alexandre,Rosin, Nicolas Augusto,et al. High resolution middle eastern soil attributes mapping via open data and cloud computing[J],2021,385. |
| APA | Poppiel, Raul Roberto.,Melo Dematte, Jose Alexandre.,Rosin, Nicolas Augusto.,Campos, Lucas Rabelo.,Tayebi, Mahboobeh.,...&Rahmati, Mehdi.(2021).High resolution middle eastern soil attributes mapping via open data and cloud computing.GEODERMA,385. |
| MLA | Poppiel, Raul Roberto,et al."High resolution middle eastern soil attributes mapping via open data and cloud computing".GEODERMA 385(2021). |
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