High resolution middle eastern soil attributes mapping via open data and cloud computing

• Published in: Geoderma 2021-03, Vol.385, p.114890, Article 114890
• Main Authors: Poppiel, Raúl Roberto, Demattê, José Alexandre Melo, Rosin, Nícolas 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
• Format: Article
• Language: English
• Subjects: Google earth engine; Pedometry; Random forest; Soil science; Uncertainty
• ISSN: 0016-7061; 1872-6259
• DOI: 10.1016/j.geoderma.2020.114890

[Display omitted] •We explored the power of cloud computing for digital soil mapping.•Multi-source environmental covariates had a highly explanatory power.•We achieved accurate topsoil clay, silt, sand, OC, pH and CaCO3 content maps.•Uncertainty maps suggest where additional fieldwork is necessary.•More studies are needed to further explore the potential of GEE for DSM. 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.