Volume 27, Issue 3 (Fall 2023)
jwss 2023, 27(3): 17-34 |
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Bandak S, Movhedei naeani A, Komaki C, kakooei M, Verrlest J. Predicting and Mapping Soil Organic Carbon Using Remote Sensing and Machine Learning Algorithms. jwss 2023; 27 (3) :17-34
URL: http://jstnar.iut.ac.ir/article-1-4314-en.html
URL: http://jstnar.iut.ac.ir/article-1-4314-en.html
Gorgan University of Agricultural Sciences and Natural Resources , soraya.bandak@gmail.com
Abstract: (1300 Views)
Soil organic carbon (SOC) is one of the most important components of soil physical and chemical properties that have an important role in sustainable production in agriculture and preventing soil degradation and erosion. Data mining approaches and spatial modeling besides machine learning techniques to investigate the amount of soil organic carbon using remote sensing data have been widely considered. The objective of the present study was the evaluation of SOC using the remote sensing technique compared with field methods in some areas of the Gonbad Kavous and Neli forests of Azadshar. The soil samples were collected from the soil surface (0-10 cm depth) to estimate the SOC. Data were categorized into two categories: 70% for training and 30% for validation. Three machine learning algorithms including Random forest (RF), support vector machine, extra tree decision, and XGBoost were used to prepare the organic soil carbon map. In the present study, auxiliary variables for predicting SOC included bands related to Lands 8 OLI and sentinel 2 measurement images, topography, and climate. The results showed that the extraction of the components related to the bands along with the calculation of indicators such as normalized vegetation difference, wetness index, and the MrVBF index as auxiliary variables play an important role in more correct estimation of the amount of soil organic matter. Comparison of different estimation regressions showed that the Sentinel 2 random forest model and in Landsat8 with the values of coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MEA) of 0.64, 0.05, and 0.17, respectively, was the best performance ratio compared to other approaches used in the study to estimate the organic carbon content of surface soil in the study area. In general, the results of this study indicated the ability of remote sensing techniques and learning models in the spatial estimation of soil organic carbon. So, this method can be used as an alternative to laboratory methods in determining soil organic carbon.
Type of Study: Research |
Subject:
Ggeneral
Received: 2022/11/20 | Accepted: 2023/03/15 | Published: 2023/12/1
Received: 2022/11/20 | Accepted: 2023/03/15 | Published: 2023/12/1
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