Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran

Emadi, Mostafa; Taghizadeh‐Mehrjardi, Ruhollah; Cherati, Ali; Danesh, Majid; Mosavi, Amir; Scholten, Thomas

doi:10.3390/rs12142234

Cited by 142 publications

(79 citation statements)

References 106 publications

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“…The influence of land use type on SOC prediction and the importance of optical and radar data under different land use types were analyzed. The overall prediction result of the model is better than some existing researches based on satellite remote sensing data [16,21,[23][24][25]33], and is similar to the result of Taghizadeh-Mehrjardi et al [17]. Among the three land use types, the prediction results of orchard (R 2 = 0.86 and MSE = 0.004%) are better than dry land and paddy field.…”

Section: Discussionsupporting

confidence: 74%

“…Therefore, it is necessary to explore the contribution of remote sensing data to SOC prediction. There have been some studies on SOC prediction based on remote sensing data that achieved good prediction results, especially optical data (e.g., Sentinel-2 [15][16][17][18][19][20][21], Landsat [22][23][24][25][26][27][28], and MODIS satellite data [29][30][31]); their bands cover from visible to short-wave infrared, providing more information. However, the application of optical data is susceptible to weather conditions, especially in the Sichuan Basin where clouds occur most frequently [32], so the available optical data are very limited.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Soil Organic Carbon under Different Land Use Types Using Sentinel-1/-2 Data in a Small Watershed

Wang

Zhang

et al. 2021

Remote Sensing

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Soil organic carbon (SOC) is a key property for evaluating soil quality. SOC is thus an important parameter of agricultural soils and needs to be regularly monitored. The aim of this study is to explore the potential of synthetic aperture radar (SAR) satellite imagery (Sentinel-1), optical satellite imagery (Sentinel-2), and digital elevation model (DEM) data to estimate the SOC content under different land use types. The extreme gradient boosting (XGboost) algorithm was used to predict the SOC content and evaluate the importance of feature variables under different land use types. For this purpose, 290 topsoil samples were collected and 49 features were derived from remote sensing images and DEM. Feature selection was carried out to prevent data redundancy. Coefficient of determination (R2), mean absolute error (MAE), mean squared error (MSE), percent root mean squared error (%RMSE), ratio of performance to interquartile range (RPIQ), and corrected akaike information criterion (AICc) were employed for evaluating model performance. The results showed that Sentinel-1 and Sentinel-2 data were both important for the prediction of SOC and the prediction accuracy of the model differed with land use types. Among them, the prediction accuracy of this model is the best for orchard (R2 = 0.86 and MSE = 0.004%), good for dry land (R2 = 0.74 and MSE = 0.008%) and paddy field (R2 = 0.66 and MSE = 0.009%). The prediction model of SOC content is effective and can provide support for the application of remote sensing data to soil property monitoring.

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Section: Discussionsupporting

confidence: 74%

Section: Introductionmentioning

confidence: 99%

Prediction of Soil Organic Carbon under Different Land Use Types Using Sentinel-1/-2 Data in a Small Watershed

Wang

Zhang

et al. 2021

Remote Sensing

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“…Gholizadeh, A. et al (2018) showed that GNDVI and SATVI indices provided the strongest correlation with SOC on agricultural plots. Also, several studies conclude that vegetation indices are the most important variables in predicting soil properties (Gopp, N.V. et al 2017;Chen, D. et al 2019;Emadi, M. et al 2020).…”

Section: Remote Sensing Datamentioning

confidence: 99%

Mapping soil organic carbon under erosion processes using remote sensing

et al. 2021

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This study aimed to map soil organic carbon under erosion processes on an arable field in the Republic of Bashkortostan (Russia). To estimate the spatial distribution of organic carbon in the Haplic Chernozem topsoil, we applied Sentinel-2A satellite data and the linear regression method. We used 13 satellite bands and 15 calculated spectral indices for regression modelling. A regression model with an average prediction level has been created (R2 = 0.58, RMSE = 0.56, RPD = 1.61). Based on the regression model, cartographic materials for organic carbon content have been created. Water flows and erosion processes were determined using the calculated Flow Accumulation model. The relationship between organic carbon, biological activity, and erosion conditions is shown. The 13C-NMR spectroscopy method was used to estimate the content and nature of humic substances of different soil samples. Based on the 213C-NMR analysis, a correlation was established with the spectral reflectivity of eroded and non-eroded soils. It was revealed that the effect of soil organic carbon on spectral reflectivity depends not only on the quantity but also on the quality of humic substances and soil formation conditions.

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“…Soil bulk density is a function of organic matter content [ 26 ]. Organic matter content and NDVI are highly dependent on the natural vegetation cover structure and the plant residue left after plant harvesting [ 111 ]. Therefore, organic matter content vividly explains the link between bulk density and NDVI.…”

Section: Discussionmentioning

confidence: 99%

Machine-Learning Classification of Soil Bulk Density in Salt Marsh Environments

Hikouei

Kim

Mishra

2021

Sensors

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Remotely sensed data from both in situ and satellite platforms in visible, near-infrared, and shortwave infrared (VNIR–SWIR, 400–2500 nm) regions have been widely used to characterize and model soil properties in a direct, cost-effective, and rapid manner at different scales. In this study, we assess the performance of machine-learning algorithms including random forest (RF), extreme gradient boosting machines (XGBoost), and support vector machines (SVM) to model salt marsh soil bulk density using multispectral remote-sensing data from the Landsat-7 Enhanced Thematic Mapper Plus (ETM+) platform. To our knowledge, use of remote-sensing data for estimating salt marsh soil bulk density at the vegetation rooting zone has not been investigated before. Our study reveals that blue (band 1; 450–520 nm) and NIR (band 4; 770–900 nm) bands of Landsat-7 ETM+ ranked as the most important spectral features for bulk density prediction by XGBoost and RF, respectively. According to XGBoost, band 1 and band 4 had relative importance of around 41% and 39%, respectively. We tested two soil bulk density classes in order to differentiate salt marshes in terms of their capability to support vegetation that grows in either low (0.032 to 0.752 g/cm3) or high (0.752 g/cm3 to 1.893 g/cm3) bulk density areas. XGBoost produced a higher classification accuracy (88%) compared to RF (87%) and SVM (86%), although discrepancies in accuracy between these models were small (<2%). XGBoost correctly classified 178 out of 186 soil samples labeled as low bulk density and 37 out of 62 soil samples labeled as high bulk density. We conclude that remote-sensing-based machine-learning models can be a valuable tool for ecologists and engineers to map the soil bulk density in wetlands to select suitable sites for effective restoration and successful re-establishment practices.

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Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran

Cited by 142 publications

References 106 publications

Prediction of Soil Organic Carbon under Different Land Use Types Using Sentinel-1/-2 Data in a Small Watershed

Prediction of Soil Organic Carbon under Different Land Use Types Using Sentinel-1/-2 Data in a Small Watershed

Mapping soil organic carbon under erosion processes using remote sensing

Machine-Learning Classification of Soil Bulk Density in Salt Marsh Environments

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