Exploring the driving forces and digital mapping of soil organic carbon using remote sensing and soil texture
Introduction
Soil organic carbon (SOC) is one of the major indicators of soil quality and productivity (Herrick and Wander, 1997). The presence of SOC improves the physicochemical properties of soils because it facilitates the maintenance of nutrients, increases the water infiltration rate and controls soil resilience against environmental degradation through improved soil aggregation, porosity and structure (Jegajeevagan et al., 2013; Parras-Alcantara et al., 2016). SOC also plays an important role in regulating the global carbon cycle and controlling the emission of greenhouse gases (Bradford et al., 2016; Filippi et al., 2016; Lal, 2004). It is more important in arid and semi-arid regions where lack of water is a critical problem and soil particles can easily be moved by wind owing to the absence of SOC (Bruun et al., 2015; Saia et al., 2014).
Urmia Plain, situated in the west of Urmia Lake, is one of the biggest plains in Iran and an important agricultural hub in the region. In the last few decades, this region has experienced several episodes of environmental degradation as exemplified by drying up of Urmia Lake. Therefore, maintaining agricultural production yields at optimum level with the least water usage and minimum damage to the environment has become a crucial issue. To achieve this, the lands must be kept in the best condition. This is not possible unless SOC, which can compensate for the unnecessary use of chemical fertilizers leading to soil salinization and environmental pollution, is taken seriously. Thus, owing to the limited amount of suitable land for the food production, mapping and monitoring SOC content is vital from both agricultural and environmental perspectives (Dono et al., 2016; Novara et al., 2017).
Geostatistical methods have been one of the common ways for spatial estimation of SOC. Kriging, known as the best linear unbiased predictor, has been upgraded in different forms and successfully calibrated for SOC prediction worldwide (Dai et al., 2014; Liu et al., 2017; Mirzaee et al., 2016; Mishra et al., 2017; Zeng et al., 2016). Such methods have shown superlative performance where considerable SOC measurements are available (Hoffmann et al., 2014; Piccini et al., 2014). However, field sampling and laboratory measurements are expensive and time consuming (Miklos et al., 2010; Mulder et al., 2011). In addition, SOC shows high degrees of variability in response to soil factors that ignoring them reduces the accuracy of the SOC predictions (Allen et al., 2010; Jandl et al., 2013; Viaud et al., 2010). According to the literature, topographic data, land cover, soil structure and texture, available water capacity, parent material, cation exchange capacity and soil type have been used as environmental auxiliaries in SOC mapping (Aksoy et al., 2016; Rial et al., 2017; Schillaci et al., 2017; Sindayihebura et al., 2017; Yigini and Panagos, 2016). Covariates such as topographic factors, the spectral behavior of soil properties, vegetation condition and soil moisture can mostly be extracted from remote sensing (RS) data. RS technology is one of the best alternatives for gaining quick and cost-effective information regarding soil properties while covering wide areas including inaccessible locations. The correlation between soil properties (for instance organic matter and moisture content) and RS covariates has been reported by the previous studies notably those on soil salinity (Bouaziz et al., 2011; Rahmati and Hamzehpour, 2016; Wang and Xu, 2008), soil erosion and runoff (Li et al., 2009; King et al., 2005), soil surface temperature (Mao et al., 2005), and soil nutrients (Xu et al., 2017, Xu et al., 2018). However, the relationship between the outlined covariates and soil properties is often complex.
Parallel to the advancements in RS technology, progress in machine learning (ML) models such as Gradient Boosting Machine (GBM, also known as Boosted Regression Tress (BRT)), Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Networks (ANNs) has greatly improved the estimation of soil parameters through a procedure known as digital soil mapping (DSM; McBratney et al., 2003; Mulder et al., 2011; Sindayihebura et al., 2017; Yang et al., 2016; G. Zhang et al., 2017; Keskin et al., 2019). Guo et al. 2015) integrated RF and residual kriging to estimate SOC at regional scale where their proposed framework resulted in a better performance than stepwise linear regression that was used as a benchmark. Schillaci et al. (2017) used the BRT together with soil properties, topographic and RS data to map SOC at a semi-arid Mediterranean region. Wang et al. (2018) used a set of ML techniques to map SOC in the semi-arid rangelands of eastern Australia. Their findings showed higher performance of RF and BRT models than SVM. Keskin et al. (2019) reviewed eight ML and statistical techniques for digital mapping of soil carbon and found that RF, SVM and BRT yielded the highest accuracy. Nevertheless, previous applications of ML techniques have focused on evaluating the models based on their accuracy while the relative influence and interaction effect of the covariates have been less investigated.
In attempt to model the SOC variation, contribution of this paper is to profoundly understand how the SOC changes in relation to the variation of its underlying factors. Hence, the aim of this research was twofold besides considering extra variables mined from RS imagery. Firstly, to evaluate the relative influence and interaction of predictors such as soil properties, RS covariates and topographic factors on the spatial variation of SOC. Secondly, to predict SOC for the unsampled locations using GBM and creating their associated uncertainty map.
Section snippets
Study area
Study area covers 900 km2 of the Urmia Plain, west of Lake Urmia in northwestern Iran (Fig. 1) which extends between 45° 13′ to 45° 55′ E and 37° 20′ to 37° 53′ N. The dominant land cover types include forest, grassland and cultivated land. The mean annual precipitation rate is approximately 367 mm while the mean annual temperature for the coldest and warmest months are −5.2 °C and 32 °C, respectively. Potential evaporation in the area is approximately 900–1170 mm. Elevation above sea level in
Feature selection
As a result of collinearity and multicollinearity examination, NDVI, greenness, clay, and elevation were removed and the remaining factors were taken into consideration. Fig. 2 shows the correlation plot between the remaining factors including silt, sand, slope, EVI, brightness, wetness, and latitude and longitude. Since the land cover is a categorical predictor, it is not shown on Fig. 2. Results from the VIF exploration measurement indicated that VIF was below 6 for all the selected factors.
Enhanced vegetation index
GBM can be used to generate the partial response curve, a graph showing the relationship between the variability of SOC and each of the explanatory factors. As shown in Fig. 4, EVI with 35.5%, was the most important factor influencing SOC variation. EVI is not only chlorophyll-sensitive, indicating the greenness of the plant canopy, but also pertinent to the structural variations of the canopy, for example plant physiognomy and canopy architecture (Huete et al., 2002). The response curve of SOC
Conclusion
Along with advances in RS technology, DSM (in particular SOC mapping) has gained an unprecedented importance. Time and cost savings and widespread coverage of satellite imageries are among the advantages that highlight the role of RS in the field of soil sciences. On the other hand, calibrating well-established ML models (i.e., GBM and RF), increase the precision of SOC mapping and boost our understanding regarding the underlying drivers of SOC variation. In this paper, a combination of soil
Acknowledgments
This research was granted by Tarbiat Modares University, Grant Number IG-39713.
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