Role of environmental variables in the spatial distribution of soil carbon (C), nitrogen (N), and C:N ratio from the northeastern coastal agroecosystems in China

Wang, Shuai; Adhikari, Kabindra; Wang, Qiubing; Jin, Xinxin; Li, Hongdan

doi:10.1016/j.ecolind.2017.08.046

Cited by 100 publications

(36 citation statements)

References 57 publications

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“…However, the predicted values of SOC (TN) for each depth seemed to underestimate the measured values which were more than 20 g kg -1 (1 g kg -1 ) and to overestimate the measured values which were less than 20 g kg -1 (1 g kg -1 ). This conclusion was in agreement with results obtained in other studies owing to the nature of the algorithms, which aimed to achieve unbiased predictions of mean values (Liu et al 2013;Guan et al 2017;Wang et al 2018;Yao et al 2019). The slopes of the model were close to each other, irrespective of soil depth (Fig.…”

Section: Spatial Variability In Soc and Tn Contentssupporting

confidence: 91%

“…In addition, anthropogenic activities in these areas have caused the degradation of terrestrial ecosystems (Bai et al 2014). In most cases, these anthropogenic activities have profound effects on soil properties, especially on soil organic carbon (SOC) and total nitrogen (TN) (Xin et al 2016;Zhang et al 2016;Yuan et al 2017;Wang et al 2018).…”

Section: Introductionmentioning

confidence: 99%

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Spatial variability of soil organic carbon and total nitrogen in the hilly red soil region of Southern China

Yao

Deng

et al. 2019

J. For. Res.

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To obtain accurate spatial distribution maps of soil organic carbon (SOC) and total nitrogen (TN) in the Hetian Town in Fujian Province, China, soil samples from three depths (0-20, 20-40, and 40-60 cm) at 59 sampling sites were sampled by using traditional analysis and geostatistical approach. The SOC and TN ranged from 2.26 to 47.54 g kg -1 , and from 0.28 to 2.71 g kg -1 , respectively. The coefficient of variation for SOC and TN was moderate at 49.02-55.87% for all depths. According to the nuggetto-sill ratio values, a moderate spatial dependence of SOC content and a strong spatial dependence of TN content were found in different soil depths, demonstrating that SOC content was affected by both extrinsic and intrinsic factors while TN content was mainly influenced by intrinsic factors. Indices of cross-validation, such as mean error, mean standardized error, were close to zero, indicating that ordinary kriging interpolation is a reliable method to predict the spatial distribution of SOC and TN in different soil depths. Interpolation using ordinary kriging indicated the spatial pattern of SOC and TN were characterized by higher in the periphery and lower in the middle. To improve the accuracy of spatial interpolation for soil properties, it is necessary and important to incorporate a probabilistic and machine learning methods in the future study.

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Section: Spatial Variability In Soc and Tn Contentssupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Spatial variability of soil organic carbon and total nitrogen in the hilly red soil region of Southern China

Yao

Deng

et al. 2019

J. For. Res.

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“…The difference between mean simulated and mean predicted SOC at current climate was also calculated in grams per kilogram. Following the method of Wang et al (2018), we considered the SD of 50 simulated SOC maps derived for projected climate scenarios as the model uncertainty in our prediction (Supplemental Fig. S1).…”

Section: Modeling Soil Organic Carbon Spatial Distributionmentioning

confidence: 99%

Projecting Soil Organic Carbon Distribution in Central Chile under Future Climate Scenarios

2018

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Soil organic C (SOC) is the largest terrestrial C pool, and it influences diverse soil properties and processes in a landscape. At global scales, SOC is related to climate; as climate changes, we expect that SOC will change at broad scales as well, but how SOC will respond to climate change in diverse environments is complex and highly uncertain. To evaluate the potential impact of predicted changes in temperature and precipitation across central Chile, we first estimated current SOC content using pedon descriptions and environmental variables (temperature, rainfall, land use, topography, soil types, and geology) as predictors. A random forest statistical model was used to predict SOC content by pedon data. Maps were created for six standard depths of the GlobalSoilMap project. Results showed mean SOC of 54 g kg at a depth interval of 0 to 5 cm, 51 g kg at 5 to 15 cm, 42 g kg at 15 to 30 cm, 29 g kg at 30 to 60 cm, 17 g kg at 60 to 100 cm, and 11 g kg at 100 to 200 cm. Model validation, withholding 25% of pedons, showed values of 0.70, 0.73, 0.75, 0.65, 0.56, and 0.29 for six depths, respectively. Two future temperature and precipitation for climate change scenarios, representative concentration pathways RCP4.5 and RCP8.5 from the NASA GISS-E2-R models, were considered in predicting SOC in 2050 and 2080. We found that central Chile would experience a loss of SOC in the depth range of 0 to 30 cm, averaging 9.7% for RCP4.5 and 12.9% for the RCP8.5 scenarios by the year 2050, with additional decreases of 8% in the RCP4.5 scenario and 16.5% under RCP8.5 by 2080.

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“…Digital soil mapping provides a low-cost and efficient method for predicting the spatial distribution of soil nutrients. Most digital soil mapping methods are based on soil landscape models, which establish mathematical or statistical relationships between soil properties and related environmental variables [5,6]. Many digital soil mapping methods have been used to predict soil properties, including generalized linear model (GLM), random forest (RF), artificial neural networks (ANNs), boosted regression trees (BRTs), support vector machine (SVM), and regression kriging (RK).…”

Section: Introductionmentioning

confidence: 99%

Mapping of Soil Total Nitrogen Content in the Middle Reaches of the Heihe River Basin in China Using Multi-Source Remote Sensing-Derived Variables

et al. 2019

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Soil total nitrogen (STN) is an important indicator of soil quality and plays a key role in global nitrogen cycling. Accurate prediction of STN content is essential for the sustainable use of soil resources. Synthetic aperture radar (SAR) provides a promising source of data for soil monitoring because of its all-weather, all-day monitoring, but it has rarely been used for STN mapping. In this study, we explored the potential of multi-temporal Sentinel-1 data to predict STN by evaluating and comparing the performance of boosted regression trees (BRTs), random forest (RF), and support vector machine (SVM) models in STN mapping in the middle reaches of the Heihe River Basin in northwestern China. Fifteen predictor variables were used to construct models, including land use/land cover, multi-source remote sensing-derived variables, and topographic and climatic variables. We evaluated the prediction accuracy of the models based on a cross-validation procedure. Results showed that tree-based models (RF and BRT) outperformed SVM. Compared to the model that only used optical data, the addition of multi-temporal Sentinel-1A data using the BRT method improved the root mean square error (RMSE) and the mean absolute error (MAE) by 17.2% and 17.4%, respectively. Furthermore, the combination of all predictor variables using the BRT model had the best predictive performance, explaining 57% of the variation in STN, with the highest R2 (0.57) value and the lowest RMSE (0.24) and MAE (0.18) values. Remote sensing variables were the most important environmental variables for STN mapping, with 59% and 50% relative importance in the RF and BRT models, respectively. Our results show the potential of using multi-temporal Sentinel-1 data to predict STN, broadening the data source for future digital soil mapping. In addition, we propose that the SVM, RF, and BRT models should be calibrated and evaluated to obtain the best results for STN content mapping in similar landscapes.

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Role of environmental variables in the spatial distribution of soil carbon (C), nitrogen (N), and C:N ratio from the northeastern coastal agroecosystems in China

Cited by 100 publications

References 57 publications

Spatial variability of soil organic carbon and total nitrogen in the hilly red soil region of Southern China

Spatial variability of soil organic carbon and total nitrogen in the hilly red soil region of Southern China

Projecting Soil Organic Carbon Distribution in Central Chile under Future Climate Scenarios

Mapping of Soil Total Nitrogen Content in the Middle Reaches of the Heihe River Basin in China Using Multi-Source Remote Sensing-Derived Variables

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