Mapping stocks of soil organic carbon and soil total nitrogen in Liaoning Province of China

Wang, Shuai; Zhuang, Qianlai; Wang, Qiubing; Jin, Xinxin; Cui, Han

doi:10.1016/j.geoderma.2017.05.048

Cited by 133 publications

(87 citation statements)

References 67 publications

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“…This means that sandier (coarser) soils may be more susceptible to organic matter losses. We also found elevation to be a significant predictor of SOC and TN responses, which is supported by previous work (Powers and Schlesinger, 2002; Wang et al, 2017). This may be due to differences in soil mineralogy and weathering across elevational gradients (Drever and Zobrist, 1992; Velbel, 1993; Szramek et al, 2007; Kramer and Chadwick, 2016).…”

Section: Discussionsupporting

confidence: 91%

A Global Meta‐Analysis of Grazing Impacts on Soil Health Indicators

et al. 2018

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Grazing lands support the livelihoods of millions of people across nearly one-half of the globe. Soils are the backbone of stability and resilience in these systems. To determine livestock grazing impacts on soil health, we conducted a global meta-analysis of soil organic carbon (SOC), total N, C/N ratio, and bulk density responses to grazing strategies (continuous, rotational, and no grazing) and intensities (heavy, moderate, and light grazing) from 64 studies around the world. Across all studies and grazing intensities, continuous grazing significantly reduced SOC, C/N, and total N compared with no grazing. Soil compaction (i.e., increased bulk density) was greater under both continuous and rotational grazing compared with no grazing; however, rotational grazing had lower bulk density than continuous grazing. Rotational grazing had greater SOC than continuous grazing and was not different from no grazing. The positive responses of SOC to rotational grazing could create climate change mitigation opportunities. Grazing strategy comparisons were minimally conditioned by aridity class (i.e., arid, subhumid, and humid); however, complete observations were notably limited or missing for many rotational grazing comparisons. For continuous and no grazing strategy comparisons, we found that grazing management can significantly influence soil function and health outcomes; however, site-specific environmental factors play important moderating roles. Greater coordination across regional, national, and global efforts, as well as consistent guidelines for soil health evaluation, would help overcome these knowledge gaps and vastly improve our collective understanding of grazing impacts on soil health, providing greater management and policy impacts.

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Section: Discussionsupporting

confidence: 91%

A Global Meta‐Analysis of Grazing Impacts on Soil Health Indicators

et al. 2018

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“…Soil organic carbon (SOC), which is an essential nutrient of crop growth and the main carbon source and sink of greenhouse gases, influences agricultural production and global climate change [1][2][3][4][5]. The identification of the spatial distribution characteristics of SOC contributes to the investigation of the role of SOC in precision agriculture and the carbon cycle of the ecosystem.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, numerous soil-forming factors have been used to develop soil-landscape models. Wang et al [4] estimated the SOC distribution by using nine environmental variables (e.g., precipitation, temperature, land use, and elevation) with boosted regression trees. Song et al [24] mapped the SOC content through geographically weighted regression using several environmental predictors (e.g., slope, aspect, elevation, land use, and normalized difference vegetation index [NDVI]) in a case study of Heihe River Basin, China.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Soil Organic Carbon based on Landsat 8 Monthly NDVI Data for the Jianghan Plain in Hubei Province, China

et al. 2019

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High-precision maps of soil organic carbon (SOC) are beneficial for managing soil fertility and understanding the global carbon cycle. Digital soil mapping plays an important role in efficiently obtaining the spatial distribution of SOC, which contributes to precision agriculture. However, traditional soil-forming factors (i.e., terrain or climatic factors) have weak variability in low-relief areas, such as plains, and cannot reflect the spatial variation of soil attributes. Meanwhile, vegetation cover hinders the acquisition of the direct information of farmland soil. Thus, useful environmental variables should be utilized for SOC prediction and the digital mapping of such areas. SOC has an important effect on crop growth status, and remote sensing data can record the apparent spectral characteristics of crops. The normalized difference vegetation index (NDVI) is an important index reflecting crop growth and biomass. This study used NDVI time series data rather than traditional soil-forming factors to map SOC. Honghu City, located in the middle of the Jianghan Plain, was selected as the study region, and the NDVI time series data extracted from Landsat 8 were used as the auxiliary variables. SOC maps were estimated through stepwise linear regression (SLR), partial least squares regression (PLSR), support vector machine (SVM), and artificial neural network (ANN). Ordinary kriging (OK) was used as the reference model, while root mean square error of prediction (RMSE P ) and coefficient of determination of prediction (R 2 P ) were used to evaluate the model performance. Results showed that SOC had a significant positive correlation in July and August (0.17, 0.29) and a significant negative correlation in January, April, and December (−0.23, −0.27, and −0.23) with NDVI time series data. The best model for SOC prediction was generated by ANN, with the lowest RMSE P of 3.718 and highest R 2 P of 0.391, followed by SVM (RMSE P = 3.753, R 2 P = 0.361) and PLSR (RMSE P = 4.087, R 2 P = 0.283). The SLR model was the worst model, with the lowest R 2 P of 0.281 and highest RMSE P of 3.930. ANN and SVM were better than OK (RMSE P = 3.727, R 2 P = 0.372), whereas PLSR and SLR were worse than OK. Moreover, the prediction results using single-data NDVI or short time series NDVI showed low accuracy. The effect of the terrain factor on SOC prediction represented unsatisfactory results. All these results indicated that the NDVI time series data can be used for SOC mapping in plain areas and that the ANN model can maximally extract additional associated information between NDVI time series data and SOC. This study presented an effective method to overcome the selection of auxiliary variables for digital soil mapping in plain areas when the soil was covered with vegetation. This finding indicated that the time series characteristics of NDVI were conducive for predicting SOC in plains.

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“…A significant correlation between elevation and MAT and MAP was observed in a study of soil mapping in the HRB [24], which further demonstrates that elevation affects regional climate variables. In previous digital soil mapping studies, elevation was also reported to be the most effective topographical variable [55,78]. In addition, slope, aspect, and TWI were also found to be important environmental variables affecting STN distribution in previous studies [79,80].…”

Section: Importance Of Predictor Variablesmentioning

confidence: 86%

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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Mapping stocks of soil organic carbon and soil total nitrogen in Liaoning Province of China

Cited by 133 publications

References 67 publications

A Global Meta‐Analysis of Grazing Impacts on Soil Health Indicators

A Global Meta‐Analysis of Grazing Impacts on Soil Health Indicators

Prediction of Soil Organic Carbon based on Landsat 8 Monthly NDVI Data for the Jianghan Plain in Hubei Province, China

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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