Spatial-Temporal Changes in Soil Organic Carbon and pH in the Liaoning Province of China: A Modeling Analysis Based on Observational Data

Qi, Li; Wang, Shuai; Zhuang, Qianlai; Yang, Zijiao; Bai, Shubin; Jin, Xinxin; Lei, Guangyu

doi:10.3390/su11133569

Cited by 26 publications

(22 citation statements)

References 42 publications

(101 reference statements)

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“…Terrain-related variables are traditionally one of the widely used environmental variables in DSM. In this research, the digital elevation model (DEM) was downloaded from Geospatial Data Cloud site, Computer Network Information Center, Chinese Academy of Sciences [30]. ArcGIS 10.2 software was used to produce elevation and slope gradient, and then SAGA GIS software was used to obtain TWI.…”

Section: Environmental Variablesmentioning

confidence: 99%

“…MAT and MAP are the main climatic variables, obtained from the Meteorological Data Service Center of China [30]. Based on the observation data, historical data, and other auxiliary data, the spatial Forests 2019, 10, 1023 5 of 14 distribution map of MAT and MAP needs to be made by using the climate data spatial interpolation Anusplin software [31] to predict the spatial distribution of MAT and MAP.…”

Section: Environmental Variablesmentioning

confidence: 99%

“…The soil in this part of the area was strongly disturbed by human activities, and the land use pattern changed rapidly, which caused the decrease of organic carbon storage in the forest surface soil. Qi et al [30] considered the topsoil SOC stocks in the same area. They believed that reclamation and other human activities were the main factors causing the decrease of SOC stocks in this area, which was confirmed in the studies of Bae and Ryu [37].…”

Section: Estimates Of Soc Stocksmentioning

confidence: 99%

“…Climate variables were the key environmental variables affecting SOC stocks [11,20]. Previous studies [18,22,30,46,48] had revealed that climate-related variables were closely related to SOC stocks, especially on the regional scale [18], and MAP and MAT were considered to be the main climate variables controlling the spatial variability of SOC stocks. The correlation between MAP and SOC stocks decreased with the decrease of spatial scale, while the correlation between MAP and SOC stocks was not obvious with the change of spatial scale, and there was a strong regional difference [42].…”

Section: Controls Of Soc Stocksmentioning

confidence: 99%

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Temporal and Spatial Changes of Soil Organic Carbon Stocks in the Forest Area of Northeastern China

Wang

Zhuang

Yang

et al. 2019

Forests

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Forest soil organic carbon (SOC) accounts for a large portion of global soil carbon stocks. Accurately mapping forest SOC stocks is a necessity for quantifying forest carbon cycling and forest soil sustainable management. In this study, we used a boosted regression trees (BRT) model to predict the spatial distribution of SOC stocks during two time periods (1990 and 2015) and calculated their spatiotemporal changes during 25 years in Liaoning Province, China. A total of 367 (1990) and 539 (2015) sampling sites and 9 environmental variables (climate, topography, remote sensing) were used in the BRT model. The ten-fold cross-validation technique was used to evaluate the prediction performance and uncertainty of the BRT model in two periods. It was found that the BRT model could account for 65% and 59% of SOC stocks, respectively for the two periods. MAP and NDVI were the main environmental variables controlling the spatial variability of SOC stocks. Over the 25-year period, the average SOC stocks increased from 5.66 to 6.61 kg m −2 . In the whole study area, the SOC stocks were the highest in the northeast, followed by the southwest, and the lowest in the middle of the spatial distribution pattern in the two periods. Our accurate mapping of SOC stocks, their spatial distribution characteristics, influencing factors, and main controlling factors in forest areas will assist soil management and help assess environmental changes in the region. soil properties are affected by many environmental factors, it is difficult to predict soil properties accurately and efficiently at the regional scale [8]. As an efficient and low-cost method, digital soil mapping (DSM) technology is able to accurately describe the spatial variability of soil attributes in a region by using a limited amount of sampling data and environmental variables [9]. In order to accurately predict the SOC stocks in a region and analyze its key environmental factors, scholars have carried out a lot of researches [10][11][12]. In fact, the direct relationship between soil properties and environmental factors is non-linear and complex [13,14]. Therefore, machine learning algorithms which can effectively avoid this kind of problem are widely used in DSM mapping [11,15]. For example, on Barro Colorado Island, Panama, Grimm et al. [15] used a random forest model to simulate SOC stocks in 0-10 cm, 10-20 cm, 20-30 cm, and 30-50 cm layers, respectively. Giasson et al. [16] applied a multiple logistic regression model to predict the occurrence of soil types in southern Brazil. Dorji et al. [17] selected regression kriging and equal-area spline profile function to predict the SOC stocks at depths 0-5, 5-15, 15-30, 30-60, and 60-100 cm in montane ecosystems, Eastern Himalayas. In Denmark, Adhikari et al. [18] used regression kriging and 12 environmental variables to simulate the spatial distribution of SOC stocks at five soil layers. Were et al. [19] compared the performance of support vector regression, artificial neural network, and random forest models in predicti...

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Section: Environmental Variablesmentioning

confidence: 99%

Section: Environmental Variablesmentioning

confidence: 99%

Section: Estimates Of Soc Stocksmentioning

confidence: 99%

Section: Controls Of Soc Stocksmentioning

confidence: 99%

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Temporal and Spatial Changes of Soil Organic Carbon Stocks in the Forest Area of Northeastern China

Wang

Zhuang

Yang

et al. 2019

Forests

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“…It is a challenging task to accurately predict the SOC stock in the region. Also, due to the high cost for data collection, it is difficult to inventory SOC by intensive sampling.With the development of remote sensing (RS), global position system (GPS) and geographic information system (GIS) have opened up new approaches for the study of SOC stocks [7]. Those had been widely used in the field of global and regional SOC stocks, spatial distribution difference of organic carbon density and dynamic change of organic carbon [8].…”

mentioning

confidence: 99%

Multispectral Remote Sensing Data Are Effective and Robust in Mapping Regional Forest Soil Organic Carbon Stocks in a Northeast Forest Region in China

et al. 2020

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Accurately mapping the spatial distribution information of soil organic carbon (SOC) stocks is a key premise for soil resource management and environment protection. Rapid development of satellite remote sensing provides a great opportunity for monitoring SOC stocks at a large scale. In this study, based on 12 environmental variables of multispectral remote sensing, topography and climate and 236 soil sampling data, three different boosted regression tree (BRT) models were compared to obtain the most accurate map of SOC stocks covering the forest area of Lvshun District in the Northeast China. Four validation indexes, including mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2), and Lin’s concordance correlation coefficient (LCCC) were calculated to evaluate the performance of the three models. The results showed that the full variable model performed the best, except the model using multispectral remote sensing variables. In the full variable model, the regional SOC stocks are primarily determined by multispectral remote sensing variables, followed by topographic and climatic variables, with the relative importance of variables in the model being 63%, 28%, and 9%, respectively. The average prediction results of full variables model and only multispectral remote sensing variables model were 8.99 and 9.32 kg m−2, respectively. Our results indicated that there is a strong dependence of SOC stocks on multispectral remote sensing data when forest ecosystems have dense natural vegetation. Our study suggests that the multispectral remote sensing variables should be used to map SOC stocks of forest ecosystems in our study region.

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Spatial prediction of soil organic carbon stocks in an arid rangeland using machine learning algorithms

Rostaminia

Rahmani

Mousavi

et al. 2021

Environ Monit Assess

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method. Robust and popular random Forest (RF), cubist (CB) along with random forest-ordinary kriging (RF-OK), and cubist-ordinary kriging (CB-OK) hybrid ML models were applied to the prediction of SOCS. Ten-fold CV was implemented for modeling performance and uncertainty map. According to data analysis, the maximum, minimum, and average values of SOCS are 44.50, 10.50, and 20.50 (ton. ha −1 ) at the surface depth (0-30 cm), respectively. In general, normalized and standardized height covariates had a higher effect related to other predictors. On the other hand, two remote sensing (RS) indices, including salinity ratio (salinity) and GNDVI index, had a better impact on SOCS variability. The external validation of model performance indicated that RF-OK with (R 2 = 0.75, RMSE = 6.33 ton. ha −1 ) with the high and low uncertainty range (3.33-9.50 ton. ha −1 ) was the outperformed ML model in compare with other models as RF (R 2 = 0.65, RMSE = 7.38 ton. ha −1 ), CB-OK (R 2 = 0.56, RMSE = 9.22 ton. ha −1 ), and CB (R 2 = 0.33, RMSE = 10.42 ton. ha −1 ). In general, the hybrid models improved the accuracy of RF and CB with increased 0.11 until 0.23 of R 2 , and 1.05 to 1.2 (ton. ha −1 ) decreased RMSE of model's prediction. Hence, we conclude that the topographic attributes (especially normalized and standardized height) were the most critical factors in controlling surface SOCS in arid rangelands when combining with robust RF ML model, and optimized soil sampling methods like RF-cLHS can prepare acceptable soil properties maps.

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Spatial-Temporal Changes in Soil Organic Carbon and pH in the Liaoning Province of China: A Modeling Analysis Based on Observational Data

Cited by 26 publications

References 42 publications

Temporal and Spatial Changes of Soil Organic Carbon Stocks in the Forest Area of Northeastern China

Temporal and Spatial Changes of Soil Organic Carbon Stocks in the Forest Area of Northeastern China

Multispectral Remote Sensing Data Are Effective and Robust in Mapping Regional Forest Soil Organic Carbon Stocks in a Northeast Forest Region in China

Spatial prediction of soil organic carbon stocks in an arid rangeland using machine learning algorithms

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