Nothing Special   »   [go: up one dir, main page]
Next Article in Journal
Differential Spatiotemporal Patterns of Major Ions and Dissolved Organic Carbon Variations from Non-Permafrost to Permafrost Arctic Basins: Insights from the Severnaya Dvina, Pechora and Taz Rivers
Previous Article in Journal
Planning Public Space Climate Comfortability: A GIS-Based Algorithm for the Compact Cities of the Far North
You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Land
Volume 13
Issue 11
10.3390/land13111764
land-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Dynamic Changes in and Driving Factors of Soil Organic Carbon in China from 2001 to 2020

by
Fuyan Zou
1,2,
Min Yan
1,2,
Liankai Zhang
1,2,
Jinjiang Yang
1,2,
Guiren Chen
1,2,
Keqiang Shan
1,2,
Chen Zhang
1,2,
Xiongwei Xu
1,2,
Zhenhui Wang
1,2 and
Can Xu
1,2,*
1
Kunming General Survey of Natural Resources Center China Geological Survey, Kunming 650100, China
2
Technology Innovation Center for Natural Ecosystem Carbon Sink, Ministry of Natural Resources, Kunming 650100, China
*
Author to whom correspondence should be addressed.
Land 2024, 13(11), 1764; https://doi.org/10.3390/land13111764
Submission received: 19 August 2024 / Revised: 14 October 2024 / Accepted: 24 October 2024 / Published: 27 October 2024
(This article belongs to the Section Land Systems and Global Change)
Browse Figures
Figure 1
<p>Distribution of sample values of soil carbon density in soil layers at 0–20 cm (<b>a</b>) and 0–100 cm (<b>b</b>) depths. Sampled data refer to the soil data obtained from our project team’s field survey. Collected data refer to the collection of data from the literature and databases.</p> "> Figure 2
<p>Scatter plots of estimated and predicted SOCD values: depths of 0–20 cm (<b>a</b>) and 0–100 cm (<b>b</b>). R<sup>2</sup>, coefficient of determination; RMSE, root mean squared error; LCCC, Lin’s concordance correlation coefficient.</p> "> Figure 3
<p>Order of importance of ECs used to predict SOCD in random forest: depths of 0–20 cm (<b>a</b>) and 0–100 cm (<b>b</b>).</p> "> Figure 4
<p>Spatial distribution of and temporal variation in 0–20 cm SOCD. (<b>a</b>–<b>e</b>) represent SOCD distribution maps; (<b>f</b>) shows the change in SOCD. When slope &gt; 0, the SOCD of the time series shows an increasing trend; when slope &lt; 0, the SOCD of the time series shows a decreasing trend.</p> "> Figure 5
<p>Spatial distribution of and temporal variation in 0–100 cm SOCD. (<b>a</b>–<b>e</b>) represent SOCD spatial distribution; (<b>f</b>) shows the change in SOCD. When slope &gt; 0, the SOCD of the time series shows an increasing trend; when slope &lt; 0, the SOCD of the time series shows a decreasing trend.</p> "> Figure 6
<p>Zonal statistics for SOCS, mean SOCD, and mean Theil–Sen median slope: depth of 0–20 cm (<b>a</b>) and 0–100 cm (<b>b</b>).</p> "> Figure 6 Cont.
<p>Zonal statistics for SOCS, mean SOCD, and mean Theil–Sen median slope: depth of 0–20 cm (<b>a</b>) and 0–100 cm (<b>b</b>).</p> "> Figure 7
<p>Spatial pattern of partial correlation and correlation coefficients of SOCD at a depth between 0 and 20 cm and influencing factors from 2001 to 2020. These factors include temperature (<b>a</b>), precipitation (<b>b</b>), land use disturbance intensity (<b>c</b>), and (<b>d</b>) the percentage of partial correlation and correlation between SOCD at a 0 to 20 cm depth and the three influencing factors. Upward and downward bars indicate percentages of positive and negative correlation, respectively. Colored areas indicate correlation coefficients greater than 0.5 or less than −0.5.</p> "> Figure 8
<p>Spatial pattern of partial correlation and correlation coefficients of SOCD at a depth between 0 and 100 cm and influencing factors from 2001 to 2020. These factors include temperature (<b>a</b>), precipitation (<b>b</b>), land use disturbance intensity (<b>c</b>), and (<b>d</b>) the percentage of partial correlation and correlation between SOCD at a 0 to 100 cm depth and the three influencing factors. Upward and downward bars indicate percentages of positive and negative correlation, respectively. Colored areas indicate correlation coefficients greater than 0.5 or less than −0.5.</p> "> Figure 9
<p>Land use change from 2001 to 2020 (the left side is the area transferred out of different land types, and the right side is the area transferred in from different land types; area unit is km<sup>2</sup>).</p> ">
Versions Notes

Abstract

:
It remains unclear what changes have occurred in the distribution pattern of and trend in soil organic carbon (SOC) in China against the background of climate and land use change. Clarifying the dynamic changes in SOC and their driving factors in different regions of China is therefore crucial for assessing the global carbon cycle. In this study, we collected and supplemented a large amount of soil organic carbon density (SOCD) data in China from 2001 to 2020 and extracted data on environmental covariates (ECs) for the corresponding years. A random forest model was used to estimate the SOCD at a depth of 0–20 cm and 0–100 cm in China for the years 2001, 2005, 2010, 2015, and 2020, and we explored the trend of SOCD changes and their key driving factors. The results showed the following: (1) Compared with previous studies, the predictive ability of the 0–100 cm depth model was greatly improved; the coefficient of determination (R2) was 0.61 and Lin’s concordance correlation coefficient (LCCC) was =0.76. (2) From 2001 to 2020, China’s soil organic carbon stocks (SOCS) were 38.11, 39.11, 39.88, 40.16, and 41.12 Pg C for the 0–20 cm depth and 110.49, 112.67, 112.80, 113.06, and 114.96 Pg C for the 0–100 cm depth, respectively. (3) The effects of temperature and precipitation on SOCD in China showed obvious regional variability, and land use changes had mainly positive effects on SOCD in all regions of China, which was related to the large-scale implementation of ecological protection and restoration and the policy of returning farmland to forests and grasslands in China. This study provides strong scientific support for addressing climate change and rationalizing the use of land resources.

1. Introduction

Organic carbon storage is a key function of soil, both for climate regulation and prediction of land use potential [1,2]. Soil is the largest carbon reservoir in terrestrial ecosystems, with global SOC stocks at a 2 m depth amounting to 2400 Gt, three times the amount of carbon in the atmosphere [3,4]. Soil plays a vital role in mitigating climate change by absorbing and storing CO2 [1,4], and SOC is also an important indicator of soil fertility, playing a key role in improving soil quality and crop yields [5,6]. Meanwhile, climate change and changes in land use types also have profound effects on soil carbon pools, thus leading to uncertainty in soil carbon stocks [7,8,9], with one study showing global warming leads to soil carbon loss [10]. Regarding the global carbon cycle assessment, carbon emission due to land use change, along with fossil fuel emission, is another important source of CO2 [11]. Clarifying the feedbacks of soil carbon pools to climate and land use change on the spatial and temporal scales is therefore an important issue in combating global warming.
The current understanding of soil carbon feedbacks to climate change is inconsistent [12,13]. On the one hand, studies have shown that temperature increases elevate microbial activity and promote the release of soil carbon from decomposition [14,15]. On the other hand, it has also been shown that global warming promotes vegetation growth and increases the input of biological carbon to the soil [16,17], facilitating carbon sequestration in the soil. Regarding the feedback of soil carbon to land use disturbance, studies have mainly dealt with the effects of different land use types (e.g., farmland, woodland, and grassland) and the intensity of land use change (e.g., tillage, crop rotation) on SOC content and dynamics [4,18]. In general, existing studies have mainly explored the feedback of soil carbon based on a single factor, which is insufficient when exploring the dynamic effects both of climate change and different land disturbance intensities on SOC in different regions, especially at the continental scale.
As a country with a diverse climate and complex land use types, China has experienced significant changes in regional climate in the context of global warming [19,20]. In recent decades, with the rapid economic and social development and the need for ecological restoration and protection, land, as a uniformly controlled resource, has undergone dramatic changes in its utilization types, leading to continuous alterations to soil carbon pools [21,22,23,24]. As a result, there are significant spatial differences in the changes in SOC and their driving factors in China, but the current research in this area lacks depth.
In summary, the objectives of this study were as follows: (1) to construct a spatial and temporal prediction model for SOCD at depths of 0–20 cm and 0–100 cm in China from 2001 to 2020 and to analyze the trend of SOC change in different regions of China; (2) to reveal the regional differences in the impacts of climate and land use change on SOC in China. The results of this study help to clarify the impacts of climate and land use change on the historical evolution of the SOC pool in China and are important for achieving carbon neutrality and assessing the global carbon cycle.

2. Materials and Methods

2.1. Inversion of SOCD Spatial Distribution

2.1.1. SOCD Sample Data Sources and Processing

The main sources of SOCD data are literature collection, database downloads, and field surveys. The SOC data from 2000 to 2014 were collected mainly from the carbon density dataset reported for China’s terrestrial ecosystems [25], which was obtained by collecting and organizing carbon density data from literature and experimental data, and comprised 4536 and 3147 samples at a depth of 0–20 cm and 0–100 cm, respectively. The 2015–2019 data were mainly obtained from the forest soil test data in China, which was downloaded from the National Earth System Science Data Center (https://www.geodata.cn, URL (accessed on 28 October 2022)) and contained 1195 and 575 SOCD data samples at a depth of 0–20 cm and 0–100 cm, respectively. The 376 0–20 cm depth SOCD samples were selected from a topsoil organic carbon density dataset for agricultural ecosystem field stations from the Chinese Ecosystem Research Network (2005–2015) [26]. The 97 0–20 cm and 73 0–100 cm depth samples were collected from the literature [27,28,29]. The reported SOCD values for the 0–30 cm depth were adjusted for the 0–20 cm depth using SOCD0–30 cm = 1.34 SOCD0–20 cm [30,31]. To increase the sample density in regions with complex environments with regard to climate, topography, and vegetation, we investigated 1940 and 263 soil samples at a depth of 0–20 cm and 0–100 cm, respectively, in the Tibetan Plateau, Yunnan–Guizhou Plateau, and the southern hilly areas and obtained the SOCD for the sampling sites via assay analysis and calculation (Appendix A).

2.1.2. Data Collection and Processing of ECs

This study collected data on 21 ECs that may have significant impacts on SOC, including climate, vegetation, soil, and terrain. The specific indicators are shown in Table A1. MODIS data for 2001–2022 were downloaded from GEE (https://code.earthengine.google.com, URL (accessed on 17 October 2023)) and the LAADS DAAC platform (https://ladsweb.modaps.eosdis.nasa.gov/, URL (accessed on 15 October 2023)) and contained surface reflectance (7 bands), normalized difference vegetation index, enhanced vegetation index, net primary productivity, and gross primary productivity at 500 m spatial resolution. Climate data (including annual mean temperature and annual precipitation, with a spatial resolution of 1 km) and DEM (ASTER DEMv3) data (with a spatial resolution of 30 m) for the years 2001–2022 were downloaded from the National Earth System Science Data Center (http://www.geodata.cn, URL (accessed on 15 October 2023)). Soil physicochemical property data were obtained from the database established by Shangguan et al. (2013) and included soil cation exchange capacity, percentage of clay, soil pH, soil porosity, and percentage of silt at a spatial resolution of 1 km [32]. Glacier and lake areas were excluded because remote sensing images of these areas lacked direct or indirect reflectance information from the soil.
The downloaded data were imported into ArcGIS 10.8 (Esri, Redlands, CA, USA) software for mosaicking, clipping, and resampling. DEM data were used to extract the slope and aspect data.

2.1.3. Extraction of ECs

The soil survey points were separated by year, the point data were imported into ArcGIS 10.8 (Esri, Redlands, CA, USA), and the extraction of statistical values of the survey points and 21 ECs was completed by utilizing the “Multi-value Extraction to Points” function in the spatial analysis tool. The SOCD survey points were attached with ECs corresponding to the time series (e.g., temperature, precipitation, GPP, NPP, EVI, NDVI, and MODIS data for the corresponding year were extracted from soil samples in 2001). After removing coordinate anomalies, unclear survey dates, and missing values, the maximum and minimum values of two soil depth confidence intervals ranging from 1% to 99% were counted, and 7243 and 3236 soil samples were ultimately retained for the 0–20 cm and 0–100 cm depths, respectively (Figure 1).

2.1.4. Model Building and Evaluation

A random forest model was used to construct relationships between SOCD and 21 environmental factors of soil at a depth of 0–20 cm and 0–100 cm. The random forest model allows for the construction of multiple decision trees that can be fused into a more accurate and stable model that measures feature interactions based on similarities between samples [33]. When extracting the training and test samples, the sample data from different years were randomly extracted by geographic partition in ArcGIS 10.8 (Esri, Redlands, CA, USA), so that the training samples (70%) and the test samples (30%) contained samples from each year and were evenly distributed in the geographic space in order to improve the accuracy of the model. In this study, a grid search method was used to find the optimal combination of mleaf and ntree, with ntree varying between 50 and 1000 at intervals of 100, and mleaf varying between 2 and 10 at intervals of 1. Taking the best prediction accuracy of the inverse model as a criterion, ntree = 290 and mtry = 3 were finally used for 0–20 cm, and ntree = 270 and mtry = 4 were used for 0–100 cm as the optimal parameters for the random forest model. The final validation dataset coefficient of determination (R2test), root mean square error (RMSEtest) and Lin’s concordance correlation coefficient (LCCC) were used to assess the predictive ability of each model.
R 2 = 1 − ∑ i S O C D X i − S O C D X ^ i 2 ∑ i S O C D X i − S O C D X ¯ i 2
R M S E = 1 n ∑ i = 1 n S O C D X ^ i − S O C D X i 2
L C C C = 2 C C × S D s × S D o M S O C D X s ¯ − M S O C D X o ¯ 2 + S D s 2 + S D o 2
where n = the size of the observations and SOCD ( X i ) and SOCD ( X i ^ ) are the field measured and predicted values, respectively. X ¯ i is the mean measured values. C C is the correlation coefficient between the measured and predicted values, respectively; S D s and S D o are the standard deviations of the measured and predicted values, respectively; and M S O C D X s ¯ and M S O C D X o ¯ are mean measured and predicted values, respectively.
The estimation of SOCD based on the inversion of the constructed model and mapping was carried out in MATLAB R2022a (MathWorks, Natick, MA, USA) software.

2.2. Changes in SOCD Trends and Attribution Analysis

A Theil–Sen median trend analysis was utilized to investigate the SOCD of soil at a depth of 0–20 cm and 0–100 cm, i.e., its spatial distribution, temporal variation characteristics, and trend changes. Theil–Sen median trend analysis is a robust nonparametric statistical trend computation method which computes the time series of all n(n − 1)/2 paired combinations of data, and it is particularly effective for small series trend estimation [34,35]. The slope of the Theil–Sen median can represent the increase or decrease in SOCD at the pixel scale for the years 2001, 2005, 2010, 2015, and 2020. Its calculation formula is as follows:
S S O C D = m e d i a n   S O C D j − S O C D i j − i ,   2001 ≤ i < j ≤ 2020 ;
SSOCD, which is used to quantify a monotonic trend, is the median of the slope of the n(n − 1)/2 data combinations, and SOCDj and SOCDi represent the SOCD values in years i and j. When SSOCD > 0, the SOCD of the time series shows an increasing trend; when SSOCD < 0, the SOCD of the time series shows a decreasing trend.

2.2.1. Land Use Change Analysis and Intensity Detection

The land use data were MODIS land cover products (MCD12Q1, V051) for the years 2001, 2005, 2010, 2015, and 2020 with a spatial resolution of 500 m downloaded from the LAADS DAAC platform (https://ladsweb.modaps.eosdis.nasa.gov/, URL (accessed on 15 October 2023)). The land use transfer matrix is an analytical method that describes the mutual shifts between different land classes. In this study, to facilitate the analysis, the land use data were reclassified into six categories (forestland, grassland, cultivated land, built-up land, barren land, and other) using ArcGIS 10.8 (Esri, Redlands, CA, USA) software, and then land use transfer matrix analysis was performed for the years 2001–2020.
The extent of land use change was detected using a change vector analysis. CVA is a change detection method for preclassification detection, where the image is subjected to rigorous pre-processing (radiometric correction, geometric alignment) before change detection [36,37]. As shown in the following equations, the extent of change is indicated by the magnitude of the vectors between the two temporal phases, and the changed/unchanged region between the two temporal phases is determined [38,39]. This study used MODIS bands to detect the intensity of land use change in the years 2001–2005, 2005–2010, 2010–2015, and 2015–2020.
∆ v i = r i − s i
∆v is the variation vector, r is the time-phase one image, and s is the time-phase two image.

2.2.2. Partial Correlation and Correlation Analysis

Pixel-by-pixel partial correlation analysis is a spatial data analysis technique used primarily to measure the correlation between two or more variables at each pixel point in a raster dataset while controlling for the effects of other variables. In this study, pixel-by-pixel partial correlation analysis was performed for temperature, precipitation, and SOCD for the years 2001, 2005, 2010, 2015, and 2020. Based on the partial correlation coefficients, the correlations between temperature changes, precipitation changes, and SOCD changes were described.
Since the land use change intensity data were generated after change detection for the periods 2001–2005, 2005–2010, 2010–2015, and 2015–2020, only four sets of raster data were generated. To analyze the relationship between the intensity of land use change and SOCD change, we also processed the SOCD for the periods 2001 to 2005, 2005 to 2010, 2010 to 2015, and 2015 to 2020, and used their change amounts for correlation analysis. Finally, a pixel-by-pixel correlation analysis was conducted for land use change intensity and SOCD change, and, based on the correlation coefficients, the correlation between the intensity of land use change and SOCD change was described.

3. Results

3.1. Model Performance

Our study evaluated the performance of the model with 30% of field measurements. The 0–20 cm depth model achieved R2 = 0.55, RMSE = 2.18 kg/m2, and LCCC = 0.71, and the 0–100 cm depth model achieved R2 = 0.61 RMSE = 5.57 kg/m2, and LCCC = 0.76 (Figure 2). In addition, the order of importance of ECs showed that temperature and precipitation were the highest contributing variables for both the 0–20 cm and 0–100 cm SOCD models (Figure 3).

3.2. Spatial Distribution and Dynamics of SOCD

The results showed that the highest 0–20 cm depth SOCD value in China was in the northeast, followed by the southwest, with the lowest value detected in the northwest (Figure 4); the highest 0–100 cm depth SOCD value was in the southwest, followed by the northeast, with the lowest in the north (Figure 5). In terms of trend change, the slope of the Theil–Sen median of the 0–20 cm depth SOCD ranges from −1 to 1 kg/km2/5 year, and that of the 0–100 cm depth is −2 to 2 kg/km2/5 year from 2001 to 2020.
From 2001 to 2020, the variation range of China’s 0–20 cm depth SOCS was 38.11~41.12 Pg C, and that of the 0–100 cm depth was 110.49~114.96 Pg C (Figure 6). From 2001 to 2020, the slopes of the mean Theil–Sen median values for the 0–20 cm and 0–100 cm depths in the northwest, southwest, south-central, north, and east regions were all positive, and the SOCD showed an increasing trend, while those in the northeast were negative, and the SOCD showed a decreasing trend (Figure 6).

3.3. Impacts of Climate Change and Land Use Disturbance on SOCD

We performed partial correlation and correlation analyses of SOCD with temperature, precipitation, and land use disturbance intensity (Figure 7a–c and Figure 8a–c). Overall, the correlation of SOCD with temperature showed north–south differences, the correlation with precipitation mainly showed east–west differences, and the correlation with the intensity of land use disturbance was relatively evenly distributed. We further partitioned the correlation coefficients by the number of rasters occupied by the four ranges of −1–0.5, −0.5–0, 0–0.5, and 0.5–1 (Figure 7d and Figure 8d). The results showed that for the 0–20 cm depth, the correlation between SOCD changes and temperature was mainly negative in the southwest, northwest, north, and northeast, and positive in the south and east; the correlation between SOCD changes and precipitation was mainly positive in each region, especially in the east and south, with more than 70% of the area positively correlated; and the correlation between SOCD changes and the intensity of land use disturbance was mainly positive in each region. The correlation between SOCD changes and land use disturbance intensity was mainly positive. For the 0–100 cm depth, the correlations between temperature, precipitation, land use disturbance intensity, and SOCD in each region were similar as those for 0–20 cm.
To further understand the land use changes in each region, we also conducted a land use transfer matrix analysis (Figure 9). The results show that over the past 20 years, land use change has generally been a mutual shift between forest land and grassland, and between cultivated land and grassland, with the grassland⟶forestland shift area being much higher than that of the forest land⟶grassland shift in all regions; however, in the northwest and north regions, the cultivated land⟶grassland shift area is significantly higher than that of the grassland⟶cultivated land shift. In addition, the barren land⟶grassland shift area is much higher than that of barren land⟶grassland in the northwest and north, and the expansion of built-up land in the east region is more obvious than in other regions.

4. Discussion

4.1. Model Performance and Variable Selection

In this study, 21 variables were considered ECs, and a national 0–20 and 0–100 depth SOCD random forest model was developed. It was found that the 0–100 cm depth model (R2 = 0.61, LCCC = 0.76) had a higher accuracy than the 0–20 cm depth model (R2 = 0.55, LCCC =0.71). In particular, the R2 of the 0–100 cm depth model was greatly improved compared to previous studies [40,41,42]. The Lin’s CCC values of both models were higher than 0.7, which is at a relatively high level compared with that reported in previous studies [43,44]. This indicates that the SOC model used in this study has high stability and reliability.
On the one hand, the random forest model was able to fully utilize its intrinsic stochasticity and integrated learning to more comprehensively consider a variety of potential variables and reduce the model’s over-reliance on specific features [45,46]. By integrating the results of multiple decision trees, random forests were able to provide more stable and accurate predictions, which reduces the errors in the inversion process, giving them a significant advantage in dealing with the complex problem of predicting SOC changes across time [41,47,48]. On the other hand, most studies on soil samples have time errors when matching with environmental covariates (ECs) [40,41,42]. This study extracted ECs in the corresponding year based on the SOCD sampling year, which increases the reliability of the relationship between SOCD and ECs. Finally, current studies on SOC time series in China mainly focus on the period before 2015 [40,41]. To reduce the errors due to spatio-temporal heterogeneity, we supplemented the data for 3097 and 770 soil samples at a depth of 0–20 cm and 0–100 cm, respectively, after 2015 by collecting and field survey soil samples. In 2022, our project team conducted field surveys in typical areas across the country, from which we obtained 1940 and 263 soil samples at a depth of 0–20 cm and 0–100 cm, respectively, which greatly increased the sample density. These advantages thus enhance the accuracy and reliability of our assessment results.
Our results show that from 2001 to 2020, the variation in SOCS in China ranged from 38.11 to 41.12 Pg C for a depth of 0–20 cm and from 110.49 to 114.96 Pg C for a depth of 0–100 cm. Regarding the study of soil carbon pools, Fang et al. (1996) utilized the information from 745 soil profile surveys across the country, and the earliest estimation of China’s soil carbon stock was 185.7 Pg C [49]. Wang and Zhou (1999) estimated the SOC pool in China to be about 100.18 Pg C based on the second national soil census data using GIS technology [50]. Most other estimates of soil carbon stocks in China based on the second national soil census data were in the range of 69 ~ 92 Pg C [51]. Compared to most studies that only assessed SOC in forest, grassland, farmland, and wetland ecosystems [52,53], SOC in desert, urban, and other terrestrial ecosystems, which are also non-negligible in terms of soil carbon stock, were included in our study, except for glaciers and lakes, which are also important reasons for the larger estimation [51]. In addition, the accumulation of SOC over recent decades is not negligible.
Overall, our study has improved the simulation methodology and data processing, and the simulation results are reliable. However, model simulations inevitably introduce errors, and the dependence on data quality and completeness, as well as the limited ability to characterize complex soil processes, deserve more in-depth exploration in the future.

4.2. Driving Factors for SOC

4.2.1. Impact of Climate Change on SOC

Our study shows that temperature and precipitation within the study period were positively correlated with SOC in eastern and southern China, and the warm and humid climatic background promoted the continuous accumulation of SOC, which is consistent with previous studies [54]. On the one hand, studies have shown that plants are an important source of SOC [55,56], and increased temperature promotes plant productivity and the allocation of organic carbon to the subsurface [16,57]. In addition, increased precipitation improves soil moisture conditions, and a moist soil environment promotes microbial growth and reproduction, accelerates the decomposition and transformation of organic matter, and further increases the organic carbon content in the soil [58,59].
On the other hand, the contribution of temperature and precipitation to SOC accumulation is not absolute, and studies have shown that climate warming enhances soil respiration, leading to soil carbon loss [15,60]. Warmer temperatures also alleviate the temperature limitation of microbial intracellular and extracellular enzyme activities, increase the microbial availability of apoplastic material, and accelerate CO2 release [12,61,62]. This tends to occur at high altitudes and high latitudes. Our results also suggest a tendency for the accelerated loss of SOC in northeastern China in the context of global warming and humidification.
Finally, it has been shown that when the temperature reaches a certain level, the higher the temperature, the slower the rate of carbon sequestration, even resulting in carbon loss [10,54]. In semi-arid and arid regions, moisture is a key factor limiting vegetation growth, which in turn significantly affects SOC accumulation [63]. Under global warming, frequent extreme heat and drought events can exacerbate the loss of SOC [64]. Zhang et al. showed that SOC in the arid northwestern region of China has decreased by 585.50 Tg, with an average decrease of 19.52 Tg C yr−1, in the past 30 years and more [65]. Consistent with previous studies, our study also demonstrates that in the arid and semi-arid Northwest China, the effects of temperature and precipitation on SOC exhibited significant variability, with temperature mainly having negative effects and precipitation playing a positive role. However, the effects of human activities are also not negligible, especially in the areas of the Maowuosu Desert, Loess Plateau, and Alashan, where SOC shows a continuous increase due to continuous afforestation and vegetation protection and restoration [23,66,67].

4.2.2. Impact of Land Use Change on SOC

This study quantitatively detected the intensity of land use change and also conducted a land use transfer matrix analysis to elucidate the impact of land use change on SOC. Overall, the impact of land use change on SOC in the past 20 years has been positive, mainly due to the conversion of large areas of grassland to forest land and cultivated land to grassland. Although the area of grassland converted into arable land is also large, the SOC content of arable land can be greatly increased by adopting appropriate management measures in the long term [68]. Therefore, adopting conservation tillage measures such as no-tillage and straw return, on the other hand, can help to maintain the stability of soil aggregates and reduce the loss of SOC [69,70]. Fundamental changes in land use patterns have more far-reaching effects on SOC; for example, the conversion of forest and grassland to farmland and the expansion of built-up areas significantly reduced SOC content [71,72,73]. On the contrary, the adoption of restoration measures for long-term vegetation, such as returning farmland to forest and grassland and afforestation, contributes to the accumulation and recovery of SOC [74,75].
However, land use changes in different regions can lead to differences in organic carbon changes. The overall SOCD is declining in northeast China, and although there is also a large conversion of cultivated land to grassland and grassland to forest, it is not enough to compensate for the loss of carbon from the former conversion. Forest clearing to obtain cultivated land is the main reason for the decrease in soil content in the northeast [72,73,76]. As reported by Wang et al. (2023), 35 years of tillage led to a decrease in SOC content of about 3.07 g kg−1 in the northeast, and about 64% of cultivated land had a negative increase in SOC from 1985 to 2020 [72]. In the Golden-Maize-Belt Counties in Jilin Province, northeast China, the maximum decrease in SOC content even reached 24.83 g kg−1 [77]. In the eastern and south-central regions, the main land use disturbances have been dominated by agricultural activities, and the increase in SOC was mainly due to the adjustment of agricultural planting structure, application of more organic fertilizers, return of straw to the field, conservation tillage, and other measures [69,70,78,79]. In addition to the conversion of large areas of grassland to woodland in the northern, northwestern, and southwestern regions, these three regions also have large areas of barren land converted to grassland, with the most obvious in the northwestern region. The increase in SOCD in these regions is largely attributed to both the reduction in anthropogenic activity intensity and ecological restoration, the former including the control of grazing intensity and the prohibition of logging [59,80,81] and the latter including natural vegetation restoration, returning farmland to forest and grassland, and soil and water conservation [74,82,83,84].

4.3. Positive Role of National Land Policies

In the past 20 years, ecological projects and land management policies implemented in China have played a significant and positive role in SOC changes, including the Three-North Protective Forest Project, the Return of Farmland to Forests and Grassland Project, soil and water conservation ecological restoration, comprehensive management of rocky desertification control, conservation tillage, and returning straw to the fields, among others [23,75,79]. Public statistics show that the Three-North Project completed afforestation of 9.7057 million hectares from 2001 to 2020; and the return of a total of 34.33 million hectares of farmland to forests and grasslands was implemented nationwide from 1999 to 2019 (as noted in the white paper “Twenty Years of Returning Farmland to Forest and Grassland in China (1999–2019)”, issued by the State Forestry and Grassland Administration). By 2020, the national soil erosion area was 269.27 km2, a decrease of 86.7 km2 compared with 1999; by 2016, the area of rocky desertification in karst areas was 100,700 km2, a decrease of 28,920 km2 compared with 2005 [85].
These policies promote SOC accumulation mainly through anthropogenic land management measures such as afforestation and vegetation restoration. Studies have shown that large-scale changes in land use after returning farmland to forest significantly increased soil carbon stocks in China [86]. Hong et al. (2023) found that large-scale afforestation in northern China contributed about 913 Tg C, of which 26% originated from the fixation of SOC [75]. A study by Li et al. (2022) found that vegetation restoration increased SOC stocks in the Loess Plateau’s SOC storage by 16.12 Tg C [23]. In addition, conservation tillage and straw return are important management practices for agricultural land in China [87], and studies have shown that conservation tillage and straw return can significantly increase the rate of soil carbon sequestration [88]. Similarly, these conclusions have been proved in the practice of other countries, such as the planting pattern model of crop rotation and fallow adopted in the United States, and the implementation of land management measures such as afforestation and straw return implemented in European countries, which also promote the recovery and accumulation of organic carbon [89,90,91].

5. Conclusions

Our study analyzed SOCS in China over the past 20 years, and the results showed that SOCS in southern, eastern, southwestern, northern, and northwestern China have maintained a stable increasing trend, while in northeastern China, there has been a decreasing trend over the past 10 years. The SOC changes, on the other hand, showed strong regional variability due to different degrees of influence from temperature, precipitation, and land use disturbances. Overall, the effect of temperature on SOCD showed north–south differences, and the effect of precipitation on SOC mainly showed east–west differences. In addition to this, our study demonstrated the positive effect of anthropogenic activities on SOC accumulation. Although temperature and precipitation limited SOC accumulation in northwest, north, and southwest regions, anthropogenic activities, especially afforestation, grass planting, and vegetation restoration and protection in desert areas, played a significant positive role. This study also emphasized that although land use change will directly cause the loss of organic carbon, this loss is often transient, and the long-term sustained carbon input from surface plants will promote the accumulation of SOC. This implies that a series of land management policies implemented in China over the past 20 years not only led to the continuous expansion of the plant carbon pool but also played a significant positive role in the accumulation of SOC. The results of this study can be used as a reference for the development of soil management strategies and for coping with future climate change.

Author Contributions

Conceptualization, C.X. and F.Z.; methodology, M.Y. and F.Z.; software, M.Y. and F.Z.; investigation, X.X., C.Z., and Z.W.; writing—original draft preparation, F.Z. and C.X.; writing—review and editing, F.Z. and C.X.; visualization, F.Z.; supervision, J.Y. and G.C.; project administration, L.Z. and K.S.; funding acquisition, L.Z. and G.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the China Geological Survey Project, the Ministry of Natural Resources (grant Nos. DD20220877(ZD20220133)), the Guangxi Science and Technology Base and Talent Project (grant Nos. AD20297090), the China Geological Survey Project, the Ministry of Natural Resources (grant Nos. DD20220879), and the Science and Technology Innovation Project of Kunming General Survey of Natural Resources Center China Geological Survey (grant Nos. [2023]10-2302-05).

Data Availability Statement

The original survey data are not publicly available due to the privacy and continuity of this research.

Acknowledgments

We thank the comprehensive investigation and potential evaluation of natural resource carbon sinks in typical regions group of the Kunming General Survey of Natural Resources Center for providing the carbon data. We are also grateful to the data support from the “National Earth System Science Data Center (https://www.geodata.cn, accessed on 20 October 2024)”.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Investigation and analysis of soil samples and calculation of SOCD.
To increase the sample density, sampling sites were selected to be evenly set up in different types of land use (forest, grassland, and cropland) in June to September 2022 in the Tibetan Plateau, the Yunnan–Guizhou Plateau, and the southern hills.
Soil sampling: Within the survey plots, one topsoil mixture (0–20 cm) and four deeper mixtures (20–40 cm, 40–60 cm, 60–80 cm, and 80–100 cm) need to be collected and the soil bulk weight of each layer determined. The topmost layer (0–20 cm), due to its high organic carbon content, loose soil texture, and high variability, needs to be sampled independently by directly using a soil auger with a depth of 20 cm and an inner diameter of >3 cm, randomly selecting six points within the sample plot, removing small soil bodies, and mixing them to form a single mixed sample. In the process of taking surface samples, two points should be noted: one is to try to maintain the integrity of each small soil body; the second is that the sample bag should be opened in the field to allow water to evaporate as early as possible. Four deep mixed samples (20–40 cm, 40–60 cm, 60–80 cm, 80–100 cm) were collected by digging 1 m deep soil profiles at no fewer than three randomly selected locations in the sample plots of the forest survey sites, and each 20–40 cm, 40–60 cm, 60–80 cm, and 80–100 cm depth soil sample was collected from the downslope side of each profile, and the three samples from the same layer depth were evenly mixed to form a mixed sample. The samples were air-dried, sieved, and sent to the Chemical Analysis Laboratory of the China Geological Survey Command Center for Comprehensive Investigation of Kunming General Survey of Natural Resources Center China Geological Survey for the determination of the SOC content after removing plant roots and residues.
SOC content measurement: A total of 0.5 g of soil passed through a 0.15 mm sieve was added to a glass test tube that had been cauterized at 500 °C. Then, 20 mL of 0.5 mol/L HCl was added, shaken well, oscillated for 1 h, and left to stand for 12 h to remove inorganic carbon. Deionized water was added and centrifuged at 3000 r/min for 5 min, and excess HCl was washed several times until the solution was neutral. The recovered soil samples were freeze-dried, and 0.03 g was taken to determine the SOC content using an elemental analyzer (Thermo Scientific, Waltham, MA, USA).
Calculating SOCD: The SOCD calculation formula is as follows:
SOCD = ∑ i = 1 n S O C i × B D i × S D i × 1 − C i 100
where the SOCD unit is kg C·m−2; SOCi is the SOC content of layer i (g·kg−1); BDi is the soil bulk density of layer i (g·cm−3); SDi is the soil depth of layer i (cm); and Ci is the gravel content of layer i (%).

Appendix B

Table A1. The 21 environmental variables used to predict SOCD.
Table A1. The 21 environmental variables used to predict SOCD.
CategoriesVariablesDescription
MODIS dataBand 1Surface reflectance band 1 (620–670 nm)
Band 2Surface reflectance band 2 (841–876 nm)
Band 3Surface reflectance band 3 (459–479 nm)
Band 4Surface reflectance band 4 (545–565 nm)
Band 5Surface reflectance band 5 (1230–1250 nm)
Band 6Surface reflectance band 6 (1628–1652 nm)
Band 7Surface reflectance band 7 (2105–2155 nm)
NDVINormalized difference vegetation index
EVIEnhanced vegetation index
NPPNet primary production
GPPGross primary productivity
Climate factorsTemperatureAnnual mean temperature
PrecipitationAnnual precipitation
Topographic factorsDEMDEM elevation
Slope—
Aspect—
Soil factorsCECSoil cation exchange capacity
ClayPercentage of clay in the soil
pH_H2OSoil pH
PorositySoil porosity
SiltPercentage of silt in the soil

References

  1. Wiesmeier, M.; Urbanski, L.; Hobley, E.; Lang, B.; von Lützow, M.; Marin-Spiotta, E.; van Wesemael, B.; Rabot, E.; Ließ, M.; Garcia-Franco, N.; et al. Soil organic carbon storage as a key function of soils—A review of drivers and indicators at various scales. Geoderma 2019, 333, 149–162. [Google Scholar] [CrossRef]
  2. Bossio, D.A.; Cook-Patton, S.C.; Ellis, P.W.; Fargione, J.; Sanderman, J.; Smith, P.; Wood, S.; Zomer, R.J.; von Unger, M.; Emmer, I.M.; et al. The role of soil carbon in natural climate solutions. Nat. Sustain. 2020, 3, 398. [Google Scholar] [CrossRef]
  3. Batjes, N.H. Total carbon and nitrogen in the soils of the world. Eur. J. Soil Sci. 1996, 47, 151–163. [Google Scholar] [CrossRef]
  4. Beillouin, D.; Corbeels, M.; Demenois, J.; Berre, D.; Boyer, A.; Fallot, A.; Feder, F.; Cardinael, R. A global meta-analysis of soil organic carbon in the Anthropocene. Nat. Commun. 2023, 14, 3700. [Google Scholar] [CrossRef]
  5. Ma, Y.Q.; Woolf, D.; Fan, M.S.; Qiao, L.; Li, R.; Lehmann, J. Global crop production increase by soil organic carbon. Nat. Geosci. 2023, 16, 1159–1165. [Google Scholar] [CrossRef]
  6. Siddique, K.H.; Bolan, N.; Rehman, A.; Farooq, M. Enhancing crop productivity for recarbonizing soil. Soil Tillage Res. 2024, 235, 105863. [Google Scholar] [CrossRef]
  7. Beillouin, D.; Demenois, J.; Cardinael, R.; Berre, D.; Corbeels, M.; Fallot, A.; Boyer, A.; Feder, F. A global database of land management, land-use change and climate change effects on soil organic carbon. Sci. Data 2022, 9, 228. [Google Scholar] [CrossRef]
  8. Tilahun, E.; Haile, M.; Gebresamuel, G.; Zeleke, G. Spatial and temporal dynamics of soil organic carbon stock and carbon sequestration affected by major land-use conversions in Northwestern highlands of Ethiopia. Geoderma 2022, 406, 115506. [Google Scholar] [CrossRef]
  9. Chen, B.; Lu, Q.K.; Wei, L.F.; Fu, W.Q.; Wei, Z.Y.; Tian, S. Global predictions of topsoil organic carbon stocks under changing climate in the 21st century. Sci. Total Environ. 2024, 908, 168448. [Google Scholar] [CrossRef]
  10. Wang, M.M.; Guo, X.W.; Zhang, S.; Xiao, L.J.; Mishra, U.; Yang, Y.H.; Zhu, B.; Wang, G.C.; Mao, X.L.; Qian, T. Global soil profiles indicate depth-dependent soil carbon losses under a warmer climate. Nat. Commun. 2022, 13, 5514. [Google Scholar] [CrossRef]
  11. Friedlingstein, P.; O’Sullivan, M.; Jones, M.W.; Andrew, R.M.; Bakker, D.C.E.; Hauck, J.; Landschützer, P.; Le Quéré, C.; Luijkx, I.T.; Peters, G.P.; et al. Global Carbon Budget 2023. Earth System Science Data Discussions 2023, 15, 5369. [Google Scholar] [CrossRef]
  12. Bradford, M.A.; Wieder, W.R.; Bonan, G.B.; Fierer, N.; Raymond, P.A.; Crowther, T.W. Managing uncertainty in soil carbon feedbacks to climate change. Nat. Clim. Chang. 2016, 6, 751–758. [Google Scholar] [CrossRef]
  13. Ding, X.L.; Chen, S.Y.; Zhang, B.; Liang, C.; He, H.B.; Horwath, W.R. Warming increases microbial residue contribution to soil organic carbon in an alpine meadow. Soil Biol. 2019, 135, 13–19. [Google Scholar] [CrossRef]
  14. Wang, C.; Morrissey, E.M.; Mau, R.L.; Hayer, M.; Piñeiro, J.; Mack, M.C.; Marks, J.C.; Bell, S.L.; Miller, S.N.; Schwartz, E. The temperature sensitivity of soil: Microbial biodiversity, growth, and carbon mineralization. ISME J. 2021, 15, 2738–2747. [Google Scholar] [CrossRef] [PubMed]
  15. Soong, J.L.; Castanha, C.; Hicks Pries, C.E.; Ofiti, N.; Porras, R.C.; Riley, W.J.; Schmidt, M.W.; Torn, M.S. Five years of whole-soil warming led to loss of subsoil carbon stocks and increased CO2 efflux. Sci. Adv. 2021, 7, eabd1343. [Google Scholar] [CrossRef]
  16. Wang, N.; Quesada, B.; Xia, L.L.; Butterbach-Bahl, K.; Goodale, C.L.; Kiese, R. Effects of climate warming on carbon fluxes in grasslands—A global meta analysis. Glob. Chang. Biol. 2019, 25, 1839–1851. [Google Scholar] [CrossRef]
  17. Craig, M.E.; Geyer, K.M.; Beidler, K.V.; Brzostek, E.R.; Frey, S.D.; Stuart Grandy, A.; Liang, C.; Phillips, R.P. Fast-decaying plant litter enhances soil carbon in temperate forests but not through microbial physiological traits. Nat. Commun. 2022, 13, 1229. [Google Scholar] [CrossRef]
  18. Man, M.; Wagner-Riddle, C.; Dunfield, K.E.; Deen, B.; Simpson, M.J. Long-term crop rotation and different tillage practices alter soil organic matter composition and degradation. Soil Tillage Res. 2021, 209, 104960. [Google Scholar] [CrossRef]
  19. Wang, M.; Jiang, C.; Sun, O.J. Spatially differentiated changes in regional climate and underlying drivers in southwestern China. J. For. Res. 2022, 33, 755–765. [Google Scholar] [CrossRef]
  20. Jouberton, A.; Shaw, T.E.; Miles, E.; McCarthy, M.; Fugger, S.; Ren, S.; Dehecq, A.; Yang, W.; Pellicciotti, F. Warming-induced monsoon precipitation phase change intensifies glacier mass loss in the southeastern Tibetan Plateau. Proc. Natl. Acad. Sci. USA 2022, 119, e2109796119. [Google Scholar] [CrossRef]
  21. Ding, Q.; Shao, H.; Chen, X.; Zhang, C. Urban land conversion reduces soil organic carbon density under impervious surfaces. Glob. Biogeochem. Cycles 2022, 36, e2021GB007293. [Google Scholar] [CrossRef]
  22. Luo, Y.L.; Shen, J.; Chen, A.F.; Tao, Q.; Li, Q.Q.; White, P.J.; Li, T.Q.; Li, B.; Chen, L.; Li, H.X. Loss of organic carbon in suburban soil upon urbanization of Chengdu megacity, China. Sci. Total Environ. 2021, 785, 147209. [Google Scholar] [CrossRef] [PubMed]
  23. Li, H.W.; Wu, Y.P.; Liu, S.G.; Zhao, W.Z.; Xiao, J.F.; Winowiecki, L.A.; Vågen, T.G.; Xu, J.C.; Yin, X.W.; Wang, F. The Grain-for-Green project offsets warming-induced soil organic carbon loss and increases soil carbon stock in Chinese Loess Plateau. Sci. Total Environ. 2022, 837, 155469. [Google Scholar] [CrossRef] [PubMed]
  24. Yang, Y.; Dou, Y.X.; Wang, B.R.; Wang, Y.Q.; Liang, C.; An, S.S.; Soromotin, A.; Kuzyakov, Y. Increasing contribution of microbial residues to soil organic carbon in grassland restoration chronosequence. Soil Biol. Biochem. 2022, 170, 108688. [Google Scholar] [CrossRef]
  25. Xu, L.; He, N.P.; Rui, Y.G. A dataset of carbon density in Chinese terrestrial ecosystems (2010s). China Sci. Data 2019, 4. [Google Scholar] [CrossRef]
  26. Wen, S.; Lan, C.C.; Hui, D.T.; Ling, F.Y.; Xang, H.X.; Tao, H.Y.; Hong, K.F.; Dong, L.Z.; Zhen, L.G.; Jun, L.X.; et al. A dataset of topsoil organic carbon density for agricultural ecosystem field stations of Chinese Ecosystem Research Network (2005–2015). China Sci. Data 2020, 5, 56–68. [Google Scholar]
  27. Chen, L.T.; Jing, X.; Flynn, D.F.B.; Shi, Y.; Kühn, P.; Scholten, T.; He, J.S. Changes of carbon stocks in alpine grassland soils from 2002 to 2011 on the Tibetan Plateau and their climatic causes. Geoderma 2017, 288, 166–174. [Google Scholar] [CrossRef]
  28. Wu, X.D.; Fang, H.B.; Zhao, Y.H.; Smoak, J.M.; Li, W.P.; Shi, W.; Sheng, Y.; Zhao, L.; Ding, Y.J. A conceptual model of the controlling factors of soil organic carbon and nitrogen densities in a permafrost-affected region on the eastern Qinghai-Tibetan Plateau. J. Geophys. Res. Biogeosci. 2017, 122, 1705–1717. [Google Scholar] [CrossRef]
  29. Shen, K.H.; Wei, S.G.; Li, L.; Chu, X.X.; Zhong, J.J.; Zhou, J.G.; Zhao, Y. Spatial distribution patterns of soil organic carbon in karst forests of the Lijiang river basin and Its driving factors. Environ. Sci. 2024, 45, 323–334. [Google Scholar] [CrossRef]
  30. Yu, Y.Q.; Huang, Y.; Zhang, W. Modeling soil organic carbon change in croplands of China, 1980–2009. Glob. Planet. Chang. 2012, 82–83, 115–128. [Google Scholar] [CrossRef]
  31. Wang, S.; Huang, M.; Shao, X.M.; Mickler, R.A.; Li, K.R.; Ji, J.J. Vertical Distribution of Soil Organic Carbon in China. Environ. Manag. 2004, 33, S200–S209. [Google Scholar] [CrossRef]
  32. Shangguan, W.; Dai, Y.; Liu, B.; Zhu, A.; Duan, Q.; Wu, L.; Ji, D.; Ye, A.; Yuan, H.; Zhang, Q.; et al. A China dataset of soil properties for land surface modeling. J. Adv. Model. Earth Syst. 2013, 5, 212–224. [Google Scholar] [CrossRef]
  33. Breiman, L. Random forests. Mach. Learn. 2001, 45, 5–32. [Google Scholar] [CrossRef]
  34. Theil, H. A Rank-Invariant Method of Linear and Polynomial Regression Analysis. Indag. Math. 1992, 12, 173. [Google Scholar]
  35. Sen, P.K. Estimates of the regression coefficient based on Kendall’s tau. J. Am. Stat. Assoc. 1968, 63, 1379–1389. [Google Scholar] [CrossRef]
  36. Nackaerts, K.; Vaesen, K.; Muys, B.; Coppin, P. Comparative performance of a modified change vector analysis in forest change detection. Int. J. Remote Sens. 2005, 26, 839–852. [Google Scholar] [CrossRef]
  37. Karnieli, A.; Qin, Z.H.; Wu, B.; Panov, N.; Yan, F. Spatio-temporal dynamics of land-use and land-cover in the Mu Us sandy land, China, using the change vector analysis technique. Remote Sens. 2014, 6, 9316–9339. [Google Scholar] [CrossRef]
  38. Malila, W.A. Change vector analysis: An approach for detecting forest changes with Landsat. In LARS Symposia; Purdue University Libraries: West Lafayette, IN, USA, 1980; p. 385. [Google Scholar]
  39. Chen, J.; Gong, P.; He, C.Y.; Pu, R.L.; Shi, P.J. Land-use/land-cover change detection using improved change-vector analysis. Photogramm. Eng. Remote Sens. 2003, 69, 369–379. [Google Scholar] [CrossRef]
  40. Yang, R.M.; Huang, L.M.; Zhang, X.; Zhu, C.M.; Xu, L. Mapping the distribution, trends, and drivers of soil organic carbon in China from 1982 to 2019. Geoderma 2023, 429, 116232. [Google Scholar] [CrossRef]
  41. Zhang, Z.P.; Ding, J.L.; Zhu, C.M.; Wang, J.J.; Ge, X.Y.; Li, X.; Han, L.J.; Chen, X.Y.; Wang, J.Z. Historical and future variation of soil organic carbon in China. Geoderma 2023, 436, 116557. [Google Scholar] [CrossRef]
  42. Zhang, Z.P.; Ding, J.L.; Zhu, C.M.; Wang, J.J.; Li, X.; Ge, X.Y.; Han, L.J.; Chen, X.Y.; Wang, J.Z. Exploring the inter-decadal variability of soil organic carbon in China. Catena 2023, 230, 107242. [Google Scholar] [CrossRef]
  43. Chi, Y.; Liu, D.H.; Gao, J.H.; Sun, J.K.; Zhang, Z.W.; Xing, W.X.; Qu, Y.B.; Ma, X.J.; Zha, B. Coastal surface soil carbon stocks have distinctly increased under extensive ecological restoration in northern China. Commun. Earth Environ. 2023, 4, 373. [Google Scholar] [CrossRef]
  44. Sahu, B.; Ghosh, A.K. Deterministic and geostatistical models for predicting soil organic carbon in a 60 ha farm on Inceptisol in Varanasi, India. Geoderma Reg. 2021, 26, e00413. [Google Scholar] [CrossRef]
  45. Liu, W.J.; Xu, C.; Zhang, Z.M.; De Boeck, H.; Wang, Y.F.; Zhang, L.K.; Xu, X.W.; Zhang, C.; Chen, G.R.; Xu, C. Machine learning-based grassland aboveground biomass estimation and its response to climate variation in Southwest China. Front. Ecol. Evol. 2023, 11, 1146850. [Google Scholar] [CrossRef]
  46. Huang, T.B.; Ou, G.L.; Wu, Y.; Zhang, X.L.; Liu, Z.H.; Xu, H.; Xu, X.W.; Wang, Z.H.; Xu, C. Estimating the aboveground biomass of various forest types with high heterogeneity at the provincial scale based on multi-source data. Remote Sens. 2023, 15, 3550. [Google Scholar] [CrossRef]
  47. Wang, S.; Zhuang, Q.; Yang, Z.; Yu, N.; Jin, X. Temporal and spatial changes of soil organic carbon stocks in the forest area of northeastern China. Forests 2019, 10, 1023. [Google Scholar] [CrossRef]
  48. Zhang, Y.C.S.; Guo, L.; Chen, Y.Y.; Shi, T.Z.; Luo, M.; Ju, Q.L.; Zhang, H.T.; Wang, S.Q. Prediction of soil organic carbon based on Landsat 8 monthly NDVI data for the Jianghan Plain in Hubei Province, China. Remote Sens. 2019, 11, 1683. [Google Scholar] [CrossRef]
  49. Fang, J.Y.; Liu, G.H.; Ling, X.S. Carbon reservoir of terrestrial ecosystem in China. In Monitoring and Relevant Process of Greenhouse Gas Concentration and Emission; Wang, G.C., Pu, W.Y., Eds.; China Environment Sciences Publishing House: Beijing, China, 1996; pp. 109–128. [Google Scholar]
  50. Wang, S.Q.; Zhou, C.H. Estimating soil carbon reservior of terrestrial ecosystem in China. Geogr. Res. 1999, 18, 349–356. [Google Scholar]
  51. Yang, Y.H.; Shi, Y.; Shun, W.J.; Chang, J.F.; Zhu, J.X.; Chen, L.Y.; Wang, X.; Guo, Y.P.; Zhang, H.T.; Yu, L.F.; et al. Terrestrial carbon sinks in China and around the world and their contribution to carbon neutrality. Sci. China-Life Sci. 2022, 52, 534–574. [Google Scholar]
  52. Tang, X.L.; Zhao, X.; Bai, Y.F.; Tang, Z.Y.; Wang, W.T.; Zhao, Y.C.; Wan, H.W.; Xie, Z.Q.; Shi, X.Z.; Wu, B.F. Carbon pools in China’s terrestrial ecosystems: New estimates based on an intensive field survey. Proc. Natl. Acad. Sci. USA 2018, 115, 4021–4026. [Google Scholar] [CrossRef]
  53. Xu, L.; Yu, G.R.; He, N.P. Increased soil organic carbon storage in Chinese terrestrial ecosystems from the 1980s to the 2010s. J. Geogr. Sci. 2019, 29, 49–66. [Google Scholar] [CrossRef]
  54. Zhang, L.M.; Zheng, Q.F.; Liu, Y.L.; Liu, S.G.; Yu, D.S.; Shi, X.Z.; Xing, S.H.; Chen, H.Y.; Fan, X.Y. Combined effects of temperature and precipitation on soil organic carbon changes in the uplands of eastern China. Geoderma 2019, 337, 1105–1115. [Google Scholar] [CrossRef]
  55. Feng, J.G.; He, K.Y.; Zhang, Q.F.; Han, M.G.; Zhu, B. Changes in plant inputs alter soil carbon and microbial communities in forest ecosystems. Glob. Change Biol. 2022, 28, 3426–3440. [Google Scholar] [CrossRef] [PubMed]
  56. Domeignoz-Horta, L.A.; Pold, G.; Erb, H.; Sebag, D.; Verrecchia, E.; Northen, T.; Louie, K.; Eloe-Fadrosh, E.; Pennacchio, C.; Knorr, M.A. Substrate availability and not thermal-acclimation controls microbial temperature sensitivity response to long term warming. Glob. Change Biol. 2022, 29, 1574–1590. [Google Scholar] [CrossRef]
  57. Yang, Y.; Tilman, D.; Furey, G.; Lehman, C. Soil carbon sequestration accelerated by restoration of grassland biodiversity. Nat. Commun. 2019, 10, 718. [Google Scholar] [CrossRef]
  58. Xu, S.; Wang, J.J.; Sayer, E.J.; Lam, S.K.; Lai, D.Y. Precipitation change affects forest soil carbon inputs and pools: A global meta-analysis. Sci. Total Environ. 2024, 908, 168171. [Google Scholar] [CrossRef]
  59. Jiang, M.D.; Li, H.L.; Zhang, W.; Liu, J.B.; Zhang, Q. Effects of climate change and grazing on the soil organic carbon stock of alpine wetlands on the Tibetan Plateau from 2000 to 2018. Catena 2024, 238, 107870. [Google Scholar] [CrossRef]
  60. Song, J.; Wan, S.Q.; Piao, S.L.; Knapp, A.K.; Classen, A.T.; Vicca, S.; Ciais, P.; Hovenden, M.J.; Leuzinger, S.; Beier, C.; et al. A meta-analysis of 1,119 manipulative experiments on terrestrial carbon-cycling responses to global change. Nat. Ecol. 2019, 3, 1320. [Google Scholar] [CrossRef]
  61. Nottingham, A.T.; Whitaker, J.; Ostle, N.J.; Bardgett, R.D.; McNamara, N.P.; Fierer, N.; Salinas, N.; Ccahuana, A.J.; Turner, B.L.; Meir, P. Microbial responses to warming enhance soil carbon loss following translocation across a tropical forest elevation gradient. Ecol. Lett. 2019, 22, 1889–1899. [Google Scholar] [CrossRef]
  62. Chen, J.; Elsgaard, L.; van Groenigen, K.J.; Olesen, J.E.; Liang, Z.; Jiang, Y.; Lærke, P.E.; Zhang, Y.F.; Luo, Y.Q.; Hungate, B.A. Soil carbon loss with warming: New evidence from carbon-degrading enzymes. Glob. Chang. Biol. 2020, 26, 1944–1952. [Google Scholar] [CrossRef]
  63. Yu, Y.Y.; Ru, J.Y.; Lei, B.H.; Han, S.J.; Wan, S.Q.; Zheng, J.Q. Distinct response patterns of soil micro-eukaryotic communities to early-season and late-season precipitation in a semiarid grassland. Soil Biol. Biochem. 2024, 194, 109427. [Google Scholar] [CrossRef]
  64. Wang, M.M.; Zhang, S.; Guo, X.W.; Xiao, L.J.; Yang, Y.H.; Luo, Y.Q.; Mishra, U.; Luo, Z.K. Responses of soil organic carbon to climate extremes under warming across global biomes. Nat. Clim. Chang. 2024, 14, 98–105. [Google Scholar] [CrossRef]
  65. Zhang, Z.; Ding, J.; Zhu, C.; Shi, H.; Chen, X.; Han, L.; Wang, J. Changes in soil organic carbon stocks from 1980-1990 and 2010-2020 in the northwest arid zone of China. Land Degrad. Dev. 2022, 33, 2713–2727. [Google Scholar] [CrossRef]
  66. Bai, L.H.; Zhang, H.; Zhang, J.G.; Li, X.; Wang, B.; Miao, H.Z.; Sial, T.A.; Dong, Q.; Fu, G.J.; Li, L.M. Long-term vegetation restoration increases carbon sequestration of different soil particles in a semi-arid desert. Ecosphere 2021, 12, 109427. [Google Scholar] [CrossRef]
  67. Li, Y.F.; Xie, T.; Yang, H.T.; Li, X.J. Revegetation enhances soil organic carbon mineralization and its temperature sensitivity in the Tengger Desert, North China. Catena 2022, 218, 106541. [Google Scholar] [CrossRef]
  68. Lin, B.J.; Li, R.C.; Liu, K.C.; Oladele, O.P.; Xu, Z.Y.; Lal, R.; Zhao, X.; Zhang, H.L. Management-induced changes in soil organic carbon and related crop yield dynamics in China’s cropland. Glob. Chang. Biol. 2023, 29, 3575–3590. [Google Scholar] [CrossRef]
  69. Xie, E.Z.; Zhang, Y.X.; Huang, B.; Zhao, Y.C.; Shi, X.Z.; Hu, W.Y.; Qu, M.K. Spatiotemporal variations in soil organic carbon and their drivers in southeastern China during 1981–2011. Soil Tillage Res. 2021, 205, 104763. [Google Scholar] [CrossRef]
  70. Zhang, C.Z.; Li, W.; Zhao, Z.H.; Zhou, Y.F.; Zhang, J.B.; Wu, Q.C. Spatiotemporal variability and related factors of soil organic carbon in henan province. Vadose Zone J. 2018, 17, 1–10. [Google Scholar] [CrossRef]
  71. Wang, Q.; Yang, C.H.; Wang, M.L.; Zhao, L.; Zhao, Y.C.; Zhang, Q.P.; Zhang, C.Y. Decoupling analysis to assess the impact of land use patterns on carbon emissions: A case study in the Yellow River Delta efficient eco-economic zone, China. J. Clean. Prod. 2023, 412, 137415. [Google Scholar] [CrossRef]
  72. Wang, X.; Li, S.J.; Wang, L.P.; Zheng, M.; Wang, Z.M.; Song, K.S. Effects of cropland reclamation on soil organic carbon in China’s black soil region over the past 35 years. Glob. Chang. Biol. 2023, 29, 5460–5477. [Google Scholar] [CrossRef]
  73. Liu, H.B.; Li, S.T.; Zhou, Y.P.; Health, P. Spatial-temporal variability of soil organic matter in urban fringe over 30 years: A case study in Northeast China. Int. J. Environ. Res. Public Health 2020, 17, 292. [Google Scholar] [CrossRef] [PubMed]
  74. He, X.X.; Sheng, M.Y.; Wang, L.J.; Zhang, S.L.; Luo, N.N. Effects on soil organic carbon accumulation and mineralization of long-term vegetation restoration in Southwest China karst. Ecol. Indic. 2022, 145, 109622. [Google Scholar] [CrossRef]
  75. Hong, S.B.; Ding, J.Z.; Kan, F.; Xu, H.; Chen, S.Y.; Yao, Y.T.; Piao, S.L. Asymmetry of carbon sequestrations by plant and soil after forestation regulated by soil nitrogen. Nat. Commun. 2023, 14, 3196. [Google Scholar] [CrossRef] [PubMed]
  76. Wang, S.; Zhuang, Q.L.; Zhou, M.Y.; Jin, X.X.; Yu, N.; Yuan, T. Temporal and spatial changes in soil organic carbon and soil inorganic carbon stocks in the semi-arid area of northeast China. Ecol. Indic. 2023, 146, 109776. [Google Scholar] [CrossRef]
  77. Ou, Y.; Rousseau, A.N.; Wang, L.X.; Yan, B.X. Spatio-temporal patterns of soil organic carbon and pH in relation to environmental factors—A case study of the Black Soil Region of Northeastern China. Agric. Ecosyst. Environ. 2017, 245, 22–31. [Google Scholar] [CrossRef]
  78. Huang, B.; Sun, W.X.; Zhao, Y.C.; Zhu, J.; Yang, R.Q.; Zou, Z.; Ding, F.; Su, J.P. Temporal and spatial variability of soil organic matter and total nitrogen in an agricultural ecosystem as affected by farming practices. Geoderma 2007, 139, 336–345. [Google Scholar] [CrossRef]
  79. Qi, J.Y.; Wang, X.; Zhao, X.; Pu, C.; Kan, Z.R.; Li, C.; Liu, P.; Xiao, X.P.; Lal, R.; Zhang, H.L. Temporal variability of soil organic carbon in paddies during 13-year conservation tillage. Land Degrad. Dev. 2019, 30, 1840–1850. [Google Scholar] [CrossRef]
  80. Wang, G.Y.; Mao, J.F.; Fan, L.L.; Ma, X.X.; Li, Y.M. Effects of climate and grazing on the soil organic carbon dynamics of the grasslands in Northern Xinjiang during the past twenty years. Glob. Ecol. Conserv. 2022, 34, e02039. [Google Scholar] [CrossRef]
  81. Hu, L.N.; Li, Q.; Yan, J.H.; Liu, C.; Zhong, J.X. Vegetation restoration facilitates belowground microbial network complexity and recalcitrant soil organic carbon storage in southwest China karst region. Sci. Total Environ. 2022, 820, 153137. [Google Scholar] [CrossRef]
  82. Cheng, S.L.; Ouyang, H.; Niu, H.S.; Wang, L.; Zhang, F.; Gao, J.Q.; Tian, Y.Q. Spatial and temporal dynamics of soil organic carbon in reserved desertification area: A case study in Yulin City, Shaanxi Province, China. Chin. Geogr. Sci. 2004, 14, 245–250. [Google Scholar] [CrossRef]
  83. Chen, T.; Chang, Q.R.; Liu, J.; Clevers, J.G. Spatio-temporal variability of farmland soil organic matter and total nitrogen in the southern Loess Plateau, China: A case study in Heyang County. Environ. Earth Sci. 2016, 75, 1–15. [Google Scholar] [CrossRef]
  84. Ye, H.C.; Huang, Y.F.; Chen, P.F.; Huang, W.J.; Zhang, S.W.; Huang, S.Y.; Sen, H. Effects of land use change on the spatiotemporal variability of soil organic carbon in an urban-rural ecotone of Beijing, China. J. Integr. Agric. 2016, 15, 918–928. [Google Scholar] [CrossRef]
  85. Wang, X.H.; He, J.; Mou, X.J.; Zhu, Z.X.; Chai, H.X.; Liu, G.H.; Rao, S.; Zhang, X. 20 Years of Ecological Protection and Restoration in China:Review and Prospect. Chin. J. Environ. Manag. 2021, 13, 85–92. [Google Scholar]
  86. Deng, L.; Liu, G.B.; Shangguan, Z.P. Land-use conversion and changing soil carbon stocks in China’s ‘Grain-for-Green’ Program: A synthesis. Glob. Chang. Biol. 2014, 20, 3544–3556. [Google Scholar] [CrossRef]
  87. Zhao, Y.C.; Wang, M.Y.; Hu, S.J.; Zhang, X.D.; Ouyang, Z.; Zhang, G.L.; Huang, B.; Zhao, S.W.; Wu, J.S.; Xie, D.T. Economics-and policy-driven organic carbon input enhancement dominates soil organic carbon accumulation in Chinese croplands. Proc. Natl. Acad. Sci. USA 2018, 115, 4045–4050. [Google Scholar] [CrossRef]
  88. Mo, F.; Yang, D.Y.; Wang, X.K.; Crowther, T.W.; Vinay, N.; Luo, Z.K.; Yu, K.L.; Sun, S.K.; Zhang, F.; Xiong, Y.C.; et al. Nutrient limitation of soil organic carbon stocks under straw return. Soil Biol. Biochem. 2024, 192, 109360. [Google Scholar] [CrossRef]
  89. Stella, T.; Mouratiadou, I.; Gaiser, T.; Berg-Mohnicke, M.; Wallor, E.; Ewert, F.; Nendel, C. Estimating the contribution of crop residues to soil organic carbon conservation. Environ. Res. Lett. 2019, 14, 094008. [Google Scholar] [CrossRef]
  90. Bárcena, T.G.; Kiær, L.P.; Vesterdal, L.; Stefánsdóttir, H.M.; Gundersen, P.; Sigurdsson, B.D. Soil carbon stock change following afforestation in Northern Europe: A meta-analysis. Glob. Chang. Biol. 2014, 20, 2393–2405. [Google Scholar] [CrossRef]
  91. Nash, P.R.; Gollany, H.T.; Liebig, M.A.; Halvorson, J.J.; Archer, D.W.; Tanaka, D.L. Simulated Soil Organic Carbon Responses to Crop Rotation, Tillage, and Climate Change in North Dakota. J. Environ. Qual. 2018, 47, 654–662. [Google Scholar] [CrossRef]
Land 13 01764 g001
Figure 1. Distribution of sample values of soil carbon density in soil layers at 0–20 cm (a) and 0–100 cm (b) depths. Sampled data refer to the soil data obtained from our project team’s field survey. Collected data refer to the collection of data from the literature and databases.
Figure 1. Distribution of sample values of soil carbon density in soil layers at 0–20 cm (a) and 0–100 cm (b) depths. Sampled data refer to the soil data obtained from our project team’s field survey. Collected data refer to the collection of data from the literature and databases.
Land 13 01764 g001
Land 13 01764 g002
Figure 2. Scatter plots of estimated and predicted SOCD values: depths of 0–20 cm (a) and 0–100 cm (b). R2, coefficient of determination; RMSE, root mean squared error; LCCC, Lin’s concordance correlation coefficient.
Figure 2. Scatter plots of estimated and predicted SOCD values: depths of 0–20 cm (a) and 0–100 cm (b). R2, coefficient of determination; RMSE, root mean squared error; LCCC, Lin’s concordance correlation coefficient.
Land 13 01764 g002
Land 13 01764 g003
Figure 3. Order of importance of ECs used to predict SOCD in random forest: depths of 0–20 cm (a) and 0–100 cm (b).
Figure 3. Order of importance of ECs used to predict SOCD in random forest: depths of 0–20 cm (a) and 0–100 cm (b).
Land 13 01764 g003
Land 13 01764 g004
Figure 4. Spatial distribution of and temporal variation in 0–20 cm SOCD. (a–e) represent SOCD distribution maps; (f) shows the change in SOCD. When slope > 0, the SOCD of the time series shows an increasing trend; when slope < 0, the SOCD of the time series shows a decreasing trend.
Figure 4. Spatial distribution of and temporal variation in 0–20 cm SOCD. (a–e) represent SOCD distribution maps; (f) shows the change in SOCD. When slope > 0, the SOCD of the time series shows an increasing trend; when slope < 0, the SOCD of the time series shows a decreasing trend.
Land 13 01764 g004
Land 13 01764 g005
Figure 5. Spatial distribution of and temporal variation in 0–100 cm SOCD. (a–e) represent SOCD spatial distribution; (f) shows the change in SOCD. When slope > 0, the SOCD of the time series shows an increasing trend; when slope < 0, the SOCD of the time series shows a decreasing trend.
Figure 5. Spatial distribution of and temporal variation in 0–100 cm SOCD. (a–e) represent SOCD spatial distribution; (f) shows the change in SOCD. When slope > 0, the SOCD of the time series shows an increasing trend; when slope < 0, the SOCD of the time series shows a decreasing trend.
Land 13 01764 g005
Land 13 01764 g006aLand 13 01764 g006b
Figure 6. Zonal statistics for SOCS, mean SOCD, and mean Theil–Sen median slope: depth of 0–20 cm (a) and 0–100 cm (b).
Figure 6. Zonal statistics for SOCS, mean SOCD, and mean Theil–Sen median slope: depth of 0–20 cm (a) and 0–100 cm (b).
Land 13 01764 g006aLand 13 01764 g006b
Land 13 01764 g007
Figure 7. Spatial pattern of partial correlation and correlation coefficients of SOCD at a depth between 0 and 20 cm and influencing factors from 2001 to 2020. These factors include temperature (a), precipitation (b), land use disturbance intensity (c), and (d) the percentage of partial correlation and correlation between SOCD at a 0 to 20 cm depth and the three influencing factors. Upward and downward bars indicate percentages of positive and negative correlation, respectively. Colored areas indicate correlation coefficients greater than 0.5 or less than −0.5.
Figure 7. Spatial pattern of partial correlation and correlation coefficients of SOCD at a depth between 0 and 20 cm and influencing factors from 2001 to 2020. These factors include temperature (a), precipitation (b), land use disturbance intensity (c), and (d) the percentage of partial correlation and correlation between SOCD at a 0 to 20 cm depth and the three influencing factors. Upward and downward bars indicate percentages of positive and negative correlation, respectively. Colored areas indicate correlation coefficients greater than 0.5 or less than −0.5.
Land 13 01764 g007
Land 13 01764 g008
Figure 8. Spatial pattern of partial correlation and correlation coefficients of SOCD at a depth between 0 and 100 cm and influencing factors from 2001 to 2020. These factors include temperature (a), precipitation (b), land use disturbance intensity (c), and (d) the percentage of partial correlation and correlation between SOCD at a 0 to 100 cm depth and the three influencing factors. Upward and downward bars indicate percentages of positive and negative correlation, respectively. Colored areas indicate correlation coefficients greater than 0.5 or less than −0.5.
Figure 8. Spatial pattern of partial correlation and correlation coefficients of SOCD at a depth between 0 and 100 cm and influencing factors from 2001 to 2020. These factors include temperature (a), precipitation (b), land use disturbance intensity (c), and (d) the percentage of partial correlation and correlation between SOCD at a 0 to 100 cm depth and the three influencing factors. Upward and downward bars indicate percentages of positive and negative correlation, respectively. Colored areas indicate correlation coefficients greater than 0.5 or less than −0.5.
Land 13 01764 g008
Land 13 01764 g009
Figure 9. Land use change from 2001 to 2020 (the left side is the area transferred out of different land types, and the right side is the area transferred in from different land types; area unit is km2).
Figure 9. Land use change from 2001 to 2020 (the left side is the area transferred out of different land types, and the right side is the area transferred in from different land types; area unit is km2).
Land 13 01764 g009
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Zou, F.; Yan, M.; Zhang, L.; Yang, J.; Chen, G.; Shan, K.; Zhang, C.; Xu, X.; Wang, Z.; Xu, C. Dynamic Changes in and Driving Factors of Soil Organic Carbon in China from 2001 to 2020. Land 2024, 13, 1764. https://doi.org/10.3390/land13111764

AMA Style

Zou F, Yan M, Zhang L, Yang J, Chen G, Shan K, Zhang C, Xu X, Wang Z, Xu C. Dynamic Changes in and Driving Factors of Soil Organic Carbon in China from 2001 to 2020. Land. 2024; 13(11):1764. https://doi.org/10.3390/land13111764

Chicago/Turabian Style

Zou, Fuyan, Min Yan, Liankai Zhang, Jinjiang Yang, Guiren Chen, Keqiang Shan, Chen Zhang, Xiongwei Xu, Zhenhui Wang, and Can Xu. 2024. "Dynamic Changes in and Driving Factors of Soil Organic Carbon in China from 2001 to 2020" Land 13, no. 11: 1764. https://doi.org/10.3390/land13111764

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop