Improving the Spatial Prediction of Soil Organic Carbon Content Using Phenological Factors: A Case Study in the Middle and Upper Reaches of Heihe River Basin, China
The accurate mapping of soil organic carbon (SOC) distribution is important for carbon sequestration and land management strategies, contributing to mitigating climate change and ensuring agricultural productivity. The Heihe River Basin in China is an important region that has immense potential for...
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| Format: | Article |
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MDPI AG
2023-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/7/1847 |
| _version_ | 1797607120133160960 |
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| author | Xinyu Liu Jian Wang Xiaodong Song |
| author_facet | Xinyu Liu Jian Wang Xiaodong Song |
| author_sort | Xinyu Liu |
| collection | DOAJ |
| description | The accurate mapping of soil organic carbon (SOC) distribution is important for carbon sequestration and land management strategies, contributing to mitigating climate change and ensuring agricultural productivity. The Heihe River Basin in China is an important region that has immense potential for SOC storage. Phenological variables are effective indicators of vegetation growth, and hence are closely related to SOC. However, few studies have incorporated phenological variables in SOC prediction, especially in alpine areas such as the Heihe River Basin. This study used random forest (RF) and extreme gradient boosting (XGBoost) to study the effects of phenological variables (e.g., Greenup, Dormancy, etc.) obtained from MODIS (i.e., Moderate Resolution Imaging Spectroradiometer) product (MCD12Q2) on SOC content prediction in the middle and upper reaches of Heihe River Basin. The current study also identified the dominating variables in SOC prediction and compared model performance using a cross validation procedure. The results indicate that: (1) when phenological variables were considered, the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi mathvariant="normal">R</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> (coefficient of determination) of RF and XGBoost were 0.68 and 0.56, respectively, and RF consistently outperforms XGBoost in various cross validation experiments; (2) the environmental variables MAT, MAP, DEM and NDVI play the most important roles in SOC prediction; (3) the phenological variables can account for 32–39% of the spatial variability of SOC in both the RF and XGBoost models, and hence were the most important factor among the five categories of predictive variables. This study proved that the introduction of phenological variables can significantly improve the performance of SOC prediction. They should be used as indispensable variables for accurately modeling SOC in related studies. |
| first_indexed | 2024-03-11T05:25:48Z |
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| language | English |
| last_indexed | 2024-03-11T05:25:48Z |
| publishDate | 2023-03-01 |
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| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-659b839889ac4ed5b9599ed36a78868d2023-11-17T17:29:49ZengMDPI AGRemote Sensing2072-42922023-03-01157184710.3390/rs15071847Improving the Spatial Prediction of Soil Organic Carbon Content Using Phenological Factors: A Case Study in the Middle and Upper Reaches of Heihe River Basin, ChinaXinyu Liu0Jian Wang1Xiaodong Song2College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, ChinaCollege of Earth Sciences, Chengdu University of Technology, Chengdu 610059, ChinaState Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, ChinaThe accurate mapping of soil organic carbon (SOC) distribution is important for carbon sequestration and land management strategies, contributing to mitigating climate change and ensuring agricultural productivity. The Heihe River Basin in China is an important region that has immense potential for SOC storage. Phenological variables are effective indicators of vegetation growth, and hence are closely related to SOC. However, few studies have incorporated phenological variables in SOC prediction, especially in alpine areas such as the Heihe River Basin. This study used random forest (RF) and extreme gradient boosting (XGBoost) to study the effects of phenological variables (e.g., Greenup, Dormancy, etc.) obtained from MODIS (i.e., Moderate Resolution Imaging Spectroradiometer) product (MCD12Q2) on SOC content prediction in the middle and upper reaches of Heihe River Basin. The current study also identified the dominating variables in SOC prediction and compared model performance using a cross validation procedure. The results indicate that: (1) when phenological variables were considered, the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi mathvariant="normal">R</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula> (coefficient of determination) of RF and XGBoost were 0.68 and 0.56, respectively, and RF consistently outperforms XGBoost in various cross validation experiments; (2) the environmental variables MAT, MAP, DEM and NDVI play the most important roles in SOC prediction; (3) the phenological variables can account for 32–39% of the spatial variability of SOC in both the RF and XGBoost models, and hence were the most important factor among the five categories of predictive variables. This study proved that the introduction of phenological variables can significantly improve the performance of SOC prediction. They should be used as indispensable variables for accurately modeling SOC in related studies.https://www.mdpi.com/2072-4292/15/7/1847soil organic carbonspatial predictionphenological variablesRFXGBoostHeihe River Basin |
| spellingShingle | Xinyu Liu Jian Wang Xiaodong Song Improving the Spatial Prediction of Soil Organic Carbon Content Using Phenological Factors: A Case Study in the Middle and Upper Reaches of Heihe River Basin, China Remote Sensing soil organic carbon spatial prediction phenological variables RF XGBoost Heihe River Basin |
| title | Improving the Spatial Prediction of Soil Organic Carbon Content Using Phenological Factors: A Case Study in the Middle and Upper Reaches of Heihe River Basin, China |
| title_full | Improving the Spatial Prediction of Soil Organic Carbon Content Using Phenological Factors: A Case Study in the Middle and Upper Reaches of Heihe River Basin, China |
| title_fullStr | Improving the Spatial Prediction of Soil Organic Carbon Content Using Phenological Factors: A Case Study in the Middle and Upper Reaches of Heihe River Basin, China |
| title_full_unstemmed | Improving the Spatial Prediction of Soil Organic Carbon Content Using Phenological Factors: A Case Study in the Middle and Upper Reaches of Heihe River Basin, China |
| title_short | Improving the Spatial Prediction of Soil Organic Carbon Content Using Phenological Factors: A Case Study in the Middle and Upper Reaches of Heihe River Basin, China |
| title_sort | improving the spatial prediction of soil organic carbon content using phenological factors a case study in the middle and upper reaches of heihe river basin china |
| topic | soil organic carbon spatial prediction phenological variables RF XGBoost Heihe River Basin |
| url | https://www.mdpi.com/2072-4292/15/7/1847 |
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