Leveraging Remote Sensing-Derived Dynamic Crop Growth Information for Improved Soil Property Prediction in Farmlands
文献类型：期刊论文
| 作者 | Geng, Jing3,4; Tan, Qiuyuan4; Zhang, Ying4; Lv, Junwei4; Yu, Yong4; Fang, Huajun2,5; Guo, Yifan2; Cheng, Shulan1 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2024-08-01 |
| 卷号 | 16期号:15页码:2731 |
| 关键词 | soil properties mapping crop growth factor time-series NDVI farmland soils |
| DOI | 10.3390/rs16152731 |
| 产权排序 | 3 |
| 文献子类 | Article |
| 英文摘要 | Rapid and accurate mapping of soil properties in farmlands is crucial for guiding agricultural production and maintaining food security. Traditional methods using spectral features from remote sensing prove valuable for estimating soil properties, but are restricted to short periods of bare soil occurrence within agricultural settings. Addressing the challenge of predicting soil properties under crop cover, this study proposed an improved soil modeling framework that integrates dynamic crop growth information with machine learning techniques. The methodology's robustness was tested on six key soil properties in an agricultural region of China, including soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), dissolved organic carbon (DOC), dissolved organic nitrogen (DON), and pH. Four experimental scenarios were established to assess the impact of crop growth information, represented by the normalized difference vegetation index (NDVI) and phenological parameters. Specifically, Scenario I utilized only natural factors (terrain and climate data); Scenario II added phenological parameters based on Scenario I; Scenario III incorporated time-series NDVI based on Scenario I; and Scenario IV combined all variables (traditional natural factors and crop growth information). These were evaluated using three advanced machine learning models: random forest (RF), Cubist, and Extreme Gradient Boosting (XGBoost). Results demonstrated that incorporating phenological parameters and time-series NDVI significantly improved model accuracy, enhancing predictions by up to 36% over models using only natural factors. Moreover, although both are crop growth factors, the contribution of the time-series NDVI variable to model accuracy surpassed that of the phenological variable for most soil properties. Relative importance analysis suggested that the crop growth information, derived from time-series NDVI and phenology data, collectively explained 14-45% of the spatial variation in soil properties. This study highlights the significant benefits of integrating remote sensing-based crop growth factors into soil property inversion under crop-covered conditions, providing valuable insights for digital soil mapping. |
| WOS关键词 | ORGANIC-MATTER ; SYSTEMS ; CARBON ; PRODUCTIVITY ; MANAGEMENT ; RESOLUTION ; LAND |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS记录号 | WOS:001287214000001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/206916]  |
| 专题 | 生态系统网络观测与模拟院重点实验室_外文论文 |
| 通讯作者 | Fang, Huajun |
| 作者单位 | 1.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China 3.Minist Nat Resources, Key Lab Nat Resources Monitoring Trop & Subtrop Ar, Zhuhai 519082, Peoples R China 4.Sun Yat Sen Univ, Sch Geospatial Engn & Sci, Zhuhai 519082, Peoples R China 5.Zhongke Jian Inst Ecoenvironm Sci, Jian 343000, Peoples R China |
| 推荐引用方式 GB/T 7714 | Geng, Jing,Tan, Qiuyuan,Zhang, Ying,et al. Leveraging Remote Sensing-Derived Dynamic Crop Growth Information for Improved Soil Property Prediction in Farmlands[J]. REMOTE SENSING,2024,16(15):2731. |
| APA | Geng, Jing.,Tan, Qiuyuan.,Zhang, Ying.,Lv, Junwei.,Yu, Yong.,...&Cheng, Shulan.(2024).Leveraging Remote Sensing-Derived Dynamic Crop Growth Information for Improved Soil Property Prediction in Farmlands.REMOTE SENSING,16(15),2731. |
| MLA | Geng, Jing,et al."Leveraging Remote Sensing-Derived Dynamic Crop Growth Information for Improved Soil Property Prediction in Farmlands".REMOTE SENSING 16.15(2024):2731. |
入库方式： OAI收割
来源：地理科学与资源研究所
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