An integrated solar-induced chlorophyll fluorescence model for more accurate soil organic carbon content estimation in an Alpine agricultural area
文献类型：期刊论文
| 作者 | Yu, Qing; Lu, Hongwei; Yao, Tianci; Feng, Wei; Xue, Yuxuan |
| 刊名 | PLANT AND SOIL
 |
| 出版日期 | 2023 |
| 卷号 | N/A |
| ISSN号 | 0032-079X |
| 关键词 | Soil organic carbon Solar-induced chlorophyll fluorescence Land attributes Landsat 8 operational land Imager data Precision agriculture |
| DOI | 10.1007/s11104-022-05863-x |
| 文献子类 | J |
| 英文摘要 | Background and aims Solar-induced chlorophyll fluorescence (SIF) is closely related to vegetation photosynthesis and can sensitively reflect the growth and health of vegetation. Using the advantages of SIF in photosynthetic physiological diagnosis, this study carried out a collaborative study of SIF, land attrib-utes and image reflectance spectra to estimate soil organic carbon (SOC) content in typical agricultural areas of the Qinghai-Tibet plateau (QTP).Methods The spectral reflectance (R), first deriva-tive of reflectance (FDR), second derivative of reflectance (SDR) of spectral band of Landsat 8 Operational Land Imager (OLI) data were selected together with land attributes (i.e. elevation, slope, soil temperature, and soil moisture content) and SIF index and vegetation indices to establish the SOC content estimation models using the random forest (RF), back propagation neural network (BPNN) and partial least squares regression (PLSR), respectively.Results SIF index can significantly improve the SOC content estimation compared to the veg-etation indices. The accuracy of the BPNN model established by combining SIF index with the FDR of Landsat 8 OLI data and land attributes was the highest (R-2 = 0.977, RMSEC = 2.069 gmiddotkg(- 1), MAE = 0.945 gmiddotkg(- 1), RPD = 3.970, d-factor = 0.010). Conclusion This study confirmed the good effect of BPNN model driven by SIF index, land attributes, and Landsat 8 OLI data on the estimation of SOC content, which can provide a new way for the accu-rate estimation of the soil internal components in the agricultural areas. |
| WOS关键词 | INFRARED SPECTROSCOPY ; RANDOM FOREST ; MATTER ; REFLECTANCE ; RETRIEVAL ; STOCKS ; PREDICTION ; GOME-2 ; REGION ; SEQUESTRATION |
| 资助项目 | Second Tibetan Plateau Scientific Expedition and Research Program (STEP) ; CAS Interdisciplinary Innovation Team ; Strategic Priority Research Program of the Chinese Academy of Sciences ; [2019QZKK1003] ; [JCTD-2019-04] ; [XDA20040301] |
| WOS研究方向 | Agriculture ; Plant Sciences |
| 出版者 | SPRINGER |
| WOS记录号 | WOS:000909485200001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/188575]  |
| 专题 | 陆地水循环及地表过程院重点实验室_外文论文 |
| 作者单位 | 1.Guangzhou Institute of Geography, Guangdong Academy of Sciences 2.Guangdong Academy of Sciences 3.University of Chinese Academy of Sciences, CAS 4.Institute of Geographic Sciences & Natural Resources Research, CAS 5.Chinese Academy of Sciences |
| 推荐引用方式 GB/T 7714 | Yu, Qing,Lu, Hongwei,Yao, Tianci,et al. An integrated solar-induced chlorophyll fluorescence model for more accurate soil organic carbon content estimation in an Alpine agricultural area[J]. PLANT AND SOIL,2023,N/A. |
| APA | Yu, Qing,Lu, Hongwei,Yao, Tianci,Feng, Wei,&Xue, Yuxuan.(2023).An integrated solar-induced chlorophyll fluorescence model for more accurate soil organic carbon content estimation in an Alpine agricultural area.PLANT AND SOIL,N/A. |
| MLA | Yu, Qing,et al."An integrated solar-induced chlorophyll fluorescence model for more accurate soil organic carbon content estimation in an Alpine agricultural area".PLANT AND SOIL N/A(2023). |
入库方式： OAI收割
来源：地理科学与资源研究所
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