| 第一作者： | Luo, Chong |
|---|---|
| 英文第一作者： | Luo, Chong |
| 联系作者： | Liu, Huanjun |
| 英文联系作者： | Liu, Huanjun |
| 发表年度： | 2022 |
| 卷： | 211 |
| 摘要： | Accurate assessment of the spatial distribution of soil organic matter (SOM) is of great significance for regional sustainable development. However, due to the strong spatial variability in soil, there is still a lack of robust SOM mapping methods. The use of remote sensing satellite technology to map soil parameters has been effectively applied in many areas. Soil mapping in Northeast China with less cloud cover, higher planting intensity and longer bare soil period of cultivated land is expected. In this study, multiyear Landsat 8 and Sentinel-2 images of bare soil periods were used to generate median synthetic images according to different time intervals in Google Earth Engine (GEE). Then, the spectral index and image band were used as input variables, and the prediction accuracies of different combinations were evaluated by the random forest (RF) algorithm. Finally, the best combination of two partitioning methods (based on different soil types and cascade simple K-means clustering) was used for SOM prediction of local regression and mapping. The results show that 1) the best time window for SOM prediction in the Songnen Plain is in May, but precipitation will affect the prediction accuracy of SOM; 2) Sentinel-2 synthetic images are not superior to Landsat 8 synthetic images for SOM mapping, although Sentinel-2 has better temporal and spatial resolution; and 3) compared with the global regression model, the local regression method based on two partitioning methods can improve the accuracy of SOM mapping, but the actual mapping effect based on the soil type partitioning algorithms is affected by the distribution of soil samples. This study extends the application of GEE to soil mapping and improve the accuracy of large-scale regional SOM mapping by partitioning images. |
| 刊物名称： | Catena |
| 参与作者： | C. Luo, Y. A. Wang, X. L. Zhang, W. Q. Zhang and H. J. Liu |
删除或更新信息，请邮件至freekaoyan#163.com(#换成@)
Spatial prediction of soil organic matter content using multiyear synthetic images and partitioning
本站小编 Free考研考试/2024-01-13
Spatial prediction of soil organic matter content using multiyear synthetic images and partitioning algorithms
相关话题/
领限时大额优惠券,享本站正版考研考试资料!
优惠券领取后72小时内有效，10万种最新考研考试考证类电子打印资料任你选。涵盖全国500余所院校考研专业课、200多种职业资格考试、1100多种经典教材，产品类型包含电子书、题库、全套资料以及视频，无论您是考研复习、考证刷题，还是考前冲刺等，不同类型的产品可满足您学习上的不同需求。 ...考试优惠券 本站小编 Free壹佰分学习网 2022-09-19
