您好，欢迎访问新疆农业科学院 机构知识库！
新疆农业科学院机构知识库

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

Inversion of coastal cultivated soil salt content based on multi-source spectra and environmental variables

文献类型： 外文期刊

作者： Jia, Pingping 1 ; He, Wei 1 ; Hu, Yi 1 ; Liang, Yanning 1 ; Liang, Yinku 3 ; Xue, Lihua 4 ; Zamanian, Kazem 5 ; Zhao, Xiaoning 1 ;

作者机构： 1.Nanjing Univ Informat Sci & Technol, Sch Geog Sci, Nanjing 210044, Peoples R China

2.Yunnan Climate Ctr, Kunming 650034, Peoples R China

3.Shaanxi Univ Technol, Sch Biol Sci & Engn, Shaanxi Prov Key Lab Bioresources, Hanzhong 723000, Peoples R China

4.Xinjiang Acad Agr Sci, Inst Grain Crops, Urumqi 830091, Xinjiang, Peoples R China

5.Leibniz Univ Hannover, Inst Soil Sci, D-30419 Hannover, Germany

关键词： Coastal area; Landsat 9; Sentinel 2; Sustainable land use; Soil health; Remote sensing

期刊名称：SOIL & TILLAGE RESEARCH （ 影响因子：6.5； 五年影响因子：7.3 ）

ISSN： 0167-1987

年卷期： 2024 年 241 卷

页码：

收录情况： SCI

摘要： Soil salinization seriously hinders the development of efficient ecological agriculture in coastal areas. The use of Landsat, Sentinel series and hyperspectral data is an ideal way for assessing soil salinity indicators. However, environmental data (e.g. climate, terrain and parent material) are important factors for estimating such indicators. It is necessary to find the advantages and limitations of a combination of satellite images, hyperspectral data and environmental variables (ENVI) for assessing soil salinity accurately. Various data or their combinations ([I] remote sensing [RS], i.e. bands and salinity indices of Landsat 9 and Sentinel 2; [II] ENVI, including soil attributes, climate and topography; and [III] RS + ENVI) were used to construct the salinity inversion model using random forest (RF) and extremely randomized trees (ERT) for cultivated areas in the coastal plain of Dongtai City, China. The hyperspectral data were also resampled to match the range of the image bands. RF performed better than ERT for all types of analyzed data, and RS + ENVI exhibited the best performance for Sentinel 2 (R2 = 0.86). Compared with the RS data alone, Landsat 9 and Sentinel 2 provided higher salinity simulations (41% and 126%, respectively) after combination with ENVI, and salinity mapping was closer to the actual soil salinity measurements. The variables of slope, salinity index (SIT), difference index and SIT had the highest contribution in Landsat 9, Sentinel 2 and resampled hyperspectrum based on Landsat 9 and Sentinel 2, respectively. In conclusion, RS + ENVI based on Sentinel 2 data is the recommended approach for monitoring the salt content of coastal cultivated soil.

  • 相关文献
作者其他论文 更多>>
置顶
购物车
反馈

© 京ICP备09089781号-29 主办单位：新疆农业科学院

学术资源： 中国农业科学院 农业专业知识服务系统 农科联盟资源共建共享平台 中国农业科技文献与信息服务平台 中国农业期刊集成服务平台