您好，欢迎访问中国农业科学院 机构知识库！

全部

  • 全部
  • 标题
  • 学者
  • 机构
  • 关键词

SPTS: Single Pixel in Time-Series Triangle Model for Estimating Surface Soil Moisture

文献类型： 外文期刊

作者： Ma, Tian 1 ; Leng, Pei 1 ; Gao, Yu-Xin 2 ; Aliyu Kasim, Abba 1 ; Guo, Xiaonan 2 ; Zhang, Xia 2 ; Shang, Guo-Fei 2 ; Li, Zhao-Liang 1 ;

作者机构： 1.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Key Lab Efficient Utilizat Arid & Semiarid Arable, Beijing 100081, Peoples R China

2.Hebei GEO Univ, Sch Land Resources & Urban Rural Planning, Shijiazhuang 050031, Peoples R China

3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China

关键词： Land surface temperature (LST); Landsat; single pixel in time series (SPTS); soil moisture

期刊名称：IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING （ 影响因子：8.6； 五年影响因子：8.8 ）

ISSN： 0196-2892

年卷期： 2025 年 63 卷

页码：

收录情况： SCI

摘要： Surface soil moisture (SSM) is essential for understanding the interactions between the atmosphere and Earth's surface. The rapid development of remote sensing technology in recent decades has provided feasible alternatives for SSM retrieval. The triangle model is one such method that uses the relationship between land surface temperature (LST) and vegetation index (VI) on a triangular space to estimate SSM. However, the traditional LST-VI triangle models inherently suffer from two major drawbacks. First, the subjective requirements for a sufficient number of pixels are characterized by a wide range of vegetation and SSM under uniform atmospheric conditions. Second, this is the need for date-to-date calibration. To overcome these limitations, the present study proposed a novel scheme of the feature space, the single pixel in time-series (SPTS) triangle model. The basic assumption of this feature space is that a given satellite pixel for cropland or grassland will undergo distinct vegetation cover and SSM status due to natural growth and soil moisture dynamics over a relatively long period. Unique triangles for 44 sites in two networks of the International Soil Moisture Network (ISMN)-the TxSon (US) dominated by grassland and REMEDHUS (Spain) dominated by cropland-were constructed based on Landsat data over a period of similar to 10 years (2013-2023). Compared to the traditional triangle model, the proposed model reveals enhanced skills for SSM retrieval, with a decrease in root-mean-square error (RMSE) by 13.5% (similar to 0.050 m(3)/m(3)) over the study sites.

  • 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：中国农业科学院

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