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UAV based soil moisture remote sensing in a karst mountainous catchment
Wei Luo;  Xianli Xu;  Wen Liu;  Meixian Liu;  Zhenwei Li;  Tao Peng;  Chaohao Xu;  Yaohua Zhang;  Rongfei Zhang
2019
Source PublicationCatena
Volume174Pages:478-489
Abstract

Spatial distribution of soil moisture (SM) is a prerequisite for research and management of agriculture and ecology. However, it is still a challenge to retrieve SM data in highly heterogeneous landscapes. By investigating environmental factors (soil, vegetation and topography) and comparing different remote sensing sources (Landsat-8, Radarsat-2, ASTER Global Digital Elevation Model (DEM) V002 (ASTGTM2), unmanned aerial vehicle (UAV)) for karst mountainous catchments of southwest China, this study identified key controlling factors on the spatial distribution of SM and built a remote sensing model for SM estimation in highly heterogeneous landscapes. Results showed that vegetation type (35.7%), aspect (7.7%), height index (4.2%), soil bulk density (3.3%), soil total nitrogen (3.1%), aspect interact with vegetation type (3.4%) and soil total phosphorous (1.3%) totally explained 58.8% of the SM variability. The correlations between SM and topographic derivatives varied with DEM resolutions (1–50 m), and generally reached their highest values at 7 m for height index, slope gradient, and aspect, 16 m for flow accumulation and topographic wetness index, and 43 m for curvature. Partial least-squares regression analysis showed that optical and infrared bands from Landsat-8 and topographic derivatives from UAV photogrammetry DEM were more strongly correlated with SM than other datasets. An empirical model (SM = 9.27 ∗ 10−2HI − 1.82 ∗ 10−5B5 + 0.519) with only height index and B5 band from Landsat-8 as inputs is proposed, as it shows acceptable performance (R2 = 0.36; RMSE = 0.076). The results of this study provide useful information for SM remote sensing in karst mountainous area and similar heterogeneous landscapes.

KeywordEarth Critical Zone Ecohydrology Landscape Ecology Uav Photogrammetry Soil Hydrology
Indexed BySCI
Language英语
Document Type期刊论文
Identifierhttp://ir.gyig.ac.cn/handle/42920512-1/10903
Collection环境地球化学国家重点实验室
Affiliation1.Huanjiang Observation and Research Station for Karst Ecosystem, Key Laboratory for Agro-ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, Hunan, China
2.University of Chinese Academy of Sciences, Beijing 100049, China
3.College of Resources and Environmental Sciences, Hunan Normal University, Changsha 410081, China
4.State Key Laboratory of Environmental Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang, Guizhou 550002, China
5.Puding Karst Ecosystem Research Station, Puding, Guizhou, 562100, China
Recommended Citation
GB/T 7714
Wei Luo;Xianli Xu;Wen Liu;Meixian Liu;Zhenwei Li;Tao Peng;Chaohao Xu;Yaohua Zhang;Rongfei Zhang. UAV based soil moisture remote sensing in a karst mountainous catchment[J]. Catena,2019,174:478-489.
APA Wei Luo;Xianli Xu;Wen Liu;Meixian Liu;Zhenwei Li;Tao Peng;Chaohao Xu;Yaohua Zhang;Rongfei Zhang.(2019).UAV based soil moisture remote sensing in a karst mountainous catchment.Catena,174,478-489.
MLA Wei Luo;Xianli Xu;Wen Liu;Meixian Liu;Zhenwei Li;Tao Peng;Chaohao Xu;Yaohua Zhang;Rongfei Zhang."UAV based soil moisture remote sensing in a karst mountainous catchment".Catena 174(2019):478-489.
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