Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring.

ABSTRACT: Soil moisture content (SMC) is an important factor that affects agricultural development in arid regions. Compared with the space-borne remote sensing system, the unmanned aerial vehicle (UAV) has been widely used because of its stronger controllability and higher resolution. It also provides a more convenient method for monitoring SMC than normal measurement methods that includes field sampling and oven-drying techniques. However, research based on UAV hyperspectral data has not yet formed a standard procedure in arid regions. Therefore, a universal processing scheme is required. We hypothesized that combining pretreatments of UAV hyperspectral imagery under optimal indices and a set of field observations within a machine learning framework will yield a highly accurate estimate of SMC. Op

SUBMITTER: Ge X 

PROVIDER: S-EPMC6501779 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature