Estimation of the spatial distribution of heavy metal in agricultural soils using airborne hyperspectral imaging and random forest.

Tan, Kun; Wang, Huimin; Chen, Lihan; Du, Qian; Du, Peijun; Pan, Cencen

Tan, Kun; Wang, Huimin; Chen, Lihan; Du, Qian; Du, Peijun; Pan, Cencen.

Afiliación

Tan K; Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China; School of Geographic Sciences, East China Normal University, Shanghai 200241, China; Key Laboratory for Land Environment and Disaster Monitoring of NASG, China University o
Wang H; Key Laboratory for Land Environment and Disaster Monitoring of NASG, China University of Mining and Technology, Xuzhou 221116, China.
Chen L; Key Laboratory for Land Environment and Disaster Monitoring of NASG, China University of Mining and Technology, Xuzhou 221116, China.
Du Q; Department of Electrical and Computer Engineering, Mississippi State University, MS 39762, USA. Electronic address: du@ece.msstate.edu.
Du P; Key Laboratory for Satellite Mapping Technology and Applications of NASG, Nanjing University, Nanjing 210023, China. Electronic address: dupjrs@126.com.
Pan C; Key Laboratory for Land Environment and Disaster Monitoring of NASG, China University of Mining and Technology, Xuzhou 221116, China.

J Hazard Mater ; 382: 120987, 2020 01 15.

Article en En | MEDLINE | ID: mdl-31454609

Palabras clave

Airborneâ¯hyperspectral remote sensing; Random forest; Soil heavy metal

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Texto completo: 1 Bases de datos: MEDLINE Tipo de estudio: Clinical_trials / Prognostic_studies Idioma: En Revista: J Hazard Mater Año: 2020 Tipo del documento: Article