Mapping Organic Carbon Content in Black Soil Using UAV Hyperspectral Remote Sensing and Deep Learning
Yang Hanshui
Ma Lin
Wang Ruizhen
Chen Weitao
Wang Lizhe
Abstract：Black soil in northeastern China is an important agricultural resource but has been increasingly degraded due to long-term development.The use of satellite remote sensing technology to retrieve the organic carbon content in black soil offers technical support for the protection and sustainable use.However,satellite hyperspectral data suffer from low spatial resolution,and the retrieval accuracy for organic carbon content remains limited in fine-scaled study sites.To address these challenges,this study utilized UAV-based hyperspectral data and soil geochemical data instead.We proposed and compared four models based on one-dimensional convolutional neural networks(1DCNN)-MDS-1DCNN,LLE-1DCNN,PLSR-1DCNN,and KPCA-1DCNN,for organic carbon content retrieval using the Wudalianchi region in Heilongjiang Province as a case study.The results show that the LLE-1DCNN model outperforms the others,achieving an R² of 0.806 and an RMSE of 0.572%on the validation set.This approach offers promising potential for accurately retrieving organic carbon content in black soil and supporting its conservation and management.
Keywords：black soilsoil organic carbonUAV hyperspectraldeep learningremote sensing
Publication Date：2025-08-30
Online Publishing Date：2025-08-22(First online date of this platform, not the publication date of the document)
Pages：9( 3144-3152 )