Integration of Machine Learning and Remote-Sensing Reflectance Spectra for Regional SOC Mapping: A Case Study on the Agricultural Lands of the Loess Plateau

Yiping Wu; Fangyan Zhang; You Tu; Changshun Sun; Sergey Kivalov; Georgii Alexandrov; Weiqin Dang; Shuguang Liu; Wende Yan; Ji Chen; Xiaowei Yin; Zexin Meng; Shantao An; Fan Wang; Huiwen Li; Yu Deng; Pengcheng Sun; Jiarui Dong; Xiankai Luo; Guangchuang Zhang; Wendan Liu; Fubo Zhao; Caiqing Qin; Linjing Qiu; Xudong Yang; Yanqing Lian; Zhao Jin; Yongming Han; Zhangdong Jin

doi:10.1016/j.eng.2026.07.004

Engineering ›› :202607004 DOI: 10.1016/j.eng.2026.07.004

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Integration of Machine Learning and Remote-Sensing Reflectance Spectra for Regional SOC Mapping: A Case Study on the Agricultural Lands of the Loess Plateau

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^a Institute of Global Environmental Change, Xi’an Jiaotong University , Xi’an 710049, China

^b School of Soil and Water Conservation, Central South University of Forestry and Technology , Changsha 410004, China

^c National Observation and Research Station of Regional Ecological Environment Change and Comprehensive Management in the Guanzhong Plain , Xi’an 710061, China

^d Shaanxi Provincial Academy of Environmental Sciences , Xi’an 710049, China

^e Institute of Physicochemical and Biological Problems in Soil Science, Russian Academy of Sciences , Pushchino 142290, Russia

^f Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences , Moscow 119017, Russia

^g Suide Soil and Water Conservation Scientific Experimental Station, Yellow River Water Conservancy Commission , Yulin 719000, China

^h School of Ecology, Hainan University , Hainan 570228, China

ⁱ Department of Civil Engineering, The University of Hong Kong , Hong Kong 999077, China

^j Key Laboratory of Soil and Water Conservation on the Loess Plateau of the Ministry of Water Resources, Yellow River Institute of Hydraulic Research , Zhengzhou 450003, China

^k Yangtze Institute for Conservation and Development, Hohai University , Nanjing 210098, China

^l State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences , Xi’an 710061, China

^*E-mail address:rocky.ypwu@gmail.com (Y. Wu)

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Abstract

Soil organic carbon (SOC) is a key component of the terrestrial carbon cycle and is essential for soil fertility, directly influencing climate change and human well-being. However, it remains unclear how different spectral data sources and machine learning (ML) models can jointly influence SOC prediction performance, especially across large and heterogeneous agricultural landscapes. This study systematically evaluates combinations of spectral data sources, feature-selection methods, and ML models for SOC prediction in the farmland of the Loess Plateau (LP), a region

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Yiping Wu, Fangyan Zhang, You Tu, Changshun Sun, Sergey Kivalov, Georgii Alexandrov, Weiqin Dang, Shuguang Liu, Wende Yan, Ji Chen, Xiaowei Yin, Zexin Meng, Shantao An, Fan Wang, Huiwen Li, Yu Deng, Pengcheng Sun, Jiarui Dong, Xiankai Luo, Guangchuang Zhang, Wendan Liu, Fubo Zhao, Caiqing Qin, Linjing Qiu, Xudong Yang, Yanqing Lian, Zhao Jin, Yongming Han, Zhangdong Jin. Integration of Machine Learning and Remote-Sensing Reflectance Spectra for Regional SOC Mapping: A Case Study on the Agricultural Lands of the Loess Plateau. Engineering 202607004 DOI:10.1016/j.eng.2026.07.004

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