中文

Journal of Applied Sciences ›› 2025, Vol. 43 ›› Issue (3): 519-529.doi: 10.3969/j.issn.0255-8297.2025.03.012

• Signal and Information Processing • Previous Articles    

Prediction of Soil Organic Carbon for Cultivated Lands in Jianyang District of Nanping City Based on Soil Texture

YE Qing1, XU Yehui2, LI Huichuan1, MA Dan1, ZHANG Liming1   

  1. 1. College of Resource and Environment, Fujian Agriculture and Forestry University, Fuzhou 350002, Fujian, China;
    2. Fujian Province Cartographic Publishing House, Fuzhou 350001, Fujian, China
  • Received:2024-09-09 Published:2025-06-23

PDF

4

Abstract: The prediction accuracy of soil organic carbon contents in cultivated lands across hilly and mountainous areas is relatively low. This paper proposes a digital soil mapping method based on multi-temporal remote sensing and hybrid random forest (RF) models for soil texture. A total of 979 soil samples collected in 2008, Landsat 5 TM images with 30-meter spatial resolution during 2007—2010, a digital elevation model (DEM) with 12.5-meter spatial resolution and meteorological data with 1-kilometer spatial resolution were used as data sources. Remote sensing factors, relief factors and meteorological factors were extracted from those data sources. Then these factors were used to construct hybrid RF models for soil texture types and for classification probability of soil texture, respectively. The SOC predicting accuracy of the global RF models were analyzed compared with single-temporal and multi-temporal remote sensing factors. Furthermore, the accuracy of two hybrid RF models were also compared against that of the global RF model. Finally, the best-performing model was employed to predict SOC contents for cultivated lands in Jianyang District of Nanping City. The results showed that SOC prediction accuracy was higher for the multi-temporal synthetic Landsat 5 TM images (2007—2010) compared to single-temporal images. Specifically, the R2 of hybrid RF model for classification probabilistic of soil texture improved by 53.57%, while the RMSE decreased by 11.20% relative to the global RF model. The spatial distributions of SOC contents generally exhibited higher levels in western regions and lower levels in central-eastern regions. The SOC map in study area became much smoother and more continuous in boundary regions. This study demonstrates that hybrid RF model for classification probabilistic of soil texture combined with multi-temporal synthetic Landsat 5 TM image can significantly improve SOC mapping accuracy in hilly and mountainous areas.

Key words: soil organic carbon, soil texture, hybrid random forest model, multi-temporal remote sensing, hilly and mountainous areas

CLC Number: