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应用科学学报 ›› 2025, Vol. 43 ›› Issue (3): 519-529.doi: 10.3969/j.issn.0255-8297.2025.03.012

• 信号与信息处理 • 上一篇    

基于土壤质地的南平市建阳区耕地土壤有机碳预测

叶晴1, 许业辉2, 李慧川1, 马丹1, 张黎明1   

  1. 1. 福建农林大学 资源与环境学院, 福建 福州 350002;
    2. 福建省地图出版社, 福建 福州 350001
  • 收稿日期:2024-09-09 发布日期:2025-06-23
  • 通信作者: 马丹，副教授，研究方向为遥感技术应用及多学科交叉。E-mail:madam_yurou@163.com E-mail:madam_yurou@163.com
  • 基金资助:
    国家自然科学基金（No.41971050）；福建省社会科学基金（No.JAS160179）；福建农林大学重点项目（No.111420015,No.KCXTF052A）

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

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摘要： 针对丘陵山地耕地土壤有机碳预测精度低的问题，研究了一种基于多时相遥感影像构建土壤质地的混合随机森林（random forest,RF）模型的数字土壤制图方法。以2008年979个土壤样点数据、2007—2010年30 m的Landsat 5 TM影像、12.5 m的数字高程模型（digital elevation model,DEM）、1 km的气象数据为数据源，提取遥感因子、地形因子和气象因子，然后分别构建基于土壤质地类型的RF模型和基于土壤质地分类概率的混合RF模型，对比分析单时相与多时相的全局RF模型精度，并进一步对比分析混合RF与全局RF预测土壤有机碳（soil organic carbon,SOC）的精度，最后筛选最佳模型预测南平市建阳区耕地SOC含量。结果表明：引入多时相合成的Landsat 5 TM遥感影像后，整体上SOC预测精度比单时相更高；与全局RF相比，基于土壤质地分类概率的混合RF模型的精度显著提高，R2提升53.57%，RMSE下降11.20%； SOC总体上呈现西部高而中东部低的空间特征，在边界区域较为平滑和连续。证明引入多时相的Landsat5 TM影像和基于土壤质地分类概率的混合RF模型可有效提高丘陵山地耕地SOC的制图精度。

关键词: 土壤有机碳, 土壤质地, 混合随机森林模型, 多时相遥感, 丘陵山地

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

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