数字农科院2.0

Integrating Historical Crop Rotation Changes Into Soil Organic Matter Mapping in the Cropland of Southeastern China

文献类型: 外文期刊

作者: Furong Zhou;Jie Xue;Zheng Wang;Wuze Jin;Zhou Shi;Qiangyi Yu;Lianqing Zhou;Songchao Chen

作者机构:

关键词: crop rotation changes;digital soil mapping;machine learning;remote sensing;soil organic matter

期刊名称: Earth's Future

ISSN: 2328-4277

年卷期: 2025 年 13 卷 8 期

页码:

收录情况: SCIE(2025版)

摘要: The prediction of cropland soil organic matter (SOM) is crucial for understanding ecosystem services such as food production and carbon sequestration. However, the influence of historical agricultural management practices on soil properties is often overlooked in current SOM digital mapping studies. Crop rotation, a critical agricultural practice, significantly influences the spatiotemporal dynamics of SOM. This study investigates the cumulative effects of dynamic crop rotation changes on SOM using digital soil mapping in a multi-cropping region in southeastern China, where 202 topsoil samples were collected. Annual crop rotations from 2019 to 2023 were mapped by integrating Sentinel-2 data with expert knowledge, enabling the extraction of historical crop rotation changes. The added value of these legacy changes in SOM digital mapping was assessed by combining traditional environmental covariates. Results showed that integrating historical crop rotation changes significantly enhanced SOM prediction accuracy, with the coefficient of determination (R2) increased by 14.86% and the root mean square error reduced by 26.43% compared to models using traditional covariates and annual crop rotations alone. Notably, five-year changes in crop rotation emerged as the dominant factor in SOM prediction. The spatial distribution of SOM exhibited distinct heterogeneity, with high-value regions primarily located in regions under relatively stable crop rotation change frequency. In conclusion, this study underscores the importance and effectiveness of incorporating historical crop rotation changes into SOM mapping. The findings highlight the impact of historical agricultural practices on SOM distribution and provide a scientific foundation for enhancing agricultural strategies that promote ecosystem services.

分类号:

  • 相关文献

[1]Prediction of soil organic matter using Landsat 8 data and machine learning algorithms in typical karst cropland in China. Naijie Chang,Di Chen. 2024

[2]Prediction potential of remote sensing-related variables in the topsoil organic carbon density of liaohekou coastal wetlands, northeast china. Shuai Wang,Mingyi Zhou,Qianlai Zhuang,Liping Guo. 2021

[3]Significant Improvement in Soil Organic Carbon Estimation Using Data-Driven Machine Learning Based on Habitat Patches. Yu W.,Zhou W.,Wang T.,Xiao J.,Peng Y.,Li H.,Li Y.. 2024

[4]Predicting surface soil pH spatial distribution based on three machine learning methods: a case study of Heilongjiang Province. Huang, Pu,Huang, Qing,Wang, Jingtian,Shi, Yuhan. 2025

[5]Comparing Machine Learning Algorithms for Pixel/Object-Based Classifications of Semi-Arid Grassland in Northern China Using Multisource Medium Resolution Imageries. Wu N.,Crusiol L.G.T.,Liu G.,Wuyun D.,Han G.. 2023

[6]Estimation of sugar content in sugar beet root based on UAV multi-sensor data. Wang Q.,Che Y.,Shao K.,Zhu J.,Wang R.,Sui Y.,Guo Y.,Li B.,Meng L.,Ma Y.. 2022

[7]Entropy Weight Ensemble Framework for Yield Prediction of Winter Wheat Under Different Water Stress Treatments Using Unmanned Aerial Vehicle-Based Multispectral and Thermal Data. Shuaipeng Fei,Muhammad Adeel Hassan,Yuntao Ma,Meiyan Shu,Qian Cheng,Zongpeng Li,Zhen Chen,Yonggui Xiao. 2021

[8]Spatial-Temporal Characteristics and Driving Forces of Aboveground Biomass in Desert Steppes of Inner Mongolia, China in the Past 20 Years. Wu, Nitu,Liu, Guixiang,Wuyun, Deji,Yi, Bole,Du, Wala,Han, Guodong. 2023

[9]A Method for Estimating Alfalfa (Medicago sativa L.) Forage Yield Based on Remote Sensing Data. Jingsi Li,Ruifeng Wang,Mengjie Zhang,Xu Wang,Yuchun Yan,Xinbo Sun,Dawei Xu. 2023

[10]Editorial: Remote sensing for field-based crop phenotyping. Jiangang Liu,Zhenjiang Zhou,Bo Li. 2024

[11]Review of GNSS-R Technology for Soil Moisture Inversion. Yang C.,Mao K.,Guo Z.,Shi J.,Bateni S.M.,Yuan Z.. 2024

[12]County-Level Cultivated Land Quality Evaluation Using Multi-Temporal Remote Sensing and Machine Learning Models: From the Perspective of National Standard. Dingding Duan,Xinru Li,Yanghua Liu,Qingyan Meng,Chengming Li,Guotian Lin,Linlin Guo,Peng Guo,Tingting Tang,Huan Su,Weifeng Ma,Shikang Ming,Yadong Yang. 2024

[13]Improved random patches and model transfer for deriving leaf mass per area across multispecies from spectral reflectance. Shuaipeng Fei,Shunfu Xiao,Demin Xu,Meiyan Shu,Hong Sun,Puyu Feng,Yonggui Xiao,Yuntao Ma. 2024

[14]Spatial Mapping of Soil CO2 Flux in the Yellow River Delta Farmland of China Using Multi-Source Optical Remote Sensing Data. Wenqing Yu,Shuo Chen,Weihao Yang,Yingqiang Song,Miao Lu. 2024

[15]Potential erosion and sedimentation based on land use change by using cellular automata-artificial neural network. Aditya Nugraha Putra,Istika Nita,Kurniawan Sigit Wicaksono,Novandi Rizky Prasetya,Michelle Talisia Sugiarto,Fahmi Hidayat,Zainal Alim,Sugik Edy Sartono,Pandham Giri Sasangka,Tiar Ranu Kusuma,Bilawal Abbasi,Alena Gessert,Mohd Hasmadi Ismail,Watit Khokthong. 2025

[16]Predicting the greenhouse crop morphological parameters based on RGB-D Computer Vision. Ziqiu Kang,Bo Zhou,Shulang Fei,Nan Wang. 2025

[17]Evaluation of Machine Learning Models for Estimating Grassland Pasture Yield Using Landsat-8 Imagery. Linming Huang,Fen Zhao,Guozheng Hu,Hasbagan Ganjurjav,Rihan Wu,Qingzhu Gao. 2025

[18]Spatial aggregation trends of cultivated land quality and landscape patterns: A remote sensing analysis☆. Tang, Mengmeng,Cheng, Wenlong,Gao, Zhengbao,Jiang, Fahui,Han, Shang,Xu, Danyang,Bu, Rongyan,Tang, Shan,Zhu, Rui,Li, Min,Wang, Hui,Lu, Changai,Wu, Ji. 2025

[19]Quantifying Grazing Intensity from Aboveground Biomass Differences Using Satellite Data and Machine Learning. Ritu Su,Yong Yang,Shujuan Chang,A. Gudamu,Xiangjun Yun,Xiangyang Song,Aijun Liu. 2025

[20]Utilising the Potential of a Robust Three-Band Hyperspectral Vegetation Index for Monitoring Plant Moisture Content in a Summer Maize-Winter Wheat Crop Rotation Farming System. Kanneh, James E.,Li, Caixia,Ma, Yanchuan,Li, Shenglin,Be, Madjebi Collela,Wang, Zuji,Zhong, Daokuan,Han, Zhiguo,Li, Hao,Wang, Jinglei. 2026

作者其他论文 更多>>