数字农科院2.0

Spatial aggregation trends of cultivated land quality and landscape patterns: A remote sensing analysis☆

文献类型： 外文期刊

作者： 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

作者机构：

关键词： Cultivated land;Machine learning;Remote sensing;Ecological landscape pattern;Spatial aggregation trends

期刊名称： APPLIED SOIL ECOLOGY

ISSN： 0929-1393

年卷期： 2025 年 217 卷

页码：

收录情况： SCIE（2025版）

摘要： Cultivated land is a vital resource for agricultural production and plays an important role in ensuring food security and maintaining ecological balance. Effective use of cultivated land resources requires long-term, largescale land quality evaluations that are rapid and accurate, along with targeted management measures. Remote sensing technology has the advantages of a wide monitoring range, short revisit period, and low cost and is a valuable tool for large-scale cultivated land quality monitoring. In this study, Landsat 8 OLI data were used to extract spectra and vegetation indices, and a remote sensing monitoring and evaluation model of cultivated land quality was established using soil sample data in the study area from 2018 and 2021. Based on the optimal random forest model (R2 = 0.91, RMSE = 0.59, MAE = 0.46), the distribution of cultivated land quality in the study area in 2024 was predicted, enabling analysis of ecological landscape patterns and spatial aggregation trends of land quality. According to these patterns and trends in the study area, management zones were delineated, including restricted construction areas, comprehensive improvement areas, and key improvement areas, with corresponding suggestions for the management measures of cultivated land quality zoning. This study aims to clarify the spatial distribution of cultivated land quality grades over large areas, identify the optimal machine learning model and input parameters for cultivated land quality evaluation, enhance evaluation efficiency, and provide a scientific basis for formulating reasonable land use policies and precise measures to improve cultivated land quality.

分类号：

  • 相关文献

[1]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

[2]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

[3]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

[4]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

[5]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

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

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

[8]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

[9]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

[10]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

[11]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

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

[13]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

[14]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

[15]Integrating Historical Crop Rotation Changes Into Soil Organic Matter Mapping in the Cropland of Southeastern China. Furong Zhou,Jie Xue,Zheng Wang,Wuze Jin,Zhou Shi,Qiangyi Yu,Lianqing Zhou,Songchao Chen. 2025

[16]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

[17]Hyperspectral inversion of leaf nitrogen content in wheat by integrating CWT-SPA feature optimization and XGBoost-SSA model. Gu, Chen,Liu, Huaiyang,You, Yunhao,Zeng, Qianghao,Zhou, Zhenxiang,Song, Ming,Shi, Yun,Tian, Tong. 2025

[18]Influences of landscape pattern evolution on regional crop water requirements in regions of large-scale agricultural operations. 韩洋,王景雷,李平. 2021

[19]Dynamic trends and driving forces of land use intensification of cultivated land in China. Wang Guogang,Chen Yangfen,Wang Guogang,Liu Yansui,Li Yurui. 2015

[20]Estimation of soil organic carbon stock and its controlling factors in cropland of Yunnan Province, China. Tao SUN,Wen jie TONG,Nai jie CHANG,Ai xing DENG,Zhong long LIN,Xing bing FENG,Jun ying LI,Zhen wei SONG. 2022

作者其他论文 更多>>