基于多源时序影像的水稻分布提取及变化分析OA

Efficient and accurate rice distribution extraction is crucial for analyzing crop yields and adjusting agricultural planning and management.Based on Google Earth Engine(GEE)cloud platform,this study uses multi-source data including multi-temporal Sentinel images,to supervise and classify Panjin City in 2016 through various machine learning algorithms,and updates the spatial and temporal distribution of rice from 2017 to 2024 through CCDC algorithm.Additionally,it further explores the dynamic changes of rice in different years.The results show that:(1)The comprehensive use of remote sensing-terrain features to build a rice extraction model and the time-division feature optimization method effectively improves the performance and accuracy of the classification model.(2)The tile-based random forest classification method(TB_RF)demonstrates high accuracy and applicability in rice extraction.The extracted distribution range of rice in Panjin City in 2016 achieves the highest precision,with an Overall Accuracy(OA)of 95.4%and a Kappa coefficient of 0.91.The error between the calculated rice planting area and the actual area is less than 1%.(3)CCDC algorithm performs well in the long-time series extraction in Panjin City using Sentinel images.The classification results for 2021 have the lowest accuracy,with an OA of 95%and a Kappa coefficient of 0.90.The difference between the calculated rice planting area and the actual area across the years remains below 0.5%.(4)From 2016 to 2024,the distribution range of rice in Panjin City shows no significant changes,with a slow expansion in area,reflecting an evolution trend characterized by"overall stability,local adjustment and gradual expansion".The findings of this study can provide a scientific reference for the planning,monitoring,and analysis of rice cultivation development.