摘要
三江平原分布着广阔肥沃的黑土地,是中国重要的商品粮基地,人均耕地面积和人均粮食产量是全国平均水平的5倍。准确的作物种植面积信息对于了解三江平原区域粮食安全和农业发展规划具有重要意义。本文利用时间序列的Sentinel-2卫星遥感数据,结合三江平原地面实地典型地物调查数据,筛选主要农作物及周边典型地物的特征波段,采用随机森林分类算法,提取了2020–2022年三江平原主要农作物(水稻、玉米和大豆)种植分布遥感监测数据集。经实地调查数据验证,2020年、2021年和2022年三大农作物提取的总体精度分别为95.18%,95.0%,94.5%,Kappa系数为0.924、0.925和0.919。本数据集不但可以作为三江平原农作物种植格局时空变化分析的基础数据,同时也为三江平原农业生产管理决策提供信息支撑,服务区域农业信息化建设以及黑土地保护与利用。
Sanjiang Plain,renowned for its expansive and fertile black soil,serves as a crucial hub for commodity grain production in China.The per capita cultivated land area and per capita grain output in the region are five times the national average.Accurate crop acreage information is of great significance for understanding regional food security and agricultural development planning in Sanjiang Plain.In this paper,we used the Sentinel-2 satellite remote sensing data of time series and the survey data of typical ground features in Sanjiang Plain to screen the feature bands of major crops and typical ground features of surrounding areas.Using the random forest classification algorithm,we extracted the data to produce a dataset of remote sensing monitoring of planting distribution for major crops(rice,corn and soybean)in Sanjiang Plain from 2020 to 2022.Based on field survey data,it has been verified that the overall accuracy of the three crops extraction in 2020,2021 and 2022 stands at 95.18%,95.0%and 94.5%,and the Kappa coefficients of these three years are 0.924,0.925 and 0.919.This dataset can not only offer basic data for the analysis of temporal and spatial changes of crop planting distribution in Sanjiang Plain,but also contribute to informed decision-making in agricultural production management for the region.Furthermore,it can support the development of agricultural informatization as well as the preservation and utilization of black soil resources.
作者
乔树亭
叶回春
刘荣豪
郭安廷
张冰瑞
钱彬祥
魏鹏
黄文江
QIAO Shuting;YE Huichun;LIU Ronghao;GUO Anting;ZHANG Binrui;QIAN Binxiang;WEI Peng;HUANG Wenjiang(International Research Center of Big Data forSustainable Development Goals,Beijing 100094,P.R.China;Key Laboratory of Digital Earth Science,Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,P.R.China;Key Laboratory of Earth Observation of Hainan Province,Hainan Research Institute,Aerospace Information Research Institute,Chinese Academy of Sciences,Sanya 572029,P.R.China;College of Water Resources Science and Engineering,Taiyuan University of Technology,Taiyuan 030024,P.R.China;College of Geoscience and Surveying Engineering,China University of Mining and Technology,Beijing 100083,P.R.China)
基金
中国科学院战略性先导科技专项(XDA28100500)
中国科学院青年创新促进会项目(2021119)
国家自然科学基金项目(42001384)。