2019–2021年河南省越冬作物分布数据集  被引量:2

A dataset of winter crop distribution maps in Henan Province from 2019 to 2021

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作  者:马晓岩 张兴旺 乔龙鑫 郑泽琳 潘力 彭洁 杨涵璐 夏浩铭 MA Xiaoyan;ZHANG Xingwang;QIAO Longxin;ZHENG Zhelin;PAN Li;PENG Jie;YANG Hanlu;XIA Haoming(School of Geography and Environment,Henan University,Kaifeng 475004,P.R.China;Henan Dabie Mountain Forest Ecosystem National Field Scientific Observation and Research Station,Kaifeng 475004,P.R.China;Henan Provincial Key Laboratory of Earth System Observation and Simulation,Kaifeng 475004,P.R.China;Ministry of Education Key Laboratory of Digital Geography Technology in the Middle and Lower reaches of the Yellow River(Henan University),Kaifeng 475004,P.R.China)

机构地区:[1]河南大学地理与环境学院,河南开封475004 [2]河南大别山森林生态系统国家野外科学观测研究站,河南开封475004 [3]河南省地球系统观测与模拟重点实验室,河南开封475004 [4]黄河中下游数字地理技术教育部重点实验室(河南大学),河南开封475004

出  处:《中国科学数据(中英文网络版)》2022年第3期285-297,共13页China Scientific Data

基  金:国家自然科学基金重点项目(32130066);河南省科技攻关计划项目(212102310019)。

摘  要:本数据集基于Google Earth Engine(GEE)云计算平台,融合多源遥感影像,构建了一种物候算法,生成了2019–2021年河南省越冬作物数据集。首先,融合Landsat 7/8和Sentinel-2 A/B影像构建了高时空分辨率时间序列数据集。其次,分析不同物候期下越冬作物和其他作物的物候差异,提取用于分类的物候指标,包括生长季节始期(start of season,SOS)、生长高峰期(start date of peak,SDP)、生长季节末期(end of season,EOS)、绿化速度(green-up speed,GUS)和生长季节长度(growing-season length,GSL)。最后,基于这些物候指标构建决策树模型,在像元尺度上对越冬作物进行提取。本研究构建的物候算法能够准确提取越冬作物的种植面积,其用户精度、生产者精度、总体精度和Kappa系数分别为98.00%、98.36%、97.77%和0.94。本数据集可以为越冬作物生长监测和产量预测提供基础数据,帮助决策者和生产者制定合理的政策和风险管理策略,也可为相关领域的科研人员提供数据参考。On the basis of the Google Earth Engine(GEE)cloud computing platform,we integrated multisource remote sensing images to construct a phenology algorithm to generate a dataset of winter crop distribution maps in Henan Province from 2019 to 2021.First,we prepared a dataset of high spatiotemporal resolution time series by fusing Landsat 7/8 images with Sentinel-2A/B images.Second,we analyzed the phenological differences between winter crops and other crops under different phenological periods,and extracted phenological indicators for classification,including the start of season(SOS),the start date of peak(SDP),and the end of season(EOS),green-up speed(GUS)and growing-season length(GSL).Finally,based on these phenological indicators,we constructed a decision tree model to extract winter crops at the pixel scale.The phenological algorithm constructed in this study can accurately extract the planting areas of winter crops.And the user accuracy,producer accuracy,overall accuracy and Kappa coefficient are 98.00%,98.36%,97.77%and 0.94,respectively.This dataset can provide basic data for the growth monitoring and yield prediction of winter crops,help decision-makers and producers to formulate reasonable policies and risk management strategies.It can also serve as a data reference for researchers in related fields.

关 键 词:越冬作物 河南省 Google Earth Engine LANDSAT Sentinel-2 物候算法 

分 类 号:S127[农业科学—农业基础科学]

 

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