水稻遥感制图研究综述  

Review of paddy rice mapping with remote sensing technology

作　　者：高心怡 池泓 黄进良 凌峰 韩逸飞 贾小凤 李一凡 黄端 董金玮[4] GAO Xinyi;CHI Hong;HUANG Jinliang;LING Feng;HAN Yifei;JIA Xiaofeng;LI Yifan;HUANG Duan;DONG Jinwei(Hubei Provincial Key Laboratory of Environment and Disaster Monitoring and Assessment,Institute of Precision Measurement Science and Technology Innovation,Chinese Academy of Sciences,Wuhan 430077;University of Chinese Academy of Sciences,Beijing 100049;School of Surveying and Mapping Engineering,East China University of Technology,Nanchang 330013;Key Laboratory of Land Surface Pattern and Simulation,Institute of Geographic Sciences and Resources,Chinese Academy of Sciences,Beijing 100101)

机构地区：[1]中国科学院精密测量科学与技术创新研究院环境与灾害监测评估湖北省重点实验室,武汉430077 [2]中国科学院大学,北京100049 [3]东华理工大学测绘工程学院,南昌330013 [4]中国科学院地理科学与资源研究所中国科学院陆地表层格局与模拟重点实验室,北京100101

出　　处：《遥感学报》2024年第9期2144-2169,共26页NATIONAL REMOTE SENSING BULLETIN

基　　金：国家自然科学基金(重点项目)(编号:U22A20567);资源与环境信息系统国家重点实验室开放基金(编号:202026)。

摘　　要：水稻是人类的主要粮食作物之一,及时准确的获取水稻面积分布和时空变化对粮食政策制定具有重要的参考意义。本文围绕“水稻遥感制图”研究主题,首先回顾调研国内外文献资料,系统梳理了水稻的生理生长过程和主要的种植模式。全球范围内,水稻种植集中在东南亚地区;从全国范围看,单季稻产区主要位于东北地区和长江中下游地区;双季稻和三季稻产区位于湖南、江西、广东等华南省份。其次,受云雨影响,早期水稻制图以雷达数据为主,随着遥感数据源日益丰富,光学和雷达数据协同应用于水稻遥感制图;在重点分析水稻的“(遥感)信号—空间—时间”特征的基础上,探讨了水稻遥感制图中典型光学植被指数和雷达后向散射系数;并从传统机器学习和深度学习两个方面总结了现阶段水稻遥感制图的主流方法。然后,从机器学习模型、多源遥感数据融合以及遥感计算云平台3个方面归纳了水稻遥感制图的应用现状。总结发现目前水稻制图研究存在以下难点:(1)由于相似生长周期植物的存在导致水稻的漏分、错分;(2)光学和雷达数据都存在时序观测不连续的现象;(3)地形破碎区域或多季、轮作水稻种植地区的制图困难较大;(4)制图方法的泛化问题。针对这些问题,本文从水稻物候特征发掘、水稻时序观测数据获取手段、水稻遥感制图空间分辨率改进等方面探讨了水稻遥感制图的发展方向:(1)水稻物候期遥感信号特征挖掘;(2)覆盖水稻完整生长期的时序遥感数据获取;(3)水稻遥感制图空间分辨率提升;(4)光学和雷达数据的协同应用。Rice is one of the main staple foods of human beings.Timely and accurate access to information about the distribution of paddy rice crop areas and its spatial-temporal variations are crucial for food policy formulation.Focusing on the topic “paddy rice remote sensing mapping,” we first summarized the physiological growing process and primary cropping patterns of paddy rice systematically,following a survey of domestic and foreign literature.Globally,rice cultivation is concentrated in Southeast Asia.In China,single-cropping rice production areas are mainly located in the northeastern region and the middle and lower reaches of the Yangtze River.The double-and triplecropping rice production areas are located in southern provinces,such as Hunan,Jiangxi,and Guangdong.Second,rice mapping primarily relied on radar data in the early stage due to the effect of clouds and rain.With the abundance of remote sensing data sources,optical and radar data were synergistically applied to rice mapping.On the basis of the highlighted features of paddy rice's “remote sensing signal-spatialtemporal properties,” we discussed typical vegetation index and the radar backscatter coefficient in rice mapping and concluded with mainstream methods of rice mapping in terms of traditional machine learning and deep learning.Afterward,the rice mapping application status was summed up in three ways:using a standard machine learning model,fusing multisource remote sensing data,and using a cloudbased remote sensing computing platform.Results indicate that the existing issues on rice mapping has the following problems:(1) Rice is misclassified due to the plants(aquatic vegetation such as wetlands) with comparable phenological stages.(2) Optical and radar data hardly provide entire observations in phenology stages of paddy rice.(3) Rice mapping in terrain fragmental areas and multiple cropping or rotation regions is still a huge challenge.(4) Generalization of mapping algorithms in rice mapping remains an issue.With an aim to solve these issue

关 键 词：水稻 遥感制图 (遥感)信号—空间—时间 多源遥感数据 机器学习 

分 类 号：P2[天文地球—测绘科学与技术]