A review of paddy rice mapping with remote sensing technology
- Pages: 1-27(2023)
Published Online： 20 October 2023
DOI： 10.11834/jrs.20233014
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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三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。
Abstract
Rice is one of the main staple foods of human beings. Timely and accurate access to information about the distribution of paddy rice cropped area and its spatial-temporal variations are of great significance for food policy formulation. According to the method of literature statistics
we used the topic ('paddy rice mapping' or 'paddy rice classification') and topic ('remote sensing') to search on the web of science (between 01/012000 and 02/28/2023). The results showed that 776 literatures related to the topic
and the number of papers published after 2010 accounted for 86.8 % of the total. Focusing on the research topic of "paddy rice remote sensing mapping"
we firstly systematically summarized the physiological growing process and primary cropping patterns of paddy rice following a survey of domestic and foreign literature. Globally
rice cultivation is concentrated in Southeast Asia. In China
the single cropping rice production areas are mainly located in the northeastern region and the middle and lower reaches of the Yangtze River. The double cropping and triple-cropping rice production areas are located in southern provinces
such as Hunan
Jiangxi
and Guangdong. Secondly
rice mapping was primarily relied on radar data in the early stage due to the impacted by clouds and rain. With the abundance of remote sensing data sources
optical and radar data were synergistically applied to rice mapping. Based on the highlighted features of paddy rice’s ‘(remote sensing) signal-spatial-temporal' properties
we discussed typical vegetation index and the radar backscatter coefficient in rice mapping
and concluded mainstream methods of rice mapping in terms of traditional machine learning and deep learning. After that
the rice mapping application status was summed up in three ways: using a standard machine learning model
fusing multi-source remote sensing data and using a cloud-based remote sensing computing platform. It is concluded that the existing issues on rice mapping has the following problem: (1) Rice is misclassified due to the plants (aquatic vegetation such as wetlands) with comparable phenological stages; (2) It’s difficult for optical and radar data to provide entire observations in phenology stages of paddy rice; (3) Rice mapping in terrain fragmental area and multiple cropping or rotation regions is still a huge challenge; (4) Generalization of mapping algorithms in rice mapping Aiming at these issues
the next steps of rice mapping was explored from the perspectives of rice phenological feature mining
techniques for collection paddy rice time-series observations
and enhancements to finer spatial resolution in rice mapping
specifically for future researches: (1) focus on the characteristics exploration of remote sensing signals in phenological stages of paddy rice; (2) various acquisition methods of temporal remote sensing data covering the entire phenological stages of paddy rice; (3) improving the spatial resolution of paddy rice mapping via finer spatial-resolution data or multiple data fusion model; and (4) taking fully advantages of optical imagery and radar data for integrated mapping of paddy rice and general algorithms are highly encouraged in application.
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
Keywords
paddy riceremote sensing mappingmulti-sources remote sensing datasignal-spatial-temporalmachine learning
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