水稻遥感制图研究综述

高心怡; 池泓; 黄进良; 凌峰; 韩逸飞; 贾小凤; 李一凡; 黄端; 董金玮

doi:10.11834/jrs.20233014

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水稻遥感制图研究综述
A review of paddy rice mapping with remote sensing technology
2023年页码：1-27
网络出版日期： 2023-10-20 ，
DOI： 10.11834/jrs.20233014

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高心怡，池泓，黄进良，凌峰，韩逸飞，贾小凤，李一凡，黄端，董金玮.XXXX.水稻遥感制图研究综述.遥感学报，XX（XX）： 1-27

Gao Xinyi，Chi Hong，Huang Jinliang，Ling Feng，Han Yifei，Jia Xiaofeng，Li Yifan，Huang Duan，Dong Jinwei. XXXX. A review of paddy rice mapping with remote sensing technology. National Remote Sensing Bulletin， XX（XX）：1-27
高心怡，池泓，黄进良，凌峰，韩逸飞，贾小凤，李一凡，黄端，董金玮.XXXX.水稻遥感制图研究综述.遥感学报，XX（XX）： 1-27 DOI： 10.11834/jrs.20233014.

Gao Xinyi，Chi Hong，Huang Jinliang，Ling Feng，Han Yifei，Jia Xiaofeng，Li Yifan，Huang Duan，Dong Jinwei. XXXX. A review of paddy rice mapping with remote sensing technology. National Remote Sensing Bulletin， XX（XX）：1-27 DOI： 10.11834/jrs.20233014.

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

references

Adrian J, Sagan V and Maimaitijiang M. 2021. Sentinel SAR-optical fusion for crop type mapping using deep learning and Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing, 175:215-235 [DOI: 10.1016/j.isprsjprs.2021.02.018http://dx.doi.org/10.1016/j.isprsjprs.2021.02.018]

Afaq Y and Manocha A. 2022. Multi-Resolution-Based deep learning approach for rice field monitoring. Canadian Journal of Remote Sensing, 48 (2):278-298 [DOI: 10.1080/07038992.2021.2010036http://dx.doi.org/10.1080/07038992.2021.2010036]

Arjasakusuma S, Kusuma S S, Rafif R, Saringatin S and Wicaksono P. 2020. Combination of Landsat 8 OLI and Sentinel-1 SAR time-series data for mapping paddy fields in parts of west and central Java Provinces, Indonesia. ISPRS International Journal of Geo-Information, 9 (11):663-679 [DOI: ARTN 66310.3390/ijgi9110663http://dx.doi.org/ARTN66310.3390/ijgi9110663]

Bazzi H, Baghdadi N, El Hajj M, Zribi M, Minh D H T, Ndikumana E, Courault D and Belhouchette H. 2019. Mapping paddy rice using Sentinel-1 SAR time series in Camargue, France. Remote Sensing, 11 (7):887-902 [DOI: ARTN 88710.3390/rs11070887http://dx.doi.org/ARTN88710.3390/rs11070887]

Bennasar M, Hicks Y and Setchi R. 2015. Feature selection using Joint Mutual Information Maximisation. Expert Systems with Applications, 42 (22):8520-8532 [DOI: 10.1016/j.eswa.2015.07.007http://dx.doi.org/10.1016/j.eswa.2015.07.007]

Bey A, Jetimane J, Lisboa S N, Ribeiro N, Sitoe A and Meyfroidt P. 2020. Mapping smallholder and large-scale cropland dynamics with a flexible classification system and pixel-based composites in an emerging frontier of Mozambique. Remote Sensing of Environment, 239 [DOI: 10.1016/j.rse.2019.111611http://dx.doi.org/10.1016/j.rse.2019.111611]

Boschetti M, Busetto L, Manfron G, Laborte A, Asilo S, Pazhanivelan S and Nelson A. 2017. PhenoRice: a method for automatic extraction of spatial-temporal information on rice crops using satellite data time series. Remote Sensing of Environment, 194:347-365 [DOI: 10.1016/j.rse.2017.03.029http://dx.doi.org/10.1016/j.rse.2017.03.029]

Bouvet A and Thuy L T. 2011. Use of ENVISAT/ASAR wide-swath data for timely rice fields mapping in the Mekong River Delta. Remote Sensing of Environment, 115 (4):1090-1101 [DOI: 10.1016/j.rse.2010.12.014http://dx.doi.org/10.1016/j.rse.2010.12.014]

Bouvet A, Thuy L T and Nguyen LD. 2009. Monitoring of the rice cropping system in the Mekong Delta using ENVISAT/ASAR dual polarization data. IEEE Transactions on Geoscience and Remote Sensing, 47 (2):517-526 [DOI: 10.1109/tgrs.2008.2007963http://dx.doi.org/10.1109/tgrs.2008.2007963]

Cai Y T, Lin H and Zhang M. 2019. Mapping paddy rice by the object-based random forest method using time series Sentinel-1/Sentinel-2 data. Advances in Space Research, 64 (11):2233-2244 [DOI: 10.1016/j.asr.2019.08.042http://dx.doi.org/10.1016/j.asr.2019.08.042]

Cai Y T, Liu S T, Lin H and Zhang M. 2020. Extraction of paddy rice based on convolutional neural network using multi-source remote sensing data. Remote Sensing for Land and Resources, 32(4) : 97-104

蔡耀通, 刘书彤, 林辉, 张猛.2020.基于多源遥感数据的CNN水稻提取研究.国土资源遥感, 32 (04):97-104 [DOI: 10.6046 /gtzyyg.2020.04.14http://dx.doi.org/10.6046/gtzyyg.2020.04.14]

Campos-Taberner M, Garcia-Haro F J, Martinez B, Izquierdo-Verdiguier E, Atzberger C, Camps-Valls G and Gilabert M A. 2020. Understanding deep learning in land use classification based on Sentinel-2 time series. Scientific Reports, 10 (1):17188 [DOI: 10.1038/s41598-020-74215-5http://dx.doi.org/10.1038/s41598-020-74215-5]

Cao J J, Cai X L, Tan J W, Cui Y L, Xie H W, Liu F P, Yang L and Luo Y F. 2020. Mapping paddy rice using Landsat time series data in the Ganfu Plain irrigation system, Southern China, from 1988-2017. International Journal of Remote Sensing, 42 (4):1556-1576 [DOI: 10.1080/01431161.2020.1841321http://dx.doi.org/10.1080/01431161.2020.1841321]

Chandrasekar K, Sai M V R S, Roy P S and Dwevedi R S. 2010. Land Surface Water Index (LSWI) response to rainfall and NDVI using the MODIS vegetation index product. International Journal of Remote Sensing, 31 (15):3987-4005 [DOI: Pii 92683988110.1080/01431160802575653http://dx.doi.org/Pii92683988110.1080/01431160802575653]

Chen A X and Li Y C. 2020. Rice recognition of different growth stages based on Sentinel-2 images in mountainous areas of Southwest China. Transactions of the Chinese Society of Agricultural Engineering, 36 (07):192-199

陈安旭, 李月臣. 2020. 基于Sentinel-2影像的西南山区不同生长期水稻识别. 农业工程学报, 36 (07):192-199 [DOI: 10.11975/j.issn.1002-6819.2020.07.022http://dx.doi.org/10.11975/j.issn.1002-6819.2020.07.022]

Chen N C, Yu L X A, Zhang X, Shen Y L, Zeng L L, Hu Q and Niyogi D. 2020. Mapping paddy rice fields by combining multi-temporal vegetation index and synthetic aperture radar remote sensing data using google earth engine machine learning platform. Remote Sensing, 12 (18):2992[DOI: ARTN 299210.3390/rs12182992http://dx.doi.org/ARTN299210.3390/rs12182992]

Clauss K, Ottinger M, Leinenkugel P and Kuenzer C. 2018. Estimating rice production in the Mekong Delta, Vietnam, utilizing time series of Sentinel-1 SAR data. International Journal of Applied Earth Observation and Geoinformation, 73:574-585 [DOI: 10.1016/j.jag.2018.07.022http://dx.doi.org/10.1016/j.jag.2018.07.022]

Clauss K, Yan H M and Kuenzer C. 2016. Mapping paddy rice in china in 2002, 2005, 2010 and 2014 with MODIS time series. Remote Sensing, 8(5):434 [DOI: ARTN 43410.3390/rs8050434http://dx.doi.org/ARTN43410.3390/rs8050434]

Crisóstomo de Castro Filho H, Abílio de Carvalho Júnior O, Ferreira de Carvalho O L, Pozzobon de Bem P, dos Santos de Moura R, Olino de Albuquerque A, Rosa Silva C, Guimarães Ferreira P H, Fontes Guimarães R and Trancoso Gomes R A. 2020. Rice crop detection using LSTM, Bi-LSTM, and machine learning models from Sentinel-1 time series. Remote Sensing, 12 (16):2665 [DOI: 10.3390/rs12162655http://dx.doi.org/10.3390/rs12162655]

Degenhardt F, Seifert S and Szymczak S. 2019. Evaluation of variable selection methods for random forests and omics data sets. Brief Bioinform, 20(2):492-503 [DOI: 10.1093/bib/bbx124http://dx.doi.org/10.1093/bib/bbx124]

dela Torre D M G, Gao J, Macinnis-Ng C and Shi Y. 2021. Phenology-based delineation of irrigated and rain-fed paddy fields with Sentinel-2 imagery in Google Earth Engine. Geo-Spatial Information Science, 24 (4):695-710 [DOI: 10.1080/10095020.2021.1984183http://dx.doi.org/10.1080/10095020.2021.1984183]

Ding M J, Guan Q H, Li L H, Zhang H M, Liu C and Zhang L. 2020. Phenology-based rice paddy mapping using multi-source satellite imagery and a fusion algorithm applied to the poyang lake plain, Southern China. Remote Sensing, 12 (6) :22 [DOI: ARTN 102210.3390/rs12061022http://dx.doi.org/ARTN102210.3390/rs12061022]

Dong J W and Xiao X M. 2016. Evolution of regional to global paddy rice mapping methods: a review. ISPRS Journal of Photogrammetry and Remote Sensing, 119:214-227 [DOI: 10.1016/j.isprsjprs.2016.05.010http://dx.doi.org/10.1016/j.isprsjprs.2016.05.010]

Dong J W, Xiao X M, Kou W L, Qin Y W, Zhang G L, Li L, Jin C, Zhou Y T, Wang J, Biradar C, Liu J Y and Moore B. 2015. Tracking the dynamics of paddy rice planting area in 1986–2010 through time series Landsat images and phenology-based algorithms.Remote Sensing of Environment, 160:99-113 [DOI: 10.1016/j.rse.2015.01.004http://dx.doi.org/10.1016/j.rse.2015.01.004]

Dong J W, Xiao X M, Menarguez M A, Zhang G, Qin Y, Thau D, Biradar C and Moore B Ⅲ. 2016. Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine.Remote Sens Environ, 185:142-54 [DOI: 10.1016/j.rse.2016.02.016http://dx.doi.org/10.1016/j.rse.2016.02.016]

Fatchurrachman, Rudiyanto, Soh N C, Shah R M, Giap S G E, Setiawan B I and Minasny B. 2022. High-resolution mapping of paddy rice extent and growth stages across peninsular Malaysia using a fusion of Sentinel-1 and 2 time series data in Google Earth Engine. Remote Sensing, 14 (8):1875 [DOI: ARTN 187510.3390/rs14081875http://dx.doi.org/ARTN187510.3390/rs14081875]

Feng G, Masek J, Schwaller M and Hall F. 2006. On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 44 (8):2207-2218 [DOI: 10.1109/tgrs.2006.872081http://dx.doi.org/10.1109/tgrs.2006.872081]

Feng Q L, Niu B W, Zhu D H, Chen B A, Zhang C and Yang J Y. 2022. Review for deep learning in land use and land cover remote sensing classification. Transactions of the Chinese Society of Agricultural Machinery, 53 (03):1-17

冯权泷, 牛博文, 朱德海, 陈泊安, 张超, 杨建宇. 2022. 土地利用/覆被深度学习遥感分类研究综述. 农业机械学报, 53 (03):1-17 [DOI: 10.6041/j.issn.1000-1298.2022.03.001http://dx.doi.org/10.6041/j.issn.1000-1298.2022.03.001]

Fiorillo E, Di Giuseppe E, Fontanelli G and Maselli F. 2020. Lowland rice mapping in Sédhiou region (Senegal) using Sentinel 1 and Sentinel 2 data and random forest. Remote Sensing, 12 (20):3403 [DOI: 10.3390/rs12203403http://dx.doi.org/10.3390/rs12203403]

Frolking S, Qiu J J, Boles S, Xiao X M, Liu J Y, Zhuang Y H, Li C S and Qin X G. 2002. Combining remote sensing and ground census data to develop new maps of the distribution of rice agriculture in China. Global Biogeochemical Cycles, 16 (4):38-1--10 [DOI: Artn 109110.1029/2001gb001425http://dx.doi.org/Artn109110.1029/2001gb001425]

Fu D J, Xiao H, Su F Z, Zhou C H, Dong J W, Zeng Y L, Yan K, Li S W, Wu J, Wu W Z and Yan F Q. 2021. Remote sensing cloud computing platform development and Earth science application. National Remote Sensing Bulletin, 25(1):220-230

付东杰, 肖寒, 苏奋振, 周成虎, 董金玮, 曾也鲁, 闫凯, 李世卫, 吴进, 吴文周, 颜凤芹. 2021. 遥感云计算平台发展及地球科学应用. 遥感学报, 25 (01):220-230 [DOI: 10.11834/jrs.20210447http://dx.doi.org/10.11834/jrs.20210447]

Fuller D Q, Qin L, Zheng Y F, Zhao Z J, Chen X, Hosoya L A and Sun G P. 2009. The domestication process and domestication rate in rice: Spikelet Bases from the Lower Yangtze. SCIENCE, 1607-1610 [DOI: 10.1126/science.1166605http://dx.doi.org/10.1126/science.1166605]

Gao B-C. 1995. NDWI A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3):257–266 [DOI:10.1016/s0034-4257(96)00067-3http://dx.doi.org/10.1016/s0034-4257(96)00067-3]

Georganos S, Grippa T, Vanhuysse S, Lennert M, Shimoni M, Kalogirou S and Wolff E. 2017. Less is more: Optimizing classification performance through feature selection in a very-high-resolution remote sensing object-based urban application. GIScience & Remote Sensing, 55 (2):221-242 [DOI: 10.1080/15481603.2017.1408892http://dx.doi.org/10.1080/15481603.2017.1408892]

Guan K Y, Li Z, Rao L N, Gao F, Xie D H, Hien N T and Zeng Z Z. 2018. Mapping paddy rice area and yields over Thai Binh Province in Viet Nam from MODIS, Landsat, and ALOS-2/PALSAR-2. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11 (7):2238-2252 [DOI: 10.1109/Jstars.2018.2834383http://dx.doi.org/10.1109/Jstars.2018.2834383]

Gumma M K, Thenkabail P S, Panjala P, Teluguntla P, Yamano T and Mohammed I. 2022. Multiple agricultural cropland products of South Asia developed using Landsat-8 30 m and MODIS 250 m data using machine learning on the Google Earth Engine (GEE) cloud and spectral matching techniques (SMTs) in support of food and water security. GIScience & Remote Sensing, 59 (1):1048-1077 [DOI: 10.1080/15481603.2022.2088651http://dx.doi.org/10.1080/15481603.2022.2088651]

Guo X L, Price P and Stiles J. 2003. Grasslands discriminant analysis using Landsat TM single and multitemporal data. Photogrammetric Engineering and Remote Sensing, 69 (11):1255-1262 [DOI: 10.14358/Pers.69.11.1255http://dx.doi.org/10.14358/Pers.69.11.1255]

Hall D K, Riggs G A and Salomonson V V. 1995. Development of methods for mapping global snow cover using moderate resolution imaging spectroradiometer data. Remote Sensing of Environment, 54 (2):127-140 [DOI: 10.1016/0034-4257(95)00137-Phttp://dx.doi.org/10.1016/0034-4257(95)00137-P]

Han J C, Zhang Z, Luo Y C A, Cao J, Zhang L L, Cheng F, Zhuang H M, Zhang J and Tao F L. 2021. NESEA-Rice10: High-resolution annual paddy rice maps for Northeast and Southeast Asia from 2017 to 2019. Earth System Science Data, 13 (12):5969-5986 [DOI: 10.5194/essd-13-5969-2021http://dx.doi.org/10.5194/essd-13-5969-2021]

He Y L, Dong J W, Liao X Y, Sun L, Wang Z P, You N S, Li Z C and Fu P. 2021. Examining rice distribution and cropping intensity in a mixed single- and double-cropping region in South China using all available Sentinel 1/ 2 images. International Journal of Applied Earth Observation and Geoinformation, 101 [DOI: ARTN 10235110.1016/j.jag.2021.102351http://dx.doi.org/ARTN10235110.1016/j.jag.2021.102351]

He Z and Li S H. XXXX. Research progress on radar remote sensing for rice growth monitoring. National Remote Sensing Bulletin, XX(XX):1-20

何泽, 李世华.XXXX.水稻雷达遥感监测研究进展.遥感学报:XX(XX):1-20 [DOI: 10.11834/jrs. 20221701http://dx.doi.org/10.11834/jrs.20221701]

He Z X, Zhang M, Wu B F, X Q. 2019. Extraction of summer crop in Jiangsu based on Google Earth Engine. Journal of Geo-information Science, 21(5):752-766

何昭欣, 张淼, 吴炳方, 刑强. 2019. Google Earth Engine 支持下的江苏省夏收作物遥感提取.地球信息科学学报, 21(5):752-766 [DOI:10.12082/dqxxkx.2019.180420http://dx.doi.org/10.12082/dqxxkx.2019.180420]

Hedayati A, Vahidnia M H and Behzadi S. 2022. Paddy lands detection using Landsat-8 satellite images and object-based classification in Rasht city, Iran. Egyptian Journal of Remote Sensing and Space Sciences, 25 (1):73-84 [DOI: 10.1016/j.ejrs.2021.12.008http://dx.doi.org/10.1016/j.ejrs.2021.12.008]

Hong S Z, Hui L and Yu L. 2015. Impacts of feature normalization on optical and SAR data fusion for land use/land cover classification. IEEE Geoscience and Remote Sensing Letters, 12 (5):1061-1065 [DOI: 10.1109/lgrs.2014.2377722http://dx.doi.org/10.1109/lgrs.2014.2377722]

Housman I W, Chastain R A and Finco M V. 2018. An evaluation of forest health insect and disease survey data and satellite-based remote sensing forest change detection methods: Case studies in the united states. Remote Sensing, 10 (8):1184 [DOI: ARTN 118410.3390/rs10081184http://dx.doi.org/ARTN118410.3390/rs10081184]

Huang C, Xu Z X, Zhang C C, Li H, Liu Q S, Yang Z K and Liu G H. 2020. Extraction of rice planting structure in tropical region based on Sentinel-1 temporal features integration. Transactions of the Chinese Society of Agricultural Engineering, 36(9): 177-184

黄翀, 许照鑫, 张晨晨, 李贺, 刘庆生, 杨振坤, 刘高焕. 2020. 基于Sentinel-1数据时序特征的热带地区水稻种植结构提取方法.农业工程学报, 36 (09):177-184 [DOI: 10.11975/j.issn.1002-6819.2020.09.020http://dx.doi.org/10.11975/j.issn.1002-6819.2020.09.020]

Huang J F, Chen L, Wang J and Wang X Z. 2013. Uncertainty analysis of rice planting area extraction based on different classifiers using Landsat data. Transactions of the Chinese Society of Agricultural Engineering, 29(6): 166-176

黄敬峰, 陈拉, 王晶, 王秀珍. 2013. 水稻种植面积遥感估算的不确定性研究. 农业工程学报, 29 (06):166-176 [DOI: 10.3969/j.issn.1002-6819.2013.06.021http://dx.doi.org/10.3969/j.issn.1002-6819.2013.06.021]

Huang W, Huang J L, Wang L H, Hu Y X, Han P P and Wang J L. 2014. Detection of late rice’s planting area change in Pingle County based on multi-temporal remote sensing images. Transactions of the Chinese Society of Agricultural Engineering, 30(21): 174-183

黄维, 黄进良, 王立辉, 胡砚霞, 韩鹏鹏, 王久玲.2014.多时相遥感影像检测平乐县晚稻种植面积变化.农业工程学报, 30 (21):174-183 [DOI: 10.3969/j.issn.1002-6819.2014.21.021http://dx.doi.org/10.3969/j.issn.1002-6819.2014.21.021]

Ienco D, Interdonato R, Gaetano R and Minh D H T. 2019. Combining Sentinel-1 and Sentinel-2 satellite image time series for land cover mapping via a multi-source deep learning architecture. ISPRS Journal of Photogrammetry and Remote Sensing, 158:11-22 [DOI: 10.1016/j.isprsjprs.2019.09.016http://dx.doi.org/10.1016/j.isprsjprs.2019.09.016]

Jiang M, Xin L J, Li X B, Tan M H and Wang R J. 2018. Decreasing rice cropping intensity in Southern China from 1990 to 2015. Remote Sensing, 11 (1):35-51 [DOI: 10.3390/rs11010035http://dx.doi.org/10.3390/rs11010035]

Jiang R, Sanchez-Azofeifa A, Laakso K, Xu Y, Zhou Z Y, Luo X W, Huang J H, Chen X and Zang Y. 2021. Cloud cover throughout all the paddy rice fields in Guangdong, China: Impacts on Sentinel 2 MSI and Landsat 8 OLI optical observations. Remote Sensing, 13 (15):2961 [DOI: ARTN 296110.3390/rs13152961http://dx.doi.org/ARTN296110.3390/rs13152961]

Jin C, Xiao X M, Merbold L, Arneth A, Veenendaal E and Kutsch W L. 2013. Phenology and gross primary production of two dominant savanna woodland ecosystems in Southern Africa. Remote Sensing of Environment, 135:189-201 [DOI: 10.1016/j.rse.2013.03.033http://dx.doi.org/10.1016/j.rse.2013.03.033]

Kang J M, Yang X M, Wang Z H, Huang C and Wang J. 2022. Collaborative extraction of paddy planting areas with multi-source information based on Google Earth Engine: A case study of Cambodia. Remote Sensing, 14 (8):1823 [DOI: ARTN 182310.3390/rs14081823http://dx.doi.org/ARTN182310.3390/rs14081823]

Kim H O, Yeom J M. 2014. Effect of red-edge and texture features for object-based paddy rice crop classification using RapidEye multi-spectral satellite image data. International Journal of Remote Sensing, 35(19): 7046-7068 [DOI: 10.1080/01431161.2014.965285http://dx.doi.org/10.1080/01431161.2014.965285]

Kontgis C, Schneider A and Ozdogan M. 2015. Mapping rice paddy extent and intensification in the Vietnamese Mekong River Delta with dense time stacks of Landsat data. Remote Sensing of Environment, 169:255-269 [DOI: 10.1016/j.rse.2015.08.004http://dx.doi.org/10.1016/j.rse.2015.08.004]

Liang J, Zheng Z W, Xia S T, Zhang X T and Tang Y Y. 2020. Crop recognition and evaluation using red edge features of GF-6 satellite. Journal of Remote Sensing(Chinese), 24(10):1168-1179

梁继, 郑镇炜, 夏诗婷, 张晓彤, 唐媛媛. 2020. 高分六号红边特征的农作物识别与评估. 遥感学报, 24 (10):1168-1179 [DOI: 10.11834/jrs.20209289http://dx.doi.org/10.11834/jrs.20209289]

Ling F L, Li Z Y, Bai L N, Tian X, Chen E X and Yang Y T. 2011. Rice mapping using ALOS PALSAR dual polarization data. Journal of Remote Sensing, 15(6): 1215-1227

凌飞龙, 李增元, 白黎娜, 田昕, 陈尔学, 杨永恬. 2011. ALOS PALSAR双极化数据水稻制图. 遥感学报, 15 (06):1215-1227

Liu J H, Li L, Huang X, Liu Y M and Li T S. 2018. Mapping paddy rice in Jiangsu Province, China, based on phenological parameters and a decision tree model. Frontiers of Earth Science, 13 (1):111-123 [DOI: 10.1007/s11707-018-0723-yhttp://dx.doi.org/10.1007/s11707-018-0723-y]

Liu L, Xiao X M, Qin Y W, Wang J, Xu X L, Hu Y M and Qiao Z. 2020. Mapping cropping intensity in China using time series Landsat and Sentinel-2 images and Google Earth Engine. Remote Sensing of Environment, 239 [DOI: ARTN 11162410.1016/j.rse.2019.111624http://dx.doi.org/ARTN11162410.1016/j.rse.2019.111624]

Liu R Q, Zhang G L, Dong J W, Zhou Y, You N S, He Y L and Xiao X M. 2022. Evaluating effects of medium-resolution optical data availability on phenology-based rice mapping in China. Remote Sensing, 14 (13):3134-3142 [DOI: ARTN 313410.3390/rs14133134http://dx.doi.org/ARTN313410.3390/rs14133134]

Liu S S, Chen Y R, Ma Y T, Kong X X, Zhang X Y and Zhang D Y. 2020. Mapping ratoon rice planting area in Central China using Sentinel-2 time stacks and the phenology-based algorithm. Remote Sensing, 12 (20):3400 [DOI: ARTN 340010.3390/rs12203400http://dx.doi.org/ARTN340010.3390/rs12203400]

Liu W, Dong J, Xiang K L, Wang S, Han W and Yuan W P. 2018. A sub-pixel method for estimating planting fraction of paddy rice in Northeast China. Remote Sensing of Environment, 205:305-314 [DOI: 10.1016/j.rse.2017.12.001http://dx.doi.org/10.1016/j.rse.2017.12.001]

Lu J, Huang J L, Wang L H and Pei Y Y. 2017. Paddy rice planting information extraction based on spatial and temporal data fusion approach in Jianghan plain. Resources and Environment in the Yangtze Basin, 26(6): 874-881

陆俊, 黄进良, 王立辉, 裴艳艳. 2017. 基于时空数据融合的江汉平原水稻种植信息提取. 长江流域资源与环境, 26(6): 874-881 [DOI:10.11870/cjlyzyyhj201706010http://dx.doi.org/10.11870/cjlyzyyhj201706010]

Lu Z R, Xiong Q X, Zhou Y S, Deng Q Y, Ji S W and Ding L. 2020. Extraction of spatial distribution information of rice and shrimp fields by Sentinel-1 A time series data and dynamic time warping algorithm. Jiangsu Agricultural Sciences, 48(18): 230-236

卢泽如, 熊勤学, 周雨顺, 邓琪云, 纪绍威, 丁璐. 2020. 运用Sentinel-1A时序数据和动态时间规整算法提取稻虾田空间分布信息.江苏农业科学, 48(18): 230-236 [DOI:10.15889/j.issn.1002-1302.2020.18.046http://dx.doi.org/10.15889/j.issn.1002-1302.2020.18.046]

Ma X F, Hong Y T, Song Y Z and Chen Y J. 2019. A super-resolution convolutional-neural-network-based approach for subpixel mapping of hyperspectral images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12 (12):4930-4939 [DOI: 10.1109/Jstars.2019.2941089http://dx.doi.org/10.1109/Jstars.2019.2941089]

Maiti A, Acharya P, Sannigrahi S, Zhang Q, Bar S, Chakraborti S, Gayen B K, Barik G, Ghosh S and Punia M. 2022. Mapping active paddy rice area over monsoon Asia using time-series Sentinel-2 images in Google Earth Engine:A case study over lower gangetic plain. Geocarto International:1-24 [DOI: 10.1080/10106049.2022.2032396http://dx.doi.org/10.1080/10106049.2022.2032396]

Mansaray L R, Huang W J, Zhang D D, Huang J F and Li J. 2017. Mapping rice fields in urban Shanghai, Southeast China, using Sentinel-1A and Landsat 8 datasets. Remote Sensing, 9 (3):257 [DOI: ARTN 25710.3390/rs9030257http://dx.doi.org/ARTN25710.3390/rs9030257]

Mansaray L R, Yang L B, Kabba V T S, Kanu A S, Huang J F and Wang F M. 2019. Optimising rice mapping in cloud-prone environments by combining quad-source optical with Sentinel-1A microwave satellite imagery. GIScience & Remote Sensing, 56 (8):1333-1354 [DOI: 10.1080/15481603.2019.1646978http://dx.doi.org/10.1080/15481603.2019.1646978]

Mou X L, Li H, Huang C, Liu Q S and Liu G H. 2021. Application progress of Google Earth Engine in land use and land cover remote sensing information extraction. Remote Sensing for Land and Resources, 33(2) : 01-10

牟晓莉, 李贺, 黄翀, 刘庆生, 刘高焕. 2021. Google Earth Engine在土地覆被遥感信息提取中的研究进展. 国土资源遥感, 33 (02):01-10 [DOI: 10.6046 /gtzyyg.2020189http://dx.doi.org/10.6046/gtzyyg.2020189]

Mullissa A, Andreas V, Christelle O, Bart S, Johannes B, Yaqing G, Noel G, and Johannes R. 2021. Sentinel-1 SAR backscatter analysis ready data preparation in Google Earth Engine. Remote Sensing, 13 (10) [DOI: 10.3390/rs13101954]

Nguyen D B, Gruber A and Wagner W. 2016. Mapping rice extent and cropping scheme in the Mekong Delta using Sentinel-1A data. Remote Sensing Letters, 7 (12):1209-1218 [DOI: 10.1080/2150704x.2016.1225172http://dx.doi.org/10.1080/2150704x.2016.1225172]

Nguyen T T H, De Bie C A J M, Ali A, Smaling E M A and Chu T H.2011.Mapping the irrigated rice cropping patterns of the Mekong delta, Vietnam, through hyper-temporal SPOT NDVI image analysis.International Journal of Remote Sensing, 33 (2):415-34 [DOI: 10.1080/01431161.2010.532826http://dx.doi.org/10.1080/01431161.2010.532826]

Ni R G, Tian J Y, Li X J, Yin D M, Li J W, Gong H L, Zhang J, Zhu L and Wu D L. 2021. An enhanced pixel-based phenological feature for accurate paddy rice mapping with Sentinel-2 imagery in Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing, 178:282-296 [DOI: 10.1016/j.isprsjprs.2021.06.018http://dx.doi.org/10.1016/j.isprsjprs.2021.06.018]

Pan X Z, Uchida S, Liang Y, Hirano A and Sun B. 2010. Discriminating different landuse types by using multitemporal NDXI in a rice planting area. International Journal of Remote Sensing, 31 (3):585-596 [DOI: 10.1080/01431160902894442http://dx.doi.org/10.1080/01431160902894442]

Park S, Im J, Park S, Yoo C, Han H and Rhee J. 2018. Classification and mapping of paddy rice by combining Landsat and SAR time series data. Remote Sensing, 10 (3):447 [DOI: ARTN 44710.3390/rs10030447http://dx.doi.org/ARTN44710.3390/rs10030447]

Peng D L, Huete A R, Huang J F, Wang F M and Sun H S. 2011. Detection and estimation of mixed paddy rice cropping patterns with MODIS data. International Journal of Applied Earth Observation and Geoinformation, 13 (1):13-23 [DOI: 10.1016/j.jag.2010.06.001http://dx.doi.org/10.1016/j.jag.2010.06.001]

Phan H, Le Toan T, Bouvet A, Nguyen L D, Pham Duy T and Zribi M. 2018. Mapping of rice varieties and sowing date using X-Band SAR data. Sensors (Basel), 18 (1) [DOI: 10.3390/s18010316]

Phiri D, Simwanda M, Salekin S, Nyirenda V R, Murayama Y and Ranagalage M. 2020. Sentinel-2 data for land cover/use mapping: A review. Remote Sensing, 12 (14):2291 [DOI: ARTN 229110.3390/rs12142291http://dx.doi.org/ARTN229110.3390/rs12142291]

Pradhan S, Dhar A and Tiwari K N. 2022. Sustainability of Boro rice cultivation in the canal irrigated command area of India. Journal of Water and Climate Change, 13 (8):3083-3099 [DOI: 10.2166/wcc.2022.125http://dx.doi.org/10.2166/wcc.2022.125]

Qin Y W, Xiao X M, Dong J W, Zhou Y T, Zhu Z, Zhang G L, Du G M, Jin C, Kou W L, Wang J and Li X P. 2015. Mapping paddy rice planting area in cold temperate climate region through analysis of time series Landsat 8 (OLI), Landsat 7 (ETM+) and MODIS imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 105:220-233 [DOI: 10.1016/j.isprsjprs.2015.04.008http://dx.doi.org/10.1016/j.isprsjprs.2015.04.008]

Qiu B W, Li W J, Tang Z H, Chen C C and Qi W. 2015. Mapping paddy rice areas based on vegetation phenology and surface moisture conditions. Ecological Indicators, 56:79-86 [DOI: 10.1016/j.ecolind.2015.03.039http://dx.doi.org/10.1016/j.ecolind.2015.03.039]

Rasel S M M, Chang H C, Ralph T J, Saintilan N and Diti I J. 2019. Application of feature selection methods and machine learning algorithms for saltmarsh biomass estimation using Worldview-2 imagery. Geocarto International, 36 (10):1075-1099 [DOI: 10.1080/10106049.2019.1624988http://dx.doi.org/10.1080/10106049.2019.1624988]

Rudiyanto, Minasny B, Shah R M, Soh N C, Arif C and Setiawan B I. 2019. Automated near-real-time mapping and monitoring of rice extent, cropping patterns, and growth stages in Southeast Asia using Sentinel-1 time series on a Google Earth Engine platform. Remote Sensing, 11 (14):1666 [DOI: ARTN 166610.3390/rs11141666http://dx.doi.org/ARTN166610.3390/rs11141666]

Sakamoto T, Van Nguyen N, Kotera A, Ohno H, Ishitsuka N and Yokozawa M. 2007. Detecting temporal changes in the extent of annual flooding within the Cambodia and the Vietnamese Mekong Delta from MODIS time-series imagery. Remote Sensing of Environment, 109 (3):295-313 [DOI: 10.1016/j.rse.2007.01.011http://dx.doi.org/10.1016/j.rse.2007.01.011]

Sanchez A H, Picoli M C A, Camara G, Andrade P R, Chaves M E D, Lechler S, Soares A R, Marujo R F B, Simões R E O, Ferreira K R and Queiroz G R. 2020. Comparison of cloud cover detection algorithms on Sentinel–2 images of the Amazon tropical forest. Remote Sensing, 12 (8):1284 [DOI: 10.3390/rs12081284http://dx.doi.org/10.3390/rs12081284]

Sang G Q, Tang Z G, Mao K B, Deng G, Wang J W and Li J. 2022. High-resolution paddy rice mapping using Sentinel data based on GEE platform: A case study of Hunan province, China. Acta Agronomica Sinica, 48(9): 2409-2420

桑国庆, 唐志光, 毛克彪, 邓刚, 王靖文, 李佳. 2022. 基于GEE云平台与Sentinel数据的高分辨率水稻种植范围提取——以湖南省为例.作物学报, 48(9): 2409-2420 [DOI:10.3724/SP.J.1006.2022.12066http://dx.doi.org/10.3724/SP.J.1006.2022.12066]

Shao Y, Fan X T, Liu H, Xiao J H, Ross S, Brisco B, Brown R, Staples G. 2001. Rice monitoring and production estimation using multitemporal RADARSAT. Remote Sensing of Environment, 76(3):310–325 [DOI:10.1016/s0034-4257(00)00212-1http://dx.doi.org/10.1016/s0034-4257(00)00212-1]

Sibanda M, Mutanga O, Dube T, S Vundla T and L Mafongoya P. 2018. Estimating LAI and mapping canopy storage capacity for hydrological applications in wattle infested ecosystems using Sentinel-2 MSI derived red edge bands. GIScience & Remote Sensing, 56 (1):68-86 [DOI: 10.1080/15481603.2018.1492213http://dx.doi.org/10.1080/15481603.2018.1492213]

Singha M and Sarmah S. 2018. Incorporating crop phenological trajectory and texture for paddy rice detection with time series MODIS, HJ-1A and ALOS PALSAR imagery. European Journal of Remote Sensing, 52 (1):73-87 [DOI: 10.1080/22797254.2018.1556568http://dx.doi.org/10.1080/22797254.2018.1556568]

Singha M, Dong J W, Zhang G and Xiao X M. 2019. High resolution paddy rice maps in cloud-prone Bangladesh and Northeast India using Sentinel-1 data. Scientific Data, 6 (1):26 [DOI: 10.1038/s41597-019-0036-3http://dx.doi.org/10.1038/s41597-019-0036-3]

Singha M, Wu B and Zhang M. 2016. Object-Based Paddy rice mapping using HJ-1A/B data and temporal features extracted from time series MODIS NDVI data.Sensors (Basel), 17 (1):10 [DOI: 10.3390/s17010010http://dx.doi.org/10.3390/s17010010]

Song B Z, Liu P, Li J, Wang L Z, Zhang L, He G J, Chen L J and Liu J B. 2022. MLFF-GAN: A multilevel feature fusion with GAN for spatiotemporal remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 60:1-16 [DOI: Artn 441081610.1109/Tgrs.2022.3169916http://dx.doi.org/Artn441081610.1109/Tgrs.2022.3169916]

Song Y J, Wu C, Li Z Y, Tang S, Li G H, Wang S H and Ding Y F. 2021. Differential responses of grain yields to high temperature in different stages of reproductive growth in rice. Chinese Journal of Rice Science, 35(2): 177-186

宋有金, 吴超, 李子煜, 唐设, 李刚华, 王绍华, 丁艳锋. 2021. 水稻产量对生殖生长阶段不同时期高温的响应差异.中国水稻科学, 35(2): 177-186 [DOI:10.16819/j.1001-7216.2021.0203http://dx.doi.org/10.16819/j.1001-7216.2021.0203]

Sun H S, Huang J F and Peng D L. 2009. Detecting major growth stages of paddy rice using MODIS data. Journal of Remote Sensing, 13(6): 1122-1137

孙华生, 黄敬峰, 彭代亮. 2009. 利用MODIS数据识别水稻关键生长发育期. 遥感学报, 13(6): 1122-1137

Talhelm T, Zhang X, Oishi S, Shimin C, Duan D, Lan X and Kitayama S. 2014. Large-scale psychological differences within china explained by rice versus wheat agriculture. SCIENCE, 344 (6184):603-608 [DOI: 10.1126/science.1246850http://dx.doi.org/10.1126/science.1246850]

Tan S, Wu B F and Zhang X.2019.Mapping paddy rice in the Hainan Province using both Google Earth Engine and remote sensing images.Journal of Geo-information Science, 21(06):937-947

谭深, 吴炳方, 张鑫.2019.基于Google Earth Engine与多源遥感数据的海南水稻分类研究.地球信息科学学报, 21 (06):937-947 [DOI: 10.12082/dqxxkx.2019.180423http://dx.doi.org/10.12082/dqxxkx.2019.180423]

Thorp K R and Drajat D. 2021. Deep machine learning with Sentinel satellite data to map paddy rice production stages across West Java, Indonesia. Remote Sensing of Environment, 265 [DOI: ARTN 11267910.1016/j.rse.2021.112679http://dx.doi.org/ARTN11267910.1016/j.rse.2021.112679]

Tian H F, Wu M Q, Wang L and Niu Z. 2018. Mapping early, middle and late rice extent using Sentinel-1A and Landsat-8 data in the Poyang Lake Plain, China. Sensors (Basel), 18 (1) :185[DOI: 10.3390/s18010185http://dx.doi.org/10.3390/s18010185]

Torbick N, Chowdhury D, Salas W and Qi J G. 2017. Monitoring rice agriculture across Myanmar using time series Sentinel-1 assisted by Landsat-8 and PALSAR-2. Remote Sensing, 9 (2):119 [DOI: ARTN 11910.3390/rs9020119http://dx.doi.org/ARTN11910.3390/rs9020119]

Tseng H H, Yang M D, Saminathan R, Hsu Y C, Yang C Y and Wu D H. 2022. Rice seedling detection in UAV images using transfer learning and machine learning. Remote Sensing, 14 (12):2837 [DOI: ARTN 283710.3390/rs14122837http://dx.doi.org/ARTN283710.3390/rs14122837]

Tu Y, Lang W, Yu L, Li Y, Jiang J H, Qin Y W, Wu J M, Chen T T and Xu B. 2020. Improved mapping results of 10 m resolution land cover classification in Guangdong, China using multisource remote sensing data with Google Earth Engine. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13:5384-5397 [DOI: 10.1109/Jstars.2020.3022210http://dx.doi.org/10.1109/Jstars.2020.3022210]

Vali A, Comai S and Matteucci M. 2020. Deep learning for land use and land cover classification based on hyperspectral and multispectral earth observation data: A review. Remote Sensing, 12 (15) :2495 [DOI: ARTN 249510.3390/rs12152495http://dx.doi.org/ARTN249510.3390/rs12152495]

Wang M, Wang J and Chen L. 2020. Mapping paddy rice using weakly supervised long short-term memory network with time series sentinel optical and SAR images. Agriculture-Basel, 10 (10):483 [DOI: ARTN 48310.3390/agriculture10100483http://dx.doi.org/ARTN48310.3390/agriculture10100483]

Wang X N, Tian J Y, Li X J, Wang L, Gong H L, Chen B B, Li X C and Guo J H. 2022. Benefits of Google Earth Engine in remote sensing. National Remote Sensing Bulletin, 26(2):299-309

王小娜, 田金炎, 李小娟, 王乐, 宫辉力, 陈蓓蓓, 李向彩, 郭婧涵. 2022. Google Earth Engine云平台对遥感发展的改变.遥感学报, 26 (02):299-309 [DOI: 10.11834/jrs.20211317http://dx.doi.org/10.11834/jrs.20211317]

Wang X Q, Wang Q M, Shi X M, Ling F L and Zhu X L. 2008. Rice field mapping and monitoring using ASAR data based on principal component analysis. Transactions of the Chinese Society of Agricultural Engineering, 24(10):122-126

汪小钦, 王钦敏, 史晓明, 凌飞龙, 朱晓铃. 2008. 基于主成分变换的ASAR数据水稻种植面积提取. 农业工程学报, (10):122-126

Wang X Y, Wang J, Li H Y and Hao X H.2017.Combination of NDSI and NDFSI for snow cover mapping in a mountainous and forested region. Journal of Remote Sensing, 21(2):310-317

王晓艳, 王建, 李弘毅, 郝晓华.2017.NDSI与NDFSI结合的山区林地积雪制图方法.遥感学报, 21 (02):310-317 [DOI: 10.11834/jrs.20176211http://dx.doi.org/10.11834/jrs.20176211]

Wei J, Cui Y L, Luo W Q and Luo Y F. 2022. Mapping paddy rice distribution and cropping intensity in China from 2014 to 2019 with Landsat images, Effective Flood Signals, and Google Earth Engine. Remote Sensing, 14 (3):759 [DOI: ARTN 75910.3390/rs14030759http://dx.doi.org/ARTN75910.3390/rs14030759]

Wei P L, Chai D F, Huang R, Peng D L, Lin T, Sha J M, Sun W W and Huang J F. 2022. Rice mapping based on Sentinel-1 images using the coupling of prior knowledge and deep semantic segmentation network: A case study in Northeast China from 2019 to 2021. International Journal of Applied Earth Observation and Geoinformation, 112 [DOI: ARTN 10294810.1016/j.jag.2022.102948http://dx.doi.org/ARTN10294810.1016/j.jag.2022.102948]

Wei P L, Chai D F, Lin T, Tang C, Du M Q and Huang J F. 2021. Large-scale rice mapping under different years based on time-series Sentinel-1 images using deep semantic segmentation model. ISPRS Journal of Photogrammetry and Remote Sensing, 174:198-214 [DOI: 10.1016/j.isprsjprs.2021.02.011http://dx.doi.org/10.1016/j.isprsjprs.2021.02.011]

Wei P L, Huang R, Lin T and Huang J F. 2022. Rice mapping in training sample shortage regions using a deep semantic segmentation model trained on pseudo-labels. Remote Sensing, 14 (2):328-340 [DOI: ARTN 32810.3390/rs14020328http://dx.doi.org/ARTN32810.3390/rs14020328]

Wu B F, Zhang F, Liu C L, Zhang L and Luo Z M. 2004. An integrated method for crop condition monitoring. Journal of Remote Sensing, 8(6): 498-514

吴炳方, 张峰, 刘成林, 张磊, 罗治敏. 2004. 农作物长势综合遥感监测方法. 遥感学报, 8(6): 498-514

Wu F M, Zhang M and Wu B F. 2019. Object-oriented rapid estimation of rice acreage estimation from UAV imagery. Journal of Geo-information Science, 21(5):789-798

吴方明, 张淼, 吴炳方.2019.无人机影像的面向对象水稻种植面积快速提取.地球信息科学学报, 21 (05):789-798 [DOI: 10.12082/dqxxkx.2019.180418http://dx.doi.org/10.12082/dqxxkx.2019.180418]

Wu J S, Liu H L and Zhang J S. 2018. Paddy planting acreage estimation in city level based on UAV images and object-oriented classification method. Transactions of the Chinese Society of Agricultural Engineering , 34(1):70-77

吴金胜, 刘红利, 张锦水.2018.无人机遥感影像面向对象分类方法估算市域水稻面积.农业工程学报, 34 (01):70-77 [DOI: 10.11975/j.issn.1002-6819.2018.01.010http://dx.doi.org/10.11975/j.issn.1002-6819.2018.01.010]

Wu M Q, Wang C Y and Niu Z.2010.Mapping paddy fields in large areas, based on time series multi-sensors data. Transactions of the Chinese Society of Agricultural Engineering, 26(7): 240-244

邬明权, 王长耀, 牛铮.2010.利用多源时序遥感数据提取大范围水稻种植面积.农业工程学报, 26 (07):240-244 [DOI:10.3969/j.issn.1002-6819.2010.07.041http://dx.doi.org/10.3969/j.issn.1002-6819.2010.07.041]

Xi W Q, Du S H and Du S J. 2022. Multi-temporal cultivated land cover extraction and change analysis: a spatiotemporal context method combining remote sensing and spatial statistics. Journal of Geo- information Science, 24(2):310-325

习文强, 杜世宏, 杜守基.2022.多时相耕地覆盖提取和变化分析:一种结合遥感和空间统计的时空上下文方法.地球信息科学学报, 24(2):310-325.[ DOI:10.12082/dqxxkx.2022.210034]

Xia L, Zhao F, Chen J, Yu L, Lu M, Yu Q Y, Liang S F, Fan L L, Sun X, Wu S R, Wu W B and Yang P. 2022. A full resolution deep learning network for paddy rice mapping using Landsat data. ISPRS Journal of Photogrammetry and Remote Sensing, 194:91-107 [DOI: 10.1016/j.isprsjprs.2022.10.005http://dx.doi.org/10.1016/j.isprsjprs.2022.10.005]

Xiao W, Xu S C and He T T. 2021. Mapping paddy rice with Sentinel-1/2 and phenology-object-based algorithm—a implementation in Hangjiahu Plain in China using GEE platform. Remote Sensing, 13 (5):990 [DOI: 10.3390/rs13050990http://dx.doi.org/10.3390/rs13050990]

Xiao X M, Boles S, Frolking S, Li C S, Babu J Y, Salas W and Moore B. 2006. Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images. Remote Sensing of Environment, 100 (1):95-113 [DOI: 10.1016/j.rse.2005.10.004http://dx.doi.org/10.1016/j.rse.2005.10.004]

Xiao X M, Boles S, Liu J Y, Zhuang D F, Frolking S, Li C S, Salas W and Moore B. 2005. Mapping paddy rice agriculture in southern China using multi-temporal MODIS images. Remote Sensing of Environment, 95 (4):480-492 [DOI: 10.1016/j.rse.2004.12.009http://dx.doi.org/10.1016/j.rse.2004.12.009]

Xiao X M, Zhang Q Y, Braswell B, Urbanski S, Boles S, Wofsy S, Moore B and Ojima D. 2004. Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data. Remote Sensing of Environment, 91 (2):256-270 [DOI: 10.1016/j.rse.2004.03.010http://dx.doi.org/10.1016/j.rse.2004.03.010]

Xu D and Zhang M.2022.Mapping paddy rice using an adaptive stacking algorithm and Sentinel-1/2 images based on Google Earth Engine. Remote Sensing Letters, 13 (4):373-382 [DOI: 10.1080/2150704x.2022.2027543http://dx.doi.org/10.1080/2150704x.2022.2027543]

Xu L, Zhang H, Wang C, Wei S S, Zhang B, Wu F and Tang Y X. 2021. Paddy rice mapping in Thailand using time-series Sentinel-1 data and deep learning model. Remote Sensing, 13 (19) :3994 [DOI: ARTN 399410.3390/rs13193994http://dx.doi.org/ARTN399410.3390/rs13193994]

Xu S, Zhu X L, Chen J, Zhu X L, Duan M J, Qiu B W, Wan L M, Tan X Y, Xu Y N and Cao R Y. 2023. A robust index to extract paddy fields in cloudy regions from SAR time series. Remote Sensing of Environment, 285 [DOI: 10.1016/j.rse.2022.113374http://dx.doi.org/10.1016/j.rse.2022.113374]

Yang H J, Pan B, Li N, Wang W, Zhang J and Zhang X L. 2021. A systematic method for spatio-temporal phenology estimation of paddy rice using time series Sentinel-1 images. Remote Sensing of Environment, 259 [DOI: ARTN 11239410.1016/j.rse.2021.112394http://dx.doi.org/ARTN11239410.1016/j.rse.2021.112394]

Yang J Y, Wu T X, Wang S D, Zhao X and Xiong H. 2022. Extraction of multiple cropping information at the Sub-pixel scale based on phenology and MODIS NDVI time-series: A case study in Henan Province, China. Geocarto International:1-21 [DOI: 10.1080/10106049.2022.2104390http://dx.doi.org/10.1080/10106049.2022.2104390]

Yang L B, Huang R, Huang J F, Lin T, Wang L M, Mijiti R, Wei P L, Tang C, Shao J, Li Q Z and Du X. 2022. Semantic segmentation based on temporal features: Learning of temporal–spatial information from time-series SAR images for paddy rice mapping. IEEE Transactions on Geoscience and Remote Sensing, 60:1-16 [DOI: 10.1109/tgrs.2021.3099522http://dx.doi.org/10.1109/tgrs.2021.3099522]

Yang X M, Yang T B, Liu H M, Ghebrezgabher M G, Wang Q and Wei H D. 2016. Vegetation variation in the north hemisphere under climate warming in the last 30 years. Arid Zone Research, 33(2): 379-391

杨雪梅, 杨太保, 刘海猛, Ghebrezgabher M G, 王倩, 魏怀东. 2016. 气候变暖背景下近30a北半球植被变化研究综述. 干旱区研究, 33(2): 379-391 [DOI:10.13866/j.azr.2016.02.21http://dx.doi.org/10.13866/j.azr.2016.02.21]

Yang Y J, Huang Y, Tian Q J, Wang L, Geng J and Yang R R. 2015. The extraction model of paddy rice information based on GF-1 satellite WFV images. Spectroscopy and Spectral Analysis, 11: 3255-3261

杨闫君, 黄彦, 田庆久, 王磊, 耿君, 杨冉冉. 2015. 基于高分一号卫星WFV影像的水稻信息提取模式.光谱学与光谱分析, 11: 3255-3261 [DOI:10.3964/j.issn.1000-0593(2015)11-3255-07http://dx.doi.org/10.3964/j.issn.1000-0593(2015)11-3255-07]

Yin Q, Liu M L, Cheng J Y, Ke Y H and Chen X W. 2019. Mapping paddy rice planting area in Northeastern China using spatiotemporal data fusion and phenology-based method. Remote Sensing, 11 (14):1699 [DOI: ARTN 169910.3390/rs11141699http://dx.doi.org/ARTN169910.3390/rs11141699]

Zeng Y L, Hao D L, Huete A, Dechant B, Berry J, Chen J M, Joiner J, Frankenberg C, Bond-Lamberty B, Ryu Y, Xiao J F, Asrar G R and Chen M. 2022. Optical vegetation indices for monitoring terrestrial ecosystems globally. Nature Reviews Earth & Environment, 3 (7):477-493 [DOI: 10.1038/s43017-022-00298-5http://dx.doi.org/10.1038/s43017-022-00298-5]

Zhan P, Zhu W Q and Li N. 2021. An automated rice mapping method based on flooding signals in synthetic aperture radar time series. Remote Sensing of Environment, 252 [DOI: ARTN 11211210.1016/j.rse.2020.112112http://dx.doi.org/ARTN11211210.1016/j.rse.2020.112112]

Zhang C K, Zhang H Y and Zhang L P. 2021. Spatial domain bridge transfer: An automated paddy rice mapping method with no training data required and decreased image inputs for the large cloudy area. Computers and Electronics in Agriculture, 181 [DOI: ARTN 10597810.1016/j.compag.2020.105978http://dx.doi.org/ARTN10597810.1016/j.compag.2020.105978]

Zhang C, Dong J W, Xie Y H, Zhang X Z and Ge Q S. 2022. Mapping irrigated croplands in China using a synergetic training sample generating method, machine learning classifier, and Google Earth Engine. International Journal of Applied Earth Observation and Geoinformation, 112 [DOI: ARTN 10288810.1016/j.jag.2022.102888http://dx.doi.org/ARTN10288810.1016/j.jag.2022.102888]

Zhang H G, He B B and Xing J. 2022. Mapping paddy rice in complex landscapes with Landsat time series data and super pixel-based deep learning method. Remote Sensing, 14 (15) [DOI: ARTN 372110.3390/rs14153721]

Zhang M, Lin H, Wang G X, Sun H and Fu J. 2018. Mapping paddy rice using a Convolutional Neural Network (CNN) with Landsat 8 datasets in the Dongting Lake Area, China. Remote Sensing, 10 (11):1840 [DOI: ARTN 184010.3390/rs10111840http://dx.doi.org/ARTN184010.3390/rs10111840]

Zhang X, Wu B F, Ponce-Campos G E, Zhang M, Chang S and Tian F Y. 2018. Mapping up-to-date paddy rice extent at 10 m resolution in China through the integration of optical and synthetic aperture radar images. Remote Sensing, 10 (8) :1200 [DOI: ARTN 120010.3390/rs10081200http://dx.doi.org/ARTN120010.3390/rs10081200]

Zhang Y Q, Li R P, Mu X H and Ren H R. 2021. Extraction of paddy rice planting areas based on multi-temporal GF-6 remote sensing images. Transactions of the Chinese Society of Agricultural Engineering, 37(17): 189-196

张悦琦, 李荣平, 穆西晗, 任鸿瑞.2021.基于多时相GF-6遥感影像的水稻种植面积提取.农业工程学报, 37(17):189-196 [DOI: 10.11975/j.issn.1002-6819.2021.17.021http://dx.doi.org/10.11975/j.issn.1002-6819.2021.17.021]

Zhang Y S, Yuan L F and Yao Y H. 2007. Study on extraction of paddy rice fields from multitemporal MODIS images. Journal of Remote Sensing, 11(2): 282-288

张友水, 原立峰, 姚永慧. 2007. 多时相MODIS影像水田信息提取研究. 遥感学报, 11(2): 282-288

Zhang Z J, Hua L, Zheng X and Li J L. 2022. Extraction of cropping patterns in Jianghan Plain based on GEE and Sentinel-NDVI time series data. Transactions of the Chinese Society of Agricultural Engineering, 38(01): 196-202

张紫荆, 华丽, 郑萱, 李嘉麟. 2022. 基于GEE平台与Sentinel-NDVI时序数据江汉平原种植模式提取. 农业工程学报, 38 (01):196-202 [DOI: 10.11975/j.issn.1002-6819.2022.01.022http://dx.doi.org/10.11975/j.issn.1002-6819.2022.01.022]

Zhao H W, Chen Z X, Jiang H, Jing W L, Sun L and Feng M. 2019. Evaluation of three deep learning models for early crop classification using Sentinel-1A imagery time series—A case study in Zhanjiang, China. Remote Sensing, 11 (22) [DOI: 10.3390/rs11222673]

Zhao R K, Li Y C and Ma M G. 2021. Mapping paddy rice with satellite remote sensing: A review. Sustainability, 13 (2):503 [DOI: ARTN 50310.3390/su13020503http://dx.doi.org/ARTN50310.3390/su13020503]

Zhao R K, Li Y C, Chen J, Ma M G, Fan L and Lu W. 2021. Mapping a paddy rice area in a cloudy and rainy region using spatiotemporal data fusion and a phenology-based algorithm. Remote Sensing, 13 (21):4400-4419 [DOI: ARTN 440010.3390/rs13214400http://dx.doi.org/ARTN440010.3390/rs13214400]

Zhao S, Liu X N, Ding C, Liu S Y, Wu C S and Wu L. 2020. Mapping rice paddies in complex landscapes with convolutional neural networks and phenological metrics. GIScience & Remote Sensing, 57 (1):37-48 [DOI: 10.1080/15481603.2019.1658960http://dx.doi.org/10.1080/15481603.2019.1658960]

Zhao Y J, Huang J L, Wang L H, Chi H and Yin H M. 2020. Extraction of rice planting areas in Jianghan plain based on spatiotemporal fusion NDVI and phenological characteristics. Resources and Environment in the Yangtze Basin, 29(2): 424-433

赵亚杰, 黄进良, 王立辉, 池泓, 阴海明.2020.基于时空融合NDVI及物候特征的江汉平原水稻种植区提取研究.长江流域资源与环境, 29(2): 424-433 [DOI:10.11870/cjlyzyyhj202002015http://dx.doi.org/10.11870/cjlyzyyhj202002015]

Zhao Y S. 2003. Principles and Methods of Remote Sensing Application Analysis. Beijing: Science Press. 493

赵英时. 2003. 遥感应用分析原理与方法. 北京:科学出版社. 493

Zheng Z C, Wang X Z and Huang J F. 2009. Mapping paddy rice planting area in Zhejiang province using multi-temporal MODIS images. Journal of Zhejiang University(Agriculture & Life Sciences), 35(1): 98-104

郑长春, 王秀珍, 黄敬峰. 2009. 多时相MODIS影像的浙江省水稻种植面积信息提取方法研究. 浙江大学学报(农业与生命科学版), 35(1): 98-104 [DOI:10.3785/j.issn.1008-9209.2009.01.015http://dx.doi.org/10.3785/j.issn.1008-9209.2009.01.015]

Zhou P C，Cheng G，Yao X W and Han J W. 2021. Machine learning paradigms in high-resolution remote sensing image interpretation. National Remote Sensing Bulletin，25（1）：182-197

周培诚，程塨，姚西文，韩军伟 .2021. 高分辨率遥感影像解译中的机器学习范式 . 遥感学报，25（1）：182-197 [DOI：10.11834/jrs.20210164http://dx.doi.org/10.11834/jrs.20210164]

Zhou Y N, Luo J C, Feng L, Yang Y P, Chen Y H and Wu W. 2019. Long-short-term-memory-based crop classification using high-resolution optical images and multi-temporal SAR data. GIScience & Remote Sensing, 56 (8):1170-1191 [DOI: 10.1080/15481603.2019.1628412http://dx.doi.org/10.1080/15481603.2019.1628412]

Zhou Y T, Xiao X M, Qin Y W, Dong J W, Zhang G L, Kou W L, Jin C, Wang J and Li X P. 2016. Mapping paddy rice planting area in rice-wetland coexistent areas through analysis of Landsat 8 OLI and MODIS images. International Journal of Applied Earth Observation and Geoinformation, 46:1-12 [DOI: 10.1016/j.jag.2015.11.001http://dx.doi.org/10.1016/j.jag.2015.11.001]

Zhu A X, Zhao F H, Pan H B and Liu J Z. 2021. Mapping rice paddy distribution using remote sensing by coupling deep learning with phenological characteristics. Remote Sensing, 13 (7):1360 [DOI: ARTN 136010.3390/rs13071360http://dx.doi.org/ARTN136010.3390/rs13071360]

Zhu L H, Liu X N, Wu L, Liu M L, Lin Y, Meng Y Y, Ye L, Zhang Q and Li Y M. 2021. Detection of paddy rice cropping systems in southern China with time series Landsat images and phenology-based algorithms. GIScience & Remote Sensing, 58 (5):733-755 [DOI: 10.1080/15481603.2021.1943214http://dx.doi.org/10.1080/15481603.2021.1943214]

Zhu X L, Chen J, Gao F, Chen X H and Masek J G. 2010. An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions. Remote Sensing of Environment, 114 (11):2610-2623 [DOI: 10.1016/j.rse.2010.05.032http://dx.doi.org/10.1016/j.rse.2010.05.032]

Zhu Z and Woodcock C E. 2012. Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment, 118:83-94 [DOI: 10.1016/j.rse.2011.10.028http://dx.doi.org/10.1016/j.rse.2011.10.028]

Zhu Z, Wang S X and Woodcock C E. 2015. Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images. Remote Sensing of Environment, 159:269-277 [DOI: 10.1016/j.rse.2014.12.014http://dx.doi.org/10.1016/j.rse.2014.12.014]

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