多源中高分辨率影像协同下时间合成窗口对农作物识别的影响

doi:10.3864/j.issn.0578-1752.2024.02.003

中国农业科学 ›› 2024, Vol. 57 ›› Issue (2): 250-263.doi: 10.3864/j.issn.0578-1752.2024.02.003

• 耕作栽培·生理生化·农业信息技术 • 上一篇下一篇

多源中高分辨率影像协同下时间合成窗口对农作物识别的影响

童婉婷¹(), 魏浩东², 杨靖雅³, 金文捷¹, 宋茜³(), 胡琼⁴, 尹高飞⁵, 徐保东¹()

¹ 华中农业大学资源与环境学院/宏观农业研究院，武汉 430070
² 华中农业大学植物科学技术学院，武汉 430070
³ 中国农业科学院农业资源与农业区划研究所/北方干旱半干旱耕地高效利用全国重点实验室，北京 100081
⁴ 华中师范大学城市与环境科学学院，武汉 430079
⁵ 西南交通大学地球科学与环境工程学院，成都 610031

收稿日期:2023-05-25 接受日期:2023-08-17 出版日期:2024-01-16 发布日期:2024-01-19
通信作者:
宋茜，E-mail：songqian01@caas.cn。
徐保东，E-mail：xubaodong@mail.hzau.edu.cn
联系方式: 童婉婷，E-mail：twt@webmail.hzau.edu.cn。
基金资助:
国家重点研发计划(2021YFD1600503); 国家自然科学基金(42271360); 国家自然科学基金(42001303); 中央高校基本科研业务费专项基金(2662021JC013); 中央高校基本科研业务费专项基金(CCNU22JC013); 中央高校基本科研业务费专项基金(CCNU22QN018); 四川省杰出青年科技人才项目(2021JDJQ0007); 中央级公益性科研院所基本科研业务费专项(1610132021010)

Exploring the Impacts of Temporal Composition Window for Integrating Multi-Source Decametric-Resolution Images on Crop Type Identification

TONG WanTing¹(), WEI HaoDong², YANG JingYa³, JIN WenJie¹, SONG Qian³(), HU Qiong⁴, YIN GaoFei⁵, XU BaoDong¹()

¹ College of Resources and Environment/Macro Agriculture Research Institute, Huazhong Agricultural University, Wuhan 430070
² College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070
³ Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences/State Key Laboratory of Efficient Utilization of Arid and Semi-Arid Arable Land in Northern China, Beijing 100081
⁴ School of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079
⁵ Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031

Received:2023-05-25 Accepted:2023-08-17 Published:2024-01-16 Online:2024-01-19

摘要/Abstract

摘要：

【背景】中高空间分辨率（≤30 m）影像是在耕地破碎、种植结构复杂的中国南方开展农作物遥感识别研究的重要数据。然而，要克服中高分辨率传感器重访周期较长以及南方多云雨天气的影响，对影像进行时间窗口合成以协同使用多源中高分辨率遥感数据，是获取时空连续农作物制图结果的必要保障。由于不同卫星影像获取的周期不同，且不同农作物物候季相节律存在较大差异，如何选择影像合成时间窗口是农作物准确识别的关键前提。【目的】通过探究影像合成时间窗口对于农作物识别的影响机制，为大尺度复杂农作物种植结构制图提供重要参考依据。【方法】以农作物类型多样且云雨天气频繁的湖北省江汉平原为研究区，通过协同Landsat-8和Sentinel-2A/2B卫星影像，设置7种时间合成窗口（15、20、25、30、40、50和60 d）情景，分别从影像的覆盖度、不同农作物时序光谱特征以及不同农作物分类精度等角度，深入分析影像时间合成窗口对农作物遥感识别的影响。【结果】在影像20 d时间合成窗口的情景下，江汉平原农作物（冬油菜、冬小麦、水稻、稻虾田和其他作物）的总体分类精度最高，为93.13%。对比而言，在影像较短时间合成窗口（如15 d）的情景下，时间序列密集但高质量影像覆盖度较低，农作物总体分类精度较低（90.91%）；而在影像较长时间合成窗口（如60 d）的情景下，影像覆盖度高但时间序列稀疏，导致农作物识别的关键物候信息丢失，降低了总体分类精度（86.06%）。此外，不同农作物的识别效果受影像时间合成窗口的影响程度不同，依次为其他作物>冬油菜>水稻>冬小麦>稻虾田。其他作物类内时序光谱特征变异性较大，因此对时间窗口极其敏感。油菜准确识别的关键物候期为开花期，该时期长度较短，影像合成时间超过30 d会极大降低其识别效果，主要体现为与小麦的混淆。区分稻虾田与单季稻的关键物候期为稻虾田的稻闲季淹水期，其持续时间较长，受时间合成窗口影响较小。【结论】影像时间合成窗口20 d时可兼顾高质量影像覆盖度和捕获农作物识别的关键物候特征，但不同作物识别的最优时间合成窗口受到作物关键物候期影响。研究结果可为多源中高分辨率影像协同下时间合成窗口的选择提供理论参考和方法支撑，进而有效服务于宏观尺度农作物高精度遥感制图研究。

关键词: 农作物遥感识别, 时间合成窗口, 随机森林, Sentinel-2, Landsat-8

Abstract:

【Background】 The decametric-resolution (≤30 m) image is an important data source to identify crop types in South China dominated by fragmented croplands and complex cropping patterns. Due to the relative long revisit frequency of decametric-resolution sensors and persistent rainy/cloud weather in South China, it is critical to integrate multi-source decametric-resolution images using the temporal composition method for the generation of spatiotemporal continuous crop type map. Due to the different temporal resolutions of different satellites, and the significant differences in phenological quaternal rhythms of various crop types, selecting the optimal temporal composition window for integrating multi-source images is vital to map crop type distribution accurately.【Objective】 This study aims to explore the impact of image temporal composition windows on crop type identification, and to provide significant references for large-scale crop type mapping in regions with complex terrain.【Method】 In this study, Landat-8 and Sentinel-2 data were integrated to extract the crop type distribution in the Jianghan Plain, Hubei Province, characterized by the various crop types and cloudy and rainy weather. Then, seven scenarios (15, 20, 25, 30, 40, 50, and 60 d) were established to analyze the effect of different temporal composition windows on crop type identification. Specifically, three aspects, including image coverage rate, spectral-temporal feature curves for different crops and classification accuracies, were combined to understand the performances of different scenarios comprehensively.【Result】 The crop type mapping using 20-day composition window performed the best, with the overall accuracy (OA) of 93.13%. In contrast, the scenarios that used narrower temporal composition window derived lower accuracy of crop type identification (e.g., OA=90.91% for the 15-day composition window), which can be primarily attributed to the low coverage rate of good observations in the study area. Meanwhile, since time series images composited in the wide window blurred the key phenological information for different crops, the classification accuracy of crop type mapping scenarios using wide temporal interval was also lower (e.g., OA=86.06% for the 60-day composition window). Additionally, the effect of temporal interval on different crops classification was ranked as following: other crops>rapeseed>rice>wheat>rice-crayfish. In detail, the reason why the classification performance of other crops was the most sensitive to the temporal composition window can be due to the high intra-class phenological variance of this type. Flowering period is the key phenology window to identify rapeseed, therefore, the classification accuracy of rapeseed decreased while the temporal composition window exceeds 30-day, and rapeseed was easily confused with wheat. Furthermore, because the key phenology window to distinguish rice-crayfish from single-cropping rice (i.e., the flooding stage of rice-crayfish fields) lasted a long period (e.g., from October 2020 to June 2021), the classification accuracy of rice-crayfish was less sensitive to the temporal composition window.【Conclusion】 In general, the 20-day impact of the temporal composition window can take into account the high-quality image coverage and capture of key phenological characteristics of crop identification, but the optimal temporal composition window of different crops identification is affected by the key phenological period of crops. This study provides theoretical reference and method support for selecting the optimal temporal composition window to generate multi-source image time series, which is promising to improve the efficiency and accuracy of large-scale crop type mapping.

Key words: crop type mapping, temporal composition window, random forest, Sentinel-2, Landsat-8

童婉婷, 魏浩东, 杨靖雅, 金文捷, 宋茜, 胡琼, 尹高飞, 徐保东. 多源中高分辨率影像协同下时间合成窗口对农作物识别的影响[J]. 中国农业科学, 2024, 57(2): 250-263.

TONG WanTing, WEI HaoDong, YANG JingYa, JIN WenJie, SONG Qian, HU Qiong, YIN GaoFei, XU BaoDong. Exploring the Impacts of Temporal Composition Window for Integrating Multi-Source Decametric-Resolution Images on Crop Type Identification[J]. Scientia Agricultura Sinica, 2024, 57(2): 250-263.

0
/ / 推荐

导出引用管理器 EndNote|Reference Manager|ProCite|BibTeX|RefWorks

链接本文: https://www.chinaagrisci.com/CN/10.3864/j.issn.0578-1752.2024.02.003

https://www.chinaagrisci.com/CN/Y2024/V57/I2/250

图/表 8

图1

图2

表1

图3

图4

图5

图6

图7

参考文献 56

[1]	胡琼, 吴文斌, 宋茜, 余强毅, 杨鹏, 唐华俊. 农作物种植结构遥感提取研究进展. 中国农业科学, 2015, 48(10): 1900-1914. doi: 10.3864/j.issn.0578-1752.2015.10.004.
	HU Q, WU W B, SONG Q, YU Q Y, YANG P, TANG H J. Recent progresses in research of crop patterns mapping by using remote sensing. Scientia Agricultura Sinica, 2015, 48(10): 1900-1914. doi: 10.3864/j.issn.0578-1752.2015.10.004. (in Chinese)
[2]	LUO C, QI B S, LIU H J, GUO D, LU L P, FU Q, SHAO Y Q. Using time series Sentinel-1 images for object-oriented crop classification in Google earth engine. Remote Sensing, 2021, 13(4): 561. doi: 10.3390/rs13040561
[3]	ZHANG W T, WANG M, GUO J, LOU S T. Crop classification using MSCDN classifier and sparse auto-encoders with non-negativity constraints for multi-temporal, Quad-Pol SAR data. Remote Sensing, 2021, 13(14): 2749. doi: 10.3390/rs13142749
[4]	GELLA G W, BIJKER W, BELGIU M. Mapping crop types in complex farming areas using SAR imagery with dynamic time warping. ISPRS Journal of Photogrammetry and Remote Sensing, 2021, 175: 171-183. doi: 10.1016/j.isprsjprs.2021.03.004
[5]	KANG Y, HU X, MENG Q, ZOU Y F, ZHANG L L, LIU M, ZHAO M F. Land cover and crop classification based on red edge indices features of GF-6 WFV time series data. Remote Sensing, 2021, 13(22): 4522. doi: 10.3390/rs13224522
[6]	WANG N, ZHAI Y, ZHANG L. Automatic cotton mapping using time series of Sentinel-2 images. Remote Sensing, 2021, 13(7): 1355. doi: 10.3390/rs13071355
[7]	GUO Y, XIA H M, ZHAO X Y, QIAO L X, QIN Y C. Estimate the earliest phenophase for garlic mapping using time series Landsat 8/9 images. Remote Sensing, 2022, 14(18): 4476. doi: 10.3390/rs14184476
[8]	CLAUSS K, YAN H, KUENZER C. Mapping paddy rice in China in 2002, 2005, 2010 and 2014 with MODIS time series. Remote Sensing, 2016, 8(5): 434. doi: 10.3390/rs8050434
[9]	张馨予, 蔡志文, 杨靖雅, 王聪, 魏浩东, 胡琼, 徐保东. 时序滤波对农作物遥感识别的影响. 农业工程学报, 2022, 38(4): 215-224.
	ZHANG X Y, CAI Z W, YANG J Y, WANG C, WEI H D, HU Q, XU B D. Impacts of temporal smoothing methods on crop type identification. Transactions of the Chinese Society of Agricultural Engineering, 2022, 38(4): 215-224. (in Chinese)
[10]	何真, 胡洁, 蔡志文, 王文静, 胡琼. 协同多时相国产GF-1和GF-6卫星影像的艾草遥感识别. 农业工程学报, 2022, 38(1): 186-195.
	HE Z, HU J, CAI Z W, WANG W J, HU Q. Remote sensing identification for Artemisia argyi integrating multi-temporal GF-1 and GF-6 images. Transactions of the Chinese Society of Agricultural Engineering, 2022, 38(1): 186-195. (in Chinese)
[11]	杨靖雅, 胡琼, 魏浩东, 蔡志文, 张馨予, 宋茜, 徐保东. 基于Sentinel-1/2数据的中国南方单双季稻识别结果一致性分析. 中国农业科学, 2022, 55(16): 3093-3109. doi: 10.3864/j.issn.0578-1752.2022.16.003.
	YANG J Y, HU Q, WEI H D, CAI Z W, ZHANG X Y, SONG Q, XU B D. Consistency analysis of classification results for single and double cropping rice in Southern China based on Sentinel-1/2imagery. Scientia Agricultura Sinica, 2022, 55(16): 3093-3109. doi: 10.3864/j.issn.0578-1752.2022.16.003. (in Chinese)
[12]	QU C, LI P, ZHANG C. A spectral index for winter wheat mapping using multi-temporal Landsat NDVI data of key growth stages. ISPRS Journal of Photogrammetry and Remote Sensing, 2021, 175: 431-447. doi: 10.1016/j.isprsjprs.2021.03.015
[13]	XIAO W, XU S, HE T. Mapping paddy rice with Sentinel-1/2 and phenology-, object-based algorithm—A implementation in Hangjiahu Plain in China using GEE platform. Remote Sensing, 2021, 13(5): 990. doi: 10.3390/rs13050990
[14]	THENKABAIL P S, WU Z. An automated cropland classification algorithm (ACCA) for Tajikistan by combining Landsat, MODIS, and secondary data. Remote Sensing, 2012, 4(10): 2890-2918. doi: 10.3390/rs4102890
[15]	WANG J, XIAO X M, LIU L, WU X C, QIN Y W, STEINER J L, DONG J W. Mapping sugarcane plantation dynamics in Guangxi, China, by time series Sentinel-1, Sentinel-2 and Landsat images. Remote Sensing of Environment, 2020, 247: 111951. doi: 10.1016/j.rse.2020.111951
[16]	LI Q, WANG C, ZHANG B, LU L. Object-based crop classification with Landsat-MODIS enhanced time-series data. Remote Sensing, 2015, 7(12): 16091-16107. doi: 10.3390/rs71215820
[17]	ZHANG H, LIU W, ZHANG L. Seamless and automated rapeseed mapping for large cloudy regions using time-series optical satellite imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 2022, 184: 45-62. doi: 10.1016/j.isprsjprs.2021.12.001
[18]	宋茜, 周清波, 吴文斌, 胡琼, 余强毅, 唐华俊. 农作物遥感识别中的多源数据融合研究进展. 中国农业科学, 2015, 48(6): 1122-1135. doi: 10.3864/j.issn.0578-1752.2015.06.09.
	SONG Q, ZHOU Q B, WU W B, HU Q, YU Q Y, TANG H J. Recent progresses in research of integrating multi-source remote sensing data for crop mapping. Scientia Agricultura Sinica, 2015, 48(6): 1122-1135. doi: 10.3864/j.issn.0578-1752.2015.06.09. (in Chinese)
[19]	LIU L, XIAO X, QIN Y, WANG J, XU X, HU Y, QIAO Z. Mapping cropping intensity in China using time series Landsat and Sentinel-2 images and Google earth engine. Remote Sensing of Environment, 2020, 239: 111624. doi: 10.1016/j.rse.2019.111624
[20]	PAN L, XIA H, ZHAO X, GUO Y, QIN Y. Mapping winter crops using a phenology algorithm, time-series Sentinel-2 and Landsat-7/8 images, and Google earth engine. Remote Sensing, 2021, 13(13): 2510. doi: 10.3390/rs13132510
[21]	LI Q, LIU G, CHEN W. Toward a simple and generic approach for identifying multi-year cotton cropping patterns using Landsat and Sentinel-2 time series. Remote Sensing, 2021, 13(24): 5183. doi: 10.3390/rs13245183
[22]	魏浩东, 杨靖雅, 蔡志文, 陈云坪, 张馨予, 徐保东, 胡琼. 物候窗口和多源中高分辨率影像的稻虾田提取. 遥感学报, 2022, 26(7): 1423-1436.
	WEI H D, YANG J Y, CAI Z W, CHEN Y P, ZHANG X Y, XU B D, HU Q. Phenology windows and multi-source medium-/high-resolution image extraction for rice-crayfish paddy fields mapping. National Remote Sensing Bulletin, 2022, 26(7): 1423-1436. (in Chinese) doi: 10.11834/jrs.20211070
[23]	TIAN H F, HUANG N, NIU Z, QIN Y C, PEI J, WANG J. Mapping winter crops in China with multi-source satellite imagery and phenology-based algorithm. Remote Sensing, 2019, 11(7): 820. doi: 10.3390/rs11070820
[24]	YOU N, DONG J. Examining earliest identifiable timing of crops using all available Sentinel 1/2 imagery and Google earth engine. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 161: 109-123. doi: 10.1016/j.isprsjprs.2020.01.001
[25]	HUANG X D, HUANG J X, LI X C, SHEN Q R, CHEN Z C. Early mapping of winter wheat in Henan Province of China using time series of Sentinel-2 data. GIScience & Remote Sensing, 2022, 59(1): 1534-1549.
[26]	XU F, LI Z F, ZHANG S Y, HUANG N T, QUAN Z Y, ZHANG W M, LIU X J, JIANG X S, PAN J J, PRISHCHEPOV A V. Mapping winter wheat with combinations of temporally aggregated Sentinel-2 and Landsat-8 data in Shandong Province, China. Remote Sensing, 2020, 12(12): 2065. doi: 10.3390/rs12122065
[27]	周惠慧, 王楠, 黄瑶, 王晋年, 张立福. 不同时间间隔下的遥感时间序列重构模型比较分析. 地球信息科学学报, 2016, 18(10): 1410-1417. doi: 10.3724/SP.J.1047.2016.01410
	ZHOU H H, WANG N, HUANG Y, WANG J N, ZHANG L F. Comparison and analysis of remotely sensed time series of reconstruction models at various intervals. Journal of Geo-Information Science, 2016, 18(10): 1410-1417. (in Chinese)
[28]	张紫荆, 华丽, 郑萱, 李嘉麟. 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, 2022, 38(1): 196-202.
	ZHANG Z J, HUA L, ZHENG X, LI J L. Extraction of cropping patterns in Jianghan Plain based on GEE and Sentinel-NDVI time series data. Transactions of the Chinese Society of Agricultural Engineering, 2022, 38(1): 196-202. (in Chinese)
[29]	FILIPPUCCI P, BROCCA L, BONAFONI S, SALTALIPPI C, WAGNER W, TARPANELLI A. Sentinel-2 high-resolution data for river discharge monitoring. Remote Sensing of Environment, 2022, 281: 113255. doi: 10.1016/j.rse.2022.113255
[30]	DONG J W, XIAO X M, MENARGUEZ M A, ZHANG G L, QIN Y W, THAU D, BIRADAR C, MOORE B. Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google earth engine. Remote Sensing of Environment, 2016, 185: 142-154. doi: 10.1016/j.rse.2016.02.016 pmid: 28025586
[31]	SCARAMUZZA P L, BOUCHARD M A, DWYER J L. Development of the Landsat data continuity mission cloud-cover assessment algorithms. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(4): 1140-1154. doi: 10.1109/TGRS.2011.2164087
[32]	ROY D P, KOVALSKYY V, ZHANG H K, VERMOTE E F, YAN L, KUMAR S S, EGOROV A. Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. Remote Sensing of Environment, 2016, 185(1): 57-70. doi: 10.1016/j.rse.2015.12.024
[33]	ZHANG H K, ROY D P, YAN L, LI Z B, HUANG H Y, VERMOTE E, SKAKUN S, ROGER J C. Characterization of Sentinel-2A and Landsat-8 top of atmosphere, surface, and nadir BRDF adjusted reflectance and NDVI differences. Remote Sensing of Environment, 2018, 215: 482-494. doi: 10.1016/j.rse.2018.04.031
[34]	CHEN J, CHEN J, LIAO A P, CAO X, CHEN L J, CHEN X H, HE C Y, HAN G, PENG S, LU M, ZHANG W W, TONG X H, MILLS J. Global land cover mapping at 30 m resolution: A POK-based operational approach. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 103: 7-27. doi: 10.1016/j.isprsjprs.2014.09.002
[35]	XU L, ZHANG H, WANG C, WEI S, ZHANG B, WU F, TANG Y. Paddy rice mapping in Thailand using time-series Sentinel-1 data and deep learning model. Remote Sensing, 2021, 13(19): 3994. doi: 10.3390/rs13193994
[36]	HU J, CHEN Y, CAI Z, WEI H, ZHANG X, ZHOU W, WANG C, YOU L, XU B. Mapping diverse paddy rice cropping patterns in South China using harmonized Landsat and Sentinel-2 data. Remote Sensing, 2023, 15(4): 1034. doi: 10.3390/rs15041034
[37]	LU M, WU W, ZHANG L, LIAO A, PENG S, TANG H. A comparative analysis of five global cropland datasets in China. Science China Earth Sciences, 2016, 59: 2307-2317. doi: 10.1007/s11430-016-5327-3
[38]	CHEN J, CAO X, PENG S, REN H. Analysis and applications of GlobeLand30: A review. ISPRS International Journal of Geo- Information, 2017, 6: 230. doi: 10.3390/ijgi6080230
[39]	CAO B, YU L, NAIPAL V, CIAIS P, LI W, ZHAO Y Y, WEI W, CHEN D, LIU Z, GONG P. A 30 m terrace mapping in China using Landsat 8 imagery and digital elevation model based on the Google earth engine. Earth System Science Data, 2021, 13(5): 2437-2456. doi: 10.5194/essd-13-2437-2021
[40]	JIANG Z, HUETE A R, CHEN J, CHEN Y, LI J, YAN G, ZHANG X. Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction. Remote Sensing of Environment, 2006, 101: 366-378. doi: 10.1016/j.rse.2006.01.003
[41]	ARVOR D, JONATHAN M, MEIRELLES M S P, DUBREUIL V, DURIEUX L. Classification of MODIS EVI time series for crop mapping in the state of Mato Grosso, Brazil. International Journal of Remote Sensing, 2011, 32: 7847-7871. doi: 10.1080/01431161.2010.531783
[42]	EASTMAN J, SANGERMANO F, MACHADO E, ROGAN J, ANYAMBA A. Global trends in seasonality of normalized difference vegetation index (NDVI), 1982-2011. Remote Sensing, 2013, 5: 4799-4818. doi: 10.3390/rs5104799
[43]	WEI H, HU Q, CAI Z, YANG J, SONG Q, YIN G, XU B D. An object- and topology-based analysis (OTBA) method for mapping rice-crayfish fields in South China. Remote Sensing, 2021, 13(22): 4666. doi: 10.3390/rs13224666
[44]	KUNKEL V R, WELLS T, HANCOCK G R. Modelling soil organic carbon using vegetation indices across large catchments in eastern Australia. Science of the Total Environment, 2022, 817: 152690. doi: 10.1016/j.scitotenv.2021.152690
[45]	HUETE A, DIDAN K, MIURA T, RODRIGUEZ E P, GAO X, FERREIRA L G. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 2002, 83: 195-213. doi: 10.1016/S0034-4257(02)00096-2
[46]	PEÑA-BARRAGÁN J, NGUGI M, PLANT R, SIX J. Object-based crop identification using multiple vegetation indices, textural features and crop phenology. Remote Sensing of Environment, 2011, 115(6): 1301-1316. doi: 10.1016/j.rse.2011.01.009
[47]	GAO B C. NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 1996, 58: 257-266. doi: 10.1016/S0034-4257(96)00067-3
[48]	霍轩琳, 牛振国, 张波, 刘林崧, 李霞. 高寒湿地分类的遥感特征优选研究. 遥感学报, 2023, 27(4): 1045-1060.
	HUO X L, NIU Z G, ZHANG B, LIU L S, LI X. Remote sensing feature selection for alpine wetland classification. National Remote Sensing Bulletin, 2023, 27(4): 1045-1060. (in Chinese) doi: 10.11834/jrs.20222080
[49]	XIAO X, BOLES S, FROLKING S, LI C, BABU J Y, SALAS W, MOORE B. Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images. Remote Sensing of Environment, 2006, 100: 95-113. doi: 10.1016/j.rse.2005.10.004
[50]	HE Y L, DONG J W, LIAO X Y, SUN L, WANG Z P, YOU N S, LI Z C, FU P. 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, 2021, 101: 102351. doi: 10.1016/j.jag.2021.102351
[51]	ZHANG X, WU B F, PONCE-CAMPOS G, ZHANG M, CHANG S, TIAN F Y. 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, 2018, 10(8): 1200. doi: 10.3390/rs10081200
[52]	YOU N S, DONG J W, HUANG J X, DU G M, ZHANG G L, HE Y L, YANG T, DI Y Y, XIAO X M. The 10-m crop type maps in Northeast China during 2017-2019. Scientific Data, 2021, 8(1): 41. doi: 10.1038/s41597-021-00827-9 pmid: 33531510
[53]	LUCIANO A C, PICOLI M C A, ROCHA J V, DUFT D G, LAMPARELLI R A C, LEAL M R L V, LE MAIRE G. A generalized space-time OBIA classification scheme to map sugarcane areas at regional scale, using Landsat images time-series and the random forest algorithm. International Journal of Applied Earth Observation and Geoinformation, 2019, 80: 127-136. doi: 10.1016/j.jag.2019.04.013
[54]	CAI Z, HU Q, ZHANG X, YANG J, WEI H, HE Z, SONG Q, WANG C, YIN G, XU B. An adaptive image segmentation method with automatic selection of optimal scale for extracting cropland parcels in smallholder farming systems. Remote Sensing, 2022, 14(13): 3067. doi: 10.3390/rs14133067
[55]	RODRIGUEZ-GALIANO V F, GHIMIRE B, ROGAN J, CHICA- OLMO M, RIGOL-SANCHEZ J P. An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS Journal of Photogrammetry and Remote Sensing, 2012, 67: 93-104. doi: 10.1016/j.isprsjprs.2011.11.002
[56]	LIU W B, ZHANG H Y. Mapping annual 10 m rapeseed extent using multisource data in the Yangtze River Economic Belt of China (2017-2021) on Google earth engine. International Journal of Applied Earth Observation and Geoinformation, 2023, 117: 103198. doi: 10.1016/j.jag.2023.103198

植被指数 Vegetation index	公式 Equation	参考文献 Reference
归一化植被指数 NDVI	$N D V I=\frac{\rho_{N I R}-\rho_{R E D}}{\rho_{N I R}+\rho_{R E D}}$	[40]
陆表水分指数 LSWI	$L S W I=\frac{\rho_{\text {NIR }}-\rho_{\text {SWIR } 1}}{\rho_{\text {NIR }}+\rho_{\text {SWIR } 1}}$	[47]
增强型植被指数 EVI	$E V I=\frac{2.5 \times\left(\rho_{\text {SWIR1 }}-\rho_{\text {RED }}\right)}{\rho_{\text {NIR }}+6 \times \rho_{\text {RED }}-7.5 \times \rho_{\text {BLUE }}+1}$	[45]
绿度指数 VIgreen	$\text { VIgreen }=\frac{\rho_{\text {GREEN }}-\rho_{\text {RED }}}{\rho_{\text {GREEN }}+\rho_{\text {RED }}}$	[46]

多源中高分辨率影像协同下时间合成窗口对农作物识别的影响

Exploring the Impacts of Temporal Composition Window for Integrating Multi-Source Decametric-Resolution Images on Crop Type Identification

RichHTML

PDF

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 8

参考文献 56

相关文章 14

Metrics

本文评价

推荐阅读 0

[1]	王佳玥, 蔡志文, 王文静, 魏浩东, 王聪, 李泽萱, 李秀妮, 胡琼. 协同多源国产高分影像和面向对象方法的南方农作物遥感识别[J]. 中国农业科学, 2023, 56(13): 2474-2490.
[2]	王淑婷, 孔雨光, 张赞, 陈红艳, 刘鹏. 基于星-机光谱融合的棉花叶片SPAD值反演[J]. 中国农业科学, 2022, 55(24): 4823-4839.
[3]	吴俊,郭大千,李果,郭熙,钟亮,朱青,国佳欣,叶英聪. 基于CARS-BPNN的江西省土壤有机碳含量高光谱预测[J]. 中国农业科学, 2022, 55(19): 3738-3750.
[4]	郭灿,岳晓凤,白艺珍,张良晓,张奇,李培武. 花生黄曲霉毒素平衡取样-随机森林风险预警模型的应用研究[J]. 中国农业科学, 2022, 55(17): 3426-3436.
[5]	申哲,张认连,龙怀玉,徐爱国. 基于机器学习方法的宁夏南部土壤质地空间分布研究[J]. 中国农业科学, 2022, 55(15): 2961-2972.
[6]	周珂, 柳乐, 张俨娜, 苗茹, 杨阳. GEE支持下的河南省冬小麦面积提取及长势监测[J]. 中国农业科学, 2021, 54(11): 2302-2318.
[7]	张振华,丁建丽,王敬哲,葛翔宇,王瑾杰,田美玲,赵启东. 集成土壤-环境关系与机器学习的干旱区土壤属性数字制图[J]. 中国农业科学, 2020, 53(3): 563-573.
[8]	奚雪,赵庚星,高鹏,崔昆,李涛. 基于Sentinel卫星及无人机多光谱的滨海冬小麦种植区土壤盐分反演研究——以黄三角垦利区为例[J]. 中国农业科学, 2020, 53(24): 5005-5016.
[9]	夏树凤,王凡,王龙俊,周琴,蔡剑,王笑,黄梅,戴廷波,姜东. 江苏省小麦籽粒蛋白质达标弱筋小麦的适生性分析与评价[J]. 中国农业科学, 2020, 53(24): 4992-5004.
[10]	申哲,张认连,龙怀玉,王转,朱国龙,石乾雄,喻科凡,徐爱国. 基于3种空间预测方法的黄土区土壤颗粒组成空间分布研究—以宁夏海原县为例[J]. 中国农业科学, 2020, 53(18): 3716-3728.
[11]	张春兰，杨贵军，李贺丽，汤伏全，刘畅，张丽妍. 基于随机森林算法的冬小麦叶面积指数遥感反演研究[J]. 中国农业科学, 2018, 51(5): 855-867.
[12]	王飞,杨胜天,魏阳,杨晓东,丁建丽. 基于RF和SGT算法的子区优先建模对绿洲尺度土壤盐度预测精度的影响[J]. 中国农业科学, 2018, 51(24): 4659-4676.
[13]	贺英，邓磊，毛智慧，孙杰. 基于数码相机的玉米冠层SPAD遥感估算[J]. 中国农业科学, 2018, 51(15): 2886-2897.
[14]	刘亚秋，陈红艳，王瑞燕，常春艳，陈哲. 基于可见/近红外光谱的黄河口区土壤盐分及其主要离子的定量分析[J]. 中国农业科学, 2016, 49(10): 1925-1935.