物候窗口和多源中高分辨率影像的稻虾田提取

魏浩东; 杨靖雅; 蔡志文; 陈云坪; 张馨予; 徐保东; 胡琼

doi:10.11834/jrs.20211070

农业遥感 | 浏览量 : 0 下载量: 275 CSCD: 3 更多指标

R-PDF
PDF
导出
分享
收藏
专辑

物候窗口和多源中高分辨率影像的稻虾田提取
Phenology windows and multi-source medium-/high-resolution image extraction for rice-crayfish paddy fields mapping
2022年26卷第7期页码：1423-1436
纸质出版日期： 2022-07-07 ，
DOI： 10.11834/jrs.20211070

扫描看全文

魏浩东，杨靖雅，蔡志文，陈云坪，张馨予，徐保东，胡琼.2022.物候窗口和多源中高分辨率影像的稻虾田提取.遥感学报，26（7）： 1423-1436

Wei H D，Yang J Y，Cai Z W，Chen Y P，Zhang X Y，Xu B D and Hu Q. 2022. Phenology windows and multi-source medium-/high-resolution image extraction for rice-crayfish paddy fields mapping. National Remote Sensing Bulletin， 26（7）：1423-1436
魏浩东，杨靖雅，蔡志文，陈云坪，张馨予，徐保东，胡琼.2022.物候窗口和多源中高分辨率影像的稻虾田提取.遥感学报，26（7）： 1423-1436 DOI： 10.11834/jrs.20211070.

Wei H D，Yang J Y，Cai Z W，Chen Y P，Zhang X Y，Xu B D and Hu Q. 2022. Phenology windows and multi-source medium-/high-resolution image extraction for rice-crayfish paddy fields mapping. National Remote Sensing Bulletin， 26（7）：1423-1436 DOI： 10.11834/jrs.20211070.

摘要

由于显著的经济效益和生态效益，近年来稻虾共作模式分布面积迅猛扩张。准确获取稻虾田空间分布信息，对于水稻种植结构调整、产量估算和水资源管理具有重要意义。本文以“小龙虾之乡”——湖北省潜江市为研究区域，基于Google Earth Engine平台协同Landsat 7/8和Sentinel-2卫星数据，通过分析稻虾田的农业耕作管理和季相节律特征，提取了稻虾田区别其他农作物的关键“水淹”信号和“植被”信号。基于实地稻虾田样本统计分析关键特征的阈值，构建稻虾田规则集识别模型，提取了湖北省潜江市2019年稻虾田空间分布。最后，基于实地样本验证该物候窗口特征方法的精度，并评估和比较了该方法与随机森林和基于水体季相差异方法的表现。结果表明：物候窗口1月1日—4月30日内的水淹信号（LSWI>NDVI或EVI）、物候窗口7月15日—9月30日内的植被信号（NDVI或EVI>LSWI）和物候窗口11月10日—12月31日内的水淹信号是稻虾田遥感识别的典型特征。基于该方法提取的2019年潜江市稻虾田制图精度和用户精度分别为90.74%、94.69%，显著高于水体季相差异方法和随机森林方法的精度。基于关键物候窗口的稻虾田提取方法具有较高的泛化能力，能以较少的实地样本进行时空尺度的延展，从而为大尺度长时序稻虾田遥感制图提供重要的方法支撑。

Abstract

Rice-crayfish co-culture is a kind of comprehensive ecological agriculture pattern. Rice-crayfish co-culture has expanded rapidly in China in the past decade due to its outstanding ecological and economic benefits. The accurate spatial distribution information of this newly emerging agricultural pattern is crucial for growth monitoring

yield estimation

and water resource management. However

most studies have focused on field-level research on the farmland ecosystem

and rice-crayfish mapping at regional or larger scales has received less attention.

In this study

Qianjiang City in Hubei Province

known as the “hometown of crayfish” was selected as the test area. The cloud computing approach was used for all available Landsat 7/8 and Sentinel-2 imagery in 2019 with the Google Earth Engine (GEE) platform. By analyzing farming characteristics and the spectral curves of rice-crayfish fields

we identified the crucial phenology windows and classification features (i.e.

flooding and vegetation signals) for rice-crayfish mapping. On the basis of the key phenological characteristics and associated frequency thresholds derived from field samples

we developed a rule-based algorithm for rice-crayfish mapping and generated the rice-crayfish map of Qianjiang City in 2019. To further evaluate the potential of our proposed method

we compared it with the random forest method and a method based on seasonal differences of water bodies.

The spectral analysis of time series images showed that the unique phenological characteristics of rice-crayfish co-culture were flooding signal (LSWI

NDVI or LSWI

EVI) in phenology window 1 (from January 1 to April 30)

vegetation signal (NDVI

LSWI or EVI

LSWI) in phenology window 2 (from July 15 to September 30)

and flooding signal in phenology window 3 (from November 10 to December 31). With the mapping results

the total area for rice-crayfish planting in Qianjiang City in 2019 was estimated to 575.58 km

and rice-crayfish plots were mainly distributed in the southwest. The producer’s accuracy of the classification result was 90.74%

the user’s accuracy was 94.69%

and the overall accuracy was 95.23%. The method based on phenology window features had fewer commission errors compared with the random forest method and fewer omission errors compared with the method based on water body seasonal differences. Among the three methods

the proposed method presented the highest overall classification accuracy.

The rice-crayfish mapping method based on phenology windows

flooding signal

and vegetation signal showed high separability. The method based on phenology windows can be easily generalized to other regions and other images because of its strong physical interpretation for rice-crayfish. On the one hand

this method has relatively low dependence on training samples. On the other hand

as long as the key phenology window can be obtained

other medium-high resolution images

such as GF-1 and GF-6

can achieve high-accuracy mapping results for rice-crayfish. Therefore

the method based on phenology windows can be effectively extended to large areas and long time series. It can provide essential information for rice production management and decision-making in the crayfish industry.

关键词

遥感稻虾田作物提取Google Earth Engine物候窗口LandsatSentinel-2

Keywords

remote sensingrice-crayfishcrop extractionGoogle Earth Enginephenology windowLandsatSentinel-2

references

Cao C G, Jiang Y, Wang J P, Yuan P L and Chen S W. 2017. “Dual character” of rice-crayfish culture and strategies for its sustainable development. Chinese Journal of Eco-Agriculture, 25(9): 1245-1253

曹凑贵, 江洋, 汪金平, 袁鹏丽, 陈松文. 2017. 稻虾共作模式的“双刃性”及可持续发展策略. 中国生态农业学报, 25(9): 1245-1253 [DOI: 10.13930/j.cnki.cjea.170739http://dx.doi.org/10.13930/j.cnki.cjea.170739]

Chen J, Ban Y F and Li S N. 2014. Open access to Earth land-cover map. Nature, 514(7523): 434 [DOI: 10.1038/514434chttp://dx.doi.org/10.1038/514434c]

Chen S W, Jiang Y, Wang J P and Cao C G. 2020. Situation and countermeasures of integrated rice-crayfish farming in Hubei Province. Journal of Huazhong Agricultural University, 39(2): 1-7

陈松文, 江洋, 汪金平, 曹凑贵. 2020. 湖北省稻虾模式发展现状与对策分析. 华中农业大学学报, 39(2): 1-7 [DOI: 10.13300/j.cnki.hnlkxb.2020.02.001http://dx.doi.org/10.13300/j.cnki.hnlkxb.2020.02.001]

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): 1022 [DOI: 10.3390/rs12061022http://dx.doi.org/10.3390/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 III 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 L, Qin Y W, Thau D, Biradar C and Moore III B. 2016. Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine. Remote Sensing of Environment, 185: 142-154 [DOI: 10.1016/j.rse.2016.02.016http://dx.doi.org/10.1016/j.rse.2016.02.016]

Fisheries and Fisheries Administration of the Ministry of Agriculture and Rural Affairs, National Aquatic Technology Extension Station and China Fisheries Society. 2018. China crayfish industry development report (2018). China Fisheries, (7): 20-27

农业农村部渔业渔政管理局, 全国水产技术推广总站, 中国水产学会. 2018. 中国小龙虾产业发展报告 (2018). 中国水产, (7): 20-27

Fisheries and Fisheries Administration of the Ministry of Agriculture and Rural Affairs, National Aquatic Technology Extension Station and China Fisheries Society. 2020. China crayfish industry development report in 2020. China Fisheries, (7): 8-17

农业农村部渔业渔政管理局, 全国水产技术推广总站, 中国水产学会. 2020. 2020中国小龙虾产业发展报告. 中国水产, (7): 8-17

Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D and Moore R. 2017. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202: 18-27 [DOI: 10.1016/j.rse.2017.06.031http://dx.doi.org/10.1016/j.rse.2017.06.031]

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: 10.3390/rs10081184http://dx.doi.org/10.3390/rs10081184]

Hu Q, Wu W B, Song Q, Yu Q Y, Lu M, Yang P, Tang H J and Long Y Q. 2016. Extending the pairwise separability index for multicrop identification using time-series modis images. IEEE Transactions on Geoscience and Remote Sensing, 54(11): 6349-6361 [DOI: 10.1109/TGRS.2016.2581210http://dx.doi.org/10.1109/TGRS.2016.2581210]

Hu Q, Wu W B, Song Q, Yu Q Y, Yang P and Tang H J. 2015. Recent progresses in research of crop patterns mapping by using remote sensing. Scientia Agricultura Sinica, 48(10): 1900-1914

胡琼, 吴文斌, 宋茜, 余强毅, 杨鹏, 唐华俊. 2015. 农作物种植结构遥感提取研究进展. 中国农业科学, 48(10): 1900-1914 [DOI: 10.3864/j.issn.0578-1752.2015.10.004http://dx.doi.org/10.3864/j.issn.0578-1752.2015.10.004]

Liu C H, Hu N J, Song W X, Chen Q and Zhu L Q. 2019. Aquaculture feeds Can be outlaws for eutrophication when hidden in rice fields? A case study in Qianjiang, China. International Journal of Environmental Research and Public Health, 16(22): 4471 [DOI: 10.3390/ijerph16224471http://dx.doi.org/10.3390/ijerph16224471]

Liu M W, Ozdogan M and Zhu X J. 2014. Crop type classification by simultaneous use of satellite images of different resolutions. IEEE Transactions on Geoscience and Remote Sensing, 52(6): 3637-3649 [DOI: 10.1109/TGRS.2013.2274431http://dx.doi.org/10.1109/TGRS.2013.2274431]

Liu M X, Liu X N, Wu L, Zou X Y, Jiang T and Zhao B Y. 2018. A modified spatiotemporal fusion algorithm using phenological information for predicting reflectance of paddy rice in Southern China. Remote Sensing, 10(5): 772 [DOI: 10.3390/rs10050772http://dx.doi.org/10.3390/rs10050772]

Löw F, Michel U, Dech S and Conrad C. 2013. Impact of feature selection on the accuracy and spatial uncertainty of per-field crop classification using support vector machines. Isprs Journal of Photogrammetry and Remote Sensing, 85: 102-119 [DOI: 10.1016/j.isprsjprs.2013.08.007http://dx.doi.org/10.1016/j.isprsjprs.2013.08.007]

Mansaray L R, Wang F M, Huang J F, Yang L B and Kanu A S. 2020. Accuracies of support vector machine and random forest in rice mapping with Sentinel-1A, Landsat-8 and Sentinel-2A datasets. Geocarto International, 35(10): 1088-1108 [DOI: 10.1080/10106049. 2019.1568586http://dx.doi.org/10.1080/10106049.2019.1568586]

Qin Z W. 2016. Propel the innovation of system and mechanism with the “Crayfish-Rice Cooperation” model-observation and reflection of comprehensive reform in Qianjiang as a nationwide small-medium city. China Development, 16(6): 51-56

秦尊文. 2016. 以“虾稻共作”模式为抓手推进体制机制创新——潜江市全国中小城市综合改革的观察与思考. 中国发展, 16(6): 51-56 [DOI: 10.15885/j.cnki.cn11-4683/z.2016.06.010http://dx.doi.org/10.15885/j.cnki.cn11-4683/z.2016.06.010]

Roy D P, Kovalskyy V, Zhang H K, Vermote E F, Yan L, Kumar S S and Egorov A. 2016. Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. Remote Sensing of Environment, 185: 57-70 [DOI: 10.1016/j.rse.2015.12.024http://dx.doi.org/10.1016/j.rse.2015.12.024]

Roy D P, Wulder M A, Loveland T R, Woodcock C E, Allen R G, Anderson M C, Helder D, Irons J R, Johnson D M, Kennedy R, Scambos T A, Schaaf C B, Schott J R, Sheng Y, Vermote E F, Belward A S, Bindschadler R, Cohen W B, Gao F, Hipple J D, Hostert P, Huntington J, Justice C O, Kilic A, Kovalskyy V, Lee Z P, Lymburner L, Masek J G, McCorkel J, Shuai Y, Trezza R, Vogelmann J, Wynne R H and Zhu Z. 2014. Landsat-8: science and product vision for terrestrial global change research. Remote Sensing of Environment, 145: 154-172 [DOI: 10.1016/j.rse.2014.02.001http://dx.doi.org/10.1016/j.rse.2014.02.001]

Scaramuzza P L, Bouchard M A and Dwyer J L. 2012. Development of the landsat data continuity mission cloud-cover assessment algorithms. IEEE Transactions on Geoscience and Remote Sensing, 50(4): 1140-1154 [DOI: 10.1109/tgrs.2011.2164087http://dx.doi.org/10.1109/tgrs.2011.2164087]

Wang J, Xiao X M, Qin Y W, Dong J W, Zhang G L, Kou W L, Jin C, Zhou Y T and Zhang Y. 2015. Mapping paddy rice planting area in wheat-rice double-cropped areas through integration of Landsat-8 OLI, MODIS, and PALSAR images. Scientific Reports, 5: 10088 [DOI: 10.1038/srep10088http://dx.doi.org/10.1038/srep10088]

Wei Y B, Lu M and Wu W B. 2019. Study on extraction method of rice-crayfish based on seasonal difference of water. Chinese Journal of Agricultural Resources and Regional Planning, 40(3): 14-20, 34

魏妍冰, 陆苗, 吴文斌. 2019. 基于水体季相差异的稻虾共作提取方法研究. 中国农业资源与区划, 40(3): 14-20, 34 [DOI: 10.7621/cjarrp.1005-9121.20190303http://dx.doi.org/10.7621/cjarrp.1005-9121.20190303]

Wu W B, Yu Q Y, Peter V H, You L Z, Yang P and Tang H J. 2014. How could agricultural land systems contribute to raise food production under global change? Journal of Integrative Agriculture, 13(7): 1432-1442 [DOI: 10.1016/S2095-3119(14)60819-4http://dx.doi.org/10.1016/S2095-3119(14)60819-4]

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]

Xie D F, Zhang J S, Pan Y Z, Sun P J and Yuan Z M Q. 2015. Fusion of MODIS and Landsat 8 images to generate high spatial-temporal resolution data for mapping autumn crop distribution. Journal of Remote Sensing, 19(5): 791-805

谢登峰, 张锦水, 潘耀忠, 孙佩军, 袁周米琪. 2015. Landsat 8和MODIS融合构建高时空分辨率数据识别秋粮作物. 遥感学报, 19(5): 791-805 [DOI: 10.11834/jrs.20154213http://dx.doi.org/10.11834/jrs.20154213]

Xiong J, Thenkabail P S, Gumma M K, Teluguntla P, Poehnelt J, Congalton R G, Yadav K and Thau D. 2017. Automated cropland mapping of continental Africa using Google Earth Engine cloud computing. Isprs Journal of Photogrammetry and Remote Sensing, 126: 225-244 [DOI: 10.1016/j.isprsjprs.2017.01.019http://dx.doi.org/10.1016/j.isprsjprs.2017.01.019]

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: 10.3390/rs11141699http://dx.doi.org/10.3390/rs11141699]

You N S and Dong J W. 2020. Examining earliest identifiable timing of crops using all available Sentinel 1/2 imagery and Google Earth Engine. Isprs Journal of Photogrammetry and Remote Sensing, 161: 109-123 [DOI: 10.1016/j.isprsjprs.2020.01.001http://dx.doi.org/10.1016/j.isprsjprs.2020.01.001]

Zhang G L, Xiao X M, Biradar C M, Dong J W, Qin Y W, Menarguez M A, Zhou Y T, Zhang Y, Jin C, Wang J, Doughty R B, Ding M J and Moore III B. 2017. Spatiotemporal patterns of paddy rice croplands in China and India from 2000 to 2015. Science of the Total Environment, 579: 82-92 [DOI: 10.1016/j.scitotenv.2016.10.223http://dx.doi.org/10.1016/j.scitotenv.2016.10.223]

Zhang H K, Roy D P, Yan L, Li Z B, Huang H Y, Vermote E, Skakun S and Roger J C. 2018b. Characterization of Sentinel-2A and Landsat-8 top of atmosphere, surface, and nadir BRDF adjusted reflectance and NDVI differences. Remote Sensing of Environment, 215: 482-494 [DOI: 10.1016/j.rse.2018.04.031http://dx.doi.org/10.1016/j.rse.2018.04.031]

Zhang X, Wu B F, Ponce-Campos G E, Zhang M, Chang S and Tian F Y. 2018a. 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: 10.3390/rs10081200http://dx.doi.org/10.3390/rs10081200]

文章被引用时，请邮件提醒。

提交

中国红树林制图的遥感时空概率阈值方法

联合GEE与多源遥感数据的黑龙江流域沼泽湿地信息提取

基于哨兵二号数据的矿区叶面降尘信息提取及尘源识别

多源多特征集成的南美洲典型地区湿地制图