Extraction of paddy rice based on convolutional neural network using multi-source remote sensing data

doi:10.6046/gtzyyg.2020.04.14

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Abstract

Rice is one of the most widely planted food crops in China. Therefore, timely and accurate rice identification and monitoring is of great significance to the national food security and the evolution of agricultural land spatial pattern. In this study, multi-temporal Sentinel-2A multispectral images, vegetation indices, vegetation abundance and Landsat 8 derived LST on the critical period of rice phenology were used. The CNN, SVM and RF classifiers were applied to extracting the paddy rice and finally the paddy rice map was obtained. The result shows that using multi-temporal and multi-source remote sensing data with the CNN algorithm can effectively extract rice information in high heterogeneity region. The overall accuracy of rice classification and Kappa coefficient are over 92% and 0.90 respectively. This study has demonstrated the potential of using moderate spatial resolution images combined with CNN to map the paddy rice in highly heterogeneous area.

Keywords paddy rice Sentinel-2A Landsat8 convolutional neural network(CNN) Changsha-Zhuzhou-Xiangtan Area

TP79

Corresponding Authors: ZHANG Meng E-mail: yaotongcai@csuft.edu.cn;mengzhang@csuft.edu.cn

Issue Date: 23 December 2020

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	Yaotong CAI
	Shutong LIU
	Hui LIN
	Meng ZHANG

Cite this article:

Yaotong CAI,Shutong LIU,Hui LIN, et al. Extraction of paddy rice based on convolutional neural network using multi-source remote sensing data[J]. Remote Sensing for Land & Resources, 2020, 32(4): 97-104.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020.04.14 OR https://www.gtzyyg.com/EN/Y2020/V32/I4/97

Fig.1 Study area

Tab.1 Phenology of rice

Tab.2 Parameters of remote sensing data set

Fig.2 Flowchart

Tab.3 Training sample information for double-cropping rice extraction(个)

Fig.3 Paddy rice extracted results by three classifiers

Tab.4 Classification accuracy of CNN, SVM and RF

Fig.4-1 Classification results in three typical regions by different classifiers

Fig.4-2 Classification results in three typical regions by different classifiers

[1]	罗观长. 南方稻作、地块特征与农户种植模式——基于南方五省稻农调查数据实证分析[D]. 广州:华南农业大学, 2016.
[1]	Luo G C. Rice in south China,land characteristics,farmers planting patterns:Empirical analysis based on survey data of five southern provinces rice farmers[D]. Guangzhou:South China Agricultural University, 2016.
[2]	Dong J, Xiao X. Evolution of regional to global paddy rice mapping methods:A review[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016,119(1):214-227.
[3]	Bachelet D. Rice paddy inventory in a few provinces of China using AVHRR data[J]. Geocarto International, 1995,10(1):23-38.
[4]	Xiao X, Boles S, Frolking S, et al. Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images[J]. Remote Sensing of Environment, 2006,100(1):95-113.
[5]	Cai Y T, Zhang M, Lin H. Mapping paddy rice by the object-based random forest method using time series Sentinel-1/Sentinel-2 data[J]. Advance in Space Research, 2019,64(11):2233-2244.
[6]	Thenkabail P S. Mapping rice areas of south Asia using MODIS multitemporal data[J]. Journal of Applied Remote Sensing, 2011,5(4):863-871.
[7]	Dong J, Xiao X, Kou W, et al. Tracking the dynamics of paddy rice planting area in 1986—2010 through time series Landsat images and phenology-based algorithms[J]. Remote Sensing of Environment, 2015,160(160):99-113.
[8]	张猛, 曾永年. 基于多时相Landsat数据融合的洞庭湖区水稻面积提取[J]. 农业工程学报, 2015,31(13):178-185.
[8]	Zhang M, Zeng Y N. Mapping paddy fields of Dongting Lake area by fusing Landsat and MODIS data[J]. Transactions of the Chinese Society of Agricultural Engineering, 2015,31(13):178-185.
[9]	杨斌, 李丹, 高桂胜, 等. Sentinel-2A卫星数据处理分析及再干旱河谷提取中的应用[J]. 国土资源遥感, 2018,30(3):128-135.doi: 10.6046/gtzyyg.2018.03.18.
[9]	Yang B, Li D, Gao G S, et al. Processing analysis of Sentinel-2A data and application to arid valleys extraction[J]. Remote Sensing for Land and Resources, 2018,30(3):128-135.doi: 10.6046/gtzyyg.2018.03.18.
[10]	Lambert M, Traore P, Blaes X, et al. Estimating smallholder crops production at village level from Sentinel-2 time series in Mali’s cotton belt[J]. Remote Sensing of Environment, 2018,216(1):647-657.
[11]	Toshihiro S, Masayuki Y, Hitoshi T, et al. A crop phenology detection method using time-series MODIS data[J]. Remote Sensing of Environment, 2005,96(3):366-374.
[12]	Wardlow B D, Egbert S L. Large-area crop mapping using time-series MODIS 250 m NDVI data:An assessment for the U.S. Central Great Plains[J]. Remote Sensing of Environment, 2008,112(3):1096-1116.
[13]	国贤玉, 李坤, 王志勇, 等. 基于SVM+SFS策略的多时相紧致极化SAR水稻精细分类[J]. 国土资源遥感, 2018,30(4):20-27.doi: 10.6046/gtzyyg.2018.04.04.
[13]	Guo X Y, Li K, Wang Z Y, et al. Fine classification of rice with multi-temporal compact polarimetric SAR based on SVM+SFS strategy[J]. Remote Sensing for Land and Resources, 2018,30(4):20-27.doi: 10.6046/gtzyyg.2018.04.04.
[14]	Qin Y, Xiao X, Dong J, et al. Mapping paddy rice planting area in cold temperate climate region through analysis of time series Landsat8 (OLI),Landsat7 (ETM+) and MODIS imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015,105:220-233. doi: 10.1016/j.isprsjprs.2015.04.008 pmid: 27695195 url: https://www.ncbi.nlm.nih.gov/pubmed/27695195
[15]	Erinjery J, Singh M, Kent R. Mapping and assessment of vegetation types in the tropical rainforests of the Western Ghats using multispectral Sentinel-2 and SAR Sentinel-1 satellite[J]. Remote Sensing of Environment, 2018,216(1):345-354.
[16]	Romero A, Gatta C, Camps-vall G. Unsupervised deep feature extraction for remote sensing image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016,54(3):1349-1362.
[17]	Maggiori E, Tarabalka Y, Charpiat G, et al. Convolutional neural networks for large-scale remote-sensing image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016,55(2):645-657. doi: 10.1109/TGRS.2016.2612821 url: http://ieeexplore.ieee.org/document/7592858/
[18]	Pan X, Zhao J. High-resolution remote sensing image classification method based on convolutional neural network and restricted conditional random field[J]. Remote Sensing, 2018,920(10):1-20.
[19]	蔡耀通, 林辉, 孙华, 等. 基于TanDEM-X数据的林分平均高反演方法研究[J]. 西南林业大学学报(自然科学), 2019,39(5):110-117.
[19]	Cai Y T, Lin H, Sun H, et al. Stand allocation high inversion method based on TanDEM-X data[J]. Journal of Southwest Forestry University (Natural Sciences), 2019,39(5):110-117.
[20]	赵莲, 张锦水, 胡潭高, 等. 变端元混合像元分解冬小麦种植面积测量方法[J]. 国土资源遥感, 2011,23(1):66-72.doi: 10.6046/gtzyyg.2011.01.13.
[20]	Zhao L, Zhang J S, Hu T G, et al. The application of the dynamic endmember linear spectral unmixing model to winter wheat area estimation[J]. Remote Sensing for Land and Resources, 2011,23(1):66-72.doi: 10.6046/gtzyyg.2011.01.13.
[21]	Zhang M, Lin H, Wang G, et al. Mapping paddy rice using a convolutional neural network (CNN) with Landsat8 datasets in the Dongting Lake area,China[J]. Remote Sensing, 2018,10(11), 1840.
[22]	徐涵秋. 新型Landsat8卫星影像的反射率和地表温度反演[J]. 地球物理学报, 2015,58(3):741-747.
[22]	Xu H Q. Retrieval of the reflectance and land surface temperature of the newly-launched Landsat8 satellite[J]. Chinese Journal of Geophysics, 2015,58(3):741-747.
[23]	Azizpour H, Razavian A S, Sullivan J, et al. Factors of transferability for a generic ConvNet representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015,38(9):1790-1802. doi: 10.1109/TPAMI.2015.2500224 pmid: 26584488 url: https://www.ncbi.nlm.nih.gov/pubmed/26584488
[24]	Kontgis C, Schneider A, Ozdogan M. Mapping rice paddy extent and intensification in the Vietnamese Mekong River Delta with dense time stacks of Landsat data[J]. Remote Sensing of Environment, 2015,169:255-269. doi: 10.1016/j.rse.2015.08.004 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425715300936

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