Identification of Winter Wheat in Huang-Huai-Hai Plain Based on Different Time Scales and Feature Preference
Abstract
Monitoring regional crop acreage accurately and promptly is critical for ensuring food security and promoting sustainable agricultural development in China. The Google Earth Engine (GEE) cloud platform, along with fused Sentinel-1 SAR radar and Sentinel-2 SR surface reflectance imagery were employed to classify winter wheat in 2021 within the Huang-Huai-Hai Plain. The Sentinel time series data were synthesized and smoothed across various temporal scales, and a prioritization of polarization features, spectral features, vegetation index, harmonic coefficients, and textural features were conducted to explore their impacts on the accuracy and generalization ability of winter wheat identification in the region. The results showed that the feature optimization process improved the classification accuracy of the model, and the spectral features were the most significant, followed by harmonic coefficients, polarization, and textural features. Reducing the time scale of image sequences led to higher classification accuracy, with overall accuracies of 95.4%, 95.6%, and 96.4% for 30 d, 20 d and 10 d scales, respectively. However, this also resulted in a decrease in generalization ability, with corresponding scores of 0.935, 0.919, and 0.918. Shorter time scales captured finer details of ground features, achieving higher classification accuracy but showing less adaptability to data variations. Moreover, the model’s generalization ability demonstrated a spatial pattern of ‘the closer it was, the more relevant they were’. The identification of winter wheat areas using the GEE platform and Sentinel imagery was highly accurate, with overall accuracy and F1 scores of the confusion matrix exceeding 90%, and classification results were highly consistent in spatial detail with high-resolution images. Furthermore, the estimated areas of winter wheat showed a strong correlation with official municipal statistics (coefficient of determination R2>0.9).
Keywords:winter wheat, remote sensing imagery identification, Google Earth Engine, random forest, time scale, feature preference
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ZHOU Litao, SUN Shuang, ZHANG Zhentao, et al. High yield and water use efficiency synergistical improvement irrigation scheme of winter wheat in North China Plain based on Meta-analvsis [J], Chinese Journal of Agrometeorology, 2022, 43(7) ; 515 -526.
CUI K, SHOEMAKER S P. A look at food security in China[J]. NPJ Science of Food, 2018, 2(1 ) : 4.
PAN Z, YANG S, ZHAO L, et al. Temporal and spatial variability of antibiotics in agricultural soils from Huang — Huai — Hai Plain, northern China [J]. Chemosphere, 2021, 272: 129803.
ZHANG Chao,GAO Lulu,YUN Wenjun, et al. Research progress on obtaining cultivated land quality evaluation indexes by remote sensing [J] . Transactions of the Chinese Society for Agricultural Machinery, 2022,53( 1) ;1 - 13.
НАО Weiping, MEI Xurong, CAI Xueliang, et al. Crop planting extraction based on multi-temporal remote sensing data in Northeast China [J] . Transactions of the CSAE,2011 ,27( 1 ) :201 -207,393. (in Chinese).
WU Zhifeng,LUO Jiancheng, SUN Yingwei, et al. Research on precision agricultural based on the spatial-temporal remote sensing collaboration [J]. Journal of Geo-information Science ,2020 ,22 (4 ) ; 731 -742. (in Chinese)
TANG Huajun. Progress and prospect of agricultural remote sensing research [J] . Journal of Agriculture, 2018, 30(5) : 167 - 171. (in Chinese)
GAO Yongdao,QIAO Rongrong,JI Shuxin, et al. Changes and driving factors of crops planting structure in Hetao Irrigation Region in Inner Mongolia [J]. Journal of Desert Research, 2021 , 41(3) ;110 — 117. (in Chinese)
HU Qiong. Research on methods of crop identification using MODIS time series data! Beijing: Chinese Academy of Agricultural Sciences,2018. (in Chinese)
HAO Pengvu,NIU Zheng, WANG Li, et al. Multi-source automatic crop pattern mapping based on historical vegetation index profiles [J]. Transactions of the CSAE ,2012 ,28 (23 ) : 123 - 131 ,297. (in Chinese)
LI Xinchuan,XU Xin'gang, WANG Jihua, et al. Crop classification recognition based on time-series images from 11J satellite [J] . Transactions of the CSAE ,2013 ,29(2) ; 169 - 176, 298. (in Chinese)
XU AN F, DONG Y, LI J, et al. Mapping crop type in Northeast China during 2013—2021 using automatic sampling and tile- based image classification[J]. International Journal of Applied Earth Observation and Geoinformation, 2023, 117; 103178.
ZHAO Q, YU L, LI X, et al. Progress and trends in the application of Google Earth and Google Earth Engine [J]. Remote Sensing, 2021 , 13( 18) : 3778.
MCNAIRN H, SHANG J. A review of multitemporal synthetic aperture radar ( SAR ) for crop monitoring. Multitemporal Remote Sensing; Methods and Applications, 2016(1): 317 -340.
REN S, GUO B, WU X, et al. W inter wheat planted area monitoring and yield modeling using MODIS data in the Huang — Huai — Hai Plain, China [J]. Computers and Electronics in Agriculture, 2021 , 182; 106049.
SHI W, TAO F, LIU J. Changes in quantity and quality of cropland and the implications for grain production in the Huang — Huai — Hai Plain of China [J ]. Food Security, 2013(5) : 69 -82.
TAMIMININI II, SALEIII B, MAHDIANPARI M, et al. Google Earth Engine for geo-big data applications: a Meta-analvsis and systematic review[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 164: 152 - 170.
LI Wen, LI Jiatian,LU Mei, et al. Spatio-temporal information weighted SG time series filtering method based on Dixon test [J]. Remote Sensing Information ,2023 ,38 (I) : 112 - 120.
SON N T, CHEN С F, CHEN С R , et al. A comparative analysis of multitemporal MODIS EVI and NDVI data for large-scale rice yield estimation [ J ]. Agricultural and Forest Meteorology, 2014, 197; 52 -64.
GAO В С. NDWI—a normalized difference water index for remote sensing of vegetation liquid water from space [J] . Remote Sensing of Environment, 1996, 58(3) ; 257 -266.
JOHN R, CHEN J. LU N, et al. Predicting plant diversity based on remote sensing products in the semi-arid region of Inner Mongolia [J]. Remote Sensing of Environment, 2008, 112(5) ; 2018 -2032.
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