首页|A phenology-based vegetation index for improving ratoon rice mapping using harmonized Landsat and Sentinel-2 data

A phenology-based vegetation index for improving ratoon rice mapping using harmonized Landsat and Sentinel-2 data

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  • 国家科技期刊平台
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Ratoon rice,which refers to a second harvest of rice obtained from the regenerated tillers originating from the stubble of the first harvested crop,plays an important role in both food security and agroecology while requiring minimal agricultural inputs.However,accurately identifying ratoon rice crops is challenging due to the similarity of its spectral features with other rice cropping systems(e.g.,double rice).Moreover,images with a high spatiotemporal resolution are essential since ratoon rice is generally cultivated in fragmented croplands within regions that frequently exhibit cloudy and rainy weather.In this study,taking Qichun County in Hubei Province,China as an example,we developed a new phenology-based ratoon rice vegetation index(PRVI)for the purpose of ratoon rice mapping at a 30 m spatial resolution using a robust time series generated from Harmonized Landsat and Sentinel-2(HLS)images.The PRVI that incorporated the red,near-infrared,and shortwave infrared 1 bands was developed based on the analysis of spectro-phenological separability and feature selection.Based on actual field samples,the performance of the PRVI for ratoon rice mapping was carefully evaluated by comparing it to several vegetation indices,including normalized difference vegetation index(NDVI),enhanced vegetation index(EVI)and land surface water index(LSWI).The results suggested that the PRVI could sufficiently capture the specific characteristics of ratoon rice,leading to a favorable separability between ratoon rice and other land cover types.Furthermore,the PRVI showed the best performance for identifying ratoon rice in the phenological phases characterized by grain filling and harvesting to tillering of the ratoon crop(GHS-TS2),indicating that only several images are required to obtain an accurate ratoon rice map.Finally,the PRVI performed better than NDVI,EVI,LSWI and their combination at the GHS-TS2 stages,with producer's accuracy and user's accuracy of 92.22 and 89.30%,respectively.These results demonstrate that the proposed PRVI based on HLS data can effectively identify ratoon rice in fragmented croplands at crucial phenological stages,which is promising for identifying the earliest timing of ratoon rice planting and can provide a fundamental dataset for crop management activities.

ratoon ricephenology-based ratoon rice vegetation index(PRVI)phenological phasefeature selectionHarmonized Landsat Sentinel-2 data

Yunping Chen、Jie Hu、Zhiwen Cai、Jingya Yang、Wei Zhou、Qiong Hu、Cong Wang、Liangzhi You、Baodong Xu

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Macro Agriculture Research Institute,College of Plant Science and Technology,Huazhong Agricultural University,Wuhan 430070,China

College of Resources and Environment,Huazhong Agricultural University,Wuhan 430070,China

Key Laboratory of Agricultural Remote Sensing,Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning,Chinese Academy of Agricultural Sciences,Beijing 100081,China

Key Laboratory for Geographical Process Analysis&Simulation of Hubei Province/College of Urban and Environmental Sciences,Central China Normal University,Wuhan 430079,China

International Food Policy Research Institute,NW,Washington,D.C.20005,USA

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国家自然科学基金国家自然科学基金Young Elite Scientists Sponsorship Program by China Association for Science and Technology(CAST)中央高校基本科研业务费专项中央高校基本科研业务费专项

42271360422713992020QNRC0012662021JC013CCNU22QN018

2024

10.1016/j.jia.2023.05.035

农业科学学报(英文)
中国农业科学院农业信息研究所

农业科学学报(英文)

CSTPCD
影响因子：0.576
ISSN：2095-3119
年,卷(期)：2024.23(4)
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