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

Journal of Integrative Agriculture 2024年第4期23卷 1164-1178页

作者： Yunping Chen Jie Hu Zhiwen Cai Jingya Yang Wei Zhou Qiong Hu Cong Wang Liangzhi You Baodong Xu Macro Agriculture Research Institute College of Plant Science and TechnologyHuazhong Agricultural UniversityWuhan 430070China College of Resources and Environment Huazhong Agricultural UniversityWuhan 430070China Key Laboratory of Agricultural Remote Sensing Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional PlanningChinese Academy of Agricultural SciencesBeijing 100081China Key Laboratory for Geographical Process Analysis&Simulation of Hubei Province/College of Urban and Environmental Sciences Central China Normal UniversityWuhan 430079China International Food Policy Research Institute NWWashingtonD.C.20005USA

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 ***,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 *** 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)*** PRVI that incorporated the red,near-infrared,and shortwave infrared 1 bands was developed based on the analysis of spectro-phenological separability and feature *** 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 ***,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 ***,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

关键词： ratoon rice phenology-based ratoon rice vegetation index(PRVI) phenological phase feature selection harmonized landsat sentinel-2 data

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