Journal of Integrative Agriculture ›› 2019, Vol. 18 ›› Issue (12): 2883-2897.DOI: 10.1016/S2095-3119(19)62599-2

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  • 收稿日期:2018-08-15 出版日期:2019-12-01 发布日期:2019-12-23

High resolution crop intensity mapping using harmonized Landsat-8 and Sentinel-2 data

HAO Peng-yu1, 2, TANG Hua-jun1, CHEN Zhong-xin1, YU Le3, WU Ming-quan4 
  

  1. 1 Key Laboratory of Agricultural Remote Sensing, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China
    2 Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and GeoInformation/Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, P.R.China
    3 Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, P.R.China
    4 State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, P.R.China
  • Received:2018-08-15 Online:2019-12-01 Published:2019-12-23
  • Contact: Correspondence TANG Hua-jun, E-mail: tanghuajun@caas.cn
  • About author: HAO Peng-yu, Mobile: +86-13718668296, E-mail: haopy8296 @163.com;
  • Supported by:
    This research was supported by the China Postdoctoral Science Foundation (2017M620075 and BX201700286) and the National Natural Science Foundation of China (NSFC-61661136006).

Abstract: An increase in crop intensity could improve crop yield but may also lead to a series of environmental problems, such as depletion of ground water and increased soil salinity.  The generation of high resolution (30 m) crop intensity maps is an important method used to monitor these changes, but this is challenging because the temporal resolution of the 30-m image time series is low due to the long satellite revisit period and high cloud coverage.  The recently launched Sentinel-2 satellite could provide optical images at 10–60 m resolution and thus improve the temporal resolution of the 30-m image time series.  This study used harmonized Landsat Sentinel-2 (HLS) data to identify crop intensity.  The sixth polynomial function was used to fit the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) curves.  Then, 15-day NDVI and EVI time series were then generated from the fitted curves and used to generate the extent of croplands.  Lastly, the first derivative of the fitted VI curves were used to calculate the VI peaks; spurious peaks were removed using artificially defined thresholds and crop intensity was generated by counting the number of remaining VI peaks.  The proposed methods were tested in four study regions, with results showing that 15-day time series generated from the fitted curves could accurately identify cropland extent.  Overall accuracy of cropland identification was higher than 95%.  In addition, both the harmonized NDVI and EVI time series identified crop intensity accurately as the overall accuracies, producer’s accuracies and user’s accuracies of non-cropland, single crop cycle and double crop cycle were higher than 85%.  NDVI outperformed EVI as identifying double crop cycle fields more accurately.

Key words: crop intensity ,  time series ,  sixth polynomial function ,  harmonized Landsat-8 and Sentinel-2