Detecting Rice Phenology in Paddy Fields with Complex Cropping Pattern Using Time Series MODIS Data


  • Dewi Kania Sari 1 Study Program of Geodesy & Geomatics, Faculty of Earth Science & Technology, ITB Jl. Ganesha 10, Bandung 40132 Indonesia. E-mail: 2 Department of Geodetic Engineering, Institute of Technology Nasional (Itenas) Jl. P.H.H. Mustafa 23, Bandung 40124 Indonesia
  • Ishak H. Ismullah 1 Study Program of Geodesy & Geomatics, Faculty of Earth Science & Technology, ITB Jl. Ganesha 10, Bandung 40132 Indonesia.
  • Widyo N. Sulasdi Study Program of Geodesy & Geomatics, Faculty of Earth Science & Technology, ITB Jl. Ganesha 10, Bandung 40132 Indonesia.
  • Agung B. Harto Study Program of Geodesy & Geomatics, Faculty of Earth Science & Technology, ITB Jl. Ganesha 10, Bandung 40132 Indonesia.



Monitoring paddy rice phenology and cropping schedules over wide areas is essential for many applications. Remote sensing provides a viable means to meet the requirement of improved regional-scale data set of paddy rice fields, such as phenological stages. A number of methods have been developed for detecting seasonal vegetation changes by using satellite images. Development of such methods to paddy fields with complex cropping pattern is still challenging. In this study, we developed a method for remotely determining phenological stages of paddy rice that uses time series of two vegetation indices (EVI and LSWI) obtained from MODIS data. We ran the algorithm to determine the planting date, heading date, and harvesting date of paddy rice in 5 districts of West Java Province, using the 8-day composite MODIS Surface Reflectance products (500-m spatial resolution) in 2004. Estimated harvesting dates were then used to calculate paddy rice harvested area. We validated the performance of the method against statistical data in 13 subdistricts. The root mean square errors of the estimated paddy rice harvested area against the statistical data were: 851 Ha for monthly data, 1227 Ha for quarterly data, and 2433 Ha for yearly data. Subdistrict-level comparisons of paddy rice harvested area between the MODIS estimation and statistical data showed moderate correlation, with coefficient of determination (r2) 0.6, 0.7, and 0.6 for monthly, quarterly and yearly data, respectively. The results of this study indicated that the MODIS-based paddy rice phenological detection algorithm could potentially be applied at large spatial scales to monitor paddy rice agriculture on a timely and frequent basis.


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