Journal of Geo-information Science ›› 2019, Vol. 21 ›› Issue (6): 937-947.doi: 10.12082/dqxxkx.2019.180423.

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Mapping Paddy Rice in the Hainan Province Using both Google Earth Engine and Remote Sensing Images

Shen TAN1,2(), Bingfang WU1,2,*(), Xin ZHANG1   

  1. 1. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Science, Beijing 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2018-08-30 Revised:2018-12-24 Online:2019-06-15 Published:2019-06-15
  • Contact: Bingfang WU;
  • Supported by:
    Science and Technology Service Network Initiative(STS), No.KFJ-STS-ZDTP-009;National Key R&D Program of China, No.2016YFA0600301, 2016YFA0600302;Strategic Priority Research Program of the Chinese Academy of Sciences(A), No.XDA19030201;National Natural Science Foundation of China, No.41561144013, 41701496


Rice is one of the main grain crops in China and East Asia, including China. The annual yield of rice has a significant influence on domestic livelihood. Therefore, timely and accurate assessment of rice distribution information is crucial for forecasting rice yields and optimize the allocation of agricultural resources. Remote sensing (RS) images can provide time series surface spectral, and other electro-magnetic, dynamics over a large-scale land surface, which are commonly used for large-scale crop monitoring. However, routine rice classifying strategies provided by the RS images during key growth stages, require spectral patterns at high frequency. This method appears to be impractical within South China, as the number of high quality RS images are difficult to obtain due to cloud contamination caused by the hot and wet weather. A combination of various RS images of rice classification from multi-platforms provide an indirect way of reducing the revisit period in routine rice classification, thus enabling successful crop mapping in cloudy regions. However, this causes difficulty with data manipulation and storage, especially when conducting classification work at province or large area levels. To address these issues, this research utilizes Google Earth Engine (a cloud-based geospatial analysis platform running on the Google server)to collect online optic RS data and micro-wave RS data at diverse resolutions for rice mapping. A distribution map of paddy rice at 10-m spatial resolution in the Hainan Province in 2016 was made by using the combined methods of random forest (RF) classification and a pattern-matching strategy based on conjunct features extracted at monthly level and histogram value distribution. Results showed this method was suitable for rice mapping in Hainan and could show clear feature divergence between the different land surface cover types. Spatial distribution results corresponded well with the actual edges of the field, along with texture information. The rice classification result of the Hainan Province was validated using sample points captured on the ground and achieved overall accuracy of 93.2%, indicating reliability for practical application. Overall, the automatic rice classifying strategy was able to map paddy rice with high efficiency and sufficient accuracy in the Hainan Province, and could be applied to other vast areas.

Key words: Mapping paddy rice, Google Earth Engine, SAR, random forest, remote sensing images, Hainan Province