地球信息科学学报 ›› 2019, Vol. 21 ›› Issue (6): 937-947.doi: 10.12082/dqxxkx.2019.180423.

• 遥感科学与应用技术 • 上一篇    下一篇

基于Google Earth Engine与多源遥感数据的海南水稻分类研究

谭深1,2(), 吴炳方1,2,*(), 张鑫1   

  1. 1. 中国科学院遥感与数字地球研究所 遥感科学重点实验室,北京 100101
    2. 中国科学院大学,北京 100049
  • 收稿日期:2018-08-30 修回日期:2018-12-24 出版日期:2019-06-15 发布日期:2019-06-15
  • 通讯作者: 吴炳方 E-mail:tanshen@radi.ac.cn;wubf@radi.ac.cn
  • 作者简介:

    作者简介:谭深(1992-),男,辽宁丹东人,博士生,主要从事农业与水资源遥感研究。E-mail: tanshen@radi.ac.cn

  • 基金资助:

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 E-mail:tanshen@radi.ac.cn;wubf@radi.ac.cn
  • 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


水稻是中国乃至亚洲的重要粮食作物之一,稻米产量关系到民生福祉。及时、准确的水稻分布信息是监测水稻产量、调控农业资源配给的基础。遥感(Remote Sensing)技术能够提供大范围地表的时间序列光谱变化特征,常用于大尺度范围的作物监测。然而,传统基于水稻生长关键时期光谱特征的分类、提取方法对遥感数据的时间分辨率要求较高。由于我国南方水稻产区湿热,雨季云污染降低了遥感数据的有效时间分辨率,因此上述方法在该地难以推广。融合多源遥感数据的分类方案变相缩短了卫星的重访周期,使多云气候区基于遥感影像的水稻分类成为可能。然而,集成多源数据所需更高的数据处理效率和存储需求也成为限制省级乃至更大范围水稻分类的主要因素。本研究基于谷歌地球引擎(Google Earth Engine)云平台,在线调用中分辨率的光学、微波遥感数据,创新性地采用了按月提取、按直方图大小提取特征的方式,采用随机森林分类器,绘制海南省2016年10 m分辨率水稻种植分布图。实验结果证明,该方法可以用于南方多云地区水稻分类,提取结果能够体现不同地类之间的差异,且与实际地表的地块边界、纹理符合良好。经过地表样本点的验证,总体精度为93.2%,满足实际应用需求。因此,本研究采用的自动分类流程能够准确、高效地提取海南省的水稻种植范围,可以向其他地区大范围推广。

关键词: 水稻分类, 谷歌地球引擎云平台, 微波数据, 随机森林, 多源遥感数据, 海南省


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