предыдущий|следующий

Indicative features for identifying corn and soybean using remote sensing imagery at middle and later growth season

  • role： First author第一作者
  • Affiliation：

    National Engineering Laboratory for Satellite Remote Sensing Applications (NELRS), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environmental, University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：shenyu@radi.ac.cn
  • Introduction：沈宇，研究方向为农作物遥感识别方法与指示性特征。E-mail： shenyu@radi.ac.cn
SHEN Yu12,  
  • role： Corresponding author通信作者
  • Affiliation：

    National Engineering Laboratory for Satellite Remote Sensing Applications (NELRS), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

  • Email：liqz@aircas.ac.cn
  • Introduction：李强子，研究方向为农业遥感。E-mail： liqz@aircas.ac.cn
LI Qiangzi1*,  
  • Affiliation：

    National Engineering Laboratory for Satellite Remote Sensing Applications (NELRS), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

DU Xin1,  
  • Affiliation：

    National Engineering Laboratory for Satellite Remote Sensing Applications (NELRS), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

WANG Hongyan1,  
  • Affiliation：

    National Engineering Laboratory for Satellite Remote Sensing Applications (NELRS), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

ZHANG Yuan1

реферат

Corn and soybean are two major crops maintaining food security, and thus the timely and accurate monitoring of their planting areas are of great importance to forecasting their production and market prices. The objectives of this study were to use a remote-sensing technology in exploring indicative features that can effectively identify corn and soybean in their middle and late growth seasons and provide technical support for the broad geographical application of corn and soybean mapping. This study can facilitate the early release of corn and soybean planting acreages for policy makers. In this study, two typical planting areas of corn and soybean in the provinces of Heilongjiang and Anhui were selected. GaoFen-1 satellite images with 30 m spatial resolution were acquired in the middle and latter growth stages and used as data sources for calculating various vegetation indices and textural features. Then, a feature optimization method was used in evaluating the relative importance scores of input features, and optimal feature combinations for identifying corn and soybean were determined. The random forest classification algorithm was used in analyzing the relationship between the number of input features and classification accuracy, and then the best feature groups in different experimental areas were identified. Finally, according to similarities and differences among the selected features in different regions, the indicative features for mapping corn and soybean in the middle and latter stages were established. The validity and stability were confirmed using our experimental designs. The following results were obtained: (1) indicative remote sensing features for efficiently identifying corn and soybean in their middle and late growing seasons were identified; (2) the classification performance of the indicative features of corn and soybean in both experimental areas was approximately 10% higher than that when original spectral band combinations were used. In different planting areas, high classification accuracy was obtained using the indicative features of corn and soybean as the optimal features selected in individual local area. Our selected indicative features for soybean and corn mapping were found stable, effective, and useful for large areas of implementation. These features included Ratio Vegetation Index (RVI), Difference Vegetation Index (DVI), Conversion Vegetation Index (TVI), improved chlorophyll absorption ratio index (MCARI), and the second moment and entropy in Gray Level Co-occurrence Matrix (GLCM).

ключеви́че слова́

remote sensing;corn;soybean;remote sensing identification;satellite feature;classification;GF-1

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