سابِق|تال

Mapping rice paddy distribution by using time series HJ blend data and phenological parameters

  • role： First author第一作者
  • Affiliation：

    Department of Survey and Remote Sensing, School of Geosciences and Info-physics, Central South University, Changsha 410083, China

    Center for Geomatics and Regional Sustainable Development Research, Central South University, Changsha 410083, China

  • Email：liuwenjiers@126.com
  • Introduction：柳文杰(1994—　)，男，硕士研究生，研究方向为遥感技术应用。E-mail：liuwenjiers@126.com
Wenjie LIU12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Department of Survey and Remote Sensing, School of Geosciences and Info-physics, Central South University, Changsha 410083, China

    Center for Geomatics and Regional Sustainable Development Research, Central South University, Changsha 410083, China

  • Email：ynzeng@mail.csu.edu.cn
  • Introduction：曾永年(1959—　)，男，教授，研究方向为遥感及地理信息系统及其环境变化研究。E-mail：ynzeng@mail.csu.edu.cn
Yongnian ZENG12*,  
  • Affiliation：

    Department of Survey and Remote Sensing, School of Geosciences and Info-physics, Central South University, Changsha 410083, China

    Center for Geomatics and Regional Sustainable Development Research, Central South University, Changsha 410083, China

Meng ZHANG12

ملخص

Extracting regional rice paddy areas with high precision by using remote sensing data is crucial to agricultural configuration and decision making. However, obtaining images with high temporal and spatial resolutions is difficult because of satellite recycling and rainy weather, which affect the accuracy of rice paddy field extraction. This study developed a new method for mapping rice paddies in cloud-prone areas by using time-series-fused HJ-1B NDVI and phenological parameters. A case study of Dongting Lake area in the middle reaches of Yangtze River, China, was conducted to test this approach. First, the spatial and temporal adaptive reflectance fusion model algorithm was employed to acquire time series NDVI (30 m) by blending HJ-1B NDVI and MODIS13Q1 NDVI. Second, the Savitzky-Golay filter was applied to smooth the time-series-fused NDVI and calculate the phenological parameters. We selected the most effective NDVI dataset by using the Jeffries-Matusita distance to reduce data redundancy. Finally, we combined the selected NDVI with phenological parameters to extract the distribution of rice paddies and evaluated the precision of the results. Results indicated that the spatial detail information of the fused NDVI data was more abundant than that of MODIS data. The true and fused images presented only slight differences visually and had high correlation coefficients. The accuracies of the results obtained using the method proposed were as follows. The producer accuracy of single cropping rice was 85.80%, and the user accuracy was 86.66%. The producer accuracy of double cropping rice was 86.62%, and the user accuracy was 89.14%. The total classification accuracy was 91.71%, and the kappa coefficient was 0.9024. For comparison, the visible and near-infrared (VNIR) data of HJ-1B, the selected NDVI, the phenological parameters, and the dataset that included the previously mentioned three types were used in the control test. For VNIR data, the producer accuracy of single cropping rice was 71.80%, and the user accuracy was 76.81%. Meanwhile, the producer accuracy of double cropping rice was 71.69%, and the user accuracy was 65.33%. The total classification accuracy was 86.36%, and the kappa coefficient was 0.8407. For the selected NDVI, the producer accuracy of single cropping rice was 74.15%, and the user accuracy was 76.38%. Meanwhile, the producer accuracy of double cropping rice was 74.65%, and the user accuracy was 78.41%. The total classification accuracy was 88.54%, and the kappa coefficient was 0.8765. For the phenological parameters, the producer accuracy of single cropping rice was 82.66%, and the user accuracy was 80.59%. Moreover, the producer accuracy of double cropping rice was 81.15%, and the user accuracy was 83.74%. The total classification accuracy was 82.35%, and the kappa coefficient was 0.7933. For the comprehensive dataset, the producer accuracy of single cropping rice was 84.93%, and the user accuracy was 88.76%. Meanwhile, the producer accuracy of double cropping rice was 87.73%, and the user accuracy was 88.77%. The total classification accuracy was 91.50%, and the kappa coefficient was 0.8988. The control test showed that the dataset combining the selected NDVI with the phenological parameters had minimal data redundancy and exhibited the highest accuracy in rice paddy area extraction. In summary, the proposed method can be widely used in regional rice paddy extraction and can effectively solve the problem of missing remote sensing data caused by weather conditions. This method can also be applied to the extraction of other land cover types.

مفهوم

rice;HJ-1B CCD;NDVI;spatial and temporal data fusion;phenological parameters;classification

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