地球信息科学学报 ›› 2022, Vol. 24 ›› Issue (5): 996-1008.doi: 10.12082/dqxxkx.2022.210527

• 遥感科学与应用技术 • 上一篇    下一篇

融合多源遥感数据的黑河中游地区生长季早期作物识别

杨泽航(), 王文*(), 鲍健雄   

  1. 河海大学水文水资源与水利工程科学国家重点实验室,南京 210098
  • 收稿日期:2021-08-31 修回日期:2021-11-08 出版日期:2022-05-25 发布日期:2022-07-25
  • 通讯作者: * 王 文(1967— ),男,江苏泰州人,教授,博士,主要从事全球变化、水文遥感与水文过程模拟研究。 E-mail: wangwen@hhu.edu.cn
  • 作者简介:杨泽航(1997— ),男,浙江浦江人,硕士生,研究方向为水文遥感。E-mail: 445627176@qq.com
  • 基金资助:
    国家自然科学基金国际(地区)合作与交流项目(41961134003)

Identifying Crop Types in Early Growing Season in the Middle Reaches of Heihe River by Fusing Multi-Source Remote Sensing Data

YANG Zehang(), WANG Wen*(), BAO Jianxiong   

  1. State Key Laboratory of Hydrology- water Resources and Hydraulic Engineering science, Hohai University, Nanjing 210098, China
  • Received:2021-08-31 Revised:2021-11-08 Online:2022-05-25 Published:2022-07-25
  • Supported by:
    International (Regional) Cooperation and Exchange Program of National Natural Science Foundation of China(41961134003)

摘要:

在生长季早期获取作物的种植情况,对于农业水资源管理,尤其是缺水地区的水量分配等具有重大的意义。本文利用改进型时空自适应融合模型(ESTARFM),将作物生长早期3—6月的Sentinel 2影像与MOD09GQ数据计算得到的NDVI数据进行融合,建立NDVI时间序列,并利用随机森林分类方法对2019年黑河流域中游地区作物种植结构进行早期识别。利用3-6月Sentinel-2 NDVI与时空融合NDVI相结合建立的时间序列,作物分类精度达到91.42%,kappa系数为0.85,相比仅使用Sentinel-2 NDVI时间序列的作物分类精度提高1.05%,kappa系数提高0.02。与使用整个作物生长期(3—10月)Sentinel-2 NDVI时间序列的作物分类结果相比,精度仅低1.53%,kappa系数仅低0.03。利用Gini系数对利用Sentinel-2 NDVI与时空融合NDVI相结合建立的时间序列进行特征重要性评估,发现Gini系数得分高于平均值的10期NDVI影像中,有6期为时空融合影像,说明时空融合获取的NDVI数据利于提高分类精度的有效性。对比使用不同长度NDVI时间序列对作物种植结构进行早期识别的精度发现,最早可在4月中旬与4月下旬分别实现对苜蓿和玉米的早期识别;玉米的分类精度受NDVI时间序列长度的影响较大,可在5月下旬实现对玉米的早期识别。

关键词: 作物识别, 遥感, 时空融合, NDVI时间序列, 黑河流域中游地区

Abstract:

Obtaining the condition of crops in the early growing season is important for the agricultural water resources management, especially for water allocation in water shortage areas. In this paper, the Enhanced Spatiotemporal Adaptive Fusion Model (ESTARFM) was used to fuse Sentinel-2 NDVI data with NDVI data calculated from MOD09GQ in the early growing season in the middle reaches of the Heihe River basin from March to June to construct NDVI time series, and then the random forest classification method was used to identify the crop structure in 2019. By using the Sentinel-2 NDVI time series and spatiotemporal fused NDVI time series jointly in the early growing season (i.e., from March to June), the crop classification accuracy reached 91.42 % with a kappa coefficient of 0.85. Compared with the classification results obtained using only Sentinel-2 NDVI time series, the accuracy improved by 1.01 % and the kappa coefficient improved by 0.02. The accuracy was only 1.53 % lower and the kappa coefficient was only 0.03 lower than the results obtained using Sentinel-2 NDVI time series in the whole crop growth period (from March to October). The Gini coefficient was used to evaluate the feature importance of the time series constructed by combining the Sentinel-2 NDVI time series and fused NDVI series. It was found that six of the 10 NDVI images with Gini coefficient scores higher than the average were spatiotemporally fused images, indicating that the NDVI data obtained by spatiotemporal fusion could improve the classification accuracy. The NDVI time series of different lengths were also established separately for early identification of crop structures. Results show that early identification of alfalfa and corn can be achieved as early as mid-April and late April, respectively. In addition, the classification accuracy of corn was strongly influenced by the length of NDVI time series, and early identification of corn can be achieved in late May.

Key words: crops identification, remote sensing, spatial-temporal data fusion, NDVI time series, the middle reaches of the Heihe River basin