Phenology-Based Rice Paddy Mapping Using Multi-Source Satellite Imagery and a Fusion Algorithm Applied to the Poyang Lake Plain, Southern China
Phenology-Based Rice Paddy Mapping Using Multi-Source Satellite Imagery and a Fusion Algorithm Applied to the Poyang Lake Plain, Southern China
复制标题
基于物候学的多源卫星图像稻田测绘和融合算法应用于中国南方鄱阳湖平原
DOI：
10.3390/rs12061022
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发表时间：
2020-03-01
期刊：
影响因子：
5
通讯作者：
Zhang, Le
中科院分区：
文献类型：
--
作者：
Ding, Mingjun;Guan, Qihui;Zhang, Le
准确的稻田时空格局信息对于粮食安全评估、农业资源管理和生态系统可持续性至关重要。但是，目前还缺乏精确的精细分辨率稻田和复种空间数据集。自20世纪80年代以来，陆地卫星观测是遥感数据的主要来源，以30米空间分辨率连续绘制区域稻田图。然而，用于水稻研究的陆地卫星数据显示出一些挑战，特别是数据质量问题（例如云量）。在这里，我们提出了一种算法，该算法将时间序列Landsat和MODIS（中分辨率成像光谱仪）图像与基于物候的方法（ILMP）相结合，以绘制水稻种植区和多作模式。首先，使用MODIS和Landsat数据的融合来减少云污染，这为Landsat时间序列数据增加了更多信息。其次，利用稻田在洪涝期和开冠期的独特生物物理特征（可通过植被指数动态捕捉）来识别稻田区和复种区。该算法于2015年在南昌县进行了测试，南昌县位于中国南部的鄱阳湖平原。我们使用地面真实数据和谷歌地球图像评估了稻田和复种系统的合成图。水稻种植面积的总体精度为93.66%，kappa系数为0.85。复种区的总体精度和kappa系数分别为92.95%和0.89。此外，我们的算法比2015年的国家土地覆盖数据集（NLCD）更能捕获耕地破碎化地区的详细信息。这些结果表明，我们的算法在中国南方复杂景观的稻田制图和多熟指数应用方面具有巨大的潜力。
Accurate information about the spatiotemporal patterns of rice paddies is essential for the assessment of food security, management of agricultural resources, and sustainability of ecosystems. However, accurate spatial datasets of rice paddy fields and multi-cropping at fine resolution are still lacking. Landsat observation is the primary source of remote sensing data that has continuously mapped regional rice paddy fields at a 30-m spatial resolution since the 1980s. However, Landsat data used for rice paddy studies reveals some challenges, especially data quality issues (e.g., cloud cover). Here, we present an algorithm that integrates time-series Landsat and MODIS (Moderate-resolution Imaging Spectroradiometer) images with a phenology-based approach (ILMP) to map rice paddy planting fields and multi-cropping patterns. First, a fusion of MODIS and Landsat data was used to reduce the cloud contamination, which added more information to the Landsat time series data. Second, the unique biophysical features of rice paddies during the flooding and open-canopy periods (which can be captured by the dynamics of the vegetation indices) were used to identify rice paddy regions as well as those of multi-cropping. This algorithm was tested for 2015 in Nanchang County, which is located on the Poyang Lake plain in southern China. We evaluated the resultant map of the rice paddy and multi-cropping systems using ground-truth data and Google Earth images. The overall accuracy and kappa coefficient of the rice paddy planting areas were 93.66% and 0.85, respectively. The overall accuracy and kappa coefficient of the multi-cropping regions were 92.95% and 0.89, respectively. In addition, our algorithm was more capable of capturing detailed information about areas with fragmented cropland than that of the National Land Cover Dataset (NLCD) from 2015. These results demonstrated the great potential of our algorithm for mapping rice paddy fields and using the multi-cropping index in complex landscapes in southern China.