Mapping paddy rice planting area in cold temperate climate region through analysis of time series Landsat 8 (OLI), Landsat 7 (ETM+) and MODIS imagery.
Mapping paddy rice planting area in cold temperate climate region through analysis of time series Landsat 8 (OLI), Landsat 7 (ETM+) and MODIS imagery.
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DOI：
10.1016/j.isprsjprs.2015.04.008
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发表时间：
2015-07
期刊：
影响因子：
--
通讯作者：
Li X
中科院分区：
文献类型：
--
作者：
Qin Y;Xiao X;Dong J;Zhou Y;Zhu Z;Zhang G;Du G;Jin C;Kou W;Wang J;Li X
准确、及时、空间分辨率高的稻田地图将大大提高我们对稻田农业对温室气体排放、粮食和水安全以及人类健康影响的认识。稻田地图是使用具有高时间分辨率和粗空间分辨率的光学图像（例如，中分辨率成像光谱仪（MODIS））或低时间分辨率和高空间分辨率（例如，Landsat TM/ETM+）。过去，由于数据的可获得性差和基于图像的算法，限制了高空间分辨率下稻田制图的精度和效率。本文利用时间序列的MODIS和Landsat ETM+/OLI影像，采用基于像元和物候的算法，对水稻种植面积进行了制图。利用植被指数的动态变化特征，捕捉水稻田在淹水/开盖期间的独特物理特征，进而识别水稻田。该算法于2013年在中国三江平原（路径/行114/27）进行了测试。由Landsat ETM+和OLI产生的水稻种植面积的地图的整体精度为97.3%，与来自地理参考的实地照片的兴趣区（AOI）进行评估。水稻种植面积图也与国营农场一级的官方统计数据相当吻合（R2 = 0.94）。这些结果表明，高空间分辨率图像和基于物候的算法相结合，可以提供一个简单的，强大的，和自动化的方法来映射水稻农业在一年中的分布。
Accurate and timely rice paddy field maps with a fine spatial resolution would greatly improve our understanding of the effects of paddy rice agriculture on greenhouse gases emissions, food and water security, and human health. Rice paddy field maps were developed using optical images with high temporal resolution and coarse spatial resolution (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS)) or low temporal resolution and high spatial resolution (e.g., Landsat TM/ETM+). In the past, the accuracy and efficiency for rice paddy field mapping at fine spatial resolutions were limited by the poor data availability and image-based algorithms. In this paper, time series MODIS and Landsat ETM+/OLI images, and the pixel- and phenology-based algorithm are used to map paddy rice planting area. The unique physical features of rice paddy fields during the flooding/open-canopy period are captured with the dynamics of vegetation indices, which are then used to identify rice paddy fields. The algorithm is tested in the Sanjiang Plain (path/row 114/27) in China in 2013. The overall accuracy of the resulted map of paddy rice planting area generated by both Landsat ETM+ and OLI is 97.3%, when evaluated with areas of interest (AOIs) derived from geo-referenced field photos. The paddy rice planting area map also agrees reasonably well with the official statistics at the level of state farms (R2 = 0.94). These results demonstrate that the combination of fine spatial resolution images and the phenology-based algorithm can provide a simple, robust, and automated approach to map the distribution of paddy rice agriculture in a year.
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