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A Rice Mapping Method Based on Time-Series Landsat Data for the Extraction of Growth Period Characteristics

机译:基于时间序列Landsat数据的水稻生育期特征提取方法

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The rapid and accurate acquisition of rice cultivation information is very important for the management and assessment of rice agriculture and for research on food security, the use of agricultural water resources, and greenhouse gas emissions. Rice mapping methods based on phenology have been widely used but further studies are needed to clearly quantify the rice characteristics during the growth cycle. This paper selected the area where rice agriculture has undergone tremendous changes as the observation object. The rice areas were mapped in three time periods during the period from 1993 to 2016 by combining the characteristics of the harvested areas, flooded areas, and the time interval when harvesting and flooding occurred. An error matrix was used to determine the mapping accuracy. After exclusion of clouds and cloud shadows, the overall accuracy of the paddy fields was higher than 90% (90.5% and 93.5% in period 1 and period 3, respectively). Mixed pixels, image quality, and image acquisition time are important factors affecting the accuracy of rice mapping. The rapid economic development led to an adjustment of people’s diets and presumably this is the main reason why rice cultivation is no longer the main agricultural production activity in the study area.
机译:快速准确地获取水稻种植信息对于水稻农业的管理和评估以及粮食安全,农业水资源的利用和温室气体排放的研究非常重要。基于物候学的水稻作图方法已被广泛使用,但需要进一步研究以清楚地量化生长周期中的水稻特性。本文选择稻作农业发生巨大变化的地区作为观察对象。结合收获地区,洪灾地区和收获洪灾的时间间隔的特点,在1993年至2016年的三个时间段内绘制了水稻面积图。使用误差矩阵来确定映射精度。排除云和云影后,稻田的总体精度高于90%(第1阶段和第3阶段分别为90.5%和93.5%)。混合像素,图像质量和图像获取时间是影响水稻定位精度的重要因素。经济的飞速发展导致人们饮食结构的调整,大概这就是水稻种植不再是研究区域主要农业生产活动的主要原因。

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