Institute of Agricultural Remote Sensing and Information Application, Zhejiang University, Hangzhou 310058, China; Key Laboratory of Agricultural Remote Sensing and Information System of Zhejiang Province, Hangzhou 310058, China; Ministry of Education Key Laboratory of Environmental Remediation and Ecological Health, Zhejiang University, Hangzhou 310058, China.
J Zhejiang Univ Sci B. 2013 Oct;14(10):934-46. doi: 10.1631/jzus.B1200352.
The objective of this study was to investigate the tempo-spatial distribution of paddy rice in Northeast China using moderate resolution imaging spectroradiometer (MODIS) data. We developed an algorithm for detection and estimation of the transplanting and flooding periods of paddy rice with a combination of enhanced vegetation index (EVI) and land surface water index with a central wavelength at 2130 nm (LSWI2130). In two intensive sites in Northeast China, fine resolution satellite imagery was used to validate the performance of the algorithm at pixel and 3×3 pixel window levels, respectively. The commission and omission errors in both of the intensive sites were approximately less than 20%. Based on the algorithm, annual distribution of paddy rice in Northeast China from 2001 to 2009 was mapped and analyzed. The results demonstrated that the MODIS-derived area was highly correlated with published agricultural statistical data with a coefficient of determination (R(2)) value of 0.847. It also revealed a sharp decline in 2003, especially in the Sanjiang Plain located in the northeast of Heilongjiang Province, due to the oversupply and price decline of rice in 2002. These results suggest that the approaches are available for accurate and reliable monitoring of rice cultivated areas and variation on a large scale.
本研究旨在利用中分辨率成像光谱仪（MODIS）数据调查中国东北地区水稻的时空分布。我们结合增强植被指数（EVI）和地面水指数（LSWI2130）的中心波长 2130nm，开发了一种用于检测和估计水稻移栽和淹水期的算法。在东北地区的两个密集站点，分别使用高分辨率卫星图像在像素和 3×3 像素窗口级别验证算法的性能。两个密集站点的误差率（commission 和 omission）均小于 20%。基于该算法，绘制和分析了 2001 年至 2009 年中国东北地区水稻的年度分布。结果表明，MODIS 衍生的面积与已公布的农业统计数据高度相关，决定系数（R²）值为 0.847。它还揭示了 2003 年的急剧下降，特别是在黑龙江省东北部的三江平原，这是由于 2002 年大米供过于求和价格下跌。这些结果表明，这些方法可用于准确可靠地监测大面积水稻种植区和变化。
