Mosleh Mostafa K, Hassan Quazi K, Chowdhury Ehsan H
Department of Geomatics Engineering, Schulich School of Engineering, University of Calgary, 2500 University Dr NW, Calgary, Alberta T2N 1N4, Canada.
Sensors (Basel). 2015 Jan 5;15(1):769-91. doi: 10.3390/s150100769.
Rice is one of the staple foods for more than three billion people worldwide. Rice paddies accounted for approximately 11.5% of the World's arable land area during 2012. Rice provided ~19% of the global dietary energy in recent times and its annual average consumption per capita was ~65 kg during 2010-2011. Therefore, rice area mapping and forecasting its production is important for food security, where demands often exceed production due to an ever increasing population. Timely and accurate estimation of rice areas and forecasting its production can provide invaluable information for governments, planners, and decision makers in formulating policies in regard to import/export in the event of shortfall and/or surplus. The aim of this paper was to review the applicability of the remote sensing-based imagery for rice area mapping and forecasting its production. Recent advances on the resolutions (i.e., spectral, spatial, radiometric, and temporal) and availability of remote sensing imagery have allowed us timely collection of information on the growth and development stages of the rice crop. For elaborative understanding of the application of remote sensing sensors, following issues were described: the rice area mapping and forecasting its production using optical and microwave imagery, synergy between remote sensing-based methods and other developments, and their implications as an operational one. The overview of the studies to date indicated that remote sensing-based methods using optical and microwave imagery found to be encouraging. However, there were having some limitations, such as: (i) optical remote sensing imagery had relatively low spatial resolution led to inaccurate estimation of rice areas; and (ii) radar imagery would suffer from speckles, which potentially would degrade the quality of the images; and also the brightness of the backscatters were sensitive to the interacting surface. In addition, most of the methods used in forecasting rice yield were empirical in nature, so thus it would require further calibration and validation prior to implement over other geographical locations.
水稻是全球超过30亿人的主食之一。2012年，稻田面积约占世界耕地面积的11.5%。近年来，水稻提供了约19%的全球膳食能量，2010 - 2011年期间其人均年消费量约为65千克。因此，水稻种植面积测绘及其产量预测对于粮食安全至关重要，由于人口不断增长，粮食需求往往超过产量。及时、准确地估算水稻种植面积并预测其产量，可为政府、规划者和决策者在出现短缺和/或过剩情况时制定进出口政策提供宝贵信息。本文旨在综述基于遥感影像在水稻种植面积测绘及其产量预测方面的适用性。遥感影像在分辨率（即光谱、空间、辐射和时间分辨率）和可获取性方面的最新进展，使我们能够及时收集有关水稻作物生长发育阶段的信息。为详细了解遥感传感器的应用，描述了以下问题：利用光学和微波影像进行水稻种植面积测绘及其产量预测、基于遥感的方法与其他技术发展之间的协同作用，以及它们作为一种可操作方法的意义。对迄今为止的研究综述表明，利用光学和微波影像的基于遥感的方法令人鼓舞。然而，存在一些局限性，例如：（i）光学遥感影像的空间分辨率相对较低，导致水稻种植面积估算不准确；（ii）雷达影像会受到斑点影响，这可能会降低图像质量；而且后向散射的亮度对相互作用的表面很敏感。此外多数用于预测水稻产量的方法本质上是经验性的，因此在应用于其他地理位置之前需要进一步校准和验证。
