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首页> 外文期刊>ISPRS Journal of Photogrammetry and Remote Sensing >Monitoring interannual variation in global crop yield using long-term AVHRR and MODIS observations
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Monitoring interannual variation in global crop yield using long-term AVHRR and MODIS observations

机译:使用长期AVHRR和MODIS监测全球作物产量的年际变化

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Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) data have been extensively applied for crop yield prediction because of their daily temporal resolution and a global coverage. This study investigated global crop yield using daily two band Enhanced Vegetation Index (EVI2) derived from AVHRR (1981-1999) and MODIS (2000-2013) observations at a spatial resolution of 0.05 degrees (similar to 5 km). Specifically, EVI2 temporal trajectory of crop growth was simulated using a hybrid piecewise logistic model (HPLM) for individual pixels, which was used to detect crop phenological metrics. The derived crop phenology was then applied to calculate crop greenness defined as EVI2 amplitude and EVI2 integration during annual crop growing seasons, which was further aggregated for croplands in each country, respectively. The interannual variations in EVI2 amplitude and EVI2 integration were combined to correlate to the variation in cereal yield from 1982-2012 for individual countries using a stepwise regression model, respectively. The results show that the confidence level of the established regression models was higher than 90% (P value < 0.1) in most countries in the northern hemisphere although it was relatively poor in the southern hemisphere (mainly in Africa). The error in the yield predication was relatively smaller in America, Europe and East Asia than that in Africa. In the 10 countries with largest cereal production across the world, the prediction error was less than 9% during past three decades. This suggests that crop phenology-controlled greenness from coarse resolution satellite data has the capability of predicting national crop yield across the world, which could provide timely and reliable crop information for global agricultural trade and policymakers. (C) 2016 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.
机译:先进的超高分辨率辐射计(AVHRR)和中等分辨率成像光谱仪(MODIS)数据由于其日常的时间分辨率和全球覆盖范围而被广泛用于作物产量的预测。这项研究使用每日两波段增强植被指数(EVI2)来调查全球作物产量,该指数来自AVHRR(1981-1999)和MODIS(2000-2013)观测值,空间分辨率为0.05度(约5 km)。具体而言,使用混合分段逻辑模型(HPLM)对单个像素模拟了作物生长的EVI2时间轨迹,该模型用于检测作物物候指标。然后将得出的作物物候态应用于计算作物的绿色度,定义为年度作物生长期中的EVI2振幅和EVI2积分,分别将其分别汇总到每个国家的农田中。分别使用逐步回归模型,将EVI2振幅和EVI2积分的年际变化相结合,以分别与1982-2012年各个国家的谷物产量变化相关。结果表明,尽管在南半球(主要在非洲)相对较差,但在北半球的大多数国家中,已建立的回归模型的置信度高于90%(P值<0.1)。在美洲,欧洲和东亚,单产预测的误差相对小于非洲。在全球谷物产量最高的10个国家中,过去三十年的预测误差小于9%。这表明,从粗分辨率卫星数据获得的作物物候控制的绿色度可以预测世界范围内的国家作物产量,这可以为全球农业贸易和政策制定者提供及时,可靠的作物信息。 (C)2016国际摄影测量与遥感学会(ISPRS)。由Elsevier B.V.发布。保留所有权利。

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