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首页> 外文会议>International Conference on Agro-Geoinformatics >Crop Fraction Layer (CFL) datasets derived through MODIS and LandSat for the Continental US from year 2000 - 2016
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Crop Fraction Layer (CFL) datasets derived through MODIS and LandSat for the Continental US from year 2000 - 2016

机译：从2000 - 2016年度，通过Modis和Landsat获得的作物分数层（CFL）数据集来自美国大陆的美国大陆

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With ever growing population and shrinkage of agricultural land, food security is an extremely important research topic. Besides human activates, natural disasters such as flood, drought also play adverse effect on food productivity. Understanding impact of these disaster on crop yield and making early estimation could help planning for any global food crisis. Among various available crop yield estimation methods, remote sensing platform provides numerous indices on crop monitoring. Moderate Resolution Imaging Spectroradiometer (MODIS) based vegetation indices are among the most extensively used parameter and can provide very high (250m) spatial resolution products with daily coverage which is ideal for crop growth monitoring, however it lacks crop type information. Depending on a single spectral pattern to identify the crop type is also not effective as it suffer from mix-pixel issue. To avoid the issue of mix-pixel, this research aims to provide pixel level crop percentage data, Crop Fraction Layer (CFL), derived by combining 250m MODIS dataset with higher spatial resolution LandSat land cover product The annual CFL will be available between years 2000 to 2016 for 10 major crops in the continental US. Additionally these CFL datasets are made accessible to the end-user through web-based application.
机译：随着农业用地的人口和收缩，粮食安全是一个极其重要的研究课题。除了人类激活，洪水等自然灾害，干旱也对食品生产力发挥不利影响。了解这些灾难对作物产量的影响，提前估计可以帮助计划任何全球粮食危机。在各种可用的作物产量估计方法中，遥感平台在作物监测中提供了许多指标。适量的分辨率成像分光镜（MODIS）基于植被指数是最广泛使用的参数，可以提供非常高（250米）的空间分辨率，每日覆盖率是作物生长监测的理想选择，但它缺乏作物类型信息。根据单个光谱模式，以识别作物类型也是没有生效的，因为它遭受混合像素问题。为了避免混合像素的问题，本研究旨在提供通过将250M Modis数据集组合具有较高空间分辨率Landsat Land Poceder的250M Modis DataSet来提供像素级别作物百分比数据，年度CFL在2000年之间可用到2016年，美国大陆的10个主要作物。另外，这些CFL数据集可以通过基于Web的应用程序访问最终用户。

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