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Impact of uncertainties on assessment and validation of MODIS leaf area index products.

机译:不确定性对MODIS叶面积指数产品评估和验证的影响。

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摘要

Green leaf area of vegetation governs the exchanges of energy, mass (e.g., water and CO2) and momentum between the Earth's surface and the atmosphere. Therefore, it is being operationally derived from measurements of the MODerate resolution Imaging Spectroradiometer (MODIS) onboard NASA's Terra and Aqua spacecrafts. The validation of such remote sensing products is significant because it provides accuracy information to users and serves as a reference for further product refinement. The objectives of this research are to validate and refine the MODIS leaf area index (LAI) product, with emphasis on the effects of uncertainties on algorithm performance and validation. The uncertainties are related to MODIS data and products as well as the field and reference data used in validation.; A mismatch between reflectances modeled by the algorithm and MODIS measurements led to anomalies in the Collection 3 MODIS LAI products. Following revision for Collection 4, the main algorithm generates over 80% of the retrievals and LAI estimates agree well with several sets of field measurements. Validation of the Collection 4 LAI product over grasses and cereal crops using data from Alpilles, France suggests that the leaf area product has an accuracy of 0.3 LAI with a precision and uncertainty of 0.23 LAI and 0.25 LAI, respectively.; The existence of geolocation offsets between MODIS observations and predefined MODIS storage bins complicates product validation. The value in a bin may only partially be derived from the location of the bin. To minimize the impact of geolocation offsets, validation of the MODIS LAI product should occur at a resolution at least 8 times the native resolution. Validating multidate composite products can be more complex because bias results from the use of spectral information as the compositing criterion.; In conclusion, validation of the MODIS LAI product yields: (a) valuable insights that lead to algorithm improvements; (b) measures of the overall accuracy of the product; and (c) insights into geolocation issues pertinent to the validation of many MODIS products.
机译:植被的绿叶面积决定着地球表面与大气之间的能量,质量(例如水和二氧化碳)和动量的交换。因此,它是从NASA的Terra和Aqua航天器上的中等分辨率成像光谱仪(MODIS)的测量中获得的。此类遥感产品的验证意义重大,因为它可以为用户提供准确性信息,并为进一步完善产品提供参考。这项研究的目的是验证和完善MODIS叶面积指数(LAI)产品,重点在于不确定性对算法性能和验证的影响。不确定性与MODIS数据和产品以及验证中使用的现场和参考数据有关。该算法建模的反射率与MODIS测量值之间的不匹配导致Collection 3 MODIS LAI产品出现异常。在对Collection 4进行修订之后,主要算法生成了80%以上的检索结果,并且LAI估计值与几组现场测量结果非常吻合。使用法国Alpilles的数据对草和谷类作物上的Collection 4 LAI产品进行验证,表明叶面积产品的精度为0.3 LAI,精确度和不确定度分别为0.23 LAI和0.25 LAI。 MODIS观测值和预定义的MODIS储存仓之间存在地理位置偏移,使产品验证变得复杂。仓中的值只能部分地从仓的位置中得出。为了最大程度地降低地理位置偏移的影响,对MODIS LAI产品的验证应至少以原始分辨率的8倍进行。验证多日期复合产品可能会更加复杂,因为使用光谱信息作为合成标准会导致偏差。总而言之,对MODIS LAI产品的验证产生了:(a)有助于改进算法的宝贵见解; (b)衡量产品整体准确性的措施; (c)对与许多MODIS产品的验证有关的地理位置问题的见解。

著录项

  • 作者

    Tan, Bin.;

  • 作者单位

    Boston University.;

  • 授予单位 Boston University.;
  • 学科 Physical Geography.; Remote Sensing.
  • 学位 Ph.D.
  • 年度 2005
  • 页码 141 p.
  • 总页数 141
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 自然地理学;遥感技术;
  • 关键词

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