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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Spatial Sampling Design for Estimating Regional GPP With Spatial Heterogeneities
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Spatial Sampling Design for Estimating Regional GPP With Spatial Heterogeneities

机译：用于估计具有空间异质性的区域GPP的空间采样设计

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摘要

The estimation of regional gross primary production (GPP) is a crucial issue in carbon cycle studies. One commonly used way to estimate the characteristics of GPP is to infer the total amount of GPP by collecting field samples. In this process, the spatial sampling design will affect the error variance of GPP estimation. This letter uses geostatistical model-based sampling to optimize the sampling locations in a spatial heterogeneous area. The approach is illustrated with a real-world application of designing a sampling strategy for estimating the regional GPP in the Babao river basin, China. By considering the heterogeneities in the spatial distribution of the GPP, the sampling locations were optimized by minimizing the spatially averaged interpolation error variance. To accelerate the optimization process, a spatial simulated annealing search algorithm was employed. Compared with a sampling design without considering stratification and anisotropies, the proposed sampling method reduced the error variance of regional GPP estimation.
机译：在碳循环研究中，区域初级生产总值（GPP）的估算是至关重要的问题。估计GPP特性的一种常用方法是通过收集现场样本来推断GPP的总量。在此过程中，空间采样设计将影响GPP估计的误差方差。这封信使用基于地统计模型的采样来优化空间异构区域中的采样位置。该方法在设计采样策略以估算中国八宝河流域区域GPP的实际应用中得到了说明。通过考虑GPP空间分布中的异质性，通过最小化空间平均插值误差方差来优化采样位置。为了加快优化过程，采用了空间模拟退火搜索算法。与不考虑分层和各向异性的抽样设计相比，所提出的抽样方法减少了区域GPP估计的误差方差。

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