Comparison of spatial sampling strategies for ground sampling and validation of MODIS LAI products
Comparison of spatial sampling strategies for ground sampling and validation of MODIS LAI products
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DOI：
10.1080/01431161.2014.967889
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发表时间：
2014-10-18
影响因子：
3.4
通讯作者：
Zhao, Kai
中科院分区：
文献类型：
--
作者：
Ding, Yanling;Ge, Yong;Zhao, Kai
制定有效的地面采样策略对于评估与中等或粗分辨率遥感产品有关的不确定性至关重要。本文介绍了在1km(2)空间尺度下，利用空间随机采样（SRS）、块克里格（BK）和非均匀性表面（MSN）方法从精细空间分辨率图像中估计空间均值的比较。为此，我们重点研究了地面数据测量的采样策略，并对上述三种方法估算的空间均值验证的MODIS LAI产品进行了评估。研究结果表明：(1)基于有效的分层策略和最小均方估计误差准则，MSN在估计分层非均匀表面均值时具有最小的均方估计误差；(2) BK能有效地估计均匀曲面的均值，且无偏差，且均方估计误差最小。MODIS LAI产品采用基于Landsat 8 OLI和SPOT HRV精细分辨率LAI图的SRS、BK和MSN估算的均值进行评估。对于非均质曲面，与BK和SRS相比，MSN的MODIS LAI产品RMSE低，精度高，而对于均匀曲面，这三种方法输出的统计参数相似。这些结果表明，MSN是估计非均质表面空间平均值的有效方法。三种方法评估的MODIS LAI产品精度存在差异。
The development of an efficient ground sampling strategy is critical to assess uncertainties associated with moderate- or coarse-resolution remote-sensing products. This work presents a comparison of estimating spatial means from fine spatial resolution images using spatial random sampling (SRS), Block Kriging (BK), and Means of Surface with Nonhomogeneity (MSN) at 1km(2) spatial scale. Towards this goal, we focus on the sampling strategies for ground data measurements and provide an assessment of the MODIS LAI product validated by the spatial means estimated by the above-mentioned three methods. The results of this study indicate that: (1) for its effective stratification strategies and its criteria of minimum mean square estimation error, MSN demonstrates the lowest mean squared estimation error for estimating the means of stratified nonhomogeneous surface; (2) BK is efficient in estimating the means of homogeneous surfaces without bias and with minimum mean squared estimation errors. The MODIS LAI product is assessed using the means estimated by SRS, BK, and MSN based on Landsat 8 OLI and SPOT HRV fine-resolution LAI maps. For heterogeneous surfaces, MSN results in low RMSE and high accuracy of MODIS LAI product compared with BK and SRS, whereas for homogeneous surfaces, the statistical parameters outputted by these three methods are similar. These results reveal that MSN is an effective method for estimating the spatial means for heterogeneous surfaces. There are differences in the accuracies of MODIS LAI product assessed by these three methods.