Enhanced scaling effects significantly lower the ability of MODIS normalized difference snow index to estimate fractional and binary snow cover on the Tibetan Plateau
Enhanced scaling effects significantly lower the ability of MODIS normalized difference snow index to estimate fractional and binary snow cover on the Tibetan Plateau
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增强的缩放效应显着降低了 MODIS 标准化差异积雪指数估计青藏高原分数和二元积雪的能力
DOI：
10.1016/j.jhydrol.2020.125795
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发表时间：
2021
影响因子：
6.4
通讯作者：
Sien Li
中科院分区：
文献类型：
--
作者：
Hongbo Zhang;Fan Zhang;Guoqing Zhang;Wei Yan;Sien Li
在青藏高原地区，积雪变化与气候变暖密切相关，利用MODIS分数（FSC）和二值（BSC）雪盖数据可以获得准确的积雪时空变化信息。然而，在最新发布的中分辨率成像分光仪雪盖产品版本（v6）中不再提供FSC和BSC数据，取而代之的是归一化差异积雪指数数据。最近的研究已经观察到明显较低的精度的中分辨率成像分光仪积雪数据的TP比其他地区，可能意味着有很强的缩放效应，由于复杂的地形和土地覆盖，这是不是很好地理解。共353 Landsat-8场景覆盖的TP的大部分地区被用来建立一个新的经验关系FSC和MODIS NDSI FSC估计和一个新的NDSI阈值BSC估计。结果表明，新的回归模型（平均均方根偏差（RMSD）：0.22）具有比先前使用的全局参考方程更好的FSC估计精度（平均RMSD：0.24），新的NDSI阈值为0.29（平均Cohen's Kappa（CK）：0.49）在BSC估计中优于全局参考NDSI阈值0.4（平均CK：0.40）。相对较低的准确度可能是由于增强的缩放效应。30米的Landsat-8 NDSI数据放大到500米（MODIS）的FSC和BSC使用MODIS NDSI估计分析尺度效应。我们发现，使用MODIS NDSI的方法有低得多的估计精度，为FSC和BSC，与那些使用放大Landsat-8 NDSI。方差分析（ANOVA）测试，考虑了512个组合的外观，坡度和归一化植被指数（NDVI）进一步表明，增强的尺度效应主要是由地形因素（即外观和坡度）。估计BSC的最佳NDSI阈值通常随坡度增加而增加，随方位从东南向西北变化而减小。这项研究具有重要的意义，最佳使用的MODIS NDSI积雪数据的TP和突出的重要性，开发更先进的方法，考虑到更多的因素。
MODIS fractional (FSC) and binary (BSC) snow-cover data are important for obtaining accurate spatiotemporal snow-cover information for the Tibetan Plateau (TP) where rapid warming is closely related to snow-cover changes. However, FSC and BSC data are no longer provided in the newly released version (v6) of the MODIS snow cover product, having been replaced with normalized difference snow index (NDSI) data. Recent studies have observed clearly lower accuracy of MODIS snow cover data on the TP than in other areas, possibly implying that there are strong scaling effects due to the complex terrain and land cover which are not well understood. A total of 353 Landsat-8 scenes covering most parts of the TP are used to establish a new empirical relationship between FSC and MODIS NDSI for FSC estimation and a new NDSI threshold for BSC estimation. The results indicate that the new regression model (mean root-mean-squared-deviation (RMSD): 0.22) has a better FSC estimation accuracy than the previously used global reference equation (mean RMSD: 0.24) and that the new NDSI threshold of 0.29 (mean Cohen’s Kappa (CK): 0.49) outperforms the global reference NDSI threshold of 0.4 (mean CK: 0.40) in BSC estimation. The relatively low accuracy could be due to an enhanced scaling effect. The 30-m Landsat-8 NDSI data are upscaled to 500-m (MODIS) to analyze the scaling effects on FSC and BSC estimates made using MODIS NDSI. We find that the methods using MODIS NDSI have much lower estimation accuracy, for both FSC and BSC, compared with those using upscaled Landsat-8 NDSI. An analysis of variance (ANOVA) test which considers 512 combinations of aspect, slope and normalized difference vegetation index (NDVI) further demonstrates that the enhanced scaling effects are mainly caused by terrain factors (i.e. aspect and slope). The optimal NDSI threshold for estimating BSC generally increases with slope and decreases as the aspect varies from the southeast to northwest. This study has important implications for the optimal use of MODIS NDSI snow cover data on the TP and highlights the importance of developing more advanced methods which take more factors into account.
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影响因子：
13.5
作者：
Czyzowska-Wisniewski, Elzbieta H.;van Leeuwen, Willem J. D.;Wisniewski, Wit T.
通讯作者：
Wisniewski, Wit T.
DOI：
10.3390/rs12060962
发表时间：
2020-03
期刊：
Remote. Sens.
影响因子：
--
作者：
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通讯作者：
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影响因子：
13.5
作者：
Zhang Guoqing;Yao T;ong;Chen Wenfeng;Zheng Guoxiong;Shum C K;Yang Kun;Piao Shilong;Sheng Yongwei;Yi Shuang;Li Junli;O'Reilly Catherine M;Qi Shuhua;Shen Samuel S P;Zhang Hongbo;Jia Yuanyuan
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DOI：
10.1109/jstars.2018.2810094
发表时间：
2018
影响因子：
5.5
作者：
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影响因子：
1.7
作者：
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通讯作者：
Zhiguang Tang;Jian Wang;Hongyi Li;Lili Yan