Comparing vegetation indices from Sentinel-2 and Landsat 8 under different vegetation gradients based on a controlled grazing experiment
Comparing vegetation indices from Sentinel-2 and Landsat 8 under different vegetation gradients based on a controlled grazing experiment
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基于受控放牧实验比较不同植被梯度下 Sentinel-2 和 Landsat 8 的植被指数
DOI：
10.1016/j.ecolind.2021.108363
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发表时间：
2021-12
影响因子：
6.9
通讯作者：
Xin Xiaoping
中科院分区：
文献类型：
--
作者：
Qin Qi;Xu Dawei;Hou Lulu;Shen Beibei;Xin Xiaoping
草原对全球碳循环和畜牧生产做出了巨大贡献。然而，世界上许多草原正在遭受退化，这主要是由于过度放牧造成的。遥感方法是监测和估算草原植被参数的有效工具。在本研究中，我们比较了两种不同传感器获得的植被指数（VI）的性能，以根据长期控制放牧实验形成的典型草地生物量梯度来估计不同植被和土壤条件下的草地植被参数。选择 Sentinel-2 和 Landsat 8 作为数据源来估计两个植被参数：新鲜地上生物量（AGB）和叶面积指数（LAI）。 2019年在内蒙古呼伦贝尔市不同放牧强度（GI）实验草地现场测量的新鲜AGB和LAI数据。在VI和现场测量之间建立单变量线性混合模型，并将放牧强度视为随机因素。结果证实，植被参数（AGB 和 LAI）和 VI 随 GI 的增加而降低；然而，当GI超过0.69 Au/ha时，下降趋势并不显着。来自 Sentinel-2 和 Landsat 8 的 VI 估计新 AGB 和 LAI 的准确度为 80%。对于新鲜 AGB 和 LAI，Sentinel-2 衍生的 VI 的预测精度高于 Landsat 8。与其他VI反演模型相比，基于Sentinel-2影像的归一化差异物候指数对植被参数的估算最为有效和准确，新鲜AGB估算的决定系数（R2）为0.625，相对均方根误差（RMSE%）为18.105%；LAI估算的R2为0.559，RMSE%为14.953%。
Grasslands contribute considerably to the global carbon cycle and livestock production. However, many of the world grasslands suffer from degradation caused mainly by overgrazing. Remote sensing methods are effective tools for monitoring and estimating grassland vegetation parameters. In this study, we compared the performance of vegetation indices (VIs) obtained from two different sensors to estimate grassland vegetation parameters under different vegetation and soil conditions based on typical grassland biomass gradients formed by long-term controlled grazing experiments. Sentinel-2 and Landsat 8 were selected as data sources to estimate two vegetation parameters, fresh aboveground biomass (AGB) and leaf area index (LAI). Field-measured fresh AGB and LAI data were collected from experimental grasslands with different grazing intensities (GI) in Hulunber, Inner Mongolia, China in 2019. Univariate linear mixed models were established between VIs and field measurements, and grazing intensities were considered as random factors. The results confirmed that vegetation parameters (AGB and LAI) and VIs decreased with increasing GI; however, the decreasing trend was insignificant when the GI exceeded 0.69 Au/ha. VIs derived from Sentinel-2 and Landsat 8 estimated fresh AGB and LAI at 80% accuracy. Sentinel-2 derived VIs yielded higher predictive accuracy than Landsat 8 for both fresh AGB and LAI. Comparing with the other VI inversion models, the normalised difference phenology index derived from Sentinel-2 images estimated the vegetation parameters the most effectively and accurately, with a coefficient of determination (R2) of 0.625 and relative root mean square error (RMSE%) of 18.105% for fresh AGB estimation and R2of 0.559 and RMSE% of 14.953% for LAI estimation.
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DOI：
10.3390/s110707063
发表时间：
2011
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Delegido J;Verrelst J;Alonso L;Moreno J
通讯作者：
Moreno J
DOI：
10.3390/rs12172760
发表时间：
2020-08
期刊：
Remote. Sens.
影响因子：
--
作者：
G. Misra;F. Cawkwell;A. Wingler
通讯作者：
G. Misra;F. Cawkwell;A. Wingler
影响因子：
13.5
作者：
Djamai, Najib;Fernandes, Richard;Goita, Kalifa
通讯作者：
Goita, Kalifa
从Sentinel-2遥感影像中检索作物生物物理参数
DOI：
10.1016/j.jag.2019.04.019
发表时间：
2019
影响因子：
7.5
作者：
Qiaoyun Xie;Jadun;an Dash;Alfredo Huete;Aihui Jiang;Gaofei Yin;Yanling Ding;Dailiang Peng;Christopher Hall;Luke A. Brown;Yue Shi;Huichun Ye;Yingying Dong;Wenjiang Huang
通讯作者：
Wenjiang Huang
DOI：
10.1016/j.rama.2014.12.001
发表时间：
2014-09
期刊：
--
影响因子：
--
作者：
R. Yan;X. Xin;Yuchun Yan;Xu Wang;B. Zhang;Guixia Yang;Shimin Liu;Yu Deng;Linghao Li
通讯作者：
R. Yan;X. Xin;Yuchun Yan;Xu Wang;B. Zhang;Guixia Yang;Shimin Liu;Yu Deng;Linghao Li