Mapping daily leaf area index at 30 m resolution over a meadow steppe area by fusing Landsat, Sentinel-2A and MODIS data
Mapping daily leaf area index at 30 m resolution over a meadow steppe area by fusing Landsat, Sentinel-2A and MODIS data
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通过融合 Landsat、Sentinel-2A 和 MODIS 数据，绘制草甸草原地区 30 m 分辨率的每日叶面积指数图
DOI：
10.1080/01431161.2018.1504342
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发表时间：
2018-10
影响因子：
3.4
通讯作者：
Ruirui Yan
中科院分区：
文献类型：
--
作者：
Zhenwang Li;Chengquan Huang;Zhiliang Zhu;Feng Gao;Huan Tang;Xiaoping Xin;Lei Ding;Beibei Shen;Jinxun Liu;Baorui Chen;Xu Wang;Ruirui Yan
叶面积指数（LAI）是植被冠层结构的关键参数，与植被光合、蒸腾和能量平衡密切相关。开发具有高时间分辨率（日）的景观尺度LAI数据集对于在野外尺度上捕获快速变化的植被结构和支持区域生物物理建模工作至关重要。基于时空自适应反射融合模型（STARFM）和LAI反演辐射传输模型（PROSAIL），对中国北方某草甸草原地区2014 - 2016年2个30 m日LAI时间序列进行了分析。利用填隙Landsat 7、Landsat 8和Sentinel-2A地表反射率（SR）影像，采用PROSAIL查表法生成精细分辨率LAI地图。利用两个每日500 m中分辨率成像光谱辐射计（MODIS） LAI产品-现有的MCD15A3H LAI产品和一个由MCD43A4 SR产品和PROSAIL模型生成的LAI产品，提供LAI的时间连续变化。然后利用STARFM模型将精细分辨率LAI图与两个500 m LAI产品分别融合，生成两个每日30 m LAI时间序列。利用2014-2015年的地面测量数据，对三种草地类型（刈割草地、放牧草地和围栏草地）的两种结果进行了评估。结果表明，prosail生成的LAI图均具有较高的精度，Landsat 7和Landsat 8 LAI与地面实测LAI的均方根误差（rmse）分别为0.33和0.28。Landsat LAI图与Sentinel-2A LAI图也表现出较好的一致性和相似的空间格局，平均差值为±0.5。MCD43A4_PROSPECT LAI产品表现出与地面测量数据以及Landsat和Sentinel-2A LAI相似的季节变化，这些数据也比空白填充的MCD15A3H LAI产品更平滑，包含更少的噪点。与地面测量结果相比，由精细分辨率LAI图和PROSPECT生成的MODIS LAI产品融合的日30 m LAI时间序列的RMSE为0.44，平均绝对误差（MAE）为0.34，优于由精细分辨率LAI图和现有MCD15A3H LAI产品融合的LAI时间序列（RMSE为0.56，MAE为0.42）。后一个数据集也表现出异常的时间波动，这可能是由插值方法引起的。结果还表明，与较小斑块大小的围栏牧场相比，STARFM模型在均匀表面的放牧和刈割草地上的表现非常好。Sentinel-2A数据提供了更多的景观植被观测频率，并提供了在Landsat立交桥日期之间发生的冠层变化的时间信息。本研究提出的方案可作为区域植被动态研究的参考，并可在更大范围内应用，以提高草地的建模效果。
ABSTRACT The leaf area index (LAI) is a key vegetation canopy structure parameter and is closely associated with vegetation photosynthesis, transpiration, and energy balance. Developing a landscape-scale LAI dataset with a high temporal resolution (daily) is essential for capturing rapidly changing vegetation structure at field scales and supporting regional biophysical modeling efforts. In this study, two daily 30 m LAI time series from 2014 to 2016 over a meadow steppe site in northern China were generated using a spatial and temporal adaptive reflectance fusion model (STARFM) combined with an LAI retrieval radiative transfer model (PROSAIL). Gap-filled Landsat 7, Landsat 8 and Sentinel-2A surface reflectance (SR) images were used to generate fine-resolution LAI maps with the PROSAIL look-up table method. Two daily 500 m moderate-resolution imaging spectroradiometer (MODIS) LAI product-the existing MCD15A3H LAI product and one was generated from the MCD43A4 SR product and the PROSAIL model, were used to provide temporally continuous LAI variations. The STARFM model was then used to fuse the fine-resolution LAI maps with the two 500 m LAI products separately to generate two daily 30 m LAI time series. Both results were assessed for three types of pasture (mowed pasture, grazing pasture, and fenced pasture) using ground measurements from 2014–2015. The results showed that the PROSAIL-generated LAI maps all exhibited a high accuracy, and the root mean squared errors (RMSEs) for the Landsat 7 LAI and Landsat 8 LAI compared to the ground-measured LAI were 0.33 and 0.28 respectively. The Landsat LAI maps also showed good agreement and similar spatial patterns with the Sentinel-2A LAI with mean differences between ± 0.5. The MCD43A4_PROSPECT LAI product exhibited similar seasonal variability to the ground measurements and to the Landsat and Sentinel-2A LAIs, and these data are also smoother and contain fewer noisy points than the gap-filled MCD15A3H LAI product. Compared to the ground measurements, the daily 30 m LAI time series fused from the fine-resolution LAI maps and PROSPECT generated MODIS LAI product demonstrated better performance with an RMSE of 0.44 and a mean absolute error (MAE) of 0.34, which is an improvement from the LAI time series fused from the fine-resolution LAI maps and the existing MCD15A3H LAI product (RMSE of 0.56 and MAE of 0.42). The latter dataset also exhibited abnormal temporal fluctuations, which may have been caused by the interpolation method. The results also demonstrated the very good performance of the STARFM model in grazing and mowed pasture with homogeneous surfaces compared to fenced pasture with smaller patch sizes. The Sentinel-2A data offers increased landscape vegetation observation frequency and provides temporal information about canopy changes that occur between Landsat overpass dates. The scheme developed in this study can be used as a reference for regional vegetation dynamic studies and can be applied to larger areas to improve grassland modeling efforts.
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DOI：
10.3390/rs6076242
发表时间：
2014-07
期刊：
Remote. Sens.
影响因子：
--
作者：
Zhenwang Li;Huanli Tang;X. Xin;B. Zhang;Dongliang Wang
通讯作者：
Zhenwang Li;Huanli Tang;X. Xin;B. Zhang;Dongliang Wang
影响因子：
3.4
作者：
KoganFelix;GoldbergMitch;SchottTom;GuoWei
通讯作者：
KoganFelix;GoldbergMitch;SchottTom;GuoWei
影响因子：
3.4
作者：
M. François;S. Santandrea;K. Mellab;D. Vrancken;J. Versluys
通讯作者：
M. François;S. Santandrea;K. Mellab;D. Vrancken;J. Versluys
影响因子：
13.5
作者：
Hilker, Thomas;Wulder, Michael A.;White, Joanne C.
通讯作者：
White, Joanne C.
影响因子：
5.1
作者：
J. Theunis;M. Stevens;D. Botteldooren
通讯作者：
J. Theunis;M. Stevens;D. Botteldooren