Intercomparison of Landsat albedo retrieval techniques and evaluation against in situ measurements across the US SURFRAD network
Intercomparison of Landsat albedo retrieval techniques and evaluation against in situ measurements across the US SURFRAD network
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DOI：
10.1016/j.rse.2014.07.019
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发表时间：
2014-09-01
影响因子：
13.5
通讯作者：
Claverie, M.
中科院分区：
文献类型：
--
作者：
Franch, B.;Vermote, E. F.;Claverie, M.
地表温度不仅是发展气候模式的一个基本参数，而且也是大多数能量平衡研究的一个基本参数。虽然气候模式通常以粗分辨率应用，但主要侧重于农业应用的能量平衡研究需要高空间分辨率。在这方面，大地卫星是最常用的遥感传感器之一，通过双向反射分布函数（BRDF）的角度积分估计的反射率，需要对表面进行适当的角度采样。本文提出了一种基于中分辨率成像光谱仪（MODIS）气候模拟网格（CMG）地表反射率产品估算的BRDF参数的陆地卫星地表反射率产品反演算法（M{0，Y} D 09），使用VJB方法（Vermote，Justice，& Breon，2009）。我们的方法基于Landsat无监督分类，将BRDF参数分解为Landsat空间分辨率。我们在美国地面辐射（SURFRAD）网络的五个不同站点上测试了所提出的算法，并将我们的结果与Shuai，Masek，Gao和Schaaf（2011）方法进行了比较，该方法也提供了Landsat的数据。结果表明，该方法可以得到的表面粗糙度的均方根误差（RMSE）为0.015（7%）。该结果假设与Shuai等人相比，RMSE提高了5%。的方法（RMSE为0.024，12%），主要通过校正负偏差（比原位数据更低的检索到的数据）来确定。(C)2014爱思唯尔公司All rights reserved.
Surface albedo is an essential parameter not only for developing climate models, but also for most energy balance studies. While climate models are usually applied at coarse resolution, the energy balance studies, which are mainly focused on agricultural applications, require a high spatial resolution. In this context Landsat is one of the most used remote sensing sensors.The albedo, estimated through the angular integration of the Bidirectional Reflectance Distribution Function (BRDF), requires an appropriate angular sampling of the surface. However, Landsat sampling characteristics, with nearly constant observation geometry and low illumination variation, prevent from deriving a surface albedo product.In this paper we present an algorithm to derive a Landsat surface albedo based on the BRDF parameters estimated from the MODerate Resolution Imaging Spectroradiometer (MODIS) Climate Modeling Grid (CMG) surface reflectance product (M{O,Y}D09) using the VJB method (Vermote, Justice, & Breon, 2009). We base our method on Landsat unsupervised classification to disaggregate the BRDF parameters to the Landsat spatial resolution. We tested the proposed algorithm over five different sites of the US Surface Radiation (SURFRAD) network and inter-compare our results with Shuai, Masek, Gao, and Schaaf (2011) method, which also provides Landsat albedo. The results show that with the proposed method we can derive the surface albedo with a Root Mean Square Error (RMSE) of 0.015 (7%). This result supposes an improvement of 5% in the RMSE compared to Shuai et al.'s (2011) method (with a RMSE of 0.024, 12%) that is mainly determined by the correction of the negative bias (lower retrieved albedo than in situ data). (C) 2014 Elsevier Inc. All rights reserved.