stimating land-surface temperature under clouds using MSG / SEVIRI bservations
stimating land-surface temperature under clouds using MSG / SEVIRI bservations
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发表时间：
2011
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通讯作者：
ei Lua;Valentijn Venusb;Andrew Skidmoreb;Tiejun Wangb;Geping Luoa
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作者：
ei Lua;Valentijn Venusb;Andrew Skidmoreb;Tiejun Wangb;Geping Luoa
众所周知，从热红外卫星传感器观测中恢复地表温度会受到云层的污染。因此，很少有研究集中在多云条件下的LST反演。本文提出了一种利用地球静止卫星观测提供的时间域(即MSG/SEVIRI)重建LST日周期的时间邻近像元方法，即使在卫星传感器只能记录云顶温度的阴天时刻也能得到LST估计。与Kim和Dickinson(2002)提出的邻域像元方法相比，我们的方法自然满足各种空间均质性假设，因此更适合于土地利用方式分散的地表。根据在非洲两个验证点获得的红外地表温度的现场测量进行了验证。结果不同，肯尼亚验证站的偏差为3.68K，均方根误差为5.55K，而在布基纳法索的验证站的结果更令人鼓舞，偏差为0.37K，均方根误差为5.11K。误差分析表明，多云天空低层温度估计的不确定性归因于对下层晴空低层温度、全天空全球辐射的估计误差，以及“邻近像素”方案本身固有的不准确。应用于该方法的误差传播模型表明，在最佳情况下，得到的多云天空LST的绝对误差小于1.5K，并且不确定性随晴空LST的绝对误差线性增加。尽管存在这种不确定性，所提出的方法在多云条件下对LST的反演是实用的，并且该方法有望用于重建LST
The retrieval of land-surface temperature (LST) from thermal infrared satellite sensor observations is known to suffer from cloud contamination. Hence few studies focus on LST retrieval under cloudy conditions. In this paper a temporal neighboring-pixel approach is presented that reconstructs the diurnal cycle of LST by exploiting the temporal domain offered by geo-stationary satellite observations (i.e. MSG/SEVIRI), and yields LST estimates even for overcast moments when satellite sensor can only record cloud-top temperatures. Contrasting to the neighboring pixel approach as presented by Jin and Dickinson (2002), our approach naturally satisfies all sorts of spatial homogeneity assumptions and is hence more suited for earth surfaces characterized by scattered land-use practices. Validation is performed against in situ measurements of infrared land-surface temperature obtained at two validation sites in Africa. Results vary and show a bias of −3.68 K and a RMSE of 5.55 K for the validation site in Kenya, while results obtained over the site in Burkina Faso are more encouraging with a bias of 0.37 K and RMSE of 5.11 K. Error analysis reveals that uncertainty of the estimation of cloudy sky LST is attributed to errors in estimation of the underlying clear sky LST, all-sky global radiation, and inaccuracies inherent to the ‘neighboring pixel’ scheme itself. An error propagation model applied for the proposed temporal neighboring-pixel approach reveals that the absolute error of the obtained cloudy sky LST is less than 1.5 K in the best case scenario, and the uncertainty increases linearly with the absolute error of clear sky LST. Despite this uncertainty, the proposed method is practical for retrieving the LST under a cloudy sky condition, and it ct diu is promising to reconstru