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Thermal remote sensing of near-surface water vapor

机译:近地表水蒸气的热遥感

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In this study, four approaches to estimate atmospheric water vapor from Advanced Very High Resolution Radiometer (AVHRR) observations were tested with data from the Boreal Ecosystem - Atmosphere Study (BOREAS) and the Oklahoma Mesonetwork. The approaches studied were (i) the split-window difference of the thermal channels (Channel 4: 10.3- 11.3μm and Channel 5:11.5-12.5μm) by Dalu [Int. J. Remote Sens. 7 (1986)1089.], (ii) the ratio of variances by Jedlovec [J. Appl. Meteorol. 29 (1990) 863.], (iii) the regression slope by Goward et al.[Ecol. Appl. 4 (1994) 322.], and (iv) a look-up table derived from radiative transfer model output. Although these techniques were primarily developed to estimate total column precipitable water, we used them to estimate near-surface water vapor, within a few meters of the surface. Near-surface water vapor is needed for hydrologic and biospheric modeling. Analysis showed the total column precipitable water to be highly correlated (r{sup}2 = .79) with near-surface absolute humidity for clear-sky conditions at the BOREAS and the Oklahoma study sites. Correlation of all the retrieval techniques with ground observations was very low. For the split-window approach, water vapor can only be estimated on a per pixel basis and is ambiguous for anything but a single site. The regression slope and variance ratio techniques showed very little correlation with ground observations with r{sup}2 = .02 when compared with data from BOREAS, and .17 for the variance ratio and .24 for the regression slope when compared with Mesonet data. The spatial variability of water vapor across the landscape hampers the use of these contextual approaches. The highest correlation was for the look-up table approach, with r{sup}2 = .36 when compared with data from the BOREAS site. The look-up table was applied using AVHRR Channels 4 and 5 brightness temperatures, surface temperature, and near-surface air temperature. Surface temperature and air temperature were both estimated from the satellite readings. Combining the satellite data with air temperature measured at meteorological ground stations improved the correlation to .50. The relatively low r{sup}2 values were at least partly due to spatial and temporal mismatches between surface and satellite measurements. Simulation of Moderate Resolution Imaging Spectrometer (MODIS) thermal Channels 29(8.4-8.7μm), 31 (10.78-11.28μm), and 32 (11.77-12.27μm) brightness temperatures showed that Channels 31 and 32 provide similar information as AVHRR Channels 4 and 5. The additional thermal information provided by Channel 29 shows promise for future water vapor detection efforts.
机译:在这项研究中,使用来自北方生态系统-大气研究(BOREAS)和俄克拉荷马州细观网络的数据,测试了四种通过高级超高分辨率辐射计(AVHRR)估算大气水汽的方法。研究的方法是:(i)Dalu的热通道(通道4:10.3-11.3μm和通道5:11.5-12.5μm)的分割窗口差异[Int。 J. Remote Sens。7(1986)1089。],(ii)Jedlovec的差异比率[J.应用陨石29(1990)863。],(iii)Goward等人的回归斜率[Ecol。应用参见图4(1994)322。],和(iv)从辐射传递模型输出得到的查找表。尽管最初开发这些技术是为了估计塔中的总可沉淀水量,但我们还是使用它们来估计地表几米内的近地表水蒸气。水文和生物圈模拟需要近地表水蒸气。分析显示,在BOREAS和俄克拉荷马州研究地点晴朗的天空条件下,总柱可沉淀水与近地表绝对湿度高度相关(r {sup} 2 = .79)。所有检索技术与地面观测的相关性非常低。对于分割窗口方法,只能以每个像素为单位估算水蒸气,并且除了单个站点外,水蒸气都是模糊的。与来自BOREAS的数据相比,回归斜率和方差比技术与地面观测的相关性很小,r {sup} 2 = .02,与Mesonet数据相比,方差比为.17,回归斜率为.24。整个景观中水蒸气的空间变化会阻碍这些上下文方法的使用。与查找表方法相关性最高,与BOREAS网站的数据相比,r {sup} 2 = 0.36。使用AVHRR通道4和5的亮度温度,表面温度和近地表空气温度应用查找表。表面温度和空气温度均由卫星读数估算得出。将卫星数据与气象地面站测得的气温结合起来,相关系数提高到0.50。相对较低的r {sup} 2值至少部分是由于地面和卫星测量值之间的空间和时间不匹配所致。中分辨率成像光谱仪(MODIS)热通道29(8.4-8.7μm),31(10.78-11.28μm)和32(11.77-12.27μm)亮度温度的仿真表明,通道31和32提供的信息与AVHRR通道4类似5.通道29提供的附加热信息显示了对未来水蒸气检测工作的希望。

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