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基于葵花-8卫星大气产品的地表下行短波辐射计算 总被引：2，自引：2，他引：0
地表下行短波辐射DSSR(Downward Surface Shortwave Radiation)的准确估算在气候变化研究和地表太阳能估算等领域具有重要作用。新一代静止气象卫星葵花-8(Himawari-8)具有高达10 min的对地观测能力,为DSSR近实时估算提供了新机遇。然而,日本宇宙航空研究开发机构(JAXA)对外公开的葵花-8辐射产品中,没有将其反演的云、气溶胶产品作为DSSR的输入参数,从而没有形成一整套的DSSR估算算法流程,缺乏产品输出的一致性。大气中的云、气溶胶是DSSR的重要影响因子,本文重点考虑云、气溶胶对太阳辐射的影响,基于大气辐射传输模式RSTAR构建了DSSR查找表,开发了DSSR的快速计算方法,进而将JAXA葵花-8二级云、气溶胶产品(光学厚度,粒子有效半径等)作为快速化计算方法的输入参量,计算得到了DSSR。通过与JAXA葵花-8二级DSSR产品(JAXA DSSR)对比,发现两者具有很好的空间一致性。为了进一步评价本文的DSSR计算精度,分别选取了陆地(Yonsei)和海洋(0n_165e)的观测数据验证了2016年4、7、10和12月本文计算的DSSR和同时期的JAXA DSSR产品,验证结果显示两者的DSSR在两个观测站点均具有非常高的相关性(全天空、晴空和云天条件下的相关系数R均大于0.88)。在两个站点云天条件下的验证结果中,考虑了云相态并在冰云模型中使用了非球形冰晶粒子(六棱柱)来计算DSSR,获得了比JAXA DSSR更小的偏差。本文提出的快速化计算方法能快速准确地计算DSSR,可为计算地表辐射收支等研究提供重要数据支撑。 相似文献
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介绍了采用现代较为常用的模拟退火算法对基于LUT结构的FPGA进行工艺映射的计算。首次采用点可见的编码方法对工艺映射的结果进行编码，在此基础上采用模拟退火算法进行计算，获得了较好的效果和收敛速度。 相似文献
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介绍了采用曝光时间小于 10的高速CCD以及相应的图像卡成功实现白天强背景光下实时显示激光束的方法 ,该方法可以有效提高卫星激光测距的白天测距能力。 相似文献
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Information about pigment and water contents provides comprehensive insights for evaluating photosynthetic potential and activity of agricultural crops. In this study, we present the concept of using spectral integral ratios (SIR) to retrieve three biochemical traits, namely chlorophyll a and b (Cab), carotenoids (Ccx), and water (Cw) content, simultaneously from hyperspectral measurements in the wavelength range 460−1100 nm. The SIR concept is based on automatic separation of respective absorption features through local peak and intercept analysis between log-transformed reflectance and convex hulls. The algorithm was tested on two synthetically established databases using a physiologically constrained look-up-table (LUT) generated by (i) the leaf optical properties model PROSPECT and (ii) the canopy radiative transfer model (RTM) PROSAIL. LUT constraints were realized based on natural Ccx-Cab relations and green peak locations identified in the leaf optical database ANGERS. Linear regression between obtained SIRs and model parameters resulted in coefficients of determination (R²) of 0.66 (i and ii) for Ccx, R2 = 0.85 (i) and 0.53 (ii) for Cab, and R2 = 0.97 (i) and 0.67 (ii) for Cw, respectively. Using the model established from the PROSPECT LUT, leaf level validation was carried out based on ANGERS data with reasonable results both in terms of goodness of fit and root mean square error (RMSE) (Ccx: R2 = 0.86, RMSE = 2.1 μg cm−2; Cab: R2 = 0.67, RMSE = 12.5 μg cm-2; Cw: R2 = 0.89, RMSE = 0.007 cm). The algorithm was applied to airborne spectrometric HyMap data acquired on 12th July 2003 in Barrax, Spain and to AVIRIS-NG data recorded on 2nd July 2018 southwest of Munich, Germany. Mapping of the SIR results as multiband images (3-segment SIR) allows for intuitive visualization of dominant absorptions with respect to the three considered biochemical variables. Barrax in situ validation using linear regression models derived from PROSAIL LUT showed satisfactory results regarding Cab (R2 = 0.84; RMSE = 9.06 μg cm-2) and canopy water content (CWC, R2 = 0.70; RMSE = 0.05 cm). Retrieved Ccx values were reasonable according to Cab-Ccx-dependence plausibility analysis. Hence, the presented SIR algorithm allows for computationally efficient and RTM supported robust retrievals of the two most important vegetation pigments as well as of water content and is ready to be applied on satellite imaging spectroscopy data available in the near future. The algorithm is publicly available as an interface supported tool within the 'Agricultural Applications' of the EnMAP-Box 3 hyperspectral remote sensing software suite. 相似文献
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