Estimation of net radiation from the MODIS data under all sky conditions: Southern Great Plains case study

Net radiation is a key component in the surface radiation budget. Numerous studies have developed frameworks to estimate net radiation or its components (upwelling or downwelling longwave and/or shortwave radiation) from remote sensing data for clear sky conditions. Application of existing methodologies to estimate net radiation for cloudy sky conditions from remote sensing sensors remains a significant challenge. In this paper, we present a framework to estimate instantaneous and daily average net radiation under all sky conditions from using the data from the MODerate Resolution Imaging Spectroradiometer (MODIS), onboard from the Terra satellites. Bisht et al. (2005) methodology is used for the clear sky portion of the MODIS overpass; while for cloudy portion of the MODIS overpass an extension of Bisht et al. (2005) methodology is applied. The extension of Bisht et al. (2005) methodology utilizes the MODIS cloud data product (MOD06_L2) for cloud top temperature, cloud fraction, cloud emissivity, cloud optical thickness and land surface temperature for cloudy days. The methodology is applied over the Southern Great Plains (SGP) for a time period covering all seasons of 2006. During the MODIS-Terra overpasses in 2006 over the SGP, only 24% of day-overpasses and 9% of night-overpasses had 75% or more of the study region as cloud free. Thus, this proposed study is applicable to a large portion of the MODIS-Terra overpasses. The root mean square errors (RMSE) of instantaneous and daily average net radiation estimated under cloudy conditions using the MOD06_L2 product, comparing to ground-based measurements are 37 W m^− 2 and 38 W m^− 2, respectively. The strength of the proposed methodology is that it can rely exclusively on remote sensing data in the absence of ancillary ground observations, thus it has a potential to estimate surface energy budget globally.     

Improving the SMAC atmospheric correction code by analysis of Meteosat Second Generation NDVI and surface reflectance data

In order to obtain high quality data, the correction of atmospheric perturbations acting upon land surface reflectance measurements recorded by a space-based sensor is an important topic within remote sensing. For many years the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer model and the Simplified Method for Atmospheric Correction (SMAC) codes have been used for this atmospheric correction, but previous studies have shown that in a number of situations the quality of correction provided by the SMAC is low. This paper describes a method designed to improve the quality of the SMAC atmospheric correction algorithm through a slight increase in its computational complexity. Data gathered from the SEVIRI aboard Meteosat Second Generation (MSG) is used to validate the additions to SMAC, both by comparison to simulated data corrected using the highly accurate 6S method and by comparison to in-situ and 6S corrected SEVIRI data gathered for two field sites in Africa. The additions to the SMAC are found to greatly increase the quality of atmospheric correction performed, as well as broaden the range of atmospheric conditions under which the SMAC can be applied. When examining the Normalised Difference Vegetation Index (NDVI), the relative difference between SMAC and in-situ values decreases by 1.5% with the improvements in place. Similarly, the mean relative difference between SMAC and 6S reflectance values decreases by a mean of 13, 14.5 and 8.5% for Channels 1, 2 and 3 respectively. Furthermore, the processing speed of the SMAC is found to remain largely unaffected, with only a small increase in the time taken to process a full SEVIRI scene. Whilst the method described within this paper is only applicable to SEVIRI data, a similar approach can be applied to other data sources than SEVIRI, and should result in a similar accuracy improvement no matter which instrument supplies the original data.     

Switching model predictive attitude control for a quadrotor helicopter subject to atmospheric disturbances

In this article a switching model predictive attitude controller for an unmanned quadrotor helicopter subject to atmospheric disturbances is presented. The proposed control scheme is computed based on a piecewise affine (PWA) model of the quadrotor's attitude dynamics, where the effects of the atmospheric turbulence are taken into consideration as additive disturbances. The switchings among the PWA models are ruled by the rate of the rotation angles and for each PWA system a corresponding model predictive controller is computed. The suggested algorithm is verified in experimental studies in the execution of sudden maneuvers subject to forcible wind disturbances. The quadrotor rejects the induced wind disturbances while performing accurate attitude tracking.     

An assessment of photosynthetic light use efficiency from space: Modeling the atmospheric and directional impacts on PRI reflectance

Estimation of photosynthetic light use efficiency (ε) from satellite observations is an important component of climate change research. The photochemical reflectance index, a narrow waveband index based on the reflectance at 531 and 570 nm, allows sampling of the photosynthetic activity of leaves; upscaling of these measurements to landscape and global scales, however, remains challenging. Only a few studies have used spaceborne observations of PRI so far, and research has largely focused on the MODIS sensor. Its daily global coverage and the capacity to detect a narrow reflectance band at 531 nm make it the best available choice for sensing ε from space. Previous results however, have identified a number of key issues with MODIS-based observations of PRI. First, the differences between the footprint of eddy covariance (EC) measurements and the MODIS footprint, which is determined by the sensor's observation geometry make a direct comparison between both data sources challenging and second, the PRI reflectance bands are affected by atmospheric scattering effects confounding the existing physiological signal. In this study we introduce a new approach for upscaling EC based ε measurements to MODIS. First, EC-measured ε values were “translated” into a tower-level optical PRI signal using AMSPEC, an automated multi-angular, tower-based spectroradiometer instrument. AMSPEC enabled us to adjust tower-measured PRI values to the individual viewing geometry of each MODIS overpass. Second, MODIS data were atmospherically corrected using a Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, which uses a time series approach and an image-based rather than pixel-based processing for simultaneous retrievals of atmospheric aerosol and surface bidirectional reflectance (BRDF). Using this approach, we found a strong relationship between tower-based and spaceborne reflectance measurements (r² = 0.74, p < 0.01) throughout the vegetation period of 2006. Swath (non-gridded) observations yielded stronger correlations than gridded data (r² = 0.58, p < 0.01) both of which included forward and backscatter observations. Spaceborne PRI values were strongly related to canopy shadow fractions and varied with different levels of ε. We conclude that MAIAC-corrected MODIS observations were able to track the site-level physiological changes from space throughout the observation period.     

Utility of an image-based canopy reflectance modeling tool for remote estimation of LAI and leaf chlorophyll content at the field scale

This paper presents a physically-based approach for estimating critical variables describing land surface vegetation canopies, relying on remotely sensed data that can be acquired from operational satellite sensors. The REGularized canopy reFLECtance (REGFLEC) modeling tool couples leaf optics (PROSPECT), canopy reflectance (ACRM), and atmospheric radiative transfer (6SV1) model components, facilitating the direct use of at-sensor radiances in green, red and near-infrared wavelengths for the inverse retrieval of leaf chlorophyll content (C_ab) and total one-sided leaf area per unit ground area (LAI). The inversion of the canopy reflectance model is constrained by assuming limited variability of leaf structure, vegetation clumping, and leaf inclination angle within a given crop field and by exploiting the added radiometric information content of pixels belonging to the same field. A look-up-table with a suite of pre-computed spectral reflectance relationships, each a function of canopy characteristics, soil background effects and external conditions, is accessed for fast pixel-wise biophysical parameter retrievals. Using 1 m resolution aircraft and 10 m resolution SPOT-5 imagery, REGFLEC effectuated robust biophysical parameter retrievals for a corn field characterized by a wide range in leaf chlorophyll levels and intermixed green and senescent leaf material. Validation against in-situ observations yielded relative root-mean-square deviations (RMSD) on the order of 10% for the 1 m resolution LAI (RMSD = 0.25) and C_ab (RMSD = 4.4 μg cm^− 2) estimates, due in part to an efficient correction for background influences. LAI and C_ab retrieval accuracies at the SPOT 10 m resolution were characterized by relative RMSDs of 13% (0.3) and 17% (7.1 μg cm^− 2), respectively, and the overall intra-field pattern in LAI and C_ab was well established at this resolution. The developed method has utility in agricultural fields characterized by widely varying distributions of model variables and holds promise as a valuable operational tool for precision crop management. Work is currently in progress to extend REGFLEC to regional scales.     

不同大气校正方法对中小湖泊蓝藻遥感动态监测的影响

环境小卫星可实现中小湖泊蓝藻动态监测,但不同大气校正方法对于相同影像的处理结果有很大差异。研究利用多种大气校正方法对环境小卫星影像进行辐射校正处理,利用多个感兴趣区的全局和局部特征以及多个统计量对处理结果进行分析,比较其在蓝藻动态监测中的作用。基于多光谱植被指数计算蓝藻生物量的思路,分析了影像经不同大气校正算法处理后,其归一化植被指数的差异性来源及其对蓝藻生物量计算结果的影响。结果表明:蓝藻动态监测的定量描述会因大气校正算法不同而不一致,进而对几种大气校正算法在蓝藻生物量监测和定量分析中所产生的不确定性进行了比较分析,并对各算法的有效利用提出了建议。     

Comparison of three SeaWiFS atmospheric correction algorithms for turbid waters using AERONET-OC measurements

The use of satellites to monitor the color of the ocean requires effective removal of the atmospheric signal. This can be performed by extrapolating the aerosol optical properties in the visible from the near-infrared (NIR) spectral region assuming that the seawater is totally absorbant in this latter part of the spectrum. However, the non-negligible water-leaving radiance in the NIR which is characteristic of turbid waters may lead to an overestimate of the atmospheric radiance in the whole visible spectrum with increasing severity at shorter wavelengths. This may result in significant errors, if not complete failure, of various algorithms for the retrieval of chlorophyll-a concentration, inherent optical properties and biogeochemical parameters of surface waters.This paper presents results of an inter-comparison study of three methods that compensate for NIR water-leaving radiances and that are based on very different hypothesis: 1) the standard SeaWiFS algorithm (Stumpf et al., 2003; Bailey et al., 2010) based on a bio-optical model and an iterative process; 2) the algorithm developed by Ruddick et al. (2000) based on the spatial homogeneity of the NIR ratios of the aerosol and water-leaving radiances; and 3) the algorithm of Kuchinke et al. (2009) based on a fully coupled atmosphere-ocean spectral optimization inversion. They are compared using normalized water-leaving radiance nL_w in the visible. The reference source for comparison is ground-based measurements from three AERONET-Ocean Color sites, one in the Adriatic Sea and two in the East Coast of USA.Based on the matchup exercise, the best overall estimates of the nL_w are obtained with the latest SeaWiFS standard algorithm version with relative error varying from 14.97% to 35.27% for λ = 490 nm and λ = 670 nm respectively. The least accurate estimates are given by the algorithm of Ruddick, the relative errors being between 16.36% and 42.92% for λ = 490 nm and λ = 412 nm, respectively. The algorithm of Kuchinke appears to be the most accurate algorithm at 412 nm (30.02%), 510 (15.54%) and 670 nm (32.32%) using its default optimization and bio-optical model coefficient settings.Similar conclusions are obtained for the aerosol optical properties (aerosol optical thickness τ(865) and the Ångström exponent, α(510, 865)). Those parameters are retrieved more accurately with the SeaWiFS standard algorithm (relative error of 33% and 54.15% for τ(865) and α(510, 865)).A detailed analysis of the hypotheses of the methods is given for explaining the differences between the algorithms. The determination of the aerosol parameters is critical for the algorithm of Ruddick et al. (2000) while the bio-optical model is critical for the algorithm of Stumpf et al. (2003) utilized in the standard SeaWiFS atmospheric correction and both aerosol and bio-optical model for the coupled atmospheric-ocean algorithm of Kuchinke. The Kuchinke algorithm presents model aerosol-size distributions that differ from real aerosol-size distribution pertaining to the measurements. In conclusion, the results show that for the given atmospheric and oceanic conditions of this study, the SeaWiFS atmospheric correction algorithm is most appropriate for estimating the marine and aerosol parameters in the given turbid waters regions.     

SODA: A new method of in-scene atmospheric water vapor estimation and post-flight spectral recalibration for hyperspectral sensors: Application to the HyMap sensor at two locations

A new method of estimating per-pixel atmospheric column water vapor (ACWV) and potential differences in the reported band center wavelengths of the HyMap sensor has been developed. The new method uses variations of a second order derivative algorithm (SODA) to assess the impact of atmospheric residual features on calculated surface reflectance spectra after atmospheric compensation. The SODA method provides an alternative to the current band ratio techniques of ACWV estimation and also allows the same form of algorithm to be used for the estimation of possible band shifts. A comparison of in-situ measured surface reflectance at two field sites in Western Australia demonstrates improvement in the resulting spectra when post-flight updates are made to the reported HyMap band center wavelengths and applied during the atmospheric compensation process. The same SODA methodology was varied to estimate the ACWV on a per-pixel basis and found to significantly reduce the appearance of the underlying surface structure on the resulting ACWV images as well as improve the overall accuracy of the estimation. The ACWV estimated from the HyMap imagery at the two field sites was found to agree with in-situ atmospheric ACWV measurements to within 2% and represented a two fold increase in accuracy over a 3 band ratio Continuum Interpolated Band Ratio (CIBR) technique of ACWV estimation.     

潮流场中质点运动的可视化*

提出了一种适用于非结构化网格系统中质点运动轨迹的跟踪算法。利用数值计算方法的稳定性限制条件对质点位置的搜索算法进行了优化,极大地降低了算法的时间复杂度,提高了计算效率。用Visual Fortran语言实现了质点运动的动态显示。将其用于渤海潮流场中质点运动规律的研究,初步得出了渤海中质点运动和水交换的一些规律。     

利用光反射探测流化床中颗粒的动力学行为

采用光导纤维探测了二维流化床中运动颗粒的反射光.光探头包含三根光纤,中间一根用于发射光,上、下各一根接收粒子反射光.颗粒速度由两路反射光强度的相关分析给出.测量给出的光脉冲信号幅值和宽度随颗粒反射面积的增加而增大.三种流化数下测得的颗粒上升速度范围为0.1—0.5 m/s.床中心和过渡区测得的颗粒上升速度随流化数的增大而增大.近壁区内颗粒主要显示下降运动以及偶然的上升,尤其是在流化数较大时颗粒下降运动更明显.