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    Surface Shortwave Net Radiation Estimation from FengYun-3 MERSI Data
    (MDPI, 2015-05-19) Wang, Dongdong; Liang, Shunlin; He, Tao; Cao, Yunfeng; Jiang, Bo
    The Medium-Resolution Spectral Imager (MERSI) is one of the major payloads of China’s second-generation polar-orbiting meteorological satellite, FengYun-3 (FY-3), and it is similar to the Moderate-Resolution Imaging Spectroradiometer (MODIS). The MERSI data are suitable for mapping terrestrial, atmospheric and oceanographic variables at continental to global scales. This study presents a direct-estimation method to retrieve surface shortwave net radiation (SSNR) data from MERSI top-of-atmosphere (TOA) reflectance and cloud mask products. This study is the first attempt to use the MERSI to retrieve SSNR data. Several critical issues concerning remote sensing of SSNR were investigated, including scale effects in validating SSNR data, impacts of the MERSI calibration update on the estimation of SSNR and the dependency of the retrieval accuracy of SSNR data on view geometry. We also incorporated data from twin MODIS sensors to assess how time and the number of satellite overpasses affect the retrieval of SSNR data. Validation against one-year data over seven Surface Radiation Budget Network (SURFRAD) stations showed that the presented algorithm estimated daily SSNR at the original resolution of the MERSI with a root mean square error (RMSE) of 41.9 W/m2 and a bias of −1.6 W/m2. Aggregated to a spatial resolution of 161 km, the RMSE of MERSI retrievals can be reduced by approximately 10 W/m2. Combined with MODIS data, the RMSE of daily SSNR estimation can be further reduced to 22.2 W/m2. Compared with that of daily SSNR, estimation of monthly SSNR is less affected by the number of satellite overpasses per day. The RMSE of monthly SSNR from a single MERSI sensor is as small as 13.5 W/m2.
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    Intercomparison of Machine-Learning Methods for Estimating Surface Shortwave and Photosynthetically Active Radiation
    (MDPI, 2020-01-23) Brown, Meredith G. L.; Skakun, Sergii; He, Tao; Liang, Shunlin
    Satellite-derived estimates of downward surface shortwave radiation (SSR) and photosynthetically active radiation (PAR) are a part of the surface radiation budget, an essential climate variable (ECV) required by climate and vegetation models. Ground measurements are insufficient for generating long-term, global measurements of surface radiation, primarily due to spatial limitations; however, remotely sensed Earth observations offer freely available, multi-day, global coverage of radiance that can be used to derive SSR and PAR estimates. Satellite-derived SSR and PAR estimates are generated by computing the radiative transfer inversion of top-of-atmosphere (TOA) measurements, and require ancillary data on the atmospheric condition. To reduce computational costs, often the radiative transfer calculations are done offline and large look-up tables (LUTs) are generated to derive estimates more quickly. Recently studies have begun exploring the use of machine-learning techniques, such as neural networks, to try to improve computational efficiency. Here, nine machine-learning methods were tested to model SSR and PAR using minimal input data from the Moderate Resolution Imaging Spectrometer (MODIS) observations at 1 km spatial resolution. The aim was to reduce the input data requirements to create the most robust model possible. The bootstrap aggregated decision tree (Bagged Tree), Gaussian Process Regression, and Neural Network yielded the best results with minimal training data requirements: an 𝑅2 of 0.77, 0.78, and 0.78 respectively, a bias of 0 ± 6, 0 ± 6, and 0 ± 5 W/m2, and an RMSE of 140 ± 7, 135 ± 8, and 138 ± 7 W/m2, respectively, for all-sky condition total surface shortwave radiation and viewing angles less than 55°. Viewing angles above 55° were excluded because the residual analysis showed exponential error growth above 55°. A simple, robust model for estimating SSR and PAR using machine-learning methods is useful for a variety of climate system studies. Future studies may focus on developing high temporal resolution direct and diffuse estimates of SSR and PAR as most current models estimate only total SSR or PAR.
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    Calculation of the Angular Radiance Distribution for a Coupled Atmosphere and Canopy
    (Institute of Electrical and Electronics Engineers, 1993-03) Liang, Shunlin; Strahler, Alan H.
    The radiative transfer equations for a coupled atmosphere and canopy are solved numerically by an improved Gause-Seidel iteration algorithm. The radiation field is decomposed into three components: unscattered sunlight, single scattering, and multiple scattering radiance for which the corresponding equations and boundary conditions are set up and their analytical or iterational solutions are explicitly derived. The classic Guass-Seidel algorithm has been widely applied in atomospheric research. This is its first application for calculating the multiple scattering radiance of a coupled atmosphere and canopy. This algorithm enables us to obtain the internal radiation field as well as radiances at boundaries. Any form of bidirectional reflectance distribution function (BRDF) as a boundary condition can be easily incorporated into the iteration procedure. The hotspot effect of the canopy is accommodated by means of the modification of the extiniction coefficients of upward single scattering radiation and unscatteered sunlight using the formulation of Nilson and Kuusk. To reduce the computation for the case of large optical thickness, an improved iteration formula is derived to speed convergence. The upwelling radiances have been evaluated for different atmospheric conditions, leaf area index (LAI), leaf angle distribution (LAD), leaf size and so on. The formulation presented in this paper is also well suited to analyze the relative magnitude of multiple scattering radiance and single scattering radiance in both the visible and near infrared regions.
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    Retrieving Leaf Area Index With a Neural Network Method: Simulation and Validation
    (Institute of Electrical and Electronics Engineers, 2003-09) Liang, Shunlin; Fang, Hongliang
    Leaf area index () is a crucial biophysical parameter that is indispensable for many biophysical and climatic models. A neural network algorithm in conjunction with extensive canopy and atmospheric radiative transfer simulations is presented in this paper to estimateLAIfromLandsat-7 Enhanced ThematicMapper Plus data. Two schemes were explored; the first was based on surface reflectance, and the second on top-of-atmosphere (TOA) radiance. The implication of the second scheme is that atmospheric corrections are not needed for estimating the surface LAI. A soil reflectance index (SRI) was proposed to account for variable soil background reflectances. Ground-measured LAI data acquired at Beltsville, MD were used to validate both schemes. The results indicate that both methods can be used to estimate LAI accurately. The experiments also showed that the use of SRI is very critical.
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    An Analytic BRDF Model of Canopy Radiative Transfer and Its Inversion
    (Institute of Electrical and Electronics Engineers, 1993-09) Liang, Shunlin; Strahler, Alan H.
    Radiative transfer modeling of the bidirectional reflectance distribution function (BRDF) of leaf canopies is a powerful tool to relate multiangle remotely sensed data to biophysical parameters of the leaf canopy and to retrieve such parameters from multiangle imagery. However, the approximate approaches for multiple scattering that are used in the inversion of existing models are quite limited, and the sky radiance frequently is simply treated as isotropic. This paper presents an analytical model based on a rigorous canopy radiative transfer equation in which the multiple-scattering component is approximated by asymptotic theory and the single-scattering calculation, which requires numerical integration to properly accommodate the hotspot effect, is also simplified. Because the model is sensitive to angular variation in sky radiance, we further provide an accompanying new formulation for directional radiance in which the unscattered solar radiance and single-scattering radiance are calculated exactly, and multiple-scattering is approximated by the well-known two-stream Dirac delta function approach. A series of validations against exact calculations indicates that both models are quite accurate, especially when the viewing angle is smaller than 55 degrees. The Powell algorithm is then used to retrieve biophysical parameters from multiangle observations based on both the canopy and the sky radiance distribution models. The results using the soybean data of Ranson et al. to recover four of nine soybean biophysical parameters indicate that inversion of the present canopy model retrieves leaf area index well. Leaf angle distribution was not retrieved as accurately for the same dataset, perhaps because these measurements do not describe the hotspot well. Further experiments are required to explore the applicability of this canopy model.