Estimation of high-resolution land surface net shortwave radiation from AVIRIS data: Algorithm development and preliminary results

Abstract

Hyperspectral remote sensing provides unique and abundant spectral information for quantification of the land surface shortwave radiation budget, which can be used to calibrate climate models and to estimate surface energy budget for monitoring agriculture and urban environment. However, only single broadband or multispectral data have been used in previous studies. In the present study, two methods are proposed to estimate the instantaneous land surface net shortwave radiation (NSR) with high spatial resolutions using hyperspectral remote sensing observations from the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data. Method A calculates the NSR based on separate estimation of downward radiation and surface broadband albedo, which requires ancillary information for aerosol optical depth; and Method B directly estimates the NSR from the observed radiance. Results based on radiative transfer simulations showed that the use of hyperspectral data can significantly improve NSR estimation compared with the multispectral data method. Atmospheric water vapor correction was applied to adjust the surface radiation estimation. Validation of AVIRIS NSR estimates against ground measurements from two flux networks for the period of 2006-2014 showed that the two methods were similar and had consistent accuracy in the all-sky instantaneous NSR estimation with root-mean-square-errors (RMSEs) of approximately 28-56 W/m 2 . The pixel-based water vapor content estimation from AVIRIS data provided slightly different results than those obtained using coarse resolution remote sensing data. A simplified topographic correction algorithm was found to be able to improve the results generated from Method A; however, the degree of improvement provided by Method B was unclear, possibly because of the lack of consideration of horizontal atmospheric scattering effects from adjacent pixels. In general, hyperspectral remote sensing data have been shown to improve the NSR estimation accuracies compared with results obtained in previous studies. Additional efforts are needed to refine the NSR estimation for application to future satellite hyperspectral data.

Solar Radiation Received by Slopes Using COMS Imagery, a Physically Based Radiation Model, and GLOBE

“…These were developed using a look-up table (LUT) containing data 2 Journal of Sensors from polar and geostationary satellites for global coverage. He et al [9] and Bisht and Bras [10] developed a physics-based algorithm for estimating incident shortwave radiation using satellite data, demonstrating its usefulness in a separate but related context. The use of these data, in conjunction with quality solar ground datasets and other meteorological data, has become an effective way of developing site-time specific solar resource assessment over large areas.…”

Estimation of Daily Solar Radiation Budget at Kilometer Resolution over the Tibetan Plateau by Integrating MODIS Data Products and a DEM

“…The use of satellite remote sensing data allows us to monitor land surface solar radiative fluxes over large areas at a satisfying spatial and temporal resolution, which meets the requirements of current studies on climate and land surface processes. Numerous algorithms have been developed to estimate solar radiative fluxes using satellite radiometric measurements [6][7][8][9][10][11][12][13][14] and some have led to the production of regional or global datasets [15]. To the knowledge of the authors, among the best known are the Surface Radiation Budget project (SRB) [16], the Clouds and the Earth's Radiant Energy System (CERES) [17], the International Satellite 2 of 24 Cloud Climatology Project (ISCCP) [18], and the Global LAnd Surface Satellite (GLASS) radiation products [19].…”

Estimation of Daily Surface Shortwave Net Radiation From the Combined MODIS Data

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