Remote sensing of earth’s energy budget: synthesis and review

Liang, Shunlin; Wang, Dongdong; He, Tao; Yu, Yunyue

doi:10.1080/17538947.2019.1597189

Cited by 149 publications

(61 citation statements)

References 382 publications

(392 reference statements)

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“…Earth's weather and climate are driven by the energy received at the surface, which is mainly affected by the contents of the atmosphere, including clouds and aerosols [1,2]. Despite the importance of downward surface radiation fluxes, studies have shown they are not always well estimated from satellite observations nor modeled by climate models [3], and the number of ground sites measuring radiation fluxes is itself a variable number.…”

Section: Introductionmentioning

confidence: 99%

Intercomparison of Machine-Learning Methods for Estimating Surface Shortwave and Photosynthetically Active Radiation

Brown

Skakun

et al. 2020

Remote Sensing

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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 R 2 of 0.77, 0.78, and 0.78 respectively, a bias of 0 ± 6, 0 ± 6, and 0 ± 5 W / m 2 , and an RMSE of 140 ± 7, 135 ± 8, and 138 ± 7 W / m 2 , 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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Section: Introductionmentioning

confidence: 99%

Intercomparison of Machine-Learning Methods for Estimating Surface Shortwave and Photosynthetically Active Radiation

Brown

Skakun

et al. 2020

Remote Sensing

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“…Land surface albedo is one of the most important variables in the study of the surface radiation budget, climate change, and the hydrologic cycle [1]. Remotely sensed albedo products from geostationary satellites provide continuous and consistent measurements over a large region with a very high temporal resolution [2].…”

Section: Introductionmentioning

confidence: 99%

Developing Land Surface Directional Reflectance and Albedo Products from Geostationary GOES-R and Himawari Data: Theoretical Basis, Operational Implementation, and Validation

Zhang

Liang

et al. 2019

Remote Sensing

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The new generation of geostationary satellite sensors is producing an unprecedented amount of Earth observations with high temporal, spatial and spectral resolutions, which enable us to detect and assess abrupt surface changes. In this study, we developed the land surface directional reflectance and albedo products from Geostationary Operational Environment Satellite-R (GOES-R) Advanced Baseline Imager (ABI) data using a method that was prototyped with the Moderate Resolution Imaging Spectroradiometer (MODIS) data in a previous study, and was also tested with data from the Advanced Himawari Imager (AHI) onboard Himawari-8. Surface reflectance is usually retrieved through atmospheric correction that requires the input of aerosol optical depth (AOD). We first estimated AOD and the surface bidirectional reflectance factor (BRF) model parameters simultaneously based on an atmospheric radiative transfer formulation with surface anisotropy, and then calculated the “blue-sky” surface broadband albedo and directional reflectance. This algorithm was implemented operationally by the National Oceanic and Atmospheric Administration (NOAA) to generate the GOES-R land surface albedo product suite with a daily updated clear-sky satellite observation database. The “operational” land surface albedo estimation from ABI and AHI data was validated against ground measurements at the SURFRAD sites and OzFlux sites and compared with the existing satellite products, including MODIS, Visible infrared Imaging Radiometer (VIIRS), and Global Land Surface Satellites (GLASS) albedo products, where good agreement was found with bias values of −0.001 (ABI) and 0.020 (AHI) and root-mean-square-errors (RMSEs) less than 0.065 for the hourly albedo estimation. Directional surface reflectance estimation, evaluated at more than 74 sites from the Aerosol Robotic Network (AERONET), was proven to be reliable as well, with an overall bias very close to zero and RMSEs within 0.042 (ABI) and 0.039 (AHI). Results show that the albedo and reflectance estimation can satisfy the NOAA accuracy requirements for operational climate and meteorological applications.

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“…They attributed higher albedo and lower incoming solar radiation obtained in wet season to the rain-bearing clouds which pervaded the sky in the rainy season compared to the dry season. It has also been reported that natural phenomena such as wild fire could alter surface albedo [12,[16][17][18].…”

Section: Introductionmentioning

confidence: 98%

“…It is a fundamental component of the processes that govern the magnitude, distribution and variability of Earth's climate and climate change [5,6]. Surface albedo is highly variable, depending on many factors, such as landcover types, snow/ice cover, surface roughness or textural characteristics, mineral composition soil moisture content and human activities, such as deforestation, agriculture, irrigation, pollution and urbanization [7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Impacts of cloudiness on near surface radiation and temperature in Nigeria, West Africa

et al. 2020

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This study investigates impacts of cloudiness on surface radiation budget and temperature from 1983 to 2018 in Nigeria, West Africa. The National Aeronautics and Space Administration meteorological datasets were used to achieve this objective. Net radiation (R) increased latitudinally from the coast to the inland areas. The least value of R was obtained in wet season which coincided with the period when cloud cover was at its optimal. We obtained significant decreasing trends (− 0.110 to − 0.536 Wm −2 year −1) in R but significant increasing trends in cloud cover in most parts of the region. Under clear-sky conditions, however, significant increasing trends (0.256-0.807 Wm −2 year −1) in R were obtained. Annual mean temperature showed significant upward trends under all-sky (0.009-0.059 °C year −1) and clear-sky (0.019-0.072 °C year −1) conditions. Furthermore, significant negative correlations (− 0.70 ≤ r ≤ − 0.95) were obtained between monthly values of cloud cover and R over the country. However, r values for cloud cover and temperature were positive over Sahel (r ~ 0.65) but negative over Guinea (r ~ 0.45); both significant at p < 0.05. Changes in R were accounted for by 21 to 26% variations in cloud cover, while fluctuation in temperature was explained by about 14% variation in cloud cover over Sahel region at p < 0.05. Our results revealed that the surface radiation budget was significantly perturbed by variations in cloudiness which resulted in net cloud-radiative cooling (− 30 Wm −2) over Guinea but warming over Savannah (25 Wm −2) and Sahel (50 Wm −2) zones.

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Remote sensing of earth’s energy budget: synthesis and review

Cited by 149 publications

References 382 publications

Intercomparison of Machine-Learning Methods for Estimating Surface Shortwave and Photosynthetically Active Radiation

Intercomparison of Machine-Learning Methods for Estimating Surface Shortwave and Photosynthetically Active Radiation

Developing Land Surface Directional Reflectance and Albedo Products from Geostationary GOES-R and Himawari Data: Theoretical Basis, Operational Implementation, and Validation

Impacts of cloudiness on near surface radiation and temperature in Nigeria, West Africa

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