Generating Global LAnd Surface Satellite incident shortwave radiation and photosynthetically active radiation products from multiple satellite data

Zhang, Xiaolin; Liang, Shunlin; Zhou, Guiling; Wu, Haoran; Zhao, Xiang

doi:10.1016/j.rse.2014.07.003

Cited by 165 publications

(72 citation statements)

References 46 publications

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“…Figure 7 illustrates the monthly PAR calculated from the GLASS PAR product. Zhang et al (2013) validated the products using ground measurements at eight sites (two SURFRAD sites, three AERONET sites, one CarbonEuropeIP site, and two sites in China), the overall R 2 , bias, and RMSE is 0.85, 14.3 W m (2 , and 47.5 W m (2 , respectively. More validation studies are under way.…”

Section: Glass Par Productmentioning

confidence: 97%

“…The daily estimates have an average RMSE of 29.16 W m (2 (17.7%) under all-sky conditions, which is relatively comparable to the results of other studies on using GMS-5 (Tanahashi et al 2001;Kawai and Kawamura 2005) The GLASS insolation algorithm is almost identical to that discussed in Section 4.2 with a few differences, and the preliminary validation results are very encouraging. Zhang et al (2013) validated the instantaneous insolation product using ground measurements at eight sites (two SURFRAD sites, three AERONET sites, one CarbonEuropeIP site, and two sites in China), the overall R 2 , bias, and RMSE is 0.87, 7.5 W m (2 , and 103.6 W m (2 , respectively. Huang et al (2013) validated the instantaneous GLASS insolation product using ground measurements at 22 sites in the arid and semi-arid regions of China and found the R 2 at every site except one is larger than 0.8, and RMSE ranges from around 90 to 130 W m (2 .…”

Section: Incident Shortwave Solar Radiation Productmentioning

confidence: 97%

“…Combinations of these data provide frequently refreshed global coverage. A detailed description of the algorithms for generating the GLASS solar radiation products is given by Zhang et al (2013). The basic algorithms are based on the approach developed in previous studies with a series of refinements suitable for operational production.…”

Section: Glass Par Productmentioning

confidence: 99%

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A long-term Global LAnd Surface Satellite (GLASS) data-set for environmental studies

Liang

Zhao

Liu

et al. 2013

International Journal of Digital Earth

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Recently, five Global LAnd Surface Satellite (GLASS) products have been released: leaf area index (LAI), shortwave broadband albedo, longwave broadband emissivity, incident short radiation, and photosynthetically active radiation (PAR). The first three products cover the years 1982Á2012 (LAI) and 1981Á2010 (albedo and emissivity) at 1Á5 km and 8-day resolutions, and the last two radiation products span the period 2008Á2010 at 5 km and 3-h resolutions. These products have been evaluated and validated, and the preliminary results indicate that they are of higher quality and accuracy than the existing products. In particular, the first three products have much longer time series, and are therefore highly suitable for various environmental studies. This paper outlines the algorithms, product characteristics, preliminary validation results, potential applications and some examples of initial analysis of these products.

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Section: Glass Par Productmentioning

confidence: 97%

Section: Incident Shortwave Solar Radiation Productmentioning

confidence: 97%

Section: Glass Par Productmentioning

confidence: 99%

See 1 more Smart Citation

A long-term Global LAnd Surface Satellite (GLASS) data-set for environmental studies

Liang

Zhao

Liu

et al. 2013

International Journal of Digital Earth

Self Cite

431

200

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“…However, observational measurements provide the best estimate of the state of R s where they are taken. Currently, a number of gridded global R s products exist from remote sensing [2,[5][6][7] and reanalysis [8][9][10][11][12]. Satellite remote sensing is one of the most practical ways to derive R s with relatively higher spatial resolution and accuracy, but the temporal coverage is always limited.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Reanalysis Surface Incident Shortwave Radiation Products from NCEP, ECMWF, GSFC, and JMA Using Satellite and Surface Observations

et al. 2016

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Solar radiation incident at the Earth's surface (R s ) is an essential component of the total energy exchange between the atmosphere and the surface. Reanalysis data have been widely used, but a comprehensive validation using surface measurements is still highly needed. In this study, we evaluated the R s estimates from six current representative global reanalyses (NCEP-NCAR, NCEP-DOE; CFSR; ERA-Interim; MERRA; and JRA-55) using surface measurements from different observation networks [GEBA; BSRN; GC-NET; Buoy; and CMA] (674 sites in total) and the Earth's Radiant Energy System (CERES) EBAF product from 2001 to 2009. The global mean biases between the reanalysis R s and surface measurements at all sites ranged from 11.25 W/m 2 to 49.80 W/m 2 . Comparing with the CERES-EBAF R s product, all the reanalyses overestimate R s , except for ERA-Interim, with the biases ranging from´2.98 W/m 2 to 21.97 W/m 2 over the globe. It was also found that the biases of cloud fraction (CF) in the reanalyses caused the overestimation of R s . After removing the averaged bias of CERES-EBAF, weighted by the area of the latitudinal band, a global annual mean R s values of 184.6 W/m 2 , 180.0 W/m 2 , and 182.9 W/m 2 were obtained over land, ocean, and the globe, respectively.

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“…It is extremely difficult to estimate Rn directly from satellite data because frequent cloud coverage can block surface information. However, satellite data can fortunately be used to estimate incoming solar radiation under all-sky conditions [10,11]. Therefore, an important research goal is to develop robust methods for estimating accurate, high tempo-spatial resolution Rn from incoming shortwave radiation or shortwave net radiation, producing estimates that are location independent and universally applicable.…”

Section: Introductionmentioning

confidence: 99%

Surface Daytime Net Radiation Estimation Using Artificial Neural Networks

et al. 2014

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Net all-wave surface radiation (Rn) is one of the most important fundamental parameters in various applications. However, conventional Rn measurements are difficult to collect because of the high cost and ongoing maintenance of recording instruments. Therefore, various empirical Rn estimation models have been developed. This study presents the results of two artificial neural network (ANN) models (general regression neural networks (GRNN) and Neuroet) to estimate Rn globally from multi-source data, including remotely sensed products, surface measurements, and meteorological reanalysis products. Rn estimates provided by the two ANNs were tested against in-situ radiation measurements obtained from 251 global sites between 1991-2010 both in global mode (all data were used to fit the models) and in conditional mode (the data were divided into four subsets and the models were fitted separately). Based on the results obtained from extensive experiments, it has been proved that the two ANNs were superior to linear-based empirical models in both global and conditional modes and that the GRNN performed better and was more stable than Neuroet. The GRNN estimates had a determination coefficient (R

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Generating Global LAnd Surface Satellite incident shortwave radiation and photosynthetically active radiation products from multiple satellite data

Cited by 165 publications

References 46 publications

A long-term Global LAnd Surface Satellite (GLASS) data-set for environmental studies

A long-term Global LAnd Surface Satellite (GLASS) data-set for environmental studies

Evaluation of the Reanalysis Surface Incident Shortwave Radiation Products from NCEP, ECMWF, GSFC, and JMA Using Satellite and Surface Observations

Surface Daytime Net Radiation Estimation Using Artificial Neural Networks

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