Validation of the Surface Daytime Net Radiation Product From Version 4.0 GLASS Product Suite

Jiang, Bo; Liang, Shunlin; Jia, Aolin; Xu, Jianglei; Zhang, Xiaolin; Xiao, Zhiqiang; Zhao, Xiang; Jia, Kun; Yao, Yunjun

doi:10.1109/lgrs.2018.2877625

Cited by 23 publications

(15 citation statements)

References 24 publications

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“…algorithms were selected to produce the GLASS daytime (Jiang et al 2019) and daily Rn products. The DSR, normalized difference vegetation index (NDVI), and albedo from the GLASS products and meteorological values from the MERRA2 (Modern-Era Retrospective analysis for Research and Applications, Version 2) reanalysis data (Gelaro, et al, 2017) are the primary inputs.…”

Section: All-wave Net Radiationmentioning

confidence: 99%

The Global Land Surface Satellite (GLASS) Product Suite

Liang

Cheng

Jia

et al. 2021

Bulletin of the American Meteorological Society

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291

137

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Section: All-wave Net Radiationmentioning

confidence: 99%

The Global Land Surface Satellite (GLASS) Product Suite

Liang

Cheng

Jia

et al. 2021

Bulletin of the American Meteorological Society

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291

137

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“…Here, we use sun-induced fluorescence (SIF, [16]), normalised by incident shortwave radiation (WATCH-WFDEI data [71]), and the evaporative fraction (EF), defined as the ratio of evapotranspiration (ALEXI-ET data [27]) over net radiation (GLASS data [32]), as two alternative proxies for water-constrained vegetation activity X. ET and net radiation data are both provided at 0.05 • and daily resolution.…”

Section: Estimating S 0 From Sif and Efmentioning

confidence: 99%

Global distribution of the rooting zone water storage capacity reflects plant adaptation to the environment

Stocker

Tumber‐Dávila

Konings

et al. 2021

Preprint

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The rooting zone water storage capacity (S0) extends from the soil surface to the weathered bedrock (the Critical Zone) and determines land-atmosphere exchange during dry periods. Despite its importance to land-surface modeling, variations of S0 across space are largely unknown as they cannot be observed directly. We developed a method to diagnose global variations of S0 from the relationship between vegetation activity (measured by sun-induced fluorescence and by the evaporative fraction) and the cumulative water deficit (CWD). We then show that spatial variations in S0 can be predicted from the assumption that plants are adapted to sustain CWD extremes occurring with a return period that is related to the life form of dominant plants and the large-scale topographical setting. Predicted biome-level S0 distributions, translated to an apparent rooting depth (zr) by accounting for soil texture, are consistent with observations from a comprehensive zr dataset. Large spatial variations in S0 across the globe reflect adaptation of zr to the hydroclimate and topography and implies large heterogeneity in the sensitivity of vegetation activity to drought. The magnitude of S0 inferred for most of the Earth's vegetated regions and particularly for those with a large seasonality in their hydroclimate indicates an important role for plant access to water stored at depth - beyond the soil layers commonly considered in land-surface models.

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“…Rn distributions at global scale. In the following analysis, the GLASS Rn retrievals were used as the main comparison because of their high accuracy and reasonable spatiotemporal variation Jiang et al, 2018).…”

Section: Assessment Of the Rcnn Modelmentioning

confidence: 99%

“…the GLASS Rn product can provide seamless global land surface Rn estimates with a 0.05° resolution. Several studies have used in situ measurements to conduct evaluation studies, illustrating high accuracy performance as well as good application potential (Jiang et al, 2018;Guo et al, 2020). Thus, we used the GLASS daily Rn product covering 2000 to 2018 as a reference to help select reliable sites and validate the results from this study.…”

mentioning

confidence: 99%

A global long term (1981–2019) daily land surface radiation budget product from AVHRR satellite data using a residual convolutional neural network

Liang

Jiang

2021

Preprint

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Abstract. The surface radiation budget, also known as all-wave net radiation (Rn), is a key parameter for various land surface processes including hydrological, ecological, agricultural, and biogeochemical processes. Satellite data can be effectively used to estimate Rn, but existing satellite products have coarse spatial resolutions and limited temporal coverage. In this study, a point-surface matching estimation (PSME) method is proposed to estimate surface Rn using a residual convolutional neural network (RCNN) integrating spatially adjacent information to improve the accuracy of retrievals. A global high-resolution (0.05°) long-term (1981–2019) Rn product was subsequently generated from Advanced Very High-Resolution Radiometer (AVHRR) data. Specifically, the RCNN was employed to establish a nonlinear relationship between globally distributed ground measurements from 537 sites and AVHRR top of atmosphere (TOA) observations. Extended triplet collocation (ETC) technology was applied to address the spatial scale mismatch issue resulting from the low spatial support of ground measurements within the AVHRR footprint by selecting reliable sites for model training. The overall independent validation results show that the generated AVHRR Rn product is highly accurate, with R2, root-mean-square error (RMSE), and bias of 0.84, 26.66 Wm−2 (31.66 %), and 1.59 Wm−2 (1.89 %), respectively. Inter-comparisons with three other Rn products, i.e., the 5 km Global Land Surface Satellite (GLASS), the 1° Clouds and the Earth's Radiant Energy System (CERES), and the 0.5° × 0.625° Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2), illustrate that our AVHRR Rn retrievals have the best accuracy under all of the considered surface and atmospheric conditions, especially thick cloud or hazy conditions. The spatiotemporal analyses of these four Rn datasets indicate that the AVHRR Rn product reasonably replicates the spatial pattern and temporal evolution trends of Rn observations. This dataset is freely available at https://doi.org/10.5281/zenodo.5509854 for 1981–2019 (Xu et al., 2021).

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Validation of the Surface Daytime Net Radiation Product From Version 4.0 GLASS Product Suite

Cited by 23 publications

References 24 publications

The Global Land Surface Satellite (GLASS) Product Suite

The Global Land Surface Satellite (GLASS) Product Suite

Global distribution of the rooting zone water storage capacity reflects plant adaptation to the environment

A global long term (1981–2019) daily land surface radiation budget product from AVHRR satellite data using a residual convolutional neural network

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