The Global LAnd Surface Satellite (GLASS) products

1. Introduction

The GLASS product suite initially included 5 products (Liang et al. 2013a; Liang et al. 2013b), but has been recently expanded into 12 products, such as leaf area index, broadband albedo, broadband longwave emissivity, downward shortwave radiation and photosynthetically active radiation, land surface temperature, longwave net radiation, daytime all-wave net radiation, fraction of absorbed photosynetically active radiation absorbed by green vegetation, fraction of vegetation cover, gross primary productivity, and evapotranspiration. Their characteristics are summarized in the following table. Note these products are being continuously updated.

The GLASS products have some unique features, for example,

· many of these products span about 35 years from 1981-present, highly suitable for long-term environmental change studies;

· the radiation products have the spatial resolution of 5km, much finer than the commonly used products (e.g., CERES, GEWEX);

· all products are spatially and temporally continuous with no gaps and missing values;

· The broadband longwave emissivity product is the first product of this kind in the world at 8-day temporal resolution and 1km spatial resolution; and

· the GLASS products have been demonstrated to have high accuracies.

As one example, figure 1 shows that the GLASS albedo product has the longest temporal coverage compared with other existing global albedo products.

Efforts are being made to generate 15 land Climate Data Records (CDRs) and other 20 land products (Liang et al. 2016).

2. Product descriptions

1). LAI

It has two versions: GLASS-MODIS LAI and GLASS-AVHRR LAI. The GLASS-MODIS LAI product is generated using the general regression neural networks (GRNNs)(Xiao et al. 2014), and the GLASS-AVHRR LAI product is generated using the same GRNN algorithm (Xiao et al. 2016a) from the re-processed Long-Term Data Record (LTDR) AVHRR data for over 35 years(Xiao et al. 2017; Xiao et al. 2015b).

The GLASS LAI product has been widely used, for example, verifying the Earth system model simulation accuracy (Bao et al. 2014)，evaluating the vegetation responses to climate change in the arid and semi-arid region (Jiapaer et al. 2015)，computing GPP and ET (Liu et al. 2015; Tian et al. 2015), FAPAR (Xiao et al. 2015a) and FVC (Xiao et al. 2016b), assessing hydrological simulations (Tesemma et al. 2015)，determining overestimation of forest GPP(Ma et al. 2015)，and monitoring the greening trends over China (Piao et al. 2015) and globe (Zhu et al. 2016).

2). FAPAR

The GLASS FAPAR product is calculated from the GLASS LAI product and other information (Xiao et al. 2015a), so it shares the similar characteristics to the GLASS LAI products, and these two products are physically consistent.

3). Fraction of vegetation cover (FVC)

The GLASS FVC product is generated using a machine learning technique (Jia et al. 2015).

4). Incident shortwave radiation

The GLASS MODIS shortwave radiation product is based on conversion of shortwave net radiation and land surface albedo, and the GLASS-AVHRR shortwave radiation is estimated from the refined look-up table (LUT) method (Zhang et al. 2014). Its spatial resolution (5km) is significantly higher than other global products with the spatial resolutions coarser than 1°.

5). Incident PAR

The GLASS PAR product is produced from the GLASS shortwave radiation product as well as the LUT method (Liang et al. 2006). Its spatial resolution (5km) is significantly higher than other global products with the spatial resolutions coarser than 1°. Cai et al. (2014) demonstrated that the input of GLASS PAR product with better accuracy can result in the improved calculation of GPP.

6). Broadband albedo

The GLASS albedo product includes shortwave, visible and near-IR broadband albedo. It is an integration (Liu et al. 2013a; Liu et al. 2013b) of two intermediate products estimated from surface reflectance and top-of-atmosphere (TOA) reflectance (Qu et al. 2014) using the “direct estimation algorithm” (Liang 2003; Liang et al. 1999; Liang et al. 2005), which is different from the conventional methods consisting of atmospheric correction, BRDF modeling, narrowband to broadband conversion (Schaaf et al. 2002).

The GLASS albedo has been used for assessing the Greenland albedo dependence on the climate change (He et al. 2013), determining the global albedo climatology (He et al. 2014), and computing the radiative forcing due to snow cover changes (Chen et al. 2016a; Chen et al. 2016b; Chen et al. 2015; Chen et al. 2017) and forest disturbances (Zhang and Liang 2014).

7). LST

The GLASS LST product is generated using the split-window approach (Zhou et al. 2014) based on extensive radiative transfer simulations(Huang et al. 2016).

8). Broadband emissivity

The GLASS longwave broadband emissivity product is based on conversion of shortwave spectral albedos for soils (Cheng and Liang 2013a; Cheng and Liang 2013b; Cheng et al. 2011) and radiative transfer calculations for vegetation (Cheng et al. 2016). The ocean emissivity product will be also produced(Cheng et al. 2017a).

9). Longwave net radiation

Downward and upwelling longwave radiation are estimated separately. Upwelling longwave radiation can be calculated from LST and broadband longwave emissivity, but the uncertainties of these two components may cause large errors (Wang and Liang 2009). The direct estimation methods have been developed for estimating upwelling, downward and net longwave radiation (Cheng and Liang 2016; Cheng et al. 2017b).

10). All-wave day-time net radiation

Instead of adding all components whose uncertainties may be accumulated, the GLASS net radiation product is based on conversion of shortwave net radiation in conjunction with other information. After comparing different linear formulae (Jiang et al. 2015) and machine learning techniques (Jiang et al. 2014), The MARS algorithm is used for producing the global day-time all-wave net radiation (Jiang et al. 2016). The validation and comparisons with other products are also carried out (Jia et al. 2016; Jia et al. 2017). Its spatial resolution (5km) is significantly higher than other global products with the spatial resolutions coarser than 1°.

11). ET

The GLASS ET product is generated by merging five process-based algorithms using the Bayesian model averaging (BMA) method(Yao et al. 2014). The five algorithms include the MODIS ET product algorithm, the revisedremote-sensing-based Penman-Monteith ET algorithm, the Priestley-Taylor-based ET algorithm, the modified satellite-based Priestley-Taylor ET algorithm, and the semi-empirical Penman ET algorithm. The validation results demonstrated that the integrated product is more accurate than any individual algorithm estimate.

12). GPP

The GLASS GPP product is also using the BMA to integrate the estimates from 8 light use efficiency models. The EC-LUE (Eddy Covariance- Light Use Efficiency) model is one of them developed by the GLASS GPP developer (Yuan et al. 2007; Yuan et al. 2010).

3. Product distribution

The GLASS products are being distributed through the Data Center for Global Change Processing and Analysis at Beijing Normal University (http://www.bnu-datacenter.com/) and the Global Land Cover Facility at the University of Maryland (www.landcover.org).

4. References

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