Global LAnd Surface Satellite (GLASS) Products

Liang, Shunlin; Zhang, Xiaolin; Xiao, Zhiqiang; Cheng, Jie; Liu, Qiang; Zhao, Xiang

doi:10.1007/978-3-319-02588-9

Cited by 57 publications

(51 citation statements)

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“…The retrieval methods of land surface variables were validated in other areas. For example, the albedo algorithm was previously applied to retrieve Global Land Surface Satellite (GLASS) products (Liang et al, 2014), the LST retrieval algorithm was validated in the Haihe River basin in northern China (H. , and the soil heat flux correction algorithm was validated in the GAME-Tibet campaign (Yang and Wang, 2008). Since the surface of the Heihe River basin is very heterogeneous, additional comparisons of our algorithm in other areas would be helpful.…”

Section: Discussionmentioning

confidence: 99%

Remote sensing algorithm for surface evapotranspiration considering landscape and statistical effects on mixed pixels

Peng

Xin

Jiao

et al. 2016

Hydrol. Earth Syst. Sci.

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Abstract. Evapotranspiration (ET) plays an important role in surface-atmosphere interactions and can be monitored using remote sensing data. However, surface heterogeneity, including the inhomogeneity of landscapes and surface variables, significantly affects the accuracy of ET estimated from satellite data. The objective of this study is to assess and reduce the uncertainties resulting from surface heterogeneity in remotely sensed ET using Chinese HJ-1B satellite data, which is of 30 m spatial resolution in VIS/NIR bands and 300 m spatial resolution in the thermal-infrared (TIR) band. A temperature-sharpening and flux aggregation scheme (TSFA) was developed to obtain accurate heat fluxes from the HJ-1B satellite data. The IPUS (input parameter upscaling) and TRFA (temperature resampling and flux aggregation) methods were used to compare with the TSFA in this study. The three methods represent three typical schemes used to handle mixed pixels from the simplest to the most complex. IPUS handles all surface variables at coarse resolution of 300 m in this study, TSFA handles them at 30 m resolution, and TRFA handles them at 30 and 300 m resolution, which depends on the actual spatial resolution. Analyzing and comparing the three methods can help us to get a better understanding of spatial-scale errors in remote sensing of surface heat fluxes. In situ data collected during HiWATER-MUSOEXE (Multi-Scale Observation Experiment on Evapotranspiration over heterogeneous land surfaces of the Heihe Watershed Allied Telemetry Experimental Research) were used to validate and analyze the methods. ET estimated by TSFA exhibited the best agreement with in situ observations, and the footprint validation results showed that the R 2 , MBE, and RMSE values of the sensible heat flux (H ) were 0.61, 0.90, and 50.99 W m −2 , respectively, and those for the latent heat flux (LE) were 0.82, −20.54, and 71.24 W m −2 , respectively. IPUS yielded the largest errors in ET estimation. The RMSE of LE between the TSFA and IPUS methods was 51.30 W m −2 , and the RMSE of LE between the TSFA and TRFA methods was 16.48 W m −2 . Furthermore, additional analysis showed that the TSFA method can capture the subpixel variations of land surface temperature and the influences of various landscapes within mixed pixels.

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Section: Discussionmentioning

confidence: 99%

Remote sensing algorithm for surface evapotranspiration considering landscape and statistical effects on mixed pixels

Peng

Xin

Jiao

et al. 2016

Hydrol. Earth Syst. Sci.

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“…The GLASS products contain long time series of global land surface characteristics based on various remotely sensed data. These products include LAI, land surface albedo, broadband emissivity, downward shortwave radiation and photosynthetically active radiation [47]. The GLASS LAI product has an eight-day temporal resolution with a spatial resolution of 1 km in a sinusoidal projection system.…”

Section: Other Auxiliary Datamentioning

confidence: 99%

Estimating Time Series Soil Moisture by Applying Recurrent Nonlinear Autoregressive Neural Networks to Passive Microwave Data over the Heihe River Basin, China

et al. 2017

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Abstract:A method using a nonlinear auto-regressive neural network with exogenous input (NARXnn) to retrieve time series soil moisture (SM) that is spatially and temporally continuous and high quality over the Heihe River Basin (HRB) in China was investigated in this study. The input training data consisted of the X-band dual polarization brightness temperature (TB) and the Ka-band V polarization TB from the Advanced Microwave Scanning Radiometer II (AMSR2), Global Land Satellite product (GLASS) Leaf Area Index (LAI), precipitation from the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM), and a global 30 arc-second elevation (GTOPO-30). The output training data were generated from fused SM products of the Japan Aerospace Exploration Agency (JAXA) and the Land Surface Parameter Model (LPRM). The reprocessed fused SM from two years (2013 and 2014) was inputted into the NARXnn for training; subsequently, SM during a third year (2015) was estimated. Direct and indirect validations were then performed during the period 2015 by comparing with in situ measurements, SM from JAXA, LPRM and the Global Land Data Assimilation System (GLDAS), as well as precipitation data from TRMM and GPM. The results showed that the SM predictions from NARXnn performed best, as indicated by their higher correlation coefficients (R ≥ 0.85 for the whole year of 2015), lower Bias values (absolute value of Bias ≤ 0.02) and root mean square error values (RMSE ≤ 0.06), and their improved response to precipitation. This method is being used to produce the NARXnn SM product over the HRB in China.

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“…The Global Land Surface Satellite (GLASS) albedo product from 1981-2010 at 8-day temporal resolution and 0.05° spatial resolution was also used. It has been demonstrated that the GLASS albedo product is superior to most of the albedo products available today and its accuracy can satisfy various applications [52][53][54][55]. NDVI and albedo time-series data were extracted for each site.…”

Section: In-situ Datamentioning

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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Global LAnd Surface Satellite (GLASS) Products

Cited by 57 publications

References 0 publications

Remote sensing algorithm for surface evapotranspiration considering landscape and statistical effects on mixed pixels

Remote sensing algorithm for surface evapotranspiration considering landscape and statistical effects on mixed pixels

Estimating Time Series Soil Moisture by Applying Recurrent Nonlinear Autoregressive Neural Networks to Passive Microwave Data over the Heihe River Basin, China

Surface Daytime Net Radiation Estimation Using Artificial Neural Networks

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