The Global Land Surface Satellite (GLASS) Remote Sensing Data Processing System and Products

Zhao, Xiang; Liang, Shunlin; Liu, Suhong; Yuan, Wenping; Xiao, Zhiqiang; Cheng, Jie; Zhang, Xiaolin; Tang, Hairong; Xin, Zhang; Liu, Qiang; Zhou, Guiling; Xu, Shuai; Yu, Kai

doi:10.3390/rs5052436

Cited by 69 publications

(45 citation statements)

References 42 publications

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“…In this study, we introduced the GLASS LAI product to assess the influences of FPAR on the effectiveness of MO17A2H GPP. In previous studies [37][38][39], it had been proven that the GLASS LAI product could provide better estimates of LAI than existing MODIS and CYCLOPES LAI datasets. For each EC flux tower site, the GLASS LAI subsets (eight-day interval at a spatial resolution of 0.05 • ×0.05 • ) during 2001-2014 were downloaded from the Generation and Applications of Global Products of Essential Land Variables website [40].…”

Section: Glass Lai Datamentioning

confidence: 99%

Evaluation of the Latest MODIS GPP Products across Multiple Biomes Using Global Eddy Covariance Flux Data

et al. 2017

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Abstract:The latest MODIS GPP (gross primary productivity) product, MOD17A2H, has great advantages over the previous version, MOD17A2, because the resolution increased from 1000 m to 500 m. In this study, MOD17A2H GPP was assessed using the latest eddy covariance (EC) flux data (FLUXNET2015 Dataset) at eighteen sites in six ecosystems across the globe. The sensitivity of MOD17A2H GPP to the meteorology dataset and the fractional photosynthetically-active radiation (FPAR) product was explored by introducing site meteorology observations and improved Global Land Surface Satellite (GLASS) Leaf Area Index (LAI) products. The results showed that MOD17A2H GPP underestimated flux-derived GPP at most sites. Its performance in estimating annual GPP was poor (R 2 = 0.62) and even worse over eight days (R 2 = 0.52). For the MOD17A2H algorithm, replacing the reanalysis meteorological datasets with the site meteorological measurements failed to improve the estimation accuracies. However, great improvements in estimating the site-based GPP were gained by replacing MODIS FPAR with GLASS FPAR. This indicated that in the existing MOD17A2H product, the errors were originated more from FPAR than the meteorological data. We further examined the potential error contributions from land cover classification and maximum light use efficiency (ε max ). It was found that the current land cover classification scheme exhibited frequent misclassification errors. Moreover, the ε max value assigned in MOD17A2H was much smaller than the inferred ε max value. Therefore, the qualities of FPAR and land cover classification datasets should be upgraded, and the ε max value needs to be adjusted to provide more accurate GPP estimates using MOD17A2H for global ecosystems.

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Section: Glass Lai Datamentioning

confidence: 99%

Evaluation of the Latest MODIS GPP Products across Multiple Biomes Using Global Eddy Covariance Flux Data

et al. 2017

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“…The GLASS products, which are spatiotemporally continuous, have long temporal ranges and higher spatial resolutions than the similar satellite products. They are produced from multiple satellite data but mainly from MODIS data for the period of 2000-2013 [42].…”

Section: Data For Attribution Analysismentioning

confidence: 99%

Validation and Spatiotemporal Analysis of CERES Surface Net Radiation Product

et al. 2016

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Abstract:The Clouds and the Earth's Radiant Energy System (CERES) generates one of the few global satellite radiation products. The CERES ARM Validation Experiment (CAVE) has been providing long-term in situ observations for the validation of the CERES products. However, the number of these sites is low and their distribution is globally sparse, and particularly the surface net radiation product has not been rigorously validated yet. Therefore, additional validation efforts are highly required to determine the accuracy of the CERES radiation products. In this study, global land surface measurements were comprehensively collected for use in the validation of the CERES net radiation (R n ) product on a daily (340 sites) and a monthly (260 sites) basis, respectively. The validation results demonstrated that the CERES R n product was, overall, highly accurate. The daily validations had a Mean Bias Error (MBE) of 3.43 W¨m´2, Root Mean Square Error (RMSE) of 33.56 W¨m´2, and R 2 of 0.79, and the monthly validations had an MBE of 3.40 W¨m´2, RMSE of 25.57 W¨m´2, and R 2 of 0.84. The accuracy was slightly lower for the high latitudes. Following the validation, the monthly CERES R n product, from March 2000 to July 2014, was used for a further analysis. The global spatiotemporal variation of the R n , which occurred during the measurement period, was analyzed. In addition, two hot spot regions, the southern Great Plains and south-central Africa, were then selected for use in determining the driving factors or attribution of the R n variation. We determined that R n over the southern Great Plains decreased by´0.33 W¨m´2 per year, which was mainly driven by changes in surface green vegetation and precipitation. In south-central Africa, R n decreased at a rate of´0.63 W¨m´2 per year, the major driving factor of which was surface green vegetation.

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“…It was derived by overlay analysis of the 2000 and 2008 land-cover maps. Because the earliest satellite estimates of the Normalized Difference Vegetation Index (NDVI, spatial resolution 1 km) were available in 1982, LAI derived from the Global Land Surface Satellite (GLASS) NDVI series (spatial resolution 1 km, Zhao et al, 2013; www.landcover.org) was used to indicate varied growth conditions and plant structure in the period of 1982-2009. Details on the relationship between NDVI and LAI are given by Feng et al (2012).…”

Section: Datasetsmentioning

confidence: 99%

The role of climatic and anthropogenic stresses on long-term runoff reduction from the Loess Plateau, China

Feng

Cheng

et al. 2016

Science of The Total Environment

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Human intervention has strongly altered patterns of river runoff. Yet, few studies have addressed the complexity and nonlinearity of the anthropogenic stresses on runoff or their interaction with climate. We study the Loess Plateau in China, whose river runoff contributes 65% of the discharge to the middle reach of the Yellow River; this landscape has been shaped by human activity and is intensively managed. Our purpose is to characterize the interactive roles of climate and human activities in defining river runoff from the Loess Plateau. Applying a transient analysis to discover the time-varying runoff trend and impact factors, we found that the average runoff in the Loess Plateau decreased continuously during the period 1961-2009 (average rate of − 0.9 mm year −1 , P b 0.001). This long-term decrease in runoff mainly occurred in three stages, with transitions in 1970, 1981 and 1996. Reduced precipitation was the main reason for the decrease in runoff over the entire study period. However, human intervention played a dominant role in creating the transition points. Water yield (i.e., the ratio of runoff to precipitation) decreased following each anthropogenic transition, causing a 56% reduction in available freshwater resources during the period 1961-2009. These findings highlight the need for studies that address the dynamic and nonlinear processes controlling the availability of freshwater resources in the light of anthropogenic influences applied under a changing climate. Such studies are essential if we are to meet the human water demand in the Loess Plateau region.

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The Global Land Surface Satellite (GLASS) Remote Sensing Data Processing System and Products

Cited by 69 publications

References 42 publications

Evaluation of the Latest MODIS GPP Products across Multiple Biomes Using Global Eddy Covariance Flux Data

Evaluation of the Latest MODIS GPP Products across Multiple Biomes Using Global Eddy Covariance Flux Data

Validation and Spatiotemporal Analysis of CERES Surface Net Radiation Product

The role of climatic and anthropogenic stresses on long-term runoff reduction from the Loess Plateau, China

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