Long-Term Global Land Surface Satellite (GLASS) Fractional Vegetation Cover Product Derived From MODIS and AVHRR Data

2019 ◽  
Vol 12 (2) ◽  
pp. 508-518 ◽  
Author(s):  
Kun Jia ◽  
Linqing Yang ◽  
Shunlin Liang ◽  
Zhiqiang Xiao ◽  
Xiang Zhao ◽  
...  
Keyword(s):  
Vegetation Cover ◽  
Land Surface ◽  
Fractional Vegetation Cover ◽  
Global Land ◽  
Avhrr Data

scholarly journals Spatiotemporal Comparison and Validation of Three Global-Scale Fractional Vegetation Cover Products

2019 ◽  
Vol 11 (21) ◽  
pp. 2524 ◽  
Author(s):  
Duanyang Liu ◽  
Kun Jia ◽  
Xiangqin Wei ◽  
Mu Xia ◽  
Xiwang Zhang ◽  
...  
Keyword(s):  
Vegetation Cover ◽  
Land Surface ◽  
Large Scale ◽  
High Spatial Resolution ◽  
Remote Sensing Data ◽  
Global Scale ◽  
Glass Product ◽  
Fractional Vegetation Cover ◽  
Temporal Profiles

Fractional vegetation cover (FVC) is an important parameter for many environmental and ecological models. Large-scale and long-term FVC products are critical for various applications. Currently, several global-scale FVC products have been generated with remote sensing data, such as VGT bioGEOphysical product Version 2 (GEOV2), PROBA-V bioGEOphysical product Version 3 (GEOV3) and Global LAnd Surface Satellite (GLASS) FVC products. However, studies comparing and validating these global-scale FVC products are rare. Therefore, in this study, the performances of three global-scale time series FVC products, including the GEOV2, GEOV3, and GLASS FVC products, are investigated to assess their spatial and temporal consistencies. Furthermore, reference FVC data generated from high-spatial-resolution data are used to directly evaluate the accuracy of these FVC products. The results show that these three FVC products achieve general agreements in terms of spatiotemporal consistencies over most regions. In addition, the GLASS and GEOV2 FVC products have reliable spatial and temporal completeness, whereas the GEOV3 FVC product contains much missing data over high-latitude regions, especially during wintertime. Furthermore, the GEOV3 FVC product presents higher FVC values than GEOV2 and GLASS FVC products over the equator. The main differences between the GEOV2 and GLASS FVC products occur over deciduous forests, for which the GLASS product presents slightly higher FVC values than the GEOV2 product during wintertime. Finally, temporal profiles of the GEOV2 and GLASS FVC products show better consistency than the GEOV3 FVC product, and the GLASS FVC product presents more reliable accuracy (R2 = 0.7878, RMSE = 0.1212) compared with the GEOV2 (R2 = 0.5798, RMSE = 0.1921) and GEOV3 (R2 = 0.7744, RMSE = 0.2224) FVC products over these reference FVC data.

Validation of Global LAnd Surface Satellite (GLASS) fractional vegetation cover product from MODIS data in an agricultural region

2018 ◽  
Vol 9 (9) ◽  
pp. 847-856 ◽  
Author(s):  
Kun Jia ◽  
Shunlin Liang ◽  
Xiangqin Wei ◽  
Yunjun Yao ◽  
Linqing Yang ◽  
...  
Keyword(s):  
Vegetation Cover ◽  
Land Surface ◽  
Agricultural Region ◽  
Modis Data ◽  
Fractional Vegetation Cover ◽  
Global Land

scholarly journals Observed Changes in Extreme Temperature over the Global Land Based on a Newly Developed Station Daily Dataset

2019 ◽  
Vol 32 (24) ◽  
pp. 8489-8509 ◽  
Author(s):  
Panfeng Zhang ◽  
Guoyu Ren ◽  
Yan Xu ◽  
Xiaolan L. Wang ◽  
Yun Qin ◽  
...  
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Extreme Temperature ◽  
Significant Trend ◽  
Daily Maximum ◽  
China Meteorological Administration ◽  
Global Land ◽  
Temperature Indices ◽  
Maximum Temperatures

Abstract This paper presents an analysis of changes in global land extreme temperature indices (1951–2015) based on the new global land surface daily air temperature dataset recently developed by the China Meteorological Administration (CMA). The linear trends of the gridpoint time series and global land mean time series were calculated by using a Mann–Kendall method that accounts for the lag-1 autocorrelation in the time series of annual extreme temperature indices. The results, which are generally consistent with previous studies, showed that the global land average annual and seasonal mean extreme temperature indices series all experienced significant long-term changes associated with warming, with cold threshold indices (frost days, icing days, cold nights, and cold days) decreasing, warm threshold indices (summer days, tropical nights, and warm days) increasing, and all absolute indices (TXx, TXn, TNx, and TNn) also increasing, over the last 65 years. The extreme temperature indices series based on daily minimum temperatures generally had a stronger and more significant trend than those based on daily maximum temperatures. The strongest warming occurred after the mid-1970s, and a few extreme temperature indices showed no significant trend over the period from 1951 to the mid-1970s. Most parts of the global land experienced significant warming trends over the period 1951–2015 as a whole, and the largest trends appeared in mid- to high latitudes of the Eurasian continent.

scholarly journals Estimation of Long-Term Surface Downward Longwave Radiation over the Global Land from 2000 to 2018

2021 ◽  
Vol 13 (9) ◽  
pp. 1848
Author(s):  
Chunjie Feng ◽  
Xiaotong Zhang ◽  
Yu Wei ◽  
Weiyu Zhang ◽  
Ning Hou ◽  
...  
Keyword(s):  
Water Vapor ◽  
Spatial Resolution ◽  
Land Surface ◽  
Longwave Radiation ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Mean Bias Error ◽  
Downward Longwave Radiation ◽  
Global Land

It is of great importance for climate change studies to construct a worldwide, long-term surface downward longwave radiation (Ld, 4–100 μm) dataset. Although a number of global Ld datasets are available, their low accuracies and coarse spatial resolutions limit their applications. This study generated a daily Ld dataset with a 5-km spatial resolution over the global land surface from 2000 to 2018 using atmospheric parameters, which include 2-m air temperature (Ta), relative humidity (RH) at 1000 hPa, total column water vapor (TCWV), surface downward shortwave radiation (Sd), and elevation, based on the gradient boosting regression tree (GBRT) method. The generated Ld dataset was evaluated using ground measurements collected from AmeriFlux, AsiaFlux, baseline surface radiation network (BSRN), surface radiation budget network (SURFRAD), and FLUXNET networks. The validation results showed that the root mean square error (RMSE), mean bias error (MBE), and correlation coefficient (R) values of the generated daily Ld dataset were 17.78 W m−2, 0.99 W m−2, and 0.96 (p < 0.01). Comparisons with other global land surface radiation products indicated that the generated Ld dataset performed better than the clouds and earth’s radiant energy system synoptic (CERES-SYN) edition 4.1 dataset and ERA5 reanalysis product at the selected sites. In addition, the analysis of the spatiotemporal characteristics for the generated Ld dataset showed an increasing trend of 1.8 W m−2 per decade (p < 0.01) from 2003 to 2018, which was closely related to Ta and water vapor pressure. In general, the generated Ld dataset has a higher spatial resolution and accuracy, which can contribute to perfect the existing radiation products.

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