与国际上其它现有全球陆表卫星产品相比，GLASS产品具有一系列独特的特点：

1.有些产品是世界上独一无二的，比如高分辨率（1 km）发射率产品；

2.GLASS产品时间跨度超过40年，把目前国际主流同类产品（DSR、地表反照率、BBE、上下行长波辐射、净辐射、LAI、FAPAR、FVC、GPP、NPP、潜热）的时间范围向前推了近20年；

3.辐射产品（DSR、长波辐射、净辐射）为全球海陆覆盖，空间分辨率为5km，比国际同类产品提高了一个数量级（例如GEWEX、CERES、ISCCP，空间分辨在100 km左右）；

4.LAI，FAPAR，反照率等是世界上空间分辨率最高的长时间序列全球产品；GLASS LAI、FAPAR、地表反照率产品的分辨率为250 m，而国外长时间序列全球覆盖的同类产品的空间分辨率最高为500 m；

5.GLASS产品具有高质量和高精度，空间连续，无数据缺失，方便有户使用；基于地面测量的直接验证和多产品的比较，GLASS产品具有更高的精度、质量和时空一致性。

GLASS产品使用说明下载：

附件1：使用说明文档

附件2：使用插件

GLASS产品代表性文献

1. Liang S, Cheng J, Jia K, Jiang B, Liu Q, Xiao Z, Yao Y, Yuan W, Zhang X, Zhao X and Zhou J. 2021. The Global Land Surface Satellite(GLASS) Product Suite. Bulletin of the American Meteorological Society. 102(2): E323-E337. DOI: 10.1175/bams-d-180341.1. https://doi.org/10.1175/BAMS-D-18-0341.1 [PDF]

2. Liang, S., Zhao, X., Yuan, W., Liu, S., Cheng, X., Xiao, Z., Zhang, X., Liu, Q., Cheng, J., Tang, H., Qu, Y.H., Bo, Y., Qu, Y., Ren, H., Yu, K., & Townshend, J. 2013. A Long-term Global LAnd Surface Satellite (GLASS) Dataset for Environmental Studies. International Journal of Digital Earth, 6, 5-33 DOI: https://doi.org/10.1080/17538947.2013.805262[PDF]

3. Liang, S., Zhang, X., Xiao, Z., Cheng, J., Liu, Q., & Zhao, X. 2013. Global LAnd Surface Satellite (GLASS) products: Algorithms, validation and analysis. Springer [PDF]

4. Chen X, Liang S, He L, Yang Y and Yin C. 2021. A Temporally Consistent 8-Day 0.05° Gap-Free Snow Cover Extent Dataset over the Northern Hemisphere for the Period 1981-2019. Earth System Science Data Discussions. 2021: 1-30. DOI: 10.5194/essd-2021-279. https://essd.copernicus.org/preprints/essd-2021-279/ [PDF]

5. Chen Y, Liang S, Ma H, Li B, He T and Wang Q. 2021. An all-sky 1 km daily land surface air temperature product over mainland China for 2003-2019 from MODIS and ancillary data. Earth System Science Data. 13(8): 4241-4261. DOI: 10.5194/essd-13-4241-2021. https://doi.org/10.5194/essd-13-4241-2021 [PDF]

6. Cheng J, Meng X, Dong S and Liang S. 2021. Generating the 30-M Land Surface Temperature Product over Continental China and USA from Landsat 5/7/8 Data. Science of Remote Sensing. 4:100032. DOI: 10.1016/j.srs.2021.100032. https://doi.org/10.1016/j.srs.2021.100032 [PDF]

7. Jia K, Yang L, Liang S, Xiao Z, Zhao X, Yao Y, Zhang X, Jiang B and Liu D. 2019. Long-Term Global Land Surface Satellite (GLASS) Fractional Vegetation Cover Product Derived from MODIS and AVHRR Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 12(2): 508-518. DOI: 10.1109/JSTARS.2018.2854293. https://ieeexplore.ieee.org/document/8417967 [PDF]

8. Jiang B, Liang S, Ma H, Zhang X, Xiao Z, Zhao X, Jia K, Yao Y and Jia A. 2016. Glass Daytime All-Wave Netradiation Product: Algorithm Development and Preliminary Validation. Remote Sensing. 8(3): 222. DOI: 10.3390/rs8030222. https://doi.org/10.3390/rs8030222 [PDF]

9. Li B, Liang S, Liu X, Ma H, Chen Y, Liang T and He T. 2021. Estimation of All-Sky 1 km Land Surface Temperature over the Conterminous United States. Remote Sensing of Environment. 266: 112707. DOI: 10.1016/j.rse.2021.112707. https://doi.org/10.1016/j.rse.2021.112707 [PDF]

10. Liu Q, Wang L Z, Qu Y, Liu N F, Liu S H, Tang H R, and Liang S L. 2013. Preliminary Evaluation of the Long-term GLASS Albedo Product, International Journal of Digital Earth, 6(s1):69-95, DOI: 10.1080/17538947.2013.804601. https://doi.org/10.1080/17538947.2013.804601 [PDF]

11. Liu X, Tang B-H, Yan G, Li Z-L and Liang S. 2019. Retrieval of Global Orbit Drift Corrected Land Surface Temperature from Long-Term AVHRR Data. Remote Sensing. 11(23): 2843. DOI: 10.3390/rs11232843. https://www.mdpi.com/2072-4292/11/23/2843 [PDF]

12. Ma H and Liang S. 2022. Development of the Glass 250-M Leaf Area Index Product (Version 6) from Modis Data Using the Bidirectional LSTM Deep Learning Model. Remote Sensing of Environment. 273: 112985. DOI: 10.1016/j.rse.2022.112985. [PDF]

13. Ma H, Liang S, Xiong C, Wang Q and Jia A. 2022. Global Land Surface 250 m 8 d Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) Product from 2000 to 2020. Earth System Science Data. 14(12): 5333-5347. DOI: 10.5194/essd-2022-131. https://doi.org/10.1016/j.rse.2022.112985 [PDF]

14. Ma J, Zhou J, Gottsche F-M, Liang S, Wang S and Li M. 2020. A Global Long-Term (1981 – 2000) Land Surface Temperature Product for NOAA AVHRR. Earth System Science Data. 12,: 3247–3268. DOI: 10.5194/essd-12-3247-2020. https://doi.org/10.5194/essd-12-3247-2020 [PDF]

15. Xu J, Liang S, Ma H and He T. 2022. Generating 5 km Resolution 1981 – 2018 Daily Global Land Surface Longwave Radiation Products from AVHRR Shortwave and Longwave Observations Using Densely Connected Convolutional Neural Networks. Remote Sensing of Environment. 280: 113223. DOI: 10.1016/j.rse.2022.113223. https://doi.org/10.1016/j.rse.2022.113223 [PDF]

16. Yao Y, Liang S, Li X, Hong Y, Fisher J B, Zhang N, Chen J, Cheng J, Zhao S, Zhang X, Jiang B, Sun L, Jia K, Wang K, Chen Y, Mu Q and Feng F. 2014. Bayesian Multimodel Estimation of Global Terrestrial Latent Heat Flux from Eddy Covariance, Meteorological, and Satellite Observations. Journal of Geophysical Research: Atmospheres. 119(8): 4521-4545. DOI: 10.1002/2013JD020864. https://doi.org/10.1002/2013JD020864 [PDF]