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.GLASS产品2023年新增植被聚集指数,中国建筑物屋顶数据(CBRA),国际湿地城市数据,森林覆盖度(FFC),土壤湿度数据;
2.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;
3.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;
4.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5 km,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);
5.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;
6.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 2023年新增产品种类
产品名称 | 空间分辨率 | 时间分辨率 | 时间序列 | |
---|---|---|---|---|
植被聚集指数 | 500m | 8天、月、季度 | 2000-2020 | |
中国建筑物屋顶数据 | 2.5m | 1年 | 2016-2021 | |
国际湿地城市数据 | 10m | 1年 | 2015、2022 | |
森林覆盖度 | 250m | 8天 | 2000-2020 | |
土壤湿度数据 | 0.0625度 | 1天 | 1970-2022 |
GLASS 2022年之前发布产品种类
产品名称 | 空间分辨率 | 时间分辨率 | 时间序列 | |
---|---|---|---|---|
叶面积指数 | 0.05D/0.5D/1km/500m/250m | 8天 | 1981-2021/2000-2021 | |
反照率 | 0.05D/0.5D/1km/500m/250m | 8天 | 1981-2021 | |
发射率 | 0.05D/0.5D/1km/500m | 8天 | 1982-2021/2000-2021 | |
光和有效辐射 | 0.05D/0.5D | 天均 | 2000-2021 | |
下行短波辐射 | 0.05D/0.5D | 天均 | 2000-2021 | |
长波辐射 | 1km/0.05 | 瞬时 | 2000-2020 | |
净辐射 | 0.05D/0.5D | 天均 | 2000-2020 | |
光合有效辐射吸收比 | 0.05D/0.5D/1km/500m/250m | 8天 | 1982-2018/2000-2021 | |
植被覆盖度 | 0.05D/500m | 8天 | 1982-2018/2000-2018 | |
潜热 | 0.05D/1km | 8天 | 1982-2018/2000-2018 | |
植被总初级生产力 | 0.05D/500m | 8天/年均 | 1982-2020/2000-2020 | |
地表温度 | 0.05D | 天均/瞬时 | 1981-2020 |
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]