Updates on Global LAnd Surface Satellite （GLASS） products suite
- Vol. 27, Issue 4, Pages: 831-856(2023)
Received：09 September 2022，
Published：07 April 2023
DOI： 10.11834/jrs.20232462
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摘要
GLASS（Global LAnd Surface Satellite）产品集是在中国国家高新技术研究和发展项目“十一五”和“十二五”863计划及“十三五”国家重点研发计划的支持下，经十余年努力研发而生成的多种陆表特征参数的高级卫星数据产品。与国际上同类产品相比较，它们具有一系列的独特特性，正得到国内外1000多家单位研究人员的使用，总下载量超过1.7 PB。本文概述了GLASS产品集算法的发展，产品特征，精度验证，以及这些产品的一些初步应用示例。同时还介绍了30 m分辨率的Hi-GLASS产品集，以及将来继续完善和发展GLASS产品的一些考虑。
Abstract
The Global LAnd Surface Satellite (GLASS) products suite includes high-level satellite products of land surface essential variables from multiple universities and research institutes. Producing the GLASS products suite has been undertaken since 2010. The suite spans from the initial five products to the current 16 products
which are generated mostly from the Advanced Very High-Resolution Radiometer and/or Moderate Resolution Imaging Spectroradiometer data. Some of the products have been previously introduced in the literature
and this study provides an update on the algorithm developments
validation accuracies
and their typical applications in all 16 products. This study also describes the Hi-GLASS products at 30 m resolution and some perspectives for further future improvement and development of the GLASS products.
Estimating land surface variables from satellite observations is an “ill-posed” inversion problem. For each pixel
the number of multispectral bands is usually smaller than the number of environmental variables
and the values of many spectral bands are highly correlated. Some novel solutions have been proposed to address the insufficient information in generating reliable GLASS products. We can identify at least four approaches. The first is based on the temporal signature of the satellite observations. A typical example is the MODIS Leaf Area Index (LAI) and the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) products generated using two-year observations simultaneously. The second uses an algorithm ensemble. A typical example is the evapotranspiration product based on integrating five estimation algorithms. The third uses multiple satellite observations. For example
the forest aboveground biomass product is based on optical
Lidar
and microwave data products. The last incorporates the physical model to generate the products
such as the gross primary production product.
The GLASS products have several unique features compared with similar products on the market
including the following:
(1) Several products are unique
such as the high-resolution (1 km) broadband emissivity and time-series forest aboveground biomass products.
(2) Most products have long time series (i.e.
over 40 years)
while most other similar global products start from approximately the year 2000
with a period of approximately 20 years.
(3) The radiation products
covering the world’s land and ocean surfaces
have a spatial resolution of 5 km
which is an order of magnitude higher than other such products in wide use
for example
the Global Energy and Water Exchanges
the Clouds and the Earth’s Radiant Energy System
and the International Satellite Cloud Climatology Project
which have spatial resolutions coarser than 100 km.
(4) Several long-time-series global products have the highest spatial resolution in the world
such as 250 m for the LAI
FAPAR
and albedo products and 5 km for snow cover extent. Moreover
the all-weather LST and near-surface air temperature products have a 1-km resolution.
(5) GLASS products are of high quality and accuracy.
Over 2000 peer-reviewed papers based on the GLASS products have been published. Their applications are distributed in many scientific disciplines and societal benefits areas.We will continue to improve the quality and accuracy of the existing GLASS products and produce more GLASS products with higher spatial resolutions.
关键词
Keywords
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CHEN Xiaona 中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室
CHEN Yan 武汉大学 遥感信息工程学院
CHENG Jie 北京师范大学地理科学学部 遥感科学国家重点实验室;北京师范大学地理科学学部 北京市陆表遥感数据产品工程技术研究中心
JIA Kun 北京师范大学地理科学学部 遥感科学国家重点实验室;北京师范大学地理科学学部 北京市陆表遥感数据产品工程技术研究中心
JIANG Bo 北京师范大学地理科学学部 遥感科学国家重点实验室;北京师范大学地理科学学部 北京市陆表遥感数据产品工程技术研究中心
LI Bing 武汉大学 遥感信息工程学院
LIU Qiang 鹏城实验室
MA Han 香港大学 地理系
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School of Automation and Electrical Engineering, University of Science and Technology Beijing
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Spatio-temporal Big Data Center, Mindu Innovation Laboratory
Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences
Jianghuai Advanced Technology Center
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