Suhong Liu

A long-term Global LAnd Surface Satellite (GLASS) data-set for environmental studies

Recently, five Global LAnd Surface Satellite (GLASS) products have been released: leaf area index (LAI), shortwave broadband albedo, longwave broadband emissivity, incident short radiation, and photosynthetically active radiation (PAR). The first three products cover the years 1982–2012 (LAI) and 1981–2010 (albedo and emissivity) at 1–5 km and 8-day resolutions, and the last two radiation products span the period 2008–2010 at 5 km and 3-h resolutions. These products have been evaluated and validated, and the preliminary results indicate that they are of higher quality and accuracy than the existing products. In particular, the first three products have much longer time series, and are therefore highly suitable for various environmental studies. This paper outlines the algorithms, product characteristics, preliminary validation results, potential applications and some examples of initial analysis of these products.

Direct-Estimation Algorithm for Mapping Daily Land-Surface Broadband Albedo From MODIS Data

Land surface albedo is a critical parameter in surface-energy budget studies. Over the past several decades, many albedo products are generated from remote-sensing data sets. The Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF)/Albedo algorithm is used to routinely produce eight day (16-day composite), 1-km resolution MODIS albedo products. When some natural processes or human activities occur, the land-surface broadband albedo can change rapidly, so it is necessary to enhance the temporal resolution of albedo product. We present a direct-estimation algorithm for mapping daily land-surface broadband albedo from MODIS data. The polarization and directionality of the Earths reflectance-3/polarization and anisotropy of reflectances for atmospheric sciences coupled with observations from a Lidar BRDF database is employed as a training data set, and the 6S atmospheric radiative transfer code is used to simulate the top-of-atmosphere (TOA) reflectances. Then a relationship between TOA reflectances and land-surface broadband albedos is developed using an angular bin regression method. The robustness of this method for different angular bins, aerosol conditions, and land-cover types is analyzed. Simulation results show that the absolute error of this algorithm is ~ 0.009 for vegetation, 0.012 for soil, and 0.030 for snow/ice. Validation of the direct-estimation algorithm against in situ measurement data shows that the proposed method is capable of characterizing the temporal variation of albedo, especially when the land-surface BRDF changes rapidly.

Preliminary evaluation of the long-term GLASS albedo product

Land surface albedo is an important parameter to describe the radiant forcing in the climate system. A long-time series of global albedo products is needed to understand the mechanism of climate change. Aiming to support global change and Earth system studies, GLASS (Global LAnd Surface Satellites) provides long-term global land surface albedo product from 1981 to 2010, which are generated from multisource remote sensing data and newly developed algorithms. It is critical to assess the quality of the GLASS product when it is released to the public. This paper first introduced the algorithms and then analyzed the integrity, accuracy, and robustness of the GLASS albedo product. The results show that the GLASS albedo product is a gapless, long-term continuous, and self-consistent data-set with an accuracy similar to that of the widely acknowledged MODIS MCD43 product. The quality flag, which is provided along with the black-sky and white-sky albedo, gives a pertinent indication of the expected uncertainty in the product.

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

Using remotely sensed satellite products is the most efficient way to monitor global land, water, and forest resource changes, which are believed to be the main factors for understanding global climate change and its impacts. A reliable remotely sensed product should be retrieved quantitatively through models or statistical methods. However, producing global products requires a complex computing system and massive volumes of multi-sensor and multi-temporal remotely sensed data. This manuscript describes the ground Global LAnd Surface Satellite (GLASS) product generation system that can be used to generate long-sequence time series of global land surface data products based on various remotely sensed data. To ensure stabilization and efficiency in running the system, we used the methods of task management, parallelization, and multi I/O channels. An array of GLASS remote sensing products related to global land surface parameters are currently being produced and distributed by the Center for Global Change Data Processing and Analysis at Beijing Normal University in Beijing, China. These products include Leaf Area Index (LAI), land surface albedo, and broadband emissivity (BBE) from the years 1981 to 2010, downward shortwave radiation (DSR) and photosynthetically active radiation (PAR) from the years 2008 to 2010.

Global Land Surface Fractional Vegetation Cover Estimation Using General Regression Neural Networks From MODIS Surface Reflectance

Fractional vegetation cover (FVC) plays an important role in earth surface process simulations, climate modeling, and global change studies. Several global FVC products have been generated using medium spatial resolution satellite data. However, the validation results indicate inconsistencies, as well as spatial and temporal discontinuities of the current FVC products. The objective of this paper is to develop a reliable estimation algorithm to operationally produce a high-quality global FVC product from the Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance. The high-spatial-resolution FVC data were first generated using Landsat TM/ETM+ data at the global sampling locations, and then, the general regression neural networks (GRNNs) were trained using the high-spatial-resolution FVC data and the reprocessed MODIS surface reflectance data. The direct validation using ground reference data from validation of land European Remote Sensing instruments sites indicated that the performance of the proposed method (R2=0.809, RMSE =0.157) was comparable with that of the GEOV1 FVC product (R2=0.775, RMSE =0.166), which is currently considered to be the best global FVC product from SPOT VEGETATION data. Further comparison indicated that the spatial and temporal continuity of the estimates from the proposed method was superior to that of the GEOV1 FVC product.

Stable isotopic observation of water use sources of Pinus sylvestris var. mongolica in Horqin Sandy Land, China

Since the late 1950s, Mongolian pine (Pinus sylvestris var. mongolica) has been widely planted for vegetation restoration in arid and semi-arid areas in North China. We used stable isotope signals from precipitation, soil water, and xylem water of Mongolian pine trees, which were planted in early 1980s on sand dunes in the east-southern margin of Horqin Sandy Land, to identify water uptake sources of this tree. (1) Stable 18O isotope composition of the xylem water exhibited little seasonality, suggesting that the trees use a relatively stable water source; (2) the water source of the pine trees primarily came from a soil depth of 20–60xa0cm (sampling depth up to 60xa0cm in this study) and the trees might use groundwater when soil moisture became extremely low; and (3) there was not much difference in water sources used by the pine trees grown at the top of the fixed dune and in the inter dune lowland, although these two sites had 3–8xa0m elevation difference. This study suggests that it is critical for Mongolian pine trees to access relatively reliable and stable water sources to grow in sandy land habitats, and timely recharging of rainwater to the trees’ rooting depth is requisite for avoiding and/or reducing their degradation caused by water shortage.

Mapping Surface Broadband Albedo from Satellite Observations: A Review of Literatures on Algorithms and Products

Surface albedo is one of the key controlling geophysical parameters in the surface energy budget studies, and its temporal and spatial variation is closely related to the global climate change and regional weather system due to the albedo feedback mechanism. As an efficient tool for monitoring the surfaces of the Earth, remote sensing is widely used for deriving long-term surface broadband albedo with various geostationary and polar-orbit satellite platforms in recent decades. Moreover, the algorithms for estimating surface broadband albedo from satellite observations, including narrow-to-broadband conversions, bidirectional reflectance distribution function (BRDF) angular modeling, direct-estimation algorithm and the algorithms for estimating albedo from geostationary satellite data, are developed and improved. In this paper, we present a comprehensive literature review on algorithms and products for mapping surface broadband albedo with satellite observations and provide a discussion of different algorithms and products in a historical perspective based on citation analysis of the published literature. This paper shows that the observation technologies and accuracy requirement of applications are important, and long-term, global fully-covered (including land, ocean, and sea-ice surfaces), gap-free, surface broadband albedo products with higher spatial and temporal resolution are required for climate change, surface energy budget, and hydrological studies.

Pinpointing the sources and measuring the lengths of the principal rivers of the world

Abstract Cultures throughout the world are associating with the rivers. People depend upon the rivers and their tributaries for food, water, transport, and many other aspects of their daily lives. Unfortunately, human beings have not calculated the accurate lengths for the great rivers even today. The lengths of the rivers are very different in popular textbooks, magazines, atlases and encyclopedias, etc. To accurately determine the lengths of the principal rivers of the world, the combination of satellite image analysis and field investigations to the source regions is proposed in this paper. The lengths of the Nile, Amazon, Yangtze, Mississippi, Yellow, Ob, Yenisey, Amur, Congo and Mekong, with lengths over or close to 5000 km, were calculated using the proposed method. The results may represent the most reliable and accurate lengths of the principal rivers of the world that are currently achievable.

Satellite-Based Analysis of Evapotranspiration and Water Balance in the Grassland Ecosystems of Dryland East Asia

The regression tree method is used to upscale evapotranspiration (ET) measurements at eddy-covariance (EC) towers to the grassland ecosystems over the Dryland East Asia (DEA). The regression tree model was driven by satellite and meteorology datasets, and explained 82% and 76% of the variations of ET observations in the calibration and validation datasets, respectively. The annual ET estimates ranged from 222.6 to 269.1 mm yr−1 over the DEA region with an average of 245.8 mm yr−1 from 1982 through 2009. Ecosystem ET showed decreased trends over 61% of the DEA region during this period, especially in most regions of Mongolia and eastern Inner Mongolia due to decreased precipitation. The increased ET occurred primarily in the western and southern DEA region. Over the entire study area, water balance (the difference between precipitation and ecosystem ET) decreased substantially during the summer and growing season. Precipitation reduction was an important cause for the severe water deficits. The drying trend occurring in the grassland ecosystems of the DEA region can exert profound impacts on a variety of terrestrial ecosystem processes and functions.

A tasseled cap transformation for Landsat 8 OLI TOA reflectance images

The Tasseled Cap Transformation (TCT) has been widely used in the remote sensing community. However, TCT is sensor dependent, so a new sensor requires a reworking of the TCT starting with analysis of data structure of images. The purpose of this paper is to derive the TCT parameters for the Landsat 8 OLI TOA Reflectance images, and compare the differences between the Tasseled Cap Transformation parameters derived from the spring and autumn images. The results from this paper suggest that the TCT parameters derived from the image from October is most appropriate for regional remote sensing applications over the Yellow River Delta, China where atmospheric correction is not feasible.