Research on inversion method of surface albedo based on HJ -1 satellite data

2011 ◽  
Author(s):  
Zhanliang Yuan ◽  
Huifang Li ◽  
Jie Han ◽  
Erwei Qi ◽  
Jiaguo Li ◽  
...  
Keyword(s):  
Satellite Data ◽  
Surface Albedo ◽  
Inversion Method

scholarly journals High-Precision Soil Moisture Mapping Based on Multi-Model Coupling and Background Knowledge, Over Vegetated Areas Using Chinese GF-3 and GF-1 Satellite Data

2020 ◽  
Vol 12 (13) ◽  
pp. 2123 ◽  
Author(s):  
Leran Han ◽  
Chunmei Wang ◽  
Tao Yu ◽  
Xingfa Gu ◽  
Qiyue Liu
Keyword(s):  
Soil Moisture ◽  
High Precision ◽  
Satellite Data ◽  
Solution Method ◽  
Pearson Correlation ◽  
Optimal Solution ◽  
Background Knowledge ◽  
Optical Data ◽  
Inversion Method ◽  
Synthetic Aperture Radar Data

This paper proposes a combined approach comprising a set of methods for the high-precision mapping of soil moisture in a study area located in Jiangsu Province of China, based on the Chinese C-band synthetic aperture radar data of GF-3 and high spatial-resolution optical data of GF-1, in situ experimental datasets and background knowledge. The study was conducted in three stages: First, in the process of eliminating the effect of vegetation canopy, an empirical vegetation water content model and a water cloud model with localized parameters were developed to obtain the bare soil backscattering coefficient. Second, four commonly used models (advanced integral equation model (AIEM), look-up table (LUT) method, Oh model, and the Dubois model) were coupled to acquire nine soil moisture retrieval maps and algorithms. Finally, a simple and effective optimal solution method was proposed to select and combine the nine algorithms based on classification strategies devised using three types of background knowledge. A comprehensive evaluation was carried out on each soil moisture map in terms of the root-mean-square-error (RMSE), Pearson correlation coefficient (PCC), mean absolute error (MAE), and mean bias (bias). The results show that for the nine individual algorithms, the estimated model constructed using the AIEM (mv1) was significantly more accurate than those constructed using the other models (RMSE = 0.0321 cm³/cm³, MAE = 0.0260 cm³/cm³, and PCC = 0.9115), followed by the Oh model (m_v5) and LUT inversion method under HH polarization (mv2). Compared with the independent algorithms, the optimal solution methods have significant advantages; the soil moisture map obtained using the classification strategy based on the percentage content of clay was the most satisfactory (RMSE = 0.0271 cm³/cm³, MAE = 0.0225 cm³/cm³, and PCC = 0.9364). This combined method could not only effectively integrate the optical and radar satellite data but also couple a variety of commonly used inversion models, and at the same time, background knowledge was introduced into the optimal solution method. Thus, we provide a new method for the high-precision mapping of soil moisture in areas with a complex underlying surface.

Climate changes impact the surface albedo of a forest ecosystem based on MODIS satellite data

2007 ◽  
Author(s):  
M. A. Zoran ◽  
A. V. Nemuc
Keyword(s):  
Satellite Data ◽  
Forest Ecosystem ◽  
Climate Changes ◽  
Surface Albedo

scholarly journals Comparison of total water vapour content in the Arctic derived from GNSS, AIRS, MODIS and SCIAMACHY

2018 ◽  
Vol 11 (5) ◽  
pp. 2949-2965 ◽  
Author(s):  
Dunya Alraddawi ◽  
Alain Sarkissian ◽  
Philippe Keckhut ◽  
Olivier Bock ◽  
Stefan Noël ◽  
...  
Keyword(s):  
Time Series ◽  
Water Vapour ◽  
Satellite Data ◽  
Cloud Cover ◽  
Surface Albedo ◽  
Hydrological Cycle ◽  
Radiation Budget ◽  
The Arctic ◽  
Gnss Measurements ◽  
Gnss Time Series

Abstract. Atmospheric water vapour plays a key role in the Arctic radiation budget, hydrological cycle and hence climate, but its measurement with high accuracy remains an important challenge. Total column water vapour (TCWV) datasets derived from ground-based GNSS measurements are used to assess the quality of different existing satellite TCWV datasets, namely from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Atmospheric Infrared Sounder (AIRS) and the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY). The comparisons between GNSS and satellite data are carried out for three reference Arctic observation sites (Sodankylä, Ny-Ålesund and Thule) where long homogeneous GNSS time series of more than a decade (2001–2014) are available. We select hourly GNSS data that are coincident with overpasses of the different satellites over the three sites and then average them into monthly means that are compared with monthly mean satellite products for different seasons. The agreement between GNSS and satellite time series is generally within 5 % at all sites for most conditions. The weakest correlations are found during summer. Among all the satellite data, AIRS shows the best agreement with GNSS time series, though AIRS TCWV is often slightly too high in drier atmospheres (i.e. high-latitude stations during autumn and winter). SCIAMACHY TCWV data are generally drier than GNSS measurements at all the stations during the summer. This study suggests that these biases are associated with cloud cover, especially at Ny-Ålesund and Thule. The dry biases of MODIS and SCIAMACHY observations are most pronounced at Sodankylä during the snow season (from October to March). Regarding SCIAMACHY, this bias is possibly linked to the fact that the SCIAMACHY TCWV retrieval does not take accurately into account the variations in surface albedo, notably in the presence of snow with a nearby canopy as in Sodankylä. The MODIS bias at Sodankylä is found to be correlated with cloud cover fraction and is also expected to be affected by other atmospheric or surface albedo changes linked for instance to the presence of forests or anthropogenic emissions. Overall, the results point out that a better estimation of seasonally dependent surface albedo and a better consideration of vertically resolved cloud cover are recommended if biases in satellite measurements are to be reduced in the polar regions.

scholarly journals FRESCO-B: A fast cloud retrieval algorithm using oxygen B-band measurements from GOME-2

2018 ◽  
Author(s):  
Marine Desmons ◽  
Ping Wang ◽  
Piet Stammes ◽  
L. Gijsbert Tilstra
Keyword(s):  
Data Processing ◽  
Satellite Data ◽  
Surface Albedo ◽  
Cloud Fraction ◽  
Mean Difference ◽  
Retrieval Algorithm ◽  
Robust Algorithm ◽  
Retrieval Scheme ◽  
The Difference

Abstract. The FRESCO (Fast Retrieval Scheme for Clouds from the Oxygen A-band) algorithm is a simple, fast and robust algorithm used to retrieve cloud information in operational satellite data processing. It has been applied to GOME-1, SCIAMACHY, GOME-2 and more recently to TROPOMI. FRESCO retrieves effective cloud fraction and cloud pressure from measurements in the oxygen A-band around 761 nm. In this paper, we propose a new version of the algorithm, called FRESCO-B, which is based on measurements in the oxygen B-band around 687 nm. Such a method is interesting for vegetated surfaces where the surface albedo is much lower in the B-band than in the A-band, which limits the ground contribution to the top-of-atmosphere reflectances. In this study we first perform retrieval simulations. These show that the retrieved cloud pressures from FRESCO-B and FRESCO differ only between −10 hPa and +10 hPa, except for high thin clouds over vegetation where the difference is larger, about +15 to +30 hPa, with FRESCO-B yielding higher pressures. Next, inter-comparison between FRESCO-B and FRESCO retrievals over one month of GOME-2B data reveals that the effective cloud fractions retrieved in the O2 A and B bands are very similar (mean difference of 0.003) while the cloud pressures show a mean difference of 11.5 hPa, with FRESCO-B retrieving higher pressures than FRESCO. This agrees with the simulations and is partly due to deeper photons penetrations of O2 B-band in clouds as compared to the O2 A-band photons, and partly due to the surface albedo bias in FRESCO. Finally, validation with ground-based measurements shows that the FRESCO-B cloud pressure represents an altitude within the cloud boundaries for clouds that are not too far from the Lambertian reflector model, which occurs in about 50 % of the cases.

Spatial distributions of surface albedo from satellite data in arid oasis

2007 ◽  
Author(s):  
Aniwaer Amut ◽  
Lu Gong ◽  
Zhenyan Yuan
Keyword(s):  
Satellite Data ◽  
Surface Albedo ◽  
Spatial Distributions

Deriving surface albedo measurements from narrow band satellite data

1987 ◽  
Vol 8 (3) ◽  
pp. 351-367 ◽  
Author(s):  
CHRISTOPHER L. BREST ◽  
SAMUEL N. GOWARD
Keyword(s):  
Satellite Data ◽  
Surface Albedo ◽  
Narrow Band

scholarly journals Land Surface Albedo from Geostationary Satelites: A Multiagency Collaboration within SCOPE-CM

2013 ◽  
Vol 94 (2) ◽  
pp. 205-214 ◽  
Author(s):  
Alessio Lattanzio ◽  
Jörg Schulz ◽  
Jessica Matthews ◽  
Arata Okuyama ◽  
Bertrand Theodore ◽  
...  
Keyword(s):  
Satellite Data ◽  
Land Surface ◽  
Surface Albedo ◽  
Global Climate ◽  
The United States ◽  
Japan Meteorological Agency ◽  
Climate Data ◽  
Climate Monitoring ◽  
Governmental Institutions

Climate has been recognized to have direct and indirect impact on society and economy, both in the long term and daily life. The challenge of understanding the climate system, with its variability and changes, is enormous and requires a joint long-term international commitment from research and governmental institutions. An important international body to coordinate worldwide climate monitoring efforts is the World Meteorological Organization (WMO). The Global Climate Observing System (GCOS) has the mission to provide coordination and the requirements for global observations and essential climate variables (ECVs) to monitor climate changes. The WMO-led activity on Sustained, Coordinated Processing of Environmental Satellite Data for Climate Monitoring (SCOPE-CM) is responding to these requirements by ensuring a continuous and sustained generation of climate data records (CDRs) from satellite data in compliance with the principles and guidelines of GCOS. SCOPE-CM represents a new partnership between operational space agencies to coordinate the generation of CDRs. To this end, pilot projects for different ECVs, such as surface albedo, cloud properties, water vapor, atmospheric motion winds, and upper-tropospheric humidity, have been initiated. The coordinated activity on land surface albedo involves the operational meteorological satellite agencies in Europe [European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)], in Japan [the Japan Meteorological Agency (JMA)], and in the United States [National Oceanic and Atmospheric Administration (NOAA)]. This paper presents the first results toward the generation of a unique land surface albedo CDR, involving five different geostationary satellite positions and approximately three decades of data starting in the 1980s, and combining close to 30 different satellite instruments.

Impact of climate and anthropogenic changes on urban surface albedo assessed from time-series MODIS satellite data

2015 ◽  
Author(s):  
Maria A. Zoran ◽  
Adrian I. Dida ◽  
Liviu Florin V. Zoran
Keyword(s):  
Time Series ◽  
Satellite Data ◽  
Surface Albedo ◽  
Urban Surface ◽  
Anthropogenic Changes

Land surface albedo retrieval via kernel-based BRDF modeling: I. Statistical inversion method and model comparison

2003 ◽  
Vol 84 (1) ◽  
pp. 100-119 ◽  
Author(s):  
Oleg Pokrovsky ◽  
Jean-Louis Roujean
Keyword(s):  
Land Surface ◽  
Surface Albedo ◽  
Model Comparison ◽  
Inversion Method
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