Evaluation of satellite land surface albedo products over China using ground-measurements

ABSTRACT Land surface albedo (LSA) is an important parameter in surface energy balance and global climate change. It has been used in the fields of energy budgets, climate dynamics, and land surface processes. To apply satellite LSA products more widely, the product accuracy needs to be evaluated at different scales and under atmospheric and surface conditions. This study validates and analyzes the errors of the LSA datasets from the Global LAnd Surface Satellites (GLASS) product, the European Space Agency’s Earth Observation Envelope Programme (GlobAlbedo), the Quality Assurance for Essential Climate Variables (QA4ECV) project, the Gap-filled Snow-free Bidirectional Reflectance Distribution Function (BRDF) parameters product (MCD43GF), and the Satellite Application Facility on Climate Monitoring (CM SAF) Albedo dataset from the AVHRR data (CLARA-SAL) against the Chinese Ecosystem Research Network (CERN) measurements at different spatiotemporal scales over China from 2005 to 2015. The results show that LSA estimated by GLASS agrees well with the CERN measurements on a continental scale. The GLASS product is characterized by a correlation coefficient of 0.80, a root-mean-square error of 0.09, and a mean absolute error of 0.06. The consistency between GLASS, GlobAlbedo, and CLARA-SAL is slightly lower over the regions with high aerosol optical depth (AOD) (e.g. Sichuan Basin, northern China) and high cloud cover compared with that in regions with lower AOD and low cloud cover. The estimation errors are related to varying atmospheric and surface conditions and increase with increasing AOD and cloud cover and decreasing enhanced vegetation index. Therefore, algorithms under complex atmospheric and surface conditions (e.g. high AOD, sparse vegetation) should be optimized to improve the accuracy of LSA products.

[1]  C. Ke,et al.  Winter sea ice albedo variations in the Bohai Sea of China , 2017, Acta Oceanologica Sinica.

[2]  K. Davis,et al.  The MODIS (Collection V005) BRDF/albedo product: Assessment of spatial representativeness over forested landscapes , 2009 .

[3]  Zhongfeng Qiu,et al.  Global Validation of MODIS C6 and C6.1 Merged Aerosol Products over Diverse Vegetated Surfaces , 2018, Remote. Sens..

[4]  Zhanqing Li,et al.  MODIS Collection 6.1 3 km resolution aerosol optical depth product: global evaluation and uncertainty analysis , 2020, Atmospheric Environment.

[5]  A. Lacis,et al.  Calculation of radiative fluxes from the surface to top of atmosphere based on ISCCP and other global data sets: Refinements of the radiative transfer model and the input data , 2004 .

[6]  Peter R. J. North,et al.  The ESA GlobAlbedo Project for mapping the Earth's land surface albedo for 15 Years from European Sensors. , 2012, IGARSS 2012.

[7]  R. Dickinson.  Land Surface Processes and Climate—Surface Albedos and Energy Balance , 1983 .

[8]  Per Jönsson,et al.  TIMESAT - a program for analyzing time-series of satellite sensor data , 2004, Comput. Geosci..

[9]  T. Painter,et al.  Reflectance quantities in optical remote sensing - definitions and case studies , 2006 .

[10]  Christopher O. Ilori,et al.  Evaluation of atmospheric correction methods for low to high resolutions satellite remote sensing data , 2021 .

[11]  C. Woodcock,et al.  Consistency of MODIS surface bidirectional reflectance distribution function and albedo retrievals: 2. Validation , 2003 .

[12]  Christophe Ley,et al.  Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median , 2013 .

[13]  Shunlin Liang,et al.  A direct algorithm for estimating land surface broadband albedos from MODIS imagery , 2003, IEEE Trans. Geosci. Remote. Sens..

[14]  Lin Li,et al.  Analysis of Global Land Surface Shortwave Broadband Albedo From Multiple Data Sources , 2010, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[15]  Alan H. Strahler,et al.  Validation of the MODIS bidirectional reflectance distribution function and albedo retrievals using combined observations from the aqua and terra platforms , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[16]  Nadine Gobron,et al.  Land ECVs from QA4ECV using an optimal estimation framework , 2017 .

[17]  Qing Xiao,et al.  Assessment of NPP VIIRS Albedo Over Heterogeneous Crop Land in Northern China , 2017 .

[18]  Ozgur Kisi,et al.  Modeling and comparison of hourly photosynthetically active radiation in different ecosystems , 2016 .

[19]  Chad J. Shuey,et al.  Validating MODIS land surface reflectance and albedo products: methods and preliminary results , 2002 .

[20]  Jerry Y. Pan,et al.  Intercomparison of MODIS albedo retrievals and in situ measurements across the global FLUXNET network , 2012 .

[21]  C. Woodcock,et al.  Evaluation of Moderate-resolution Imaging Spectroradiometer (MODIS) snow albedo product (MCD43A) over tundra , 2012 .

[22]  Ming Zhang,et al.  Performance of the NPP-VIIRS and aqua-MODIS Aerosol Optical Depth Products over the Yangtze River Basin , 2018, Remote. Sens..

[23]  Karl-Göran Karlsson,et al.  CLARA-A1: a cloud, albedo, and radiation dataset from 28 yr of global AVHRR data , 2013 .

[24]  Lunche Wang,et al.  Prediction of diffuse solar radiation based on multiple variables in China , 2019, Renewable and Sustainable Energy Reviews.

[25]  T. Manninen,et al.  CLARA-SAL: a global 28 yr timeseries of Earth's black-sky surface albedo , 2012 .

[26]  Jean-Louis Roujean,et al.  Analysis of MODIS albedo changes over steady woody covers in France during the period of 2001–2013 , 2017 .

[27]  Suhong Liu,et al.  Mapping Surface Broadband Albedo from Satellite Observations: A Review of Literatures on Algorithms and Products , 2015, Remote. Sens..

[28]  Dazhi Yang,et al.  Worldwide validation of CAMS and MERRA-2 reanalysis aerosol optical depth products using 15 years of AERONET observations , 2020 .

[29]  Qiang Liu,et al.  Greenland surface albedo changes in July 1981–2012 from satellite observations , 2013 .

[30]  Shunlin Liang,et al.  Evaluation of Four Reanalysis Surface Albedo Data Sets in Arctic Using a Satellite Product , 2016, IEEE Geoscience and Remote Sensing Letters.

[31]  Lizhao Wang,et al.  A temporal filtering algorithm to reconstruct daily albedo series based on GLASS albedo product , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.

[32]  S. Liang,et al.  Evaluating land surface albedo estimation from Landsat MSS, TM, ETM +, and OLI data based on the unified direct estimation approach , 2018 .

[33]  Bo Hu,et al.  Spatiotemporal characteristics of photosynthetically active radiation in China , 2007 .

[34]  Philip Lewis,et al.  The ESA globAlbedo project: Algorithm , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[35]  Nektarios Chrysoulakis,et al.  Estimation of the Land Surface Albedo Changes in the Broader Mediterranean Area, Based on 12 Years of Satellite Observations , 2015, Remote. Sens..

[36]  Ottar Michelsen,et al.  Biogenic CO2 fluxes, changes in surface albedo and biodiversity impacts from establishment of a miscanthus plantation. , 2014, Journal of environmental management.

[37]  Qiang Liu,et al.  Multi-scale validation strategy for satellite albedo products and its uncertainty analysis , 2015, Science China Earth Sciences.

[38]  Qiang Liu,et al.  Coarse scale in situ albedo observations over heterogeneous snow-free land surfaces and validation strategy: A case of MODIS albedo products preliminary validation over northern China , 2016 .

[39]  Yanxu Liu,et al.  Seasonal contrast of the dominant factors for spatial distribution of land surface temperature in urban areas , 2018, Remote Sensing of Environment.

[40]  F. Arif,et al.  Resampling air borne sensed data using bilinear interpolation algorithm , 2005, IEEE International Conference on Mechatronics, 2005. ICM '05..

[41]  Irma J. Terpenning,et al.  STL : A Seasonal-Trend Decomposition Procedure Based on Loess , 1990 .

[42]  R. Purser,et al.  Three-Dimensional Variational Analysis with Spatially Inhomogeneous Covariances , 2002 .

[43]  Muhammad Bilal,et al.  Evaluation of Terra-MODIS C6 and C6.1 Aerosol Products against Beijing, XiangHe, and Xinglong AERONET Sites in China during 2004-2014 , 2019, Remote. Sens..

[44]  Yanmin Shuai,et al.  Validation of Moderate Resolution Imaging Spectroradiometer (MODIS) albedo retrieval algorithm: Dependence of albedo on solar zenith angle , 2009 .

[45]  Charles S. Zender,et al.  MODIS snow albedo bias at high solar zenith angles relative to theory and to in situ observations in Greenland , 2009 .

[46]  Muhammad Bilal,et al.  A Simplified and Robust Surface Reflectance Estimation Method (SREM) for Use over Diverse Land Surfaces Using Multi-Sensor Data , 2019, Remote. Sens..

[47]  Zhuosen Wang,et al.  Use of In Situ and Airborne Multiangle Data to Assess MODIS- and Landsat-Based Estimates of Directional Reflectance and Albedo , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[48]  K. Gui,et al.  Variation in MERRA-2 aerosol optical depth and absorption aerosol optical depth over China from 1980 to 2017 , 2019, Journal of Atmospheric and Solar-Terrestrial Physics.

[49]  T. Crowther,et al.  Quantitative analysis of the links between forest structure and land surface albedo on a global scale , 2020 .

[50]  Qiang Liu,et al.  Global LAnd Surface Satellite (GLASS) Products: Algorithms, Validation and Analysis , 2014 .

[51]  Qiang Liu,et al.  Preliminary evaluation of the long-term GLASS albedo product , 2013 .

[52]  N. Lu,et al.  Hourly 5-km surface total and diffuse solar radiation in China, 2007–2018 , 2020, Scientific data.

[53]  C. Woodcock,et al.  Evaluation of MODIS albedo product (MCD43A) over grassland, agriculture and forest surface types during dormant and snow-covered periods , 2014 .

[54]  Zhan Li,et al.  Evaluation of the global MODIS 30 arc-second spatially and temporally complete snow-free land surface albedo and reflectance anisotropy dataset , 2017, Int. J. Appl. Earth Obs. Geoinformation.

[55]  Feng Gao,et al.  Assessing the coupling between surface albedo derived from MODIS and the fraction of diffuse skylight over spatially-characterized landscapes , 2010 .