Evaluation of HJ-1A/B CCD Surface Reflectance Products Using the VNIR and MODIS-Based Atmospheric Correction Approaches

The growing development of medium- to high-resolution satellites in China has led to a considerable increase in the quantitative applications using the data; therefore, it is important to produce standard surface reflectance (SR) products operationally from such data. However, there is still a lack of relevant SR systems and SR products. We applied two atmospheric correction algorithms, adaptive to most multispectral satellites with visible and near-infrared (VNIR) bands, to HJ-1A/B charge-coupled device (CCD) instrument data, namely, the VNIR method and the MODIS-based method, with both methods being based on the Second Simulation of the Satellite Signal in the Solar Spectrum, Vector (6SV) code, and the look-up tables. We evaluated the accuracy of the SR by these two approaches for HJ-1A/B CCD images compared with the AErosol RObotic NETwork (AERONET) corrected reflectance for the period July 2011 to June 2012 over China and surrounding regions. We assessed more than 12 million pixels for the 49 spatial circular subsets, with a radius of 5 km, centered at 12 AERONET sites. The evaluation results indicated that both methods were suitable for operational flow, and that the MODIS-based method had better accuracy than the VNIR method, except for the near-infrared band. This conclusion was also validated by comparison with the normalized difference vegetation index products derived from the MODIS-based SR, VNIR SR, and AERONET SR. Additionally, the MODIS-based method showed superior accuracy when the overpass time of HJ-1A/B was more approximate to that of Terra MODIS.

[1]  Qing Li,et al.  Chinese HJ-1A/B satellites and data characteristics , 2010 .

[2]  Yong Hu,et al.  A Landsat-5 Atmospheric Correction Based on MODIS Atmosphere Products and 6S Model , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[3]  Alexei Lyapustin,et al.  Radiative transfer codes for atmospheric correction and aerosol retrieval: intercomparison study. , 2008, Applied optics.

[4]  Michael D. King,et al.  Aerosol properties over bright-reflecting source regions , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[5]  A. Kokhanovsky,et al.  Radiative transfer through terrestrial atmosphere and ocean: Software package SCIATRAN , 2014 .

[6]  J. M. Anderson,et al.  The applicability of LOWTRAN 5 computer code to aerial thermographic data correction , 1986 .

[7]  José F. Moreno,et al.  A method for accurate geometric correction of NOAA AVHRR HRPT data , 1993, IEEE Trans. Geosci. Remote. Sens..

[8]  M. Claverie,et al.  Evaluation of the Landsat-5 TM and Landsat-7 ETM+ surface reflectance products , 2015 .

[9]  Paul E. Lewis,et al.  MODTRAN4-based atmospheric correction algorithm: FLAASH (fast line-of-sight atmospheric analysis of spectral hypercubes) , 2002, SPIE Defense + Commercial Sensing.

[10]  T. Eck,et al.  Variability of Absorption and Optical Properties of Key Aerosol Types Observed in Worldwide Locations , 2002 .

[11]  Michael D. King,et al.  Deep Blue Retrievals of Asian Aerosol Properties During ACE-Asia , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[12]  B. Gao NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space , 1996 .

[13]  A. Goetz,et al.  Software for the derivation of scaled surface reflectances from AVIRIS data , 1992 .

[14]  R. Richter,et al.  Geo-atmospheric processing of airborne imaging spectrometry data. Part 2: Atmospheric/topographic correction , 2002 .

[15]  Xavier Pons,et al.  Radiometric Correction of Simultaneously Acquired Landsat-7/Landsat-8 and Sentinel-2A Imagery Using Pseudoinvariant Areas (PIA): Contributing to the Landsat Time Series Legacy , 2017, Remote. Sens..

[16]  D. Roy,et al.  Web-enabled Landsat Data (WELD): Landsat ETM+ composited mosaics of the conterminous United States , 2010 .

[17]  Yoram J. Kaufman,et al.  Atmospheric correction against algorithm for NOAA-AVHRR products: theory and application , 1992, IEEE Trans. Geosci. Remote. Sens..

[18]  Magdalena Main-Knorn,et al.  CALIBRATION AND VALIDATION PLAN FOR THE L2A PROCESSOR AND PRODUCTS OF THE SENTINEL-2 MISSION , 2015 .

[19]  Y. Kaufman,et al.  Passive remote sensing of tropospheric aerosol and atmospheric , 1997 .

[20]  E. Vermote,et al.  Validation of a vector version of the 6S radiative transfer code for atmospheric correction of satellite data. Part I: path radiance. , 2006, Applied optics.

[21]  B. Holben,et al.  Validation of MODIS aerosol optical depth retrieval over land , 2002 .

[22]  Didier Tanré,et al.  Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: an overview , 1997, IEEE Trans. Geosci. Remote. Sens..

[23]  Craig J. Miller,et al.  Performance assessment of ACORN atmospheric correction algorithm , 2002, SPIE Defense + Commercial Sensing.

[24]  A. Ångström The parameters of atmospheric turbidity , 1964 .

[25]  Mahesh Pun,et al.  Land Use Classification: A Surface Energy Balance and Vegetation Index Application to Map and Monitor Irrigated Lands , 2017, Remote. Sens..

[26]  Arve Kylling,et al.  The libRadtran software package for radiative transfer calculations (version 2.0.1) , 2015 .

[27]  Paul E. Lewis,et al.  FLAASH, a MODTRAN4-based atmospheric correction algorithm, its application and validation , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[28]  Bernhard Mayer,et al.  Atmospheric Chemistry and Physics Technical Note: the Libradtran Software Package for Radiative Transfer Calculations – Description and Examples of Use , 2022 .

[29]  A. Smirnov,et al.  AERONET-a federated instrument network and data archive for aerosol Characterization , 1998 .

[30]  Yong Xue,et al.  Validation and analysis of aerosol optical thickness retrieval over land , 2012 .

[31]  D. Roy,et al.  Conterminous United States demonstration and characterization of MODIS-based Landsat ETM+ atmospheric correction☆ , 2014 .

[32]  Xavier Pons,et al.  Automatic and improved radiometric correction of Landsat imagery using reference values from MODIS surface reflectance images , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[33]  Robert E. Wolfe,et al.  A Landsat surface reflectance dataset for North America, 1990-2000 , 2006, IEEE Geoscience and Remote Sensing Letters.

[34]  Liangyun Liu,et al.  Development and validation of the Landsat-8 surface reflectance products using a MODIS-based per-pixel atmospheric correction method , 2016 .

[35]  M. Claverie,et al.  Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. , 2016, Remote sensing of environment.

[36]  Molly E. Brown,et al.  Evaluation of the consistency of long-term NDVI time series derived from AVHRR,SPOT-vegetation, SeaWiFS, MODIS, and Landsat ETM+ sensors , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[37]  E. Vermote,et al.  Second‐generation operational algorithm: Retrieval of aerosol properties over land from inversion of Moderate Resolution Imaging Spectroradiometer spectral reflectance , 2007 .

[38]  Yong Xue,et al.  SAHARA: A Simplified AtmospHeric Correction AlgoRithm for Chinese gAofen Data: 1. Aerosol Algorithm , 2017, Remote. Sens..

[39]  Daniel Schläpfer,et al.  An automatic atmospheric correction algorithm for visible/NIR imagery , 2006 .

[40]  Hao Sun,et al.  Evaluation of Typical Spectral Vegetation Indices for Drought Monitoring in Cropland of the North China Plain , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[41]  D. Roy,et al.  Continental-scale Validation of MODIS-based and LEDAPS Landsat ETM+ Atmospheric Correction Methods , 2012 .

[42]  D. C. Robertson,et al.  MODTRAN cloud and multiple scattering upgrades with application to AVIRIS , 1998 .

[43]  P. Chavez Image-Based Atmospheric Corrections - Revisited and Improved , 1996 .

[44]  D. Tanré,et al.  Strategy for direct and indirect methods for correcting the aerosol effect on remote sensing: From AVHRR to EOS-MODIS , 1996 .

[45]  E. Vermote,et al.  Validation of a vector version of the 6S radiative transfer code for atmospheric correction of satellite data. Part II. Homogeneous Lambertian and anisotropic surfaces. , 2007, Applied optics.

[46]  Tang-Huang Lin,et al.  Applying SPOT data to estimate the aerosol optical depth and air quality , 2002, Environ. Model. Softw..

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

[48]  E. Vermote,et al.  Operational Atmospheric Correction of MODIS Visible to Middle Infrared Land Surface Data in the Case of an Infinite Lambertian Target , 2006 .

[49]  Bing Zhang,et al.  Estimating Winter Wheat Leaf Area Index From Ground and Hyperspectral Observations Using Vegetation Indices , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[50]  Zhaoming Zhang,et al.  A practical DOS model-based atmospheric correction algorithm , 2010 .