Estimation of all-sky instantaneous surface incident shortwave radiation from Moderate Resolution Imaging Spectroradiometer data using optimization method

Details

• Journal Title:
  Remote Sensing of Environment
• Personal Author:
  Zhang, Yi ; He, Tao ; Liang, Shunlin ; Wang, Dongdong ; Yu, Yunyue
• NOAA Program & Office:
  NOAA General Documents
• Description:
  Surface incident shortwave radiation (ISR) is a crucial parameter in the land surface radiation budget. Many reanalysis, observation-based, and satellite-derived global radiation products have been developed but often have insufficient accuracy and spatial resolution for many applications. In this paper, we propose a method based on a radiative transfer model for estimating surface ISR from Moderate Resolution Imaging Spectroradiometer (MODIS) Top of Atmosphere (TOA) observations by optimizing the surface and atmospheric variables with a cost function. This approach consisted of two steps: retrieving surface bidirectional reflectance distribution function parameters, aerosol optical depth (AOD), and cloud optical depth (COD); and subsequently calculating surface ISR. Validation against measurements at seven Surface Radiation Budget Network (SURFRAD) sites resulted in an R2 of 0.91, a bias of −6.47 W/m2, and a root mean square error (RMSE) of 84.17 W/m2 (15.12%) for the instantaneous results. Validation at eight high-latitude snow-covered Greenland Climate Network (GC-Net) sites resulted in an R2 of 0.86, a bias of −21.40 W/m2, and an RMSE of 84.77 W/m2 (20.96%). These validation results show that the proposed method is much more accurate than the previous studies (usually with RMSEs of 80-150 W/m2). We further investigated whether incorporating additional satellite products, such as the MODIS surface broadband albedo (MCD43), aerosol (MOD/MYD04), and cloud products (MOD/MYD06), as constraints in the cost function would improve the accuracy. When the AOD and COD estimates were constrained, RMSEs were reduced to 62.19 W/m2 (12.12%) and 71.70 W/m2 (17.74%) at the SURFRAD and GC-Net sites, respectively. This algorithm could estimate surface ISR with MODIS TOA observations over both snow-free and seasonal/permanent snow-covered surfaces. The algorithm performed well at high-latitude sites, which is very useful for radiation budget research in the polar regions.
• Keywords:
  Computers In Earth Sciences Geology Soil Science Computers In Earth Sciences Computers In Earth Sciences
• Source:
  Remote Sensing of Environment, 209, 468-479
• DOI:
  https://doi.org/10.1016/j.rse.2018.02.052
• ISSN:
  0034-4257
• Format:
  PDF
• Publisher:
  Elsevier BV
• Document Type:
  Journal Article
• Rights Information:
  Accepted Manuscript
• Compliance:
  Library
• Main Document Checksum:
  urn:sha256:c98064eccf003dcbc825e17cc94d79edb5e882a6612589468b2a3a115a46cf4f
• Download URL:
  https://repository.library.noaa.gov/view/noaa/57957/noaa_57957_DS1.pdf
• File Type:
  [PDF - 3.96 MB ]