Item request has been placed! ×
Item request cannot be made. ×
loading  Processing Request

Global-scale assessment and combination of SMAP with ASCAT (active) and AMSR2 (passive) soil moisture products

Item request has been placed! ×
Item request cannot be made. ×
loading   Processing Request
اقرأ أكثر حفظ في قائمتي
  • معلومة اضافية
    • Contributors:
      University of South Carolina, USA; VanderSat, The Netherlands; Stanford University, USA; #PLACEHOLDER_PARENT_METADATA_VALUE#; USDA-ARS-Hydrology and Remote Sensing Laboratory, Beltsville, USA; Sungkyunkwan University, Suwon, Republic of Korea
    • بيانات النشر:
      Elsevier
    • الموضوع:
      2021
    • Collection:
      TU Wien: reposiTUm
    • نبذة مختصرة :
      Global-scale surface soil moisture (SSM) products retrieved from active and passive microwave remote sensing provide an effective method for monitoring near-real-time SSM content with nearly daily temporal resolution. In the present study, we first inter-compared global-scale error patterns and combined the Soil Moisture Active Passive (SMAP), Advanced Scatterometer (ASCAT), and Advanced Microwave Scanning Radiometer 2 (AMSR2) SSM products using a triple collocation (TC) analysis and the maximized Pearson correlation coefficient (R) method from April 2015 to December 2016. The Global Land Data Assimilation System (GLDAS) and global in situ observations were utilized to investigate and to compare the quality of satellite-based SSM products. The average R-values of SMAP, ASCAT, and AMSR2 were 0.74, 0.64, and 0.65 when they compared with in situ networks, respectively. The ubRMSD values were (0.0411, 0.0625, and 0.0708) m3 m− 3; and the bias values were (− 0.0460, 0.0010, and 0.0418) m3 m− 3 for SMAP, ASCAT, and AMSR2, respectively. The highest average R-values from SMAP against the in situ results are very encouraging; only SMAP showed higher R-values than GLDAS in several in situ networks with low ubRMSD (0.0438 m3 m− 3). Overall, SMAP showed a dry bias (− 0.0460 m3 m− 3) and AMSR2 had a wet bias (0.0418 m3 m− 3); while ASCAT showed the least bias (0.0010 m3 m− 3) among all the products. Each product was evaluated using TC metrics with respect to the different ranges of vegetation optical depth (VOD). Under vegetation scarce conditions (VOD < 0.10), such as desert and semi-desert regions, all products have difficulty obtaining SSM information. In regions with moderately vegetated areas (0.10 < VOD < 0.40), SMAP showed the highest Signal-to-Noise Ratio. Over highly vegetated regions (VOD > 0.40) ASCAT showed comparatively better performance than did the other products. Using the maximized R method, SMAP, ASCAT, and AMSR2 products were combined one by one using the GLDAS dataset for reference SSM ...
    • ISSN:
      0034-4257
      1879-0704
    • Relation:
      Remote Sensing of Environment; NRF-2016R1A2B4008312; NRF-2014M1A3A3A02034789; http://hdl.handle.net/20.500.12708/18281; https://doi.org/10.34726/1321; 2-s2.0-85031814972; https://api.elsevier.com/content/abstract/scopus_id/85031814972
    • الرقم المعرف:
      10.34726/1321
    • Rights:
      open
    • الرقم المعرف:
      edsbas.62D9F4F9