Paper Info
Title
Evaluation Of Smap Level 2, 3, And 4 Soil Moisture Datasets Over The Great Lakes Region
Abstract
Satellite sensor systems for soil moisture measurements have been continuously evolving. The Soil Moisture Active Passive (SMAP) mission represents one of the latest advances in this regard. Thus far, much of our knowledge of the accuracy of SMAP soil moisture over the Great Lakes region of North America has originated from evaluation studies using in situ data from the U.S. Department of Agriculture (USDA) Natural Resources Conservation Service Soil Climate Analysis Network and/or the U.S. Climate Reference Network, which provide only several in situ sensor stations for this region. As such, these results typically underrepresent the accuracy of SMAP soil moisture in this region, which is characterized by a relatively large soil moisture variability and is one of the least studied regions. In this work, SMAP Level 2-4 soil moisture products: SMAP/Sentinel-1 L2 Radiometer/Radar Soil Moisture (SPL2SMAP_S), SMAP Enhanced L3 Radiometer Soil Moisture (SPL3SMP_E), and SMAP L4 Surface and Root-Zone Soil Moisture Analysis Update (SPL4SMAU) are evaluated over the southern portion of the Great Lakes region using in situ measurements from Michigan State University's Enviro-weather Automated Weather Station Network. The unbiased root-mean-square error (ubRMSE) values for both SPL4SMAU surface and root zone soil moisture estimates are below 0.04 m(3) m(-3) at the 36-km scale, with an average ubRMSE of 0.045 m(3) m(-3) (0.037 m(3) m(-3)) for the surface (root-zone) soil moisture against the sparse network. The ubRMSE values for SPL3SMP_E a.m. (i.e., descending overpasses) soil moisture retrievals are close to or below 0.04 m(3) m(-3) at the 36-km scale, with an average ubRMSE of similar to 0.06 m(3) m(-3) against the sparse network. The average ubRMSE values are similar to 0.05-0.06 m(3) m(-3) for high-resolution SPL2SMAP_S soil moisture retrievals against the sparse network, with the skill of the baseline algorithm-based soil moisture retrievals exceeding that of the optional algorithm-based counterparts. Clearly, the skill of SPL4SMAU surface soil moisture exceeds that of the SPL3SMP_E and SPL2SMAP_S soil moisture retrievals.
Year
DOI
Venue
2020
10.3390/rs12223785
REMOTE SENSING
Keywords
DocType
Volume
soil moisture active passive (SMAP), soil moisture, unbiased root-mean-square error (ubRMSE)
Journal
12
Issue
Citations 
PageRank 
22
0
0.34
References 
Authors
0
1
Name
Order
Citations
PageRank
Xiaoyong Xu101.01