TY - JOUR
T1 - GCOM-W AMSR2 soil moisture product validation using core validation sites
AU - Bindlish, Rajat
AU - Cosh, Michael H.
AU - Jackson, Thomas J.
AU - Koike, Toshio
AU - Fujii, Hideyuki
AU - Chan, Steven K.
AU - Asanuma, Jun
AU - Berg, Aaron A.
AU - Bosch, David D.
AU - Caldwell, Todd G.
AU - Collins, Chandra Holifield
AU - McNairn, Heather
AU - Martinez-Fernandez, Jose
AU - Prueger, John H.
AU - Rowlandson, Tracy
AU - Seyfried, Mark
AU - Starks, Patrick J.
AU - Thibeault, Marc
AU - van der Velde, R.
AU - Walker, Jeffrey P.
AU - Coopersmith, Evan J.
PY - 2018/1/1
Y1 - 2018/1/1
N2 - The Advanced microwave scanning radiometer 2 (AMSR2) is part of the global change observationmission-water (GCOM-W). AMSR2 has filled the gap in passive microwave observations left by the loss of theAMSR-earth observing system (AMSR-E) after almost ten years of observations. Both missions provide brightness temperature observations that are used to retrieve soil moisture estimates at the near surface. A merged AMSR-E and AMSR2 data product will help build a consistent long-term dataset; however, before this can be done, it is necessary to conduct a thorough validation and assessment of the AMSR2 soil moisture products. This study focuses on the validation of the AMSR2 soil moisture products by comparison with in situ reference data from a set of core validation sites around the world. A total of three soil moisture products that rely on different algorithms were evaluated; the Japan Aerospace Exploration Agency (JAXA) soil moisture algorithm, the land parameter retrieval model (LPRM), and the single channel algorithm (SCA). JAXA, SCA, and LPRM soil moisture estimates capture the overall climatological features. The spatial features of the three products have similar overall spatial structure. The JAXA soil moisture product shows a lower dynamic range in the retrieved soil moisture with a satisfactory performance matrix when compared to in situ observations [unbiased root mean square error (ubRMSE) = 0.059 m3/m3, Bias = -0.083 m3/m3, R = 0.465]. The SCA performs well over low and moderately vegetated areas (ubRMSE = 0.053 m3/m3, Bias = -0.039 m3/m3, R = 0.549). The LPRM product has a large dynamic range compared to in situ observations with a wet bias (ubRMSE = 0.094 m3/m3, Bias = 0.091 m3/m3, R = 0.577). Some of the error is due to the difference in observation depth between the in situ sensors (5 cm) and satellite estimates (1 cm). Results indicate that overall the JAXA and SCA have the best performance based upon the metrics considered.
AB - The Advanced microwave scanning radiometer 2 (AMSR2) is part of the global change observationmission-water (GCOM-W). AMSR2 has filled the gap in passive microwave observations left by the loss of theAMSR-earth observing system (AMSR-E) after almost ten years of observations. Both missions provide brightness temperature observations that are used to retrieve soil moisture estimates at the near surface. A merged AMSR-E and AMSR2 data product will help build a consistent long-term dataset; however, before this can be done, it is necessary to conduct a thorough validation and assessment of the AMSR2 soil moisture products. This study focuses on the validation of the AMSR2 soil moisture products by comparison with in situ reference data from a set of core validation sites around the world. A total of three soil moisture products that rely on different algorithms were evaluated; the Japan Aerospace Exploration Agency (JAXA) soil moisture algorithm, the land parameter retrieval model (LPRM), and the single channel algorithm (SCA). JAXA, SCA, and LPRM soil moisture estimates capture the overall climatological features. The spatial features of the three products have similar overall spatial structure. The JAXA soil moisture product shows a lower dynamic range in the retrieved soil moisture with a satisfactory performance matrix when compared to in situ observations [unbiased root mean square error (ubRMSE) = 0.059 m3/m3, Bias = -0.083 m3/m3, R = 0.465]. The SCA performs well over low and moderately vegetated areas (ubRMSE = 0.053 m3/m3, Bias = -0.039 m3/m3, R = 0.549). The LPRM product has a large dynamic range compared to in situ observations with a wet bias (ubRMSE = 0.094 m3/m3, Bias = 0.091 m3/m3, R = 0.577). Some of the error is due to the difference in observation depth between the in situ sensors (5 cm) and satellite estimates (1 cm). Results indicate that overall the JAXA and SCA have the best performance based upon the metrics considered.
KW - In situ networks
KW - Passive microwave
KW - Soil moisture
KW - Validation
KW - ITC-ISI-JOURNAL-ARTICLE
U2 - 10.1109/JSTARS.2017.2754293
DO - 10.1109/JSTARS.2017.2754293
M3 - Article
AN - SCOPUS:85034412651
SN - 1939-1404
VL - 11
SP - 209
EP - 219
JO - IEEE Journal of selected topics in applied earth observations and remote sensing
JF - IEEE Journal of selected topics in applied earth observations and remote sensing
IS - 1
M1 - 8233417
ER -