A Performance Analysis of Soil Dielectric Models over Organic Soils in Alaska for Passive Microwave Remote Sensing of Soil Moisture

被引:10
作者
Zhang, Runze [1 ]
Chan, Steven [2 ]
Bindlish, Rajat [3 ]
Lakshmi, Venkataraman [1 ]
机构
[1] Univ Virginia, Dept Engn Syst & Environm, Charlottesville, VA 22904 USA
[2] CALTECH, NASA Jet Prop Lab, Pasadena, CA 91109 USA
[3] NASA Goddard Space Flight Ctr, Greenbelt, MD 20771 USA
关键词
soil moisture; dielectric models; SMAP; soil organic matter; WATER-CONTENT; EMPIRICAL-MODEL; SNOW COVER; WET SOIL; RETRIEVAL; CALIBRATION; SMOS; PERMITTIVITY; SENSITIVITY; VALIDATION;
D O I
10.3390/rs15061658
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Passive microwave remote sensing of soil moisture (SM) requires a physically based dielectric model that quantitatively converts the volumetric SM into the soil bulk dielectric constant. Mironov 2009 is the dielectric model used in the operational SM retrieval algorithms of the NASA Soil Moisture Active Passive (SMAP) and the ESA Soil Moisture and Ocean Salinity (SMOS) missions. However, Mironov 2009 suffers a challenge in deriving SM over organic soils, as it does not account for the impact of soil organic matter (SOM) on the soil bulk dielectric constant. To this end, we presented a comparative performance analysis of nine advanced soil dielectric models over organic soil in Alaska, four of which incorporate SOM. In the framework of the SMAP single-channel algorithm at vertical polarization (SCA-V), SM retrievals from different dielectric models were derived using an iterative optimization scheme. The skills of the different dielectric models over organic soils were reflected by the performance of their respective SM retrievals, which was measured by four conventional statistical metrics, calculated by comparing satellite-based SM time series with in-situ benchmarks. Overall, SM retrievals of organic-soil-based dielectric models tended to overestimate, while those from mineral-soil-based models displayed dry biases. All the models showed comparable values of unbiased root-mean-square error (ubRMSE) and Pearson Correlation (R), but Mironov 2019 exhibited a slight but consistent edge over the others. An integrated consideration of the model inputs, the physical basis, and the validated accuracy indicated that the separate use of Mironov 2009 and Mironov 2019 in the SMAP SCA-V for mineral soils (SOM <15%) and organic soils (SOM >= 15%) would be the preferred option.
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页数:19
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