The Characterization of the Vertical Distribution of Surface Soil Moisture Using ISMN Multilayer In Situ Data and Their Comparison with SMOS and SMAP Soil Moisture Products

Author:

Yang Na1ORCID,Xiang Feng2,Zhang Hengjie1

Affiliation:

1. School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China

2. College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454000, China

Abstract

In this paper, we investigated the vertical distribution characteristics of surface soil moisture based on ISMN (International Soil Moisture Network) multilayer in situ data (5, 10, and 20 cm; 2, 4, and 8 in) and performed comparisons between the in situ data and four microwave satellite remote sensing products (SMOS L2, SMOS-IC, SMAP L2, and SMAP L4). The results showed that the mean soil moisture difference between layers can be −0.042~−0.024 (for the centimeter group)/−0.067~−0.044 (for the inch group) m3/m3 in negative terms and 0.020~0.028 (for the centimeter group)/0.036~0.040 (for the inch group) m3/m3 in positive terms. The surface soil moisture was found to have very significant stratification characteristics, and the interlayer difference was close to or beyond the SMOS and SMAP 0.04 m3/m3 nominal retrieval accuracy. Comparisons revealed that the satellite retrievals had a higher correlation with the field measurements of 5 cm/2 in, and SMAP L4 had the smallest difference with the in situ data. The mean difference caused by using 10 cm/4 in and 20 cm/8 in in situ data instead of the 5 cm/2 in data could be about −0.019~−0.018/−0.18~−0.015 m3/m3 and −0.026~−0.023/−0.043~−0.039 m3/m3, respectively, meaning that there would be a potential depth mismatch in the data validation.

Funder

National Natural Science Foundation of China

Double First-Class Project Cultivation Special Project—Key Technology for Intelligent Equipment and Intelligent Processing of Spatiotemporal Information

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

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1. Microwave Remote Sensing of Soil Moisture;Remote Sensing;2023-08-29

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