ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-3-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 125–131, 2020
https://doi.org/10.5194/isprs-annals-V-3-2020-125-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 125–131, 2020
https://doi.org/10.5194/isprs-annals-V-3-2020-125-2020

  03 Aug 2020

03 Aug 2020

DEVELOPMENT AND VALIDATION OF A NEW PASSIVE MICROWAVE BASED SOIL MOISTURE INDEX

J. Zeng1, K.-S. Chen1, C. Cui1,2, and H. Bi3 J. Zeng et al.
  • 1State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
  • 2Suzhou Industrial Park Surveying, Mapping, and Geoinformation Company, Ltd., Suzhou 215000, China
  • 3State Key Laboratory of Earthquake Dynamics, Institute of Geology, China Earthquake Administration, Beijing 100029, China

Keywords: Soil moisture, Passive microwave, SMAP, Temporal variability, Vegetation, Surface roughness

Abstract. Knowledge on the spatial-temporal variation of soil moisture is essential to many hydrometeorology applications. In this study, we proposed a new soil moisture index (SMI) from passive microwave observations, aiming to capture the soil moisture variability. The new SMI is developed based on the underlying physical basis that vegetation and surface roughness exert similar effects on the variation of land surface emissivity and microwave polarization difference radio (MPDI), but they act in an opposite way compared with soil moisture. Hence, we can obtain the SMI value in a two-dimensional space by combining use of land surface emissivity and MPDI to isolate the contribution of soil moisture and that of vegetation and surface roughness. We calculated the SMI by using the L-band SMAP Level-3 datasets and validated it with five well calibrated and dense soil moisture networks and also compared it with SMAP and ESA CCI soil moisture products. The results show the SMI exhibits the highest R (0.87) and lowest RMSE (0.028 m3 m−3) value after removing the systematic bias by using the cumulative distribution function (CDF) matching technique among the satellite products during the whole study period, thus demonstrating its good capability of tracking the temporal variation of soil moisture and its potential usage in various hydrometeorology applications.