Assessment of SMOS Root Zone Soil Moisture: A Comparative Study Using SMAP, ERA5, and GLDAS
Nitu Ojha, Ali Mahmoodi, Arnaud Mialon, Philippe Richaume, Sylvain Ferrant, Yann H. Kerr
Abstract
Root zone soil moisture (RZSM) refers to the amount of water present in the soil layer where plants can freely absorb water, and its information is crucial for various applications such as hydrology and agriculture. SMOS and SMAP remote sensing satellites provide soil moisture (SM) data on a global scale but limit their sensing capability to a depth of approximately 5 cm. However, for a comprehensive understanding of soil water content in the root zone, a deeper insight into RZSM, which extends from 5 cm to 100 cm. Hence, to bridge the gap between the surface SM and RZSM, SMOS surface SM information ( 5 cm) was integrated into the root zone ( 100 cm) using a simple subsurface physical model to estimate RZSM. SMOS RZSM data are available on a global scale with 25 km sampling on EASE grid 2, which provides a daily temporal scale from 2010 to the present. The main aims of this study were to i) check the efficacy of a simple model-based approach and ii) investigate the importance of remote sensing SM observations for the retrieval of RZSM. Here, we investigate the benefit of the simple model-based approach by comparing SMOS RZSM (simple model) and SMAP, ERA5, and GLDAS RZSM (complex model or data-assimilation) with in-situ SM. We then investigated the role of remote sensing SM observation in the retrieval of RZSM by comparing SMOS RZSM (direct use of surface SM) and SMAP RZSM (temperature brightness for SM information) products over rice-irrigated areas for dry seasons (minimal rainfall) in Telangana, South India. First, SMOS RZSM was evaluated with in-situ SM data for four distinct networks: SCAN, HOBE, SMOSMANIA, and Amma catch from 2011 to 2017. The results between SMOS RZSM and in-situ SM show an average correlation coefficient between 0.54 and 0.8 with an average sample standard deviation (SSD) within the threshold of 0.04m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> . The average correlation coefficient between the RZSM and in-situSMfor the SMOS and SMAP RZSM shows better performance in the range (0.55 to 0.93) - because of the inclusion of SM information by satellite observation - than the ERA5 and GLDAS RZSM in the range (0.20 to 0.93). Finally, the outcomes of SMOS and SMAP RZSM over irrigated areas show that only SMOS RZSM captures changes in SM dynamics due to irrigation, particularly during the dry season.